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
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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 — Technical Use Cases
Pydantic AI is purpose-built for Python developers working across the full spectrum of modern AI application patterns:
- Typed AI agents: Build single or multi-agent systems with structured, validated inputs and outputs using Pydantic schemas — eliminating untyped JSON wrangling and runtime schema errors.
- Data extraction pipelines: Use agents with typed output models to reliably extract structured data from unstructured text, documents, or API responses.
- Durable workflows: Integrate with Temporal, DBOS, Prefect, Restate, or Apache Airflow to run long-running, fault-tolerant agent workflows with guaranteed execution semantics.
- Realtime voice applications: Build voice agents using native voice pipeline support and the ElevenLabs integration for speech synthesis.
- Image generation: Invoke image generation models through the same unified agent interface used for text and embeddings.
- Embeddings & retrieval: Generate embeddings natively for use in vector search, RAG pipelines, and semantic similarity workflows.
- Multi-environment CI/CD agents: Run agents inside GitHub Actions or other automation environments without environment-specific code paths.
- Serverless AI functions: Deploy agents to AWS Lambda, Modal, or Daytona with minimal configuration overhead.
- Sandboxed code execution: Use the E2B Sandbox integration to safely execute agent-generated code in isolated environments.
- Agent evaluation & regression testing: Use Pydantic Evals to write deterministic and LLM-graded evaluations over outputs and tool-call trajectories, enabling CI-integrated quality gates.
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