/pydantic-ai

name: pydantic-ai
description: "Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support."
metadata:
  category: ai-agents
  risk: safe
  source: community
  date_added: "2026-03-18"
  author: suhaibjanjua
  tags: [pydantic-ai, ai-agents, llm, openai, anthropic, gemini, tool-use, structured-output, python]
  tools: [claude, cursor, gemini]

PydanticAI — Typed AI Agents in Python

Overview

PydanticAI is a Python agent framework from the Pydantic team that brings the same type-safety and validation guarantees as Pydantic to LLM-based applications. It supports structured outputs (validated with Pydantic models), dependency injection for testability, streamed responses, multi-turn conversations, and tool use — across OpenAI, Anthropic, Google Gemini, Groq, Mistral, and Ollama. Use this skill when building production AI agents, chatbots, or LLM pipelines where correctness and testability matter.

When to Use This Skill

• Use when building Python AI agents that call tools and return structured data
• Use when you need validated, typed LLM outputs (not raw strings)
• Use when you want to write unit tests for agent logic without hitting a real LLM
• Use when switching between LLM providers without rewriting agent code
• Use when the user asks about `Agent`, `@agent.tool`, `RunContext`, `ModelRetry`, or `result_type`

How It Works

Step 1: Installation

pip install pydantic-ai

# Install extras for specific providers
pip install 'pydantic-ai[openai]'       # OpenAI / Azure OpenAI
pip install 'pydantic-ai[anthropic]'    # Anthropic Claude
pip install 'pydantic-ai[gemini]'       # Google Gemini
pip install 'pydantic-ai[groq]'         # Groq
pip install 'pydantic-ai[vertexai]'     # Google Vertex AI

Step 2: A Minimal Agent

from pydantic_ai import Agent

# Simple agent — returns a plain string
agent = Agent(
    'anthropic:claude-sonnet-4-6',
    system_prompt='You are a helpful assistant. Be concise.',
)

result = agent.run_sync('What is the capital of Japan?')
print(result.data)  # "Tokyo"
print(result.usage())  # Usage(requests=1, request_tokens=..., response_tokens=...)

Step 3: Structured Output with Pydantic Models

from pydantic import BaseModel
from pydantic_ai import Agent

class MovieReview(BaseModel):
    title: str
    year: int
    rating: float  # 0.0 to 10.0
    summary: str
    recommended: bool

agent = Agent(
    'openai:gpt-4o',
    result_type=MovieReview,
    system_prompt='You are a film critic. Return structured reviews.',
)

result = agent.run_sync('Review Inception (2010)')
review = result.data  # Fully typed MovieReview instance
print(f"{review.title} ({review.year}): {review.rating}/10")
print(f"Recommended: {review.recommended}")

Step 4: Tool Use

Register tools with `@agent.tool` — the LLM can call them during a run:

from pydantic_ai import Agent, RunContext
from pydantic import BaseModel
import httpx

class WeatherReport(BaseModel):
    city: str
    temperature_c: float
    condition: str

weather_agent = Agent(
    'anthropic:claude-sonnet-4-6',
    result_type=WeatherReport,
    system_prompt='Get current weather for the requested city.',
)

@weather_agent.tool
async def get_temperature(ctx: RunContext, city: str) -> dict:
    """Fetch the current temperature for a city from the weather API."""
    async with httpx.AsyncClient() as client:
        r = await client.get(f'https://wttr.in/{city}?format=j1')
        data = r.json()
        return {
            'temp_c': float(data['current_condition'][0]['temp_C']),
            'description': data['current_condition'][0]['weatherDesc'][0]['value'],
        }

import asyncio
result = asyncio.run(weather_agent.run('What is the weather in Tokyo?'))
print(result.data)

Step 5: Dependency Injection

Inject services (database, HTTP clients, config) into agents for testability:

from dataclasses import dataclass
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel

@dataclass
class Deps:
    db: Database
    user_id: str

class SupportResponse(BaseModel):
    message: str
    escalate: bool

support_agent = Agent(
    'openai:gpt-4o-mini',
    deps_type=Deps,
    result_type=SupportResponse,
    system_prompt='You are a support agent. Use the tools to help customers.',
)

@support_agent.tool
async def get_order_history(ctx: RunContext[Deps]) -> list[dict]:
    """Fetch recent orders for the current user."""
    return await ctx.deps.db.get_orders(ctx.deps.user_id, limit=5)

@support_agent.tool
async def create_refund(ctx: RunContext[Deps], order_id: str, reason: str) -> dict:
    """Initiate a refund for a specific order."""
    return await ctx.deps.db.create_refund(order_id, reason, ctx.deps.user_id)

# Usage
async def handle_support(user_id: str, message: str):
    deps = Deps(db=get_db(), user_id=user_id)
    result = await support_agent.run(message, deps=deps)
    return result.data

Step 6: Testing with TestModel

Write unit tests without real LLM calls:

from pydantic_ai.models.test import TestModel

def test_support_agent_escalates():
    with support_agent.override(model=TestModel()):
        # TestModel returns a minimal valid response matching result_type
        result = support_agent.run_sync(
            'I want to cancel my account',
            deps=Deps(db=FakeDb(), user_id='user-123'),
        )
    # Test the structure, not the LLM's exact words
    assert isinstance(result.data, SupportResponse)
    assert isinstance(result.data.escalate, bool)

**FunctionModel** for deterministic test responses:

from pydantic_ai.models.function import FunctionModel, ModelContext

def my_model(messages, info):
    return ModelResponse(parts=[TextPart('Always this response')])

with agent.override(model=FunctionModel(my_model)):
    result = agent.run_sync('anything')

Step 7: Stre