A GenAI Agent Framework built by the Pydantic team with type safety and Pydantic validation at its core, supporting 20+ model providers, MCP/A2A protocols, and Logfire observability for building production-grade Generative AI applications.
Overview#
Pydantic AI is a Python GenAI Agent Framework maintained by the official Pydantic team (Samuel Colvin, dmontagu, etc.), designed to bring FastAPI-like development experience to GenAI applications and Agent development—built on Pydantic validation layer and modern Python features (like type hints).
Core Features#
- Model-agnostic Architecture: Supports 20+ providers including OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, Perplexity, Azure AI Foundry, Amazon Bedrock, Google Vertex AI, Ollama, LiteLLM, Groq, OpenRouter, Together AI, Fireworks AI, Cerebras, Hugging Face, GitHub, Heroku, Vercel, Nebius, OVHcloud, Alibaba Cloud, SambaNova, Outlines; custom model implementation supported
- Fully Type-safe: Provides maximum context for IDEs and AI coding assistants, compile-time type checking
- Pydantic Validation: Serves as the validation layer for OpenAI SDK, Anthropic SDK, LangChain, LlamaIndex, and more
- Logfire Observability: Tight integration with Pydantic Logfire (OpenTelemetry platform), real-time debugging, performance monitoring, behavior tracing, cost tracking
- Powerful Evals: Systematic testing and evaluation of Agent system performance and accuracy
- Protocol Support: MCP (Model Context Protocol), A2A (Agent2Agent), multiple UI event stream standards
- Human-in-the-Loop: Approval control for specific tool calls
- Durable Execution: Build persistent agents that maintain progress across API failures, application errors, or restarts
- Streamed Outputs: Continuous streaming of structured outputs with immediate validation
- Graph Support: Define graphs using type hints for complex workflows
Core Abstractions#
| Component | Description |
|---|---|
| Agent | Core abstraction, generic Agent[DepsType, OutputType] |
| RunContext | Runtime context with dependency injection |
| Tools | Registered via @agent.tool decorator, parameters validated by Pydantic |
| Dependencies | Type-safe dependency injection mechanism |
| Output Types | Structured output based on Pydantic Models, auto-generated JSON Schema |
Quick Start#
pip install pydantic-ai
from pydantic_ai import Agent
agent = Agent(
'openai:gpt-4o',
instructions='Be concise, reply with one sentence.',
)
result = agent.run_sync('Where does "hello world" come from?')
print(result.output)
Use Cases#
- Conversational Agents (chatbots with tool-calling capabilities)
- Business Process Automation (banking support, order processing requiring structured output)
- Multi-Agent Collaboration Systems
- Data Analysis and Reporting
- Production-grade LLM Applications (requiring observability, evaluation, durable execution)
Requirements#
- Python ≥ 3.10 (supports 3.10, 3.11, 3.12, 3.13, 3.14)
- MIT License