Pydantic AI — Production AI Agent Framework

What is Pydantic AI?

Pydantic AI is a Python agent framework built by the Pydantic team. It brings type safety, structured outputs, and dependency injection to AI agent development — making agents as reliable and testable as regular Python code.

Answer-Ready: Pydantic AI is a production-grade AI agent framework by the Pydantic team that provides type-safe structured outputs, dependency injection, streaming, and multi-model support for building reliable AI agents in Python.

Best for: Python developers building production AI agents who need type safety and testability. Works with: Claude, GPT, Gemini, Groq, Mistral, Ollama. Setup time: Under 2 minutes.

Core Features

1. Structured Outputs with Pydantic Models

from pydantic import BaseModel
from pydantic_ai import Agent

class CityInfo(BaseModel):
    name: str
    country: str
    population: int

agent = Agent('claude-sonnet-4-20250514', result_type=CityInfo)
result = agent.run_sync('Tell me about Tokyo')
print(result.output)
# CityInfo(name='Tokyo', country='Japan', population=13960000)

2. Tool Definitions

from pydantic_ai import Agent, RunContext

agent = Agent('claude-sonnet-4-20250514')

@agent.tool
async def get_weather(ctx: RunContext[str], city: str) -> str:
    # Fetch weather data
    return f"Sunny, 22C in {city}"

result = agent.run_sync('Weather in London?')

3. Dependency Injection

from dataclasses import dataclass

@dataclass
class Deps:
    db: Database
    user_id: str

agent = Agent('claude-sonnet-4-20250514', deps_type=Deps)

@agent.tool
async def get_orders(ctx: RunContext[Deps]) -> list:
    return await ctx.deps.db.get_orders(ctx.deps.user_id)

result = agent.run_sync('Show my orders', deps=Deps(db=db, user_id='123'))

4. Streaming

async with agent.run_stream('Write a poem') as response:
    async for text in response.stream_text():
        print(text, end='', flush=True)

5. Multi-Model Support

from pydantic_ai.models import OpenAIModel, AnthropicModel

agent = Agent(OpenAIModel('gpt-4o'))         # OpenAI
agent = Agent(AnthropicModel('claude-sonnet-4-20250514'))  # Anthropic
agent = Agent('gemini-1.5-pro')              # Google
agent = Agent(OpenAIModel('llama3', base_url='http://localhost:11434/v1'))  # Ollama

FAQ

Q: How does it compare to LangChain? A: Pydantic AI is more opinionated and type-safe. LangChain is broader but more complex. Pydantic AI focuses on making agents testable and production-ready.

Q: Can I use it with Claude? A: Yes, Anthropic Claude is a first-class supported provider.

Q: Is it production ready? A: Yes, built by the Pydantic team (whose validation library is used by 80% of Python AI projects).