DSPy LLM analytics installation

  1. Install the PostHog SDK

    Required

    Setting up analytics starts with installing the PostHog SDK. The DSPy integration uses PostHog's LiteLLM callback.

    pip install posthog

  2. Install DSPy and LiteLLM

    Required

    Install DSPy and LiteLLM. DSPy uses LiteLLM natively for provider access, and PostHog integrates with LiteLLM's callback system.

    pip install dspy litellm

  3. Configure PostHog with LiteLLM

    Required

    Set your PostHog project API key and host as environment variables, then configure LiteLLM to use PostHog as a callback handler. You can find your API key in your project settings.

    import os
    import dspy
    import litellm
    

    # Set PostHog environment variables
    os.environ["POSTHOG_API_KEY"] = "<ph_project_api_key>"
    os.environ["POSTHOG_API_URL"] = "https://us.i.posthog.com"
    

    # Enable PostHog callbacks in LiteLLM
    litellm.success_callback = ["posthog"]
    litellm.failure_callback = ["posthog"]
    

    # Configure DSPy to use an LLM
    lm = dspy.LM("openai/gpt-4o-mini", api_key="your_openai_api_key")
    dspy.configure(lm=lm)
    How this works

    DSPy uses LiteLLM under the hood for LLM provider access. By configuring PostHog as a LiteLLM callback, all LLM calls made through DSPy are automatically captured as $ai_generation events.

  4. Run DSPy modules

    Required

    Use DSPy as normal. PostHog automatically captures an $ai_generation event for each LLM call made through LiteLLM.

    # Define a simple signature
    class QA(dspy.Signature):
        """Answer the question."""
        question: str = dspy.InputField()
        answer: str = dspy.OutputField()
    

    # Create and run a module
    predictor = dspy.Predict(QA)
    result = predictor(
        question="What is a fun fact about hedgehogs?"
    )
    

    print(result.answer)

    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_time_to_first_tokenTime to first token in seconds (streaming only)
    $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

    Recommended
    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

  6. Next steps

    Recommended

    Now that you're capturing AI conversations, continue with the resources below to learn what else LLM Analytics enables within the PostHog platform.

    ResourceDescription
    BasicsLearn the basics of how LLM calls become events in PostHog.
    GenerationsRead about the $ai_generation event and its properties.
    TracesExplore the trace hierarchy and how to use it to debug LLM calls.
    SpansReview spans and their role in representing individual operations.
    Anaylze LLM performanceLearn how to create dashboards to analyze LLM performance.

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