Pydantic AI
Free tierGenAI Agent Framework, the Pydantic way — build production-grade AI apps with confidence
Free·Technical·Powered by Model-agnostic (OpenAI, Anthropic, Gemini, Mistral, Cohere, Groq, Ollama, and more)·API available·Open source
Key strengths
Model-agnostic support for virtually every major LLM providerFully type-safe with IDE auto-completion and compile-time error catchingSeamless observability via Pydantic Logfire (OpenTelemetry)Composable, extensible agent capabilities (tools, hooks, MCP, A2A)Built-in evaluation framework (Pydantic Evals) for systematic performance testing
Completely free
London, United Kingdom
Founded 2024
Self-hostable
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Developer Documentation
Pydantic AI exposes a rich Python API built around the Agent class, typed RunContext, and a dependency injection system inspired by FastAPI.
Core concepts:
- Agents: Defined via
Agent(model, deps_type=..., output_type=..., instructions=...). Supports both sync (run_sync) and async (run) execution modes, with streaming viarun_stream. - Dependency Injection: Pass runtime dependencies (e.g., DB connections, user IDs) through typed
RunContext[DepsType]— fully type-checked and IDE-friendly. - Tools: Decorate functions with
@agent.toolto expose them as callable tools. Supports deferred, native, and third-party toolsets. - Structured Output: Define
output_type=SomeBaseModelto get validated, typed outputs directly from the LLM. - MCP Support: Built-in MCP client and FastMCP client/server for connecting to external tool/data sources.
- Evals:
pydantic_evalsprovides dataset management, built-in and custom evaluators, LLM-as-judge, span-based online evaluation, and Logfire integration. - Graph Workflows:
pydantic_graphsupports complex state-machine-style multi-agent workflows with type-hint-defined nodes, joins, parallel execution, and Mermaid diagram export. - Durable Execution: Native integrations with Temporal, DBOS, Prefect, and Restate for fault-tolerant, long-running agent workflows.