Pydantic AI
Type-safe agent framework from the Pydantic team
Pydantic AI is profiled here as a LLM tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Pydantic AI is an agent framework from the team behind Pydantic, led by Samuel Colvin, and released in late 2024. It applies the type-safe ergonomics of Pydantic and FastAPI to LLM applications: agents return validated structured outputs, dependencies inject through typed contexts, and mistakes surface at development time. The framework stays model-agnostic across OpenAI, Anthropic, Google, and local providers. Agents defined once can serve chat apps, batch jobs, and evaluation harnesses, and the framework's test models make agent logic unit-testable without network calls.
Key Capabilities:
Type-safe agents with Pydantic-validated structured outputs
Model-agnostic support across major and local LLM providers
Dependency injection for tools, connections, and context
MCP support for external tool servers
Durable execution through the Temporal integration
Native observability through Pydantic Logfire and OpenTelemetry
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