OpenRouter 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 OpenAI SDK

    Required

    Install the OpenAI SDK:

    pip install openai

  3. Initialize PostHog and OpenAI client

    Required

    We call OpenRouter through the OpenAI client and generate a response. We’ll use PostHog’s OpenAI provider to capture all the details of the call.

    Initialize PostHog with your PostHog project API key and host from your project settings, then pass the PostHog client along with the OpenRouter config (the base URL and API key) to our OpenAI wrapper.

    from posthog.ai.openai import OpenAI
    from posthog import Posthog
    

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

    client = OpenAI(
        baseURL="https://openrouter.ai/api/v1",
        api_key="<openrouter_api_key>",
        posthog_client=posthog # This is an optional parameter. If it is not provided, a default client will be used.
    )

    Note: This also works with the AsyncOpenAI client.

    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.

  4. Call OpenRouter

    Required

    Now, when you call OpenRouter with the OpenAI SDK, PostHog automatically captures an $ai_generation event.

    You can also capture or modify additional properties with the distinct ID, trace ID, properties, groups, and privacy mode parameters.

    response = client.responses.create(
        model="gpt-5-mini",
        input=[
            {"role": "user", "content": "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.choices[0].message.content)

    Notes:

    • We also support the old chat.completions API.
    • This works with responses where stream=True.
    • 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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Privacy mode

To avoid storing potentially sensitive prompt and completion data, you can enable privacy mode. This excludes the $ai_input and $ai_output_choices properties from being captured. SDK config This can be done by setting the privacy_mode config option in the SDK like this: Request parameter It can also be set at the request level by setting the privacy_mode parameter to True in the request. The exact setup depends on the LLM platform you're using:

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