PydanticAI
Agent framework by the Pydantic team that brings full Python type safety to building AI agents, with first-class OpenAI, Anthropic, and Gemini support.
PydanticAI is an open-source agent framework released by Samuel Colvin and the Pydantic team in November 2024, designed to apply Pydantic's type-safe philosophy to LLM applications and autonomous agents. Developers define agent inputs, outputs, and tool schemas as standard Pydantic models, enabling IDE autocompletion and runtime validation without additional marshaling code. The framework includes a novel dependency injection system that makes agents fully unit-testable without mocking LLM API calls. PydanticAI crossed 10,000 GitHub stars within weeks of release and integrates natively with OpenAI, Anthropic, Google Gemini, Groq, and Ollama.
Key Features
- Full Python type safety via Pydantic models for agent inputs, outputs, and tool schemas
- Dependency injection system for building fully unit-testable agent components
- Streaming support with structured output validation as tokens arrive
- Multi-turn conversation handling with typed message history
- Native integration with OpenAI, Anthropic, Gemini, Groq, and Ollama providers
- Graph-based agent workflows for complex multi-step orchestration with shared state
Use Cases
- Python developers building type-safe AI agents that catch schema mismatches at development time
- Teams reusing existing Pydantic schemas as agent input and output contracts
- Engineers writing testable AI code using dependency injection without mocking LLM APIs
- Developers migrating from LangChain who prefer Pydantic ergonomics over chain abstractions
Pros
- Full type safety via Pydantic - IDE catches schema mismatches before runtime
- Dependency injection makes agents unit-testable without any LLM API mocking
- Built by the Pydantic team - deeply integrated with the ecosystem most Python devs already use
Cons
- Very new framework (2024) - ecosystem and documentation still maturing compared to LangChain
- Requires Pydantic V2 knowledge which differs significantly from V1 patterns
- Fewer community tutorials and production case studies than more established agent frameworks
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