# building-pydantic-ai-agents

Guides coding assistants in writing production Pydantic AI applications with tools and structured output. Useful for building typed agent workflows.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 5700 installs · ★ 145
- Author: pydantic
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/pydantic/skills/tree/HEAD/skills/building-pydantic-ai-agents/SKILL.md
- Updated: 2026-10-16
- HTML: https://affpapa.org/en/nevibecode/skills/building-pydantic-ai-agents

## What it does

This skill equips coding assistants with patterns for building applications using Pydantic AI. It covers `Agent` setup and dependency injection via `RunContext` alongside structured outputs typed by `BaseModel`. The assistant uses native agent patterns instead of treating the framework as standard Pydantic schema validation.

Included examples show how to register functions via `@agent.tool` and `@agent.tool_plain`, cache prompt prefixes with `Caching()`, and handle streaming. For observability, the skill configures Logfire instrumentation to track token costs and tool calls, with support for generic OpenTelemetry backends and unified provider access via Pydantic AI Gateway.

The framework requires Python 3.11+. Running it on older versions causes typing and runtime failures. It is not intended for projects that only need Pydantic schema validation or other frameworks like LangChain.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add pydantic/skills --skill building-pydantic-ai-agents -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/building-pydantic-ai-agents/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/building-pydantic-ai-agents/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add pydantic/skills --skill building-pydantic-ai-agents -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/building-pydantic-ai-agents/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add pydantic/skills --skill building-pydantic-ai-agents -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/building-pydantic-ai-agents/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install pydantic/skills building-pydantic-ai-agents
```

2. **Manually: put the files in place**
- File: `.github/skills/building-pydantic-ai-agents/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add pydantic/skills --skill building-pydantic-ai-agents -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/pydantic/skills --path skills/building-pydantic-ai-agents
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/building-pydantic-ai-agents/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add pydantic/skills --skill building-pydantic-ai-agents -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/pydantic/skills/tree/HEAD/skills/building-pydantic-ai-agents/SKILL.md

## When to use it
- build a Pydantic AI agent with structured BaseModel outputs
- inject runtime dependencies into tools using RunContext
- instrument model calls and token tracking with Logfire
- route multi-provider LLM requests through Pydantic AI Gateway

## Example prompts
- Create a Pydantic AI agent that returns responses in a CityLocation schema
- Add a tool with RunContext dependency injection to my dice game agent
- Set up Logfire instrumentation and prompt caching for a new Pydantic AI project

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: c403d792f4c5d17b.

BEGIN THIRD-PARTY FILE c403d792f4c5d17b
````text
---
name: building-pydantic-ai-agents
description: Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, attach a workspace, defer capability loading, stream output, define agents from YAML, or test agent behavior.
license: MIT
compatibility: Requires Python 3.11+
metadata:
  version: "1.1.2"
  author: pydantic
---

# Building AI Agents with Pydantic AI

Pydantic AI is a Python agent framework for building production-grade Generative AI applications.
This skill provides patterns, architecture guidance, and tested code examples for building applications with Pydantic AI.

## When to Use This Skill

Invoke this skill when:
- User asks to build an AI agent, create an LLM-powered app, or mentions Pydantic AI
- User wants to add tools, capabilities (thinking, web search), or structured output to an agent
- User asks to define agents from YAML/JSON specs or use template strings
- User wants to stream agent events, delegate between agents, or test agent behavior
- Code imports `pydantic_ai` or references Pydantic AI classes (`Agent`, `RunContext`, `Tool`)
- User asks about hooks, lifecycle interception, or agent observability with Logfire
- User wants to reach models from several providers with one API key (the Pydantic AI Gateway)
- User wants an agent to run commands or access files in an attached workspace
- The agent design includes optional instructions, specialist workflows, long-tail tools, or any context the model does not need on most turns

Do **not** use this skill for:
- The Pydantic validation library alone (`pydantic`/`BaseModel` without agents)
- Other AI frameworks (LangChain, LlamaIndex, CrewAI, AutoGen)
- General Python development unrelated to AI agents

## Quick-Start Patterns

### Create a Basic Agent

Start new applications with Logfire instrumentation and prompt caching in place, so the first run is already visible and repeated prompt prefixes are read from the cache (see [Set Up Observability and Model Access](#set-up-observability-and-model-access) for credentials and alternatives):

```python
import logfire

from pydantic_ai import Agent
from pydantic_ai.capabilities import Caching

logfire.configure()
logfire.instrument_pydantic_ai()

agent = Agent(
    'anthropic:claude-fable-5-1',
    name='hello_world_agent',
    instructions='Be concise, reply with one sentence.',
    capabilities=[Caching()],
)

result = agent.run_sync('Where does "hello world" come from?')
print(result.output)
"""
````
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