feat: support gemini (#237)

.github/workflows/release.yaml

@@ -1,38 +1,43 @@
-name: 'Release'
+name: "Release"
 
 on:
-  - workflow_dispatch
+  push:
+    branches:
+      - master
+    paths:
+      - "posthog/version.py"
+  workflow_dispatch:
 
 jobs:
-    release:
-        name: Publish release
-        runs-on: ubuntu-latest
+  release:
+    name: Publish release
+    runs-on: ubuntu-latest
+    env:
+      TWINE_USERNAME: __token__
+      TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+    steps:
+      - name: Checkout the repository
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+          token: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+
+      - name: Detect version
+        run: echo "REPO_VERSION=$(python3 posthog/version.py)" >> $GITHUB_ENV
+
+      - name: Prepare for building release
+        run: pip install -U pip setuptools wheel twine
+
+      - name: Push release to PyPI
+        run: make release && make release_analytics
+
+      - name: Create GitHub release
+        uses: actions/create-release@v1
         env:
-            TWINE_USERNAME: __token__
-            TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
-        steps:
-            - name: Checkout the repository
-              uses: actions/checkout@v2
-              with:
-                  fetch-depth: 0
-                  token: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
-
-            - name: Set up Python
-              uses: actions/setup-python@v2
-
-            - name: Detect version
-              run: echo "REPO_VERSION=$(python3 posthog/version.py)" >> $GITHUB_ENV
-
-            - name: Prepare for building release
-              run: pip install -U pip setuptools wheel twine
-
-            - name: Push release to PyPI
-              run: make release && make release_analytics
-
-            - name: Create GitHub release
-              uses: actions/create-release@v1
-              env:
-                  GITHUB_TOKEN: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
-              with:
-                  tag_name: v${{ env.REPO_VERSION }}
-                  release_name: ${{ env.REPO_VERSION }}
+          GITHUB_TOKEN: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
+        with:
+          tag_name: v${{ env.REPO_VERSION }}
+          release_name: ${{ env.REPO_VERSION }}

CHANGELOG.md

@@ -1,3 +1,16 @@
+## 4.2.0 - 2025-05-22
+
+Add support for google gemini
+
+## 4.1.0 - 2025-05-22
+
+Moved ai openai package to a composition approach over inheritance.
+
+## 4.0.1 – 2025-04-29
+
+1. Remove deprecated `monotonic` library. Use Python's core `time.monotonic` function instead
+2. Clarify Python 3.9+ is required
+
 ## 4.0.0 - 2025-04-24
 
 1. Added new method `get_feature_flag_result` which returns a `FeatureFlagResult` object. This object breaks down the result of a feature flag into its enabled state, variant, and payload. The benefit of this method is it allows you to retrieve the result of a feature flag and its payload in a single API call. You can call `get_value` on the result to get the value of the feature flag, which is the same value returned by `get_feature_flag` (aka the string `variant` if the flag is a multivariate flag or the `boolean` value if the flag is a boolean flag).

posthog/ai/gemini/__init__.py

@@ -0,0 +1,11 @@
+from .gemini import Client
+
+
+# Create a genai-like module for perfect drop-in replacement
+class _GenAI:
+    Client = Client
+
+
+genai = _GenAI()
+
+__all__ = ["Client", "genai"]

posthog/ai/gemini/gemini.py

@@ -0,0 +1,336 @@
+import os
+import time
+import uuid
+from typing import Any, Dict, Optional
+
+try:
+    from google import genai
+except ImportError:
+    raise ModuleNotFoundError("Please install the Google Gemini SDK to use this feature: 'pip install google-genai'")
+
+from posthog.ai.utils import call_llm_and_track_usage, get_model_params, with_privacy_mode
+from posthog.client import Client as PostHogClient
+
+
+class Client:
+    """
+    A drop-in replacement for genai.Client that automatically sends LLM usage events to PostHog.
+
+    Usage:
+        client = Client(
+            api_key="your_api_key",
+            posthog_client=posthog_client,
+            posthog_distinct_id="default_user",  # Optional defaults
+            posthog_properties={"team": "ai"}    # Optional defaults
+        )
+        response = client.models.generate_content(
+            model="gemini-2.0-flash",
+            contents=["Hello world"],
+            posthog_distinct_id="specific_user"  # Override default
+        )
+    """
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        posthog_client: Optional[PostHogClient] = None,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable
+            posthog_client: PostHog client for tracking usage
+            posthog_distinct_id: Default distinct ID for all calls (can be overridden per call)
+            posthog_properties: Default properties for all calls (can be overridden per call)
+            posthog_privacy_mode: Default privacy mode for all calls (can be overridden per call)
+            posthog_groups: Default groups for all calls (can be overridden per call)
+            **kwargs: Additional arguments (for future compatibility)
+        """
+        if posthog_client is None:
+            raise ValueError("posthog_client is required for PostHog tracking")
+
+        self.models = Models(
+            api_key=api_key,
+            posthog_client=posthog_client,
+            posthog_distinct_id=posthog_distinct_id,
+            posthog_properties=posthog_properties,
+            posthog_privacy_mode=posthog_privacy_mode,
+            posthog_groups=posthog_groups,
+            **kwargs,
+        )
+
+
+class Models:
+    """
+    Models interface that mimics genai.Client().models with PostHog tracking.
+    """
+
+    _ph_client: PostHogClient  # Not None after __init__ validation
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        posthog_client: Optional[PostHogClient] = None,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: bool = False,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            api_key: Google AI API key. If not provided, will use GOOGLE_API_KEY or API_KEY environment variable
+            posthog_client: PostHog client for tracking usage
+            posthog_distinct_id: Default distinct ID for all calls
+            posthog_properties: Default properties for all calls
+            posthog_privacy_mode: Default privacy mode for all calls
+            posthog_groups: Default groups for all calls
+            **kwargs: Additional arguments (for future compatibility)
+        """
+        if posthog_client is None:
+            raise ValueError("posthog_client is required for PostHog tracking")
+
+        self._ph_client = posthog_client
+
+        # Store default PostHog settings
+        self._default_distinct_id = posthog_distinct_id
+        self._default_properties = posthog_properties or {}
+        self._default_privacy_mode = posthog_privacy_mode
+        self._default_groups = posthog_groups
+
+        # Handle API key - try parameter first, then environment variables
+        if api_key is None:
+            api_key = os.environ.get("GOOGLE_API_KEY") or os.environ.get("API_KEY")
+
+        if api_key is None:
+            raise ValueError(
+                "API key must be provided either as parameter or via GOOGLE_API_KEY/API_KEY environment variable"
+            )
+
+        self._client = genai.Client(api_key=api_key)
+        self._base_url = "https://generativelanguage.googleapis.com"
+
+    def _merge_posthog_params(
+        self,
+        call_distinct_id: Optional[str],
+        call_trace_id: Optional[str],
+        call_properties: Optional[Dict[str, Any]],
+        call_privacy_mode: Optional[bool],
+        call_groups: Optional[Dict[str, Any]],
+    ):
+        """Merge call-level PostHog parameters with client defaults."""
+        # Use call-level values if provided, otherwise fall back to defaults
+        distinct_id = call_distinct_id if call_distinct_id is not None else self._default_distinct_id
+        privacy_mode = call_privacy_mode if call_privacy_mode is not None else self._default_privacy_mode
+        groups = call_groups if call_groups is not None else self._default_groups
+
+        # Merge properties: default properties + call properties (call properties override)
+        properties = dict(self._default_properties)
+        if call_properties:
+            properties.update(call_properties)
+
+        if call_trace_id is None:
+            call_trace_id = str(uuid.uuid4())
+
+        return distinct_id, call_trace_id, properties, privacy_mode, groups
+
+    def generate_content(
+        self,
+        model: str,
+        contents,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: Optional[bool] = None,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        """
+        Generate content using Gemini's API while tracking usage in PostHog.
+
+        This method signature exactly matches genai.Client().models.generate_content()
+        with additional PostHog tracking parameters.
+
+        Args:
+            model: The model to use (e.g., 'gemini-2.0-flash')
+            contents: The input content for generation
+            posthog_distinct_id: ID to associate with the usage event (overrides client default)
+            posthog_trace_id: Trace UUID for linking events (auto-generated if not provided)
+            posthog_properties: Extra properties to include in the event (merged with client defaults)
+            posthog_privacy_mode: Whether to redact sensitive information (overrides client default)
+            posthog_groups: Group analytics properties (overrides client default)
+            **kwargs: Arguments passed to Gemini's generate_content
+        """
+        # Merge PostHog parameters
+        distinct_id, trace_id, properties, privacy_mode, groups = self._merge_posthog_params(
+            posthog_distinct_id, posthog_trace_id, posthog_properties, posthog_privacy_mode, posthog_groups
+        )
+
+        kwargs_with_contents = {"model": model, "contents": contents, **kwargs}
+
+        return call_llm_and_track_usage(
+            distinct_id,
+            self._ph_client,
+            "gemini",
+            trace_id,
+            properties,
+            privacy_mode,
+            groups,
+            self._base_url,
+            self._client.models.generate_content,
+            **kwargs_with_contents,
+        )
+
+    def _generate_content_streaming(
+        self,
+        model: str,
+        contents,
+        distinct_id: Optional[str],
+        trace_id: Optional[str],
+        properties: Optional[Dict[str, Any]],
+        privacy_mode: bool,
+        groups: Optional[Dict[str, Any]],
+        **kwargs: Any,
+    ):
+        start_time = time.time()
+        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        accumulated_content = []
+
+        kwargs_without_stream = {"model": model, "contents": contents, **kwargs}
+        response = self._client.models.generate_content_stream(**kwargs_without_stream)
+
+        def generator():
+            nonlocal usage_stats
+            nonlocal accumulated_content  # noqa: F824
+            try:
+                for chunk in response:
+                    if hasattr(chunk, "usage_metadata") and chunk.usage_metadata:
+                        usage_stats = {
+                            "input_tokens": getattr(chunk.usage_metadata, "prompt_token_count", 0),
+                            "output_tokens": getattr(chunk.usage_metadata, "candidates_token_count", 0),
+                        }
+
+                    if hasattr(chunk, "text") and chunk.text:
+                        accumulated_content.append(chunk.text)
+
+                    yield chunk
+
+            finally:
+                end_time = time.time()
+                latency = end_time - start_time
+                output = "".join(accumulated_content)
+
+                self._capture_streaming_event(
+                    model,
+                    contents,
+                    distinct_id,
+                    trace_id,
+                    properties,
+                    privacy_mode,
+                    groups,
+                    kwargs,
+                    usage_stats,
+                    latency,
+                    output,
+                )
+
+        return generator()
+
+    def _capture_streaming_event(
+        self,
+        model: str,
+        contents,
+        distinct_id: Optional[str],
+        trace_id: Optional[str],
+        properties: Optional[Dict[str, Any]],
+        privacy_mode: bool,
+        groups: Optional[Dict[str, Any]],
+        kwargs: Dict[str, Any],
+        usage_stats: Dict[str, int],
+        latency: float,
+        output: str,
+    ):
+        if trace_id is None:
+            trace_id = str(uuid.uuid4())
+
+        event_properties = {
+            "$ai_provider": "gemini",
+            "$ai_model": model,
+            "$ai_model_parameters": get_model_params(kwargs),
+            "$ai_input": with_privacy_mode(
+                self._ph_client,
+                privacy_mode,
+                self._format_input(contents),
+            ),
+            "$ai_output_choices": with_privacy_mode(
+                self._ph_client,
+                privacy_mode,
+                [{"content": output, "role": "assistant"}],
+            ),
+            "$ai_http_status": 200,
+            "$ai_input_tokens": usage_stats.get("input_tokens", 0),
+            "$ai_output_tokens": usage_stats.get("output_tokens", 0),
+            "$ai_latency": latency,
+            "$ai_trace_id": trace_id,
+            "$ai_base_url": self._base_url,
+            **(properties or {}),
+        }
+
+        if distinct_id is None:
+            event_properties["$process_person_profile"] = False
+
+        if hasattr(self._ph_client, "capture"):
+            self._ph_client.capture(
+                distinct_id=distinct_id,
+                event="$ai_generation",
+                properties=event_properties,
+                groups=groups,
+            )
+
+    def _format_input(self, contents):
+        """Format input contents for PostHog tracking"""
+        if isinstance(contents, str):
+            return [{"role": "user", "content": contents}]
+        elif isinstance(contents, list):
+            formatted = []
+            for item in contents:
+                if isinstance(item, str):
+                    formatted.append({"role": "user", "content": item})
+                elif hasattr(item, "text"):
+                    formatted.append({"role": "user", "content": item.text})
+                else:
+                    formatted.append({"role": "user", "content": str(item)})
+            return formatted
+        else:
+            return [{"role": "user", "content": str(contents)}]
+
+    def generate_content_stream(
+        self,
+        model: str,
+        contents,
+        posthog_distinct_id: Optional[str] = None,
+        posthog_trace_id: Optional[str] = None,
+        posthog_properties: Optional[Dict[str, Any]] = None,
+        posthog_privacy_mode: Optional[bool] = None,
+        posthog_groups: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ):
+        # Merge PostHog parameters
+        distinct_id, trace_id, properties, privacy_mode, groups = self._merge_posthog_params(
+            posthog_distinct_id, posthog_trace_id, posthog_properties, posthog_privacy_mode, posthog_groups
+        )
+
+        return self._generate_content_streaming(
+            model,
+            contents,
+            distinct_id,
+            trace_id,
+            properties,
+            privacy_mode,
+            groups,
+            **kwargs,
+        )

posthog/ai/openai/openai.py

@@ -4,7 +4,6 @@ from typing import Any, Dict, List, Optional
 
 try:
     import openai
-    import openai.resources
 except ImportError:
     raise ModuleNotFoundError("Please install the OpenAI SDK to use this feature: 'pip install openai'")
 
@@ -29,14 +28,37 @@ class OpenAI(openai.OpenAI):
         """
         super().__init__(**kwargs)
         self._ph_client = posthog_client
-        self.chat = WrappedChat(self)
-        self.embeddings = WrappedEmbeddings(self)
-        self.beta = WrappedBeta(self)
-        self.responses = WrappedResponses(self)
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = WrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = WrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = WrappedBeta(self, self._original_beta)
+
+        if self._original_responses is not None:
+            self.responses = WrappedResponses(self, self._original_responses)
 
 
-class WrappedResponses(openai.resources.responses.Responses):
-    _client: OpenAI
+class WrappedResponses:
+    """Wrapper for OpenAI responses that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_responses):
+        self._client = client
+        self._original = original_responses
+
+    def __getattr__(self, name):
+        """Fallback to original responses object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     def create(
         self,
@@ -69,7 +91,7 @@ class WrappedResponses(openai.resources.responses.Responses):
             posthog_privacy_mode,
             posthog_groups,
             self._client.base_url,
-            super().create,
+            self._original.create,
             **kwargs,
         )
 
@@ -85,7 +107,7 @@ class WrappedResponses(openai.resources.responses.Responses):
         start_time = time.time()
         usage_stats: Dict[str, int] = {}
         final_content = []
-        response = super().create(**kwargs)
+        response = self._original.create(**kwargs)
 
         def generator():
             nonlocal usage_stats
@@ -195,16 +217,32 @@ class WrappedResponses(openai.resources.responses.Responses):
             )
 
 
-class WrappedChat(openai.resources.chat.Chat):
-    _client: OpenAI
+class WrappedChat:
+    """Wrapper for OpenAI chat that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_chat):
+        self._client = client
+        self._original = original_chat
+
+    def __getattr__(self, name):
+        """Fallback to original chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     @property
     def completions(self):
-        return WrappedCompletions(self._client)
+        return WrappedCompletions(self._client, self._original.completions)
 
 
-class WrappedCompletions(openai.resources.chat.completions.Completions):
-    _client: OpenAI
+class WrappedCompletions:
+    """Wrapper for OpenAI chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_completions):
+        self._client = client
+        self._original = original_completions
+
+    def __getattr__(self, name):
+        """Fallback to original completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     def create(
         self,
@@ -237,7 +275,7 @@ class WrappedCompletions(openai.resources.chat.completions.Completions):
             posthog_privacy_mode,
             posthog_groups,
             self._client.base_url,
-            super().create,
+            self._original.create,
             **kwargs,
         )
 
@@ -257,7 +295,7 @@ class WrappedCompletions(openai.resources.chat.completions.Completions):
         if "stream_options" not in kwargs:
             kwargs["stream_options"] = {}
         kwargs["stream_options"]["include_usage"] = True
-        response = super().create(**kwargs)
+        response = self._original.create(**kwargs)
 
         def generator():
             nonlocal usage_stats
@@ -383,8 +421,16 @@ class WrappedCompletions(openai.resources.chat.completions.Completions):
             )
 
 
-class WrappedEmbeddings(openai.resources.embeddings.Embeddings):
-    _client: OpenAI
+class WrappedEmbeddings:
+    """Wrapper for OpenAI embeddings that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_embeddings):
+        self._client = client
+        self._original = original_embeddings
+
+    def __getattr__(self, name):
+        """Fallback to original embeddings object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     def create(
         self,
@@ -402,6 +448,8 @@ class WrappedEmbeddings(openai.resources.embeddings.Embeddings):
             posthog_distinct_id: Optional ID to associate with the usage event.
             posthog_trace_id: Optional trace UUID for linking events.
             posthog_properties: Optional dictionary of extra properties to include in the event.
+            posthog_privacy_mode: Whether to anonymize the input and output.
+            posthog_groups: Optional dictionary of groups to associate with the event.
             **kwargs: Any additional parameters for the OpenAI Embeddings API.
 
         Returns:
@@ -411,7 +459,7 @@ class WrappedEmbeddings(openai.resources.embeddings.Embeddings):
             posthog_trace_id = str(uuid.uuid4())
 
         start_time = time.time()
-        response = super().create(**kwargs)
+        response = self._original.create(**kwargs)
         end_time = time.time()
 
         # Extract usage statistics if available
@@ -452,24 +500,48 @@ class WrappedEmbeddings(openai.resources.embeddings.Embeddings):
         return response
 
 
-class WrappedBeta(openai.resources.beta.Beta):
-    _client: OpenAI
+class WrappedBeta:
+    """Wrapper for OpenAI beta features that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_beta):
+        self._client = client
+        self._original = original_beta
+
+    def __getattr__(self, name):
+        """Fallback to original beta object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     @property
     def chat(self):
-        return WrappedBetaChat(self._client)
+        return WrappedBetaChat(self._client, self._original.chat)
 
 
-class WrappedBetaChat(openai.resources.beta.chat.Chat):
-    _client: OpenAI
+class WrappedBetaChat:
+    """Wrapper for OpenAI beta chat that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_beta_chat):
+        self._client = client
+        self._original = original_beta_chat
+
+    def __getattr__(self, name):
+        """Fallback to original beta chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     @property
     def completions(self):
-        return WrappedBetaCompletions(self._client)
+        return WrappedBetaCompletions(self._client, self._original.completions)
 
 
-class WrappedBetaCompletions(openai.resources.beta.chat.completions.Completions):
-    _client: OpenAI
+class WrappedBetaCompletions:
+    """Wrapper for OpenAI beta chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: OpenAI, original_beta_completions):
+        self._client = client
+        self._original = original_beta_completions
+
+    def __getattr__(self, name):
+        """Fallback to original beta completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     def parse(
         self,
@@ -489,6 +561,6 @@ class WrappedBetaCompletions(openai.resources.beta.chat.completions.Completions)
             posthog_privacy_mode,
             posthog_groups,
             self._client.base_url,
-            super().parse,
+            self._original.parse,
             **kwargs,
         )

posthog/ai/openai/openai_async.py

@@ -4,7 +4,6 @@ from typing import Any, Dict, List, Optional
 
 try:
     import openai
-    import openai.resources
 except ImportError:
     raise ModuleNotFoundError("Please install the OpenAI SDK to use this feature: 'pip install openai'")
 
@@ -23,19 +22,43 @@ class AsyncOpenAI(openai.AsyncOpenAI):
         """
         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").
+            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)
-        self.beta = WrappedBeta(self)
-        self.responses = WrappedResponses(self)
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = WrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = WrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = WrappedBeta(self, self._original_beta)
+
+        if self._original_responses is not None:
+            self.responses = WrappedResponses(self, self._original_responses)
 
 
-class WrappedResponses(openai.resources.responses.Responses):
-    _client: AsyncOpenAI
+class WrappedResponses:
+    """Async wrapper for OpenAI responses that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_responses):
+        self._client = client
+        self._original = original_responses
+
+    def __getattr__(self, name):
+        """Fallback to original responses object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     async def create(
         self,
@@ -68,7 +91,7 @@ class WrappedResponses(openai.resources.responses.Responses):
             posthog_privacy_mode,
             posthog_groups,
             self._client.base_url,
-            super().create,
+            self._original.create,
             **kwargs,
         )
 
@@ -84,7 +107,7 @@ class WrappedResponses(openai.resources.responses.Responses):
         start_time = time.time()
         usage_stats: Dict[str, int] = {}
         final_content = []
-        response = await super().create(**kwargs)
+        response = await self._original.create(**kwargs)
 
         async def async_generator():
             nonlocal usage_stats
@@ -186,7 +209,7 @@ class WrappedResponses(openai.resources.responses.Responses):
             event_properties["$process_person_profile"] = False
 
         if hasattr(self._client._ph_client, "capture"):
-            await 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,
@@ -194,16 +217,32 @@ class WrappedResponses(openai.resources.responses.Responses):
             )
 
 
-class WrappedChat(openai.resources.chat.AsyncChat):
-    _client: AsyncOpenAI
+class WrappedChat:
+    """Async wrapper for OpenAI chat that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_chat):
+        self._client = client
+        self._original = original_chat
+
+    def __getattr__(self, name):
+        """Fallback to original chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     @property
     def completions(self):
-        return WrappedCompletions(self._client)
+        return WrappedCompletions(self._client, self._original.completions)
 
 
-class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
-    _client: AsyncOpenAI
+class WrappedCompletions:
+    """Async wrapper for OpenAI chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_completions):
+        self._client = client
+        self._original = original_completions
+
+    def __getattr__(self, name):
+        """Fallback to original completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     async def create(
         self,
@@ -237,7 +276,7 @@ class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
             posthog_privacy_mode,
             posthog_groups,
             self._client.base_url,
-            super().create,
+            self._original.create,
             **kwargs,
         )
         return response
@@ -247,21 +286,25 @@ class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
         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,
+        posthog_privacy_mode: bool,
+        posthog_groups: Optional[Dict[str, Any]],
         **kwargs: Any,
     ):
         start_time = time.time()
         usage_stats: Dict[str, int] = {}
         accumulated_content = []
         accumulated_tools = {}
+
         if "stream_options" not in kwargs:
             kwargs["stream_options"] = {}
         kwargs["stream_options"]["include_usage"] = True
-        response = await super().create(**kwargs)
+        response = await self._original.create(**kwargs)
 
         async def async_generator():
-            nonlocal usage_stats, accumulated_content, accumulated_tools  # noqa: F824
+            nonlocal usage_stats
+            nonlocal accumulated_content  # noqa: F824
+            nonlocal accumulated_tools  # noqa: F824
+
             try:
                 async for chunk in response:
                     if hasattr(chunk, "usage") and chunk.usage:
@@ -280,6 +323,11 @@ class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
                         ):
                             usage_stats["cache_read_input_tokens"] = chunk.usage.prompt_tokens_details.cached_tokens
 
+                        if hasattr(chunk.usage, "output_tokens_details") and hasattr(
+                            chunk.usage.output_tokens_details, "reasoning_tokens"
+                        ):
+                            usage_stats["reasoning_tokens"] = chunk.usage.output_tokens_details.reasoning_tokens
+
                     if hasattr(chunk, "choices") and chunk.choices and len(chunk.choices) > 0:
                         if chunk.choices[0].delta and chunk.choices[0].delta.content:
                             content = chunk.choices[0].delta.content
@@ -350,6 +398,7 @@ class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
             "$ai_input_tokens": usage_stats.get("prompt_tokens", 0),
             "$ai_output_tokens": usage_stats.get("completion_tokens", 0),
             "$ai_cache_read_input_tokens": usage_stats.get("cache_read_input_tokens", 0),
+            "$ai_reasoning_tokens": usage_stats.get("reasoning_tokens", 0),
             "$ai_latency": latency,
             "$ai_trace_id": posthog_trace_id,
             "$ai_base_url": str(self._client.base_url),
@@ -367,7 +416,7 @@ class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
             event_properties["$process_person_profile"] = False
 
         if hasattr(self._client._ph_client, "capture"):
-            await 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,
@@ -375,8 +424,16 @@ class WrappedCompletions(openai.resources.chat.completions.AsyncCompletions):
             )
 
 
-class WrappedEmbeddings(openai.resources.embeddings.AsyncEmbeddings):
-    _client: AsyncOpenAI
+class WrappedEmbeddings:
+    """Async wrapper for OpenAI embeddings that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_embeddings):
+        self._client = client
+        self._original = original_embeddings
+
+    def __getattr__(self, name):
+        """Fallback to original embeddings object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     async def create(
         self,
@@ -394,8 +451,8 @@ class WrappedEmbeddings(openai.resources.embeddings.AsyncEmbeddings):
             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.
+            posthog_privacy_mode: Whether to anonymize the input and output.
+            posthog_groups: Optional dictionary of groups to associate with the event.
             **kwargs: Any additional parameters for the OpenAI Embeddings API.
 
         Returns:
@@ -405,7 +462,7 @@ class WrappedEmbeddings(openai.resources.embeddings.AsyncEmbeddings):
             posthog_trace_id = str(uuid.uuid4())
 
         start_time = time.time()
-        response = await super().create(**kwargs)
+        response = await self._original.create(**kwargs)
         end_time = time.time()
 
         # Extract usage statistics if available
@@ -446,24 +503,48 @@ class WrappedEmbeddings(openai.resources.embeddings.AsyncEmbeddings):
         return response
 
 
-class WrappedBeta(openai.resources.beta.AsyncBeta):
-    _client: AsyncOpenAI
+class WrappedBeta:
+    """Async wrapper for OpenAI beta features that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_beta):
+        self._client = client
+        self._original = original_beta
+
+    def __getattr__(self, name):
+        """Fallback to original beta object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     @property
     def chat(self):
-        return WrappedBetaChat(self._client)
+        return WrappedBetaChat(self._client, self._original.chat)
 
 
-class WrappedBetaChat(openai.resources.beta.chat.AsyncChat):
-    _client: AsyncOpenAI
+class WrappedBetaChat:
+    """Async wrapper for OpenAI beta chat that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_beta_chat):
+        self._client = client
+        self._original = original_beta_chat
+
+    def __getattr__(self, name):
+        """Fallback to original beta chat object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     @property
     def completions(self):
-        return WrappedBetaCompletions(self._client)
+        return WrappedBetaCompletions(self._client, self._original.completions)
 
 
-class WrappedBetaCompletions(openai.resources.beta.chat.completions.AsyncCompletions):
-    _client: AsyncOpenAI
+class WrappedBetaCompletions:
+    """Async wrapper for OpenAI beta chat completions that tracks usage in PostHog."""
+
+    def __init__(self, client: AsyncOpenAI, original_beta_completions):
+        self._client = client
+        self._original = original_beta_completions
+
+    def __getattr__(self, name):
+        """Fallback to original beta completions object for any methods we don't explicitly handle."""
+        return getattr(self._original, name)
 
     async def parse(
         self,
@@ -483,6 +564,6 @@ class WrappedBetaCompletions(openai.resources.beta.chat.completions.AsyncComplet
             posthog_privacy_mode,
             posthog_groups,
             self._client.base_url,
-            super().parse,
+            self._original.parse,
             **kwargs,
         )

posthog/ai/openai/openai_providers.py

@@ -1,13 +1,13 @@
 try:
     import openai
-    import openai.resources
 except ImportError:
     raise ModuleNotFoundError("Please install the Open AI SDK to use this feature: 'pip install openai'")
 
-from posthog.ai.openai.openai import WrappedBeta, WrappedChat, WrappedEmbeddings
+from posthog.ai.openai.openai import WrappedBeta, WrappedChat, WrappedEmbeddings, WrappedResponses
 from posthog.ai.openai.openai_async import WrappedBeta as AsyncWrappedBeta
 from posthog.ai.openai.openai_async import WrappedChat as AsyncWrappedChat
 from posthog.ai.openai.openai_async import WrappedEmbeddings as AsyncWrappedEmbeddings
+from posthog.ai.openai.openai_async import WrappedResponses as AsyncWrappedResponses
 from posthog.client import Client as PostHogClient
 
 
@@ -19,23 +19,70 @@ class AzureOpenAI(openai.AzureOpenAI):
     _ph_client: PostHogClient
 
     def __init__(self, posthog_client: PostHogClient, **kwargs):
+        """
+        Args:
+            api_key: Azure OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead
+                            of the global posthog.
+            **openai_config: Any additional keyword args to set on Azure OpenAI (e.g. azure_endpoint="xxx").
+        """
         super().__init__(**kwargs)
         self._ph_client = posthog_client
-        self.chat = WrappedChat(self)
-        self.embeddings = WrappedEmbeddings(self)
-        self.beta = WrappedBeta(self)
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = WrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = WrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = WrappedBeta(self, self._original_beta)
+
+        if self._original_responses is not None:
+            self.responses = WrappedResponses(self, self._original_responses)
 
 
 class AsyncAzureOpenAI(openai.AsyncAzureOpenAI):
     """
-    A wrapper around the Azure OpenAI SDK that automatically sends LLM usage events to PostHog.
+    An async wrapper around the Azure OpenAI SDK that automatically sends LLM usage events to PostHog.
     """
 
     _ph_client: PostHogClient
 
     def __init__(self, posthog_client: PostHogClient, **kwargs):
+        """
+        Args:
+            api_key: Azure OpenAI API key.
+            posthog_client: If provided, events will be captured via this client instead
+                            of the global posthog.
+            **openai_config: Any additional keyword args to set on Azure OpenAI (e.g. azure_endpoint="xxx").
+        """
         super().__init__(**kwargs)
         self._ph_client = posthog_client
-        self.chat = AsyncWrappedChat(self)
-        self.embeddings = AsyncWrappedEmbeddings(self)
-        self.beta = AsyncWrappedBeta(self)
+
+        # Store original objects after parent initialization (only if they exist)
+        self._original_chat = getattr(self, "chat", None)
+        self._original_embeddings = getattr(self, "embeddings", None)
+        self._original_beta = getattr(self, "beta", None)
+        self._original_responses = getattr(self, "responses", None)
+
+        # Replace with wrapped versions (only if originals exist)
+        if self._original_chat is not None:
+            self.chat = AsyncWrappedChat(self, self._original_chat)
+
+        if self._original_embeddings is not None:
+            self.embeddings = AsyncWrappedEmbeddings(self, self._original_embeddings)
+
+        if self._original_beta is not None:
+            self.beta = AsyncWrappedBeta(self, self._original_beta)
+
+        # Only add responses if available (newer OpenAI versions)
+        if self._original_responses is not None:
+            self.responses = AsyncWrappedResponses(self, self._original_responses)

posthog/ai/utils.py

@@ -73,6 +73,21 @@ def get_usage(response, provider: str) -> Dict[str, Any]:
             "cache_read_input_tokens": cached_tokens,
             "reasoning_tokens": reasoning_tokens,
         }
+    elif provider == "gemini":
+        input_tokens = 0
+        output_tokens = 0
+
+        if hasattr(response, "usage_metadata") and response.usage_metadata:
+            input_tokens = getattr(response.usage_metadata, "prompt_token_count", 0)
+            output_tokens = getattr(response.usage_metadata, "candidates_token_count", 0)
+
+        return {
+            "input_tokens": input_tokens,
+            "output_tokens": output_tokens,
+            "cache_read_input_tokens": 0,
+            "cache_creation_input_tokens": 0,
+            "reasoning_tokens": 0,
+        }
     return {
         "input_tokens": 0,
         "output_tokens": 0,
@@ -93,6 +108,8 @@ def format_response(response, provider: str):
         return format_response_anthropic(response)
     elif provider == "openai":
         return format_response_openai(response)
+    elif provider == "gemini":
+        return format_response_gemini(response)
     return output
 
 
@@ -170,6 +187,40 @@ def format_response_openai(response):
     return output
 
 
+def format_response_gemini(response):
+    output = []
+    if hasattr(response, "candidates") and response.candidates:
+        for candidate in response.candidates:
+            if hasattr(candidate, "content") and candidate.content:
+                content_text = ""
+                if hasattr(candidate.content, "parts") and candidate.content.parts:
+                    for part in candidate.content.parts:
+                        if hasattr(part, "text") and part.text:
+                            content_text += part.text
+                if content_text:
+                    output.append(
+                        {
+                            "role": "assistant",
+                            "content": content_text,
+                        }
+                    )
+            elif hasattr(candidate, "text") and candidate.text:
+                output.append(
+                    {
+                        "role": "assistant",
+                        "content": candidate.text,
+                    }
+                )
+    elif hasattr(response, "text") and response.text:
+        output.append(
+            {
+                "role": "assistant",
+                "content": response.text,
+            }
+        )
+    return output
+
+
 def format_tool_calls(response, provider: str):
     if provider == "anthropic":
         if hasattr(response, "tools") and response.tools and len(response.tools) > 0:
@@ -198,6 +249,22 @@ def merge_system_prompt(kwargs: Dict[str, Any], provider: str):
         if kwargs.get("system") is None:
             return messages
         return [{"role": "system", "content": kwargs.get("system")}] + messages
+    elif provider == "gemini":
+        contents = kwargs.get("contents", [])
+        if isinstance(contents, str):
+            return [{"role": "user", "content": contents}]
+        elif isinstance(contents, list):
+            formatted = []
+            for item in contents:
+                if isinstance(item, str):
+                    formatted.append({"role": "user", "content": item})
+                elif hasattr(item, "text"):
+                    formatted.append({"role": "user", "content": item.text})
+                else:
+                    formatted.append({"role": "user", "content": str(item)})
+            return formatted
+        else:
+            return [{"role": "user", "content": str(contents)}]
 
     # For OpenAI, handle both Chat Completions and Responses API
     if kwargs.get("messages") is not None:
@@ -271,7 +338,7 @@ def call_llm_and_track_usage(
         if posthog_trace_id is None:
             posthog_trace_id = str(uuid.uuid4())
 
-        if response and hasattr(response, "usage"):
+        if response and (hasattr(response, "usage") or (provider == "gemini" and hasattr(response, "usage_metadata"))):
             usage = get_usage(response, provider)
 
         messages = merge_system_prompt(kwargs, provider)
@@ -366,7 +433,7 @@ async def call_llm_and_track_usage_async(
         if posthog_trace_id is None:
             posthog_trace_id = str(uuid.uuid4())
 
-        if response and hasattr(response, "usage"):
+        if response and (hasattr(response, "usage") or (provider == "gemini" and hasattr(response, "usage_metadata"))):
             usage = get_usage(response, provider)
 
         messages = merge_system_prompt(kwargs, provider)

posthog/consumer.py

@@ -1,9 +1,9 @@
 import json
 import logging
+import time
 from threading import Thread
 
 import backoff
-import monotonic
 
 from posthog.request import APIError, DatetimeSerializer, batch_post
 
@@ -96,11 +96,11 @@ class Consumer(Thread):
         queue = self.queue
         items = []
 
-        start_time = monotonic.monotonic()
+        start_time = time.monotonic()
         total_size = 0
 
         while len(items) < self.flush_at:
-            elapsed = monotonic.monotonic() - start_time
+            elapsed = time.monotonic() - start_time
             if elapsed >= self.flush_interval:
                 break
             try:

posthog/test/ai/gemini/test_gemini.py

@@ -0,0 +1,296 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+try:
+    from google import genai as google_genai
+
+    from posthog.ai.gemini import Client
+
+    GEMINI_AVAILABLE = True
+except ImportError:
+    GEMINI_AVAILABLE = False
+
+pytestmark = pytest.mark.skipif(not GEMINI_AVAILABLE, reason="Google Gemini package is not available")
+
+
+@pytest.fixture
+def mock_client():
+    with patch("posthog.client.Client") as mock_client:
+        mock_client.privacy_mode = False
+        yield mock_client
+
+
+@pytest.fixture
+def mock_gemini_response():
+    mock_response = MagicMock()
+    mock_response.text = "Test response from Gemini"
+
+    mock_usage = MagicMock()
+    mock_usage.prompt_token_count = 20
+    mock_usage.candidates_token_count = 10
+    mock_response.usage_metadata = mock_usage
+
+    mock_candidate = MagicMock()
+    mock_candidate.text = "Test response from Gemini"
+    mock_content = MagicMock()
+    mock_part = MagicMock()
+    mock_part.text = "Test response from Gemini"
+    mock_content.parts = [mock_part]
+    mock_candidate.content = mock_content
+    mock_response.candidates = [mock_candidate]
+
+    return mock_response
+
+
+@pytest.fixture
+def mock_google_genai_client():
+    """Mock for the new google-genai Client"""
+    with patch.object(google_genai, "Client") as mock_client_class:
+        mock_client_instance = MagicMock()
+        mock_models = MagicMock()
+        mock_client_instance.models = mock_models
+        mock_client_class.return_value = mock_client_instance
+        yield mock_client_instance
+
+
+def test_new_client_basic_generation(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test the new Client/Models API structure"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    response = client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Tell me a fun fact about hedgehogs"],
+        posthog_distinct_id="test-id",
+        posthog_properties={"foo": "bar"},
+    )
+
+    assert response == mock_gemini_response
+    assert mock_client.capture.call_count == 1
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.0-flash"
+    assert props["$ai_input_tokens"] == 20
+    assert props["$ai_output_tokens"] == 10
+    assert props["foo"] == "bar"
+    assert "$ai_trace_id" in props
+    assert props["$ai_latency"] > 0
+
+
+def test_new_client_streaming_with_generate_content_stream(mock_client, mock_google_genai_client):
+    """Test the new generate_content_stream method"""
+
+    def mock_streaming_response():
+        mock_chunk1 = MagicMock()
+        mock_chunk1.text = "Hello "
+        mock_usage1 = MagicMock()
+        mock_usage1.prompt_token_count = 10
+        mock_usage1.candidates_token_count = 5
+        mock_chunk1.usage_metadata = mock_usage1
+
+        mock_chunk2 = MagicMock()
+        mock_chunk2.text = "world!"
+        mock_usage2 = MagicMock()
+        mock_usage2.prompt_token_count = 10
+        mock_usage2.candidates_token_count = 10
+        mock_chunk2.usage_metadata = mock_usage2
+
+        yield mock_chunk1
+        yield mock_chunk2
+
+    # Mock the generate_content_stream method
+    mock_google_genai_client.models.generate_content_stream.return_value = mock_streaming_response()
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    response = client.models.generate_content_stream(
+        model="gemini-2.0-flash",
+        contents=["Write a short story"],
+        posthog_distinct_id="test-id",
+        posthog_properties={"feature": "streaming"},
+    )
+
+    chunks = list(response)
+    assert len(chunks) == 2
+    assert chunks[0].text == "Hello "
+    assert chunks[1].text == "world!"
+
+    # Check that the streaming event was captured
+    assert mock_client.capture.call_count == 1
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "test-id"
+    assert call_args["event"] == "$ai_generation"
+    assert props["$ai_provider"] == "gemini"
+    assert props["$ai_model"] == "gemini-2.0-flash"
+    assert props["$ai_input_tokens"] == 10
+    assert props["$ai_output_tokens"] == 10
+    assert props["feature"] == "streaming"
+    assert isinstance(props["$ai_latency"], float)
+
+
+def test_new_client_groups(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test groups functionality with new Client API"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+        posthog_groups={"company": "company_123"},
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    assert call_args["groups"] == {"company": "company_123"}
+
+
+def test_new_client_privacy_mode_local(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test local privacy mode with new Client API"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+        posthog_privacy_mode=True,
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] is None
+    assert props["$ai_output_choices"] is None
+
+
+def test_new_client_privacy_mode_global(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test global privacy mode with new Client API"""
+    mock_client.privacy_mode = True
+
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] is None
+    assert props["$ai_output_choices"] is None
+
+
+def test_new_client_different_input_formats(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test different input formats with new Client API"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    # Test string input
+    client.models.generate_content(model="gemini-2.0-flash", contents="Hello", posthog_distinct_id="test-id")
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [{"role": "user", "content": "Hello"}]
+
+    # Test list input
+    mock_client.capture.reset_mock()
+    mock_part = MagicMock()
+    mock_part.text = "List item"
+    client.models.generate_content(model="gemini-2.0-flash", contents=[mock_part], posthog_distinct_id="test-id")
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_input"] == [{"role": "user", "content": "List item"}]
+
+
+def test_new_client_model_parameters(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test model parameters with new Client API"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(api_key="test-key", posthog_client=mock_client)
+
+    client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="test-id",
+        temperature=0.7,
+        max_tokens=100,
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+    assert props["$ai_model_parameters"]["temperature"] == 0.7
+    assert props["$ai_model_parameters"]["max_tokens"] == 100
+
+
+def test_new_client_default_settings(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test client with default PostHog settings"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(
+        api_key="test-key",
+        posthog_client=mock_client,
+        posthog_distinct_id="default_user",
+        posthog_properties={"team": "ai"},
+        posthog_privacy_mode=False,
+        posthog_groups={"company": "acme_corp"},
+    )
+
+    # Call without overriding defaults
+    client.models.generate_content(model="gemini-2.0-flash", contents=["Hello"])
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    assert call_args["distinct_id"] == "default_user"
+    assert call_args["groups"] == {"company": "acme_corp"}
+    assert props["team"] == "ai"
+
+
+def test_new_client_override_defaults(mock_client, mock_google_genai_client, mock_gemini_response):
+    """Test overriding client defaults per call"""
+    mock_google_genai_client.models.generate_content.return_value = mock_gemini_response
+
+    client = Client(
+        api_key="test-key",
+        posthog_client=mock_client,
+        posthog_distinct_id="default_user",
+        posthog_properties={"team": "ai"},
+        posthog_privacy_mode=False,
+        posthog_groups={"company": "acme_corp"},
+    )
+
+    # Override defaults in call
+    client.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=["Hello"],
+        posthog_distinct_id="specific_user",
+        posthog_properties={"feature": "chat", "urgent": True},
+        posthog_privacy_mode=True,
+        posthog_groups={"organization": "special_org"},
+    )
+
+    call_args = mock_client.capture.call_args[1]
+    props = call_args["properties"]
+
+    # Check overrides
+    assert call_args["distinct_id"] == "specific_user"
+    assert call_args["groups"] == {"organization": "special_org"}
+    assert props["$ai_input"] is None  # privacy mode was overridden
+
+    # Check merged properties (defaults + call-specific)
+    assert props["team"] == "ai"  # from defaults
+    assert props["feature"] == "chat"  # from call
+    assert props["urgent"] is True  # from call

posthog/version.py

@@ -1,4 +1,4 @@
-VERSION = "4.0.0"
+VERSION = "4.2.0"
 
 if __name__ == "__main__":
     print(VERSION, end="")  # noqa: T201

setup.cfg

@@ -3,3 +3,4 @@ universal = 1
 
 [tool:pytest]
 asyncio_mode = auto
+asyncio_default_fixture_loop_scope = function

setup.py

@@ -12,14 +12,15 @@ from version import VERSION
 
 long_description = """
 PostHog is developer-friendly, self-hosted product analytics. posthog-python is the python package.
+
+This package requires Python 3.9 or higher.
 """
 
 install_requires = [
     "requests>=2.7,<3.0",
     "six>=1.5",
-    "monotonic>=1.5",
+    "python-dateutil>=2.2",
     "backoff>=1.10.0",
-    "python-dateutil>2.1",
     "distro>=1.5.0",  # Required for Linux OS detection in Python 3.9+
 ]
 
@@ -57,6 +58,7 @@ extras_require = {
         "langchain-community>=0.2.0",
         "langchain-openai>=0.2.0",
         "langchain-anthropic>=0.2.0",
+        "google-genai",
         "pydantic",
         "parameterized>=0.8.1",
     ],
@@ -79,6 +81,7 @@ setup(
         "posthog.ai.langchain",
         "posthog.ai.openai",
         "posthog.ai.anthropic",
+        "posthog.ai.gemini",
         "posthog.test",
         "posthog.sentry",
         "posthog.exception_integrations",

setup_analytics.py

@@ -12,14 +12,15 @@ from version import VERSION
 
 long_description = """
 PostHog is developer-friendly, self-hosted product analytics. posthog-python is the python package.
+
+This package requires Python 3.9 or higher.
 """
 
 install_requires = [
     "requests>=2.7,<3.0",
     "six>=1.5",
-    "monotonic>=1.5",
+    "python-dateutil>=2.2",
     "backoff>=1.10.0",
-    "python-dateutil>2.1",
     "distro>=1.5.0",  # Required for Linux OS detection in Python 3.9+
 ]
 
@@ -40,6 +41,7 @@ setup(
         "posthoganalytics.ai.langchain",
         "posthoganalytics.ai.openai",
         "posthoganalytics.ai.anthropic",
+        "posthoganalytics.ai.gemini",
         "posthoganalytics.test",
         "posthoganalytics.sentry",
         "posthoganalytics.exception_integrations",
@@ -58,19 +60,10 @@ setup(
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
         "Programming Language :: Python",
-        "Programming Language :: Python :: 2",
-        "Programming Language :: Python :: 2.6",
-        "Programming Language :: Python :: 2.7",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.2",
-        "Programming Language :: Python :: 3.3",
-        "Programming Language :: Python :: 3.4",
-        "Programming Language :: Python :: 3.5",
-        "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",
+        "Programming Language :: Python :: 3.12",
+        "Programming Language :: Python :: 3.13",
     ],
 )