WIP

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)

.env.example

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

.github/dependabot.yml

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

.github/workflows/generate-references.yml

@@ -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/

.github/workflows/release.yml

@@ -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

CHANGELOG.md

@@ -1,3 +1,89 @@
+# 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

bin/docs_scripts/doc_constant.py

@@ -3,50 +3,11 @@ Constants for PostHog Python SDK documentation generation.
 """
 
 from typing import Dict, Union
-
-# Types that are built-in to Python and don't need to be documented
-NO_DOCS_TYPES = [
-    "Client",
-    "any",
-    "int",
-    "float",
-    "bool",
-    "dict",
-    "list",
-    "str",
-    "tuple",
-    "set",
-    "frozenset",
-    "bytes",
-    "bytearray",
-    "memoryview",
-    "range",
-    "slice",
-    "complex",
-    "Union",
-    "Optional",
-    "Any",
-    "Callable",
-    "Type",
-    "TypeVar",
-    "Generic",
-    "Literal",
-    "ClassVar",
-    "Final",
-    "Annotated",
-    "NotRequired",
-    "Required",
-    "None",
-    "NoneType",
-    "object",
-    "Unpack",
-    "BaseException",
-    "Exception",
-]
+from posthog.version import VERSION
 
 # Documentation generation metadata
 DOCUMENTATION_METADATA = {
-    "hogRef": "0.1",
+    "hogRef": "0.3",
     "slugPrefix": "posthog-python",
     "specUrl": "https://github.com/PostHog/posthog-python",
 }
@@ -67,8 +28,9 @@ DOCSTRING_PATTERNS = {
 
 # Output file configuration
 OUTPUT_CONFIG: Dict[str, Union[str, int]] = {
-    "output_dir": ".",
-    "filename": "posthog-python-references.json",
+    "output_dir": "./references",
+    "filename": f"posthog-python-references-{VERSION}.json",
+    "filename_latest": "posthog-python-references-latest.json",
     "indent": 2,
 }
 

bin/docs_scripts/generate_json_schemas.py

@@ -11,7 +11,6 @@ from dataclasses import is_dataclass, fields
 from typing import get_origin, get_args, Union
 from textwrap import dedent
 from doc_constant import (
-    NO_DOCS_TYPES,
     DOCUMENTATION_METADATA,
     DOCSTRING_PATTERNS,
     OUTPUT_CONFIG,
@@ -187,7 +186,7 @@ def analyze_parameter(param: inspect.Parameter, docstring: str = "") -> dict:
         param_type = get_type_name(type(param.default))
 
     # Extract parameter description from Args section
-    param_description = f"Parameter: {param.name}"
+    param_description = ""
     if docstring:
         # Look for Args section and extract description for this parameter
         args_section_match = re.search(
@@ -378,6 +377,14 @@ def generate_sdk_documentation():
             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 = []
 
@@ -420,14 +427,28 @@ def generate_sdk_documentation():
             }
         )
 
+    # 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,
-        "noDocsTypes": NO_DOCS_TYPES,
         "types": types_list,
         "classes": classes_list,
+        "categories": categories,
     }
 
     return result
@@ -439,12 +460,23 @@ if __name__ == "__main__":
     try:
         documentation = generate_sdk_documentation()
 
-        # Write to file
+        # 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}")
 
@@ -459,12 +491,6 @@ if __name__ == "__main__":
         print(f"   • {classes_count} classes documented")
         print(f"   • {total_functions} functions documented")
 
-        no_docs = documentation["noDocsTypes"]
-        if no_docs:
-            print(
-                f"   • {len(no_docs)} types without documentation: {', '.join(no_docs[:5])}{'...' if len(no_docs) > 5 else ''}"
-            )
-
     except Exception as e:
         print(f"❌ Error generating documentation: {e}")
         import traceback

bin/fmt

@@ -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

example.py

@@ -1,175 +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(
-    "event",
-    distinct_id="distinct_id",
-    properties={"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(
-    "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
-posthog.set(
-    distinct_id="new_distinct_id", properties={"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(distinct_id="new_distinct_id", properties={"self_serve_signup": True})
-
-
-posthog.set_once(
-    distinct_id="new_distinct_id", properties={"self_serve_signup": False}
-)  # this will not change the property (because it was already set)
-
-posthog.set(distinct_id="new_distinct_id", properties={"current_browser": "Chrome"})
-posthog.set(distinct_id="new_distinct_id", properties={"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()

mypy-baseline.txt

@@ -36,11 +36,5 @@ 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]
-example.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"?
 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]

posthog/__init__.py

@@ -2,7 +2,13 @@ import datetime  # noqa: F401
 from typing import Callable, Dict, Optional, Any  # noqa: F401
 from typing_extensions import Unpack
 
-from posthog.args import OptionalCaptureArgs, OptionalSetArgs, ExceptionArg
+from posthog.args import (
+    OptionalCaptureArgs,
+    OptionalSetArgs,
+    OptionalCaptureAIArgs,
+    AI_EVENT_TYPE,
+    ExceptionArg,
+)
 from posthog.client import Client
 from posthog.contexts import (
     new_context as inner_new_context,
@@ -11,7 +17,15 @@ from posthog.contexts import (
     set_context_session as inner_set_context_session,
     identify_context as inner_identify_context,
 )
-from posthog.types import FeatureFlag, FlagsAndPayloads
+from posthog.feature_flags import (
+    InconclusiveMatchError as InconclusiveMatchError,
+    RequiresServerEvaluation as RequiresServerEvaluation,
+)
+from posthog.types import (
+    FeatureFlag,
+    FlagsAndPayloads,
+    FeatureFlagResult as FeatureFlagResult,
+)
 from posthog.version import VERSION
 
 __version__ = VERSION
@@ -385,12 +399,87 @@ def capture_exception(
     return _proxy("capture_exception", exception=exception, **kwargs)
 
 
+def capture_ai(
+    event: AI_EVENT_TYPE, **kwargs: Unpack[OptionalCaptureAIArgs]
+) -> Optional[str]:
+    """
+    Capture an AI event to the dedicated AI endpoint with support for large payloads.
+
+    This method sends AI events (like $ai_generation, $ai_trace, etc.) to PostHog's
+    specialized AI endpoint (/i/v0/ai) which supports large payloads through multipart/form-data
+    and blob storage in S3.
+
+    Args:
+        event: The AI event type. Must be one of: "$ai_generation", "$ai_trace", "$ai_span",
+               "$ai_embedding", "$ai_metric", "$ai_feedback"
+        distinct_id: The distinct ID of the user.
+        properties: A dictionary of AI event properties. Must include required properties based on event type:
+            - All events: "$ai_model" (required)
+            - $ai_generation: "$ai_provider", "$ai_trace_id" (required)
+            - $ai_trace: "$ai_trace_id" (required)
+            - $ai_span: "$ai_trace_id", "$ai_span_id" (required)
+            - $ai_embedding: "$ai_provider", "$ai_trace_id" (required)
+        blob_properties: List of property names to send as blobs (large data stored in S3).
+            Common blob properties: "$ai_input", "$ai_output_choices", "$ai_input_state", "$ai_output_state"
+            If not provided, defaults to common blob properties based on event type.
+        timestamp: The timestamp of the event.
+        uuid: A unique identifier for the event.
+        groups: A dictionary of group information.
+        disable_geoip: Whether to disable GeoIP for this event.
+
+    Examples:
+        ```python
+        # $ai_generation event with blobs
+        from posthog import capture_ai
+        capture_ai(
+            "$ai_generation",
+            distinct_id="user_123",
+            properties={
+                "$ai_model": "gpt-4",
+                "$ai_provider": "openai",
+                "$ai_trace_id": "trace_abc123",
+                "$ai_input": {
+                    "messages": [
+                        {"role": "user", "content": "Hello!"}
+                    ]
+                },
+                "$ai_output_choices": {
+                    "choices": [{"message": {"role": "assistant", "content": "Hi there!"}}]
+                },
+                "$ai_completion_tokens": 150,
+                "$ai_prompt_tokens": 50
+            },
+            blob_properties=["$ai_input", "$ai_output_choices"]
+        )
+        ```
+
+        ```python
+        # $ai_trace event
+        from posthog import capture_ai
+        capture_ai(
+            "$ai_trace",
+            distinct_id="user_123",
+            properties={
+                "$ai_model": "gpt-4",
+                "$ai_trace_id": "trace_abc123"
+            }
+        )
+        ```
+
+    Category:
+        AI Events
+
+    Note: This method sends events synchronously to the AI endpoint, bypassing the queue system.
+    """
+    return _proxy("capture_ai", event, **kwargs)
+
+
 def feature_enabled(
     key,  # type: str
     distinct_id,  # type: str
-    groups={},  # type: dict
-    person_properties={},  # type: dict
-    group_properties={},  # type: dict
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
     only_evaluate_locally=False,  # type: bool
     send_feature_flag_events=True,  # type: bool
     disable_geoip=None,  # type: Optional[bool]
@@ -427,9 +516,9 @@ def feature_enabled(
         "feature_enabled",
         key=key,
         distinct_id=distinct_id,
-        groups=groups,
-        person_properties=person_properties,
-        group_properties=group_properties,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
         only_evaluate_locally=only_evaluate_locally,
         send_feature_flag_events=send_feature_flag_events,
         disable_geoip=disable_geoip,
@@ -439,9 +528,9 @@ def feature_enabled(
 def get_feature_flag(
     key,  # type: str
     distinct_id,  # type: str
-    groups={},  # type: dict
-    person_properties={},  # type: dict
-    group_properties={},  # type: dict
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
     only_evaluate_locally=False,  # type: bool
     send_feature_flag_events=True,  # type: bool
     disable_geoip=None,  # type: Optional[bool]
@@ -477,9 +566,9 @@ def get_feature_flag(
         "get_feature_flag",
         key=key,
         distinct_id=distinct_id,
-        groups=groups,
-        person_properties=person_properties,
-        group_properties=group_properties,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
         only_evaluate_locally=only_evaluate_locally,
         send_feature_flag_events=send_feature_flag_events,
         disable_geoip=disable_geoip,
@@ -488,9 +577,9 @@ def get_feature_flag(
 
 def get_all_flags(
     distinct_id,  # type: str
-    groups={},  # type: dict
-    person_properties={},  # type: dict
-    group_properties={},  # type: dict
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
     only_evaluate_locally=False,  # type: bool
     disable_geoip=None,  # type: Optional[bool]
 ) -> Optional[dict[str, FeatureFlag]]:
@@ -520,21 +609,64 @@ def get_all_flags(
     return _proxy(
         "get_all_flags",
         distinct_id=distinct_id,
-        groups=groups,
-        person_properties=person_properties,
-        group_properties=group_properties,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
         only_evaluate_locally=only_evaluate_locally,
         disable_geoip=disable_geoip,
     )
 
 
+def get_feature_flag_result(
+    key,
+    distinct_id,
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
+    only_evaluate_locally=False,
+    send_feature_flag_events=True,
+    disable_geoip=None,  # type: Optional[bool]
+):
+    # type: (...) -> Optional[FeatureFlagResult]
+    """
+    Get a FeatureFlagResult object which contains the flag result and payload.
+
+    This method evaluates a feature flag and returns a FeatureFlagResult object containing:
+    - enabled: Whether the flag is enabled
+    - variant: The variant value if the flag has variants
+    - payload: The payload associated with the flag (automatically deserialized from JSON)
+    - key: The flag key
+    - reason: Why the flag was enabled/disabled
+
+    Example:
+    ```python
+    result = posthog.get_feature_flag_result('beta-feature', 'distinct_id')
+    if result and result.enabled:
+        # Use the variant and payload
+        print(f"Variant: {result.variant}")
+        print(f"Payload: {result.payload}")
+    ```
+    """
+    return _proxy(
+        "get_feature_flag_result",
+        key=key,
+        distinct_id=distinct_id,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
+        only_evaluate_locally=only_evaluate_locally,
+        send_feature_flag_events=send_feature_flag_events,
+        disable_geoip=disable_geoip,
+    )
+
+
 def get_feature_flag_payload(
     key,
     distinct_id,
     match_value=None,
-    groups={},
-    person_properties={},
-    group_properties={},
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
     only_evaluate_locally=False,
     send_feature_flag_events=True,
     disable_geoip=None,  # type: Optional[bool]
@@ -544,9 +676,9 @@ def get_feature_flag_payload(
         key=key,
         distinct_id=distinct_id,
         match_value=match_value,
-        groups=groups,
-        person_properties=person_properties,
-        group_properties=group_properties,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
         only_evaluate_locally=only_evaluate_locally,
         send_feature_flag_events=send_feature_flag_events,
         disable_geoip=disable_geoip,
@@ -575,18 +707,18 @@ def get_remote_config_payload(
 
 def get_all_flags_and_payloads(
     distinct_id,
-    groups={},
-    person_properties={},
-    group_properties={},
+    groups=None,  # type: Optional[dict]
+    person_properties=None,  # type: Optional[dict]
+    group_properties=None,  # type: Optional[dict]
     only_evaluate_locally=False,
     disable_geoip=None,  # type: Optional[bool]
 ) -> FlagsAndPayloads:
     return _proxy(
         "get_all_flags_and_payloads",
         distinct_id=distinct_id,
-        groups=groups,
-        person_properties=person_properties,
-        group_properties=group_properties,
+        groups=groups or {},
+        person_properties=person_properties or {},
+        group_properties=group_properties or {},
         only_evaluate_locally=only_evaluate_locally,
         disable_geoip=disable_geoip,
     )

posthog/ai/anthropic/__init__.py

@@ -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",
 ]

posthog/ai/anthropic/anthropic.py

@@ -8,14 +8,21 @@ except ImportError:
 
 import time
 import uuid
-from typing import Any, Dict, Optional, cast
+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
 
@@ -61,6 +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())
 
@@ -118,35 +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,
@@ -157,7 +196,8 @@ class WrappedMessages(Messages):
                     kwargs,
                     usage_stats,
                     latency,
-                    output,
+                    content_blocks,
+                    accumulated_content,
                 )
 
         return generator()
@@ -170,49 +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)

posthog/ai/anthropic/anthropic_async.py

@@ -8,15 +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
 
 
@@ -61,6 +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())
 
@@ -118,35 +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,
@@ -157,7 +201,8 @@ class AsyncWrappedMessages(AsyncMessages):
                     kwargs,
                     usage_stats,
                     latency,
-                    output,
+                    content_blocks,
+                    accumulated_content,
                 )
 
         return generator()
@@ -170,13 +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"),
@@ -184,12 +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),
@@ -206,6 +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
 

posthog/ai/anthropic/anthropic_converter.py

@@ -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}],
+            }
+        ]

posthog/ai/gemini/__init__.py

@@ -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",
+]

posthog/ai/gemini/gemini.py

@@ -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:
@@ -13,9 +16,15 @@ except ImportError:
 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
 
 
@@ -42,6 +51,12 @@ class Client:
     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,
@@ -51,7 +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)
@@ -59,6 +80,7 @@ class Client:
             posthog_groups: Default groups for all calls (can be overridden per call)
             **kwargs: Additional arguments (for future compatibility)
         """
+
         self._ph_client = posthog_client or setup()
 
         if self._ph_client is None:
@@ -66,6 +88,12 @@ class Client:
 
         self.models = Models(
             api_key=api_key,
+            vertexai=vertexai,
+            credentials=credentials,
+            project=project,
+            location=location,
+            debug_config=debug_config,
+            http_options=http_options,
             posthog_client=self._ph_client,
             posthog_distinct_id=posthog_distinct_id,
             posthog_properties=posthog_properties,
@@ -85,6 +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,
@@ -94,7 +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
@@ -102,6 +142,7 @@ class Models:
             posthog_groups: Default groups for all calls
             **kwargs: Additional arguments (for future compatibility)
         """
+
         self._ph_client = posthog_client or setup()
 
         if self._ph_client is None:
@@ -113,16 +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(
@@ -134,6 +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
@@ -149,6 +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)
 
@@ -184,6 +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(
@@ -222,7 +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}
@@ -233,25 +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,
@@ -264,7 +337,7 @@ class Models:
                     kwargs,
                     usage_stats,
                     latency,
-                    output,
+                    accumulated_content,
                 )
 
         return generator()
@@ -279,63 +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,

posthog/ai/gemini/gemini_converter.py

@@ -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": ""}]}]

posthog/ai/langchain/callbacks.py

@@ -37,6 +37,7 @@ from pydantic import BaseModel
 
 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")
@@ -480,11 +481,12 @@ class CallbackHandler(BaseCallbackHandler):
         event_properties = {
             "$ai_trace_id": trace_id,
             "$ai_input_state": with_privacy_mode(
-                self._ph_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["$ai_parent_id"] = parent_run_id
@@ -550,18 +552,16 @@ class CallbackHandler(BaseCallbackHandler):
             "$ai_model": run.model,
             "$ai_model_parameters": run.model_params,
             "$ai_input": with_privacy_mode(
-                self._ph_client, self._privacy_mode, run.input
+                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["$ai_tools"] = with_privacy_mode(
-                self._ph_client,
-                self._privacy_mode,
-                run.tools,
-            )
+            event_properties["$ai_tools"] = run.tools
 
         if isinstance(output, BaseException):
             event_properties["$ai_http_status"] = _get_http_status(output)
@@ -587,7 +587,8 @@ class CallbackHandler(BaseCallbackHandler):
                 ]
             else:
                 completions = [
-                    _extract_raw_esponse(generation) for generation in generation_result
+                    _extract_raw_response(generation)
+                    for generation in generation_result
                 ]
             event_properties["$ai_output_choices"] = with_privacy_mode(
                 self._ph_client, self._privacy_mode, completions
@@ -618,7 +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() != "":

posthog/ai/openai/__init__.py

@@ -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",
+]

posthog/ai/openai/openai.py

@@ -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:
@@ -11,9 +13,17 @@ 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
 
@@ -32,6 +42,7 @@ class OpenAI(openai.OpenAI):
             posthog_client: If provided, events will be captured via this client instead of the global `posthog`.
             **openai_config: Any additional keyword args to set on openai (e.g. organization="xxx").
         """
+
         super().__init__(**kwargs)
         self._ph_client = posthog_client or setup()
 
@@ -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),

posthog/ai/openai/openai_async.py

@@ -1,6 +1,8 @@
 import time
 import uuid
-from typing import Any, Dict, List, Optional, cast
+from typing import Any, Dict, List, Optional
+
+from posthog.ai.types import TokenUsage
 
 try:
     import openai
@@ -12,9 +14,19 @@ except ImportError:
 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
 
 
@@ -33,6 +45,7 @@ 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 or setup()
 
@@ -65,6 +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(
@@ -112,7 +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)
 
@@ -122,35 +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
 
@@ -158,6 +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,
@@ -168,6 +165,7 @@ class WrappedResponses:
                     usage_stats,
                     latency,
                     output,
+                    extract_available_tool_calls("openai", kwargs),
                 )
 
         return async_generator()
@@ -180,10 +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())
@@ -193,12 +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),
@@ -213,12 +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
@@ -342,9 +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"] = {}
@@ -354,70 +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,
@@ -427,8 +393,9 @@ class WrappedCompletions:
                     kwargs,
                     usage_stats,
                     latency,
-                    output,
-                    tools,
+                    accumulated_content,
+                    tool_calls_list,
+                    extract_available_tool_calls("openai", kwargs),
                 )
 
         return async_generator()
@@ -441,10 +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())
@@ -454,16 +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
             ),
@@ -474,12 +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
@@ -502,6 +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(
@@ -527,6 +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())
 
@@ -535,12 +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
 
@@ -549,10 +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),
@@ -583,6 +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
@@ -599,6 +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
@@ -615,6 +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(

posthog/ai/openai/openai_converter.py

@@ -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")

posthog/ai/sanitization.py

@@ -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)

posthog/ai/types.py

@@ -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]]

posthog/ai/utils.py

@@ -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"),
+        )

posthog/args.py

@@ -1,4 +1,4 @@
-from typing import TypedDict, Optional, Any, Dict, Union, Tuple, Type
+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
@@ -69,3 +69,33 @@ ExcInfo = Union[
 ]
 
 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]]

posthog/feature_flags.py

@@ -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:
@@ -101,22 +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":
-                log.warning(
-                    "Flag dependency filters are not supported in local evaluation. "
-                    "Skipping condition for flag '%s' with dependency on flag '%s'",
-                    feature_flag.get("key", "unknown"),
-                    prop.get("key", "unknown"),
+                matches = evaluate_flag_dependency(
+                    prop,
+                    flags_by_key,
+                    evaluation_cache,
+                    distinct_id,
+                    properties,
+                    cohort_properties,
                 )
-                continue
             else:
                 matches = match_property(prop, properties)
             if not matches:
@@ -264,7 +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": {
@@ -276,15 +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
 
@@ -301,7 +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
@@ -309,6 +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
@@ -324,14 +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":
-                    log.warning(
-                        "Flag dependency filters are not supported in local evaluation. "
-                        "Skipping condition with dependency on flag '%s'",
-                        prop.get("key", "unknown"),
+                    matches = evaluate_flag_dependency(
+                        prop,
+                        flags_by_key,
+                        evaluation_cache,
+                        distinct_id,
+                        property_values,
+                        cohort_properties,
                     )
-                    continue
                 else:
                     matches = match_property(prop, property_values)
 
@@ -349,6 +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

posthog/integrations/django.py

@@ -1,10 +1,24 @@
 from typing import TYPE_CHECKING, cast
-from posthog import contexts, capture_exception
+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  # noqa: F401
+    from typing import Callable, Dict, Any, Optional, Union, Awaitable  # noqa: F401
 
 
 class PosthogContextMiddleware:
@@ -31,11 +45,24 @@ class PosthogContextMiddleware:
     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: (Callable[[HttpRequest], HttpResponse]) -> None
+        # 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
 
@@ -158,24 +185,67 @@ class PosthogContextMiddleware:
         return user_id, email
 
     def __call__(self, request):
-        # type: (HttpRequest) -> HttpResponse
+        # 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 self.get_response(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 self.get_response(request)
+            return await self.get_response(request)
 
     def process_exception(self, request, exception):
+        # type: (HttpRequest, Exception) -> None
+        """
+        Process exceptions from views and downstream middleware.
+
+        Django calls this WHILE still inside the context created by __call__,
+        so request tags have already been extracted and set. This method just
+        needs to capture the exception directly.
+
+        Django converts view exceptions into responses before they propagate through
+        the middleware stack, so the context manager in __call__/__acall__ never sees them.
+
+        Note: Django's process_exception is always synchronous, even for async views.
+        """
         if self.request_filter and not self.request_filter(request):
             return
 
         if not self.capture_exceptions:
             return
 
+        # Context and tags already set by __call__ or __acall__
+        # Just capture the exception
         if self.client:
             self.client.capture_exception(exception)
         else:
+            from posthog import capture_exception
+
             capture_exception(exception)

posthog/request.py

@@ -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,
     )

posthog/test/ai/anthropic/test_anthropic.py

@@ -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

posthog/test/ai/gemini/test_gemini.py

@@ -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

posthog/test/ai/langchain/test_callbacks.py

@@ -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"] == [
@@ -1790,3 +1791,89 @@ def test_convert_message_to_dict_tool_calls():
             },
         }
     ]
+
+
+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

posthog/test/ai/openai/test_openai.py

@@ -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

posthog/test/ai/test_sanitization.py

@@ -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()

posthog/test/ai/test_system_prompts.py

@@ -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"])

posthog/test/integrations/test_middleware.py

@@ -4,7 +4,21 @@ from posthog.contexts import (
     get_context_distinct_id,
 )
 import unittest
-from unittest.mock import Mock
+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
 
@@ -38,14 +52,33 @@ class TestPosthogContextMiddleware(unittest.TestCase):
         request_filter=None,
         tag_map=None,
         capture_exceptions=True,
+        get_response=None,
     ):
-        """Helper to create middleware instance without calling __init__"""
-        middleware = PosthogContextMiddleware.__new__(PosthogContextMiddleware)
-        middleware.get_response = Mock()
-        middleware.extra_tags = extra_tags
-        middleware.request_filter = request_filter
-        middleware.tag_map = tag_map
-        middleware.capture_exceptions = capture_exceptions
+        """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):
@@ -168,6 +201,348 @@ class TestPosthogContextMiddleware(unittest.TestCase):
 
         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()

posthog/test/test_client.py

@@ -2,17 +2,18 @@ import time
 import unittest
 from datetime import datetime
 from uuid import uuid4
-from posthog.contexts import get_context_session_id, set_context_session, new_context
 
 import mock
 import six
 from parameterized import parameterized
 
 from posthog.client import Client
+from posthog.contexts import get_context_session_id, new_context, set_context_session
 from posthog.request import APIError
 from posthog.test.test_utils import FAKE_TEST_API_KEY
 from posthog.types import FeatureFlag, LegacyFlagMetadata
 from posthog.version import VERSION
+from posthog.contexts import tag
 
 
 class TestClient(unittest.TestCase):
@@ -647,8 +648,8 @@ class TestClient(unittest.TestCase):
                 timeout=3,
                 distinct_id="distinct_id",
                 groups={},
-                person_properties=None,
-                group_properties=None,
+                person_properties={},
+                group_properties={},
                 geoip_disable=True,
             )
 
@@ -711,8 +712,8 @@ class TestClient(unittest.TestCase):
                 timeout=12,
                 distinct_id="distinct_id",
                 groups={},
-                person_properties=None,
-                group_properties=None,
+                person_properties={},
+                group_properties={},
                 geoip_disable=False,
             )
 
@@ -1741,6 +1742,7 @@ class TestClient(unittest.TestCase):
             person_properties={"distinct_id": "some_id"},
             group_properties={},
             geoip_disable=True,
+            flag_keys_to_evaluate=["random_key"],
         )
         patch_flags.reset_mock()
         client.feature_enabled(
@@ -1755,6 +1757,7 @@ class TestClient(unittest.TestCase):
             person_properties={"distinct_id": "feature_enabled_distinct_id"},
             group_properties={},
             geoip_disable=True,
+            flag_keys_to_evaluate=["random_key"],
         )
         patch_flags.reset_mock()
         client.get_all_flags_and_payloads("all_flags_payloads_id")
@@ -1815,6 +1818,7 @@ class TestClient(unittest.TestCase):
                 "instance": {"$group_key": "app.posthog.com"},
             },
             geoip_disable=False,
+            flag_keys_to_evaluate=["random_key"],
         )
 
         patch_flags.reset_mock()
@@ -1841,6 +1845,7 @@ class TestClient(unittest.TestCase):
                 "instance": {"$group_key": "app.posthog.com"},
             },
             geoip_disable=False,
+            flag_keys_to_evaluate=["random_key"],
         )
 
         patch_flags.reset_mock()
@@ -2057,7 +2062,7 @@ class TestClient(unittest.TestCase):
 
     def test_set_context_session_override_in_capture(self):
         """Test that explicit session ID overrides context session ID in capture"""
-        from posthog.contexts import set_context_session, new_context
+        from posthog.contexts import new_context, set_context_session
 
         with mock.patch("posthog.client.batch_post") as mock_post:
             client = Client(FAKE_TEST_API_KEY, on_error=self.set_fail, sync_mode=True)
@@ -2158,6 +2163,7 @@ class TestClient(unittest.TestCase):
         self.assertEqual(result, {"test": "payload"})
         patch_remote_config.assert_called_once_with(
             "test-personal-key",
+            FAKE_TEST_API_KEY,
             client.host,
             "test-flag",
             timeout=client.feature_flags_request_timeout_seconds,
@@ -2185,6 +2191,7 @@ class TestClient(unittest.TestCase):
             "only_evaluate_locally": None,
             "person_properties": None,
             "group_properties": None,
+            "flag_keys_filter": None,
         }
         self.assertEqual(result, expected)
 
@@ -2195,6 +2202,7 @@ class TestClient(unittest.TestCase):
             "only_evaluate_locally": None,
             "person_properties": None,
             "group_properties": None,
+            "flag_keys_filter": None,
         }
         self.assertEqual(result, expected)
 
@@ -2210,6 +2218,7 @@ class TestClient(unittest.TestCase):
             "only_evaluate_locally": True,
             "person_properties": {"plan": "premium"},
             "group_properties": {"company": {"type": "enterprise"}},
+            "flag_keys_filter": None,
         }
         self.assertEqual(result, expected)
 
@@ -2221,6 +2230,7 @@ class TestClient(unittest.TestCase):
             "only_evaluate_locally": None,
             "person_properties": {"user_id": "123"},
             "group_properties": None,
+            "flag_keys_filter": None,
         }
         self.assertEqual(result, expected)
 
@@ -2231,6 +2241,7 @@ class TestClient(unittest.TestCase):
             "only_evaluate_locally": None,
             "person_properties": None,
             "group_properties": None,
+            "flag_keys_filter": None,
         }
         self.assertEqual(result, expected)
 
@@ -2246,3 +2257,212 @@ class TestClient(unittest.TestCase):
         with self.assertRaises(TypeError) as cm:
             client._parse_send_feature_flags(None)
         self.assertIn("Invalid type for send_feature_flags", str(cm.exception))
+
+    @mock.patch("posthog.client.flags")
+    def test_capture_with_send_feature_flags_flag_keys_filter(self, patch_flags):
+        """Test that SendFeatureFlagsOptions with flag_keys_filter only evaluates specified flags"""
+        # When flag_keys_to_evaluate is provided, the API should only return the requested flags
+        patch_flags.return_value = {
+            "featureFlags": {
+                "flag1": "value1",
+                "flag3": "value3",
+            }
+        }
+
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(
+                FAKE_TEST_API_KEY,
+                on_error=self.set_fail,
+                personal_api_key=FAKE_TEST_API_KEY,
+                sync_mode=True,
+            )
+
+            send_options = {
+                "flag_keys_filter": ["flag1", "flag3"],
+                "person_properties": {"subscription": "pro"},
+            }
+
+            msg_uuid = client.capture(
+                "test event", distinct_id="distinct_id", send_feature_flags=send_options
+            )
+
+            self.assertIsNotNone(msg_uuid)
+            self.assertFalse(self.failed)
+
+            # Verify flags() was called with flag_keys_to_evaluate
+            patch_flags.assert_called_once()
+            call_args = patch_flags.call_args[1]
+            self.assertEqual(call_args["flag_keys_to_evaluate"], ["flag1", "flag3"])
+            self.assertEqual(call_args["person_properties"], {"subscription": "pro"})
+
+            # Check the message includes only the filtered flags
+            mock_post.assert_called_once()
+            batch_data = mock_post.call_args[1]["batch"]
+            msg = batch_data[0]
+
+            self.assertEqual(msg["properties"]["$feature/flag1"], "value1")
+            self.assertEqual(msg["properties"]["$feature/flag3"], "value3")
+            # flag2 should not be included since it wasn't requested
+            self.assertNotIn("$feature/flag2", msg["properties"])
+
+    @mock.patch("posthog.client.batch_post")
+    def test_get_feature_flag_result_with_empty_string_payload(self, patch_batch_post):
+        """Test that get_feature_flag_result returns a FeatureFlagResult when payload is empty string"""
+        client = Client(
+            FAKE_TEST_API_KEY,
+            personal_api_key="test_personal_api_key",
+            sync_mode=True,
+        )
+
+        # Set up local evaluation with a flag that has empty string payload
+        client.feature_flags = [
+            {
+                "id": 1,
+                "name": "Test flag",
+                "key": "test-flag",
+                "is_simple_flag": False,
+                "active": True,
+                "rollout_percentage": None,
+                "filters": {
+                    "groups": [
+                        {
+                            "properties": [],
+                            "rollout_percentage": None,
+                            "variant": "empty-variant",
+                        }
+                    ],
+                    "multivariate": {
+                        "variants": [
+                            {
+                                "key": "empty-variant",
+                                "name": "Empty Variant",
+                                "rollout_percentage": 100,
+                            }
+                        ]
+                    },
+                    "payloads": {"empty-variant": ""},  # Empty string payload
+                },
+            }
+        ]
+
+        # Test get_feature_flag_result
+        result = client.get_feature_flag_result(
+            "test-flag", "test-user", only_evaluate_locally=True
+        )
+
+        # Should return a FeatureFlagResult, not None
+        self.assertIsNotNone(result)
+        self.assertEqual(result.key, "test-flag")
+        self.assertEqual(result.get_value(), "empty-variant")
+        self.assertEqual(result.payload, "")  # Should be empty string, not None
+
+    @mock.patch("posthog.client.batch_post")
+    def test_get_all_flags_and_payloads_with_empty_string(self, patch_batch_post):
+        """Test that get_all_flags_and_payloads includes flags with empty string payloads"""
+        client = Client(
+            FAKE_TEST_API_KEY,
+            personal_api_key="test_personal_api_key",
+            sync_mode=True,
+        )
+
+        # Set up multiple flags with different payload types
+        client.feature_flags = [
+            {
+                "id": 1,
+                "name": "Flag with empty payload",
+                "key": "empty-payload-flag",
+                "is_simple_flag": False,
+                "active": True,
+                "filters": {
+                    "groups": [{"properties": [], "variant": "variant1"}],
+                    "multivariate": {
+                        "variants": [{"key": "variant1", "rollout_percentage": 100}]
+                    },
+                    "payloads": {"variant1": ""},  # Empty string
+                },
+            },
+            {
+                "id": 2,
+                "name": "Flag with normal payload",
+                "key": "normal-payload-flag",
+                "is_simple_flag": False,
+                "active": True,
+                "filters": {
+                    "groups": [{"properties": [], "variant": "variant2"}],
+                    "multivariate": {
+                        "variants": [{"key": "variant2", "rollout_percentage": 100}]
+                    },
+                    "payloads": {"variant2": "normal payload"},
+                },
+            },
+        ]
+
+        result = client.get_all_flags_and_payloads(
+            "test-user", only_evaluate_locally=True
+        )
+
+        # Check that both flags are included
+        self.assertEqual(result["featureFlags"]["empty-payload-flag"], "variant1")
+        self.assertEqual(result["featureFlags"]["normal-payload-flag"], "variant2")
+
+        # Check that empty string payload is included (not filtered out)
+        self.assertIn("empty-payload-flag", result["featureFlagPayloads"])
+        self.assertEqual(result["featureFlagPayloads"]["empty-payload-flag"], "")
+        self.assertEqual(
+            result["featureFlagPayloads"]["normal-payload-flag"], "normal payload"
+        )
+
+    def test_context_tags_added(self):
+        with mock.patch("posthog.client.batch_post") as mock_post:
+            client = Client(FAKE_TEST_API_KEY, on_error=self.set_fail, sync_mode=True)
+
+            with new_context():
+                tag("random_tag", 12345)
+                client.capture("python test event", distinct_id="distinct_id")
+
+            batch_data = mock_post.call_args[1]["batch"]
+            msg = batch_data[0]
+            self.assertEqual(msg["properties"]["$context_tags"], ["random_tag"])
+
+    @mock.patch(
+        "posthog.client.Client._enqueue", side_effect=Exception("Unexpected error")
+    )
+    def test_methods_handle_exceptions(self, mock_enqueue):
+        """Test that all decorated methods handle exceptions gracefully."""
+        client = Client("test-key")
+
+        test_cases = [
+            ("capture", ["test_event"], {}),
+            ("set", [], {"distinct_id": "some-id", "properties": {"a": "b"}}),
+            ("set_once", [], {"distinct_id": "some-id", "properties": {"a": "b"}}),
+            ("group_identify", ["group-type", "group-key"], {}),
+            ("alias", ["some-id", "new-id"], {}),
+        ]
+
+        for method_name, args, kwargs in test_cases:
+            with self.subTest(method=method_name):
+                method = getattr(client, method_name)
+                result = method(*args, **kwargs)
+                self.assertEqual(result, None)
+
+    @mock.patch(
+        "posthog.client.Client._enqueue", side_effect=Exception("Expected error")
+    )
+    def test_debug_flag_re_raises_exceptions(self, mock_enqueue):
+        """Test that methods re-raise exceptions when debug=True."""
+        client = Client("test-key", debug=True)
+
+        test_cases = [
+            ("capture", ["test_event"], {}),
+            ("set", [], {"distinct_id": "some-id", "properties": {"a": "b"}}),
+            ("set_once", [], {"distinct_id": "some-id", "properties": {"a": "b"}}),
+            ("group_identify", ["group-type", "group-key"], {}),
+            ("alias", ["some-id", "new-id"], {}),
+        ]
+
+        for method_name, args, kwargs in test_cases:
+            with self.subTest(method=method_name):
+                method = getattr(client, method_name)
+                with self.assertRaises(Exception) as cm:
+                    method(*args, **kwargs)
+                self.assertEqual(str(cm.exception), "Expected error")

posthog/test/test_module.py

@@ -18,14 +18,6 @@ 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("python module event", distinct_id="distinct_id")
         self._assert_enqueue_result(res)

posthog/types.py

@@ -9,6 +9,7 @@ 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.
 
@@ -22,9 +23,11 @@ class SendFeatureFlagsOptions(TypedDict, total=False):
             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)
@@ -110,7 +113,7 @@ class FeatureFlag:
             variant=variant,
             reason=None,
             metadata=LegacyFlagMetadata(
-                payload=payload if payload else None,
+                payload=payload,
             ),
         )
 
@@ -178,7 +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,
         )
 
@@ -219,6 +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,
@@ -296,5 +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
     }

posthog/version.py

@@ -1,4 +1,4 @@
-VERSION = "6.3.1"
+VERSION = "6.7.11"
 
 if __name__ == "__main__":
     print(VERSION, end="")  # noqa: T201

remote_config_example.py

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

test_capture_ai_local.py

@@ -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.")