pydantic-ai
Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.
npx skills add majiayu000/claude-skill-registry --skill pydantic-ai-sickn33-antigravity-awesome --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PydanticAI — Typed AI Agents in Python ## Overview PydanticAI is a Python agent framework from the Pydantic team that brings the same type-safety and validation guarantees as Pydantic to LLM-based applications. It supports structured outputs (validated with Pydantic models), dependency injection for testability, streamed responses, multi-turn conversations, and tool use — across OpenAI, Anthropic, Google Gemini, Groq, Mistral, and Ollama. Use this skill when building production AI agents, chatbots, or LLM pipelines where correctness and testability matter. ## When to Use This Skill - Use when building Python AI agents that call tools and return structured data - Use when you need validated, typed LLM outputs (not raw strings) - Use when you want to write unit tests for agent logic without hitting a real LLM - Use when switching between LLM providers without rewriting agent code - Use when the user asks about `Agent`, `@agent.tool`, `RunContext`, `ModelRetry`, or `result_type` ## How It Works ### Step 1: Installation ```bash pip install pydantic-ai # Install extras for specific providers pip install 'pydantic-ai[openai]' # OpenAI / Azure OpenAI pip install 'pydantic-ai[anthropic]'
- Overview
- When to Use This Skill
- How It Works
- Step 1: Installation
- Step 2: A Minimal Agent
- Step 3: Structured Output with Pydantic Models
- Step 4: Tool Use
- Step 5: Dependency Injection
- Step 6: Testing with TestModel
- Step 7: Streaming Responses
- Step 8: Multi-Turn Conversations
- Examples
- Example 1: Code Review Agent
- Example 2: Agent with Retry Logic
pip install pydantic-ai Install extras for specific providers pip install 'pydantic-ai[openai]' # OpenAI / Azure OpenAI pip install 'pydantic-ai[anthropic]' # Anthropic Claude pip install 'pydantic-ai[gemini]' # Google Gemini pip install 'pydantic-ai[groq]' # Groq pip install 'pydantic-ai[vertexai]' # Google Vertex AI
What does the pydantic-ai skill do?
Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pydantic-ai-sickn33-antigravity-awesome --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
