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

# Pydantic AI

> Auto-track LLM calls in Pydantic AI agents

## Installation

```bash theme={null}
pip install meshai-sdk[pydantic-ai]
```

## Usage

```python theme={null}
from meshai import MeshAI
from meshai.integrations.pydantic_ai import track_pydantic_ai
from pydantic_ai import Agent

client = MeshAI(api_key="msh_...", agent_name="my-pydantic-agent")
client.register(framework="pydantic-ai")

# Enable global tracking
track_pydantic_ai(client)

# Run your agents as normal
agent = Agent("openai:gpt-4o", system_prompt="You are a helpful assistant")

# Both async and sync are tracked
result = agent.run_sync("What is AI governance?")
print(result.data)
# Model and tokens tracked automatically
```

## How It Works

`track_pydantic_ai` patches `Agent.run` and `Agent.run_sync` to intercept LLM calls. After each agent run, it:

1. Extracts the **model name** from the agent's model configuration
2. Extracts **token counts** from `RunResult.usage()` (`RunUsage` object)
3. Infers the **provider** from the model string prefix
4. Sends the usage event to MeshAI (buffered, non-blocking)

Works with all Pydantic AI-supported models: OpenAI, Anthropic, Gemini, Groq, Mistral, and others.

## Alternative: Proxy (Zero-Code)

If your Pydantic AI agents use OpenAI or Anthropic, route through the proxy:

```bash theme={null}
export OPENAI_BASE_URL=https://proxy.meshai.dev/v1/openai/k/msh_YOUR_PROXY_KEY
```