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pydantic-ai

Design and operate Pydantic AI agents with dependency injection and structured outputs.

1|Updated Jan 13, 2026
Search Tags:#AI agents#build#observability#dependency injection#Pydantic AI#structured outputs
One-click install
npx skills add https://github.com/osocode/code-tools-bootstrap --skill pydantic-ai-osocode
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic-ai
Source: https://github.com/osocode/code-tools-bootstrap/tree/main/skills/generated/claude-code/pydantic-ai
Command: npx skills add https://github.com/osocode/code-tools-bootstrap --skill pydantic-ai-osocode

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Developers struggle to implement robust AI agents with reliable architectures, tool integration, and observability across multi-agent systems. This Skill provides a framework to express architecture, dependency injection, tool design, structured outputs, streaming, and production-grade observability using Pydantic AI.

Core Features & Use Cases

  • Dependency injection for clean, testable agent code and easy context sharing.
  • Structured outputs and streaming for reliable data exchange with LLMs and tools.
  • Graph-based and multi-agent workflows for complex problem solving (e.g., orchestrating planning and execution across tools).
  • Observability patterns, logging, and tracing to monitor agent execution in cloud-native environments.
  • Use Case: Build an agent that coordinates multiple tools to collect data, process it, and return a structured result.

Quick Start

  1. Install the pydantic-ai package and import Agent. 2) Define a minimal dependency type and create an Agent instance. 3) Run a simple prompt to see a structured output. Example: from pydantic_ai import Agent; class MyDeps: pass; agent = Agent('openai:gpt-4o', deps_type=MyDeps); result = agent.run_sync('Summarize data')

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 dependency injection?▼

AI agents with dependency injection are built by defining a dependency type and passing it to the Agent instance. This approach ensures clean, testable agent code and easy context sharing across multi-agent systems.

How do I get structured outputs from AI agents?▼

Structured outputs from AI agents are obtained by running prompts through an Agent instance. This mechanism provides reliable data exchange with LLMs and tools, ensuring predictable data formats for downstream processing.

What is the best way to orchestrate multi-agent workflows?▼

Multi-agent workflows are orchestrated using graph-based architectures. This approach coordinates planning and execution across multiple tools, enabling agents to collect data, process it, and return structured results for complex problem solving.

How does observability work for production-grade AI agents?▼

Observability for production-grade AI agents works through built-in logging and tracing patterns. These features monitor agent execution in cloud-native environments, allowing developers to track tool orchestration and system reliability.

Can I use streaming with structured outputs in Pydantic AI?▼

Streaming is supported alongside structured outputs in Pydantic AI. This combination enables reliable data exchange with LLMs and tools while processing continuous data streams during agent execution.

Do I need to define dependencies to test AI agent code?▼

Defining dependencies is required to test AI agent code effectively. Dependency injection creates clean, testable architectures by separating context sharing and tool integration from core agent logic.

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