fix(llm-observability): capture system prompt for anthropic (#167)

Co-authored-by: Peter Kirkham <peter@posthog.com>

.github/workflows/ci.yml

@@ -18,7 +18,7 @@ jobs:
               with:
                   python-version: 3.8
 
-            - uses: actions/cache@v1
+            - uses: actions/cache@v3
               with:
                   path: ~/.cache/pip
                   key: ${{ runner.os }}-pip-${{ hashFiles('setup.py') }}
@@ -33,7 +33,7 @@ jobs:
             - name: Check formatting with black
               run: |
                   black --check .
-            
+
             - name: Lint with flake8
               run: |
                   flake8 posthog --ignore E501
@@ -47,14 +47,14 @@ jobs:
         runs-on: ubuntu-latest
 
         steps:
-            - uses: actions/checkout@v1
+            - uses: actions/checkout@v2
               with:
                   fetch-depth: 1
 
-            - name: Set up Python 3.7
-              uses: actions/setup-python@v1
+            - name: Set up Python 3.9
+              uses: actions/setup-python@v2
               with:
-                  python-version: 3.7
+                  python-version: 3.9
 
             - name: Install requirements.txt dependencies with pip
               run: |

.gitignore

@@ -14,4 +14,6 @@ pylint.out
 posthog-analytics
 .idea
 .python-version
-.coverage
+.coverage
+pyrightconfig.json
+.env

CHANGELOG.md

@@ -1,3 +1,64 @@
+## 3.8.4 - 2025-01-17
+
+1. Add Anthropic support for LLM Observability.
+2. Update LLM Observability to use output_choices.
+
+## 3.8.3 - 2025-01-14
+
+1. Fix setuptools to include the `posthog.ai.openai` and `posthog.ai.langchain` packages for the `posthoganalytics` package.
+
+## 3.8.2 - 2025-01-14
+
+1. Fix setuptools to include the `posthog.ai.openai` and `posthog.ai.langchain` packages.
+
+## 3.8.1 - 2025-01-14
+
+1. Add LLM Observability with support for OpenAI and Langchain callbacks.
+
+## 3.7.5 - 2025-01-03
+
+1. Add `distinct_id` to group_identify
+
+## 3.7.4 - 2024-11-25
+
+1. Fix bug where this SDK incorrectly sent feature flag events with null values when calling `get_feature_flag_payload`.
+
+## 3.7.3 - 2024-11-25
+
+1. Use personless mode when sending an exception without a provided `distinct_id`.
+
+## 3.7.2 - 2024-11-19
+
+1. Add `type` property to exception stacks.
+
+## 3.7.1 - 2024-10-24
+
+1. Add `platform` property to each frame of exception stacks.
+
+## 3.7.0 - 2024-10-03
+
+1. Adds a new `super_properties` parameter on the client that are appended to every /capture call.
+
+## 3.6.7 - 2024-09-24
+
+1. Remove deprecated datetime.utcnow() in favour of datetime.now(tz=tzutc())
+
+## 3.6.6 - 2024-09-16
+
+1. Fix manual capture support for in app frames
+
+## 3.6.5 - 2024-09-10
+
+1. Fix django integration support for manual exception capture.
+
+## 3.6.4 - 2024-09-05
+
+1. Add manual exception capture.
+
+## 3.6.3 - 2024-09-03
+
+1. Make sure setup.py for posthoganalytics package also discovers the new exception integration package.
+
 ## 3.6.2 - 2024-09-03
 
 1. Make sure setup.py discovers the new exception integration package.
@@ -46,7 +107,6 @@
 
 1. Return success/failure with all capture calls from module functions
 
-
 ## 3.3.1 - 2024-01-10
 
 1. Make sure we don't override any existing feature flag properties when adding locally evaluated feature flag properties.
@@ -72,6 +132,7 @@
 
 1. Restore how feature flags work when the client library is disabled: All requests return `None` and no events are sent when the client is disabled.
 2. Add a `feature_flag_definitions()` debug option, which returns currently loaded feature flag definitions. You can use this to more cleverly decide when to request local evaluation of feature flags.
+
 ## 3.0.0 - 2023-04-14
 
 Breaking change:
@@ -112,7 +173,6 @@ posthog = Posthog('api_key', disable_geoip=False)
 1. Log instead of raise error on posthog personal api key errors
 2. Remove upper bound on backoff dependency
 
-
 ## 2.3.0 - 2023-01-31
 
 1. Add support for returning payloads of matched feature flags
@@ -128,6 +188,7 @@ Changes:
 Changes:
 
 1. Fixes issues with date comparison.
+
 ## 2.1.1 - 2022-09-14
 
 Changes:
@@ -140,6 +201,7 @@ Changes:
 
 1. Feature flag defaults have been removed
 2. Setup logging only when debug mode is enabled.
+
 ## 2.0.1 - 2022-08-04
 
 - Make poll_interval configurable
@@ -152,7 +214,7 @@ Breaking changes:
 
 1. The minimum version requirement for PostHog servers is now 1.38. If you're using PostHog Cloud, you satisfy this requirement automatically.
 2. Feature flag defaults apply only when there's an error fetching feature flag results. Earlier, if the default was set to `True`, even if a flag resolved to `False`, the default would override this.
-**Note: These are removed in 2.0.2**
+   **Note: These are removed in 2.0.2**
 3. Feature flag remote evaluation doesn't require a personal API key.
 
 New Changes:
@@ -160,18 +222,20 @@ New Changes:
 1. You can now evaluate feature flags locally (i.e. without sending a request to your PostHog servers) by setting a personal API key, and passing in groups and person properties to `is_feature_enabled` and `get_feature_flag` calls.
 2. Introduces a `get_all_flags` method that returns all feature flags. This is useful for when you want to seed your frontend with some initial flags, given a user ID.
 
-
-
 ## 1.4.9 - 2022-06-13
+
 - Support for sending feature flags with capture calls
 
 ## 1.4.8 - 2022-05-12
+
 - Support multi variate feature flags
 
 ## 1.4.7 - 2022-04-25
+
 - Allow feature flags usage without project_api_key
 
 ## 1.4.1 - 2021-05-28
+
 - Fix packaging issues with Sentry integrations
 
 ## 1.4.0 - 2021-05-18

CODEOWNERS

@@ -1 +1 @@
-@PostHog/team-feature-success
+@PostHog/team-feature-flags

example.py

@@ -1,7 +1,7 @@
 # PostHog Python library example
 
 # Import the library
-import time
+# import time
 
 import posthog
 

llm_observability_examples.py

@@ -0,0 +1,196 @@
+import os
+import uuid
+
+import posthog
+from posthog.ai.openai import AsyncOpenAI, OpenAI
+
+# Example credentials - replace these with your own or use environment variables
+posthog.project_api_key = os.getenv("POSTHOG_PROJECT_API_KEY", "your-project-api-key")
+posthog.personal_api_key = os.getenv("POSTHOG_PERSONAL_API_KEY", "your-personal-api-key")
+posthog.host = os.getenv("POSTHOG_HOST", "http://localhost:8000")  # Or https://app.posthog.com
+posthog.debug = True
+# change this to False to see usage events
+# posthog.privacy_mode = True
+
+openai_client = OpenAI(
+    api_key=os.getenv("OPENAI_API_KEY", "your-openai-api-key"),
+    posthog_client=posthog,
+)
+
+async_openai_client = AsyncOpenAI(
+    api_key=os.getenv("OPENAI_API_KEY", "your-openai-api-key"),
+    posthog_client=posthog,
+)
+
+
+def main_sync():
+    trace_id = str(uuid.uuid4())
+    print("Trace ID:", trace_id)
+    distinct_id = "test2_distinct_id"
+    properties = {"test_property": "test_value"}
+    groups = {"company": "test_company"}
+
+    try:
+        basic_openai_call(distinct_id, trace_id, properties, groups)
+        streaming_openai_call(distinct_id, trace_id, properties, groups)
+        embedding_openai_call(distinct_id, trace_id, properties, groups)
+        image_openai_call()
+    except Exception as e:
+        print("Error during OpenAI call:", str(e))
+
+
+async def main_async():
+    trace_id = str(uuid.uuid4())
+    print("Trace ID:", trace_id)
+    distinct_id = "test_distinct_id"
+    properties = {"test_property": "test_value"}
+    groups = {"company": "test_company"}
+
+    try:
+        await basic_async_openai_call(distinct_id, trace_id, properties, groups)
+        await streaming_async_openai_call(distinct_id, trace_id, properties, groups)
+        await embedding_async_openai_call(distinct_id, trace_id, properties, groups)
+        await image_async_openai_call()
+    except Exception as e:
+        print("Error during OpenAI call:", str(e))
+
+
+def basic_openai_call(distinct_id, trace_id, properties, groups):
+    response = openai_client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a complex problem solver."},
+            {"role": "user", "content": "Explain quantum computing in simple terms."},
+        ],
+        max_tokens=100,
+        temperature=0.7,
+        posthog_distinct_id=distinct_id,
+        posthog_trace_id=trace_id,
+        posthog_properties=properties,
+        posthog_groups=groups,
+    )
+    print(response)
+    if response and response.choices:
+        print("OpenAI response:", response.choices[0].message.content)
+    else:
+        print("No response or unexpected format returned.")
+    return response
+
+
+async def basic_async_openai_call(distinct_id, trace_id, properties, groups):
+    response = await async_openai_client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a complex problem solver."},
+            {"role": "user", "content": "Explain quantum computing in simple terms."},
+        ],
+        max_tokens=100,
+        temperature=0.7,
+        posthog_distinct_id=distinct_id,
+        posthog_trace_id=trace_id,
+        posthog_properties=properties,
+        posthog_groups=groups,
+    )
+    if response and hasattr(response, "choices"):
+        print("OpenAI response:", response.choices[0].message.content)
+    else:
+        print("No response or unexpected format returned.")
+    return response
+
+
+def streaming_openai_call(distinct_id, trace_id, properties, groups):
+
+    response = openai_client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a complex problem solver."},
+            {"role": "user", "content": "Explain quantum computing in simple terms."},
+        ],
+        max_tokens=100,
+        temperature=0.7,
+        stream=True,
+        posthog_distinct_id=distinct_id,
+        posthog_trace_id=trace_id,
+        posthog_properties=properties,
+        posthog_groups=groups,
+    )
+
+    for chunk in response:
+        if hasattr(chunk, "choices") and chunk.choices and len(chunk.choices) > 0:
+            print(chunk.choices[0].delta.content or "", end="")
+
+    return response
+
+
+async def streaming_async_openai_call(distinct_id, trace_id, properties, groups):
+    response = await async_openai_client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a complex problem solver."},
+            {"role": "user", "content": "Explain quantum computing in simple terms."},
+        ],
+        max_tokens=100,
+        temperature=0.7,
+        stream=True,
+        posthog_distinct_id=distinct_id,
+        posthog_trace_id=trace_id,
+        posthog_properties=properties,
+        posthog_groups=groups,
+    )
+
+    async for chunk in response:
+        if hasattr(chunk, "choices") and chunk.choices and len(chunk.choices) > 0:
+            print(chunk.choices[0].delta.content or "", end="")
+
+    return response
+
+
+# none instrumented
+def image_openai_call():
+    response = openai_client.images.generate(model="dall-e-3", prompt="A cute baby hedgehog", n=1, size="1024x1024")
+    print(response)
+    return response
+
+
+# none instrumented
+async def image_async_openai_call():
+    response = await async_openai_client.images.generate(
+        model="dall-e-3", prompt="A cute baby hedgehog", n=1, size="1024x1024"
+    )
+    print(response)
+    return response
+
+
+def embedding_openai_call(posthog_distinct_id, posthog_trace_id, posthog_properties, posthog_groups):
+    response = openai_client.embeddings.create(
+        input="The hedgehog is cute",
+        model="text-embedding-3-small",
+        posthog_distinct_id=posthog_distinct_id,
+        posthog_trace_id=posthog_trace_id,
+        posthog_properties=posthog_properties,
+        posthog_groups=posthog_groups,
+    )
+    print(response)
+    return response
+
+
+async def embedding_async_openai_call(posthog_distinct_id, posthog_trace_id, posthog_properties, posthog_groups):
+    response = await async_openai_client.embeddings.create(
+        input="The hedgehog is cute",
+        model="text-embedding-3-small",
+        posthog_distinct_id=posthog_distinct_id,
+        posthog_trace_id=posthog_trace_id,
+        posthog_properties=posthog_properties,
+        posthog_groups=posthog_groups,
+    )
+    print(response)
+    return response
+
+
+# HOW TO RUN:
+# comment out one of these to run the other
+
+if __name__ == "__main__":
+    main_sync()
+
+# asyncio.run(main_async())

posthog/__init__.py

@@ -20,9 +20,14 @@ project_api_key = None  # type: Optional[str]
 poll_interval = 30  # type: int
 disable_geoip = True  # type: bool
 feature_flags_request_timeout_seconds = 3  # type: int
+super_properties = None  # type: Optional[Dict]
 # Currently alpha, use at your own risk
 enable_exception_autocapture = False  # type: bool
 exception_autocapture_integrations = []  # type: List[Integrations]
+# Used to determine in app paths for exception autocapture. Defaults to the current working directory
+project_root = None  # type: Optional[str]
+# Used for our AI observability feature to not capture any prompt or output just usage + metadata
+privacy_mode = False  # type: bool
 
 default_client = None  # type: Optional[Client]
 
@@ -251,6 +256,50 @@ def alias(
     )
 
 
+def capture_exception(
+    exception=None,  # type: Optional[BaseException]
+    distinct_id=None,  # type: Optional[str]
+    properties=None,  # type: Optional[Dict]
+    context=None,  # type: Optional[Dict]
+    timestamp=None,  # type: Optional[datetime.datetime]
+    uuid=None,  # type: Optional[str]
+    groups=None,  # type: Optional[Dict]
+):
+    # type: (...) -> Tuple[bool, dict]
+    """
+    capture_exception allows you to capture exceptions that happen in your code. This is useful for debugging and understanding what errors your users are encountering.
+    This function never raises an exception, even if it fails to send the event.
+
+    A `capture_exception` call does not require any fields, but we recommend sending:
+    - `distinct id` which uniquely identifies your user for which this exception happens
+    - `exception` to specify the exception to capture. If not provided, the current exception is captured via `sys.exc_info()`
+
+    Optionally you can submit
+    - `properties`, which can be a dict with any information you'd like to add
+    - `groups`, which is a dict of group type -> group key mappings
+
+    For example:
+    ```python
+    try:
+        1 / 0
+    except Exception as e:
+        posthog.capture_exception(e, 'my specific distinct id')
+        posthog.capture_exception(distinct_id='my specific distinct id')
+
+    ```
+    """
+    return _proxy(
+        "capture_exception",
+        exception=exception,
+        distinct_id=distinct_id,
+        properties=properties,
+        context=context,
+        timestamp=timestamp,
+        uuid=uuid,
+        groups=groups,
+    )
+
+
 def feature_enabled(
     key,  # type: str
     distinct_id,  # type: str
@@ -458,6 +507,7 @@ def _proxy(method, *args, **kwargs):
             disabled=disabled,
             disable_geoip=disable_geoip,
             feature_flags_request_timeout_seconds=feature_flags_request_timeout_seconds,
+            super_properties=super_properties,
             # TODO: Currently this monitoring begins only when the Client is initialised (which happens when you do something with the SDK)
             # This kind of initialisation is very annoying for exception capture. We need to figure out a way around this,
             # or deprecate this proxy option fully (it's already in the process of deprecation, no new clients should be using this method since like 5-6 months)

posthog/ai/anthropic/__init__.py

@@ -0,0 +1,12 @@
+from .anthropic import Anthropic
+from .anthropic_async import AsyncAnthropic
+from .anthropic_providers import AnthropicBedrock, AnthropicVertex, AsyncAnthropicBedrock, AsyncAnthropicVertex
+
+__all__ = [
+    "Anthropic",
+    "AsyncAnthropic",
+    "AnthropicBedrock",
+    "AsyncAnthropicBedrock",
+    "AnthropicVertex",
+    "AsyncAnthropicVertex",
+]

posthog/ai/anthropic/anthropic.py

@@ -0,0 +1,202 @@
+try:
+    import anthropic
+    from anthropic.resources import Messages
+except ImportError:
+    raise ModuleNotFoundError("Please install the Anthropic SDK to use this feature: 'pip install anthropic'")
+
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+from posthog.ai.utils import call_llm_and_track_usage, get_model_params, merge_system_prompt, with_privacy_mode
+from posthog.client import Client as PostHogClient
+
+
+class Anthropic(anthropic.Anthropic):
+    """
+    A wrapper around the Anthropic SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        """
+        Args:
+            posthog_client: PostHog client for tracking usage
+            **kwargs: Additional arguments passed to the Anthropic client
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.messages = WrappedMessages(self)
+
+
+class WrappedMessages(Messages):
+    _client: Anthropic
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create a message using Anthropic's API while tracking usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event
+            posthog_trace_id: Optional trace UUID for linking events
+            posthog_properties: Optional dictionary of extra properties to include in the event
+            posthog_privacy_mode: Whether to redact sensitive information in tracking
+            posthog_groups: Optional group analytics properties
+            **kwargs: Arguments passed to Anthropic's messages.create
+        """
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        if kwargs.get("stream", False):
+            return self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "anthropic",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            super().create,
+            **kwargs,
+        )
+
+    def stream(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        return self._create_streaming(
+            posthog_distinct_id,
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            **kwargs,
+        )
+
+    def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        accumulated_content = []
+        response = super().create(**kwargs)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+            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",
+                            ]
+                        }
+
+                    if hasattr(event, "content") and event.content:
+                        accumulated_content.append(event.content)
+
+                    yield event
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = "".join(accumulated_content)
+
+                self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: Dict[str, int],
+        latency: float,
+        output: str,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        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),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **(posthog_properties or {}),
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        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,
+            )

posthog/ai/anthropic/anthropic_async.py

@@ -0,0 +1,202 @@
+try:
+    import anthropic
+    from anthropic.resources import AsyncMessages
+except ImportError:
+    raise ModuleNotFoundError("Please install the Anthropic SDK to use this feature: 'pip install anthropic'")
+
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+from posthog.ai.utils import call_llm_and_track_usage_async, get_model_params, merge_system_prompt, with_privacy_mode
+from posthog.client import Client as PostHogClient
+
+
+class AsyncAnthropic(anthropic.AsyncAnthropic):
+    """
+    An async wrapper around the Anthropic SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        """
+        Args:
+            posthog_client: PostHog client for tracking usage
+            **kwargs: Additional arguments passed to the Anthropic client
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.messages = AsyncWrappedMessages(self)
+
+
+class AsyncWrappedMessages(AsyncMessages):
+    _client: AsyncAnthropic
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create a message using Anthropic's API while tracking usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event
+            posthog_trace_id: Optional trace UUID for linking events
+            posthog_properties: Optional dictionary of extra properties to include in the event
+            posthog_privacy_mode: Whether to redact sensitive information in tracking
+            posthog_groups: Optional group analytics properties
+            **kwargs: Arguments passed to Anthropic's messages.create
+        """
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        if kwargs.get("stream", False):
+            return await self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "anthropic",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            super().create,
+            **kwargs,
+        )
+
+    async def stream(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        return await self._create_streaming(
+            posthog_distinct_id,
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            **kwargs,
+        )
+
+    async def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        accumulated_content = []
+        response = await super().create(**kwargs)
+
+        async def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+            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",
+                            ]
+                        }
+
+                    if hasattr(event, "content") and event.content:
+                        accumulated_content.append(event.content)
+
+                    yield event
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = "".join(accumulated_content)
+
+                await self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                )
+
+        return generator()
+
+    async def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: Dict[str, int],
+        latency: float,
+        output: str,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        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),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **(posthog_properties or {}),
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        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,
+            )

posthog/ai/anthropic/anthropic_providers.py

@@ -0,0 +1,60 @@
+try:
+    import anthropic
+except ImportError:
+    raise ModuleNotFoundError("Please install the Anthropic SDK to use this feature: 'pip install anthropic'")
+
+from posthog.ai.anthropic.anthropic import WrappedMessages
+from posthog.ai.anthropic.anthropic_async import AsyncWrappedMessages
+from posthog.client import Client as PostHogClient
+
+
+class AnthropicBedrock(anthropic.AnthropicBedrock):
+    """
+    A wrapper around the Anthropic Bedrock SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.messages = WrappedMessages(self)
+
+
+class AsyncAnthropicBedrock(anthropic.AsyncAnthropicBedrock):
+    """
+    A wrapper around the Anthropic Bedrock SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.messages = AsyncWrappedMessages(self)
+
+
+class AnthropicVertex(anthropic.AnthropicVertex):
+    """
+    A wrapper around the Anthropic Vertex SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.messages = WrappedMessages(self)
+
+
+class AsyncAnthropicVertex(anthropic.AsyncAnthropicVertex):
+    """
+    A wrapper around the Anthropic Vertex SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.messages = AsyncWrappedMessages(self)

posthog/ai/langchain/__init__.py

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

posthog/ai/langchain/callbacks.py

@@ -0,0 +1,419 @@
+try:
+    import langchain  # noqa: F401
+except ImportError:
+    raise ModuleNotFoundError("Please install LangChain to use this feature: 'pip install langchain'")
+
+import logging
+import time
+import uuid
+from typing import (
+    Any,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    TypedDict,
+    Union,
+    cast,
+)
+from uuid import UUID
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage, HumanMessage, SystemMessage, ToolMessage
+from langchain_core.outputs import ChatGeneration, LLMResult
+from pydantic import BaseModel
+
+from posthog.ai.utils import get_model_params, with_privacy_mode
+from posthog.client import Client
+
+log = logging.getLogger("posthog")
+
+
+class RunMetadata(TypedDict, total=False):
+    messages: Union[List[Dict[str, Any]], List[str]]
+    provider: str
+    model: str
+    model_params: Dict[str, Any]
+    base_url: str
+    start_time: float
+    end_time: float
+
+
+RunStorage = Dict[UUID, RunMetadata]
+
+
+class CallbackHandler(BaseCallbackHandler):
+    """
+    A callback handler for LangChain that sends events to PostHog LLM Observability.
+    """
+
+    _client: Client
+    """PostHog client instance."""
+    _distinct_id: Optional[Union[str, int, float, UUID]]
+    """Distinct ID of the user to associate the trace with."""
+    _trace_id: Optional[Union[str, int, float, UUID]]
+    """Global trace ID to be sent with every event. Otherwise, the top-level run ID is used."""
+    _properties: Optional[Dict[str, Any]]
+    """Global properties to be sent with every event."""
+    _runs: RunStorage
+    """Mapping of run IDs to run metadata as run metadata is only available on the start of generation."""
+    _parent_tree: Dict[UUID, UUID]
+    """
+    A dictionary that maps chain run IDs to their parent chain run IDs (parent pointer tree),
+    so the top level can be found from a bottom-level run ID.
+    """
+
+    def __init__(
+        self,
+        client: Client,
+        distinct_id: Optional[Union[str, int, float, UUID]] = None,
+        trace_id: Optional[Union[str, int, float, UUID]] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        privacy_mode: bool = False,
+        groups: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Args:
+            client: PostHog client instance.
+            distinct_id: Optional distinct ID of the user to associate the trace with.
+            trace_id: Optional trace ID to use for the event.
+            properties: Optional additional metadata to use for the trace.
+            privacy_mode: Whether to redact the input and output of the trace.
+            groups: Optional additional PostHog groups to use for the trace.
+        """
+        self._client = client
+        self._distinct_id = distinct_id
+        self._trace_id = trace_id
+        self._properties = properties or {}
+        self._privacy_mode = privacy_mode
+        self._groups = groups or {}
+        self._runs = {}
+        self._parent_tree = {}
+
+    def on_chain_start(
+        self,
+        serialized: Dict[str, Any],
+        inputs: Dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs,
+    ):
+        self._set_parent_of_run(run_id, parent_run_id)
+
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[BaseMessage]],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs,
+    ):
+        self._set_parent_of_run(run_id, parent_run_id)
+        input = [_convert_message_to_dict(message) for row in messages for message in row]
+        self._set_run_metadata(serialized, run_id, input, **kwargs)
+
+    def on_llm_start(
+        self,
+        serialized: Dict[str, Any],
+        prompts: List[str],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._set_parent_of_run(run_id, parent_run_id)
+        self._set_run_metadata(serialized, run_id, prompts, **kwargs)
+
+    def on_chain_end(
+        self,
+        outputs: Dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        **kwargs: Any,
+    ):
+        self._pop_parent_of_run(run_id)
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        **kwargs: Any,
+    ):
+        """
+        The callback works for both streaming and non-streaming runs. For streaming runs, the chain must set `stream_usage=True` in the LLM.
+        """
+        trace_id = self._get_trace_id(run_id)
+        self._pop_parent_of_run(run_id)
+        run = self._pop_run_metadata(run_id)
+        if not run:
+            return
+
+        latency = run.get("end_time", 0) - run.get("start_time", 0)
+        input_tokens, output_tokens = _parse_usage(response)
+
+        generation_result = response.generations[-1]
+        if isinstance(generation_result[-1], ChatGeneration):
+            output = [
+                _convert_message_to_dict(cast(ChatGeneration, generation).message) for generation in generation_result
+            ]
+        else:
+            output = [_extract_raw_esponse(generation) for generation in generation_result]
+
+        event_properties = {
+            "$ai_provider": run.get("provider"),
+            "$ai_model": run.get("model"),
+            "$ai_model_parameters": run.get("model_params"),
+            "$ai_input": with_privacy_mode(self._client, self._privacy_mode, run.get("messages")),
+            "$ai_output_choices": with_privacy_mode(self._client, self._privacy_mode, output),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": input_tokens,
+            "$ai_output_tokens": output_tokens,
+            "$ai_latency": latency,
+            "$ai_trace_id": trace_id,
+            "$ai_base_url": run.get("base_url"),
+            **self._properties,
+        }
+        if self._distinct_id is None:
+            event_properties["$process_person_profile"] = False
+        self._client.capture(
+            distinct_id=self._distinct_id or trace_id,
+            event="$ai_generation",
+            properties=event_properties,
+            groups=self._groups,
+        )
+
+    def on_chain_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ):
+        self._pop_parent_of_run(run_id)
+
+    def on_llm_error(
+        self,
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        **kwargs: Any,
+    ):
+        trace_id = self._get_trace_id(run_id)
+        self._pop_parent_of_run(run_id)
+        run = self._pop_run_metadata(run_id)
+        if not run:
+            return
+
+        latency = run.get("end_time", 0) - run.get("start_time", 0)
+        event_properties = {
+            "$ai_provider": run.get("provider"),
+            "$ai_model": run.get("model"),
+            "$ai_model_parameters": run.get("model_params"),
+            "$ai_input": with_privacy_mode(self._client, self._privacy_mode, run.get("messages")),
+            "$ai_http_status": _get_http_status(error),
+            "$ai_latency": latency,
+            "$ai_trace_id": trace_id,
+            "$ai_base_url": run.get("base_url"),
+            **self._properties,
+        }
+        if self._distinct_id is None:
+            event_properties["$process_person_profile"] = False
+        self._client.capture(
+            distinct_id=self._distinct_id or trace_id,
+            event="$ai_generation",
+            properties=event_properties,
+            groups=self._groups,
+        )
+
+    def _set_parent_of_run(self, run_id: UUID, parent_run_id: Optional[UUID] = None):
+        """
+        Set the parent run ID for a chain run. If there is no parent, the run is the root.
+        """
+        if parent_run_id is not None:
+            self._parent_tree[run_id] = parent_run_id
+
+    def _pop_parent_of_run(self, run_id: UUID):
+        """
+        Remove the parent run ID for a chain run.
+        """
+        try:
+            self._parent_tree.pop(run_id)
+        except KeyError:
+            pass
+
+    def _find_root_run(self, run_id: UUID) -> UUID:
+        """
+        Finds the root ID of a chain run.
+        """
+        id: UUID = run_id
+        while id in self._parent_tree:
+            id = self._parent_tree[id]
+        return id
+
+    def _set_run_metadata(
+        self,
+        serialized: Dict[str, Any],
+        run_id: UUID,
+        messages: Union[List[Dict[str, Any]], List[str]],
+        metadata: Optional[Dict[str, Any]] = None,
+        invocation_params: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        run: RunMetadata = {
+            "messages": messages,
+            "start_time": time.time(),
+        }
+        if isinstance(invocation_params, dict):
+            run["model_params"] = get_model_params(invocation_params)
+        if isinstance(metadata, dict):
+            if model := metadata.get("ls_model_name"):
+                run["model"] = model
+            if provider := metadata.get("ls_provider"):
+                run["provider"] = provider
+        try:
+            base_url = serialized["kwargs"]["openai_api_base"]
+            if base_url is not None:
+                run["base_url"] = base_url
+        except KeyError:
+            pass
+        self._runs[run_id] = run
+
+    def _pop_run_metadata(self, run_id: UUID) -> Optional[RunMetadata]:
+        end_time = time.time()
+        try:
+            run = self._runs.pop(run_id)
+        except KeyError:
+            log.warning(f"No run metadata found for run {run_id}")
+            return None
+        run["end_time"] = end_time
+        return run
+
+    def _get_trace_id(self, run_id: UUID):
+        trace_id = self._trace_id or self._find_root_run(run_id)
+        if not trace_id:
+            trace_id = uuid.uuid4()
+        return trace_id
+
+
+def _extract_raw_esponse(last_response):
+    """Extract the response from the last response of the LLM call."""
+    # We return the text of the response if not empty
+    if last_response.text is not None and last_response.text.strip() != "":
+        return last_response.text.strip()
+    elif hasattr(last_response, "message"):
+        # Additional kwargs contains the response in case of tool usage
+        return last_response.message.additional_kwargs
+    else:
+        # Not tool usage, some LLM responses can be simply empty
+        return ""
+
+
+def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
+    # assistant message
+    if isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, ToolMessage):
+        message_dict = {"role": "tool", "content": message.content}
+    elif isinstance(message, FunctionMessage):
+        message_dict = {"role": "function", "content": message.content}
+    else:
+        message_dict = {"role": message.type, "content": str(message.content)}
+
+    if message.additional_kwargs:
+        message_dict.update(message.additional_kwargs)
+
+    return message_dict
+
+
+def _parse_usage_model(usage: Union[BaseModel, Dict]) -> Tuple[Union[int, None], Union[int, None]]:
+    if isinstance(usage, BaseModel):
+        usage = usage.__dict__
+
+    conversion_list = [
+        # https://pypi.org/project/langchain-anthropic/ (works also for Bedrock-Anthropic)
+        ("input_tokens", "input"),
+        ("output_tokens", "output"),
+        # https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/get-token-count
+        ("prompt_token_count", "input"),
+        ("candidates_token_count", "output"),
+        # Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/monitoring-cw.html#runtime-cloudwatch-metrics
+        ("inputTokenCount", "input"),
+        ("outputTokenCount", "output"),
+        # langchain-ibm https://pypi.org/project/langchain-ibm/
+        ("input_token_count", "input"),
+        ("generated_token_count", "output"),
+    ]
+
+    parsed_usage = {}
+    for model_key, type_key in conversion_list:
+        if model_key in usage:
+            captured_count = usage[model_key]
+            final_count = (
+                sum(captured_count) if isinstance(captured_count, list) else captured_count
+            )  # For Bedrock, the token count is a list when streamed
+
+            parsed_usage[type_key] = final_count
+
+    return parsed_usage.get("input"), parsed_usage.get("output")
+
+
+def _parse_usage(response: LLMResult):
+    # langchain-anthropic uses the usage field
+    llm_usage_keys = ["token_usage", "usage"]
+    llm_usage: Tuple[Union[int, None], Union[int, None]] = (None, None)
+    if response.llm_output is not None:
+        for key in llm_usage_keys:
+            if response.llm_output.get(key):
+                llm_usage = _parse_usage_model(response.llm_output[key])
+                break
+
+    if hasattr(response, "generations"):
+        for generation in response.generations:
+            for generation_chunk in generation:
+                if generation_chunk.generation_info and ("usage_metadata" in generation_chunk.generation_info):
+                    llm_usage = _parse_usage_model(generation_chunk.generation_info["usage_metadata"])
+                    break
+
+                message_chunk = getattr(generation_chunk, "message", {})
+                response_metadata = getattr(message_chunk, "response_metadata", {})
+
+                bedrock_anthropic_usage = (
+                    response_metadata.get("usage", None)  # for Bedrock-Anthropic
+                    if isinstance(response_metadata, dict)
+                    else None
+                )
+                bedrock_titan_usage = (
+                    response_metadata.get("amazon-bedrock-invocationMetrics", None)  # for Bedrock-Titan
+                    if isinstance(response_metadata, dict)
+                    else None
+                )
+                ollama_usage = getattr(message_chunk, "usage_metadata", None)  # for Ollama
+
+                chunk_usage = bedrock_anthropic_usage or bedrock_titan_usage or ollama_usage
+                if chunk_usage:
+                    llm_usage = _parse_usage_model(chunk_usage)
+                    break
+
+    return llm_usage
+
+
+def _get_http_status(error: BaseException) -> int:
+    # OpenAI: https://github.com/openai/openai-python/blob/main/src/openai/_exceptions.py
+    # Anthropic: https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/_exceptions.py
+    # Google: https://github.com/googleapis/python-api-core/blob/main/google/api_core/exceptions.py
+    status_code = getattr(error, "status_code", getattr(error, "code", 0))
+    return status_code

posthog/ai/openai/__init__.py

@@ -0,0 +1,4 @@
+from .openai import OpenAI
+from .openai_async import AsyncOpenAI
+
+__all__ = ["OpenAI", "AsyncOpenAI"]

posthog/ai/openai/openai.py

@@ -0,0 +1,251 @@
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+try:
+    import openai
+    import openai.resources
+except ImportError:
+    raise ModuleNotFoundError("Please install the OpenAI SDK to use this feature: 'pip install openai'")
+
+from posthog.ai.utils import call_llm_and_track_usage, get_model_params, with_privacy_mode
+from posthog.client import Client as PostHogClient
+
+
+class OpenAI(openai.OpenAI):
+    """
+    A wrapper around the OpenAI SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        """
+        Args:
+            api_key: OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead
+                            of the global posthog.
+            **openai_config: Any additional keyword args to set on openai (e.g. organization="xxx").
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.chat = WrappedChat(self)
+        self.embeddings = WrappedEmbeddings(self)
+
+
+class WrappedChat(openai.resources.chat.Chat):
+    _client: OpenAI
+
+    @property
+    def completions(self):
+        return WrappedCompletions(self._client)
+
+
+class WrappedCompletions(openai.resources.chat.completions.Completions):
+    _client: OpenAI
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        if kwargs.get("stream", False):
+            return self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        return call_llm_and_track_usage(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            posthog_privacy_mode,
+            posthog_groups,
+            self._client.base_url,
+            super().create,
+            **kwargs,
+        )
+
+    def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: Dict[str, int] = {}
+        accumulated_content = []
+        if "stream_options" not in kwargs:
+            kwargs["stream_options"] = {}
+        kwargs["stream_options"]["include_usage"] = True
+        response = super().create(**kwargs)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content
+            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",
+                            ]
+                        }
+
+                    if hasattr(chunk, "choices") and chunk.choices and len(chunk.choices) > 0:
+                        content = chunk.choices[0].delta.content
+                        if content:
+                            accumulated_content.append(content)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = "".join(accumulated_content)
+                self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: Dict[str, int],
+        latency: float,
+        output: str,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        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),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **posthog_properties,
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+
+class WrappedEmbeddings(openai.resources.embeddings.Embeddings):
+    _client: OpenAI
+
+    def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create an embedding using OpenAI's 'embeddings.create' method, but also track usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event.
+            posthog_trace_id: Optional trace UUID for linking events.
+            posthog_properties: Optional dictionary of extra properties to include in the event.
+            **kwargs: Any additional parameters for the OpenAI Embeddings API.
+
+        Returns:
+            The response from OpenAI's embeddings.create call.
+        """
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        start_time = time.time()
+        response = super().create(**kwargs)
+        end_time = time.time()
+
+        # Extract usage statistics if available
+        usage_stats = {}
+        if hasattr(response, "usage") and response.usage:
+            usage_stats = {
+                "prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
+                "total_tokens": getattr(response.usage, "total_tokens", 0),
+            }
+
+        latency = end_time - start_time
+
+        # Build the event properties
+        event_properties = {
+            "$ai_provider": "openai",
+            "$ai_model": kwargs.get("model"),
+            "$ai_input": with_privacy_mode(self._client._ph_client, posthog_privacy_mode, kwargs.get("input")),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **posthog_properties,
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        # Send capture event for embeddings
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_embedding",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+        return response

posthog/ai/openai/openai_async.py

@@ -0,0 +1,250 @@
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+try:
+    import openai
+    import openai.resources
+except ImportError:
+    raise ModuleNotFoundError("Please install the OpenAI SDK to use this feature: 'pip install openai'")
+
+from posthog.ai.utils import call_llm_and_track_usage_async, get_model_params, with_privacy_mode
+from posthog.client import Client as PostHogClient
+
+
+class AsyncOpenAI(openai.AsyncOpenAI):
+    """
+    An async wrapper around the OpenAI SDK that automatically sends LLM usage events to PostHog.
+    """
+
+    _ph_client: PostHogClient
+
+    def __init__(self, posthog_client: PostHogClient, **kwargs):
+        """
+        Args:
+            api_key: OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instance.
+            **openai_config: Additional keyword args (e.g. organization="xxx").
+        """
+        super().__init__(**kwargs)
+        self._ph_client = posthog_client
+        self.chat = WrappedChat(self)
+        self.embeddings = WrappedEmbeddings(self)
+
+
+class WrappedChat(openai.resources.chat.AsyncChat):
+    _client: AsyncOpenAI
+
+    @property
+    def completions(self):
+        return WrappedCompletions(self._client)
+
+
+class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
+    _client: AsyncOpenAI
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        # If streaming, handle streaming specifically
+        if kwargs.get("stream", False):
+            return await self._create_streaming(
+                posthog_distinct_id,
+                posthog_trace_id,
+                posthog_properties,
+                posthog_privacy_mode,
+                posthog_groups,
+                **kwargs,
+            )
+
+        response = await call_llm_and_track_usage_async(
+            posthog_distinct_id,
+            self._client._ph_client,
+            "openai",
+            posthog_trace_id,
+            posthog_properties,
+            self._client.base_url,
+            super().create,
+            **kwargs,
+        )
+        return response
+
+    async def _create_streaming(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: Dict[str, int] = {}
+        accumulated_content = []
+        if "stream_options" not in kwargs:
+            kwargs["stream_options"] = {}
+        kwargs["stream_options"]["include_usage"] = True
+        response = await super().create(**kwargs)
+
+        async def async_generator():
+            nonlocal usage_stats, accumulated_content
+            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",
+                            ]
+                        }
+                    if hasattr(chunk, "choices") and chunk.choices and len(chunk.choices) > 0:
+                        content = chunk.choices[0].delta.content
+                        if content:
+                            accumulated_content.append(content)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = "".join(accumulated_content)
+                await self._capture_streaming_event(
+                    posthog_distinct_id,
+                    posthog_trace_id,
+                    posthog_properties,
+                    posthog_privacy_mode,
+                    posthog_groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                )
+
+        return async_generator()
+
+    async def _capture_streaming_event(
+        self,
+        posthog_distinct_id: Optional[str],
+        posthog_trace_id: Optional[str],
+        posthog_properties: Optional[Dict[str, Any]],
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: Dict[str, int],
+        latency: float,
+        output: str,
+    ):
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        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),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **posthog_properties,
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+
+class WrappedEmbeddings(openai.resources.embeddings.AsyncEmbeddings):
+    _client: AsyncOpenAI
+
+    async def create(
+        self,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Create an embedding using OpenAI's 'embeddings.create' method, but also track usage in PostHog.
+
+        Args:
+            posthog_distinct_id: Optional ID to associate with the usage event.
+            posthog_trace_id: Optional trace UUID for linking events.
+            posthog_properties: Optional dictionary of extra properties to include in the event.
+            posthog_privacy_mode: Whether to store input and output in PostHog.
+            posthog_groups: Optional dictionary of groups to include in the event.
+            **kwargs: Any additional parameters for the OpenAI Embeddings API.
+
+        Returns:
+            The response from OpenAI's embeddings.create call.
+        """
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        start_time = time.time()
+        response = await super().create(**kwargs)
+        end_time = time.time()
+
+        # Extract usage statistics if available
+        usage_stats = {}
+        if hasattr(response, "usage") and response.usage:
+            usage_stats = {
+                "prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
+                "total_tokens": getattr(response.usage, "total_tokens", 0),
+            }
+
+        latency = end_time - start_time
+
+        # Build the event properties
+        event_properties = {
+            "$ai_provider": "openai",
+            "$ai_model": kwargs.get("model"),
+            "$ai_input": with_privacy_mode(self._client._ph_client, posthog_privacy_mode, kwargs.get("input")),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(self._client.base_url),
+            **posthog_properties,
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        # Send capture event for embeddings
+        if hasattr(self._client._ph_client, "capture"):
+            self._client._ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_embedding",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+        return response

posthog/ai/utils.py

@@ -0,0 +1,245 @@
+import time
+import uuid
+from typing import Any, Callable, Dict, Optional
+
+from httpx import URL
+
+from posthog.client import Client as PostHogClient
+
+
+def get_model_params(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Extracts model parameters from the kwargs dictionary.
+    """
+    model_params = {}
+    for param in [
+        "temperature",
+        "max_tokens",  # Deprecated field
+        "max_completion_tokens",
+        "top_p",
+        "frequency_penalty",
+        "presence_penalty",
+        "n",
+        "stop",
+        "stream",  # OpenAI-specific field
+        "streaming",  # Anthropic-specific field
+    ]:
+        if param in kwargs and kwargs[param] is not None:
+            model_params[param] = kwargs[param]
+    return model_params
+
+
+def get_usage(response, provider: str) -> Dict[str, Any]:
+    if provider == "anthropic":
+        return {
+            "input_tokens": response.usage.input_tokens,
+            "output_tokens": response.usage.output_tokens,
+        }
+    elif provider == "openai":
+        return {
+            "input_tokens": response.usage.prompt_tokens,
+            "output_tokens": response.usage.completion_tokens,
+        }
+    return {
+        "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)
+    elif provider == "openai":
+        return format_response_openai(response)
+    return output
+
+
+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 = []
+    for choice in response.choices:
+        if choice.message.content:
+            output.append(
+                {
+                    "content": choice.message.content,
+                    "role": choice.message.role,
+                }
+            )
+    return output
+
+
+def merge_system_prompt(kwargs: Dict[str, Any], provider: str):
+    if provider != "anthropic":
+        return kwargs.get("messages")
+    messages = kwargs.get("messages") or []
+    if kwargs.get("system") is None:
+        return messages
+    return [{"role": "system", "content": kwargs.get("system")}] + messages
+
+
+def call_llm_and_track_usage(
+    posthog_distinct_id: Optional[str],
+    ph_client: PostHogClient,
+    provider: str,
+    posthog_trace_id: Optional[str],
+    posthog_properties: Optional[Dict[str, Any]],
+    posthog_privacy_mode: bool,
+    posthog_groups: Optional[Dict[str, Any]],
+    base_url: URL,
+    call_method: Callable[..., Any],
+    **kwargs: Any,
+) -> Any:
+    """
+    Common usage-tracking logic for both sync and async calls.
+    call_method: the llm call method (e.g. openai.chat.completions.create)
+    """
+    start_time = time.time()
+    response = None
+    error = None
+    http_status = 200
+    usage: Dict[str, Any] = {}
+
+    try:
+        response = call_method(**kwargs)
+    except Exception as exc:
+        error = exc
+        http_status = getattr(exc, "status_code", 0)  # default to 0 becuase its likely an SDK error
+    finally:
+        end_time = time.time()
+        latency = end_time - start_time
+
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        if response and hasattr(response, "usage"):
+            usage = get_usage(response, provider)
+
+        messages = merge_system_prompt(kwargs, 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),
+            "$ai_output_choices": with_privacy_mode(
+                ph_client, posthog_privacy_mode, format_response(response, provider)
+            ),
+            "$ai_http_status": http_status,
+            "$ai_input_tokens": usage.get("input_tokens", 0),
+            "$ai_output_tokens": usage.get("output_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(base_url),
+            **(posthog_properties or {}),
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        # send the event to posthog
+        if hasattr(ph_client, "capture") and callable(ph_client.capture):
+            ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+    if error:
+        raise error
+
+    return response
+
+
+async def call_llm_and_track_usage_async(
+    posthog_distinct_id: Optional[str],
+    ph_client: PostHogClient,
+    provider: str,
+    posthog_trace_id: Optional[str],
+    posthog_properties: Optional[Dict[str, Any]],
+    posthog_privacy_mode: bool,
+    posthog_groups: Optional[Dict[str, Any]],
+    base_url: URL,
+    call_async_method: Callable[..., Any],
+    **kwargs: Any,
+) -> Any:
+    start_time = time.time()
+    response = None
+    error = None
+    http_status = 200
+    usage: Dict[str, Any] = {}
+
+    try:
+        response = await call_async_method(**kwargs)
+    except Exception as exc:
+        error = exc
+        http_status = getattr(exc, "status_code", 0)  # default to 0 because its likely an SDK error
+    finally:
+        end_time = time.time()
+        latency = end_time - start_time
+
+        if posthog_trace_id is None:
+            posthog_trace_id = uuid.uuid4()
+
+        if response and hasattr(response, "usage"):
+            usage = get_usage(response, provider)
+
+        messages = merge_system_prompt(kwargs, provider)
+
+        event_properties = {
+            "$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_output_choices": with_privacy_mode(
+                ph_client, posthog_privacy_mode, format_response(response, provider)
+            ),
+            "$ai_http_status": http_status,
+            "$ai_input_tokens": usage.get("input_tokens", 0),
+            "$ai_output_tokens": usage.get("output_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": posthog_trace_id,
+            "$ai_base_url": str(base_url),
+            **(posthog_properties or {}),
+        }
+
+        if posthog_distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        # send the event to posthog
+        if hasattr(ph_client, "capture") and callable(ph_client.capture):
+            ph_client.capture(
+                distinct_id=posthog_distinct_id or posthog_trace_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=posthog_groups,
+            )
+
+    if error:
+        raise error
+
+    return response
+
+
+def with_privacy_mode(ph_client: PostHogClient, privacy_mode: bool, value: Any):
+    if ph_client.privacy_mode or privacy_mode:
+        return None
+    return value

posthog/client.py

@@ -1,18 +1,21 @@
 import atexit
 import logging
 import numbers
+import os
+import sys
 from datetime import datetime, timedelta
-from uuid import UUID
+from uuid import UUID, uuid4
 
 from dateutil.tz import tzutc
 from six import string_types
 
 from posthog.consumer import Consumer
 from posthog.exception_capture import ExceptionCapture
+from posthog.exception_utils import exc_info_from_error, exceptions_from_error_tuple, handle_in_app
 from posthog.feature_flags import InconclusiveMatchError, match_feature_flag_properties
 from posthog.poller import Poller
-from posthog.request import APIError, batch_post, decide, determine_server_host, get
-from posthog.utils import SizeLimitedDict, clean, guess_timezone
+from posthog.request import DEFAULT_HOST, APIError, batch_post, decide, determine_server_host, get
+from posthog.utils import SizeLimitedDict, clean, guess_timezone, remove_trailing_slash
 from posthog.version import VERSION
 
 try:
@@ -52,8 +55,11 @@ class Client(object):
         disable_geoip=True,
         historical_migration=False,
         feature_flags_request_timeout_seconds=3,
+        super_properties=None,
         enable_exception_autocapture=False,
         exception_autocapture_integrations=None,
+        project_root=None,
+        privacy_mode=False,
     ):
         self.queue = queue.Queue(max_queue_size)
 
@@ -67,7 +73,7 @@ class Client(object):
         self.send = send
         self.sync_mode = sync_mode
         # Used for session replay URL generation - we don't want the server host here.
-        self.raw_host = host
+        self.raw_host = host or DEFAULT_HOST
         self.host = determine_server_host(host)
         self.gzip = gzip
         self.timeout = timeout
@@ -82,9 +88,19 @@ class Client(object):
         self.disabled = disabled
         self.disable_geoip = disable_geoip
         self.historical_migration = historical_migration
+        self.super_properties = super_properties
         self.enable_exception_autocapture = enable_exception_autocapture
         self.exception_autocapture_integrations = exception_autocapture_integrations
         self.exception_capture = None
+        self.privacy_mode = privacy_mode
+
+        if project_root is None:
+            try:
+                project_root = os.getcwd()
+            except Exception:
+                project_root = None
+
+        self.project_root = project_root
 
         # personal_api_key: This should be a generated Personal API Key, private
         self.personal_api_key = personal_api_key
@@ -159,6 +175,15 @@ class Client(object):
         resp_data = self.get_decide(distinct_id, groups, person_properties, group_properties, disable_geoip)
         return resp_data["featureFlagPayloads"]
 
+    def get_feature_flags_and_payloads(
+        self, distinct_id, groups=None, person_properties=None, group_properties=None, disable_geoip=None
+    ):
+        resp_data = self.get_decide(distinct_id, groups, person_properties, group_properties, disable_geoip)
+        return {
+            "featureFlags": resp_data["featureFlags"],
+            "featureFlagPayloads": resp_data["featureFlagPayloads"],
+        }
+
     def get_decide(self, distinct_id, groups=None, person_properties=None, group_properties=None, disable_geoip=None):
         require("distinct_id", distinct_id, ID_TYPES)
 
@@ -281,6 +306,7 @@ class Client(object):
         timestamp=None,
         uuid=None,
         disable_geoip=None,
+        distinct_id=None,
     ):
         properties = properties or {}
         context = context or {}
@@ -288,6 +314,11 @@ class Client(object):
         require("group_key", group_key, ID_TYPES)
         require("properties", properties, dict)
 
+        if distinct_id:
+            require("distinct_id", distinct_id, ID_TYPES)
+        else:
+            distinct_id = "${}_{}".format(group_type, group_key)
+
         msg = {
             "event": "$groupidentify",
             "properties": {
@@ -295,7 +326,7 @@ class Client(object):
                 "$group_key": group_key,
                 "$group_set": properties,
             },
-            "distinct_id": "${}_{}".format(group_type, group_key),
+            "distinct_id": distinct_id,
             "timestamp": timestamp,
             "context": context,
             "uuid": uuid,
@@ -345,6 +376,65 @@ class Client(object):
 
         return self._enqueue(msg, disable_geoip)
 
+    def capture_exception(
+        self,
+        exception=None,
+        distinct_id=None,
+        properties=None,
+        context=None,
+        timestamp=None,
+        uuid=None,
+        groups=None,
+    ):
+        # this function shouldn't ever throw an error, so it logs exceptions instead of raising them.
+        # this is important to ensure we don't unexpectedly re-raise exceptions in the user's code.
+        try:
+            properties = properties or {}
+
+            # if there's no distinct_id, we'll generate one and set personless mode
+            # via $process_person_profile = false
+            if distinct_id is None:
+                properties["$process_person_profile"] = False
+                distinct_id = uuid4()
+
+            require("distinct_id", distinct_id, ID_TYPES)
+            require("properties", properties, dict)
+
+            if exception is not None:
+                exc_info = exc_info_from_error(exception)
+            else:
+                exc_info = sys.exc_info()
+
+            if exc_info is None or exc_info == (None, None, None):
+                self.log.warning("No exception information available")
+                return
+
+            # Format stack trace for cymbal
+            all_exceptions_with_trace = exceptions_from_error_tuple(exc_info)
+
+            # Add in-app property to frames in the exceptions
+            event = handle_in_app(
+                {
+                    "exception": {
+                        "values": all_exceptions_with_trace,
+                    },
+                },
+                project_root=self.project_root,
+            )
+            all_exceptions_with_trace_and_in_app = event["exception"]["values"]
+
+            properties = {
+                "$exception_type": all_exceptions_with_trace_and_in_app[0].get("type"),
+                "$exception_message": all_exceptions_with_trace_and_in_app[0].get("value"),
+                "$exception_list": all_exceptions_with_trace_and_in_app,
+                "$exception_personURL": f"{remove_trailing_slash(self.raw_host)}/project/{self.api_key}/person/{distinct_id}",
+                **properties,
+            }
+
+            return self.capture(distinct_id, "$exception", properties, context, timestamp, uuid, groups)
+        except Exception as e:
+            self.log.exception(f"Failed to capture exception: {e}")
+
     def _enqueue(self, msg, disable_geoip):
         """Push a new `msg` onto the queue, return `(success, msg)`"""
 
@@ -353,7 +443,7 @@ class Client(object):
 
         timestamp = msg["timestamp"]
         if timestamp is None:
-            timestamp = datetime.utcnow().replace(tzinfo=tzutc())
+            timestamp = datetime.now(tz=tzutc())
 
         require("timestamp", timestamp, datetime)
         require("context", msg["context"], dict)
@@ -379,6 +469,9 @@ class Client(object):
         if disable_geoip:
             msg["properties"]["$geoip_disable"] = True
 
+        if self.super_properties:
+            msg["properties"] = {**msg["properties"], **self.super_properties}
+
         msg["distinct_id"] = stringify_id(msg.get("distinct_id", None))
 
         msg = clean(msg)
@@ -477,7 +570,7 @@ class Client(object):
             )
             self.log.warning(e)
 
-        self._last_feature_flag_poll = datetime.utcnow().replace(tzinfo=tzutc())
+        self._last_feature_flag_poll = datetime.now(tz=tzutc())
 
     def load_feature_flags(self):
         if not self.personal_api_key:
@@ -670,23 +763,52 @@ class Client(object):
                 groups=groups,
                 person_properties=person_properties,
                 group_properties=group_properties,
-                send_feature_flag_events=send_feature_flag_events,
-                only_evaluate_locally=True,
+                send_feature_flag_events=False,
+                # Disable automatic sending of feature flag events because we're manually handling event dispatch.
+                # This prevents sending events with empty data when `get_feature_flag` cannot be evaluated locally.
+                only_evaluate_locally=True,  # Enable local evaluation of feature flags to avoid making multiple requests to `/decide`.
                 disable_geoip=disable_geoip,
             )
 
         response = None
+        payload = None
 
         if match_value is not None:
-            response = self._compute_payload_locally(key, match_value)
+            payload = self._compute_payload_locally(key, match_value)
 
-        if response is None and not only_evaluate_locally:
-            decide_payloads = self.get_feature_payloads(
-                distinct_id, groups, person_properties, group_properties, disable_geoip
+        flag_was_locally_evaluated = payload is not None
+        if not flag_was_locally_evaluated and not only_evaluate_locally:
+            try:
+                responses_and_payloads = self.get_feature_flags_and_payloads(
+                    distinct_id, groups, person_properties, group_properties, disable_geoip
+                )
+                response = responses_and_payloads["featureFlags"].get(key, None)
+                payload = responses_and_payloads["featureFlagPayloads"].get(str(key).lower(), None)
+            except Exception as e:
+                self.log.exception(f"[FEATURE FLAGS] Unable to get feature flags and payloads: {e}")
+
+        feature_flag_reported_key = f"{key}_{str(response)}"
+
+        if (
+            feature_flag_reported_key not in self.distinct_ids_feature_flags_reported[distinct_id]
+            and send_feature_flag_events  # noqa: W503
+        ):
+            self.capture(
+                distinct_id,
+                "$feature_flag_called",
+                {
+                    "$feature_flag": key,
+                    "$feature_flag_response": response,
+                    "$feature_flag_payload": payload,
+                    "locally_evaluated": flag_was_locally_evaluated,
+                    f"$feature/{key}": response,
+                },
+                groups=groups,
+                disable_geoip=disable_geoip,
             )
-            response = decide_payloads.get(str(key).lower(), None)
+            self.distinct_ids_feature_flags_reported[distinct_id].add(feature_flag_reported_key)
 
-        return response
+        return payload
 
     def _compute_payload_locally(self, key, match_value):
         payload = None

posthog/exception_capture.py

@@ -4,9 +4,6 @@ import threading
 from enum import Enum
 from typing import TYPE_CHECKING, List, Optional
 
-from posthog.exception_utils import exceptions_from_error_tuple, handle_in_app
-from posthog.utils import remove_trailing_slash
-
 if TYPE_CHECKING:
     from posthog.client import Client
 
@@ -15,9 +12,6 @@ class Integrations(str, Enum):
     Django = "django"
 
 
-DEFAULT_DISTINCT_ID = "python-exceptions"
-
-
 class ExceptionCapture:
     # TODO: Add client side rate limiting to prevent spamming the server with exceptions
 
@@ -49,52 +43,22 @@ class ExceptionCapture:
 
     def exception_handler(self, exc_type, exc_value, exc_traceback):
         # don't affect default behaviour.
-        self.capture_exception(exc_type, exc_value, exc_traceback)
+        self.capture_exception((exc_type, exc_value, exc_traceback))
         self.original_excepthook(exc_type, exc_value, exc_traceback)
 
     def thread_exception_handler(self, args):
-        self.capture_exception(args.exc_type, args.exc_value, args.exc_traceback)
+        self.capture_exception((args.exc_type, args.exc_value, args.exc_traceback))
 
     def exception_receiver(self, exc_info, extra_properties):
         if "distinct_id" in extra_properties:
             metadata = {"distinct_id": extra_properties["distinct_id"]}
         else:
             metadata = None
-        self.capture_exception(exc_info[0], exc_info[1], exc_info[2], metadata)
+        self.capture_exception((exc_info[0], exc_info[1], exc_info[2]), metadata)
 
-    def capture_exception(self, exc_type, exc_value, exc_traceback, metadata=None):
+    def capture_exception(self, exception, metadata=None):
         try:
-            # if hasattr(sys, "ps1"):
-            #     # Disable the excepthook for interactive Python shells
-            #     return
-
-            # Format stack trace like sentry
-            all_exceptions_with_trace = exceptions_from_error_tuple((exc_type, exc_value, exc_traceback))
-
-            # Add in-app property to frames in the exceptions
-            event = handle_in_app(
-                {
-                    "exception": {
-                        "values": all_exceptions_with_trace,
-                    },
-                }
-            )
-            all_exceptions_with_trace_and_in_app = event["exception"]["values"]
-
-            distinct_id = metadata.get("distinct_id") if metadata else DEFAULT_DISTINCT_ID
-            # Make sure we have a distinct_id if its empty in metadata
-            distinct_id = distinct_id or DEFAULT_DISTINCT_ID
-
-            properties = {
-                "$exception_type": all_exceptions_with_trace_and_in_app[0].get("type"),
-                "$exception_message": all_exceptions_with_trace_and_in_app[0].get("value"),
-                "$exception_list": all_exceptions_with_trace_and_in_app,
-                "$exception_personURL": f"{remove_trailing_slash(self.client.raw_host)}/project/{self.client.api_key}/person/{distinct_id}",
-            }
-
-            # TODO: What distinct id should we attach these server-side exceptions to?
-            # Any heuristic seems prone to errors - how can we know if exception occurred in the context of a user that captured some other event?
-
-            self.client.capture(distinct_id, "$exception", properties=properties)
+            distinct_id = metadata.get("distinct_id") if metadata else None
+            self.client.capture_exception(exception, distinct_id)
         except Exception as e:
             self.log.exception(f"Failed to capture exception: {e}")

posthog/exception_utils.py

@@ -369,6 +369,7 @@ def serialize_frame(
         tb_lineno = frame.f_lineno
 
     rv = {
+        "platform": "python",
         "filename": filename_for_module(module, abs_path) or None,
         "abs_path": os.path.abspath(abs_path) if abs_path else None,
         "function": function or "<unknown>",
@@ -417,7 +418,7 @@ def current_stacktrace(
 
     frames.reverse()
 
-    return {"frames": frames}
+    return {"frames": frames, "type": "raw"}
 
 
 def get_errno(exc_value):
@@ -503,7 +504,7 @@ def single_exception_from_error_tuple(
     ]
 
     if frames:
-        exception_value["stacktrace"] = {"frames": frames}
+        exception_value["stacktrace"] = {"frames": frames, "type": "raw"}
 
     return exception_value
 

posthog/request.py

@@ -37,7 +37,7 @@ def post(
     """Post the `kwargs` to the API"""
     log = logging.getLogger("posthog")
     body = kwargs
-    body["sentAt"] = datetime.utcnow().replace(tzinfo=tzutc()).isoformat()
+    body["sentAt"] = datetime.now(tz=tzutc()).isoformat()
     url = remove_trailing_slash(host or DEFAULT_HOST) + path
     body["api_key"] = api_key
     data = json.dumps(body, cls=DatetimeSerializer)

posthog/test/ai/anthropic/test_anthropic.py

@@ -0,0 +1,327 @@
+import os
+import time
+from unittest.mock import patch
+
+import pytest
+from anthropic.types import Message, Usage
+
+from posthog.ai.anthropic import Anthropic, AsyncAnthropic
+
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+
+
+@pytest.fixture
+def mock_client():
+    with patch("posthog.client.Client") as mock_client:
+        mock_client.privacy_mode = False
+        yield mock_client
+
+
+@pytest.fixture
+def mock_anthropic_response():
+    return Message(
+        id="msg_123",
+        type="message",
+        role="assistant",
+        content=[{"type": "text", "text": "Test response"}],
+        model="claude-3-opus-20240229",
+        usage=Usage(
+            input_tokens=20,
+            output_tokens=10,
+        ),
+        stop_reason="end_turn",
+        stop_sequence=None,
+    )
+
+
+@pytest.fixture
+def mock_anthropic_stream():
+    class MockStreamEvent:
+        def __init__(self, content, usage=None):
+            self.content = content
+            self.usage = usage
+
+    def stream_generator():
+        yield MockStreamEvent("A")
+        yield MockStreamEvent("B")
+        yield MockStreamEvent(
+            "C",
+            usage=Usage(
+                input_tokens=20,
+                output_tokens=10,
+            ),
+        )
+
+    return stream_generator()
+
+
+def test_basic_completion(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)
+        response = client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[{"role": "user", "content": "Hello"}],
+            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["$ai_model"] == "claude-3-opus-20240229"
+        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
+        assert props["$ai_output_choices"] == [{"role": "assistant", "content": "Test response"}]
+        assert props["$ai_input_tokens"] == 20
+        assert props["$ai_output_tokens"] == 10
+        assert props["$ai_http_status"] == 200
+        assert props["foo"] == "bar"
+        assert isinstance(props["$ai_latency"], float)
+
+
+def test_streaming(mock_client, mock_anthropic_stream):
+    with patch("anthropic.resources.Messages.create", return_value=mock_anthropic_stream):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[{"role": "user", "content": "Hello"}],
+            stream=True,
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+        )
+
+        # Consume the stream
+        chunks = list(response)
+        assert len(chunks) == 3
+        assert chunks[0].content == "A"
+        assert chunks[1].content == "B"
+        assert chunks[2].content == "C"
+
+        # Wait a bit to ensure the capture is called
+        time.sleep(0.1)
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$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["$ai_latency"], float)
+        assert props["foo"] == "bar"
+
+
+def test_groups(mock_client, mock_anthropic_response):
+    with patch("anthropic.resources.Messages.create", return_value=mock_anthropic_response):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[{"role": "user", "content": "Hello"}],
+            posthog_distinct_id="test-id",
+            posthog_groups={"company": "test_company"},
+        )
+
+        assert response == mock_anthropic_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        assert call_args["groups"] == {"company": "test_company"}
+
+
+def test_privacy_mode_local(mock_client, mock_anthropic_response):
+    with patch("anthropic.resources.Messages.create", return_value=mock_anthropic_response):
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[{"role": "user", "content": "Hello"}],
+            posthog_distinct_id="test-id",
+            posthog_privacy_mode=True,
+        )
+
+        assert response == mock_anthropic_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+        assert props["$ai_input"] is None
+        assert props["$ai_output_choices"] is None
+
+
+def test_privacy_mode_global(mock_client, mock_anthropic_response):
+    with patch("anthropic.resources.Messages.create", return_value=mock_anthropic_response):
+        mock_client.privacy_mode = True
+        client = Anthropic(api_key="test-key", posthog_client=mock_client)
+        response = client.messages.create(
+            model="claude-3-opus-20240229",
+            messages=[{"role": "user", "content": "Hello"}],
+            posthog_distinct_id="test-id",
+            posthog_privacy_mode=False,
+        )
+
+        assert response == mock_anthropic_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+        assert props["$ai_input"] is None
+        assert props["$ai_output_choices"] is None
+
+
+@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
+def test_basic_integration(mock_client):
+    client = Anthropic(posthog_client=mock_client)
+    client.messages.create(
+        model="claude-3-opus-20240229",
+        messages=[{"role": "user", "content": "Foo"}],
+        max_tokens=1,
+        temperature=0,
+        posthog_distinct_id="test-id",
+        posthog_properties={"foo": "bar"},
+        system="You must always answer with 'Bar'.",
+    )
+
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "anthropic"
+    assert props["$ai_model"] == "claude-3-opus-20240229"
+    assert props["$ai_input"] == [
+        {"role": "system", "content": "You must always answer with 'Bar'."},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert props["$ai_output_choices"][0]["role"] == "assistant"
+    assert props["$ai_output_choices"][0]["content"] == "Bar"
+    assert props["$ai_input_tokens"] == 18
+    assert props["$ai_output_tokens"] == 1
+    assert props["$ai_http_status"] == 200
+    assert props["foo"] == "bar"
+    assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
+async def test_basic_async_integration(mock_client):
+    client = AsyncAnthropic(posthog_client=mock_client)
+    await client.messages.create(
+        model="claude-3-opus-20240229",
+        messages=[{"role": "user", "content": "You must always answer with 'Bar'."}],
+        max_tokens=1,
+        temperature=0,
+        posthog_distinct_id="test-id",
+        posthog_properties={"foo": "bar"},
+    )
+
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "anthropic"
+    assert props["$ai_model"] == "claude-3-opus-20240229"
+    assert props["$ai_input"] == [{"role": "user", "content": "You must always answer with 'Bar'."}]
+    assert props["$ai_output_choices"][0]["role"] == "assistant"
+    assert props["$ai_input_tokens"] == 16
+    assert props["$ai_output_tokens"] == 1
+    assert props["$ai_http_status"] == 200
+    assert props["foo"] == "bar"
+    assert isinstance(props["$ai_latency"], float)
+
+
+def test_streaming_system_prompt(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",
+            system="Foo",
+            messages=[{"role": "user", "content": "Bar"}],
+            stream=True,
+        )
+
+        # Consume the stream
+        list(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"]
+
+        assert props["$ai_input"] == [{"role": "system", "content": "Foo"}, {"role": "user", "content": "Bar"}]
+
+
+@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
+async def test_async_streaming_system_prompt(mock_client, mock_anthropic_stream):
+    client = AsyncAnthropic(posthog_client=mock_client)
+    response = await client.messages.create(
+        model="claude-3-opus-20240229",
+        system="You must always answer with 'Bar'.",
+        messages=[{"role": "user", "content": "Foo"}],
+        stream=True,
+        max_tokens=1,
+    )
+
+    # Consume the stream
+    [c async for c in response]
+
+    # Wait a bit to ensure the capture is called
+    time.sleep(0.1)
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert props["$ai_input"] == [
+        {"role": "system", "content": "You must always answer with 'Bar'."},
+        {"role": "user", "content": "Foo"},
+    ]

posthog/test/ai/langchain/__init__.py

@@ -0,0 +1,4 @@
+import pytest
+
+pytest.importorskip("langchain")
+pytest.importorskip("langchain_community")

posthog/test/ai/langchain/test_callbacks.py

@@ -0,0 +1,773 @@
+import math
+import os
+import time
+import uuid
+from unittest.mock import patch
+
+import pytest
+from langchain_anthropic.chat_models import ChatAnthropic
+from langchain_community.chat_models.fake import FakeMessagesListChatModel
+from langchain_community.llms.fake import FakeListLLM, FakeStreamingListLLM
+from langchain_core.messages import AIMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableLambda
+from langchain_openai.chat_models import ChatOpenAI
+
+from posthog.ai.langchain import CallbackHandler
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+
+
+@pytest.fixture(scope="function")
+def mock_client():
+    with patch("posthog.client.Client") as mock_client:
+        mock_client.privacy_mode = False
+        yield mock_client
+
+
+def test_parent_capture(mock_client):
+    callbacks = CallbackHandler(mock_client)
+    parent_run_id = uuid.uuid4()
+    run_id = uuid.uuid4()
+    callbacks._set_parent_of_run(run_id, parent_run_id)
+    assert callbacks._parent_tree == {run_id: parent_run_id}
+    callbacks._pop_parent_of_run(run_id)
+    assert callbacks._parent_tree == {}
+    callbacks._pop_parent_of_run(parent_run_id)  # should not raise
+
+
+def test_find_root_run(mock_client):
+    callbacks = CallbackHandler(mock_client)
+    root_run_id = uuid.uuid4()
+    parent_run_id = uuid.uuid4()
+    run_id = uuid.uuid4()
+    callbacks._set_parent_of_run(run_id, parent_run_id)
+    callbacks._set_parent_of_run(parent_run_id, root_run_id)
+    assert callbacks._find_root_run(run_id) == root_run_id
+    new_run_id = uuid.uuid4()
+    assert callbacks._find_root_run(new_run_id) == new_run_id
+
+
+def test_trace_id_generation(mock_client):
+    callbacks = CallbackHandler(mock_client)
+    run_id = uuid.uuid4()
+    with patch("uuid.uuid4", return_value=run_id):
+        assert callbacks._get_trace_id(run_id) == run_id
+    run_id = uuid.uuid4()
+    callbacks = CallbackHandler(mock_client, trace_id=run_id)
+    assert callbacks._get_trace_id(uuid.uuid4()) == run_id
+
+
+def test_metadata_capture(mock_client):
+    callbacks = CallbackHandler(mock_client)
+    run_id = uuid.uuid4()
+    with patch("time.time", return_value=1234567890):
+        callbacks._set_run_metadata(
+            {"kwargs": {"openai_api_base": "https://us.posthog.com"}},
+            run_id,
+            messages=[{"role": "user", "content": "Who won the world series in 2020?"}],
+            invocation_params={"temperature": 0.5},
+            metadata={"ls_model_name": "hog-mini", "ls_provider": "posthog"},
+        )
+    expected = {
+        "model": "hog-mini",
+        "messages": [{"role": "user", "content": "Who won the world series in 2020?"}],
+        "start_time": 1234567890,
+        "model_params": {"temperature": 0.5},
+        "provider": "posthog",
+        "base_url": "https://us.posthog.com",
+    }
+    assert callbacks._runs[run_id] == expected
+    with patch("time.time", return_value=1234567891):
+        run = callbacks._pop_run_metadata(run_id)
+    assert run == {**expected, "end_time": 1234567891}
+    assert callbacks._runs == {}
+    callbacks._pop_run_metadata(uuid.uuid4())  # should not raise
+
+
+@pytest.mark.parametrize("stream", [True, False])
+def test_basic_chat_chain(mock_client, stream):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", "You are a helpful assistant."),
+            ("user", "Who won the world series in 2020?"),
+        ]
+    )
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="The Los Angeles Dodgers won the World Series in 2020.",
+                usage_metadata={"input_tokens": 10, "output_tokens": 10, "total_tokens": 20},
+            )
+        ]
+    )
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model
+    if stream:
+        result = [m for m in chain.stream({}, config={"callbacks": callbacks})][0]
+    else:
+        result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "The Los Angeles Dodgers won the World Series in 2020."
+    assert mock_client.capture.call_count == 1
+    args = mock_client.capture.call_args[1]
+    props = args["properties"]
+
+    assert args["event"] == "$ai_generation"
+    assert "distinct_id" in args
+    assert "$ai_model" in props
+    assert "$ai_provider" in props
+    assert props["$ai_input"] == [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Who won the world series in 2020?"},
+    ]
+    assert props["$ai_output_choices"] == [
+        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."}
+    ]
+    assert props["$ai_input_tokens"] == 10
+    assert props["$ai_output_tokens"] == 10
+    assert props["$ai_http_status"] == 200
+    assert props["$ai_trace_id"] is not None
+    assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.parametrize("stream", [True, False])
+async def test_async_basic_chat_chain(mock_client, stream):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", "You are a helpful assistant."),
+            ("user", "Who won the world series in 2020?"),
+        ]
+    )
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="The Los Angeles Dodgers won the World Series in 2020.",
+                usage_metadata={"input_tokens": 10, "output_tokens": 10, "total_tokens": 20},
+            )
+        ]
+    )
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model
+    if stream:
+        result = [m async for m in chain.astream({}, config={"callbacks": callbacks})][0]
+    else:
+        result = await chain.ainvoke({}, config={"callbacks": callbacks})
+    assert result.content == "The Los Angeles Dodgers won the World Series in 2020."
+    assert mock_client.capture.call_count == 1
+
+    args = mock_client.capture.call_args[1]
+    props = args["properties"]
+    assert args["event"] == "$ai_generation"
+    assert "distinct_id" in args
+    assert "$ai_model" in props
+    assert "$ai_provider" in props
+    assert props["$ai_input"] == [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Who won the world series in 2020?"},
+    ]
+    assert props["$ai_output_choices"] == [
+        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."}
+    ]
+    assert props["$ai_input_tokens"] == 10
+    assert props["$ai_output_tokens"] == 10
+    assert props["$ai_http_status"] == 200
+    assert props["$ai_trace_id"] is not None
+    assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.parametrize(
+    "Model,stream",
+    [(FakeListLLM, True), (FakeListLLM, False), (FakeStreamingListLLM, True), (FakeStreamingListLLM, False)],
+)
+def test_basic_llm_chain(mock_client, Model, stream):
+    model = Model(responses=["The Los Angeles Dodgers won the World Series in 2020."])
+    callbacks: list[CallbackHandler] = [CallbackHandler(mock_client)]
+
+    if stream:
+        result = "".join(
+            [m for m in model.stream("Who won the world series in 2020?", config={"callbacks": callbacks})]
+        )
+    else:
+        result = model.invoke("Who won the world series in 2020?", config={"callbacks": callbacks})
+    assert result == "The Los Angeles Dodgers won the World Series in 2020."
+
+    assert mock_client.capture.call_count == 1
+    args = mock_client.capture.call_args[1]
+    props = args["properties"]
+
+    assert args["event"] == "$ai_generation"
+    assert "distinct_id" in args
+    assert "$ai_model" in props
+    assert "$ai_provider" in props
+    assert props["$ai_input"] == ["Who won the world series in 2020?"]
+    assert props["$ai_output_choices"] == ["The Los Angeles Dodgers won the World Series in 2020."]
+    assert props["$ai_http_status"] == 200
+    assert props["$ai_trace_id"] is not None
+    assert isinstance(props["$ai_latency"], float)
+
+
+@pytest.mark.parametrize(
+    "Model,stream",
+    [(FakeListLLM, True), (FakeListLLM, False), (FakeStreamingListLLM, True), (FakeStreamingListLLM, False)],
+)
+async def test_async_basic_llm_chain(mock_client, Model, stream):
+    model = Model(responses=["The Los Angeles Dodgers won the World Series in 2020."])
+    callbacks: list[CallbackHandler] = [CallbackHandler(mock_client)]
+
+    if stream:
+        result = "".join(
+            [m async for m in model.astream("Who won the world series in 2020?", config={"callbacks": callbacks})]
+        )
+    else:
+        result = await model.ainvoke("Who won the world series in 2020?", config={"callbacks": callbacks})
+    assert result == "The Los Angeles Dodgers won the World Series in 2020."
+
+    assert mock_client.capture.call_count == 1
+    args = mock_client.capture.call_args[1]
+    props = args["properties"]
+
+    assert args["event"] == "$ai_generation"
+    assert "distinct_id" in args
+    assert "$ai_model" in props
+    assert "$ai_provider" in props
+    assert props["$ai_input"] == ["Who won the world series in 2020?"]
+    assert props["$ai_output_choices"] == ["The Los Angeles Dodgers won the World Series in 2020."]
+    assert props["$ai_http_status"] == 200
+    assert props["$ai_trace_id"] is not None
+    assert isinstance(props["$ai_latency"], float)
+
+
+def test_trace_id_for_multiple_chains(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("user", "Foo"),
+        ]
+    )
+    model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    callbacks = [CallbackHandler(mock_client)]
+    chain = prompt | model | RunnableLambda(lambda x: [x]) | model
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 2
+
+    first_call_args = mock_client.capture.call_args_list[0][1]
+    first_call_props = first_call_args["properties"]
+    assert first_call_args["event"] == "$ai_generation"
+    assert "distinct_id" in first_call_args
+    assert "$ai_model" in first_call_props
+    assert "$ai_provider" in first_call_props
+    assert first_call_props["$ai_input"] == [{"role": "user", "content": "Foo"}]
+    assert first_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert first_call_props["$ai_http_status"] == 200
+    assert first_call_props["$ai_trace_id"] is not None
+    assert isinstance(first_call_props["$ai_latency"], float)
+
+    second_call_args = mock_client.capture.call_args_list[1][1]
+    second_call_props = second_call_args["properties"]
+    assert second_call_args["event"] == "$ai_generation"
+    assert "distinct_id" in second_call_args
+    assert "$ai_model" in second_call_props
+    assert "$ai_provider" in second_call_props
+    assert second_call_props["$ai_input"] == [{"role": "assistant", "content": "Bar"}]
+    assert second_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert second_call_props["$ai_http_status"] == 200
+    assert second_call_props["$ai_trace_id"] is not None
+    assert isinstance(second_call_props["$ai_latency"], float)
+
+    # Check that the trace_id is the same as the first call
+    assert first_call_props["$ai_trace_id"] == second_call_props["$ai_trace_id"]
+
+
+def test_personless_mode(mock_client):
+    prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+    chain = prompt | FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    chain.invoke({}, config={"callbacks": [CallbackHandler(mock_client)]})
+    assert mock_client.capture.call_count == 1
+    args = mock_client.capture.call_args_list[0][1]
+    assert args["properties"]["$process_person_profile"] is False
+
+    id = uuid.uuid4()
+    chain.invoke({}, config={"callbacks": [CallbackHandler(mock_client, distinct_id=id)]})
+    assert mock_client.capture.call_count == 2
+    args = mock_client.capture.call_args_list[1][1]
+    assert "$process_person_profile" not in args["properties"]
+    assert args["distinct_id"] == id
+
+
+def test_personless_mode_exception(mock_client):
+    prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+    chain = prompt | ChatOpenAI(api_key="test", model="gpt-4o-mini")
+    callbacks = CallbackHandler(mock_client)
+    with pytest.raises(Exception):
+        chain.invoke({}, config={"callbacks": [callbacks]})
+    assert mock_client.capture.call_count == 1
+    args = mock_client.capture.call_args_list[0][1]
+    assert args["properties"]["$process_person_profile"] is False
+
+    id = uuid.uuid4()
+    with pytest.raises(Exception):
+        chain.invoke({}, config={"callbacks": [CallbackHandler(mock_client, distinct_id=id)]})
+    assert mock_client.capture.call_count == 2
+    args = mock_client.capture.call_args_list[1][1]
+    assert "$process_person_profile" not in args["properties"]
+    assert args["distinct_id"] == id
+
+
+def test_metadata(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("user", "Foo"),
+        ]
+    )
+    model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    callbacks = [
+        CallbackHandler(mock_client, trace_id="test-trace-id", distinct_id="test_id", properties={"foo": "bar"})
+    ]
+    chain = prompt | model
+    result = chain.invoke({}, config={"callbacks": callbacks})
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    assert first_call_args["distinct_id"] == "test_id"
+
+    first_call_props = first_call_args["properties"]
+    assert first_call_args["event"] == "$ai_generation"
+    assert first_call_props["$ai_trace_id"] == "test-trace-id"
+    assert first_call_props["foo"] == "bar"
+    assert first_call_props["$ai_input"] == [{"role": "user", "content": "Foo"}]
+    assert first_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert first_call_props["$ai_http_status"] == 200
+    assert isinstance(first_call_props["$ai_latency"], float)
+
+
+def test_callbacks_logic(mock_client):
+    prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+    model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    callbacks = CallbackHandler(mock_client, trace_id="test-trace-id", distinct_id="test_id", properties={"foo": "bar"})
+    chain = prompt | model
+
+    chain.invoke({}, config={"callbacks": [callbacks]})
+    assert callbacks._runs == {}
+    assert callbacks._parent_tree == {}
+
+    def assert_intermediary_run(m):
+        assert callbacks._runs == {}
+        assert len(callbacks._parent_tree.items()) == 1
+        return [m]
+
+    (chain | RunnableLambda(assert_intermediary_run) | model).invoke({}, config={"callbacks": [callbacks]})
+    assert callbacks._runs == {}
+    assert callbacks._parent_tree == {}
+
+
+def test_exception_in_chain(mock_client):
+    def runnable(_):
+        raise ValueError("test")
+
+    callbacks = CallbackHandler(mock_client)
+    with pytest.raises(ValueError):
+        RunnableLambda(runnable).invoke({}, config={"callbacks": [callbacks]})
+
+    assert callbacks._runs == {}
+    assert callbacks._parent_tree == {}
+    assert mock_client.capture.call_count == 0
+
+
+def test_openai_error(mock_client):
+    prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+    chain = prompt | ChatOpenAI(api_key="test", model="gpt-4o-mini")
+    callbacks = CallbackHandler(mock_client)
+
+    # 401
+    with pytest.raises(Exception):
+        chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert callbacks._runs == {}
+    assert callbacks._parent_tree == {}
+    assert mock_client.capture.call_count == 1
+    args = mock_client.capture.call_args[1]
+    props = args["properties"]
+    assert props["$ai_http_status"] == 401
+    assert props["$ai_input"] == [{"role": "user", "content": "Foo"}]
+    assert "$ai_output_choices" not in props
+
+
+@pytest.mark.skipif(not OPENAI_API_KEY, reason="OpenAI API key not set")
+def test_openai_chain(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    chain = prompt | ChatOpenAI(
+        api_key=OPENAI_API_KEY,
+        model="gpt-4o-mini",
+        temperature=0,
+        max_tokens=1,
+    )
+    callbacks = CallbackHandler(mock_client, trace_id="test-trace-id", distinct_id="test_id", properties={"foo": "bar"})
+    start_time = time.time()
+    result = chain.invoke({}, config={"callbacks": [callbacks]})
+    approximate_latency = math.floor(time.time() - start_time)
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    first_call_props = first_call_args["properties"]
+    assert first_call_args["event"] == "$ai_generation"
+    assert first_call_props["$ai_trace_id"] == "test-trace-id"
+    assert first_call_props["$ai_provider"] == "openai"
+    assert first_call_props["$ai_model"] == "gpt-4o-mini"
+    assert first_call_props["foo"] == "bar"
+
+    # langchain-openai for langchain v3
+    if "max_completion_tokens" in first_call_props["$ai_model_parameters"]:
+        assert first_call_props["$ai_model_parameters"] == {
+            "temperature": 0.0,
+            "max_completion_tokens": 1,
+            "stream": False,
+        }
+    else:
+        assert first_call_props["$ai_model_parameters"] == {
+            "temperature": 0.0,
+            "max_tokens": 1,
+            "n": 1,
+            "stream": False,
+        }
+    assert first_call_props["$ai_input"] == [
+        {"role": "system", "content": 'You must always answer with "Bar".'},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert first_call_props["$ai_output_choices"] == [
+        {
+            "role": "assistant",
+            "content": "Bar",
+            "additional_kwargs": {"refusal": None},
+        }
+    ]
+    assert first_call_props["$ai_http_status"] == 200
+    assert isinstance(first_call_props["$ai_latency"], float)
+    assert min(approximate_latency - 1, 0) <= math.floor(first_call_props["$ai_latency"]) <= approximate_latency
+    assert first_call_props["$ai_input_tokens"] == 20
+    assert first_call_props["$ai_output_tokens"] == 1
+
+
+@pytest.mark.skipif(not OPENAI_API_KEY, reason="OpenAI API key not set")
+def test_openai_captures_multiple_generations(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    chain = prompt | ChatOpenAI(
+        api_key=OPENAI_API_KEY,
+        model="gpt-4o-mini",
+        temperature=0,
+        max_tokens=1,
+        n=2,
+    )
+    callbacks = CallbackHandler(mock_client)
+    result = chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    first_call_props = first_call_args["properties"]
+    assert first_call_props["$ai_input"] == [
+        {"role": "system", "content": 'You must always answer with "Bar".'},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert first_call_props["$ai_output_choices"] == [
+        {
+            "role": "assistant",
+            "content": "Bar",
+            "additional_kwargs": {"refusal": None},
+        },
+        {
+            "role": "assistant",
+            "content": "Bar",
+        },
+    ]
+
+    # langchain-openai for langchain v3
+    if "max_completion_tokens" in first_call_props["$ai_model_parameters"]:
+        assert first_call_props["$ai_model_parameters"] == {
+            "temperature": 0.0,
+            "max_completion_tokens": 1,
+            "stream": False,
+            "n": 2,
+        }
+    else:
+        assert first_call_props["$ai_model_parameters"] == {
+            "temperature": 0.0,
+            "max_tokens": 1,
+            "stream": False,
+            "n": 2,
+        }
+    assert first_call_props["$ai_http_status"] == 200
+
+
+@pytest.mark.skipif(not OPENAI_API_KEY, reason="OpenAI API key not set")
+def test_openai_streaming(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    chain = prompt | ChatOpenAI(
+        api_key=OPENAI_API_KEY, model="gpt-4o-mini", temperature=0, max_tokens=1, stream=True, stream_usage=True
+    )
+    callbacks = CallbackHandler(mock_client)
+    result = [m for m in chain.stream({}, config={"callbacks": [callbacks]})]
+    result = sum(result[1:], result[0])
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    first_call_props = first_call_args["properties"]
+
+    assert first_call_props["$ai_model_parameters"]["stream"]
+    assert first_call_props["$ai_input"] == [
+        {"role": "system", "content": 'You must always answer with "Bar".'},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert first_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert first_call_props["$ai_http_status"] == 200
+    assert first_call_props["$ai_input_tokens"] == 20
+    assert first_call_props["$ai_output_tokens"] == 1
+
+
+@pytest.mark.skipif(not OPENAI_API_KEY, reason="OpenAI API key not set")
+async def test_async_openai_streaming(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    chain = prompt | ChatOpenAI(
+        api_key=OPENAI_API_KEY, model="gpt-4o-mini", temperature=0, max_tokens=1, stream=True, stream_usage=True
+    )
+    callbacks = CallbackHandler(mock_client)
+    result = [m async for m in chain.astream({}, config={"callbacks": [callbacks]})]
+    result = sum(result[1:], result[0])
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    first_call_props = first_call_args["properties"]
+
+    assert first_call_props["$ai_model_parameters"]["stream"]
+    assert first_call_props["$ai_input"] == [
+        {"role": "system", "content": 'You must always answer with "Bar".'},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert first_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert first_call_props["$ai_http_status"] == 200
+    assert first_call_props["$ai_input_tokens"] == 20
+    assert first_call_props["$ai_output_tokens"] == 1
+
+
+def test_base_url_retrieval(mock_client):
+    prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+    chain = prompt | ChatOpenAI(
+        api_key="test",
+        model="posthog-mini",
+        base_url="https://test.posthog.com",
+    )
+    callbacks = CallbackHandler(mock_client)
+    with pytest.raises(Exception):
+        chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert mock_client.capture.call_count == 1
+    call = mock_client.capture.call_args[1]
+    assert call["properties"]["$ai_base_url"] == "https://test.posthog.com"
+
+
+def test_groups(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    chain = prompt | model
+    callbacks = CallbackHandler(mock_client, groups={"company": "test_company"})
+    chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert mock_client.capture.call_count == 1
+    call = mock_client.capture.call_args[1]
+    assert call["groups"] == {"company": "test_company"}
+
+
+def test_privacy_mode_local(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    chain = prompt | model
+    callbacks = CallbackHandler(mock_client, privacy_mode=True)
+    chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert mock_client.capture.call_count == 1
+    call = mock_client.capture.call_args[1]
+    assert call["properties"]["$ai_input"] is None
+    assert call["properties"]["$ai_output_choices"] is None
+
+
+def test_privacy_mode_global(mock_client):
+    mock_client.privacy_mode = True
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    model = FakeMessagesListChatModel(responses=[AIMessage(content="Bar")])
+    chain = prompt | model
+    callbacks = CallbackHandler(mock_client)
+    chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert mock_client.capture.call_count == 1
+    call = mock_client.capture.call_args[1]
+    assert call["properties"]["$ai_input"] is None
+    assert call["properties"]["$ai_output_choices"] is None
+
+
+@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
+def test_anthropic_chain(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    chain = prompt | ChatAnthropic(
+        api_key=ANTHROPIC_API_KEY,
+        model="claude-3-opus-20240229",
+        temperature=0,
+        max_tokens=1,
+    )
+    callbacks = CallbackHandler(mock_client, trace_id="test-trace-id", distinct_id="test_id", properties={"foo": "bar"})
+    start_time = time.time()
+    result = chain.invoke({}, config={"callbacks": [callbacks]})
+    approximate_latency = math.floor(time.time() - start_time)
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    first_call_props = first_call_args["properties"]
+    assert first_call_args["event"] == "$ai_generation"
+    assert first_call_props["$ai_trace_id"] == "test-trace-id"
+    assert first_call_props["$ai_provider"] == "anthropic"
+    assert first_call_props["$ai_model"] == "claude-3-opus-20240229"
+    assert first_call_props["foo"] == "bar"
+
+    assert first_call_props["$ai_model_parameters"] == {
+        "temperature": 0.0,
+        "max_tokens": 1,
+        "streaming": False,
+    }
+    assert first_call_props["$ai_input"] == [
+        {"role": "system", "content": 'You must always answer with "Bar".'},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert first_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert first_call_props["$ai_http_status"] == 200
+    assert isinstance(first_call_props["$ai_latency"], float)
+    assert min(approximate_latency - 1, 0) <= math.floor(first_call_props["$ai_latency"]) <= approximate_latency
+    assert first_call_props["$ai_input_tokens"] == 17
+    assert first_call_props["$ai_output_tokens"] == 1
+
+
+@pytest.mark.skipif(not ANTHROPIC_API_KEY, reason="ANTHROPIC_API_KEY is not set")
+async def test_async_anthropic_streaming(mock_client):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", 'You must always answer with "Bar".'),
+            ("user", "Foo"),
+        ]
+    )
+    chain = prompt | ChatAnthropic(
+        api_key=ANTHROPIC_API_KEY,
+        model="claude-3-opus-20240229",
+        temperature=0,
+        max_tokens=1,
+        streaming=True,
+        stream_usage=True,
+    )
+    callbacks = CallbackHandler(mock_client)
+    result = [m async for m in chain.astream({}, config={"callbacks": [callbacks]})]
+    result = sum(result[1:], result[0])
+
+    assert result.content == "Bar"
+    assert mock_client.capture.call_count == 1
+
+    first_call_args = mock_client.capture.call_args[1]
+    first_call_props = first_call_args["properties"]
+    assert first_call_props["$ai_model_parameters"]["streaming"]
+    assert first_call_props["$ai_input"] == [
+        {"role": "system", "content": 'You must always answer with "Bar".'},
+        {"role": "user", "content": "Foo"},
+    ]
+    assert first_call_props["$ai_output_choices"] == [{"role": "assistant", "content": "Bar"}]
+    assert first_call_props["$ai_http_status"] == 200
+    assert first_call_props["$ai_input_tokens"] == 17
+    assert first_call_props["$ai_output_tokens"] is not None
+
+
+def test_tool_calls(mock_client):
+    prompt = ChatPromptTemplate.from_messages([("user", "Foo")])
+    model = FakeMessagesListChatModel(
+        responses=[
+            AIMessage(
+                content="Bar",
+                additional_kwargs={
+                    "tool_calls": [
+                        {
+                            "type": "function",
+                            "id": "123",
+                            "function": {
+                                "name": "test",
+                                "args": '{"a": 1}',
+                            },
+                        }
+                    ]
+                },
+            )
+        ]
+    )
+    chain = prompt | model
+    callbacks = CallbackHandler(mock_client)
+    chain.invoke({}, config={"callbacks": [callbacks]})
+
+    assert mock_client.capture.call_count == 1
+    call = mock_client.capture.call_args[1]
+    assert call["properties"]["$ai_output_choices"][0]["tool_calls"] == [
+        {
+            "type": "function",
+            "id": "123",
+            "function": {
+                "name": "test",
+                "args": '{"a": 1}',
+            },
+        }
+    ]
+    assert "additional_kwargs" not in call["properties"]["$ai_output_choices"][0]

posthog/test/ai/openai/test_openai.py

@@ -0,0 +1,175 @@
+import time
+from unittest.mock import patch
+
+import pytest
+from openai.types.chat import ChatCompletion, ChatCompletionMessage
+from openai.types.chat.chat_completion import Choice
+from openai.types.completion_usage import CompletionUsage
+from openai.types.create_embedding_response import CreateEmbeddingResponse, Usage
+from openai.types.embedding import Embedding
+
+from posthog.ai.openai import OpenAI
+
+
+@pytest.fixture
+def mock_client():
+    with patch("posthog.client.Client") as mock_client:
+        mock_client.privacy_mode = False
+        yield mock_client
+
+
+@pytest.fixture
+def mock_openai_response():
+    return ChatCompletion(
+        id="test",
+        model="gpt-4",
+        object="chat.completion",
+        created=int(time.time()),
+        choices=[
+            Choice(
+                finish_reason="stop",
+                index=0,
+                message=ChatCompletionMessage(
+                    content="Test response",
+                    role="assistant",
+                ),
+            )
+        ],
+        usage=CompletionUsage(
+            completion_tokens=10,
+            prompt_tokens=20,
+            total_tokens=30,
+        ),
+    )
+
+
+@pytest.fixture
+def mock_embedding_response():
+    return CreateEmbeddingResponse(
+        data=[
+            Embedding(
+                embedding=[0.1, 0.2, 0.3],
+                index=0,
+                object="embedding",
+            )
+        ],
+        model="text-embedding-3-small",
+        object="list",
+        usage=Usage(
+            prompt_tokens=10,
+            total_tokens=10,
+        ),
+    )
+
+
+def test_basic_completion(mock_client, mock_openai_response):
+    with patch("openai.resources.chat.completions.Completions.create", return_value=mock_openai_response):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": "Hello"}],
+            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["$ai_model"] == "gpt-4"
+        assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
+        assert props["$ai_output_choices"] == [{"role": "assistant", "content": "Test response"}]
+        assert props["$ai_input_tokens"] == 20
+        assert props["$ai_output_tokens"] == 10
+        assert props["$ai_http_status"] == 200
+        assert props["foo"] == "bar"
+        assert isinstance(props["$ai_latency"], float)
+
+
+def test_embeddings(mock_client, mock_embedding_response):
+    with patch("openai.resources.embeddings.Embeddings.create", return_value=mock_embedding_response):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.embeddings.create(
+            model="text-embedding-3-small",
+            input="Hello world",
+            posthog_distinct_id="test-id",
+            posthog_properties={"foo": "bar"},
+        )
+
+        assert response == mock_embedding_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+        props = call_args["properties"]
+
+        assert call_args["distinct_id"] == "test-id"
+        assert call_args["event"] == "$ai_embedding"
+        assert props["$ai_provider"] == "openai"
+        assert props["$ai_model"] == "text-embedding-3-small"
+        assert props["$ai_input"] == "Hello world"
+        assert props["$ai_input_tokens"] == 10
+        assert props["$ai_http_status"] == 200
+        assert props["foo"] == "bar"
+        assert isinstance(props["$ai_latency"], float)
+
+
+def test_groups(mock_client, mock_openai_response):
+    with patch("openai.resources.chat.completions.Completions.create", return_value=mock_openai_response):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": "Hello"}],
+            posthog_distinct_id="test-id",
+            posthog_groups={"company": "test_company"},
+        )
+
+        assert response == mock_openai_response
+        assert mock_client.capture.call_count == 1
+
+        call_args = mock_client.capture.call_args[1]
+
+        assert call_args["groups"] == {"company": "test_company"}
+
+
+def test_privacy_mode_local(mock_client, mock_openai_response):
+    with patch("openai.resources.chat.completions.Completions.create", return_value=mock_openai_response):
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": "Hello"}],
+            posthog_distinct_id="test-id",
+            posthog_privacy_mode=True,
+        )
+
+        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 props["$ai_input"] is None
+        assert props["$ai_output_choices"] is None
+
+
+def test_privacy_mode_global(mock_client, mock_openai_response):
+    with patch("openai.resources.chat.completions.Completions.create", return_value=mock_openai_response):
+        mock_client.privacy_mode = True
+        client = OpenAI(api_key="test-key", posthog_client=mock_client)
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": "Hello"}],
+            posthog_distinct_id="test-id",
+            posthog_privacy_mode=False,
+        )
+
+        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 props["$ai_input"] is None
+        assert props["$ai_output_choices"] is None

posthog/test/test_client.py

@@ -84,6 +84,184 @@ class TestClient(unittest.TestCase):
         self.assertEqual(msg["properties"]["$lib"], "posthog-python")
         self.assertEqual(msg["properties"]["$lib_version"], VERSION)
 
+    def test_basic_super_properties(self):
+        client = Client(FAKE_TEST_API_KEY, super_properties={"source": "repo-name"})
+
+        _, msg = client.capture("distinct_id", "python test event")
+        client.flush()
+
+        self.assertEqual(msg["event"], "python test event")
+        self.assertEqual(msg["properties"]["source"], "repo-name")
+
+        _, msg = client.identify("distinct_id", {"trait": "value"})
+        client.flush()
+
+        self.assertEqual(msg["$set"]["trait"], "value")
+        self.assertEqual(msg["properties"]["source"], "repo-name")
+
+    def test_basic_capture_exception(self):
+
+        with mock.patch.object(Client, "capture", return_value=None) as patch_capture:
+            client = self.client
+            exception = Exception("test exception")
+            client.capture_exception(exception, distinct_id="distinct_id")
+
+            self.assertTrue(patch_capture.called)
+            capture_call = patch_capture.call_args[0]
+            self.assertEqual(capture_call[0], "distinct_id")
+            self.assertEqual(capture_call[1], "$exception")
+            self.assertEqual(
+                capture_call[2],
+                {
+                    "$exception_type": "Exception",
+                    "$exception_message": "test exception",
+                    "$exception_list": [
+                        {
+                            "mechanism": {"type": "generic", "handled": True},
+                            "module": None,
+                            "type": "Exception",
+                            "value": "test exception",
+                        }
+                    ],
+                    "$exception_personURL": "https://us.i.posthog.com/project/random_key/person/distinct_id",
+                },
+            )
+
+    def test_basic_capture_exception_with_distinct_id(self):
+
+        with mock.patch.object(Client, "capture", return_value=None) as patch_capture:
+            client = self.client
+            exception = Exception("test exception")
+            client.capture_exception(exception, "distinct_id")
+
+            self.assertTrue(patch_capture.called)
+            capture_call = patch_capture.call_args[0]
+            self.assertEqual(capture_call[0], "distinct_id")
+            self.assertEqual(capture_call[1], "$exception")
+            self.assertEqual(
+                capture_call[2],
+                {
+                    "$exception_type": "Exception",
+                    "$exception_message": "test exception",
+                    "$exception_list": [
+                        {
+                            "mechanism": {"type": "generic", "handled": True},
+                            "module": None,
+                            "type": "Exception",
+                            "value": "test exception",
+                        }
+                    ],
+                    "$exception_personURL": "https://us.i.posthog.com/project/random_key/person/distinct_id",
+                },
+            )
+
+    def test_basic_capture_exception_with_correct_host_generation(self):
+
+        with mock.patch.object(Client, "capture", return_value=None) as patch_capture:
+            client = Client(FAKE_TEST_API_KEY, on_error=self.set_fail, host="https://aloha.com")
+            exception = Exception("test exception")
+            client.capture_exception(exception, "distinct_id")
+
+            self.assertTrue(patch_capture.called)
+            capture_call = patch_capture.call_args[0]
+            self.assertEqual(capture_call[0], "distinct_id")
+            self.assertEqual(capture_call[1], "$exception")
+            self.assertEqual(
+                capture_call[2],
+                {
+                    "$exception_type": "Exception",
+                    "$exception_message": "test exception",
+                    "$exception_list": [
+                        {
+                            "mechanism": {"type": "generic", "handled": True},
+                            "module": None,
+                            "type": "Exception",
+                            "value": "test exception",
+                        }
+                    ],
+                    "$exception_personURL": "https://aloha.com/project/random_key/person/distinct_id",
+                },
+            )
+
+    def test_basic_capture_exception_with_correct_host_generation_for_server_hosts(self):
+
+        with mock.patch.object(Client, "capture", return_value=None) as patch_capture:
+            client = Client(FAKE_TEST_API_KEY, on_error=self.set_fail, host="https://app.posthog.com")
+            exception = Exception("test exception")
+            client.capture_exception(exception, "distinct_id")
+
+            self.assertTrue(patch_capture.called)
+            capture_call = patch_capture.call_args[0]
+            self.assertEqual(capture_call[0], "distinct_id")
+            self.assertEqual(capture_call[1], "$exception")
+            self.assertEqual(
+                capture_call[2],
+                {
+                    "$exception_type": "Exception",
+                    "$exception_message": "test exception",
+                    "$exception_list": [
+                        {
+                            "mechanism": {"type": "generic", "handled": True},
+                            "module": None,
+                            "type": "Exception",
+                            "value": "test exception",
+                        }
+                    ],
+                    "$exception_personURL": "https://app.posthog.com/project/random_key/person/distinct_id",
+                },
+            )
+
+    def test_basic_capture_exception_with_no_exception_given(self):
+
+        with mock.patch.object(Client, "capture", return_value=None) as patch_capture:
+            client = self.client
+            try:
+                raise Exception("test exception")
+            except Exception:
+                client.capture_exception(distinct_id="distinct_id")
+
+            self.assertTrue(patch_capture.called)
+            capture_call = patch_capture.call_args[0]
+            self.assertEqual(capture_call[0], "distinct_id")
+            self.assertEqual(capture_call[1], "$exception")
+            self.assertEqual(capture_call[2]["$exception_type"], "Exception")
+            self.assertEqual(capture_call[2]["$exception_message"], "test exception")
+            self.assertEqual(capture_call[2]["$exception_list"][0]["mechanism"]["type"], "generic")
+            self.assertEqual(capture_call[2]["$exception_list"][0]["mechanism"]["handled"], True)
+            self.assertEqual(capture_call[2]["$exception_list"][0]["module"], None)
+            self.assertEqual(capture_call[2]["$exception_list"][0]["type"], "Exception")
+            self.assertEqual(capture_call[2]["$exception_list"][0]["value"], "test exception")
+            self.assertEqual(
+                capture_call[2]["$exception_list"][0]["stacktrace"]["type"],
+                "raw",
+            )
+            self.assertEqual(
+                capture_call[2]["$exception_list"][0]["stacktrace"]["frames"][0]["filename"],
+                "posthog/test/test_client.py",
+            )
+            self.assertEqual(
+                capture_call[2]["$exception_list"][0]["stacktrace"]["frames"][0]["function"],
+                "test_basic_capture_exception_with_no_exception_given",
+            )
+            self.assertEqual(
+                capture_call[2]["$exception_list"][0]["stacktrace"]["frames"][0]["module"], "posthog.test.test_client"
+            )
+            self.assertEqual(capture_call[2]["$exception_list"][0]["stacktrace"]["frames"][0]["in_app"], True)
+
+    def test_basic_capture_exception_with_no_exception_happening(self):
+
+        with mock.patch.object(Client, "capture", return_value=None) as patch_capture:
+            with self.assertLogs("posthog", level="WARNING") as logs:
+
+                client = self.client
+                client.capture_exception()
+
+                self.assertFalse(patch_capture.called)
+                self.assertEqual(
+                    logs.output[0],
+                    "WARNING:posthog:No exception information available",
+                )
+
     @mock.patch("posthog.client.decide")
     def test_basic_capture_with_feature_flags(self, patch_decide):
         patch_decide.return_value = {"featureFlags": {"beta-feature": "random-variant"}}
@@ -537,6 +715,25 @@ class TestClient(unittest.TestCase):
         self.assertTrue(isinstance(msg["timestamp"], str))
         self.assertIsNone(msg.get("uuid"))
 
+    def test_basic_group_identify_with_distinct_id(self):
+        success, msg = self.client.group_identify("organization", "id:5", distinct_id="distinct_id")
+        self.assertTrue(success)
+        self.assertEqual(msg["event"], "$groupidentify")
+        self.assertEqual(msg["distinct_id"], "distinct_id")
+        self.assertEqual(
+            msg["properties"],
+            {
+                "$group_type": "organization",
+                "$group_key": "id:5",
+                "$group_set": {},
+                "$lib": "posthog-python",
+                "$lib_version": VERSION,
+                "$geoip_disable": True,
+            },
+        )
+        self.assertTrue(isinstance(msg["timestamp"], str))
+        self.assertIsNone(msg.get("uuid"))
+
     def test_advanced_group_identify(self):
         success, msg = self.client.group_identify(
             "organization", "id:5", {"trait": "value"}, {"ip": "192.168.0.1"}, datetime(2014, 9, 3), "new-uuid"
@@ -559,6 +756,35 @@ class TestClient(unittest.TestCase):
         self.assertEqual(msg["timestamp"], "2014-09-03T00:00:00+00:00")
         self.assertEqual(msg["context"]["ip"], "192.168.0.1")
 
+    def test_advanced_group_identify_with_distinct_id(self):
+        success, msg = self.client.group_identify(
+            "organization",
+            "id:5",
+            {"trait": "value"},
+            {"ip": "192.168.0.1"},
+            datetime(2014, 9, 3),
+            "new-uuid",
+            distinct_id="distinct_id",
+        )
+
+        self.assertTrue(success)
+        self.assertEqual(msg["event"], "$groupidentify")
+        self.assertEqual(msg["distinct_id"], "distinct_id")
+
+        self.assertEqual(
+            msg["properties"],
+            {
+                "$group_type": "organization",
+                "$group_key": "id:5",
+                "$group_set": {"trait": "value"},
+                "$lib": "posthog-python",
+                "$lib_version": VERSION,
+                "$geoip_disable": True,
+            },
+        )
+        self.assertEqual(msg["timestamp"], "2014-09-03T00:00:00+00:00")
+        self.assertEqual(msg["context"]["ip"], "192.168.0.1")
+
     def test_basic_alias(self):
         client = self.client
         success, msg = client.alias("previousId", "distinct_id")

posthog/test/test_exception_capture.py

@@ -29,7 +29,7 @@ def test_excepthook(tmpdir):
     assert b"LOL" in output
     assert b"DEBUG:posthog:data uploaded successfully" in output
     assert (
-        b'"$exception_list": [{"mechanism": {"type": "generic", "handled": true}, "module": null, "type": "ZeroDivisionError", "value": "division by zero", "stacktrace": {"frames": [{"filename": "app.py", "abs_path"'
+        b'"$exception_list": [{"mechanism": {"type": "generic", "handled": true}, "module": null, "type": "ZeroDivisionError", "value": "division by zero", "stacktrace": {"frames": [{"platform": "python", "filename": "app.py", "abs_path"'
         in output
     )
 
@@ -58,6 +58,6 @@ def test_trying_to_use_django_integration(tmpdir):
     assert b"LOL" in output
     assert b"DEBUG:posthog:data uploaded successfully" in output
     assert (
-        b'"$exception_list": [{"mechanism": {"type": "generic", "handled": true}, "module": null, "type": "ZeroDivisionError", "value": "division by zero", "stacktrace": {"frames": [{"filename": "app.py", "abs_path"'
+        b'"$exception_list": [{"mechanism": {"type": "generic", "handled": true}, "module": null, "type": "ZeroDivisionError", "value": "division by zero", "stacktrace": {"frames": [{"platform": "python", "filename": "app.py", "abs_path"'
         in output
     )

posthog/test/test_feature_flags.py

@@ -1632,9 +1632,10 @@ class TestLocalEvaluation(unittest.TestCase):
         )
         self.assertEqual(patch_decide.call_count, 0)
 
+    @mock.patch.object(Client, "capture")
     @mock.patch("posthog.client.decide")
-    def test_boolean_feature_flag_payload_decide(self, patch_decide):
-        patch_decide.return_value = {"featureFlagPayloads": {"person-flag": 300}}
+    def test_boolean_feature_flag_payload_decide(self, patch_decide, patch_capture):
+        patch_decide.return_value = {"featureFlags": {"person-flag": True}, "featureFlagPayloads": {"person-flag": 300}}
         self.assertEqual(
             self.client.get_feature_flag_payload(
                 "person-flag", "some-distinct-id", person_properties={"region": "USA"}
@@ -1649,6 +1650,8 @@ class TestLocalEvaluation(unittest.TestCase):
             300,
         )
         self.assertEqual(patch_decide.call_count, 2)
+        self.assertEqual(patch_capture.call_count, 1)
+        patch_capture.reset_mock()
 
     @mock.patch("posthog.client.decide")
     def test_multivariate_feature_flag_payloads(self, patch_decide):
@@ -2334,6 +2337,88 @@ class TestCaptureCalls(unittest.TestCase):
             disable_geoip=None,
         )
 
+    @mock.patch.object(Client, "capture")
+    @mock.patch("posthog.client.decide")
+    def test_capture_is_called_in_get_feature_flag_payload(self, patch_decide, patch_capture):
+        patch_decide.return_value = {
+            "featureFlags": {"person-flag": True},
+            "featureFlagPayloads": {"person-flag": 300},
+        }
+        client = Client(api_key=FAKE_TEST_API_KEY, personal_api_key=FAKE_TEST_API_KEY)
+
+        client.feature_flags = [
+            {
+                "id": 1,
+                "name": "Beta Feature",
+                "key": "person-flag",
+                "is_simple_flag": False,
+                "active": True,
+                "filters": {
+                    "groups": [
+                        {
+                            "properties": [{"key": "region", "value": "USA"}],
+                            "rollout_percentage": 100,
+                        }
+                    ],
+                },
+            }
+        ]
+
+        # Call get_feature_flag_payload with match_value=None to trigger get_feature_flag
+        client.get_feature_flag_payload(
+            key="person-flag", distinct_id="some-distinct-id", person_properties={"region": "USA", "name": "Aloha"}
+        )
+
+        # Assert that capture was called once, with the correct parameters
+        self.assertEqual(patch_capture.call_count, 1)
+        patch_capture.assert_called_with(
+            "some-distinct-id",
+            "$feature_flag_called",
+            {
+                "$feature_flag": "person-flag",
+                "$feature_flag_response": True,
+                "$feature_flag_payload": 300,
+                "locally_evaluated": False,
+                "$feature/person-flag": True,
+            },
+            groups={},
+            disable_geoip=None,
+        )
+
+        # Reset mocks for further tests
+        patch_capture.reset_mock()
+        patch_decide.reset_mock()
+
+        # Call get_feature_flag_payload again for the same user; capture should not be called again because we've already reported an event for this distinct_id + flag
+        client.get_feature_flag_payload(
+            key="person-flag", distinct_id="some-distinct-id", person_properties={"region": "USA", "name": "Aloha"}
+        )
+
+        self.assertEqual(patch_capture.call_count, 0)
+        patch_capture.reset_mock()
+
+        # Call get_feature_flag_payload for a different user; capture should be called
+        client.get_feature_flag_payload(
+            key="person-flag", distinct_id="some-distinct-id2", person_properties={"region": "USA", "name": "Aloha"}
+        )
+
+        self.assertEqual(patch_capture.call_count, 1)
+        patch_capture.assert_called_with(
+            "some-distinct-id2",
+            "$feature_flag_called",
+            {
+                "$feature_flag": "person-flag",
+                "$feature_flag_response": True,
+                "$feature_flag_payload": 300,
+                "locally_evaluated": False,
+                "$feature/person-flag": True,
+            },
+            groups={},
+            disable_geoip=None,
+        )
+
+        patch_capture.reset_mock()
+
     @mock.patch.object(Client, "capture")
     @mock.patch("posthog.client.decide")
     def test_disable_geoip_get_flag_capture_call(self, patch_decide, patch_capture):

posthog/version.py

@@ -1,4 +1,4 @@
-VERSION = "3.6.2"
+VERSION = "3.8.4"
 
 if __name__ == "__main__":
     print(VERSION, end="")  # noqa: T201

setup.cfg

@@ -1,2 +1,5 @@
 [bdist_wheel]
 universal = 1
+
+[tool:pytest]
+asyncio_mode = auto

setup.py

@@ -14,7 +14,13 @@ long_description = """
 PostHog is developer-friendly, self-hosted product analytics. posthog-python is the python package.
 """
 
-install_requires = ["requests>=2.7,<3.0", "six>=1.5", "monotonic>=1.5", "backoff>=1.10.0", "python-dateutil>2.1"]
+install_requires = [
+    "requests>=2.7,<3.0",
+    "six>=1.5",
+    "monotonic>=1.5",
+    "backoff>=1.10.0",
+    "python-dateutil>2.1",
+]
 
 extras_require = {
     "dev": [
@@ -24,8 +30,24 @@ extras_require = {
         "flake8-print",
         "pre-commit",
     ],
-    "test": ["mock>=2.0.0", "freezegun==0.3.15", "pylint", "flake8", "coverage", "pytest", "pytest-timeout", "django"],
+    "test": [
+        "mock>=2.0.0",
+        "freezegun==0.3.15",
+        "pylint",
+        "flake8",
+        "coverage",
+        "pytest",
+        "pytest-timeout",
+        "pytest-asyncio",
+        "django",
+        "openai",
+        "anthropic",
+        "langchain-community>=0.2.0",
+        "langchain-openai>=0.2.0",
+        "langchain-anthropic>=0.2.0",
+    ],
     "sentry": ["sentry-sdk", "django"],
+    "langchain": ["langchain>=0.2.0"],
 }
 
 setup(
@@ -37,7 +59,16 @@ setup(
     maintainer="PostHog",
     maintainer_email="hey@posthog.com",
     test_suite="posthog.test.all",
-    packages=["posthog", "posthog.test", "posthog.sentry", "posthog.exception_integrations"],
+    packages=[
+        "posthog",
+        "posthog.ai",
+        "posthog.ai.langchain",
+        "posthog.ai.openai",
+        "posthog.ai.anthropic",
+        "posthog.test",
+        "posthog.sentry",
+        "posthog.exception_integrations",
+    ],
     license="MIT License",
     install_requires=install_requires,
     extras_require=extras_require,
@@ -60,5 +91,8 @@ setup(
         "Programming Language :: Python :: 3.6",
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
     ],
 )

setup_analytics.py

@@ -27,7 +27,16 @@ setup(
     maintainer="PostHog",
     maintainer_email="hey@posthog.com",
     test_suite="posthoganalytics.test.all",
-    packages=["posthoganalytics", "posthoganalytics.test", "posthoganalytics.sentry"],
+    packages=[
+        "posthoganalytics",
+        "posthoganalytics.ai",
+        "posthoganalytics.ai.langchain",
+        "posthoganalytics.ai.openai",
+        "posthoganalytics.ai.anthropic",
+        "posthoganalytics.test",
+        "posthoganalytics.sentry",
+        "posthoganalytics.exception_integrations",
+    ],
     license="MIT License",
     install_requires=install_requires,
     tests_require=tests_require,
@@ -53,5 +62,8 @@ setup(
         "Programming Language :: Python :: 3.6",
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
     ],
 )