ObservabilityIntegrations

Pydantic AI

Pydantic AI is a Python agent framework designed to build production grade applications with Generative AI.

Pydantic AI’s primary advantage is its integration of Pydantic’s type-safe data validation, ensuring structured and reliable responses in AI applications.

Pydantic AI tracing

Getting started

To use the Pydantic AI integration with Opik, you will need to have Pydantic AI and logfire installed:

$pip install --upgrade pydantic-ai logfire 'logfire[httpx]'

In addition you will need to set the following environment variables to make sure the data is logged to Opik:

If you are using Opik Cloud, you will need to set the following environment variables:

$export OTEL_EXPORTER_OTLP_ENDPOINT=https://www.comet.com/opik/api/v1/private/otel
>export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default'

To log the traces to a specific project, you can add the projectName parameter to the OTEL_EXPORTER_OTLP_HEADERS environment variable:

$export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default,projectName=<your-project-name>'

You can also update the Comet-Workspace parameter to a different value if you would like to log the data to a different workspace.

Using Opik with Pydantic AI

To track your Pydantic AI agents, you will need to configure logfire as this is the framework used by Pydantic AI to enable tracing.

1import logfire
2
3logfire.configure(
4    send_to_logfire=False,
5)
6logfire.instrument_httpx(capture_all=True)

Advanced usage

You can reduce the amount of data logged to Opik by setting capture_all to False:

1import logfire
2
3logfire.configure(
4    send_to_logfire=False,
5)
6logfire.instrument_httpx(capture_all=False)

When this parameter is set to False, we will not log the exact request made to the LLM provider.

Further improvements

If you would like to see us improve this integration, simply open a new feature request on Github.