PydanticAI

PydanticAI:

FastAPI revolutionized web development by offering an innovative and ergonomic design, built on the foundation of Pydantic.

Similarly, virtually every agent framework and LLM library in Python uses Pydantic, yet when we began to use LLMs in Pydantic Logfire, we couldn’t find anything that gave us the same feeling.

We built PydanticAI with one simple aim: to bring that FastAPI feeling to GenAI app development.

PydanticAI:

• Built by the Pydantic Team: Built by the team behind  Pydantic (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more).
• Model-agnostic: Supports OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral, and there is a simple interface to implement support for  other models.
• Pydantic Logfire Integration: Seamlessly  integrates with  Pydantic Logfire for real-time debugging, performance monitoring, and behavior tracking of your LLM-powered applications.
• Type-safe: Designed to make  type checking as powerful and informative as possible for you.
• Python-centric Design: Leverages Python’s familiar control flow and agent composition to build your AI-driven projects, making it easy to apply standard Python best practices you’d use in any other (non-AI) project.
• Structured Responses: Harnesses the power of  Pydantic to  validate and structure model outputs, ensuring responses are consistent across runs.
• Dependency Injection System: Offers an optional  dependency injection system to provide data and services to your agent’s  system prompts,  tools and  result validators. This is useful for testing and eval-driven iterative development.
• Streamed Responses: Provides the ability to  stream LLM outputs continuously, with immediate validation, ensuring rapid and accurate results.
• Graph Support:  Pydantic Graph provides a powerful way to define graphs using typing hints, this is useful in complex applications where standard control flow can degrade to spaghetti code.