> ## Documentation Index
> Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# PydanticAI Instrumentation

> Connect PydanticAI applications to LangWatch using the Python SDK to trace calls, debug structured outputs, and improve AI agent evaluations.

LangWatch integrates with PydanticAI through its built-in OpenTelemetry support to capture traces of agent runs and model interactions.

## Installation

<CodeGroup>
  ```bash pip theme={null}
  pip install langwatch pydantic-ai
  ```

  ```bash uv theme={null}
  uv add langwatch pydantic-ai
  ```
</CodeGroup>

## Usage

<Info>
  The LangWatch API key is configured by default via the `LANGWATCH_API_KEY` environment variable.
</Info>

Initialize LangWatch and create your PydanticAI agent. PydanticAI's built-in OpenTelemetry support will automatically send traces to LangWatch.

```python  theme={null}
from pydantic_ai import Agent
import langwatch

langwatch.setup()

agent = Agent(
    "openai:gpt-5",
    instructions="Be funny, but not too funny.",
)

if __name__ == "__main__":
    result = agent.run_sync("Tell me a joke")
    print(result.output)
```

LangWatch automatically captures all PydanticAI agent runs and model interactions through PydanticAI's built-in OpenTelemetry support. Use `@langwatch.trace()` decorators to add custom traces and metadata as needed.

## Related

* [Capturing RAG](/integration/python/tutorials/capturing-rag) - Learn how to capture RAG data from retrievers and tools
* [Capturing Metadata and Attributes](/integration/python/tutorials/capturing-metadata) - Add custom metadata and attributes to your traces and spans
* [Capturing Evaluations & Guardrails](/integration/python/tutorials/capturing-evaluations-guardrails) - Log evaluations and implement guardrails in your PydanticAI applications