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

Pydantic AI is a Python framework for building LLM agents that return validated, structured outputs using Pydantic models. Instead of parsing raw strings from LLMs, you get type-safe objects with automatic validation. If you’ve used FastAPI or Pydantic before, then you’ll recognize the familiar pattern of defining schemas with type hints and letting the framework handle the type validation for you. By the end of this tutorial, you’ll understand that: 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 structure