WIP

fix(django): restore process_exception to capture view exceptions (#350 )
Restores the process_exception method that was removed in v6.7.5 (PR #328), which broke exception capture from Django views and downstream middleware. Django converts view exceptions into responses before they propagate through the middleware stack's __call__ method, so the context manager's exception handler never sees them. Django provides these exceptions via the process_exception hook instead. Changes: - Add process_exception method to capture exceptions from views and downstream middleware with proper request context and tags - Add tests verifying process_exception behavior and settings (capture_exceptions, request_filter)
2025-10-31 15:12:27 -04:00 · 2025-10-29 10:40:08 +00:00 · 2025-10-28 13:06:45 +00:00 · 2025-10-28 13:05:50 +00:00 · 2025-10-28 09:59:20 +00:00 · 2025-10-24 14:17:27 +00:00
84 changed files with 26334 additions and 6199 deletions
@@ -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
@@ -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"
@@ -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
@@ -0,0 +1,48 @@
+name: "Generate References"
+
+on:
+  workflow_dispatch:
+
+jobs:
+  docs-generation:
+    name: Generate references
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout the repository
+        uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
+        with:
+          fetch-depth: 0
+          token: ${{ secrets.POSTHOG_BOT_PAT }}
+
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+        with:
+          python-version: 3.11.11
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        with:
+            enable-cache: true
+            pyproject-file: 'pyproject.toml'
+
+      - name: Generate references
+        run: |
+          uv run bin/docs generate-references
+
+      - name: Check for changes in references
+        id: changes
+        run: |
+          if [ -n "$(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/
@@ -20,7 +20,7 @@ jobs:
        uses: actions/checkout@85e6279cec87321a52edac9c87bce653a07cf6c2
        with:
          fetch-depth: 0
-          token: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
+          token: ${{ secrets.POSTHOG_BOT_PAT }}

      - name: Set up Python
        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
@@ -45,7 +45,13 @@ jobs:
      - name: Create GitHub release
        uses: actions/create-release@0cb9c9b65d5d1901c1f53e5e66eaf4afd303e70e # v1
        env:
-          GITHUB_TOKEN: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
+          GITHUB_TOKEN: ${{ secrets.POSTHOG_BOT_PAT }}
        with:
          tag_name: v${{ env.REPO_VERSION }}
          release_name: ${{ env.REPO_VERSION }}
+      
+      - name: Dispatch generate-references for posthog-python
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          gh workflow run generate-references.yml --ref master
@@ -18,3 +18,4 @@ posthog-analytics
 pyrightconfig.json
 .env
 .DS_Store
+posthog-python-references.json
@@ -1,3 +1,177 @@
+# Unreleased
+
+- fix(django): Restore process_exception method to capture view and downstream middleware exceptions (fixes #329)
+
+# 6.7.11 - 2025-10-28
+
+- feat(ai): Add `$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
@@ -35,8 +35,23 @@ release_analytics:
 e2e_test:
 	.buildscripts/e2e.sh

-django_example:
-	python -m pip install -e ".[sentry]"
-	cd sentry_django_example && python manage.py runserver 8080
+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 release e2e_test
+.PHONY: test lint release e2e_test prep_local
@@ -32,7 +32,7 @@ We recommend using [uv](https://docs.astral.sh/uv/). It's super fast.
 ```bash
 uv python install 3.9.19
 uv python pin 3.9.19
-uv venv env
+uv venv
 source env/bin/activate
 uv sync --extra dev --extra test
 pre-commit install
@@ -43,24 +43,24 @@ make test

 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.

-### Running the Django Sentry Integration Locally
-
-There's a sample Django project included, called `sentry_django_example`, which explains how to use PostHog with Sentry.
-
-There's 2 places of importance (Changes required are all marked with TODO in the sample project directory)
-
-1. Settings.py
-    1. Input your Sentry DSN
-    2. Input your Sentry Org and ProjectID details into `PosthogIntegration()`
-    3. Add `POSTHOG_DJANGO` to settings.py. This allows the `PosthogDistinctIdMiddleware` to get the distinct_ids
-
-2. urls.py
-    1. This includes the `sentry-debug/` endpoint, which generates an exception
-
-To run things: `make django_example`. This installs the posthog-python library with the sentry-sdk add-on, and then runs the django app.
-Also start the PostHog app locally.
-Then navigate to `http://127.0.0.1:8080/sentry-debug/` and you should get an event in both Sentry and PostHog, with links to each other.
-
 ### Releasing Versions

-Updated are released using GitHub Actions: after bumping `version.py` in `master` and adding to `CHANGELOG.md`, go to [our release workflow's page](https://github.com/PostHog/posthog-python/actions/workflows/release.yaml) and dispatch it manually, using workflow from `master`.
+Updates are released automatically using GitHub Actions when `version.py` is updated on `master`. After bumping `version.py` in `master` and adding to `CHANGELOG.md`, the [release workflow](https://github.com/PostHog/posthog-python/blob/master/.github/workflows/release.yaml) will automatically trigger and deploy the new version.
+
+If you need to check the latest runs or manually trigger a release, you can go to [our release workflow's page](https://github.com/PostHog/posthog-python/actions/workflows/release.yaml) and dispatch it manually, using workflow from `master`.
+
+
+### 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" }
+```
+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.
@@ -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 "$0")/docs_scripts/generate_json_schemas.py" "$@" 
@@ -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,
+}
@@ -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()
@@ -6,9 +6,7 @@ set_source_and_root_dir
 ensure_virtual_env

 if [[ "$1" == "--check" ]]; then
-  black --check .
-  isort --check-only .
+  ruff format --check .
 else
-  black .
-  isort .
+  ruff format .
 fi
@@ -1,171 +1,502 @@
 # PostHog Python library example
-import argparse
+#
+# 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 posthog

-# Add argument parsing
-parser = argparse.ArgumentParser(description="PostHog Python library example")
-parser.add_argument(
-    "--flag",
-    default="person-on-events-enabled",
-    help="Feature flag key to check (default: person-on-events-enabled)",
-)
-args = parser.parse_args()

-posthog.debug = True
+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())

-# You can find this key on the /setup page in PostHog
-posthog.project_api_key = "phc_gtWmTq3Pgl06u4sZY3TRcoQfp42yfuXHKoe8ZVSR6Kh"
-posthog.personal_api_key = "phx_fiRCOQkTA3o2ePSdLrFDAILLHjMu2Mv52vUi8MNruIm"

-# Where you host PostHog, with no trailing /.
-# You can remove this line if you're using posthog.com
-posthog.host = "http://localhost:8000"
-posthog.poll_interval = 10
+# Load .env file if it exists
+load_env_file()

-print(
-    posthog.feature_enabled(
-        args.flag,  # Use the flag from command line arguments
-        "12345",
-        groups={"organization": str("0182ee91-8ef7-0000-4cb9-fedc5f00926a")},
-        group_properties={
-            "organization": {
-                "id": "0182ee91-8ef7-0000-4cb9-fedc5f00926a",
-                "created_at": "2022-06-30 11:44:52.984121+00:00",
-            }
-        },
+# 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 credentials are provided
+if not project_key or not personal_api_key:
+    print("❌ Missing PostHog credentials!")
+    print(
+        "   Please set POSTHOG_PROJECT_API_KEY and POSTHOG_PERSONAL_API_KEY environment variables"
+    )
+    print("   or copy .env.example to .env and fill in your values")
+    exit(1)
+
+# Test authentication before proceeding
+print("🔑 Testing PostHog authentication...")
+
+try:
+    # Configure PostHog with credentials
+    posthog.debug = False  # Keep quiet during auth test
+    posthog.api_key = project_key
+    posthog.project_api_key = project_key
+    posthog.personal_api_key = personal_api_key
+    posthog.host = host
+    posthog.poll_interval = 10
+
+    # Test by attempting to get feature flags (this validates both keys)
+    # This will fail if credentials are invalid
+    test_flags = posthog.get_all_flags("test_user", only_evaluate_locally=True)
+
+    # If we get here without exception, credentials work
+    print("✅ Authentication successful!")
+    print(f"   Project API Key: {project_key[:9]}...")
+    print("   Personal API Key: [REDACTED]")
+    print(f"   Host: {host}\n\n")
+
+except Exception as e:
+    print("❌ Authentication failed!")
+    print(f"   Error: {e}")
+    print("\n   Please check your credentials:")
+    print("   - POSTHOG_PROJECT_API_KEY: Project API key from PostHog settings")
+    print(
+        "   - POSTHOG_PERSONAL_API_KEY: Personal API key (required for local evaluation)"
+    )
+    print("   - POSTHOG_HOST: Your PostHog instance URL")
+    exit(1)
+
+# Display menu and get user choice
+print("🚀 PostHog Python SDK Demo - Choose an example to run:\n")
+print("1. Identify and capture examples")
+print("2. Feature flag local evaluation examples")
+print("3. Feature flag payload examples")
+print("4. Flag dependencies examples")
+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":
+    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":
+    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}")

-
-# Capture an event
-posthog.capture(
-    "distinct_id",
-    "event",
-    {"property1": "value", "property2": "value"},
-    send_feature_flags=True,
-)
-
-print(posthog.feature_enabled("beta-feature", "distinct_id"))
-print(
-    posthog.feature_enabled(
-        "beta-feature-groups", "distinct_id", groups={"company": "id:5"}
-    )
-)
-
-print(posthog.feature_enabled("beta-feature", "distinct_id"))
-
-# get payload
-print(posthog.get_feature_flag_payload("beta-feature", "distinct_id"))
-print(posthog.get_all_flags_and_payloads("distinct_id"))
-exit()
-# # Alias a previous distinct id with a new one
-
-posthog.alias("distinct_id", "new_distinct_id")
-
-posthog.capture(
-    "new_distinct_id", "event2", {"property1": "value", "property2": "value"}
-)
-posthog.capture(
-    "new_distinct_id",
-    "event-with-groups",
-    {"property1": "value", "property2": "value"},
-    groups={"company": "id:5"},
-)
-
-# # Add properties to the person
-posthog.identify("new_distinct_id", {"email": "something@something.com"})
-
-# Add properties to a group
-posthog.group_identify("company", "id:5", {"employees": 11})
-
-# properties set only once to the person
-posthog.set_once("new_distinct_id", {"self_serve_signup": True})
-
-
-posthog.set_once(
-    "new_distinct_id", {"self_serve_signup": False}
-)  # this will not change the property (because it was already set)
-
-posthog.set("new_distinct_id", {"current_browser": "Chrome"})
-posthog.set("new_distinct_id", {"current_browser": "Firefox"})
-
-
-# #############################################################################
-# Make sure you have a personal API key for the examples below
-
-# Local Evaluation
-
-# If flag has City=Sydney, this call doesn't go to `/decide`
-print(
-    posthog.feature_enabled(
-        "test-flag",
-        "distinct_id_random_22",
-        person_properties={"$geoip_city_name": "Sydney"},
-    )
-)
-
-print(
-    posthog.feature_enabled(
-        "test-flag",
-        "distinct_id_random_22",
-        person_properties={"$geoip_city_name": "Sydney"},
+    # 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}")

-
-print(posthog.get_all_flags("distinct_id_random_22"))
-print(posthog.get_all_flags("distinct_id_random_22", only_evaluate_locally=True))
-print(
-    posthog.get_all_flags(
-        "distinct_id_random_22",
-        person_properties={"$geoip_city_name": "Sydney"},
+    # Test beta-feature directly for comparison
+    beta1 = posthog.feature_enabled(
+        "beta-feature",
+        "example_user",
+        person_properties={"email": "user@example.com"},
        only_evaluate_locally=True,
    )
-)
-print(posthog.get_remote_config_payload("encrypted_payload_flag_key"))
+    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")

-# You can add tags to a context, and these are automatically added to any events (including exceptions) captured
-# within that context.
+    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("")

-# 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.
-with posthog.new_context():
-    posthog.tag("transaction_id", "abc123")
-    posthog.tag("some_arbitrary_value", {"tags": "can be dicts"})
+    # 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")

-    # This event will be captured with the tags set above
-    posthog.capture("order_processed")
-    # This exception will be captured with the tags set above
-    raise Exception("Order processing failed")
+    # 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,
+        )

-# Use fresh=True to start with a clean context (no inherited tags)
-with posthog.new_context(fresh=True):
-    posthog.tag("session_id", "xyz789")
-    # Only session_id tag will be present, no inherited tags
-    raise Exception("Session handling failed")
+        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'}")

-# 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)
-    # Exception will be captured and tagged automatically
-    raise Exception("Order processing 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)

-# 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)
-    # Only payment_id tag will be present, no inherited tags
-    raise Exception("Payment processing failed")
+    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...")
+
+    # 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
+    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
+    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()
@@ -22,19 +22,19 @@ posthog/client.py:0: error: Library stubs not installed for "six"  [import-untyp
 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: Item "None" of "Any | None" has no attribute "get"  [union-attr]
-simulator.py:0: error: Unexpected keyword argument "anonymous_id" for "capture"  [call-arg]
-posthog/__init__.py:0: note: "capture" defined here
-simulator.py:0: error: Unexpected keyword argument "anonymous_id" for "identify"  [call-arg]
-posthog/__init__.py:0: note: "identify" defined here
-simulator.py:0: error: Unexpected keyword argument "traits" for "identify"  [call-arg]
-posthog/__init__.py:0: note: "identify" defined here
-example.py:0: error: Statement is unreachable  [unreachable]
-posthog/sentry/posthog_integration.py:0: error: Statement is unreachable  [unreachable]
-posthog/ai/utils.py:0: error: Need type annotation for "output" (hint: "output: list[<type>] = ...")  [var-annotated]
-posthog/ai/utils.py:0: error: Function "builtins.any" is not valid as a type  [valid-type]
-posthog/ai/utils.py:0: note: Perhaps you meant "typing.Any" instead of "any"?
-posthog/ai/utils.py:0: error: Function "builtins.any" is not valid as a type  [valid-type]
-posthog/ai/utils.py:0: note: Perhaps you meant "typing.Any" instead of "any"?
-sentry_django_example/sentry_django_example/settings.py:0: error: Need type annotation for "ALLOWED_HOSTS" (hint: "ALLOWED_HOSTS: list[<type>] = ...")  [var-annotated]
-sentry_django_example/sentry_django_example/settings.py:0: error: Incompatible types in assignment (expression has type "str", variable has type "None")  [assignment]
+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: Incompatible types in assignment (expression has type "Any | dict[Any, Any]", variable has type "None")  [assignment]
+posthog/client.py:0: error: Incompatible types in assignment (expression has type "Any | dict[Any, Any]", variable has type "None")  [assignment]
+posthog/client.py:0: error: Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "None")  [assignment]
+posthog/client.py:0: error: Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "None")  [assignment]
+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: "None" has no attribute "get"  [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]
@@ -6,6 +6,12 @@ from .anthropic_providers import (
    AsyncAnthropicBedrock,
    AsyncAnthropicVertex,
 )
+from .anthropic_converter import (
+    format_anthropic_response,
+    format_anthropic_input,
+    extract_anthropic_tools,
+    format_anthropic_streaming_content,
+)

 __all__ = [
    "Anthropic",
@@ -14,4 +20,8 @@ __all__ = [
    "AsyncAnthropicBedrock",
    "AnthropicVertex",
    "AsyncAnthropicVertex",
+    "format_anthropic_response",
+    "format_anthropic_input",
+    "extract_anthropic_tools",
+    "format_anthropic_streaming_content",
 ]
@@ -8,15 +8,23 @@ except ImportError:

 import time
 import uuid
-from typing import Any, Dict, Optional
+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,
-    get_model_params,
-    merge_system_prompt,
-    with_privacy_mode,
+    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):
@@ -26,14 +34,14 @@ class Anthropic(anthropic.Anthropic):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    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
+        self._ph_client = posthog_client or setup()
        self.messages = WrappedMessages(self)


@@ -60,6 +68,7 @@ class WrappedMessages(Messages):
            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())

@@ -117,35 +126,66 @@ class WrappedMessages(Messages):
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
-        accumulated_content = []
+        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  # noqa: F824
+            nonlocal accumulated_content
+            nonlocal content_blocks
+            nonlocal tools_in_progress
+            nonlocal current_text_block
+
            try:
                for event in response:
-                    if hasattr(event, "usage") and event.usage:
-                        usage_stats = {
-                            k: getattr(event.usage, k, 0)
-                            for k in [
-                                "input_tokens",
-                                "output_tokens",
-                                "cache_read_input_tokens",
-                                "cache_creation_input_tokens",
-                            ]
-                        }
+                    # Extract usage stats from event
+                    event_usage = extract_anthropic_usage_from_event(event)
+                    merge_usage_stats(usage_stats, event_usage)

-                    if hasattr(event, "content") and event.content:
-                        accumulated_content.append(event.content)
+                    # 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
-                output = "".join(accumulated_content)

                self._capture_streaming_event(
                    posthog_distinct_id,
@@ -156,7 +196,8 @@ class WrappedMessages(Messages):
                    kwargs,
                    usage_stats,
                    latency,
-                    output,
+                    content_blocks,
+                    accumulated_content,
                )

        return generator()
@@ -169,49 +210,39 @@ class WrappedMessages(Messages):
        posthog_privacy_mode: bool,
        posthog_groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
-        output: str,
+        content_blocks: List[StreamingContentBlock],
+        accumulated_content: str,
    ):
-        if posthog_trace_id is None:
-            posthog_trace_id = str(uuid.uuid4())
+        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

-        event_properties = {
-            "$ai_provider": "anthropic",
-            "$ai_model": kwargs.get("model"),
-            "$ai_model_parameters": get_model_params(kwargs),
-            "$ai_input": with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                merge_system_prompt(kwargs, "anthropic"),
-            ),
-            "$ai_output_choices": with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                [{"content": output, "role": "assistant"}],
-            ),
-            "$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_cache_creation_input_tokens": usage_stats.get(
-                "cache_creation_input_tokens", 0
-            ),
-            "$ai_latency": latency,
-            "$ai_trace_id": posthog_trace_id,
-            "$ai_base_url": str(self._client.base_url),
-            **(posthog_properties or {}),
-        }
+        # Prepare standardized event data
+        formatted_input = format_anthropic_streaming_input(kwargs)
+        sanitized_input = sanitize_anthropic(formatted_input)

-        if posthog_distinct_id is None:
-            event_properties["$process_person_profile"] = False
+        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,
+        )

-        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,
-            )
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)
@@ -8,14 +8,27 @@ except ImportError:

 import time
 import uuid
-from typing import Any, Dict, Optional
+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,
+    extract_available_tool_calls,
    get_model_params,
    merge_system_prompt,
+    merge_usage_stats,
    with_privacy_mode,
 )
+from posthog.ai.anthropic.anthropic_converter import (
+    format_anthropic_streaming_content,
+    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


@@ -26,14 +39,14 @@ class AsyncAnthropic(anthropic.AsyncAnthropic):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    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
+        self._ph_client = posthog_client or setup()
        self.messages = AsyncWrappedMessages(self)


@@ -60,6 +73,7 @@ class AsyncWrappedMessages(AsyncMessages):
            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())

@@ -117,35 +131,66 @@ class AsyncWrappedMessages(AsyncMessages):
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
-        accumulated_content = []
+        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  # noqa: F824
+            nonlocal accumulated_content
+            nonlocal content_blocks
+            nonlocal tools_in_progress
+            nonlocal current_text_block
+
            try:
                async for event in response:
-                    if hasattr(event, "usage") and event.usage:
-                        usage_stats = {
-                            k: getattr(event.usage, k, 0)
-                            for k in [
-                                "input_tokens",
-                                "output_tokens",
-                                "cache_read_input_tokens",
-                                "cache_creation_input_tokens",
-                            ]
-                        }
+                    # Extract usage stats from event
+                    event_usage = extract_anthropic_usage_from_event(event)
+                    merge_usage_stats(usage_stats, event_usage)

-                    if hasattr(event, "content") and event.content:
-                        accumulated_content.append(event.content)
+                    # 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
-                output = "".join(accumulated_content)

                await self._capture_streaming_event(
                    posthog_distinct_id,
@@ -156,7 +201,8 @@ class AsyncWrappedMessages(AsyncMessages):
                    kwargs,
                    usage_stats,
                    latency,
-                    output,
+                    content_blocks,
+                    accumulated_content,
                )

        return generator()
@@ -169,13 +215,29 @@ class AsyncWrappedMessages(AsyncMessages):
        posthog_privacy_mode: bool,
        posthog_groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
-        output: str,
+        content_blocks: List[StreamingContentBlock],
+        accumulated_content: str,
    ):
        if posthog_trace_id is None:
            posthog_trace_id = str(uuid.uuid4())

+        # Format output using converter
+        formatted_content = format_anthropic_streaming_content(content_blocks)
+        formatted_output = []
+
+        if formatted_content:
+            formatted_output = [{"role": "assistant", "content": formatted_content}]
+        else:
+            # Fallback to accumulated content if no blocks
+            formatted_output = [
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": accumulated_content}],
+                }
+            ]
+
        event_properties = {
            "$ai_provider": "anthropic",
            "$ai_model": kwargs.get("model"),
@@ -183,12 +245,12 @@ class AsyncWrappedMessages(AsyncMessages):
            "$ai_input": with_privacy_mode(
                self._client._ph_client,
                posthog_privacy_mode,
-                merge_system_prompt(kwargs, "anthropic"),
+                sanitize_anthropic(merge_system_prompt(kwargs, "anthropic")),
            ),
            "$ai_output_choices": with_privacy_mode(
                self._client._ph_client,
                posthog_privacy_mode,
-                [{"content": output, "role": "assistant"}],
+                formatted_output,
            ),
            "$ai_http_status": 200,
            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
@@ -205,6 +267,12 @@ class AsyncWrappedMessages(AsyncMessages):
            **(posthog_properties or {}),
        }

+        # Add tools if available
+        available_tools = extract_available_tool_calls("anthropic", kwargs)
+
+        if available_tools:
+            event_properties["$ai_tools"] = available_tools
+
        if posthog_distinct_id is None:
            event_properties["$process_person_profile"] = False

@@ -0,0 +1,403 @@
+"""
+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,
+)
+
+
+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_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
+
+    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
+            )
+
+    # Handle usage stats from message_delta event
+    if hasattr(event, "usage") and event.usage:
+        usage["output_tokens"] = getattr(event.usage, "output_tokens", 0)
+
+    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}],
+            }
+        ]
@@ -5,9 +5,12 @@ except ImportError:
        "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):
@@ -17,9 +20,9 @@ class AnthropicBedrock(anthropic.AnthropicBedrock):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        super().__init__(**kwargs)
-        self._ph_client = posthog_client
+        self._ph_client = posthog_client or setup()
        self.messages = WrappedMessages(self)


@@ -30,9 +33,9 @@ class AsyncAnthropicBedrock(anthropic.AsyncAnthropicBedrock):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        super().__init__(**kwargs)
-        self._ph_client = posthog_client
+        self._ph_client = posthog_client or setup()
        self.messages = AsyncWrappedMessages(self)


@@ -43,9 +46,9 @@ class AnthropicVertex(anthropic.AnthropicVertex):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        super().__init__(**kwargs)
-        self._ph_client = posthog_client
+        self._ph_client = posthog_client or setup()
        self.messages = WrappedMessages(self)


@@ -56,7 +59,7 @@ class AsyncAnthropicVertex(anthropic.AsyncAnthropicVertex):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        super().__init__(**kwargs)
-        self._ph_client = posthog_client
+        self._ph_client = posthog_client or setup()
        self.messages = AsyncWrappedMessages(self)
@@ -1,4 +1,9 @@
 from .gemini import Client
+from .gemini_converter import (
+    format_gemini_input,
+    format_gemini_response,
+    extract_gemini_tools,
+)


 # Create a genai-like module for perfect drop-in replacement
@@ -8,4 +13,10 @@ class _GenAI:

 genai = _GenAI()

-__all__ = ["Client", "genai"]
+__all__ = [
+    "Client",
+    "genai",
+    "format_gemini_input",
+    "format_gemini_response",
+    "extract_gemini_tools",
+]
@@ -3,6 +3,9 @@ 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:
@@ -10,11 +13,18 @@ except ImportError:
        "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,
-    get_model_params,
-    with_privacy_mode,
+    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


@@ -36,9 +46,17 @@ class Client:
        )
    """

+    _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,
@@ -48,7 +66,13 @@ class Client:
    ):
        """
        Args:
-            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable
+            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)
@@ -56,12 +80,21 @@ class Client:
            posthog_groups: Default groups for all calls (can be overridden per call)
            **kwargs: Additional arguments (for future compatibility)
        """
-        if posthog_client is None:
+
+        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,
-            posthog_client=posthog_client,
+            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,
@@ -80,6 +113,12 @@ class Models:
    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,
@@ -89,7 +128,13 @@ class Models:
    ):
        """
        Args:
-            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable
+            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
@@ -97,10 +142,11 @@ class Models:
            posthog_groups: Default groups for all calls
            **kwargs: Additional arguments (for future compatibility)
        """
-        if posthog_client is None:
-            raise ValueError("posthog_client is required for PostHog tracking")

-        self._ph_client = posthog_client
+        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
@@ -108,16 +154,46 @@ class Models:
        self._default_privacy_mode = posthog_privacy_mode
        self._default_groups = posthog_groups

-        # Handle API key - try parameter first, then environment variables
-        if api_key is None:
-            api_key = os.environ.get("GOOGLE_API_KEY") or os.environ.get("API_KEY")
+        # Build genai.Client arguments
+        client_args: Dict[str, Any] = {}

-        if api_key is None:
-            raise ValueError(
-                "API key must be provided either as parameter or via GOOGLE_API_KEY/API_KEY environment variable"
-            )
+        # Add Vertex AI parameters if provided
+        if vertexai is not None:
+            client_args["vertexai"] = vertexai

-        self._client = genai.Client(api_key=api_key)
+        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(
@@ -129,6 +205,7 @@ class Models:
        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
@@ -144,6 +221,7 @@ class Models:

        # Merge properties: default properties + call properties (call properties override)
        properties = dict(self._default_properties)
+
        if call_properties:
            properties.update(call_properties)

@@ -179,6 +257,7 @@ class Models:
            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(
@@ -217,7 +296,7 @@ class Models:
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
        accumulated_content = []

        kwargs_without_stream = {"model": model, "contents": contents, **kwargs}
@@ -228,25 +307,24 @@ class Models:
            nonlocal accumulated_content  # noqa: F824
            try:
                for chunk in response:
-                    if hasattr(chunk, "usage_metadata") and chunk.usage_metadata:
-                        usage_stats = {
-                            "input_tokens": getattr(
-                                chunk.usage_metadata, "prompt_token_count", 0
-                            ),
-                            "output_tokens": getattr(
-                                chunk.usage_metadata, "candidates_token_count", 0
-                            ),
-                        }
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_gemini_usage_from_chunk(chunk)

-                    if hasattr(chunk, "text") and chunk.text:
-                        accumulated_content.append(chunk.text)
+                    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
-                output = "".join(accumulated_content)

                self._capture_streaming_event(
                    model,
@@ -259,7 +337,7 @@ class Models:
                    kwargs,
                    usage_stats,
                    latency,
-                    output,
+                    accumulated_content,
                )

        return generator()
@@ -274,63 +352,39 @@ class Models:
        privacy_mode: bool,
        groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
-        output: str,
+        output: Any,
    ):
-        if trace_id is None:
-            trace_id = str(uuid.uuid4())
+        # Prepare standardized event data
+        formatted_input = self._format_input(contents, **kwargs)
+        sanitized_input = sanitize_gemini(formatted_input)

-        event_properties = {
-            "$ai_provider": "gemini",
-            "$ai_model": model,
-            "$ai_model_parameters": get_model_params(kwargs),
-            "$ai_input": with_privacy_mode(
-                self._ph_client,
-                privacy_mode,
-                self._format_input(contents),
-            ),
-            "$ai_output_choices": with_privacy_mode(
-                self._ph_client,
-                privacy_mode,
-                [{"content": output, "role": "assistant"}],
-            ),
-            "$ai_http_status": 200,
-            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
-            "$ai_output_tokens": usage_stats.get("output_tokens", 0),
-            "$ai_latency": latency,
-            "$ai_trace_id": trace_id,
-            "$ai_base_url": self._base_url,
-            **(properties or {}),
-        }
+        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,
+        )

-        if distinct_id is None:
-            event_properties["$process_person_profile"] = False
+        # Use the common capture function
+        capture_streaming_event(self._ph_client, event_data)

-        if hasattr(self._ph_client, "capture"):
-            self._ph_client.capture(
-                distinct_id=distinct_id,
-                event="$ai_generation",
-                properties=event_properties,
-                groups=groups,
-            )
-
-    def _format_input(self, contents):
+    def _format_input(self, contents, **kwargs):
        """Format input contents for PostHog tracking"""
-        if isinstance(contents, str):
-            return [{"role": "user", "content": contents}]
-        elif isinstance(contents, list):
-            formatted = []
-            for item in contents:
-                if isinstance(item, str):
-                    formatted.append({"role": "user", "content": item})
-                elif hasattr(item, "text"):
-                    formatted.append({"role": "user", "content": item.text})
-                else:
-                    formatted.append({"role": "user", "content": str(item)})
-            return formatted
-        else:
-            return [{"role": "user", "content": str(contents)}]
+
+        # 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,
@@ -0,0 +1,516 @@
+"""
+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,
+)
+
+
+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 _extract_text_from_parts(parts: List[Any]) -> str:
+    """
+    Extract and concatenate text from a parts array.
+
+    Args:
+        parts: List of parts that may contain text content
+
+    Returns:
+        Concatenated text from all parts
+    """
+
+    content_parts = []
+
+    for part in parts:
+        if isinstance(part, dict) and "text" in part:
+            content_parts.append(part["text"])
+
+        elif isinstance(part, str):
+            content_parts.append(part)
+
+        elif hasattr(part, "text"):
+            # Get the text attribute value
+            text_value = getattr(part, "text", "")
+            content_parts.append(text_value if text_value else str(part))
+
+        else:
+            content_parts.append(str(part))
+
+    return "".join(content_parts)
+
+
+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 = _extract_text_from_parts(item["parts"])
+        return {"role": item.get("role", "user"), "content": content}
+
+    # Handle dict with content field
+    if "content" in item:
+        content = item["content"]
+
+        if isinstance(content, list):
+            # If content is a list, extract text from it
+            content = _extract_text_from_parts(content)
+
+        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 = _extract_text_from_parts(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}
+
+    # 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 = _extract_text_from_parts(content)
+
+        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,
+                                    },
+                                }
+                            )
+
+                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_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
+
+    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)
+
+    return _extract_usage_from_metadata(response.usage_metadata)
+
+
+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()
+
+    if not hasattr(chunk, "usage_metadata") or not chunk.usage_metadata:
+        return usage
+
+    # Use the shared helper to extract usage
+    usage = _extract_usage_from_metadata(chunk.usage_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": ""}]}]
@@ -5,6 +5,7 @@ except ImportError:
        "Please install LangChain to use this feature: 'pip install langchain'"
    )

+import json
 import logging
 import time
 from dataclasses import dataclass
@@ -14,7 +15,6 @@ from typing import (
    List,
    Optional,
    Sequence,
-    Tuple,
    Union,
    cast,
 )
@@ -30,12 +30,14 @@ from langchain_core.messages import (
    HumanMessage,
    SystemMessage,
    ToolMessage,
+    ToolCall,
 )
 from langchain_core.outputs import ChatGeneration, LLMResult
 from pydantic import BaseModel

-from posthog import default_client
+from posthog import setup
 from posthog.ai.utils import get_model_params, with_privacy_mode
+from posthog.ai.sanitization import sanitize_langchain
 from posthog.client import Client

 log = logging.getLogger("posthog")
@@ -82,10 +84,10 @@ class CallbackHandler(BaseCallbackHandler):
    The PostHog LLM observability callback handler for LangChain.
    """

-    _client: Client
+    _ph_client: Client
    """PostHog client instance."""

-    _distinct_id: Optional[Union[str, int, float, UUID]]
+    _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]]
@@ -113,7 +115,7 @@ class CallbackHandler(BaseCallbackHandler):
        self,
        client: Optional[Client] = None,
        *,
-        distinct_id: Optional[Union[str, int, float, UUID]] = 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,
@@ -128,10 +130,7 @@ class CallbackHandler(BaseCallbackHandler):
            privacy_mode: Whether to redact the input and output of the trace.
            groups: Optional additional PostHog groups to use for the trace.
        """
-        posthog_client = client or default_client
-        if posthog_client is None:
-            raise ValueError("PostHog client is required")
-        self._client = posthog_client
+        self._ph_client = client or setup()
        self._distinct_id = distinct_id
        self._trace_id = trace_id
        self._properties = properties or {}
@@ -482,11 +481,12 @@ class CallbackHandler(BaseCallbackHandler):
        event_properties = {
            "$ai_trace_id": trace_id,
            "$ai_input_state": with_privacy_mode(
-                self._client, self._privacy_mode, run.input
+                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["$ai_parent_id"] = parent_run_id
@@ -498,13 +498,13 @@ class CallbackHandler(BaseCallbackHandler):
            event_properties["$ai_is_error"] = True
        elif outputs is not None:
            event_properties["$ai_output_state"] = with_privacy_mode(
-                self._client, self._privacy_mode, outputs
+                self._ph_client, self._privacy_mode, outputs
            )

        if self._distinct_id is None:
            event_properties["$process_person_profile"] = False

-        self._client.capture(
+        self._ph_client.capture(
            distinct_id=self._distinct_id or run_id,
            event=event_name,
            properties=event_properties,
@@ -551,17 +551,17 @@ class CallbackHandler(BaseCallbackHandler):
            "$ai_provider": run.provider,
            "$ai_model": run.model,
            "$ai_model_parameters": run.model_params,
-            "$ai_input": with_privacy_mode(self._client, self._privacy_mode, run.input),
+            "$ai_input": with_privacy_mode(
+                self._ph_client, self._privacy_mode, sanitize_langchain(run.input)
+            ),
            "$ai_http_status": 200,
            "$ai_latency": run.latency,
            "$ai_base_url": run.base_url,
+            "$ai_framework": "langchain",
        }
+
        if run.tools:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                self._client,
-                self._privacy_mode,
-                run.tools,
-            )
+            event_properties["$ai_tools"] = run.tools

        if isinstance(output, BaseException):
            event_properties["$ai_http_status"] = _get_http_status(output)
@@ -569,9 +569,14 @@ class CallbackHandler(BaseCallbackHandler):
            event_properties["$ai_is_error"] = True
        else:
            # Add usage
-            input_tokens, output_tokens = _parse_usage(output)
-            event_properties["$ai_input_tokens"] = input_tokens
-            event_properties["$ai_output_tokens"] = output_tokens
+            usage = _parse_usage(output)
+            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]
@@ -582,10 +587,11 @@ class CallbackHandler(BaseCallbackHandler):
                ]
            else:
                completions = [
-                    _extract_raw_esponse(generation) for generation in generation_result
+                    _extract_raw_response(generation)
+                    for generation in generation_result
                ]
            event_properties["$ai_output_choices"] = with_privacy_mode(
-                self._client, self._privacy_mode, completions
+                self._ph_client, self._privacy_mode, completions
            )

        if self._properties:
@@ -594,7 +600,7 @@ class CallbackHandler(BaseCallbackHandler):
        if self._distinct_id is None:
            event_properties["$process_person_profile"] = False

-        self._client.capture(
+        self._ph_client.capture(
            distinct_id=self._distinct_id or trace_id,
            event="$ai_generation",
            properties=event_properties,
@@ -613,7 +619,7 @@ class CallbackHandler(BaseCallbackHandler):
        )


-def _extract_raw_esponse(last_response):
+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() != "":
@@ -626,12 +632,35 @@ def _extract_raw_esponse(last_response):
        return ""


-def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
+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):
@@ -644,12 +673,24 @@ def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
    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],
-) -> Tuple[Union[int, None], Union[int, None]]:
+    usage: Union[BaseModel, dict],
+) -> ModelUsage:
    if isinstance(usage, BaseModel):
        usage = usage.__dict__

@@ -657,15 +698,23 @@ def _parse_usage_model(
        # 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"),
@@ -683,13 +732,45 @@ def _parse_usage_model(

            parsed_usage[type_key] = final_count

-    return parsed_usage.get("input"), parsed_usage.get("output")
+    # 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",
+    }
+    return ModelUsage(
+        **{
+            dataclass_key: parsed_usage.get(mapped_key) or 0
+            for mapped_key, dataclass_key in field_mapping.items()
+        },
+    )


-def _parse_usage(response: LLMResult):
+def _parse_usage(response: LLMResult) -> ModelUsage:
    # langchain-anthropic uses the usage field
    llm_usage_keys = ["token_usage", "usage"]
-    llm_usage: Tuple[Union[int, None], Union[int, None]] = (None, None)
+    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):
@@ -1,5 +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"]
+__all__ = [
+    "OpenAI",
+    "AsyncOpenAI",
+    "AzureOpenAI",
+    "AsyncAzureOpenAI",
+    "format_openai_response",
+    "format_openai_input",
+    "extract_openai_tools",
+    "format_openai_streaming_content",
+]
@@ -2,6 +2,8 @@ import time
 import uuid
 from typing import Any, Dict, List, Optional

+from posthog.ai.types import TokenUsage
+
 try:
    import openai
 except ImportError:
@@ -11,10 +13,19 @@ except ImportError:

 from posthog.ai.utils import (
    call_llm_and_track_usage,
-    get_model_params,
+    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):
@@ -24,16 +35,16 @@ class OpenAI(openai.OpenAI):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    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.
+            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
+        self._ph_client = posthog_client or setup()

        # Store original objects after parent initialization (only if they exist)
        self._original_chat = getattr(self, "chat", None)
@@ -111,7 +122,7 @@ class WrappedResponses:
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {}
+        usage_stats: TokenUsage = TokenUsage()
        final_content = []
        response = self._original.create(**kwargs)

@@ -121,35 +132,17 @@ class WrappedResponses:

            try:
                for chunk in response:
-                    if hasattr(chunk, "type") and chunk.type == "response.completed":
-                        res = chunk.response
-                        if res.output and len(res.output) > 0:
-                            final_content.append(res.output[0])
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "responses")

-                    if hasattr(chunk, "usage") and chunk.usage:
-                        usage_stats = {
-                            k: getattr(chunk.usage, k, 0)
-                            for k in [
-                                "input_tokens",
-                                "output_tokens",
-                                "total_tokens",
-                            ]
-                        }
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)

-                        # Add support for cached tokens
-                        if hasattr(chunk.usage, "output_tokens_details") and hasattr(
-                            chunk.usage.output_tokens_details, "reasoning_tokens"
-                        ):
-                            usage_stats["reasoning_tokens"] = (
-                                chunk.usage.output_tokens_details.reasoning_tokens
-                            )
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "responses")

-                        if hasattr(chunk.usage, "input_tokens_details") and hasattr(
-                            chunk.usage.input_tokens_details, "cached_tokens"
-                        ):
-                            usage_stats["cache_read_input_tokens"] = (
-                                chunk.usage.input_tokens_details.cached_tokens
-                            )
+                    if content is not None:
+                        final_content.append(content)

                    yield chunk

@@ -167,6 +160,7 @@ class WrappedResponses:
                    usage_stats,
                    latency,
                    output,
+                    None,  # Responses API doesn't have tools
                )

        return generator()
@@ -179,56 +173,40 @@ class WrappedResponses:
        posthog_privacy_mode: bool,
        posthog_groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
        output: Any,
-        tool_calls: Optional[List[Dict[str, Any]]] = None,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
    ):
-        if posthog_trace_id is None:
-            posthog_trace_id = str(uuid.uuid4())
+        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

-        event_properties = {
-            "$ai_provider": "openai",
-            "$ai_model": kwargs.get("model"),
-            "$ai_model_parameters": get_model_params(kwargs),
-            "$ai_input": with_privacy_mode(
-                self._client._ph_client, posthog_privacy_mode, kwargs.get("input")
-            ),
-            "$ai_output_choices": with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                output,
-            ),
-            "$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 {}),
-        }
+        # Prepare standardized event data
+        formatted_input = format_openai_streaming_input(kwargs, "responses")
+        sanitized_input = sanitize_openai_response(formatted_input)

-        if tool_calls:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                tool_calls,
-            )
+        event_data = StreamingEventData(
+            provider="openai",
+            model=kwargs.get("model", "unknown"),
+            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,
+        )

-        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,
-            )
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)

    def parse(
        self,
@@ -339,9 +317,9 @@ class WrappedCompletions:
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {}
+        usage_stats: TokenUsage = TokenUsage()
        accumulated_content = []
-        accumulated_tools = {}
+        accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}
        if "stream_options" not in kwargs:
            kwargs["stream_options"] = {}
        kwargs["stream_options"]["include_usage"] = True
@@ -350,70 +328,42 @@ class WrappedCompletions:
        def generator():
            nonlocal usage_stats
            nonlocal accumulated_content  # noqa: F824
-            nonlocal accumulated_tools  # noqa: F824
+            nonlocal accumulated_tool_calls

            try:
                for chunk in response:
-                    if hasattr(chunk, "usage") and chunk.usage:
-                        usage_stats = {
-                            k: getattr(chunk.usage, k, 0)
-                            for k in [
-                                "prompt_tokens",
-                                "completion_tokens",
-                                "total_tokens",
-                            ]
-                        }
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "chat")

-                        # Add support for cached tokens
-                        if hasattr(chunk.usage, "prompt_tokens_details") and hasattr(
-                            chunk.usage.prompt_tokens_details, "cached_tokens"
-                        ):
-                            usage_stats["cache_read_input_tokens"] = (
-                                chunk.usage.prompt_tokens_details.cached_tokens
-                            )
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)

-                        if hasattr(chunk.usage, "output_tokens_details") and hasattr(
-                            chunk.usage.output_tokens_details, "reasoning_tokens"
-                        ):
-                            usage_stats["reasoning_tokens"] = (
-                                chunk.usage.output_tokens_details.reasoning_tokens
-                            )
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "chat")

-                    if (
-                        hasattr(chunk, "choices")
-                        and chunk.choices
-                        and len(chunk.choices) > 0
-                    ):
-                        if chunk.choices[0].delta and chunk.choices[0].delta.content:
-                            content = chunk.choices[0].delta.content
-                            if content:
-                                accumulated_content.append(content)
+                    if content is not None:
+                        accumulated_content.append(content)

-                        # Process tool calls
-                        tool_calls = getattr(chunk.choices[0].delta, "tool_calls", None)
-                        if tool_calls:
-                            for tool_call in tool_calls:
-                                index = tool_call.index
-                                if index not in accumulated_tools:
-                                    accumulated_tools[index] = tool_call
-                                else:
-                                    # Append arguments for existing tool calls
-                                    if hasattr(tool_call, "function") and hasattr(
-                                        tool_call.function, "arguments"
-                                    ):
-                                        accumulated_tools[
-                                            index
-                                        ].function.arguments += (
-                                            tool_call.function.arguments
-                                        )
+                    # 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
-                output = "".join(accumulated_content)
-                tools = list(accumulated_tools.values()) if accumulated_tools else None
+
+                # 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,
@@ -423,8 +373,9 @@ class WrappedCompletions:
                    kwargs,
                    usage_stats,
                    latency,
-                    output,
-                    tools,
+                    accumulated_content,
+                    tool_calls_list,
+                    extract_available_tool_calls("openai", kwargs),
                )

        return generator()
@@ -437,56 +388,41 @@ class WrappedCompletions:
        posthog_privacy_mode: bool,
        posthog_groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
        output: Any,
        tool_calls: Optional[List[Dict[str, Any]]] = None,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
    ):
-        if posthog_trace_id is None:
-            posthog_trace_id = str(uuid.uuid4())
+        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

-        event_properties = {
-            "$ai_provider": "openai",
-            "$ai_model": kwargs.get("model"),
-            "$ai_model_parameters": get_model_params(kwargs),
-            "$ai_input": with_privacy_mode(
-                self._client._ph_client, posthog_privacy_mode, kwargs.get("messages")
-            ),
-            "$ai_output_choices": with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                [{"content": output, "role": "assistant"}],
-            ),
-            "$ai_http_status": 200,
-            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
-            "$ai_output_tokens": usage_stats.get("completion_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 {}),
-        }
+        # Prepare standardized event data
+        formatted_input = format_openai_streaming_input(kwargs, "chat")
+        sanitized_input = sanitize_openai(formatted_input)

-        if tool_calls:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                tool_calls,
-            )
+        event_data = StreamingEventData(
+            provider="openai",
+            model=kwargs.get("model", "unknown"),
+            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,
+        )

-        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,
-            )
+        # Use the common capture function
+        capture_streaming_event(self._client._ph_client, event_data)


 class WrappedEmbeddings:
@@ -523,6 +459,7 @@ class WrappedEmbeddings:
        Returns:
            The response from OpenAI's embeddings.create call.
        """
+
        if posthog_trace_id is None:
            posthog_trace_id = str(uuid.uuid4())

@@ -545,7 +482,9 @@ class WrappedEmbeddings:
            "$ai_provider": "openai",
            "$ai_model": kwargs.get("model"),
            "$ai_input": with_privacy_mode(
-                self._client._ph_client, posthog_privacy_mode, kwargs.get("input")
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai_response(kwargs.get("input")),
            ),
            "$ai_http_status": 200,
            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
@@ -2,6 +2,8 @@ import time
 import uuid
 from typing import Any, Dict, List, Optional

+from posthog.ai.types import TokenUsage
+
 try:
    import openai
 except ImportError:
@@ -9,11 +11,22 @@ except ImportError:
        "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


@@ -24,7 +37,7 @@ class AsyncOpenAI(openai.AsyncOpenAI):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        """
        Args:
            api_key: OpenAI API key.
@@ -32,8 +45,9 @@ class AsyncOpenAI(openai.AsyncOpenAI):
                            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
+        self._ph_client = posthog_client or setup()

        # Store original objects after parent initialization (only if they exist)
        self._original_chat = getattr(self, "chat", None)
@@ -64,6 +78,7 @@ class WrappedResponses:

    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(
@@ -111,7 +126,7 @@ class WrappedResponses:
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {}
+        usage_stats: TokenUsage = TokenUsage()
        final_content = []
        response = await self._original.create(**kwargs)

@@ -121,35 +136,17 @@ class WrappedResponses:

            try:
                async for chunk in response:
-                    if hasattr(chunk, "type") and chunk.type == "response.completed":
-                        res = chunk.response
-                        if res.output and len(res.output) > 0:
-                            final_content.append(res.output[0])
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "responses")

-                    if hasattr(chunk, "usage") and chunk.usage:
-                        usage_stats = {
-                            k: getattr(chunk.usage, k, 0)
-                            for k in [
-                                "input_tokens",
-                                "output_tokens",
-                                "total_tokens",
-                            ]
-                        }
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)

-                        # Add support for cached tokens
-                        if hasattr(chunk.usage, "output_tokens_details") and hasattr(
-                            chunk.usage.output_tokens_details, "reasoning_tokens"
-                        ):
-                            usage_stats["reasoning_tokens"] = (
-                                chunk.usage.output_tokens_details.reasoning_tokens
-                            )
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "responses")

-                        if hasattr(chunk.usage, "input_tokens_details") and hasattr(
-                            chunk.usage.input_tokens_details, "cached_tokens"
-                        ):
-                            usage_stats["cache_read_input_tokens"] = (
-                                chunk.usage.input_tokens_details.cached_tokens
-                            )
+                    if content is not None:
+                        final_content.append(content)

                    yield chunk

@@ -157,6 +154,7 @@ class WrappedResponses:
                end_time = time.time()
                latency = end_time - start_time
                output = final_content
+
                await self._capture_streaming_event(
                    posthog_distinct_id,
                    posthog_trace_id,
@@ -167,6 +165,7 @@ class WrappedResponses:
                    usage_stats,
                    latency,
                    output,
+                    extract_available_tool_calls("openai", kwargs),
                )

        return async_generator()
@@ -179,10 +178,10 @@ class WrappedResponses:
        posthog_privacy_mode: bool,
        posthog_groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
        output: Any,
-        tool_calls: Optional[List[Dict[str, Any]]] = None,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
    ):
        if posthog_trace_id is None:
            posthog_trace_id = str(uuid.uuid4())
@@ -192,12 +191,14 @@ class WrappedResponses:
            "$ai_model": kwargs.get("model"),
            "$ai_model_parameters": get_model_params(kwargs),
            "$ai_input": with_privacy_mode(
-                self._client._ph_client, posthog_privacy_mode, kwargs.get("input")
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai_response(kwargs.get("input")),
            ),
            "$ai_output_choices": with_privacy_mode(
                self._client._ph_client,
                posthog_privacy_mode,
-                output,
+                format_openai_streaming_output(output, "responses"),
            ),
            "$ai_http_status": 200,
            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
@@ -212,12 +213,8 @@ class WrappedResponses:
            **(posthog_properties or {}),
        }

-        if tool_calls:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                tool_calls,
-            )
+        if available_tool_calls:
+            event_properties["$ai_tools"] = available_tool_calls

        if posthog_distinct_id is None:
            event_properties["$process_person_profile"] = False
@@ -341,9 +338,9 @@ class WrappedCompletions:
        **kwargs: Any,
    ):
        start_time = time.time()
-        usage_stats: Dict[str, int] = {}
+        usage_stats: TokenUsage = TokenUsage()
        accumulated_content = []
-        accumulated_tools = {}
+        accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}

        if "stream_options" not in kwargs:
            kwargs["stream_options"] = {}
@@ -353,70 +350,40 @@ class WrappedCompletions:
        async def async_generator():
            nonlocal usage_stats
            nonlocal accumulated_content  # noqa: F824
-            nonlocal accumulated_tools  # noqa: F824
+            nonlocal accumulated_tool_calls

            try:
                async for chunk in response:
-                    if hasattr(chunk, "usage") and chunk.usage:
-                        usage_stats = {
-                            k: getattr(chunk.usage, k, 0)
-                            for k in [
-                                "prompt_tokens",
-                                "completion_tokens",
-                                "total_tokens",
-                            ]
-                        }
+                    # Extract usage stats from chunk
+                    chunk_usage = extract_openai_usage_from_chunk(chunk, "chat")
+                    if chunk_usage:
+                        merge_usage_stats(usage_stats, chunk_usage)

-                        # Add support for cached tokens
-                        if hasattr(chunk.usage, "prompt_tokens_details") and hasattr(
-                            chunk.usage.prompt_tokens_details, "cached_tokens"
-                        ):
-                            usage_stats["cache_read_input_tokens"] = (
-                                chunk.usage.prompt_tokens_details.cached_tokens
-                            )
+                    # Extract content from chunk
+                    content = extract_openai_content_from_chunk(chunk, "chat")
+                    if content is not None:
+                        accumulated_content.append(content)

-                        if hasattr(chunk.usage, "output_tokens_details") and hasattr(
-                            chunk.usage.output_tokens_details, "reasoning_tokens"
-                        ):
-                            usage_stats["reasoning_tokens"] = (
-                                chunk.usage.output_tokens_details.reasoning_tokens
-                            )
-
-                    if (
-                        hasattr(chunk, "choices")
-                        and chunk.choices
-                        and len(chunk.choices) > 0
-                    ):
-                        if chunk.choices[0].delta and chunk.choices[0].delta.content:
-                            content = chunk.choices[0].delta.content
-                            if content:
-                                accumulated_content.append(content)
-
-                        # Process tool calls
-                        tool_calls = getattr(chunk.choices[0].delta, "tool_calls", None)
-                        if tool_calls:
-                            for tool_call in tool_calls:
-                                index = tool_call.index
-                                if index not in accumulated_tools:
-                                    accumulated_tools[index] = tool_call
-                                else:
-                                    # Append arguments for existing tool calls
-                                    if hasattr(tool_call, "function") and hasattr(
-                                        tool_call.function, "arguments"
-                                    ):
-                                        accumulated_tools[
-                                            index
-                                        ].function.arguments += (
-                                            tool_call.function.arguments
-                                        )
+                    # 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
-                output = "".join(accumulated_content)
-                tools = list(accumulated_tools.values()) if accumulated_tools else None
+
+                # 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,
@@ -426,8 +393,9 @@ class WrappedCompletions:
                    kwargs,
                    usage_stats,
                    latency,
-                    output,
-                    tools,
+                    accumulated_content,
+                    tool_calls_list,
+                    extract_available_tool_calls("openai", kwargs),
                )

        return async_generator()
@@ -440,10 +408,11 @@ class WrappedCompletions:
        posthog_privacy_mode: bool,
        posthog_groups: Optional[Dict[str, Any]],
        kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
        latency: float,
        output: Any,
        tool_calls: Optional[List[Dict[str, Any]]] = None,
+        available_tool_calls: Optional[List[Dict[str, Any]]] = None,
    ):
        if posthog_trace_id is None:
            posthog_trace_id = str(uuid.uuid4())
@@ -453,16 +422,18 @@ class WrappedCompletions:
            "$ai_model": kwargs.get("model"),
            "$ai_model_parameters": get_model_params(kwargs),
            "$ai_input": with_privacy_mode(
-                self._client._ph_client, posthog_privacy_mode, kwargs.get("messages")
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai(kwargs.get("messages")),
            ),
            "$ai_output_choices": with_privacy_mode(
                self._client._ph_client,
                posthog_privacy_mode,
-                [{"content": output, "role": "assistant"}],
+                format_openai_streaming_output(output, "chat", tool_calls),
            ),
            "$ai_http_status": 200,
-            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
-            "$ai_output_tokens": usage_stats.get("completion_tokens", 0),
+            "$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
            ),
@@ -473,12 +444,8 @@ class WrappedCompletions:
            **(posthog_properties or {}),
        }

-        if tool_calls:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                self._client._ph_client,
-                posthog_privacy_mode,
-                tool_calls,
-            )
+        if available_tool_calls:
+            event_properties["$ai_tools"] = available_tool_calls

        if posthog_distinct_id is None:
            event_properties["$process_person_profile"] = False
@@ -501,6 +468,7 @@ class WrappedEmbeddings:

    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(
@@ -526,6 +494,7 @@ class WrappedEmbeddings:
        Returns:
            The response from OpenAI's embeddings.create call.
        """
+
        if posthog_trace_id is None:
            posthog_trace_id = str(uuid.uuid4())

@@ -534,12 +503,13 @@ class WrappedEmbeddings:
        end_time = time.time()

        # Extract usage statistics if available
-        usage_stats = {}
+        usage_stats: TokenUsage = TokenUsage()
+
        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),
-            }
+            usage_stats = TokenUsage(
+                input_tokens=getattr(response.usage, "prompt_tokens", 0),
+                output_tokens=getattr(response.usage, "completion_tokens", 0),
+            )

        latency = end_time - start_time

@@ -548,10 +518,12 @@ class WrappedEmbeddings:
            "$ai_provider": "openai",
            "$ai_model": kwargs.get("model"),
            "$ai_input": with_privacy_mode(
-                self._client._ph_client, posthog_privacy_mode, kwargs.get("input")
+                self._client._ph_client,
+                posthog_privacy_mode,
+                sanitize_openai_response(kwargs.get("input")),
            ),
            "$ai_http_status": 200,
-            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
+            "$ai_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),
@@ -582,6 +554,7 @@ class WrappedBeta:

    def __getattr__(self, name):
        """Fallback to original beta object for any methods we don't explicitly handle."""
+
        return getattr(self._original, name)

    @property
@@ -598,6 +571,7 @@ class WrappedBetaChat:

    def __getattr__(self, name):
        """Fallback to original beta chat object for any methods we don't explicitly handle."""
+
        return getattr(self._original, name)

    @property
@@ -614,6 +588,7 @@ class WrappedBetaCompletions:

    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(
@@ -0,0 +1,611 @@
+"""
+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,
+)
+
+
+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,
+                                },
+                            }
+                        )
+
+        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_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
+
+    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":
+        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
+            )
+
+    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
+                    )
+
+    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")
@@ -15,7 +15,10 @@ 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):
@@ -25,7 +28,7 @@ class AzureOpenAI(openai.AzureOpenAI):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        """
        Args:
            api_key: Azure OpenAI API key.
@@ -34,7 +37,7 @@ class AzureOpenAI(openai.AzureOpenAI):
            **openai_config: Any additional keyword args to set on Azure OpenAI (e.g. azure_endpoint="xxx").
        """
        super().__init__(**kwargs)
-        self._ph_client = posthog_client
+        self._ph_client = posthog_client or setup()

        # Store original objects after parent initialization (only if they exist)
        self._original_chat = getattr(self, "chat", None)
@@ -63,7 +66,7 @@ class AsyncAzureOpenAI(openai.AsyncAzureOpenAI):

    _ph_client: PostHogClient

-    def __init__(self, posthog_client: PostHogClient, **kwargs):
+    def __init__(self, posthog_client: Optional[PostHogClient] = None, **kwargs):
        """
        Args:
            api_key: Azure OpenAI API key.
@@ -72,7 +75,7 @@ class AsyncAzureOpenAI(openai.AsyncAzureOpenAI):
            **openai_config: Any additional keyword args to set on Azure OpenAI (e.g. azure_endpoint="xxx").
        """
        super().__init__(**kwargs)
-        self._ph_client = posthog_client
+        self._ph_client = posthog_client or setup()

        # Store original objects after parent initialization (only if they exist)
        self._original_chat = getattr(self, "chat", None)
@@ -0,0 +1,226 @@
+import re
+from typing import Any
+from urllib.parse import urlparse
+
+REDACTED_IMAGE_PLACEHOLDER = "[base64 image redacted]"
+
+
+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 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") == "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"]),
+            },
+        }
+
+    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 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"]
+    ):
+        # For Anthropic, if the source type is "base64", we should always redact the data
+        # The provider is explicitly telling us this is base64 data
+        return {
+            **item,
+            "source": {
+                **item["source"],
+                "data": REDACTED_IMAGE_PLACEHOLDER,
+            },
+        }
+
+    return item
+
+
+def sanitize_gemini_part(part: Any) -> Any:
+    if not isinstance(part, dict):
+        return part
+
+    if (
+        "inline_data" in part
+        and isinstance(part["inline_data"], dict)
+        and "data" in part["inline_data"]
+    ):
+        # For Gemini, the inline_data structure indicates base64 data
+        # We should redact any string data in this context
+        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"]
+    ):
+        # Anthropic style - raw base64 in structured format, always redact
+        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)
@@ -0,0 +1,124 @@
+"""
+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]
+
+
+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]]
@@ -1,10 +1,74 @@
 import time
 import uuid
-from typing import Any, Callable, Dict, List, Optional
-
-from httpx import URL
+from typing import Any, Callable, Dict, List, Optional, cast

 from posthog.client import Client as PostHogClient
+from posthog.ai.types import FormattedMessage, StreamingEventData, TokenUsage
+from posthog.ai.sanitization import (
+    sanitize_openai,
+    sanitize_anthropic,
+    sanitize_gemini,
+    sanitize_langchain,
+)
+
+
+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
+    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"]
+    else:
+        raise ValueError(f"Invalid mode: {mode}. Must be 'incremental' or 'cumulative'")


 def get_model_params(kwargs: Dict[str, Any]) -> Dict[str, Any]:
@@ -29,285 +93,135 @@ def get_model_params(kwargs: Dict[str, Any]) -> Dict[str, Any]:
    return model_params


-def get_usage(response, provider: str) -> Dict[str, Any]:
+def get_usage(response, provider: str) -> TokenUsage:
+    """
+    Extract usage statistics from response based on provider.
+    Delegates to provider-specific converter functions.
+    """
    if provider == "anthropic":
-        return {
-            "input_tokens": response.usage.input_tokens,
-            "output_tokens": response.usage.output_tokens,
-            "cache_read_input_tokens": response.usage.cache_read_input_tokens,
-            "cache_creation_input_tokens": response.usage.cache_creation_input_tokens,
-        }
+        from posthog.ai.anthropic.anthropic_converter import (
+            extract_anthropic_usage_from_response,
+        )
+
+        return extract_anthropic_usage_from_response(response)
    elif provider == "openai":
-        cached_tokens = 0
-        input_tokens = 0
-        output_tokens = 0
-        reasoning_tokens = 0
+        from posthog.ai.openai.openai_converter import (
+            extract_openai_usage_from_response,
+        )

-        # responses api
-        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
-        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
-
-        return {
-            "input_tokens": input_tokens,
-            "output_tokens": output_tokens,
-            "cache_read_input_tokens": cached_tokens,
-            "reasoning_tokens": reasoning_tokens,
-        }
+        return extract_openai_usage_from_response(response)
    elif provider == "gemini":
-        input_tokens = 0
-        output_tokens = 0
+        from posthog.ai.gemini.gemini_converter import (
+            extract_gemini_usage_from_response,
+        )

-        if hasattr(response, "usage_metadata") and response.usage_metadata:
-            input_tokens = getattr(response.usage_metadata, "prompt_token_count", 0)
-            output_tokens = getattr(
-                response.usage_metadata, "candidates_token_count", 0
-            )
+        return extract_gemini_usage_from_response(response)

-        return {
-            "input_tokens": input_tokens,
-            "output_tokens": output_tokens,
-            "cache_read_input_tokens": 0,
-            "cache_creation_input_tokens": 0,
-            "reasoning_tokens": 0,
-        }
-    return {
-        "input_tokens": 0,
-        "output_tokens": 0,
-        "cache_read_input_tokens": 0,
-        "cache_creation_input_tokens": 0,
-        "reasoning_tokens": 0,
-    }
+    return TokenUsage(input_tokens=0, output_tokens=0)


 def format_response(response, provider: str):
    """
    Format a regular (non-streaming) response.
    """
-    output = []
-    if response is None:
-        return output
    if provider == "anthropic":
-        return format_response_anthropic(response)
+        from posthog.ai.anthropic.anthropic_converter import format_anthropic_response
+
+        return format_anthropic_response(response)
    elif provider == "openai":
-        return format_response_openai(response)
+        from posthog.ai.openai.openai_converter import format_openai_response
+
+        return format_openai_response(response)
    elif provider == "gemini":
-        return format_response_gemini(response)
-    return output
+        from posthog.ai.gemini.gemini_converter import format_gemini_response
+
+        return format_gemini_response(response)
+    return []


-def format_response_anthropic(response):
-    output = []
-    for choice in response.content:
-        if choice.text:
-            output.append(
-                {
-                    "role": "assistant",
-                    "content": choice.text,
-                }
-            )
-    return output
-
-
-def format_response_openai(response):
-    output = []
-    if hasattr(response, "choices"):
-        for choice in response.choices:
-            # Handle Chat Completions response format
-            if hasattr(choice, "message") and choice.message and choice.message.content:
-                output.append(
-                    {
-                        "content": choice.message.content,
-                        "role": choice.message.role,
-                    }
-                )
-    # Handle Responses API format
-    if hasattr(response, "output"):
-        for item in response.output:
-            if item.type == "message":
-                # Extract text content from the content list
-                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")
-                        ):
-                            output.append(
-                                {
-                                    "content": content_item.text,
-                                    "role": item.role,
-                                }
-                            )
-                        elif hasattr(content_item, "text"):
-                            output.append(
-                                {
-                                    "content": content_item.text,
-                                    "role": item.role,
-                                }
-                            )
-                        elif (
-                            hasattr(content_item, "type")
-                            and content_item.type == "input_image"
-                            and hasattr(content_item, "image_url")
-                        ):
-                            output.append(
-                                {
-                                    "content": {
-                                        "type": "image",
-                                        "image": content_item.image_url,
-                                    },
-                                    "role": item.role,
-                                }
-                            )
-                else:
-                    output.append(
-                        {
-                            "content": item.content,
-                            "role": item.role,
-                        }
-                    )
-    return output
-
-
-def format_response_gemini(response):
-    output = []
-    if hasattr(response, "candidates") and response.candidates:
-        for candidate in response.candidates:
-            if hasattr(candidate, "content") and candidate.content:
-                content_text = ""
-                if hasattr(candidate.content, "parts") and candidate.content.parts:
-                    for part in candidate.content.parts:
-                        if hasattr(part, "text") and part.text:
-                            content_text += part.text
-                if content_text:
-                    output.append(
-                        {
-                            "role": "assistant",
-                            "content": content_text,
-                        }
-                    )
-            elif hasattr(candidate, "text") and candidate.text:
-                output.append(
-                    {
-                        "role": "assistant",
-                        "content": candidate.text,
-                    }
-                )
-    elif hasattr(response, "text") and response.text:
-        output.append(
-            {
-                "role": "assistant",
-                "content": response.text,
-            }
-        )
-    return output
-
-
-def format_tool_calls(response, provider: str):
+def extract_available_tool_calls(provider: str, kwargs: Dict[str, Any]):
+    """
+    Extract available tool calls for the given provider.
+    """
    if provider == "anthropic":
-        if hasattr(response, "tools") and response.tools and len(response.tools) > 0:
-            return response.tools
-    elif provider == "openai":
-        # Handle both Chat Completions and Responses API
-        if hasattr(response, "choices") and response.choices:
-            # Check for tool_calls in message (Chat Completions format)
-            if (
-                hasattr(response.choices[0], "message")
-                and hasattr(response.choices[0].message, "tool_calls")
-                and response.choices[0].message.tool_calls
-            ):
-                return response.choices[0].message.tool_calls
+        from posthog.ai.anthropic.anthropic_converter import extract_anthropic_tools

-            # Check for tool_calls directly in response (Responses API format)
-            if (
-                hasattr(response.choices[0], "tool_calls")
-                and response.choices[0].tool_calls
-            ):
-                return response.choices[0].tool_calls
+        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):
-    messages: List[Dict[str, Any]] = []
+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 []
-        if kwargs.get("system") is None:
-            return messages
-        return [{"role": "system", "content": kwargs.get("system")}] + messages
+        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", [])
-        if isinstance(contents, str):
-            return [{"role": "user", "content": contents}]
-        elif isinstance(contents, list):
-            formatted = []
-            for item in contents:
-                if isinstance(item, str):
-                    formatted.append({"role": "user", "content": item})
-                elif hasattr(item, "text"):
-                    formatted.append({"role": "user", "content": item.text})
-                else:
-                    formatted.append({"role": "user", "content": str(item)})
-            return formatted
-        else:
-            return [{"role": "user", "content": str(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
-    if kwargs.get("messages") is not None:
-        messages = list(kwargs.get("messages", []))
+        # For OpenAI, handle both Chat Completions and Responses API
+        messages_param = kwargs.get("messages")
+        input_param = kwargs.get("input")

-    if kwargs.get("input") is not None:
-        input_data = kwargs.get("input")
-        if isinstance(input_data, list):
-            messages.extend(input_data)
-        else:
-            messages.append({"role": "user", "content": input_data})
+        # 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:
-            messages = [{"role": "system", "content": kwargs.get("system")}] + messages
+        # 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')}"
+        # 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,
            )
-        else:
-            # Create a new system message with instructions
-            messages = [
-                {"role": "system", "content": kwargs.get("instructions")}
-            ] + messages

-    return messages
+            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(
@@ -318,7 +232,7 @@ def call_llm_and_track_usage(
    posthog_properties: Optional[Dict[str, Any]],
    posthog_privacy_mode: bool,
    posthog_groups: Optional[Dict[str, Any]],
-    base_url: URL,
+    base_url: str,
    call_method: Callable[..., Any],
    **kwargs: Any,
 ) -> Any:
@@ -330,8 +244,8 @@ def call_llm_and_track_usage(
    response = None
    error = None
    http_status = 200
-    usage: Dict[str, Any] = {}
-    error_params: Dict[str, any] = {}
+    usage: TokenUsage = TokenUsage()
+    error_params: Dict[str, Any] = {}

    try:
        response = call_method(**kwargs)
@@ -358,12 +272,15 @@ def call_llm_and_track_usage(
            usage = get_usage(response, provider)

        messages = merge_system_prompt(kwargs, provider)
+        sanitized_messages = sanitize_messages(messages, provider)

        event_properties = {
            "$ai_provider": provider,
            "$ai_model": kwargs.get("model"),
            "$ai_model_parameters": get_model_params(kwargs),
-            "$ai_input": with_privacy_mode(ph_client, posthog_privacy_mode, messages),
+            "$ai_input": with_privacy_mode(
+                ph_client, posthog_privacy_mode, sanitized_messages
+            ),
            "$ai_output_choices": with_privacy_mode(
                ph_client, posthog_privacy_mode, format_response(response, provider)
            ),
@@ -377,33 +294,22 @@ def call_llm_and_track_usage(
            **(error_params or {}),
        }

-        tool_calls = format_tool_calls(response, provider)
-        if tool_calls:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                ph_client, posthog_privacy_mode, tool_calls
-            )
+        available_tool_calls = extract_available_tool_calls(provider, kwargs)

-        if (
-            usage.get("cache_read_input_tokens") is not None
-            and usage.get("cache_read_input_tokens", 0) > 0
-        ):
-            event_properties["$ai_cache_read_input_tokens"] = usage.get(
-                "cache_read_input_tokens", 0
-            )
+        if available_tool_calls:
+            event_properties["$ai_tools"] = available_tool_calls

-        if (
-            usage.get("cache_creation_input_tokens") is not None
-            and usage.get("cache_creation_input_tokens", 0) > 0
-        ):
-            event_properties["$ai_cache_creation_input_tokens"] = usage.get(
-                "cache_creation_input_tokens", 0
-            )
+        cache_read = usage.get("cache_read_input_tokens")
+        if cache_read is not None and cache_read > 0:
+            event_properties["$ai_cache_read_input_tokens"] = cache_read

-        if (
-            usage.get("reasoning_tokens") is not None
-            and usage.get("reasoning_tokens", 0) > 0
-        ):
-            event_properties["$ai_reasoning_tokens"] = usage.get("reasoning_tokens", 0)
+        cache_creation = usage.get("cache_creation_input_tokens")
+        if cache_creation is not None and cache_creation > 0:
+            event_properties["$ai_cache_creation_input_tokens"] = cache_creation
+
+        reasoning = usage.get("reasoning_tokens")
+        if reasoning is not None and reasoning > 0:
+            event_properties["$ai_reasoning_tokens"] = reasoning

        if posthog_distinct_id is None:
            event_properties["$process_person_profile"] = False
@@ -437,7 +343,7 @@ async def call_llm_and_track_usage_async(
    posthog_properties: Optional[Dict[str, Any]],
    posthog_privacy_mode: bool,
    posthog_groups: Optional[Dict[str, Any]],
-    base_url: URL,
+    base_url: str,
    call_async_method: Callable[..., Any],
    **kwargs: Any,
 ) -> Any:
@@ -445,8 +351,8 @@ async def call_llm_and_track_usage_async(
    response = None
    error = None
    http_status = 200
-    usage: Dict[str, Any] = {}
-    error_params: Dict[str, any] = {}
+    usage: TokenUsage = TokenUsage()
+    error_params: Dict[str, Any] = {}

    try:
        response = await call_async_method(**kwargs)
@@ -473,12 +379,15 @@ async def call_llm_and_track_usage_async(
            usage = get_usage(response, provider)

        messages = merge_system_prompt(kwargs, provider)
+        sanitized_messages = sanitize_messages(messages, provider)

        event_properties = {
            "$ai_provider": provider,
            "$ai_model": kwargs.get("model"),
            "$ai_model_parameters": get_model_params(kwargs),
-            "$ai_input": with_privacy_mode(ph_client, posthog_privacy_mode, messages),
+            "$ai_input": with_privacy_mode(
+                ph_client, posthog_privacy_mode, sanitized_messages
+            ),
            "$ai_output_choices": with_privacy_mode(
                ph_client, posthog_privacy_mode, format_response(response, provider)
            ),
@@ -492,27 +401,18 @@ async def call_llm_and_track_usage_async(
            **(error_params or {}),
        }

-        tool_calls = format_tool_calls(response, provider)
-        if tool_calls:
-            event_properties["$ai_tools"] = with_privacy_mode(
-                ph_client, posthog_privacy_mode, tool_calls
-            )
+        available_tool_calls = extract_available_tool_calls(provider, kwargs)

-        if (
-            usage.get("cache_read_input_tokens") is not None
-            and usage.get("cache_read_input_tokens", 0) > 0
-        ):
-            event_properties["$ai_cache_read_input_tokens"] = usage.get(
-                "cache_read_input_tokens", 0
-            )
+        if available_tool_calls:
+            event_properties["$ai_tools"] = available_tool_calls

-        if (
-            usage.get("cache_creation_input_tokens") is not None
-            and usage.get("cache_creation_input_tokens", 0) > 0
-        ):
-            event_properties["$ai_cache_creation_input_tokens"] = usage.get(
-                "cache_creation_input_tokens", 0
-            )
+        cache_read = usage.get("cache_read_input_tokens")
+        if cache_read is not None and cache_read > 0:
+            event_properties["$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:
+            event_properties["$ai_cache_creation_input_tokens"] = cache_creation

        if posthog_distinct_id is None:
            event_properties["$process_person_profile"] = False
@@ -538,7 +438,122 @@ async def call_llm_and_track_usage_async(
    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"$ai_{field}"] = value
+
+    # Handle provider-specific fields
+    if (
+        event_data["provider"] == "openai"
+        and event_data["kwargs"].get("instructions") is not None
+    ):
+        event_properties["$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"),
+        )
@@ -0,0 +1,101 @@
+from typing import TypedDict, Optional, Any, Dict, List, 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]
+
+
+# AI Event Types (literal strings to enforce valid event types)
+AI_EVENT_TYPE = Union[
+    str,  # Allow str for flexibility but document the expected values
+]  # "$ai_generation", "$ai_trace", "$ai_span", "$ai_embedding", "$ai_metric", "$ai_feedback"
+
+
+class OptionalCaptureAIArgs(TypedDict):
+    """Optional arguments for the capture_ai method.
+
+    Args:
+        distinct_id: Unique identifier for the person associated with this AI event. If not set, the context
+            distinct_id is used, if available, otherwise a UUID is generated.
+        properties: Dictionary of AI event properties to track. Must include required properties for the event type.
+        blob_properties: List of property names that should be sent as blobs (e.g., '$ai_input', '$ai_output_choices').
+            These properties will be extracted from `properties` and sent as multipart blobs.
+        timestamp: When the event occurred (defaults to current time)
+        uuid: Unique identifier for this specific event. If not provided, one is generated.
+        groups: Group identifiers to associate with this event (format: {group_type: group_key})
+        disable_geoip: Whether to disable GeoIP lookup for this event.
+    """
+
+    distinct_id: NotRequired[Optional[ID_TYPES]]
+    properties: NotRequired[Optional[Dict[str, Any]]]
+    blob_properties: NotRequired[Optional[List[str]]]
+    timestamp: NotRequired[Optional[Union[datetime, str]]]
+    uuid: NotRequired[Optional[str]]
+    groups: NotRequired[Optional[Dict[str, str]]]
+    disable_geoip: NotRequired[Optional[bool]]
@@ -0,0 +1,284 @@
+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.tags: Dict[str, Any] = {}
+
+    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 add_tag(self, key: str, value: Any):
+        self.tags[key] = value
+
+    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 collect_tags(self) -> Dict[str, Any]:
+        tags = self.tags.copy()
+        if self.parent and not self.fresh:
+            # We want child tags to take precedence over parent tags,
+            # so we can't use a simple update here, instead collecting
+            # the parent tags and then updating with the child tags.
+            new_tags = self.parent.collect_tags()
+            tags.update(new_tags)
+        return tags
+
+
+_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
+
+
+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
@@ -6,47 +6,25 @@
 import logging
 import sys
 import threading
-from enum import Enum
-from typing import TYPE_CHECKING, List, Optional
+from typing import TYPE_CHECKING

 if TYPE_CHECKING:
    from posthog.client import Client


-class Integrations(str, Enum):
-    Django = "django"
-
-
 class ExceptionCapture:
    # TODO: Add client side rate limiting to prevent spamming the server with exceptions

    log = logging.getLogger("posthog")

-    def __init__(
-        self, client: "Client", integrations: Optional[List[Integrations]] = None
-    ):
+    def __init__(self, client: "Client"):
        self.client = client
        self.original_excepthook = sys.excepthook
        sys.excepthook = self.exception_handler
        threading.excepthook = self.thread_exception_handler
-        self.enabled_integrations = []
-
-        for integration in integrations or []:
-            # TODO: Maybe find a better way of enabling integrations
-            # This is very annoying currently if we had to add any configuration per integration
-            if integration == Integrations.Django:
-                try:
-                    from posthog.exception_integrations.django import DjangoIntegration
-
-                    enabled_integration = DjangoIntegration(self.exception_receiver)
-                    self.enabled_integrations.append(enabled_integration)
-                except Exception as e:
-                    self.log.exception(f"Failed to enable Django integration: {e}")

    def close(self):
        sys.excepthook = self.original_excepthook
-        for integration in self.enabled_integrations:
-            integration.uninstall()

    def exception_handler(self, exc_type, exc_value, exc_traceback):
        # don't affect default behaviour.
@@ -66,6 +44,6 @@ class ExceptionCapture:
    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)
+            self.client.capture_exception(exception, distinct_id=distinct_id)
        except Exception as e:
            self.log.exception(f"Failed to capture exception: {e}")
@@ -1,5 +0,0 @@
-class IntegrationEnablingError(Exception):
-    """
-    The integration could not be enabled due to a user error like
-    `django` not being installed for the `DjangoIntegration`.
-    """
@@ -1,117 +0,0 @@
-# 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 re
-import sys
-from typing import TYPE_CHECKING
-
-from posthog.exception_integrations import IntegrationEnablingError
-
-try:
-    from django import VERSION as DJANGO_VERSION
-    from django.core import signals
-
-except ImportError:
-    raise IntegrationEnablingError("Django not installed")
-
-
-if TYPE_CHECKING:
-    from typing import Any, Dict  # noqa: F401
-
-    from django.core.handlers.wsgi import WSGIRequest  # noqa: F401
-
-
-class DjangoIntegration:
-    # TODO: Abstract integrations one we have more and can see patterns
-    """
-    Autocapture errors from a Django application.
-    """
-
-    identifier = "django"
-
-    def __init__(self, capture_exception_fn=None):
-        if DJANGO_VERSION < (4, 2):
-            raise IntegrationEnablingError("Django 4.2 or newer is required.")
-
-        # TODO: Right now this seems too complicated / overkill for us, but seems like we can automatically plug in middlewares
-        # which is great for users (they don't need to do this) and everything should just work.
-        # We should consider this in the future, but for now we can just use the middleware and signals handlers.
-        # See: https://github.com/getsentry/sentry-python/blob/269d96d6e9821122fbff280e6a26956e5ed03c0b/sentry_sdk/integrations/django/__init__.py
-
-        self.capture_exception_fn = capture_exception_fn
-
-        def _got_request_exception(request=None, **kwargs):
-            # type: (WSGIRequest, **Any) -> None
-
-            extra_props = {}
-            if request is not None:
-                # get headers metadata
-                extra_props = DjangoRequestExtractor(request).extract_person_data()
-
-            self.capture_exception_fn(sys.exc_info(), extra_props)
-
-        signals.got_request_exception.connect(_got_request_exception)
-
-    def uninstall(self):
-        pass
-
-
-class DjangoRequestExtractor:
-    def __init__(self, request):
-        # type: (Any) -> None
-        self.request = request
-
-    def extract_person_data(self):
-        headers = self.headers()
-
-        # Extract traceparent and tracestate headers
-        traceparent = headers.get("Traceparent")
-        tracestate = headers.get("Tracestate")
-
-        # Extract the distinct_id from tracestate
-        distinct_id = None
-        if tracestate:
-            # TODO: Align on the format of the distinct_id in tracestate
-            # We can't have comma or equals in header values here, so maybe we should base64 encode it?
-            match = re.search(r"posthog-distinct-id=([^,]+)", tracestate)
-            if match:
-                distinct_id = match.group(1)
-
-        return {
-            **self.user(),
-            "distinct_id": distinct_id,
-            "ip": headers.get("X-Forwarded-For"),
-            "user_agent": headers.get("User-Agent"),
-            "traceparent": traceparent,
-            "$request_path": self.request.path,
-        }
-
-    def user(self):
-        user_data: dict[str, str] = {}
-
-        user = getattr(self.request, "user", None)
-
-        if user is None or not user.is_authenticated:
-            return user_data
-
-        try:
-            user_id = str(user.pk)
-            if user_id:
-                user_data.setdefault("$user_id", user_id)
-        except Exception:
-            pass
-
-        try:
-            email = str(user.email)
-            if email:
-                user_data.setdefault("email", email)
-        except Exception:
-            pass
-
-        return user_data
-
-    def headers(self):
-        # type: () -> Dict[str, str]
-        return dict(self.request.headers)
@@ -9,8 +9,26 @@ import linecache
 import os
 import re
 import sys
+import types
 from datetime import datetime
-from typing import TYPE_CHECKING
+from types import FrameType, TracebackType  # noqa: F401
+from typing import (  # noqa: F401
+    Any,
+    Dict,
+    Iterator,
+    List,
+    Literal,
+    Optional,
+    Set,
+    Tuple,
+    TypedDict,
+    TypeVar,
+    Union,
+    cast,
+    TYPE_CHECKING,
+)
+
+from posthog.args import ExcInfo, ExceptionArg  # noqa: F401

 try:
    # Python 3.11
@@ -22,85 +40,61 @@ except ImportError:

 DEFAULT_MAX_VALUE_LENGTH = 1024

+LogLevelStr = Literal["fatal", "critical", "error", "warning", "info", "debug"]

-if TYPE_CHECKING:
-    from types import FrameType, TracebackType
-    from typing import (  # noqa: F401
-        Any,
-        Callable,
-        Dict,
-        Iterator,
-        List,
-        Literal,
-        Optional,
-        Set,
-        Tuple,
-        Type,
-        TypedDict,
-        TypeVar,
-        Union,
-        cast,
-    )
-
-    ExcInfo = Union[
-        Tuple[Type[BaseException], BaseException, Optional[TracebackType]],
-        Tuple[None, None, None],
-    ]
-    LogLevelStr = Literal["fatal", "critical", "error", "warning", "info", "debug"]
-
-    Event = TypedDict(
-        "Event",
-        {
-            "breadcrumbs": Dict[
-                Literal["values"], List[Dict[str, Any]]
-            ],  # TODO: We can expand on this type
-            "check_in_id": str,
-            "contexts": Dict[str, Dict[str, object]],
-            "dist": str,
-            "duration": Optional[float],
-            "environment": str,
-            "errors": List[Dict[str, Any]],  # TODO: We can expand on this type
-            "event_id": str,
-            "exception": Dict[
-                Literal["values"], List[Dict[str, Any]]
-            ],  # TODO: We can expand on this type
-            # "extra": MutableMapping[str, object],
-            # "fingerprint": List[str],
-            "level": LogLevelStr,
-            # "logentry": Mapping[str, object],
-            "logger": str,
-            # "measurements": Dict[str, MeasurementValue],
-            "message": str,
-            "modules": Dict[str, str],
-            # "monitor_config": Mapping[str, object],
-            "monitor_slug": Optional[str],
-            "platform": Literal["python"],
-            "profile": object,  # Should be sentry_sdk.profiler.Profile, but we can't import that here due to circular imports
-            "release": str,
-            "request": Dict[str, object],
-            # "sdk": Mapping[str, object],
-            "server_name": str,
-            "spans": List[Dict[str, object]],
-            "stacktrace": Dict[
-                str, object
-            ],  # We access this key in the code, but I am unsure whether we ever set it
-            "start_timestamp": datetime,
-            "status": Optional[str],
-            # "tags": MutableMapping[
-            #     str, str
-            # ],  # Tags must be less than 200 characters each
-            "threads": Dict[
-                Literal["values"], List[Dict[str, Any]]
-            ],  # TODO: We can expand on this type
-            "timestamp": Optional[datetime],  # Must be set before sending the event
-            "transaction": str,
-            # "transaction_info": Mapping[str, Any],  # TODO: We can expand on this type
-            "type": Literal["check_in", "transaction"],
-            "user": Dict[str, object],
-            "_metrics_summary": Dict[str, object],
-        },
-        total=False,
-    )
+Event = TypedDict(
+    "Event",
+    {
+        "breadcrumbs": Dict[
+            Literal["values"], List[Dict[str, Any]]
+        ],  # TODO: We can expand on this type
+        "check_in_id": str,
+        "contexts": Dict[str, Dict[str, object]],
+        "dist": str,
+        "duration": Optional[float],
+        "environment": str,
+        "errors": List[Dict[str, Any]],  # TODO: We can expand on this type
+        "event_id": str,
+        "exception": Dict[
+            Literal["values"], List[Dict[str, Any]]
+        ],  # TODO: We can expand on this type
+        # "extra": MutableMapping[str, object],
+        # "fingerprint": List[str],
+        "level": LogLevelStr,
+        # "logentry": Mapping[str, object],
+        "logger": str,
+        # "measurements": Dict[str, MeasurementValue],
+        "message": str,
+        "modules": Dict[str, str],
+        # "monitor_config": Mapping[str, object],
+        "monitor_slug": Optional[str],
+        "platform": Literal["python"],
+        "profile": object,
+        "release": str,
+        "request": Dict[str, object],
+        # "sdk": Mapping[str, object],
+        "server_name": str,
+        "spans": List[Dict[str, object]],
+        "stacktrace": Dict[
+            str, object
+        ],  # We access this key in the code, but I am unsure whether we ever set it
+        "start_timestamp": datetime,
+        "status": Optional[str],
+        # "tags": MutableMapping[
+        #     str, str
+        # ],  # Tags must be less than 200 characters each
+        "threads": Dict[
+            Literal["values"], List[Dict[str, Any]]
+        ],  # TODO: We can expand on this type
+        "timestamp": Optional[datetime],  # Must be set before sending the event
+        "transaction": str,
+        # "transaction_info": Mapping[str, Any],  # TODO: We can expand on this type
+        "type": Literal["check_in", "transaction"],
+        "user": Dict[str, object],
+        "_metrics_summary": Dict[str, object],
+    },
+    total=False,
+)


 epoch = datetime(1970, 1, 1)
@@ -136,9 +130,6 @@ def event_hint_with_exc_info(exc_info=None):
 class AnnotatedValue:
    """
    Meta information for a data field in the event payload.
-    This is to tell Relay that we have tampered with the fields value.
-    See:
-    https://github.com/getsentry/relay/blob/be12cd49a0f06ea932ed9b9f93a655de5d6ad6d1/relay-general/src/types/meta.rs#L407-L423
    """

    __slots__ = ("value", "metadata")
@@ -364,12 +355,9 @@ def filename_for_module(module, abs_path):
 def serialize_frame(
    frame,
    tb_lineno=None,
-    include_local_variables=True,
-    include_source_context=True,
    max_value_length=None,
-    custom_repr=None,
 ):
-    # type: (FrameType, Optional[int], bool, bool, Optional[int], Optional[Callable[..., Optional[str]]]) -> Dict[str, Any]
+    # type: (FrameType, Optional[int], Optional[int]) -> Dict[str, Any]
    f_code = getattr(frame, "f_code", None)
    if not f_code:
        abs_path = None
@@ -394,50 +382,13 @@ def serialize_frame(
        "lineno": tb_lineno,
    }  # type: Dict[str, Any]

-    if include_source_context:
-        rv["pre_context"], rv["context_line"], rv["post_context"] = get_source_context(
-            frame, tb_lineno, max_value_length
-        )
-
-    if include_local_variables:
-        # TODO(nk): Sort out this current invalid import
-        # from sentry_sdk.serializer import serialize
-
-        # rv["vars"] = serialize(
-        #     dict(frame.f_locals), is_vars=True, custom_repr=custom_repr
-        # )
-        pass
+    rv["pre_context"], rv["context_line"], rv["post_context"] = get_source_context(
+        frame, tb_lineno, max_value_length
+    )

    return rv


-def current_stacktrace(
-    include_local_variables=True,  # type: bool
-    include_source_context=True,  # type: bool
-    max_value_length=None,  # type: Optional[int]
-):
-    # type: (...) -> Dict[str, Any]
-    __tracebackhide__ = True
-    frames = []
-
-    f = sys._getframe()  # type: Optional[FrameType]
-    while f is not None:
-        if not should_hide_frame(f):
-            frames.append(
-                serialize_frame(
-                    f,
-                    include_local_variables=include_local_variables,
-                    include_source_context=include_source_context,
-                    max_value_length=max_value_length,
-                )
-            )
-        f = f.f_back
-
-    frames.reverse()
-
-    return {"frames": frames, "type": "raw"}
-
-
 def get_errno(exc_value):
    # type: (BaseException) -> Optional[Any]
    return getattr(exc_value, "errno", None)
@@ -445,18 +396,19 @@ def get_errno(exc_value):

 def get_error_message(exc_value):
    # type: (Optional[BaseException]) -> str
-    return (
+    message = (
        getattr(exc_value, "message", "")
        or getattr(exc_value, "detail", "")
-        or safe_str(exc_value)
+        or exc_value
    )

+    return safe_str(message)
+

 def single_exception_from_error_tuple(
    exc_type,  # type: Optional[type]
    exc_value,  # type: Optional[BaseException]
    tb,  # type: Optional[TracebackType]
-    client_options=None,  # type: Optional[Dict[str, Any]]
    mechanism=None,  # type: Optional[Dict[str, Any]]
    exception_id=None,  # type: Optional[int]
    parent_id=None,  # type: Optional[int]
@@ -464,10 +416,7 @@ def single_exception_from_error_tuple(
 ):
    # type: (...) -> Dict[str, Any]
    """
-    Creates a dict that goes into the events `exception.values` list and is ingestible by Sentry.
-
-    See the Exception Interface documentation for more details:
-    https://develop.sentry.dev/sdk/event-payloads/exception/
+    Creates a dict that goes into the events `exception.values` list
    """
    exception_value = {}  # type: Dict[str, Any]
    exception_value["mechanism"] = (
@@ -507,25 +456,13 @@ def single_exception_from_error_tuple(
    exception_value["type"] = get_type_name(exc_type)
    exception_value["value"] = get_error_message(exc_value)

-    if client_options is None:
-        include_local_variables = True
-        include_source_context = True
-        max_value_length = DEFAULT_MAX_VALUE_LENGTH  # fallback
-        custom_repr = None
-    else:
-        include_local_variables = client_options["include_local_variables"]
-        include_source_context = client_options["include_source_context"]
-        max_value_length = client_options["max_value_length"]
-        custom_repr = client_options.get("custom_repr")
+    max_value_length = DEFAULT_MAX_VALUE_LENGTH  # fallback

    frames = [
        serialize_frame(
            tb.tb_frame,
            tb_lineno=tb.tb_lineno,
-            include_local_variables=include_local_variables,
-            include_source_context=include_source_context,
            max_value_length=max_value_length,
-            custom_repr=custom_repr,
        )
        for tb in iter_stacks(tb)
    ]
@@ -581,7 +518,6 @@ def exceptions_from_error(
    exc_type,  # type: Optional[type]
    exc_value,  # type: Optional[BaseException]
    tb,  # type: Optional[TracebackType]
-    client_options=None,  # type: Optional[Dict[str, Any]]
    mechanism=None,  # type: Optional[Dict[str, Any]]
    exception_id=0,  # type: int
    parent_id=0,  # type: int
@@ -591,16 +527,12 @@ def exceptions_from_error(
    """
    Creates the list of exceptions.
    This can include chained exceptions and exceptions from an ExceptionGroup.
-
-    See the Exception Interface documentation for more details:
-    https://develop.sentry.dev/sdk/event-payloads/exception/
    """

    parent = single_exception_from_error_tuple(
        exc_type=exc_type,
        exc_value=exc_value,
        tb=tb,
-        client_options=client_options,
        mechanism=mechanism,
        exception_id=exception_id,
        parent_id=parent_id,
@@ -628,7 +560,6 @@ def exceptions_from_error(
                exc_type=type(cause),
                exc_value=cause,
                tb=getattr(cause, "__traceback__", None),
-                client_options=client_options,
                mechanism=mechanism,
                exception_id=exception_id,
                source="__cause__",
@@ -649,7 +580,6 @@ def exceptions_from_error(
                exc_type=type(context),
                exc_value=context,
                tb=getattr(context, "__traceback__", None),
-                client_options=client_options,
                mechanism=mechanism,
                exception_id=exception_id,
                source="__context__",
@@ -664,7 +594,6 @@ def exceptions_from_error(
                exc_type=type(e),
                exc_value=e,
                tb=getattr(e, "__traceback__", None),
-                client_options=client_options,
                mechanism=mechanism,
                exception_id=exception_id,
                parent_id=parent_id,
@@ -677,7 +606,6 @@ def exceptions_from_error(

 def exceptions_from_error_tuple(
    exc_info,  # type: ExcInfo
-    client_options=None,  # type: Optional[Dict[str, Any]]
    mechanism=None,  # type: Optional[Dict[str, Any]]
 ):
    # type: (...) -> List[Dict[str, Any]]
@@ -692,7 +620,6 @@ def exceptions_from_error_tuple(
            exc_type=exc_type,
            exc_value=exc_value,
            tb=tb,
-            client_options=client_options,
            mechanism=mechanism,
            exception_id=0,
            parent_id=0,
@@ -702,9 +629,7 @@ def exceptions_from_error_tuple(
        exceptions = []
        for exc_type, exc_value, tb in walk_exception_chain(exc_info):
            exceptions.append(
-                single_exception_from_error_tuple(
-                    exc_type, exc_value, tb, client_options, mechanism
-                )
+                single_exception_from_error_tuple(exc_type, exc_value, tb, mechanism)
            )

    exceptions.reverse()
@@ -793,11 +718,42 @@ def set_in_app_in_frames(frames, in_app_exclude, in_app_include, project_root=No
    return frames


+def exception_is_already_captured(error):
+    # type: (ExceptionArg) -> bool
+    if isinstance(error, BaseException):
+        return hasattr(error, "__posthog_exception_captured")
+    # Autocaptured exceptions are passed as a tuple from our system hooks,
+    # the second item is the exception value (the first is the exception type)
+    elif isinstance(error, tuple) and len(error) > 1:
+        return error[1] is not None and hasattr(
+            error[1], "__posthog_exception_captured"
+        )
+    else:
+        return False  # type: ignore[unreachable]
+
+
+def mark_exception_as_captured(error, uuid):
+    # type: (ExceptionArg, str) -> None
+    if isinstance(error, BaseException):
+        setattr(error, "__posthog_exception_captured", True)
+        setattr(error, "__posthog_exception_uuid", uuid)
+    # Autocaptured exceptions are passed as a tuple from our system hooks,
+    # the second item is the exception value (the first is the exception type)
+    elif isinstance(error, tuple) and len(error) > 1:
+        if error[1] is not None:
+            setattr(error[1], "__posthog_exception_captured", True)
+            setattr(error[1], "__posthog_exception_uuid", uuid)
+
+
 def exc_info_from_error(error):
-    # type: (Union[BaseException, ExcInfo]) -> ExcInfo
+    # type: (ExceptionArg) -> ExcInfo
    if isinstance(error, tuple) and len(error) == 3:
        exc_type, exc_value, tb = error
    elif isinstance(error, BaseException):
+        try:
+            construct_artificial_traceback(error)
+        except Exception:
+            pass
        tb = getattr(error, "__traceback__", None)
        if tb is not None:
            exc_type = type(error)
@@ -822,25 +778,29 @@ def exc_info_from_error(error):
    return exc_info


-def event_from_exception(
-    exc_info,  # type: Union[BaseException, ExcInfo]
-    client_options=None,  # type: Optional[Dict[str, Any]]
-    mechanism=None,  # type: Optional[Dict[str, Any]]
-):
-    # type: (...) -> Tuple[Event, Dict[str, Any]]
-    exc_info = exc_info_from_error(exc_info)
-    hint = event_hint_with_exc_info(exc_info)
-    return (
-        {
-            "level": "error",
-            "exception": {
-                "values": exceptions_from_error_tuple(
-                    exc_info, client_options, mechanism
-                )
-            },
-        },
-        hint,
-    )
+def construct_artificial_traceback(e):
+    # type: (BaseException) -> None
+    if getattr(e, "__traceback__", None) is not None:
+        return
+
+    depth = 0
+    frames = []
+    while True:
+        try:
+            frame = sys._getframe(depth)
+            depth += 1
+        except ValueError:
+            break
+
+        frames.append(frame)
+
+    frames.reverse()
+
+    tb = None
+    for frame in frames:
+        tb = types.TracebackType(tb, frame, frame.f_lasti, frame.f_lineno)
+
+    setattr(e, "__traceback__", tb)


 def _module_in_list(name, items):
@@ -22,6 +22,18 @@ 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 distinct_id and a feature flag key and returns a float between 0 and 1.
 # Given the same distinct_id 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
@@ -55,8 +67,161 @@ def variant_lookup_table(feature_flag):
    return lookup_table


+def evaluate_flag_dependency(
+    property, flags_by_key, evaluation_cache, distinct_id, properties, cohort_properties
+):
+    """
+    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
+
+    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_result = match_feature_flag_properties(
+                            dep_flag,
+                            distinct_id,
+                            properties,
+                            cohort_properties,
+                            flags_by_key,
+                            evaluation_cache,
+                        )
+                        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 match_feature_flag_properties(
-    flag, distinct_id, properties, cohort_properties=None
+    flag,
+    distinct_id,
+    properties,
+    cohort_properties=None,
+    flags_by_key=None,
+    evaluation_cache=None,
 ) -> FlagValue:
    flag_conditions = (flag.get("filters") or {}).get("groups") or []
    is_inconclusive = False
@@ -67,19 +232,18 @@ def match_feature_flag_properties(
    ) or []
    valid_variant_keys = [variant["key"] for variant in flag_variants]

-    # Stable sort conditions with variant overrides to the top. This ensures that if overrides are present, they are
-    # evaluated first, and the variant override is applied to the first matching condition.
-    sorted_flag_conditions = sorted(
-        flag_conditions,
-        key=lambda condition: 0 if condition.get("variant") else 1,
-    )
-
-    for condition in sorted_flag_conditions:
+    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
+                flag,
+                distinct_id,
+                condition,
+                properties,
+                cohort_properties,
+                flags_by_key,
+                evaluation_cache,
            ):
                variant_override = condition.get("variant")
                if variant_override and variant_override in valid_variant_keys:
@@ -87,7 +251,12 @@ def match_feature_flag_properties(
                else:
                    variant = get_matching_variant(flag, distinct_id)
                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:
@@ -101,14 +270,36 @@ def match_feature_flag_properties(


 def is_condition_match(
-    feature_flag, distinct_id, condition, properties, cohort_properties
+    feature_flag,
+    distinct_id,
+    condition,
+    properties,
+    cohort_properties,
+    flags_by_key=None,
+    evaluation_cache=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)
+                matches = match_cohort(
+                    prop,
+                    properties,
+                    cohort_properties,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                )
+            elif property_type == "flag":
+                matches = evaluate_flag_dependency(
+                    prop,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                    properties,
+                    cohort_properties,
+                )
            else:
                matches = match_property(prop, properties)
            if not matches:
@@ -256,7 +447,14 @@ def match_property(property, property_values) -> bool:
    raise InconclusiveMatchError(f"Unknown operator {operator}")


-def match_cohort(property, property_values, cohort_properties) -> bool:
+def match_cohort(
+    property,
+    property_values,
+    cohort_properties,
+    flags_by_key=None,
+    evaluation_cache=None,
+    distinct_id=None,
+) -> bool:
    # Cohort properties are in the form of property groups like this:
    # {
    #     "cohort_id": {
@@ -268,15 +466,29 @@ def match_cohort(property, property_values, cohort_properties) -> bool:
    # }
    cohort_id = str(property.get("value"))
    if cohort_id not in cohort_properties:
-        raise InconclusiveMatchError(
-            "can't match cohort without a given cohort property value"
+        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)
+    return match_property_group(
+        property_group,
+        property_values,
+        cohort_properties,
+        flags_by_key,
+        evaluation_cache,
+        distinct_id,
+    )


-def match_property_group(property_group, property_values, cohort_properties) -> bool:
+def match_property_group(
+    property_group,
+    property_values,
+    cohort_properties,
+    flags_by_key=None,
+    evaluation_cache=None,
+    distinct_id=None,
+) -> bool:
    if not property_group:
        return True

@@ -293,7 +505,14 @@ def match_property_group(property_group, property_values, cohort_properties) ->
        # a nested property group
        for prop in properties:
            try:
-                matches = match_property_group(prop, property_values, cohort_properties)
+                matches = match_property_group(
+                    prop,
+                    property_values,
+                    cohort_properties,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                )
                if property_group_type == "AND":
                    if not matches:
                        return False
@@ -301,6 +520,9 @@ def match_property_group(property_group, property_values, cohort_properties) ->
                    # 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
@@ -316,7 +538,23 @@ def match_property_group(property_group, property_values, cohort_properties) ->
        for prop in properties:
            try:
                if prop.get("type") == "cohort":
-                    matches = match_cohort(prop, property_values, cohort_properties)
+                    matches = match_cohort(
+                        prop,
+                        property_values,
+                        cohort_properties,
+                        flags_by_key,
+                        evaluation_cache,
+                        distinct_id,
+                    )
+                elif prop.get("type") == "flag":
+                    matches = evaluate_flag_dependency(
+                        prop,
+                        flags_by_key,
+                        evaluation_cache,
+                        distinct_id,
+                        property_values,
+                        cohort_properties,
+                    )
                else:
                    matches = match_property(prop, property_values)

@@ -334,6 +572,9 @@ def match_property_group(property_group, property_values, cohort_properties) ->
                        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
@@ -0,0 +1,251 @@
+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]
+        tags = {}
+
+        (user_id, user_email) = self.extract_request_user(request)
+
+        # 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):
+        user_id = None
+        email = None
+
+        user = getattr(request, "user", None)
+
+        if user and getattr(user, "is_authenticated", False):
+            try:
+                user_id = str(user.pk)
+            except Exception:
+                pass
+
+            try:
+                email = str(user.email)
+            except Exception:
+                pass
+
+        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.
+        """
+        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 self.extract_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)
@@ -132,12 +132,16 @@ def flags(


 def remote_config(
-    personal_api_key: str, host: Optional[str] = None, key: str = "", timeout: int = 15
+    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"""
    return get(
        personal_api_key,
-        f"/api/projects/@current/feature_flags/{key}/remote_config/",
+        f"/api/projects/@current/feature_flags/{key}/remote_config?token={project_api_key}",
        host,
        timeout,
    )
@@ -1,127 +0,0 @@
-import contextvars
-from contextlib import contextmanager
-from typing import Any, Callable, Dict, TypeVar, cast
-
-_context_stack: contextvars.ContextVar[list] = contextvars.ContextVar(
-    "posthog_context_stack", default=[{}]
-)
-
-
-def _get_current_context() -> Dict[str, Any]:
-    return _context_stack.get()[-1]
-
-
-@contextmanager
-def new_context(fresh=False, capture_exceptions=True):
-    """
-    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 from parent context.
-               If True, starts with no tags.
-        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.
-
-    Examples:
-        # 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")
-
-        # 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")
-
-    """
-    from posthog import capture_exception
-
-    current_tags = _get_current_context().copy()
-    current_stack = _context_stack.get()
-    new_stack = current_stack + [{}] if fresh else current_stack + [current_tags]
-    token = _context_stack.set(new_stack)
-
-    try:
-        yield
-    except Exception as e:
-        if capture_exceptions:
-            capture_exception(e)
-        raise
-    finally:
-        _context_stack.reset(token)
-
-
-def tag(key: str, value: Any) -> None:
-    """
-    Add a tag to the current context.
-
-    Args:
-        key: The tag key
-        value: The tag value
-
-    Example:
-        posthog.tag("user_id", "123")
-    """
-    _get_current_context()[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
-    """
-    return _get_current_context().copy()
-
-
-def clear_tags() -> None:
-    """Clear all tags in the current context."""
-    _get_current_context().clear()
-
-
-F = TypeVar("F", bound=Callable[..., Any])
-
-
-def scoped(fresh=False, capture_exceptions=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()
-    """
-
-    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
@@ -1 +0,0 @@
-POSTHOG_ID_TAG = "posthog_distinct_id"
@@ -1,28 +0,0 @@
-from django.conf import settings
-from sentry_sdk import configure_scope
-
-from posthog.sentry import POSTHOG_ID_TAG
-
-GET_DISTINCT_ID = getattr(settings, "POSTHOG_DJANGO", {}).get("distinct_id")
-
-
-def get_distinct_id(request):
-    if not GET_DISTINCT_ID:
-        return None
-    try:
-        return GET_DISTINCT_ID(request)
-    except:  # noqa: E722
-        return None
-
-
-class PosthogDistinctIdMiddleware:
-    def __init__(self, get_response):
-        self.get_response = get_response
-
-    def __call__(self, request):
-        with configure_scope() as scope:
-            distinct_id = get_distinct_id(request)
-            if distinct_id:
-                scope.set_tag(POSTHOG_ID_TAG, distinct_id)
-            response = self.get_response(request)
-        return response
@@ -1,57 +0,0 @@
-from sentry_sdk._types import MYPY
-from sentry_sdk.hub import Hub
-from sentry_sdk.integrations import Integration
-from sentry_sdk.scope import add_global_event_processor
-from sentry_sdk.utils import Dsn
-
-from posthog import capture, host
-from posthog.request import DEFAULT_HOST
-from posthog.sentry import POSTHOG_ID_TAG
-
-if MYPY:
-    from typing import Optional  # noqa: F401
-
-    from sentry_sdk._types import Event, Hint  # noqa: F401
-
-
-class PostHogIntegration(Integration):
-    identifier = "posthog-python"
-    organization = None  # The Sentry organization, used to send a direct link from PostHog to Sentry
-    project_id = (
-        None  # The Sentry project id, used to send a direct link from PostHog to Sentry
-    )
-    prefix = "https://sentry.io/organizations/"  # URL of a hosted sentry instance (default: https://sentry.io/organizations/)
-
-    @staticmethod
-    def setup_once():
-        @add_global_event_processor
-        def processor(event, hint):
-            # type: (Event, Optional[Hint]) -> Optional[Event]
-            if Hub.current.get_integration(PostHogIntegration) is not None:
-                if event.get("level") != "error":
-                    return event
-
-                if event.get("tags", {}).get(POSTHOG_ID_TAG):
-                    posthog_distinct_id = event["tags"][POSTHOG_ID_TAG]
-                    event["tags"]["PostHog URL"] = (
-                        f"{host or DEFAULT_HOST}/person/{posthog_distinct_id}"
-                    )
-
-                    properties = {
-                        "$sentry_event_id": event["event_id"],
-                        "$sentry_exception": event["exception"],
-                    }
-
-                    if PostHogIntegration.organization:
-                        project_id = PostHogIntegration.project_id or (
-                            not not Hub.current.client.dsn
-                            and Dsn(Hub.current.client.dsn).project_id
-                        )
-                        if project_id:
-                            properties["$sentry_url"] = (
-                                f"{PostHogIntegration.prefix}{PostHogIntegration.organization}/issues/?project={project_id}&query={event['event_id']}"
-                            )
-
-                    capture(posthog_distinct_id, "$exception", properties)
-
-            return event
@@ -1,5 +1,4 @@
 import os
-import time
 from unittest.mock import patch

 import pytest
@@ -13,14 +12,95 @@ try:
 except ImportError:
    ANTHROPIC_AVAILABLE = False

-ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
-
 # Skip all tests if Anthropic is not available
 pytestmark = pytest.mark.skipif(
    not ANTHROPIC_AVAILABLE, reason="Anthropic package is not available"
 )


+# =======================
+# Reusable Mock Helpers
+# =======================
+
+
+class MockContent:
+    """Reusable mock content class for Anthropic responses."""
+
+    def __init__(self, text="Bar", content_type="text"):
+        self.type = content_type
+        self.text = text
+
+
+class MockUsage:
+    """Reusable mock usage class for Anthropic responses."""
+
+    def __init__(
+        self,
+        input_tokens=18,
+        output_tokens=1,
+        cache_read_input_tokens=0,
+        cache_creation_input_tokens=0,
+    ):
+        self.input_tokens = input_tokens
+        self.output_tokens = output_tokens
+        self.cache_read_input_tokens = cache_read_input_tokens
+        self.cache_creation_input_tokens = cache_creation_input_tokens
+
+
+class MockResponse:
+    """Reusable mock response class for Anthropic messages."""
+
+    def __init__(
+        self,
+        content_text="Bar",
+        model="claude-3-opus-20240229",
+        input_tokens=18,
+        output_tokens=1,
+        cache_read=0,
+        cache_creation=0,
+    ):
+        self.content = [MockContent(text=content_text)]
+        self.model = model
+        self.usage = MockUsage(
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            cache_read_input_tokens=cache_read,
+            cache_creation_input_tokens=cache_creation,
+        )
+
+
+def create_mock_response(**kwargs):
+    """Factory function to create mock responses with custom parameters."""
+    return MockResponse(**kwargs)
+
+
+# Streaming mock helpers
+class MockStreamEvent:
+    """Reusable mock event class for streaming responses."""
+
+    def __init__(self, event_type=None, **kwargs):
+        self.type = event_type
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+class MockContentBlock:
+    """Reusable mock content block for streaming."""
+
+    def __init__(self, block_type, **kwargs):
+        self.type = block_type
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+class MockDelta:
+    """Reusable mock delta for streaming events."""
+
+    def __init__(self, **kwargs):
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
@pytest.fixture
 def mock_client():
    with patch("posthog.client.Client") as mock_client:
@@ -46,22 +126,77 @@ def mock_anthropic_response():


@pytest.fixture
-def mock_anthropic_stream():
-    class MockStreamEvent:
-        def __init__(self, content, usage=None):
-            self.content = content
-            self.usage = usage
+def mock_anthropic_stream_with_tools():
+    """Mock stream events for tool calls."""
+
+    class MockMessage:
+        def __init__(self):
+            self.usage = MockUsage(
+                input_tokens=50,
+                cache_creation_input_tokens=0,
+                cache_read_input_tokens=5,
+            )

    def stream_generator():
-        yield MockStreamEvent("A")
-        yield MockStreamEvent("B")
-        yield MockStreamEvent(
-            "C",
-            usage=Usage(
-                input_tokens=20,
-                output_tokens=10,
-            ),
+        # Message start with usage
+        event = MockStreamEvent("message_start")
+        event.message = MockMessage()
+        yield event
+
+        # Text block start
+        event = MockStreamEvent("content_block_start")
+        event.content_block = MockContentBlock("text")
+        event.index = 0
+        yield event
+
+        # Text delta
+        event = MockStreamEvent("content_block_delta")
+        event.delta = MockDelta(text="I'll check the weather for you.")
+        event.index = 0
+        yield event
+
+        # Text block stop
+        event = MockStreamEvent("content_block_stop")
+        event.index = 0
+        yield event
+
+        # Tool use block start
+        event = MockStreamEvent("content_block_start")
+        event.content_block = MockContentBlock(
+            "tool_use", id="toolu_stream123", name="get_weather"
        )
+        event.index = 1
+        yield event
+
+        # Tool input delta 1
+        event = MockStreamEvent("content_block_delta")
+        event.delta = MockDelta(
+            type="input_json_delta", partial_json='{"location": "San'
+        )
+        event.index = 1
+        yield event
+
+        # Tool input delta 2
+        event = MockStreamEvent("content_block_delta")
+        event.delta = MockDelta(
+            type="input_json_delta", partial_json=' Francisco", "unit": "celsius"}'
+        )
+        event.index = 1
+        yield event
+
+        # Tool block stop
+        event = MockStreamEvent("content_block_stop")
+        event.index = 1
+        yield event
+
+        # Message delta with final usage
+        event = MockStreamEvent("message_delta")
+        event.usage = MockUsage(output_tokens=25)
+        yield event
+
+        # Message stop
+        event = MockStreamEvent("message_stop")
+        yield event

    return stream_generator()

@@ -88,6 +223,56 @@ def mock_anthropic_response_with_cached_tokens():
    )


+@pytest.fixture
+def mock_anthropic_response_with_tool_calls():
+    return Message(
+        id="msg_456",
+        type="message",
+        role="assistant",
+        content=[
+            {"type": "text", "text": "I'll help you check the weather."},
+            {"type": "text", "text": " Let me look that up."},
+            {
+                "type": "tool_use",
+                "id": "toolu_abc123",
+                "name": "get_weather",
+                "input": {"location": "San Francisco"},
+            },
+        ],
+        model="claude-3-5-sonnet-20241022",
+        usage=Usage(
+            input_tokens=25,
+            output_tokens=15,
+        ),
+        stop_reason="tool_use",
+        stop_sequence=None,
+    )
+
+
+@pytest.fixture
+def mock_anthropic_response_tool_calls_only():
+    return Message(
+        id="msg_789",
+        type="message",
+        role="assistant",
+        content=[
+            {
+                "type": "tool_use",
+                "id": "toolu_def456",
+                "name": "get_weather",
+                "input": {"location": "New York", "unit": "fahrenheit"},
+            }
+        ],
+        model="claude-3-5-sonnet-20241022",
+        usage=Usage(
+            input_tokens=30,
+            output_tokens=12,
+        ),
+        stop_reason="tool_use",
+        stop_sequence=None,
+    )
+
+
 def test_basic_completion(mock_client, mock_anthropic_response):
    with patch(
        "anthropic.resources.Messages.create", return_value=mock_anthropic_response
@@ -112,7 +297,10 @@ def test_basic_completion(mock_client, mock_anthropic_response):
        assert props["$ai_model"] == "claude-3-opus-20240229"
        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
        assert props["$ai_output_choices"] == [
-            {"role": "assistant", "content": "Test response"}
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
        ]
        assert props["$ai_input_tokens"] == 20
        assert props["$ai_output_tokens"] == 10
@@ -121,83 +309,6 @@ def test_basic_completion(mock_client, mock_anthropic_response):
        assert isinstance(props["$ai_latency"], float)


-def test_streaming(mock_client, mock_anthropic_stream):
-    with patch(
-        "anthropic.resources.Messages.create", return_value=mock_anthropic_stream
-    ):
-        client = Anthropic(api_key="test-key", posthog_client=mock_client)
-        response = client.messages.create(
-            model="claude-3-opus-20240229",
-            messages=[{"role": "user", "content": "Hello"}],
-            stream=True,
-            posthog_distinct_id="test-id",
-            posthog_properties={"foo": "bar"},
-        )
-
-        # Consume the stream
-        chunks = list(response)
-        assert len(chunks) == 3
-        assert chunks[0].content == "A"
-        assert chunks[1].content == "B"
-        assert chunks[2].content == "C"
-
-        # Wait a bit to ensure the capture is called
-        time.sleep(0.1)
-        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"] == "anthropic"
-        assert props["$ai_model"] == "claude-3-opus-20240229"
-        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
-        assert props["$ai_output_choices"] == [{"role": "assistant", "content": "ABC"}]
-        assert props["$ai_input_tokens"] == 20
-        assert props["$ai_output_tokens"] == 10
-        assert isinstance(props["$ai_latency"], float)
-        assert props["foo"] == "bar"
-
-
-def test_streaming_with_stream_endpoint(mock_client, mock_anthropic_stream):
-    with patch(
-        "anthropic.resources.Messages.create", return_value=mock_anthropic_stream
-    ):
-        client = Anthropic(api_key="test-key", posthog_client=mock_client)
-        response = client.messages.stream(
-            model="claude-3-opus-20240229",
-            messages=[{"role": "user", "content": "Hello"}],
-            posthog_distinct_id="test-id",
-            posthog_properties={"foo": "bar"},
-        )
-
-        # Consume the stream
-        chunks = list(response)
-        assert len(chunks) == 3
-        assert chunks[0].content == "A"
-        assert chunks[1].content == "B"
-        assert chunks[2].content == "C"
-
-        # Wait a bit to ensure the capture is called
-        time.sleep(0.1)
-        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"] == "anthropic"
-        assert props["$ai_model"] == "claude-3-opus-20240229"
-        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
-        assert props["$ai_output_choices"] == [{"role": "assistant", "content": "ABC"}]
-        assert props["$ai_input_tokens"] == 20
-        assert props["$ai_output_tokens"] == 10
-        assert isinstance(props["$ai_latency"], float)
-        assert props["foo"] == "bar"
-
-
 def test_groups(mock_client, mock_anthropic_response):
    with patch(
        "anthropic.resources.Messages.create", return_value=mock_anthropic_response
@@ -260,18 +371,23 @@ def test_privacy_mode_global(mock_client, mock_anthropic_response):
        assert props["$ai_output_choices"] is None


-@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
 def test_basic_integration(mock_client):
-    client = Anthropic(posthog_client=mock_client)
-    client.messages.create(
-        model="claude-3-opus-20240229",
-        messages=[{"role": "user", "content": "Foo"}],
-        max_tokens=1,
-        temperature=0,
-        posthog_distinct_id="test-id",
-        posthog_properties={"foo": "bar"},
-        system="You must always answer with 'Bar'.",
-    )
+    """Test basic non-streaming integration."""
+
+    with patch(
+        "anthropic.resources.Messages.create",
+        return_value=create_mock_response(),
+    ):
+        client = Anthropic(posthog_client=mock_client)
+        client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[{"role": "user", "content": "Foo"}],
+            max_tokens=1,
+            temperature=0,
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+            system="You must always answer with 'Bar'.",
+        )

    assert mock_client.capture.call_count == 1

@@ -286,7 +402,9 @@ def test_basic_integration(mock_client):
        {"role": "user", "content": "Foo"},
    ]
    assert props["$ai_output_choices"][0]["role"] == "assistant"
-    assert props["$ai_output_choices"][0]["content"] == "Bar"
+    assert props["$ai_output_choices"][0]["content"] == [
+        {"type": "text", "text": "Bar"}
+    ]
    assert props["$ai_input_tokens"] == 18
    assert props["$ai_output_tokens"] == 1
    assert props["$ai_http_status"] == 200
@@ -294,17 +412,28 @@ def test_basic_integration(mock_client):
    assert isinstance(props["$ai_latency"], float)


-@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
 async def test_basic_async_integration(mock_client):
-    client = AsyncAnthropic(posthog_client=mock_client)
-    await client.messages.create(
-        model="claude-3-opus-20240229",
-        messages=[{"role": "user", "content": "You must always answer with 'Bar'."}],
-        max_tokens=1,
-        temperature=0,
-        posthog_distinct_id="test-id",
-        posthog_properties={"foo": "bar"},
-    )
+    """Test async non-streaming integration."""
+
+    # Make the mock async
+    async def mock_async_create(**kwargs):
+        return create_mock_response(input_tokens=16)
+
+    with patch(
+        "anthropic.resources.messages.AsyncMessages.create",
+        side_effect=mock_async_create,
+    ):
+        client = AsyncAnthropic(posthog_client=mock_client)
+        await client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[
+                {"role": "user", "content": "You must always answer with 'Bar'."}
+            ],
+            max_tokens=1,
+            temperature=0,
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+        )

    assert mock_client.capture.call_count == 1

@@ -326,52 +455,50 @@ async def test_basic_async_integration(mock_client):
    assert isinstance(props["$ai_latency"], float)


-def test_streaming_system_prompt(mock_client, mock_anthropic_stream):
+async def test_async_streaming_system_prompt(mock_client):
+    """Test async streaming with system prompt."""
+
+    # Create a simple mock async stream using reusable helpers
+    async def mock_async_stream():
+        # Yield some events
+        yield MockStreamEvent(type="message_start")
+        yield MockStreamEvent(type="content_block_start")
+        yield MockStreamEvent(type="content_block_delta", text="Bar")
+
+        # Final message with usage
+        final_msg = MockStreamEvent(type="message_delta")
+        final_msg.usage = MockUsage(
+            input_tokens=10,
+            output_tokens=5,
+            cache_read_input_tokens=0,
+            cache_creation_input_tokens=0,
+        )
+        yield final_msg
+
+    # Mock create to return a coroutine that yields the async generator
+    # This matches the actual behavior when stream=True with await
+    async def async_create_wrapper(**kwargs):
+        return mock_async_stream()
+
    with patch(
-        "anthropic.resources.Messages.create", return_value=mock_anthropic_stream
+        "anthropic.resources.messages.AsyncMessages.create",
+        side_effect=async_create_wrapper,
    ):
-        client = Anthropic(api_key="test-key", posthog_client=mock_client)
-        response = client.messages.create(
+        client = AsyncAnthropic(posthog_client=mock_client)
+        response = await client.messages.create(
            model="claude-3-opus-20240229",
-            system="Foo",
-            messages=[{"role": "user", "content": "Bar"}],
+            system="You must always answer with 'Bar'.",
+            messages=[{"role": "user", "content": "Foo"}],
            stream=True,
+            max_tokens=1,
        )

-        # Consume the stream
-        list(response)
+        # Consume the stream - async finally block completes before this returns
+        [c async for c in response]

-        # Wait a bit to ensure the capture is called
-        time.sleep(0.1)
+        # Capture happens in the async finally block before generator completes
        assert mock_client.capture.call_count == 1

-        call_args = mock_client.capture.call_args[1]
-        props = call_args["properties"]
-
-        assert props["$ai_input"] == [
-            {"role": "system", "content": "Foo"},
-            {"role": "user", "content": "Bar"},
-        ]
-
-
-@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
-async def test_async_streaming_system_prompt(mock_client, mock_anthropic_stream):
-    client = AsyncAnthropic(posthog_client=mock_client)
-    response = await client.messages.create(
-        model="claude-3-opus-20240229",
-        system="You must always answer with 'Bar'.",
-        messages=[{"role": "user", "content": "Foo"}],
-        stream=True,
-        max_tokens=1,
-    )
-
-    # Consume the stream
-    [c async for c in response]
-
-    # Wait a bit to ensure the capture is called
-    time.sleep(0.1)
-    assert mock_client.capture.call_count == 1
-
    call_args = mock_client.capture.call_args[1]
    props = call_args["properties"]

@@ -425,7 +552,10 @@ def test_cached_tokens(mock_client, mock_anthropic_response_with_cached_tokens):
        assert props["$ai_model"] == "claude-3-opus-20240229"
        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
        assert props["$ai_output_choices"] == [
-            {"role": "assistant", "content": "Test response"}
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
        ]
        assert props["$ai_input_tokens"] == 20
        assert props["$ai_output_tokens"] == 10
@@ -434,3 +564,473 @@ def test_cached_tokens(mock_client, mock_anthropic_response_with_cached_tokens):
        assert props["$ai_http_status"] == 200
        assert props["foo"] == "bar"
        assert isinstance(props["$ai_latency"], float)
+
+
+def test_tool_definition(mock_client, mock_anthropic_response):
+    with patch(
+        "anthropic.resources.Messages.create",
+        return_value=mock_anthropic_response,
+    ):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+
+        tools = [
+            {
+                "name": "get_weather",
+                "description": "Get the current weather for a specific location",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city or location name to get weather for",
+                        }
+                    },
+                    "required": ["location"],
+                },
+            }
+        ]
+
+        response = client.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=200,
+            temperature=0.7,
+            tools=tools,
+            messages=[{"role": "user", "content": "hey"}],
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+        )
+
+        assert response == mock_anthropic_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_generation"
+        assert props["$ai_provider"] == "anthropic"
+        assert props["$ai_model"] == "claude-3-5-sonnet-20241022"
+        assert props["$ai_input"] == [{"role": "user", "content": "hey"}]
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
+        ]
+        assert props["$ai_input_tokens"] == 20
+        assert props["$ai_output_tokens"] == 10
+        assert props["$ai_http_status"] == 200
+        assert props["foo"] == "bar"
+        assert isinstance(props["$ai_latency"], float)
+        # Verify that tools are captured in the $ai_tools property
+        assert props["$ai_tools"] == tools
+
+
+def test_tool_calls_in_output_choices(
+    mock_client, mock_anthropic_response_with_tool_calls
+):
+    with patch(
+        "anthropic.resources.Messages.create",
+        return_value=mock_anthropic_response_with_tool_calls,
+    ):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=200,
+            messages=[
+                {"role": "user", "content": "What's the weather in San Francisco?"}
+            ],
+            tools=[
+                {
+                    "name": "get_weather",
+                    "description": "Get weather",
+                    "input_schema": {
+                        "type": "object",
+                        "properties": {"location": {"type": "string"}},
+                        "required": ["location"],
+                    },
+                }
+            ],
+            posthog_distinct_id="test-id",
+        )
+
+        assert response == mock_anthropic_response_with_tool_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"] == "anthropic"
+        assert props["$ai_model"] == "claude-3-5-sonnet-20241022"
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll help you check the weather."},
+                    {"type": "text", "text": " Let me look that up."},
+                    {
+                        "type": "function",
+                        "id": "toolu_abc123",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "San Francisco"},
+                        },
+                    },
+                ],
+            }
+        ]
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 25
+        assert props["$ai_output_tokens"] == 15
+        assert props["$ai_http_status"] == 200
+
+
+def test_tool_calls_only_no_content(
+    mock_client, mock_anthropic_response_tool_calls_only
+):
+    with patch(
+        "anthropic.resources.Messages.create",
+        return_value=mock_anthropic_response_tool_calls_only,
+    ):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=200,
+            messages=[{"role": "user", "content": "Get weather for New York"}],
+            tools=[
+                {
+                    "name": "get_weather",
+                    "description": "Get weather",
+                    "input_schema": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string"},
+                            "unit": {"type": "string"},
+                        },
+                        "required": ["location"],
+                    },
+                }
+            ],
+            posthog_distinct_id="test-id",
+        )
+
+        assert response == mock_anthropic_response_tool_calls_only
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_generation"
+        assert props["$ai_provider"] == "anthropic"
+        assert props["$ai_model"] == "claude-3-5-sonnet-20241022"
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "function",
+                        "id": "toolu_def456",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "New York", "unit": "fahrenheit"},
+                        },
+                    }
+                ],
+            }
+        ]
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 30
+        assert props["$ai_output_tokens"] == 12
+        assert props["$ai_http_status"] == 200
+
+
+def test_async_tool_calls_in_output_choices(
+    mock_client, mock_anthropic_response_with_tool_calls
+):
+    import asyncio
+
+    async def mock_async_create(**kwargs):
+        return mock_anthropic_response_with_tool_calls
+
+    with patch(
+        "anthropic.resources.AsyncMessages.create",
+        side_effect=mock_async_create,
+    ):
+        async_client = AsyncAnthropic(api_key="test-key", posthog_client=mock_client)
+
+        async def run_test():
+            return await async_client.messages.create(
+                model="claude-3-5-sonnet-20241022",
+                max_tokens=200,
+                messages=[
+                    {"role": "user", "content": "What's the weather in San Francisco?"}
+                ],
+                tools=[
+                    {
+                        "name": "get_weather",
+                        "description": "Get weather",
+                        "input_schema": {
+                            "type": "object",
+                            "properties": {"location": {"type": "string"}},
+                            "required": ["location"],
+                        },
+                    }
+                ],
+                posthog_distinct_id="test-id",
+            )
+
+        response = asyncio.run(run_test())
+
+        assert response == mock_anthropic_response_with_tool_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"] == "anthropic"
+        assert props["$ai_model"] == "claude-3-5-sonnet-20241022"
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll help you check the weather."},
+                    {"type": "text", "text": " Let me look that up."},
+                    {
+                        "type": "function",
+                        "id": "toolu_abc123",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "San Francisco"},
+                        },
+                    },
+                ],
+            }
+        ]
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 25
+        assert props["$ai_output_tokens"] == 15
+        assert props["$ai_http_status"] == 200
+
+
+def test_streaming_with_tool_calls(mock_client, mock_anthropic_stream_with_tools):
+    """Test that tool calls are properly captured in streaming mode."""
+    with patch(
+        "anthropic.resources.Messages.create",
+        return_value=mock_anthropic_stream_with_tools,
+    ):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            system="You are a helpful weather assistant.",
+            messages=[
+                {"role": "user", "content": "What's the weather in San Francisco?"}
+            ],
+            tools=[
+                {
+                    "name": "get_weather",
+                    "description": "Get weather information",
+                    "input_schema": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string"},
+                            "unit": {"type": "string"},
+                        },
+                        "required": ["location"],
+                    },
+                }
+            ],
+            stream=True,
+            posthog_distinct_id="test-id",
+        )
+
+        # Consume the stream - this triggers the finally block synchronously
+        list(response)
+
+        # Capture happens synchronously when generator is exhausted
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_generation"
+        assert props["$ai_provider"] == "anthropic"
+        assert props["$ai_model"] == "claude-3-5-sonnet-20241022"
+
+        # Verify system prompt is included in input
+        assert props["$ai_input"] == [
+            {"role": "system", "content": "You are a helpful weather assistant."},
+            {"role": "user", "content": "What's the weather in San Francisco?"},
+        ]
+
+        # Verify that tools are captured in the properties
+        assert props["$ai_tools"] == [
+            {
+                "name": "get_weather",
+                "description": "Get weather information",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string"},
+                        "unit": {"type": "string"},
+                    },
+                    "required": ["location"],
+                },
+            }
+        ]
+
+        # Verify output contains both text and tool call
+        output_choices = props["$ai_output_choices"]
+        assert len(output_choices) == 1
+
+        assistant_message = output_choices[0]
+        assert assistant_message["role"] == "assistant"
+
+        content = assistant_message["content"]
+        assert isinstance(content, list)
+        assert len(content) == 2
+
+        # Verify text block
+        text_block = content[0]
+        assert text_block["type"] == "text"
+        assert text_block["text"] == "I'll check the weather for you."
+
+        # Verify tool call block
+        tool_block = content[1]
+        assert tool_block["type"] == "function"
+        assert tool_block["id"] == "toolu_stream123"
+        assert tool_block["function"]["name"] == "get_weather"
+        assert tool_block["function"]["arguments"] == {
+            "location": "San Francisco",
+            "unit": "celsius",
+        }
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 50
+        assert props["$ai_output_tokens"] == 25
+        assert props["$ai_cache_read_input_tokens"] == 5
+        assert props["$ai_cache_creation_input_tokens"] == 0
+
+
+def test_async_streaming_with_tool_calls(mock_client, mock_anthropic_stream_with_tools):
+    """Test that tool calls are properly captured in async streaming mode."""
+    import asyncio
+
+    async def mock_async_generator():
+        # Convert regular generator to async generator
+        for event in mock_anthropic_stream_with_tools:
+            yield event
+
+    async def mock_async_create(**kwargs):
+        # Return the async generator (to be awaited by the implementation)
+        return mock_async_generator()
+
+    with patch(
+        "anthropic.resources.AsyncMessages.create",
+        side_effect=mock_async_create,
+    ):
+        async_client = AsyncAnthropic(api_key="test-key", posthog_client=mock_client)
+
+        async def run_test():
+            response = await async_client.messages.create(
+                model="claude-3-5-sonnet-20241022",
+                system="You are a helpful weather assistant.",
+                messages=[
+                    {"role": "user", "content": "What's the weather in San Francisco?"}
+                ],
+                tools=[
+                    {
+                        "name": "get_weather",
+                        "description": "Get weather information",
+                        "input_schema": {
+                            "type": "object",
+                            "properties": {
+                                "location": {"type": "string"},
+                                "unit": {"type": "string"},
+                            },
+                            "required": ["location"],
+                        },
+                    }
+                ],
+                stream=True,
+                posthog_distinct_id="test-id",
+            )
+
+            # Consume the async stream
+            [event async for event in response]
+
+        # asyncio.run() waits for all async operations to complete
+        asyncio.run(run_test())
+
+        # Capture completes before asyncio.run() returns
+        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["$ai_provider"] == "anthropic"
+        assert props["$ai_model"] == "claude-3-5-sonnet-20241022"
+
+        # Verify system prompt is included in input
+        assert props["$ai_input"] == [
+            {"role": "system", "content": "You are a helpful weather assistant."},
+            {"role": "user", "content": "What's the weather in San Francisco?"},
+        ]
+
+        # Verify that tools are captured in the properties
+        assert props["$ai_tools"] == [
+            {
+                "name": "get_weather",
+                "description": "Get weather information",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string"},
+                        "unit": {"type": "string"},
+                    },
+                    "required": ["location"],
+                },
+            }
+        ]
+
+        # Verify output contains both text and tool call
+        output_choices = props["$ai_output_choices"]
+        assert len(output_choices) == 1
+
+        assistant_message = output_choices[0]
+        assert assistant_message["role"] == "assistant"
+
+        content = assistant_message["content"]
+        assert isinstance(content, list)
+        assert len(content) == 2
+
+        # Verify text block
+        text_block = content[0]
+        assert text_block["type"] == "text"
+        assert text_block["text"] == "I'll check the weather for you."
+
+        # Verify tool call block
+        tool_block = content[1]
+        assert tool_block["type"] == "function"
+        assert tool_block["id"] == "toolu_stream123"
+        assert tool_block["function"]["name"] == "get_weather"
+        assert tool_block["function"]["arguments"] == {
+            "location": "San Francisco",
+            "unit": "celsius",
+        }
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 50
+        assert props["$ai_output_tokens"] == 25
+        assert props["$ai_cache_read_input_tokens"] == 5
+        assert props["$ai_cache_creation_input_tokens"] == 0
@@ -31,6 +31,9 @@ def mock_gemini_response():
    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()
@@ -56,6 +59,91 @@ def mock_google_genai_client():
        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."
+    # Make hasattr(part, "text") return True
+    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 - need to ensure hasattr() works correctly
+    mock_function_part = MagicMock()
+    mock_function_part.function_call = mock_function_call
+    # Make hasattr(part, "function_call") return True
+    type(mock_function_part).function_call = mock_function_part.function_call
+    # Ensure hasattr(part, "text") returns False for the function part
+    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
+
+
+@pytest.fixture
+def mock_gemini_response_function_calls_only():
+    mock_response = MagicMock()
+
+    # Mock usage metadata
+    mock_usage = MagicMock()
+    mock_usage.prompt_token_count = 30
+    mock_usage.candidates_token_count = 12
+    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": "New York", "unit": "fahrenheit"}
+
+    # Mock function call part (no text part) - need to ensure hasattr() works correctly
+    mock_function_part = MagicMock()
+    mock_function_part.function_call = mock_function_call
+    # Make hasattr(part, "function_call") return True
+    type(mock_function_part).function_call = mock_function_part.function_call
+    # Ensure hasattr(part, "text") returns False for the function part
+    del mock_function_part.text
+
+    # Mock content with only function call part
+    mock_content = MagicMock()
+    mock_content.parts = [mock_function_part]
+
+    # Mock candidate
+    mock_candidate = MagicMock()
+    mock_candidate.content = mock_content
+    mock_response.candidates = [mock_candidate]
+
+    return mock_response
+
+
 def test_new_client_basic_generation(
    mock_client, mock_google_genai_client, mock_gemini_response
 ):
@@ -99,6 +187,8 @@ def test_new_client_streaming_with_generate_content_stream(
        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

        mock_chunk2 = MagicMock()
@@ -106,6 +196,8 @@ def test_new_client_streaming_with_generate_content_stream(
        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_chunk1
@@ -145,6 +237,91 @@ def test_new_client_streaming_with_generate_content_stream(
    assert isinstance(props["$ai_latency"], float)


+def test_new_client_streaming_with_tools(mock_client, mock_google_genai_client):
+    """Test that tools are captured in streaming mode"""
+
+    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
+
+        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_chunk1
+        yield mock_chunk2
+
+    # Mock the generate_content_stream method
+    mock_google_genai_client.models.generate_content_stream.return_value = (
+        mock_streaming_response()
+    )
+
+    client = Client(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 = 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 = list(response)
+    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]
+
+
 def test_new_client_groups(mock_client, mock_google_genai_client, mock_gemini_response):
    """Test groups functionality with new Client API"""
    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
@@ -221,12 +398,32 @@ def test_new_client_different_input_formats(
    props = call_args["properties"]
    assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]

-    # Test list input
-    mock_client.capture.reset_mock()
-    mock_part = MagicMock()
-    mock_part.text = "List item"
+    # Test Gemini-specific format with parts array (like in the screenshot)
+    mock_client.reset_mock()
    client.models.generate_content(
-        model="gemini-2.0-flash", contents=[mock_part], posthog_distinct_id="test-id"
+        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["$ai_input"] == [{"role": "user", "content": "hey"}]
+
+    # Test multiple parts in the parts array
+    mock_client.reset_mock()
+    client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=[{"role": "user", "parts": [{"text": "Hello "}, {"text": "world"}]}],
+        posthog_distinct_id="test-id",
+    )
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [{"role": "user", "content": "Hello world"}]
+
+    # Test list input with string
+    mock_client.capture.reset_mock()
+    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"]
@@ -318,3 +515,325 @@ def test_new_client_override_defaults(
    assert props["team"] == "ai"  # from defaults
    assert props["feature"] == "chat"  # from call
    assert props["urgent"] is True  # from call
+
+
+def test_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.models.generate_content.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
+    Client(
+        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,
+    )
+
+
+def test_api_key_mode(mock_client, mock_google_genai_client):
+    """Test API key authentication mode"""
+
+    # Create client with just API key (traditional mode)
+    Client(
+        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")
+
+
+def test_vertex_ai_mode_with_optional_api_key(
+    mock_client, mock_google_genai_client, mock_gemini_response
+):
+    """Test Vertex AI mode with optional API key"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    mock_credentials = MagicMock()
+
+    # Create client with Vertex AI + API key
+    Client(
+        vertexai=True,
+        api_key="test-api-key",
+        credentials=mock_credentials,
+        project="test-project",
+        posthog_client=mock_client,
+    )
+
+    # Verify genai.Client was called with both Vertex AI params and API key
+    google_genai.Client.assert_called_once_with(
+        vertexai=True,
+        api_key="test-api-key",
+        credentials=mock_credentials,
+        project="test-project",
+    )
+
+
+def test_tool_use_response(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test that tools defined in config are captured in $ai_tools property"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    # Create mock tools configuration
+    mock_tool = MagicMock()
+    mock_tool.function_declarations = [
+        MagicMock(
+            name="get_current_weather",
+            description="Gets the current weather for a given location.",
+            parameters=MagicMock(
+                type="OBJECT",
+                properties={
+                    "location": MagicMock(
+                        type="STRING",
+                        description="The city and state, e.g. San Francisco, CA",
+                    )
+                },
+                required=["location"],
+            ),
+        )
+    ]
+
+    mock_config = MagicMock()
+    mock_config.tools = [mock_tool]
+    # Explicitly specify this config doesn't have system_instruction
+    del mock_config.system_instruction
+
+    response = client.models.generate_content(
+        model="gemini-2.5-flash",
+        contents=["hey"],
+        config=mock_config,
+        posthog_distinct_id="test-id",
+        posthog_properties={"foo": "bar"},
+    )
+
+    assert response == mock_gemini_response
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.5-flash"
+    assert props["$ai_input"] == [{"role": "user", "content": "hey"}]
+    assert props["$ai_output_choices"] == [
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": "Test response from Gemini"}],
+        }
+    ]
+    assert props["$ai_input_tokens"] == 20
+    assert props["$ai_output_tokens"] == 10
+    assert props["$ai_http_status"] == 200
+    assert props["foo"] == "bar"
+    assert isinstance(props["$ai_latency"], float)
+    # Verify that tools are captured in the $ai_tools property
+    assert props["$ai_tools"] == [mock_tool]
+
+
+def test_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 $ai_output_choices"""
+    mock_google_genai_client.models.generate_content.return_value = (
+        mock_gemini_response_with_function_calls
+    )
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    response = client.models.generate_content(
+        model="gemini-2.5-flash",
+        contents=["What's the weather in San Francisco?"],
+        posthog_distinct_id="test-id",
+    )
+
+    assert response == mock_gemini_response_with_function_calls
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$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
+
+
+def test_function_calls_only_no_content(
+    mock_client, mock_google_genai_client, mock_gemini_response_function_calls_only
+):
+    """Test function calls without text content in $ai_output_choices"""
+    mock_google_genai_client.models.generate_content.return_value = (
+        mock_gemini_response_function_calls_only
+    )
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    response = client.models.generate_content(
+        model="gemini-2.5-flash",
+        contents=["Get weather for New York"],
+        posthog_distinct_id="test-id",
+    )
+
+    assert response == mock_gemini_response_function_calls_only
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.5-flash"
+    assert props["$ai_output_choices"] == [
+        {
+            "role": "assistant",
+            "content": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_current_weather",
+                        "arguments": {"location": "New York", "unit": "fahrenheit"},
+                    },
+                }
+            ],
+        }
+    ]
+
+    # Check token usage
+    assert props["$ai_input_tokens"] == 30
+    assert props["$ai_output_tokens"] == 12
+    assert props["$ai_http_status"] == 200
+
+
+def test_cache_and_reasoning_tokens(mock_client, mock_google_genai_client):
+    """Test that cache and reasoning tokens are properly extracted"""
+    # 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.models.generate_content.return_value = mock_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    response = 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
+
+
+def test_streaming_cache_and_reasoning_tokens(mock_client, mock_google_genai_client):
+    """Test that cache and reasoning tokens are properly extracted in streaming"""
+    # 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
+
+    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
+
+    mock_stream = iter([chunk1, chunk2])
+    mock_google_genai_client.models.generate_content_stream.return_value = mock_stream
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    response = client.models.generate_content_stream(
+        model="gemini-2.5-pro",
+        contents="Test streaming with cache",
+        posthog_distinct_id="test-id",
+    )
+
+    # Consume the stream
+    result = list(response)
+    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
@@ -5,7 +5,7 @@ import os
 import time
 import uuid
 from typing import List, Literal, Optional, TypedDict, Union
-from unittest.mock import patch
+from unittest.mock import patch, MagicMock

 import pytest

@@ -204,6 +204,7 @@ def test_basic_chat_chain(mock_client, stream):
    # Generation is second
    assert generation_args["event"] == "$ai_generation"
    assert "distinct_id" in generation_args
+    assert generation_props["$ai_framework"] == "langchain"
    assert "$ai_model" in generation_props
    assert "$ai_provider" in generation_props
    assert generation_props["$ai_input"] == [
@@ -1378,11 +1379,11 @@ def test_langgraph_agent(mock_client):
    )
    graph.invoke(inputs, config={"callbacks": [cb]})
    calls = [call[1] for call in mock_client.capture.call_args_list]
-    assert len(calls) == 21
+    assert len(calls) == 15
    for call in calls:
        assert call["properties"]["$ai_trace_id"] == "test-trace-id"
    assert len([call for call in calls if call["event"] == "$ai_generation"]) == 2
-    assert len([call for call in calls if call["event"] == "$ai_span"]) == 18
+    assert len([call for call in calls if call["event"] == "$ai_span"]) == 12
    assert len([call for call in calls if call["event"] == "$ai_trace"]) == 1


@@ -1435,11 +1436,13 @@ def test_span_set_parent_ids_for_third_level_run(mock_client, trace_id):

    assert mock_client.capture.call_count == 3

-    span2, span1, trace = [
-        call[1]["properties"] for call in mock_client.capture.call_args_list
-    ]
-    assert span2["$ai_parent_id"] == span1["$ai_span_id"]
-    assert span1["$ai_parent_id"] == trace["$ai_trace_id"]
+    calls = mock_client.capture.call_args_list
+    span_props_2 = calls[0][1]["properties"]
+    span_props_1 = calls[1][1]["properties"]
+    trace_props = calls[2][1]["properties"]
+
+    assert span_props_2["$ai_parent_id"] == span_props_1["$ai_span_id"]
+    assert span_props_1["$ai_parent_id"] == trace_props["$ai_trace_id"]


 def test_captures_error_with_details_in_span(mock_client):
@@ -1478,3 +1481,399 @@ def test_captures_error_without_details_in_span(mock_client):
        == "ValueError"
    )
    assert mock_client.capture.call_args_list[1][1]["properties"]["$ai_is_error"]
+
+
+def test_openai_reasoning_tokens(mock_client):
+    """Test that OpenAI reasoning tokens are captured correctly."""
+    prompt = ChatPromptTemplate.from_messages(
+        [("user", "Think step by step about this problem")]
+    )
+
+    # Mock response with reasoning tokens in output_token_details
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="Let me think through this step by step...",
+                usage_metadata={
+                    "input_tokens": 10,
+                    "output_tokens": 25,
+                    "total_tokens": 35,
+                    "output_token_details": {"reasoning": 15},  # 15 reasoning tokens
+                },
+            )
+        ]
+    )
+
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Let me think through this step by step..."
+    assert mock_client.capture.call_count == 3
+
+    generation_args = mock_client.capture.call_args_list[1][1]
+    generation_props = generation_args["properties"]
+
+    assert generation_args["event"] == "$ai_generation"
+    assert generation_props["$ai_input_tokens"] == 10
+    assert generation_props["$ai_output_tokens"] == 25
+    assert generation_props["$ai_reasoning_tokens"] == 15
+
+
+def test_anthropic_cache_write_and_read_tokens(mock_client):
+    """Test that Anthropic cache creation and read tokens are captured correctly."""
+    prompt = ChatPromptTemplate.from_messages([("user", "Analyze this large document")])
+
+    # First call with cache creation
+    model_write = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="I've analyzed the document and cached the context.",
+                usage_metadata={
+                    "total_tokens": 1050,
+                    "input_tokens": 1000,
+                    "output_tokens": 50,
+                    "cache_creation_input_tokens": 800,  # Anthropic cache write
+                },
+            )
+        ]
+    )
+
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model_write
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "I've analyzed the document and cached the context."
+    assert mock_client.capture.call_count == 3
+
+    generation_args = mock_client.capture.call_args_list[1][1]
+    generation_props = generation_args["properties"]
+
+    assert generation_args["event"] == "$ai_generation"
+    assert generation_props["$ai_input_tokens"] == 1000
+    assert generation_props["$ai_output_tokens"] == 50
+    assert generation_props["$ai_cache_creation_input_tokens"] == 800
+    assert generation_props["$ai_cache_read_input_tokens"] == 0
+    assert generation_props["$ai_reasoning_tokens"] == 0
+
+    # Reset mock for second call
+    mock_client.reset_mock()
+
+    # Second call with cache read
+    model_read = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="Using cached analysis to provide quick response.",
+                usage_metadata={
+                    "input_tokens": 200,
+                    "output_tokens": 30,
+                    "total_tokens": 1030,
+                    "cache_read_input_tokens": 800,  # Anthropic cache read
+                },
+            )
+        ]
+    )
+
+    chain = prompt | model_read
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Using cached analysis to provide quick response."
+    assert mock_client.capture.call_count == 3
+
+    generation_args = mock_client.capture.call_args_list[1][1]
+    generation_props = generation_args["properties"]
+
+    assert generation_args["event"] == "$ai_generation"
+    assert generation_props["$ai_input_tokens"] == 200
+    assert generation_props["$ai_output_tokens"] == 30
+    assert generation_props["$ai_cache_creation_input_tokens"] == 0
+    assert generation_props["$ai_cache_read_input_tokens"] == 800
+    assert generation_props["$ai_reasoning_tokens"] == 0
+
+
+def test_openai_cache_read_tokens(mock_client):
+    """Test that OpenAI cache read tokens are captured correctly."""
+    prompt = ChatPromptTemplate.from_messages(
+        [("user", "Use the cached prompt for this request")]
+    )
+
+    # Mock response with cache read tokens in input_token_details
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="Response using cached prompt context.",
+                usage_metadata={
+                    "input_tokens": 150,
+                    "output_tokens": 40,
+                    "total_tokens": 190,
+                    "input_token_details": {
+                        "cache_read": 100,  # 100 tokens read from cache
+                        "cache_creation": 0,
+                    },
+                },
+            )
+        ]
+    )
+
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Response using cached prompt context."
+    assert mock_client.capture.call_count == 3
+
+    generation_args = mock_client.capture.call_args_list[1][1]
+    generation_props = generation_args["properties"]
+
+    assert generation_args["event"] == "$ai_generation"
+    assert generation_props["$ai_input_tokens"] == 150
+    assert generation_props["$ai_output_tokens"] == 40
+    assert generation_props["$ai_cache_read_input_tokens"] == 100
+    assert generation_props["$ai_cache_creation_input_tokens"] == 0
+    assert generation_props["$ai_reasoning_tokens"] == 0
+
+
+def test_openai_cache_creation_tokens(mock_client):
+    """Test that OpenAI cache creation tokens are captured correctly."""
+    prompt = ChatPromptTemplate.from_messages(
+        [("user", "Create a cache for this large prompt context")]
+    )
+
+    # Mock response with cache creation tokens in input_token_details
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="Created cache for the prompt context.",
+                usage_metadata={
+                    "input_tokens": 2000,
+                    "output_tokens": 25,
+                    "total_tokens": 2025,
+                    "input_token_details": {
+                        "cache_creation": 1500,  # 1500 tokens written to cache
+                        "cache_read": 0,
+                    },
+                },
+            )
+        ]
+    )
+
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Created cache for the prompt context."
+    assert mock_client.capture.call_count == 3
+
+    generation_args = mock_client.capture.call_args_list[1][1]
+    generation_props = generation_args["properties"]
+
+    assert generation_args["event"] == "$ai_generation"
+    assert generation_props["$ai_input_tokens"] == 2000
+    assert generation_props["$ai_output_tokens"] == 25
+    assert generation_props["$ai_cache_creation_input_tokens"] == 1500
+    assert generation_props["$ai_cache_read_input_tokens"] == 0
+    assert generation_props["$ai_reasoning_tokens"] == 0
+
+
+def test_combined_reasoning_and_cache_tokens(mock_client):
+    """Test that both reasoning tokens and cache tokens can be captured together."""
+    prompt = ChatPromptTemplate.from_messages(
+        [("user", "Think through this cached problem")]
+    )
+
+    # Mock response with both reasoning and cache tokens
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="Let me reason through this using cached context...",
+                usage_metadata={
+                    "input_tokens": 500,
+                    "output_tokens": 100,
+                    "total_tokens": 600,
+                    "input_token_details": {"cache_read": 300, "cache_creation": 0},
+                    "output_token_details": {"reasoning": 60},  # 60 reasoning tokens
+                },
+            )
+        ]
+    )
+
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Let me reason through this using cached context..."
+    assert mock_client.capture.call_count == 3
+
+    generation_args = mock_client.capture.call_args_list[1][1]
+    generation_props = generation_args["properties"]
+
+    assert generation_args["event"] == "$ai_generation"
+    assert generation_props["$ai_input_tokens"] == 500
+    assert generation_props["$ai_output_tokens"] == 100
+    assert generation_props["$ai_cache_read_input_tokens"] == 300
+    assert generation_props["$ai_cache_creation_input_tokens"] == 0
+    assert generation_props["$ai_reasoning_tokens"] == 60
+
+
+@pytest.mark.skipif(not OPENAI_API_KEY, reason="OPENAI_API_KEY is not set")
+def test_openai_reasoning_tokens(mock_client):
+    model = ChatOpenAI(
+        api_key=OPENAI_API_KEY, model="o4-mini", max_completion_tokens=10
+    )
+    cb = CallbackHandler(
+        mock_client, trace_id="test-trace-id", distinct_id="test-distinct-id"
+    )
+    model.invoke("what is the weather in sf", config={"callbacks": [cb]})
+    call = mock_client.capture.call_args_list[0][1]
+    assert call["properties"]["$ai_reasoning_tokens"] is not None
+    assert call["properties"]["$ai_input_tokens"] is not None
+    assert call["properties"]["$ai_output_tokens"] is not None
+
+
+def test_callback_handler_without_client():
+    """Test that CallbackHandler works properly when no PostHog client is passed."""
+    with patch("posthog.ai.langchain.callbacks.setup") as mock_setup:
+        mock_client = mock_setup.return_value
+
+        callbacks = CallbackHandler()
+
+        # Verify that setup() was called
+        mock_setup.assert_called_once()
+
+        # Verify that the client was set to the result of setup()
+        assert callbacks._ph_client == mock_client
+
+        # Test that the callback handler works with a simple chain
+        prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+        model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+        chain = prompt | model
+
+        # This should work and call the mock client
+        result = chain.invoke({}, config={"callbacks": [callbacks]})
+        assert result.content == "Bar"
+
+        # Verify that the mock client was used for capturing events
+        assert mock_client.capture.call_count == 3
+
+
+def test_convert_message_to_dict_tool_calls():
+    """Test that _convert_message_to_dict properly converts tool calls in AIMessage."""
+    from posthog.ai.langchain.callbacks import _convert_message_to_dict
+    from langchain_core.messages import AIMessage
+    from langchain_core.messages.tool import ToolCall
+
+    # Create an AIMessage with tool calls
+    tool_calls = [
+        ToolCall(
+            id="call_123",
+            name="get_weather",
+            args={"city": "San Francisco", "units": "celsius"},
+        )
+    ]
+
+    ai_message = AIMessage(
+        content="I'll check the weather for you.", tool_calls=tool_calls
+    )
+
+    # Convert to dict
+    result = _convert_message_to_dict(ai_message)
+
+    # Verify the conversion
+    assert result["role"] == "assistant"
+    assert result["content"] == "I'll check the weather for you."
+    assert result["tool_calls"] == [
+        {
+            "type": "function",
+            "id": "call_123",
+            "function": {
+                "name": "get_weather",
+                "arguments": '{"city": "San Francisco", "units": "celsius"}',
+            },
+        }
+    ]
+
+
+def test_tool_definition(mock_client):
+    """Test that tools defined in invocation parameters are captured in $ai_tools property"""
+    callbacks = CallbackHandler(mock_client)
+    run_id = uuid.uuid4()
+
+    # Define tools to be passed to the invocation parameters
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get the current weather for a specific location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city or location name to get weather for",
+                        }
+                    },
+                    "required": ["location"],
+                },
+            },
+        }
+    ]
+
+    with patch("time.time", return_value=1234567890):
+        callbacks._set_llm_metadata(
+            {"kwargs": {"openai_api_base": "https://api.openai.com/v1"}},
+            run_id,
+            messages=[{"role": "user", "content": "hey"}],
+            invocation_params={"temperature": 0.7, "tools": tools},
+            metadata={"ls_model_name": "gpt-4o-mini", "ls_provider": "openai"},
+            name="test",
+        )
+
+    expected = GenerationMetadata(
+        model="gpt-4o-mini",
+        input=[{"role": "user", "content": "hey"}],
+        start_time=1234567890,
+        model_params={"temperature": 0.7},
+        provider="openai",
+        base_url="https://api.openai.com/v1",
+        name="test",
+        tools=tools,
+        end_time=None,
+    )
+    assert callbacks._runs[run_id] == expected
+
+    with patch("time.time", return_value=1234567891):
+        run = callbacks._pop_run_metadata(run_id)
+    expected.end_time = 1234567891
+    assert run == expected
+    assert callbacks._runs == {}
+
+    # Now test that the tools are properly captured in the PostHog event
+    mock_response = MagicMock()
+    mock_response.generations = [[MagicMock()]]
+
+    callbacks._capture_generation(
+        trace_id=run_id,
+        run_id=run_id,
+        run=run,
+        output=mock_response,
+        parent_run_id=None,
+    )
+
+    assert mock_client.capture.call_count == 1
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == run_id
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "openai"
+    assert props["$ai_model"] == "gpt-4o-mini"
+    assert props["$ai_input"] == [{"role": "user", "content": "hey"}]
+    assert props["$ai_model_parameters"] == {"temperature": 0.7}
+    assert props["$ai_base_url"] == "https://api.openai.com/v1"
+    assert props["$ai_span_name"] == "test"
+    assert props["$ai_span_id"] == run_id
+    assert props["$ai_trace_id"] == run_id
+    assert props["$ai_latency"] == 1.0
+    # Verify that tools are captured in the $ai_tools property
+    assert props["$ai_tools"] == tools
@@ -1,6 +1,5 @@
-import json
 import time
-from unittest.mock import patch
+from unittest.mock import AsyncMock, patch

 import pytest

@@ -26,6 +25,7 @@ try:
        ResponseOutputMessage,
        ResponseOutputText,
        ResponseUsage,
+        ResponseFunctionToolCall,
        ParsedResponse,
    )
    from openai.types.responses.parsed_response import (
@@ -34,6 +34,7 @@ try:
    )

    from posthog.ai.openai import OpenAI
+    from posthog.ai.openai.openai_async import AsyncOpenAI

    OPENAI_AVAILABLE = True
 except ImportError:
@@ -218,6 +219,106 @@ def mock_openai_response_with_cached_tokens():
    )


+@pytest.fixture
+def streaming_tool_call_chunks():
+    return [
+        ChatCompletionChunk(
+            id="chunk1",
+            model="gpt-4",
+            object="chat.completion.chunk",
+            created=1234567890,
+            choices=[
+                ChoiceChunk(
+                    index=0,
+                    delta=ChoiceDelta(
+                        role="assistant",
+                        tool_calls=[
+                            ChoiceDeltaToolCall(
+                                index=0,
+                                id="call_abc123",
+                                type="function",
+                                function=ChoiceDeltaToolCallFunction(
+                                    name="get_weather",
+                                    arguments='{"location": "',
+                                ),
+                            )
+                        ],
+                    ),
+                    finish_reason=None,
+                )
+            ],
+        ),
+        ChatCompletionChunk(
+            id="chunk2",
+            model="gpt-4",
+            object="chat.completion.chunk",
+            created=1234567891,
+            choices=[
+                ChoiceChunk(
+                    index=0,
+                    delta=ChoiceDelta(
+                        tool_calls=[
+                            ChoiceDeltaToolCall(
+                                index=0,
+                                id="call_abc123",
+                                type="function",
+                                function=ChoiceDeltaToolCallFunction(
+                                    arguments='San Francisco"',
+                                ),
+                            )
+                        ],
+                    ),
+                    finish_reason=None,
+                )
+            ],
+        ),
+        ChatCompletionChunk(
+            id="chunk3",
+            model="gpt-4",
+            object="chat.completion.chunk",
+            created=1234567892,
+            choices=[
+                ChoiceChunk(
+                    index=0,
+                    delta=ChoiceDelta(
+                        tool_calls=[
+                            ChoiceDeltaToolCall(
+                                index=0,
+                                id="call_abc123",
+                                type="function",
+                                function=ChoiceDeltaToolCallFunction(
+                                    arguments=', "unit": "celsius"}',
+                                ),
+                            )
+                        ],
+                    ),
+                    finish_reason=None,
+                )
+            ],
+        ),
+        ChatCompletionChunk(
+            id="chunk4",
+            model="gpt-4",
+            object="chat.completion.chunk",
+            created=1234567893,
+            choices=[
+                ChoiceChunk(
+                    index=0,
+                    delta=ChoiceDelta(
+                        content="The weather in San Francisco is 15°C.",
+                    ),
+                    finish_reason=None,
+                )
+            ],
+            usage=CompletionUsage(
+                prompt_tokens=20,
+                completion_tokens=15,
+                total_tokens=35,
+            ),
+        ),
+    ]
+
+
@pytest.fixture
 def mock_openai_response_with_tool_calls():
    return ChatCompletion(
@@ -227,11 +328,27 @@ def mock_openai_response_with_tool_calls():
        created=int(time.time()),
        choices=[
            Choice(
-                finish_reason="tool_calls",
+                finish_reason="stop",
                index=0,
                message=ChatCompletionMessage(
                    content="I'll check the weather for you.",
                    role="assistant",
+                ),
+            ),
+            Choice(
+                finish_reason="stop",
+                index=1,
+                message=ChatCompletionMessage(
+                    content=" Let me look that up.",
+                    role="assistant",
+                ),
+            ),
+            Choice(
+                finish_reason="tool_calls",
+                index=2,
+                message=ChatCompletionMessage(
+                    content=None,
+                    role="assistant",
                    tool_calls=[
                        ChatCompletionMessageToolCall(
                            id="call_abc123",
@@ -243,7 +360,7 @@ def mock_openai_response_with_tool_calls():
                        )
                    ],
                ),
-            )
+            ),
        ],
        usage=CompletionUsage(
            completion_tokens=15,
@@ -253,6 +370,97 @@ def mock_openai_response_with_tool_calls():
    )


+@pytest.fixture
+def mock_openai_response_tool_calls_only():
+    return ChatCompletion(
+        id="test",
+        model="gpt-4",
+        object="chat.completion",
+        created=int(time.time()),
+        choices=[
+            Choice(
+                finish_reason="tool_calls",
+                index=0,
+                message=ChatCompletionMessage(
+                    content=None,
+                    role="assistant",
+                    tool_calls=[
+                        ChatCompletionMessageToolCall(
+                            id="call_def456",
+                            type="function",
+                            function=Function(
+                                name="get_weather",
+                                arguments='{"location": "New York"}',
+                            ),
+                        )
+                    ],
+                ),
+            )
+        ],
+        usage=CompletionUsage(
+            completion_tokens=10,
+            prompt_tokens=25,
+            total_tokens=35,
+        ),
+    )
+
+
+@pytest.fixture
+def mock_responses_api_with_tool_calls():
+    return Response(
+        id="resp_123",
+        object="response",
+        created_at=int(time.time()),
+        model="gpt-4o-mini",
+        status="completed",
+        error=None,
+        incomplete_details=None,
+        instructions=None,
+        max_output_tokens=None,
+        tools=[],
+        tool_choice="auto",
+        parallel_tool_calls=True,
+        output=[
+            ResponseOutputMessage(
+                id="msg_456",
+                type="message",
+                role="assistant",
+                status="completed",
+                content=[
+                    ResponseOutputText(
+                        type="output_text",
+                        text="I'll help you with the weather.",
+                        annotations=[],
+                    ),
+                    ResponseOutputText(
+                        type="output_text",
+                        text=" Let me check that for you.",
+                        annotations=[],
+                    ),
+                ],
+            ),
+            ResponseFunctionToolCall(
+                id="fc_789",
+                type="function_call",
+                name="get_weather",
+                call_id="call_xyz789",
+                arguments='{"location": "Chicago"}',
+                status="completed",
+            ),
+        ],
+        usage=ResponseUsage(
+            input_tokens=30,
+            output_tokens=20,
+            input_tokens_details={"prompt_tokens": 30, "cached_tokens": 0},
+            output_tokens_details={"reasoning_tokens": 0},
+            total_tokens=50,
+        ),
+        previous_response_id=None,
+        user=None,
+        metadata={},
+    )
+
+
 def test_basic_completion(mock_client, mock_openai_response):
    with patch(
        "openai.resources.chat.completions.Completions.create",
@@ -278,7 +486,10 @@ def test_basic_completion(mock_client, mock_openai_response):
        assert props["$ai_model"] == "gpt-4"
        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
        assert props["$ai_output_choices"] == [
-            {"role": "assistant", "content": "Test response"}
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
        ]
        assert props["$ai_input_tokens"] == 20
        assert props["$ai_output_tokens"] == 10
@@ -427,7 +638,10 @@ def test_cached_tokens(mock_client, mock_openai_response_with_cached_tokens):
        assert props["$ai_model"] == "gpt-4"
        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
        assert props["$ai_output_choices"] == [
-            {"role": "assistant", "content": "Test response"}
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
        ]
        assert props["$ai_input_tokens"] == 20
        assert props["$ai_output_tokens"] == 10
@@ -475,24 +689,34 @@ def test_tool_calls(mock_client, mock_openai_response_with_tool_calls):
            {"role": "user", "content": "What's the weather in San Francisco?"}
        ]
        assert props["$ai_output_choices"] == [
-            {"role": "assistant", "content": "I'll check the weather for you."}
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll check the weather for you."},
+                    {"type": "text", "text": " Let me look that up."},
+                    {
+                        "type": "function",
+                        "id": "call_abc123",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"location": "San Francisco", "unit": "celsius"}',
+                        },
+                    },
+                ],
+            }
        ]

-        # Check that tool calls are properly captured
+        # Check that defined tools are properly captured in $ai_tools
        assert "$ai_tools" in props
-        tool_calls = props["$ai_tools"]
-        assert len(tool_calls) == 1
+        defined_tools = props["$ai_tools"]
+        assert len(defined_tools) == 1

-        # Verify the tool call details
-        tool_call = tool_calls[0]
-        assert tool_call.id == "call_abc123"
-        assert tool_call.type == "function"
-        assert tool_call.function.name == "get_weather"
-
-        # Verify the arguments
-        arguments = tool_call.function.arguments
-        parsed_args = json.loads(arguments)
-        assert parsed_args == {"location": "San Francisco", "unit": "celsius"}
+        # Verify the defined tool details
+        defined_tool = defined_tools[0]
+        assert defined_tool["type"] == "function"
+        assert defined_tool["function"]["name"] == "get_weather"
+        assert defined_tool["function"]["description"] == "Get weather"
+        assert defined_tool["function"]["parameters"] == {}

        # Check token usage
        assert props["$ai_input_tokens"] == 20
@@ -500,109 +724,122 @@ def test_tool_calls(mock_client, mock_openai_response_with_tool_calls):
        assert props["$ai_http_status"] == 200


-def test_streaming_with_tool_calls(mock_client):
-    # Create mock tool call chunks that will be returned in sequence
-    tool_call_chunks = [
-        ChatCompletionChunk(
-            id="chunk1",
+def test_tool_calls_only_no_content(mock_client, mock_openai_response_tool_calls_only):
+    with patch(
+        "openai.resources.chat.completions.Completions.create",
+        return_value=mock_openai_response_tool_calls_only,
+    ):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.chat.completions.create(
            model="gpt-4",
-            object="chat.completion.chunk",
-            created=1234567890,
-            choices=[
-                ChoiceChunk(
-                    index=0,
-                    delta=ChoiceDelta(
-                        role="assistant",
-                        tool_calls=[
-                            ChoiceDeltaToolCall(
-                                index=0,
-                                id="call_abc123",
-                                type="function",
-                                function=ChoiceDeltaToolCallFunction(
-                                    name="get_weather",
-                                    arguments='{"location": "',
-                                ),
-                            )
-                        ],
-                    ),
-                    finish_reason=None,
-                )
+            messages=[{"role": "user", "content": "Get weather for New York"}],
+            tools=[
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "description": "Get weather",
+                        "parameters": {},
+                    },
+                }
            ],
-        ),
-        ChatCompletionChunk(
-            id="chunk2",
-            model="gpt-4",
-            object="chat.completion.chunk",
-            created=1234567891,
-            choices=[
-                ChoiceChunk(
-                    index=0,
-                    delta=ChoiceDelta(
-                        tool_calls=[
-                            ChoiceDeltaToolCall(
-                                index=0,
-                                id="call_abc123",
-                                type="function",
-                                function=ChoiceDeltaToolCallFunction(
-                                    arguments='San Francisco"',
-                                ),
-                            )
-                        ],
-                    ),
-                    finish_reason=None,
-                )
-            ],
-        ),
-        ChatCompletionChunk(
-            id="chunk3",
-            model="gpt-4",
-            object="chat.completion.chunk",
-            created=1234567892,
-            choices=[
-                ChoiceChunk(
-                    index=0,
-                    delta=ChoiceDelta(
-                        tool_calls=[
-                            ChoiceDeltaToolCall(
-                                index=0,
-                                id="call_abc123",
-                                type="function",
-                                function=ChoiceDeltaToolCallFunction(
-                                    arguments=', "unit": "celsius"}',
-                                ),
-                            )
-                        ],
-                    ),
-                    finish_reason=None,
-                )
-            ],
-        ),
-        ChatCompletionChunk(
-            id="chunk4",
-            model="gpt-4",
-            object="chat.completion.chunk",
-            created=1234567893,
-            choices=[
-                ChoiceChunk(
-                    index=0,
-                    delta=ChoiceDelta(
-                        content="The weather in San Francisco is 15°C.",
-                    ),
-                    finish_reason=None,
-                )
-            ],
-            usage=CompletionUsage(
-                prompt_tokens=20,
-                completion_tokens=15,
-                total_tokens=35,
-            ),
-        ),
-    ]
+            posthog_distinct_id="test-id",
+        )

+        assert response == mock_openai_response_tool_calls_only
+        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"] == "openai"
+        assert props["$ai_model"] == "gpt-4"
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "function",
+                        "id": "call_def456",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"location": "New York"}',
+                        },
+                    }
+                ],
+            }
+        ]
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 25
+        assert props["$ai_output_tokens"] == 10
+        assert props["$ai_http_status"] == 200
+
+
+def test_responses_api_tool_calls(mock_client, mock_responses_api_with_tool_calls):
+    with patch(
+        "openai.resources.responses.Responses.create",
+        return_value=mock_responses_api_with_tool_calls,
+    ):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.responses.create(
+            model="gpt-4o-mini",
+            input=[{"role": "user", "content": "What's the weather in Chicago?"}],
+            tools=[
+                {
+                    "name": "get_weather",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "description": "Get weather",
+                        "parameters": {},
+                    },
+                }
+            ],
+            posthog_distinct_id="test-id",
+        )
+
+        assert response == mock_responses_api_with_tool_calls
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_generation"
+        assert props["$ai_provider"] == "openai"
+        assert props["$ai_model"] == "gpt-4o-mini"
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll help you with the weather."},
+                    {"type": "text", "text": " Let me check that for you."},
+                    {
+                        "type": "function",
+                        "id": "call_xyz789",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"location": "Chicago"}',
+                        },
+                    },
+                ],
+            }
+        ]
+
+        # Check token usage
+        assert props["$ai_input_tokens"] == 30
+        assert props["$ai_output_tokens"] == 20
+        assert props["$ai_http_status"] == 200
+
+
+def test_streaming_with_tool_calls(mock_client, streaming_tool_call_chunks):
    # Mock the create method to return our chunks
    with patch("openai.resources.chat.completions.Completions.create") as mock_create:
        # Set up the mock to return our chunks when iterated
-        mock_create.return_value = tool_call_chunks
+        mock_create.return_value = streaming_tool_call_chunks

        client = OpenAI(api_key="test-key", posthog_client=mock_client)

@@ -631,7 +868,7 @@ def test_streaming_with_tool_calls(mock_client):

        # Verify the chunks were returned correctly
        assert len(chunks) == 4
-        assert chunks == tool_call_chunks
+        assert chunks == streaming_tool_call_chunks

        # Verify the capture was called with the right arguments
        assert mock_client.capture.call_count == 1
@@ -644,26 +881,41 @@ def test_streaming_with_tool_calls(mock_client):
        assert props["$ai_provider"] == "openai"
        assert props["$ai_model"] == "gpt-4"

-        # Check that the tool calls were properly accumulated
+        # Check that defined tools are properly captured in $ai_tools
        assert "$ai_tools" in props
-        tool_calls = props["$ai_tools"]
-        assert len(tool_calls) == 1
+        defined_tools = props["$ai_tools"]
+        assert len(defined_tools) == 1

-        # Verify the complete tool call was properly assembled
-        tool_call = tool_calls[0]
-        assert tool_call.id == "call_abc123"
-        assert tool_call.type == "function"
-        assert tool_call.function.name == "get_weather"
+        # Verify the defined tool details
+        defined_tool = defined_tools[0]
+        assert defined_tool["type"] == "function"
+        assert defined_tool["function"]["name"] == "get_weather"
+        assert defined_tool["function"]["description"] == "Get weather"
+        assert defined_tool["function"]["parameters"] == {}

-        # Verify the arguments were concatenated correctly
-        arguments = tool_call.function.arguments
-        parsed_args = json.loads(arguments)
-        assert parsed_args == {"location": "San Francisco", "unit": "celsius"}
+        # Check that both text content and tool calls were accumulated
+        output_content = props["$ai_output_choices"][0]["content"]

-        # Check that the content was also accumulated
+        # Find text content and tool call in the output
+        text_content = None
+        tool_call_content = None
+        for item in output_content:
+            if item["type"] == "text":
+                text_content = item
+            elif item["type"] == "function":
+                tool_call_content = item
+
+        # Verify text content
+        assert text_content is not None
+        assert text_content["text"] == "The weather in San Francisco is 15°C."
+
+        # Verify tool call was captured
+        assert tool_call_content is not None
+        assert tool_call_content["id"] == "call_abc123"
+        assert tool_call_content["function"]["name"] == "get_weather"
        assert (
-            props["$ai_output_choices"][0]["content"]
-            == "The weather in San Francisco is 15°C."
+            tool_call_content["function"]["arguments"]
+            == '{"location": "San Francisco", "unit": "celsius"}'
        )

        # Check token usage
@@ -696,7 +948,10 @@ def test_responses_api(mock_client, mock_openai_response_with_responses_api):
        assert props["$ai_model"] == "gpt-4o-mini"
        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
        assert props["$ai_output_choices"] == [
-            {"role": "assistant", "content": "Test response"}
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
        ]
        assert props["$ai_input_tokens"] == 10
        assert props["$ai_output_tokens"] == 10
@@ -765,7 +1020,12 @@ def test_responses_parse(mock_client, mock_parsed_response):
        assert props["$ai_output_choices"] == [
            {
                "role": "assistant",
-                "content": '{"name": "Science Fair", "date": "Friday", "participants": ["Alice", "Bob"]}',
+                "content": [
+                    {
+                        "type": "text",
+                        "text": '{"name": "Science Fair", "date": "Friday", "participants": ["Alice", "Bob"]}',
+                    }
+                ],
            }
        ]
        assert props["$ai_input_tokens"] == 15
@@ -774,3 +1034,308 @@ def test_responses_parse(mock_client, mock_parsed_response):
        assert props["$ai_http_status"] == 200
        assert props["foo"] == "bar"
        assert isinstance(props["$ai_latency"], float)
+
+
+def test_responses_api_streaming_with_tokens(mock_client):
+    """Test that Responses API streaming properly captures token usage from response.usage."""
+    from openai.types.responses import ResponseUsage
+    from unittest.mock import MagicMock
+
+    # Create mock response chunks with usage data in the correct location
+    chunks = []
+
+    # First chunk - just content, no usage
+    chunk1 = MagicMock()
+    chunk1.type = "response.text.delta"
+    chunk1.text = "Test "
+    chunks.append(chunk1)
+
+    # Second chunk - more content
+    chunk2 = MagicMock()
+    chunk2.type = "response.text.delta"
+    chunk2.text = "response"
+    chunks.append(chunk2)
+
+    # Final chunk - completed event with usage in response.usage
+    chunk3 = MagicMock()
+    chunk3.type = "response.completed"
+    chunk3.response = MagicMock()
+    chunk3.response.usage = ResponseUsage(
+        input_tokens=25,
+        output_tokens=30,
+        total_tokens=55,
+        input_tokens_details={"prompt_tokens": 25, "cached_tokens": 0},
+        output_tokens_details={"reasoning_tokens": 0},
+    )
+    chunk3.response.output = ["Test response"]
+    chunks.append(chunk3)
+
+    captured_kwargs = {}
+
+    def mock_streaming_response(**kwargs):
+        # Capture the kwargs to verify stream_options was NOT added
+        captured_kwargs.update(kwargs)
+        return iter(chunks)
+
+    with patch(
+        "openai.resources.responses.Responses.create",
+        side_effect=mock_streaming_response,
+    ):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+
+        # Consume the streaming response
+        response = client.responses.create(
+            model="gpt-4o-mini",
+            input=[{"role": "user", "content": "Test message"}],
+            stream=True,
+            posthog_distinct_id="test-id",
+            posthog_properties={"test": "streaming"},
+        )
+
+        # Consume all chunks
+        list(response)
+
+        # Verify stream_options was NOT added (Responses API doesn't support it)
+        assert "stream_options" not in captured_kwargs
+
+        # Verify capture was called
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        # Verify tokens are captured correctly from response.usage (not 0)
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_generation"
+        assert props["$ai_provider"] == "openai"
+        assert props["$ai_model"] == "gpt-4o-mini"
+        assert props["$ai_input_tokens"] == 25  # Should not be 0
+        assert props["$ai_output_tokens"] == 30  # Should not be 0
+        assert props["test"] == "streaming"
+        assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.asyncio
+async def test_async_chat_streaming_with_tool_calls(
+    mock_client, streaming_tool_call_chunks
+):
+    captured_kwargs = {}
+
+    async def mock_create(self, **kwargs):
+        captured_kwargs["kwargs"] = kwargs
+
+        async def chunk_iterable():
+            for chunk in streaming_tool_call_chunks:
+                yield chunk
+
+        return chunk_iterable()
+
+    with patch(
+        "openai.resources.chat.completions.AsyncCompletions.create", new=mock_create
+    ):
+        client = AsyncOpenAI(api_key="test-key", posthog_client=mock_client)
+
+        response_stream = await client.chat.completions.create(
+            model="gpt-4",
+            messages=[
+                {"role": "user", "content": "What's the weather in San Francisco?"}
+            ],
+            tools=[
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "description": "Get weather",
+                        "parameters": {},
+                    },
+                }
+            ],
+            stream=True,
+            posthog_distinct_id="test-id",
+        )
+
+        chunks = []
+        async for chunk in response_stream:
+            chunks.append(chunk)
+
+    kwargs = captured_kwargs["kwargs"]
+    assert kwargs["stream_options"]["include_usage"] is True
+
+    assert len(chunks) == len(streaming_tool_call_chunks)
+    assert chunks == streaming_tool_call_chunks
+
+    assert mock_client.capture.call_count == 1
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "openai"
+    assert props["$ai_model"] == "gpt-4"
+    assert props["$ai_output_tokens"] == 15
+    assert props["$ai_input_tokens"] == 20
+    assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.asyncio
+async def test_async_responses_streaming_with_tokens(mock_client):
+    from openai.types.responses import ResponseUsage
+    from unittest.mock import MagicMock
+
+    chunks = []
+
+    chunk1 = MagicMock()
+    chunk1.type = "response.text.delta"
+    chunk1.text = "Test "
+    chunks.append(chunk1)
+
+    chunk2 = MagicMock()
+    chunk2.type = "response.text.delta"
+    chunk2.text = "response"
+    chunks.append(chunk2)
+
+    chunk3 = MagicMock()
+    chunk3.type = "response.completed"
+    chunk3.response = MagicMock()
+    chunk3.response.usage = ResponseUsage(
+        input_tokens=25,
+        output_tokens=30,
+        total_tokens=55,
+        input_tokens_details={"prompt_tokens": 25, "cached_tokens": 0},
+        output_tokens_details={"reasoning_tokens": 0},
+    )
+    chunk3.response.output = ["Test response"]
+    chunks.append(chunk3)
+
+    captured_kwargs = {}
+
+    async def mock_create(self, **kwargs):
+        captured_kwargs["kwargs"] = kwargs
+
+        async def chunk_iterable():
+            for chunk in chunks:
+                yield chunk
+
+        return chunk_iterable()
+
+    with patch("openai.resources.responses.AsyncResponses.create", new=mock_create):
+        client = AsyncOpenAI(api_key="test-key", posthog_client=mock_client)
+
+        response_stream = await client.responses.create(
+            model="gpt-4o-mini",
+            input=[{"role": "user", "content": "Test message"}],
+            stream=True,
+            posthog_distinct_id="test-id",
+            posthog_properties={"test": "streaming"},
+        )
+
+        async for _ in response_stream:
+            pass
+
+    kwargs = captured_kwargs["kwargs"]
+    assert "stream_options" not in kwargs
+
+    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["$ai_provider"] == "openai"
+    assert props["$ai_model"] == "gpt-4o-mini"
+    assert props["$ai_input_tokens"] == 25
+    assert props["$ai_output_tokens"] == 30
+    assert props["test"] == "streaming"
+    assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.asyncio
+async def test_async_embeddings_create(mock_client, mock_embedding_response):
+    mock_create = AsyncMock(return_value=mock_embedding_response)
+
+    with patch("openai.resources.embeddings.AsyncEmbeddings.create", new=mock_create):
+        client = AsyncOpenAI(api_key="test-key", posthog_client=mock_client)
+
+        response = await client.embeddings.create(
+            model="text-embedding-3-small",
+            input="Hello world",
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+        )
+
+    assert response == mock_embedding_response
+    assert mock_create.await_count == 1
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_embedding"
+    assert props["$ai_provider"] == "openai"
+    assert props["$ai_model"] == "text-embedding-3-small"
+    assert props["foo"] == "bar"
+    assert isinstance(props["$ai_latency"], float)
+
+
+def test_tool_definition(mock_client, mock_openai_response):
+    """Test that tools defined in the create function are captured in $ai_tools property"""
+    with patch(
+        "openai.resources.chat.completions.Completions.create",
+        return_value=mock_openai_response,
+    ):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+
+        # Define tools to be passed to the create function
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the current weather for a specific location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city or location name to get weather for",
+                            }
+                        },
+                        "required": ["location"],
+                    },
+                },
+            }
+        ]
+
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": "hey"}],
+            tools=tools,
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+        )
+
+        assert response == mock_openai_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_generation"
+        assert props["$ai_provider"] == "openai"
+        assert props["$ai_model"] == "gpt-4o-mini"
+        assert props["$ai_input"] == [{"role": "user", "content": "hey"}]
+        assert props["$ai_output_choices"] == [
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": "Test response"}],
+            }
+        ]
+        assert props["$ai_input_tokens"] == 20
+        assert props["$ai_output_tokens"] == 10
+        assert props["$ai_http_status"] == 200
+        assert props["foo"] == "bar"
+        assert isinstance(props["$ai_latency"], float)
+        # Verify that tools are captured in the $ai_tools property
+        assert props["$ai_tools"] == tools
@@ -0,0 +1,335 @@
+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_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
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,354 @@
+"""
+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 patch, MagicMock
+
+
+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 = 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 posthog.ai.openai import OpenAI
+            from openai.types.chat import ChatCompletion, ChatCompletionMessage
+            from openai.types.chat.chat_completion import Choice
+            from openai.types.completion_usage import CompletionUsage
+        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.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    def test_openai_separate_system_parameter(self):
+        """Test OpenAI with system prompt as separate parameter."""
+        try:
+            from posthog.ai.openai import OpenAI
+            from openai.types.chat import ChatCompletion, ChatCompletionMessage
+            from openai.types.chat.chat_completion import Choice
+            from openai.types.completion_usage import CompletionUsage
+        except ImportError:
+            self.skipTest("OpenAI package not available")
+
+        mock_response = ChatCompletion(
+            id="test",
+            model="gpt-4",
+            object="chat.completion",
+            created=int(time.time()),
+            choices=[
+                Choice(
+                    finish_reason="stop",
+                    index=0,
+                    message=ChatCompletionMessage(
+                        content=self.test_response, role="assistant"
+                    ),
+                )
+            ],
+            usage=CompletionUsage(
+                completion_tokens=10, prompt_tokens=20, total_tokens=30
+            ),
+        )
+
+        with patch(
+            "openai.resources.chat.completions.Completions.create",
+            return_value=mock_response,
+        ):
+            client = OpenAI(posthog_client=self.client, api_key="test")
+
+            messages = [{"role": "user", "content": self.test_user_message}]
+
+            client.chat.completions.create(
+                model="gpt-4",
+                messages=messages,
+                system=self.test_system_prompt,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    def test_openai_streaming_system_parameter(self):
+        """Test OpenAI streaming with system parameter."""
+        try:
+            from posthog.ai.openai import OpenAI
+            from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
+            from openai.types.chat.chat_completion_chunk import Choice as ChoiceChunk
+            from openai.types.chat.chat_completion_chunk import ChoiceDelta
+            from openai.types.completion_usage import CompletionUsage
+        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.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    # Anthropic Tests
+    def test_anthropic_messages_array_system_prompt(self):
+        """Test Anthropic with system prompt in messages array."""
+        try:
+            from posthog.ai.anthropic import Anthropic
+        except ImportError:
+            self.skipTest("Anthropic package not available")
+
+        with patch("anthropic.resources.messages.Messages.create") as mock_create:
+            mock_response = MagicMock()
+            mock_response.usage.input_tokens = 20
+            mock_response.usage.output_tokens = 10
+            mock_response.usage.cache_read_input_tokens = None
+            mock_response.usage.cache_creation_input_tokens = None
+            mock_create.return_value = mock_response
+
+            client = Anthropic(posthog_client=self.client, api_key="test")
+
+            messages = [
+                {"role": "system", "content": self.test_system_prompt},
+                {"role": "user", "content": self.test_user_message},
+            ]
+
+            client.messages.create(
+                model="claude-3-5-sonnet-20241022",
+                messages=messages,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$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.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
+
+    # Gemini Tests
+    def test_gemini_contents_array_system_prompt(self):
+        """Test Gemini with system prompt in contents array."""
+        try:
+            from posthog.ai.gemini import Client
+        except ImportError:
+            self.skipTest("Gemini package not available")
+
+        with patch("google.genai.Client") as mock_genai_class:
+            mock_response = MagicMock()
+            mock_response.candidates = [MagicMock()]
+            mock_response.candidates[0].content.parts = [MagicMock()]
+            mock_response.candidates[0].content.parts[0].text = self.test_response
+            mock_response.usage_metadata.prompt_token_count = 20
+            mock_response.usage_metadata.candidates_token_count = 10
+            mock_response.usage_metadata.cached_content_token_count = None
+            mock_response.usage_metadata.thoughts_token_count = None
+
+            mock_client_instance = MagicMock()
+            mock_models_instance = MagicMock()
+            mock_models_instance.generate_content.return_value = mock_response
+            mock_client_instance.models = mock_models_instance
+            mock_genai_class.return_value = mock_client_instance
+
+            client = Client(posthog_client=self.client, api_key="test")
+
+            contents = [
+                {"role": "system", "content": self.test_system_prompt},
+                {"role": "user", "content": self.test_user_message},
+            ]
+
+            client.models.generate_content(
+                model="gemini-2.0-flash",
+                contents=contents,
+                posthog_distinct_id="test-user",
+            )
+
+            self.assertEqual(len(self.client.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$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.capture.call_args_list), 1)
+            properties = self.client.capture.call_args_list[0][1]["properties"]
+            self._assert_system_prompt_captured(properties["$ai_input"])
@@ -1,3 +0,0 @@
-import pytest
-
-pytest.importorskip("django")
@@ -1,121 +0,0 @@
-from posthog.exception_integrations.django import DjangoRequestExtractor
-from django.test import RequestFactory
-from django.conf import settings
-from django.core.management import call_command
-import django
-
-DEFAULT_USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
-
-# setup a test app
-if not settings.configured:
-    settings.configure(
-        SECRET_KEY="test",
-        DEFAULT_CHARSET="utf-8",
-        INSTALLED_APPS=[
-            "django.contrib.auth",
-            "django.contrib.contenttypes",
-        ],
-        DATABASES={
-            "default": {
-                "ENGINE": "django.db.backends.sqlite3",
-                "NAME": ":memory:",
-            }
-        },
-    )
-    django.setup()
-
-    call_command("migrate", verbosity=0, interactive=False)
-
-
-def mock_request_factory(override_headers):
-    factory = RequestFactory(
-        headers={
-            "User-Agent": DEFAULT_USER_AGENT,
-            "Referrer": "http://example.com",
-            "X-Forwarded-For": "193.4.5.12",
-            **(override_headers or {}),
-        }
-    )
-
-    request = factory.get("/api/endpoint")
-    return request
-
-
-def test_request_extractor_with_no_trace():
-    request = mock_request_factory(None)
-    extractor = DjangoRequestExtractor(request)
-    assert extractor.extract_person_data() == {
-        "ip": "193.4.5.12",
-        "user_agent": DEFAULT_USER_AGENT,
-        "traceparent": None,
-        "distinct_id": None,
-        "$request_path": "/api/endpoint",
-    }
-
-
-def test_request_extractor_with_trace():
-    request = mock_request_factory(
-        {"traceparent": "00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01"}
-    )
-
-    extractor = DjangoRequestExtractor(request)
-    assert extractor.extract_person_data() == {
-        "ip": "193.4.5.12",
-        "user_agent": DEFAULT_USER_AGENT,
-        "traceparent": "00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01",
-        "distinct_id": None,
-        "$request_path": "/api/endpoint",
-    }
-
-
-def test_request_extractor_with_tracestate():
-    request = mock_request_factory(
-        {
-            "traceparent": "00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01",
-            "tracestate": "posthog-distinct-id=1234",
-        }
-    )
-    extractor = DjangoRequestExtractor(request)
-    assert extractor.extract_person_data() == {
-        "ip": "193.4.5.12",
-        "user_agent": DEFAULT_USER_AGENT,
-        "traceparent": "00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01",
-        "distinct_id": "1234",
-        "$request_path": "/api/endpoint",
-    }
-
-
-def test_request_extractor_with_complicated_tracestate():
-    request = mock_request_factory(
-        {"tracestate": "posthog-distinct-id=alohaMountainsXUYZ,rojo=00f067aa0ba902b7"}
-    )
-    extractor = DjangoRequestExtractor(request)
-    assert extractor.extract_person_data() == {
-        "ip": "193.4.5.12",
-        "user_agent": DEFAULT_USER_AGENT,
-        "traceparent": None,
-        "distinct_id": "alohaMountainsXUYZ",
-        "$request_path": "/api/endpoint",
-    }
-
-
-def test_request_extractor_with_request_user():
-    from django.contrib.auth.models import User
-
-    user = User.objects.create_user(
-        username="test", email="test@posthog.com", password="top_secret"
-    )
-
-    request = mock_request_factory(None)
-    request.user = user
-
-    extractor = DjangoRequestExtractor(request)
-    assert extractor.extract_person_data() == {
-        "ip": "193.4.5.12",
-        "user_agent": DEFAULT_USER_AGENT,
-        "traceparent": None,
-        "distinct_id": None,
-        "$request_path": "/api/endpoint",
-        "email": "test@posthog.com",
-        "$user_id": "1",
-    }
@@ -0,0 +1,548 @@
+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
+        request_filter = lambda req: 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())
+
+
+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()
@@ -40,16 +40,30 @@ class TestClient(unittest.TestCase):
            event["properties"]["processed_by_before_send"] = True
            return event

-        client = Client(
-            FAKE_TEST_API_KEY, on_error=self.set_fail, before_send=my_before_send
-        )
-        success, msg = client.capture("user1", "test_event", {"original": "value"})
+        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.assertTrue(success)
-        self.assertEqual(msg["properties"]["processed_by_before_send"], True)
-        self.assertEqual(msg["properties"]["original"], "value")
-        self.assertEqual(len(processed_events), 1)
-        self.assertEqual(processed_events[0]["event"], "test_event")
+            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."""
@@ -59,20 +73,27 @@ class TestClient(unittest.TestCase):
                return None
            return event

-        client = Client(
-            FAKE_TEST_API_KEY, on_error=self.set_fail, before_send=drop_test_events
-        )
+        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
-        success, msg = client.capture("user1", "test_drop_me")
-        self.assertTrue(success)
-        self.assertIsNone(msg)
+            # Event should be dropped
+            msg_uuid = client.capture("test_drop_me", distinct_id="user1")
+            self.assertIsNone(msg_uuid)

-        # Event should go through
-        success, msg = client.capture("user1", "keep_me")
-        self.assertTrue(success)
-        self.assertIsNotNone(msg)
-        self.assertEqual(msg["event"], "keep_me")
+            # 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."""
@@ -80,18 +101,26 @@ class TestClient(unittest.TestCase):
        def buggy_before_send(event):
            raise ValueError("Oops!")

-        client = Client(
-            FAKE_TEST_API_KEY, on_error=self.set_fail, before_send=buggy_before_send
-        )
-        success, msg = client.capture("user1", "robust_event")
+        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.assertTrue(success)
-        self.assertIsNotNone(msg)
-        self.assertEqual(msg["event"], "robust_event")
+            # 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, identify, set, etc."""
+        """Test that before_send works with capture, set, etc."""

        def add_marker(event):
            if "properties" not in event:
@@ -99,38 +128,46 @@ class TestClient(unittest.TestCase):
            event["properties"]["marked"] = True
            return event

-        client = Client(
-            FAKE_TEST_API_KEY, on_error=self.set_fail, before_send=add_marker
-        )
+        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
-        success, msg = client.capture("user1", "event")
-        self.assertTrue(success)
-        self.assertTrue(msg["properties"]["marked"])
+            # Test capture
+            msg_uuid = client.capture("event", distinct_id="user1")
+            self.assertIsNotNone(msg_uuid)

-        # Test identify
-        success, msg = client.identify("user1", {"trait": "value"})
-        self.assertTrue(success)
-        self.assertTrue(msg["properties"]["marked"])
+            # Test set
+            msg_uuid = client.set(distinct_id="user1", properties={"prop": "value"})
+            self.assertIsNotNone(msg_uuid)

-        # Test set
-        success, msg = client.set("user1", {"prop": "value"})
-        self.assertTrue(success)
-        self.assertTrue(msg["properties"]["marked"])
-
-        # Test page
-        success, msg = client.page("user1", "https://example.com")
-        self.assertTrue(success)
-        self.assertTrue(msg["properties"]["marked"])
+            # 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."""
-        client = Client(FAKE_TEST_API_KEY, on_error=self.set_fail, before_send=None)
-        success, msg = client.capture("user1", "normal_event")
+        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)

-        self.assertTrue(success)
-        self.assertIsNotNone(msg)
-        self.assertEqual(msg["event"], "normal_event")
+            # 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."""
@@ -152,20 +189,30 @@ class TestClient(unittest.TestCase):

            return event

-        client = Client(
-            FAKE_TEST_API_KEY, on_error=self.set_fail, before_send=scrub_pii
-        )
-        success, msg = client.capture(
-            "user1",
-            "form_submit",
-            {
-                "email": "user@example.com",
-                "credit_card": "1234-5678-9012-3456",
-                "form_name": "contact",
-            },
-        )
+        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.assertTrue(success)
-        self.assertEqual(msg["properties"]["email"], "***@example.com")
-        self.assertNotIn("credit_card", msg["properties"])
-        self.assertEqual(msg["properties"]["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")
@@ -0,0 +1,207 @@
+import unittest
+from unittest.mock import patch
+
+from posthog.contexts import (
+    get_tags,
+    new_context,
+    scoped,
+    tag,
+    identify_context,
+    set_context_session,
+    get_context_session_id,
+    get_context_distinct_id,
+)
+
+
+class TestContexts(unittest.TestCase):
+    def test_tag_and_get_tags(self):
+        with new_context(fresh=True):
+            tag("key1", "value1")
+            tag("key2", 2)
+
+            tags = get_tags()
+            assert tags["key1"] == "value1"
+            assert tags["key2"] == 2
+
+    def test_new_context_isolation(self):
+        with new_context(fresh=True):
+            # Set tag in outer context
+            tag("outer", "value")
+
+            with new_context(fresh=True):
+                # Inner context should start empty
+                assert get_tags() == {}
+
+                # Set tag in inner context
+                tag("inner", "value")
+                assert get_tags()["inner"] == "value"
+
+                # Outer tag should not be visible
+                self.assertNotIn("outer", get_tags())
+
+            with new_context(fresh=False):
+                # Inner context should inherit outer tag
+                assert get_tags() == {"outer": "value"}
+
+            # After exiting context, inner tag should be gone
+            self.assertNotIn("inner", get_tags())
+
+            # Outer tag should still be there
+            assert get_tags()["outer"] == "value"
+
+    def test_nested_contexts(self):
+        with new_context(fresh=True):
+            tag("level1", "value1")
+
+            with new_context(fresh=True):
+                tag("level2", "value2")
+
+                with new_context(fresh=True):
+                    tag("level3", "value3")
+                    assert get_tags() == {"level3": "value3"}
+
+                # Back to level 2
+                assert get_tags() == {"level2": "value2"}
+
+            # Back to level 1
+            assert get_tags() == {"level1": "value1"}
+
+    @patch("posthog.capture_exception")
+    def test_scoped_decorator_success(self, mock_capture):
+        @scoped()
+        def successful_function(x, y):
+            tag("x", x)
+            tag("y", y)
+            return x + y
+
+        result = successful_function(1, 2)
+
+        # Function should execute normally
+        assert result == 3
+
+        # No exception should be captured
+        mock_capture.assert_not_called()
+
+        # Context should be cleared after function execution
+        assert get_tags() == {}
+
+    @patch("posthog.capture_exception")
+    def test_scoped_decorator_exception(self, mock_capture):
+        test_exception = ValueError("Test exception")
+
+        def check_context_on_capture(exception, **kwargs):
+            # Assert tags are available when capture_exception is called
+            current_tags = get_tags()
+            assert current_tags.get("important_context") == "value"
+
+        mock_capture.side_effect = check_context_on_capture
+
+        @scoped()
+        def failing_function():
+            tag("important_context", "value")
+            raise test_exception
+
+        # Function should raise the exception
+        with self.assertRaises(ValueError):
+            failing_function()
+
+        # Verify capture_exception was called
+        mock_capture.assert_called_once_with(test_exception)
+
+        # Context should be cleared after function execution
+        assert get_tags() == {}
+
+    @patch("posthog.capture_exception")
+    def test_new_context_exception_handling(self, mock_capture):
+        test_exception = RuntimeError("Context exception")
+
+        def check_context_on_capture(exception, **kwargs):
+            # Assert inner context tags are available when capture_exception is called
+            current_tags = get_tags()
+            assert current_tags.get("inner_context") == "inner_value"
+
+        mock_capture.side_effect = check_context_on_capture
+
+        # Set up outer context
+        with new_context():
+            tag("outer_context", "outer_value")
+
+            try:
+                with new_context():
+                    tag("inner_context", "inner_value")
+                    raise test_exception
+            except RuntimeError:
+                pass  # Expected exception
+
+            # Outer context should still be intact
+            assert get_tags()["outer_context"] == "outer_value"
+
+        # Verify capture_exception was called
+        mock_capture.assert_called_once_with(test_exception)
+
+    def test_identify_context(self):
+        with new_context(fresh=True):
+            # Initially no distinct ID
+            assert get_context_distinct_id() is None
+
+            # Set distinct ID
+            identify_context("user123")
+            assert get_context_distinct_id() == "user123"
+
+    def test_set_context_session(self):
+        with new_context(fresh=True):
+            # Initially no session ID
+            assert get_context_session_id() is None
+
+            # Set session ID
+            set_context_session("session456")
+            assert get_context_session_id() == "session456"
+
+    def test_context_inheritance_fresh_context(self):
+        with new_context(fresh=True):
+            identify_context("user123")
+            set_context_session("session456")
+
+            with new_context(fresh=True):
+                # Fresh context should not inherit
+                assert get_context_distinct_id() is None
+                assert get_context_session_id() is None
+
+            # Original context should still have values
+            assert get_context_distinct_id() == "user123"
+            assert get_context_session_id() == "session456"
+
+    def test_context_inheritance_non_fresh_context(self):
+        with new_context(fresh=True):
+            identify_context("user123")
+            set_context_session("session456")
+
+            with new_context(fresh=False):
+                # Non-fresh context should inherit
+                assert get_context_distinct_id() == "user123"
+                assert get_context_session_id() == "session456"
+
+                # Override in child context
+                identify_context("user789")
+                set_context_session("session999")
+                assert get_context_distinct_id() == "user789"
+                assert get_context_session_id() == "session999"
+
+            # Original context should still have original values
+            assert get_context_distinct_id() == "user123"
+            assert get_context_session_id() == "session456"
+
+    def test_scoped_decorator_with_context_ids(self):
+        @scoped()
+        def function_with_context():
+            identify_context("user456")
+            set_context_session("session789")
+            return get_context_distinct_id(), get_context_session_id()
+
+        distinct_id, session_id = function_with_context()
+        assert distinct_id == "user456"
+        assert session_id == "session789"
+
+        # Context should be cleared after function execution
+        assert get_context_distinct_id() is None
+        assert get_context_session_id() is None
@@ -32,32 +32,3 @@ def test_excepthook(tmpdir):
        b'"$exception_list": [{"mechanism": {"type": "generic", "handled": true}, "module": null, "type": "ZeroDivisionError", "value": "division by zero", "stacktrace": {"frames": [{"platform": "python", "filename": "app.py", "abs_path"'
        in output
    )
-
-
-def test_trying_to_use_django_integration(tmpdir):
-    app = tmpdir.join("app.py")
-    app.write(
-        dedent(
-            """
-    from posthog import Posthog, Integrations
-    posthog = Posthog('phc_x', host='https://eu.i.posthog.com', enable_exception_autocapture=True, exception_autocapture_integrations=[Integrations.Django], debug=True, on_error=lambda e, batch: print('error handling batch: ', e, batch))
-
-    # frame_value = "LOL"
-
-    1/0
-    """
-        )
-    )
-
-    with pytest.raises(subprocess.CalledProcessError) as excinfo:
-        subprocess.check_output([sys.executable, str(app)], stderr=subprocess.STDOUT)
-
-    output = excinfo.value.output
-
-    assert b"ZeroDivisionError" in output
-    assert b"LOL" in output
-    assert b"DEBUG:posthog:data uploaded successfully" in output
-    assert (
-        b'"$exception_list": [{"mechanism": {"type": "generic", "handled": true}, "module": null, "type": "ZeroDivisionError", "value": "division by zero", "stacktrace": {"frames": [{"platform": "python", "filename": "app.py", "abs_path"'
-        in output
-    )
@@ -229,9 +229,9 @@ class TestGetFeatureFlagResult(unittest.TestCase):
        self.assertEqual(flag_result.variant, None)
        self.assertEqual(flag_result.payload, 300)
        patch_capture.assert_called_with(
-            "some-distinct-id",
            "$feature_flag_called",
-            {
+            distinct_id="some-distinct-id",
+            properties={
                "$feature_flag": "person-flag",
                "$feature_flag_response": True,
                "locally_evaluated": True,
@@ -283,9 +283,9 @@ class TestGetFeatureFlagResult(unittest.TestCase):
        self.assertEqual(flag_result.payload, {"some": "value"})

        patch_capture.assert_called_with(
-            "distinct_id",
            "$feature_flag_called",
-            {
+            distinct_id="distinct_id",
+            properties={
                "$feature_flag": "person-flag",
                "$feature_flag_response": "variant-1",
                "locally_evaluated": True,
@@ -305,9 +305,9 @@ class TestGetFeatureFlagResult(unittest.TestCase):
        self.assertIsNone(another_flag_result.payload)

        patch_capture.assert_called_with(
-            "another-distinct-id",
            "$feature_flag_called",
-            {
+            distinct_id="another-distinct-id",
+            properties={
                "$feature_flag": "person-flag",
                "$feature_flag_response": "variant-2",
                "locally_evaluated": True,
@@ -345,9 +345,9 @@ class TestGetFeatureFlagResult(unittest.TestCase):
        self.assertEqual(flag_result.variant, None)
        self.assertEqual(flag_result.payload, 300)
        patch_capture.assert_called_with(
-            "some-distinct-id",
            "$feature_flag_called",
-            {
+            distinct_id="some-distinct-id",
+            properties={
                "$feature_flag": "person-flag",
                "$feature_flag_response": True,
                "locally_evaluated": False,
@@ -388,9 +388,9 @@ class TestGetFeatureFlagResult(unittest.TestCase):
        self.assertEqual(flag_result.get_value(), "variant-1")
        self.assertEqual(flag_result.payload, [1, 2, 3])
        patch_capture.assert_called_with(
-            "distinct_id",
            "$feature_flag_called",
-            {
+            distinct_id="distinct_id",
+            properties={
                "$feature_flag": "person-flag",
                "$feature_flag_response": "variant-1",
                "locally_evaluated": False,
@@ -431,9 +431,9 @@ class TestGetFeatureFlagResult(unittest.TestCase):

        self.assertIsNone(flag_result)
        patch_capture.assert_called_with(
-            "some-distinct-id",
            "$feature_flag_called",
-            {
+            distinct_id="some-distinct-id",
+            properties={
                "$feature_flag": "no-person-flag",
                "$feature_flag_response": None,
                "locally_evaluated": False,
@@ -7,8 +7,7 @@ class TestModule(unittest.TestCase):
    posthog = None

    def _assert_enqueue_result(self, result):
-        self.assertEqual(type(result[0]), bool)
-        self.assertEqual(type(result[1]), dict)
+        self.assertEqual(type(result[0]), str)

    def failed(self):
        self.failed = True
@@ -19,21 +18,8 @@ class TestModule(unittest.TestCase):
            "testsecret", host="http://localhost:8000", on_error=self.failed
        )

-    def test_no_api_key(self):
-        self.posthog.api_key = None
-        self.assertRaises(Exception, self.posthog.capture)
-
-    def test_no_host(self):
-        self.posthog.host = None
-        self.assertRaises(Exception, self.posthog.capture)
-
    def test_track(self):
-        res = self.posthog.capture("distinct_id", "python module event")
-        self._assert_enqueue_result(res)
-        self.posthog.flush()
-
-    def test_identify(self):
-        res = self.posthog.identify("distinct_id", {"email": "user@email.com"})
+        res = self.posthog.capture("python module event", distinct_id="distinct_id")
        self._assert_enqueue_result(res)
        self.posthog.flush()

@@ -42,9 +28,5 @@ class TestModule(unittest.TestCase):
        self._assert_enqueue_result(res)
        self.posthog.flush()

-    def test_page(self):
-        self.posthog.page("distinct_id", "https://posthog.com/contact")
-        self.posthog.flush()
-
    def test_flush(self):
        self.posthog.flush()
@@ -1,138 +0,0 @@
-import unittest
-from unittest.mock import patch
-
-from posthog.scopes import clear_tags, get_tags, new_context, scoped, tag
-
-
-class TestScopes(unittest.TestCase):
-    def setUp(self):
-        # Reset any context between tests
-        clear_tags()
-
-    def test_tag_and_get_tags(self):
-        tag("key1", "value1")
-        tag("key2", 2)
-
-        tags = get_tags()
-        assert tags["key1"] == "value1"
-        assert tags["key2"] == 2
-
-    def test_clear_tags(self):
-        tag("key1", "value1")
-        assert get_tags()["key1"] == "value1"
-
-        clear_tags()
-        assert get_tags() == {}
-
-    def test_new_context_isolation(self):
-        # Set tag in outer context
-        tag("outer", "value")
-
-        with new_context(fresh=True):
-            # Inner context should start empty
-            assert get_tags() == {}
-
-            # Set tag in inner context
-            tag("inner", "value")
-            assert get_tags()["inner"] == "value"
-
-            # Outer tag should not be visible
-            self.assertNotIn("outer", get_tags())
-
-        with new_context(fresh=False):
-            # Inner context should start empty
-            assert get_tags() == {"outer": "value"}
-
-        # After exiting context, inner tag should be gone
-        self.assertNotIn("inner", get_tags())
-
-        # Outer tag should still be there
-        assert get_tags()["outer"] == "value"
-
-    def test_nested_contexts(self):
-        tag("level1", "value1")
-
-        with new_context(fresh=True):
-            tag("level2", "value2")
-
-            with new_context(fresh=True):
-                tag("level3", "value3")
-                assert get_tags() == {"level3": "value3"}
-
-            # Back to level 2
-            assert get_tags() == {"level2": "value2"}
-
-        # Back to level 1
-        assert get_tags() == {"level1": "value1"}
-
-    @patch("posthog.capture_exception")
-    def test_scoped_decorator_success(self, mock_capture):
-        @scoped()
-        def successful_function(x, y):
-            tag("x", x)
-            tag("y", y)
-            return x + y
-
-        result = successful_function(1, 2)
-
-        # Function should execute normally
-        assert result == 3
-
-        # No exception should be captured
-        mock_capture.assert_not_called()
-
-        # Context should be cleared after function execution
-        assert get_tags() == {}
-
-    @patch("posthog.capture_exception")
-    def test_scoped_decorator_exception(self, mock_capture):
-        test_exception = ValueError("Test exception")
-
-        def check_context_on_capture(exception, **kwargs):
-            # Assert tags are available when capture_exception is called
-            current_tags = get_tags()
-            assert current_tags.get("important_context") == "value"
-
-        mock_capture.side_effect = check_context_on_capture
-
-        @scoped()
-        def failing_function():
-            tag("important_context", "value")
-            raise test_exception
-
-        # Function should raise the exception
-        with self.assertRaises(ValueError):
-            failing_function()
-
-        # Verify capture_exception was called
-        mock_capture.assert_called_once_with(test_exception)
-
-        # Context should be cleared after function execution
-        assert get_tags() == {}
-
-    @patch("posthog.capture_exception")
-    def test_new_context_exception_handling(self, mock_capture):
-        test_exception = RuntimeError("Context exception")
-
-        def check_context_on_capture(exception, **kwargs):
-            # Assert inner context tags are available when capture_exception is called
-            current_tags = get_tags()
-            assert current_tags.get("inner_context") == "inner_value"
-
-        mock_capture.side_effect = check_context_on_capture
-
-        # Set up outer context
-        tag("outer_context", "outer_value")
-
-        try:
-            with new_context():
-                tag("inner_context", "inner_value")
-                raise test_exception
-        except RuntimeError:
-            pass  # Expected exception
-
-        # Verify capture_exception was called
-        mock_capture.assert_called_once_with(test_exception)
-
-        # Outer context should still be intact
-        assert get_tags()["outer_context"] == "outer_value"
@@ -1,3 +1,4 @@
+import time
 import unittest
 from dataclasses import dataclass
 from datetime import date, datetime, timedelta
@@ -12,6 +13,7 @@ from pydantic import BaseModel
 from pydantic.v1 import BaseModel as BaseModelV1

 from posthog import utils
+from posthog.types import FeatureFlagResult

 TEST_API_KEY = "kOOlRy2QlMY9jHZQv0bKz0FZyazBUoY8Arj0lFVNjs4"
 FAKE_TEST_API_KEY = "random_key"
@@ -173,3 +175,124 @@ class TestUtils(unittest.TestCase):
                "inner_optional": None,
            },
        }
+
+
+class TestFlagCache(unittest.TestCase):
+    def setUp(self):
+        self.cache = utils.FlagCache(max_size=3, default_ttl=1)
+        self.flag_result = FeatureFlagResult.from_value_and_payload(
+            "test-flag", True, None
+        )
+
+    def test_cache_basic_operations(self):
+        distinct_id = "user123"
+        flag_key = "test-flag"
+        flag_version = 1
+
+        # Test cache miss
+        result = self.cache.get_cached_flag(distinct_id, flag_key, flag_version)
+        assert result is None
+
+        # Test cache set and hit
+        self.cache.set_cached_flag(
+            distinct_id, flag_key, self.flag_result, flag_version
+        )
+        result = self.cache.get_cached_flag(distinct_id, flag_key, flag_version)
+        assert result is not None
+        assert result.get_value()
+
+    def test_cache_ttl_expiration(self):
+        distinct_id = "user123"
+        flag_key = "test-flag"
+        flag_version = 1
+
+        # Set flag in cache
+        self.cache.set_cached_flag(
+            distinct_id, flag_key, self.flag_result, flag_version
+        )
+
+        # Should be available immediately
+        result = self.cache.get_cached_flag(distinct_id, flag_key, flag_version)
+        assert result is not None
+
+        # Wait for TTL to expire (1 second + buffer)
+        time.sleep(1.1)
+
+        # Should be expired
+        result = self.cache.get_cached_flag(distinct_id, flag_key, flag_version)
+        assert result is None
+
+    def test_cache_version_invalidation(self):
+        distinct_id = "user123"
+        flag_key = "test-flag"
+        old_version = 1
+        new_version = 2
+
+        # Set flag with old version
+        self.cache.set_cached_flag(distinct_id, flag_key, self.flag_result, old_version)
+
+        # Should hit with old version
+        result = self.cache.get_cached_flag(distinct_id, flag_key, old_version)
+        assert result is not None
+
+        # Should miss with new version
+        result = self.cache.get_cached_flag(distinct_id, flag_key, new_version)
+        assert result is None
+
+        # Invalidate old version
+        self.cache.invalidate_version(old_version)
+
+        # Should miss even with old version after invalidation
+        result = self.cache.get_cached_flag(distinct_id, flag_key, old_version)
+        assert result is None
+
+    def test_stale_cache_functionality(self):
+        distinct_id = "user123"
+        flag_key = "test-flag"
+        flag_version = 1
+
+        # Set flag in cache
+        self.cache.set_cached_flag(
+            distinct_id, flag_key, self.flag_result, flag_version
+        )
+
+        # Wait for TTL to expire
+        time.sleep(1.1)
+
+        # Should not get fresh cache
+        result = self.cache.get_cached_flag(distinct_id, flag_key, flag_version)
+        assert result is None
+
+        # Should get stale cache (within 1 hour default)
+        stale_result = self.cache.get_stale_cached_flag(distinct_id, flag_key)
+        assert stale_result is not None
+        assert stale_result.get_value()
+
+    def test_lru_eviction(self):
+        # Cache has max_size=3, so adding 4 users should evict the LRU one
+        flag_version = 1
+
+        # Add 3 users
+        for i in range(3):
+            user_id = f"user{i}"
+            self.cache.set_cached_flag(
+                user_id, "test-flag", self.flag_result, flag_version
+            )
+
+        # Access user0 to make it recently used
+        self.cache.get_cached_flag("user0", "test-flag", flag_version)
+
+        # Add 4th user, should evict user1 (least recently used)
+        self.cache.set_cached_flag("user3", "test-flag", self.flag_result, flag_version)
+
+        # user0 should still be there (was recently accessed)
+        result = self.cache.get_cached_flag("user0", "test-flag", flag_version)
+        assert result is not None
+
+        # user2 should still be there (was recently added)
+        result = self.cache.get_cached_flag("user2", "test-flag", flag_version)
+        assert result is not None
+
+        # user3 should be there (just added)
+        result = self.cache.get_cached_flag("user3", "test-flag", flag_version)
+        assert result is not None
@@ -9,6 +9,27 @@ FlagValue = Union[bool, str]
 BeforeSendCallback = Callable[[dict[str, Any]], Optional[dict[str, Any]]]


+# Type alias for the send_feature_flags parameter
+class SendFeatureFlagsOptions(TypedDict, total=False):
+    """Options for sending feature flags with capture events.
+
+    Args:
+        only_evaluate_locally: Whether to only use local evaluation for feature flags.
+            If True, only flags that can be evaluated locally will be included.
+            If False, remote evaluation via /flags API will be used when needed.
+        person_properties: Properties to use for feature flag evaluation specific to this event.
+            These properties will be merged with any existing person properties.
+        group_properties: Group properties to use for feature flag evaluation specific to this event.
+            Format: { group_type_name: { group_properties } }
+    """
+
+    should_send: bool
+    only_evaluate_locally: Optional[bool]
+    person_properties: Optional[dict[str, Any]]
+    group_properties: Optional[dict[str, dict[str, Any]]]
+    flag_keys_filter: Optional[list[str]]
+
+
@dataclass(frozen=True)
 class FlagReason:
    code: str
@@ -92,7 +113,7 @@ class FeatureFlag:
            variant=variant,
            reason=None,
            metadata=LegacyFlagMetadata(
-                payload=payload if payload else None,
+                payload=payload,
            ),
        )

@@ -160,7 +181,9 @@ class FeatureFlagResult:
            key=key,
            enabled=enabled,
            variant=variant,
-            payload=json.loads(payload) if isinstance(payload, str) else payload,
+            payload=json.loads(payload)
+            if isinstance(payload, str) and payload
+            else payload,
            reason=None,
        )

@@ -201,6 +224,7 @@ class FeatureFlagResult:
            payload=(
                json.loads(details.metadata.payload)
                if isinstance(details.metadata.payload, str)
+                and details.metadata.payload
                else details.metadata.payload
            ),
            reason=details.reason.description if details.reason else None,
@@ -278,5 +302,7 @@ def to_payloads(response: FlagsResponse) -> Optional[dict[str, str]]:
    return {
        key: value.metadata.payload
        for key, value in response.get("flags", {}).items()
-        if isinstance(value, FeatureFlag) and value.enabled and value.metadata.payload
+        if isinstance(value, FeatureFlag)
+        and value.enabled
+        and value.metadata.payload is not None
    }
@@ -1,12 +1,17 @@
+import json
 import logging
 import numbers
 import re
+import time
 from collections import defaultdict
 from dataclasses import asdict, is_dataclass
 from datetime import date, datetime, timezone
 from decimal import Decimal
 from typing import Any, Optional
 from uuid import UUID
+import sys
+import platform
+import distro  # For Linux OS detection

 import six
 from dateutil.tz import tzlocal, tzutc
@@ -154,6 +159,266 @@ class SizeLimitedDict(defaultdict):
        super().__setitem__(key, value)


+class FlagCacheEntry:
+    def __init__(self, flag_result, flag_definition_version, timestamp=None):
+        self.flag_result = flag_result
+        self.flag_definition_version = flag_definition_version
+        self.timestamp = timestamp or time.time()
+
+    def is_valid(self, current_time, ttl, current_flag_version):
+        time_valid = (current_time - self.timestamp) < ttl
+        version_valid = self.flag_definition_version == current_flag_version
+        return time_valid and version_valid
+
+    def is_stale_but_usable(self, current_time, max_stale_age=3600):
+        return (current_time - self.timestamp) < max_stale_age
+
+
+class FlagCache:
+    def __init__(self, max_size=10000, default_ttl=300):
+        self.cache = {}  # distinct_id -> {flag_key: FlagCacheEntry}
+        self.access_times = {}  # distinct_id -> last_access_time
+        self.max_size = max_size
+        self.default_ttl = default_ttl
+
+    def get_cached_flag(self, distinct_id, flag_key, current_flag_version):
+        current_time = time.time()
+
+        if distinct_id not in self.cache:
+            return None
+
+        user_flags = self.cache[distinct_id]
+        if flag_key not in user_flags:
+            return None
+
+        entry = user_flags[flag_key]
+        if entry.is_valid(current_time, self.default_ttl, current_flag_version):
+            self.access_times[distinct_id] = current_time
+            return entry.flag_result
+
+        return None
+
+    def get_stale_cached_flag(self, distinct_id, flag_key, max_stale_age=3600):
+        current_time = time.time()
+
+        if distinct_id not in self.cache:
+            return None
+
+        user_flags = self.cache[distinct_id]
+        if flag_key not in user_flags:
+            return None
+
+        entry = user_flags[flag_key]
+        if entry.is_stale_but_usable(current_time, max_stale_age):
+            return entry.flag_result
+
+        return None
+
+    def set_cached_flag(
+        self, distinct_id, flag_key, flag_result, flag_definition_version
+    ):
+        current_time = time.time()
+
+        # Evict LRU users if we're at capacity
+        if distinct_id not in self.cache and len(self.cache) >= self.max_size:
+            self._evict_lru()
+
+        # Initialize user cache if needed
+        if distinct_id not in self.cache:
+            self.cache[distinct_id] = {}
+
+        # Store the flag result
+        self.cache[distinct_id][flag_key] = FlagCacheEntry(
+            flag_result, flag_definition_version, current_time
+        )
+        self.access_times[distinct_id] = current_time
+
+    def invalidate_version(self, old_version):
+        users_to_remove = []
+
+        for distinct_id, user_flags in self.cache.items():
+            flags_to_remove = []
+            for flag_key, entry in user_flags.items():
+                if entry.flag_definition_version == old_version:
+                    flags_to_remove.append(flag_key)
+
+            # Remove invalidated flags
+            for flag_key in flags_to_remove:
+                del user_flags[flag_key]
+
+            # Remove user entirely if no flags remain
+            if not user_flags:
+                users_to_remove.append(distinct_id)
+
+        # Clean up empty users
+        for distinct_id in users_to_remove:
+            del self.cache[distinct_id]
+            if distinct_id in self.access_times:
+                del self.access_times[distinct_id]
+
+    def _evict_lru(self):
+        if not self.access_times:
+            return
+
+        # Remove 20% of least recently used entries
+        sorted_users = sorted(self.access_times.items(), key=lambda x: x[1])
+        to_remove = max(1, len(sorted_users) // 5)
+
+        for distinct_id, _ in sorted_users[:to_remove]:
+            if distinct_id in self.cache:
+                del self.cache[distinct_id]
+            if distinct_id in self.access_times:
+                del self.access_times[distinct_id]
+
+    def clear(self):
+        self.cache.clear()
+        self.access_times.clear()
+
+
+class RedisFlagCache:
+    def __init__(
+        self, redis_client, default_ttl=300, stale_ttl=3600, key_prefix="posthog:flags:"
+    ):
+        self.redis = redis_client
+        self.default_ttl = default_ttl
+        self.stale_ttl = stale_ttl
+        self.key_prefix = key_prefix
+        self.version_key = f"{key_prefix}version"
+
+    def _get_cache_key(self, distinct_id, flag_key):
+        return f"{self.key_prefix}{distinct_id}:{flag_key}"
+
+    def _serialize_entry(self, flag_result, flag_definition_version, timestamp=None):
+        if timestamp is None:
+            timestamp = time.time()
+
+        # Use clean to make flag_result JSON-serializable for cross-platform compatibility
+        serialized_result = clean(flag_result)
+
+        entry = {
+            "flag_result": serialized_result,
+            "flag_version": flag_definition_version,
+            "timestamp": timestamp,
+        }
+        return json.dumps(entry)
+
+    def _deserialize_entry(self, data):
+        try:
+            entry = json.loads(data)
+            flag_result = entry["flag_result"]
+            return FlagCacheEntry(
+                flag_result=flag_result,
+                flag_definition_version=entry["flag_version"],
+                timestamp=entry["timestamp"],
+            )
+        except (json.JSONDecodeError, KeyError, ValueError):
+            # If deserialization fails, treat as cache miss
+            return None
+
+    def get_cached_flag(self, distinct_id, flag_key, current_flag_version):
+        try:
+            cache_key = self._get_cache_key(distinct_id, flag_key)
+            data = self.redis.get(cache_key)
+
+            if data:
+                entry = self._deserialize_entry(data)
+                if entry and entry.is_valid(
+                    time.time(), self.default_ttl, current_flag_version
+                ):
+                    return entry.flag_result
+
+            return None
+        except Exception:
+            # Redis error - return None to fall back to normal evaluation
+            return None
+
+    def get_stale_cached_flag(self, distinct_id, flag_key, max_stale_age=None):
+        try:
+            if max_stale_age is None:
+                max_stale_age = self.stale_ttl
+
+            cache_key = self._get_cache_key(distinct_id, flag_key)
+            data = self.redis.get(cache_key)
+
+            if data:
+                entry = self._deserialize_entry(data)
+                if entry and entry.is_stale_but_usable(time.time(), max_stale_age):
+                    return entry.flag_result
+
+            return None
+        except Exception:
+            # Redis error - return None
+            return None
+
+    def set_cached_flag(
+        self, distinct_id, flag_key, flag_result, flag_definition_version
+    ):
+        try:
+            cache_key = self._get_cache_key(distinct_id, flag_key)
+            serialized_entry = self._serialize_entry(
+                flag_result, flag_definition_version
+            )
+
+            # Set with TTL for automatic cleanup (use stale_ttl for total lifetime)
+            self.redis.setex(cache_key, self.stale_ttl, serialized_entry)
+
+            # Update the current version
+            self.redis.set(self.version_key, flag_definition_version)
+
+        except Exception:
+            # Redis error - silently fail, don't break flag evaluation
+            pass
+
+    def invalidate_version(self, old_version):
+        try:
+            # For Redis, we use a simple approach: scan for keys with old version
+            # and delete them. This could be expensive with many keys, but it's
+            # necessary for correctness.
+
+            cursor = 0
+            pattern = f"{self.key_prefix}*"
+
+            while True:
+                cursor, keys = self.redis.scan(cursor, match=pattern, count=100)
+
+                for key in keys:
+                    if key.decode() == self.version_key:
+                        continue
+
+                    try:
+                        data = self.redis.get(key)
+                        if data:
+                            entry_dict = json.loads(data)
+                            if entry_dict.get("flag_version") == old_version:
+                                self.redis.delete(key)
+                    except (json.JSONDecodeError, KeyError):
+                        # If we can't parse the entry, delete it to be safe
+                        self.redis.delete(key)
+
+                if cursor == 0:
+                    break
+
+        except Exception:
+            # Redis error - silently fail
+            pass
+
+    def clear(self):
+        try:
+            # Delete all keys matching our pattern
+            cursor = 0
+            pattern = f"{self.key_prefix}*"
+
+            while True:
+                cursor, keys = self.redis.scan(cursor, match=pattern, count=100)
+                if keys:
+                    self.redis.delete(*keys)
+                if cursor == 0:
+                    break
+        except Exception:
+            # Redis error - silently fail
+            pass
+
+
 def convert_to_datetime_aware(date_obj):
    if date_obj.tzinfo is None:
        date_obj = date_obj.replace(tzinfo=timezone.utc)
@@ -198,3 +463,57 @@ def str_iequals(value, comparand):
        False
    """
    return str(value).casefold() == str(comparand).casefold()
+
+
+def get_os_info():
+    """
+    Returns standardized OS name and version information.
+    Similar to how user agent parsing works in JS.
+    """
+    os_name = ""
+    os_version = ""
+
+    platform_name = sys.platform
+
+    if platform_name.startswith("win"):
+        os_name = "Windows"
+        if hasattr(platform, "win32_ver"):
+            win_version = platform.win32_ver()[0]
+            if win_version:
+                os_version = win_version
+
+    elif platform_name == "darwin":
+        os_name = "Mac OS X"
+        if hasattr(platform, "mac_ver"):
+            mac_version = platform.mac_ver()[0]
+            if mac_version:
+                os_version = mac_version
+
+    elif platform_name.startswith("linux"):
+        os_name = "Linux"
+        linux_info = distro.info()
+        if linux_info["version"]:
+            os_version = linux_info["version"]
+
+    elif platform_name.startswith("freebsd"):
+        os_name = "FreeBSD"
+        if hasattr(platform, "release"):
+            os_version = platform.release()
+
+    else:
+        os_name = platform_name
+        if hasattr(platform, "release"):
+            os_version = platform.release()
+
+    return os_name, os_version
+
+
+def system_context() -> dict[str, Any]:
+    os_name, os_version = get_os_info()
+
+    return {
+        "$python_runtime": platform.python_implementation(),
+        "$python_version": "%s.%s.%s" % (sys.version_info[:3]),
+        "$os": os_name,
+        "$os_version": os_version,
+    }
@@ -1,4 +1,4 @@
-VERSION = "4.8.0"
+VERSION = "6.7.11"

 if __name__ == "__main__":
    print(VERSION, end="")  # noqa: T201
@@ -8,7 +8,7 @@ dynamic = ["version"]
 description = "Integrate PostHog into any python application."
 authors = [{ name = "PostHog", email = "hey@posthog.com" }]
 maintainers = [{ name = "PostHog", email = "hey@posthog.com" }]
-license = {text = "MIT"}
+license = { text = "MIT" }
 readme = "README.md"
 requires-python = ">=3.9"
 classifiers = [
@@ -29,6 +29,7 @@ dependencies = [
    "python-dateutil>=2.2",
    "backoff>=1.10.0",
    "distro>=1.5.0",
+    "typing-extensions>=4.2.0",
 ]

 [project.urls]
@@ -36,7 +37,6 @@ Homepage = "https://github.com/posthog/posthog-python"
 Repository = "https://github.com/posthog/posthog-python"

 [project.optional-dependencies]
-sentry = ["sentry-sdk", "django"]
 langchain = ["langchain>=0.2.0"]
 dev = [
    "django-stubs",
@@ -52,7 +52,7 @@ dev = [
    "pydantic",
    "ruff",
    "setuptools",
-    "packaging", 
+    "packaging",
    "wheel",
    "twine",
    "tomli",
@@ -68,10 +68,11 @@ test = [
    "django",
    "openai",
    "anthropic",
-    "langgraph",
-    "langchain-community>=0.2.0",
-    "langchain-openai>=0.2.0",
-    "langchain-anthropic>=0.2.0",
+    "langgraph>=0.4.8",
+    "langchain-core>=0.3.65",
+    "langchain-community>=0.3.25",
+    "langchain-openai>=0.3.22",
+    "langchain-anthropic>=0.3.15",
    "google-genai",
    "pydantic",
    "parameterized>=0.8.1",
@@ -86,8 +87,7 @@ packages = [
    "posthog.ai.anthropic",
    "posthog.ai.gemini",
    "posthog.test",
-    "posthog.sentry",
-    "posthog.exception_integrations",
+    "posthog.integrations",
 ]

 [tool.setuptools.dynamic]
@@ -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()
@@ -1,23 +0,0 @@
-#!/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", "sentry_django_example.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()
@@ -1,16 +0,0 @@
-"""
-ASGI config for sentry_django_example 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/3.2/howto/deployment/asgi/
-"""
-
-import os
-
-from django.core.asgi import get_asgi_application
-
-os.environ.setdefault("DJANGO_SETTINGS_MODULE", "sentry_django_example.settings")
-
-application = get_asgi_application()
@@ -1,171 +0,0 @@
-"""
-Django settings for sentry_django_example project.
-
-Generated by 'django-admin startproject' using Django 3.2.2.
-
-For more information on this file, see
-https://docs.djangoproject.com/en/3.2/topics/settings/
-
-For the full list of settings and their values, see
-https://docs.djangoproject.com/en/3.2/ref/settings/
-"""
-
-from pathlib import Path
-from uuid import uuid4
-
-# 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/3.2/howto/deployment/checklist/
-
-# SECURITY WARNING: keep the secret key used in production secret!
-SECRET_KEY = "django-insecure-4kzfiq7vb(t0+jbl#vq)u=%06ouf)n*=l%730c8=tk(wkm9i9o"
-
-# SECURITY WARNING: don't run with debug turned on in production!
-DEBUG = True
-
-ALLOWED_HOSTS = []
-
-
-# PostHog Setup (can be a separate app)
-import posthog  # noqa: E402
-
-# You can find this key on the /setup page in PostHog
-posthog.api_key = (
-    "LXP6nQXvo-2TCqGVrWvPah8uJIyVykoMmhnEkEBi5PA"  # TODO: replace with your api key
-)
-
-posthog.personal_api_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"
-
-from posthog.sentry.posthog_integration import PostHogIntegration  # noqa: E402
-
-PostHogIntegration.organization = "posthog"  # TODO: your sentry organization
-# PostHogIntegration.prefix = # TODO: your self hosted Sentry url. (default: https://sentry.io/organizations/)
-
-# Since Sentry doesn't allow Integrations configuration (see https://github.com/getsentry/sentry-python/blob/master/sentry_sdk/integrations/__init__.py#L171-L183)
-# we work around this by setting static class variables beforehand
-
-# Sentry Setup
-import sentry_sdk  # noqa: E402
-from sentry_sdk.integrations.django import DjangoIntegration  # noqa: E402
-
-sentry_sdk.init(
-    dsn="https://27ac54f7f4cf484abf1335436b0c52e5@o344752.ingest.sentry.io/5624115",  # TODO: your Sentry DSN here
-    integrations=[DjangoIntegration(), PostHogIntegration()],
-    # Set traces_sample_rate to 1.0 to capture 100%
-    # of transactions for performance monitoring.
-    # We recommend adjusting this value in production.
-    traces_sample_rate=1.0,
-    # If you wish to associate users to errors (assuming you are using
-    # django.contrib.auth) you may enable sending PII data.
-    send_default_pii=True,
-)
-
-POSTHOG_DJANGO = {
-    "distinct_id": lambda request: str(
-        uuid4()
-    )  # TODO: your logic for generating unique ID, given the request object
-}
-
-# 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.sentry.django.PosthogDistinctIdMiddleware",
-]
-
-ROOT_URLCONF = "sentry_django_example.urls"
-
-TEMPLATES = [
-    {
-        "BACKEND": "django.template.backends.django.DjangoTemplates",
-        "DIRS": [],
-        "APP_DIRS": True,
-        "OPTIONS": {
-            "context_processors": [
-                "django.template.context_processors.debug",
-                "django.template.context_processors.request",
-                "django.contrib.auth.context_processors.auth",
-                "django.contrib.messages.context_processors.messages",
-            ],
-        },
-    },
-]
-
-WSGI_APPLICATION = "sentry_django_example.wsgi.application"
-
-
-# Database
-# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
-
-DATABASES = {
-    "default": {
-        "ENGINE": "django.db.backends.sqlite3",
-        "NAME": BASE_DIR / "db.sqlite3",
-    }
-}
-
-
-# Password validation
-# https://docs.djangoproject.com/en/3.2/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/3.2/topics/i18n/
-
-LANGUAGE_CODE = "en-us"
-
-TIME_ZONE = "UTC"
-
-USE_I18N = True
-
-USE_L10N = True
-
-USE_TZ = True
-
-
-# Static files (CSS, JavaScript, Images)
-# https://docs.djangoproject.com/en/3.2/howto/static-files/
-
-STATIC_URL = "/static/"
-
-# Default primary key field type
-# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
-
-DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
@@ -1,28 +0,0 @@
-"""sentry_django_example URL Configuration
-
-The `urlpatterns` list routes URLs to views. For more information please see:
-    https://docs.djangoproject.com/en/3.2/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
-
-
-def trigger_error(request):
-    division_by_zero = 1 / 0
-
-
-urlpatterns = [
-    path("admin/", admin.site.urls),
-    path("sentry-debug/", trigger_error),
-]
@@ -1,16 +0,0 @@
-"""
-WSGI config for sentry_django_example 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/3.2/howto/deployment/wsgi/
-"""
-
-import os
-
-from django.core.wsgi import get_wsgi_application
-
-os.environ.setdefault("DJANGO_SETTINGS_MODULE", "sentry_django_example.settings")
-
-application = get_wsgi_application()
@@ -1,112 +0,0 @@
-import argparse
-import json
-import logging
-
-import posthog
-
-__name__ = "simulator.py"
-__version__ = "0.0.1"
-__description__ = "scripting simulator"
-
-
-def json_hash(str):
-    if str:
-        return json.loads(str)
-
-
-# posthog -method=<method> -posthog-write-key=<posthogWriteKey> [options]
-
-
-parser = argparse.ArgumentParser(description="send a posthog message")
-
-parser.add_argument("--writeKey", help="the posthog writeKey")
-parser.add_argument("--type", help="The posthog message type")
-
-parser.add_argument("--distinct_id", help="the user id to send the event as")
-parser.add_argument("--anonymousId", help="the anonymous user id to send the event as")
-
-parser.add_argument("--event", help="the event name to send with the event")
-parser.add_argument("--properties", help="the event properties to send (JSON-encoded)")
-
-parser.add_argument(
-    "--name", help="name of the screen or page to send with the message"
-)
-
-parser.add_argument("--traits", help="the identify/group traits to send (JSON-encoded)")
-
-parser.add_argument("--groupId", help="the group id")
-
-options = parser.parse_args()
-
-
-def failed(status, msg):
-    raise Exception(msg)
-
-
-def capture():
-    posthog.capture(
-        options.distinct_id,
-        options.event,
-        anonymous_id=options.anonymousId,
-        properties=json_hash(options.properties),
-    )
-
-
-def page():
-    posthog.page(
-        options.distinct_id,
-        name=options.name,
-        anonymous_id=options.anonymousId,
-        properties=json_hash(options.properties),
-    )
-
-
-def identify():
-    posthog.identify(
-        options.distinct_id,
-        anonymous_id=options.anonymousId,
-        traits=json_hash(options.traits),
-    )
-
-
-def set_once():
-    posthog.set_once(
-        options.distinct_id,
-        properties=json_hash(options.traits),
-    )
-
-
-def set():
-    posthog.set(
-        options.distinct_id,
-        properties=json_hash(options.traits),
-    )
-
-
-def unknown():
-    print()
-
-
-posthog.api_key = options.writeKey
-posthog.on_error = failed
-posthog.debug = True
-
-log = logging.getLogger("posthog")
-ch = logging.StreamHandler()
-ch.setLevel(logging.DEBUG)
-log.addHandler(ch)
-
-switcher = {
-    "capture": capture,
-    "page": page,
-    "identify": identify,
-    "set_once": set_once,
-    "set": set,
-}
-
-func = switcher.get(options.type)
-if func:
-    func()
-    posthog.shutdown()
-else:
-    print("Invalid Message Type " + options.type)
@@ -0,0 +1,326 @@
+#!/usr/bin/env python3
+"""
+Test script to send capture_ai events to localhost:8010.
+This script tests the actual network request to a local PostHog instance.
+"""
+
+from posthog import Posthog
+from uuid import uuid4
+
+# Create a client pointing to localhost:8010
+posthog = Posthog(
+    "test-api-key",  # Use your actual project API key if needed
+    host="http://localhost:8010",
+    debug=True,  # Enable debug mode to see detailed logs
+)
+
+print("Testing capture_ai with localhost:8010")
+print("=" * 60)
+
+# Test 1: $ai_generation event with blobs
+print("\n1. Testing $ai_generation event with blobs...")
+print("-" * 60)
+
+trace_id = f"trace_{uuid4().hex[:8]}"
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_generation",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "gpt-4",
+            "$ai_provider": "openai",
+            "$ai_trace_id": trace_id,
+            "$ai_input": {
+                "messages": [
+                    {
+                        "role": "system",
+                        "content": "You are a helpful assistant that answers questions about Python.",
+                    },
+                    {
+                        "role": "user",
+                        "content": "What is the difference between a list and a tuple?",
+                    },
+                ],
+                "temperature": 0.7,
+                "max_tokens": 500,
+            },
+            "$ai_output_choices": {
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": "A list is mutable (can be changed) while a tuple is immutable (cannot be changed after creation). Lists use square brackets [] and tuples use parentheses ().",
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+                "model": "gpt-4",
+                "usage": {
+                    "prompt_tokens": 45,
+                    "completion_tokens": 32,
+                    "total_tokens": 77,
+                },
+            },
+            "$ai_completion_tokens": 32,
+            "$ai_prompt_tokens": 45,
+            "$ai_total_tokens": 77,
+            "$ai_latency": 1.234,
+        },
+        blob_properties=["$ai_input", "$ai_output_choices"],
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: $ai_generation event sent")
+        print(f"  UUID: {event_uuid}")
+        print(f"  Trace ID: {trace_id}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 2: $ai_trace event
+print("\n2. Testing $ai_trace event...")
+print("-" * 60)
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_trace",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "gpt-4",
+            "$ai_trace_id": trace_id,
+            "$ai_trace_name": "python_qa_session",
+            "$ai_input_state": {
+                "session_id": "session_123",
+                "user_context": "learning Python",
+            },
+            "$ai_output_state": {"questions_answered": 1, "satisfaction_score": 5},
+        },
+        blob_properties=["$ai_input_state", "$ai_output_state"],
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: $ai_trace event sent")
+        print(f"  UUID: {event_uuid}")
+        print(f"  Trace ID: {trace_id}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 3: $ai_span event
+print("\n3. Testing $ai_span event...")
+print("-" * 60)
+
+span_id = f"span_{uuid4().hex[:8]}"
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_span",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "gpt-4",
+            "$ai_trace_id": trace_id,
+            "$ai_span_id": span_id,
+            "$ai_span_name": "answer_generation",
+            "$ai_parent_id": trace_id,
+            "$ai_span_kind": "llm",
+            "$ai_latency": 0.8,
+        },
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: $ai_span event sent")
+        print(f"  UUID: {event_uuid}")
+        print(f"  Span ID: {span_id}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 4: $ai_embedding event
+print("\n4. Testing $ai_embedding event...")
+print("-" * 60)
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_embedding",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "text-embedding-ada-002",
+            "$ai_provider": "openai",
+            "$ai_trace_id": trace_id,
+            "$ai_input": {
+                "text": "What is the difference between a list and a tuple in Python?"
+            },
+            "$ai_embedding_dimension": 1536,
+            "$ai_latency": 0.123,
+        },
+        blob_properties=["$ai_input"],
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: $ai_embedding event sent")
+        print(f"  UUID: {event_uuid}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 5: $ai_metric event
+print("\n5. Testing $ai_metric event...")
+print("-" * 60)
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_metric",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "gpt-4",
+            "$ai_trace_id": trace_id,
+            "$ai_metric_name": "response_quality",
+            "$ai_metric_value": "0.95",
+        },
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: $ai_metric event sent")
+        print(f"  UUID: {event_uuid}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 6: $ai_feedback event
+print("\n6. Testing $ai_feedback event...")
+print("-" * 60)
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_feedback",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "gpt-4",
+            "$ai_trace_id": trace_id,
+            "$ai_feedback_text": "Great explanation! Very clear and helpful.",
+            "$ai_feedback_rating": 5,
+        },
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: $ai_feedback event sent")
+        print(f"  UUID: {event_uuid}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 7: Test with custom blob properties
+print("\n7. Testing with custom blob properties...")
+print("-" * 60)
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_generation",
+        distinct_id="test_user_123",
+        properties={
+            "$ai_model": "claude-3-opus",
+            "$ai_provider": "anthropic",
+            "$ai_trace_id": trace_id,
+            "$ai_input": {
+                "messages": [{"role": "user", "content": "Write a haiku about Python"}]
+            },
+            "$ai_output_choices": {
+                "choices": [
+                    {
+                        "message": {
+                            "role": "assistant",
+                            "content": "Snake glides through code\nSimple syntax, powerful tools\nDevelopers smile",
+                        }
+                    }
+                ]
+            },
+            "$ai_custom_data": {
+                "large_context": "This is some large custom data that should be sent as a blob"
+            },
+            "$ai_completion_tokens": 20,
+            "$ai_prompt_tokens": 10,
+        },
+        # Custom blob properties - including the default ones plus a custom one
+        blob_properties=["$ai_input", "$ai_output_choices", "$ai_custom_data"],
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: Event with custom blob properties sent")
+        print(f"  UUID: {event_uuid}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 8: Test with groups
+print("\n8. Testing with groups...")
+print("-" * 60)
+
+try:
+    event_uuid = posthog.capture_ai(
+        "$ai_generation",
+        distinct_id="test_user_123",
+        groups={"company": "posthog_inc", "team": "engineering"},
+        properties={
+            "$ai_model": "gpt-4",
+            "$ai_provider": "openai",
+            "$ai_trace_id": trace_id,
+            "$ai_input": {"messages": [{"role": "user", "content": "test"}]},
+            "$ai_output_choices": {
+                "choices": [{"message": {"role": "assistant", "content": "response"}}]
+            },
+        },
+    )
+
+    if event_uuid:
+        print("✓ SUCCESS: Event with groups sent")
+        print(f"  UUID: {event_uuid}")
+    else:
+        print("✗ FAILED: No UUID returned")
+
+except Exception as e:
+    print(f"✗ ERROR: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+print("\n" + "=" * 60)
+print("All tests completed!")
+print("\nMake sure your local PostHog instance is running on http://localhost:8010")
+print("and that the /i/v0/ai endpoint is available.")