What Is Pydantic AI?

Pydantic AI brings structured outputs, dependency injection, and type checking to LLM applications. Instead of parsing free-form text, you declare a result model and the framework validates every response against it, raising clear errors when the model deviates.

How to Use Pydantic AI: Step by Step

1. Install the library

Install Pydantic AI and the provider extra for the model you plan to use. A virtual environment keeps your dependencies reproducible.

2. Set your API key

Export your model provider key as an environment variable. The framework reads standard provider variables, so no extra configuration is needed.

3. Define an output model

Create a Pydantic model describing exactly the data you want back. Field descriptions double as instructions to the model, improving accuracy.

4. Create an agent

Instantiate an agent with a system prompt, a model name, and your output type. The agent is a plain Python object you can call with await.

5. Add tools

Register Python functions as tools with type-annotated arguments. The model can request these functions, and the framework validates the arguments before calling them.

6. Use dependency injection

Pass runtime dependencies such as database connections into the agent via a typed context, keeping your tools testable and decoupled.

7. Test with the test model

Use the built-in test model to simulate responses without hitting a real API. This makes unit tests fast, deterministic, and free.

Tips for Getting the Most Out of Pydantic AI

• Let validation errors re-prompt the model automatically for a self-correction loop.
• Prefer small, focused output models over one giant schema.
• Write unit tests against the test model, then smoke-test once against a real provider.
• Keep tool functions pure so they are easy to mock in dependency injection.

Final Thoughts

Pydantic AI removes the friction between an idea and a working result, whether you are automating research, writing code, or orchestrating multi-step AI workflows. Start with a small, concrete task, review every step the agent takes, and expand from there. With the workflow above you can go from installation to a finished, verified result in a single session.