Pydantic AI Agent Builder

What problem does it solve?

This skill provides comprehensive guidance for building production-grade AI agents using the Pydantic AI framework, solving challenges related to unstructured LLM outputs, lack of type safety, and complex agent orchestration. It enables developers to create reliable, scalable, and maintainable AI applications.

Core Features & Use Cases

• Type-Safe Agents: Define structured outputs with Pydantic models for guaranteed valid responses.
• Agent with Tools: Integrate external APIs and databases using tool calling with dependency injection.
• Multi-Step & Multi-Agent Systems: Orchestrate complex workflows and compose specialized agents for advanced tasks.
• Streaming & Retry Logic: Implement real-time responses and robust error handling with automatic retries on validation failures.
• RAG Implementation: Build Retrieval Augmented Generation (RAG) agents by integrating with vector databases.
• Testing & Production Patterns: Learn best practices for testing agents, error handling, logging, rate limiting, and prompt management.
• Use Case: Develop a multi-agent system where a "Researcher" agent gathers information using RAG, a "Summarizer" agent condenses findings, and a "Formatter" agent prepares a final report, all with type-safe outputs and robust error handling.

Quick Start

Show me how to create a simple Pydantic AI agent that recommends movies with a structured output.