LangChain LLM analytics installation

  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 LangChain and OpenAI SDKs

    Required

    Install the LangChain and OpenAI Python SDKs:

    pip install langchain openai langchain-openai
    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 LangChain

    Required

    Initialize PostHog with your project API key and host from your project settings, then pass it to the LangChain CallbackHandler wrapper.

    Optionally, you can provide a user distinct ID, trace ID, PostHog properties, groups, and privacy mode.

    from posthog.ai.langchain import CallbackHandler
    from langchain_openai import ChatOpenAI
    from langchain_core.prompts import ChatPromptTemplate
    from posthog import Posthog
    

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

    callback_handler = CallbackHandler(
        client=posthog, # This is an optional parameter. If it is not provided, a default client will be used.
        distinct_id="user_123", # optional
        trace_id="trace_456", # optional
        properties={"conversation_id": "abc123"} # optional
        groups={"company": "company_id_in_your_db"} # optional
        privacy_mode=False # optional
    )

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

  4. Call LangChain

    Required

    When you invoke your chain, pass the callback_handler in the config as part of your callbacks:

    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful assistant."),
        ("user", "{input}")
    ])
    

    model = ChatOpenAI(openai_api_key="your_openai_api_key")
    chain = prompt | model
    

    # Execute the chain with the callback handler
    response = chain.invoke(
        {"input": "Tell me a joke about programming"},
        config={"callbacks": [callback_handler]}
    )
    

    print(response.content)

    PostHog automatically captures an $ai_generation event along with these 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

    It also automatically creates a trace hierarchy based on how LangChain components are nested.

  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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