Pydantic AI Agent Builder
Expert guidance for building AI agents with Pydantic AI framework. Use when creating multi-agent systems, AI orchestration workflows, or structured LLM applications with type safety and validation.
npx skills add majiayu000/claude-skill-registry --skill pydantic-ai-agent-builder --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.
What it does
Provides a comprehensive framework and patterns for constructing production-ready AI agents using Pydantic AI, with emphasis on type safety, structured outputs, and enterprise practices across simple agents, tool-enabled agents, multi-step stateful agents, streaming, retries, RAG, custom models, testing, and production patterns.
How it works
- Describes core concepts of Pydantic AI and its features: type-safe runtime validation, model-agnostic compatibility, structured outputs, dependency injection, streaming, and tool/function calling.
- Presents basic agent patterns including: 1) Simple Agent (define response model, instantiate Agent with model and system_prompt, run with input), 2) Agent with Tools (define dependencies, expose tool functions with @agent.tool, pass deps when running), 3) Multi-Step Agent with State (define state and result models, use tools that mutate state, run with initial state), 4) Agent with Structured Output (define a Pydantic model for outputs like CodeReview), and 5) Multi-Agent System (orchestrator coordinating specialized agents).
- Provides advanced patterns such as streaming (agent.run_stream with real-time chunks), retry logic (ModelRetry, @agent.result_validator), RAG (Retrieval Augmented Generation) with a RunContext and deps, custom model usage with infer_model, and agent testing scaffolds.
- Covers production patterns including error handling & logging, rate limiting & cost control, caching, and prompt management using templates.
When to use it
Use when building multi-agent systems, AI orchestration workflows, or structured LLM applications that require type safety, validated outputs, modularity, and enterprise-friendly features.
What it can touch
- Tools declared in examples: @agent.tool decorated functions (e.g., search_web, search_database, search_knowledge_base).
- Dependencies via RunContext[Deps] and a deps_type class (e.g., SearchDeps, RAGDeps).
- External integrations and services through tool calls, data fetches, and database interactions as shown in the examples.
Caveats
- License is MIT for the skill; use within licensing terms.
- The content describes usage patterns and code structure but does not guarantee runtime success; actual behavior depends on the underlying model and environment.
# Pydantic AI Agent Builder Comprehensive system for building production-grade AI agents using Pydantic AI with type safety, structured outputs, and enterprise patterns. ## Core Concepts Pydantic AI is a Python agent framework designed to make it less painful to build production-grade applications with Generative AI. ### Key Features - **Type-safe**: Built on Pydantic for runtime validation - **Model-agnostic**: Works with OpenAI, Anthropic, Gemini, Ollama - **Structured outputs**: Guaranteed valid responses - **Dependency injection**: Clean testing and modularity - **Streaming support**: Real-time responses - **Tool/function calling**: External integrations ## Basic Agent Patterns ### 1. Simple Agent ```python from pydantic_ai import Agent from pydantic import BaseModel # Define response model class MovieRecommendation(BaseModel): title: str year: int genre: str reason: str # Create agent agent = Agent( 'openai:gpt-4o', result_type=MovieRecommendation, system_prompt='You are a movie recommendation expert.', ) # Run agent async def get_recommendation(preferences: str): result = await agent.run(preferences) return result.data # Usage recommendation = await get_recommendation("sci-fi
- Core Concepts
- Key Features
- Basic Agent Patterns
- 1. Simple Agent
- 2. Agent with Tools
- 3. Multi-Step Agent with State
- 4. Agent with Structured Output
- Advanced Patterns
- 5. Multi-Agent System
- 6. Agent with Streaming
- 7. Agent with Retry Logic
- 8. Agent with RAG (Retrieval Augmented Generation)
- 9. Agent with Custom Model
- 10. Agent Testing
What does the Pydantic AI Agent Builder skill do?
Expert guidance for building AI agents with Pydantic AI framework. Use when creating multi-agent systems, AI orchestration workflows, or structured LLM applications with type safety and validation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pydantic-ai-agent-builder --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.