chore: Release v7.9.3

* fix: Avoid setting dynamic version

Version is now fixed because of sampo, so we can get rid of this

* feat: add changeset

* docs: Add new RELEASING section to README

.env.example

@@ -0,0 +1,11 @@
+# PostHog API Configuration
+# Copy this file to .env and update with your actual values
+
+# Your project API key (found on the /setup page in PostHog)
+POSTHOG_PROJECT_API_KEY=phc_your_project_api_key_here
+
+# Your personal API key (for local evaluation and other advanced features)
+POSTHOG_PERSONAL_API_KEY=phx_your_personal_api_key_here
+
+# PostHog host URL (remove this line if using posthog.com)
+POSTHOG_HOST=http://localhost:8000

.github/dependabot.yml

@@ -0,0 +1,36 @@
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "daily"
+      time: "10:00"
+      timezone: "UTC"
+    groups:
+      ai-providers:
+        patterns:
+          - "openai"
+          - "anthropic"
+          - "google-genai"
+          - "langchain-core"
+          - "langchain-community"
+          - "langchain-openai"
+          - "langchain-anthropic"
+          - "langgraph"
+    allow:
+      - dependency-name: "openai"
+      - dependency-name: "anthropic"
+      - dependency-name: "google-genai"
+      - dependency-name: "langchain-core"
+      - dependency-name: "langchain-community"
+      - dependency-name: "langchain-openai"
+      - dependency-name: "langchain-anthropic"
+      - dependency-name: "langgraph"
+    open-pull-requests-limit: 1
+    reviewers:
+      - "PostHog/team-llm-analytics"
+    # Uncomment below to enable auto-merge for minor updates when CI passes
+    # pull-request-branch-name:
+    #   separator: "/"
+    # assignees:
+    #   - "PostHog/ai-team"

.github/workflows/call-flags-project-board.yml

@@ -0,0 +1,17 @@
+# This workflow is used to call the flags-project-board workflow when a pull request is opened, ready for review, review requested, synchronized, converted to draft, or reopened.
+# It is used to update the feature flags project board with the pull request information.
+
+name: Call Feature Flags Project Workflow
+
+on:
+    pull_request:
+        types: [opened, ready_for_review, review_requested, synchronize, converted_to_draft, reopened]
+
+jobs:
+    call-flags-project:
+        uses: PostHog/.github/.github/workflows/flags-project-board.yml@main
+        with:
+            pr_number: ${{ github.event.pull_request.number }}
+            pr_node_id: ${{ github.event.pull_request.node_id }}
+            is_draft: ${{ github.event.pull_request.draft }}
+        secrets: inherit

.github/workflows/ci.yml

@@ -0,0 +1,131 @@
+name: CI
+
+on:
+    - pull_request
+
+permissions:
+    contents: read
+
+jobs:
+    code-quality:
+        name: Code quality checks
+        runs-on: ubuntu-latest
+
+        steps:
+            - uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
+              with:
+                  fetch-depth: 1
+
+            - name: Set up Python 3.11
+              uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+              with:
+                  python-version: 3.11.11
+
+            - name: Install uv
+              uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+              with:
+                  enable-cache: true
+                  pyproject-file: 'pyproject.toml'
+
+            - name: Install dev dependencies
+              shell: bash
+              run: |
+                  UV_PROJECT_ENVIRONMENT=$pythonLocation uv sync --extra dev
+
+            - name: Check formatting with ruff
+              run: |
+                  ruff format --check .
+
+            - name: Lint with ruff
+              run: |
+                  ruff check .
+
+            - name: Check types with mypy
+              run: |
+                  mypy --no-site-packages --config-file mypy.ini  . | mypy-baseline filter
+
+    tests:
+        name: Python ${{ matrix.python-version }} tests
+        runs-on: ubuntu-latest
+        strategy:
+            matrix:
+                python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
+
+        steps:
+            - uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
+              with:
+                  fetch-depth: 1
+
+            - name: Set up Python ${{ matrix.python-version }}
+              uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+              with:
+                  python-version: ${{ matrix.python-version }}
+
+            - name: Install uv
+              uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+              with:
+                  enable-cache: true
+                  pyproject-file: 'pyproject.toml'
+
+            - name: Install test dependencies
+              shell: bash
+              run: |
+                  UV_PROJECT_ENVIRONMENT=$pythonLocation uv sync --extra test
+
+            - name: Run posthog tests
+              run: |
+                  pytest --verbose --timeout=30
+
+    import-check:
+        name: Python ${{ matrix.python-version }} import check
+        runs-on: ubuntu-latest
+        strategy:
+            matrix:
+                python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
+
+        steps:
+            - uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
+              with:
+                  fetch-depth: 1
+
+            - name: Set up Python ${{ matrix.python-version }}
+              uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+              with:
+                  python-version: ${{ matrix.python-version }}
+
+            - name: Install posthog
+              run: pip install .
+
+            - name: Check import produces no warnings
+              run: python -W error -c "import posthog"
+
+    django5-integration:
+        name: Django 5 integration tests
+        runs-on: ubuntu-latest
+
+        steps:
+            - uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
+              with:
+                  fetch-depth: 1
+
+            - name: Set up Python 3.12
+              uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+              with:
+                  python-version: 3.12
+
+            - name: Install uv
+              uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+              with:
+                  enable-cache: true
+                  pyproject-file: 'integration_tests/django5/pyproject.toml'
+
+            - name: Install Django 5 test project dependencies
+              shell: bash
+              working-directory: integration_tests/django5
+              run: |
+                  UV_PROJECT_ENVIRONMENT=$pythonLocation uv sync
+
+            - name: Run Django 5 middleware integration tests
+              working-directory: integration_tests/django5
+              run: |
+                  uv run pytest test_middleware.py test_exception_capture.py --verbose

.github/workflows/codeql.yml

@@ -0,0 +1,45 @@
+name: 'CodeQL Advanced'
+
+on:
+  push:
+    branches: ['master']
+  pull_request:
+    branches: ['master']
+  schedule:
+    - cron: '32 13 * * 1'
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    runs-on: 'ubuntu-latest'
+    permissions:
+      security-events: write
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - language: actions
+            build-mode: none
+          - language: python
+            build-mode: none
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@5d4e8d1aca955e8d8589aabd499c5cae939e33c7 # v4.31.9
+        with:
+          languages: ${{ matrix.language }}
+          build-mode: ${{ matrix.build-mode }}
+          # Disable TRAP caching - it creates a new cache per commit SHA which
+          # is never reused, causing wasted cache space.
+          # See: https://github.com/github/codeql-action/issues/2030
+          trap-caching: false
+
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@5d4e8d1aca955e8d8589aabd499c5cae939e33c7 # v4.31.9
+        with:
+          category: '/language:${{matrix.language}}'

.github/workflows/generate-references.yml

@@ -0,0 +1,49 @@
+name: "Generate References"
+
+on:
+  workflow_dispatch:
+
+jobs:
+  docs-generation:
+    name: Generate references
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - name: Checkout the repository
+        uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+        with:
+          python-version: 3.11.11
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        with:
+            enable-cache: true
+            pyproject-file: 'pyproject.toml'
+
+      - name: Generate references
+        run: |
+          uv run bin/docs generate-references
+
+      - name: Check for changes in references
+        id: changes
+        run: |
+          if [ -n "$(git status --porcelain references/)" ]; then
+            echo "changed=true" >> $GITHUB_OUTPUT
+            echo "New references generated in references directory:"
+            git status --porcelain references/
+          else
+            echo "changed=false" >> $GITHUB_OUTPUT
+            echo "No new references generated in references directory"
+          fi
+
+      - uses: stefanzweifel/git-auto-commit-action@778341af668090896ca464160c2def5d1d1a3eb0
+        if: steps.changes.outputs.changed == 'true'
+        with:
+          commit_message: "Update generated references"
+          file_pattern: references/

.github/workflows/release.yml

@@ -0,0 +1,249 @@
+name: "Release"
+
+on:
+  pull_request:
+    types: [closed]
+    branches: [master]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+# Concurrency control: only one release process can run at a time
+# This prevents race conditions if multiple PRs with 'release' label merge simultaneously
+concurrency:
+  group: release
+  cancel-in-progress: false
+
+jobs:
+  check-release-label:
+    name: Check for release label
+    runs-on: ubuntu-latest
+    # Run when PR with 'release' label is merged to master
+    if: |
+      github.event_name == 'workflow_dispatch' ||
+       (github.event_name == 'pull_request' &&
+        github.event.pull_request.merged == true &&
+        contains(github.event.pull_request.labels.*.name, 'release'))
+    outputs:
+      should-release: ${{ steps.check.outputs.should-release }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          ref: master
+          fetch-depth: 0
+
+      - name: Check release conditions
+        id: check
+        run: |
+          changeset_count=$(find .sampo/changesets -name '*.md' 2>/dev/null | wc -l)
+          if [ "$changeset_count" -gt 0 ]; then
+            echo "should-release=true" >> "$GITHUB_OUTPUT"
+            echo "Found $changeset_count changeset(s), ready to release"
+          else
+            echo "should-release=false" >> "$GITHUB_OUTPUT"
+            echo "No changesets to release"
+          fi
+
+  notify-approval-needed:
+    name: Notify Slack - Approval Needed
+    needs: check-release-label
+    if: needs.check-release-label.outputs.should-release == 'true'
+    uses: posthog/.github/.github/workflows/notify-approval-needed.yml@main
+    with:
+      slack_channel_id: ${{ vars.SLACK_APPROVALS_CLIENT_LIBRARIES_CHANNEL_ID }}
+      slack_user_group_id: ${{ vars.GROUP_CLIENT_LIBRARIES_SLACK_GROUP_ID }}
+    secrets:
+      slack_bot_token: ${{ secrets.SLACK_CLIENT_LIBRARIES_BOT_TOKEN }}
+      posthog_project_api_key: ${{ secrets.POSTHOG_PROJECT_API_KEY }}
+
+  release:
+    name: Release and publish
+    needs: [check-release-label, notify-approval-needed]
+    runs-on: ubuntu-latest
+    # Use `always()` to ensure the job runs even if notify-approval-needed is skipped,
+    # but still depend on it to access `needs.notify-approval-needed.outputs.slack_ts`
+    if: always() && needs.check-release-label.outputs.should-release == 'true'
+    environment: "Release" # This will require an approval from a maintainer, they are notified in Slack above
+    permissions:
+      contents: write
+      actions: write
+      id-token: write
+    steps:
+      - name: Notify Slack - Approved
+        if: needs.notify-approval-needed.outputs.slack_ts != ''
+        uses: posthog/.github/.github/actions/slack-thread-reply@main
+        with:
+          slack_bot_token: ${{ secrets.SLACK_CLIENT_LIBRARIES_BOT_TOKEN }}
+          slack_channel_id: ${{ vars.SLACK_APPROVALS_CLIENT_LIBRARIES_CHANNEL_ID }}
+          thread_ts: ${{ needs.notify-approval-needed.outputs.slack_ts }}
+          message: "✅ Release approved! Version bump in progress..."
+          emoji_reaction: "white_check_mark"
+
+      - name: Get GitHub App token
+        id: releaser
+        uses: actions/create-github-app-token@v2
+        with:
+          app-id: ${{ secrets.GH_APP_POSTHOG_PYTHON_RELEASER_APP_ID }}
+          private-key: ${{ secrets.GH_APP_POSTHOG_PYTHON_RELEASER_PRIVATE_KEY }}
+
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          ref: master
+          fetch-depth: 0
+          token: ${{ steps.releaser.outputs.token }}
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.11.11
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+          pyproject-file: "pyproject.toml"
+
+      - name: Install Rust
+        uses: dtolnay/rust-toolchain@0b1efabc08b657293548b77fb76cc02d26091c7e
+        with:
+          toolchain: 1.91.1
+          components: cargo
+
+      - name: Cache Sampo CLI
+        id: cache-sampo
+        uses: actions/cache@v3
+        with:
+          path: ~/.cargo/bin/sampo
+          key: sampo-${{ runner.os }}-${{ runner.arch }}
+
+      - name: Install Sampo CLI
+        if: steps.cache-sampo.outputs.cache-hit != 'true'
+        run: cargo install sampo
+
+      - name: Install dependencies
+        run: uv sync --extra dev
+
+      - name: Configure Git
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+
+      - name: Prepare release with Sampo
+        id: sampo-release
+        env:
+          GITHUB_TOKEN: ${{ steps.releaser.outputs.token }}
+        run: |
+          sampo release
+          new_version=$(python3 -c "import tomllib; print(tomllib.load(open('pyproject.toml', 'rb'))['project']['version'])")
+          echo "new_version=$new_version" >> "$GITHUB_OUTPUT"
+
+      - name: Sync version to posthog/version.py
+        run: |
+          echo 'VERSION = "${{ steps.sampo-release.outputs.new_version }}"' > posthog/version.py
+
+      - name: Commit release changes
+        id: commit-release
+        env:
+          GITHUB_TOKEN: ${{ steps.releaser.outputs.token }}
+        run: |
+          git add -A
+          if git diff --staged --quiet; then
+            echo "No changes to commit"
+            echo "committed=false" >> "$GITHUB_OUTPUT"
+          else
+            git commit -m "chore: Release v${{ steps.sampo-release.outputs.new_version }}"
+            git push origin master
+            echo "committed=true" >> "$GITHUB_OUTPUT"
+          fi
+
+      # Publishing is done manually (not via `sampo publish`) because we need to
+      # publish both `posthog` and `posthoganalytics` packages to PyPI.
+      # Sampo only knows about the `posthog` package, so we handle both here.
+      # Both packages use PyPI OIDC trusted publishing (no API tokens needed).
+      - name: Build posthog
+        if: steps.commit-release.outputs.committed == 'true'
+        run: uv run make build_release
+
+      - name: Publish posthog to PyPI
+        if: steps.commit-release.outputs.committed == 'true'
+        uses: pypa/gh-action-pypi-publish@release/v1
+
+      # The `posthoganalytics` package is a mirror of `posthog` published under
+      # a different name for backwards compatibility. The make target handles
+      # copying, renaming imports, and building the dist automatically.
+      - name: Build posthoganalytics
+        if: steps.commit-release.outputs.committed == 'true'
+        run: uv run make build_release_analytics
+
+      - name: Publish posthoganalytics to PyPI
+        if: steps.commit-release.outputs.committed == 'true'
+        uses: pypa/gh-action-pypi-publish@release/v1
+
+      # We skip `sampo publish` (which normally creates the tag) because we
+      # need to publish both posthog and posthoganalytics manually, so we
+      # create the tag ourselves.
+      - name: Tag release
+        if: steps.commit-release.outputs.committed == 'true'
+        run: git tag "v${{ steps.sampo-release.outputs.new_version }}"
+
+      - name: Push tags
+        if: steps.commit-release.outputs.committed == 'true'
+        run: git push origin --tags
+
+      - name: Create GitHub Release
+        if: steps.commit-release.outputs.committed == 'true'
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: gh release create "v${{ steps.sampo-release.outputs.new_version }}" --generate-notes
+
+      - name: Dispatch generate-references
+        if: steps.commit-release.outputs.committed == 'true'
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: gh workflow run generate-references.yml --ref master
+
+      # Notify in case of a failure
+      - name: Send failure event to PostHog
+        if: ${{ failure() }}
+        uses: PostHog/posthog-github-action@v0.1
+        with:
+          posthog-token: "${{ secrets.POSTHOG_PROJECT_API_KEY }}"
+          event: "posthog-python-github-release-workflow-failure"
+          properties: >-
+            {
+              "commitSha": "${{ github.sha }}",
+              "jobStatus": "${{ job.status }}",
+              "ref": "${{ github.ref }}",
+              "version": "v${{ steps.sampo-release.outputs.new_version }}"
+            }
+
+      - name: Notify Slack - Failed
+        if: ${{ failure() && needs.notify-approval-needed.outputs.slack_ts != '' }}
+        uses: posthog/.github/.github/actions/slack-thread-reply@main
+        with:
+          slack_bot_token: ${{ secrets.SLACK_CLIENT_LIBRARIES_BOT_TOKEN }}
+          slack_channel_id: ${{ vars.SLACK_APPROVALS_CLIENT_LIBRARIES_CHANNEL_ID }}
+          thread_ts: ${{ needs.notify-approval-needed.outputs.slack_ts }}
+          message: "❌ Failed to release `posthog-python@v${{ steps.sampo-release.outputs.new_version }}`! <https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}|View logs>"
+          emoji_reaction: "x"
+
+  notify-released:
+    name: Notify Slack - Released
+    needs: [check-release-label, notify-approval-needed, release]
+    runs-on: ubuntu-latest
+    if: always() && needs.release.result == 'success' && needs.notify-approval-needed.outputs.slack_ts != ''
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Notify Slack - Released
+        uses: posthog/.github/.github/actions/slack-thread-reply@main
+        with:
+          slack_bot_token: ${{ secrets.SLACK_CLIENT_LIBRARIES_BOT_TOKEN }}
+          slack_channel_id: ${{ vars.SLACK_APPROVALS_CLIENT_LIBRARIES_CHANNEL_ID }}
+          thread_ts: ${{ needs.notify-approval-needed.outputs.slack_ts }}
+          message: "🚀 posthog-python released successfully!"
+          emoji_reaction: "rocket"

.github/workflows/sdk-compliance.yml

@@ -0,0 +1,21 @@
+name: SDK Compliance Tests
+
+permissions:
+  contents: read
+  packages: read
+  pull-requests: write
+
+on:
+  pull_request:
+  push:
+    branches:
+      - master
+
+jobs:
+  compliance:
+    name: PostHog SDK compliance tests
+    uses: PostHog/posthog-sdk-test-harness/.github/workflows/test-sdk-action.yml@main
+    with:
+      adapter-dockerfile: "sdk_compliance_adapter/Dockerfile"
+      adapter-context: "."
+      test-harness-version: "latest"

.gitignore

@@ -1,14 +1,22 @@
 **sublime**
 *.pyc
-dist
+dist/
 *.egg-info
-dist
 MANIFEST
-build
-.eggs
+build/
+.eggs/
 .coverage
 .vscode/
-env
+env/
+venv/
 flake8.out
 pylint.out
 posthog-analytics
+.idea
+.python-version
+.coverage
+pyrightconfig.json
+.env
+.DS_Store
+posthog-python-references.json
+.claude/settings.local.json

.pre-commit-config.yaml

@@ -0,0 +1,10 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.11.12
+  hooks:
+    # Run the linter.
+    - id: ruff-check
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format

.sampo/config.toml

@@ -0,0 +1,19 @@
+# Sampo configuration
+version = 1
+
+[git]
+default_branch = "master"
+short_tags = "posthog"    # Tag with v1.2.3 rather than posthog-v1.2.3
+
+[github]
+repository = "posthog/posthog-python"
+
+[changelog]
+# Options for release notes generation.
+# show_commit_hash = true (default)
+# show_acknowledgments = true (default)
+
+[packages]
+# Options for package discovery and filtering.
+# ignore_unpublished = false (default)
+# ignore = ["internal-*", "examples/*"]

BEFORE_SEND.md

@@ -0,0 +1,237 @@
+# Before Send Hook
+
+The `before_send` parameter allows you to modify or filter events before they are sent to PostHog. This is useful for:
+
+- **Privacy**: Removing or masking sensitive data (PII)
+- **Filtering**: Dropping unwanted events (test events, internal users, etc.)
+- **Enhancement**: Adding custom properties to all events
+- **Transformation**: Modifying event names or property formats
+
+## Basic Usage
+
+```python
+import posthog
+from typing import Optional, Dict, Any
+
+def my_before_send(event: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """
+    Process event before sending to PostHog.
+    
+    Args:
+        event: The event dictionary containing 'event', 'distinct_id', 'properties', etc.
+    
+    Returns:
+        Modified event dictionary to send, or None to drop the event
+    """
+    # Your processing logic here
+    return event
+
+# Initialize client with before_send hook
+client = posthog.Client(
+    api_key="your-project-api-key",
+    before_send=my_before_send
+)
+```
+
+## Common Use Cases
+
+### 1. Filter Out Events
+
+```python
+from typing import Optional, Any
+
+def filter_events_by_property_or_event_name(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    """Drop events from internal users or test environments."""
+    properties = event.get("properties", {})
+    
+    # Choose some property from your events
+    event_source = properties.get("event_source", "")
+    if event_source.endswith("internal"):
+        return None  # Drop the event
+    
+    # Filter out test events
+    if event.get("event") == "test_event":
+        return None
+    
+    return event
+```
+
+### 2. Remove/Mask PII Data
+
+```python
+from typing import Optional, Any
+
+def scrub_pii(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    """Remove or mask personally identifiable information."""
+    properties = event.get("properties", {})
+    
+    # Mask email but keep domain for analytics
+    if "email" in properties:
+        email = properties["email"]
+        if "@" in email:
+            domain = email.split("@")[1]
+            properties["email"] = f"***@{domain}"
+        else:
+            properties["email"] = "***"
+    
+    # Remove sensitive fields entirely
+    sensitive_fields = ["my_business_info", "secret_things"]
+    for field in sensitive_fields:
+        properties.pop(field, None)
+    
+    return event
+```
+
+### 3. Add Custom Properties
+
+```python
+from typing import Optional, Any
+
+from datetime import datetime
+from typing import Optional, Any
+
+def add_context(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    """Add custom properties to all events."""
+    if "properties" not in event:
+        event["properties"] = {}
+    
+    event["properties"].update({
+        "app_version": "2.1.0",
+        "environment": "production", 
+        "processed_at": datetime.now().isoformat()
+    })
+    
+    return event
+```
+
+### 4. Transform Event Names
+
+```python
+from typing import Optional, Any
+
+def normalize_event_names(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    """Convert event names to a consistent format."""
+    original_event = event.get("event")
+    if original_event:
+        # Convert to snake_case
+        normalized = original_event.lower().replace(" ", "_").replace("-", "_")
+        event["event"] = f"app_{normalized}"
+    
+    return event
+```
+
+### 5. Log and drop in "dev" mode
+
+When running in local dev often, you want to log but drop all events
+
+
+```python
+from typing import Optional, Any
+
+def log_and_drop_all(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    """Convert event names to a consistent format."""
+    print(event)
+    
+    return None
+```
+
+### 6. Combined Processing
+
+```python
+from typing import Optional, Any
+
+def comprehensive_processor(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    """Apply multiple transformations in sequence."""
+    
+    # Step 1: Filter unwanted events
+    if should_drop_event(event):
+        return None
+    
+    # Step 2: Scrub PII
+    event = scrub_pii(event)
+    
+    # Step 3: Add context
+    event = add_context(event)
+    
+    # Step 4: Normalize names
+    event = normalize_event_names(event)
+    
+    return event
+
+def should_drop_event(event: dict[str, Any]) -> bool:
+    """Determine if event should be dropped."""
+    # Your filtering logic
+    return False
+```
+
+## Error Handling
+
+If your `before_send` function raises an exception, PostHog will:
+
+1. Log the error 
+2. Continue with the original, unmodified event
+3. Not crash your application
+
+```python
+from typing import Optional, Any
+
+def risky_before_send(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    # If this raises an exception, the original event will be sent
+    risky_operation()
+    return event
+```
+
+## Complete Example
+
+```python
+import posthog
+from typing import Optional, Any
+import re
+
+def production_before_send(event: dict[str, Any]) -> Optional[dict[str, Any]]:
+    try:
+        properties = event.get("properties", {})
+        
+        # 1. Filter out bot traffic
+        user_agent = properties.get("$user_agent", "")
+        if re.search(r'bot|crawler|spider', user_agent, re.I):
+            return None
+        
+        # 2. Filter out internal traffic  
+        ip = properties.get("$ip", "")
+        if ip.startswith("192.168.") or ip.startswith("10."):
+            return None
+        
+        # 3. Scrub email PII but keep domain
+        if "email" in properties:
+            email = properties["email"]
+            if "@" in email:
+                domain = email.split("@")[1]
+                properties["email"] = f"***@{domain}"
+        
+        # 4. Add custom context
+        properties.update({
+            "app_version": "1.0.0",
+            "build_number": "123"
+        })
+        
+        # 5. Normalize event name
+        if event.get("event"):
+            event["event"] = event["event"].lower().replace(" ", "_")
+        
+        return event
+        
+    except Exception as e:
+        # Log error but don't crash
+        print(f"Error in before_send: {e}")
+        return event  # Return original event on error
+
+# Usage
+client = posthog.Client(
+    api_key="your-api-key",
+    before_send=production_before_send
+)
+
+# All events will now be processed by your before_send function
+client.capture("user_123", "Page View", {"url": "/home"})
+```

CHANGELOG.md

@@ -0,0 +1,819 @@
+# posthog
+
+## 7.9.3 — 2026-02-18
+
+### Patch changes
+
+- [9f9553a](https://github.com/posthog/posthog-python/commit/9f9553a420d22e5e6435b775993f61a059280c2a) Fix posthoganalytics release, previously broken — Thanks @rafaeelaudibert!
+
+## 7.9.2 — 2026-02-18
+
+### Patch changes
+
+- [f1dc4d7](https://github.com/posthog/posthog-python/commit/f1dc4d73914712983a7f715ee4fe1b70e66e770a) Add sampo to the project — Thanks @rafaeelaudibert!
+
+## 7.9.1 - 2026-02-17
+
+fix(llma): make prompt fetches deterministic by requiring project_api_key and sending it as token query param
+
+## 7.9.0 - 2026-02-17
+
+feat: Support device_id as bucketing identifier for local evaluation
+
+## 7.8.6 - 2026-02-09
+
+fix: limit collections scanning in code variables
+
+## 7.8.5 - 2026-02-09
+
+fix: further optimize code variables pattern matching
+
+## 7.8.4 - 2026-02-09
+
+fix: do not pattern match long values in code variables
+
+## 7.8.3 - 2026-02-06
+
+fix: openAI input image sanitization
+
+## 7.8.2 - 2026-02-04
+
+fix(llma): fix prompts default url
+
+## 7.8.1 - 2026-02-03
+
+fix(llma): small fixes for prompt management
+
+## 7.8.0 - 2026-01-28
+
+feat(llma): add prompt management
+
+Adds the Prompt Management feature. At the time of release, this feature is in a closed alpha.
+
+## 7.7.0 - 2026-01-15
+
+feat(ai): Add OpenAI Agents SDK integration
+
+Automatic tracing for agent workflows, handoffs, tool calls, guardrails, and custom spans. Includes `$ai_total_tokens`, `$ai_error_type` categorization, and `$ai_framework` property.
+
+## 7.6.0 - 2026-01-12
+
+feat: add device_id to flags request payload
+
+Add device_id parameter to all feature flag methods, allowing the server to track device identifiers for flag evaluation. The device_id can be passed explicitly or set via context using `set_context_device_id()`.
+
+## 7.5.1 - 2026-01-07
+
+fix: avoid return from finally block to fix Python 3.14 SyntaxWarning (#361) - thanks @jodal
+
+## 7.5.0 - 2026-01-06
+
+feat: Capture Langchain, OpenAI and Anthropic errors as exceptions (if exception autocapture is enabled)
+feat: Add reference to exception in LLMA trace and span events
+
+## 7.4.3 - 2026-01-02
+
+Fixes cache creation cost for Langchain with Anthropic
+
+## 7.4.2 - 2025-12-22
+
+feat: add `in_app_modules` option to control code variables capturing
+
+## 7.4.1 - 2025-12-19
+
+fix: extract model from response for OpenAI stored prompts
+
+When using OpenAI stored prompts, the model is defined in the OpenAI dashboard rather than passed in the API request. This fix adds a fallback to extract the model from the response object when not provided in kwargs, ensuring generations show up with the correct model and enabling cost calculations.
+
+## 7.4.0 - 2025-12-16
+
+feat: Add automatic retries for feature flag requests
+
+Feature flag API requests now automatically retry on transient failures:
+
+- Network errors (connection refused, DNS failures, timeouts)
+- Server errors (500, 502, 503, 504)
+- Up to 2 retries with exponential backoff (0.5s, 1s delays)
+
+Rate limit (429) and quota (402) errors are not retried.
+
+## 7.3.1 - 2025-12-06
+
+fix: remove unused $exception_message and $exception_type
+
+## 7.3.0 - 2025-12-05
+
+feat: improve code variables capture masking
+
+## 7.2.0 - 2025-12-01
+
+feat: add $feature_flag_evaluated_at properties to $feature_flag_called events
+
+## 7.1.0 - 2025-11-26
+
+Add support for the async version of Gemini.
+
+## 7.0.2 - 2025-11-18
+
+Add support for Python 3.14.
+Projects upgrading to Python 3.14 should ensure any Pydantic models passed into the SDK use Pydantic v2, as Pydantic v1 is not compatible with Python 3.14.
+
+## 7.0.1 - 2025-11-15
+
+Try to use repr() when formatting code variables
+
+## 7.0.0 - 2025-11-11
+
+NB Python 3.9 is no longer supported
+
+- chore(llma): update LLM provider SDKs to latest major versions
+  - openai: 1.102.0 → 2.7.1
+  - anthropic: 0.64.0 → 0.72.0
+  - google-genai: 1.32.0 → 1.49.0
+  - langchain-core: 0.3.75 → 1.0.3
+  - langchain-openai: 0.3.32 → 1.0.2
+  - langchain-anthropic: 0.3.19 → 1.0.1
+  - langchain-community: 0.3.29 → 0.4.1
+  - langgraph: 0.6.6 → 1.0.2
+
+## 6.9.3 - 2025-11-10
+
+- feat(ph-ai): PostHog properties dict in GenerationMetadata
+
+## 6.9.2 - 2025-11-10
+
+- fix(llma): fix cache token double subtraction in Langchain for non-Anthropic providers causing negative costs
+
+## 6.9.1 - 2025-11-07
+
+- fix(error-tracking): pass code variables config from init to client
+
+## 6.9.0 - 2025-11-06
+
+- feat(error-tracking): add local variables capture
+
+## 6.8.0 - 2025-11-03
+
+- feat(llma): send web search calls to be used for LLM cost calculations
+
+## 6.7.14 - 2025-11-03
+
+- fix(django): Handle request.user access in async middleware context to prevent SynchronousOnlyOperation errors in Django 5+ (fixes #355)
+- test(django): Add Django 5 integration test suite with real ASGI application testing async middleware behavior
+
+## 6.7.13 - 2025-11-02
+
+- fix(llma): cache cost calculation in the LangChain callback
+
+## 6.7.12 - 2025-11-02
+
+- fix(django): Restore process_exception method to capture view and downstream middleware exceptions (fixes #329)
+- fix(ai/langchain): Add LangChain 1.0+ compatibility for CallbackHandler imports (fixes #362)
+
+## 6.7.11 - 2025-10-28
+
+- feat(ai): Add `$ai_framework` property for framework integrations (e.g. LangChain)
+
+## 6.7.10 - 2025-10-24
+
+- fix(django): Make middleware truly hybrid - compatible with both sync (WSGI) and async (ASGI) Django stacks without breaking sync-only deployments
+
+## 6.7.9 - 2025-10-22
+
+- fix(flags): multi-condition flags with static cohorts returning wrong variants
+
+## 6.7.8 - 2025-10-16
+
+- fix(llma): missing async for OpenAI's streaming implementation
+
+## 6.7.7 - 2025-10-14
+
+- fix: remove deprecated attribute $exception_personURL from exception events
+
+## 6.7.6 - 2025-09-16
+
+- fix: don't sort condition sets with variant overrides to the top
+- fix: Prevent core Client methods from raising exceptions
+
+## 6.7.5 - 2025-09-16
+
+- feat: Django middleware now supports async request handling.
+
+## 6.7.4 - 2025-09-05
+
+- fix: Missing system prompts for some providers
+
+## 6.7.3 - 2025-09-04
+
+- fix: missing usage tokens in Gemini
+
+## 6.7.2 - 2025-09-03
+
+- fix: tool call results in streaming providers
+
+## 6.7.1 - 2025-09-01
+
+- fix: Add base64 inline image sanitization
+
+## 6.7.0 - 2025-08-26
+
+- feat: Add support for feature flag dependencies
+
+## 6.6.1 - 2025-08-21
+
+- fix: Prevent `NoneType` error when `group_properties` is `None`
+
+## 6.6.0 - 2025-08-15
+
+- feat: Add `flag_keys_to_evaluate` parameter to optimize feature flag evaluation performance by only evaluating specified flags
+- feat: Add `flag_keys_filter` option to `send_feature_flags` for selective flag evaluation in capture events
+
+## 6.5.0 - 2025-08-08
+
+- feat: Add `$context_tags` to an event to know which properties were included as tags
+
+## 6.4.1 - 2025-08-06
+
+- fix: Always pass project API key in `remote_config` requests for deterministic project routing
+
+## 6.4.0 - 2025-08-05
+
+- feat: support Vertex AI for Gemini
+
+## 6.3.4 - 2025-08-04
+
+- fix: set `$ai_tools` for all providers and `$ai_output_choices` for all non-streaming provider flows properly
+
+## 6.3.3 - 2025-08-01
+
+- fix: `get_feature_flag_result` now correctly returns FeatureFlagResult when payload is empty string instead of None
+
+## 6.3.2 - 2025-07-31
+
+- fix: Anthropic's tool calls are now handled properly
+
+## 6.3.0 - 2025-07-22
+
+- feat: Enhanced `send_feature_flags` parameter to accept `SendFeatureFlagsOptions` object for declarative control over local/remote evaluation and custom properties
+
+## 6.2.1 - 2025-07-21
+
+- feat: make `posthog_client` an optional argument in PostHog AI providers wrappers (`posthog.ai.*`), intuitively using the default client as the default
+
+## 6.1.1 - 2025-07-16
+
+- fix: correctly capture exceptions processed by Django from views or middleware
+
+## 6.1.0 - 2025-07-10
+
+- feat: decouple feature flag local evaluation from personal API keys; support decrypting remote config payloads without relying on the feature flags poller
+
+## 6.0.4 - 2025-07-09
+
+- fix: add POSTHOG_MW_CLIENT setting to django middleware, to support custom clients for exception capture.
+
+## 6.0.3 - 2025-07-07
+
+- feat: add a feature flag evaluation cache (local storage or redis) to support returning flag evaluations when the service is down
+
+## 6.0.2 - 2025-07-02
+
+- fix: send_feature_flags changed to default to false in `Client::capture_exception`
+
+## 6.0.1
+
+- fix: response `$process_person_profile` property when passed to capture
+
+## 6.0.0
+
+This release contains a number of major breaking changes:
+
+- feat: make distinct_id an optional parameter in posthog.capture and related functions
+- feat: make capture and related functions return `Optional[str]`, which is the UUID of the sent event, if it was sent
+- fix: remove `identify` (prefer `posthog.set()`), and `page` and `screen` (prefer `posthog.capture()`)
+- fix: delete exception-capture specific integrations module. Prefer the general-purpose django middleware as a replacement for the django `Integration`.
+
+To migrate to this version, you'll mostly just need to switch to using named keyword arguments, rather than positional ones. For example:
+
+```python
+# Old calling convention
+posthog.capture("user123", "button_clicked", {"button_id": "123"})
+# New calling convention
+posthog.capture(distinct_id="user123", event="button_clicked", properties={"button_id": "123"})
+
+# Better pattern
+with posthog.new_context():
+    posthog.identify_context("user123")
+
+    # The event name is the first argument, and can be passed positionally, or as a keyword argument in a later position
+    posthog.capture("button_pressed")
+```
+
+Generally, arguments are now appropriately typed, and docstrings have been updated. If something is unclear, please open an issue, or submit a PR!
+
+## 5.4.0 - 2025-06-20
+
+- feat: add support to session_id context on page method
+
+## 5.3.0 - 2025-06-19
+
+- fix: safely handle exception values
+
+## 5.2.0 - 2025-06-19
+
+- feat: construct artificial stack traces if no traceback is available on a captured exception
+
+## 5.1.0 - 2025-06-18
+
+- feat: session and distinct ID's can now be associated with contexts, and are used as such
+- feat: django http request middleware
+
+## 5.0.0 - 2025-06-16
+
+- fix: removed deprecated sentry integration
+
+## 4.10.0 - 2025-06-13
+
+- fix: no longer fail in autocapture.
+
+## 4.9.0 - 2025-06-13
+
+- feat(ai): track reasoning and cache tokens in the LangChain callback
+
+## 4.8.0 - 2025-06-10
+
+- fix: export scoped, rather than tracked, decorator
+- feat: allow use of contexts without error tracking
+
+## 4.7.0 - 2025-06-10
+
+- feat: add support for parse endpoint in responses API (no longer beta)
+
+## 4.6.2 - 2025-06-09
+
+- fix: replace `import posthog` with direct method imports
+
+## 4.6.1 - 2025-06-09
+
+- fix: replace `import posthog` in `posthoganalytics` package
+
+## 4.6.0 - 2025-06-09
+
+- feat: add additional user and request context to captured exceptions via the Django integration
+- feat: Add `setup()` function to initialise default client
+
+## 4.5.0 - 2025-06-09
+
+- feat: add before_send callback (#249)
+
+## 4.4.2- 2025-06-09
+
+- empty point release to fix release automation
+
+## 4.4.1 2025-06-09
+
+- empty point release to fix release automation
+
+## 4.4.0 - 2025-06-09
+
+- Use the new `/flags` endpoint for all feature flag evaluations (don't fall back to `/decide` at all)
+
+## 4.3.2 - 2025-06-06
+
+1. Add context management:
+
+- New context manager with `posthog.new_context()`
+- Tag functions: `posthog.tag()`, `posthog.get_tags()`, `posthog.clear_tags()`
+- Function decorator:
+  - `@posthog.scoped` - Creates context and captures exceptions thrown within the function
+- Automatic deduplication of exceptions to ensure each exception is only captured once
+
+2. fix: feature flag request use geoip_disable (#235)
+3. chore: pin actions versions (#210)
+4. fix: opinionated setup and clean fn fix (#240)
+5. fix: release action failed (#241)
+
+## 4.2.0 - 2025-05-22
+
+Add support for google gemini
+
+## 4.1.0 - 2025-05-22
+
+Moved ai openai package to a composition approach over inheritance.
+
+## 4.0.1 – 2025-04-29
+
+1. Remove deprecated `monotonic` library. Use Python's core `time.monotonic` function instead
+2. Clarify Python 3.9+ is required
+
+## 4.0.0 - 2025-04-24
+
+1. Added new method `get_feature_flag_result` which returns a `FeatureFlagResult` object. This object breaks down the result of a feature flag into its enabled state, variant, and payload. The benefit of this method is it allows you to retrieve the result of a feature flag and its payload in a single API call. You can call `get_value` on the result to get the value of the feature flag, which is the same value returned by `get_feature_flag` (aka the string `variant` if the flag is a multivariate flag or the `boolean` value if the flag is a boolean flag).
+
+Example:
+
+```python
+result = posthog.get_feature_flag_result("my-flag", "distinct_id")
+print(result.enabled)     # True or False
+print(result.variant)     # 'the-variant-value' or None
+print(result.payload)     # {'foo': 'bar'}
+print(result.get_value()) # 'the-variant-value' or True or False
+print(result.reason)      # 'matched condition set 2' (Not available for local evaluation)
+```
+
+Breaking change:
+
+1. `get_feature_flag_payload` now deserializes payloads from JSON strings to `Any`. Previously, it returned the payload as a JSON encoded string.
+
+Before:
+
+```python
+payload = get_feature_flag_payload('key', 'distinct_id') # "{\"some\": \"payload\"}"
+```
+
+After:
+
+```python
+payload = get_feature_flag_payload('key', 'distinct_id') # {"some": "payload"}
+```
+
+## 3.25.0 – 2025-04-15
+
+1. Roll out new `/flags` endpoint to 100% of `/decide` traffic, excluding the top 10 customers.
+
+## 3.24.3 – 2025-04-15
+
+1. Fix hash inclusion/exclusion for flag rollout
+
+## 3.24.2 – 2025-04-15
+
+1. Roll out new /flags endpoint to 10% of /decide traffic
+
+## 3.24.1 – 2025-04-11
+
+1. Add `log_captured_exceptions` option to proxy setup
+
+## 3.24.0 – 2025-04-10
+
+1. Add config option to `log_captured_exceptions`
+
+## 3.23.0 – 2025-03-26
+
+1. Expand automatic retries to include read errors (e.g. RemoteDisconnected)
+
+## 3.22.0 – 2025-03-26
+
+1. Add more information to `$feature_flag_called` events.
+2. Support for the `/decide?v=4` endpoint which contains more information about feature flags.
+
+## 3.21.0 – 2025-03-17
+
+1. Support serializing dataclasses.
+
+## 3.20.0 – 2025-03-13
+
+1. Add support for OpenAI Responses API.
+
+## 3.19.2 – 2025-03-11
+
+1. Fix install requirements for analytics package
+
+## 3.19.1 – 2025-03-11
+
+1. Fix bug where None is sent as delta in azure
+
+## 3.19.0 – 2025-03-04
+
+1. Add support for tool calls in OpenAI and Anthropic.
+2. Add support for cached tokens.
+
+## 3.18.1 – 2025-03-03
+
+1. Improve quota-limited feature flag logs
+
+## 3.18.0 - 2025-02-28
+
+1. Add support for Azure OpenAI.
+
+## 3.17.0 - 2025-02-27
+
+1. The LangChain handler now captures tools in `$ai_generation` events, in property `$ai_tools`. This allows for displaying tools provided to the LLM call in PostHog UI. Note that support for `$ai_tools` in OpenAI and Anthropic SDKs is coming soon.
+
+## 3.16.0 - 2025-02-26
+
+1. feat: add some platform info to events (#198)
+
+## 3.15.1 - 2025-02-23
+
+1. Fix async client support for OpenAI.
+
+## 3.15.0 - 2025-02-19
+
+1. Support quota-limited feature flags
+
+## 3.14.2 - 2025-02-19
+
+1. Evaluate feature flag payloads with case sensitivity correctly. Fixes <https://github.com/PostHog/posthog-python/issues/178>
+
+## 3.14.1 - 2025-02-18
+
+1. Add support for Bedrock Anthropic Usage
+
+## 3.13.0 - 2025-02-12
+
+1. Automatically retry connection errors
+
+## 3.12.1 - 2025-02-11
+
+1. Fix mypy support for 3.12.0
+2. Deprecate `is_simple_flag`
+
+## 3.12.0 - 2025-02-11
+
+1. Add support for OpenAI beta parse API.
+2. Deprecate `context` parameter
+
+## 3.11.1 - 2025-02-06
+
+1. Fix LangChain callback handler to capture parent run ID.
+
+## 3.11.0 - 2025-01-28
+
+1. Add the `$ai_span` event to the LangChain callback handler to capture the input and output of intermediary chains.
+
+   > LLM observability naming change: event property `$ai_trace_name` is now `$ai_span_name`.
+
+2. Fix serialiazation of Pydantic models in methods.
+
+## 3.10.0 - 2025-01-24
+
+1. Add `$ai_error` and `$ai_is_error` properties to LangChain callback handler, OpenAI, and Anthropic.
+
+## 3.9.3 - 2025-01-23
+
+1. Fix capturing of multiple traces in the LangChain callback handler.
+
+## 3.9.2 - 2025-01-22
+
+1. Fix importing of LangChain callback handler under certain circumstances.
+
+## 3.9.0 - 2025-01-22
+
+1. Add `$ai_trace` event emission to LangChain callback handler.
+
+## 3.8.4 - 2025-01-17
+
+1. Add Anthropic support for LLM Observability.
+2. Update LLM Observability to use output_choices.
+
+## 3.8.3 - 2025-01-14
+
+1. Fix setuptools to include the `posthog.ai.openai` and `posthog.ai.langchain` packages for the `posthoganalytics` package.
+
+## 3.8.2 - 2025-01-14
+
+1. Fix setuptools to include the `posthog.ai.openai` and `posthog.ai.langchain` packages.
+
+## 3.8.1 - 2025-01-14
+
+1. Add LLM Observability with support for OpenAI and Langchain callbacks.
+
+## 3.7.5 - 2025-01-03
+
+1. Add `distinct_id` to group_identify
+
+## 3.7.4 - 2024-11-25
+
+1. Fix bug where this SDK incorrectly sent feature flag events with null values when calling `get_feature_flag_payload`.
+
+## 3.7.3 - 2024-11-25
+
+1. Use personless mode when sending an exception without a provided `distinct_id`.
+
+## 3.7.2 - 2024-11-19
+
+1. Add `type` property to exception stacks.
+
+## 3.7.1 - 2024-10-24
+
+1. Add `platform` property to each frame of exception stacks.
+
+## 3.7.0 - 2024-10-03
+
+1. Adds a new `super_properties` parameter on the client that are appended to every /capture call.
+
+## 3.6.7 - 2024-09-24
+
+1. Remove deprecated datetime.utcnow() in favour of datetime.now(tz=tzutc())
+
+## 3.6.6 - 2024-09-16
+
+1. Fix manual capture support for in app frames
+
+## 3.6.5 - 2024-09-10
+
+1. Fix django integration support for manual exception capture.
+
+## 3.6.4 - 2024-09-05
+
+1. Add manual exception capture.
+
+## 3.6.3 - 2024-09-03
+
+1. Make sure setup.py for posthoganalytics package also discovers the new exception integration package.
+
+## 3.6.2 - 2024-09-03
+
+1. Make sure setup.py discovers the new exception integration package.
+
+## 3.6.1 - 2024-09-03
+
+1. Adds django integration to exception autocapture in alpha state. This feature is not yet stable and may change in future versions.
+
+## 3.6.0 - 2024-08-28
+
+1. Adds exception autocapture in alpha state. This feature is not yet stable and may change in future versions.
+
+## 3.5.2 - 2024-08-21
+
+1. Guard for None values in local evaluation
+
+## 3.5.1 - 2024-08-13
+
+1. Remove "-api" suffix from ingestion hostnames
+
+## 3.5.0 - 2024-02-29
+
+1. - Adds a new `feature_flags_request_timeout_seconds` timeout parameter for feature flags which defaults to 3 seconds, updated from the default 10s for all other API calls.
+
+## 3.4.2 - 2024-02-20
+
+1. Add `historical_migration` option for bulk migration to PostHog Cloud.
+
+## 3.4.1 - 2024-02-09
+
+1. Use new hosts for event capture as well
+
+## 3.4.0 - 2024-02-05
+
+1. Point given hosts to new ingestion hosts
+
+## 3.3.4 - 2024-01-30
+
+1. Update type hints for module variables to work with newer versions of mypy
+
+## 3.3.3 - 2024-01-26
+
+1. Remove new relative date operators, combine into regular date operators
+
+## 3.3.2 - 2024-01-19
+
+1. Return success/failure with all capture calls from module functions
+
+## 3.3.1 - 2024-01-10
+
+1. Make sure we don't override any existing feature flag properties when adding locally evaluated feature flag properties.
+
+## 3.3.0 - 2024-01-09
+
+1. When local evaluation is enabled, we automatically add flag information to all events sent to PostHog, whenever possible. This makes it easier to use these events in experiments.
+
+## 3.2.0 - 2024-01-09
+
+1. Numeric property handling for feature flags now does the expected: When passed in a number, we do a numeric comparison. When passed in a string, we do a string comparison. Previously, we always did a string comparison.
+2. Add support for relative date operators for local evaluation.
+
+## 3.1.0 - 2023-12-04
+
+1. Increase maximum event size and batch size
+
+## 3.0.2 - 2023-08-17
+
+1. Returns the current flag property with $feature_flag_called events, to make it easier to use in experiments
+
+## 3.0.1 - 2023-04-21
+
+1. Restore how feature flags work when the client library is disabled: All requests return `None` and no events are sent when the client is disabled.
+2. Add a `feature_flag_definitions()` debug option, which returns currently loaded feature flag definitions. You can use this to more cleverly decide when to request local evaluation of feature flags.
+
+## 3.0.0 - 2023-04-14
+
+Breaking change:
+
+All events by default now send the `$geoip_disable` property to disable geoip lookup in app. This is because usually we don't
+want to update person properties to take the server's location.
+
+The same now happens for feature flag requests, where we discard the IP address of the server for matching on geoip properties like city, country, continent.
+
+To restore previous behaviour, you can set the default to False like so:
+
+```python
+posthog.disable_geoip = False
+
+# // and if using client instantiation:
+posthog = Posthog('api_key', disable_geoip=False)
+
+```
+
+## 2.5.0 - 2023-04-10
+
+1. Add option for instantiating separate client object
+
+## 2.4.2 - 2023-03-30
+
+1. Update backoff dependency for posthoganalytics package to be the same as posthog package
+
+## 2.4.1 - 2023-03-17
+
+1. Removes accidental print call left in for decide response
+
+## 2.4.0 - 2023-03-14
+
+1. Support evaluating all cohorts in feature flags for local evaluation
+
+## 2.3.1 - 2023-02-07
+
+1. Log instead of raise error on posthog personal api key errors
+2. Remove upper bound on backoff dependency
+
+## 2.3.0 - 2023-01-31
+
+1. Add support for returning payloads of matched feature flags
+
+## 2.2.0 - 2022-11-14
+
+Changes:
+
+1. Add support for feature flag variant overrides with local evaluation
+
+## 2.1.2 - 2022-09-15
+
+Changes:
+
+1. Fixes issues with date comparison.
+
+## 2.1.1 - 2022-09-14
+
+Changes:
+
+1. Feature flags local evaluation now supports date property filters as well. Accepts both strings and datetime objects.
+
+## 2.1.0 - 2022-08-11
+
+Changes:
+
+1. Feature flag defaults have been removed
+2. Setup logging only when debug mode is enabled.
+
+## 2.0.1 - 2022-08-04
+
+- Make poll_interval configurable
+- Add `send_feature_flag_events` parameter to feature flag calls, which determine whether the `$feature_flag_called` event should be sent or not.
+- Add `only_evaluate_locally` parameter to feature flag calls, which determines whether the feature flag should only be evaluated locally or not.
+
+## 2.0.0 - 2022-08-02
+
+Breaking changes:
+
+1. The minimum version requirement for PostHog servers is now 1.38. If you're using PostHog Cloud, you satisfy this requirement automatically.
+2. Feature flag defaults apply only when there's an error fetching feature flag results. Earlier, if the default was set to `True`, even if a flag resolved to `False`, the default would override this.
+   **Note: These are removed in 2.0.2**
+3. Feature flag remote evaluation doesn't require a personal API key.
+
+New Changes:
+
+1. You can now evaluate feature flags locally (i.e. without sending a request to your PostHog servers) by setting a personal API key, and passing in groups and person properties to `is_feature_enabled` and `get_feature_flag` calls.
+2. Introduces a `get_all_flags` method that returns all feature flags. This is useful for when you want to seed your frontend with some initial flags, given a user ID.
+
+## 1.4.9 - 2022-06-13
+
+- Support for sending feature flags with capture calls
+
+## 1.4.8 - 2022-05-12
+
+- Support multi variate feature flags
+
+## 1.4.7 - 2022-04-25
+
+- Allow feature flags usage without project_api_key
+
+## 1.4.1 - 2021-05-28
+
+- Fix packaging issues with Sentry integrations
+
+## 1.4.0 - 2021-05-18
+
+- Improve support for `project_api_key` (#32)
+- Resolve polling issues with feature flags (#29)
+- Add Sentry (and Sentry+Django) integrations (#13)
+- Fix feature flag issue with no percentage rollout (#30)
+
+## 1.3.1 - 2021-05-07
+
+- Add `$set` and `$set_once` support (#23)
+- Add distinct ID to `$create_alias` event (#27)
+- Add `UUID` to `ID_TYPES` (#26)
+
+## 1.2.1 - 2021-02-05
+
+Initial release logged in CHANGELOG.md.

CODEOWNERS

@@ -0,0 +1 @@
+@PostHog/team-feature-flags

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2020 PostHog (part of Hiberly Inc)
+Copyright (c) 2023 PostHog (part of Hiberly Inc)
 
 Copyright (c) 2013 Segment Inc. friends@segment.com
 
@@ -20,3 +20,29 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 
+---
+
+Some files in this codebase contain code from getsentry/sentry-javascript by Software, Inc. dba Sentry.
+In such cases it is explicitly stated in the file header. This license only applies to the relevant code in such cases.
+
+MIT License
+
+Copyright (c) 2012 Functional Software, Inc. dba Sentry
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

@@ -1,29 +1,70 @@
-test:
-	pylint --rcfile=.pylintrc --reports=y --exit-zero analytics | tee pylint.out
-	flake8 --max-complexity=10 --statistics analytics > flake8.out || true
-	coverage run --branch --include=analytics/\* --omit=*/test* setup.py test
+lint:
+	uvx ruff format
 
-release:
+test:
+	coverage run -m pytest
+	coverage report
+
+build_release:
 	rm -rf dist/*
 	python setup.py sdist bdist_wheel
-	twine upload dist/*
 
-release_analytics:
+# Builds the `posthoganalytics` PyPI package, which is a mirror of `posthog`
+# published under a different name for internal use by posthog/posthog.
+#
+# The process works in three phases:
+#   1. posthog -> posthoganalytics: Copy the source, rewrite all imports,
+#      remove the original posthog/ dir, and build the dist.
+#   2. posthoganalytics -> posthog: Reverse the import rewrites, copy
+#      everything back into posthog/, and clean up.
+#   3. Restore pyproject.toml from backup (setup_analytics.py modifies it).
+#
+# This ensures the working tree is left in the same state it started in.
+#
+# NOTE: This target clears dist/ before building. In the release workflow,
+# `build_release` (posthog) must be published BEFORE running this target,
+# otherwise the posthog dist artifacts will be lost.
+build_release_analytics:
 	rm -rf dist
 	rm -rf build
 	rm -rf posthoganalytics
 	mkdir posthoganalytics
 	cp -r posthog/* posthoganalytics/
-	find ./posthoganalytics -type f -exec sed -i '' -e 's/from posthog\./from posthoganalytics\./g' {} \;
+	find ./posthoganalytics -type f -name "*.py" -exec sed -i.bak -e 's/from posthog /from posthoganalytics /g' {} \;
+	find ./posthoganalytics -type f -name "*.py" -exec sed -i.bak -e 's/from posthog\./from posthoganalytics\./g' {} \;
+	find ./posthoganalytics -name "*.bak" -delete
 	rm -rf posthog
 	python setup_analytics.py sdist bdist_wheel
-	twine upload dist/*
 	mkdir posthog
-	find ./posthoganalytics -type f -exec sed -i '' -e 's/from posthoganalytics\./from posthog\./g' {} \;
+	find ./posthoganalytics -type f -name "*.py" -exec sed -i.bak -e 's/from posthoganalytics /from posthog /g' {} \;
+	find ./posthoganalytics -type f -name "*.py" -exec sed -i.bak -e 's/from posthoganalytics\./from posthog\./g' {} \;
+	find ./posthoganalytics -name "*.bak" -delete
 	cp -r posthoganalytics/* posthog/
 	rm -rf posthoganalytics
+	rm -f pyproject.toml
+	cp pyproject.toml.backup pyproject.toml
+	rm -f pyproject.toml.backup
 
 e2e_test:
 	.buildscripts/e2e.sh
 
-.PHONY: test release e2e_test
+prep_local:
+	rm -rf ../posthog-python-local
+	mkdir ../posthog-python-local
+	cp -r . ../posthog-python-local/
+	cd ../posthog-python-local && rm -rf dist build posthoganalytics .git
+	cd ../posthog-python-local && mkdir posthoganalytics
+	cd ../posthog-python-local && cp -r posthog/* posthoganalytics/
+	cd ../posthog-python-local && find ./posthoganalytics -type f -name "*.py" -exec sed -i.bak -e 's/from posthog /from posthoganalytics /g' {} \;
+	cd ../posthog-python-local && find ./posthoganalytics -type f -name "*.py" -exec sed -i.bak -e 's/from posthog\./from posthoganalytics\./g' {} \;
+	cd ../posthog-python-local && find ./posthoganalytics -name "*.bak" -delete
+	cd ../posthog-python-local && rm -rf posthog
+	cd ../posthog-python-local && sed -i.bak 's/from version import VERSION/from posthoganalytics.version import VERSION/' setup_analytics.py
+	cd ../posthog-python-local && rm setup_analytics.py.bak
+	cd ../posthog-python-local && sed -i.bak 's/"posthog"/"posthoganalytics"/' setup.py
+	cd ../posthog-python-local && rm setup.py.bak
+	cd ../posthog-python-local && python -c "import setup_analytics" 2>/dev/null || true
+	@echo "Local copy created at ../posthog-python-local"
+	@echo "Install with: pip install -e ../posthog-python-local"
+
+.PHONY: test lint build_release build_release_analytics e2e_test prep_local

README.md

@@ -1,120 +1,80 @@
 # PostHog Python
 
-Official PostHog Python library to capture and send events to any PostHog instance (including PostHog.com).
+<p align="center">
+  <img alt="posthoglogo" src="https://user-images.githubusercontent.com/65415371/205059737-c8a4f836-4889-4654-902e-f302b187b6a0.png">
+</p>
+<p align="center">
+   <a href="https://pypi.org/project/posthog/"><img alt="pypi installs" src="https://img.shields.io/pypi/v/posthog"/></a>
+   <img alt="GitHub contributors" src="https://img.shields.io/github/contributors/posthog/posthog-python">
+  <img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/m/posthog/posthog-python"/>
+  <img alt="GitHub closed issues" src="https://img.shields.io/github/issues-closed/posthog/posthog-python"/>
+</p>
 
-This library uses an internal queue to make calls non-blocking and fast. It also batches requests and flushes asynchronously, making it perfect to use in any part of your web app or other server side application that needs performance.
+Please see the [Python integration docs](https://posthog.com/docs/integrations/python-integration) for details.
 
-## Installation 
+## Python Version Support
+
+| SDK Version   | Python Versions Supported    | Notes                      |
+| ------------- | ---------------------------- | -------------------------- |
+| 7.3.1+        | 3.10, 3.11, 3.12, 3.13, 3.14 | Added Python 3.14 support  |
+| 7.0.0 - 7.0.1 | 3.10, 3.11, 3.12, 3.13       | Dropped Python 3.9 support |
+| 4.0.1 - 6.x   | 3.9, 3.10, 3.11, 3.12, 3.13  | Python 3.9+ required       |
+
+## Development
+
+### Testing Locally
+
+We recommend using [uv](https://docs.astral.sh/uv/). It's super fast.
+
+1. Run `uv venv env` (creates virtual environment called "env")
+   - or `python3 -m venv env`
+2. Run `source env/bin/activate` (activates the virtual environment)
+3. Run `uv sync --extra dev --extra test` (installs the package in develop mode, along with test dependencies)
+   - or `pip install -e ".[dev,test]"`
+4. you have to run `pre-commit install` to have auto linting pre commit
+5. Run `make test`
+6. To run a specific test do `pytest -k test_no_api_key`
+
+## PostHog recommends `uv` so...
 
 ```bash
-pip install posthog
+uv python install 3.12
+uv python pin 3.12
+uv venv
+source env/bin/activate
+uv sync --extra dev --extra test
+pre-commit install
+make test
 ```
 
-In your app, import the posthog library and set your api key **before** making any calls.
+### Running Locally
 
-```python
-import posthog
+Assuming you have a [local version of PostHog](https://posthog.com/docs/developing-locally) running, you can run `python3 example.py` to see the library in action.
 
-posthog.api_key = 'YOUR API KEY'
+### Testing changes locally with the PostHog app
+
+You can run `make prep_local`, and it'll create a new folder alongside the SDK repo one called `posthog-python-local`, which you can then import into the posthog project by changing pyproject.toml to look like this:
+
+```toml
+dependencies = [
+    ...
+    "posthoganalytics" #NOTE: no version number
+    ...
+]
+...
+[tools.uv.sources]
+posthoganalytics = { path = "../posthog-python-local" }
 ```
 
-You can find your key in the /setup page in PostHog.
+This'll let you build and test SDK changes fully locally, incorporating them into your local posthog app stack. It mainly takes care of the `posthog -> posthoganalytics` module renaming. You'll need to re-run `make prep_local` each time you make a change, and re-run `uv sync --active` in the posthog app project.
 
-To debug, you can set debug mode.
-```python
-posthog.debug = True
-```
+## Releasing
 
-## Making calls
+This repository uses [Sampo](https://github.com/bruits/sampo) for versioning, changelogs, and publishing to crates.io.
 
-### Capture
+1. When making changes, include a changeset: `sampo add`
+2. Create a PR with your changes and the changeset file
+3. Add the `release` label and merge to `main`
+4. Approve the release in Slack when prompted — this triggers version bump, crates.io publish, git tag, and GitHub Release
 
-Capture allows you to capture anything a user does within your system, which you can later use in PostHog to find patterns in usage, work out which features to improve or where people are giving up.
-
-A `capture` call requires
- - `distinct id` which uniquely identifies your user
- - `event name` to make sure 
-   - We recommend using [verb] [noun], like `movie played` or `movie updated` to easily identify what your events mean later on.
-
-Optionally you can submit
-- `properties`, which can be a dict with any information you'd like to add
-
-For example:
-```python
-posthog.capture('distinct id', 'movie played', {'movie_id': '123', 'category': 'romcom'})
-```
-
-### Identify
-Identify lets you add metadata on your users so you can more easily identify who they are in PostHog, and even do things like segment users by these properties.
-
-An `identify` call requires
-- `distinct id` which uniquely identifies your user
-- `properties` with a dict with any key: value pairs 
-
-For example:
-```python
-posthog.identify('distinct id', {
-    'email': 'dwayne@gmail.com',
-    'name': 'Dwayne Johnson'
-})
-```
-
-The most obvious place to make this call is whenever a user signs up, or when they update their information.
-
-### Alias
-
-To marry up whatever a user does before they sign up or log in with what they do after you need to make an alias call. This will allow you to answer questions like "Which marketing channels leads to users churning after a month?" or "What do users do on our website before signing up?"
-
-In a purely back-end implementation, this means whenever an anonymous user does something, you'll want to send a session ID ([Django](https://stackoverflow.com/questions/526179/in-django-how-can-i-find-out-the-request-session-sessionid-and-use-it-as-a-vari), [Flask](https://stackoverflow.com/questions/15156132/flask-login-how-to-get-session-id)) with the capture call. Then, when that users signs up, you want to do an alias call with the session ID and the newly created user ID.
-
-The same concept applies for when a user logs in.
-
-If you're using PostHog in the front-end and back-end, doing the `identify` call in the frontend will be enough.
-
-An `alias` call requires
-- `previous distinct id` the unique ID of the user before
-- `distinct id` the current unique id
-
-For example:
-```python
-posthog.alias('anonymous session id', 'distinct id')
-```
-
-## Django
-
-For Django, you can do the initialisation of the key in the AppConfig, so that it's available everywhere.
-
-in `yourapp/apps.py`
-```python
-from django.apps import AppConfig
-import posthog
-
-class YourAppConfig(AppConfig):
-    def ready(self):
-        posthog.api_key = 'your key'
-```
-
-Then, anywhere else in your app you can do
-```python
-import posthog
-
-def homepage(request):
-    # example capture
-    posthog.capture(request.session.session_key, 'page view', ....)
-```
-
-# Development
-
-## Naming confusion
-
-As our open source project [PostHog](https://github.com/PostHog/posthog) shares the same module name, we create a special `posthog-analytics` package, mostly for internal use to avoid module collision. It is the exact same.
-
-## How to release
-1. Increase `VERSION` in `posthog/version.py`
-2. run `make release` and `make release_analytics`
-3. `git commit -am "Release X.Y.Z."` (where X.Y.Z is the new version)
-4. `git tag -a X.Y.Z -m "Version X.Y.Z"` (where X.Y.Z is the new version).
-
-## Thank you
-
-This library is largely based on the `analytics-python` package.
+You can also trigger a release manually via the workflow's `workflow_dispatch` trigger (still requires pending changesets).

README_ANALYTICS.md

@@ -0,0 +1,11 @@
+# posthoganalytics
+
+> **Do not use this package.** Use [`posthog`](https://pypi.org/project/posthog/) instead.
+
+```bash
+pip install posthog
+```
+
+This package exists solely for internal use by [posthog/posthog](https://github.com/posthog/posthog) to avoid import conflicts with the local `posthog` package in that repository. It is an automatically generated mirror of `posthog` — same code, same versions, just published under a different name.
+
+If you are not working on the PostHog main repository, you should never need this package. All documentation, issues, and development happen in [`posthog-python`](https://github.com/posthog/posthog-python).

bin/build

@@ -0,0 +1,8 @@
+#!/usr/bin/env bash
+#/ Usage: bin/build
+#/ Description: Runs linter and mypy
+source bin/helpers/_utils.sh
+set_source_and_root_dir
+
+flake8 posthog --ignore E501,W503
+mypy --no-site-packages --config-file mypy.ini . | mypy-baseline filter

bin/docs

@@ -0,0 +1,8 @@
+#!/usr/bin/env bash
+#/ Usage: bin/docs
+#/ Description: Generate documentation for the PostHog Python SDK
+source bin/helpers/_utils.sh
+set_source_and_root_dir
+ensure_virtual_env
+
+exec python3 "$(dirname "$0")/docs_scripts/generate_json_schemas.py" "$@" 

bin/docs_scripts/doc_constant.py

@@ -0,0 +1,43 @@
+"""
+Constants for PostHog Python SDK documentation generation.
+"""
+
+from typing import Dict, Union
+from posthog.version import VERSION
+
+# Documentation generation metadata
+DOCUMENTATION_METADATA = {
+    "hogRef": "0.3",
+    "slugPrefix": "posthog-python",
+    "specUrl": "https://github.com/PostHog/posthog-python",
+}
+
+# Docstring parsing patterns for new format
+DOCSTRING_PATTERNS = {
+    "examples_section": r"Examples:\s*\n(.*?)(?=\n\s*\n\s*Category:|\Z)",
+    "args_section": r"Args:\s*\n(.*?)(?=\n\s*\n\s*Examples:|\n\s*\n\s*Details:|\n\s*\n\s*Category:|\Z)",
+    "details_section": r"Details:\s*\n(.*?)(?=\n\s*\n\s*Examples:|\n\s*\n\s*Category:|\Z)",
+    "category_section": r"Category:\s*\n\s*(.+?)\s*(?:\n|$)",
+    "code_block": r"```(?:python)?\n(.*?)```",
+    "param_description": r"^\s*{param_name}:\s*(.+?)(?=\n\s*\w+:|\Z)",
+    "args_marker": r"\n\s*Args:\s*\n",
+    "examples_marker": r"\n\s*Examples:\s*\n",
+    "details_marker": r"\n\s*Details:\s*\n",
+    "category_marker": r"\n\s*Category:\s*\n",
+}
+
+# Output file configuration
+OUTPUT_CONFIG: Dict[str, Union[str, int]] = {
+    "output_dir": "./references",
+    "filename": f"posthog-python-references-{VERSION}.json",
+    "filename_latest": "posthog-python-references-latest.json",
+    "indent": 2,
+}
+
+# Documentation structure defaults
+DOC_DEFAULTS = {
+    "showDocs": True,
+    "releaseTag": "public",
+    "return_type_void": "None",
+    "max_optional_params": 3,
+}

bin/docs_scripts/generate_json_schemas.py

@@ -0,0 +1,498 @@
+#!/usr/bin/env python3
+"""
+Generate comprehensive SDK documentation JSON from PostHog Python SDK.
+This script inspects the code and docstrings to create documentation in the specified format.
+"""
+
+import json
+import inspect
+import re
+from dataclasses import is_dataclass, fields
+from typing import get_origin, get_args, Union
+from textwrap import dedent
+from doc_constant import (
+    DOCUMENTATION_METADATA,
+    DOCSTRING_PATTERNS,
+    OUTPUT_CONFIG,
+    DOC_DEFAULTS,
+)
+import os
+
+
+def extract_examples_from_docstring(docstring: str) -> list:
+    """Extract code examples from docstring."""
+    if not docstring:
+        return []
+
+    examples = []
+
+    # Look for Examples section in the new format
+    examples_section_match = re.search(
+        DOCSTRING_PATTERNS["examples_section"], docstring, re.DOTALL
+    )
+    if examples_section_match:
+        examples_content = examples_section_match.group(1).strip()
+        # Extract code blocks from the Examples section
+        code_blocks = re.findall(
+            DOCSTRING_PATTERNS["code_block"], examples_content, re.DOTALL
+        )
+        for i, code_block in enumerate(code_blocks):
+            # Remove common leading whitespace while preserving relative indentation
+            code = dedent(code_block).strip()
+
+            # Extract name from first comment line if present
+            lines = code.split("\n")
+            name = f"Example {i + 1}"  # Default fallback
+
+            if lines and lines[0].strip().startswith("#"):
+                # Extract name from first comment, keep the comment in the code
+                comment_text = lines[0].strip()[1:].strip()
+                if comment_text:
+                    name = comment_text
+
+            examples.append({"id": f"example_{i + 1}", "name": name, "code": code})
+
+    return examples
+
+
+def extract_details_from_docstring(docstring: str) -> str:
+    """Extract details section from docstring."""
+    if not docstring:
+        return ""
+
+    # Look for Details section
+    details_match = re.search(
+        DOCSTRING_PATTERNS["details_section"], docstring, re.DOTALL
+    )
+    if details_match:
+        details_content = details_match.group(1).strip()
+        # Clean up formatting
+        return details_content.replace("\n", " ")
+
+    return ""
+
+
+def parse_docstring_tags(docstring: str) -> dict:
+    """Parse tags from docstring Category section."""
+    if not docstring:
+        return {}
+
+    tags = {}
+
+    # Extract Category section
+    category_match = re.search(DOCSTRING_PATTERNS["category_section"], docstring)
+    if category_match:
+        category_value = category_match.group(1).strip()
+        tags["category"] = category_value
+
+    return tags
+
+
+def extract_description_from_docstring(docstring: str) -> str:
+    """Extract main description from docstring."""
+    if not docstring:
+        return ""
+
+    # Clean up the docstring
+    cleaned = dedent(docstring).strip()
+
+    # Find the end of the description by looking for first section marker
+    # Check for Args:, Examples:, Details:, or Category: sections
+    section_patterns = [
+        DOCSTRING_PATTERNS["args_marker"],
+        DOCSTRING_PATTERNS["examples_marker"],
+        DOCSTRING_PATTERNS["details_marker"],
+        DOCSTRING_PATTERNS["category_marker"],
+    ]
+
+    end_pos = len(cleaned)
+    for pattern in section_patterns:
+        match = re.search(pattern, cleaned)
+        if match:
+            end_pos = min(end_pos, match.start())
+
+    # Extract description up to the first section marker
+    description = cleaned[:end_pos].strip()
+
+    # Remove one level of \n since it will be rendered as markdown
+    # and \n will be padded in later steps
+    description = description.replace("\n", " ")
+
+    return description
+
+
+def get_type_name(type_annotation) -> str:
+    """Convert type annotation to string name."""
+    if type_annotation is None or type_annotation is type(None):
+        return "any"
+
+    # Handle typing constructs
+    origin = get_origin(type_annotation)
+    if origin is not None:
+        # Handle Union types (including Optional)
+        if origin is Union:
+            args = get_args(type_annotation)
+            if len(args) == 2 and type(None) in args:
+                # This is Optional[Type] - get the non-None type
+                non_none_type = next(arg for arg in args if arg is not type(None))
+                return f"Optional[{get_type_name(non_none_type)}]"
+            else:
+                # Regular Union - list all types
+                type_names = [get_type_name(arg) for arg in args]
+                return f"Union[{', '.join(type_names)}]"
+
+        # Handle other generic types (List, Dict, etc.)
+        origin_name = getattr(origin, "__name__", str(origin))
+        args = get_args(type_annotation)
+        if args:
+            arg_names = [get_type_name(arg) for arg in args]
+            return f"{origin_name}[{', '.join(arg_names)}]"
+        else:
+            return origin_name
+
+    # Handle regular types
+    elif hasattr(type_annotation, "__name__"):
+        return type_annotation.__name__
+    else:
+        return str(type_annotation)
+
+
+def analyze_parameter(param: inspect.Parameter, docstring: str = "") -> dict:
+    """Analyze a function parameter and return its documentation."""
+    # Determine if parameter is optional (has default value)
+    is_optional = param.default == inspect.Parameter.empty
+
+    # Get the type annotation
+    type_annotation = param.annotation
+    param_type = "any"
+
+    if type_annotation != inspect.Parameter.empty:
+        # Handle Union/Optional types first
+        origin = get_origin(type_annotation)
+        if origin is Union:
+            args = get_args(type_annotation)
+            if len(args) == 2 and type(None) in args:
+                # This is Optional[Type]
+                non_none_type = next(arg for arg in args if arg is not type(None))
+                param_type = get_type_name(non_none_type)
+                is_optional = True
+            else:
+                # Other Union types, use first type
+                param_type = get_type_name(args[0]) if args else "any"
+        else:
+            param_type = get_type_name(type_annotation)
+    elif param.default != inspect.Parameter.empty:
+        # No type annotation, but has default value - infer type from default
+        param_type = get_type_name(type(param.default))
+
+    # Extract parameter description from Args section
+    param_description = ""
+    if docstring:
+        # Look for Args section and extract description for this parameter
+        args_section_match = re.search(
+            DOCSTRING_PATTERNS["args_section"], docstring, re.DOTALL
+        )
+        if args_section_match:
+            args_content = args_section_match.group(1)
+            # Look for the parameter description
+            param_pattern = DOCSTRING_PATTERNS["param_description"].format(
+                param_name=re.escape(param.name)
+            )
+            param_match = re.search(
+                param_pattern, args_content, re.MULTILINE | re.DOTALL
+            )
+            if param_match:
+                param_description = param_match.group(1).strip().replace("\n", " ")
+
+    param_info = {
+        "name": param.name,
+        "description": param_description,
+        "isOptional": is_optional,
+        "type": param_type,
+    }
+
+    return param_info
+
+
+def analyze_function(func, name: str) -> dict:
+    """Analyze a function and return its documentation."""
+    try:
+        sig = inspect.signature(func)
+        docstring = inspect.getdoc(func) or ""
+
+        # Skip functions with empty docstrings
+        if not docstring.strip():
+            return {}
+
+        # Extract parameters (excluding 'self')
+        params = []
+        for param_name, param in sig.parameters.items():
+            if param_name != "self":
+                params.append(analyze_parameter(param, docstring))
+
+        # Special handling for constructor
+        display_name = name
+        if name == "__init__":
+            display_name = func.__qualname__.split(".")[0]
+
+        # Parse tags from docstring
+        tags = parse_docstring_tags(docstring)
+
+        category = tags.get("category", None)
+
+        # Extract description
+        description = extract_description_from_docstring(docstring)
+
+        # Skip if no meaningful description
+        if not description.strip():
+            return {}
+
+        # Extract details section (only if it exists)
+        details = extract_details_from_docstring(docstring)
+
+        # Get examples from docstring, do not generate fallback examples
+        examples = extract_examples_from_docstring(docstring)
+        # If no examples, do not include the examples key or set to empty list
+
+        result = {
+            "id": name,
+            "title": display_name,
+            "description": description,
+            "details": details,
+            "category": category,
+            "params": params,
+            "showDocs": DOC_DEFAULTS["showDocs"],
+            "releaseTag": DOC_DEFAULTS["releaseTag"],
+            "returnType": {
+                "id": "return_type",
+                "name": get_type_name(sig.return_annotation)
+                if sig.return_annotation != inspect.Signature.empty
+                else DOC_DEFAULTS["return_type_void"],
+            },
+        }
+        if examples:
+            result["examples"] = examples
+        return result
+    except Exception as e:
+        print(f"Error analyzing function {name}: {e}")
+        return {}
+
+
+def analyze_class(cls) -> dict:
+    """Analyze a class and return its documentation."""
+    class_doc = inspect.getdoc(cls) or f"Class: {cls.__name__}"
+
+    # Get all public methods and constructor
+    functions = []
+    for method_name in dir(cls):
+        if method_name.startswith("_") and method_name != "__init__":
+            continue
+
+        method = getattr(cls, method_name)
+        if callable(method):
+            func_info = analyze_function(method, method_name)
+            if func_info:  # Only add if not None (empty docstring check)
+                functions.append(func_info)
+
+    return {
+        "id": cls.__name__,
+        "title": cls.__name__,
+        "description": extract_description_from_docstring(class_doc),
+        "functions": functions,
+    }
+
+
+def analyze_type(cls) -> dict:
+    """Analyze a type/dataclass and return its documentation."""
+    type_info = {
+        "id": cls.__name__,
+        "name": cls.__name__,
+        "path": f"{cls.__module__}.{cls.__name__}",
+        "properties": [],
+        "example": "",
+    }
+
+    if is_dataclass(cls):
+        # Handle dataclass
+        for field in fields(cls):
+            prop = {
+                "name": field.name,
+                "type": get_type_name(field.type),
+                "description": f"Field: {field.name}",
+            }
+            type_info["properties"].append(prop)
+    elif hasattr(cls, "__annotations__"):
+        # Handle TypedDict or annotated class
+        for field_name, field_type in cls.__annotations__.items():
+            prop = {
+                "name": field_name,
+                "type": get_type_name(field_type),
+                "description": f"Field: {field_name}",
+            }
+            type_info["properties"].append(prop)
+
+    return type_info
+
+
+def generate_sdk_documentation():
+    """Generate complete SDK documentation in the requested format."""
+
+    # Import PostHog components
+    import posthog
+    from posthog.client import Client
+    import posthog.types as types_module
+    import posthog.args as args_module
+    from posthog.version import VERSION
+
+    # Main SDK info
+    sdk_info = {
+        "version": VERSION,
+        "id": "posthog-python",
+        "title": "PostHog Python SDK",
+        "description": "Integrate PostHog into any python application.",
+        "slugPrefix": DOCUMENTATION_METADATA["slugPrefix"],
+        "specUrl": DOCUMENTATION_METADATA["specUrl"],
+    }
+
+    # Collect types
+    types_list = []
+
+    # Types from posthog.types
+    for name in dir(types_module):
+        obj = getattr(types_module, name)
+        if inspect.isclass(obj) and not name.startswith("_"):
+            try:
+                type_info = analyze_type(obj)
+                types_list.append(type_info)
+            except Exception as e:
+                print(f"Error analyzing type {name}: {e}")
+
+    # Types from posthog.args
+    for name in dir(args_module):
+        obj = getattr(args_module, name)
+        if inspect.isclass(obj) and not name.startswith("_"):
+            try:
+                type_info = analyze_type(obj)
+                types_list.append(type_info)
+            except Exception as e:
+                print(f"Error analyzing type {name}: {e}")
+
+    # Clean types of empty types
+
+    # Remove types that have no properties and no examples
+    # Remove types that have no properties and no examples
+    types_list = [
+        t for t in types_list if len(t["properties"]) > 0 or t["example"] != ""
+    ]
+
+    # Collect classes
+    classes_list = []
+
+    # Main PostHog class (renamed from Client)
+    client_class = analyze_class(Client)
+    client_class["id"] = "PostHog"
+    client_class["title"] = "PostHog"
+    classes_list.append(client_class)
+
+    # Global module functions (functions callable as posthog.function_name)
+    global_functions = []
+    for func_name in dir(posthog):
+        # Skip private functions and non-callables
+        if func_name.startswith("_") or not callable(getattr(posthog, func_name)):
+            continue
+
+        func = getattr(posthog, func_name)
+        # Only include functions actually defined in the posthog module (not imported)
+        # and exclude class references
+        if (
+            func_name not in ["Client", "Posthog"]
+            and hasattr(func, "__module__")
+            and func.__module__ == "posthog"
+        ):
+            try:
+                func_info = analyze_function(func, func_name)
+                if func_info:  # Only add if not None (has proper docstring)
+                    global_functions.append(func_info)
+            except Exception:
+                continue
+
+    # Add global functions as a "class"
+    if global_functions:
+        classes_list.append(
+            {
+                "id": "PostHogModule",
+                "title": "PostHog Module Functions",
+                "description": "Global functions available in the PostHog module",
+                "functions": global_functions,
+            }
+        )
+
+    # Collect categories from functions
+    categories = ["Initialization", "Identification", "Capture"]
+    seen_categories = set(categories)
+    for class_info in classes_list:
+        if "functions" in class_info:
+            for func in class_info["functions"]:
+                if (
+                    "category" in func
+                    and func["category"] not in seen_categories
+                    and func["category"]
+                ):
+                    categories.append(func["category"])
+                    seen_categories.add(func["category"])
+
+    # Create the final structure
+    result = {
+        "id": "posthog-python",
+        "hogRef": DOCUMENTATION_METADATA["hogRef"],
+        "info": sdk_info,
+        "types": types_list,
+        "classes": classes_list,
+        "categories": categories,
+    }
+
+    return result
+
+
+if __name__ == "__main__":
+    print("Generating PostHog Python SDK documentation...")
+
+    try:
+        documentation = generate_sdk_documentation()
+
+        # Ensure output directory exists
+        output_dir = str(OUTPUT_CONFIG["output_dir"])
+        os.makedirs(output_dir, exist_ok=True)
+
+        output_file = os.path.join(
+            str(OUTPUT_CONFIG["output_dir"]), str(OUTPUT_CONFIG["filename"])
+        )
+        output_file_latest = os.path.join(
+            str(OUTPUT_CONFIG["output_dir"]), str(OUTPUT_CONFIG["filename_latest"])
+        )
+
+        # Write to current version
+        with open(output_file, "w") as f:
+            json.dump(documentation, f, indent=int(OUTPUT_CONFIG["indent"]))
+        # Write to latest
+        with open(output_file_latest, "w") as f:
+            json.dump(documentation, f, indent=int(OUTPUT_CONFIG["indent"]))
+
+        print(f"✓ Generated {output_file}")
+
+        # Print summary
+        types_count = len(documentation["types"])
+        classes_count = len(documentation["classes"])
+
+        total_functions = sum(len(cls["functions"]) for cls in documentation["classes"])
+
+        print("📊 Documentation Summary:")
+        print(f"   • {types_count} types documented")
+        print(f"   • {classes_count} classes documented")
+        print(f"   • {total_functions} functions documented")
+
+    except Exception as e:
+        print(f"❌ Error generating documentation: {e}")
+        import traceback
+
+        traceback.print_exc()

bin/fmt

@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+#/ Usage: bin/fmt
+#/ Description: Formats and lints the code
+source bin/helpers/_utils.sh
+set_source_and_root_dir
+ensure_virtual_env
+
+if [[ "$1" == "--check" ]]; then
+  ruff format --check .
+else
+  ruff format .
+fi

bin/helpers/_utils.sh

@@ -0,0 +1,26 @@
+error() {
+    echo "$@" >&2
+}
+
+fatal() {
+    error "$@"
+    exit 1
+}
+
+set_source_and_root_dir() {
+    { set +x; } 2>/dev/null
+    source_dir="$( cd -P "$( dirname "$0" )" >/dev/null 2>&1 && pwd )"
+    root_dir=$(cd "$source_dir" && cd ../ && pwd)
+    cd "$root_dir"
+}
+
+ensure_virtual_env() {
+    if [ -z "$VIRTUAL_ENV" ]; then
+        echo "Virtual environment not activated. Activating now..."
+        if [ ! -f env/bin/activate ]; then
+            echo "Virtual environment not found. Please run 'python -m venv env' first."
+            exit 1
+        fi
+        source env/bin/activate
+    fi
+}

bin/setup

@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+#/ Usage: bin/setup
+#/ Description: Sets up the dependencies needed to develop this project
+source bin/helpers/_utils.sh
+set_source_and_root_dir
+
+if [ ! -d "env" ]; then
+  python3 -m venv env
+fi
+
+source env/bin/activate
+pip install -e ".[dev,test]"

bin/test

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+#/ Usage: bin/test
+#/ Description: Runs all the unit tests for this project
+source bin/helpers/_utils.sh
+set_source_and_root_dir
+
+ensure_virtual_env
+
+# Pass through all arguments to pytest
+pytest "$@"

example.py

@@ -1,20 +1,508 @@
 # PostHog Python library example
+#
+# This script demonstrates various PostHog Python SDK capabilities including:
+# - Basic event capture and user identification
+# - Feature flag local evaluation
+# - Feature flag payloads
+# - Context management and tagging
+#
+# Setup:
+# 1. Copy .env.example to .env and fill in your PostHog credentials
+# 2. Run this script and choose from the interactive menu
+
+import os
 
-# Import the library
 import posthog
 
-# You can find this key on the /setup page in PostHog
-posthog.api_key = '<your key>'
 
-# Where you host PostHog, with no trailing /.
-# You can remove this line if you're using posthog.com
-posthog.host = 'http://127.0.0.1:8000' 
+def load_env_file():
+    """Load environment variables from .env file if it exists."""
+    env_path = os.path.join(os.path.dirname(__file__), ".env")
+    if os.path.exists(env_path):
+        with open(env_path, "r") as f:
+            for line in f:
+                line = line.strip()
+                if line and not line.startswith("#") and "=" in line:
+                    key, value = line.split("=", 1)
+                    os.environ.setdefault(key.strip(), value.strip())
 
-# Capture an event
-posthog.capture('distinct_id', 'event', {'property1': 'value', 'property2': 'value'})
 
-# Alias a previous distinct id with a new one
-posthog.alias('distinct_id', 'new_distinct_id')
+# Load .env file if it exists
+load_env_file()
 
-# Add properties to the person
-posthog.identify('distinct_id', {'email': 'something@something.com'})
+# Get configuration
+project_key = os.getenv("POSTHOG_PROJECT_API_KEY", "")
+personal_api_key = os.getenv("POSTHOG_PERSONAL_API_KEY", "")
+host = os.getenv("POSTHOG_HOST", "http://localhost:8000")
+
+# Check if project key is provided (required)
+if not project_key:
+    print("❌ Missing PostHog project API key!")
+    print("   Please set POSTHOG_PROJECT_API_KEY environment variable")
+    print("   or copy .env.example to .env and fill in your values")
+    exit(1)
+
+# Configure PostHog with credentials
+posthog.debug = False
+posthog.api_key = project_key
+posthog.project_api_key = project_key
+posthog.host = host
+posthog.poll_interval = 10
+
+# Check if personal API key is available for local evaluation
+local_eval_available = bool(personal_api_key)
+if personal_api_key:
+    posthog.personal_api_key = personal_api_key
+
+print("🔑 PostHog Configuration:")
+print(f"   Project API Key: {project_key[:9]}...")
+if local_eval_available:
+    print("   Personal API Key: [SET]")
+else:
+    print("   Personal API Key: [NOT SET] - Local evaluation examples will be skipped")
+print(f"   Host: {host}\n")
+
+# Display menu and get user choice
+print("🚀 PostHog Python SDK Demo - Choose an example to run:\n")
+print("1. Identify and capture examples")
+local_eval_note = "" if local_eval_available else " [requires personal API key]"
+print(f"2. Feature flag local evaluation examples{local_eval_note}")
+print("3. Feature flag payload examples")
+print(f"4. Flag dependencies examples{local_eval_note}")
+print("5. Context management and tagging examples")
+print("6. Run all examples")
+print("7. Exit")
+choice = input("\nEnter your choice (1-7): ").strip()
+
+if choice == "1":
+    print("\n" + "=" * 60)
+    print("IDENTIFY AND CAPTURE EXAMPLES")
+    print("=" * 60)
+
+    posthog.debug = True
+
+    # Capture an event
+    print("📊 Capturing events...")
+    posthog.capture(
+        "event",
+        distinct_id="distinct_id",
+        properties={"property1": "value", "property2": "value"},
+        send_feature_flags=True,
+    )
+
+    # Alias a previous distinct id with a new one
+    print("🔗 Creating alias...")
+    posthog.alias("distinct_id", "new_distinct_id")
+
+    posthog.capture(
+        "event2",
+        distinct_id="new_distinct_id",
+        properties={"property1": "value", "property2": "value"},
+    )
+    posthog.capture(
+        "event-with-groups",
+        distinct_id="new_distinct_id",
+        properties={"property1": "value", "property2": "value"},
+        groups={"company": "id:5"},
+    )
+
+    # Add properties to the person
+    print("👤 Identifying user...")
+    posthog.set(
+        distinct_id="new_distinct_id", properties={"email": "something@something.com"}
+    )
+
+    # Add properties to a group
+    print("🏢 Identifying group...")
+    posthog.group_identify("company", "id:5", {"employees": 11})
+
+    # Properties set only once to the person
+    print("🔒 Setting properties once...")
+    posthog.set_once(
+        distinct_id="new_distinct_id", properties={"self_serve_signup": True}
+    )
+
+    # This will not change the property (because it was already set)
+    posthog.set_once(
+        distinct_id="new_distinct_id", properties={"self_serve_signup": False}
+    )
+
+    print("🔄 Updating properties...")
+    posthog.set(distinct_id="new_distinct_id", properties={"current_browser": "Chrome"})
+    posthog.set(
+        distinct_id="new_distinct_id", properties={"current_browser": "Firefox"}
+    )
+
+elif choice == "2":
+    if not local_eval_available:
+        print("\n❌ This example requires a personal API key for local evaluation.")
+        print(
+            "   Set POSTHOG_PERSONAL_API_KEY environment variable to run this example."
+        )
+        posthog.shutdown()
+        exit(1)
+
+    print("\n" + "=" * 60)
+    print("FEATURE FLAG LOCAL EVALUATION EXAMPLES")
+    print("=" * 60)
+
+    posthog.debug = True
+
+    print("🏁 Testing basic feature flags...")
+    print(
+        f"beta-feature for 'distinct_id': {posthog.feature_enabled('beta-feature', 'distinct_id')}"
+    )
+    print(
+        f"beta-feature for 'new_distinct_id': {posthog.feature_enabled('beta-feature', 'new_distinct_id')}"
+    )
+    print(
+        f"beta-feature with groups: {posthog.feature_enabled('beta-feature-groups', 'distinct_id', groups={'company': 'id:5'})}"
+    )
+
+    print("\n🌍 Testing location-based flags...")
+    # Assume test-flag has `City Name = Sydney` as a person property set
+    print(
+        f"Sydney user: {posthog.feature_enabled('test-flag', 'random_id_12345', person_properties={'$geoip_city_name': 'Sydney'})}"
+    )
+
+    print(
+        f"Sydney user (local only): {posthog.feature_enabled('test-flag', 'distinct_id_random_22', person_properties={'$geoip_city_name': 'Sydney'}, only_evaluate_locally=True)}"
+    )
+
+    print("\n📋 Getting all flags...")
+    print(f"All flags: {posthog.get_all_flags('distinct_id_random_22')}")
+    print(
+        f"All flags (local): {posthog.get_all_flags('distinct_id_random_22', only_evaluate_locally=True)}"
+    )
+    print(
+        f"All flags with properties: {posthog.get_all_flags('distinct_id_random_22', person_properties={'$geoip_city_name': 'Sydney'}, only_evaluate_locally=True)}"
+    )
+
+elif choice == "3":
+    print("\n" + "=" * 60)
+    print("FEATURE FLAG PAYLOAD EXAMPLES")
+    print("=" * 60)
+
+    posthog.debug = True
+
+    print("📦 Testing feature flag payloads...")
+    print(
+        f"beta-feature payload: {posthog.get_feature_flag_payload('beta-feature', 'distinct_id')}"
+    )
+    print(
+        f"All flags and payloads: {posthog.get_all_flags_and_payloads('distinct_id')}"
+    )
+    print(
+        f"Remote config payload: {posthog.get_remote_config_payload('encrypted_payload_flag_key')}"
+    )
+
+    # Get feature flag result with all details (enabled, variant, payload, key, reason)
+    print("\n🔍 Getting detailed flag result...")
+    result = posthog.get_feature_flag_result("beta-feature", "distinct_id")
+    if result:
+        print(f"Flag key: {result.key}")
+        print(f"Flag enabled: {result.enabled}")
+        print(f"Variant: {result.variant}")
+        print(f"Payload: {result.payload}")
+        print(f"Reason: {result.reason}")
+        # get_value() returns the variant if it exists, otherwise the enabled value
+        print(f"Value (variant or enabled): {result.get_value()}")
+
+elif choice == "4":
+    if not local_eval_available:
+        print("\n❌ This example requires a personal API key for local evaluation.")
+        print(
+            "   Set POSTHOG_PERSONAL_API_KEY environment variable to run this example."
+        )
+        posthog.shutdown()
+        exit(1)
+
+    print("\n" + "=" * 60)
+    print("FLAG DEPENDENCIES EXAMPLES")
+    print("=" * 60)
+    print("🔗 Testing flag dependencies with local evaluation...")
+    print(
+        "   Flag structure: 'test-flag-dependency' depends on 'beta-feature' being enabled"
+    )
+    print("")
+    print("📋 Required setup (if 'test-flag-dependency' doesn't exist):")
+    print("   1. Create feature flag 'beta-feature':")
+    print("      - Condition: email contains '@example.com'")
+    print("      - Rollout: 100%")
+    print("   2. Create feature flag 'test-flag-dependency':")
+    print("      - Condition: flag 'beta-feature' is enabled")
+    print("      - Rollout: 100%")
+    print("")
+
+    posthog.debug = True
+
+    # Test @example.com user (should satisfy dependency if flags exist)
+    result1 = posthog.feature_enabled(
+        "test-flag-dependency",
+        "example_user",
+        person_properties={"email": "user@example.com"},
+        only_evaluate_locally=True,
+    )
+    print(f"✅ @example.com user (test-flag-dependency): {result1}")
+
+    # Test non-example.com user (dependency should not be satisfied)
+    result2 = posthog.feature_enabled(
+        "test-flag-dependency",
+        "regular_user",
+        person_properties={"email": "user@other.com"},
+        only_evaluate_locally=True,
+    )
+    print(f"❌ Regular user (test-flag-dependency): {result2}")
+
+    # Test beta-feature directly for comparison
+    beta1 = posthog.feature_enabled(
+        "beta-feature",
+        "example_user",
+        person_properties={"email": "user@example.com"},
+        only_evaluate_locally=True,
+    )
+    beta2 = posthog.feature_enabled(
+        "beta-feature",
+        "regular_user",
+        person_properties={"email": "user@other.com"},
+        only_evaluate_locally=True,
+    )
+    print(f"📊 Beta feature comparison - @example.com: {beta1}, regular: {beta2}")
+
+    print("\n🎯 Results Summary:")
+    print(
+        f"   - Flag dependencies evaluated locally: {'✅ YES' if result1 != result2 else '❌ NO'}"
+    )
+    print("   - Zero API calls needed: ✅ YES (all evaluated locally)")
+    print("   - Python SDK supports flag dependencies: ✅ YES")
+
+    print("\n" + "-" * 60)
+    print("PRODUCTION-STYLE MULTIVARIATE DEPENDENCY CHAIN")
+    print("-" * 60)
+    print("🔗 Testing complex multivariate flag dependencies...")
+    print(
+        "   Structure: multivariate-root-flag -> multivariate-intermediate-flag -> multivariate-leaf-flag"
+    )
+    print("")
+    print("📋 Required setup (if flags don't exist):")
+    print(
+        "   1. Create 'multivariate-leaf-flag' with fruit variants (pineapple, mango, papaya, kiwi)"
+    )
+    print("      - pineapple: email = 'pineapple@example.com'")
+    print("      - mango: email = 'mango@example.com'")
+    print(
+        "   2. Create 'multivariate-intermediate-flag' with color variants (blue, red)"
+    )
+    print("      - blue: depends on multivariate-leaf-flag = 'pineapple'")
+    print("      - red: depends on multivariate-leaf-flag = 'mango'")
+    print(
+        "   3. Create 'multivariate-root-flag' with show variants (breaking-bad, the-wire)"
+    )
+    print("      - breaking-bad: depends on multivariate-intermediate-flag = 'blue'")
+    print("      - the-wire: depends on multivariate-intermediate-flag = 'red'")
+    print("")
+
+    # Test pineapple -> blue -> breaking-bad chain
+    dependent_result3 = posthog.get_feature_flag(
+        "multivariate-root-flag",
+        "regular_user",
+        person_properties={"email": "pineapple@example.com"},
+        only_evaluate_locally=True,
+    )
+    if str(dependent_result3) != "breaking-bad":
+        print(
+            f"     ❌ Something went wrong evaluating 'multivariate-root-flag' with pineapple@example.com. Expected 'breaking-bad', got '{dependent_result3}'"
+        )
+    else:
+        print("✅ 'multivariate-root-flag' with email pineapple@example.com succeeded")
+
+    # Test mango -> red -> the-wire chain
+    dependent_result4 = posthog.get_feature_flag(
+        "multivariate-root-flag",
+        "regular_user",
+        person_properties={"email": "mango@example.com"},
+        only_evaluate_locally=True,
+    )
+    if str(dependent_result4) != "the-wire":
+        print(
+            f"     ❌ Something went wrong evaluating multivariate-root-flag with mango@example.com. Expected 'the-wire', got '{dependent_result4}'"
+        )
+    else:
+        print("✅ 'multivariate-root-flag' with email mango@example.com succeeded")
+
+    # Show the complete chain evaluation
+    print("\n🔍 Complete dependency chain evaluation:")
+    for email, expected_chain in [
+        ("pineapple@example.com", ["pineapple", "blue", "breaking-bad"]),
+        ("mango@example.com", ["mango", "red", "the-wire"]),
+    ]:
+        leaf = posthog.get_feature_flag(
+            "multivariate-leaf-flag",
+            "regular_user",
+            person_properties={"email": email},
+            only_evaluate_locally=True,
+        )
+        intermediate = posthog.get_feature_flag(
+            "multivariate-intermediate-flag",
+            "regular_user",
+            person_properties={"email": email},
+            only_evaluate_locally=True,
+        )
+        root = posthog.get_feature_flag(
+            "multivariate-root-flag",
+            "regular_user",
+            person_properties={"email": email},
+            only_evaluate_locally=True,
+        )
+
+        actual_chain = [str(leaf), str(intermediate), str(root)]
+        chain_success = actual_chain == expected_chain
+
+        print(f"   📧 {email}:")
+        print(f"      Expected: {' -> '.join(map(str, expected_chain))}")
+        print(f"      Actual:   {' -> '.join(map(str, actual_chain))}")
+        print(f"      Status:   {'✅ SUCCESS' if chain_success else '❌ FAILED'}")
+
+    print("\n🎯 Multivariate Chain Summary:")
+    print("   - Complex dependency chains: ✅ SUPPORTED")
+    print("   - Multivariate flag dependencies: ✅ SUPPORTED")
+    print("   - Local evaluation of chains: ✅ WORKING")
+
+elif choice == "5":
+    print("\n" + "=" * 60)
+    print("CONTEXT MANAGEMENT AND TAGGING EXAMPLES")
+    print("=" * 60)
+
+    posthog.debug = True
+
+    print("🏷️ Testing context management...")
+    print(
+        "You can add tags to a context, and these are automatically added to any events captured within that context."
+    )
+
+    # You can enter a new context using a with statement. Any exceptions thrown in the context will be captured,
+    # and tagged with the context tags. Other events captured will also be tagged with the context tags. By default,
+    # the new context inherits tags from the parent context.
+    try:
+        with posthog.new_context():
+            posthog.tag("transaction_id", "abc123")
+            posthog.tag("some_arbitrary_value", {"tags": "can be dicts"})
+
+            # This event will be captured with the tags set above
+            posthog.capture("order_processed")
+            print("✅ Event captured with inherited context tags")
+            # This exception will be captured with the tags set above
+            # raise Exception("Order processing failed")
+    except Exception as e:
+        print(f"Exception captured: {e}")
+
+    # Use fresh=True to start with a clean context (no inherited tags)
+    try:
+        with posthog.new_context(fresh=True):
+            posthog.tag("session_id", "xyz789")
+            # Only session_id tag will be present, no inherited tags
+            posthog.capture("session_event")
+            print("✅ Event captured with fresh context tags")
+            # raise Exception("Session handling failed")
+    except Exception as e:
+        print(f"Exception captured: {e}")
+
+    # You can also use the `@posthog.scoped()` decorator to enter a new context.
+    # By default, it inherits tags from the parent context
+    @posthog.scoped()
+    def process_order(order_id):
+        posthog.tag("order_id", order_id)
+        posthog.capture("order_step_completed")
+        print(f"✅ Order {order_id} processed with scoped context")
+        # Exception will be captured and tagged automatically
+        # raise Exception("Order processing failed")
+
+    # Use fresh=True to start with a clean context (no inherited tags)
+    @posthog.scoped(fresh=True)
+    def process_payment(payment_id):
+        posthog.tag("payment_id", payment_id)
+        posthog.capture("payment_processed")
+        print(f"✅ Payment {payment_id} processed with fresh scoped context")
+        # Only payment_id tag will be present, no inherited tags
+        # raise Exception("Payment processing failed")
+
+    process_order("12345")
+    process_payment("67890")
+
+elif choice == "6":
+    print("\n🔄 Running all examples...")
+    if not local_eval_available:
+        print("   (Skipping local evaluation examples - no personal API key set)\n")
+
+    # Run example 1
+    print(f"\n{'🔸' * 20} IDENTIFY AND CAPTURE {'🔸' * 20}")
+    posthog.debug = True
+    print("📊 Capturing events...")
+    posthog.capture(
+        "event",
+        distinct_id="distinct_id",
+        properties={"property1": "value", "property2": "value"},
+        send_feature_flags=True,
+    )
+    print("🔗 Creating alias...")
+    posthog.alias("distinct_id", "new_distinct_id")
+    print("👤 Identifying user...")
+    posthog.set(
+        distinct_id="new_distinct_id", properties={"email": "something@something.com"}
+    )
+
+    # Run example 2 (requires local evaluation)
+    if local_eval_available:
+        print(f"\n{'🔸' * 20} FEATURE FLAGS {'🔸' * 20}")
+        print("🏁 Testing basic feature flags...")
+        print(f"beta-feature: {posthog.feature_enabled('beta-feature', 'distinct_id')}")
+        print(
+            f"Sydney user: {posthog.feature_enabled('test-flag', 'random_id_12345', person_properties={'$geoip_city_name': 'Sydney'})}"
+        )
+
+    # Run example 3
+    print(f"\n{'🔸' * 20} PAYLOADS {'🔸' * 20}")
+    print("📦 Testing payloads...")
+    print(f"Payload: {posthog.get_feature_flag_payload('beta-feature', 'distinct_id')}")
+
+    # Run example 4 (requires local evaluation)
+    if local_eval_available:
+        print(f"\n{'🔸' * 20} FLAG DEPENDENCIES {'🔸' * 20}")
+        print("🔗 Testing flag dependencies...")
+        result1 = posthog.feature_enabled(
+            "test-flag-dependency",
+            "demo_user",
+            person_properties={"email": "user@example.com"},
+            only_evaluate_locally=True,
+        )
+        result2 = posthog.feature_enabled(
+            "test-flag-dependency",
+            "demo_user2",
+            person_properties={"email": "user@other.com"},
+            only_evaluate_locally=True,
+        )
+        print(f"✅ @example.com user: {result1}, regular user: {result2}")
+
+    # Run example 5
+    print(f"\n{'🔸' * 20} CONTEXT MANAGEMENT {'🔸' * 20}")
+    print("🏷️ Testing context management...")
+    with posthog.new_context():
+        posthog.tag("demo_run", "all_examples")
+        posthog.capture("demo_completed")
+        print("✅ Demo completed with context tags")
+
+elif choice == "7":
+    print("👋 Goodbye!")
+    posthog.shutdown()
+    exit()
+
+else:
+    print("❌ Invalid choice. Please run again and select 1-7.")
+    posthog.shutdown()
+    exit()
+
+print("\n" + "=" * 60)
+print("✅ Example completed!")
+print("=" * 60)
+
+posthog.shutdown()

examples/redis_flag_cache.py

@@ -0,0 +1,144 @@
+"""
+Redis-based distributed cache for PostHog feature flag definitions.
+
+This example demonstrates how to implement a FlagDefinitionCacheProvider
+using Redis for multi-instance deployments (leader election pattern).
+
+Usage:
+    import redis
+    from posthog import Posthog
+
+    redis_client = redis.Redis(host='localhost', port=6379, decode_responses=True)
+    cache = RedisFlagCache(redis_client, service_key="my-service")
+
+    posthog = Posthog(
+        "<project_api_key>",
+        personal_api_key="<personal_api_key>",
+        flag_definition_cache_provider=cache,
+    )
+
+Requirements:
+    pip install redis
+"""
+
+import json
+import uuid
+
+from posthog import FlagDefinitionCacheData, FlagDefinitionCacheProvider
+from redis import Redis
+from typing import Optional
+
+
+class RedisFlagCache(FlagDefinitionCacheProvider):
+    """
+    A distributed cache for PostHog feature flag definitions using Redis.
+
+    In a multi-instance deployment (e.g., multiple serverless functions or containers),
+    we want only ONE instance to poll PostHog for flag updates, while all instances
+    share the cached results. This prevents N instances from making N redundant API calls.
+
+    The implementation uses leader election:
+    - One instance "wins" and becomes responsible for fetching
+    - Other instances read from the shared cache
+    - If the leader dies, the lock expires (TTL) and another instance takes over
+
+    Uses Lua scripts for atomic operations, following Redis distributed lock best practices:
+    https://redis.io/docs/latest/develop/clients/patterns/distributed-locks/
+    """
+
+    LOCK_TTL_MS = 60 * 1000  # 60 seconds, should be longer than the flags poll interval
+    CACHE_TTL_SECONDS = 60 * 60 * 24  # 24 hours
+
+    # Lua script: acquire lock if free, or extend if we own it
+    _LUA_TRY_LEAD = """
+        local current = redis.call('GET', KEYS[1])
+        if current == false then
+            redis.call('SET', KEYS[1], ARGV[1], 'PX', ARGV[2])
+            return 1
+        elseif current == ARGV[1] then
+            redis.call('PEXPIRE', KEYS[1], ARGV[2])
+            return 1
+        end
+        return 0
+    """
+
+    # Lua script: release lock only if we own it
+    _LUA_STOP_LEAD = """
+        if redis.call('GET', KEYS[1]) == ARGV[1] then
+            return redis.call('DEL', KEYS[1])
+        end
+        return 0
+    """
+
+    def __init__(self, redis: Redis[str], service_key: str):
+        """
+        Initialize the Redis flag cache.
+
+        Args:
+            redis: A redis-py client instance. Must be configured with
+                   decode_responses=True for correct string handling.
+            service_key: A unique identifier for this service/environment.
+                         Used to scope Redis keys, allowing multiple services
+                         or environments to share the same Redis instance.
+                         Examples: "my-api-prod", "checkout-service", "staging".
+
+        Redis Keys Created:
+            - posthog:flags:{service_key} - Cached flag definitions (JSON)
+            - posthog:flags:{service_key}:lock - Leader election lock
+
+        Example:
+            redis_client = redis.Redis(
+                host='localhost',
+                port=6379,
+                decode_responses=True
+            )
+            cache = RedisFlagCache(redis_client, service_key="my-api-prod")
+        """
+        self._redis = redis
+        self._cache_key = f"posthog:flags:{service_key}"
+        self._lock_key = f"posthog:flags:{service_key}:lock"
+        self._instance_id = str(uuid.uuid4())
+        self._try_lead = self._redis.register_script(self._LUA_TRY_LEAD)
+        self._stop_lead = self._redis.register_script(self._LUA_STOP_LEAD)
+
+    def get_flag_definitions(self) -> Optional[FlagDefinitionCacheData]:
+        """
+        Retrieve cached flag definitions from Redis.
+
+        Returns:
+            Cached flag definitions if available, None otherwise.
+        """
+        cached = self._redis.get(self._cache_key)
+        return json.loads(cached) if cached else None
+
+    def should_fetch_flag_definitions(self) -> bool:
+        """
+        Determines if this instance should fetch flag definitions from PostHog.
+
+        Atomically either:
+        - Acquires the lock if no one holds it, OR
+        - Extends the lock TTL if we already hold it
+
+        Returns:
+            True if this instance is the leader and should fetch, False otherwise.
+        """
+        result = self._try_lead(
+            keys=[self._lock_key],
+            args=[self._instance_id, self.LOCK_TTL_MS],
+        )
+        return result == 1
+
+    def on_flag_definitions_received(self, data: FlagDefinitionCacheData) -> None:
+        """
+        Store fetched flag definitions in Redis.
+
+        Args:
+            data: The flag definitions to cache.
+        """
+        self._redis.set(self._cache_key, json.dumps(data), ex=self.CACHE_TTL_SECONDS)
+
+    def shutdown(self) -> None:
+        """
+        Release leadership if we hold it. Safe to call even if not the leader.
+        """
+        self._stop_lead(keys=[self._lock_key], args=[self._instance_id])

examples/remote_config.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+"""
+Simple test script for PostHog remote config endpoint.
+"""
+
+import posthog
+
+# Initialize PostHog client
+posthog.api_key = "phc_..."
+posthog.personal_api_key = "phs_..."  # or "phx_..."
+posthog.host = "http://localhost:8000"  # or "https://us.posthog.com"
+posthog.debug = True
+
+
+def test_remote_config():
+    """Test remote config payload retrieval."""
+    print("Testing remote config endpoint...")
+
+    # Test feature flag key - replace with an actual flag key from your project
+    flag_key = "unencrypted-remote-config-setting"
+
+    try:
+        # Get remote config payload
+        payload = posthog.get_remote_config_payload(flag_key)
+        print(f"✅ Success! Remote config payload for '{flag_key}': {payload}")
+
+    except Exception as e:
+        print(f"❌ Error getting remote config: {e}")
+
+
+if __name__ == "__main__":
+    test_remote_config()

integration_tests/django5/.gitignore

@@ -0,0 +1,4 @@
+db.sqlite3
+*.pyc
+__pycache__/
+.pytest_cache/

integration_tests/django5/manage.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+
+import os
+import sys
+
+
+def main():
+    """Run administrative tasks."""
+    os.environ.setdefault("DJANGO_SETTINGS_MODULE", "testdjango.settings")
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == "__main__":
+    main()

integration_tests/django5/pyproject.toml

@@ -0,0 +1,19 @@
+[project]
+name = "test-django5"
+version = "0.1.0"
+requires-python = ">=3.12"
+dependencies = [
+    "django~=5.2.7",
+    "uvicorn[standard]~=0.38.0",
+    "posthog",
+    "pytest~=8.4.2",
+    "pytest-asyncio~=1.2.0",
+    "pytest-django~=4.11.1",
+    "httpx~=0.28.1",
+]
+
+[tool.uv]
+required-version = ">=0.5"
+
+[tool.uv.sources]
+posthog = { path = "../..", editable = true }

integration_tests/django5/test_exception_capture.py

@@ -0,0 +1,111 @@
+"""
+Test that verifies exception capture functionality.
+
+These tests verify that exceptions are actually captured to PostHog, not just that
+500 responses are returned.
+
+Without process_exception(), view exceptions are NOT captured to PostHog (v6.7.11 and earlier).
+With process_exception(), Django calls this method to capture exceptions before
+converting them to 500 responses.
+"""
+
+import os
+import django
+
+# Setup Django before importing anything else
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "testdjango.settings")
+django.setup()
+
+import pytest  # noqa: E402
+from httpx import AsyncClient, ASGITransport  # noqa: E402
+from django.core.asgi import get_asgi_application  # noqa: E402
+
+
+@pytest.fixture(scope="session")
+def asgi_app():
+    """Shared ASGI application for all tests."""
+    return get_asgi_application()
+
+
+@pytest.mark.asyncio
+async def test_async_exception_is_captured(asgi_app):
+    """
+    Test that async view exceptions are captured to PostHog.
+
+    The middleware's process_exception() method ensures exceptions are captured.
+    Without it (v6.7.11 and earlier), exceptions are NOT captured even though 500 is returned.
+    """
+    from unittest.mock import patch
+
+    # Track captured exceptions
+    captured = []
+
+    def mock_capture(exception, **kwargs):
+        """Mock capture_exception to record calls."""
+        captured.append(
+            {
+                "exception": exception,
+                "type": type(exception).__name__,
+                "message": str(exception),
+            }
+        )
+
+    # Patch at the posthog module level where middleware imports from
+    with patch("posthog.capture_exception", side_effect=mock_capture):
+        async with AsyncClient(
+            transport=ASGITransport(app=asgi_app), base_url="http://testserver"
+        ) as ac:
+            response = await ac.get("/test/async-exception")
+
+        # Django returns 500
+        assert response.status_code == 500
+
+        # CRITICAL: Verify PostHog captured the exception
+        assert len(captured) > 0, "Exception was NOT captured to PostHog!"
+
+        # Verify it's the right exception
+        exception_data = captured[0]
+        assert exception_data["type"] == "ValueError"
+        assert "Test exception from Django 5 async view" in exception_data["message"]
+
+
+@pytest.mark.asyncio
+async def test_sync_exception_is_captured(asgi_app):
+    """
+    Test that sync view exceptions are captured to PostHog.
+
+    The middleware's process_exception() method ensures exceptions are captured.
+    Without it (v6.7.11 and earlier), exceptions are NOT captured even though 500 is returned.
+    """
+    from unittest.mock import patch
+
+    # Track captured exceptions
+    captured = []
+
+    def mock_capture(exception, **kwargs):
+        """Mock capture_exception to record calls."""
+        captured.append(
+            {
+                "exception": exception,
+                "type": type(exception).__name__,
+                "message": str(exception),
+            }
+        )
+
+    # Patch at the posthog module level where middleware imports from
+    with patch("posthog.capture_exception", side_effect=mock_capture):
+        async with AsyncClient(
+            transport=ASGITransport(app=asgi_app), base_url="http://testserver"
+        ) as ac:
+            response = await ac.get("/test/sync-exception")
+
+        # Django returns 500
+        assert response.status_code == 500
+
+        # CRITICAL: Verify PostHog captured the exception
+        assert len(captured) > 0, "Exception was NOT captured to PostHog!"
+
+        # Verify it's the right exception
+        exception_data = captured[0]
+        assert exception_data["type"] == "ValueError"
+        assert "Test exception from Django 5 sync view" in exception_data["message"]

integration_tests/django5/test_middleware.py

@@ -0,0 +1,170 @@
+"""
+Tests for PostHog Django middleware in async context.
+
+These tests verify that the middleware correctly handles:
+1. Async user access (request.auser() in Django 5)
+2. Exception capture in both sync and async views
+3. No SynchronousOnlyOperation errors in async context
+
+Tests run directly against the ASGI application without needing a server.
+"""
+
+import os
+import django
+
+# Setup Django before importing anything else
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "testdjango.settings")
+django.setup()
+
+import pytest  # noqa: E402
+from httpx import AsyncClient, ASGITransport  # noqa: E402
+from django.core.asgi import get_asgi_application  # noqa: E402
+
+
+@pytest.fixture(scope="session")
+def asgi_app():
+    """Shared ASGI application for all tests."""
+    return get_asgi_application()
+
+
+@pytest.mark.asyncio
+async def test_async_user_access(asgi_app):
+    """
+    Test that middleware can access request.user in async context.
+
+    In Django 5, this requires using await request.auser() instead of request.user
+    to avoid SynchronousOnlyOperation error.
+
+    Without authentication, request.user is AnonymousUser which doesn't
+    trigger the lazy loading bug. This test verifies the middleware works
+    in the common case.
+    """
+    async with AsyncClient(
+        transport=ASGITransport(app=asgi_app), base_url="http://testserver"
+    ) as ac:
+        response = await ac.get("/test/async-user")
+
+    assert response.status_code == 200
+    data = response.json()
+    assert data["status"] == "success"
+    assert "django_version" in data
+
+
+@pytest.mark.django_db(transaction=True)
+@pytest.mark.asyncio
+async def test_async_authenticated_user_access(asgi_app):
+    """
+    Test that middleware can access an authenticated user in async context.
+
+    This is the critical test that triggers the SynchronousOnlyOperation bug
+    in v6.7.11. When AuthenticationMiddleware sets request.user to a
+    SimpleLazyObject wrapping a database query, accessing user.pk or user.email
+    in async context causes the error.
+
+    In v6.7.11, extract_request_user() does getattr(user, "is_authenticated", False)
+    which triggers the lazy object evaluation synchronously.
+
+    The fix uses await request.auser() instead to avoid this.
+    """
+    from django.contrib.auth import get_user_model
+    from django.test import Client
+    from asgiref.sync import sync_to_async
+    from django.test import override_settings
+
+    # Create a test user (must use sync_to_async since we're in async test)
+    User = get_user_model()
+
+    @sync_to_async
+    def create_or_get_user():
+        user, created = User.objects.get_or_create(
+            username="testuser",
+            defaults={
+                "email": "test@example.com",
+            },
+        )
+        if created:
+            user.set_password("testpass123")
+            user.save()
+        return user
+
+    user = await create_or_get_user()
+
+    # Create a session with authenticated user (sync operation)
+    @sync_to_async
+    def create_session():
+        client = Client()
+        client.force_login(user)
+        return client.cookies.get("sessionid")
+
+    session_cookie = await create_session()
+
+    if not session_cookie:
+        pytest.skip("Could not create authenticated session")
+
+    # Make request with session cookie - this should trigger the bug in v6.7.11
+    # Disable exception capture to see the SynchronousOnlyOperation clearly
+    with override_settings(POSTHOG_MW_CAPTURE_EXCEPTIONS=False):
+        async with AsyncClient(
+            transport=ASGITransport(app=asgi_app),
+            base_url="http://testserver",
+            cookies={"sessionid": session_cookie.value},
+        ) as ac:
+            response = await ac.get("/test/async-user")
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["status"] == "success"
+        assert data["user_authenticated"]
+
+
+@pytest.mark.asyncio
+async def test_sync_user_access(asgi_app):
+    """
+    Test that middleware works with sync views.
+
+    This should always work regardless of middleware version.
+    """
+    async with AsyncClient(
+        transport=ASGITransport(app=asgi_app), base_url="http://testserver"
+    ) as ac:
+        response = await ac.get("/test/sync-user")
+
+    assert response.status_code == 200
+    data = response.json()
+    assert data["status"] == "success"
+
+
+@pytest.mark.asyncio
+async def test_async_exception_capture(asgi_app):
+    """
+    Test that middleware handles exceptions from async views.
+
+    The middleware's process_exception() method captures view exceptions to PostHog
+    before Django converts them to 500 responses. This test verifies the exception
+    causes a 500 response. See test_exception_capture.py for tests that verify
+    actual exception capture to PostHog.
+    """
+    async with AsyncClient(
+        transport=ASGITransport(app=asgi_app), base_url="http://testserver"
+    ) as ac:
+        response = await ac.get("/test/async-exception")
+
+    # Django returns 500 for unhandled exceptions
+    assert response.status_code == 500
+
+
+@pytest.mark.asyncio
+async def test_sync_exception_capture(asgi_app):
+    """
+    Test that middleware handles exceptions from sync views.
+
+    The middleware's process_exception() method captures view exceptions to PostHog.
+    This test verifies the exception causes a 500 response.
+    """
+    async with AsyncClient(
+        transport=ASGITransport(app=asgi_app), base_url="http://testserver"
+    ) as ac:
+        response = await ac.get("/test/sync-exception")
+
+    # Django returns 500 for unhandled exceptions
+    assert response.status_code == 500

integration_tests/django5/testdjango/asgi.py

@@ -0,0 +1,16 @@
+"""
+ASGI config for testdjango project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.2/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "testdjango.settings")
+
+application = get_asgi_application()

integration_tests/django5/testdjango/settings.py

@@ -0,0 +1,129 @@
+"""
+Django settings for testdjango project.
+
+Generated by 'django-admin startproject' using Django 5.2.7.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.2/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/5.2/ref/settings/
+"""
+
+from pathlib import Path
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/5.2/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = "django-insecure-q5(&wfw@_lb)noyowbfl$2ls8c82hl__0f9s5(mohlh2)aas#3"
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = ["*"]
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    "django.contrib.admin",
+    "django.contrib.auth",
+    "django.contrib.contenttypes",
+    "django.contrib.sessions",
+    "django.contrib.messages",
+    "django.contrib.staticfiles",
+]
+
+MIDDLEWARE = [
+    "django.middleware.security.SecurityMiddleware",
+    "django.contrib.sessions.middleware.SessionMiddleware",
+    "django.middleware.common.CommonMiddleware",
+    "django.middleware.csrf.CsrfViewMiddleware",
+    "django.contrib.auth.middleware.AuthenticationMiddleware",
+    "django.contrib.messages.middleware.MessageMiddleware",
+    "django.middleware.clickjacking.XFrameOptionsMiddleware",
+    "posthog.integrations.django.PosthogContextMiddleware",  # Test PostHog middleware
+]
+
+ROOT_URLCONF = "testdjango.urls"
+
+TEMPLATES = [
+    {
+        "BACKEND": "django.template.backends.django.DjangoTemplates",
+        "DIRS": [],
+        "APP_DIRS": True,
+        "OPTIONS": {
+            "context_processors": [
+                "django.template.context_processors.request",
+                "django.contrib.auth.context_processors.auth",
+                "django.contrib.messages.context_processors.messages",
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = "testdjango.wsgi.application"
+
+
+# Database
+# https://docs.djangoproject.com/en/5.2/ref/settings/#databases
+
+DATABASES = {
+    "default": {
+        "ENGINE": "django.db.backends.sqlite3",
+        "NAME": BASE_DIR / "db.sqlite3",
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/5.2/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
+    },
+    {
+        "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
+    },
+    {
+        "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
+    },
+    {
+        "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/5.2/topics/i18n/
+
+LANGUAGE_CODE = "en-us"
+
+TIME_ZONE = "UTC"
+
+USE_I18N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/5.2/howto/static-files/
+
+STATIC_URL = "static/"
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/5.2/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
+
+
+# PostHog settings for testing
+POSTHOG_API_KEY = "test-key"
+POSTHOG_HOST = "https://app.posthog.com"
+POSTHOG_MW_CAPTURE_EXCEPTIONS = True

integration_tests/django5/testdjango/urls.py

@@ -0,0 +1,28 @@
+"""
+URL configuration for testdjango project.
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/5.2/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+
+from django.contrib import admin
+from django.urls import path
+from testdjango import views
+
+urlpatterns = [
+    path("admin/", admin.site.urls),
+    path("test/async-user", views.test_async_user),
+    path("test/sync-user", views.test_sync_user),
+    path("test/async-exception", views.test_async_exception),
+    path("test/sync-exception", views.test_sync_exception),
+]

integration_tests/django5/testdjango/views.py

@@ -0,0 +1,50 @@
+"""
+Test views for validating PostHog middleware with Django 5 ASGI.
+"""
+
+from django.http import JsonResponse
+
+
+async def test_async_user(request):
+    """
+    Async view that tests middleware with request.user access.
+
+    The middleware will access request.user (SimpleLazyObject) via auser()
+    in async context. Without the fix, this causes SynchronousOnlyOperation.
+    """
+    # The middleware has already accessed request.user via auser()
+    # If we got here, the fix works!
+    user = await request.auser()
+
+    return JsonResponse(
+        {
+            "status": "success",
+            "message": "Django 5 async middleware test passed!",
+            "django_version": "5.x",
+            "user_authenticated": user.is_authenticated if user else False,
+            "note": "Middleware used await request.auser() successfully",
+        }
+    )
+
+
+def test_sync_user(request):
+    """Sync view for comparison."""
+    return JsonResponse(
+        {
+            "status": "success",
+            "message": "Sync view works",
+            "user_authenticated": request.user.is_authenticated
+            if hasattr(request, "user")
+            else False,
+        }
+    )
+
+
+async def test_async_exception(request):
+    """Async view that raises an exception for testing exception capture."""
+    raise ValueError("Test exception from Django 5 async view")
+
+
+def test_sync_exception(request):
+    """Sync view that raises an exception for testing exception capture."""
+    raise ValueError("Test exception from Django 5 sync view")

integration_tests/django5/testdjango/wsgi.py

@@ -0,0 +1,16 @@
+"""
+WSGI config for testdjango project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.2/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "testdjango.settings")
+
+application = get_wsgi_application()

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+]

mypy-baseline.txt

@@ -0,0 +1,35 @@
+posthog/utils.py:0: error: Library stubs not installed for "six"  [import-untyped]
+posthog/utils.py:0: error: Library stubs not installed for "dateutil.tz"  [import-untyped]
+posthog/utils.py:0: error: Statement is unreachable  [unreachable]
+posthog/request.py:0: error: Library stubs not installed for "requests"  [import-untyped]
+posthog/request.py:0: note: Hint: "python3 -m pip install types-requests"
+posthog/request.py:0: error: Library stubs not installed for "dateutil.tz"  [import-untyped]
+posthog/request.py:0: error: Incompatible types in assignment (expression has type "bytes", variable has type "str")  [assignment]
+posthog/consumer.py:0: error: Name "Empty" already defined (possibly by an import)  [no-redef]
+posthog/consumer.py:0: error: Need type annotation for "items" (hint: "items: list[<type>] = ...")  [var-annotated]
+posthog/consumer.py:0: error: Unsupported operand types for <= ("int" and "str")  [operator]
+posthog/consumer.py:0: note: Right operand is of type "int | str"
+posthog/consumer.py:0: error: Unsupported operand types for < ("str" and "int")  [operator]
+posthog/consumer.py:0: note: Left operand is of type "int | str"
+posthog/feature_flags.py:0: error: Library stubs not installed for "dateutil"  [import-untyped]
+posthog/feature_flags.py:0: error: Library stubs not installed for "dateutil.relativedelta"  [import-untyped]
+posthog/feature_flags.py:0: error: Unused "type: ignore" comment  [unused-ignore]
+posthog/client.py:0: error: Library stubs not installed for "dateutil.tz"  [import-untyped]
+posthog/client.py:0: note: Hint: "python3 -m pip install types-python-dateutil"
+posthog/client.py:0: note: (or run "mypy --install-types" to install all missing stub packages)
+posthog/client.py:0: note: See https://mypy.readthedocs.io/en/stable/running_mypy.html#missing-imports
+posthog/client.py:0: error: Library stubs not installed for "six"  [import-untyped]
+posthog/client.py:0: note: Hint: "python3 -m pip install types-six"
+posthog/client.py:0: error: Name "queue" already defined (by an import)  [no-redef]
+posthog/client.py:0: error: Need type annotation for "queue"  [var-annotated]
+posthog/client.py:0: error: Incompatible types in assignment (expression has type "Any | list[Any]", variable has type "None")  [assignment]
+posthog/client.py:0: error: Incompatible types in assignment (expression has type "dict[Any, Any]", variable has type "None")  [assignment]
+posthog/client.py:0: error: "None" has no attribute "__iter__" (not iterable)  [attr-defined]
+posthog/client.py:0: error: Statement is unreachable  [unreachable]
+posthog/client.py:0: error: Right operand of "and" is never evaluated  [unreachable]
+posthog/client.py:0: error: Incompatible types in assignment (expression has type "Poller", variable has type "None")  [assignment]
+posthog/client.py:0: error: "None" has no attribute "start"  [attr-defined]
+posthog/client.py:0: error: Statement is unreachable  [unreachable]
+posthog/client.py:0: error: Statement is unreachable  [unreachable]
+posthog/client.py:0: error: Name "urlparse" already defined (possibly by an import)  [no-redef]
+posthog/client.py:0: error: Name "parse_qs" already defined (possibly by an import)  [no-redef]

mypy.ini

@@ -0,0 +1,39 @@
+[mypy]
+python_version = 3.11
+plugins =
+    pydantic.mypy
+strict_optional = True
+no_implicit_optional = True
+warn_unused_ignores = True
+check_untyped_defs = True
+warn_unreachable = True
+strict_equality = True
+ignore_missing_imports = True
+exclude = env/.*|venv/.*|build/.*
+
+[mypy-django.*]
+ignore_missing_imports = True
+
+[mypy-sentry_sdk.*]
+ignore_missing_imports = True
+
+[mypy-posthog.test.*]
+ignore_errors = True
+
+[mypy-posthog.*.test.*]
+ignore_errors = True
+
+[mypy-openai.*]
+ignore_missing_imports = True
+
+[mypy-langchain.*]
+ignore_missing_imports = True
+
+[mypy-langchain_core.*]
+ignore_missing_imports = True
+
+[mypy-anthropic.*]
+ignore_missing_imports = True
+
+[mypy-httpx.*]
+ignore_missing_imports = True

posthog/__init__.py

@@ -1,121 +1,892 @@
+import datetime  # noqa: F401
+from typing import Any, Callable, Dict, Optional  # noqa: F401
 
-from posthog.version import VERSION
+from typing_extensions import Unpack
+
+from posthog.args import ExceptionArg, OptionalCaptureArgs, OptionalSetArgs
 from posthog.client import Client
-from typing import Optional, Dict, Callable
+from posthog.contexts import (
+    identify_context as inner_identify_context,
+)
+from posthog.contexts import (
+    new_context as inner_new_context,
+)
+from posthog.contexts import (
+    scoped as inner_scoped,
+)
+from posthog.contexts import (
+    set_capture_exception_code_variables_context as inner_set_capture_exception_code_variables_context,
+)
+from posthog.contexts import (
+    set_code_variables_ignore_patterns_context as inner_set_code_variables_ignore_patterns_context,
+)
+from posthog.contexts import (
+    set_code_variables_mask_patterns_context as inner_set_code_variables_mask_patterns_context,
+)
+from posthog.contexts import (
+    set_context_device_id as inner_set_context_device_id,
+)
+from posthog.contexts import (
+    set_context_session as inner_set_context_session,
+)
+from posthog.contexts import (
+    tag as inner_tag,
+)
+from posthog.contexts import (
+    get_tags as inner_get_tags,
+)
+from posthog.exception_utils import (
+    DEFAULT_CODE_VARIABLES_IGNORE_PATTERNS,
+    DEFAULT_CODE_VARIABLES_MASK_PATTERNS,
+)
+from posthog.feature_flags import (
+    InconclusiveMatchError as InconclusiveMatchError,
+)
+from posthog.feature_flags import (
+    RequiresServerEvaluation as RequiresServerEvaluation,
+)
+from posthog.flag_definition_cache import (
+    FlagDefinitionCacheData as FlagDefinitionCacheData,
+    FlagDefinitionCacheProvider as FlagDefinitionCacheProvider,
+)
+from posthog.request import (
+    disable_connection_reuse as disable_connection_reuse,
+    enable_keep_alive as enable_keep_alive,
+    set_socket_options as set_socket_options,
+    SocketOptions as SocketOptions,
+)
+from posthog.types import (
+    FeatureFlag,
+    FlagsAndPayloads,
+)
+from posthog.types import (
+    FeatureFlagResult as FeatureFlagResult,
+)
+from posthog.version import VERSION
 
 __version__ = VERSION
 
+"""Context management."""
+
+
+def new_context(fresh=False, capture_exceptions=True, client=None):
+    """
+    Create a new context scope that will be active for the duration of the with block.
+
+    Args:
+        fresh: Whether to start with a fresh context (default: False)
+        capture_exceptions: Whether to capture exceptions raised within the context (default: True)
+        client: Optional Posthog client instance to use for this context (default: None)
+
+    Examples:
+        ```python
+        from posthog import new_context, tag, capture
+        with new_context():
+            tag("request_id", "123")
+            capture("event_name", properties={"property": "value"})
+        ```
+
+    Category:
+        Contexts
+    """
+    return inner_new_context(
+        fresh=fresh, capture_exceptions=capture_exceptions, client=client
+    )
+
+
+def scoped(fresh=False, capture_exceptions=True):
+    """
+    Decorator that creates a new context for the function.
+
+    Args:
+        fresh: Whether to start with a fresh context (default: False)
+        capture_exceptions: Whether to capture and track exceptions with posthog error tracking (default: True)
+
+    Examples:
+        ```python
+        from posthog import scoped, tag, capture
+        @scoped()
+        def process_payment(payment_id):
+            tag("payment_id", payment_id)
+            capture("payment_started")
+        ```
+
+    Category:
+        Contexts
+    """
+    return inner_scoped(fresh=fresh, capture_exceptions=capture_exceptions)
+
+
+def set_context_session(session_id: str):
+    """
+    Set the session ID for the current context.
+
+    Args:
+        session_id: The session ID to associate with the current context and its children
+
+    Examples:
+        ```python
+        from posthog import set_context_session
+        set_context_session("session_123")
+        ```
+
+    Category:
+        Contexts
+    """
+    return inner_set_context_session(session_id)
+
+
+def set_context_device_id(device_id: str):
+    """
+    Set the device ID for the current context, associating all feature flag requests
+    in this or child contexts with the given device ID.
+
+    Args:
+        device_id: The device ID to associate with the current context and its children
+
+    Examples:
+        ```python
+        from posthog import set_context_device_id
+        set_context_device_id("device_123")
+        ```
+
+    Category:
+        Contexts
+    """
+    return inner_set_context_device_id(device_id)
+
+
+def identify_context(distinct_id: str):
+    """
+    Identify the current context with a distinct ID.
+
+    Args:
+        distinct_id: The distinct ID to associate with the current context and its children
+
+    Examples:
+        ```python
+        from posthog import identify_context
+        identify_context("user_123")
+        ```
+
+    Category:
+        Identification
+    """
+    return inner_identify_context(distinct_id)
+
+
+def set_capture_exception_code_variables_context(enabled: bool):
+    """
+    Set whether code variables are captured for the current context.
+    """
+    return inner_set_capture_exception_code_variables_context(enabled)
+
+
+def set_code_variables_mask_patterns_context(mask_patterns: list):
+    """
+    Variable names matching these patterns will be masked with *** when capturing code variables.
+    """
+    return inner_set_code_variables_mask_patterns_context(mask_patterns)
+
+
+def set_code_variables_ignore_patterns_context(ignore_patterns: list):
+    """
+    Variable names matching these patterns will be ignored completely when capturing code variables.
+    """
+    return inner_set_code_variables_ignore_patterns_context(ignore_patterns)
+
+
+def tag(name: str, value: Any):
+    """
+    Add a tag to the current context.
+
+    Args:
+        name: The tag key
+        value: The tag value
+
+    Examples:
+        ```python
+        from posthog import tag
+        tag("user_id", "123")
+        ```
+
+    Category:
+        Contexts
+    """
+    return inner_tag(name, value)
+
+
+def get_tags() -> Dict[str, Any]:
+    """
+    Get all tags from the current context.
+
+    Returns:
+        Dict of all tags in the current context
+
+    Category:
+        Contexts
+    """
+    return inner_get_tags()
+
+
 """Settings."""
-api_key: str = None
-host: str = None
-on_error: Callable = None
-debug: bool = False
-send: bool = True
-sync_mode:bool = False
-disabled: bool = False
+api_key = None  # type: Optional[str]
+host = None  # type: Optional[str]
+on_error = None  # type: Optional[Callable]
+debug = False  # type: bool
+send = True  # type: bool
+sync_mode = False  # type: bool
+disabled = False  # type: bool
+personal_api_key = None  # type: Optional[str]
+project_api_key = None  # type: Optional[str]
+poll_interval = 30  # type: int
+disable_geoip = True  # type: bool
+feature_flags_request_timeout_seconds = 3  # type: int
+super_properties = None  # type: Optional[Dict]
+# Currently alpha, use at your own risk
+enable_exception_autocapture = False  # type: bool
+log_captured_exceptions = False  # type: bool
+# Used to determine in app paths for exception autocapture. Defaults to the current working directory
+project_root = None  # type: Optional[str]
+# Used for our AI observability feature to not capture any prompt or output just usage + metadata
+privacy_mode = False  # type: bool
+# Whether to enable feature flag polling for local evaluation by default. Defaults to True.
+# We recommend setting this to False if you are only using the personalApiKey for evaluating remote config payloads via `get_remote_config_payload` and not using local evaluation.
+enable_local_evaluation = True  # type: bool
 
-default_client = None
+default_client = None  # type: Optional[Client]
+
+capture_exception_code_variables = False
+code_variables_mask_patterns = DEFAULT_CODE_VARIABLES_MASK_PATTERNS
+code_variables_ignore_patterns = DEFAULT_CODE_VARIABLES_IGNORE_PATTERNS
+in_app_modules = None  # type: Optional[list[str]]
 
 
-def capture(distinct_id: str, event: str, properties: Optional[Dict]=None, context: Optional[Dict]=None,
-              timestamp: Optional[str]=None, message_id: Optional[str]=None) -> None:
+# NOTE - this and following functions take unpacked kwargs because we needed to make
+# it impossible to write `posthog.capture(distinct-id, event-name)` - basically, to enforce
+# the breaking change made between 5.3.0 and 6.0.0. This decision can be unrolled in later
+# versions, without a breaking change, to get back the type information in function signatures
+def capture(event: str, **kwargs: Unpack[OptionalCaptureArgs]) -> Optional[str]:
     """
-    Capture allows you to capture anything a user does within your system, which you can later use in PostHog to find patterns in usage, work out which features to improve or where people are giving up.
+    Capture anything a user does within your system.
 
-    A `capture` call requires
-    - `distinct id` which uniquely identifies your user
-    - `event name` to make sure 
-    - We recommend using [verb] [noun], like `movie played` or `movie updated` to easily identify what your events mean later on.
+    Args:
+        event: The event name to specify the event
+        **kwargs: Optional arguments including:
+            distinct_id: Unique identifier for the user
+            properties: Dict of event properties
+            timestamp: When the event occurred
+            groups: Dict of group types and IDs
+            disable_geoip: Whether to disable GeoIP lookup
 
-    Optionally you can submit
-    - `properties`, which can be a dict with any information you'd like to add
+    Details:
+        Capture allows you to capture anything a user does within your system, which you can later use in PostHog to find patterns in usage, work out which features to improve or where people are giving up. A capture call requires an event name to specify the event. We recommend using [verb] [noun], like `movie played` or `movie updated` to easily identify what your events mean later on. Capture takes a number of optional arguments, which are defined by the `OptionalCaptureArgs` type.
 
-    For example:
+    Examples:
+        ```python
+        # Context and capture usage
+        from posthog import new_context, identify_context, tag_context, capture
+        # Enter a new context (e.g. a request/response cycle, an instance of a background job, etc)
+        with new_context():
+            # Associate this context with some user, by distinct_id
+            identify_context('some user')
+
+            # Capture an event, associated with the context-level distinct ID ('some user')
+            capture('movie started')
+
+            # Capture an event associated with some other user (overriding the context-level distinct ID)
+            capture('movie joined', distinct_id='some-other-user')
+
+            # Capture an event with some properties
+            capture('movie played', properties={'movie_id': '123', 'category': 'romcom'})
+
+            # Capture an event with some properties
+            capture('purchase', properties={'product_id': '123', 'category': 'romcom'})
+            # Capture an event with some associated group
+            capture('purchase', groups={'company': 'id:5'})
+
+            # Adding a tag to the current context will cause it to appear on all subsequent events
+            tag_context('some-tag', 'some-value')
+
+            capture('another-event') # Will be captured with `'some-tag': 'some-value'` in the properties dict
+        ```
+        ```python
+        # Set event properties
+        from posthog import capture
+        capture(
+            "user_signed_up",
+            distinct_id="distinct_id_of_the_user",
+            properties={
+                "login_type": "email",
+                "is_free_trial": "true"
+            }
+        )
+        ```
+    Category:
+        Events
+    """
+
+    return _proxy("capture", event, **kwargs)
+
+
+def set(**kwargs: Unpack[OptionalSetArgs]) -> Optional[str]:
+    """
+    Set properties on a user record.
+
+    Details:
+        This will overwrite previous people property values. Generally operates similar to `capture`, with distinct_id being an optional argument, defaulting to the current context's distinct ID. If there is no context-level distinct ID, and no override distinct_id is passed, this function will do nothing. Context tags are folded into $set properties, so tagging the current context and then calling `set` will cause those tags to be set on the user (unlike capture, which causes them to just be set on the event).
+
+    Examples:
+        ```python
+        # Set person properties
+        from posthog import capture
+        capture(
+            'distinct_id',
+            event='event_name',
+            properties={
+                '$set': {'name': 'Max Hedgehog'},
+                '$set_once': {'initial_url': '/blog'}
+            }
+        )
+        ```
+    Category:
+        Identification
+    """
+
+    return _proxy("set", **kwargs)
+
+
+def set_once(**kwargs: Unpack[OptionalSetArgs]) -> Optional[str]:
+    """
+    Set properties on a user record, only if they do not yet exist.
+
+    Details:
+        This will not overwrite previous people property values, unlike `set`. Otherwise, operates in an identical manner to `set`.
+
+    Examples:
+        ```python
+        # Set property once
+        from posthog import capture
+        capture(
+            'distinct_id',
+            event='event_name',
+            properties={
+                '$set': {'name': 'Max Hedgehog'},
+                '$set_once': {'initial_url': '/blog'}
+            }
+        )
+
+        ```
+    Category:
+        Identification
+    """
+    return _proxy("set_once", **kwargs)
+
+
+def group_identify(
+    group_type,  # type: str
+    group_key,  # type: str
+    properties=None,  # type: Optional[Dict]
+    timestamp=None,  # type: Optional[datetime.datetime]
+    uuid=None,  # type: Optional[str]
+    disable_geoip=None,  # type: Optional[bool]
+):
+    # type: (...) -> Optional[str]
+    """
+    Set properties on a group.
+
+    Args:
+        group_type: Type of your group
+        group_key: Unique identifier of the group
+        properties: Properties to set on the group
+        timestamp: Optional timestamp for the event
+        uuid: Optional UUID for the event
+        disable_geoip: Whether to disable GeoIP lookup
+
+    Examples:
+        ```python
+        # Group identify
+        from posthog import group_identify
+        group_identify('company', 'company_id_in_your_db', {
+            'name': 'Awesome Inc.',
+            'employees': 11
+        })
+        ```
+    Category:
+        Identification
+    """
+
+    return _proxy(
+        "group_identify",
+        group_type=group_type,
+        group_key=group_key,
+        properties=properties,
+        timestamp=timestamp,
+        uuid=uuid,
+        disable_geoip=disable_geoip,
+    )
+
+
+def alias(
+    previous_id,  # type: str
+    distinct_id,  # type: str
+    timestamp=None,  # type: Optional[datetime.datetime]
+    uuid=None,  # type: Optional[str]
+    disable_geoip=None,  # type: Optional[bool]
+):
+    # type: (...) -> Optional[str]
+    """
+    Associate user behaviour before and after they e.g. register, login, or perform some other identifying action.
+
+    Args:
+        previous_id: The unique ID of the user before
+        distinct_id: The current unique id
+        timestamp: Optional timestamp for the event
+        uuid: Optional UUID for the event
+        disable_geoip: Whether to disable GeoIP lookup
+
+    Details:
+        To marry up whatever a user does before they sign up or log in with what they do after you need to make an alias call. This will allow you to answer questions like "Which marketing channels leads to users churning after a month?" or "What do users do on our website before signing up?". Particularly useful for associating user behaviour before and after they e.g. register, login, or perform some other identifying action.
+
+    Examples:
+        ```python
+        # Alias user
+        from posthog import alias
+        alias(previous_id='distinct_id', distinct_id='alias_id')
+        ```
+    Category:
+        Identification
+    """
+
+    return _proxy(
+        "alias",
+        previous_id=previous_id,
+        distinct_id=distinct_id,
+        timestamp=timestamp,
+        uuid=uuid,
+        disable_geoip=disable_geoip,
+    )
+
+
+def capture_exception(
+    exception: Optional[ExceptionArg] = None,
+    **kwargs: Unpack[OptionalCaptureArgs],
+):
+    """
+    Capture exceptions that happen in your code.
+
+    Args:
+        exception: The exception to capture. If not provided, the current exception is captured via `sys.exc_info()`
+
+    Details:
+        Capture exception is idempotent - if it is called twice with the same exception instance, only a occurrence will be tracked in posthog. This is because, generally, contexts will cause exceptions to be captured automatically. However, to ensure you track an exception, if you catch and do not re-raise it, capturing it manually is recommended, unless you are certain it will have crossed a context boundary (e.g. by existing a `with posthog.new_context():` block already). If the passed exception was raised and caught, the captured stack trace will consist of every frame between where the exception was raised and the point at which it is captured (the "traceback"). If the passed exception was never raised, e.g. if you call `posthog.capture_exception(ValueError("Some Error"))`, the stack trace captured will be the full stack trace at the moment the exception was captured. Note that heavy use of contexts will lead to truncated stack traces, as the exception will be captured by the context entered most recently, which may not be the point you catch the exception for the final time in your code. It's recommended to use contexts sparingly, for this reason. `capture_exception` takes the same set of optional arguments as `capture`.
+
+    Examples:
+        ```python
+        # Capture exception
+        from posthog import capture_exception
+        try:
+            risky_operation()
+        except Exception as e:
+            capture_exception(e)
+        ```
+    Category:
+        Events
+    """
+
+    return _proxy("capture_exception", exception=exception, **kwargs)
+
+
+def feature_enabled(
+    key,  # type: str
+    distinct_id,  # type: str
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,  # type: bool
+    send_feature_flag_events=True,  # type: bool
+    disable_geoip=None,  # type: Optional[bool]
+    device_id=None,  # type: Optional[str]
+):
+    # type: (...) -> bool
+    """
+    Use feature flags to enable or disable features for users.
+
+    Args:
+        key: The feature flag key
+        distinct_id: The user's distinct ID
+        groups: Groups mapping
+        person_properties: Person properties
+        group_properties: Group properties
+        only_evaluate_locally: Whether to evaluate only locally
+        send_feature_flag_events: Whether to send feature flag events
+        disable_geoip: Whether to disable GeoIP lookup
+
+    Details:
+        You can call `posthog.load_feature_flags()` before to make sure you're not doing unexpected requests.
+
+    Examples:
+        ```python
+        # Boolean feature flag
+        from posthog import feature_enabled, get_feature_flag_payload
+        is_my_flag_enabled = feature_enabled('flag-key', 'distinct_id_of_your_user')
+        if is_my_flag_enabled:
+            matched_flag_payload = get_feature_flag_payload('flag-key', 'distinct_id_of_your_user')
+        ```
+    Category:
+        Feature flags
+    """
+    return _proxy(
+        "feature_enabled",
+        key=key,
+        distinct_id=distinct_id,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        send_feature_flag_events=send_feature_flag_events,
+        disable_geoip=disable_geoip,
+        device_id=device_id,
+    )
+
+
+def get_feature_flag(
+    key,  # type: str
+    distinct_id,  # type: str
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,  # type: bool
+    send_feature_flag_events=True,  # type: bool
+    disable_geoip=None,  # type: Optional[bool]
+    device_id=None,  # type: Optional[str]
+) -> Optional[FeatureFlag]:
+    """
+    Get feature flag variant for users. Used with experiments.
+
+    Args:
+        key: The feature flag key
+        distinct_id: The user's distinct ID
+        groups: Groups mapping from group type to group key
+        person_properties: Person properties
+        group_properties: Group properties in format { group_type_name: { group_properties } }
+        only_evaluate_locally: Whether to evaluate only locally
+        send_feature_flag_events: Whether to send feature flag events
+        disable_geoip: Whether to disable GeoIP lookup
+
+    Details:
+        `groups` are a mapping from group type to group key. So, if you have a group type of "organization" and a group key of "5", you would pass groups={"organization": "5"}. `group_properties` take the format: { group_type_name: { group_properties } }. So, for example, if you have the group type "organization" and the group key "5", with the properties name, and employee count, you'll send these as: group_properties={"organization": {"name": "PostHog", "employees": 11}}.
+
+    Examples:
+        ```python
+        # Multivariate feature flag
+        from posthog import get_feature_flag, get_feature_flag_payload
+        enabled_variant = get_feature_flag('flag-key', 'distinct_id_of_your_user')
+        if enabled_variant == 'variant-key':
+            matched_flag_payload = get_feature_flag_payload('flag-key', 'distinct_id_of_your_user')
+        ```
+    Category:
+        Feature flags
+    """
+    return _proxy(
+        "get_feature_flag",
+        key=key,
+        distinct_id=distinct_id,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        send_feature_flag_events=send_feature_flag_events,
+        disable_geoip=disable_geoip,
+        device_id=device_id,
+    )
+
+
+def get_all_flags(
+    distinct_id,  # type: str
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,  # type: bool
+    disable_geoip=None,  # type: Optional[bool]
+    device_id=None,  # type: Optional[str]
+) -> Optional[dict[str, FeatureFlag]]:
+    """
+    Get all flags for a given user.
+
+    Args:
+        distinct_id: The user's distinct ID
+        groups: Groups mapping
+        person_properties: Person properties
+        group_properties: Group properties
+        only_evaluate_locally: Whether to evaluate only locally
+        disable_geoip: Whether to disable GeoIP lookup
+
+    Details:
+        Flags are key-value pairs where the key is the flag key and the value is the flag variant, or True, or False.
+
+    Examples:
+        ```python
+        # All flags for user
+        from posthog import get_all_flags
+        get_all_flags('distinct_id_of_your_user')
+        ```
+    Category:
+        Feature flags
+    """
+    return _proxy(
+        "get_all_flags",
+        distinct_id=distinct_id,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        disable_geoip=disable_geoip,
+        device_id=device_id,
+    )
+
+
+def get_feature_flag_result(
+    key,
+    distinct_id,
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,
+    send_feature_flag_events=True,
+    disable_geoip=None,  # type: Optional[bool]
+    device_id=None,  # type: Optional[str]
+):
+    # type: (...) -> Optional[FeatureFlagResult]
+    """
+    Get a FeatureFlagResult object which contains the flag result and payload.
+
+    This method evaluates a feature flag and returns a FeatureFlagResult object containing:
+    - enabled: Whether the flag is enabled
+    - variant: The variant value if the flag has variants
+    - payload: The payload associated with the flag (automatically deserialized from JSON)
+    - key: The flag key
+    - reason: Why the flag was enabled/disabled
+
+    Example:
     ```python
-    posthog.capture('distinct id', 'movie played', {'movie_id': '123', 'category': 'romcom'})
+    result = posthog.get_feature_flag_result('beta-feature', 'distinct_id')
+    if result and result.enabled:
+        # Use the variant and payload
+        print(f"Variant: {result.variant}")
+        print(f"Payload: {result.payload}")
     ```
     """
-    _proxy('capture', distinct_id=distinct_id, event=event, properties=properties, context=context, timestamp=timestamp, message_id=message_id)
+    return _proxy(
+        "get_feature_flag_result",
+        key=key,
+        distinct_id=distinct_id,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        send_feature_flag_events=send_feature_flag_events,
+        disable_geoip=disable_geoip,
+        device_id=device_id,
+    )
 
-def identify(distinct_id: str, properties: Optional[Dict]=None, context: Optional[Dict]=None, timestamp: Optional[str]=None,
-                message_id=None) -> None:
+
+def get_feature_flag_payload(
+    key,
+    distinct_id,
+    match_value=None,
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,
+    send_feature_flag_events=True,
+    disable_geoip=None,  # type: Optional[bool]
+    device_id=None,  # type: Optional[str]
+) -> Optional[str]:
+    return _proxy(
+        "get_feature_flag_payload",
+        key=key,
+        distinct_id=distinct_id,
+        match_value=match_value,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        send_feature_flag_events=send_feature_flag_events,
+        disable_geoip=disable_geoip,
+        device_id=device_id,
+    )
+
+
+def get_remote_config_payload(
+    key,  # type: str
+):
+    """Get the payload for a remote config feature flag.
+
+    Args:
+        key: The key of the feature flag
+
+    Returns:
+        The payload associated with the feature flag. If payload is encrypted, the return value will decrypted
+
+    Note:
+        Requires personal_api_key to be set for authentication
     """
-    Identify lets you add metadata on your users so you can more easily identify who they are in PostHog, and even do things like segment users by these properties.
+    return _proxy(
+        "get_remote_config_payload",
+        key=key,
+    )
 
-    An `identify` call requires
-    - `distinct id` which uniquely identifies your user
-    - `properties` with a dict with any key: value pairs 
 
-    For example:
-    ```python
-    posthog.capture('distinct id', {
-        'email': 'dwayne@gmail.com',
-        'name': 'Dwayne Johnson'
-    })
-    ```
+def get_all_flags_and_payloads(
+    distinct_id,
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,
+    disable_geoip=None,  # type: Optional[bool]
+    device_id=None,  # type: Optional[str]
+) -> FlagsAndPayloads:
+    return _proxy(
+        "get_all_flags_and_payloads",
+        distinct_id=distinct_id,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        disable_geoip=disable_geoip,
+        device_id=device_id,
+    )
+
+
+def feature_flag_definitions():
     """
-    _proxy('identify', distinct_id=distinct_id, properties=properties, context=context, timestamp=timestamp, message_id=message_id)
+    Returns loaded feature flags.
 
-def group(*args, **kwargs):
-    """Send a group call."""
-    _proxy('group', *args, **kwargs)
+    Details:
+        Returns loaded feature flags, if any. Helpful for debugging what flag information you have loaded.
 
+    Examples:
+        ```python
+        from posthog import feature_flag_definitions
+        definitions = feature_flag_definitions()
+        ```
 
-def alias(previous_id: str, distinct_id: str, context: Optional[Dict]=None, timestamp: Optional[str]=None, message_id: Optional[str]=None) -> None:
+    Category:
+        Feature flags
     """
-    To marry up whatever a user does before they sign up or log in with what they do after you need to make an alias call. This will allow you to answer questions like "Which marketing channels leads to users churning after a month?" or "What do users do on our website before signing up?"
+    return _proxy("feature_flag_definitions")
 
-    In a purely back-end implementation, this means whenever an anonymous user does something, you'll want to send a session ID ([Django](https://stackoverflow.com/questions/526179/in-django-how-can-i-find-out-the-request-session-sessionid-and-use-it-as-a-vari), [Flask](https://stackoverflow.com/questions/15156132/flask-login-how-to-get-session-id)) with the capture call. Then, when that users signs up, you want to do an alias call with the session ID and the newly created user ID.
 
-    The same concept applies for when a user logs in.
-
-    An `alias` call requires
-    - `previous distinct id` the unique ID of the user before
-    - `distinct id` the current unique id
-
-    For example:
-    ```python
-    posthog.alias('anonymous session id', 'distinct id')
-    ```
+def load_feature_flags():
     """
-    _proxy('alias', previous_id=previous_id, distinct_id=distinct_id, context=context, timestamp=timestamp, message_id=message_id)
+    Load feature flag definitions from PostHog.
 
+    Examples:
+        ```python
+        from posthog import load_feature_flags
+        load_feature_flags()
+        ```
 
-def page(*args, **kwargs):
-    """Send a page call."""
-    _proxy('page', *args, **kwargs)
-
-
-def screen(*args, **kwargs):
-    """Send a screen call."""
-    _proxy('screen', *args, **kwargs)
+    Category:
+        Feature flags
+    """
+    return _proxy("load_feature_flags")
 
 
 def flush():
-    """Tell the client to flush."""
-    _proxy('flush')
+    """
+    Tell the client to flush all queued events.
+
+    Examples:
+        ```python
+        from posthog import flush
+        flush()
+        ```
+
+    Category:
+        Client management
+    """
+    _proxy("flush")
 
 
 def join():
-    """Block program until the client clears the queue"""
-    _proxy('join')
+    """
+    Block program until the client clears the queue. Used during program shutdown. You should use `shutdown()` directly in most cases.
+
+    Examples:
+        ```python
+        from posthog import join
+        join()
+        ```
+
+    Category:
+        Client management
+    """
+    _proxy("join")
 
 
 def shutdown():
-    """Flush all messages and cleanly shutdown the client"""
-    _proxy('flush')
-    _proxy('join')
+    """
+    Flush all messages and cleanly shutdown the client.
+
+    Examples:
+        ```python
+        from posthog import shutdown
+        shutdown()
+        ```
+
+    Category:
+        Client management
+    """
+    _proxy("flush")
+    _proxy("join")
+
+
+def setup() -> Client:
+    global default_client
+    if not default_client:
+        if not api_key:
+            raise ValueError("API key is required")
+        default_client = Client(
+            api_key,
+            host=host,
+            debug=debug,
+            on_error=on_error,
+            send=send,
+            sync_mode=sync_mode,
+            personal_api_key=personal_api_key,
+            poll_interval=poll_interval,
+            disabled=disabled,
+            disable_geoip=disable_geoip,
+            feature_flags_request_timeout_seconds=feature_flags_request_timeout_seconds,
+            super_properties=super_properties,
+            # TODO: Currently this monitoring begins only when the Client is initialised (which happens when you do something with the SDK)
+            # This kind of initialisation is very annoying for exception capture. We need to figure out a way around this,
+            # or deprecate this proxy option fully (it's already in the process of deprecation, no new clients should be using this method since like 5-6 months)
+            enable_exception_autocapture=enable_exception_autocapture,
+            log_captured_exceptions=log_captured_exceptions,
+            enable_local_evaluation=enable_local_evaluation,
+            capture_exception_code_variables=capture_exception_code_variables,
+            code_variables_mask_patterns=code_variables_mask_patterns,
+            code_variables_ignore_patterns=code_variables_ignore_patterns,
+            in_app_modules=in_app_modules,
+        )
+
+    # always set incase user changes it
+    default_client.disabled = disabled
+    default_client.debug = debug
+
+    return default_client
 
 
 def _proxy(method, *args, **kwargs):
     """Create an analytics client if one doesn't exist and send to it."""
-    global default_client
-    if disabled:
-        return None
-    if not default_client:
-        default_client = Client(api_key, host=host, debug=debug,
-                                on_error=on_error, send=send,
-                                sync_mode=sync_mode)
+    setup()
 
     fn = getattr(default_client, method)
-    fn(*args, **kwargs)
+    return fn(*args, **kwargs)
+
+
+class Posthog(Client):
+    pass

posthog/ai/__init__.py

@@ -0,0 +1,3 @@
+from posthog.ai.prompts import Prompts
+
+__all__ = ["Prompts"]

posthog/ai/anthropic/__init__.py

@@ -0,0 +1,27 @@
+from .anthropic import Anthropic
+from .anthropic_async import AsyncAnthropic
+from .anthropic_providers import (
+    AnthropicBedrock,
+    AnthropicVertex,
+    AsyncAnthropicBedrock,
+    AsyncAnthropicVertex,
+)
+from .anthropic_converter import (
+    format_anthropic_response,
+    format_anthropic_input,
+    extract_anthropic_tools,
+    format_anthropic_streaming_content,
+)
+
+__all__ = [
+    "Anthropic",
+    "AsyncAnthropic",
+    "AnthropicBedrock",
+    "AsyncAnthropicBedrock",
+    "AnthropicVertex",
+    "AsyncAnthropicVertex",
+    "format_anthropic_response",
+    "format_anthropic_input",
+    "extract_anthropic_tools",
+    "format_anthropic_streaming_content",
+]

posthog/ai/anthropic/anthropic.py

@@ -0,0 +1,248 @@
+try:
+    import anthropic
+    from anthropic.resources import Messages
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the Anthropic SDK to use this feature: 'pip install anthropic'"
+    )
+
+import time
+import uuid
+from typing import Any, Dict, List, Optional
+
+from posthog.ai.types import StreamingContentBlock, TokenUsage, ToolInProgress
+from posthog.ai.utils import (
+    call_llm_and_track_usage,
+    merge_usage_stats,
+)
+from posthog.ai.anthropic.anthropic_converter import (
+    extract_anthropic_usage_from_event,
+    handle_anthropic_content_block_start,
+    handle_anthropic_text_delta,
+    handle_anthropic_tool_delta,
+    finalize_anthropic_tool_input,
+)
+from posthog.ai.sanitization import sanitize_anthropic
+from posthog.client import Client as PostHogClient
+from posthog import setup
+
+
+class Anthropic(anthropic.Anthropic):
+    """
+    A wrapper around the Anthropic SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        """
+        Args:
+            posthog_client: PostHog client for tracking usage
+            **kwargs: Additional arguments passed to the Anthropic client
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+        self.messages = WrappedMessages(self)
+
+
+class WrappedMessages(Messages):
+    _client: Anthropic
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create a message using Anthropic's API while tracking usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event
+            posthog_trace_id: Optional trace UUID for linking events
+            posthog_properties: Optional dictionary of extra properties to include in the event
+            posthog_privacy_mode: Whether to redact sensitive information in tracking
+            posthog_groups: Optional group analytics properties
+            **kwargs: Arguments passed to Anthropic's messages.create
+        """
+
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        if kwargs.get("stream", False):
+            return self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "anthropic",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            super().create,
+            **kwargs,
+        )
+
+    def stream(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        return self._create_streaming(
+            posthog_distinct_id,
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            **kwargs,
+        )
+
+    def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
+        accumulated_content = ""
+        content_blocks: List[StreamingContentBlock] = []
+        tools_in_progress: Dict[str, ToolInProgress] = {}
+        current_text_block: Optional[StreamingContentBlock] = None
+        response = super().create(**kwargs)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+            nonlocal content_blocks
+            nonlocal tools_in_progress
+            nonlocal current_text_block
+
+            try:
+                for event in response:
+                    # Extract usage stats from event
+                    event_usage = extract_anthropic_usage_from_event(event)
+                    merge_usage_stats(usage_stats, event_usage)
+
+                    # Handle content block start events
+                    if hasattr(event, "type") and event.type == "content_block_start":
+                        block, tool = handle_anthropic_content_block_start(event)
+
+                        if block:
+                            content_blocks.append(block)
+
+                            if block.get("type") == "text":
+                                current_text_block = block
+                            else:
+                                current_text_block = None
+
+                        if tool:
+                            tool_id = tool["block"].get("id")
+                            if tool_id:
+                                tools_in_progress[tool_id] = tool
+
+                    # Handle text delta events
+                    delta_text = handle_anthropic_text_delta(event, current_text_block)
+
+                    if delta_text:
+                        accumulated_content += delta_text
+
+                    # Handle tool input delta events
+                    handle_anthropic_tool_delta(
+                        event, content_blocks, tools_in_progress
+                    )
+
+                    # Handle content block stop events
+                    if hasattr(event, "type") and event.type == "content_block_stop":
+                        current_text_block = None
+                        finalize_anthropic_tool_input(
+                            event, content_blocks, tools_in_progress
+                        )
+
+                    yield event
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+
+                self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    content_blocks,
+                    accumulated_content,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        content_blocks: List[StreamingContentBlock],
+        accumulated_content: str,
+    ):
+        from posthog.ai.types import StreamingEventData
+        from posthog.ai.anthropic.anthropic_converter import (
+            format_anthropic_streaming_input,
+            format_anthropic_streaming_output_complete,
+        )
+        from posthog.ai.utils import capture_streaming_event
+
+        # Prepare standardized event data
+        formatted_input = format_anthropic_streaming_input(kwargs)
+        sanitized_input = sanitize_anthropic(formatted_input)
+
+        event_data = StreamingEventData(
+            provider="anthropic",
+            model=kwargs.get("model", "unknown"),
+            base_url=str(self._client.base_url),
+            kwargs=kwargs,
+            formatted_input=sanitized_input,
+            formatted_output=format_anthropic_streaming_output_complete(
+                content_blocks, accumulated_content
+            ),
+            usage_stats=usage_stats,
+            latency=latency,
+            distinct_id=posthog_distinct_id,
+            trace_id=posthog_trace_id,
+            properties=posthog_properties,
+            privacy_mode=posthog_privacy_mode,
+            groups=posthog_groups,
+        )
+
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)

posthog/ai/anthropic/anthropic_async.py

@@ -0,0 +1,248 @@
+try:
+    import anthropic
+    from anthropic.resources import AsyncMessages
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the Anthropic SDK to use this feature: 'pip install anthropic'"
+    )
+
+import time
+import uuid
+from typing import Any, Dict, List, Optional
+
+from posthog import setup
+from posthog.ai.types import StreamingContentBlock, TokenUsage, ToolInProgress
+from posthog.ai.utils import (
+    call_llm_and_track_usage_async,
+    merge_usage_stats,
+)
+from posthog.ai.anthropic.anthropic_converter import (
+    extract_anthropic_usage_from_event,
+    handle_anthropic_content_block_start,
+    handle_anthropic_text_delta,
+    handle_anthropic_tool_delta,
+    finalize_anthropic_tool_input,
+)
+from posthog.ai.sanitization import sanitize_anthropic
+from posthog.client import Client as PostHogClient
+
+
+class AsyncAnthropic(anthropic.AsyncAnthropic):
+    """
+    An async wrapper around the Anthropic SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        """
+        Args:
+            posthog_client: PostHog client for tracking usage
+            **kwargs: Additional arguments passed to the Anthropic client
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+        self.messages = AsyncWrappedMessages(self)
+
+
+class AsyncWrappedMessages(AsyncMessages):
+    _client: AsyncAnthropic
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create a message using Anthropic's API while tracking usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event
+            posthog_trace_id: Optional trace UUID for linking events
+            posthog_properties: Optional dictionary of extra properties to include in the event
+            posthog_privacy_mode: Whether to redact sensitive information in tracking
+            posthog_groups: Optional group analytics properties
+            **kwargs: Arguments passed to Anthropic's messages.create
+        """
+
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        if kwargs.get("stream", False):
+            return await self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "anthropic",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            super().create,
+            **kwargs,
+        )
+
+    async def stream(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        return await self._create_streaming(
+            posthog_distinct_id,
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            **kwargs,
+        )
+
+    async def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
+        accumulated_content = ""
+        content_blocks: List[StreamingContentBlock] = []
+        tools_in_progress: Dict[str, ToolInProgress] = {}
+        current_text_block: Optional[StreamingContentBlock] = None
+        response = await super().create(**kwargs)
+
+        async def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+            nonlocal content_blocks
+            nonlocal tools_in_progress
+            nonlocal current_text_block
+
+            try:
+                async for event in response:
+                    # Extract usage stats from event
+                    event_usage = extract_anthropic_usage_from_event(event)
+                    merge_usage_stats(usage_stats, event_usage)
+
+                    # Handle content block start events
+                    if hasattr(event, "type") and event.type == "content_block_start":
+                        block, tool = handle_anthropic_content_block_start(event)
+
+                        if block:
+                            content_blocks.append(block)
+
+                            if block.get("type") == "text":
+                                current_text_block = block
+                            else:
+                                current_text_block = None
+
+                        if tool:
+                            tool_id = tool["block"].get("id")
+                            if tool_id:
+                                tools_in_progress[tool_id] = tool
+
+                    # Handle text delta events
+                    delta_text = handle_anthropic_text_delta(event, current_text_block)
+
+                    if delta_text:
+                        accumulated_content += delta_text
+
+                    # Handle tool input delta events
+                    handle_anthropic_tool_delta(
+                        event, content_blocks, tools_in_progress
+                    )
+
+                    # Handle content block stop events
+                    if hasattr(event, "type") and event.type == "content_block_stop":
+                        current_text_block = None
+                        finalize_anthropic_tool_input(
+                            event, content_blocks, tools_in_progress
+                        )
+
+                    yield event
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+
+                await self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    content_blocks,
+                    accumulated_content,
+                )
+
+        return generator()
+
+    async def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        content_blocks: List[StreamingContentBlock],
+        accumulated_content: str,
+    ):
+        from posthog.ai.types import StreamingEventData
+        from posthog.ai.anthropic.anthropic_converter import (
+            format_anthropic_streaming_input,
+            format_anthropic_streaming_output_complete,
+        )
+        from posthog.ai.utils import capture_streaming_event
+
+        # Prepare standardized event data
+        formatted_input = format_anthropic_streaming_input(kwargs)
+        sanitized_input = sanitize_anthropic(formatted_input)
+
+        event_data = StreamingEventData(
+            provider="anthropic",
+            model=kwargs.get("model", "unknown"),
+            base_url=str(self._client.base_url),
+            kwargs=kwargs,
+            formatted_input=sanitized_input,
+            formatted_output=format_anthropic_streaming_output_complete(
+                content_blocks, accumulated_content
+            ),
+            usage_stats=usage_stats,
+            latency=latency,
+            distinct_id=posthog_distinct_id,
+            trace_id=posthog_trace_id,
+            properties=posthog_properties,
+            privacy_mode=posthog_privacy_mode,
+            groups=posthog_groups,
+        )
+
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)

posthog/ai/anthropic/anthropic_converter.py

@@ -0,0 +1,461 @@
+"""
+Anthropic-specific conversion utilities.
+
+This module handles the conversion of Anthropic API responses and inputs
+into standardized formats for PostHog tracking.
+"""
+
+import json
+from typing import Any, Dict, List, Optional, Tuple
+
+from posthog.ai.types import (
+    FormattedContentItem,
+    FormattedFunctionCall,
+    FormattedMessage,
+    FormattedTextContent,
+    StreamingContentBlock,
+    TokenUsage,
+    ToolInProgress,
+)
+from posthog.ai.utils import serialize_raw_usage
+
+
+def format_anthropic_response(response: Any) -> List[FormattedMessage]:
+    """
+    Format an Anthropic response into standardized message format.
+
+    Args:
+        response: The response object from Anthropic API
+
+    Returns:
+        List of formatted messages with role and content
+    """
+
+    output: List[FormattedMessage] = []
+
+    if response is None:
+        return output
+
+    content: List[FormattedContentItem] = []
+
+    # Process content blocks from the response
+    if hasattr(response, "content"):
+        for choice in response.content:
+            if (
+                hasattr(choice, "type")
+                and choice.type == "text"
+                and hasattr(choice, "text")
+                and choice.text
+            ):
+                text_content: FormattedTextContent = {
+                    "type": "text",
+                    "text": choice.text,
+                }
+                content.append(text_content)
+
+            elif (
+                hasattr(choice, "type")
+                and choice.type == "tool_use"
+                and hasattr(choice, "name")
+                and hasattr(choice, "id")
+            ):
+                function_call: FormattedFunctionCall = {
+                    "type": "function",
+                    "id": choice.id,
+                    "function": {
+                        "name": choice.name,
+                        "arguments": getattr(choice, "input", {}),
+                    },
+                }
+                content.append(function_call)
+
+    if content:
+        message: FormattedMessage = {
+            "role": "assistant",
+            "content": content,
+        }
+        output.append(message)
+
+    return output
+
+
+def format_anthropic_input(
+    messages: List[Dict[str, Any]], system: Optional[str] = None
+) -> List[FormattedMessage]:
+    """
+    Format Anthropic input messages with optional system prompt.
+
+    Args:
+        messages: List of message dictionaries
+        system: Optional system prompt to prepend
+
+    Returns:
+        List of formatted messages
+    """
+
+    formatted_messages: List[FormattedMessage] = []
+
+    # Add system message if provided
+    if system is not None:
+        formatted_messages.append({"role": "system", "content": system})
+
+    # Add user messages
+    if messages:
+        for msg in messages:
+            # Messages are already in the correct format, just ensure type safety
+            formatted_msg: FormattedMessage = {
+                "role": msg.get("role", "user"),
+                "content": msg.get("content", ""),
+            }
+            formatted_messages.append(formatted_msg)
+
+    return formatted_messages
+
+
+def extract_anthropic_tools(kwargs: Dict[str, Any]) -> Optional[Any]:
+    """
+    Extract tool definitions from Anthropic API kwargs.
+
+    Args:
+        kwargs: Keyword arguments passed to Anthropic API
+
+    Returns:
+        Tool definitions if present, None otherwise
+    """
+
+    return kwargs.get("tools", None)
+
+
+def format_anthropic_streaming_content(
+    content_blocks: List[StreamingContentBlock],
+) -> List[FormattedContentItem]:
+    """
+    Format content blocks from Anthropic streaming response.
+
+    Used by streaming handlers to format accumulated content blocks.
+
+    Args:
+        content_blocks: List of content block dictionaries from streaming
+
+    Returns:
+        List of formatted content items
+    """
+
+    formatted: List[FormattedContentItem] = []
+
+    for block in content_blocks:
+        if block.get("type") == "text":
+            formatted.append(
+                {
+                    "type": "text",
+                    "text": block.get("text") or "",
+                }
+            )
+
+        elif block.get("type") == "function":
+            formatted.append(
+                {
+                    "type": "function",
+                    "id": block.get("id"),
+                    "function": block.get("function") or {},
+                }
+            )
+
+    return formatted
+
+
+def extract_anthropic_web_search_count(response: Any) -> int:
+    """
+    Extract web search count from Anthropic response.
+
+    Anthropic provides exact web search counts via usage.server_tool_use.web_search_requests.
+
+    Args:
+        response: The response from Anthropic API
+
+    Returns:
+        Number of web search requests (0 if none)
+    """
+    if not hasattr(response, "usage"):
+        return 0
+
+    if not hasattr(response.usage, "server_tool_use"):
+        return 0
+
+    server_tool_use = response.usage.server_tool_use
+
+    if hasattr(server_tool_use, "web_search_requests"):
+        return max(0, int(getattr(server_tool_use, "web_search_requests", 0)))
+
+    return 0
+
+
+def extract_anthropic_usage_from_response(response: Any) -> TokenUsage:
+    """
+    Extract usage from a full Anthropic response (non-streaming).
+
+    Args:
+        response: The complete response from Anthropic API
+
+    Returns:
+        TokenUsage with standardized usage
+    """
+    if not hasattr(response, "usage"):
+        return TokenUsage(input_tokens=0, output_tokens=0)
+
+    result = TokenUsage(
+        input_tokens=getattr(response.usage, "input_tokens", 0),
+        output_tokens=getattr(response.usage, "output_tokens", 0),
+    )
+
+    if hasattr(response.usage, "cache_read_input_tokens"):
+        cache_read = response.usage.cache_read_input_tokens
+        if cache_read and cache_read > 0:
+            result["cache_read_input_tokens"] = cache_read
+
+    if hasattr(response.usage, "cache_creation_input_tokens"):
+        cache_creation = response.usage.cache_creation_input_tokens
+        if cache_creation and cache_creation > 0:
+            result["cache_creation_input_tokens"] = cache_creation
+
+    web_search_count = extract_anthropic_web_search_count(response)
+    if web_search_count > 0:
+        result["web_search_count"] = web_search_count
+
+    # Capture raw usage metadata for backend processing
+    # Serialize to dict here in the converter (not in utils)
+    serialized = serialize_raw_usage(response.usage)
+    if serialized:
+        result["raw_usage"] = serialized
+
+    return result
+
+
+def extract_anthropic_usage_from_event(event: Any) -> TokenUsage:
+    """
+    Extract usage statistics from an Anthropic streaming event.
+
+    Args:
+        event: Streaming event from Anthropic API
+
+    Returns:
+        Dictionary of usage statistics
+    """
+
+    usage: TokenUsage = TokenUsage()
+
+    # Handle usage stats from message_start event
+    if hasattr(event, "type") and event.type == "message_start":
+        if hasattr(event, "message") and hasattr(event.message, "usage"):
+            usage["input_tokens"] = getattr(event.message.usage, "input_tokens", 0)
+            usage["cache_creation_input_tokens"] = getattr(
+                event.message.usage, "cache_creation_input_tokens", 0
+            )
+            usage["cache_read_input_tokens"] = getattr(
+                event.message.usage, "cache_read_input_tokens", 0
+            )
+            # Capture raw usage metadata for backend processing
+            # Serialize to dict here in the converter (not in utils)
+            serialized = serialize_raw_usage(event.message.usage)
+            if serialized:
+                usage["raw_usage"] = serialized
+
+    # Handle usage stats from message_delta event
+    if hasattr(event, "usage") and event.usage:
+        usage["output_tokens"] = getattr(event.usage, "output_tokens", 0)
+
+        # Extract web search count from usage
+        if hasattr(event.usage, "server_tool_use"):
+            server_tool_use = event.usage.server_tool_use
+            if hasattr(server_tool_use, "web_search_requests"):
+                web_search_count = int(
+                    getattr(server_tool_use, "web_search_requests", 0)
+                )
+                if web_search_count > 0:
+                    usage["web_search_count"] = web_search_count
+
+        # Capture raw usage metadata for backend processing
+        # Serialize to dict here in the converter (not in utils)
+        serialized = serialize_raw_usage(event.usage)
+        if serialized:
+            usage["raw_usage"] = serialized
+
+    return usage
+
+
+def handle_anthropic_content_block_start(
+    event: Any,
+) -> Tuple[Optional[StreamingContentBlock], Optional[ToolInProgress]]:
+    """
+    Handle content block start event from Anthropic streaming.
+
+    Args:
+        event: Content block start event
+
+    Returns:
+        Tuple of (content_block, tool_in_progress)
+    """
+
+    if not (hasattr(event, "type") and event.type == "content_block_start"):
+        return None, None
+
+    if not hasattr(event, "content_block"):
+        return None, None
+
+    block = event.content_block
+
+    if not hasattr(block, "type"):
+        return None, None
+
+    if block.type == "text":
+        content_block: StreamingContentBlock = {"type": "text", "text": ""}
+        return content_block, None
+
+    elif block.type == "tool_use":
+        tool_block: StreamingContentBlock = {
+            "type": "function",
+            "id": getattr(block, "id", ""),
+            "function": {"name": getattr(block, "name", ""), "arguments": {}},
+        }
+        tool_in_progress: ToolInProgress = {"block": tool_block, "input_string": ""}
+        return tool_block, tool_in_progress
+
+    return None, None
+
+
+def handle_anthropic_text_delta(
+    event: Any, current_block: Optional[StreamingContentBlock]
+) -> Optional[str]:
+    """
+    Handle text delta event from Anthropic streaming.
+
+    Args:
+        event: Delta event
+        current_block: Current text block being accumulated
+
+    Returns:
+        Text delta if present
+    """
+
+    if hasattr(event, "delta") and hasattr(event.delta, "text"):
+        delta_text = event.delta.text or ""
+
+        if current_block is not None and current_block.get("type") == "text":
+            text_val = current_block.get("text")
+            if text_val is not None:
+                current_block["text"] = text_val + delta_text
+            else:
+                current_block["text"] = delta_text
+
+        return delta_text
+
+    return None
+
+
+def handle_anthropic_tool_delta(
+    event: Any,
+    content_blocks: List[StreamingContentBlock],
+    tools_in_progress: Dict[str, ToolInProgress],
+) -> None:
+    """
+    Handle tool input delta event from Anthropic streaming.
+
+    Args:
+        event: Tool delta event
+        content_blocks: List of content blocks
+        tools_in_progress: Dictionary tracking tools being accumulated
+    """
+
+    if not (hasattr(event, "type") and event.type == "content_block_delta"):
+        return
+
+    if not (
+        hasattr(event, "delta")
+        and hasattr(event.delta, "type")
+        and event.delta.type == "input_json_delta"
+    ):
+        return
+
+    if hasattr(event, "index") and event.index < len(content_blocks):
+        block = content_blocks[event.index]
+
+        if block.get("type") == "function" and block.get("id") in tools_in_progress:
+            tool = tools_in_progress[block["id"]]
+            partial_json = getattr(event.delta, "partial_json", "")
+            tool["input_string"] += partial_json
+
+
+def finalize_anthropic_tool_input(
+    event: Any,
+    content_blocks: List[StreamingContentBlock],
+    tools_in_progress: Dict[str, ToolInProgress],
+) -> None:
+    """
+    Finalize tool input when content block stops.
+
+    Args:
+        event: Content block stop event
+        content_blocks: List of content blocks
+        tools_in_progress: Dictionary tracking tools being accumulated
+    """
+
+    if not (hasattr(event, "type") and event.type == "content_block_stop"):
+        return
+
+    if hasattr(event, "index") and event.index < len(content_blocks):
+        block = content_blocks[event.index]
+
+        if block.get("type") == "function" and block.get("id") in tools_in_progress:
+            tool = tools_in_progress[block["id"]]
+
+            try:
+                block["function"]["arguments"] = json.loads(tool["input_string"])
+            except (json.JSONDecodeError, Exception):
+                # Keep empty dict if parsing fails
+                pass
+
+            del tools_in_progress[block["id"]]
+
+
+def format_anthropic_streaming_input(kwargs: Dict[str, Any]) -> Any:
+    """
+    Format Anthropic streaming input using system prompt merging.
+
+    Args:
+        kwargs: Keyword arguments passed to Anthropic API
+
+    Returns:
+        Formatted input ready for PostHog tracking
+    """
+    from posthog.ai.utils import merge_system_prompt
+
+    return merge_system_prompt(kwargs, "anthropic")
+
+
+def format_anthropic_streaming_output_complete(
+    content_blocks: List[StreamingContentBlock], accumulated_content: str
+) -> List[FormattedMessage]:
+    """
+    Format complete Anthropic streaming output.
+
+    Combines existing logic for formatting content blocks with fallback to accumulated content.
+
+    Args:
+        content_blocks: List of content blocks accumulated during streaming
+        accumulated_content: Raw accumulated text content as fallback
+
+    Returns:
+        Formatted messages ready for PostHog tracking
+    """
+    formatted_content = format_anthropic_streaming_content(content_blocks)
+
+    if formatted_content:
+        return [{"role": "assistant", "content": formatted_content}]
+    else:
+        # Fallback to accumulated content if no blocks
+        return [
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": accumulated_content}],
+            }
+        ]

posthog/ai/anthropic/anthropic_providers.py

@@ -0,0 +1,65 @@
+try:
+    import anthropic
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the Anthropic SDK to use this feature: 'pip install anthropic'"
+    )
+
+from typing import Optional
+
+from posthog.ai.anthropic.anthropic import WrappedMessages
+from posthog.ai.anthropic.anthropic_async import AsyncWrappedMessages
+from posthog.client import Client as PostHogClient
+from posthog import setup
+
+
+class AnthropicBedrock(anthropic.AnthropicBedrock):
+    """
+    A wrapper around the Anthropic Bedrock SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+        self.messages = WrappedMessages(self)
+
+
+class AsyncAnthropicBedrock(anthropic.AsyncAnthropicBedrock):
+    """
+    A wrapper around the Anthropic Bedrock SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+        self.messages = AsyncWrappedMessages(self)
+
+
+class AnthropicVertex(anthropic.AnthropicVertex):
+    """
+    A wrapper around the Anthropic Vertex SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+        self.messages = WrappedMessages(self)
+
+
+class AsyncAnthropicVertex(anthropic.AsyncAnthropicVertex):
+    """
+    A wrapper around the Anthropic Vertex SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+        self.messages = AsyncWrappedMessages(self)

posthog/ai/gemini/__init__.py

@@ -0,0 +1,25 @@
+from .gemini import Client
+from .gemini_async import AsyncClient
+from .gemini_converter import (
+    format_gemini_input,
+    format_gemini_response,
+    extract_gemini_tools,
+)
+
+
+# Create a genai-like module for perfect drop-in replacement
+class _GenAI:
+    Client = Client
+    AsyncClient = AsyncClient
+
+
+genai = _GenAI()
+
+__all__ = [
+    "Client",
+    "AsyncClient",
+    "genai",
+    "format_gemini_input",
+    "format_gemini_response",
+    "extract_gemini_tools",
+]

posthog/ai/gemini/gemini.py

@@ -0,0 +1,420 @@
+import os
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+from posthog.ai.types import TokenUsage, StreamingEventData
+from posthog.ai.utils import merge_system_prompt
+
+try:
+    from google import genai
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the Google Gemini SDK to use this feature: 'pip install google-genai'"
+    )
+
+from posthog import setup
+from posthog.ai.utils import (
+    call_llm_and_track_usage,
+    capture_streaming_event,
+    merge_usage_stats,
+)
+from posthog.ai.gemini.gemini_converter import (
+    extract_gemini_usage_from_chunk,
+    extract_gemini_content_from_chunk,
+    format_gemini_streaming_output,
+)
+from posthog.ai.sanitization import sanitize_gemini
+from posthog.client import Client as PostHogClient
+
+
+class Client:
+    """
+    A drop-in replacement for genai.Client that automatically sends LLM usage events to PostHog.
+
+    Usage:
+        client = Client(
+            api_key="your_api_key",
+            posthog_client=posthog_client,
+            posthog_distinct_id="default_user",  # Optional defaults
+            posthog_properties={"team": "ai"}    # Optional defaults
+        )
+        response = client.models.generate_content(
+            model="gemini-2.0-flash",
+            contents=["Hello world"],
+            posthog_distinct_id="specific_user"  # Override default
+        )
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        vertexai: Optional[bool] = None,
+        credentials: Optional[Any] = None,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+        debug_config: Optional[Any] = None,
+        http_options: Optional[Any] = None,
+        posthog_client: Optional[PostHogClient] = None,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable (not required for Vertex AI)
+            vertexai: Whether to use Vertex AI authentication
+            credentials: Vertex AI credentials object
+            project: GCP project ID for Vertex AI
+            location: GCP location for Vertex AI
+            debug_config: Debug configuration for the client
+            http_options: HTTP options for the client
+            posthog_client: PostHog client for tracking usage
+            posthog_distinct_id: Default distinct ID for all calls (can be overridden per call)
+            posthog_properties: Default properties for all calls (can be overridden per call)
+            posthog_privacy_mode: Default privacy mode for all calls (can be overridden per call)
+            posthog_groups: Default groups for all calls (can be overridden per call)
+            **kwargs: Additional arguments (for future compatibility)
+        """
+
+        self._ph_client = posthog_client or setup()
+
+        if self._ph_client is None:
+            raise ValueError("posthog_client is required for PostHog tracking")
+
+        self.models = Models(
+            api_key=api_key,
+            vertexai=vertexai,
+            credentials=credentials,
+            project=project,
+            location=location,
+            debug_config=debug_config,
+            http_options=http_options,
+            posthog_client=self._ph_client,
+            posthog_distinct_id=posthog_distinct_id,
+            posthog_properties=posthog_properties,
+            posthog_privacy_mode=posthog_privacy_mode,
+            posthog_groups=posthog_groups,
+            **kwargs,
+        )
+
+
+class Models:
+    """
+    Models interface that mimics genai.Client().models with PostHog tracking.
+    """
+
+    _ph_client: PostHogClient  # Not None after __init__ validation
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        vertexai: Optional[bool] = None,
+        credentials: Optional[Any] = None,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+        debug_config: Optional[Any] = None,
+        http_options: Optional[Any] = None,
+        posthog_client: Optional[PostHogClient] = None,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable (not required for Vertex AI)
+            vertexai: Whether to use Vertex AI authentication
+            credentials: Vertex AI credentials object
+            project: GCP project ID for Vertex AI
+            location: GCP location for Vertex AI
+            debug_config: Debug configuration for the client
+            http_options: HTTP options for the client
+            posthog_client: PostHog client for tracking usage
+            posthog_distinct_id: Default distinct ID for all calls
+            posthog_properties: Default properties for all calls
+            posthog_privacy_mode: Default privacy mode for all calls
+            posthog_groups: Default groups for all calls
+            **kwargs: Additional arguments (for future compatibility)
+        """
+
+        self._ph_client = posthog_client or setup()
+
+        if self._ph_client is None:
+            raise ValueError("posthog_client is required for PostHog tracking")
+
+        # Store default PostHog settings
+        self._default_distinct_id = posthog_distinct_id
+        self._default_properties = posthog_properties or {}
+        self._default_privacy_mode = posthog_privacy_mode
+        self._default_groups = posthog_groups
+
+        # Build genai.Client arguments
+        client_args: Dict[str, Any] = {}
+
+        # Add Vertex AI parameters if provided
+        if vertexai is not None:
+            client_args["vertexai"] = vertexai
+
+        if credentials is not None:
+            client_args["credentials"] = credentials
+
+        if project is not None:
+            client_args["project"] = project
+
+        if location is not None:
+            client_args["location"] = location
+
+        if debug_config is not None:
+            client_args["debug_config"] = debug_config
+
+        if http_options is not None:
+            client_args["http_options"] = http_options
+
+        # Handle API key authentication
+        if vertexai:
+            # For Vertex AI, api_key is optional
+            if api_key is not None:
+                client_args["api_key"] = api_key
+        else:
+            # For non-Vertex AI mode, api_key is required (backwards compatibility)
+            if api_key is None:
+                api_key = os.environ.get("GOOGLE_API_KEY") or os.environ.get("API_KEY")
+
+            if api_key is None:
+                raise ValueError(
+                    "API key must be provided either as parameter or via GOOGLE_API_KEY/API_KEY environment variable"
+                )
+
+            client_args["api_key"] = api_key
+
+        self._client = genai.Client(**client_args)
+        self._base_url = "https://generativelanguage.googleapis.com"
+
+    def _merge_posthog_params(
+        self,
+        call_distinct_id: Optional[str],
+        call_trace_id: Optional[str],
+        call_properties: Optional[Dict[str, Any]],
+        call_privacy_mode: Optional[bool],
+        call_groups: Optional[Dict[str, Any]],
+    ):
+        """Merge call-level PostHog parameters with client defaults."""
+
+        # Use call-level values if provided, otherwise fall back to defaults
+        distinct_id = (
+            call_distinct_id
+            if call_distinct_id is not None
+            else self._default_distinct_id
+        )
+        privacy_mode = (
+            call_privacy_mode
+            if call_privacy_mode is not None
+            else self._default_privacy_mode
+        )
+        groups = call_groups if call_groups is not None else self._default_groups
+
+        # Merge properties: default properties + call properties (call properties override)
+        properties = dict(self._default_properties)
+
+        if call_properties:
+            properties.update(call_properties)
+
+        if call_trace_id is None:
+            call_trace_id = str(uuid.uuid4())
+
+        return distinct_id, call_trace_id, properties, privacy_mode, groups
+
+    def generate_content(
+        self,
+        model: str,
+        contents,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: Optional[bool] = None,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Generate content using Gemini's API while tracking usage in PostHog.
+
+        This method signature exactly matches genai.Client().models.generate_content()
+        with additional PostHog tracking parameters.
+
+        Args:
+            model: The model to use (e.g., 'gemini-2.0-flash')
+            contents: The input content for generation
+            posthog_distinct_id: ID to associate with the usage event (overrides client default)
+            posthog_trace_id: Trace UUID for linking events (auto-generated if not provided)
+            posthog_properties: Extra properties to include in the event (merged with client defaults)
+            posthog_privacy_mode: Whether to redact sensitive information (overrides client default)
+            posthog_groups: Group analytics properties (overrides client default)
+            **kwargs: Arguments passed to Gemini's generate_content
+        """
+
+        # Merge PostHog parameters
+        distinct_id, trace_id, properties, privacy_mode, groups = (
+            self._merge_posthog_params(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+            )
+        )
+
+        kwargs_with_contents = {"model": model, "contents": contents, **kwargs}
+
+        return call_llm_and_track_usage(
+            distinct_id,
+            self._ph_client,
+            "gemini",
+            trace_id,
+            properties,
+            privacy_mode,
+            groups,
+            self._base_url,
+            self._client.models.generate_content,
+            **kwargs_with_contents,
+        )
+
+    def _generate_content_streaming(
+        self,
+        model: str,
+        contents,
+        distinct_id: Optional[str],
+        trace_id: Optional[str],
+        properties: Optional[Dict[str, Any]],
+        privacy_mode: bool,
+        groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
+        accumulated_content = []
+
+        kwargs_without_stream = {"model": model, "contents": contents, **kwargs}
+        response = self._client.models.generate_content_stream(**kwargs_without_stream)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+            try:
+                for chunk in response:
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_gemini_usage_from_chunk(chunk)
+
+                    if chunk_usage:
+                        # Gemini reports cumulative totals, not incremental values
+                        merge_usage_stats(usage_stats, chunk_usage, mode="cumulative")
+
+                    # Extract content from chunk (now returns content blocks)
+                    content_block = extract_gemini_content_from_chunk(chunk)
+
+                    if content_block is not None:
+                        accumulated_content.append(content_block)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+
+                self._capture_streaming_event(
+                    model,
+                    contents,
+                    distinct_id,
+                    trace_id,
+                    properties,
+                    privacy_mode,
+                    groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    accumulated_content,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        model: str,
+        contents,
+        distinct_id: Optional[str],
+        trace_id: Optional[str],
+        properties: Optional[Dict[str, Any]],
+        privacy_mode: bool,
+        groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        output: Any,
+    ):
+        # Prepare standardized event data
+        formatted_input = self._format_input(contents, **kwargs)
+        sanitized_input = sanitize_gemini(formatted_input)
+
+        event_data = StreamingEventData(
+            provider="gemini",
+            model=model,
+            base_url=self._base_url,
+            kwargs=kwargs,
+            formatted_input=sanitized_input,
+            formatted_output=format_gemini_streaming_output(output),
+            usage_stats=usage_stats,
+            latency=latency,
+            distinct_id=distinct_id,
+            trace_id=trace_id,
+            properties=properties,
+            privacy_mode=privacy_mode,
+            groups=groups,
+        )
+
+        # Use the common capture function
+        capture_streaming_event(self._ph_client, event_data)
+
+    def _format_input(self, contents, **kwargs):
+        """Format input contents for PostHog tracking"""
+
+        # Create kwargs dict with contents for merge_system_prompt
+        input_kwargs = {"contents": contents, **kwargs}
+        return merge_system_prompt(input_kwargs, "gemini")
+
+    def generate_content_stream(
+        self,
+        model: str,
+        contents,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: Optional[bool] = None,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        # Merge PostHog parameters
+        distinct_id, trace_id, properties, privacy_mode, groups = (
+            self._merge_posthog_params(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+            )
+        )
+
+        return self._generate_content_streaming(
+            model,
+            contents,
+            distinct_id,
+            trace_id,
+            properties,
+            privacy_mode,
+            groups,
+            **kwargs,
+        )

posthog/ai/gemini/gemini_async.py

@@ -0,0 +1,423 @@
+import os
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+from posthog.ai.types import TokenUsage, StreamingEventData
+from posthog.ai.utils import merge_system_prompt
+
+try:
+    from google import genai
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the Google Gemini SDK to use this feature: 'pip install google-genai'"
+    )
+
+from posthog import setup
+from posthog.ai.utils import (
+    call_llm_and_track_usage_async,
+    capture_streaming_event,
+    merge_usage_stats,
+)
+from posthog.ai.gemini.gemini_converter import (
+    extract_gemini_usage_from_chunk,
+    extract_gemini_content_from_chunk,
+    format_gemini_streaming_output,
+)
+from posthog.ai.sanitization import sanitize_gemini
+from posthog.client import Client as PostHogClient
+
+
+class AsyncClient:
+    """
+    An async drop-in replacement for genai.Client that automatically sends LLM usage events to PostHog.
+
+    Usage:
+        client = AsyncClient(
+            api_key="your_api_key",
+            posthog_client=posthog_client,
+            posthog_distinct_id="default_user",  # Optional defaults
+            posthog_properties={"team": "ai"}    # Optional defaults
+        )
+        response = await client.models.generate_content(
+            model="gemini-2.0-flash",
+            contents=["Hello world"],
+            posthog_distinct_id="specific_user"  # Override default
+        )
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        vertexai: Optional[bool] = None,
+        credentials: Optional[Any] = None,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+        debug_config: Optional[Any] = None,
+        http_options: Optional[Any] = None,
+        posthog_client: Optional[PostHogClient] = None,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable (not required for Vertex AI)
+            vertexai: Whether to use Vertex AI authentication
+            credentials: Vertex AI credentials object
+            project: GCP project ID for Vertex AI
+            location: GCP location for Vertex AI
+            debug_config: Debug configuration for the client
+            http_options: HTTP options for the client
+            posthog_client: PostHog client for tracking usage
+            posthog_distinct_id: Default distinct ID for all calls (can be overridden per call)
+            posthog_properties: Default properties for all calls (can be overridden per call)
+            posthog_privacy_mode: Default privacy mode for all calls (can be overridden per call)
+            posthog_groups: Default groups for all calls (can be overridden per call)
+            **kwargs: Additional arguments (for future compatibility)
+        """
+
+        self._ph_client = posthog_client or setup()
+
+        if self._ph_client is None:
+            raise ValueError("posthog_client is required for PostHog tracking")
+
+        self.models = AsyncModels(
+            api_key=api_key,
+            vertexai=vertexai,
+            credentials=credentials,
+            project=project,
+            location=location,
+            debug_config=debug_config,
+            http_options=http_options,
+            posthog_client=self._ph_client,
+            posthog_distinct_id=posthog_distinct_id,
+            posthog_properties=posthog_properties,
+            posthog_privacy_mode=posthog_privacy_mode,
+            posthog_groups=posthog_groups,
+            **kwargs,
+        )
+
+
+class AsyncModels:
+    """
+    Async Models interface that mimics genai.Client().aio.models with PostHog tracking.
+    """
+
+    _ph_client: PostHogClient  # Not None after __init__ validation
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        vertexai: Optional[bool] = None,
+        credentials: Optional[Any] = None,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+        debug_config: Optional[Any] = None,
+        http_options: Optional[Any] = None,
+        posthog_client: Optional[PostHogClient] = None,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable (not required for Vertex AI)
+            vertexai: Whether to use Vertex AI authentication
+            credentials: Vertex AI credentials object
+            project: GCP project ID for Vertex AI
+            location: GCP location for Vertex AI
+            debug_config: Debug configuration for the client
+            http_options: HTTP options for the client
+            posthog_client: PostHog client for tracking usage
+            posthog_distinct_id: Default distinct ID for all calls
+            posthog_properties: Default properties for all calls
+            posthog_privacy_mode: Default privacy mode for all calls
+            posthog_groups: Default groups for all calls
+            **kwargs: Additional arguments (for future compatibility)
+        """
+
+        self._ph_client = posthog_client or setup()
+
+        if self._ph_client is None:
+            raise ValueError("posthog_client is required for PostHog tracking")
+
+        # Store default PostHog settings
+        self._default_distinct_id = posthog_distinct_id
+        self._default_properties = posthog_properties or {}
+        self._default_privacy_mode = posthog_privacy_mode
+        self._default_groups = posthog_groups
+
+        # Build genai.Client arguments
+        client_args: Dict[str, Any] = {}
+
+        # Add Vertex AI parameters if provided
+        if vertexai is not None:
+            client_args["vertexai"] = vertexai
+
+        if credentials is not None:
+            client_args["credentials"] = credentials
+
+        if project is not None:
+            client_args["project"] = project
+
+        if location is not None:
+            client_args["location"] = location
+
+        if debug_config is not None:
+            client_args["debug_config"] = debug_config
+
+        if http_options is not None:
+            client_args["http_options"] = http_options
+
+        # Handle API key authentication
+        if vertexai:
+            # For Vertex AI, api_key is optional
+            if api_key is not None:
+                client_args["api_key"] = api_key
+        else:
+            # For non-Vertex AI mode, api_key is required (backwards compatibility)
+            if api_key is None:
+                api_key = os.environ.get("GOOGLE_API_KEY") or os.environ.get("API_KEY")
+
+            if api_key is None:
+                raise ValueError(
+                    "API key must be provided either as parameter or via GOOGLE_API_KEY/API_KEY environment variable"
+                )
+
+            client_args["api_key"] = api_key
+
+        self._client = genai.Client(**client_args)
+        self._base_url = "https://generativelanguage.googleapis.com"
+
+    def _merge_posthog_params(
+        self,
+        call_distinct_id: Optional[str],
+        call_trace_id: Optional[str],
+        call_properties: Optional[Dict[str, Any]],
+        call_privacy_mode: Optional[bool],
+        call_groups: Optional[Dict[str, Any]],
+    ):
+        """Merge call-level PostHog parameters with client defaults."""
+
+        # Use call-level values if provided, otherwise fall back to defaults
+        distinct_id = (
+            call_distinct_id
+            if call_distinct_id is not None
+            else self._default_distinct_id
+        )
+        privacy_mode = (
+            call_privacy_mode
+            if call_privacy_mode is not None
+            else self._default_privacy_mode
+        )
+        groups = call_groups if call_groups is not None else self._default_groups
+
+        # Merge properties: default properties + call properties (call properties override)
+        properties = dict(self._default_properties)
+
+        if call_properties:
+            properties.update(call_properties)
+
+        if call_trace_id is None:
+            call_trace_id = str(uuid.uuid4())
+
+        return distinct_id, call_trace_id, properties, privacy_mode, groups
+
+    async def generate_content(
+        self,
+        model: str,
+        contents,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: Optional[bool] = None,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Generate content using Gemini's API while tracking usage in PostHog.
+
+        This method signature exactly matches genai.Client().aio.models.generate_content()
+        with additional PostHog tracking parameters.
+
+        Args:
+            model: The model to use (e.g., 'gemini-2.0-flash')
+            contents: The input content for generation
+            posthog_distinct_id: ID to associate with the usage event (overrides client default)
+            posthog_trace_id: Trace UUID for linking events (auto-generated if not provided)
+            posthog_properties: Extra properties to include in the event (merged with client defaults)
+            posthog_privacy_mode: Whether to redact sensitive information (overrides client default)
+            posthog_groups: Group analytics properties (overrides client default)
+            **kwargs: Arguments passed to Gemini's generate_content
+        """
+
+        # Merge PostHog parameters
+        distinct_id, trace_id, properties, privacy_mode, groups = (
+            self._merge_posthog_params(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+            )
+        )
+
+        kwargs_with_contents = {"model": model, "contents": contents, **kwargs}
+
+        return await call_llm_and_track_usage_async(
+            distinct_id,
+            self._ph_client,
+            "gemini",
+            trace_id,
+            properties,
+            privacy_mode,
+            groups,
+            self._base_url,
+            self._client.aio.models.generate_content,
+            **kwargs_with_contents,
+        )
+
+    async def _generate_content_streaming(
+        self,
+        model: str,
+        contents,
+        distinct_id: Optional[str],
+        trace_id: Optional[str],
+        properties: Optional[Dict[str, Any]],
+        privacy_mode: bool,
+        groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
+        accumulated_content = []
+
+        kwargs_without_stream = {"model": model, "contents": contents, **kwargs}
+        response = await self._client.aio.models.generate_content_stream(
+            **kwargs_without_stream
+        )
+
+        async def async_generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+
+            try:
+                async for chunk in response:
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_gemini_usage_from_chunk(chunk)
+
+                    if chunk_usage:
+                        # Gemini reports cumulative totals, not incremental values
+                        merge_usage_stats(usage_stats, chunk_usage, mode="cumulative")
+
+                    # Extract content from chunk (now returns content blocks)
+                    content_block = extract_gemini_content_from_chunk(chunk)
+
+                    if content_block is not None:
+                        accumulated_content.append(content_block)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+
+                self._capture_streaming_event(
+                    model,
+                    contents,
+                    distinct_id,
+                    trace_id,
+                    properties,
+                    privacy_mode,
+                    groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    accumulated_content,
+                )
+
+        return async_generator()
+
+    def _capture_streaming_event(
+        self,
+        model: str,
+        contents,
+        distinct_id: Optional[str],
+        trace_id: Optional[str],
+        properties: Optional[Dict[str, Any]],
+        privacy_mode: bool,
+        groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        output: Any,
+    ):
+        # Prepare standardized event data
+        formatted_input = self._format_input(contents, **kwargs)
+        sanitized_input = sanitize_gemini(formatted_input)
+
+        event_data = StreamingEventData(
+            provider="gemini",
+            model=model,
+            base_url=self._base_url,
+            kwargs=kwargs,
+            formatted_input=sanitized_input,
+            formatted_output=format_gemini_streaming_output(output),
+            usage_stats=usage_stats,
+            latency=latency,
+            distinct_id=distinct_id,
+            trace_id=trace_id,
+            properties=properties,
+            privacy_mode=privacy_mode,
+            groups=groups,
+        )
+
+        # Use the common capture function
+        capture_streaming_event(self._ph_client, event_data)
+
+    def _format_input(self, contents, **kwargs):
+        """Format input contents for PostHog tracking"""
+
+        # Create kwargs dict with contents for merge_system_prompt
+        input_kwargs = {"contents": contents, **kwargs}
+        return merge_system_prompt(input_kwargs, "gemini")
+
+    async def generate_content_stream(
+        self,
+        model: str,
+        contents,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: Optional[bool] = None,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        # Merge PostHog parameters
+        distinct_id, trace_id, properties, privacy_mode, groups = (
+            self._merge_posthog_params(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+            )
+        )
+
+        return await self._generate_content_streaming(
+            model,
+            contents,
+            distinct_id,
+            trace_id,
+            properties,
+            privacy_mode,
+            groups,
+            **kwargs,
+        )

posthog/ai/gemini/gemini_converter.py

@@ -0,0 +1,659 @@
+"""
+Gemini-specific conversion utilities.
+
+This module handles the conversion of Gemini API responses and inputs
+into standardized formats for PostHog tracking.
+"""
+
+from typing import Any, Dict, List, Optional, TypedDict, Union
+
+from posthog.ai.types import (
+    FormattedContentItem,
+    FormattedMessage,
+    TokenUsage,
+)
+from posthog.ai.utils import serialize_raw_usage
+
+
+class GeminiPart(TypedDict, total=False):
+    """Represents a part in a Gemini message."""
+
+    text: str
+
+
+class GeminiMessage(TypedDict, total=False):
+    """Represents a Gemini message with various possible fields."""
+
+    role: str
+    parts: List[Union[GeminiPart, Dict[str, Any]]]
+    content: Union[str, List[Any]]
+    text: str
+
+
+def _format_parts_as_content_blocks(parts: List[Any]) -> List[FormattedContentItem]:
+    """
+    Format Gemini parts array into structured content blocks.
+
+    Preserves structure for multimodal content (text + images) instead of
+    concatenating everything into a string.
+
+    Args:
+        parts: List of parts that may contain text, inline_data, etc.
+
+    Returns:
+        List of formatted content blocks
+    """
+    content_blocks: List[FormattedContentItem] = []
+
+    for part in parts:
+        # Handle dict with text field
+        if isinstance(part, dict) and "text" in part:
+            content_blocks.append({"type": "text", "text": part["text"]})
+
+        # Handle string parts
+        elif isinstance(part, str):
+            content_blocks.append({"type": "text", "text": part})
+
+        # Handle dict with inline_data (images, documents, etc.)
+        elif isinstance(part, dict) and "inline_data" in part:
+            inline_data = part["inline_data"]
+            mime_type = inline_data.get("mime_type", "")
+            content_type = "image" if mime_type.startswith("image/") else "document"
+
+            content_blocks.append(
+                {
+                    "type": content_type,
+                    "inline_data": inline_data,
+                }
+            )
+
+        # Handle object with text attribute
+        elif hasattr(part, "text"):
+            text_value = getattr(part, "text", "")
+            if text_value:
+                content_blocks.append({"type": "text", "text": text_value})
+
+        # Handle object with inline_data attribute
+        elif hasattr(part, "inline_data"):
+            inline_data = part.inline_data
+            # Convert to dict if needed
+            if hasattr(inline_data, "mime_type") and hasattr(inline_data, "data"):
+                # Determine type based on mime_type
+                mime_type = inline_data.mime_type
+                content_type = "image" if mime_type.startswith("image/") else "document"
+
+                content_blocks.append(
+                    {
+                        "type": content_type,
+                        "inline_data": {
+                            "mime_type": mime_type,
+                            "data": inline_data.data,
+                        },
+                    }
+                )
+            else:
+                content_blocks.append(
+                    {
+                        "type": "image",
+                        "inline_data": inline_data,
+                    }
+                )
+
+    return content_blocks
+
+
+def _format_dict_message(item: Dict[str, Any]) -> FormattedMessage:
+    """
+    Format a dictionary message into standardized format.
+
+    Args:
+        item: Dictionary containing message data
+
+    Returns:
+        Formatted message with role and content
+    """
+
+    # Handle dict format with parts array (Gemini-specific format)
+    if "parts" in item and isinstance(item["parts"], list):
+        content_blocks = _format_parts_as_content_blocks(item["parts"])
+        return {"role": item.get("role", "user"), "content": content_blocks}
+
+    # Handle dict with content field
+    if "content" in item:
+        content = item["content"]
+
+        if isinstance(content, list):
+            # If content is a list, format it as content blocks
+            content_blocks = _format_parts_as_content_blocks(content)
+            return {"role": item.get("role", "user"), "content": content_blocks}
+
+        elif not isinstance(content, str):
+            content = str(content)
+
+        return {"role": item.get("role", "user"), "content": content}
+
+    # Handle dict with text field
+    if "text" in item:
+        return {"role": item.get("role", "user"), "content": item["text"]}
+
+    # Fallback to string representation
+    return {"role": "user", "content": str(item)}
+
+
+def _format_object_message(item: Any) -> FormattedMessage:
+    """
+    Format an object (with attributes) into standardized format.
+
+    Args:
+        item: Object that may have text or parts attributes
+
+    Returns:
+        Formatted message with role and content
+    """
+
+    # Handle object with parts attribute
+    if hasattr(item, "parts") and hasattr(item.parts, "__iter__"):
+        content_blocks = _format_parts_as_content_blocks(list(item.parts))
+        role = getattr(item, "role", "user") if hasattr(item, "role") else "user"
+
+        # Ensure role is a string
+        if not isinstance(role, str):
+            role = "user"
+
+        return {"role": role, "content": content_blocks}
+
+    # Handle object with text attribute
+    if hasattr(item, "text"):
+        role = getattr(item, "role", "user") if hasattr(item, "role") else "user"
+
+        # Ensure role is a string
+        if not isinstance(role, str):
+            role = "user"
+
+        return {"role": role, "content": item.text}
+
+    # Handle object with content attribute
+    if hasattr(item, "content"):
+        role = getattr(item, "role", "user") if hasattr(item, "role") else "user"
+
+        # Ensure role is a string
+        if not isinstance(role, str):
+            role = "user"
+
+        content = item.content
+
+        if isinstance(content, list):
+            content_blocks = _format_parts_as_content_blocks(content)
+            return {"role": role, "content": content_blocks}
+
+        elif not isinstance(content, str):
+            content = str(content)
+        return {"role": role, "content": content}
+
+    # Fallback to string representation
+    return {"role": "user", "content": str(item)}
+
+
+def format_gemini_response(response: Any) -> List[FormattedMessage]:
+    """
+    Format a Gemini response into standardized message format.
+
+    Args:
+        response: The response object from Gemini API
+
+    Returns:
+        List of formatted messages with role and content
+    """
+
+    output: List[FormattedMessage] = []
+
+    if response is None:
+        return output
+
+    if hasattr(response, "candidates") and response.candidates:
+        for candidate in response.candidates:
+            if hasattr(candidate, "content") and candidate.content:
+                content: List[FormattedContentItem] = []
+
+                if hasattr(candidate.content, "parts") and candidate.content.parts:
+                    for part in candidate.content.parts:
+                        if hasattr(part, "text") and part.text:
+                            content.append(
+                                {
+                                    "type": "text",
+                                    "text": part.text,
+                                }
+                            )
+
+                        elif hasattr(part, "function_call") and part.function_call:
+                            function_call = part.function_call
+                            content.append(
+                                {
+                                    "type": "function",
+                                    "function": {
+                                        "name": function_call.name,
+                                        "arguments": function_call.args,
+                                    },
+                                }
+                            )
+
+                        elif hasattr(part, "inline_data") and part.inline_data:
+                            # Handle audio/media inline data
+                            import base64
+
+                            inline_data = part.inline_data
+                            mime_type = getattr(inline_data, "mime_type", "audio/pcm")
+                            raw_data = getattr(inline_data, "data", b"")
+
+                            # Encode binary data as base64 string for JSON serialization
+                            if isinstance(raw_data, bytes):
+                                data = base64.b64encode(raw_data).decode("utf-8")
+                            else:
+                                # Already a string (base64)
+                                data = raw_data
+
+                            content.append(
+                                {
+                                    "type": "audio",
+                                    "mime_type": mime_type,
+                                    "data": data,
+                                }
+                            )
+
+                if content:
+                    output.append(
+                        {
+                            "role": "assistant",
+                            "content": content,
+                        }
+                    )
+
+            elif hasattr(candidate, "text") and candidate.text:
+                output.append(
+                    {
+                        "role": "assistant",
+                        "content": [{"type": "text", "text": candidate.text}],
+                    }
+                )
+
+    elif hasattr(response, "text") and response.text:
+        output.append(
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": response.text}],
+            }
+        )
+
+    return output
+
+
+def extract_gemini_system_instruction(config: Any) -> Optional[str]:
+    """
+    Extract system instruction from Gemini config parameter.
+
+    Args:
+        config: Config object or dict that may contain system instruction
+
+    Returns:
+        System instruction string if present, None otherwise
+    """
+    if config is None:
+        return None
+
+    # Handle different config formats
+    if hasattr(config, "system_instruction"):
+        return config.system_instruction
+    elif isinstance(config, dict) and "system_instruction" in config:
+        return config["system_instruction"]
+    elif isinstance(config, dict) and "systemInstruction" in config:
+        return config["systemInstruction"]
+
+    return None
+
+
+def extract_gemini_tools(kwargs: Dict[str, Any]) -> Optional[Any]:
+    """
+    Extract tool definitions from Gemini API kwargs.
+
+    Args:
+        kwargs: Keyword arguments passed to Gemini API
+
+    Returns:
+        Tool definitions if present, None otherwise
+    """
+
+    if "config" in kwargs and hasattr(kwargs["config"], "tools"):
+        return kwargs["config"].tools
+
+    return None
+
+
+def format_gemini_input_with_system(
+    contents: Any, config: Any = None
+) -> List[FormattedMessage]:
+    """
+    Format Gemini input contents into standardized message format, including system instruction handling.
+
+    Args:
+        contents: Input contents in various possible formats
+        config: Config object or dict that may contain system instruction
+
+    Returns:
+        List of formatted messages with role and content fields, with system message prepended if needed
+    """
+    formatted_messages = format_gemini_input(contents)
+
+    # Check if system instruction is provided in config parameter
+    system_instruction = extract_gemini_system_instruction(config)
+
+    if system_instruction is not None:
+        has_system = any(msg.get("role") == "system" for msg in formatted_messages)
+        if not has_system:
+            from posthog.ai.types import FormattedMessage
+
+            system_message: FormattedMessage = {
+                "role": "system",
+                "content": system_instruction,
+            }
+            formatted_messages = [system_message] + list(formatted_messages)
+
+    return formatted_messages
+
+
+def format_gemini_input(contents: Any) -> List[FormattedMessage]:
+    """
+    Format Gemini input contents into standardized message format for PostHog tracking.
+
+    This function handles various input formats:
+    - String inputs
+    - List of strings, dicts, or objects
+    - Single dict or object
+    - Gemini-specific format with parts array
+
+    Args:
+        contents: Input contents in various possible formats
+
+    Returns:
+        List of formatted messages with role and content fields
+    """
+
+    # Handle string input
+    if isinstance(contents, str):
+        return [{"role": "user", "content": contents}]
+
+    # Handle list input
+    if isinstance(contents, list):
+        formatted: List[FormattedMessage] = []
+
+        for item in contents:
+            if isinstance(item, str):
+                formatted.append({"role": "user", "content": item})
+
+            elif isinstance(item, dict):
+                formatted.append(_format_dict_message(item))
+
+            else:
+                formatted.append(_format_object_message(item))
+
+        return formatted
+
+    # Handle single dict input
+    if isinstance(contents, dict):
+        return [_format_dict_message(contents)]
+
+    # Handle single object input
+    return [_format_object_message(contents)]
+
+
+def extract_gemini_web_search_count(response: Any) -> int:
+    """
+    Extract web search count from Gemini response.
+
+    Gemini bills per request that uses grounding, not per query.
+    Returns 1 if grounding_metadata is present with actual search data, 0 otherwise.
+
+    Args:
+        response: The response from Gemini API
+
+    Returns:
+        1 if web search/grounding was used, 0 otherwise
+    """
+
+    # Check for grounding_metadata in candidates
+    if hasattr(response, "candidates"):
+        for candidate in response.candidates:
+            if (
+                hasattr(candidate, "grounding_metadata")
+                and candidate.grounding_metadata
+            ):
+                grounding_metadata = candidate.grounding_metadata
+
+                # Check if web_search_queries exists and is non-empty
+                if hasattr(grounding_metadata, "web_search_queries"):
+                    queries = grounding_metadata.web_search_queries
+
+                    if queries is not None and len(queries) > 0:
+                        return 1
+
+                # Check if grounding_chunks exists and is non-empty
+                if hasattr(grounding_metadata, "grounding_chunks"):
+                    chunks = grounding_metadata.grounding_chunks
+
+                    if chunks is not None and len(chunks) > 0:
+                        return 1
+
+            # Also check for google_search or grounding in function call names
+            if hasattr(candidate, "content") and candidate.content:
+                if hasattr(candidate.content, "parts") and candidate.content.parts:
+                    for part in candidate.content.parts:
+                        if hasattr(part, "function_call") and part.function_call:
+                            function_name = getattr(
+                                part.function_call, "name", ""
+                            ).lower()
+
+                            if (
+                                "google_search" in function_name
+                                or "grounding" in function_name
+                            ):
+                                return 1
+
+    return 0
+
+
+def _extract_usage_from_metadata(metadata: Any) -> TokenUsage:
+    """
+    Common logic to extract usage from Gemini metadata.
+    Used by both streaming and non-streaming paths.
+
+    Args:
+        metadata: usage_metadata from Gemini response or chunk
+
+    Returns:
+        TokenUsage with standardized usage
+    """
+    usage = TokenUsage(
+        input_tokens=getattr(metadata, "prompt_token_count", 0),
+        output_tokens=getattr(metadata, "candidates_token_count", 0),
+    )
+
+    # Add cache tokens if present (don't add if 0)
+    if hasattr(metadata, "cached_content_token_count"):
+        cache_tokens = metadata.cached_content_token_count
+        if cache_tokens and cache_tokens > 0:
+            usage["cache_read_input_tokens"] = cache_tokens
+
+    # Add reasoning tokens if present (don't add if 0)
+    if hasattr(metadata, "thoughts_token_count"):
+        reasoning_tokens = metadata.thoughts_token_count
+        if reasoning_tokens and reasoning_tokens > 0:
+            usage["reasoning_tokens"] = reasoning_tokens
+
+    # Capture raw usage metadata for backend processing
+    # Serialize to dict here in the converter (not in utils)
+    serialized = serialize_raw_usage(metadata)
+    if serialized:
+        usage["raw_usage"] = serialized
+
+    return usage
+
+
+def extract_gemini_usage_from_response(response: Any) -> TokenUsage:
+    """
+    Extract usage statistics from a full Gemini response (non-streaming).
+
+    Args:
+        response: The complete response from Gemini API
+
+    Returns:
+        TokenUsage with standardized usage statistics
+    """
+    if not hasattr(response, "usage_metadata") or not response.usage_metadata:
+        return TokenUsage(input_tokens=0, output_tokens=0)
+
+    usage = _extract_usage_from_metadata(response.usage_metadata)
+
+    # Add web search count if present
+    web_search_count = extract_gemini_web_search_count(response)
+    if web_search_count > 0:
+        usage["web_search_count"] = web_search_count
+
+    return usage
+
+
+def extract_gemini_usage_from_chunk(chunk: Any) -> TokenUsage:
+    """
+    Extract usage statistics from a Gemini streaming chunk.
+
+    Args:
+        chunk: Streaming chunk from Gemini API
+
+    Returns:
+        TokenUsage with standardized usage statistics
+    """
+
+    usage: TokenUsage = TokenUsage()
+
+    # Extract web search count from the chunk before checking for usage_metadata
+    # Web search indicators can appear on any chunk, not just those with usage data
+    web_search_count = extract_gemini_web_search_count(chunk)
+    if web_search_count > 0:
+        usage["web_search_count"] = web_search_count
+
+    if not hasattr(chunk, "usage_metadata") or not chunk.usage_metadata:
+        return usage
+
+    usage_from_metadata = _extract_usage_from_metadata(chunk.usage_metadata)
+
+    # Merge the usage from metadata with any web search count we found
+    usage.update(usage_from_metadata)
+
+    return usage
+
+
+def extract_gemini_content_from_chunk(chunk: Any) -> Optional[Dict[str, Any]]:
+    """
+    Extract content (text or function call) from a Gemini streaming chunk.
+
+    Args:
+        chunk: Streaming chunk from Gemini API
+
+    Returns:
+        Content block dictionary if present, None otherwise
+    """
+
+    # Check for text content
+    if hasattr(chunk, "text") and chunk.text:
+        return {"type": "text", "text": chunk.text}
+
+    # Check for function calls in candidates
+    if hasattr(chunk, "candidates") and chunk.candidates:
+        for candidate in chunk.candidates:
+            if hasattr(candidate, "content") and candidate.content:
+                if hasattr(candidate.content, "parts") and candidate.content.parts:
+                    for part in candidate.content.parts:
+                        # Check for function_call part
+                        if hasattr(part, "function_call") and part.function_call:
+                            function_call = part.function_call
+                            return {
+                                "type": "function",
+                                "function": {
+                                    "name": function_call.name,
+                                    "arguments": function_call.args,
+                                },
+                            }
+                        # Also check for text in parts
+                        elif hasattr(part, "text") and part.text:
+                            return {"type": "text", "text": part.text}
+
+    return None
+
+
+def format_gemini_streaming_output(
+    accumulated_content: Union[str, List[Any]],
+) -> List[FormattedMessage]:
+    """
+    Format the final output from Gemini streaming.
+
+    Args:
+        accumulated_content: Accumulated content from streaming (string, list of strings, or list of content blocks)
+
+    Returns:
+        List of formatted messages
+    """
+
+    # Handle legacy string input (backward compatibility)
+    if isinstance(accumulated_content, str):
+        return [
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": accumulated_content}],
+            }
+        ]
+
+    # Handle list input
+    if isinstance(accumulated_content, list):
+        content: List[FormattedContentItem] = []
+        text_parts = []
+
+        for item in accumulated_content:
+            if isinstance(item, str):
+                # Legacy support: accumulate strings
+                text_parts.append(item)
+            elif isinstance(item, dict):
+                # New format: content blocks
+                if item.get("type") == "text":
+                    text_parts.append(item.get("text", ""))
+                elif item.get("type") == "function":
+                    # If we have accumulated text, add it first
+                    if text_parts:
+                        content.append(
+                            {
+                                "type": "text",
+                                "text": "".join(text_parts),
+                            }
+                        )
+                        text_parts = []
+
+                    # Add the function call
+                    content.append(
+                        {
+                            "type": "function",
+                            "function": item.get("function", {}),
+                        }
+                    )
+
+        # Add any remaining text
+        if text_parts:
+            content.append(
+                {
+                    "type": "text",
+                    "text": "".join(text_parts),
+                }
+            )
+
+        # If we have content, return it
+        if content:
+            return [{"role": "assistant", "content": content}]
+
+    # Fallback for empty or unexpected input
+    return [{"role": "assistant", "content": [{"type": "text", "text": ""}]}]

posthog/ai/langchain/__init__.py

@@ -0,0 +1,3 @@
+from .callbacks import CallbackHandler
+
+__all__ = ["CallbackHandler"]

posthog/ai/langchain/callbacks.py

@@ -0,0 +1,948 @@
+try:
+    import langchain_core  # noqa: F401
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install LangChain to use this feature: 'pip install langchain-core'"
+    )
+
+import json
+import logging
+import time
+from dataclasses import dataclass
+from typing import (
+    Any,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Union,
+    cast,
+)
+from uuid import UUID
+
+try:
+    # LangChain 1.0+ and modern 0.x with langchain-core
+    from langchain_core.agents import AgentAction, AgentFinish
+    from langchain_core.callbacks.base import BaseCallbackHandler
+except (ImportError, ModuleNotFoundError):
+    # Fallback for older LangChain versions
+    from langchain.callbacks.base import BaseCallbackHandler
+    from langchain.schema.agent import AgentAction, AgentFinish
+from langchain_core.documents import Document
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    FunctionMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolCall,
+    ToolMessage,
+)
+from langchain_core.outputs import ChatGeneration, LLMResult
+from pydantic import BaseModel
+
+from posthog import setup
+from posthog.ai.sanitization import sanitize_langchain
+from posthog.ai.utils import get_model_params, with_privacy_mode
+from posthog.client import Client
+
+log = logging.getLogger("posthog")
+
+
+@dataclass
+class SpanMetadata:
+    name: str
+    """Name of the run: chain name, model name, etc."""
+    start_time: float
+    """Start time of the run."""
+    end_time: Optional[float]
+    """End time of the run."""
+    input: Optional[Any]
+    """Input of the run: messages, prompt variables, etc."""
+
+    @property
+    def latency(self) -> float:
+        if not self.end_time:
+            return 0
+        return self.end_time - self.start_time
+
+
+@dataclass
+class GenerationMetadata(SpanMetadata):
+    provider: Optional[str] = None
+    """Provider of the run: OpenAI, Anthropic"""
+    model: Optional[str] = None
+    """Model used in the run"""
+    model_params: Optional[Dict[str, Any]] = None
+    """Model parameters of the run: temperature, max_tokens, etc."""
+    base_url: Optional[str] = None
+    """Base URL of the provider's API used in the run."""
+    tools: Optional[List[Dict[str, Any]]] = None
+    """Tools provided to the model."""
+    posthog_properties: Optional[Dict[str, Any]] = None
+    """PostHog properties of the run."""
+
+
+RunMetadata = Union[SpanMetadata, GenerationMetadata]
+RunMetadataStorage = Dict[UUID, RunMetadata]
+
+
+class CallbackHandler(BaseCallbackHandler):
+    """
+    The PostHog LLM observability callback handler for LangChain.
+    """
+
+    _ph_client: Client
+    """PostHog client instance."""
+
+    _distinct_id: Optional[Union[str, int, UUID]]
+    """Distinct ID of the user to associate the trace with."""
+
+    _trace_id: Optional[Union[str, int, float, UUID]]
+    """Global trace ID to be sent with every event. Otherwise, the top-level run ID is used."""
+
+    _trace_input: Optional[Any]
+    """The input at the start of the trace. Any JSON object."""
+
+    _trace_name: Optional[str]
+    """Name of the trace, exposed in the UI."""
+
+    _properties: Optional[Dict[str, Any]]
+    """Global properties to be sent with every event."""
+
+    _runs: RunMetadataStorage
+    """Mapping of run IDs to run metadata as run metadata is only available on the start of generation."""
+
+    _parent_tree: Dict[UUID, UUID]
+    """
+    A dictionary that maps chain run IDs to their parent chain run IDs (parent pointer tree),
+    so the top level can be found from a bottom-level run ID.
+    """
+
+    def __init__(
+        self,
+        client: Optional[Client] = None,
+        *,
+        distinct_id: Optional[Union[str, int, UUID]] = None,
+        trace_id: Optional[Union[str, int, float, UUID]] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        privacy_mode: bool = False,
+        groups: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Args:
+            client: PostHog client instance.
+            distinct_id: Optional distinct ID of the user to associate the trace with.
+            trace_id: Optional trace ID to use for the event.
+            properties: Optional additional metadata to use for the trace.
+            privacy_mode: Whether to redact the input and output of the trace.
+            groups: Optional additional PostHog groups to use for the trace.
+        """
+        self._ph_client = client or setup()
+        self._distinct_id = distinct_id
+        self._trace_id = trace_id
+        self._properties = properties or {}
+        self._privacy_mode = privacy_mode
+        self._groups = groups or {}
+        self._runs = {}
+        self._parent_tree = {}
+
+    def on_chain_start(
+        self,
+        serialized: Dict[str, Any],
+        inputs: Dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        self._log_debug_event("on_chain_start", run_id, parent_run_id, inputs=inputs)
+        self._set_parent_of_run(run_id, parent_run_id)
+        self._set_trace_or_span_metadata(
+            serialized, inputs, run_id, parent_run_id, **kwargs
+        )
+
+    def on_chain_end(
+        self,
+        outputs: Dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._log_debug_event("on_chain_end", run_id, parent_run_id, outputs=outputs)
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, outputs)
+
+    def on_chain_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._log_debug_event("on_chain_error", run_id, parent_run_id, error=error)
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, error)
+
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[BaseMessage]],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs,
+    ):
+        self._log_debug_event(
+            "on_chat_model_start", run_id, parent_run_id, messages=messages
+        )
+        self._set_parent_of_run(run_id, parent_run_id)
+        input = [
+            _convert_message_to_dict(message) for row in messages for message in row
+        ]
+        self._set_llm_metadata(serialized, run_id, input, **kwargs)
+
+    def on_llm_start(
+        self,
+        serialized: Dict[str, Any],
+        prompts: List[str],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._log_debug_event("on_llm_start", run_id, parent_run_id, prompts=prompts)
+        self._set_parent_of_run(run_id, parent_run_id)
+        self._set_llm_metadata(serialized, run_id, prompts, **kwargs)
+
+    def on_llm_new_token(
+        self,
+        token: str,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Run on new LLM token. Only available when streaming is enabled."""
+        self._log_debug_event("on_llm_new_token", run_id, parent_run_id, token=token)
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        """
+        The callback works for both streaming and non-streaming runs. For streaming runs, the chain must set `stream_usage=True` in the LLM.
+        """
+        self._log_debug_event(
+            "on_llm_end", run_id, parent_run_id, response=response, kwargs=kwargs
+        )
+        self._pop_run_and_capture_generation(run_id, parent_run_id, response)
+
+    def on_llm_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._log_debug_event("on_llm_error", run_id, parent_run_id, error=error)
+        self._pop_run_and_capture_generation(run_id, parent_run_id, error)
+
+    def on_tool_start(
+        self,
+        serialized: Optional[Dict[str, Any]],
+        input_str: str,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._log_debug_event(
+            "on_tool_start", run_id, parent_run_id, input_str=input_str
+        )
+        self._set_parent_of_run(run_id, parent_run_id)
+        self._set_trace_or_span_metadata(
+            serialized, input_str, run_id, parent_run_id, **kwargs
+        )
+
+    def on_tool_end(
+        self,
+        output: str,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._log_debug_event("on_tool_end", run_id, parent_run_id, output=output)
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, output)
+
+    def on_tool_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[list[str]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._log_debug_event("on_tool_error", run_id, parent_run_id, error=error)
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, error)
+
+    def on_retriever_start(
+        self,
+        serialized: Optional[Dict[str, Any]],
+        query: str,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._log_debug_event("on_retriever_start", run_id, parent_run_id, query=query)
+        self._set_parent_of_run(run_id, parent_run_id)
+        self._set_trace_or_span_metadata(
+            serialized, query, run_id, parent_run_id, **kwargs
+        )
+
+    def on_retriever_end(
+        self,
+        documents: Sequence[Document],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._log_debug_event(
+            "on_retriever_end", run_id, parent_run_id, documents=documents
+        )
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, documents)
+
+    def on_retriever_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[list[str]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Run when Retriever errors."""
+        self._log_debug_event("on_retriever_error", run_id, parent_run_id, error=error)
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, error)
+
+    def on_agent_action(
+        self,
+        action: AgentAction,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Run on agent action."""
+        self._log_debug_event("on_agent_action", run_id, parent_run_id, action=action)
+        self._set_parent_of_run(run_id, parent_run_id)
+        self._set_trace_or_span_metadata(None, action, run_id, parent_run_id, **kwargs)
+
+    def on_agent_finish(
+        self,
+        finish: AgentFinish,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ) -> Any:
+        self._log_debug_event("on_agent_finish", run_id, parent_run_id, finish=finish)
+        self._pop_run_and_capture_trace_or_span(run_id, parent_run_id, finish)
+
+    def _set_parent_of_run(self, run_id: UUID, parent_run_id: Optional[UUID] = None):
+        """
+        Set the parent run ID for a chain run. If there is no parent, the run is the root.
+        """
+        if parent_run_id is not None:
+            self._parent_tree[run_id] = parent_run_id
+
+    def _pop_parent_of_run(self, run_id: UUID):
+        """
+        Remove the parent run ID for a chain run.
+        """
+        try:
+            self._parent_tree.pop(run_id)
+        except KeyError:
+            pass
+
+    def _find_root_run(self, run_id: UUID) -> UUID:
+        """
+        Finds the root ID of a chain run.
+        """
+        id: UUID = run_id
+        while id in self._parent_tree:
+            id = self._parent_tree[id]
+        return id
+
+    def _set_trace_or_span_metadata(
+        self,
+        serialized: Optional[Dict[str, Any]],
+        input: Any,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs,
+    ):
+        default_name = "trace" if parent_run_id is None else "span"
+        run_name = _get_langchain_run_name(serialized, **kwargs) or default_name
+        self._runs[run_id] = SpanMetadata(
+            name=run_name, input=input, start_time=time.time(), end_time=None
+        )
+
+    def _set_llm_metadata(
+        self,
+        serialized: Dict[str, Any],
+        run_id: UUID,
+        messages: Union[List[Dict[str, Any]], List[str]],
+        metadata: Optional[Dict[str, Any]] = None,
+        invocation_params: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        run_name = _get_langchain_run_name(serialized, **kwargs) or "generation"
+        generation = GenerationMetadata(
+            name=run_name, input=messages, start_time=time.time(), end_time=None
+        )
+        if isinstance(invocation_params, dict):
+            generation.model_params = get_model_params(invocation_params)
+            if tools := invocation_params.get("tools"):
+                generation.tools = tools
+        if isinstance(metadata, dict):
+            if model := metadata.get("ls_model_name"):
+                generation.model = model
+            if provider := metadata.get("ls_provider"):
+                generation.provider = provider
+
+            generation.posthog_properties = metadata.get("posthog_properties")
+        try:
+            base_url = serialized["kwargs"]["openai_api_base"]
+            if base_url is not None:
+                generation.base_url = base_url
+        except KeyError:
+            pass
+        self._runs[run_id] = generation
+
+    def _pop_run_metadata(self, run_id: UUID) -> Optional[RunMetadata]:
+        end_time = time.time()
+        try:
+            run = self._runs.pop(run_id)
+        except KeyError:
+            log.warning(f"No run metadata found for run {run_id}")
+            return None
+        run.end_time = end_time
+        return run
+
+    def _get_trace_id(self, run_id: UUID):
+        trace_id = self._trace_id or self._find_root_run(run_id)
+        if not trace_id:
+            return run_id
+        return trace_id
+
+    def _get_parent_run_id(
+        self, trace_id: Any, run_id: UUID, parent_run_id: Optional[UUID]
+    ):
+        """
+        Replace the parent run ID with the trace ID for second level runs when a custom trace ID is set.
+        """
+        if parent_run_id is not None and parent_run_id not in self._parent_tree:
+            return trace_id
+        return parent_run_id
+
+    def _pop_run_and_capture_trace_or_span(
+        self, run_id: UUID, parent_run_id: Optional[UUID], outputs: Any
+    ):
+        trace_id = self._get_trace_id(run_id)
+        self._pop_parent_of_run(run_id)
+        run = self._pop_run_metadata(run_id)
+        if not run:
+            return
+        if isinstance(run, GenerationMetadata):
+            log.warning(
+                f"Run {run_id} is a generation, but attempted to be captured as a trace or span."
+            )
+            return
+        self._capture_trace_or_span(
+            trace_id,
+            run_id,
+            run,
+            outputs,
+            self._get_parent_run_id(trace_id, run_id, parent_run_id),
+        )
+
+    def _capture_trace_or_span(
+        self,
+        trace_id: Any,
+        run_id: UUID,
+        run: SpanMetadata,
+        outputs: Any,
+        parent_run_id: Optional[UUID],
+    ):
+        event_name = "$ai_trace" if parent_run_id is None else "$ai_span"
+        event_properties = {
+            "$ai_trace_id": trace_id,
+            "$ai_input_state": with_privacy_mode(
+                self._ph_client, self._privacy_mode, sanitize_langchain(run.input)
+            ),
+            "$ai_latency": run.latency,
+            "$ai_span_name": run.name,
+            "$ai_span_id": run_id,
+            "$ai_framework": "langchain",
+        }
+        if parent_run_id is not None:
+            event_properties["$ai_parent_id"] = parent_run_id
+        if self._properties:
+            event_properties.update(self._properties)
+
+        if isinstance(outputs, BaseException):
+            event_properties["$ai_error"] = _stringify_exception(outputs)
+            event_properties["$ai_is_error"] = True
+            event_properties = _capture_exception_and_update_properties(
+                self._ph_client,
+                outputs,
+                self._distinct_id,
+                self._groups,
+                event_properties,
+            )
+
+        elif outputs is not None:
+            event_properties["$ai_output_state"] = with_privacy_mode(
+                self._ph_client, self._privacy_mode, outputs
+            )
+
+        if self._distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        self._ph_client.capture(
+            distinct_id=self._distinct_id or run_id,
+            event=event_name,
+            properties=event_properties,
+            groups=self._groups,
+        )
+
+    def _pop_run_and_capture_generation(
+        self,
+        run_id: UUID,
+        parent_run_id: Optional[UUID],
+        response: Union[LLMResult, BaseException],
+    ):
+        trace_id = self._get_trace_id(run_id)
+        self._pop_parent_of_run(run_id)
+        run = self._pop_run_metadata(run_id)
+        if not run:
+            return
+        if not isinstance(run, GenerationMetadata):
+            log.warning(
+                f"Run {run_id} is not a generation, but attempted to be captured as a generation."
+            )
+            return
+        self._capture_generation(
+            trace_id,
+            run_id,
+            run,
+            response,
+            self._get_parent_run_id(trace_id, run_id, parent_run_id),
+        )
+
+    def _capture_generation(
+        self,
+        trace_id: Any,
+        run_id: UUID,
+        run: GenerationMetadata,
+        output: Union[LLMResult, BaseException],
+        parent_run_id: Optional[UUID] = None,
+    ):
+        event_properties = {
+            "$ai_trace_id": trace_id,
+            "$ai_span_id": run_id,
+            "$ai_span_name": run.name,
+            "$ai_parent_id": parent_run_id,
+            "$ai_provider": run.provider,
+            "$ai_model": run.model,
+            "$ai_model_parameters": run.model_params,
+            "$ai_input": with_privacy_mode(
+                self._ph_client, self._privacy_mode, sanitize_langchain(run.input)
+            ),
+            "$ai_http_status": 200,
+            "$ai_latency": run.latency,
+            "$ai_base_url": run.base_url,
+            "$ai_framework": "langchain",
+        }
+
+        if isinstance(run.posthog_properties, dict):
+            event_properties.update(run.posthog_properties)
+
+        if run.tools:
+            event_properties["$ai_tools"] = run.tools
+
+        if self._properties:
+            event_properties.update(self._properties)
+
+        if self._distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        if isinstance(output, BaseException):
+            event_properties["$ai_http_status"] = _get_http_status(output)
+            event_properties["$ai_error"] = _stringify_exception(output)
+            event_properties["$ai_is_error"] = True
+
+            event_properties = _capture_exception_and_update_properties(
+                self._ph_client,
+                output,
+                self._distinct_id,
+                self._groups,
+                event_properties,
+            )
+        else:
+            # Add usage
+            usage = _parse_usage(output, run.provider, run.model)
+            event_properties["$ai_input_tokens"] = usage.input_tokens
+            event_properties["$ai_output_tokens"] = usage.output_tokens
+            event_properties["$ai_cache_creation_input_tokens"] = (
+                usage.cache_write_tokens
+            )
+            event_properties["$ai_cache_read_input_tokens"] = usage.cache_read_tokens
+            event_properties["$ai_reasoning_tokens"] = usage.reasoning_tokens
+
+            # Generation results
+            generation_result = output.generations[-1]
+            if isinstance(generation_result[-1], ChatGeneration):
+                completions = [
+                    _convert_message_to_dict(cast(ChatGeneration, generation).message)
+                    for generation in generation_result
+                ]
+            else:
+                completions = [
+                    _extract_raw_response(generation)
+                    for generation in generation_result
+                ]
+            event_properties["$ai_output_choices"] = with_privacy_mode(
+                self._ph_client, self._privacy_mode, completions
+            )
+
+        self._ph_client.capture(
+            distinct_id=self._distinct_id or trace_id,
+            event="$ai_generation",
+            properties=event_properties,
+            groups=self._groups,
+        )
+
+    def _log_debug_event(
+        self,
+        event_name: str,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs,
+    ):
+        log.debug(
+            f"Event: {event_name}, run_id: {str(run_id)[:5]}, parent_run_id: {str(parent_run_id)[:5]}, kwargs: {kwargs}"
+        )
+
+
+def _extract_raw_response(last_response):
+    """Extract the response from the last response of the LLM call."""
+    # We return the text of the response if not empty
+    if last_response.text is not None and last_response.text.strip() != "":
+        return last_response.text.strip()
+    elif hasattr(last_response, "message"):
+        # Additional kwargs contains the response in case of tool usage
+        return last_response.message.additional_kwargs
+    else:
+        # Not tool usage, some LLM responses can be simply empty
+        return ""
+
+
+def _convert_lc_tool_calls_to_oai(
+    tool_calls: list[ToolCall],
+) -> list[dict[str, Any]]:
+    try:
+        return [
+            {
+                "type": "function",
+                "id": tool_call["id"],
+                "function": {
+                    "name": tool_call["name"],
+                    "arguments": json.dumps(tool_call["args"]),
+                },
+            }
+            for tool_call in tool_calls
+        ]
+    except KeyError:
+        return tool_calls
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict[str, Any]:
+    # assistant message
+    if isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+        if message.tool_calls:
+            message_dict["tool_calls"] = _convert_lc_tool_calls_to_oai(
+                message.tool_calls
+            )
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, ToolMessage):
+        message_dict = {"role": "tool", "content": message.content}
+    elif isinstance(message, FunctionMessage):
+        message_dict = {"role": "function", "content": message.content}
+    else:
+        message_dict = {"role": message.type, "content": str(message.content)}
+
+    if message.additional_kwargs:
+        message_dict.update(message.additional_kwargs)
+
+    if "content" in message_dict and not message_dict["content"]:
+        message_dict["content"] = ""
+
+    return message_dict
+
+
+@dataclass
+class ModelUsage:
+    input_tokens: Optional[int]
+    output_tokens: Optional[int]
+    cache_write_tokens: Optional[int]
+    cache_read_tokens: Optional[int]
+    reasoning_tokens: Optional[int]
+
+
+def _parse_usage_model(
+    usage: Union[BaseModel, dict],
+    provider: Optional[str] = None,
+    model: Optional[str] = None,
+) -> ModelUsage:
+    if isinstance(usage, BaseModel):
+        usage = usage.__dict__
+
+    conversion_list = [
+        # https://pypi.org/project/langchain-anthropic/ (works also for Bedrock-Anthropic)
+        ("input_tokens", "input"),
+        ("output_tokens", "output"),
+        ("cache_creation_input_tokens", "cache_write"),
+        ("cache_read_input_tokens", "cache_read"),
+        # https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/get-token-count
+        ("prompt_token_count", "input"),
+        ("candidates_token_count", "output"),
+        ("cached_content_token_count", "cache_read"),
+        ("thoughts_token_count", "reasoning"),
+        # Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/monitoring-cw.html#runtime-cloudwatch-metrics
+        ("inputTokenCount", "input"),
+        ("outputTokenCount", "output"),
+        ("cacheCreationInputTokenCount", "cache_write"),
+        ("cacheReadInputTokenCount", "cache_read"),
+        # Bedrock Anthropic
+        ("prompt_tokens", "input"),
+        ("completion_tokens", "output"),
+        ("cache_creation_input_tokens", "cache_write"),
+        ("cache_read_input_tokens", "cache_read"),
+        # langchain-ibm https://pypi.org/project/langchain-ibm/
+        ("input_token_count", "input"),
+        ("generated_token_count", "output"),
+    ]
+
+    parsed_usage = {}
+    for model_key, type_key in conversion_list:
+        if model_key in usage:
+            captured_count = usage[model_key]
+            final_count = (
+                sum(captured_count)
+                if isinstance(captured_count, list)
+                else captured_count
+            )  # For Bedrock, the token count is a list when streamed
+
+            parsed_usage[type_key] = final_count
+
+    # Caching (OpenAI & langchain 0.3.9+)
+    if "input_token_details" in usage and isinstance(
+        usage["input_token_details"], dict
+    ):
+        parsed_usage["cache_write"] = usage["input_token_details"].get("cache_creation")
+        parsed_usage["cache_read"] = usage["input_token_details"].get("cache_read")
+
+    # Reasoning (OpenAI & langchain 0.3.9+)
+    if "output_token_details" in usage and isinstance(
+        usage["output_token_details"], dict
+    ):
+        parsed_usage["reasoning"] = usage["output_token_details"].get("reasoning")
+
+    field_mapping = {
+        "input": "input_tokens",
+        "output": "output_tokens",
+        "cache_write": "cache_write_tokens",
+        "cache_read": "cache_read_tokens",
+        "reasoning": "reasoning_tokens",
+    }
+    normalized_usage = ModelUsage(
+        **{
+            dataclass_key: parsed_usage.get(mapped_key) or 0
+            for mapped_key, dataclass_key in field_mapping.items()
+        },
+    )
+    # For Anthropic providers, LangChain reports input_tokens as the sum of all input tokens.
+    # Our cost calculation expects them to be separate for Anthropic, so we subtract cache tokens.
+    # Both cache_read and cache_write tokens should be subtracted since Anthropic's raw API
+    # reports input_tokens as tokens NOT read from or used to create a cache.
+    # For other providers (OpenAI, etc.), input_tokens already excludes cache tokens as expected.
+    # Match logic consistent with plugin-server: exact match on provider OR substring match on model
+    is_anthropic = False
+    if provider and provider.lower() == "anthropic":
+        is_anthropic = True
+    elif model and "anthropic" in model.lower():
+        is_anthropic = True
+
+    if is_anthropic and normalized_usage.input_tokens:
+        cache_tokens = (normalized_usage.cache_read_tokens or 0) + (
+            normalized_usage.cache_write_tokens or 0
+        )
+        if cache_tokens > 0:
+            normalized_usage.input_tokens = max(
+                normalized_usage.input_tokens - cache_tokens, 0
+            )
+    return normalized_usage
+
+
+def _parse_usage(
+    response: LLMResult, provider: Optional[str] = None, model: Optional[str] = None
+) -> ModelUsage:
+    # langchain-anthropic uses the usage field
+    llm_usage_keys = ["token_usage", "usage"]
+    llm_usage: ModelUsage = ModelUsage(
+        input_tokens=None,
+        output_tokens=None,
+        cache_write_tokens=None,
+        cache_read_tokens=None,
+        reasoning_tokens=None,
+    )
+
+    if response.llm_output is not None:
+        for key in llm_usage_keys:
+            if response.llm_output.get(key):
+                llm_usage = _parse_usage_model(
+                    response.llm_output[key], provider, model
+                )
+                break
+
+    if hasattr(response, "generations"):
+        for generation in response.generations:
+            if "usage" in generation:
+                llm_usage = _parse_usage_model(generation["usage"], provider, model)
+                break
+
+            for generation_chunk in generation:
+                if generation_chunk.generation_info and (
+                    "usage_metadata" in generation_chunk.generation_info
+                ):
+                    llm_usage = _parse_usage_model(
+                        generation_chunk.generation_info["usage_metadata"],
+                        provider,
+                        model,
+                    )
+                    break
+
+                message_chunk = getattr(generation_chunk, "message", {})
+                response_metadata = getattr(message_chunk, "response_metadata", {})
+
+                bedrock_anthropic_usage = (
+                    response_metadata.get("usage", None)  # for Bedrock-Anthropic
+                    if isinstance(response_metadata, dict)
+                    else None
+                )
+                bedrock_titan_usage = (
+                    response_metadata.get(
+                        "amazon-bedrock-invocationMetrics", None
+                    )  # for Bedrock-Titan
+                    if isinstance(response_metadata, dict)
+                    else None
+                )
+                ollama_usage = getattr(
+                    message_chunk, "usage_metadata", None
+                )  # for Ollama
+
+                chunk_usage = (
+                    bedrock_anthropic_usage or bedrock_titan_usage or ollama_usage
+                )
+                if chunk_usage:
+                    llm_usage = _parse_usage_model(chunk_usage, provider, model)
+                    break
+
+    return llm_usage
+
+
+def _capture_exception_and_update_properties(
+    client: Client,
+    exception: BaseException,
+    distinct_id: Optional[Union[str, int, UUID]],
+    groups: Optional[Dict[str, Any]],
+    event_properties: Dict[str, Any],
+):
+    if client.enable_exception_autocapture:
+        exception_id = client.capture_exception(
+            exception,
+            distinct_id=distinct_id,
+            groups=groups,
+            properties=event_properties,
+        )
+
+        if exception_id:
+            event_properties["$exception_event_id"] = exception_id
+
+    return event_properties
+
+
+def _get_http_status(error: BaseException) -> int:
+    # OpenAI: https://github.com/openai/openai-python/blob/main/src/openai/_exceptions.py
+    # Anthropic: https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/_exceptions.py
+    # Google: https://github.com/googleapis/python-api-core/blob/main/google/api_core/exceptions.py
+    status_code = getattr(error, "status_code", getattr(error, "code", 0))
+    return status_code
+
+
+def _get_langchain_run_name(
+    serialized: Optional[Dict[str, Any]], **kwargs: Any
+) -> Optional[str]:
+    """Retrieve the name of a serialized LangChain runnable.
+
+    The prioritization for the determination of the run name is as follows:
+    - The value assigned to the "name" key in `kwargs`.
+    - The value assigned to the "name" key in `serialized`.
+    - The last entry of the value assigned to the "id" key in `serialized`.
+    - "<unknown>".
+
+    Args:
+        serialized (Optional[Dict[str, Any]]): A dictionary containing the runnable's serialized data.
+        **kwargs (Any): Additional keyword arguments, potentially including the 'name' override.
+
+    Returns:
+        str: The determined name of the Langchain runnable.
+    """
+    if "name" in kwargs and kwargs["name"] is not None:
+        return kwargs["name"]
+    if serialized is None:
+        return None
+    try:
+        return serialized["name"]
+    except (KeyError, TypeError):
+        pass
+    try:
+        return serialized["id"][-1]
+    except (KeyError, TypeError):
+        pass
+    return None
+
+
+def _stringify_exception(exception: BaseException) -> str:
+    description = str(exception)
+    if description:
+        return f"{exception.__class__.__name__}: {description}"
+    return exception.__class__.__name__

posthog/ai/openai/__init__.py

@@ -0,0 +1,20 @@
+from .openai import OpenAI
+from .openai_async import AsyncOpenAI
+from .openai_providers import AsyncAzureOpenAI, AzureOpenAI
+from .openai_converter import (
+    format_openai_response,
+    format_openai_input,
+    extract_openai_tools,
+    format_openai_streaming_content,
+)
+
+__all__ = [
+    "OpenAI",
+    "AsyncOpenAI",
+    "AzureOpenAI",
+    "AsyncAzureOpenAI",
+    "format_openai_response",
+    "format_openai_input",
+    "extract_openai_tools",
+    "format_openai_streaming_content",
+]

posthog/ai/openai/openai.py

@@ -0,0 +1,600 @@
+import time
+import uuid
+from typing import Any, Dict, List, Optional
+
+from posthog.ai.types import TokenUsage
+
+try:
+    import openai
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the OpenAI SDK to use this feature: 'pip install openai'"
+    )
+
+from posthog.ai.utils import (
+    call_llm_and_track_usage,
+    extract_available_tool_calls,
+    merge_usage_stats,
+    with_privacy_mode,
+)
+from posthog.ai.openai.openai_converter import (
+    extract_openai_usage_from_chunk,
+    extract_openai_content_from_chunk,
+    extract_openai_tool_calls_from_chunk,
+    accumulate_openai_tool_calls,
+)
+from posthog.ai.sanitization import sanitize_openai, sanitize_openai_response
+from posthog.client import Client as PostHogClient
+from posthog import setup
+
+
+class OpenAI(openai.OpenAI):
+    """
+    A wrapper around the OpenAI SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        """
+        Args:
+            api_key: OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead of the global `posthog`.
+            **openai_config: Any additional keyword args to set on openai (e.g. organization="xxx").
+        """
+
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = WrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = WrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = WrappedBeta(self, self._original_beta)
+
+        if self._original_responses is not None:
+            self.responses = WrappedResponses(self, self._original_responses)
+
+
+class WrappedResponses:
+    """Wrapper for OpenAI responses that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_responses):
+        self._client = client
+        self._original = original_responses
+
+    def __getattr__(self, name):
+        """Fallback to original responses object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        if kwargs.get("stream", False):
+            return self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.create,
+            **kwargs,
+        )
+
+    def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage()
+        final_content = []
+        model_from_response: Optional[str] = None
+        response = self._original.create(**kwargs)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal final_content  # noqa: F824
+            nonlocal model_from_response
+
+            try:
+                for chunk in response:
+                    # Extract model from response object in chunk (for stored prompts)
+                    if hasattr(chunk, "response") and chunk.response:
+                        if model_from_response is None and hasattr(
+                            chunk.response, "model"
+                        ):
+                            model_from_response = chunk.response.model
+
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "responses")
+
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)
+
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "responses")
+
+                    if content is not None:
+                        final_content.append(content)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = final_content
+                self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                    None,  # Responses API doesn't have tools
+                    model_from_response,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        output: Any,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
+    ):
+        from posthog.ai.types import StreamingEventData
+        from posthog.ai.openai.openai_converter import (
+            format_openai_streaming_input,
+            format_openai_streaming_output,
+        )
+        from posthog.ai.utils import capture_streaming_event
+
+        # Prepare standardized event data
+        formatted_input = format_openai_streaming_input(kwargs, "responses")
+        sanitized_input = sanitize_openai_response(formatted_input)
+
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response or "unknown"
+
+        event_data = StreamingEventData(
+            provider="openai",
+            model=model,
+            base_url=str(self._client.base_url),
+            kwargs=kwargs,
+            formatted_input=sanitized_input,
+            formatted_output=format_openai_streaming_output(output, "responses"),
+            usage_stats=usage_stats,
+            latency=latency,
+            distinct_id=posthog_distinct_id,
+            trace_id=posthog_trace_id,
+            properties=posthog_properties,
+            privacy_mode=posthog_privacy_mode,
+            groups=posthog_groups,
+        )
+
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)
+
+    def parse(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Parse structured output using OpenAI's 'responses.parse' method, but also track usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event.
+            posthog_trace_id: Optional trace UUID for linking events.
+            posthog_properties: Optional dictionary of extra properties to include in the event.
+            posthog_privacy_mode: Whether to anonymize the input and output.
+            posthog_groups: Optional dictionary of groups to associate with the event.
+            **kwargs: Any additional parameters for the OpenAI Responses Parse API.
+
+        Returns:
+            The response from OpenAI's responses.parse call.
+        """
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.parse,
+            **kwargs,
+        )
+
+
+class WrappedChat:
+    """Wrapper for OpenAI chat that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_chat):
+        self._client = client
+        self._original = original_chat
+
+    def __getattr__(self, name):
+        """Fallback to original chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    @property
+    def completions(self):
+        return WrappedCompletions(self._client, self._original.completions)
+
+
+class WrappedCompletions:
+    """Wrapper for OpenAI chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_completions):
+        self._client = client
+        self._original = original_completions
+
+    def __getattr__(self, name):
+        """Fallback to original completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        if kwargs.get("stream", False):
+            return self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.create,
+            **kwargs,
+        )
+
+    def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage()
+        accumulated_content = []
+        accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}
+        model_from_response: Optional[str] = None
+        if "stream_options" not in kwargs:
+            kwargs["stream_options"] = {}
+        kwargs["stream_options"]["include_usage"] = True
+        response = self._original.create(**kwargs)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content  # noqa: F824
+            nonlocal accumulated_tool_calls
+            nonlocal model_from_response
+
+            try:
+                for chunk in response:
+                    # Extract model from chunk (Chat Completions chunks have model field)
+                    if model_from_response is None and hasattr(chunk, "model"):
+                        model_from_response = chunk.model
+
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "chat")
+
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)
+
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "chat")
+
+                    if content is not None:
+                        accumulated_content.append(content)
+
+                    # Extract and accumulate tool calls from chunk
+                    chunk_tool_calls = extract_openai_tool_calls_from_chunk(chunk)
+                    if chunk_tool_calls:
+                        accumulate_openai_tool_calls(
+                            accumulated_tool_calls, chunk_tool_calls
+                        )
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+
+                # Convert accumulated tool calls dict to list
+                tool_calls_list = (
+                    list(accumulated_tool_calls.values())
+                    if accumulated_tool_calls
+                    else None
+                )
+
+                self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    accumulated_content,
+                    tool_calls_list,
+                    extract_available_tool_calls("openai", kwargs),
+                    model_from_response,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        output: Any,
+        tool_calls: Optional[List[Dict[str, Any]]] = None,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
+    ):
+        from posthog.ai.types import StreamingEventData
+        from posthog.ai.openai.openai_converter import (
+            format_openai_streaming_input,
+            format_openai_streaming_output,
+        )
+        from posthog.ai.utils import capture_streaming_event
+
+        # Prepare standardized event data
+        formatted_input = format_openai_streaming_input(kwargs, "chat")
+        sanitized_input = sanitize_openai(formatted_input)
+
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response or "unknown"
+
+        event_data = StreamingEventData(
+            provider="openai",
+            model=model,
+            base_url=str(self._client.base_url),
+            kwargs=kwargs,
+            formatted_input=sanitized_input,
+            formatted_output=format_openai_streaming_output(output, "chat", tool_calls),
+            usage_stats=usage_stats,
+            latency=latency,
+            distinct_id=posthog_distinct_id,
+            trace_id=posthog_trace_id,
+            properties=posthog_properties,
+            privacy_mode=posthog_privacy_mode,
+            groups=posthog_groups,
+        )
+
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)
+
+
+class WrappedEmbeddings:
+    """Wrapper for OpenAI embeddings that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_embeddings):
+        self._client = client
+        self._original = original_embeddings
+
+    def __getattr__(self, name):
+        """Fallback to original embeddings object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create an embedding using OpenAI's 'embeddings.create' method, but also track usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event.
+            posthog_trace_id: Optional trace UUID for linking events.
+            posthog_properties: Optional dictionary of extra properties to include in the event.
+            posthog_privacy_mode: Whether to anonymize the input and output.
+            posthog_groups: Optional dictionary of groups to associate with the event.
+            **kwargs: Any additional parameters for the OpenAI Embeddings API.
+
+        Returns:
+            The response from OpenAI's embeddings.create call.
+        """
+
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        start_time = time.time()
+        response = self._original.create(**kwargs)
+        end_time = time.time()
+
+        # Extract usage statistics if available
+        usage_stats = {}
+        if hasattr(response, "usage") and response.usage:
+            usage_stats = {
+                "prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
+                "total_tokens": getattr(response.usage, "total_tokens", 0),
+            }
+
+        latency = end_time - start_time
+
+        # Build the event properties
+        event_properties = {
+            "$ai_provider": "openai",
+            "$ai_model": kwargs.get("model"),
+            "$ai_input": with_privacy_mode(
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai_response(kwargs.get("input")),
+            ),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **(posthog_properties or {}),
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        # Send capture event for embeddings
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_embedding",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+        return response
+
+
+class WrappedBeta:
+    """Wrapper for OpenAI beta features that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_beta):
+        self._client = client
+        self._original = original_beta
+
+    def __getattr__(self, name):
+        """Fallback to original beta object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    @property
+    def chat(self):
+        return WrappedBetaChat(self._client, self._original.chat)
+
+
+class WrappedBetaChat:
+    """Wrapper for OpenAI beta chat that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_beta_chat):
+        self._client = client
+        self._original = original_beta_chat
+
+    def __getattr__(self, name):
+        """Fallback to original beta chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    @property
+    def completions(self):
+        return WrappedBetaCompletions(self._client, self._original.completions)
+
+
+class WrappedBetaCompletions:
+    """Wrapper for OpenAI beta chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_beta_completions):
+        self._client = client
+        self._original = original_beta_completions
+
+    def __getattr__(self, name):
+        """Fallback to original beta completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    def parse(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.parse,
+            **kwargs,
+        )

posthog/ai/openai/openai_async.py

@@ -0,0 +1,658 @@
+import time
+import uuid
+from typing import Any, Dict, List, Optional
+
+from posthog.ai.types import TokenUsage
+
+try:
+    import openai
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the OpenAI SDK to use this feature: 'pip install openai'"
+    )
+
+from posthog import setup
+from posthog.ai.utils import (
+    call_llm_and_track_usage_async,
+    extract_available_tool_calls,
+    get_model_params,
+    merge_usage_stats,
+    with_privacy_mode,
+)
+from posthog.ai.openai.openai_converter import (
+    extract_openai_usage_from_chunk,
+    extract_openai_content_from_chunk,
+    extract_openai_tool_calls_from_chunk,
+    accumulate_openai_tool_calls,
+    format_openai_streaming_output,
+)
+from posthog.ai.sanitization import sanitize_openai, sanitize_openai_response
+from posthog.client import Client as PostHogClient
+
+
+class AsyncOpenAI(openai.AsyncOpenAI):
+    """
+    An async wrapper around the OpenAI SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        """
+        Args:
+            api_key: OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead
+                            of the global posthog.
+            **openai_config: Any additional keyword args to set on openai (e.g. organization="xxx").
+        """
+
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = WrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = WrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = WrappedBeta(self, self._original_beta)
+
+        if self._original_responses is not None:
+            self.responses = WrappedResponses(self, self._original_responses)
+
+
+class WrappedResponses:
+    """Async wrapper for OpenAI responses that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_responses):
+        self._client = client
+        self._original = original_responses
+
+    def __getattr__(self, name):
+        """Fallback to original responses object for any methods we don't explicitly handle."""
+
+        return getattr(self._original, name)
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        if kwargs.get("stream", False):
+            return await self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.create,
+            **kwargs,
+        )
+
+    async def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage()
+        final_content = []
+        model_from_response: Optional[str] = None
+        response = await self._original.create(**kwargs)
+
+        async def async_generator():
+            nonlocal usage_stats
+            nonlocal final_content  # noqa: F824
+            nonlocal model_from_response
+
+            try:
+                async for chunk in response:
+                    # Extract model from response object in chunk (for stored prompts)
+                    if hasattr(chunk, "response") and chunk.response:
+                        if model_from_response is None and hasattr(
+                            chunk.response, "model"
+                        ):
+                            model_from_response = chunk.response.model
+
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "responses")
+
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)
+
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "responses")
+
+                    if content is not None:
+                        final_content.append(content)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = final_content
+
+                await self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                    extract_available_tool_calls("openai", kwargs),
+                    model_from_response,
+                )
+
+        return async_generator()
+
+    async def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        output: Any,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response or "unknown"
+
+        event_properties = {
+            "$ai_provider": "openai",
+            "$ai_model": model,
+            "$ai_model_parameters": get_model_params(kwargs),
+            "$ai_input": with_privacy_mode(
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai_response(kwargs.get("input")),
+            ),
+            "$ai_output_choices": with_privacy_mode(
+                self._client._ph_client,
+                posthog_privacy_mode,
+                format_openai_streaming_output(output, "responses"),
+            ),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
+            "$ai_output_tokens": usage_stats.get("output_tokens", 0),
+            "$ai_cache_read_input_tokens": usage_stats.get(
+                "cache_read_input_tokens", 0
+            ),
+            "$ai_reasoning_tokens": usage_stats.get("reasoning_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **(posthog_properties or {}),
+        }
+
+        # Add web search count if present
+        web_search_count = usage_stats.get("web_search_count")
+        if (
+            web_search_count is not None
+            and isinstance(web_search_count, int)
+            and web_search_count > 0
+        ):
+            event_properties["$ai_web_search_count"] = web_search_count
+
+        if available_tool_calls:
+            event_properties["$ai_tools"] = available_tool_calls
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+    async def parse(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Parse structured output using OpenAI's 'responses.parse' method, but also track usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event.
+            posthog_trace_id: Optional trace UUID for linking events.
+            posthog_properties: Optional dictionary of extra properties to include in the event.
+            posthog_privacy_mode: Whether to anonymize the input and output.
+            posthog_groups: Optional dictionary of groups to associate with the event.
+            **kwargs: Any additional parameters for the OpenAI Responses Parse API.
+
+        Returns:
+            The response from OpenAI's responses.parse call.
+        """
+        return await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.parse,
+            **kwargs,
+        )
+
+
+class WrappedChat:
+    """Async wrapper for OpenAI chat that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_chat):
+        self._client = client
+        self._original = original_chat
+
+    def __getattr__(self, name):
+        """Fallback to original chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    @property
+    def completions(self):
+        return WrappedCompletions(self._client, self._original.completions)
+
+
+class WrappedCompletions:
+    """Async wrapper for OpenAI chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_completions):
+        self._client = client
+        self._original = original_completions
+
+    def __getattr__(self, name):
+        """Fallback to original completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        # If streaming, handle streaming specifically
+        if kwargs.get("stream", False):
+            return await self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        response = await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.create,
+            **kwargs,
+        )
+        return response
+
+    async def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: TokenUsage = TokenUsage()
+        accumulated_content = []
+        accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}
+        model_from_response: Optional[str] = None
+
+        if "stream_options" not in kwargs:
+            kwargs["stream_options"] = {}
+        kwargs["stream_options"]["include_usage"] = True
+        response = await self._original.create(**kwargs)
+
+        async def async_generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content  # noqa: F824
+            nonlocal accumulated_tool_calls
+            nonlocal model_from_response
+
+            try:
+                async for chunk in response:
+                    # Extract model from chunk (Chat Completions chunks have model field)
+                    if model_from_response is None and hasattr(chunk, "model"):
+                        model_from_response = chunk.model
+
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "chat")
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)
+
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "chat")
+                    if content is not None:
+                        accumulated_content.append(content)
+
+                    # Extract and accumulate tool calls from chunk
+                    chunk_tool_calls = extract_openai_tool_calls_from_chunk(chunk)
+                    if chunk_tool_calls:
+                        accumulate_openai_tool_calls(
+                            accumulated_tool_calls, chunk_tool_calls
+                        )
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+
+                # Convert accumulated tool calls dict to list
+                tool_calls_list = (
+                    list(accumulated_tool_calls.values())
+                    if accumulated_tool_calls
+                    else None
+                )
+
+                await self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    accumulated_content,
+                    tool_calls_list,
+                    extract_available_tool_calls("openai", kwargs),
+                    model_from_response,
+                )
+
+        return async_generator()
+
+    async def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: TokenUsage,
+        latency: float,
+        output: Any,
+        tool_calls: Optional[List[Dict[str, Any]]] = None,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
+        model_from_response: Optional[str] = None,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        # Use model from kwargs, fallback to model from response
+        model = kwargs.get("model") or model_from_response or "unknown"
+
+        event_properties = {
+            "$ai_provider": "openai",
+            "$ai_model": model,
+            "$ai_model_parameters": get_model_params(kwargs),
+            "$ai_input": with_privacy_mode(
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai(kwargs.get("messages")),
+            ),
+            "$ai_output_choices": with_privacy_mode(
+                self._client._ph_client,
+                posthog_privacy_mode,
+                format_openai_streaming_output(output, "chat", tool_calls),
+            ),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
+            "$ai_output_tokens": usage_stats.get("output_tokens", 0),
+            "$ai_cache_read_input_tokens": usage_stats.get(
+                "cache_read_input_tokens", 0
+            ),
+            "$ai_reasoning_tokens": usage_stats.get("reasoning_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **(posthog_properties or {}),
+        }
+
+        # Add web search count if present
+        web_search_count = usage_stats.get("web_search_count")
+
+        if (
+            web_search_count is not None
+            and isinstance(web_search_count, int)
+            and web_search_count > 0
+        ):
+            event_properties["$ai_web_search_count"] = web_search_count
+
+        if available_tool_calls:
+            event_properties["$ai_tools"] = available_tool_calls
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+
+class WrappedEmbeddings:
+    """Async wrapper for OpenAI embeddings that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_embeddings):
+        self._client = client
+        self._original = original_embeddings
+
+    def __getattr__(self, name):
+        """Fallback to original embeddings object for any methods we don't explicitly handle."""
+
+        return getattr(self._original, name)
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create an embedding using OpenAI's 'embeddings.create' method, but also track usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event.
+            posthog_trace_id: Optional trace UUID for linking events.
+            posthog_properties: Optional dictionary of extra properties to include in the event.
+            posthog_privacy_mode: Whether to anonymize the input and output.
+            posthog_groups: Optional dictionary of groups to associate with the event.
+            **kwargs: Any additional parameters for the OpenAI Embeddings API.
+
+        Returns:
+            The response from OpenAI's embeddings.create call.
+        """
+
+        if posthog_trace_id is None:
+            posthog_trace_id = str(uuid.uuid4())
+
+        start_time = time.time()
+        response = await self._original.create(**kwargs)
+        end_time = time.time()
+
+        # Extract usage statistics if available
+        usage_stats: TokenUsage = TokenUsage()
+
+        if hasattr(response, "usage") and response.usage:
+            usage_stats = TokenUsage(
+                input_tokens=getattr(response.usage, "prompt_tokens", 0),
+                output_tokens=getattr(response.usage, "completion_tokens", 0),
+            )
+
+        latency = end_time - start_time
+
+        # Build the event properties
+        event_properties = {
+            "$ai_provider": "openai",
+            "$ai_model": kwargs.get("model"),
+            "$ai_input": with_privacy_mode(
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai_response(kwargs.get("input")),
+            ),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **(posthog_properties or {}),
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        # Send capture event for embeddings
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_embedding",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+        return response
+
+
+class WrappedBeta:
+    """Async wrapper for OpenAI beta features that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_beta):
+        self._client = client
+        self._original = original_beta
+
+    def __getattr__(self, name):
+        """Fallback to original beta object for any methods we don't explicitly handle."""
+
+        return getattr(self._original, name)
+
+    @property
+    def chat(self):
+        return WrappedBetaChat(self._client, self._original.chat)
+
+
+class WrappedBetaChat:
+    """Async wrapper for OpenAI beta chat that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_beta_chat):
+        self._client = client
+        self._original = original_beta_chat
+
+    def __getattr__(self, name):
+        """Fallback to original beta chat object for any methods we don't explicitly handle."""
+
+        return getattr(self._original, name)
+
+    @property
+    def completions(self):
+        return WrappedBetaCompletions(self._client, self._original.completions)
+
+
+class WrappedBetaCompletions:
+    """Async wrapper for OpenAI beta chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_beta_completions):
+        self._client = client
+        self._original = original_beta_completions
+
+    def __getattr__(self, name):
+        """Fallback to original beta completions object for any methods we don't explicitly handle."""
+
+        return getattr(self._original, name)
+
+    async def parse(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        return await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            self._original.parse,
+            **kwargs,
+        )

posthog/ai/openai/openai_converter.py

@@ -0,0 +1,760 @@
+"""
+OpenAI-specific conversion utilities.
+
+This module handles the conversion of OpenAI API responses and inputs
+into standardized formats for PostHog tracking. It supports both
+Chat Completions API and Responses API formats.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from posthog.ai.types import (
+    FormattedContentItem,
+    FormattedFunctionCall,
+    FormattedImageContent,
+    FormattedMessage,
+    FormattedTextContent,
+    TokenUsage,
+)
+from posthog.ai.utils import serialize_raw_usage
+
+
+def format_openai_response(response: Any) -> List[FormattedMessage]:
+    """
+    Format an OpenAI response into standardized message format.
+
+    Handles both Chat Completions API and Responses API formats.
+
+    Args:
+        response: The response object from OpenAI API
+
+    Returns:
+        List of formatted messages with role and content
+    """
+
+    output: List[FormattedMessage] = []
+
+    if response is None:
+        return output
+
+    # Handle Chat Completions response format
+    if hasattr(response, "choices"):
+        content: List[FormattedContentItem] = []
+        role = "assistant"
+
+        for choice in response.choices:
+            if hasattr(choice, "message") and choice.message:
+                if choice.message.role:
+                    role = choice.message.role
+
+                if choice.message.content:
+                    content.append(
+                        {
+                            "type": "text",
+                            "text": choice.message.content,
+                        }
+                    )
+
+                if hasattr(choice.message, "tool_calls") and choice.message.tool_calls:
+                    for tool_call in choice.message.tool_calls:
+                        content.append(
+                            {
+                                "type": "function",
+                                "id": tool_call.id,
+                                "function": {
+                                    "name": tool_call.function.name,
+                                    "arguments": tool_call.function.arguments,
+                                },
+                            }
+                        )
+
+                # Handle audio output (gpt-4o-audio-preview)
+                if hasattr(choice.message, "audio") and choice.message.audio:
+                    # Convert Pydantic model to dict to capture all fields from OpenAI
+                    audio_dict = choice.message.audio.model_dump()
+                    content.append({"type": "audio", **audio_dict})
+
+        if content:
+            output.append(
+                {
+                    "role": role,
+                    "content": content,
+                }
+            )
+
+    # Handle Responses API format
+    if hasattr(response, "output"):
+        content = []
+        role = "assistant"
+
+        for item in response.output:
+            if item.type == "message":
+                role = item.role
+
+                if hasattr(item, "content") and isinstance(item.content, list):
+                    for content_item in item.content:
+                        if (
+                            hasattr(content_item, "type")
+                            and content_item.type == "output_text"
+                            and hasattr(content_item, "text")
+                        ):
+                            content.append(
+                                {
+                                    "type": "text",
+                                    "text": content_item.text,
+                                }
+                            )
+
+                        elif hasattr(content_item, "text"):
+                            content.append({"type": "text", "text": content_item.text})
+
+                        elif (
+                            hasattr(content_item, "type")
+                            and content_item.type == "input_image"
+                            and hasattr(content_item, "image_url")
+                        ):
+                            image_content: FormattedImageContent = {
+                                "type": "image",
+                                "image": content_item.image_url,
+                            }
+                            content.append(image_content)
+
+                elif hasattr(item, "content"):
+                    text_content = {"type": "text", "text": str(item.content)}
+                    content.append(text_content)
+
+            elif hasattr(item, "type") and item.type == "function_call":
+                content.append(
+                    {
+                        "type": "function",
+                        "id": getattr(item, "call_id", getattr(item, "id", "")),
+                        "function": {
+                            "name": item.name,
+                            "arguments": getattr(item, "arguments", {}),
+                        },
+                    }
+                )
+
+        if content:
+            output.append(
+                {
+                    "role": role,
+                    "content": content,
+                }
+            )
+
+    return output
+
+
+def format_openai_input(
+    messages: Optional[List[Dict[str, Any]]] = None, input_data: Optional[Any] = None
+) -> List[FormattedMessage]:
+    """
+    Format OpenAI input messages.
+
+    Handles both messages parameter (Chat Completions) and input parameter (Responses API).
+
+    Args:
+        messages: List of message dictionaries for Chat Completions API
+        input_data: Input data for Responses API
+
+    Returns:
+        List of formatted messages
+    """
+
+    formatted_messages: List[FormattedMessage] = []
+
+    # Handle Chat Completions API format
+    if messages is not None:
+        for msg in messages:
+            formatted_messages.append(
+                {
+                    "role": msg.get("role", "user"),
+                    "content": msg.get("content", ""),
+                }
+            )
+
+    # Handle Responses API format
+    if input_data is not None:
+        if isinstance(input_data, list):
+            for item in input_data:
+                role = "user"
+                content = ""
+
+                if isinstance(item, dict):
+                    role = item.get("role", "user")
+                    content = item.get("content", "")
+
+                elif isinstance(item, str):
+                    content = item
+
+                else:
+                    content = str(item)
+
+                formatted_messages.append({"role": role, "content": content})
+
+        elif isinstance(input_data, str):
+            formatted_messages.append({"role": "user", "content": input_data})
+
+        else:
+            formatted_messages.append({"role": "user", "content": str(input_data)})
+
+    return formatted_messages
+
+
+def extract_openai_tools(kwargs: Dict[str, Any]) -> Optional[Any]:
+    """
+    Extract tool definitions from OpenAI API kwargs.
+
+    Args:
+        kwargs: Keyword arguments passed to OpenAI API
+
+    Returns:
+        Tool definitions if present, None otherwise
+    """
+
+    # Check for tools parameter (newer API)
+    if "tools" in kwargs:
+        return kwargs["tools"]
+
+    # Check for functions parameter (older API)
+    if "functions" in kwargs:
+        return kwargs["functions"]
+
+    return None
+
+
+def format_openai_streaming_content(
+    accumulated_content: str, tool_calls: Optional[List[Dict[str, Any]]] = None
+) -> List[FormattedContentItem]:
+    """
+    Format content from OpenAI streaming response.
+
+    Used by streaming handlers to format accumulated content.
+
+    Args:
+        accumulated_content: Accumulated text content from streaming
+        tool_calls: Optional list of tool calls accumulated during streaming
+
+    Returns:
+        List of formatted content items
+    """
+    formatted: List[FormattedContentItem] = []
+
+    # Add text content if present
+    if accumulated_content:
+        text_content: FormattedTextContent = {
+            "type": "text",
+            "text": accumulated_content,
+        }
+        formatted.append(text_content)
+
+    # Add tool calls if present
+    if tool_calls:
+        for tool_call in tool_calls:
+            function_call: FormattedFunctionCall = {
+                "type": "function",
+                "id": tool_call.get("id"),
+                "function": tool_call.get("function", {}),
+            }
+            formatted.append(function_call)
+
+    return formatted
+
+
+def extract_openai_web_search_count(response: Any) -> int:
+    """
+    Extract web search count from OpenAI response.
+
+    Uses a two-tier detection strategy:
+    1. Priority 1 (exact count): Check for output[].type == "web_search_call" (Responses API)
+    2. Priority 2 (binary detection): Check for various web search indicators:
+       - Root-level citations, search_results, or usage.search_context_size (Perplexity)
+       - Annotations with type "url_citation" in choices/output (including delta for streaming)
+
+    Args:
+        response: The response from OpenAI API
+
+    Returns:
+        Number of web search requests (exact count or binary 1/0)
+    """
+
+    # Priority 1: Check for exact count in Responses API output
+    if hasattr(response, "output"):
+        web_search_count = 0
+
+        for item in response.output:
+            if hasattr(item, "type") and item.type == "web_search_call":
+                web_search_count += 1
+
+        web_search_count = max(0, web_search_count)
+
+        if web_search_count > 0:
+            return web_search_count
+
+    # Priority 2: Binary detection (returns 1 or 0)
+
+    # Check root-level indicators (Perplexity)
+    if hasattr(response, "citations"):
+        citations = getattr(response, "citations")
+
+        if citations and len(citations) > 0:
+            return 1
+
+    if hasattr(response, "search_results"):
+        search_results = getattr(response, "search_results")
+
+        if search_results and len(search_results) > 0:
+            return 1
+
+    if hasattr(response, "usage") and hasattr(response.usage, "search_context_size"):
+        if response.usage.search_context_size:
+            return 1
+
+    # Check for url_citation annotations in choices (Chat Completions)
+    if hasattr(response, "choices"):
+        for choice in response.choices:
+            # Check message.annotations (non-streaming or final chunk)
+            if hasattr(choice, "message") and hasattr(choice.message, "annotations"):
+                annotations = choice.message.annotations
+
+                if annotations:
+                    for annotation in annotations:
+                        # Support both dict and object formats
+                        annotation_type = (
+                            annotation.get("type")
+                            if isinstance(annotation, dict)
+                            else getattr(annotation, "type", None)
+                        )
+
+                        if annotation_type == "url_citation":
+                            return 1
+
+            # Check delta.annotations (streaming chunks)
+            if hasattr(choice, "delta") and hasattr(choice.delta, "annotations"):
+                annotations = choice.delta.annotations
+
+                if annotations:
+                    for annotation in annotations:
+                        # Support both dict and object formats
+                        annotation_type = (
+                            annotation.get("type")
+                            if isinstance(annotation, dict)
+                            else getattr(annotation, "type", None)
+                        )
+
+                        if annotation_type == "url_citation":
+                            return 1
+
+    # Check for url_citation annotations in output (Responses API)
+    if hasattr(response, "output"):
+        for item in response.output:
+            if hasattr(item, "content") and isinstance(item.content, list):
+                for content_item in item.content:
+                    if hasattr(content_item, "annotations"):
+                        annotations = content_item.annotations
+
+                        if annotations:
+                            for annotation in annotations:
+                                # Support both dict and object formats
+                                annotation_type = (
+                                    annotation.get("type")
+                                    if isinstance(annotation, dict)
+                                    else getattr(annotation, "type", None)
+                                )
+
+                                if annotation_type == "url_citation":
+                                    return 1
+
+    return 0
+
+
+def extract_openai_usage_from_response(response: Any) -> TokenUsage:
+    """
+    Extract usage statistics from a full OpenAI response (non-streaming).
+    Handles both Chat Completions and Responses API.
+
+    Args:
+        response: The complete response from OpenAI API
+
+    Returns:
+        TokenUsage with standardized usage statistics
+    """
+    if not hasattr(response, "usage"):
+        return TokenUsage(input_tokens=0, output_tokens=0)
+
+    cached_tokens = 0
+    input_tokens = 0
+    output_tokens = 0
+    reasoning_tokens = 0
+
+    # Responses API format
+    if hasattr(response.usage, "input_tokens"):
+        input_tokens = response.usage.input_tokens
+    if hasattr(response.usage, "output_tokens"):
+        output_tokens = response.usage.output_tokens
+    if hasattr(response.usage, "input_tokens_details") and hasattr(
+        response.usage.input_tokens_details, "cached_tokens"
+    ):
+        cached_tokens = response.usage.input_tokens_details.cached_tokens
+    if hasattr(response.usage, "output_tokens_details") and hasattr(
+        response.usage.output_tokens_details, "reasoning_tokens"
+    ):
+        reasoning_tokens = response.usage.output_tokens_details.reasoning_tokens
+
+    # Chat Completions format
+    if hasattr(response.usage, "prompt_tokens"):
+        input_tokens = response.usage.prompt_tokens
+    if hasattr(response.usage, "completion_tokens"):
+        output_tokens = response.usage.completion_tokens
+    if hasattr(response.usage, "prompt_tokens_details") and hasattr(
+        response.usage.prompt_tokens_details, "cached_tokens"
+    ):
+        cached_tokens = response.usage.prompt_tokens_details.cached_tokens
+    if hasattr(response.usage, "completion_tokens_details") and hasattr(
+        response.usage.completion_tokens_details, "reasoning_tokens"
+    ):
+        reasoning_tokens = response.usage.completion_tokens_details.reasoning_tokens
+
+    result = TokenUsage(
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+    )
+
+    if cached_tokens > 0:
+        result["cache_read_input_tokens"] = cached_tokens
+    if reasoning_tokens > 0:
+        result["reasoning_tokens"] = reasoning_tokens
+
+    web_search_count = extract_openai_web_search_count(response)
+    if web_search_count > 0:
+        result["web_search_count"] = web_search_count
+
+    # Capture raw usage metadata for backend processing
+    # Serialize to dict here in the converter (not in utils)
+    serialized = serialize_raw_usage(response.usage)
+    if serialized:
+        result["raw_usage"] = serialized
+
+    return result
+
+
+def extract_openai_usage_from_chunk(
+    chunk: Any, provider_type: str = "chat"
+) -> TokenUsage:
+    """
+    Extract usage statistics from an OpenAI streaming chunk.
+
+    Handles both Chat Completions and Responses API formats.
+
+    Args:
+        chunk: Streaming chunk from OpenAI API
+        provider_type: Either "chat" or "responses" to handle different API formats
+
+    Returns:
+        Dictionary of usage statistics
+    """
+
+    usage: TokenUsage = TokenUsage()
+
+    if provider_type == "chat":
+        # Extract web search count from the chunk before checking for usage
+        # Web search indicators (citations, annotations) can appear on any chunk,
+        # not just those with usage data
+        web_search_count = extract_openai_web_search_count(chunk)
+        if web_search_count > 0:
+            usage["web_search_count"] = web_search_count
+
+        if not hasattr(chunk, "usage") or not chunk.usage:
+            return usage
+
+        # Chat Completions API uses prompt_tokens and completion_tokens
+        # Standardize to input_tokens and output_tokens
+        usage["input_tokens"] = getattr(chunk.usage, "prompt_tokens", 0)
+        usage["output_tokens"] = getattr(chunk.usage, "completion_tokens", 0)
+
+        # Handle cached tokens
+        if hasattr(chunk.usage, "prompt_tokens_details") and hasattr(
+            chunk.usage.prompt_tokens_details, "cached_tokens"
+        ):
+            usage["cache_read_input_tokens"] = (
+                chunk.usage.prompt_tokens_details.cached_tokens
+            )
+
+        # Handle reasoning tokens
+        if hasattr(chunk.usage, "completion_tokens_details") and hasattr(
+            chunk.usage.completion_tokens_details, "reasoning_tokens"
+        ):
+            usage["reasoning_tokens"] = (
+                chunk.usage.completion_tokens_details.reasoning_tokens
+            )
+
+        # Capture raw usage metadata for backend processing
+        # Serialize to dict here in the converter (not in utils)
+        serialized = serialize_raw_usage(chunk.usage)
+        if serialized:
+            usage["raw_usage"] = serialized
+
+    elif provider_type == "responses":
+        # For Responses API, usage is only in chunk.response.usage for completed events
+        if hasattr(chunk, "type") and chunk.type == "response.completed":
+            if (
+                hasattr(chunk, "response")
+                and hasattr(chunk.response, "usage")
+                and chunk.response.usage
+            ):
+                response_usage = chunk.response.usage
+                usage["input_tokens"] = getattr(response_usage, "input_tokens", 0)
+                usage["output_tokens"] = getattr(response_usage, "output_tokens", 0)
+
+                # Handle cached tokens
+                if hasattr(response_usage, "input_tokens_details") and hasattr(
+                    response_usage.input_tokens_details, "cached_tokens"
+                ):
+                    usage["cache_read_input_tokens"] = (
+                        response_usage.input_tokens_details.cached_tokens
+                    )
+
+                # Handle reasoning tokens
+                if hasattr(response_usage, "output_tokens_details") and hasattr(
+                    response_usage.output_tokens_details, "reasoning_tokens"
+                ):
+                    usage["reasoning_tokens"] = (
+                        response_usage.output_tokens_details.reasoning_tokens
+                    )
+
+                # Extract web search count from the complete response
+                if hasattr(chunk, "response"):
+                    web_search_count = extract_openai_web_search_count(chunk.response)
+                    if web_search_count > 0:
+                        usage["web_search_count"] = web_search_count
+
+                # Capture raw usage metadata for backend processing
+                # Serialize to dict here in the converter (not in utils)
+                serialized = serialize_raw_usage(response_usage)
+                if serialized:
+                    usage["raw_usage"] = serialized
+
+    return usage
+
+
+def extract_openai_content_from_chunk(
+    chunk: Any, provider_type: str = "chat"
+) -> Optional[str]:
+    """
+    Extract content from an OpenAI streaming chunk.
+
+    Handles both Chat Completions and Responses API formats.
+
+    Args:
+        chunk: Streaming chunk from OpenAI API
+        provider_type: Either "chat" or "responses" to handle different API formats
+
+    Returns:
+        Text content if present, None otherwise
+    """
+
+    if provider_type == "chat":
+        # Chat Completions API format
+        if (
+            hasattr(chunk, "choices")
+            and chunk.choices
+            and len(chunk.choices) > 0
+            and chunk.choices[0].delta
+            and chunk.choices[0].delta.content
+        ):
+            return chunk.choices[0].delta.content
+
+    elif provider_type == "responses":
+        # Responses API format
+        if hasattr(chunk, "type") and chunk.type == "response.completed":
+            if hasattr(chunk, "response") and chunk.response:
+                res = chunk.response
+                if res.output and len(res.output) > 0:
+                    # Return the full output for responses
+                    return res.output[0]
+
+    return None
+
+
+def extract_openai_tool_calls_from_chunk(chunk: Any) -> Optional[List[Dict[str, Any]]]:
+    """
+    Extract tool calls from an OpenAI streaming chunk.
+
+    Args:
+        chunk: Streaming chunk from OpenAI API
+
+    Returns:
+        List of tool call deltas if present, None otherwise
+    """
+    if (
+        hasattr(chunk, "choices")
+        and chunk.choices
+        and len(chunk.choices) > 0
+        and chunk.choices[0].delta
+        and hasattr(chunk.choices[0].delta, "tool_calls")
+        and chunk.choices[0].delta.tool_calls
+    ):
+        tool_calls = []
+        for tool_call in chunk.choices[0].delta.tool_calls:
+            tc_dict = {
+                "index": getattr(tool_call, "index", None),
+            }
+
+            if hasattr(tool_call, "id") and tool_call.id:
+                tc_dict["id"] = tool_call.id
+
+            if hasattr(tool_call, "type") and tool_call.type:
+                tc_dict["type"] = tool_call.type
+
+            if hasattr(tool_call, "function") and tool_call.function:
+                function_dict = {}
+                if hasattr(tool_call.function, "name") and tool_call.function.name:
+                    function_dict["name"] = tool_call.function.name
+                if (
+                    hasattr(tool_call.function, "arguments")
+                    and tool_call.function.arguments
+                ):
+                    function_dict["arguments"] = tool_call.function.arguments
+                tc_dict["function"] = function_dict
+
+            tool_calls.append(tc_dict)
+        return tool_calls
+
+    return None
+
+
+def accumulate_openai_tool_calls(
+    accumulated_tool_calls: Dict[int, Dict[str, Any]],
+    chunk_tool_calls: List[Dict[str, Any]],
+) -> None:
+    """
+    Accumulate tool calls from streaming chunks.
+
+    OpenAI sends tool calls incrementally:
+    - First chunk has id, type, function.name and partial function.arguments
+    - Subsequent chunks have more function.arguments
+
+    Args:
+        accumulated_tool_calls: Dictionary mapping index to accumulated tool call data
+        chunk_tool_calls: List of tool call deltas from current chunk
+    """
+    for tool_call_delta in chunk_tool_calls:
+        index = tool_call_delta.get("index")
+        if index is None:
+            continue
+
+        # Initialize tool call if first time seeing this index
+        if index not in accumulated_tool_calls:
+            accumulated_tool_calls[index] = {
+                "id": "",
+                "type": "function",
+                "function": {
+                    "name": "",
+                    "arguments": "",
+                },
+            }
+
+        # Update with new data from delta
+        tc = accumulated_tool_calls[index]
+
+        if "id" in tool_call_delta and tool_call_delta["id"]:
+            tc["id"] = tool_call_delta["id"]
+
+        if "type" in tool_call_delta and tool_call_delta["type"]:
+            tc["type"] = tool_call_delta["type"]
+
+        if "function" in tool_call_delta:
+            func_delta = tool_call_delta["function"]
+            if "name" in func_delta and func_delta["name"]:
+                tc["function"]["name"] = func_delta["name"]
+            if "arguments" in func_delta and func_delta["arguments"]:
+                # Arguments are sent incrementally, concatenate them
+                tc["function"]["arguments"] += func_delta["arguments"]
+
+
+def format_openai_streaming_output(
+    accumulated_content: Any,
+    provider_type: str = "chat",
+    tool_calls: Optional[List[Dict[str, Any]]] = None,
+) -> List[FormattedMessage]:
+    """
+    Format the final output from OpenAI streaming.
+
+    Args:
+        accumulated_content: Accumulated content from streaming (string for chat, list for responses)
+        provider_type: Either "chat" or "responses" to handle different API formats
+        tool_calls: Optional list of accumulated tool calls
+
+    Returns:
+        List of formatted messages
+    """
+
+    if provider_type == "chat":
+        content_items: List[FormattedContentItem] = []
+
+        # Add text content if present
+        if isinstance(accumulated_content, str) and accumulated_content:
+            content_items.append({"type": "text", "text": accumulated_content})
+        elif isinstance(accumulated_content, list):
+            # If it's a list of strings, join them
+            text = "".join(str(item) for item in accumulated_content if item)
+            if text:
+                content_items.append({"type": "text", "text": text})
+
+        # Add tool calls if present
+        if tool_calls:
+            for tool_call in tool_calls:
+                if "function" in tool_call:
+                    function_call: FormattedFunctionCall = {
+                        "type": "function",
+                        "id": tool_call.get("id", ""),
+                        "function": tool_call["function"],
+                    }
+                    content_items.append(function_call)
+
+        # Return formatted message with content
+        if content_items:
+            return [{"role": "assistant", "content": content_items}]
+        else:
+            # Empty response
+            return [{"role": "assistant", "content": []}]
+
+    elif provider_type == "responses":
+        # Responses API: accumulated_content is a list of output items
+        if isinstance(accumulated_content, list) and accumulated_content:
+            # The output is already formatted, just return it
+            return accumulated_content
+        elif isinstance(accumulated_content, str):
+            return [
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": accumulated_content}],
+                }
+            ]
+
+    # Fallback for any other format
+    return [
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": str(accumulated_content)}],
+        }
+    ]
+
+
+def format_openai_streaming_input(
+    kwargs: Dict[str, Any], api_type: str = "chat"
+) -> Any:
+    """
+    Format OpenAI streaming input based on API type.
+
+    Args:
+        kwargs: Keyword arguments passed to OpenAI API
+        api_type: Either "chat" or "responses"
+
+    Returns:
+        Formatted input ready for PostHog tracking
+    """
+    from posthog.ai.utils import merge_system_prompt
+
+    return merge_system_prompt(kwargs, "openai")

posthog/ai/openai/openai_providers.py

@@ -0,0 +1,98 @@
+try:
+    import openai
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the Open AI SDK to use this feature: 'pip install openai'"
+    )
+
+from posthog.ai.openai.openai import (
+    WrappedBeta,
+    WrappedChat,
+    WrappedEmbeddings,
+    WrappedResponses,
+)
+from posthog.ai.openai.openai_async import WrappedBeta as AsyncWrappedBeta
+from posthog.ai.openai.openai_async import WrappedChat as AsyncWrappedChat
+from posthog.ai.openai.openai_async import WrappedEmbeddings as AsyncWrappedEmbeddings
+from posthog.ai.openai.openai_async import WrappedResponses as AsyncWrappedResponses
+from typing import Optional
+
+from posthog.client import Client as PostHogClient
+from posthog import setup
+
+
+class AzureOpenAI(openai.AzureOpenAI):
+    """
+    A wrapper around the Azure OpenAI SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        """
+        Args:
+            api_key: Azure OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead
+                            of the global posthog.
+            **openai_config: Any additional keyword args to set on Azure OpenAI (e.g. azure_endpoint="xxx").
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = WrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = WrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = WrappedBeta(self, self._original_beta)
+
+        if self._original_responses is not None:
+            self.responses = WrappedResponses(self, self._original_responses)
+
+
+class AsyncAzureOpenAI(openai.AsyncAzureOpenAI):
+    """
+    An async wrapper around the Azure OpenAI SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
+        """
+        Args:
+            api_key: Azure OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead
+                            of the global posthog.
+            **openai_config: Any additional keyword args to set on Azure OpenAI (e.g. azure_endpoint="xxx").
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client or setup()
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = AsyncWrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = AsyncWrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = AsyncWrappedBeta(self, self._original_beta)
+
+        # Only add responses if available (newer OpenAI versions)
+        if self._original_responses is not None:
+            self.responses = AsyncWrappedResponses(self, self._original_responses)

posthog/ai/openai_agents/__init__.py

@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Union
+
+if TYPE_CHECKING:
+    from agents.tracing import Trace
+
+    from posthog.client import Client
+
+try:
+    import agents  # noqa: F401
+except ImportError:
+    raise ModuleNotFoundError(
+        "Please install the OpenAI Agents SDK to use this feature: 'pip install openai-agents'"
+    )
+
+from posthog.ai.openai_agents.processor import PostHogTracingProcessor
+
+__all__ = ["PostHogTracingProcessor", "instrument"]
+
+
+def instrument(
+    client: Optional[Client] = None,
+    distinct_id: Optional[Union[str, Callable[[Trace], Optional[str]]]] = None,
+    privacy_mode: bool = False,
+    groups: Optional[Dict[str, Any]] = None,
+    properties: Optional[Dict[str, Any]] = None,
+) -> PostHogTracingProcessor:
+    """
+    One-liner to instrument OpenAI Agents SDK with PostHog tracing.
+
+    This registers a PostHogTracingProcessor with the OpenAI Agents SDK,
+    automatically capturing traces, spans, and LLM generations.
+
+    Args:
+        client: Optional PostHog client instance. If not provided, uses the default client.
+        distinct_id: Optional distinct ID to associate with all traces.
+            Can also be a callable that takes a trace and returns a distinct ID.
+        privacy_mode: If True, redacts input/output content from events.
+        groups: Optional PostHog groups to associate with events.
+        properties: Optional additional properties to include with all events.
+
+    Returns:
+        PostHogTracingProcessor: The registered processor instance.
+
+    Example:
+        ```python
+        from posthog.ai.openai_agents import instrument
+
+        # Simple setup
+        instrument(distinct_id="user@example.com")
+
+        # With custom properties
+        instrument(
+            distinct_id="user@example.com",
+            privacy_mode=True,
+            properties={"environment": "production"}
+        )
+
+        # Now run agents as normal - traces automatically sent to PostHog
+        from agents import Agent, Runner
+        agent = Agent(name="Assistant", instructions="You are helpful.")
+        result = Runner.run_sync(agent, "Hello!")
+        ```
+    """
+    from agents.tracing import add_trace_processor
+
+    processor = PostHogTracingProcessor(
+        client=client,
+        distinct_id=distinct_id,
+        privacy_mode=privacy_mode,
+        groups=groups,
+        properties=properties,
+    )
+    add_trace_processor(processor)
+    return processor

posthog/ai/openai_agents/processor.py

@@ -0,0 +1,863 @@
+import json
+import logging
+import time
+from datetime import datetime
+from typing import Any, Callable, Dict, Optional, Union
+
+from agents.tracing import Span, Trace
+from agents.tracing.processor_interface import TracingProcessor
+from agents.tracing.span_data import (
+    AgentSpanData,
+    CustomSpanData,
+    FunctionSpanData,
+    GenerationSpanData,
+    GuardrailSpanData,
+    HandoffSpanData,
+    MCPListToolsSpanData,
+    ResponseSpanData,
+    SpeechGroupSpanData,
+    SpeechSpanData,
+    TranscriptionSpanData,
+)
+
+from posthog import setup
+from posthog.client import Client
+
+log = logging.getLogger("posthog")
+
+
+def _ensure_serializable(obj: Any) -> Any:
+    """Ensure an object is JSON-serializable, converting to str as fallback.
+
+    Returns the original object if it's already serializable (dict, list, str,
+    int, etc.), or str(obj) for non-serializable types so that downstream
+    json.dumps() calls won't fail.
+    """
+    if obj is None:
+        return None
+    try:
+        json.dumps(obj)
+        return obj
+    except (TypeError, ValueError):
+        return str(obj)
+
+
+def _parse_iso_timestamp(iso_str: Optional[str]) -> Optional[float]:
+    """Parse ISO timestamp to Unix timestamp."""
+    if not iso_str:
+        return None
+    try:
+        dt = datetime.fromisoformat(iso_str.replace("Z", "+00:00"))
+        return dt.timestamp()
+    except (ValueError, AttributeError):
+        return None
+
+
+class PostHogTracingProcessor(TracingProcessor):
+    """
+    A tracing processor that sends OpenAI Agents SDK traces to PostHog.
+
+    This processor implements the TracingProcessor interface from the OpenAI Agents SDK
+    and maps agent traces, spans, and generations to PostHog's LLM analytics events.
+
+    Example:
+        ```python
+        from agents import Agent, Runner
+        from agents.tracing import add_trace_processor
+        from posthog.ai.openai_agents import PostHogTracingProcessor
+
+        # Create and register the processor
+        processor = PostHogTracingProcessor(
+            distinct_id="user@example.com",
+            privacy_mode=False,
+        )
+        add_trace_processor(processor)
+
+        # Run agents as normal - traces automatically sent to PostHog
+        agent = Agent(name="Assistant", instructions="You are helpful.")
+        result = Runner.run_sync(agent, "Hello!")
+        ```
+    """
+
+    def __init__(
+        self,
+        client: Optional[Client] = None,
+        distinct_id: Optional[Union[str, Callable[[Trace], Optional[str]]]] = None,
+        privacy_mode: bool = False,
+        groups: Optional[Dict[str, Any]] = None,
+        properties: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Initialize the PostHog tracing processor.
+
+        Args:
+            client: Optional PostHog client instance. If not provided, uses the default client.
+            distinct_id: Either a string distinct ID or a callable that takes a Trace
+                and returns a distinct ID. If not provided, uses the trace_id.
+            privacy_mode: If True, redacts input/output content from events.
+            groups: Optional PostHog groups to associate with all events.
+            properties: Optional additional properties to include with all events.
+        """
+        self._client = client or setup()
+        self._distinct_id = distinct_id
+        self._privacy_mode = privacy_mode
+        self._groups = groups or {}
+        self._properties = properties or {}
+
+        # Track span start times for latency calculation
+        self._span_start_times: Dict[str, float] = {}
+
+        # Track trace metadata for associating with spans
+        self._trace_metadata: Dict[str, Dict[str, Any]] = {}
+
+        # Max entries to prevent unbounded growth if on_span_end/on_trace_end
+        # is never called (e.g., due to an exception in the Agents SDK).
+        self._max_tracked_entries = 10000
+
+    def _get_distinct_id(self, trace: Optional[Trace]) -> Optional[str]:
+        """Resolve the distinct ID for a trace.
+
+        Returns the user-provided distinct ID (string or callable result),
+        or None if no user-provided ID is available. Callers should treat
+        None as a signal to use a fallback ID in personless mode.
+        """
+        if callable(self._distinct_id):
+            if trace:
+                result = self._distinct_id(trace)
+                if result:
+                    return str(result)
+            return None
+        elif self._distinct_id:
+            return str(self._distinct_id)
+        return None
+
+    def _with_privacy_mode(self, value: Any) -> Any:
+        """Apply privacy mode redaction if enabled."""
+        if self._privacy_mode or (
+            hasattr(self._client, "privacy_mode") and self._client.privacy_mode
+        ):
+            return None
+        return value
+
+    def _evict_stale_entries(self) -> None:
+        """Evict oldest entries if dicts exceed max size to prevent unbounded growth."""
+        if len(self._span_start_times) > self._max_tracked_entries:
+            # Remove oldest entries by start time
+            sorted_spans = sorted(self._span_start_times.items(), key=lambda x: x[1])
+            for span_id, _ in sorted_spans[: len(sorted_spans) // 2]:
+                del self._span_start_times[span_id]
+            log.debug(
+                "Evicted stale span start times (exceeded %d entries)",
+                self._max_tracked_entries,
+            )
+
+        if len(self._trace_metadata) > self._max_tracked_entries:
+            # Remove half the entries (oldest inserted via dict ordering in Python 3.7+)
+            keys = list(self._trace_metadata.keys())
+            for key in keys[: len(keys) // 2]:
+                del self._trace_metadata[key]
+            log.debug(
+                "Evicted stale trace metadata (exceeded %d entries)",
+                self._max_tracked_entries,
+            )
+
+    def _get_group_id(self, trace_id: str) -> Optional[str]:
+        """Get the group_id for a trace from stored metadata."""
+        if trace_id in self._trace_metadata:
+            return self._trace_metadata[trace_id].get("group_id")
+        return None
+
+    def _capture_event(
+        self,
+        event: str,
+        properties: Dict[str, Any],
+        distinct_id: Optional[str] = None,
+    ) -> None:
+        """Capture an event to PostHog with error handling.
+
+        Args:
+            distinct_id: The resolved distinct ID. When the user didn't provide
+                one, callers should pass ``user_distinct_id or fallback_id``
+                (matching the langchain/openai pattern) and separately set
+                ``$process_person_profile`` in properties.
+        """
+        try:
+            if not hasattr(self._client, "capture") or not callable(
+                self._client.capture
+            ):
+                return
+
+            final_properties = {
+                **properties,
+                **self._properties,
+            }
+
+            self._client.capture(
+                distinct_id=distinct_id or "unknown",
+                event=event,
+                properties=final_properties,
+                groups=self._groups,
+            )
+        except Exception as e:
+            log.debug(f"Failed to capture PostHog event: {e}")
+
+    def on_trace_start(self, trace: Trace) -> None:
+        """Called when a new trace begins. Stores metadata for spans; the $ai_trace event is emitted in on_trace_end."""
+        try:
+            self._evict_stale_entries()
+            trace_id = trace.trace_id
+            trace_name = trace.name
+            group_id = getattr(trace, "group_id", None)
+            metadata = getattr(trace, "metadata", None)
+
+            distinct_id = self._get_distinct_id(trace)
+
+            # Store trace metadata for later (used by spans and on_trace_end)
+            self._trace_metadata[trace_id] = {
+                "name": trace_name,
+                "group_id": group_id,
+                "metadata": metadata,
+                "distinct_id": distinct_id,
+                "start_time": time.time(),
+            }
+        except Exception as e:
+            log.debug(f"Error in on_trace_start: {e}")
+
+    def on_trace_end(self, trace: Trace) -> None:
+        """Called when a trace completes. Emits the $ai_trace event with full metadata."""
+        try:
+            trace_id = trace.trace_id
+
+            # Pop stored metadata (also cleans up)
+            trace_info = self._trace_metadata.pop(trace_id, {})
+            trace_name = trace_info.get("name") or trace.name
+            group_id = trace_info.get("group_id") or getattr(trace, "group_id", None)
+            metadata = trace_info.get("metadata") or getattr(trace, "metadata", None)
+            distinct_id = trace_info.get("distinct_id") or self._get_distinct_id(trace)
+
+            # Calculate trace-level latency
+            start_time = trace_info.get("start_time")
+            latency = (time.time() - start_time) if start_time else None
+
+            properties = {
+                "$ai_trace_id": trace_id,
+                "$ai_trace_name": trace_name,
+                "$ai_provider": "openai",
+                "$ai_framework": "openai-agents",
+            }
+
+            if latency is not None:
+                properties["$ai_latency"] = latency
+
+            # Include group_id for linking related traces (e.g., conversation threads)
+            if group_id:
+                properties["$ai_group_id"] = group_id
+
+            # Include trace metadata if present
+            if metadata:
+                properties["$ai_trace_metadata"] = _ensure_serializable(metadata)
+
+            if distinct_id is None:
+                properties["$process_person_profile"] = False
+
+            self._capture_event(
+                event="$ai_trace",
+                distinct_id=distinct_id or trace_id,
+                properties=properties,
+            )
+        except Exception as e:
+            log.debug(f"Error in on_trace_end: {e}")
+
+    def on_span_start(self, span: Span[Any]) -> None:
+        """Called when a new span begins."""
+        try:
+            self._evict_stale_entries()
+            span_id = span.span_id
+            self._span_start_times[span_id] = time.time()
+        except Exception as e:
+            log.debug(f"Error in on_span_start: {e}")
+
+    def on_span_end(self, span: Span[Any]) -> None:
+        """Called when a span completes."""
+        try:
+            span_id = span.span_id
+            trace_id = span.trace_id
+            parent_id = span.parent_id
+            span_data = span.span_data
+
+            # Calculate latency
+            start_time = self._span_start_times.pop(span_id, None)
+            if start_time:
+                latency = time.time() - start_time
+            else:
+                # Fall back to parsing timestamps
+                started = _parse_iso_timestamp(span.started_at)
+                ended = _parse_iso_timestamp(span.ended_at)
+                latency = (ended - started) if (started and ended) else 0
+
+            # Get user-provided distinct ID from trace metadata (resolved at trace start).
+            # None means no user-provided ID — use trace_id as fallback in personless mode,
+            # matching the langchain/openai pattern: `distinct_id or trace_id`.
+            trace_info = self._trace_metadata.get(trace_id, {})
+            distinct_id = trace_info.get("distinct_id") or self._get_distinct_id(None)
+
+            # Get group_id from trace metadata for linking
+            group_id = self._get_group_id(trace_id)
+
+            # Get error info if present
+            error_info = span.error
+            error_properties = {}
+            if error_info:
+                if isinstance(error_info, dict):
+                    error_message = error_info.get("message", str(error_info))
+                    error_type_raw = error_info.get("type", "")
+                else:
+                    error_message = str(error_info)
+                    error_type_raw = ""
+
+                # Categorize error type for cross-provider filtering/alerting
+                error_type = "unknown"
+                if (
+                    "ModelBehaviorError" in error_type_raw
+                    or "ModelBehaviorError" in error_message
+                ):
+                    error_type = "model_behavior_error"
+                elif "UserError" in error_type_raw or "UserError" in error_message:
+                    error_type = "user_error"
+                elif (
+                    "InputGuardrailTripwireTriggered" in error_type_raw
+                    or "InputGuardrailTripwireTriggered" in error_message
+                ):
+                    error_type = "input_guardrail_triggered"
+                elif (
+                    "OutputGuardrailTripwireTriggered" in error_type_raw
+                    or "OutputGuardrailTripwireTriggered" in error_message
+                ):
+                    error_type = "output_guardrail_triggered"
+                elif (
+                    "MaxTurnsExceeded" in error_type_raw
+                    or "MaxTurnsExceeded" in error_message
+                ):
+                    error_type = "max_turns_exceeded"
+
+                error_properties = {
+                    "$ai_is_error": True,
+                    "$ai_error": error_message,
+                    "$ai_error_type": error_type,
+                }
+
+            # Personless mode: no user-provided distinct_id, fallback to trace_id
+            if distinct_id is None:
+                error_properties["$process_person_profile"] = False
+                distinct_id = trace_id
+
+            # Dispatch based on span data type
+            if isinstance(span_data, GenerationSpanData):
+                self._handle_generation_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, FunctionSpanData):
+                self._handle_function_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, AgentSpanData):
+                self._handle_agent_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, HandoffSpanData):
+                self._handle_handoff_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, GuardrailSpanData):
+                self._handle_guardrail_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, ResponseSpanData):
+                self._handle_response_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, CustomSpanData):
+                self._handle_custom_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(
+                span_data, (TranscriptionSpanData, SpeechSpanData, SpeechGroupSpanData)
+            ):
+                self._handle_audio_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            elif isinstance(span_data, MCPListToolsSpanData):
+                self._handle_mcp_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+            else:
+                # Unknown span type - capture as generic span
+                self._handle_generic_span(
+                    span_data,
+                    trace_id,
+                    span_id,
+                    parent_id,
+                    latency,
+                    distinct_id,
+                    group_id,
+                    error_properties,
+                )
+
+        except Exception as e:
+            log.debug(f"Error in on_span_end: {e}")
+
+    def _base_properties(
+        self,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """Build the base properties dict shared by all span handlers."""
+        properties = {
+            "$ai_trace_id": trace_id,
+            "$ai_span_id": span_id,
+            "$ai_parent_id": parent_id,
+            "$ai_provider": "openai",
+            "$ai_framework": "openai-agents",
+            "$ai_latency": latency,
+            **error_properties,
+        }
+        if group_id:
+            properties["$ai_group_id"] = group_id
+        return properties
+
+    def _handle_generation_span(
+        self,
+        span_data: GenerationSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle LLM generation spans - maps to $ai_generation event."""
+        # Extract token usage
+        usage = span_data.usage or {}
+        input_tokens = usage.get("input_tokens") or usage.get("prompt_tokens") or 0
+        output_tokens = (
+            usage.get("output_tokens") or usage.get("completion_tokens") or 0
+        )
+
+        # Extract model config parameters
+        model_config = span_data.model_config or {}
+        model_params = {}
+        for param in [
+            "temperature",
+            "max_tokens",
+            "top_p",
+            "frequency_penalty",
+            "presence_penalty",
+        ]:
+            if param in model_config:
+                model_params[param] = model_config[param]
+
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_model": span_data.model,
+            "$ai_model_parameters": model_params if model_params else None,
+            "$ai_input": self._with_privacy_mode(_ensure_serializable(span_data.input)),
+            "$ai_output_choices": self._with_privacy_mode(
+                _ensure_serializable(span_data.output)
+            ),
+            "$ai_input_tokens": input_tokens,
+            "$ai_output_tokens": output_tokens,
+            "$ai_total_tokens": (input_tokens or 0) + (output_tokens or 0),
+        }
+
+        # Add optional token fields if present
+        if usage.get("reasoning_tokens"):
+            properties["$ai_reasoning_tokens"] = usage["reasoning_tokens"]
+        if usage.get("cache_read_input_tokens"):
+            properties["$ai_cache_read_input_tokens"] = usage["cache_read_input_tokens"]
+        if usage.get("cache_creation_input_tokens"):
+            properties["$ai_cache_creation_input_tokens"] = usage[
+                "cache_creation_input_tokens"
+            ]
+
+        self._capture_event("$ai_generation", properties, distinct_id)
+
+    def _handle_function_span(
+        self,
+        span_data: FunctionSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle function/tool call spans - maps to $ai_span event."""
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": span_data.name,
+            "$ai_span_type": "tool",
+            "$ai_input_state": self._with_privacy_mode(
+                _ensure_serializable(span_data.input)
+            ),
+            "$ai_output_state": self._with_privacy_mode(
+                _ensure_serializable(span_data.output)
+            ),
+        }
+
+        if span_data.mcp_data:
+            properties["$ai_mcp_data"] = _ensure_serializable(span_data.mcp_data)
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_agent_span(
+        self,
+        span_data: AgentSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle agent execution spans - maps to $ai_span event."""
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": span_data.name,
+            "$ai_span_type": "agent",
+        }
+
+        if span_data.handoffs:
+            properties["$ai_agent_handoffs"] = span_data.handoffs
+        if span_data.tools:
+            properties["$ai_agent_tools"] = span_data.tools
+        if span_data.output_type:
+            properties["$ai_agent_output_type"] = span_data.output_type
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_handoff_span(
+        self,
+        span_data: HandoffSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle agent handoff spans - maps to $ai_span event."""
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": f"{span_data.from_agent} -> {span_data.to_agent}",
+            "$ai_span_type": "handoff",
+            "$ai_handoff_from_agent": span_data.from_agent,
+            "$ai_handoff_to_agent": span_data.to_agent,
+        }
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_guardrail_span(
+        self,
+        span_data: GuardrailSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle guardrail execution spans - maps to $ai_span event."""
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": span_data.name,
+            "$ai_span_type": "guardrail",
+            "$ai_guardrail_triggered": span_data.triggered,
+        }
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_response_span(
+        self,
+        span_data: ResponseSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle OpenAI Response API spans - maps to $ai_generation event."""
+        response = span_data.response
+        response_id = response.id if response else None
+
+        # Try to extract usage from response
+        usage = getattr(response, "usage", None) if response else None
+        input_tokens = 0
+        output_tokens = 0
+        if usage:
+            input_tokens = getattr(usage, "input_tokens", 0) or 0
+            output_tokens = getattr(usage, "output_tokens", 0) or 0
+
+        # Try to extract model from response
+        model = getattr(response, "model", None) if response else None
+
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_model": model,
+            "$ai_response_id": response_id,
+            "$ai_input": self._with_privacy_mode(_ensure_serializable(span_data.input)),
+            "$ai_input_tokens": input_tokens,
+            "$ai_output_tokens": output_tokens,
+            "$ai_total_tokens": input_tokens + output_tokens,
+        }
+
+        # Extract output content from response
+        if response:
+            output_items = getattr(response, "output", None)
+            if output_items:
+                properties["$ai_output_choices"] = self._with_privacy_mode(
+                    _ensure_serializable(output_items)
+                )
+
+        self._capture_event("$ai_generation", properties, distinct_id)
+
+    def _handle_custom_span(
+        self,
+        span_data: CustomSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle custom user-defined spans - maps to $ai_span event."""
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": span_data.name,
+            "$ai_span_type": "custom",
+            "$ai_custom_data": self._with_privacy_mode(
+                _ensure_serializable(span_data.data)
+            ),
+        }
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_audio_span(
+        self,
+        span_data: Union[TranscriptionSpanData, SpeechSpanData, SpeechGroupSpanData],
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle audio-related spans (transcription, speech) - maps to $ai_span event."""
+        span_type = span_data.type  # "transcription", "speech", or "speech_group"
+
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": span_type,
+            "$ai_span_type": span_type,
+        }
+
+        # Add model info if available
+        if hasattr(span_data, "model") and span_data.model:
+            properties["$ai_model"] = span_data.model
+
+        # Add model config if available (pass-through property)
+        if hasattr(span_data, "model_config") and span_data.model_config:
+            properties["model_config"] = _ensure_serializable(span_data.model_config)
+
+        # Add time to first audio byte for speech spans (pass-through property)
+        if hasattr(span_data, "first_content_at") and span_data.first_content_at:
+            properties["first_content_at"] = span_data.first_content_at
+
+        # Add audio format info (pass-through properties)
+        if hasattr(span_data, "input_format"):
+            properties["audio_input_format"] = span_data.input_format
+        if hasattr(span_data, "output_format"):
+            properties["audio_output_format"] = span_data.output_format
+
+        # Add text input for TTS
+        if (
+            hasattr(span_data, "input")
+            and span_data.input
+            and isinstance(span_data.input, str)
+        ):
+            properties["$ai_input"] = self._with_privacy_mode(span_data.input)
+
+        # Don't include audio data (base64) - just metadata
+        if hasattr(span_data, "output") and isinstance(span_data.output, str):
+            # For transcription, output is the text
+            properties["$ai_output_state"] = self._with_privacy_mode(span_data.output)
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_mcp_span(
+        self,
+        span_data: MCPListToolsSpanData,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle MCP (Model Context Protocol) spans - maps to $ai_span event."""
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": f"mcp:{span_data.server}",
+            "$ai_span_type": "mcp_tools",
+            "$ai_mcp_server": span_data.server,
+            "$ai_mcp_tools": span_data.result,
+        }
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def _handle_generic_span(
+        self,
+        span_data: Any,
+        trace_id: str,
+        span_id: str,
+        parent_id: Optional[str],
+        latency: float,
+        distinct_id: str,
+        group_id: Optional[str],
+        error_properties: Dict[str, Any],
+    ) -> None:
+        """Handle unknown span types - maps to $ai_span event."""
+        span_type = getattr(span_data, "type", "unknown")
+
+        properties = {
+            **self._base_properties(
+                trace_id, span_id, parent_id, latency, group_id, error_properties
+            ),
+            "$ai_span_name": span_type,
+            "$ai_span_type": span_type,
+        }
+
+        # Try to export span data
+        if hasattr(span_data, "export"):
+            try:
+                exported = span_data.export()
+                properties["$ai_span_data"] = _ensure_serializable(exported)
+            except Exception:
+                pass
+
+        self._capture_event("$ai_span", properties, distinct_id)
+
+    def shutdown(self) -> None:
+        """Clean up resources when the application stops."""
+        try:
+            self._span_start_times.clear()
+            self._trace_metadata.clear()
+
+            # Flush the PostHog client if possible
+            if hasattr(self._client, "flush") and callable(self._client.flush):
+                self._client.flush()
+        except Exception as e:
+            log.debug(f"Error in shutdown: {e}")
+
+    def force_flush(self) -> None:
+        """Force immediate processing of any queued events."""
+        try:
+            if hasattr(self._client, "flush") and callable(self._client.flush):
+                self._client.flush()
+        except Exception as e:
+            log.debug(f"Error in force_flush: {e}")

posthog/ai/prompts.py

@@ -0,0 +1,286 @@
+"""
+Prompt management for PostHog AI SDK.
+
+Fetch and compile LLM prompts from PostHog with caching and fallback support.
+"""
+
+import logging
+import re
+import time
+import urllib.parse
+from typing import Any, Dict, Optional, Union
+
+from posthog.request import USER_AGENT, _get_session
+from posthog.utils import remove_trailing_slash
+
+log = logging.getLogger("posthog")
+
+APP_ENDPOINT = "https://us.posthog.com"
+DEFAULT_CACHE_TTL_SECONDS = 300  # 5 minutes
+
+PromptVariables = Dict[str, Union[str, int, float, bool]]
+
+
+class CachedPrompt:
+    """Cached prompt with metadata."""
+
+    def __init__(self, prompt: str, fetched_at: float):
+        self.prompt = prompt
+        self.fetched_at = fetched_at
+
+
+def _is_prompt_api_response(data: Any) -> bool:
+    """Check if the response is a valid prompt API response."""
+    return (
+        isinstance(data, dict)
+        and "prompt" in data
+        and isinstance(data.get("prompt"), str)
+    )
+
+
+class Prompts:
+    """
+    Fetch and compile LLM prompts from PostHog.
+
+    Can be initialized with a PostHog client or with direct options.
+
+    Examples:
+        ```python
+        from posthog import Posthog
+        from posthog.ai.prompts import Prompts
+
+        # With PostHog client
+        posthog = Posthog('phc_xxx', host='https://us.posthog.com', personal_api_key='phx_xxx')
+        prompts = Prompts(posthog)
+
+        # Or with direct options (no PostHog client needed)
+        prompts = Prompts(
+            personal_api_key='phx_xxx',
+            project_api_key='phc_xxx',
+            host='https://us.posthog.com',
+        )
+
+        # Fetch with caching and fallback
+        template = prompts.get('support-system-prompt', fallback='You are a helpful assistant.')
+
+        # Compile with variables
+        system_prompt = prompts.compile(template, {
+            'company': 'Acme Corp',
+            'tier': 'premium',
+        })
+        ```
+    """
+
+    def __init__(
+        self,
+        posthog: Optional[Any] = None,
+        *,
+        personal_api_key: Optional[str] = None,
+        project_api_key: Optional[str] = None,
+        host: Optional[str] = None,
+        default_cache_ttl_seconds: Optional[int] = None,
+    ):
+        """
+        Initialize Prompts.
+
+        Args:
+            posthog: PostHog client instance (optional if personal_api_key provided)
+            personal_api_key: Direct personal API key (optional if posthog provided)
+            project_api_key: Direct project API key (optional if posthog provided)
+            host: PostHog host (defaults to app endpoint)
+            default_cache_ttl_seconds: Default cache TTL (defaults to 300)
+        """
+        self._default_cache_ttl_seconds = (
+            default_cache_ttl_seconds or DEFAULT_CACHE_TTL_SECONDS
+        )
+        self._cache: Dict[str, CachedPrompt] = {}
+
+        if posthog is not None:
+            self._personal_api_key = getattr(posthog, "personal_api_key", None) or ""
+            self._project_api_key = getattr(posthog, "api_key", None) or ""
+            self._host = remove_trailing_slash(
+                getattr(posthog, "raw_host", None) or APP_ENDPOINT
+            )
+        else:
+            self._personal_api_key = personal_api_key or ""
+            self._project_api_key = project_api_key or ""
+            self._host = remove_trailing_slash(host or APP_ENDPOINT)
+
+    def get(
+        self,
+        name: str,
+        *,
+        cache_ttl_seconds: Optional[int] = None,
+        fallback: Optional[str] = None,
+    ) -> str:
+        """
+        Fetch a prompt by name from the PostHog API.
+
+        Caching behavior:
+        1. If cache is fresh, return cached value
+        2. If fetch fails and cache exists (stale), return stale cache with warning
+        3. If fetch fails and fallback provided, return fallback with warning
+        4. If fetch fails with no cache/fallback, raise exception
+
+        Args:
+            name: The name of the prompt to fetch
+            cache_ttl_seconds: Cache TTL in seconds (defaults to instance default)
+            fallback: Fallback prompt to use if fetch fails and no cache available
+
+        Returns:
+            The prompt string
+
+        Raises:
+            Exception: If the prompt cannot be fetched and no fallback is available
+        """
+        ttl = (
+            cache_ttl_seconds
+            if cache_ttl_seconds is not None
+            else self._default_cache_ttl_seconds
+        )
+
+        # Check cache first
+        cached = self._cache.get(name)
+        now = time.time()
+
+        if cached is not None:
+            is_fresh = (now - cached.fetched_at) < ttl
+
+            if is_fresh:
+                return cached.prompt
+
+        # Try to fetch from API
+        try:
+            prompt = self._fetch_prompt_from_api(name)
+            fetched_at = time.time()
+
+            # Update cache
+            self._cache[name] = CachedPrompt(prompt=prompt, fetched_at=fetched_at)
+
+            return prompt
+
+        except Exception as error:
+            # Fallback order:
+            # 1. Return stale cache (with warning)
+            if cached is not None:
+                log.warning(
+                    '[PostHog Prompts] Failed to fetch prompt "%s", using stale cache: %s',
+                    name,
+                    error,
+                )
+                return cached.prompt
+
+            # 2. Return fallback (with warning)
+            if fallback is not None:
+                log.warning(
+                    '[PostHog Prompts] Failed to fetch prompt "%s", using fallback: %s',
+                    name,
+                    error,
+                )
+                return fallback
+
+            # 3. Raise error
+            raise
+
+    def compile(self, prompt: str, variables: PromptVariables) -> str:
+        """
+        Replace {{variableName}} placeholders with values.
+
+        Unmatched variables are left unchanged.
+        Supports variable names with hyphens and dots (e.g., user-id, company.name).
+
+        Args:
+            prompt: The prompt template string
+            variables: Object containing variable values
+
+        Returns:
+            The compiled prompt string
+        """
+
+        def replace_variable(match: re.Match) -> str:
+            variable_name = match.group(1)
+
+            if variable_name in variables:
+                return str(variables[variable_name])
+
+            return match.group(0)
+
+        return re.sub(r"\{\{([\w.-]+)\}\}", replace_variable, prompt)
+
+    def clear_cache(self, name: Optional[str] = None) -> None:
+        """
+        Clear cached prompts.
+
+        Args:
+            name: Specific prompt to clear. If None, clears all cached prompts.
+        """
+        if name is not None:
+            self._cache.pop(name, None)
+        else:
+            self._cache.clear()
+
+    def _fetch_prompt_from_api(self, name: str) -> str:
+        """
+        Fetch prompt from PostHog API.
+
+        Endpoint: {host}/api/environments/@current/llm_prompts/name/{encoded_name}/?token={encoded_project_api_key}
+        Auth: Bearer {personal_api_key}
+
+        Args:
+            name: The name of the prompt to fetch
+
+        Returns:
+            The prompt string
+
+        Raises:
+            Exception: If the prompt cannot be fetched
+        """
+        if not self._personal_api_key:
+            raise Exception(
+                "[PostHog Prompts] personal_api_key is required to fetch prompts. "
+                "Please provide it when initializing the Prompts instance."
+            )
+        if not self._project_api_key:
+            raise Exception(
+                "[PostHog Prompts] project_api_key is required to fetch prompts. "
+                "Please provide it when initializing the Prompts instance."
+            )
+
+        encoded_name = urllib.parse.quote(name, safe="")
+        encoded_project_api_key = urllib.parse.quote(self._project_api_key, safe="")
+        url = f"{self._host}/api/environments/@current/llm_prompts/name/{encoded_name}/?token={encoded_project_api_key}"
+
+        headers = {
+            "Authorization": f"Bearer {self._personal_api_key}",
+            "User-Agent": USER_AGENT,
+        }
+
+        response = _get_session().get(url, headers=headers, timeout=10)
+
+        if not response.ok:
+            if response.status_code == 404:
+                raise Exception(f'[PostHog Prompts] Prompt "{name}" not found')
+
+            if response.status_code == 403:
+                raise Exception(
+                    f'[PostHog Prompts] Access denied for prompt "{name}". '
+                    "Check that your personal_api_key has the correct permissions and the LLM prompts feature is enabled."
+                )
+
+            raise Exception(
+                f'[PostHog Prompts] Failed to fetch prompt "{name}": HTTP {response.status_code}'
+            )
+
+        try:
+            data = response.json()
+        except Exception:
+            raise Exception(
+                f'[PostHog Prompts] Invalid response format for prompt "{name}"'
+            )
+
+        if not _is_prompt_api_response(data):
+            raise Exception(
+                f'[PostHog Prompts] Invalid response format for prompt "{name}"'
+            )
+
+        return data["prompt"]

posthog/ai/sanitization.py

@@ -0,0 +1,254 @@
+import os
+import re
+from typing import Any
+from urllib.parse import urlparse
+
+REDACTED_IMAGE_PLACEHOLDER = "[base64 image redacted]"
+
+
+def _is_multimodal_enabled() -> bool:
+    """Check if multimodal capture is enabled via environment variable."""
+    return os.environ.get("_INTERNAL_LLMA_MULTIMODAL", "").lower() in (
+        "true",
+        "1",
+        "yes",
+    )
+
+
+def is_base64_data_url(text: str) -> bool:
+    return re.match(r"^data:([^;]+);base64,", text) is not None
+
+
+def is_valid_url(text: str) -> bool:
+    try:
+        result = urlparse(text)
+        return bool(result.scheme and result.netloc)
+    except Exception:
+        pass
+
+    return text.startswith(("/", "./", "../"))
+
+
+def is_raw_base64(text: str) -> bool:
+    if is_valid_url(text):
+        return False
+
+    return len(text) > 20 and re.match(r"^[A-Za-z0-9+/]+=*$", text) is not None
+
+
+def redact_base64_data_url(value: Any) -> Any:
+    if _is_multimodal_enabled():
+        return value
+
+    if not isinstance(value, str):
+        return value
+
+    if is_base64_data_url(value):
+        return REDACTED_IMAGE_PLACEHOLDER
+
+    if is_raw_base64(value):
+        return REDACTED_IMAGE_PLACEHOLDER
+
+    return value
+
+
+def process_messages(messages: Any, transform_content_func) -> Any:
+    if not messages:
+        return messages
+
+    def process_content(content: Any) -> Any:
+        if isinstance(content, str):
+            return content
+
+        if not content:
+            return content
+
+        if isinstance(content, list):
+            return [transform_content_func(item) for item in content]
+
+        return transform_content_func(content)
+
+    def process_message(msg: Any) -> Any:
+        if not isinstance(msg, dict) or "content" not in msg:
+            return msg
+        return {**msg, "content": process_content(msg["content"])}
+
+    if isinstance(messages, list):
+        return [process_message(msg) for msg in messages]
+
+    return process_message(messages)
+
+
+def sanitize_openai_image(item: Any) -> Any:
+    if not isinstance(item, dict):
+        return item
+
+    if item.get("type") == "input_image" and isinstance(item.get("image_url"), str):
+        return {
+            **item,
+            "image_url": redact_base64_data_url(item["image_url"]),
+        }
+
+    if (
+        item.get("type") == "image_url"
+        and isinstance(item.get("image_url"), dict)
+        and "url" in item["image_url"]
+    ):
+        return {
+            **item,
+            "image_url": {
+                **item["image_url"],
+                "url": redact_base64_data_url(item["image_url"]["url"]),
+            },
+        }
+
+    if item.get("type") == "audio" and "data" in item:
+        if _is_multimodal_enabled():
+            return item
+        return {**item, "data": REDACTED_IMAGE_PLACEHOLDER}
+
+    return item
+
+
+def sanitize_openai_response_image(item: Any) -> Any:
+    if not isinstance(item, dict):
+        return item
+
+    if item.get("type") == "input_image" and "image_url" in item:
+        return {
+            **item,
+            "image_url": redact_base64_data_url(item["image_url"]),
+        }
+
+    return item
+
+
+def sanitize_anthropic_image(item: Any) -> Any:
+    if _is_multimodal_enabled():
+        return item
+
+    if not isinstance(item, dict):
+        return item
+
+    if (
+        item.get("type") == "image"
+        and isinstance(item.get("source"), dict)
+        and item["source"].get("type") == "base64"
+        and "data" in item["source"]
+    ):
+        return {
+            **item,
+            "source": {
+                **item["source"],
+                "data": REDACTED_IMAGE_PLACEHOLDER,
+            },
+        }
+
+    return item
+
+
+def sanitize_gemini_part(part: Any) -> Any:
+    if _is_multimodal_enabled():
+        return part
+
+    if not isinstance(part, dict):
+        return part
+
+    if (
+        "inline_data" in part
+        and isinstance(part["inline_data"], dict)
+        and "data" in part["inline_data"]
+    ):
+        return {
+            **part,
+            "inline_data": {
+                **part["inline_data"],
+                "data": REDACTED_IMAGE_PLACEHOLDER,
+            },
+        }
+
+    return part
+
+
+def process_gemini_item(item: Any) -> Any:
+    if not isinstance(item, dict):
+        return item
+
+    if "parts" in item and item["parts"]:
+        parts = item["parts"]
+        if isinstance(parts, list):
+            parts = [sanitize_gemini_part(part) for part in parts]
+        else:
+            parts = sanitize_gemini_part(parts)
+
+        return {**item, "parts": parts}
+
+    return item
+
+
+def sanitize_langchain_image(item: Any) -> Any:
+    if not isinstance(item, dict):
+        return item
+
+    if (
+        item.get("type") == "image_url"
+        and isinstance(item.get("image_url"), dict)
+        and "url" in item["image_url"]
+    ):
+        return {
+            **item,
+            "image_url": {
+                **item["image_url"],
+                "url": redact_base64_data_url(item["image_url"]["url"]),
+            },
+        }
+
+    if item.get("type") == "image" and "data" in item:
+        return {**item, "data": redact_base64_data_url(item["data"])}
+
+    if (
+        item.get("type") == "image"
+        and isinstance(item.get("source"), dict)
+        and "data" in item["source"]
+    ):
+        if _is_multimodal_enabled():
+            return item
+
+        return {
+            **item,
+            "source": {
+                **item["source"],
+                "data": REDACTED_IMAGE_PLACEHOLDER,
+            },
+        }
+
+    if item.get("type") == "media" and "data" in item:
+        return {**item, "data": redact_base64_data_url(item["data"])}
+
+    return item
+
+
+def sanitize_openai(data: Any) -> Any:
+    return process_messages(data, sanitize_openai_image)
+
+
+def sanitize_openai_response(data: Any) -> Any:
+    return process_messages(data, sanitize_openai_response_image)
+
+
+def sanitize_anthropic(data: Any) -> Any:
+    return process_messages(data, sanitize_anthropic_image)
+
+
+def sanitize_gemini(data: Any) -> Any:
+    if not data:
+        return data
+
+    if isinstance(data, list):
+        return [process_gemini_item(item) for item in data]
+
+    return process_gemini_item(data)
+
+
+def sanitize_langchain(data: Any) -> Any:
+    return process_messages(data, sanitize_langchain_image)

posthog/ai/types.py

@@ -0,0 +1,126 @@
+"""
+Common type definitions for PostHog AI SDK.
+
+These types are used for formatting messages and responses across different AI providers
+(Anthropic, OpenAI, Gemini, etc.) to ensure consistency in tracking and data structure.
+"""
+
+from typing import Any, Dict, List, Optional, TypedDict, Union
+
+
+class FormattedTextContent(TypedDict):
+    """Formatted text content item."""
+
+    type: str  # Literal["text"]
+    text: str
+
+
+class FormattedFunctionCall(TypedDict, total=False):
+    """Formatted function/tool call content item."""
+
+    type: str  # Literal["function"]
+    id: Optional[str]
+    function: Dict[str, Any]  # Contains 'name' and 'arguments'
+
+
+class FormattedImageContent(TypedDict):
+    """Formatted image content item."""
+
+    type: str  # Literal["image"]
+    image: str
+
+
+# Union type for all formatted content items
+FormattedContentItem = Union[
+    FormattedTextContent,
+    FormattedFunctionCall,
+    FormattedImageContent,
+    Dict[str, Any],  # Fallback for unknown content types
+]
+
+
+class FormattedMessage(TypedDict):
+    """
+    Standardized message format for PostHog tracking.
+
+    Used across all providers to ensure consistent message structure
+    when sending events to PostHog.
+    """
+
+    role: str
+    content: Union[str, List[FormattedContentItem], Any]
+
+
+class TokenUsage(TypedDict, total=False):
+    """
+    Token usage information for AI model responses.
+
+    Different providers may populate different fields.
+    """
+
+    input_tokens: int
+    output_tokens: int
+    cache_read_input_tokens: Optional[int]
+    cache_creation_input_tokens: Optional[int]
+    reasoning_tokens: Optional[int]
+    web_search_count: Optional[int]
+    raw_usage: Optional[Any]  # Raw provider usage metadata for backend processing
+
+
+class ProviderResponse(TypedDict, total=False):
+    """
+    Standardized provider response format.
+
+    Used for consistent response formatting across all providers.
+    """
+
+    messages: List[FormattedMessage]
+    usage: TokenUsage
+    error: Optional[str]
+
+
+class StreamingContentBlock(TypedDict, total=False):
+    """
+    Content block used during streaming to accumulate content.
+
+    Used for tracking text and function calls as they stream in.
+    """
+
+    type: str  # "text" or "function"
+    text: Optional[str]
+    id: Optional[str]
+    function: Optional[Dict[str, Any]]
+
+
+class ToolInProgress(TypedDict):
+    """
+    Tracks a tool/function call being accumulated during streaming.
+
+    Used by Anthropic to accumulate JSON input for tools.
+    """
+
+    block: StreamingContentBlock
+    input_string: str
+
+
+class StreamingEventData(TypedDict):
+    """
+    Standardized data for streaming events across all providers.
+
+    This type ensures consistent data structure when capturing streaming events,
+    with all provider-specific formatting already completed.
+    """
+
+    provider: str  # "openai", "anthropic", "gemini"
+    model: str
+    base_url: str
+    kwargs: Dict[str, Any]  # Original kwargs for tool extraction and special handling
+    formatted_input: Any  # Provider-formatted input ready for tracking
+    formatted_output: Any  # Provider-formatted output ready for tracking
+    usage_stats: TokenUsage
+    latency: float
+    distinct_id: Optional[str]
+    trace_id: Optional[str]
+    properties: Optional[Dict[str, Any]]
+    privacy_mode: bool
+    groups: Optional[Dict[str, Any]]

posthog/ai/utils.py

@@ -0,0 +1,696 @@
+import time
+import uuid
+from typing import Any, Callable, Dict, List, Optional, cast
+
+from posthog import get_tags, identify_context, new_context, tag
+from posthog.ai.sanitization import (
+    sanitize_anthropic,
+    sanitize_gemini,
+    sanitize_langchain,
+    sanitize_openai,
+)
+from posthog.ai.types import FormattedMessage, StreamingEventData, TokenUsage
+from posthog.client import Client as PostHogClient
+
+
+def serialize_raw_usage(raw_usage: Any) -> Optional[Dict[str, Any]]:
+    """
+    Convert raw provider usage objects to JSON-serializable dicts.
+
+    Handles Pydantic models (OpenAI/Anthropic) and protobuf-like objects (Gemini)
+    with a fallback chain to ensure we never pass unserializable objects to PostHog.
+
+    Args:
+        raw_usage: Raw usage object from provider SDK
+
+    Returns:
+        Plain dict or None if conversion fails
+    """
+    if raw_usage is None:
+        return None
+
+    # Already a dict
+    if isinstance(raw_usage, dict):
+        return raw_usage
+
+    # Try Pydantic model_dump() (OpenAI/Anthropic)
+    if hasattr(raw_usage, "model_dump") and callable(raw_usage.model_dump):
+        try:
+            return raw_usage.model_dump()
+        except Exception:
+            pass
+
+    # Try to_dict() (some protobuf objects)
+    if hasattr(raw_usage, "to_dict") and callable(raw_usage.to_dict):
+        try:
+            return raw_usage.to_dict()
+        except Exception:
+            pass
+
+    # Try __dict__ / vars() for simple objects
+    try:
+        return vars(raw_usage)
+    except Exception:
+        pass
+
+    # Last resort: convert to string representation
+    # This ensures we always return something rather than failing
+    try:
+        return {"_raw": str(raw_usage)}
+    except Exception:
+        return None
+
+
+def merge_usage_stats(
+    target: TokenUsage, source: TokenUsage, mode: str = "incremental"
+) -> None:
+    """
+    Merge streaming usage statistics into target dict, handling None values.
+
+    Supports two modes:
+    - "incremental": Add source values to target (for APIs that report new tokens)
+    - "cumulative": Replace target with source values (for APIs that report totals)
+
+    Args:
+        target: Dictionary to update with usage stats
+        source: TokenUsage that may contain None values
+        mode: Either "incremental" or "cumulative"
+    """
+    if mode == "incremental":
+        # Add new values to existing totals
+        source_input = source.get("input_tokens")
+        if source_input is not None:
+            current = target.get("input_tokens") or 0
+            target["input_tokens"] = current + source_input
+
+        source_output = source.get("output_tokens")
+        if source_output is not None:
+            current = target.get("output_tokens") or 0
+            target["output_tokens"] = current + source_output
+
+        source_cache_read = source.get("cache_read_input_tokens")
+        if source_cache_read is not None:
+            current = target.get("cache_read_input_tokens") or 0
+            target["cache_read_input_tokens"] = current + source_cache_read
+
+        source_cache_creation = source.get("cache_creation_input_tokens")
+        if source_cache_creation is not None:
+            current = target.get("cache_creation_input_tokens") or 0
+            target["cache_creation_input_tokens"] = current + source_cache_creation
+
+        source_reasoning = source.get("reasoning_tokens")
+        if source_reasoning is not None:
+            current = target.get("reasoning_tokens") or 0
+            target["reasoning_tokens"] = current + source_reasoning
+
+        source_web_search = source.get("web_search_count")
+        if source_web_search is not None:
+            current = target.get("web_search_count") or 0
+            target["web_search_count"] = max(current, source_web_search)
+
+        # Merge raw_usage to avoid losing data from earlier events
+        # For Anthropic streaming: message_start has input tokens, message_delta has output
+        # Note: raw_usage is already serialized by converters, so it's a dict
+        source_raw_usage = source.get("raw_usage")
+        if source_raw_usage is not None and isinstance(source_raw_usage, dict):
+            current_raw_value = target.get("raw_usage")
+            current_raw: Dict[str, Any] = (
+                current_raw_value if isinstance(current_raw_value, dict) else {}
+            )
+            target["raw_usage"] = {**current_raw, **source_raw_usage}
+
+    elif mode == "cumulative":
+        # Replace with latest values (already cumulative)
+        if source.get("input_tokens") is not None:
+            target["input_tokens"] = source["input_tokens"]
+        if source.get("output_tokens") is not None:
+            target["output_tokens"] = source["output_tokens"]
+        if source.get("cache_read_input_tokens") is not None:
+            target["cache_read_input_tokens"] = source["cache_read_input_tokens"]
+        if source.get("cache_creation_input_tokens") is not None:
+            target["cache_creation_input_tokens"] = source[
+                "cache_creation_input_tokens"
+            ]
+        if source.get("reasoning_tokens") is not None:
+            target["reasoning_tokens"] = source["reasoning_tokens"]
+        if source.get("web_search_count") is not None:
+            target["web_search_count"] = source["web_search_count"]
+        # Note: raw_usage is already serialized by converters, so it's a dict
+        if source.get("raw_usage") is not None:
+            target["raw_usage"] = source["raw_usage"]
+
+    else:
+        raise ValueError(f"Invalid mode: {mode}. Must be 'incremental' or 'cumulative'")
+
+
+def get_model_params(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Extracts model parameters from the kwargs dictionary.
+    """
+    model_params = {}
+    for param in [
+        "temperature",
+        "max_tokens",  # Deprecated field
+        "max_completion_tokens",
+        "top_p",
+        "frequency_penalty",
+        "presence_penalty",
+        "n",
+        "stop",
+        "stream",  # OpenAI-specific field
+        "streaming",  # Anthropic-specific field
+    ]:
+        if param in kwargs and kwargs[param] is not None:
+            model_params[param] = kwargs[param]
+    return model_params
+
+
+def get_usage(response, provider: str) -> TokenUsage:
+    """
+    Extract usage statistics from response based on provider.
+    Delegates to provider-specific converter functions.
+    """
+    if provider == "anthropic":
+        from posthog.ai.anthropic.anthropic_converter import (
+            extract_anthropic_usage_from_response,
+        )
+
+        return extract_anthropic_usage_from_response(response)
+    elif provider == "openai":
+        from posthog.ai.openai.openai_converter import (
+            extract_openai_usage_from_response,
+        )
+
+        return extract_openai_usage_from_response(response)
+    elif provider == "gemini":
+        from posthog.ai.gemini.gemini_converter import (
+            extract_gemini_usage_from_response,
+        )
+
+        return extract_gemini_usage_from_response(response)
+
+    return TokenUsage(input_tokens=0, output_tokens=0)
+
+
+def format_response(response, provider: str):
+    """
+    Format a regular (non-streaming) response.
+    """
+    if provider == "anthropic":
+        from posthog.ai.anthropic.anthropic_converter import format_anthropic_response
+
+        return format_anthropic_response(response)
+    elif provider == "openai":
+        from posthog.ai.openai.openai_converter import format_openai_response
+
+        return format_openai_response(response)
+    elif provider == "gemini":
+        from posthog.ai.gemini.gemini_converter import format_gemini_response
+
+        return format_gemini_response(response)
+    return []
+
+
+def extract_available_tool_calls(provider: str, kwargs: Dict[str, Any]):
+    """
+    Extract available tool calls for the given provider.
+    """
+    if provider == "anthropic":
+        from posthog.ai.anthropic.anthropic_converter import extract_anthropic_tools
+
+        return extract_anthropic_tools(kwargs)
+    elif provider == "gemini":
+        from posthog.ai.gemini.gemini_converter import extract_gemini_tools
+
+        return extract_gemini_tools(kwargs)
+    elif provider == "openai":
+        from posthog.ai.openai.openai_converter import extract_openai_tools
+
+        return extract_openai_tools(kwargs)
+    return None
+
+
+def merge_system_prompt(
+    kwargs: Dict[str, Any], provider: str
+) -> List[FormattedMessage]:
+    """
+    Merge system prompts and format messages for the given provider.
+    """
+    if provider == "anthropic":
+        from posthog.ai.anthropic.anthropic_converter import format_anthropic_input
+
+        messages = kwargs.get("messages") or []
+        system = kwargs.get("system")
+        return format_anthropic_input(messages, system)
+    elif provider == "gemini":
+        from posthog.ai.gemini.gemini_converter import format_gemini_input_with_system
+
+        contents = kwargs.get("contents", [])
+        config = kwargs.get("config")
+        return format_gemini_input_with_system(contents, config)
+    elif provider == "openai":
+        from posthog.ai.openai.openai_converter import format_openai_input
+
+        # For OpenAI, handle both Chat Completions and Responses API
+        messages_param = kwargs.get("messages")
+        input_param = kwargs.get("input")
+
+        # Get base formatted messages
+        messages = format_openai_input(messages_param, input_param)
+
+        # Check if system prompt is provided as a separate parameter
+        if kwargs.get("system") is not None:
+            has_system = any(msg.get("role") == "system" for msg in messages)
+            if not has_system:
+                system_msg = cast(
+                    FormattedMessage,
+                    {"role": "system", "content": kwargs.get("system")},
+                )
+                messages = [system_msg] + messages
+
+        # For Responses API, add instructions to the system prompt if provided
+        if kwargs.get("instructions") is not None:
+            # Find the system message if it exists
+            system_idx = next(
+                (i for i, msg in enumerate(messages) if msg.get("role") == "system"),
+                None,
+            )
+
+            if system_idx is not None:
+                # Append instructions to existing system message
+                system_content = messages[system_idx].get("content", "")
+                messages[system_idx]["content"] = (
+                    f"{system_content}\n\n{kwargs.get('instructions')}"
+                )
+            else:
+                # Create a new system message with instructions
+                instruction_msg = cast(
+                    FormattedMessage,
+                    {"role": "system", "content": kwargs.get("instructions")},
+                )
+                messages = [instruction_msg] + messages
+
+        return messages
+
+    # Default case - return empty list
+    return []
+
+
+def call_llm_and_track_usage(
+    posthog_distinct_id: Optional[str],
+    ph_client: PostHogClient,
+    provider: str,
+    posthog_trace_id: Optional[str],
+    posthog_properties: Optional[Dict[str, Any]],
+    posthog_privacy_mode: bool,
+    posthog_groups: Optional[Dict[str, Any]],
+    base_url: str,
+    call_method: Callable[..., Any],
+    **kwargs: Any,
+) -> Any:
+    """
+    Common usage-tracking logic for both sync and async calls.
+    call_method: the llm call method (e.g. openai.chat.completions.create)
+    """
+    start_time = time.time()
+    response = None
+    error = None
+    http_status = 200
+    usage: TokenUsage = TokenUsage()
+    error_params: Dict[str, Any] = {}
+
+    with new_context(client=ph_client, capture_exceptions=False):
+        if posthog_distinct_id:
+            identify_context(posthog_distinct_id)
+
+        try:
+            response = call_method(**kwargs)
+        except Exception as exc:
+            error = exc
+            http_status = getattr(
+                exc, "status_code", 0
+            )  # default to 0 becuase its likely an SDK error
+            error_params = {
+                "$ai_is_error": True,
+                "$ai_error": exc.__str__(),
+            }
+            # TODO: Add exception capture for OpenAI/Anthropic/Gemini wrappers when
+            # enable_exception_autocapture is True, similar to LangChain callbacks.
+            # See _capture_exception_and_update_properties in langchain/callbacks.py
+        finally:
+            end_time = time.time()
+            latency = end_time - start_time
+
+            if posthog_trace_id is None:
+                posthog_trace_id = str(uuid.uuid4())
+
+            if response and (
+                hasattr(response, "usage")
+                or (provider == "gemini" and hasattr(response, "usage_metadata"))
+            ):
+                usage = get_usage(response, provider)
+
+            messages = merge_system_prompt(kwargs, provider)
+            sanitized_messages = sanitize_messages(messages, provider)
+
+            tag("$ai_provider", provider)
+            tag("$ai_model", kwargs.get("model") or getattr(response, "model", None))
+            tag("$ai_model_parameters", get_model_params(kwargs))
+            tag(
+                "$ai_input",
+                with_privacy_mode(ph_client, posthog_privacy_mode, sanitized_messages),
+            )
+            tag(
+                "$ai_output_choices",
+                with_privacy_mode(
+                    ph_client, posthog_privacy_mode, format_response(response, provider)
+                ),
+            )
+            tag("$ai_http_status", http_status)
+            tag("$ai_input_tokens", usage.get("input_tokens", 0))
+            tag("$ai_output_tokens", usage.get("output_tokens", 0))
+            tag("$ai_latency", latency)
+            tag("$ai_trace_id", posthog_trace_id)
+            tag("$ai_base_url", str(base_url))
+
+            available_tool_calls = extract_available_tool_calls(provider, kwargs)
+
+            if available_tool_calls:
+                tag("$ai_tools", available_tool_calls)
+
+            cache_read = usage.get("cache_read_input_tokens")
+            if cache_read is not None and cache_read > 0:
+                tag("$ai_cache_read_input_tokens", cache_read)
+
+            cache_creation = usage.get("cache_creation_input_tokens")
+            if cache_creation is not None and cache_creation > 0:
+                tag("$ai_cache_creation_input_tokens", cache_creation)
+
+            reasoning = usage.get("reasoning_tokens")
+            if reasoning is not None and reasoning > 0:
+                tag("$ai_reasoning_tokens", reasoning)
+
+            web_search_count = usage.get("web_search_count")
+            if web_search_count is not None and web_search_count > 0:
+                tag("$ai_web_search_count", web_search_count)
+
+            raw_usage = usage.get("raw_usage")
+            if raw_usage is not None:
+                # Already serialized by converters
+                tag("$ai_usage", raw_usage)
+
+            if posthog_distinct_id is None:
+                tag("$process_person_profile", False)
+
+            # Process instructions for Responses API
+            if provider == "openai" and kwargs.get("instructions") is not None:
+                tag(
+                    "$ai_instructions",
+                    with_privacy_mode(
+                        ph_client, posthog_privacy_mode, kwargs.get("instructions")
+                    ),
+                )
+
+            # send the event to posthog
+            if hasattr(ph_client, "capture") and callable(ph_client.capture):
+                ph_client.capture(
+                    distinct_id=posthog_distinct_id or posthog_trace_id,
+                    event="$ai_generation",
+                    properties={
+                        **get_tags(),
+                        **(posthog_properties or {}),
+                        **(error_params or {}),
+                    },
+                    groups=posthog_groups,
+                )
+
+        if error:
+            raise error
+
+    return response
+
+
+async def call_llm_and_track_usage_async(
+    posthog_distinct_id: Optional[str],
+    ph_client: PostHogClient,
+    provider: str,
+    posthog_trace_id: Optional[str],
+    posthog_properties: Optional[Dict[str, Any]],
+    posthog_privacy_mode: bool,
+    posthog_groups: Optional[Dict[str, Any]],
+    base_url: str,
+    call_async_method: Callable[..., Any],
+    **kwargs: Any,
+) -> Any:
+    start_time = time.time()
+    response = None
+    error = None
+    http_status = 200
+    usage: TokenUsage = TokenUsage()
+    error_params: Dict[str, Any] = {}
+
+    with new_context(client=ph_client, capture_exceptions=False):
+        if posthog_distinct_id:
+            identify_context(posthog_distinct_id)
+
+        try:
+            response = await call_async_method(**kwargs)
+        except Exception as exc:
+            error = exc
+            http_status = getattr(
+                exc, "status_code", 0
+            )  # default to 0 because its likely an SDK error
+            error_params = {
+                "$ai_is_error": True,
+                "$ai_error": exc.__str__(),
+            }
+            # TODO: Add exception capture for OpenAI/Anthropic/Gemini wrappers when
+            # enable_exception_autocapture is True, similar to LangChain callbacks.
+            # See _capture_exception_and_update_properties in langchain/callbacks.py
+        finally:
+            end_time = time.time()
+            latency = end_time - start_time
+
+            if posthog_trace_id is None:
+                posthog_trace_id = str(uuid.uuid4())
+
+            if response and (
+                hasattr(response, "usage")
+                or (provider == "gemini" and hasattr(response, "usage_metadata"))
+            ):
+                usage = get_usage(response, provider)
+
+            messages = merge_system_prompt(kwargs, provider)
+            sanitized_messages = sanitize_messages(messages, provider)
+
+            tag("$ai_provider", provider)
+            tag("$ai_model", kwargs.get("model") or getattr(response, "model", None))
+            tag("$ai_model_parameters", get_model_params(kwargs))
+            tag(
+                "$ai_input",
+                with_privacy_mode(ph_client, posthog_privacy_mode, sanitized_messages),
+            )
+            tag(
+                "$ai_output_choices",
+                with_privacy_mode(
+                    ph_client, posthog_privacy_mode, format_response(response, provider)
+                ),
+            )
+            tag("$ai_http_status", http_status)
+            tag("$ai_input_tokens", usage.get("input_tokens", 0))
+            tag("$ai_output_tokens", usage.get("output_tokens", 0))
+            tag("$ai_latency", latency)
+            tag("$ai_trace_id", posthog_trace_id)
+            tag("$ai_base_url", str(base_url))
+
+            available_tool_calls = extract_available_tool_calls(provider, kwargs)
+
+            if available_tool_calls:
+                tag("$ai_tools", available_tool_calls)
+
+            cache_read = usage.get("cache_read_input_tokens")
+            if cache_read is not None and cache_read > 0:
+                tag("$ai_cache_read_input_tokens", cache_read)
+
+            cache_creation = usage.get("cache_creation_input_tokens")
+            if cache_creation is not None and cache_creation > 0:
+                tag("$ai_cache_creation_input_tokens", cache_creation)
+
+            reasoning = usage.get("reasoning_tokens")
+            if reasoning is not None and reasoning > 0:
+                tag("$ai_reasoning_tokens", reasoning)
+
+            web_search_count = usage.get("web_search_count")
+            if web_search_count is not None and web_search_count > 0:
+                tag("$ai_web_search_count", web_search_count)
+
+            raw_usage = usage.get("raw_usage")
+            if raw_usage is not None:
+                # Already serialized by converters
+                tag("$ai_usage", raw_usage)
+
+            if posthog_distinct_id is None:
+                tag("$process_person_profile", False)
+
+            # Process instructions for Responses API
+            if provider == "openai" and kwargs.get("instructions") is not None:
+                tag(
+                    "$ai_instructions",
+                    with_privacy_mode(
+                        ph_client, posthog_privacy_mode, kwargs.get("instructions")
+                    ),
+                )
+
+            # send the event to posthog
+            if hasattr(ph_client, "capture") and callable(ph_client.capture):
+                ph_client.capture(
+                    distinct_id=posthog_distinct_id or posthog_trace_id,
+                    event="$ai_generation",
+                    properties={
+                        **get_tags(),
+                        **(posthog_properties or {}),
+                        **(error_params or {}),
+                    },
+                    groups=posthog_groups,
+                )
+
+        if error:
+            raise error
+
+    return response
+
+
+def sanitize_messages(data: Any, provider: str) -> Any:
+    """Sanitize messages using provider-specific sanitization functions."""
+    if provider == "anthropic":
+        return sanitize_anthropic(data)
+    elif provider == "openai":
+        return sanitize_openai(data)
+    elif provider == "gemini":
+        return sanitize_gemini(data)
+    elif provider == "langchain":
+        return sanitize_langchain(data)
+    return data
+
+
+def with_privacy_mode(ph_client: PostHogClient, privacy_mode: bool, value: Any):
+    if ph_client.privacy_mode or privacy_mode:
+        return None
+    return value
+
+
+def capture_streaming_event(
+    ph_client: PostHogClient,
+    event_data: StreamingEventData,
+):
+    """
+    Unified streaming event capture for all LLM providers.
+
+    This function handles the common logic for capturing streaming events across all providers.
+    All provider-specific formatting should be done BEFORE calling this function.
+
+    The function handles:
+    - Building PostHog event properties
+    - Extracting and adding tools based on provider
+    - Applying privacy mode
+    - Adding special token fields (cache, reasoning)
+    - Provider-specific fields (e.g., OpenAI instructions)
+    - Sending the event to PostHog
+
+    Args:
+        ph_client: PostHog client instance
+        event_data: Standardized streaming event data containing all necessary information
+    """
+    trace_id = event_data.get("trace_id") or str(uuid.uuid4())
+
+    # Build base event properties
+    event_properties = {
+        "$ai_provider": event_data["provider"],
+        "$ai_model": event_data["model"],
+        "$ai_model_parameters": get_model_params(event_data["kwargs"]),
+        "$ai_input": with_privacy_mode(
+            ph_client,
+            event_data["privacy_mode"],
+            event_data["formatted_input"],
+        ),
+        "$ai_output_choices": with_privacy_mode(
+            ph_client,
+            event_data["privacy_mode"],
+            event_data["formatted_output"],
+        ),
+        "$ai_http_status": 200,
+        "$ai_input_tokens": event_data["usage_stats"].get("input_tokens", 0),
+        "$ai_output_tokens": event_data["usage_stats"].get("output_tokens", 0),
+        "$ai_latency": event_data["latency"],
+        "$ai_trace_id": trace_id,
+        "$ai_base_url": str(event_data["base_url"]),
+        **(event_data.get("properties") or {}),
+    }
+
+    # Extract and add tools based on provider
+    available_tools = extract_available_tool_calls(
+        event_data["provider"],
+        event_data["kwargs"],
+    )
+    if available_tools:
+        event_properties["$ai_tools"] = available_tools
+
+    # Add optional token fields
+    # For Anthropic, always include cache fields even if 0 (backward compatibility)
+    # For others, only include if present and non-zero
+    if event_data["provider"] == "anthropic":
+        # Anthropic always includes cache fields
+        cache_read = event_data["usage_stats"].get("cache_read_input_tokens", 0)
+        cache_creation = event_data["usage_stats"].get("cache_creation_input_tokens", 0)
+        event_properties["$ai_cache_read_input_tokens"] = cache_read
+        event_properties["$ai_cache_creation_input_tokens"] = cache_creation
+    else:
+        # Other providers only include if non-zero
+        optional_token_fields = [
+            "cache_read_input_tokens",
+            "cache_creation_input_tokens",
+            "reasoning_tokens",
+        ]
+
+        for field in optional_token_fields:
+            value = event_data["usage_stats"].get(field)
+            if value is not None and isinstance(value, int) and value > 0:
+                event_properties[f"$ai_{field}"] = value
+
+    # Add web search count if present (all providers)
+    web_search_count = event_data["usage_stats"].get("web_search_count")
+    if (
+        web_search_count is not None
+        and isinstance(web_search_count, int)
+        and web_search_count > 0
+    ):
+        event_properties["$ai_web_search_count"] = web_search_count
+
+    # Add raw usage metadata if present (all providers)
+    raw_usage = event_data["usage_stats"].get("raw_usage")
+    if raw_usage is not None:
+        # Already serialized by converters
+        event_properties["$ai_usage"] = raw_usage
+
+    # Handle provider-specific fields
+    if (
+        event_data["provider"] == "openai"
+        and event_data["kwargs"].get("instructions") is not None
+    ):
+        event_properties["$ai_instructions"] = with_privacy_mode(
+            ph_client,
+            event_data["privacy_mode"],
+            event_data["kwargs"]["instructions"],
+        )
+
+    if event_data.get("distinct_id") is None:
+        event_properties["$process_person_profile"] = False
+
+    # Send event to PostHog
+    if hasattr(ph_client, "capture"):
+        ph_client.capture(
+            distinct_id=event_data.get("distinct_id") or trace_id,
+            event="$ai_generation",
+            properties=event_properties,
+            groups=event_data.get("groups"),
+        )

posthog/args.py

@@ -0,0 +1,71 @@
+from typing import TypedDict, Optional, Any, Dict, Union, Tuple, Type
+from types import TracebackType
+from typing_extensions import NotRequired  # For Python < 3.11 compatibility
+from datetime import datetime
+import numbers
+from uuid import UUID
+
+from posthog.types import SendFeatureFlagsOptions
+
+ID_TYPES = Union[numbers.Number, str, UUID, int]
+
+
+class OptionalCaptureArgs(TypedDict):
+    """Optional arguments for the capture method.
+
+    Args:
+        distinct_id: Unique identifier for the person associated with this event. If not set, the context
+            distinct_id is used, if available, otherwise a UUID is generated, and the event is marked
+            as personless. Setting context-level distinct_id's is recommended.
+        properties: Dictionary of properties to track with the event
+        timestamp: When the event occurred (defaults to current time)
+        uuid: Unique identifier for this specific event. If not provided, one is generated. The event
+            UUID is returned, so you can correlate it with actions in your app (like showing users an
+            error ID if you capture an exception).
+        groups: Group identifiers to associate with this event (format: {group_type: group_key})
+        send_feature_flags: Whether to include currently active feature flags in the event properties.
+            Can be a boolean (True/False) or a SendFeatureFlagsOptions object for advanced configuration.
+            Defaults to False.
+        disable_geoip: Whether to disable GeoIP lookup for this event. Defaults to False.
+    """
+
+    distinct_id: NotRequired[Optional[ID_TYPES]]
+    properties: NotRequired[Optional[Dict[str, Any]]]
+    timestamp: NotRequired[Optional[Union[datetime, str]]]
+    uuid: NotRequired[Optional[str]]
+    groups: NotRequired[Optional[Dict[str, str]]]
+    send_feature_flags: NotRequired[
+        Optional[Union[bool, SendFeatureFlagsOptions]]
+    ]  # Updated to support both boolean and options object
+    disable_geoip: NotRequired[
+        Optional[bool]
+    ]  # As above, optional so we can tell if the user is intentionally overriding a client setting or not
+
+
+class OptionalSetArgs(TypedDict):
+    """Optional arguments for the set method.
+
+    Args:
+        distinct_id: Unique identifier for the user to set properties on. If not set, the context
+            distinct_id is used, if available, otherwise this function does nothing. Setting
+            context-level distinct_id's is recommended.
+        properties: Dictionary of properties to set on the person
+        timestamp: When the properties were set (defaults to current time)
+        uuid: Unique identifier for this operation. If not provided, one is generated. This
+            UUID is returned, so you can correlate it with actions in your app.
+        disable_geoip: Whether to disable GeoIP lookup for this operation. Defaults to False.
+    """
+
+    distinct_id: NotRequired[Optional[ID_TYPES]]
+    properties: NotRequired[Optional[Dict[str, Any]]]
+    timestamp: NotRequired[Optional[Union[datetime, str]]]
+    uuid: NotRequired[Optional[str]]
+    disable_geoip: NotRequired[Optional[bool]]
+
+
+ExcInfo = Union[
+    Tuple[Type[BaseException], BaseException, Optional[TracebackType]],
+    Tuple[None, None, None],
+]
+
+ExceptionArg = Union[BaseException, ExcInfo]

posthog/consumer.py

@@ -1,30 +1,41 @@
-import logging
-from threading import Thread
-import monotonic
-import backoff
 import json
+import logging
+import time
+from threading import Thread
 
-from posthog.request import post, APIError, DatetimeSerializer
+from posthog.request import APIError, DatetimeSerializer, batch_post
 
 try:
     from queue import Empty
 except ImportError:
     from Queue import Empty
 
-MAX_MSG_SIZE = 32 << 10
 
-# Our servers only accept batches less than 500KB. Here limit is set slightly
-# lower to leave space for extra data that will be added later, eg. "sentAt".
-BATCH_SIZE_LIMIT = 475000
+MAX_MSG_SIZE = 900 * 1024  # 900KiB per event
+
+# The maximum request body size is currently 20MiB, let's be conservative
+# in case we want to lower it in the future.
+BATCH_SIZE_LIMIT = 5 * 1024 * 1024
 
 
 class Consumer(Thread):
     """Consumes the messages from the client's queue."""
-    log = logging.getLogger('posthog')
 
-    def __init__(self, queue, api_key, flush_at=100, host=None,
-                 on_error=None, flush_interval=0.5, gzip=False, retries=10,
-                 timeout=15):
+    log = logging.getLogger("posthog")
+
+    def __init__(
+        self,
+        queue,
+        api_key,
+        flush_at=100,
+        host=None,
+        on_error=None,
+        flush_interval=0.5,
+        gzip=False,
+        retries=10,
+        timeout=15,
+        historical_migration=False,
+    ):
         """Create a consumer thread."""
         Thread.__init__(self)
         # Make consumer a daemon thread so that it doesn't block program exit
@@ -43,14 +54,15 @@ class Consumer(Thread):
         self.running = True
         self.retries = retries
         self.timeout = timeout
+        self.historical_migration = historical_migration
 
     def run(self):
         """Runs the consumer."""
-        self.log.debug('consumer is running...')
+        self.log.debug("consumer is running...")
         while self.running:
             self.upload()
 
-        self.log.debug('consumer exited.')
+        self.log.debug("consumer exited.")
 
     def pause(self):
         """Pause the consumer."""
@@ -67,42 +79,44 @@ class Consumer(Thread):
             self.request(batch)
             success = True
         except Exception as e:
-            self.log.error('error uploading: %s', e)
+            self.log.error("error uploading: %s", e)
             success = False
             if self.on_error:
-                self.on_error(e, batch)
+                try:
+                    self.on_error(e, batch)
+                except Exception as e:
+                    self.log.error("on_error handler failed: %s", e)
         finally:
             # mark items as acknowledged from queue
             for item in batch:
                 self.queue.task_done()
-            return success
+
+        return success
 
     def next(self):
         """Return the next batch of items to upload."""
         queue = self.queue
         items = []
 
-        start_time = monotonic.monotonic()
+        start_time = time.monotonic()
         total_size = 0
 
         while len(items) < self.flush_at:
-            elapsed = monotonic.monotonic() - start_time
+            elapsed = time.monotonic() - start_time
             if elapsed >= self.flush_interval:
                 break
             try:
-                item = queue.get(
-                    block=True, timeout=self.flush_interval - elapsed)
-                item_size = len(json.dumps(
-                    item, cls=DatetimeSerializer).encode())
+                item = queue.get(block=True, timeout=self.flush_interval - elapsed)
+                item_size = len(json.dumps(item, cls=DatetimeSerializer).encode())
                 if item_size > MAX_MSG_SIZE:
                     self.log.error(
-                        'Item exceeds 32kb limit, dropping. (%s)', str(item))
+                        "Item exceeds 900kib limit, dropping. (%s)", str(item)
+                    )
                     continue
                 items.append(item)
                 total_size += item_size
                 if total_size >= BATCH_SIZE_LIMIT:
-                    self.log.debug(
-                        'hit batch size limit (size: %d)', total_size)
+                    self.log.debug("hit batch size limit (size: %d)", total_size)
                     break
             except Empty:
                 break
@@ -110,25 +124,43 @@ class Consumer(Thread):
         return items
 
     def request(self, batch):
-        """Attempt to upload the batch and retry before raising an error """
+        """Attempt to upload the batch and retry before raising an error"""
 
-        def fatal_exception(exc):
+        def is_retryable(exc):
             if isinstance(exc, APIError):
                 # retry on server errors and client errors
-                # with 429 status code (rate limited),
+                # with 408 (request timeout) or 429 (rate limited),
                 # don't retry on other client errors
-                return (400 <= exc.status < 500) and exc.status != 429
+                if exc.status == "N/A":
+                    return False
+                return not ((400 <= exc.status < 500) and exc.status not in (408, 429))
             else:
                 # retry on all other errors (eg. network)
-                return False
+                return True
 
-        @backoff.on_exception(
-            backoff.expo,
-            Exception,
-            max_tries=self.retries + 1,
-            giveup=fatal_exception)
-        def send_request():
-            post(self.api_key, self.host, gzip=self.gzip,
-                 timeout=self.timeout, batch=batch)
+        last_exc = None
+        for attempt in range(self.retries + 1):
+            try:
+                batch_post(
+                    self.api_key,
+                    self.host,
+                    gzip=self.gzip,
+                    timeout=self.timeout,
+                    batch=batch,
+                    historical_migration=self.historical_migration,
+                )
+                return
+            except Exception as e:
+                last_exc = e
+                if not is_retryable(e):
+                    raise
+                if attempt < self.retries:
+                    # Respect Retry-After header if present, otherwise use exponential backoff
+                    retry_after = getattr(e, "retry_after", None)
+                    if retry_after and retry_after > 0:
+                        time.sleep(retry_after)
+                    else:
+                        time.sleep(min(2**attempt, 30))
 
-        send_request()
+        if last_exc:
+            raise last_exc

posthog/contexts.py

@@ -0,0 +1,408 @@
+import contextvars
+from contextlib import contextmanager
+from typing import Optional, Any, Callable, Dict, TypeVar, cast, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # To avoid circular imports
+    from posthog.client import Client
+
+
+class ContextScope:
+    def __init__(
+        self,
+        parent=None,
+        fresh: bool = False,
+        capture_exceptions: bool = True,
+        client: Optional["Client"] = None,
+    ):
+        self.client: Optional[Client] = client
+        self.parent = parent
+        self.fresh = fresh
+        self.capture_exceptions = capture_exceptions
+        self.session_id: Optional[str] = None
+        self.distinct_id: Optional[str] = None
+        self.device_id: Optional[str] = None
+        self.tags: Dict[str, Any] = {}
+        self.capture_exception_code_variables: Optional[bool] = None
+        self.code_variables_mask_patterns: Optional[list] = None
+        self.code_variables_ignore_patterns: Optional[list] = None
+
+    def set_session_id(self, session_id: str):
+        self.session_id = session_id
+
+    def set_distinct_id(self, distinct_id: str):
+        self.distinct_id = distinct_id
+
+    def set_device_id(self, device_id: str):
+        self.device_id = device_id
+
+    def add_tag(self, key: str, value: Any):
+        self.tags[key] = value
+
+    def set_capture_exception_code_variables(self, enabled: bool):
+        self.capture_exception_code_variables = enabled
+
+    def set_code_variables_mask_patterns(self, mask_patterns: list):
+        self.code_variables_mask_patterns = mask_patterns
+
+    def set_code_variables_ignore_patterns(self, ignore_patterns: list):
+        self.code_variables_ignore_patterns = ignore_patterns
+
+    def get_parent(self):
+        return self.parent
+
+    def get_session_id(self) -> Optional[str]:
+        if self.session_id is not None:
+            return self.session_id
+        if self.parent is not None and not self.fresh:
+            return self.parent.get_session_id()
+        return None
+
+    def get_distinct_id(self) -> Optional[str]:
+        if self.distinct_id is not None:
+            return self.distinct_id
+        if self.parent is not None and not self.fresh:
+            return self.parent.get_distinct_id()
+        return None
+
+    def get_device_id(self) -> Optional[str]:
+        if self.device_id is not None:
+            return self.device_id
+        if self.parent is not None and not self.fresh:
+            return self.parent.get_device_id()
+        return None
+
+    def collect_tags(self) -> Dict[str, Any]:
+        if self.parent and not self.fresh:
+            # We want child tags to take precedence over parent tags,
+            # so collect parent tags first, then update with child tags.
+            tags = self.parent.collect_tags()
+            tags.update(self.tags)
+            return tags
+        return self.tags.copy()
+
+    def get_capture_exception_code_variables(self) -> Optional[bool]:
+        if self.capture_exception_code_variables is not None:
+            return self.capture_exception_code_variables
+        if self.parent is not None and not self.fresh:
+            return self.parent.get_capture_exception_code_variables()
+        return None
+
+    def get_code_variables_mask_patterns(self) -> Optional[list]:
+        if self.code_variables_mask_patterns is not None:
+            return self.code_variables_mask_patterns
+        if self.parent is not None and not self.fresh:
+            return self.parent.get_code_variables_mask_patterns()
+        return None
+
+    def get_code_variables_ignore_patterns(self) -> Optional[list]:
+        if self.code_variables_ignore_patterns is not None:
+            return self.code_variables_ignore_patterns
+        if self.parent is not None and not self.fresh:
+            return self.parent.get_code_variables_ignore_patterns()
+        return None
+
+
+_context_stack: contextvars.ContextVar[Optional[ContextScope]] = contextvars.ContextVar(
+    "posthog_context_stack", default=None
+)
+
+
+def _get_current_context() -> Optional[ContextScope]:
+    return _context_stack.get()
+
+
+@contextmanager
+def new_context(
+    fresh: bool = False,
+    capture_exceptions: bool = True,
+    client: Optional["Client"] = None,
+):
+    """
+    Create a new context scope that will be active for the duration of the with block.
+    Any tags set within this scope will be isolated to this context. Any exceptions raised
+    or events captured within the context will be tagged with the context tags.
+
+    Args:
+        fresh: Whether to start with a fresh context (default: False).
+               If False, inherits tags, identity and session id's from parent context.
+               If True, starts with no state
+        capture_exceptions: Whether to capture exceptions raised within the context (default: True).
+               If True, captures exceptions and tags them with the context tags before propagating them.
+               If False, exceptions will propagate without being tagged or captured.
+        client: Optional client instance to use for capturing exceptions (default: None).
+                If provided, the client will be used to capture exceptions within the context.
+                If not provided, the default (global) client will be used. Note that the passed
+                client is only used to capture exceptions within the context - other events captured
+                within the context via `Client.capture` or `posthog.capture` will still carry the context
+                state (tags, identity, session id), but will be captured by the client directly used (or
+                the global one, in the case of `posthog.capture`)
+
+    Examples:
+        ```python
+        # Inherit parent context tags
+        with posthog.new_context():
+            posthog.tag("request_id", "123")
+            # Both this event and the exception will be tagged with the context tags
+            posthog.capture("event_name", {"property": "value"})
+            raise ValueError("Something went wrong")
+        ```
+        ```python
+        # Start with fresh context (no inherited tags)
+        with posthog.new_context(fresh=True):
+            posthog.tag("request_id", "123")
+            # Both this event and the exception will be tagged with the context tags
+            posthog.capture("event_name", {"property": "value"})
+            raise ValueError("Something went wrong")
+        ```
+
+    Category:
+        Contexts
+    """
+    from posthog import capture_exception
+
+    current_context = _get_current_context()
+    new_context = ContextScope(current_context, fresh, capture_exceptions, client)
+    _context_stack.set(new_context)
+
+    try:
+        yield
+    except Exception as e:
+        if new_context.capture_exceptions:
+            if new_context.client:
+                new_context.client.capture_exception(e)
+            else:
+                capture_exception(e)
+        raise
+    finally:
+        _context_stack.set(new_context.get_parent())
+
+
+def tag(key: str, value: Any) -> None:
+    """
+    Add a tag to the current context. All tags are added as properties to any event, including exceptions, captured
+    within the context.
+
+    Args:
+        key: The tag key
+        value: The tag value
+
+    Example:
+        ```python
+        posthog.tag("user_id", "123")
+        ```
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.add_tag(key, value)
+
+
+def get_tags() -> Dict[str, Any]:
+    """
+    Get all tags from the current context. Note, modifying
+    the returned dictionary will not affect the current context.
+
+    Returns:
+        Dict of all tags in the current context
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.collect_tags()
+    return {}
+
+
+def identify_context(distinct_id: str) -> None:
+    """
+    Identify the current context with a distinct ID, associating all events captured in this or
+    child contexts with the given distinct ID (unless identify_context is called again). This is overridden by
+    distinct id's passed directly to posthog.capture and related methods (identify, set etc). Entering a
+    fresh context will clear the context-level distinct ID. The distinct-id passed should be uniquely associated
+    with one of your users. Events captured outside of a context, or in a context with no associated distinct
+    ID, will be assigned a random UUID, and captured as "personless".
+
+    Args:
+        distinct_id: The distinct ID to associate with the current context and its children.
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.set_distinct_id(distinct_id)
+
+
+def set_context_session(session_id: str) -> None:
+    """
+    Set the session ID for the current context, associating all events captured in this or
+    child contexts with the given session ID (unless set_context_session is called again).
+    Entering a fresh context will clear the context-level session ID.
+
+    Args:
+        session_id: The session ID to associate with the current context and its children. See https://posthog.com/docs/data/sessions
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.set_session_id(session_id)
+
+
+def get_context_session_id() -> Optional[str]:
+    """
+    Get the session ID for the current context.
+
+    Returns:
+        The session ID if set, None otherwise
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.get_session_id()
+    return None
+
+
+def get_context_distinct_id() -> Optional[str]:
+    """
+    Get the distinct ID for the current context.
+
+    Returns:
+        The distinct ID if set, None otherwise
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.get_distinct_id()
+    return None
+
+
+def set_context_device_id(device_id: str) -> None:
+    """
+    Set the device ID for the current context, associating all feature flag requests in this or
+    child contexts with the given device ID (unless set_context_device_id is called again).
+    Entering a fresh context will clear the context-level device ID.
+
+    Args:
+        device_id: The device ID to associate with the current context and its children.
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.set_device_id(device_id)
+
+
+def get_context_device_id() -> Optional[str]:
+    """
+    Get the device ID for the current context.
+
+    Returns:
+        The device ID if set, None otherwise
+
+    Category:
+        Contexts
+    """
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.get_device_id()
+    return None
+
+
+def set_capture_exception_code_variables_context(enabled: bool) -> None:
+    """
+    Set whether code variables are captured for the current context.
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.set_capture_exception_code_variables(enabled)
+
+
+def set_code_variables_mask_patterns_context(mask_patterns: list) -> None:
+    """
+    Variable names matching these patterns will be masked with *** when capturing code variables.
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.set_code_variables_mask_patterns(mask_patterns)
+
+
+def set_code_variables_ignore_patterns_context(ignore_patterns: list) -> None:
+    """
+    Variable names matching these patterns will be ignored completely when capturing code variables.
+    """
+    current_context = _get_current_context()
+    if current_context:
+        current_context.set_code_variables_ignore_patterns(ignore_patterns)
+
+
+def get_capture_exception_code_variables_context() -> Optional[bool]:
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.get_capture_exception_code_variables()
+    return None
+
+
+def get_code_variables_mask_patterns_context() -> Optional[list]:
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.get_code_variables_mask_patterns()
+    return None
+
+
+def get_code_variables_ignore_patterns_context() -> Optional[list]:
+    current_context = _get_current_context()
+    if current_context:
+        return current_context.get_code_variables_ignore_patterns()
+    return None
+
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+def scoped(fresh: bool = False, capture_exceptions: bool = True):
+    """
+    Decorator that creates a new context for the function. Simply wraps
+    the function in a with posthog.new_context(): block.
+
+    Args:
+        fresh: Whether to start with a fresh context (default: False)
+        capture_exceptions: Whether to capture and track exceptions with posthog error tracking (default: True)
+
+    Example:
+        @posthog.scoped()
+        def process_payment(payment_id):
+            posthog.tag("payment_id", payment_id)
+            posthog.tag("payment_method", "credit_card")
+
+            # This event will be captured with tags
+            posthog.capture("payment_started")
+            # If this raises an exception, it will be captured with tags
+            # and then re-raised
+            some_risky_function()
+
+    Category:
+        Contexts
+    """
+
+    def decorator(func: F) -> F:
+        from functools import wraps
+
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            with new_context(fresh=fresh, capture_exceptions=capture_exceptions):
+                return func(*args, **kwargs)
+
+        return cast(F, wrapper)
+
+    return decorator

posthog/exception_capture.py

@@ -0,0 +1,49 @@
+# Portions of this file are derived from getsentry/sentry-javascript by Software, Inc. dba Sentry
+# Licensed under the MIT License
+
+# 💖open source (under MIT License)
+
+import logging
+import sys
+import threading
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from posthog.client import Client
+
+
+class ExceptionCapture:
+    # TODO: Add client side rate limiting to prevent spamming the server with exceptions
+
+    log = logging.getLogger("posthog")
+
+    def __init__(self, client: "Client"):
+        self.client = client
+        self.original_excepthook = sys.excepthook
+        sys.excepthook = self.exception_handler
+        threading.excepthook = self.thread_exception_handler
+
+    def close(self):
+        sys.excepthook = self.original_excepthook
+
+    def exception_handler(self, exc_type, exc_value, exc_traceback):
+        # don't affect default behaviour.
+        self.capture_exception((exc_type, exc_value, exc_traceback))
+        self.original_excepthook(exc_type, exc_value, exc_traceback)
+
+    def thread_exception_handler(self, args):
+        self.capture_exception((args.exc_type, args.exc_value, args.exc_traceback))
+
+    def exception_receiver(self, exc_info, extra_properties):
+        if "distinct_id" in extra_properties:
+            metadata = {"distinct_id": extra_properties["distinct_id"]}
+        else:
+            metadata = None
+        self.capture_exception((exc_info[0], exc_info[1], exc_info[2]), metadata)
+
+    def capture_exception(self, exception, metadata=None):
+        try:
+            distinct_id = metadata.get("distinct_id") if metadata else None
+            self.client.capture_exception(exception, distinct_id=distinct_id)
+        except Exception as e:
+            self.log.exception(f"Failed to capture exception: {e}")

posthog/feature_flags.py

@@ -0,0 +1,688 @@
+import datetime
+import hashlib
+import logging
+import re
+import warnings
+from typing import Optional
+
+from dateutil import parser
+from dateutil.relativedelta import relativedelta
+
+from posthog import utils
+from posthog.types import FlagValue
+from posthog.utils import convert_to_datetime_aware, is_valid_regex
+
+__LONG_SCALE__ = float(0xFFFFFFFFFFFFFFF)
+
+log = logging.getLogger("posthog")
+
+NONE_VALUES_ALLOWED_OPERATORS = ["is_not"]
+
+
+class InconclusiveMatchError(Exception):
+    pass
+
+
+class RequiresServerEvaluation(Exception):
+    """
+    Raised when feature flag evaluation requires server-side data that is not
+    available locally (e.g., static cohorts, experience continuity).
+
+    This error should propagate immediately to trigger API fallback, unlike
+    InconclusiveMatchError which allows trying other conditions.
+    """
+
+    pass
+
+
+# This function takes a bucketing value and a feature flag key and returns a float between 0 and 1.
+# Given the same bucketing value and key, it'll always return the same float. These floats are
+# uniformly distributed between 0 and 1, so if we want to show this feature to 20% of traffic
+# we can do _hash(key, bucketing_value) < 0.2
+def _hash(key: str, bucketing_value: str, salt: str = "") -> float:
+    hash_key = f"{key}.{bucketing_value}{salt}"
+    hash_val = int(hashlib.sha1(hash_key.encode("utf-8")).hexdigest()[:15], 16)
+    return hash_val / __LONG_SCALE__
+
+
+def get_matching_variant(flag, bucketing_value):
+    hash_value = _hash(flag["key"], bucketing_value, salt="variant")
+    for variant in variant_lookup_table(flag):
+        if hash_value >= variant["value_min"] and hash_value < variant["value_max"]:
+            return variant["key"]
+    return None
+
+
+def variant_lookup_table(feature_flag):
+    lookup_table = []
+    value_min = 0
+    multivariates = ((feature_flag.get("filters") or {}).get("multivariate") or {}).get(
+        "variants"
+    ) or []
+    for variant in multivariates:
+        value_max = value_min + variant["rollout_percentage"] / 100
+        lookup_table.append(
+            {"value_min": value_min, "value_max": value_max, "key": variant["key"]}
+        )
+        value_min = value_max
+    return lookup_table
+
+
+def evaluate_flag_dependency(
+    property,
+    flags_by_key,
+    evaluation_cache,
+    distinct_id,
+    properties,
+    cohort_properties,
+    device_id=None,
+):
+    """
+    Evaluate a flag dependency property according to the dependency chain algorithm.
+
+    Args:
+        property: Flag property with type="flag" and dependency_chain
+        flags_by_key: Dictionary of all flags by their key
+        evaluation_cache: Cache for storing evaluation results
+        distinct_id: The distinct ID being evaluated
+        properties: Person properties for evaluation
+        cohort_properties: Cohort properties for evaluation
+        device_id: The device ID for bucketing (optional)
+
+    Returns:
+        bool: True if all dependencies in the chain evaluate to True, False otherwise
+    """
+    if flags_by_key is None or evaluation_cache is None:
+        # Cannot evaluate flag dependencies without required context
+        raise InconclusiveMatchError(
+            f"Cannot evaluate flag dependency on '{property.get('key', 'unknown')}' without flags_by_key and evaluation_cache"
+        )
+
+    # Check if dependency_chain is present - it should always be provided for flag dependencies
+    if "dependency_chain" not in property:
+        # Missing dependency_chain indicates malformed server data
+        raise InconclusiveMatchError(
+            f"Flag dependency property for '{property.get('key', 'unknown')}' is missing required 'dependency_chain' field"
+        )
+
+    dependency_chain = property["dependency_chain"]
+
+    # Handle circular dependency (empty chain means circular)
+    if len(dependency_chain) == 0:
+        log.debug(f"Circular dependency detected for flag: {property.get('key')}")
+        raise InconclusiveMatchError(
+            f"Circular dependency detected for flag '{property.get('key', 'unknown')}'"
+        )
+
+    # Evaluate all dependencies in the chain order
+    for dep_flag_key in dependency_chain:
+        if dep_flag_key not in evaluation_cache:
+            # Need to evaluate this dependency first
+            dep_flag = flags_by_key.get(dep_flag_key)
+            if not dep_flag:
+                # Missing flag dependency - cannot evaluate locally
+                evaluation_cache[dep_flag_key] = None
+                raise InconclusiveMatchError(
+                    f"Cannot evaluate flag dependency '{dep_flag_key}' - flag not found in local flags"
+                )
+            else:
+                # Check if the flag is active (same check as in client._compute_flag_locally)
+                if not dep_flag.get("active"):
+                    evaluation_cache[dep_flag_key] = False
+                else:
+                    # Recursively evaluate the dependency
+                    try:
+                        dep_flag_filters = dep_flag.get("filters") or {}
+                        dep_aggregation_group_type_index = dep_flag_filters.get(
+                            "aggregation_group_type_index"
+                        )
+                        if dep_aggregation_group_type_index is not None:
+                            # Group flags should continue bucketing by the group key
+                            # from the current evaluation context.
+                            dep_bucketing_value = distinct_id
+                        else:
+                            dep_bucketing_value = resolve_bucketing_value(
+                                dep_flag, distinct_id, device_id
+                            )
+                        dep_result = match_feature_flag_properties(
+                            dep_flag,
+                            distinct_id,
+                            properties,
+                            cohort_properties=cohort_properties,
+                            flags_by_key=flags_by_key,
+                            evaluation_cache=evaluation_cache,
+                            device_id=device_id,
+                            bucketing_value=dep_bucketing_value,
+                        )
+                        evaluation_cache[dep_flag_key] = dep_result
+                    except InconclusiveMatchError as e:
+                        # If we can't evaluate a dependency, store None and propagate the error
+                        evaluation_cache[dep_flag_key] = None
+                        raise InconclusiveMatchError(
+                            f"Cannot evaluate flag dependency '{dep_flag_key}': {e}"
+                        ) from e
+
+        # Check the cached result
+        cached_result = evaluation_cache[dep_flag_key]
+        if cached_result is None:
+            # Previously inconclusive - raise error again
+            raise InconclusiveMatchError(
+                f"Flag dependency '{dep_flag_key}' was previously inconclusive"
+            )
+        elif not cached_result:
+            # Definitive False result - dependency failed
+            return False
+
+    # All dependencies in the chain have been evaluated successfully
+    # Now check if the final flag value matches the expected value in the property
+    flag_key = property.get("key")
+    expected_value = property.get("value")
+    operator = property.get("operator", "exact")
+
+    if flag_key and expected_value is not None:
+        # Get the actual value of the flag we're checking
+        actual_value = evaluation_cache.get(flag_key)
+
+        if actual_value is None:
+            # Flag wasn't evaluated - this shouldn't happen if dependency chain is correct
+            raise InconclusiveMatchError(
+                f"Flag '{flag_key}' was not evaluated despite being in dependency chain"
+            )
+
+        # For flag dependencies, we need to compare the actual flag result with expected value
+        # using the flag_evaluates_to operator logic
+        if operator == "flag_evaluates_to":
+            return matches_dependency_value(expected_value, actual_value)
+        else:
+            # This should never happen, but just to be defensive.
+            raise InconclusiveMatchError(
+                f"Flag dependency property for '{property.get('key', 'unknown')}' has invalid operator '{operator}'"
+            )
+
+    # If no value check needed, return True (all dependencies passed)
+    return True
+
+
+def matches_dependency_value(expected_value, actual_value):
+    """
+    Check if the actual flag value matches the expected dependency value.
+
+    This follows the same logic as the C# MatchesDependencyValue function:
+    - String variant case: check for exact match or boolean true
+    - Boolean case: must match expected boolean value
+
+    Args:
+        expected_value: The expected value from the property
+        actual_value: The actual value returned by the flag evaluation
+
+    Returns:
+        bool: True if the values match according to flag dependency rules
+    """
+    # String variant case - check for exact match or boolean true
+    if isinstance(actual_value, str) and len(actual_value) > 0:
+        if isinstance(expected_value, bool):
+            # Any variant matches boolean true
+            return expected_value
+        elif isinstance(expected_value, str):
+            # variants are case-sensitive, hence our comparison is too
+            return actual_value == expected_value
+        else:
+            return False
+
+    # Boolean case - must match expected boolean value
+    elif isinstance(actual_value, bool) and isinstance(expected_value, bool):
+        return actual_value == expected_value
+
+    # Default case
+    return False
+
+
+def resolve_bucketing_value(flag, distinct_id, device_id=None):
+    """Resolve the bucketing value for a flag based on its bucketing_identifier setting.
+
+    Returns:
+        The appropriate identifier string to use for hashing/bucketing.
+
+    Raises:
+        InconclusiveMatchError: If the flag requires device_id but none was provided.
+    """
+    flag_filters = flag.get("filters") or {}
+    bucketing_identifier = flag.get("bucketing_identifier") or flag_filters.get(
+        "bucketing_identifier"
+    )
+    if bucketing_identifier == "device_id":
+        if not device_id:
+            raise InconclusiveMatchError(
+                "Flag requires device_id for bucketing but none was provided"
+            )
+        return device_id
+    return distinct_id
+
+
+def match_feature_flag_properties(
+    flag,
+    distinct_id,
+    properties,
+    *,
+    cohort_properties=None,
+    flags_by_key=None,
+    evaluation_cache=None,
+    device_id=None,
+    bucketing_value=None,
+) -> FlagValue:
+    if bucketing_value is None:
+        warnings.warn(
+            "Calling match_feature_flag_properties() without bucketing_value is deprecated. "
+            "Pass bucketing_value explicitly. This fallback will be removed in a future major release.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        bucketing_value = resolve_bucketing_value(flag, distinct_id, device_id)
+
+    flag_filters = flag.get("filters") or {}
+    flag_conditions = flag_filters.get("groups") or []
+    is_inconclusive = False
+    cohort_properties = cohort_properties or {}
+    # Some filters can be explicitly set to null, which require accessing variants like so
+    flag_variants = (flag_filters.get("multivariate") or {}).get("variants") or []
+    valid_variant_keys = [variant["key"] for variant in flag_variants]
+
+    for condition in flag_conditions:
+        try:
+            # if any one condition resolves to True, we can shortcircuit and return
+            # the matching variant
+            if is_condition_match(
+                flag,
+                distinct_id,
+                condition,
+                properties,
+                cohort_properties,
+                flags_by_key,
+                evaluation_cache,
+                bucketing_value=bucketing_value,
+                device_id=device_id,
+            ):
+                variant_override = condition.get("variant")
+                if variant_override and variant_override in valid_variant_keys:
+                    variant = variant_override
+                else:
+                    variant = get_matching_variant(flag, bucketing_value)
+                return variant or True
+        except RequiresServerEvaluation:
+            # Static cohort or other missing server-side data - must fallback to API
+            raise
+        except InconclusiveMatchError:
+            # Evaluation error (bad regex, invalid date, missing property, etc.)
+            # Track that we had an inconclusive match, but try other conditions
+            is_inconclusive = True
+
+    if is_inconclusive:
+        raise InconclusiveMatchError(
+            "Can't determine if feature flag is enabled or not with given properties"
+        )
+
+    # We can only return False when either all conditions are False, or
+    # no condition was inconclusive.
+    return False
+
+
+def is_condition_match(
+    feature_flag,
+    distinct_id,
+    condition,
+    properties,
+    cohort_properties,
+    flags_by_key=None,
+    evaluation_cache=None,
+    *,
+    bucketing_value,
+    device_id=None,
+) -> bool:
+    rollout_percentage = condition.get("rollout_percentage")
+    if len(condition.get("properties") or []) > 0:
+        for prop in condition.get("properties"):
+            property_type = prop.get("type")
+            if property_type == "cohort":
+                matches = match_cohort(
+                    prop,
+                    properties,
+                    cohort_properties,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                    device_id=device_id,
+                )
+            elif property_type == "flag":
+                matches = evaluate_flag_dependency(
+                    prop,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                    properties,
+                    cohort_properties,
+                    device_id=device_id,
+                )
+            else:
+                matches = match_property(prop, properties)
+            if not matches:
+                return False
+
+        if rollout_percentage is None:
+            return True
+
+    if rollout_percentage is not None and _hash(
+        feature_flag["key"], bucketing_value
+    ) > (rollout_percentage / 100):
+        return False
+
+    return True
+
+
+def match_property(property, property_values) -> bool:
+    # only looks for matches where key exists in override_property_values
+    # doesn't support operator is_not_set
+    key = property.get("key")
+    operator = property.get("operator") or "exact"
+    value = property.get("value")
+
+    if key not in property_values:
+        raise InconclusiveMatchError(
+            "can't match properties without a given property value"
+        )
+
+    if operator == "is_not_set":
+        raise InconclusiveMatchError("can't match properties with operator is_not_set")
+
+    override_value = property_values[key]
+
+    if (operator not in NONE_VALUES_ALLOWED_OPERATORS) and override_value is None:
+        return False
+
+    if operator in ("exact", "is_not"):
+
+        def compute_exact_match(value, override_value):
+            if isinstance(value, list):
+                return str(override_value).casefold() in [
+                    str(val).casefold() for val in value
+                ]
+            return utils.str_iequals(value, override_value)
+
+        if operator == "exact":
+            return compute_exact_match(value, override_value)
+        else:
+            return not compute_exact_match(value, override_value)
+
+    if operator == "is_set":
+        return key in property_values
+
+    if operator == "icontains":
+        return utils.str_icontains(override_value, value)
+
+    if operator == "not_icontains":
+        return not utils.str_icontains(override_value, value)
+
+    if operator == "regex":
+        return (
+            is_valid_regex(str(value))
+            and re.compile(str(value)).search(str(override_value)) is not None
+        )
+
+    if operator == "not_regex":
+        return (
+            is_valid_regex(str(value))
+            and re.compile(str(value)).search(str(override_value)) is None
+        )
+
+    if operator in ("gt", "gte", "lt", "lte"):
+        # :TRICKY: We adjust comparison based on the override value passed in,
+        # to make sure we handle both numeric and string comparisons appropriately.
+        def compare(lhs, rhs, operator):
+            if operator == "gt":
+                return lhs > rhs
+            elif operator == "gte":
+                return lhs >= rhs
+            elif operator == "lt":
+                return lhs < rhs
+            elif operator == "lte":
+                return lhs <= rhs
+            else:
+                raise ValueError(f"Invalid operator: {operator}")
+
+        parsed_value = None
+        try:
+            parsed_value = float(value)  # type: ignore
+        except Exception:
+            pass
+
+        if parsed_value is not None and override_value is not None:
+            if isinstance(override_value, str):
+                return compare(override_value, str(value), operator)
+            else:
+                return compare(override_value, parsed_value, operator)
+        else:
+            return compare(str(override_value), str(value), operator)
+
+    if operator in ["is_date_before", "is_date_after"]:
+        try:
+            parsed_date = relative_date_parse_for_feature_flag_matching(str(value))
+
+            if not parsed_date:
+                parsed_date = parser.parse(str(value))
+                parsed_date = convert_to_datetime_aware(parsed_date)
+        except Exception as e:
+            raise InconclusiveMatchError(
+                "The date set on the flag is not a valid format"
+            ) from e
+
+        if not parsed_date:
+            raise InconclusiveMatchError(
+                "The date set on the flag is not a valid format"
+            )
+
+        if isinstance(override_value, datetime.datetime):
+            override_date = convert_to_datetime_aware(override_value)
+            if operator == "is_date_before":
+                return override_date < parsed_date
+            else:
+                return override_date > parsed_date
+        elif isinstance(override_value, datetime.date):
+            if operator == "is_date_before":
+                return override_value < parsed_date.date()
+            else:
+                return override_value > parsed_date.date()
+        elif isinstance(override_value, str):
+            try:
+                override_date = parser.parse(override_value)
+                override_date = convert_to_datetime_aware(override_date)
+                if operator == "is_date_before":
+                    return override_date < parsed_date
+                else:
+                    return override_date > parsed_date
+            except Exception:
+                raise InconclusiveMatchError("The date provided is not a valid format")
+        else:
+            raise InconclusiveMatchError(
+                "The date provided must be a string or date object"
+            )
+
+    # if we get here, we don't know how to handle the operator
+    raise InconclusiveMatchError(f"Unknown operator {operator}")
+
+
+def match_cohort(
+    property,
+    property_values,
+    cohort_properties,
+    flags_by_key=None,
+    evaluation_cache=None,
+    distinct_id=None,
+    device_id=None,
+) -> bool:
+    # Cohort properties are in the form of property groups like this:
+    # {
+    #     "cohort_id": {
+    #         "type": "AND|OR",
+    #         "values": [{
+    #            "key": "property_name", "value": "property_value"
+    #        }]
+    #     }
+    # }
+    cohort_id = str(property.get("value"))
+    if cohort_id not in cohort_properties:
+        raise RequiresServerEvaluation(
+            f"cohort {cohort_id} not found in local cohorts - likely a static cohort that requires server evaluation"
+        )
+
+    property_group = cohort_properties[cohort_id]
+    return match_property_group(
+        property_group,
+        property_values,
+        cohort_properties,
+        flags_by_key,
+        evaluation_cache,
+        distinct_id,
+        device_id=device_id,
+    )
+
+
+def match_property_group(
+    property_group,
+    property_values,
+    cohort_properties,
+    flags_by_key=None,
+    evaluation_cache=None,
+    distinct_id=None,
+    device_id=None,
+) -> bool:
+    if not property_group:
+        return True
+
+    property_group_type = property_group.get("type")
+    properties = property_group.get("values")
+
+    if not properties or len(properties) == 0:
+        # empty groups are no-ops, always match
+        return True
+
+    error_matching_locally = False
+
+    if "values" in properties[0]:
+        # a nested property group
+        for prop in properties:
+            try:
+                matches = match_property_group(
+                    prop,
+                    property_values,
+                    cohort_properties,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                    device_id=device_id,
+                )
+                if property_group_type == "AND":
+                    if not matches:
+                        return False
+                else:
+                    # OR group
+                    if matches:
+                        return True
+            except RequiresServerEvaluation:
+                # Immediately propagate - this condition requires server-side data
+                raise
+            except InconclusiveMatchError as e:
+                log.debug(f"Failed to compute property {prop} locally: {e}")
+                error_matching_locally = True
+
+        if error_matching_locally:
+            raise InconclusiveMatchError(
+                "Can't match cohort without a given cohort property value"
+            )
+        # if we get here, all matched in AND case, or none matched in OR case
+        return property_group_type == "AND"
+
+    else:
+        for prop in properties:
+            try:
+                if prop.get("type") == "cohort":
+                    matches = match_cohort(
+                        prop,
+                        property_values,
+                        cohort_properties,
+                        flags_by_key,
+                        evaluation_cache,
+                        distinct_id,
+                        device_id=device_id,
+                    )
+                elif prop.get("type") == "flag":
+                    matches = evaluate_flag_dependency(
+                        prop,
+                        flags_by_key,
+                        evaluation_cache,
+                        distinct_id,
+                        property_values,
+                        cohort_properties,
+                        device_id=device_id,
+                    )
+                else:
+                    matches = match_property(prop, property_values)
+
+                negation = prop.get("negation", False)
+
+                if property_group_type == "AND":
+                    # if negated property, do the inverse
+                    if not matches and not negation:
+                        return False
+                    if matches and negation:
+                        return False
+                else:
+                    # OR group
+                    if matches and not negation:
+                        return True
+                    if not matches and negation:
+                        return True
+            except RequiresServerEvaluation:
+                # Immediately propagate - this condition requires server-side data
+                raise
+            except InconclusiveMatchError as e:
+                log.debug(f"Failed to compute property {prop} locally: {e}")
+                error_matching_locally = True
+
+        if error_matching_locally:
+            raise InconclusiveMatchError(
+                "can't match cohort without a given cohort property value"
+            )
+
+        # if we get here, all matched in AND case, or none matched in OR case
+        return property_group_type == "AND"
+
+
+def relative_date_parse_for_feature_flag_matching(
+    value: str,
+) -> Optional[datetime.datetime]:
+    regex = r"^-?(?P<number>[0-9]+)(?P<interval>[a-z])$"
+    match = re.search(regex, value)
+    parsed_dt = datetime.datetime.now(datetime.timezone.utc)
+    if match:
+        number = int(match.group("number"))
+
+        if number >= 10_000:
+            # Guard against overflow, disallow numbers greater than 10_000
+            return None
+
+        interval = match.group("interval")
+        if interval == "h":
+            parsed_dt = parsed_dt - relativedelta(hours=number)
+        elif interval == "d":
+            parsed_dt = parsed_dt - relativedelta(days=number)
+        elif interval == "w":
+            parsed_dt = parsed_dt - relativedelta(weeks=number)
+        elif interval == "m":
+            parsed_dt = parsed_dt - relativedelta(months=number)
+        elif interval == "y":
+            parsed_dt = parsed_dt - relativedelta(years=number)
+        else:
+            return None
+
+        return parsed_dt
+    else:
+        return None

posthog/flag_definition_cache.py

@@ -0,0 +1,127 @@
+"""
+Flag Definition Cache Provider interface for multi-worker environments.
+
+EXPERIMENTAL: This API may change in future minor version bumps.
+
+This module provides an interface for external caching of feature flag definitions,
+enabling multi-worker environments (Kubernetes, load-balanced servers, serverless
+functions) to share flag definitions and reduce API calls.
+
+Usage:
+
+    from posthog import Posthog
+    from posthog.flag_definition_cache import FlagDefinitionCacheProvider
+
+    cache = RedisFlagDefinitionCache(redis_client, "my-team")
+    posthog = Posthog(
+        "<project_api_key>",
+        personal_api_key="<personal_api_key>",
+        flag_definition_cache_provider=cache,
+    )
+"""
+
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+
+from typing_extensions import Required, TypedDict
+
+
+class FlagDefinitionCacheData(TypedDict):
+    """
+    Data structure for cached flag definitions.
+
+    Attributes:
+        flags: List of feature flag definition dictionaries from the API.
+        group_type_mapping: Mapping of group type indices to group names.
+        cohorts: Dictionary of cohort definitions for local evaluation.
+    """
+
+    flags: Required[List[Dict[str, Any]]]
+    group_type_mapping: Required[Dict[str, str]]
+    cohorts: Required[Dict[str, Any]]
+
+
+@runtime_checkable
+class FlagDefinitionCacheProvider(Protocol):
+    """
+    Interface for external caching of feature flag definitions.
+
+    Enables multi-worker environments to share flag definitions, reducing API
+    calls while ensuring all workers have consistent data.
+
+    EXPERIMENTAL: This API may change in future minor version bumps.
+
+    The four methods handle the complete lifecycle of flag definition caching:
+
+    1. `should_fetch_flag_definitions()` - Called before each poll to determine
+       if this worker should fetch new definitions. Use for distributed lock
+       coordination to ensure only one worker fetches at a time.
+
+    2. `get_flag_definitions()` - Called when `should_fetch_flag_definitions()`
+       returns False. Returns cached definitions if available.
+
+    3. `on_flag_definitions_received()` - Called after successfully fetching
+       new definitions from the API. Store the data in your external cache
+       and release any locks.
+
+    4. `shutdown()` - Called when the PostHog client shuts down. Release any
+       distributed locks and clean up resources.
+
+    Error Handling:
+        All methods are wrapped in try/except. Errors will be logged but will
+        never break flag evaluation. On error:
+        - `should_fetch_flag_definitions()` errors default to fetching (fail-safe)
+        - `get_flag_definitions()` errors fall back to API fetch
+        - `on_flag_definitions_received()` errors are logged but flags remain in memory
+        - `shutdown()` errors are logged but shutdown continues
+    """
+
+    def get_flag_definitions(self) -> Optional[FlagDefinitionCacheData]:
+        """
+        Retrieve cached flag definitions.
+
+        Returns:
+            Cached flag definitions if available and valid, None otherwise.
+            Returning None will trigger a fetch from the API if this worker
+            has no flags loaded yet.
+        """
+        ...
+
+    def should_fetch_flag_definitions(self) -> bool:
+        """
+        Determine whether this instance should fetch new flag definitions.
+
+        Use this for distributed lock coordination. Only one worker should
+        return True to avoid thundering herd problems. A typical implementation
+        uses a distributed lock (e.g., Redis SETNX) that expires after the
+        poll interval.
+
+        Returns:
+            True if this instance should fetch from the API, False otherwise.
+            When False, the client will call `get_flag_definitions()` to
+            retrieve cached data instead.
+        """
+        ...
+
+    def on_flag_definitions_received(self, data: FlagDefinitionCacheData) -> None:
+        """
+        Called after successfully receiving new flag definitions from PostHog.
+
+        Use this to store the data in your external cache and release any
+        distributed locks acquired in `should_fetch_flag_definitions()`.
+
+        Args:
+            data: The flag definitions to cache, containing flags,
+                  group_type_mapping, and cohorts.
+        """
+        ...
+
+    def shutdown(self) -> None:
+        """
+        Called when the PostHog client shuts down.
+
+        Use this to release any distributed locks and clean up resources.
+        This method is called even if `should_fetch_flag_definitions()`
+        returned False, so implementations should handle the case where
+        no lock was acquired.
+        """
+        ...

posthog/integrations/django.py

@@ -0,0 +1,319 @@
+from typing import TYPE_CHECKING, cast
+from posthog import contexts
+from posthog.client import Client
+
+try:
+    from asgiref.sync import iscoroutinefunction, markcoroutinefunction
+except ImportError:
+    # Fallback for older Django versions without asgiref
+    import asyncio
+
+    iscoroutinefunction = asyncio.iscoroutinefunction
+
+    # No-op fallback for markcoroutinefunction
+    # Older Django versions without asgiref typically don't support async middleware anyway
+    def markcoroutinefunction(func):
+        return func
+
+
+if TYPE_CHECKING:
+    from django.http import HttpRequest, HttpResponse  # noqa: F401
+    from typing import Callable, Dict, Any, Optional, Union, Awaitable  # noqa: F401
+
+
+class PosthogContextMiddleware:
+    """Middleware to automatically track Django requests.
+
+    This middleware wraps all calls with a posthog context. It attempts to extract the following from the request headers:
+    - Session ID, (extracted from `X-POSTHOG-SESSION-ID`)
+    - Distinct ID, (extracted from `X-POSTHOG-DISTINCT-ID`)
+    - Request URL as $current_url
+    - Request Method as $request_method
+
+    The context will also auto-capture exceptions and send them to PostHog, unless you disable it by setting
+    `POSTHOG_MW_CAPTURE_EXCEPTIONS` to `False` in your Django settings. The exceptions are captured using the
+    global client, unless the setting `POSTHOG_MW_CLIENT` is set to a custom client instance
+
+    The middleware behaviour is customisable through 3 additional functions:
+    - `POSTHOG_MW_EXTRA_TAGS`, which is a Callable[[HttpRequest], Dict[str, Any]] expected to return a dictionary of additional tags to be added to the context.
+    - `POSTHOG_MW_REQUEST_FILTER`, which is a Callable[[HttpRequest], bool] expected to return `False` if the request should not be tracked.
+    - `POSTHOG_MW_TAG_MAP`, which is a Callable[[Dict[str, Any]], Dict[str, Any]], which you can use to modify the tags before they're added to the context.
+
+    You can use the `POSTHOG_MW_TAG_MAP` function to remove any default tags you don't want to capture, or override them with your own values.
+
+    Context tags are automatically included as properties on all events captured within a context, including exceptions.
+    See the context documentation for more information. The extracted distinct ID and session ID, if found, are used to
+    associate all events captured in the middleware context with the same distinct ID and session as currently active on the
+    frontend. See the documentation for `set_context_session` and `identify_context` for more details.
+
+    This middleware is hybrid-capable: it supports both WSGI (sync) and ASGI (async) Django applications. The middleware
+    detects at initialization whether the next middleware in the chain is async or sync, and adapts its behavior accordingly.
+    This ensures compatibility with both pure sync and pure async middleware chains, as well as mixed chains in ASGI mode.
+    """
+
+    sync_capable = True
+    async_capable = True
+
+    def __init__(self, get_response):
+        # type: (Union[Callable[[HttpRequest], HttpResponse], Callable[[HttpRequest], Awaitable[HttpResponse]]]) -> None
+        self.get_response = get_response
+        self._is_coroutine = iscoroutinefunction(get_response)
+
+        # Mark this instance as a coroutine function if get_response is async
+        # This is required for Django to correctly detect async middleware
+        if self._is_coroutine:
+            markcoroutinefunction(self)
+
+        from django.conf import settings
+
+        if hasattr(settings, "POSTHOG_MW_EXTRA_TAGS") and callable(
+            settings.POSTHOG_MW_EXTRA_TAGS
+        ):
+            self.extra_tags = cast(
+                "Optional[Callable[[HttpRequest], Dict[str, Any]]]",
+                settings.POSTHOG_MW_EXTRA_TAGS,
+            )
+        else:
+            self.extra_tags = None
+
+        if hasattr(settings, "POSTHOG_MW_REQUEST_FILTER") and callable(
+            settings.POSTHOG_MW_REQUEST_FILTER
+        ):
+            self.request_filter = cast(
+                "Optional[Callable[[HttpRequest], bool]]",
+                settings.POSTHOG_MW_REQUEST_FILTER,
+            )
+        else:
+            self.request_filter = None
+
+        if hasattr(settings, "POSTHOG_MW_TAG_MAP") and callable(
+            settings.POSTHOG_MW_TAG_MAP
+        ):
+            self.tag_map = cast(
+                "Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]",
+                settings.POSTHOG_MW_TAG_MAP,
+            )
+        else:
+            self.tag_map = None
+
+        if hasattr(settings, "POSTHOG_MW_CAPTURE_EXCEPTIONS") and isinstance(
+            settings.POSTHOG_MW_CAPTURE_EXCEPTIONS, bool
+        ):
+            self.capture_exceptions = settings.POSTHOG_MW_CAPTURE_EXCEPTIONS
+        else:
+            self.capture_exceptions = True
+
+        if hasattr(settings, "POSTHOG_MW_CLIENT") and isinstance(
+            settings.POSTHOG_MW_CLIENT, Client
+        ):
+            self.client = cast("Optional[Client]", settings.POSTHOG_MW_CLIENT)
+        else:
+            self.client = None
+
+    def extract_tags(self, request):
+        # type: (HttpRequest) -> Dict[str, Any]
+        """Extract tags from request in sync context."""
+        user_id, user_email = self.extract_request_user(request)
+        return self._build_tags(request, user_id, user_email)
+
+    def _build_tags(self, request, user_id, user_email):
+        # type: (HttpRequest, Optional[str], Optional[str]) -> Dict[str, Any]
+        """
+        Build tags dict from request and user info.
+
+        Centralized tag extraction logic used by both sync and async paths.
+        """
+        tags = {}
+
+        # Extract session ID from X-POSTHOG-SESSION-ID header
+        session_id = request.headers.get("X-POSTHOG-SESSION-ID")
+        if session_id:
+            contexts.set_context_session(session_id)
+
+        # Extract distinct ID from X-POSTHOG-DISTINCT-ID header or request user id
+        distinct_id = request.headers.get("X-POSTHOG-DISTINCT-ID") or user_id
+        if distinct_id:
+            contexts.identify_context(distinct_id)
+
+        # Extract user email
+        if user_email:
+            tags["email"] = user_email
+
+        # Extract current URL
+        absolute_url = request.build_absolute_uri()
+        if absolute_url:
+            tags["$current_url"] = absolute_url
+
+        # Extract request method
+        if request.method:
+            tags["$request_method"] = request.method
+
+        # Extract request path
+        if request.path:
+            tags["$request_path"] = request.path
+
+        # Extract IP address
+        ip_address = request.headers.get("X-Forwarded-For")
+        if ip_address:
+            tags["$ip_address"] = ip_address
+
+        # Extract user agent
+        user_agent = request.headers.get("User-Agent")
+        if user_agent:
+            tags["$user_agent"] = user_agent
+
+        # Apply extra tags if configured
+        if self.extra_tags:
+            extra = self.extra_tags(request)
+            if extra:
+                tags.update(extra)
+
+        # Apply tag mapping if configured
+        if self.tag_map:
+            tags = self.tag_map(tags)
+
+        return tags
+
+    def extract_request_user(self, request):
+        # type: (HttpRequest) -> tuple[Optional[str], Optional[str]]
+        """Extract user ID and email from request in sync context."""
+        user = getattr(request, "user", None)
+        return self._resolve_user_details(user)
+
+    async def aextract_tags(self, request):
+        # type: (HttpRequest) -> Dict[str, Any]
+        """
+        Async version of extract_tags for use in async request handling.
+
+        Uses await request.auser() instead of request.user to avoid
+        SynchronousOnlyOperation in async context.
+
+        Follows Django's naming convention for async methods (auser, asave, etc.).
+        """
+        user_id, user_email = await self.aextract_request_user(request)
+        return self._build_tags(request, user_id, user_email)
+
+    async def aextract_request_user(self, request):
+        # type: (HttpRequest) -> tuple[Optional[str], Optional[str]]
+        """
+        Async version of extract_request_user for use in async request handling.
+
+        Uses await request.auser() instead of request.user to avoid
+        SynchronousOnlyOperation in async context.
+
+        Follows Django's naming convention for async methods (auser, asave, etc.).
+        """
+        auser = getattr(request, "auser", None)
+        if callable(auser):
+            try:
+                user = await auser()
+                return self._resolve_user_details(user)
+            except Exception:
+                # If auser() fails, return empty - don't break the request
+                # Real errors (permissions, broken auth) will be logged by Django
+                return None, None
+
+        # Fallback for test requests without auser
+        return None, None
+
+    def _resolve_user_details(self, user):
+        # type: (Any) -> tuple[Optional[str], Optional[str]]
+        """
+        Extract user ID and email from a user object.
+
+        Handles both authenticated and unauthenticated users, as well as
+        legacy Django where is_authenticated was a method.
+        """
+        user_id = None
+        email = None
+
+        if user is None:
+            return user_id, email
+
+        # Handle is_authenticated (property in modern Django, method in legacy)
+        is_authenticated = getattr(user, "is_authenticated", False)
+        if callable(is_authenticated):
+            is_authenticated = is_authenticated()
+
+        if not is_authenticated:
+            return user_id, email
+
+        # Extract user primary key
+        user_pk = getattr(user, "pk", None)
+        if user_pk is not None:
+            user_id = str(user_pk)
+
+        # Extract user email
+        user_email = getattr(user, "email", None)
+        if user_email:
+            email = str(user_email)
+
+        return user_id, email
+
+    def __call__(self, request):
+        # type: (HttpRequest) -> Union[HttpResponse, Awaitable[HttpResponse]]
+        """
+        Unified entry point for both sync and async request handling.
+
+        When sync_capable and async_capable are both True, Django passes requests
+        without conversion. This method detects the mode and routes accordingly.
+        """
+        if self._is_coroutine:
+            return self.__acall__(request)
+        else:
+            # Synchronous path
+            if self.request_filter and not self.request_filter(request):
+                return self.get_response(request)
+
+            with contexts.new_context(self.capture_exceptions, client=self.client):
+                for k, v in self.extract_tags(request).items():
+                    contexts.tag(k, v)
+
+                return self.get_response(request)
+
+    async def __acall__(self, request):
+        # type: (HttpRequest) -> Awaitable[HttpResponse]
+        """
+        Asynchronous entry point for async request handling.
+
+        This method is called when the middleware chain is async.
+        Uses aextract_tags() which calls request.auser() to avoid
+        SynchronousOnlyOperation when accessing user in async context.
+        """
+        if self.request_filter and not self.request_filter(request):
+            return await self.get_response(request)
+
+        with contexts.new_context(self.capture_exceptions, client=self.client):
+            for k, v in (await self.aextract_tags(request)).items():
+                contexts.tag(k, v)
+
+            return await self.get_response(request)
+
+    def process_exception(self, request, exception):
+        # type: (HttpRequest, Exception) -> None
+        """
+        Process exceptions from views and downstream middleware.
+
+        Django calls this WHILE still inside the context created by __call__,
+        so request tags have already been extracted and set. This method just
+        needs to capture the exception directly.
+
+        Django converts view exceptions into responses before they propagate through
+        the middleware stack, so the context manager in __call__/__acall__ never sees them.
+
+        Note: Django's process_exception is always synchronous, even for async views.
+        """
+        if self.request_filter and not self.request_filter(request):
+            return
+
+        if not self.capture_exceptions:
+            return
+
+        # Context and tags already set by __call__ or __acall__
+        # Just capture the exception
+        if self.client:
+            self.client.capture_exception(exception)
+        else:
+            from posthog import capture_exception
+
+            capture_exception(exception)

posthog/poller.py

@@ -0,0 +1,20 @@
+import threading
+
+
+class Poller(threading.Thread):
+    def __init__(self, interval, execute, *args, **kwargs):
+        threading.Thread.__init__(self)
+        self.daemon = True  # Make daemon to not interfere with program exit
+        self.stopped = threading.Event()
+        self.interval = interval
+        self.execute = execute
+        self.args = args
+        self.kwargs = kwargs
+
+    def stop(self):
+        self.stopped.set()
+        self.join()
+
+    def run(self):
+        while not self.stopped.wait(self.interval.total_seconds()):
+            self.execute(*self.args, **self.kwargs)

posthog/request.py

@@ -1,69 +1,396 @@
-from datetime import date, datetime
-from dateutil.tz import tzutc
-import logging
 import json
+import logging
+import re
+import socket
+from dataclasses import dataclass
+from datetime import date, datetime, timezone
 from gzip import GzipFile
-from requests.auth import HTTPBasicAuth
-from requests import sessions
 from io import BytesIO
+from typing import Any, List, Optional, Tuple, Union
+
+import requests
+from dateutil.tz import tzutc
+from requests.adapters import HTTPAdapter  # type: ignore[import-untyped]
+from urllib3.connection import HTTPConnection
+from urllib3.util.retry import Retry
 
-from posthog.version import VERSION
 from posthog.utils import remove_trailing_slash
+from posthog.version import VERSION
 
-_session = sessions.Session()
+SocketOptions = List[Tuple[int, int, Union[int, bytes]]]
+
+KEEPALIVE_IDLE_SECONDS = 60
+KEEPALIVE_INTERVAL_SECONDS = 60
+KEEPALIVE_PROBE_COUNT = 3
+
+# TCP keepalive probes idle connections to prevent them from being dropped.
+# SO_KEEPALIVE is cross-platform, but timing options vary:
+# - Linux: TCP_KEEPIDLE, TCP_KEEPINTVL, TCP_KEEPCNT
+# - macOS: only SO_KEEPALIVE (uses system defaults)
+# - Windows: TCP_KEEPIDLE, TCP_KEEPINTVL (since Windows 10 1709)
+KEEP_ALIVE_SOCKET_OPTIONS: SocketOptions = list(
+    HTTPConnection.default_socket_options
+) + [
+    (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
+]
+for attr, value in [
+    ("TCP_KEEPIDLE", KEEPALIVE_IDLE_SECONDS),
+    ("TCP_KEEPINTVL", KEEPALIVE_INTERVAL_SECONDS),
+    ("TCP_KEEPCNT", KEEPALIVE_PROBE_COUNT),
+]:
+    if hasattr(socket, attr):
+        KEEP_ALIVE_SOCKET_OPTIONS.append((socket.SOL_TCP, getattr(socket, attr), value))
+
+# Status codes that indicate transient server errors worth retrying
+RETRY_STATUS_FORCELIST = [408, 500, 502, 503, 504]
 
 
-def post(api_key, host=None, gzip=False, timeout=15, **kwargs):
+def _mask_tokens_in_url(url: str) -> str:
+    """Mask token values in URLs for safe logging, keeping first 10 chars visible."""
+    return re.sub(r"(token=)([^&]{10})[^&]*", r"\1\2...", url)
+
+
+@dataclass
+class GetResponse:
+    """Response from a GET request with ETag support."""
+
+    data: Any
+    etag: Optional[str] = None
+    not_modified: bool = False
+
+
+class HTTPAdapterWithSocketOptions(HTTPAdapter):
+    """HTTPAdapter with configurable socket options."""
+
+    def __init__(self, *args, socket_options: Optional[SocketOptions] = None, **kwargs):
+        self.socket_options = socket_options
+        super().__init__(*args, **kwargs)
+
+    def init_poolmanager(self, *args, **kwargs):
+        if self.socket_options is not None:
+            kwargs["socket_options"] = self.socket_options
+        super().init_poolmanager(*args, **kwargs)
+
+
+def _build_session(socket_options: Optional[SocketOptions] = None) -> requests.Session:
+    """Build a session for general requests (batch, decide, etc.)."""
+    adapter = HTTPAdapterWithSocketOptions(
+        max_retries=Retry(
+            total=2,
+            connect=2,
+            read=2,
+        ),
+        socket_options=socket_options,
+    )
+    session = requests.Session()
+    session.mount("https://", adapter)
+    return session
+
+
+def _build_flags_session(
+    socket_options: Optional[SocketOptions] = None,
+) -> requests.Session:
+    """
+    Build a session for feature flag requests with POST retries.
+
+    Feature flag requests are idempotent (read-only), so retrying POST
+    requests is safe. This session retries on transient server errors
+    (408, 5xx) and network failures with exponential backoff
+    (0.5s, 1s delays between retries).
+    """
+    adapter = HTTPAdapterWithSocketOptions(
+        max_retries=Retry(
+            total=2,
+            connect=2,
+            read=2,
+            backoff_factor=0.5,
+            status_forcelist=RETRY_STATUS_FORCELIST,
+            allowed_methods=["POST"],
+        ),
+        socket_options=socket_options,
+    )
+    session = requests.Session()
+    session.mount("https://", adapter)
+    return session
+
+
+_session = _build_session()
+_flags_session = _build_flags_session()
+_socket_options: Optional[SocketOptions] = None
+_pooling_enabled = True
+
+
+def _get_session() -> requests.Session:
+    if _pooling_enabled:
+        return _session
+    return _build_session(_socket_options)
+
+
+def _get_flags_session() -> requests.Session:
+    if _pooling_enabled:
+        return _flags_session
+    return _build_flags_session(_socket_options)
+
+
+def set_socket_options(socket_options: Optional[SocketOptions]) -> None:
+    """
+    Configure socket options for all HTTP connections.
+
+    Example:
+        from posthog import set_socket_options
+        set_socket_options([(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)])
+    """
+    global _session, _flags_session, _socket_options
+    if socket_options == _socket_options:
+        return
+    _socket_options = socket_options
+    _session = _build_session(socket_options)
+    _flags_session = _build_flags_session(socket_options)
+
+
+def enable_keep_alive() -> None:
+    """Enable TCP keepalive to prevent idle connections from being dropped."""
+    set_socket_options(KEEP_ALIVE_SOCKET_OPTIONS)
+
+
+def disable_connection_reuse() -> None:
+    """Disable connection reuse, creating a fresh connection for each request."""
+    global _pooling_enabled
+    _pooling_enabled = False
+
+
+US_INGESTION_ENDPOINT = "https://us.i.posthog.com"
+EU_INGESTION_ENDPOINT = "https://eu.i.posthog.com"
+DEFAULT_HOST = US_INGESTION_ENDPOINT
+USER_AGENT = "posthog-python/" + VERSION
+
+
+def determine_server_host(host: Optional[str]) -> str:
+    """Determines the server host to use."""
+    host_or_default = host or DEFAULT_HOST
+    trimmed_host = remove_trailing_slash(host_or_default)
+    if trimmed_host in ("https://app.posthog.com", "https://us.posthog.com"):
+        return US_INGESTION_ENDPOINT
+    elif trimmed_host == "https://eu.posthog.com":
+        return EU_INGESTION_ENDPOINT
+    else:
+        return host_or_default
+
+
+def post(
+    api_key: str,
+    host: Optional[str] = None,
+    path=None,
+    gzip: bool = False,
+    timeout: int = 15,
+    session: Optional[requests.Session] = None,
+    **kwargs,
+) -> requests.Response:
     """Post the `kwargs` to the API"""
-    log = logging.getLogger('posthog')
+    log = logging.getLogger("posthog")
     body = kwargs
-    body["sentAt"] = datetime.utcnow().replace(tzinfo=tzutc()).isoformat()
-    url = remove_trailing_slash(host or 'https://t.posthog.com') + '/batch/'
-    body['api_key'] = api_key
+    body["sentAt"] = datetime.now(tz=tzutc()).isoformat()
+    url = remove_trailing_slash(host or DEFAULT_HOST) + path
+    body["api_key"] = api_key
     data = json.dumps(body, cls=DatetimeSerializer)
-    log.debug('making request: %s', data)
-    headers = {
-        'Content-Type': 'application/json',
-        'User-Agent': 'analytics-python/' + VERSION
-    }
+    log.debug("making request: %s to url: %s", data, url)
+    headers = {"Content-Type": "application/json", "User-Agent": USER_AGENT}
     if gzip:
-        headers['Content-Encoding'] = 'gzip'
+        headers["Content-Encoding"] = "gzip"
         buf = BytesIO()
-        with GzipFile(fileobj=buf, mode='w') as gz:
+        with GzipFile(fileobj=buf, mode="w") as gz:
             # 'data' was produced by json.dumps(),
             # whose default encoding is utf-8.
-            gz.write(data.encode('utf-8'))
+            gz.write(data.encode("utf-8"))
         data = buf.getvalue()
 
-    res = _session.post(url, data=data,
-                        headers=headers, timeout=timeout)
+    res = (session or _get_session()).post(
+        url, data=data, headers=headers, timeout=timeout
+    )
 
     if res.status_code == 200:
-        log.debug('data uploaded successfully')
-        return res
+        log.debug("data uploaded successfully")
+
+    return res
+
+
+def _process_response(
+    res: requests.Response, success_message: str, *, return_json: bool = True
+) -> Union[requests.Response, Any]:
+    log = logging.getLogger("posthog")
+    if res.status_code == 200:
+        log.debug(success_message)
+        response = res.json() if return_json else res
+        # Handle quota limited decide responses by raising a specific error
+        # NB: other services also put entries into the quotaLimited key, but right now we only care about feature flags
+        # since most of the other services handle quota limiting in other places in the application.
+        if (
+            isinstance(response, dict)
+            and "quotaLimited" in response
+            and isinstance(response["quotaLimited"], list)
+            and "feature_flags" in response["quotaLimited"]
+        ):
+            log.warning(
+                "[FEATURE FLAGS] PostHog feature flags quota limited, resetting feature flag data.  Learn more about billing limits at https://posthog.com/docs/billing/limits-alerts"
+            )
+            raise QuotaLimitError(res.status_code, "Feature flags quota limited")
+        return response
+    retry_after = None
+    retry_after_header = res.headers.get("Retry-After")
+    if retry_after_header:
+        try:
+            retry_after = float(retry_after_header)
+        except (ValueError, TypeError):
+            try:
+                from email.utils import parsedate_to_datetime
+
+                retry_after = max(
+                    0.0,
+                    (
+                        parsedate_to_datetime(retry_after_header)
+                        - datetime.now(timezone.utc)
+                    ).total_seconds(),
+                )
+            except (ValueError, TypeError):
+                pass
 
     try:
         payload = res.json()
-        log.debug('received response: %s', payload)
-        raise APIError(res.status_code, payload['code'], payload['message'])
-    except ValueError:
-        raise APIError(res.status_code, 'unknown', res.text)
+        log.debug("received response: %s", payload)
+        raise APIError(res.status_code, payload["detail"], retry_after=retry_after)
+    except (KeyError, ValueError):
+        raise APIError(res.status_code, res.text, retry_after=retry_after)
+
+
+def decide(
+    api_key: str,
+    host: Optional[str] = None,
+    gzip: bool = False,
+    timeout: int = 15,
+    **kwargs,
+) -> Any:
+    """Post the `kwargs to the decide API endpoint"""
+    res = post(api_key, host, "/decide/?v=4", gzip, timeout, **kwargs)
+    return _process_response(res, success_message="Feature flags decided successfully")
+
+
+def flags(
+    api_key: str,
+    host: Optional[str] = None,
+    gzip: bool = False,
+    timeout: int = 15,
+    **kwargs,
+) -> Any:
+    """Post the kwargs to the flags API endpoint with automatic retries."""
+    res = post(
+        api_key,
+        host,
+        "/flags/?v=2",
+        gzip,
+        timeout,
+        session=_get_flags_session(),
+        **kwargs,
+    )
+    return _process_response(
+        res, success_message="Feature flags evaluated successfully"
+    )
+
+
+def remote_config(
+    personal_api_key: str,
+    project_api_key: str,
+    host: Optional[str] = None,
+    key: str = "",
+    timeout: int = 15,
+) -> Any:
+    """Get remote config flag value from remote_config API endpoint"""
+    response = get(
+        personal_api_key,
+        f"/api/projects/@current/feature_flags/{key}/remote_config?token={project_api_key}",
+        host,
+        timeout,
+    )
+    return response.data
+
+
+def batch_post(
+    api_key: str,
+    host: Optional[str] = None,
+    gzip: bool = False,
+    timeout: int = 15,
+    **kwargs,
+) -> requests.Response:
+    """Post the `kwargs` to the batch API endpoint for events"""
+    res = post(api_key, host, "/batch/", gzip, timeout, **kwargs)
+    return _process_response(
+        res, success_message="data uploaded successfully", return_json=False
+    )
+
+
+def get(
+    api_key: str,
+    url: str,
+    host: Optional[str] = None,
+    timeout: Optional[int] = None,
+    etag: Optional[str] = None,
+) -> GetResponse:
+    """
+    Make a GET request with optional ETag support.
+
+    If an etag is provided, sends If-None-Match header. Returns GetResponse with:
+    - not_modified=True and data=None if server returns 304
+    - not_modified=False and data=response if server returns 200
+    """
+    log = logging.getLogger("posthog")
+    full_url = remove_trailing_slash(host or DEFAULT_HOST) + url
+    headers = {"Authorization": "Bearer %s" % api_key, "User-Agent": USER_AGENT}
+
+    if etag:
+        headers["If-None-Match"] = etag
+
+    res = _get_session().get(full_url, headers=headers, timeout=timeout)
+
+    masked_url = _mask_tokens_in_url(full_url)
+
+    # Handle 304 Not Modified
+    if res.status_code == 304:
+        log.debug(f"GET {masked_url} returned 304 Not Modified")
+        response_etag = res.headers.get("ETag")
+        return GetResponse(data=None, etag=response_etag or etag, not_modified=True)
+
+    # Handle normal response
+    data = _process_response(
+        res, success_message=f"GET {masked_url} completed successfully"
+    )
+    response_etag = res.headers.get("ETag")
+    return GetResponse(data=data, etag=response_etag, not_modified=False)
 
 
 class APIError(Exception):
-
-    def __init__(self, status, code, message):
+    def __init__(
+        self, status: Union[int, str], message: str, retry_after: Optional[float] = None
+    ):
         self.message = message
         self.status = status
-        self.code = code
+        self.retry_after = retry_after
 
     def __str__(self):
-        msg = "[PostHog] {0}: {1} ({2})"
-        return msg.format(self.code, self.message, self.status)
+        msg = "[PostHog] {0} ({1})"
+        return msg.format(self.message, self.status)
+
+
+class QuotaLimitError(APIError):
+    pass
+
+
+# Re-export requests exceptions for use in client.py
+# This keeps all requests library imports centralized in this module
+RequestsTimeout = requests.exceptions.Timeout
+RequestsConnectionError = requests.exceptions.ConnectionError
 
 
 class DatetimeSerializer(json.JSONEncoder):
-    def default(self, obj):
+    def default(self, obj: Any):
         if isinstance(obj, (date, datetime)):
             return obj.isoformat()
 

posthog/test/__init__.py

@@ -1,12 +1,12 @@
-import unittest
-import pkgutil
 import logging
+import pkgutil
 import sys
+import unittest
 
 
 def all_names():
     for _, modname, _ in pkgutil.iter_modules(__path__):
-        yield 'analytics.test.' + modname
+        yield "posthog.test." + modname
 
 
 def all():

posthog/test/ai/gemini/test_gemini_async.py

@@ -0,0 +1,853 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+try:
+    from google import genai as google_genai
+
+    from posthog.ai.gemini import AsyncClient
+
+    GEMINI_AVAILABLE = True
+except ImportError:
+    GEMINI_AVAILABLE = False
+
+pytestmark = [
+    pytest.mark.skipif(
+        not GEMINI_AVAILABLE, reason="Google Gemini package is not available"
+    ),
+    pytest.mark.asyncio,
+]
+
+
+@pytest.fixture
+def mock_client():
+    with patch("posthog.client.Client") as mock_client:
+        mock_client.privacy_mode = False
+        yield mock_client
+
+
+@pytest.fixture
+def mock_gemini_response():
+    mock_response = MagicMock()
+    mock_response.text = "Test response from Gemini"
+
+    mock_usage = MagicMock()
+    mock_usage.prompt_token_count = 20
+    mock_usage.candidates_token_count = 10
+    # Ensure cache and reasoning tokens are not present (not MagicMock)
+    mock_usage.cached_content_token_count = 0
+    mock_usage.thoughts_token_count = 0
+    mock_response.usage_metadata = mock_usage
+
+    mock_candidate = MagicMock()
+    mock_candidate.text = "Test response from Gemini"
+    mock_content = MagicMock()
+    mock_part = MagicMock()
+    mock_part.text = "Test response from Gemini"
+    mock_content.parts = [mock_part]
+    mock_candidate.content = mock_content
+    mock_response.candidates = [mock_candidate]
+
+    return mock_response
+
+
+@pytest.fixture
+def mock_google_genai_client():
+    """Mock for the google-genai Client with async support"""
+    with patch.object(google_genai, "Client") as mock_client_class:
+        mock_client_instance = MagicMock()
+        mock_models = MagicMock()
+        mock_aio = MagicMock()
+        mock_aio_models = MagicMock()
+
+        mock_client_instance.models = mock_models
+        mock_client_instance.aio = mock_aio
+        mock_aio.models = mock_aio_models
+
+        mock_client_class.return_value = mock_client_instance
+        yield mock_client_instance
+
+
+@pytest.fixture
+def mock_gemini_response_with_function_calls():
+    mock_response = MagicMock()
+
+    # Mock usage metadata
+    mock_usage = MagicMock()
+    mock_usage.prompt_token_count = 25
+    mock_usage.candidates_token_count = 15
+    mock_usage.cached_content_token_count = 0
+    mock_usage.thoughts_token_count = 0
+    mock_response.usage_metadata = mock_usage
+
+    # Mock function call
+    mock_function_call = MagicMock()
+    mock_function_call.name = "get_current_weather"
+    mock_function_call.args = {"location": "San Francisco"}
+
+    # Mock text part 1
+    mock_text_part1 = MagicMock()
+    mock_text_part1.text = "I'll check the weather for you."
+    type(mock_text_part1).text = mock_text_part1.text
+
+    # Mock text part 2
+    mock_text_part2 = MagicMock()
+    mock_text_part2.text = " Let me look that up."
+    type(mock_text_part2).text = mock_text_part2.text
+
+    # Mock function call part
+    mock_function_part = MagicMock()
+    mock_function_part.function_call = mock_function_call
+    type(mock_function_part).function_call = mock_function_part.function_call
+    del mock_function_part.text
+
+    # Mock content with 2 text parts and 1 function call part
+    mock_content = MagicMock()
+    mock_content.parts = [mock_text_part1, mock_text_part2, mock_function_part]
+
+    # Mock candidate
+    mock_candidate = MagicMock()
+    mock_candidate.content = mock_content
+    mock_response.candidates = [mock_candidate]
+
+    return mock_response
+
+
+async def test_async_client_basic_generation(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test the async Client/AsyncModels API structure"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    response = await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Tell me a fun fact about hedgehogs"],
+        posthog_distinct_id="test-id",
+        posthog_properties={"foo": "bar"},
+    )
+
+    assert response == mock_gemini_response
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.0-flash"
+    assert props["$ai_input_tokens"] == 20
+    assert props["$ai_output_tokens"] == 10
+    assert props["foo"] == "bar"
+    assert "$ai_trace_id" in props
+    assert props["$ai_latency"] > 0
+
+
+async def test_async_client_streaming_with_generate_content_stream(
+    mock_client, mock_google_genai_client
+):
+    """Test the async generate_content_stream method"""
+
+    async def mock_streaming_response():
+        mock_chunk1 = MagicMock()
+        mock_chunk1.text = "Hello "
+        mock_usage1 = MagicMock()
+        mock_usage1.prompt_token_count = 10
+        mock_usage1.candidates_token_count = 5
+        mock_usage1.cached_content_token_count = 0
+        mock_usage1.thoughts_token_count = 0
+        mock_chunk1.usage_metadata = mock_usage1
+        yield mock_chunk1
+
+        mock_chunk2 = MagicMock()
+        mock_chunk2.text = "world!"
+        mock_usage2 = MagicMock()
+        mock_usage2.prompt_token_count = 10
+        mock_usage2.candidates_token_count = 10
+        mock_usage2.cached_content_token_count = 0
+        mock_usage2.thoughts_token_count = 0
+        mock_chunk2.usage_metadata = mock_usage2
+        yield mock_chunk2
+
+    # Mock the async generate_content_stream method
+    mock_google_genai_client.aio.models.generate_content_stream = AsyncMock(
+        return_value=mock_streaming_response()
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    response = await client.models.generate_content_stream(
+        model="gemini-2.0-flash",
+        contents=["Write a short story"],
+        posthog_distinct_id="test-id",
+        posthog_properties={"feature": "streaming"},
+    )
+
+    chunks = []
+    async for chunk in response:
+        chunks.append(chunk)
+
+    assert len(chunks) == 2
+    assert chunks[0].text == "Hello "
+    assert chunks[1].text == "world!"
+
+    # Check that the streaming event was captured
+    assert mock_client.capture.call_count == 1
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.0-flash"
+    assert props["$ai_input_tokens"] == 10
+    assert props["$ai_output_tokens"] == 10
+    assert props["feature"] == "streaming"
+    assert isinstance(props["$ai_latency"], float)
+
+
+async def test_async_client_streaming_with_tools(mock_client, mock_google_genai_client):
+    """Test that tools are captured in async streaming mode"""
+
+    async def mock_streaming_response():
+        mock_chunk1 = MagicMock()
+        mock_chunk1.text = "I'll check "
+        mock_usage1 = MagicMock()
+        mock_usage1.prompt_token_count = 15
+        mock_usage1.candidates_token_count = 5
+        mock_usage1.cached_content_token_count = 0
+        mock_usage1.thoughts_token_count = 0
+        mock_chunk1.usage_metadata = mock_usage1
+        yield mock_chunk1
+
+        mock_chunk2 = MagicMock()
+        mock_chunk2.text = "the weather"
+        mock_usage2 = MagicMock()
+        mock_usage2.prompt_token_count = 15
+        mock_usage2.candidates_token_count = 10
+        mock_usage2.cached_content_token_count = 0
+        mock_usage2.thoughts_token_count = 0
+        mock_chunk2.usage_metadata = mock_usage2
+        yield mock_chunk2
+
+    # Mock the async generate_content_stream method
+    mock_google_genai_client.aio.models.generate_content_stream = AsyncMock(
+        return_value=mock_streaming_response()
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    # Create mock tools configuration
+    mock_tool = MagicMock()
+    mock_tool.function_declarations = [
+        MagicMock(
+            name="get_current_weather",
+            description="Gets the current weather for a given location.",
+            parameters=MagicMock(
+                type="OBJECT",
+                properties={
+                    "location": MagicMock(
+                        type="STRING",
+                        description="The city and state, e.g. San Francisco, CA",
+                    )
+                },
+                required=["location"],
+            ),
+        )
+    ]
+
+    mock_config = MagicMock()
+    mock_config.tools = [mock_tool]
+
+    response = await client.models.generate_content_stream(
+        model="gemini-2.0-flash",
+        contents=["What's the weather in SF?"],
+        config=mock_config,
+        posthog_distinct_id="test-id",
+        posthog_properties={"feature": "streaming_with_tools"},
+    )
+
+    chunks = []
+    async for chunk in response:
+        chunks.append(chunk)
+
+    assert len(chunks) == 2
+    assert chunks[0].text == "I'll check "
+    assert chunks[1].text == "the weather"
+
+    # Check that the streaming event was captured with tools
+    assert mock_client.capture.call_count == 1
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.0-flash"
+    assert props["$ai_input_tokens"] == 15
+    assert props["$ai_output_tokens"] == 10
+    assert props["feature"] == "streaming_with_tools"
+    assert isinstance(props["$ai_latency"], float)
+
+    # Verify that tools are captured in the $ai_tools property in streaming mode
+    assert props["$ai_tools"] == [mock_tool]
+
+
+async def test_async_client_groups(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test groups functionality with async Client API"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+        posthog_groups={"company": "company_123"},
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    assert call_args["groups"] == {"company": "company_123"}
+
+
+async def test_async_client_privacy_mode_local(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test local privacy mode with async Client API"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+        posthog_privacy_mode=True,
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] is None
+    assert props["$ai_output_choices"] is None
+
+
+async def test_async_client_privacy_mode_global(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test global privacy mode with async Client API"""
+    mock_client.privacy_mode = True
+
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] is None
+    assert props["$ai_output_choices"] is None
+
+
+async def test_async_client_different_input_formats(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test different input formats with async Client API"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    # Test string input
+    await client.models.generate_content(
+        model="gemini-2.0-flash", contents="Hello", posthog_distinct_id="test-id"
+    )
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
+
+    # Test Gemini-specific format with parts array
+    mock_client.reset_mock()
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=[{"role": "user", "parts": [{"text": "hey"}]}],
+        posthog_distinct_id="test-id",
+    )
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [
+        {"role": "user", "content": [{"type": "text", "text": "hey"}]}
+    ]
+
+    # Test multiple parts in the parts array
+    mock_client.reset_mock()
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=[{"role": "user", "parts": [{"text": "Hello "}, {"text": "world"}]}],
+        posthog_distinct_id="test-id",
+    )
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Hello "},
+                {"type": "text", "text": "world"},
+            ],
+        }
+    ]
+
+    # Test list input with string
+    mock_client.capture.reset_mock()
+    await client.models.generate_content(
+        model="gemini-2.0-flash", contents=["List item"], posthog_distinct_id="test-id"
+    )
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [{"role": "user", "content": "List item"}]
+
+
+async def test_async_client_model_parameters(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test model parameters with async Client API"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+        temperature=0.7,
+        max_tokens=100,
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_model_parameters"]["temperature"] == 0.7
+    assert props["$ai_model_parameters"]["max_tokens"] == 100
+
+
+async def test_async_client_default_settings(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test async client with default PostHog settings"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(
+        api_key="test-key",
+        posthog_client=mock_client,
+        posthog_distinct_id="default_user",
+        posthog_properties={"team": "ai"},
+        posthog_privacy_mode=False,
+        posthog_groups={"company": "acme_corp"},
+    )
+
+    # Call without overriding defaults
+    await client.models.generate_content(model="gemini-2.0-flash", contents=["Hello"])
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "default_user"
+    assert call_args["groups"] == {"company": "acme_corp"}
+    assert props["team"] == "ai"
+
+
+async def test_async_client_override_defaults(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test overriding async client defaults per call"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    client = AsyncClient(
+        api_key="test-key",
+        posthog_client=mock_client,
+        posthog_distinct_id="default_user",
+        posthog_properties={"team": "ai"},
+        posthog_privacy_mode=False,
+        posthog_groups={"company": "acme_corp"},
+    )
+
+    # Override defaults in call
+    await client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="specific_user",
+        posthog_properties={"feature": "chat", "urgent": True},
+        posthog_privacy_mode=True,
+        posthog_groups={"organization": "special_org"},
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    # Check overrides
+    assert call_args["distinct_id"] == "specific_user"
+    assert call_args["groups"] == {"organization": "special_org"}
+    assert props["$ai_input"] is None  # privacy mode was overridden
+
+    # Check merged properties (defaults + call-specific)
+    assert props["team"] == "ai"  # from defaults
+    assert props["feature"] == "chat"  # from call
+    assert props["urgent"] is True  # from call
+
+
+async def test_async_vertex_ai_parameters_passed_through(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test that Vertex AI parameters are properly passed to genai.Client"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response
+    )
+
+    # Mock credentials object
+    mock_credentials = MagicMock()
+    mock_debug_config = MagicMock()
+    mock_http_options = MagicMock()
+
+    # Create client with Vertex AI parameters
+    AsyncClient(
+        vertexai=True,
+        credentials=mock_credentials,
+        project="test-project",
+        location="us-central1",
+        debug_config=mock_debug_config,
+        http_options=mock_http_options,
+        posthog_client=mock_client,
+    )
+
+    # Verify genai.Client was called with correct parameters
+    google_genai.Client.assert_called_once_with(
+        vertexai=True,
+        credentials=mock_credentials,
+        project="test-project",
+        location="us-central1",
+        debug_config=mock_debug_config,
+        http_options=mock_http_options,
+    )
+
+
+async def test_async_api_key_mode(mock_client, mock_google_genai_client):
+    """Test API key authentication mode with async client"""
+
+    # Create async client with just API key (traditional mode)
+    AsyncClient(
+        api_key="test-api-key",
+        posthog_client=mock_client,
+    )
+
+    # Verify genai.Client was called with only api_key
+    google_genai.Client.assert_called_once_with(api_key="test-api-key")
+
+
+async def test_async_function_calls_in_output_choices(
+    mock_client, mock_google_genai_client, mock_gemini_response_with_function_calls
+):
+    """Test that function calls are properly included in $ai_output_choices with async"""
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_gemini_response_with_function_calls
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    response = await client.models.generate_content(
+        model="gemini-2.5-flash",
+        contents=["What's the weather in San Francisco?"],
+        posthog_distinct_id="test-id",
+    )
+
+    assert response == mock_gemini_response_with_function_calls
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.5-flash"
+    assert props["$ai_output_choices"] == [
+        {
+            "role": "assistant",
+            "content": [
+                {"type": "text", "text": "I'll check the weather for you."},
+                {"type": "text", "text": " Let me look that up."},
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_current_weather",
+                        "arguments": {"location": "San Francisco"},
+                    },
+                },
+            ],
+        }
+    ]
+
+    # Check token usage
+    assert props["$ai_input_tokens"] == 25
+    assert props["$ai_output_tokens"] == 15
+    assert props["$ai_http_status"] == 200
+
+
+async def test_async_cache_and_reasoning_tokens(mock_client, mock_google_genai_client):
+    """Test that cache and reasoning tokens are properly extracted with async"""
+    # Create a mock response with cache and reasoning tokens
+    mock_response = MagicMock()
+    mock_response.text = "Test response with cache"
+
+    mock_usage = MagicMock()
+    mock_usage.prompt_token_count = 100
+    mock_usage.candidates_token_count = 50
+    mock_usage.cached_content_token_count = 30  # Cache tokens
+    mock_usage.thoughts_token_count = 10  # Reasoning tokens
+    mock_response.usage_metadata = mock_usage
+
+    # Mock candidates
+    mock_candidate = MagicMock()
+    mock_candidate.text = "Test response with cache"
+    mock_response.candidates = [mock_candidate]
+
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    response = await client.models.generate_content(
+        model="gemini-2.5-pro",
+        contents="Test with cache",
+        posthog_distinct_id="test-id",
+    )
+
+    assert response == mock_response
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    # Check that all token types are present
+    assert props["$ai_input_tokens"] == 100
+    assert props["$ai_output_tokens"] == 50
+    assert props["$ai_cache_read_input_tokens"] == 30
+    assert props["$ai_reasoning_tokens"] == 10
+
+
+async def test_async_streaming_cache_and_reasoning_tokens(
+    mock_client, mock_google_genai_client
+):
+    """Test that cache and reasoning tokens are properly extracted in async streaming"""
+
+    async def mock_streaming_response():
+        # Create mock chunks with cache and reasoning tokens
+        chunk1 = MagicMock()
+        chunk1.text = "Hello "
+        chunk1_usage = MagicMock()
+        chunk1_usage.prompt_token_count = 100
+        chunk1_usage.candidates_token_count = 5
+        chunk1_usage.cached_content_token_count = 30  # Cache tokens
+        chunk1_usage.thoughts_token_count = 0
+        chunk1.usage_metadata = chunk1_usage
+        yield chunk1
+
+        chunk2 = MagicMock()
+        chunk2.text = "world!"
+        chunk2_usage = MagicMock()
+        chunk2_usage.prompt_token_count = 100
+        chunk2_usage.candidates_token_count = 10
+        chunk2_usage.cached_content_token_count = 30  # Same cache tokens
+        chunk2_usage.thoughts_token_count = 5  # Reasoning tokens
+        chunk2.usage_metadata = chunk2_usage
+        yield chunk2
+
+    mock_google_genai_client.aio.models.generate_content_stream = AsyncMock(
+        return_value=mock_streaming_response()
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    response = await client.models.generate_content_stream(
+        model="gemini-2.5-pro",
+        contents="Test streaming with cache",
+        posthog_distinct_id="test-id",
+    )
+
+    # Consume the stream
+    result = []
+    async for chunk in response:
+        result.append(chunk)
+
+    assert len(result) == 2
+
+    # Check PostHog capture was called
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    # Check that all token types are present (should use final chunk's usage)
+    assert props["$ai_input_tokens"] == 100
+    assert props["$ai_output_tokens"] == 10
+    assert props["$ai_cache_read_input_tokens"] == 30
+    assert props["$ai_reasoning_tokens"] == 5
+
+
+async def test_async_web_search_grounding(mock_client, mock_google_genai_client):
+    """Test async web search detection via grounding_metadata."""
+
+    # Create mock response with grounding metadata
+    mock_response = MagicMock()
+
+    # Mock usage metadata
+    mock_usage = MagicMock()
+    mock_usage.prompt_token_count = 60
+    mock_usage.candidates_token_count = 40
+    mock_usage.cached_content_token_count = 0
+    mock_usage.thoughts_token_count = 0
+    mock_response.usage_metadata = mock_usage
+
+    # Mock grounding metadata
+    mock_grounding_chunk = MagicMock()
+    mock_grounding_chunk.uri = "https://example.com"
+
+    mock_grounding_metadata = MagicMock()
+    mock_grounding_metadata.grounding_chunks = [mock_grounding_chunk]
+
+    # Mock text part
+    mock_text_part = MagicMock()
+    mock_text_part.text = "According to search results..."
+    type(mock_text_part).text = mock_text_part.text
+
+    # Mock content with parts
+    mock_content = MagicMock()
+    mock_content.parts = [mock_text_part]
+
+    # Mock candidate with grounding metadata
+    mock_candidate = MagicMock()
+    mock_candidate.content = mock_content
+    mock_candidate.grounding_metadata = mock_grounding_metadata
+    type(mock_candidate).grounding_metadata = mock_candidate.grounding_metadata
+
+    mock_response.candidates = [mock_candidate]
+    mock_response.text = "According to search results..."
+
+    # Mock the async generate_content method
+    mock_google_genai_client.aio.models.generate_content = AsyncMock(
+        return_value=mock_response
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+    response = await client.models.generate_content(
+        model="gemini-2.5-flash",
+        contents="What's the latest news?",
+        posthog_distinct_id="test-id",
+    )
+
+    assert response == mock_response
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    # Verify web search count is detected (binary for grounding)
+    assert props["$ai_web_search_count"] == 1
+    assert props["$ai_input_tokens"] == 60
+    assert props["$ai_output_tokens"] == 40
+
+
+async def test_async_streaming_with_web_search(mock_client, mock_google_genai_client):
+    """Test that web search count is properly captured in async streaming mode."""
+
+    async def mock_streaming_response():
+        # Create chunk 1 with grounding metadata
+        mock_chunk1 = MagicMock()
+        mock_chunk1.text = "According to "
+
+        mock_usage1 = MagicMock()
+        mock_usage1.prompt_token_count = 30
+        mock_usage1.candidates_token_count = 5
+        mock_usage1.cached_content_token_count = 0
+        mock_usage1.thoughts_token_count = 0
+        mock_chunk1.usage_metadata = mock_usage1
+
+        # Add grounding metadata to first chunk
+        mock_grounding_chunk = MagicMock()
+        mock_grounding_chunk.uri = "https://example.com"
+
+        mock_grounding_metadata = MagicMock()
+        mock_grounding_metadata.grounding_chunks = [mock_grounding_chunk]
+
+        mock_candidate1 = MagicMock()
+        mock_candidate1.grounding_metadata = mock_grounding_metadata
+        type(mock_candidate1).grounding_metadata = mock_candidate1.grounding_metadata
+
+        mock_chunk1.candidates = [mock_candidate1]
+        yield mock_chunk1
+
+        # Create chunk 2
+        mock_chunk2 = MagicMock()
+        mock_chunk2.text = "search results..."
+
+        mock_usage2 = MagicMock()
+        mock_usage2.prompt_token_count = 30
+        mock_usage2.candidates_token_count = 15
+        mock_usage2.cached_content_token_count = 0
+        mock_usage2.thoughts_token_count = 0
+        mock_chunk2.usage_metadata = mock_usage2
+
+        mock_candidate2 = MagicMock()
+        mock_chunk2.candidates = [mock_candidate2]
+        yield mock_chunk2
+
+    # Mock the async generate_content_stream method
+    mock_google_genai_client.aio.models.generate_content_stream = AsyncMock(
+        return_value=mock_streaming_response()
+    )
+
+    client = AsyncClient(api_key="test-key", posthog_client=mock_client)
+
+    response = await client.models.generate_content_stream(
+        model="gemini-2.5-flash",
+        contents="What's the latest news?",
+        posthog_distinct_id="test-id",
+    )
+
+    chunks = []
+    async for chunk in response:
+        chunks.append(chunk)
+
+    assert len(chunks) == 2
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    # Verify web search count is detected (binary for grounding)
+    assert props["$ai_web_search_count"] == 1
+    assert props["$ai_input_tokens"] == 30
+    assert props["$ai_output_tokens"] == 15

posthog/test/ai/langchain/__init__.py

@@ -0,0 +1,5 @@
+import pytest
+
+pytest.importorskip("langchain_core")
+pytest.importorskip("langchain_community")
+pytest.importorskip("langgraph")

posthog/test/ai/openai_agents/__init__.py

@@ -0,0 +1 @@
+# Tests for OpenAI Agents SDK integration

posthog/test/ai/openai_agents/test_processor.py

@@ -0,0 +1,810 @@
+import logging
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+try:
+    from agents.tracing.span_data import (
+        AgentSpanData,
+        CustomSpanData,
+        FunctionSpanData,
+        GenerationSpanData,
+        GuardrailSpanData,
+        HandoffSpanData,
+        ResponseSpanData,
+        SpeechSpanData,
+        TranscriptionSpanData,
+    )
+
+    from posthog.ai.openai_agents import PostHogTracingProcessor, instrument
+
+    OPENAI_AGENTS_AVAILABLE = True
+except ImportError:
+    OPENAI_AGENTS_AVAILABLE = False
+
+
+# Skip all tests if OpenAI Agents SDK is not available
+pytestmark = pytest.mark.skipif(
+    not OPENAI_AGENTS_AVAILABLE, reason="OpenAI Agents SDK is not available"
+)
+
+
+@pytest.fixture(scope="function")
+def mock_client():
+    client = MagicMock()
+    client.privacy_mode = False
+    logging.getLogger("posthog").setLevel(logging.DEBUG)
+    return client
+
+
+@pytest.fixture(scope="function")
+def processor(mock_client):
+    return PostHogTracingProcessor(
+        client=mock_client,
+        distinct_id="test-user",
+        privacy_mode=False,
+    )
+
+
+@pytest.fixture
+def mock_trace():
+    trace = MagicMock()
+    trace.trace_id = "trace_123456789"
+    trace.name = "Test Workflow"
+    trace.group_id = "group_123"
+    trace.metadata = {"key": "value"}
+    return trace
+
+
+@pytest.fixture
+def mock_span():
+    span = MagicMock()
+    span.trace_id = "trace_123456789"
+    span.span_id = "span_987654321"
+    span.parent_id = None
+    span.started_at = "2024-01-01T00:00:00Z"
+    span.ended_at = "2024-01-01T00:00:01Z"
+    span.error = None
+    return span
+
+
+class TestPostHogTracingProcessor:
+    """Tests for the PostHogTracingProcessor class."""
+
+    def test_initialization(self, mock_client):
+        """Test processor initializes correctly."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id="user@example.com",
+            privacy_mode=True,
+            groups={"company": "acme"},
+            properties={"env": "test"},
+        )
+
+        assert processor._client == mock_client
+        assert processor._distinct_id == "user@example.com"
+        assert processor._privacy_mode is True
+        assert processor._groups == {"company": "acme"}
+        assert processor._properties == {"env": "test"}
+
+    def test_initialization_with_callable_distinct_id(self, mock_client, mock_trace):
+        """Test processor with callable distinct_id resolver."""
+
+        def resolver(trace):
+            return trace.metadata.get("user_id", "default")
+
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id=resolver,
+        )
+
+        mock_trace.metadata = {"user_id": "resolved-user"}
+        distinct_id = processor._get_distinct_id(mock_trace)
+        assert distinct_id == "resolved-user"
+
+    def test_on_trace_start_stores_metadata(self, processor, mock_client, mock_trace):
+        """Test that on_trace_start stores metadata but does not capture an event."""
+        processor.on_trace_start(mock_trace)
+
+        mock_client.capture.assert_not_called()
+        assert mock_trace.trace_id in processor._trace_metadata
+
+    def test_on_trace_end_captures_ai_trace(self, processor, mock_client, mock_trace):
+        """Test that on_trace_end captures $ai_trace event."""
+        processor.on_trace_start(mock_trace)
+        processor.on_trace_end(mock_trace)
+
+        mock_client.capture.assert_called_once()
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_trace"
+        assert call_kwargs["distinct_id"] == "test-user"
+        assert call_kwargs["properties"]["$ai_trace_id"] == "trace_123456789"
+        assert call_kwargs["properties"]["$ai_trace_name"] == "Test Workflow"
+        assert call_kwargs["properties"]["$ai_provider"] == "openai"
+        assert call_kwargs["properties"]["$ai_framework"] == "openai-agents"
+        assert "$ai_latency" in call_kwargs["properties"]
+
+    def test_personless_mode_when_no_distinct_id(self, mock_client, mock_trace):
+        """Test that trace events use personless mode when no distinct_id is provided."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+        )
+
+        processor.on_trace_start(mock_trace)
+        processor.on_trace_end(mock_trace)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$process_person_profile"] is False
+        # Should fallback to trace_id as the distinct_id
+        assert call_kwargs["distinct_id"] == mock_trace.trace_id
+
+    def test_personless_mode_for_spans_when_no_distinct_id(
+        self, mock_client, mock_trace, mock_span
+    ):
+        """Test that span events use personless mode when no distinct_id is provided."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+        )
+
+        processor.on_trace_start(mock_trace)
+        mock_client.capture.reset_mock()
+
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$process_person_profile"] is False
+        assert call_kwargs["distinct_id"] == mock_span.trace_id
+
+    def test_personless_mode_when_callable_returns_none(
+        self, mock_client, mock_trace, mock_span
+    ):
+        """Test personless mode when callable distinct_id returns None."""
+
+        def resolver(trace):
+            return None  # Simulate no user ID available
+
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id=resolver,
+        )
+
+        processor.on_trace_start(mock_trace)
+        mock_client.capture.reset_mock()
+
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$process_person_profile"] is False
+        assert call_kwargs["distinct_id"] == mock_span.trace_id
+
+    def test_person_profile_when_distinct_id_provided(self, mock_client, mock_trace):
+        """Test that events create person profiles when distinct_id is provided."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id="real-user",
+        )
+
+        processor.on_trace_start(mock_trace)
+        processor.on_trace_end(mock_trace)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert "$process_person_profile" not in call_kwargs["properties"]
+
+    def test_on_trace_end_clears_metadata(self, processor, mock_client, mock_trace):
+        """Test that on_trace_end clears stored trace metadata."""
+        processor.on_trace_start(mock_trace)
+        assert mock_trace.trace_id in processor._trace_metadata
+
+        processor.on_trace_end(mock_trace)
+        assert mock_trace.trace_id not in processor._trace_metadata
+        # Also verify it captured the event
+        mock_client.capture.assert_called_once()
+
+    def test_on_span_start_tracks_time(self, processor, mock_span):
+        """Test that on_span_start records start time."""
+        processor.on_span_start(mock_span)
+        assert mock_span.span_id in processor._span_start_times
+
+    def test_generation_span_mapping(self, processor, mock_client, mock_span):
+        """Test GenerationSpanData maps to $ai_generation event."""
+        span_data = GenerationSpanData(
+            input=[{"role": "user", "content": "Hello"}],
+            output=[{"role": "assistant", "content": "Hi there!"}],
+            model="gpt-4o",
+            model_config={"temperature": 0.7, "max_tokens": 100},
+            usage={"input_tokens": 10, "output_tokens": 20},
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        mock_client.capture.assert_called_once()
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_generation"
+        assert call_kwargs["properties"]["$ai_trace_id"] == "trace_123456789"
+        assert call_kwargs["properties"]["$ai_span_id"] == "span_987654321"
+        assert call_kwargs["properties"]["$ai_provider"] == "openai"
+        assert call_kwargs["properties"]["$ai_framework"] == "openai-agents"
+        assert call_kwargs["properties"]["$ai_model"] == "gpt-4o"
+        assert call_kwargs["properties"]["$ai_input_tokens"] == 10
+        assert call_kwargs["properties"]["$ai_output_tokens"] == 20
+        assert call_kwargs["properties"]["$ai_input"] == [
+            {"role": "user", "content": "Hello"}
+        ]
+        assert call_kwargs["properties"]["$ai_output_choices"] == [
+            {"role": "assistant", "content": "Hi there!"}
+        ]
+
+    def test_generation_span_with_reasoning_tokens(
+        self, processor, mock_client, mock_span
+    ):
+        """Test GenerationSpanData includes reasoning tokens when present."""
+        span_data = GenerationSpanData(
+            model="o1-preview",
+            usage={
+                "input_tokens": 100,
+                "output_tokens": 500,
+                "reasoning_tokens": 400,
+            },
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_reasoning_tokens"] == 400
+
+    def test_function_span_mapping(self, processor, mock_client, mock_span):
+        """Test FunctionSpanData maps to $ai_span event with type=tool."""
+        span_data = FunctionSpanData(
+            name="get_weather",
+            input='{"city": "San Francisco"}',
+            output="Sunny, 72F",
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_name"] == "get_weather"
+        assert call_kwargs["properties"]["$ai_span_type"] == "tool"
+        assert (
+            call_kwargs["properties"]["$ai_input_state"] == '{"city": "San Francisco"}'
+        )
+        assert call_kwargs["properties"]["$ai_output_state"] == "Sunny, 72F"
+
+    def test_agent_span_mapping(self, processor, mock_client, mock_span):
+        """Test AgentSpanData maps to $ai_span event with type=agent."""
+        span_data = AgentSpanData(
+            name="CustomerServiceAgent",
+            handoffs=["TechnicalAgent", "BillingAgent"],
+            tools=["search", "get_order"],
+            output_type="str",
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_name"] == "CustomerServiceAgent"
+        assert call_kwargs["properties"]["$ai_span_type"] == "agent"
+        assert call_kwargs["properties"]["$ai_agent_handoffs"] == [
+            "TechnicalAgent",
+            "BillingAgent",
+        ]
+        assert call_kwargs["properties"]["$ai_agent_tools"] == ["search", "get_order"]
+
+    def test_handoff_span_mapping(self, processor, mock_client, mock_span):
+        """Test HandoffSpanData maps to $ai_span event with type=handoff."""
+        span_data = HandoffSpanData(
+            from_agent="TriageAgent",
+            to_agent="TechnicalAgent",
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_type"] == "handoff"
+        assert call_kwargs["properties"]["$ai_handoff_from_agent"] == "TriageAgent"
+        assert call_kwargs["properties"]["$ai_handoff_to_agent"] == "TechnicalAgent"
+        assert (
+            call_kwargs["properties"]["$ai_span_name"]
+            == "TriageAgent -> TechnicalAgent"
+        )
+
+    def test_guardrail_span_mapping(self, processor, mock_client, mock_span):
+        """Test GuardrailSpanData maps to $ai_span event with type=guardrail."""
+        span_data = GuardrailSpanData(
+            name="ContentFilter",
+            triggered=True,
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_name"] == "ContentFilter"
+        assert call_kwargs["properties"]["$ai_span_type"] == "guardrail"
+        assert call_kwargs["properties"]["$ai_guardrail_triggered"] is True
+
+    def test_custom_span_mapping(self, processor, mock_client, mock_span):
+        """Test CustomSpanData maps to $ai_span event with type=custom."""
+        span_data = CustomSpanData(
+            name="database_query",
+            data={"query": "SELECT * FROM users", "rows": 100},
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_name"] == "database_query"
+        assert call_kwargs["properties"]["$ai_span_type"] == "custom"
+        assert call_kwargs["properties"]["$ai_custom_data"] == {
+            "query": "SELECT * FROM users",
+            "rows": 100,
+        }
+
+    def test_privacy_mode_redacts_content(self, mock_client, mock_span):
+        """Test that privacy_mode redacts input/output content."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id="test-user",
+            privacy_mode=True,
+        )
+
+        span_data = GenerationSpanData(
+            input=[{"role": "user", "content": "Secret message"}],
+            output=[{"role": "assistant", "content": "Secret response"}],
+            model="gpt-4o",
+            usage={"input_tokens": 10, "output_tokens": 20},
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        # Content should be redacted
+        assert call_kwargs["properties"]["$ai_input"] is None
+        assert call_kwargs["properties"]["$ai_output_choices"] is None
+        # Token counts should still be present
+        assert call_kwargs["properties"]["$ai_input_tokens"] == 10
+        assert call_kwargs["properties"]["$ai_output_tokens"] == 20
+
+    def test_error_handling_in_span(self, processor, mock_client, mock_span):
+        """Test that span errors are captured correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {"message": "Rate limit exceeded", "data": {"code": 429}}
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["properties"]["$ai_is_error"] is True
+        assert call_kwargs["properties"]["$ai_error"] == "Rate limit exceeded"
+
+    def test_generation_span_includes_total_tokens(
+        self, processor, mock_client, mock_span
+    ):
+        """Test that $ai_total_tokens is calculated and included."""
+        span_data = GenerationSpanData(
+            model="gpt-4o",
+            usage={"input_tokens": 100, "output_tokens": 50},
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_total_tokens"] == 150
+
+    def test_error_type_categorization_model_behavior(
+        self, processor, mock_client, mock_span
+    ):
+        """Test that ModelBehaviorError is categorized correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {
+            "message": "ModelBehaviorError: Invalid JSON output",
+            "type": "ModelBehaviorError",
+        }
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_error_type"] == "model_behavior_error"
+
+    def test_error_type_categorization_user_error(
+        self, processor, mock_client, mock_span
+    ):
+        """Test that UserError is categorized correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {"message": "UserError: Tool failed", "type": "UserError"}
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_error_type"] == "user_error"
+
+    def test_error_type_categorization_input_guardrail(
+        self, processor, mock_client, mock_span
+    ):
+        """Test that InputGuardrailTripwireTriggered is categorized correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {
+            "message": "InputGuardrailTripwireTriggered: Content blocked"
+        }
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert (
+            call_kwargs["properties"]["$ai_error_type"] == "input_guardrail_triggered"
+        )
+
+    def test_error_type_categorization_output_guardrail(
+        self, processor, mock_client, mock_span
+    ):
+        """Test that OutputGuardrailTripwireTriggered is categorized correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {
+            "message": "OutputGuardrailTripwireTriggered: Response blocked"
+        }
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert (
+            call_kwargs["properties"]["$ai_error_type"] == "output_guardrail_triggered"
+        )
+
+    def test_error_type_categorization_max_turns(
+        self, processor, mock_client, mock_span
+    ):
+        """Test that MaxTurnsExceeded is categorized correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {"message": "MaxTurnsExceeded: Agent exceeded maximum turns"}
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_error_type"] == "max_turns_exceeded"
+
+    def test_error_type_categorization_unknown(self, processor, mock_client, mock_span):
+        """Test that unknown errors are categorized as unknown."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {"message": "Some random error occurred"}
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_error_type"] == "unknown"
+
+    def test_response_span_with_output_and_total_tokens(
+        self, processor, mock_client, mock_span
+    ):
+        """Test ResponseSpanData includes output choices and total tokens."""
+        # Create a mock response object
+        mock_response = MagicMock()
+        mock_response.id = "resp_123"
+        mock_response.model = "gpt-4o"
+        mock_response.output = [{"type": "message", "content": "Hello!"}]
+        mock_response.usage = MagicMock()
+        mock_response.usage.input_tokens = 25
+        mock_response.usage.output_tokens = 10
+
+        span_data = ResponseSpanData(
+            response=mock_response,
+            input="Hello, world!",
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_generation"
+        assert call_kwargs["properties"]["$ai_total_tokens"] == 35
+        assert call_kwargs["properties"]["$ai_output_choices"] == [
+            {"type": "message", "content": "Hello!"}
+        ]
+        assert call_kwargs["properties"]["$ai_response_id"] == "resp_123"
+
+    def test_speech_span_with_pass_through_properties(
+        self, processor, mock_client, mock_span
+    ):
+        """Test SpeechSpanData includes pass-through properties."""
+        span_data = SpeechSpanData(
+            input="Hello, how can I help you?",
+            output="base64_audio_data",
+            output_format="pcm",
+            model="tts-1",
+            model_config={"voice": "alloy", "speed": 1.0},
+            first_content_at="2024-01-01T00:00:00.500Z",
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_type"] == "speech"
+        assert call_kwargs["properties"]["$ai_model"] == "tts-1"
+        # Pass-through properties (no $ai_ prefix)
+        assert (
+            call_kwargs["properties"]["first_content_at"] == "2024-01-01T00:00:00.500Z"
+        )
+        assert call_kwargs["properties"]["audio_output_format"] == "pcm"
+        assert call_kwargs["properties"]["model_config"] == {
+            "voice": "alloy",
+            "speed": 1.0,
+        }
+        # Text input should be captured
+        assert call_kwargs["properties"]["$ai_input"] == "Hello, how can I help you?"
+
+    def test_transcription_span_with_pass_through_properties(
+        self, processor, mock_client, mock_span
+    ):
+        """Test TranscriptionSpanData includes pass-through properties."""
+        span_data = TranscriptionSpanData(
+            input="base64_audio_data",
+            input_format="pcm",
+            output="This is the transcribed text.",
+            model="whisper-1",
+            model_config={"language": "en"},
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+
+        assert call_kwargs["event"] == "$ai_span"
+        assert call_kwargs["properties"]["$ai_span_type"] == "transcription"
+        assert call_kwargs["properties"]["$ai_model"] == "whisper-1"
+        # Pass-through properties (no $ai_ prefix)
+        assert call_kwargs["properties"]["audio_input_format"] == "pcm"
+        assert call_kwargs["properties"]["model_config"] == {"language": "en"}
+        # Transcription output should be captured
+        assert (
+            call_kwargs["properties"]["$ai_output_state"]
+            == "This is the transcribed text."
+        )
+
+    def test_latency_calculation(self, processor, mock_client, mock_span):
+        """Test that latency is calculated correctly."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+
+        with patch("time.time") as mock_time:
+            mock_time.return_value = 1000.0
+            processor.on_span_start(mock_span)
+
+            mock_time.return_value = 1001.5  # 1.5 seconds later
+            processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_latency"] == pytest.approx(1.5, rel=0.01)
+
+    def test_groups_included_in_events(self, mock_client, mock_trace, mock_span):
+        """Test that groups are included in captured events."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id="test-user",
+            groups={"company": "acme", "team": "engineering"},
+        )
+
+        processor.on_trace_start(mock_trace)
+        processor.on_trace_end(mock_trace)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["groups"] == {"company": "acme", "team": "engineering"}
+
+    def test_additional_properties_included(self, mock_client, mock_trace):
+        """Test that additional properties are included in events."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id="test-user",
+            properties={"environment": "production", "version": "1.0"},
+        )
+
+        processor.on_trace_start(mock_trace)
+        processor.on_trace_end(mock_trace)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["environment"] == "production"
+        assert call_kwargs["properties"]["version"] == "1.0"
+
+    def test_shutdown_clears_state(self, processor):
+        """Test that shutdown clears internal state."""
+        processor._span_start_times["span_1"] = 1000.0
+        processor._trace_metadata["trace_1"] = {"name": "test"}
+
+        processor.shutdown()
+
+        assert len(processor._span_start_times) == 0
+        assert len(processor._trace_metadata) == 0
+
+    def test_force_flush_calls_client_flush(self, processor, mock_client):
+        """Test that force_flush calls client.flush()."""
+        processor.force_flush()
+        mock_client.flush.assert_called_once()
+
+    def test_generation_span_with_no_usage(self, processor, mock_client, mock_span):
+        """Test GenerationSpanData with no usage data defaults to zero tokens."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_input_tokens"] == 0
+        assert call_kwargs["properties"]["$ai_output_tokens"] == 0
+        assert call_kwargs["properties"]["$ai_total_tokens"] == 0
+
+    def test_generation_span_with_partial_usage(
+        self, processor, mock_client, mock_span
+    ):
+        """Test GenerationSpanData with only input_tokens present."""
+        span_data = GenerationSpanData(
+            model="gpt-4o",
+            usage={"input_tokens": 42},
+        )
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_input_tokens"] == 42
+        assert call_kwargs["properties"]["$ai_output_tokens"] == 0
+        assert call_kwargs["properties"]["$ai_total_tokens"] == 42
+
+    def test_error_type_categorization_by_type_field_only(
+        self, processor, mock_client, mock_span
+    ):
+        """Test error categorization works when only the type field matches."""
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+        mock_span.error = {
+            "message": "Something went wrong",
+            "type": "ModelBehaviorError",
+        }
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["properties"]["$ai_error_type"] == "model_behavior_error"
+
+    def test_distinct_id_resolved_from_trace_for_spans(
+        self, mock_client, mock_trace, mock_span
+    ):
+        """Test that spans use the distinct_id resolved at trace start."""
+
+        def resolver(trace):
+            return f"user-{trace.name}"
+
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id=resolver,
+        )
+
+        # Start trace - this resolves and stores distinct_id
+        processor.on_trace_start(mock_trace)
+        mock_client.capture.reset_mock()
+
+        # End a span - should use the stored distinct_id from trace
+        span_data = GenerationSpanData(model="gpt-4o")
+        mock_span.span_data = span_data
+
+        processor.on_span_start(mock_span)
+        processor.on_span_end(mock_span)
+
+        call_kwargs = mock_client.capture.call_args[1]
+        assert call_kwargs["distinct_id"] == "user-Test Workflow"
+
+    def test_eviction_of_stale_entries(self, mock_client):
+        """Test that stale entries are evicted when max is exceeded."""
+        processor = PostHogTracingProcessor(
+            client=mock_client,
+            distinct_id="test-user",
+        )
+        processor._max_tracked_entries = 10
+
+        # Fill beyond max
+        for i in range(15):
+            processor._span_start_times[f"span_{i}"] = float(i)
+            processor._trace_metadata[f"trace_{i}"] = {"name": f"trace_{i}"}
+
+        processor._evict_stale_entries()
+
+        # Should have evicted half
+        assert len(processor._span_start_times) <= 10
+        assert len(processor._trace_metadata) <= 10
+
+
+class TestInstrumentHelper:
+    """Tests for the instrument() convenience function."""
+
+    def test_instrument_registers_processor(self, mock_client):
+        """Test that instrument() registers a processor."""
+        with patch("agents.tracing.add_trace_processor") as mock_add:
+            processor = instrument(
+                client=mock_client,
+                distinct_id="test-user",
+            )
+
+            mock_add.assert_called_once_with(processor)
+            assert isinstance(processor, PostHogTracingProcessor)
+
+    def test_instrument_with_privacy_mode(self, mock_client):
+        """Test instrument() respects privacy_mode."""
+        with patch("agents.tracing.add_trace_processor"):
+            processor = instrument(
+                client=mock_client,
+                privacy_mode=True,
+            )
+
+            assert processor._privacy_mode is True
+
+    def test_instrument_with_groups_and_properties(self, mock_client):
+        """Test instrument() accepts groups and properties."""
+        with patch("agents.tracing.add_trace_processor"):
+            processor = instrument(
+                client=mock_client,
+                groups={"company": "acme"},
+                properties={"env": "test"},
+            )
+
+            assert processor._groups == {"company": "acme"}
+            assert processor._properties == {"env": "test"}

posthog/test/ai/test_prompts.py

@@ -0,0 +1,607 @@
+import unittest
+from unittest.mock import MagicMock, patch
+
+from posthog.ai.prompts import Prompts
+
+
+class MockResponse:
+    """Mock HTTP response for testing."""
+
+    def __init__(self, json_data=None, status_code=200, ok=True):
+        self._json_data = json_data
+        self.status_code = status_code
+        self.ok = ok
+
+    def json(self):
+        if self._json_data is None:
+            raise ValueError("No JSON data")
+        return self._json_data
+
+
+class TestPrompts(unittest.TestCase):
+    """Tests for the Prompts class."""
+
+    mock_prompt_response = {
+        "id": 1,
+        "name": "test-prompt",
+        "prompt": "Hello, {{name}}! You are a helpful assistant for {{company}}.",
+        "version": 1,
+        "created_by": "user@example.com",
+        "created_at": "2024-01-01T00:00:00Z",
+        "updated_at": "2024-01-01T00:00:00Z",
+        "deleted": False,
+    }
+
+    def create_mock_posthog(
+        self,
+        personal_api_key="phx_test_key",
+        project_api_key="phc_test_key",
+        host="https://us.posthog.com",
+    ):
+        """Create a mock PostHog client."""
+        mock = MagicMock()
+        mock.personal_api_key = personal_api_key
+        mock.api_key = project_api_key
+        mock.raw_host = host
+        return mock
+
+
+class TestPromptsGet(TestPrompts):
+    """Tests for the Prompts.get() method."""
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_successfully_fetch_a_prompt(self, mock_get_session):
+        """Should successfully fetch a prompt."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.get("test-prompt")
+
+        self.assertEqual(result, self.mock_prompt_response["prompt"])
+        mock_get.assert_called_once()
+        call_args = mock_get.call_args
+        self.assertEqual(
+            call_args[0][0],
+            "https://us.posthog.com/api/environments/@current/llm_prompts/name/test-prompt/?token=phc_test_key",
+        )
+        self.assertIn("Authorization", call_args[1]["headers"])
+        self.assertEqual(
+            call_args[1]["headers"]["Authorization"], "Bearer phx_test_key"
+        )
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.time.time")
+    def test_return_cached_prompt_when_fresh(self, mock_time, mock_get_session):
+        """Should return cached prompt when fresh (no API call)."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+        mock_time.return_value = 1000.0
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        # First call - fetches from API
+        result1 = prompts.get("test-prompt", cache_ttl_seconds=300)
+        self.assertEqual(result1, self.mock_prompt_response["prompt"])
+        self.assertEqual(mock_get.call_count, 1)
+
+        # Advance time by 60 seconds (still within TTL)
+        mock_time.return_value = 1060.0
+
+        # Second call - should use cache
+        result2 = prompts.get("test-prompt", cache_ttl_seconds=300)
+        self.assertEqual(result2, self.mock_prompt_response["prompt"])
+        self.assertEqual(mock_get.call_count, 1)  # No additional fetch
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.time.time")
+    def test_refetch_when_cache_is_stale(self, mock_time, mock_get_session):
+        """Should refetch when cache is stale."""
+        mock_get = mock_get_session.return_value.get
+        updated_prompt_response = {
+            **self.mock_prompt_response,
+            "prompt": "Updated prompt: Hello, {{name}}!",
+        }
+
+        mock_get.side_effect = [
+            MockResponse(json_data=self.mock_prompt_response),
+            MockResponse(json_data=updated_prompt_response),
+        ]
+        mock_time.return_value = 1000.0
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        # First call - fetches from API
+        result1 = prompts.get("test-prompt", cache_ttl_seconds=60)
+        self.assertEqual(result1, self.mock_prompt_response["prompt"])
+        self.assertEqual(mock_get.call_count, 1)
+
+        # Advance time past TTL
+        mock_time.return_value = 1061.0
+
+        # Second call - should refetch
+        result2 = prompts.get("test-prompt", cache_ttl_seconds=60)
+        self.assertEqual(result2, updated_prompt_response["prompt"])
+        self.assertEqual(mock_get.call_count, 2)
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.time.time")
+    @patch("posthog.ai.prompts.log")
+    def test_use_stale_cache_on_fetch_failure_with_warning(
+        self, mock_log, mock_time, mock_get_session
+    ):
+        """Should use stale cache on fetch failure with warning."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.side_effect = [
+            MockResponse(json_data=self.mock_prompt_response),
+            Exception("Network error"),
+        ]
+        mock_time.return_value = 1000.0
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        # First call - populates cache
+        result1 = prompts.get("test-prompt", cache_ttl_seconds=60)
+        self.assertEqual(result1, self.mock_prompt_response["prompt"])
+
+        # Advance time past TTL
+        mock_time.return_value = 1061.0
+
+        # Second call - should use stale cache
+        result2 = prompts.get("test-prompt", cache_ttl_seconds=60)
+        self.assertEqual(result2, self.mock_prompt_response["prompt"])
+
+        # Check warning was logged
+        mock_log.warning.assert_called()
+        warning_call = mock_log.warning.call_args
+        self.assertIn("using stale cache", warning_call[0][0])
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.log")
+    def test_use_fallback_when_no_cache_and_fetch_fails_with_warning(
+        self, mock_log, mock_get_session
+    ):
+        """Should use fallback when no cache and fetch fails with warning."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.side_effect = Exception("Network error")
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        fallback = "Default system prompt."
+        result = prompts.get("test-prompt", fallback=fallback)
+
+        self.assertEqual(result, fallback)
+
+        # Check warning was logged
+        mock_log.warning.assert_called()
+        warning_call = mock_log.warning.call_args
+        self.assertIn("using fallback", warning_call[0][0])
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_throw_when_no_cache_no_fallback_and_fetch_fails(self, mock_get_session):
+        """Should throw when no cache, no fallback, and fetch fails."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.side_effect = Exception("Network error")
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        with self.assertRaises(Exception) as context:
+            prompts.get("test-prompt")
+
+        self.assertIn("Network error", str(context.exception))
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_handle_404_response(self, mock_get_session):
+        """Should handle 404 response."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(status_code=404, ok=False)
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        with self.assertRaises(Exception) as context:
+            prompts.get("nonexistent-prompt")
+
+        self.assertIn('Prompt "nonexistent-prompt" not found', str(context.exception))
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_handle_403_response(self, mock_get_session):
+        """Should handle 403 response."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(status_code=403, ok=False)
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        with self.assertRaises(Exception) as context:
+            prompts.get("restricted-prompt")
+
+        self.assertIn(
+            'Access denied for prompt "restricted-prompt"', str(context.exception)
+        )
+
+    def test_throw_when_no_personal_api_key_configured(self):
+        """Should throw when no personal_api_key is configured."""
+        posthog = self.create_mock_posthog(personal_api_key=None)
+        prompts = Prompts(posthog)
+
+        with self.assertRaises(Exception) as context:
+            prompts.get("test-prompt")
+
+        self.assertIn(
+            "personal_api_key is required to fetch prompts", str(context.exception)
+        )
+
+    def test_throw_when_no_project_api_key_configured(self):
+        """Should throw when no project_api_key is configured."""
+        posthog = self.create_mock_posthog(project_api_key=None)
+        prompts = Prompts(posthog)
+
+        with self.assertRaises(Exception) as context:
+            prompts.get("test-prompt")
+
+        self.assertIn(
+            "project_api_key is required to fetch prompts", str(context.exception)
+        )
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_throw_when_api_returns_invalid_response_format(self, mock_get_session):
+        """Should throw when API returns invalid response format."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data={"invalid": "response"})
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        with self.assertRaises(Exception) as context:
+            prompts.get("test-prompt")
+
+        self.assertIn("Invalid response format", str(context.exception))
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_use_custom_host_from_posthog_options(self, mock_get_session):
+        """Should use custom host from PostHog options."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+
+        posthog = self.create_mock_posthog(host="https://eu.posthog.com")
+        prompts = Prompts(posthog)
+
+        prompts.get("test-prompt")
+
+        call_args = mock_get.call_args
+        self.assertTrue(
+            call_args[0][0].startswith(
+                "https://eu.posthog.com/api/environments/@current/llm_prompts/name/test-prompt/?token=phc_test_key"
+            ),
+            f"Expected URL to start with 'https://eu.posthog.com/api/environments/@current/llm_prompts/name/test-prompt/?token=phc_test_key', got {call_args[0][0]}",
+        )
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.time.time")
+    def test_use_default_cache_ttl_5_minutes(self, mock_time, mock_get_session):
+        """Should use default cache TTL (5 minutes) when not specified."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+        mock_time.return_value = 1000.0
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        # First call
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 1)
+
+        # Advance time by 4 minutes (within default 5-minute TTL)
+        mock_time.return_value = 1000.0 + (4 * 60)
+
+        # Second call - should use cache
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 1)
+
+        # Advance time past 5-minute TTL
+        mock_time.return_value = 1000.0 + (6 * 60)
+
+        # Third call - should refetch
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 2)
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.time.time")
+    def test_use_custom_default_cache_ttl_from_constructor(
+        self, mock_time, mock_get_session
+    ):
+        """Should use custom default cache TTL from constructor."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+        mock_time.return_value = 1000.0
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog, default_cache_ttl_seconds=60)
+
+        # First call
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 1)
+
+        # Advance time past custom TTL
+        mock_time.return_value = 1061.0
+
+        # Second call - should refetch
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 2)
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_url_encode_prompt_names_with_special_characters(self, mock_get_session):
+        """Should URL-encode prompt names with special characters."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        prompts.get("prompt with spaces/and/slashes")
+
+        call_args = mock_get.call_args
+        self.assertEqual(
+            call_args[0][0],
+            "https://us.posthog.com/api/environments/@current/llm_prompts/name/prompt%20with%20spaces%2Fand%2Fslashes/?token=phc_test_key",
+        )
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_work_with_direct_options_no_posthog_client(self, mock_get_session):
+        """Should work with direct options (no PostHog client)."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+
+        prompts = Prompts(
+            personal_api_key="phx_direct_key", project_api_key="phc_direct_key"
+        )
+
+        result = prompts.get("test-prompt")
+
+        self.assertEqual(result, self.mock_prompt_response["prompt"])
+        call_args = mock_get.call_args
+        self.assertEqual(
+            call_args[0][0],
+            "https://us.posthog.com/api/environments/@current/llm_prompts/name/test-prompt/?token=phc_direct_key",
+        )
+        self.assertEqual(
+            call_args[1]["headers"]["Authorization"], "Bearer phx_direct_key"
+        )
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_use_custom_host_from_direct_options(self, mock_get_session):
+        """Should use custom host from direct options."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+
+        prompts = Prompts(
+            personal_api_key="phx_direct_key",
+            project_api_key="phc_direct_key",
+            host="https://eu.posthog.com",
+        )
+
+        prompts.get("test-prompt")
+
+        call_args = mock_get.call_args
+        self.assertEqual(
+            call_args[0][0],
+            "https://eu.posthog.com/api/environments/@current/llm_prompts/name/test-prompt/?token=phc_direct_key",
+        )
+
+    @patch("posthog.ai.prompts._get_session")
+    @patch("posthog.ai.prompts.time.time")
+    def test_use_custom_default_cache_ttl_from_direct_options(
+        self, mock_time, mock_get_session
+    ):
+        """Should use custom default cache TTL from direct options."""
+        mock_get = mock_get_session.return_value.get
+        mock_get.return_value = MockResponse(json_data=self.mock_prompt_response)
+        mock_time.return_value = 1000.0
+
+        prompts = Prompts(
+            personal_api_key="phx_direct_key",
+            project_api_key="phc_direct_key",
+            default_cache_ttl_seconds=60,
+        )
+
+        # First call
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 1)
+
+        # Advance time past custom TTL
+        mock_time.return_value = 1061.0
+
+        # Second call - should refetch
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 2)
+
+
+class TestPromptsCompile(TestPrompts):
+    """Tests for the Prompts.compile() method."""
+
+    def test_replace_a_single_variable(self):
+        """Should replace a single variable."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile("Hello, {{name}}!", {"name": "World"})
+
+        self.assertEqual(result, "Hello, World!")
+
+    def test_replace_multiple_variables(self):
+        """Should replace multiple variables."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile(
+            "Hello, {{name}}! Welcome to {{company}}. Your tier is {{tier}}.",
+            {"name": "John", "company": "Acme Corp", "tier": "premium"},
+        )
+
+        self.assertEqual(
+            result, "Hello, John! Welcome to Acme Corp. Your tier is premium."
+        )
+
+    def test_handle_numbers(self):
+        """Should handle numbers."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile("You have {{count}} items.", {"count": 42})
+
+        self.assertEqual(result, "You have 42 items.")
+
+    def test_handle_booleans(self):
+        """Should handle booleans."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile("Feature enabled: {{enabled}}", {"enabled": True})
+
+        self.assertEqual(result, "Feature enabled: True")
+
+    def test_leave_unmatched_variables_unchanged(self):
+        """Should leave unmatched variables unchanged."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile(
+            "Hello, {{name}}! Your {{unknown}} is ready.", {"name": "World"}
+        )
+
+        self.assertEqual(result, "Hello, World! Your {{unknown}} is ready.")
+
+    def test_handle_prompts_with_no_variables(self):
+        """Should handle prompts with no variables."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile("You are a helpful assistant.", {})
+
+        self.assertEqual(result, "You are a helpful assistant.")
+
+    def test_handle_empty_variables_dict(self):
+        """Should handle empty variables dict."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile("Hello, {{name}}!", {})
+
+        self.assertEqual(result, "Hello, {{name}}!")
+
+    def test_handle_multiple_occurrences_of_same_variable(self):
+        """Should handle multiple occurrences of the same variable."""
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        result = prompts.compile(
+            "Hello, {{name}}! Goodbye, {{name}}!", {"name": "World"}
+        )
+
+        self.assertEqual(result, "Hello, World! Goodbye, World!")
+
+    def test_work_with_direct_options_initialization(self):
+        """Should work with direct options initialization."""
+        prompts = Prompts(
+            personal_api_key="phx_test_key", project_api_key="phc_test_key"
+        )
+
+        result = prompts.compile("Hello, {{name}}!", {"name": "World"})
+
+        self.assertEqual(result, "Hello, World!")
+
+    def test_handle_variables_with_hyphens(self):
+        """Should handle variables with hyphens."""
+        prompts = Prompts(
+            personal_api_key="phx_test_key", project_api_key="phc_test_key"
+        )
+
+        result = prompts.compile("User ID: {{user-id}}", {"user-id": "12345"})
+
+        self.assertEqual(result, "User ID: 12345")
+
+    def test_handle_variables_with_dots(self):
+        """Should handle variables with dots."""
+        prompts = Prompts(
+            personal_api_key="phx_test_key", project_api_key="phc_test_key"
+        )
+
+        result = prompts.compile("Company: {{company.name}}", {"company.name": "Acme"})
+
+        self.assertEqual(result, "Company: Acme")
+
+
+class TestPromptsClearCache(TestPrompts):
+    """Tests for the Prompts.clear_cache() method."""
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_clear_a_specific_prompt_from_cache(self, mock_get_session):
+        """Should clear a specific prompt from cache."""
+        mock_get = mock_get_session.return_value.get
+        other_prompt_response = {**self.mock_prompt_response, "name": "other-prompt"}
+
+        mock_get.side_effect = [
+            MockResponse(json_data=self.mock_prompt_response),
+            MockResponse(json_data=other_prompt_response),
+            MockResponse(json_data=self.mock_prompt_response),
+        ]
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        # Populate cache with two prompts
+        prompts.get("test-prompt")
+        prompts.get("other-prompt")
+        self.assertEqual(mock_get.call_count, 2)
+
+        # Clear only test-prompt
+        prompts.clear_cache("test-prompt")
+
+        # test-prompt should be refetched
+        prompts.get("test-prompt")
+        self.assertEqual(mock_get.call_count, 3)
+
+        # other-prompt should still be cached
+        prompts.get("other-prompt")
+        self.assertEqual(mock_get.call_count, 3)
+
+    @patch("posthog.ai.prompts._get_session")
+    def test_clear_all_prompts_from_cache(self, mock_get_session):
+        """Should clear all prompts from cache when no name is provided."""
+        mock_get = mock_get_session.return_value.get
+        other_prompt_response = {**self.mock_prompt_response, "name": "other-prompt"}
+
+        mock_get.side_effect = [
+            MockResponse(json_data=self.mock_prompt_response),
+            MockResponse(json_data=other_prompt_response),
+            MockResponse(json_data=self.mock_prompt_response),
+            MockResponse(json_data=other_prompt_response),
+        ]
+
+        posthog = self.create_mock_posthog()
+        prompts = Prompts(posthog)
+
+        # Populate cache with two prompts
+        prompts.get("test-prompt")
+        prompts.get("other-prompt")
+        self.assertEqual(mock_get.call_count, 2)
+
+        # Clear all cache
+        prompts.clear_cache()
+
+        # Both prompts should be refetched
+        prompts.get("test-prompt")
+        prompts.get("other-prompt")
+        self.assertEqual(mock_get.call_count, 4)
+
+
+if __name__ == "__main__":
+    unittest.main()

posthog/test/ai/test_sanitization.py

@@ -0,0 +1,541 @@
+import os
+import unittest
+
+from posthog.ai.sanitization import (
+    redact_base64_data_url,
+    sanitize_openai,
+    sanitize_openai_response,
+    sanitize_anthropic,
+    sanitize_gemini,
+    sanitize_langchain,
+    is_base64_data_url,
+    is_raw_base64,
+    REDACTED_IMAGE_PLACEHOLDER,
+)
+
+
+class TestSanitization(unittest.TestCase):
+    def setUp(self):
+        self.sample_base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
+        self.sample_base64_png = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUA..."
+        self.regular_url = "https://example.com/image.jpg"
+        self.raw_base64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUl=="
+
+    def test_is_base64_data_url(self):
+        self.assertTrue(is_base64_data_url(self.sample_base64_image))
+        self.assertTrue(is_base64_data_url(self.sample_base64_png))
+        self.assertFalse(is_base64_data_url(self.regular_url))
+        self.assertFalse(is_base64_data_url("regular text"))
+
+    def test_is_raw_base64(self):
+        self.assertTrue(is_raw_base64(self.raw_base64))
+        self.assertFalse(is_raw_base64("short"))
+        self.assertFalse(is_raw_base64(self.regular_url))
+        self.assertFalse(is_raw_base64("/path/to/file"))
+
+    def test_redact_base64_data_url(self):
+        self.assertEqual(
+            redact_base64_data_url(self.sample_base64_image), REDACTED_IMAGE_PLACEHOLDER
+        )
+        self.assertEqual(
+            redact_base64_data_url(self.sample_base64_png), REDACTED_IMAGE_PLACEHOLDER
+        )
+        self.assertEqual(redact_base64_data_url(self.regular_url), self.regular_url)
+        self.assertEqual(redact_base64_data_url(None), None)
+        self.assertEqual(redact_base64_data_url(123), 123)
+
+    def test_sanitize_openai(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is in this image?"},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": self.sample_base64_image,
+                            "detail": "high",
+                        },
+                    },
+                ],
+            }
+        ]
+
+        result = sanitize_openai(input_data)
+
+        self.assertEqual(result[0]["content"][0]["text"], "What is in this image?")
+        self.assertEqual(
+            result[0]["content"][1]["image_url"]["url"], REDACTED_IMAGE_PLACEHOLDER
+        )
+        self.assertEqual(result[0]["content"][1]["image_url"]["detail"], "high")
+
+    def test_sanitize_openai_input_image(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_image",
+                        "image_url": self.sample_base64_image,
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_openai(input_data)
+
+        self.assertEqual(
+            result[0]["content"][0]["image_url"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+    def test_sanitize_openai_preserves_regular_urls(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": self.regular_url},
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_openai(input_data)
+        self.assertEqual(result[0]["content"][0]["image_url"]["url"], self.regular_url)
+
+    def test_sanitize_openai_response(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_image",
+                        "image_url": self.sample_base64_image,
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_openai_response(input_data)
+        self.assertEqual(
+            result[0]["content"][0]["image_url"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+    def test_sanitize_anthropic(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is in this image?"},
+                    {
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "image/jpeg",
+                            "data": "base64data",
+                        },
+                    },
+                ],
+            }
+        ]
+
+        result = sanitize_anthropic(input_data)
+
+        self.assertEqual(result[0]["content"][0]["text"], "What is in this image?")
+        self.assertEqual(
+            result[0]["content"][1]["source"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+        self.assertEqual(result[0]["content"][1]["source"]["type"], "base64")
+        self.assertEqual(result[0]["content"][1]["source"]["media_type"], "image/jpeg")
+
+    def test_sanitize_gemini(self):
+        input_data = [
+            {
+                "parts": [
+                    {"text": "What is in this image?"},
+                    {
+                        "inline_data": {
+                            "mime_type": "image/jpeg",
+                            "data": "base64data",
+                        }
+                    },
+                ]
+            }
+        ]
+
+        result = sanitize_gemini(input_data)
+
+        self.assertEqual(result[0]["parts"][0]["text"], "What is in this image?")
+        self.assertEqual(
+            result[0]["parts"][1]["inline_data"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+        self.assertEqual(
+            result[0]["parts"][1]["inline_data"]["mime_type"], "image/jpeg"
+        )
+
+    def test_sanitize_langchain_openai_style(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": self.sample_base64_image},
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_langchain(input_data)
+        self.assertEqual(
+            result[0]["content"][0]["image_url"]["url"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+    def test_sanitize_langchain_anthropic_style(self):
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "source": {"data": "base64data"},
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_langchain(input_data)
+        self.assertEqual(
+            result[0]["content"][0]["source"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+    def test_sanitize_with_data_url_format(self):
+        # Test that data URLs are properly detected and redacted across providers
+        data_url = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD"
+
+        # OpenAI format
+        openai_data = [
+            {
+                "role": "user",
+                "content": [{"type": "image_url", "image_url": {"url": data_url}}],
+            }
+        ]
+        result = sanitize_openai(openai_data)
+        self.assertEqual(
+            result[0]["content"][0]["image_url"]["url"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+        # Anthropic format
+        anthropic_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "image/jpeg",
+                            "data": data_url,
+                        },
+                    }
+                ],
+            }
+        ]
+        result = sanitize_anthropic(anthropic_data)
+        self.assertEqual(
+            result[0]["content"][0]["source"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+        # LangChain format
+        langchain_data = [
+            {"role": "user", "content": [{"type": "image", "data": data_url}]}
+        ]
+        result = sanitize_langchain(langchain_data)
+        self.assertEqual(result[0]["content"][0]["data"], REDACTED_IMAGE_PLACEHOLDER)
+
+    def test_sanitize_with_raw_base64(self):
+        # Test that raw base64 strings (without data URL prefix) are detected
+        raw_base64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUl=="
+
+        # Test with Anthropic format
+        anthropic_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "image/png",
+                            "data": raw_base64,
+                        },
+                    }
+                ],
+            }
+        ]
+        result = sanitize_anthropic(anthropic_data)
+        self.assertEqual(
+            result[0]["content"][0]["source"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+        # Test with Gemini format
+        gemini_data = [
+            {"parts": [{"inline_data": {"mime_type": "image/png", "data": raw_base64}}]}
+        ]
+        result = sanitize_gemini(gemini_data)
+        self.assertEqual(
+            result[0]["parts"][0]["inline_data"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+    def test_sanitize_preserves_regular_content(self):
+        # Ensure non-base64 content is preserved across all providers
+        regular_url = "https://example.com/image.jpg"
+        text_content = "What do you see?"
+
+        # OpenAI
+        openai_data = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": text_content},
+                    {"type": "image_url", "image_url": {"url": regular_url}},
+                ],
+            }
+        ]
+        result = sanitize_openai(openai_data)
+        self.assertEqual(result[0]["content"][0]["text"], text_content)
+        self.assertEqual(result[0]["content"][1]["image_url"]["url"], regular_url)
+
+        # Anthropic
+        anthropic_data = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": text_content},
+                    {"type": "image", "source": {"type": "url", "url": regular_url}},
+                ],
+            }
+        ]
+        result = sanitize_anthropic(anthropic_data)
+        self.assertEqual(result[0]["content"][0]["text"], text_content)
+        # URL-based images should remain unchanged
+        self.assertEqual(result[0]["content"][1]["source"]["url"], regular_url)
+
+    def test_sanitize_handles_non_dict_content(self):
+        input_data = [{"role": "user", "content": "Just text"}]
+
+        result = sanitize_openai(input_data)
+        self.assertEqual(result, input_data)
+
+    def test_sanitize_handles_none_input(self):
+        self.assertIsNone(sanitize_openai(None))
+        self.assertIsNone(sanitize_anthropic(None))
+        self.assertIsNone(sanitize_gemini(None))
+        self.assertIsNone(sanitize_langchain(None))
+
+    def test_sanitize_handles_single_message(self):
+        input_data = {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": self.sample_base64_image},
+                }
+            ],
+        }
+
+        result = sanitize_openai(input_data)
+        self.assertEqual(
+            result["content"][0]["image_url"]["url"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+
+class TestAIMultipartRequest(unittest.TestCase):
+    """Test that _INTERNAL_LLMA_MULTIMODAL environment variable controls sanitization."""
+
+    def tearDown(self):
+        # Clean up environment variable after each test
+        if "_INTERNAL_LLMA_MULTIMODAL" in os.environ:
+            del os.environ["_INTERNAL_LLMA_MULTIMODAL"]
+
+    def test_multimodal_disabled_redacts_images(self):
+        """When _INTERNAL_LLMA_MULTIMODAL is not set, images should be redacted."""
+        if "_INTERNAL_LLMA_MULTIMODAL" in os.environ:
+            del os.environ["_INTERNAL_LLMA_MULTIMODAL"]
+
+        base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
+        result = redact_base64_data_url(base64_image)
+        self.assertEqual(result, REDACTED_IMAGE_PLACEHOLDER)
+
+    def test_multimodal_enabled_preserves_images(self):
+        """When _INTERNAL_LLMA_MULTIMODAL is true, images should be preserved."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "true"
+
+        base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
+        result = redact_base64_data_url(base64_image)
+        self.assertEqual(result, base64_image)
+
+    def test_multimodal_enabled_with_1(self):
+        """_INTERNAL_LLMA_MULTIMODAL=1 should enable multimodal."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "1"
+
+        base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
+        result = redact_base64_data_url(base64_image)
+        self.assertEqual(result, base64_image)
+
+    def test_multimodal_enabled_with_yes(self):
+        """_INTERNAL_LLMA_MULTIMODAL=yes should enable multimodal."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "yes"
+
+        base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
+        result = redact_base64_data_url(base64_image)
+        self.assertEqual(result, base64_image)
+
+    def test_multimodal_false_redacts_images(self):
+        """_INTERNAL_LLMA_MULTIMODAL=false should still redact."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "false"
+
+        base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
+        result = redact_base64_data_url(base64_image)
+        self.assertEqual(result, REDACTED_IMAGE_PLACEHOLDER)
+
+    def test_anthropic_multimodal_enabled(self):
+        """Anthropic images should be preserved when _INTERNAL_LLMA_MULTIMODAL is enabled."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "true"
+
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "image/jpeg",
+                            "data": "base64data",
+                        },
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_anthropic(input_data)
+        self.assertEqual(result[0]["content"][0]["source"]["data"], "base64data")
+
+    def test_gemini_multimodal_enabled(self):
+        """Gemini images should be preserved when _INTERNAL_LLMA_MULTIMODAL is enabled."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "true"
+
+        input_data = [
+            {
+                "parts": [
+                    {"inline_data": {"mime_type": "image/jpeg", "data": "base64data"}}
+                ]
+            }
+        ]
+
+        result = sanitize_gemini(input_data)
+        self.assertEqual(result[0]["parts"][0]["inline_data"]["data"], "base64data")
+
+    def test_langchain_anthropic_style_multimodal_enabled(self):
+        """LangChain Anthropic-style images should be preserved when _INTERNAL_LLMA_MULTIMODAL is enabled."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "true"
+
+        input_data = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "source": {"data": "base64data"},
+                    }
+                ],
+            }
+        ]
+
+        result = sanitize_langchain(input_data)
+        self.assertEqual(result[0]["content"][0]["source"]["data"], "base64data")
+
+    def test_openai_audio_redacted_by_default(self):
+        """OpenAI audio should be redacted when _INTERNAL_LLMA_MULTIMODAL is not set."""
+        if "_INTERNAL_LLMA_MULTIMODAL" in os.environ:
+            del os.environ["_INTERNAL_LLMA_MULTIMODAL"]
+
+        input_data = [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "audio", "data": "base64audiodata", "id": "audio_123"}
+                ],
+            }
+        ]
+
+        result = sanitize_openai(input_data)
+        self.assertEqual(result[0]["content"][0]["data"], REDACTED_IMAGE_PLACEHOLDER)
+        self.assertEqual(result[0]["content"][0]["id"], "audio_123")
+
+    def test_openai_audio_preserved_with_flag(self):
+        """OpenAI audio should be preserved when _INTERNAL_LLMA_MULTIMODAL is enabled."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "true"
+
+        input_data = [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "audio", "data": "base64audiodata", "id": "audio_123"}
+                ],
+            }
+        ]
+
+        result = sanitize_openai(input_data)
+        self.assertEqual(result[0]["content"][0]["data"], "base64audiodata")
+
+    def test_gemini_audio_redacted_by_default(self):
+        """Gemini audio should be redacted when _INTERNAL_LLMA_MULTIMODAL is not set."""
+        if "_INTERNAL_LLMA_MULTIMODAL" in os.environ:
+            del os.environ["_INTERNAL_LLMA_MULTIMODAL"]
+
+        input_data = [
+            {
+                "parts": [
+                    {
+                        "inline_data": {
+                            "mime_type": "audio/L16;codec=pcm;rate=24000",
+                            "data": "base64audiodata",
+                        }
+                    }
+                ]
+            }
+        ]
+
+        result = sanitize_gemini(input_data)
+        self.assertEqual(
+            result[0]["parts"][0]["inline_data"]["data"], REDACTED_IMAGE_PLACEHOLDER
+        )
+
+    def test_gemini_audio_preserved_with_flag(self):
+        """Gemini audio should be preserved when _INTERNAL_LLMA_MULTIMODAL is enabled."""
+        os.environ["_INTERNAL_LLMA_MULTIMODAL"] = "true"
+
+        input_data = [
+            {
+                "parts": [
+                    {
+                        "inline_data": {
+                            "mime_type": "audio/L16;codec=pcm;rate=24000",
+                            "data": "base64audiodata",
+                        }
+                    }
+                ]
+            }
+        ]
+
+        result = sanitize_gemini(input_data)
+        self.assertEqual(
+            result[0]["parts"][0]["inline_data"]["data"], "base64audiodata"
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

posthog/test/ai/test_system_prompts.py

@@ -0,0 +1,363 @@
+"""
+Tests for system prompt capture across all LLM providers.
+
+This test suite ensures that system prompts are correctly captured in analytics
+regardless of how they're passed to the providers:
+- As first message in messages/contents array (standard format)
+- As separate system parameter (Anthropic, OpenAI)
+- As instructions parameter (OpenAI Responses API)
+- As system_instruction parameter (Gemini)
+"""
+
+import time
+import unittest
+from unittest.mock import MagicMock, patch
+
+from posthog.client import Client
+from posthog.test.test_utils import FAKE_TEST_API_KEY
+
+
+class TestSystemPromptCapture(unittest.TestCase):
+    """Test system prompt capture for all providers."""
+
+    def setUp(self):
+        super().setUp()
+        self.test_system_prompt = "You are a helpful AI assistant."
+        self.test_user_message = "Hello, how are you?"
+        self.test_response = "I'm doing well, thank you!"
+
+        # Create mock PostHog client
+        self.client = Client(FAKE_TEST_API_KEY)
+        self.client._enqueue = MagicMock()
+        self.client.privacy_mode = False
+
+    def _assert_system_prompt_captured(self, captured_input):
+        """Helper to assert system prompt is correctly captured."""
+        self.assertEqual(
+            len(captured_input), 2, "Should have 2 messages (system + user)"
+        )
+        self.assertEqual(
+            captured_input[0]["role"], "system", "First message should be system"
+        )
+        self.assertEqual(
+            captured_input[0]["content"],
+            self.test_system_prompt,
+            "System content should match",
+        )
+        self.assertEqual(
+            captured_input[1]["role"], "user", "Second message should be user"
+        )
+        self.assertEqual(
+            captured_input[1]["content"],
+            self.test_user_message,
+            "User content should match",
+        )
+
+    # OpenAI Tests
+    def test_openai_messages_array_system_prompt(self):
+        """Test OpenAI with system prompt in messages array."""
+        try:
+            from openai.types.chat import ChatCompletion, ChatCompletionMessage
+            from openai.types.chat.chat_completion import Choice
+            from openai.types.completion_usage import CompletionUsage
+
+            from posthog.ai.openai import OpenAI
+        except ImportError:
+            self.skipTest("OpenAI package not available")
+
+        mock_response = ChatCompletion(
+            id="test",
+            model="gpt-4",
+            object="chat.completion",
+            created=int(time.time()),
+            choices=[
+                Choice(
+                    finish_reason="stop",
+                    index=0,
+                    message=ChatCompletionMessage(
+                        content=self.test_response, role="assistant"
+                    ),
+                )
+            ],
+            usage=CompletionUsage(
+                completion_tokens=10, prompt_tokens=20, total_tokens=30
+            ),
+        )
+
+        with patch(
+            "openai.resources.chat.completions.Completions.create",
+            return_value=mock_response,
+        ):
+            client = OpenAI(posthog_client=self.client, api_key="test")
+
+            messages = [
+                {"role": "system", "content": self.test_system_prompt},
+                {"role": "user", "content": self.test_user_message},
+            ]
+
+            client.chat.completions.create(
+                model="gpt-4", messages=messages, posthog_distinct_id="test-user"
+            )
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    def test_openai_separate_system_parameter(self):
+        """Test OpenAI with system prompt as separate parameter."""
+        try:
+            from openai.types.chat import ChatCompletion, ChatCompletionMessage
+            from openai.types.chat.chat_completion import Choice
+            from openai.types.completion_usage import CompletionUsage
+
+            from posthog.ai.openai import OpenAI
+        except ImportError:
+            self.skipTest("OpenAI package not available")
+
+        mock_response = ChatCompletion(
+            id="test",
+            model="gpt-4",
+            object="chat.completion",
+            created=int(time.time()),
+            choices=[
+                Choice(
+                    finish_reason="stop",
+                    index=0,
+                    message=ChatCompletionMessage(
+                        content=self.test_response, role="assistant"
+                    ),
+                )
+            ],
+            usage=CompletionUsage(
+                completion_tokens=10, prompt_tokens=20, total_tokens=30
+            ),
+        )
+
+        with patch(
+            "openai.resources.chat.completions.Completions.create",
+            return_value=mock_response,
+        ):
+            client = OpenAI(posthog_client=self.client, api_key="test")
+
+            messages = [{"role": "user", "content": self.test_user_message}]
+
+            client.chat.completions.create(
+                model="gpt-4",
+                messages=messages,
+                system=self.test_system_prompt,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    def test_openai_streaming_system_parameter(self):
+        """Test OpenAI streaming with system parameter."""
+        try:
+            from openai.types.chat.chat_completion_chunk import (
+                ChatCompletionChunk,
+                ChoiceDelta,
+            )
+            from openai.types.chat.chat_completion_chunk import Choice as ChoiceChunk
+            from openai.types.completion_usage import CompletionUsage
+
+            from posthog.ai.openai import OpenAI
+        except ImportError:
+            self.skipTest("OpenAI package not available")
+
+        chunk1 = ChatCompletionChunk(
+            id="test",
+            model="gpt-4",
+            object="chat.completion.chunk",
+            created=int(time.time()),
+            choices=[
+                ChoiceChunk(
+                    finish_reason=None,
+                    index=0,
+                    delta=ChoiceDelta(content="Hello", role="assistant"),
+                )
+            ],
+        )
+
+        chunk2 = ChatCompletionChunk(
+            id="test",
+            model="gpt-4",
+            object="chat.completion.chunk",
+            created=int(time.time()),
+            choices=[
+                ChoiceChunk(
+                    finish_reason="stop",
+                    index=0,
+                    delta=ChoiceDelta(content=" there!", role=None),
+                )
+            ],
+            usage=CompletionUsage(
+                completion_tokens=10, prompt_tokens=20, total_tokens=30
+            ),
+        )
+
+        with patch(
+            "openai.resources.chat.completions.Completions.create",
+            return_value=[chunk1, chunk2],
+        ):
+            client = OpenAI(posthog_client=self.client, api_key="test")
+
+            messages = [{"role": "user", "content": self.test_user_message}]
+
+            response_generator = client.chat.completions.create(
+                model="gpt-4",
+                messages=messages,
+                system=self.test_system_prompt,
+                stream=True,
+                posthog_distinct_id="test-user",
+            )
+
+            list(response_generator)  # Consume generator
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    # Anthropic Tests
+    def test_anthropic_messages_array_system_prompt(self):
+        """Test Anthropic with system prompt in messages array."""
+        try:
+            from posthog.ai.anthropic import Anthropic
+        except ImportError:
+            self.skipTest("Anthropic package not available")
+
+        with patch("anthropic.resources.messages.Messages.create") as mock_create:
+            mock_response = MagicMock()
+            mock_response.usage.input_tokens = 20
+            mock_response.usage.output_tokens = 10
+            mock_response.usage.cache_read_input_tokens = None
+            mock_response.usage.cache_creation_input_tokens = None
+            mock_create.return_value = mock_response
+
+            client = Anthropic(posthog_client=self.client, api_key="test")
+
+            messages = [
+                {"role": "system", "content": self.test_system_prompt},
+                {"role": "user", "content": self.test_user_message},
+            ]
+
+            client.messages.create(
+                model="claude-3-5-sonnet-20241022",
+                messages=messages,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    def test_anthropic_separate_system_parameter(self):
+        """Test Anthropic with system prompt as separate parameter."""
+        try:
+            from posthog.ai.anthropic import Anthropic
+        except ImportError:
+            self.skipTest("Anthropic package not available")
+
+        with patch("anthropic.resources.messages.Messages.create") as mock_create:
+            mock_response = MagicMock()
+            mock_response.usage.input_tokens = 20
+            mock_response.usage.output_tokens = 10
+            mock_response.usage.cache_read_input_tokens = None
+            mock_response.usage.cache_creation_input_tokens = None
+            mock_create.return_value = mock_response
+
+            client = Anthropic(posthog_client=self.client, api_key="test")
+
+            messages = [{"role": "user", "content": self.test_user_message}]
+
+            client.messages.create(
+                model="claude-3-5-sonnet-20241022",
+                messages=messages,
+                system=self.test_system_prompt,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    # Gemini Tests
+    def test_gemini_contents_array_system_prompt(self):
+        """Test Gemini with system prompt in contents array."""
+        try:
+            from posthog.ai.gemini import Client
+        except ImportError:
+            self.skipTest("Gemini package not available")
+
+        with patch("google.genai.Client") as mock_genai_class:
+            mock_response = MagicMock()
+            mock_response.candidates = [MagicMock()]
+            mock_response.candidates[0].content.parts = [MagicMock()]
+            mock_response.candidates[0].content.parts[0].text = self.test_response
+            mock_response.usage_metadata.prompt_token_count = 20
+            mock_response.usage_metadata.candidates_token_count = 10
+            mock_response.usage_metadata.cached_content_token_count = None
+            mock_response.usage_metadata.thoughts_token_count = None
+
+            mock_client_instance = MagicMock()
+            mock_models_instance = MagicMock()
+            mock_models_instance.generate_content.return_value = mock_response
+            mock_client_instance.models = mock_models_instance
+            mock_genai_class.return_value = mock_client_instance
+
+            client = Client(posthog_client=self.client, api_key="test")
+
+            contents = [
+                {"role": "system", "content": self.test_system_prompt},
+                {"role": "user", "content": self.test_user_message},
+            ]
+
+            client.models.generate_content(
+                model="gemini-2.0-flash",
+                contents=contents,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    def test_gemini_system_instruction_parameter(self):
+        """Test Gemini with system_instruction in config parameter."""
+        try:
+            from posthog.ai.gemini import Client
+        except ImportError:
+            self.skipTest("Gemini package not available")
+
+        with patch("google.genai.Client") as mock_genai_class:
+            mock_response = MagicMock()
+            mock_response.candidates = [MagicMock()]
+            mock_response.candidates[0].content.parts = [MagicMock()]
+            mock_response.candidates[0].content.parts[0].text = self.test_response
+            mock_response.usage_metadata.prompt_token_count = 20
+            mock_response.usage_metadata.candidates_token_count = 10
+            mock_response.usage_metadata.cached_content_token_count = None
+            mock_response.usage_metadata.thoughts_token_count = None
+
+            mock_client_instance = MagicMock()
+            mock_models_instance = MagicMock()
+            mock_models_instance.generate_content.return_value = mock_response
+            mock_client_instance.models = mock_models_instance
+            mock_genai_class.return_value = mock_client_instance
+
+            client = Client(posthog_client=self.client, api_key="test")
+
+            contents = [{"role": "user", "content": self.test_user_message}]
+            config = {"system_instruction": self.test_system_prompt}
+
+            client.models.generate_content(
+                model="gemini-2.0-flash",
+                contents=contents,
+                config=config,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client._enqueue.call_args_list), 1)
+            properties = self.client._enqueue.call_args_list[0][0][0]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])

posthog/test/client.py

@@ -1,314 +0,0 @@
-from datetime import date, datetime
-import unittest
-import six
-import mock
-import time
-
-from posthog.version import VERSION
-from posthog.client import Client
-
-
-class TestClient(unittest.TestCase):
-
-    def fail(self, e, batch):
-        """Mark the failure handler"""
-        self.failed = True
-
-    def setUp(self):
-        self.failed = False
-        self.client = Client('testsecret', on_error=self.fail)
-
-    def test_requires_api_key(self):
-        self.assertRaises(AssertionError, Client)
-
-    def test_empty_flush(self):
-        self.client.flush()
-
-    def test_basic_track(self):
-        client = self.client
-        success, msg = client.track('distinct_id', 'python test event')
-        client.flush()
-        self.assertTrue(success)
-        self.assertFalse(self.failed)
-
-        self.assertEqual(msg['event'], 'python test event')
-        self.assertTrue(isinstance(msg['timestamp'], str))
-        self.assertTrue(isinstance(msg['messageId'], str))
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['properties'], {})
-        self.assertEqual(msg['type'], 'track')
-
-    def test_stringifies_distinct_id(self):
-        # A large number that loses precision in node:
-        # node -e "console.log(157963456373623802 + 1)" > 157963456373623800
-        client = self.client
-        success, msg = client.track(
-            distinct_id=157963456373623802, event='python test event')
-        client.flush()
-        self.assertTrue(success)
-        self.assertFalse(self.failed)
-
-        self.assertEqual(msg['distinct_id'], '157963456373623802')
-
-    def test_advanced_track(self):
-        client = self.client
-        success, msg = client.track(
-            'distinct_id', 'python test event', {'property': 'value'},
-            {'ip': '192.168.0.1'}, datetime(2014, 9, 3),
-            'messageId')
-
-        self.assertTrue(success)
-
-        self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00')
-        self.assertEqual(msg['properties'], {'property': 'value'})
-        self.assertEqual(msg['context']['ip'], '192.168.0.1')
-        self.assertEqual(msg['event'], 'python test event')
-        self.assertEqual(msg['properties']['$lib'], 'posthog-python')
-        self.assertEqual(msg['properties']['$lib_version'], VERSION)
-        self.assertEqual(msg['messageId'], 'messageId')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'track')
-
-    def test_basic_identify(self):
-        client = self.client
-        success, msg = client.identify('distinct_id', {'trait': 'value'})
-        client.flush()
-        self.assertTrue(success)
-        self.assertFalse(self.failed)
-
-        self.assertEqual(msg['traits'], {'trait': 'value'})
-        self.assertTrue(isinstance(msg['timestamp'], str))
-        self.assertTrue(isinstance(msg['messageId'], str))
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'identify')
-
-    def test_advanced_identify(self):
-        client = self.client
-        success, msg = client.identify(
-            'distinct_id', {'trait': 'value'}, {'ip': '192.168.0.1'},
-            datetime(2014, 9, 3), 'messageId')
-
-        self.assertTrue(success)
-
-        self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00')
-        self.assertEqual(msg['context']['ip'], '192.168.0.1')
-        self.assertEqual(msg['traits'], {'trait': 'value'})
-        self.assertEqual(msg['context']['library'], {
-            'name': 'analytics-python',
-            'version': VERSION
-        })
-        self.assertTrue(isinstance(msg['timestamp'], str))
-        self.assertEqual(msg['messageId'], 'messageId')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'identify')
-
-    def test_basic_group(self):
-        client = self.client
-        success, msg = client.group('distinct_id', 'groupId')
-        client.flush()
-        self.assertTrue(success)
-        self.assertFalse(self.failed)
-
-        self.assertEqual(msg['groupId'], 'groupId')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'group')
-
-    def test_advanced_group(self):
-        client = self.client
-        success, msg = client.group(
-            'distinct_id', 'groupId', {'trait': 'value'}, {'ip': '192.168.0.1'},
-            datetime(2014, 9, 3), 'messageId')
-
-        self.assertTrue(success)
-
-        self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00')
-        self.assertEqual(msg['context']['ip'], '192.168.0.1')
-        self.assertEqual(msg['traits'], {'trait': 'value'})
-        self.assertEqual(msg['context']['library'], {
-            'name': 'analytics-python',
-            'version': VERSION
-        })
-        self.assertTrue(isinstance(msg['timestamp'], str))
-        self.assertEqual(msg['messageId'], 'messageId')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'group')
-
-    def test_basic_alias(self):
-        client = self.client
-        success, msg = client.alias('previousId', 'distinct_id')
-        client.flush()
-        self.assertTrue(success)
-        self.assertFalse(self.failed)
-        self.assertEqual(msg['previousId'], 'previousId')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-
-    def test_basic_page(self):
-        client = self.client
-        success, msg = client.page('distinct_id', name='name')
-        self.assertFalse(self.failed)
-        client.flush()
-        self.assertTrue(success)
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'page')
-        self.assertEqual(msg['name'], 'name')
-
-    def test_advanced_page(self):
-        client = self.client
-        success, msg = client.page(
-            'distinct_id', 'category', 'name', {'property': 'value'},
-            {'ip': '192.168.0.1'}, datetime(2014, 9, 3), 'messageId')
-
-        self.assertTrue(success)
-
-        self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00')
-        self.assertEqual(msg['context']['ip'], '192.168.0.1')
-        self.assertEqual(msg['properties'], {'property': 'value'})
-        self.assertEqual(msg['context']['library'], {
-            'name': 'analytics-python',
-            'version': VERSION
-        })
-        self.assertEqual(msg['category'], 'category')
-        self.assertTrue(isinstance(msg['timestamp'], str))
-        self.assertEqual(msg['messageId'], 'messageId')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'page')
-        self.assertEqual(msg['name'], 'name')
-
-    def test_basic_screen(self):
-        client = self.client
-        success, msg = client.screen('distinct_id', name='name')
-        client.flush()
-        self.assertTrue(success)
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'screen')
-        self.assertEqual(msg['name'], 'name')
-
-    def test_advanced_screen(self):
-        client = self.client
-        success, msg = client.screen(
-            'distinct_id', 'category', 'name', {'property': 'value'},
-            {'ip': '192.168.0.1'}, datetime(2014, 9, 3), 'messageId')
-
-        self.assertTrue(success)
-
-        self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00')
-        self.assertEqual(msg['context']['ip'], '192.168.0.1')
-        self.assertEqual(msg['properties'], {'property': 'value'})
-        self.assertEqual(msg['context']['library'], {
-            'name': 'analytics-python',
-            'version': VERSION
-        })
-        self.assertTrue(isinstance(msg['timestamp'], str))
-        self.assertEqual(msg['messageId'], 'messageId')
-        self.assertEqual(msg['category'], 'category')
-        self.assertEqual(msg['distinct_id'], 'distinct_id')
-        self.assertEqual(msg['type'], 'screen')
-        self.assertEqual(msg['name'], 'name')
-
-    def test_flush(self):
-        client = self.client
-        # set up the consumer with more requests than a single batch will allow
-        for i in range(1000):
-            success, msg = client.identify('distinct_id', {'trait': 'value'})
-        # We can't reliably assert that the queue is non-empty here; that's
-        # a race condition. We do our best to load it up though.
-        client.flush()
-        # Make sure that the client queue is empty after flushing
-        self.assertTrue(client.queue.empty())
-
-    def test_shutdown(self):
-        client = self.client
-        # set up the consumer with more requests than a single batch will allow
-        for i in range(1000):
-            success, msg = client.identify('distinct_id', {'trait': 'value'})
-        client.shutdown()
-        # we expect two things after shutdown:
-        # 1. client queue is empty
-        # 2. consumer thread has stopped
-        self.assertTrue(client.queue.empty())
-        for consumer in client.consumers:
-            self.assertFalse(consumer.is_alive())
-
-    def test_synchronous(self):
-        client = Client('testsecret', sync_mode=True)
-
-        success, message = client.identify('distinct_id')
-        self.assertFalse(client.consumers)
-        self.assertTrue(client.queue.empty())
-        self.assertTrue(success)
-
-    def test_overflow(self):
-        client = Client('testsecret', max_queue_size=1)
-        # Ensure consumer thread is no longer uploading
-        client.join()
-
-        for i in range(10):
-            client.identify('distinct_id')
-
-        success, msg = client.identify('distinct_id')
-        # Make sure we are informed that the queue is at capacity
-        self.assertFalse(success)
-
-    def test_success_on_invalid_api_key(self):
-        client = Client('bad_key', on_error=self.fail)
-        client.track('distinct_id', 'event')
-        client.flush()
-        self.assertFalse(self.failed)
-
-    def test_unicode(self):
-        Client(six.u('unicode_key'))
-
-    def test_numeric_distinct_id(self):
-        self.client.track(1234, 'python event')
-        self.client.flush()
-        self.assertFalse(self.failed)
-
-    def test_debug(self):
-        Client('bad_key', debug=True)
-
-    def test_identify_with_date_object(self):
-        client = self.client
-        success, msg = client.identify(
-            'distinct_id',
-            {
-                'birthdate': date(1981, 2, 2),
-            },
-        )
-        client.flush()
-        self.assertTrue(success)
-        self.assertFalse(self.failed)
-
-        self.assertEqual(msg['traits'], {'birthdate': date(1981, 2, 2)})
-
-    def test_gzip(self):
-        client = Client('testsecret', on_error=self.fail, gzip=True)
-        for _ in range(10):
-            client.identify('distinct_id', {'trait': 'value'})
-        client.flush()
-        self.assertFalse(self.failed)
-
-    def test_user_defined_flush_at(self):
-        client = Client('testsecret', on_error=self.fail,
-                        flush_at=10, flush_interval=3)
-
-        def mock_post_fn(*args, **kwargs):
-            self.assertEquals(len(kwargs['batch']), 10)
-
-        # the post function should be called 2 times, with a batch size of 10
-        # each time.
-        with mock.patch('analytics.consumer.post', side_effect=mock_post_fn) \
-                as mock_post:
-            for _ in range(20):
-                client.identify('distinct_id', {'trait': 'value'})
-            time.sleep(1)
-            self.assertEquals(mock_post.call_count, 2)
-
-    def test_user_defined_timeout(self):
-        client = Client('testsecret', timeout=10)
-        for consumer in client.consumers:
-            self.assertEquals(consumer.timeout, 10)
-
-    def test_default_timeout_15(self):
-        client = Client('testsecret')
-        for consumer in client.consumers:
-            self.assertEquals(consumer.timeout, 15)

posthog/test/consumer.py

@@ -1,198 +0,0 @@
-import unittest
-import mock
-import time
-import json
-
-try:
-    from queue import Queue
-except ImportError:
-    from Queue import Queue
-
-from posthog.consumer import Consumer, MAX_MSG_SIZE
-from posthog.request import APIError
-
-
-class TestConsumer(unittest.TestCase):
-
-    def test_next(self):
-        q = Queue()
-        consumer = Consumer(q, '')
-        q.put(1)
-        next = consumer.next()
-        self.assertEqual(next, [1])
-
-    def test_next_limit(self):
-        q = Queue()
-        flush_at = 50
-        consumer = Consumer(q, '', flush_at)
-        for i in range(10000):
-            q.put(i)
-        next = consumer.next()
-        self.assertEqual(next, list(range(flush_at)))
-
-    def test_dropping_oversize_msg(self):
-        q = Queue()
-        consumer = Consumer(q, '')
-        oversize_msg = {'m': 'x' * MAX_MSG_SIZE}
-        q.put(oversize_msg)
-        next = consumer.next()
-        self.assertEqual(next, [])
-        self.assertTrue(q.empty())
-
-    def test_upload(self):
-        q = Queue()
-        consumer = Consumer(q, 'testsecret')
-        track = {
-            'type': 'track',
-            'event': 'python event',
-            'distinct_id': 'distinct_id'
-        }
-        q.put(track)
-        success = consumer.upload()
-        self.assertTrue(success)
-
-    def test_flush_interval(self):
-        # Put _n_ items in the queue, pausing a little bit more than
-        # _flush_interval_ after each one.
-        # The consumer should upload _n_ times.
-        q = Queue()
-        flush_interval = 0.3
-        consumer = Consumer(q, 'testsecret', flush_at=10,
-                            flush_interval=flush_interval)
-        with mock.patch('analytics.consumer.post') as mock_post:
-            consumer.start()
-            for i in range(0, 3):
-                track = {
-                    'type': 'track',
-                    'event': 'python event %d' % i,
-                    'distinct_id': 'distinct_id'
-                }
-                q.put(track)
-                time.sleep(flush_interval * 1.1)
-            self.assertEqual(mock_post.call_count, 3)
-
-    def test_multiple_uploads_per_interval(self):
-        # Put _flush_at*2_ items in the queue at once, then pause for
-        # _flush_interval_. The consumer should upload 2 times.
-        q = Queue()
-        flush_interval = 0.5
-        flush_at = 10
-        consumer = Consumer(q, 'testsecret', flush_at=flush_at,
-                            flush_interval=flush_interval)
-        with mock.patch('analytics.consumer.post') as mock_post:
-            consumer.start()
-            for i in range(0, flush_at * 2):
-                track = {
-                    'type': 'track',
-                    'event': 'python event %d' % i,
-                    'distinct_id': 'distinct_id'
-                }
-                q.put(track)
-            time.sleep(flush_interval * 1.1)
-            self.assertEqual(mock_post.call_count, 2)
-
-    def test_request(self):
-        consumer = Consumer(None, 'testsecret')
-        track = {
-            'type': 'track',
-            'event': 'python event',
-            'distinct_id': 'distinct_id'
-        }
-        consumer.request([track])
-
-    def _test_request_retry(self, consumer,
-                            expected_exception, exception_count):
-
-        def mock_post(*args, **kwargs):
-            mock_post.call_count += 1
-            if mock_post.call_count <= exception_count:
-                raise expected_exception
-        mock_post.call_count = 0
-
-        with mock.patch('analytics.consumer.post',
-                        mock.Mock(side_effect=mock_post)):
-            track = {
-                'type': 'track',
-                'event': 'python event',
-                'distinct_id': 'distinct_id'
-            }
-            # request() should succeed if the number of exceptions raised is
-            # less than the retries paramater.
-            if exception_count <= consumer.retries:
-                consumer.request([track])
-            else:
-                # if exceptions are raised more times than the retries
-                # parameter, we expect the exception to be returned to
-                # the caller.
-                try:
-                    consumer.request([track])
-                except type(expected_exception) as exc:
-                    self.assertEqual(exc, expected_exception)
-                else:
-                    self.fail(
-                        "request() should raise an exception if still failing "
-                        "after %d retries" % consumer.retries)
-
-    def test_request_retry(self):
-        # we should retry on general errors
-        consumer = Consumer(None, 'testsecret')
-        self._test_request_retry(consumer, Exception('generic exception'), 2)
-
-        # we should retry on server errors
-        consumer = Consumer(None, 'testsecret')
-        self._test_request_retry(consumer, APIError(
-            500, 'code', 'Internal Server Error'), 2)
-
-        # we should retry on HTTP 429 errors
-        consumer = Consumer(None, 'testsecret')
-        self._test_request_retry(consumer, APIError(
-            429, 'code', 'Too Many Requests'), 2)
-
-        # we should NOT retry on other client errors
-        consumer = Consumer(None, 'testsecret')
-        api_error = APIError(400, 'code', 'Client Errors')
-        try:
-            self._test_request_retry(consumer, api_error, 1)
-        except APIError:
-            pass
-        else:
-            self.fail('request() should not retry on client errors')
-
-        # test for number of exceptions raise > retries value
-        consumer = Consumer(None, 'testsecret', retries=3)
-        self._test_request_retry(consumer, APIError(
-            500, 'code', 'Internal Server Error'), 3)
-
-    def test_pause(self):
-        consumer = Consumer(None, 'testsecret')
-        consumer.pause()
-        self.assertFalse(consumer.running)
-
-    def test_max_batch_size(self):
-        q = Queue()
-        consumer = Consumer(
-            q, 'testsecret', flush_at=100000, flush_interval=3)
-        track = {
-            'type': 'track',
-            'event': 'python event',
-            'distinct_id': 'distinct_id'
-        }
-        msg_size = len(json.dumps(track).encode())
-        # number of messages in a maximum-size batch
-        n_msgs = int(475000 / msg_size)
-
-        def mock_post_fn(_, data, **kwargs):
-            res = mock.Mock()
-            res.status_code = 200
-            self.assertTrue(len(data.encode()) < 500000,
-                            'batch size (%d) exceeds 500KB limit'
-                            % len(data.encode()))
-            return res
-
-        with mock.patch('analytics.request._session.post',
-                        side_effect=mock_post_fn) as mock_post:
-            consumer.start()
-            for _ in range(0, n_msgs + 2):
-                q.put(track)
-            q.join()
-            self.assertEquals(mock_post.call_count, 2)

posthog/test/integrations/test_middleware.py

@@ -0,0 +1,773 @@
+from posthog.contexts import (
+    new_context,
+    get_context_session_id,
+    get_context_distinct_id,
+)
+import unittest
+from unittest.mock import Mock, patch
+import asyncio
+
+# Configure Django settings before importing middleware
+import django
+from django.conf import settings
+
+if not settings.configured:
+    settings.configure(
+        DEBUG=True,
+        SECRET_KEY="test-secret-key",
+        INSTALLED_APPS=[],
+        MIDDLEWARE=[],
+    )
+    django.setup()
+
+from posthog.integrations.django import PosthogContextMiddleware
+
+
+class MockRequest:
+    """Mock Django HttpRequest object"""
+
+    def __init__(
+        self,
+        headers=None,
+        method="GET",
+        path="/test",
+        host="example.com",
+        is_secure=False,
+    ):
+        self.headers = headers or {}
+        self.method = method
+        self.path = path
+        self._host = host
+        self._is_secure = is_secure
+
+    def build_absolute_uri(self):
+        scheme = "https" if self._is_secure else "http"
+        return f"{scheme}://{self._host}{self.path}"
+
+
+class TestPosthogContextMiddleware(unittest.TestCase):
+    def create_middleware(
+        self,
+        extra_tags=None,
+        request_filter=None,
+        tag_map=None,
+        capture_exceptions=True,
+        get_response=None,
+    ):
+        """Helper to create middleware instance with mock Django settings"""
+        if get_response is None:
+            get_response = Mock()
+
+        with patch("django.conf.settings") as mock_settings:
+            # Configure mock settings
+            mock_settings.POSTHOG_MW_EXTRA_TAGS = extra_tags
+            mock_settings.POSTHOG_MW_REQUEST_FILTER = request_filter
+            mock_settings.POSTHOG_MW_TAG_MAP = tag_map
+            mock_settings.POSTHOG_MW_CAPTURE_EXCEPTIONS = capture_exceptions
+            mock_settings.POSTHOG_MW_CLIENT = None
+
+            # Make hasattr work correctly
+            def mock_hasattr(obj, name):
+                return name in [
+                    "POSTHOG_MW_EXTRA_TAGS",
+                    "POSTHOG_MW_REQUEST_FILTER",
+                    "POSTHOG_MW_TAG_MAP",
+                    "POSTHOG_MW_CAPTURE_EXCEPTIONS",
+                    "POSTHOG_MW_CLIENT",
+                ]
+
+            with patch("builtins.hasattr", side_effect=mock_hasattr):
+                middleware = PosthogContextMiddleware(get_response)
+
+        return middleware
+
+    def test_extract_tags_basic(self):
+        with new_context():
+            """Test basic tag extraction from request"""
+            middleware = self.create_middleware()
+            request = MockRequest(
+                headers={
+                    "X-POSTHOG-SESSION-ID": "session-123",
+                    "X-POSTHOG-DISTINCT-ID": "user-456",
+                },
+                method="POST",
+                path="/api/test",
+                host="example.com",
+                is_secure=True,
+            )
+
+            tags = middleware.extract_tags(request)
+
+            self.assertEqual(get_context_session_id(), "session-123")
+            self.assertEqual(get_context_distinct_id(), "user-456")
+            self.assertEqual(tags["$current_url"], "https://example.com/api/test")
+            self.assertEqual(tags["$request_method"], "POST")
+
+    def test_extract_tags_missing_headers(self):
+        """Test tag extraction when PostHog headers are missing"""
+
+        with new_context():
+            middleware = self.create_middleware()
+            request = MockRequest(headers={}, method="GET", path="/home")
+
+            tags = middleware.extract_tags(request)
+
+            self.assertIsNone(get_context_session_id())
+            self.assertIsNone(get_context_distinct_id())
+            self.assertEqual(tags["$current_url"], "http://example.com/home")
+            self.assertEqual(tags["$request_method"], "GET")
+
+    def test_extract_tags_partial_headers(self):
+        """Test tag extraction with only some PostHog headers present"""
+
+        with new_context():
+            middleware = self.create_middleware()
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "session-only"}, method="PUT"
+            )
+
+            tags = middleware.extract_tags(request)
+
+            self.assertEqual(get_context_session_id(), "session-only")
+            self.assertIsNone(get_context_distinct_id())
+            self.assertEqual(tags["$request_method"], "PUT")
+
+    def test_extract_tags_with_extra_tags(self):
+        """Test tag extraction with extra_tags function"""
+
+        def extra_tags_func(request):
+            return {"custom_tag": "custom_value", "user_id": "789"}
+
+        with new_context():
+            middleware = self.create_middleware(extra_tags=extra_tags_func)
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "session-123"}, method="GET"
+            )
+
+            tags = middleware.extract_tags(request)
+
+            self.assertEqual(get_context_session_id(), "session-123")
+            self.assertEqual(tags["custom_tag"], "custom_value")
+            self.assertEqual(tags["user_id"], "789")
+
+    def test_extract_tags_with_tag_map(self):
+        """Test tag extraction with tag_map function"""
+
+        def extra_tags_func(request):
+            return {"custom_tag": "custom_value", "user_id": "789"}
+
+        def tag_map_func(tags):
+            if "custom_tag" in tags:
+                tags["mapped_custom_tag"] = f"mapped_{tags['custom_tag']}"
+                del tags["custom_tag"]
+            return tags
+
+        with new_context():
+            middleware = self.create_middleware(
+                tag_map=tag_map_func, extra_tags=extra_tags_func
+            )
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "session-123"}, method="GET"
+            )
+
+            tags = middleware.extract_tags(request)
+
+            self.assertEqual(tags["mapped_custom_tag"], "mapped_custom_value")
+
+    def test_extract_tags_extra_tags_returns_none(self):
+        """Test tag extraction when extra_tags returns None"""
+
+        def extra_tags_func(request):
+            return None
+
+        middleware = self.create_middleware(extra_tags=extra_tags_func)
+        request = MockRequest(method="GET")
+
+        tags = middleware.extract_tags(request)
+
+        self.assertEqual(tags["$request_method"], "GET")
+        # Should not crash when extra_tags returns None
+
+    def test_extract_tags_extra_tags_returns_empty_dict(self):
+        """Test tag extraction when extra_tags returns empty dict"""
+
+        def extra_tags_func(request):
+            return {}
+
+        middleware = self.create_middleware(extra_tags=extra_tags_func)
+        request = MockRequest(method="PATCH")
+
+        tags = middleware.extract_tags(request)
+
+        self.assertEqual(tags["$request_method"], "PATCH")
+
+    def test_process_exception_called_during_view_exception(self):
+        """
+        Unit test verifying process_exception captures exceptions per Django's contract.
+
+        Since this is a library test (no Django runtime), we simulate how Django
+        would invoke our middleware in production:
+        1. Middleware.__call__ creates context with request tags
+        2. View raises exception inside get_response
+        3. Django's BaseHandler catches it, calls process_exception, returns error response
+        4. Exception never propagates to middleware's context manager
+
+        We manually call process_exception to simulate Django's behavior - this is
+        the only way to test the hook without a full Django integration test.
+        """
+        mock_client = Mock()
+        view_exception = ValueError("View raised this error")
+        error_response = Mock(status_code=500)
+
+        def mock_get_response(request):
+            # Simulate Django's exception handling: catches view exception,
+            # calls process_exception hook if it exists, returns error response
+            if hasattr(middleware, "process_exception"):
+                middleware.process_exception(request, view_exception)
+            return error_response
+
+        middleware = self.create_middleware(get_response=mock_get_response)
+        middleware.client = mock_client
+
+        request = MockRequest(
+            headers={"X-POSTHOG-DISTINCT-ID": "test-user"},
+            method="POST",
+            path="/api/endpoint",
+        )
+        response = middleware(request)
+
+        self.assertEqual(response.status_code, 500)
+        mock_client.capture_exception.assert_called_once_with(view_exception)
+
+    def test_process_exception_respects_capture_exceptions_false(self):
+        """Verify process_exception respects capture_exceptions=False setting"""
+        mock_client = Mock()
+        view_exception = ValueError("Should not be captured")
+
+        def mock_get_response(request):
+            if hasattr(middleware, "process_exception"):
+                middleware.process_exception(request, view_exception)
+            return Mock(status_code=500)
+
+        middleware = self.create_middleware(
+            capture_exceptions=False, get_response=mock_get_response
+        )
+        middleware.client = mock_client
+
+        request = MockRequest()
+        middleware(request)
+
+        mock_client.capture_exception.assert_not_called()
+
+    def test_process_exception_respects_request_filter(self):
+        """Verify process_exception respects request_filter setting"""
+        mock_client = Mock()
+        view_exception = ValueError("Should be filtered")
+
+        def mock_get_response(request):
+            if hasattr(middleware, "process_exception"):
+                middleware.process_exception(request, view_exception)
+            return Mock(status_code=500)
+
+        middleware = self.create_middleware(
+            request_filter=lambda req: False,
+            capture_exceptions=True,
+            get_response=mock_get_response,
+        )
+        middleware.client = mock_client
+
+        request = MockRequest()
+        middleware(request)
+
+        mock_client.capture_exception.assert_not_called()
+
+
+class TestPosthogContextMiddlewareSync(unittest.TestCase):
+    """Test synchronous middleware behavior"""
+
+    def test_sync_middleware_call(self):
+        """Test that sync middleware correctly processes requests"""
+        mock_response = Mock()
+        get_response = Mock(return_value=mock_response)
+
+        # Create middleware with sync get_response
+        middleware = PosthogContextMiddleware(get_response)
+
+        # Verify sync mode detected
+        self.assertFalse(middleware._is_coroutine)
+
+        request = MockRequest(
+            headers={"X-POSTHOG-SESSION-ID": "test-session"},
+            method="GET",
+            path="/test",
+        )
+
+        with new_context():
+            response = middleware(request)
+
+            # Verify response returned
+            self.assertEqual(response, mock_response)
+            get_response.assert_called_once_with(request)
+
+    def test_sync_middleware_with_filter(self):
+        """Test sync middleware respects request filter"""
+        mock_response = Mock()
+        get_response = Mock(return_value=mock_response)
+
+        # Create middleware with request filter that filters all requests
+        def request_filter(req):
+            return False
+
+        middleware = PosthogContextMiddleware.__new__(PosthogContextMiddleware)
+        middleware.get_response = get_response
+        middleware._is_coroutine = False
+        middleware.request_filter = request_filter
+        middleware.capture_exceptions = True
+        middleware.client = None
+
+        request = MockRequest()
+
+        # Should skip context creation and return response directly
+        response = middleware(request)
+        self.assertEqual(response, mock_response)
+        get_response.assert_called_once_with(request)
+
+    def test_view_exceptions_only_captured_via_process_exception(self):
+        """
+        Demonstrates that process_exception is required to capture view exceptions.
+
+        In production Django, view exceptions don't propagate to middleware's context
+        manager because Django's BaseHandler catches them first and converts them to
+        error responses. Django provides the exception via process_exception hook instead.
+
+        This unit test proves:
+        1. Context manager in __call__ never sees view exceptions (Django intercepts)
+        2. Only process_exception can capture them
+        3. Without process_exception, exceptions are silently lost (v6.7.5 regression)
+
+        We manually call process_exception to verify the hook works - in production,
+        Django's BaseHandler would call it when a view raises.
+        """
+        mock_client = Mock()
+        get_response = Mock(return_value=Mock(status_code=500))
+
+        middleware = PosthogContextMiddleware(get_response)
+        middleware.client = mock_client
+
+        def get_response_simulating_django(request):
+            # Simulates Django behavior: view exception converted to error response,
+            # never propagates to middleware's context manager
+            return Mock(status_code=500)
+
+        middleware._sync_get_response = get_response_simulating_django
+
+        request = MockRequest()
+
+        response = middleware(request)
+        self.assertEqual(response.status_code, 500)
+
+        # Context manager didn't capture anything - exception was intercepted by Django
+        mock_client.capture_exception.assert_not_called()
+
+        # Verify process_exception hook exists and captures exceptions when called
+        if hasattr(middleware, "process_exception"):
+            exception = ValueError("View error")
+            middleware.process_exception(request, exception)
+            mock_client.capture_exception.assert_called_once_with(exception)
+        else:
+            self.fail(
+                "process_exception missing - view exceptions will not be captured!"
+            )
+
+
+class TestPosthogContextMiddlewareAsync(unittest.TestCase):
+    """Test asynchronous middleware behavior"""
+
+    def test_async_middleware_detection(self):
+        """Test that async get_response is correctly detected"""
+
+        async def async_get_response(request):
+            return Mock()
+
+        middleware = PosthogContextMiddleware(async_get_response)
+
+        # Verify async mode detected
+        self.assertTrue(middleware._is_coroutine)
+
+    def test_async_middleware_call(self):
+        """Test that async middleware correctly processes requests"""
+
+        async def run_test():
+            mock_response = Mock()
+
+            async def async_get_response(request):
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "async-session"},
+                method="POST",
+                path="/async-test",
+            )
+
+            with new_context():
+                # Call should return the coroutine from __acall__
+                result = middleware(request)
+
+                # Verify it's a coroutine
+                self.assertTrue(asyncio.iscoroutine(result))
+
+                # Await the result
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_with_filter(self):
+        """Test async middleware respects request filter"""
+
+        async def run_test():
+            mock_response = Mock()
+
+            async def async_get_response(request):
+                return mock_response
+
+            # Properly initialize middleware
+            middleware = PosthogContextMiddleware(async_get_response)
+            # Override request filter after initialization
+            middleware.request_filter = lambda req: False
+
+            request = MockRequest()
+
+            # Should skip context creation and return response directly
+            result = middleware(request)
+            response = await result
+            self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_context_propagation(self):
+        """Test that async middleware properly propagates context"""
+
+        async def run_test():
+            mock_response = Mock()
+
+            async def async_get_response(request):
+                # Verify context is available during async processing
+                session_id = get_context_session_id()
+                self.assertEqual(session_id, "async-session-123")
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "async-session-123"},
+                method="GET",
+            )
+
+            with new_context():
+                result = middleware(request)
+                await result
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_exception_capture(self):
+        """Test that async middleware captures exceptions during request processing"""
+
+        async def run_test():
+            mock_client = Mock()
+
+            # Make async_get_response raise an exception
+            async def raise_exception(request):
+                raise ValueError("Async test exception")
+
+            # Properly initialize middleware
+            middleware = PosthogContextMiddleware(raise_exception)
+            middleware.client = mock_client  # Override with mock client
+
+            request = MockRequest()
+
+            # Should capture exception and re-raise
+            with self.assertRaises(ValueError):
+                result = middleware(request)
+                await result
+
+            # Verify exception was captured by middleware
+            mock_client.capture_exception.assert_called_once()
+            captured_exception = mock_client.capture_exception.call_args[0][0]
+            self.assertIsInstance(captured_exception, ValueError)
+            self.assertEqual(str(captured_exception), "Async test exception")
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_with_authenticated_user(self):
+        """
+        Test that async middleware correctly extracts user info in async context.
+
+        Django's request.user is a SimpleLazyObject that defers DB access.
+        In async context, accessing it directly raises SynchronousOnlyOperation.
+        The middleware should use request.auser() instead.
+
+        This tests the fix for issue #355.
+        """
+
+        async def run_test():
+            mock_response = Mock()
+            mock_user = Mock()
+            mock_user.is_authenticated = True
+            mock_user.pk = 123
+            mock_user.email = "test@example.com"
+
+            async def async_get_response(request):
+                # Verify user info was extracted and set as distinct_id
+                distinct_id = get_context_distinct_id()
+                self.assertEqual(distinct_id, "123")
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+            middleware.client = Mock()
+
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "test-session"}, method="GET"
+            )
+
+            # Mock auser() to return authenticated user
+            async def mock_auser():
+                return mock_user
+
+            request.auser = mock_auser
+
+            with new_context():
+                result = middleware(request)
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_with_unauthenticated_user(self):
+        """
+        Test that async middleware handles unauthenticated users correctly.
+        """
+
+        async def run_test():
+            mock_response = Mock()
+            mock_user = Mock()
+            mock_user.is_authenticated = False  # Not authenticated
+
+            async def async_get_response(request):
+                # Verify no distinct_id was set (no user)
+                distinct_id = get_context_distinct_id()
+                self.assertIsNone(distinct_id)
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+            middleware.client = Mock()
+
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "test-session"}, method="GET"
+            )
+
+            async def mock_auser():
+                return mock_user
+
+            request.auser = mock_auser
+
+            with new_context():
+                result = middleware(request)
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_without_user_attribute(self):
+        """
+        Test that async middleware handles requests without user attribute (no auth middleware).
+        """
+
+        async def run_test():
+            mock_response = Mock()
+
+            async def async_get_response(request):
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+            middleware.client = Mock()
+
+            # Request without auser method (no auth middleware)
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "test-session"}, method="GET"
+            )
+
+            with new_context():
+                result = middleware(request)
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_with_extra_tags(self):
+        """
+        Test that async middleware works with extra_tags callback.
+        """
+
+        async def run_test():
+            mock_response = Mock()
+
+            def extra_tags_callback(request):
+                # Simple sync callback - should work
+                return {"custom_tag": "custom_value"}
+
+            async def async_get_response(request):
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+            middleware.extra_tags = extra_tags_callback
+            middleware.client = Mock()
+
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "test-session"}, method="GET"
+            )
+
+            # Mock auser for no user
+            async def mock_auser():
+                return None
+
+            request.auser = mock_auser
+
+            with new_context():
+                result = middleware(request)
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_with_tag_map(self):
+        """
+        Test that async middleware works with tag_map callback.
+        """
+
+        async def run_test():
+            mock_response = Mock()
+
+            def tag_map_callback(tags):
+                # Simple sync callback - should work
+                tags["mapped"] = "yes"
+                return tags
+
+            async def async_get_response(request):
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+            middleware.tag_map = tag_map_callback
+            middleware.client = Mock()
+
+            request = MockRequest(
+                headers={"X-POSTHOG-SESSION-ID": "test-session"}, method="GET"
+            )
+
+            # Mock auser for no user
+            async def mock_auser():
+                return None
+
+            request.auser = mock_auser
+
+            with new_context():
+                result = middleware(request)
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+    def test_async_middleware_user_extraction_with_all_headers(self):
+        """
+        Test async middleware extracts all request info correctly.
+        """
+
+        async def run_test():
+            mock_response = Mock()
+            mock_user = Mock()
+            mock_user.is_authenticated = True
+            mock_user.pk = 456
+            mock_user.email = "async@test.com"
+
+            async def async_get_response(request):
+                # Verify all context was set correctly
+                distinct_id = get_context_distinct_id()
+                session_id = get_context_session_id()
+                self.assertEqual(distinct_id, "456")
+                self.assertEqual(session_id, "async-sess-123")
+                return mock_response
+
+            middleware = PosthogContextMiddleware(async_get_response)
+            middleware.client = Mock()
+
+            request = MockRequest(
+                headers={
+                    "X-POSTHOG-SESSION-ID": "async-sess-123",
+                    "X-Forwarded-For": "192.168.1.1",
+                    "User-Agent": "TestAgent/1.0",
+                },
+                method="POST",
+                path="/api/test",
+            )
+
+            async def mock_auser():
+                return mock_user
+
+            request.auser = mock_auser
+
+            with new_context():
+                result = middleware(request)
+                response = await result
+                self.assertEqual(response, mock_response)
+
+        asyncio.run(run_test())
+
+
+class TestPosthogContextMiddlewareHybrid(unittest.TestCase):
+    """Test hybrid middleware behavior with mixed sync/async chains"""
+
+    def test_hybrid_flags_set(self):
+        """Test that both capability flags are set"""
+        self.assertTrue(PosthogContextMiddleware.sync_capable)
+        self.assertTrue(PosthogContextMiddleware.async_capable)
+
+    def test_sync_to_async_routing(self):
+        """Test that __call__ routes to __acall__ when async"""
+
+        async def run_test():
+            async def async_get_response(request):
+                return Mock()
+
+            middleware = PosthogContextMiddleware(async_get_response)
+
+            # Verify routing happens
+            request = MockRequest()
+            result = middleware(request)
+
+            # Should be a coroutine from __acall__
+            self.assertTrue(asyncio.iscoroutine(result))
+            await result  # Clean up
+
+        asyncio.run(run_test())
+
+    def test_sync_path_direct_return(self):
+        """Test that sync path returns directly without coroutine"""
+        mock_response = Mock()
+
+        def sync_get_response(request):
+            return mock_response
+
+        middleware = PosthogContextMiddleware(sync_get_response)
+
+        request = MockRequest()
+        result = middleware(request)
+
+        # Should NOT be a coroutine
+        self.assertFalse(asyncio.iscoroutine(result))
+        self.assertEqual(result, mock_response)
+
+
+if __name__ == "__main__":
+    unittest.main()

posthog/test/module.py

@@ -1,49 +0,0 @@
-import unittest
-
-import analytics
-
-
-class TestModule(unittest.TestCase):
-
-    def failed(self):
-        self.failed = True
-
-    def setUp(self):
-        self.failed = False
-        analytics.api_key = 'testsecret'
-        analytics.on_error = self.failed
-
-    def test_no_api_key(self):
-        analytics.api_key = None
-        self.assertRaises(Exception, analytics.track)
-
-    def test_no_host(self):
-        analytics.host = None
-        self.assertRaises(Exception, analytics.track)
-
-    def test_track(self):
-        analytics.track('distinct_id', 'python module event')
-        analytics.flush()
-
-    def test_identify(self):
-        analytics.identify('distinct_id', {'email': 'user@email.com'})
-        analytics.flush()
-
-    def test_group(self):
-        analytics.group('distinct_id', 'groupId')
-        analytics.flush()
-
-    def test_alias(self):
-        analytics.alias('previousId', 'distinct_id')
-        analytics.flush()
-
-    def test_page(self):
-        analytics.page('distinct_id')
-        analytics.flush()
-
-    def test_screen(self):
-        analytics.screen('distinct_id')
-        analytics.flush()
-
-    def test_flush(self):
-        analytics.flush()

posthog/test/request.py

@@ -1,53 +0,0 @@
-from datetime import datetime, date
-import unittest
-import json
-import requests
-
-from posthog.request import post, DatetimeSerializer
-
-
-class TestRequests(unittest.TestCase):
-
-    def test_valid_request(self):
-        res = post(batch=[{
-            'distinct_id': 'distinct_id',
-            'event': 'python event',
-            'type': 'track'
-        }])
-        self.assertEqual(res.status_code, 200)
-
-    def test_invalid_request_error(self):
-        self.assertRaises(Exception, post, 'testsecret',
-                          'https://t.posthog.com', False, '[{]')
-
-    def test_invalid_host(self):
-        self.assertRaises(Exception, post, 'testsecret',
-                          't.posthog.com/', batch=[])
-
-    def test_datetime_serialization(self):
-        data = {'created': datetime(2012, 3, 4, 5, 6, 7, 891011)}
-        result = json.dumps(data, cls=DatetimeSerializer)
-        self.assertEqual(result, '{"created": "2012-03-04T05:06:07.891011"}')
-
-    def test_date_serialization(self):
-        today = date.today()
-        data = {'created': today}
-        result = json.dumps(data, cls=DatetimeSerializer)
-        expected = '{"created": "%s"}' % today.isoformat()
-        self.assertEqual(result, expected)
-
-    def test_should_not_timeout(self):
-        res = post(batch=[{
-            'distinct_id': 'distinct_id',
-            'event': 'python event',
-            'type': 'track'
-        }], timeout=15)
-        self.assertEqual(res.status_code, 200)
-
-    def test_should_timeout(self):
-        with self.assertRaises(requests.ReadTimeout):
-            post(batch=[{
-                'distinct_id': 'distinct_id',
-                'event': 'python event',
-                'type': 'track'
-            }], timeout=0.0001)

posthog/test/test_before_send.py

@@ -0,0 +1,218 @@
+import unittest
+
+import mock
+
+from posthog.client import Client
+from posthog.test.test_utils import FAKE_TEST_API_KEY
+
+
+class TestClient(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        # This ensures no real HTTP POST requests are made
+        cls.client_post_patcher = mock.patch("posthog.client.batch_post")
+        cls.consumer_post_patcher = mock.patch("posthog.consumer.batch_post")
+        cls.client_post_patcher.start()
+        cls.consumer_post_patcher.start()
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.client_post_patcher.stop()
+        cls.consumer_post_patcher.stop()
+
+    def set_fail(self, e, batch):
+        """Mark the failure handler"""
+        print("FAIL", e, batch)  # noqa: T201
+        self.failed = True
+
+    def setUp(self):
+        self.failed = False
+        self.client = Client(FAKE_TEST_API_KEY, on_error=self.set_fail)
+
+    def test_before_send_callback_modifies_event(self):
+        """Test that before_send callback can modify events."""
+        processed_events = []
+
+        def my_before_send(event):
+            processed_events.append(event.copy())
+            if "properties" not in event:
+                event["properties"] = {}
+            event["properties"]["processed_by_before_send"] = True
+            return event
+
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                before_send=my_before_send,
+                sync_mode=True,
+            )
+            msg_uuid = client.capture(
+                "test_event", distinct_id="user1", properties={"original": "value"}
+            )
+
+            self.assertIsNotNone(msg_uuid)
+
+            # Get the enqueued message from the mock
+            mock_post.assert_called_once()
+            batch_data = mock_post.call_args[1]["batch"]
+            enqueued_msg = batch_data[0]
+
+            self.assertEqual(
+                enqueued_msg["properties"]["processed_by_before_send"], True
+            )
+            self.assertEqual(enqueued_msg["properties"]["original"], "value")
+            self.assertEqual(len(processed_events), 1)
+            self.assertEqual(processed_events[0]["event"], "test_event")
+
+    def test_before_send_callback_drops_event(self):
+        """Test that before_send callback can drop events by returning None."""
+
+        def drop_test_events(event):
+            if event.get("event") == "test_drop_me":
+                return None
+            return event
+
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                before_send=drop_test_events,
+                sync_mode=True,
+            )
+
+            # Event should be dropped
+            msg_uuid = client.capture("test_drop_me", distinct_id="user1")
+            self.assertIsNone(msg_uuid)
+
+            # Event should go through
+            msg_uuid = client.capture("keep_me", distinct_id="user1")
+            self.assertIsNotNone(msg_uuid)
+
+            # Check the enqueued message
+            mock_post.assert_called_once()
+            batch_data = mock_post.call_args[1]["batch"]
+            enqueued_msg = batch_data[0]
+            self.assertEqual(enqueued_msg["event"], "keep_me")
+
+    def test_before_send_callback_handles_exceptions(self):
+        """Test that exceptions in before_send don't crash the client."""
+
+        def buggy_before_send(event):
+            raise ValueError("Oops!")
+
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                before_send=buggy_before_send,
+                sync_mode=True,
+            )
+            msg_uuid = client.capture("robust_event", distinct_id="user1")
+
+            # Event should still be sent despite the exception
+            self.assertIsNotNone(msg_uuid)
+
+            # Check the enqueued message
+            mock_post.assert_called_once()
+            batch_data = mock_post.call_args[1]["batch"]
+            enqueued_msg = batch_data[0]
+            self.assertEqual(enqueued_msg["event"], "robust_event")
+
+    def test_before_send_callback_works_with_all_event_types(self):
+        """Test that before_send works with capture, set, etc."""
+
+        def add_marker(event):
+            if "properties" not in event:
+                event["properties"] = {}
+            event["properties"]["marked"] = True
+            return event
+
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                before_send=add_marker,
+                sync_mode=True,
+            )
+
+            # Test capture
+            msg_uuid = client.capture("event", distinct_id="user1")
+            self.assertIsNotNone(msg_uuid)
+
+            # Test set
+            msg_uuid = client.set(distinct_id="user1", properties={"prop": "value"})
+            self.assertIsNotNone(msg_uuid)
+
+            # Check all events were marked
+            self.assertEqual(mock_post.call_count, 2)
+            for call in mock_post.call_args_list:
+                batch_data = call[1]["batch"]
+                enqueued_msg = batch_data[0]
+                self.assertTrue(enqueued_msg["properties"]["marked"])
+
+    def test_before_send_callback_disabled_when_none(self):
+        """Test that client works normally when before_send is None."""
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                before_send=None,
+                sync_mode=True,
+            )
+            msg_uuid = client.capture("normal_event", distinct_id="user1")
+            self.assertIsNotNone(msg_uuid)
+
+            # Check the event was sent normally
+            mock_post.assert_called_once()
+            batch_data = mock_post.call_args[1]["batch"]
+            enqueued_msg = batch_data[0]
+            self.assertEqual(enqueued_msg["event"], "normal_event")
+
+    def test_before_send_callback_pii_scrubbing_example(self):
+        """Test a realistic PII scrubbing use case."""
+
+        def scrub_pii(event):
+            properties = event.get("properties", {})
+
+            # Mask email but keep domain
+            if "email" in properties:
+                email = properties["email"]
+                if "@" in email:
+                    domain = email.split("@")[1]
+                    properties["email"] = f"***@{domain}"
+                else:
+                    properties["email"] = "***"
+
+            # Remove credit card
+            properties.pop("credit_card", None)
+
+            return event
+
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                before_send=scrub_pii,
+                sync_mode=True,
+            )
+            msg_uuid = client.capture(
+                "form_submit",
+                distinct_id="user1",
+                properties={
+                    "email": "user@example.com",
+                    "credit_card": "1234-5678-9012-3456",
+                    "form_name": "contact",
+                },
+            )
+
+            self.assertIsNotNone(msg_uuid)
+
+            # Check the enqueued message was scrubbed
+            mock_post.assert_called_once()
+            batch_data = mock_post.call_args[1]["batch"]
+            enqueued_msg = batch_data[0]
+
+            self.assertEqual(enqueued_msg["properties"]["email"], "***@example.com")
+            self.assertNotIn("credit_card", enqueued_msg["properties"])
+            self.assertEqual(enqueued_msg["properties"]["form_name"], "contact")

posthog/test/test_consumer.py

@@ -0,0 +1,256 @@
+import json
+import time
+import unittest
+from typing import Any
+
+import mock
+from parameterized import parameterized
+
+try:
+    from queue import Queue
+except ImportError:
+    from Queue import Queue
+
+from posthog.consumer import MAX_MSG_SIZE, Consumer
+from posthog.request import APIError
+from posthog.test.test_utils import TEST_API_KEY
+
+
+def _track_event(event_name: str = "python event") -> dict[str, str]:
+    return {"type": "track", "event": event_name, "distinct_id": "distinct_id"}
+
+
+class TestConsumer(unittest.TestCase):
+    def test_next(self) -> None:
+        q = Queue()
+        consumer = Consumer(q, "")
+        q.put(1)
+        next = consumer.next()
+        self.assertEqual(next, [1])
+
+    def test_next_limit(self) -> None:
+        q = Queue()
+        flush_at = 50
+        consumer = Consumer(q, "", flush_at)
+        for i in range(10000):
+            q.put(i)
+        next = consumer.next()
+        self.assertEqual(next, list(range(flush_at)))
+
+    def test_dropping_oversize_msg(self) -> None:
+        q = Queue()
+        consumer = Consumer(q, "")
+        oversize_msg = {"m": "x" * MAX_MSG_SIZE}
+        q.put(oversize_msg)
+        next = consumer.next()
+        self.assertEqual(next, [])
+        self.assertTrue(q.empty())
+
+    def test_upload(self) -> None:
+        q = Queue()
+        consumer = Consumer(q, TEST_API_KEY)
+        q.put(_track_event())
+        success = consumer.upload()
+        self.assertTrue(success)
+
+    def test_flush_interval(self) -> None:
+        # Put _n_ items in the queue, pausing a little bit more than
+        # _flush_interval_ after each one.
+        # The consumer should upload _n_ times.
+        q = Queue()
+        flush_interval = 0.3
+        consumer = Consumer(q, TEST_API_KEY, flush_at=10, flush_interval=flush_interval)
+        with mock.patch("posthog.consumer.batch_post") as mock_post:
+            consumer.start()
+            for i in range(3):
+                q.put(_track_event("python event %d" % i))
+                time.sleep(flush_interval * 1.1)
+            self.assertEqual(mock_post.call_count, 3)
+
+    def test_multiple_uploads_per_interval(self) -> None:
+        # Put _flush_at*2_ items in the queue at once, then pause for
+        # _flush_interval_. The consumer should upload 2 times.
+        q = Queue()
+        flush_interval = 0.5
+        flush_at = 10
+        consumer = Consumer(
+            q, TEST_API_KEY, flush_at=flush_at, flush_interval=flush_interval
+        )
+        with mock.patch("posthog.consumer.batch_post") as mock_post:
+            consumer.start()
+            for i in range(flush_at * 2):
+                q.put(_track_event("python event %d" % i))
+            time.sleep(flush_interval * 1.1)
+            self.assertEqual(mock_post.call_count, 2)
+
+    def test_request(self) -> None:
+        consumer = Consumer(None, TEST_API_KEY)
+        consumer.request([_track_event()])
+
+    def _run_retry_test(
+        self, exception: Exception, exception_count: int, retries: int = 10
+    ) -> None:
+        call_count = [0]
+
+        def mock_post(*args: Any, **kwargs: Any) -> None:
+            call_count[0] += 1
+            if call_count[0] <= exception_count:
+                raise exception
+
+        consumer = Consumer(None, TEST_API_KEY, retries=retries)
+        with mock.patch(
+            "posthog.consumer.batch_post", mock.Mock(side_effect=mock_post)
+        ):
+            if exception_count <= retries:
+                consumer.request([_track_event()])
+            else:
+                with self.assertRaises(type(exception)):
+                    consumer.request([_track_event()])
+
+    @parameterized.expand(
+        [
+            ("general_errors", Exception("generic exception"), 2),
+            ("server_errors", APIError(500, "Internal Server Error"), 2),
+            ("rate_limit_errors", APIError(429, "Too Many Requests"), 2),
+        ]
+    )
+    def test_request_retries_on_retriable_errors(
+        self, _name: str, exception: Exception, exception_count: int
+    ) -> None:
+        self._run_retry_test(exception, exception_count)
+
+    def test_request_does_not_retry_client_errors(self) -> None:
+        with self.assertRaises(APIError):
+            self._run_retry_test(APIError(400, "Client Errors"), 1)
+
+    def test_request_fails_when_exceptions_exceed_retries(self) -> None:
+        self._run_retry_test(APIError(500, "Internal Server Error"), 4, retries=3)
+
+    def test_pause(self) -> None:
+        consumer = Consumer(None, TEST_API_KEY)
+        consumer.pause()
+        self.assertFalse(consumer.running)
+
+    def test_max_batch_size(self) -> None:
+        q = Queue()
+        consumer = Consumer(q, TEST_API_KEY, flush_at=100000, flush_interval=3)
+        properties = {}
+        for n in range(0, 500):
+            properties[str(n)] = "one_long_property_value_to_build_a_big_event"
+        track = {
+            "type": "track",
+            "event": "python event",
+            "distinct_id": "distinct_id",
+            "properties": properties,
+        }
+        msg_size = len(json.dumps(track).encode())
+        # Let's capture 8MB of data to trigger two batches
+        n_msgs = int(8_000_000 / msg_size)
+
+        def mock_post_fn(_: str, data: str, **kwargs: Any) -> mock.Mock:
+            res = mock.Mock()
+            res.status_code = 200
+            request_size = len(data.encode())
+            # Batches close after the first message bringing it bigger than BATCH_SIZE_LIMIT, let's add 10% of margin
+            self.assertTrue(
+                request_size < (5 * 1024 * 1024) * 1.1,
+                "batch size (%d) higher than limit" % request_size,
+            )
+            return res
+
+        with mock.patch(
+            "posthog.request._session.post", side_effect=mock_post_fn
+        ) as mock_post:
+            consumer.start()
+            for _ in range(0, n_msgs + 2):
+                q.put(track)
+            q.join()
+            self.assertEqual(mock_post.call_count, 2)
+
+    def test_request_sleeps_with_retry_after(self) -> None:
+        error = APIError(429, "Too Many Requests", retry_after=5.0)
+        call_count = [0]
+
+        def mock_post(*args: Any, **kwargs: Any) -> None:
+            call_count[0] += 1
+            if call_count[0] <= 1:
+                raise error
+
+        consumer = Consumer(None, TEST_API_KEY, retries=3)
+        with (
+            mock.patch("posthog.consumer.batch_post", side_effect=mock_post),
+            mock.patch("posthog.consumer.time.sleep") as mock_sleep,
+        ):
+            consumer.request([_track_event()])
+            mock_sleep.assert_called_once_with(5.0)
+
+    def test_request_uses_exponential_backoff_without_retry_after(self) -> None:
+        error = APIError(503, "Service Unavailable")
+        call_count = [0]
+
+        def mock_post(*args: Any, **kwargs: Any) -> None:
+            call_count[0] += 1
+            if call_count[0] <= 3:
+                raise error
+
+        consumer = Consumer(None, TEST_API_KEY, retries=3)
+        with (
+            mock.patch("posthog.consumer.batch_post", side_effect=mock_post),
+            mock.patch("posthog.consumer.time.sleep") as mock_sleep,
+        ):
+            consumer.request([_track_event()])
+            self.assertEqual(
+                mock_sleep.call_args_list,
+                [
+                    mock.call(1),  # 2^0
+                    mock.call(2),  # 2^1
+                    mock.call(4),  # 2^2
+                ],
+            )
+
+    def test_request_retries_on_408(self) -> None:
+        call_count = [0]
+
+        def mock_post(*args: Any, **kwargs: Any) -> None:
+            call_count[0] += 1
+            if call_count[0] <= 1:
+                raise APIError(408, "Request Timeout")
+
+        consumer = Consumer(None, TEST_API_KEY, retries=3)
+        with (
+            mock.patch("posthog.consumer.batch_post", side_effect=mock_post),
+            mock.patch("posthog.consumer.time.sleep"),
+        ):
+            consumer.request([_track_event()])
+            self.assertEqual(call_count[0], 2)
+
+    @parameterized.expand(
+        [
+            ("on_error_succeeds", False),
+            ("on_error_raises", True),
+        ]
+    )
+    def test_upload_exception_calls_on_error_and_does_not_raise(
+        self, _name: str, on_error_raises: bool
+    ) -> None:
+        on_error_called: list[tuple[Exception, list[dict[str, str]]]] = []
+
+        def on_error(e: Exception, batch: list[dict[str, str]]) -> None:
+            on_error_called.append((e, batch))
+            if on_error_raises:
+                raise Exception("on_error failed")
+
+        q = Queue()
+        consumer = Consumer(q, TEST_API_KEY, on_error=on_error)
+        track = _track_event()
+        q.put(track)
+
+        with mock.patch.object(
+            consumer, "request", side_effect=Exception("request failed")
+        ):
+            result = consumer.upload()
+
+        self.assertFalse(result)
+        self.assertEqual(len(on_error_called), 1)
+        self.assertEqual(str(on_error_called[0][0]), "request failed")
+        self.assertEqual(on_error_called[0][1], [track])