pydantic/pydantic-ai

★ 17,050PythonAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((pydantic-ai))
    What it does
      AI agent framework
      Structured outputs
      Type-safe APIs
    Supported models
      OpenAI
      Anthropic
      Gemini
      Custom providers
    Features
      Tool calling
      Evals system
      Observability
      Human-in-the-loop
    Use Cases
      LLM backends
      AI agents
      Multi-step workflows
      Production apps

mindmap root((pydantic-ai)) What it does AI agent framework Structured outputs Type-safe APIs Supported models OpenAI Anthropic Gemini Custom providers Features Tool calling Evals system Observability Human-in-the-loop Use Cases LLM backends AI agents Multi-step workflows Production apps

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Things people build with this

USE CASE 1

Add an LLM-powered Q&A or summarization feature to a Python backend, switching between OpenAI and Anthropic without changing your application code.

USE CASE 2

Build an AI agent that calls custom tools like searching a database or sending an email, and returns structured validated outputs.

USE CASE 3

Evaluate and test AI agent response quality using the built-in evals system before shipping to production.

USE CASE 4

Build a multi-step AI workflow with human-in-the-loop approval for specific tool calls, using durable execution for reliability.

Tech stack

PythonOpenAIAnthropicGemini

Getting it running

Difficulty · moderate Time to first run · 30min

Requires an API key for at least one supported model provider such as OpenAI, Anthropic, or Gemini.

In plain English

Pydantic AI is a Python library for building applications and agents that use generative AI models, the kind of AI that produces text, answers questions, or takes actions based on instructions. An agent here means a program that wraps a language model, gives it tools, and runs it in a structured way. The library is built by the team behind Pydantic, a popular Python tool that checks the shape of data, and its stated goal is bringing the FastAPI feel to this newer category of AI apps. Conceptually, you create an Agent object, tell it which underlying model to talk to, give it instructions, and then call methods like run_sync with a prompt to get a result. From there you can add tools the agent can call, dynamic instructions, structured outputs the model must conform to, and composable capabilities like web search or step-by-step thinking. The README says the library is model-agnostic and supports many providers including OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, and others, with a path for custom ones. Other listed features include type-safe APIs for IDE help, an evals system for testing agent quality, observability through Pydantic Logfire or any OpenTelemetry backend, support for Model Context Protocol and Agent-to-Agent interop, streamed structured outputs, human-in-the-loop tool approval, durable execution, and graph definitions for complex flows. You would use Pydantic AI when writing a Python backend that needs an LLM-powered feature or agent and want validation, type safety, observability, and a single API across many providers. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

Show me how to create a Pydantic AI agent that uses OpenAI, has a tool to look up a user's order status from a dict, and returns a structured Pydantic model response.

Prompt 2

How do I add observability to a Pydantic AI agent using OpenTelemetry so I can trace each LLM call and tool invocation in my backend?

Prompt 3

Walk me through writing a Pydantic AI eval that checks whether my agent's answers to 20 test prompts meet a minimum quality threshold.

Prompt 4

How do I define a multi-agent workflow in Pydantic AI where one agent researches a topic and a second agent writes a summary, with results validated by Pydantic models?

Prompt 5

Show me how to switch a Pydantic AI agent from OpenAI GPT-4 to Anthropic Claude without changing any tool definitions or response schemas.

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