Pydantic AI: Build Type-Safe LLM Agents in Python - Real Python

"Pydantic AI uses BaseModel classes to define structured outputs that guarantee type safety and automatic validation. The @agent.tool decorator registers Python functions that LLMs can invoke based on user queries and docstrings. Dependency injection with deps_type provides type-safe runtime context like database connections without using global state."

"Validation retries automatically rerun queries when the LLM returns invalid data, which increases reliability but also API costs. Google Gemini, OpenAI, and Anthropic models support structured outputs best, while other providers have varying capabilities."

"Pydantic AI emphasizes type safety and minimal boilerplate, making it ideal if you value the FastAPI-style development experience. You need structured, validated outputs from an LLM and are building a quick prototype or single-agent app, or already use Pydantic or FastAPI."

Pydantic AI is a Python framework designed for building LLM agents that produce validated, structured outputs through Pydantic models. It provides type-safe objects with automatic validation, eliminating the need to parse raw strings from LLMs. The framework uses BaseModel classes to define structured outputs, the @agent.tool decorator to register Python functions for LLM invocation, and dependency injection with deps_type for type-safe runtime context. Validation retries automatically rerun queries when LLMs return invalid data, improving reliability though increasing API costs. Google Gemini, OpenAI, and Anthropic models support structured outputs best. Pydantic AI is ideal for projects requiring structured validated outputs, quick prototypes, or teams already using Pydantic or FastAPI, emphasizing type safety and minimal boilerplate.

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