OpenAI Agents SDK LLM analytics installation - Docs

1. 1

   Install the PostHog SDK

   Required

   Setting up analytics starts with installing the PostHog Python SDK.

   pip install posthog
   

2. 2

   Install the OpenAI Agents SDK

   Required

   Install the OpenAI Agents SDK. PostHog instruments your agent runs by registering a tracing processor. The PostHog SDK does not proxy your calls.

   pip install openai-agents
   

   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. 3

   Initialize PostHog tracing

   Required

   Initialize PostHog with your project token and host from your project settings, then call instrument() to register PostHog tracing with the OpenAI Agents SDK. This automatically captures all agent traces, spans, and LLM generations.

   from posthog import Posthog
   from posthog.ai.openai_agents import instrument
   posthog = Posthog(
       "<ph_project_token>",
       host="https://us.i.posthog.com"
   )
   instrument(
       client=posthog,
       distinct_id="user_123", # optional
       privacy_mode=False, # optional
       groups={"company": "company_id_in_your_db"}, # optional
       properties={"conversation_id": "abc123"}, # 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.

4. 4

   Run your agents

   Required

   Run your OpenAI agents as normal. PostHog automatically captures $ai_generation events for LLM calls and $ai_span events for agent execution, tool calls, and handoffs.

   from agents import Agent, Runner
   agent = Agent(
       name="Assistant",
       instructions="You are a helpful assistant.",
   )
   result = Runner.run_sync(agent, "Tell me a fun fact about hedgehogs")
   print(result.final_output)
   

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

   Property	Description
   $ai_model	The specific model, like gpt-5-mini or claude-4-sonnet
   $ai_latency	The latency of the LLM call in seconds
   $ai_time_to_first_token	Time to first token in seconds (streaming only)
   $ai_tools	Tools and functions available to the LLM
   $ai_input	List of messages sent to the LLM
   $ai_input_tokens	The number of tokens in the input (often found in response.usage)
   $ai_output_choices	List of response choices from the LLM
   $ai_output_tokens	The number of tokens in the output (often found in response.usage)
   $ai_total_cost_usd	The total cost in USD (input + output)
   [...]	See full list of properties

5. 5

   Multi-agent and tool usage

   Optional

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

   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

6. 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.

   

   Check for LLM events in PostHog

7. 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.

   Resource	Description
   Basics	Learn the basics of how LLM calls become events in PostHog.
   Generations	Read about the $ai_generation event and its properties.
   Traces	Explore the trace hierarchy and how to use it to debug LLM calls.
   Spans	Review spans and their role in representing individual operations.
   Anaylze LLM performance	Learn how to create dashboards to analyze LLM performance.

Community questions

Ask a question

Was this page useful?

HelpfulCould be better