Pydantic AI

Python agent framework that brings Pydantic-style type safety to LLM applications.

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About

From the team behind the Pydantic validation library, Pydantic AI is a Python agent framework that aims to bring a FastAPI-like developer experience to generative AI applications. Agents are fully type annotated, so IDEs and static type checkers catch mistakes early, and model responses are validated into structured Pydantic models, including streamed output validated as it arrives. It abstracts over a wide range of providers, supporting OpenAI, Anthropic, Gemini, DeepSeek, and dozens of others plus local models through Ollama, and adds tool registration via decorators, a type-safe dependency injection system, Model Context Protocol integration, and composable built-ins such as web search. Production concerns are addressed through durable execution that preserves agent progress across failures and restarts, an evals framework for systematic testing, and native tracing and cost tracking through Pydantic Logfire. The framework is open source under the MIT license and is used by Python teams building agents, chatbots, and structured extraction pipelines.

Should you use Pydantic AI?

Pick it when

Pick Pydantic AI when your Python team already likes FastAPI and Pydantic and wants agents whose dependencies and outputs are type-checked, with durable execution, evals, and Logfire tracing for production.

Look elsewhere when

Skip it if you want a visual builder or a batteries-included multi-agent platform with a runtime and control plane, where agno or crewai give more out of the box. TypeScript teams should look at mastra instead.

Alternatives to Pydantic AI

  • OpenAI Agents SDK

    Fewer concepts, with handoffs and sessions built in; quicker for simple multi-agent flows, less emphasis on static typing and durable runs.

  • Agno

    Ships a FastAPI runtime with PostgreSQL persistence, RBAC, and a control plane UI; more platform included, less type-driven design.

  • Instructor

    Just structured extraction with Pydantic and retries; pick it when you need validated outputs but no agent loop at all.

  • Mastra

    TypeScript framework with workflows, RAG, memory, and evals; the closer fit when your stack is Node rather than Python.

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