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pydantic-ai

Create AI agents with PydanticAI for structured output and tool registration.

1|Updated Feb 17, 2026
Search Tags:#pydantic#llm#ai agent#orchestration#tool use#structured output
One-click install
npx skills add https://github.com/iaac-maai/structure_compliance_check --skill pydantic-ai-iaac-maai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic-ai
Source: https://github.com/iaac-maai/structure_compliance_check/tree/main/.gemini/skills/pydantic-ai-skill
Command: npx skills add https://github.com/iaac-maai/structure_compliance_check --skill pydantic-ai-iaac-maai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the development of AI agents by providing robust tools for structured output, tool integration, and multi-agent orchestration, making AI development more predictable and efficient.

Core Features & Use Cases

  • Type-Safe Agents: Define agents with Pydantic models for predictable, structured outputs.
  • Tool Integration: Easily register and use Python functions as tools for your agents.
  • Multi-Agent Orchestration: Build complex workflows by chaining or delegating tasks between multiple agents.
  • Use Case: Develop an AI assistant that can analyze customer feedback, categorize it using Pydantic models, and then trigger different actions (like creating a support ticket or updating a CRM) based on the analysis.

Quick Start

Use the pydantic-ai skill to create a simple agent that responds to the prompt "What is PydanticAI?".

Frequently Asked Questions about pydantic-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI agents with structured output using Python?▼

You can build AI agents with structured output by using Pydantic models to define expected responses, ensuring predictable and type-safe outputs from your LLM workflows.

What is the best way to orchestrate multiple AI agents in Python?▼

Multi-agent orchestration is achieved by chaining or delegating tasks between agents, enabling complex workflows like programmatic hand-offs and task delegation within your application.

How do I register Python functions as tools for an LLM agent?▼

You can register Python functions as tools for an LLM agent by integrating them directly into the agent setup, allowing the model to call your custom functions during execution.

Does PydanticAI support Google Gemini models for agent development?▼

Yes, PydanticAI supports Google Gemini models for agent development, allowing you to configure and integrate Gemini within your multi-agent orchestration workflows.

Do I need Python 3.10 or higher to use Pydantic models for AI agents?▼

Yes, you need Python 3.10 or higher to build type-safe AI agents with Pydantic models and utilize structured output, tool registration, and orchestration features.

Can I use system prompts to configure AI agent behavior in PydanticAI?▼

Yes, you can configure AI agent behavior by defining system prompts during the agent setup, allowing you to control how the model processes inputs and generates structured outputs.

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