Pydantic AI Documentation
by Pydantic
Type-safe, production-grade agent framework that brings the FastAPI feeling to GenAI.
Overview
Pydantic AI is an open-source Python agent framework from the team behind Pydantic, built to bring the ergonomic, type-safe 'FastAPI feeling' to generative-AI and agent development. Its documentation covers a model-agnostic design that supports virtually every major provider (OpenAI, Anthropic, Google, DeepSeek, Mistral, AWS Bedrock, Azure, and more); function tools registered via decorators with automatic schema generation and validation; dependency injection; structured outputs returned as validated Pydantic models; Model Context Protocol (MCP) integration for external tools and data; Pydantic Evals for systematic testing; durable execution across failures via integrations like Temporal, DBOS, and Prefect; and first-class observability through Pydantic Logfire for tracing and cost monitoring. It targets developers who want production reliability, static type checking, and strong IDE support when building agents.
At a Glance
- Topic
- Frameworks
- Level
- Intermediate
- Format
- Documentation
- Cost
- Free
- Duration
- Reference, self-paced
- Provider
- Pydantic
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Build type-safe agents with validated, structured Pydantic outputs
- ✓Register function tools with automatic schema generation and dependency injection
- ✓Run agents across many model providers with a single model-agnostic API
- ✓Integrate MCP tools and add observability/tracing with Pydantic Logfire
- ✓Systematically test agents with Pydantic Evals and add durable execution
Highlights
- •Built by the Pydantic team — the FastAPI ergonomics applied to agents
- •Fully type-safe with strong IDE auto-completion and static checking
- •Batteries included: MCP, evals, Logfire observability, and durable execution
Who It’s For
Best For
- ✓Python engineers who want production-grade, type-safe agents
- ✓Teams standardizing on validated structured outputs
- ✓Developers wanting built-in evals and observability out of the box
Prerequisites
- •Intermediate Python and familiarity with Pydantic / type hints
- •Basic understanding of LLM tool calling
FAQ
What is Pydantic AI Documentation?
Official documentation for Pydantic AI, an open-source Python agent framework built by the Pydantic team for production-grade, type-safe agentic applications. For engineers who want validated structured outputs, dependency injection, MCP support, and built-in evals and observability.
Is Pydantic AI Documentation free?
Pydantic AI Documentation is free to access.
What level is Pydantic AI Documentation for?
Pydantic AI Documentation is aimed at a intermediate audience. Recommended background: Intermediate Python and familiarity with Pydantic / type hints, Basic understanding of LLM tool calling.
How long does Pydantic AI Documentation take?
Expect roughly Reference, self-paced. Most learners work through it at their own pace.
What will I learn from Pydantic AI Documentation?
You'll learn: Build type-safe agents with validated, structured Pydantic outputs; Register function tools with automatic schema generation and dependency injection; Run agents across many model providers with a single model-agnostic API; Integrate MCP tools and add observability/tracing with Pydantic Logfire; Systematically test agents with Pydantic Evals and add durable execution.