Pydantic AI
Type-safe agent framework from the Pydantic team
- Best for
- Python backends that treat agents as typed services
- Pricing
- Free and open-source (MIT)
- Stack layer
- AI Stack
Live preview of ai.pydantic.dev
Overview
Pydantic AI is what you reach for when you want agent code to read like a normal Python service. Inputs are typed, outputs are validated Pydantic models, dependencies are injected. No magic strings, no JSON guessing.
It's model-agnostic from day one — same code runs against OpenAI, Anthropic, Google, Groq, or local models. We use it for backend services where the agent is one component of a larger system and needs to behave like a function with predictable I/O.
The Logfire integration gives you tracing without bolting on a separate observability stack.
Key Features
Type-Safe
Pydantic models everywhere — inputs, outputs, tools
Model Agnostic
Switch between providers without rewriting the agent
Dependency Injection
Pass DBs and clients into agents cleanly
Structured Outputs
Validated responses without parsing prompts
Logfire Tracing
Built-in observability via Pydantic Logfire
Python-Native
Reads like normal Python, no DSL
Why We Recommend Pydantic AI
Type safety is the cheapest way to keep agent code maintainable as it grows. Pydantic AI bakes it in from the first line.