Pydantic AI
Type-safe Python agent framework with structured outputs, dependency injection, and support for every major LLM provider through a unified API.
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Product Overview
What is Pydantic AI?
Pydantic AI is a production-grade Python agent framework built by the Pydantic team. It treats AI agents as typed Python functions rather than prompt chains, guaranteeing validated structured outputs through Pydantic models. The framework supports virtually every LLM provider (OpenAI, Anthropic, Google, Bedrock, Azure, Groq, Mistral, xAI, Ollama, and more) through a single consistent API where models are swappable with a string. Pydantic AI brings FastAPI-style developer experience to agent development with end-to-end type safety, dependency injection, composable capabilities, OpenTelemetry-native observability, and durable execution integrations.
Key Features
Typed End-to-End
Agent inputs, outputs, tool arguments, and dependencies are all expressed with Pydantic models and type hints. Model outputs are validated automatically, moving errors from runtime to write-time and ensuring IDE, type checker, and LLM all agree on returned types.
Any Model, One API
Support for virtually every LLM provider including OpenAI, Anthropic, Google, Bedrock, Azure AI Foundry, Groq, Mistral, xAI, and Ollama. Swap models with a single string change without vendor SDK differences leaking into application code.
Composable Capabilities
Everything bundles into reusable 'capability' primitives that combine tools, instructions, hooks, and model settings. Core ships fundamentals like MCP and web search; Pydantic AI Harness adds memory, guardrails, coding agents, file system access, shell execution, and more.
Production Observability
OpenTelemetry-native instrumentation works with any OTel backend. One-line setup with Pydantic Logfire provides real-time debugging, tracing, and cost tracking. Pydantic Evals tests agent behavior like pytest tests code.
Durable Execution
First-party integrations with Temporal, DBOS, Prefect, Restate, Kitaru, and Airflow enable long-running workflows that survive restarts and failures. Agents can run for days with human-in-the-loop approval built in.
Multiple Interfaces
One agent definition runs as CLI, built-in web chat, realtime voice sessions, UI event streams (AG-UI, Vercel AI), or editor agents via ACP. Voice works on OpenAI Realtime, Gemini Live, Azure, and xAI Grok Voice with tools and capabilities included.
Use Cases
- Production Backend Agents : Build type-safe agents behind FastAPI endpoints where the agent's output type becomes the API response schema. Ideal for customer support, data processing, and business automation requiring validated structured outputs.
- Coding Agents : Deploy complete coding agents in terminal with workspace-rooted file access, allowlisted shell, repo orientation, planning, and context management. Pydantic AI Harness ships Coder capability with web search and advisor sub-agents.
- Realtime Voice Assistants : Run agents on live voice sessions with tools and capabilities included. Build voice assistants for customer service, order lookup, and interactive conversations on OpenAI Realtime, Gemini Live, and other voice providers.
- Long-Running Workflows : Execute durable agents inside Temporal, DBOS, or Prefect workflows for background queues, multi-step processes, and workflows spanning days. Survives restarts, failures, and long waits with automatic checkpointing.
- Structured Data Extraction : Extract and validate structured data from unstructured text using Pydantic models. Perfect for parsing documents, analyzing business data, generating reports, and transforming content into validated JSON schemas.
- Multi-Agent Collaboration : Orchestrate complex multi-agent workflows with sub-agents, planning capabilities, and dynamic capability loading. Build research agents, writing pipelines, and collaborative systems with typed inter-agent communication.
FAQs
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