pydanticai-docslisted

# Pydantic AI Documentation Skill ## Overview This skill provides guidance for using **Pydantic AI** - a Python agent framework for building production-grade Generative AI applications. Pydantic AI emphasizes type safety, dependency injection, and structured outputs. ## Key Concepts ### Agents Agents are the primary interface for interacting with LLMs. They contain: - **Instructions**: System prompts for the LLM - **Tools**: Functions the LLM can call - **Output Type**: Structured datatype the LLM must return - **Dependencies**: Data/services injected into tools and prompts ### Models Pydantic AI supports multiple LLM providers via model identifiers. All models that supports tool-calling can be used with pydantic-ai-skills. ### Tools Two types of tools: - `@agent.tool`: Receives `RunContext` with dependencies - `@agent.tool_plain`: Plain function without context ### Toolsets Collections of tools that can be registered with agents: - `FunctionToolset`: Group multiple tools - `MCPServerTool`: Model Context Protocol servers - Third-party toolsets (ACI.dev, etc.) ## Instructions ### 1. Fetch Full Documentation For comprehensive information, fetch the complete Pydantic AI documentation: <https://ai.pydantic.dev/llms.txt> This contains complete documentation including agents, tools, dependencies, models, and API reference. ### 2. Quick Reference #### Basic Agent Creation ```python from pydantic_ai import Agent agent = Agent('openai:gpt-5.2') result = agent.ru