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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- Typed, structured LLM output: Use
output_type=PydanticModelto get validated, schema-enforced responses from any supported LLM provider. - Multi-agent graph workflows: Define stateful pipelines using
pydantic_graphwith typed nodes, reducers, parallel execution, and durable state persistence. - MCP client/server integration: Connect agents to external tools and data sources via the Model Context Protocol using built-in client and FastMCP support.
- Eval-driven development: Write test datasets with
pydantic_evals, run LLM-judge or custom evaluators, and track regressions in Pydantic Logfire over time. - Durable, fault-tolerant agents: Wrap agent runs in Temporal, DBOS, or Prefect workflows to handle long-running tasks, retries, and human-in-the-loop approvals.
- Streaming structured outputs: Use
run_streamto incrementally yield validated partial outputs, enabling real-time UI updates and progressive rendering.