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pydantic-ai

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更新日2026年2月2日 21:42

Expert guidance on building agents and tools with Pydantic AI.

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FullFran

FullFran/Hybrid-RAG-example

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Pydantic AI Expert Skill

This skill provides patterns for defining agents, dependencies, and tools using Pydantic AI.

🤖 Agent Definition

State Management: Use StateDeps[T] to pass dependencies (DB clients, settings) to agents.
System Prompts: Define complex prompts in src/prompts.py. Use @agent.instructions for dynamic context.
Model Choice: Use OpenAIModel as a base for OpenAI-compatible providers (OpenRouter, Ollama) via OpenAIProvider.

🛠️ Tool Patterns

Return Strings: Agents work best with text. Tools should return formatted strings, not Pydantic objects.
Resource Management: Initialize and cleanup DB connections properly within the tool or via injected deps.
Error Handling: Tools should catch exceptions and return helpful error messages to the agent rather than crashing.

📺 CLI & Streaming

Rich Integration: Use Rich to display real-time streaming of agent output and tool call details.
Node Handling: Implement logic for user_prompt, model_request, call_tools, and end nodes in the CLI.
Transparency: Always show the user which tool is being called and with what parameters.

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name	pydantic-ai
description	Expert guidance on building agents and tools with Pydantic AI.

Pydantic AI Expert Skill

This skill provides patterns for defining agents, dependencies, and tools using Pydantic AI.

🤖 Agent Definition

State Management: Use StateDeps[T] to pass dependencies (DB clients, settings) to agents.
System Prompts: Define complex prompts in src/prompts.py. Use @agent.instructions for dynamic context.
Model Choice: Use OpenAIModel as a base for OpenAI-compatible providers (OpenRouter, Ollama) via OpenAIProvider.

🛠️ Tool Patterns

Return Strings: Agents work best with text. Tools should return formatted strings, not Pydantic objects.
Resource Management: Initialize and cleanup DB connections properly within the tool or via injected deps.
Error Handling: Tools should catch exceptions and return helpful error messages to the agent rather than crashing.

📺 CLI & Streaming

Rich Integration: Use Rich to display real-time streaming of agent output and tool call details.
Node Handling: Implement logic for user_prompt, model_request, call_tools, and end nodes in the CLI.
Transparency: Always show the user which tool is being called and with what parameters.