smolagents LLM analytics installation - Docs

1. 1

   Install dependencies

   Required

   Full working examples

   See the complete Python example on GitHub. If you're using the PostHog SDK wrapper instead of OpenTelemetry, see the Python wrapper example.

   Install the OpenTelemetry SDK, the OpenAI instrumentation, and smolagents.

   pip install smolagents openai opentelemetry-sdk posthog[otel] opentelemetry-instrumentation-openai-v2
   

2. 2

   Set up OpenTelemetry tracing

   Required

   Configure OpenTelemetry to auto-instrument OpenAI SDK calls and export traces to PostHog. PostHog converts gen_ai.* spans into $ai_generation events automatically.

   from opentelemetry import trace
   from opentelemetry.sdk.trace import TracerProvider
   from opentelemetry.sdk.resources import Resource, SERVICE_NAME
   from posthog.ai.otel import PostHogSpanProcessor
   from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
   resource = Resource(attributes={
       SERVICE_NAME: "my-app",
       "posthog.distinct_id": "user_123", # optional: identifies the user in PostHog
       "foo": "bar", # custom properties are passed through
   })
   provider = TracerProvider(resource=resource)
   provider.add_span_processor(
       PostHogSpanProcessor(
           api_key="<ph_project_token>",
           host="https://us.i.posthog.com",
       )
   )
   trace.set_tracer_provider(provider)
   OpenAIInstrumentor().instrument()
   

3. 3

   Run your agent

   Required

   Use smolagents as normal. PostHog automatically captures an $ai_generation event for each LLM call made through the OpenAI SDK that smolagents uses internally.

   import os
   from smolagents import CodeAgent, LiteLLMModel
   model = LiteLLMModel(model_id="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"])
   agent = CodeAgent(tools=[], model=model)
   result = agent.run("Tell me a fun fact about hedgehogs")
   print(result)
   

   Note: If you want to capture LLM events anonymously, omit the posthog.distinct_id resource attribute. See our docs on anonymous vs identified events to learn more.

   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

4. 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

5. 4

   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.

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