Pydantic AI
Free tierGenAI Agent Framework, the Pydantic way — build production-grade AI apps with confidence
Free·Technical·Powered by Multiple (OpenAI, Anthropic, Google, AWS Bedrock, Azure, Groq, Mistral, Ollama, Cohere, DeepSeek, xAI, Hugging Face, OpenRouter, Snowflake Cortex, and others)·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
Facts verified against ai.pydantic.dev on
Recent changes
- Models updated: Multiple (OpenAI, Anthropic, Google, AWS Bedrock, Azure, Groq, Mistral, Ollama, Cohere, DeepSeek, xAI, Hugging Face, OpenRouter, Snowflake Cortex, and others)
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Pydantic AI — Documentation & Developer Resources
Pydantic AI is open source, giving developers full access to source code, issues, and contribution workflows via GitHub (20,799 stars).
Key areas covered in the documentation:
- Agent construction: Defining agents with typed inputs, structured outputs, and tool registries using Pydantic models.
- Model provider configuration: Connecting to any supported provider (OpenAI, Anthropic, Google, AWS Bedrock, Azure, Groq, Mistral, Ollama, Cohere, DeepSeek, xAI, Hugging Face, OpenRouter, Snowflake Cortex, and others) via a unified API — provider switching requires only a string change.
- Capabilities system: Composing modular agent behaviors including memory, guardrails, sub-agents, planning, and context management.
- Multi-modal support: APIs for image generation, embeddings, and realtime voice alongside standard text completions.
- Workflow integrations: Guides for running agents inside Temporal, DBOS, Prefect, Restate, and Apache Airflow for durable, fault-tolerant execution.
- Serverless & sandbox deployment: Integration docs for AWS Lambda, Modal, Daytona, and E2B Sandbox.
- Observability: OpenTelemetry instrumentation setup, compatible with any OTel backend; dedicated Logfire integration.
- Evaluation (Pydantic Evals): Framework for writing evaluations against agent outputs and tool-call trajectories.
- Third-party integrations: GitHub, Linear, Macroscope, LocalStack, Google Workspace, Notion, Slack, PostHog, ElevenLabs, and more.
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