Google LLM analytics installation

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  1. Install the PostHog SDK

    Required

    Setting up analytics starts with installing the PostHog SDK for your language. LLM analytics works best with our Python and Node SDKs.

    pip install posthog

  2. Install the Google Gen AI SDK

    Required

    Install the Google Gen AI SDK:

    pip install google-genai
    Proxy note

    These SDKs do not proxy your calls. They only fire off an async call to PostHog in the background to send the data.

    You can also use LLM analytics with other SDKs or our API, but you will need to capture the data in the right format. See the schema in the manual capture section for more details.

  3. Initialize PostHog and Google Gen AI client

    Required

    Initialize PostHog with your project API key and host from your project settings, then pass it to our Google Gen AI wrapper.

    from posthog.ai.gemini import Client
    from posthog import Posthog
    

    posthog = Posthog(
        "<ph_project_api_key>",
        host="https://us.i.posthog.com"
    )
    

    client = Client(
        api_key="...", # Replace with your Gemini API key
        posthog_client=posthog # This is an optional parameter. If it is not provided, a default client will be used.
    )

  4. Call Google Gen AI LLMs

    Required

    Now, when you use the Google Gen AI SDK to call LLMs, PostHog automatically captures an $ai_generation event.

    You can enrich the event with additional data such as the trace ID, distinct ID, custom properties, groups, and privacy mode options.

    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents=["Tell me a fun fact about hedgehogs"],
        posthog_distinct_id="user_123", # optional
        posthog_trace_id="trace_123", # optional
        posthog_properties={"conversation_id": "abc123", "paid": True}, # optional
        posthog_groups={"company": "company_id_in_your_db"},  # optional 
        posthog_privacy_mode=False # optional
    )
    

    print(response.text)

    Note: If you want to capture LLM events anonymously, don't pass a distinct ID to the request. See our docs on anonymous vs identified events to learn more.

    You can expect captured $ai_generation events to have the following properties:

    PropertyDescription
    $ai_modelThe specific model, like gpt-5-mini or claude-4-sonnet
    $ai_latencyThe latency of the LLM call in seconds
    $ai_toolsTools and functions available to the LLM
    $ai_inputList of messages sent to the LLM
    $ai_input_tokensThe number of tokens in the input (often found in response.usage)
    $ai_output_choicesList of response choices from the LLM
    $ai_output_tokensThe number of tokens in the output (often found in response.usage)
    $ai_total_cost_usdThe total cost in USD (input + output)
    ...See full list of properties

  5. Verify traces and generations

    Checkpoint
    Confirm LLM events are being sent to PostHog

    Let's make sure LLM events are being captured and sent to PostHog. Under LLM analytics, you should see rows of data appear in the Traces and Generations tabs.


    LLM generations in PostHog
    Check for LLM events in PostHog

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