pydantic/pydantic-ai

Project overview

A Python agent framework that provides type safety, structured output validation via Pydantic, and model-agnostic provider support. Developers define agents programmatically in Python to build generative AI applications that validate LLM outputs and recover from transient failures.

Project type

AI Agent · Evaluation & Observability

Use cases

Coding & Development · Automation

Deployment

Refer to project documentation

License

MIT

Best for

• Developers, AI engineers, and data teams building Python-based generative AI applications who need structured output validation, type safety, the ability to switch between different LLM providers, human-in-the-loop tool approval, and durable execution to preserve progress across failures.

Key capabilities

• Supports virtually every model and provider, allowing users to easily switch or implement custom models.
• Designed to give your IDE or AI coding agent as much context as possible for auto-completion and type checking.
• Build agents from composable capabilities that bundle tools, hooks, instructions, and model settings into reusable units.
• Easily lets you flag that certain tool calls require approval before they can proceed.
• Enables you to build durable agents that can preserve their progress across transient API failures and application errors.
• Provides the ability to stream structured output continuously, with immediate validation.
• Provides a powerful way to define graphs using type hints, for use in complex applications.
• Defines the structure of the output returned by the agent and uses Pydantic validation, passing errors back to the LLM so it can retry.

Limitations and risks

• The framework is currently limited to the Python ecosystem.
• The framework has a heavy reliance on the availability and cost of external LLM APIs.

Getting started

• Setup requires a Python environment and defining agents in code. The first success path is to install the pydantic_ai package, define a simple agent using Python code, and run the agent synchronously.

Evidence and sources

• GitHub project description: AI Agent Framework, the Pydantic way
• README: Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI.
• README: Tightly integrates with Pydantic Logfire, our general-purpose OpenTelemetry observability platform, for real-time debugging, evals-based performance monitoring, and behavior, trac…
• README: Model-agnostic: Supports virtually every model and provider: OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, and Perplexity; Azure AI Foundry, Amazon Bedrock, Google C…
• README: Fully Type-safe: Designed to give your IDE or AI coding agent as much context as possible for auto-completion and type checking, moving entire classes of errors from runtime to wr…

Reviewed at: 2026-07-17T12:17:10Z