OpenAI Agents SDK LLM analytics installation

  1. Install the PostHog SDK

    Required

    Install the PostHog Python SDK with the OpenAI Agents SDK.

    Terminal
    pip install posthog openai-agents

  2. Initialize PostHog tracing

    Required

    Import and call the instrument() helper to register PostHog tracing with the OpenAI Agents SDK. This automatically captures all agent traces, spans, and LLM generations.

    Python
    from posthog import Posthog
    from posthog.ai.openai_agents import instrument
    

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

    # Register PostHog tracing with OpenAI Agents SDK
    instrument(
        client=posthog,
        distinct_id="user_123",  # optional
        privacy_mode=False,  # optional - redact inputs/outputs
        groups={"company": "company_id"},  # optional
        properties={"environment": "production"},  # optional
    )

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

  3. Run your agents

    Required

    Run your OpenAI agents as normal. PostHog automatically captures traces for agent execution, tool calls, handoffs, and LLM generations.

    Python
    from agents import Agent, Runner
    

    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant."
    )
    

    result = Runner.run_sync(agent, "Tell me a joke about programming")
    print(result.final_output)

    PostHog automatically captures $ai_generation events for LLM calls and $ai_span events for agent execution, tool calls, and handoffs.

    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

  4. Multi-agent and tool usage

    Optional

    PostHog captures the full trace hierarchy for complex agent workflows including handoffs and tool calls.

    Python
    from agents import Agent, Runner, function_tool
    

    @function_tool
    def get_weather(city: str) -> str:
        """Get the weather for a city."""
        return f"The weather in {city} is sunny, 72F"
    

    weather_agent = Agent(
        name="WeatherAgent",
        instructions="You help with weather queries.",
        tools=[get_weather]
    )
    

    triage_agent = Agent(
        name="TriageAgent",
        instructions="Route weather questions to the weather agent.",
        handoffs=[weather_agent]
    )
    

    result = Runner.run_sync(triage_agent, "What's the weather in San Francisco?")

    This captures: - Agent spans for TriageAgent and WeatherAgent - Handoff spans showing the routing between agents - Tool spans for get_weather function calls - Generation spans for all LLM calls

  5. Verify traces and generations

    Checkpoint
    Confirm LLM events are being 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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