Pydantic AI

What is Pydantic AI?

Pydantic AI is an open-source, type-safe agent framework from the team behind Pydantic, the validation library that underpins much of the Python AI ecosystem. Its goal is to bring “that FastAPI feeling” to building GenAI agents: you define an agent with Python type hints, and the framework guarantees structured, validated outputs while giving your IDE and type-checker full context. It is MIT-licensed and free, model-agnostic across OpenAI, Anthropic, Gemini, Mistral, Cohere, DeepSeek, Grok, Bedrock, Ollama and more, and integrates tightly with Pydantic Logfire for observability. It reached a stable 1.0 release in September 2025, then a backwards-incompatible 2.0 on 2026-06-23 that reorganized the framework around a new “capabilities” primitive and added official durable-execution integrations (Temporal, DBOS, Prefect, Restate) plus native AG-UI and A2A protocol support.

What does Pydantic AI do?

You create an Agent with a system prompt, a model and, optionally, a Pydantic model describing the exact output structure you want. When you call agent.run() (or run_sync), the framework runs the LLM loop, dispatches any tool calls, and validates the result against your types before returning it, so a malformed model response is caught at the boundary rather than propagating into your app. Tools are ordinary Python functions whose signatures become the schema automatically, and a type-safe dependency-injection system lets you pass database connections, API clients or config into an agent and its tools without globals. Beyond single agents it supports multi-agent handoffs, a graph library for more complex control flow, Model Context Protocol (MCP) servers, human-in-the-loop tool approval, streamed structured outputs and durable execution across failures via official Temporal, DBOS, Prefect and Restate integrations. Everything can be traced through Pydantic Logfire, an OpenTelemetry-based observability platform, for debugging, cost tracking and evals. The runtime is deliberately lightweight, plain Python with no heavy graph engine or scheduler by default, which makes it a fast, pleasant fit for the most common production case: an agent that takes input, calls a few tools and returns a validated, structured result. The framework is free; you pay only your model provider (and optionally Logfire).

Key features

• Type-safe structured outputs: Model responses are validated against your Pydantic models, so malformed outputs are caught at the boundary, not in production.
• Model-agnostic: First-class support for OpenAI, Anthropic, Gemini, Mistral, Cohere, DeepSeek, Bedrock, Vertex, Ollama and more.
• Type-safe dependency injection: Pass database connections, API clients and config into agents and tools with full type checking and no globals.
• Tools, MCP, multi-agent & graphs: Python functions become tools automatically; supports MCP servers, multi-agent handoffs and a graph library for complex flows.
• Durable, checkpointed execution: Official, co-maintained integrations with Temporal, DBOS, Prefect and Restate let an agent checkpoint progress and resume across transient failures, restarts or long human-in-the-loop pauses, you run the orchestrator, Pydantic AI wires into it.
• AG-UI and A2A interoperability: Native AG-UI protocol support (streaming, frontend tools, shared state) built into the core package, plus A2A agent-to-agent protocol support via the companion FastA2A library.
• Logfire observability: Tight integration with Pydantic Logfire (OpenTelemetry) for tracing, debugging, cost tracking and evals.

What are Pydantic AI's use cases?

• Structured extraction & classification: An agent reads free text and returns a strictly validated Pydantic object, fields, enums and types guaranteed.
• Tool-using assistant: A support or ops agent calls a few typed tools with injected dependencies and returns a structured, checked result.

What does Pydantic AI integrate with?

• Model providers (OpenAI, Anthropic, Gemini, Mistral, Cohere, DeepSeek, Bedrock, Vertex, Ollama)
• Pydantic Logfire / OpenTelemetry
• MCP servers
• Python tools via type hints
• Temporal, DBOS, Prefect and Restate (durable execution)
• AG-UI protocol (built-in) and A2A protocol (via FastA2A)

Why use Pydantic AI?

• Type safety moves whole classes of LLM-output errors from runtime to write-time, with excellent IDE support.
• Model-agnostic and MIT-licensed/free, not locked to a single provider.
• Lightweight, low-boilerplate, FastAPI-style developer experience with a gentle learning curve.
• Since 2.0, real checkpointed durability via official Temporal/DBOS/Prefect/Restate integrations, plus native AG-UI and A2A protocol support.
• First-class observability via Pydantic Logfire (OpenTelemetry).

Pros & cons

Pydantic AI pricing

• Open sourceFree
• Logfire (optional)Free tier + paid

See current pricing on ai.pydantic.dev ↗Compare Pydantic AI alternatives →

Pydantic AI review

Pydantic AI brings the same discipline that made Pydantic and FastAPI ubiquitous to agent building: define your agent and its output with Python type hints, and the framework validates every model response against them, catching malformed outputs at the boundary instead of deep in production. It is MIT-licensed and free, genuinely model-agnostic across OpenAI, Anthropic, Gemini and many more, and its dependency-injection system and lightweight runtime make for an unusually pleasant, low-boilerplate developer experience. It has matured fast: a stable 1.0 in September 2025, then a backwards-incompatible 2.0 on 2026-06-23 that added official durable-execution integrations (Temporal, DBOS, Prefect and Restate) and native AG-UI/A2A protocol support, closing much of the gap with graph-orchestration frameworks on long-running, checkpointed and human-in-the-loop workflows. The trade-offs have narrowed but not disappeared: it still ships fewer pre-built integrations than LangChain, and durable execution is something you operate yourself via an external orchestrator rather than a built-in, zero-infrastructure engine, and LangGraph’s native graph-branching model still goes deeper for the most complex, stateful orchestration. The best observability story also runs through Pydantic’s own Logfire product. Pick Pydantic AI when you want type-safe, validated outputs with minimal ceremony and are willing to wire in an orchestrator when you need production-grade durability; skip it when you want the deepest native graph-branching control or a fully built-in durability engine with nothing extra to operate.

The type-safe, FastAPI-flavoured agent framework, now backed by real checkpointed durability (Temporal/DBOS/Prefect/Restate) and native AG-UI/A2A interoperability since its 2.0 release, though LangGraph still goes deeper on native graph-branching control.

Frequently asked questions