Chapter 48 · Instrument LLM Analytics
Subchapter 48.31
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Required
Full working examples
See the complete Python example (opens in a new tab) on GitHub. If you’re using the PostHog SDK wrapper instead of OpenTelemetry, see the Python wrapper example (opens in a new tab).
Install the OpenTelemetry SDK and Pydantic AI.
pip install "pydantic-ai[openai]" opentelemetry-sdk "posthog[otel]"2
Required
Configure OpenTelemetry to export traces to PostHog and enable Pydantic AI’s built-in OTel instrumentation. PostHog converts gen_ai.* spans into $ai_generation events automatically.
import os
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 pydantic_ai import Agent
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)
# Enable automatic OTel instrumentation for all Pydantic AI agents
Agent.instrument_all()3
Required
Create a Pydantic AI agent and run it. PostHog automatically captures an $ai_generation event for each LLM call via the OTel instrumentation.
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
model = OpenAIModel("gpt-4o-mini")
agent = Agent(model, system_prompt="You are a helpful assistant.")
result = agent.run_sync("Tell me a fun fact about hedgehogs.")
print(result.output)Note: If you want to capture LLM events anonymously, omit the
posthog.distinct_idresource attribute. See our docs on anonymous vs identified events (opens in a new tab) 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) |
| […] (opens in a new tab) | See full list (opens in a new tab) of properties |
Recommended
Confirm LLM events are being sent to PostHog
Let’s make sure LLM events are being captured and sent to PostHog. Under AI Observability, you should see rows of data appear in the Traces and Generations tabs.
4
Recommended
Now that you’re capturing AI conversations, continue with the resources below to learn what else AI Observability enables within the PostHog platform.
| Resource | Description |
|---|---|
| Basics (opens in a new tab) | Learn the basics of how LLM calls become events in PostHog. |
| Generations (opens in a new tab) | Read about the $ai_generation event and its properties. |
| Traces (opens in a new tab) | Explore the trace hierarchy and how to use it to debug LLM calls. |
| Spans (opens in a new tab) | Review spans and their role in representing individual operations. |
| Anaylze LLM performance (opens in a new tab) | Learn how to create dashboards to analyze LLM performance. |
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