Pydantic AI: Python Agent Framework

Pydantic AI represents a significant advancement in Python-based AI development, bringing the proven data validation patterns of Pydantic to the world of AI agents and applications.

Key Advantages

Type-Safe AI Development

Unlike many AI frameworks that treat responses as unstructured text, Pydantic AI enforces type safety and data validation at every step, ensuring your AI applications are robust and reliable.

Production-Grade Design

Built specifically for production environments, Pydantic AI includes comprehensive error handling, monitoring capabilities, and debugging tools that are essential for enterprise AI applications.

Seamless Integration

If you're already using Pydantic in your Python applications, Pydantic AI feels natural and familiar, extending your existing validation patterns to AI workflows.

Installation

pip install pydantic-ai

Quick Start

from pydantic_ai import Agent
from pydantic import BaseModel

class WeatherResponse(BaseModel):
    location: str
    temperature: float
    conditions: str

agent = Agent(
    'openai:gpt-4',
    result_type=WeatherResponse,
)

result = agent.run_sync('What is the weather like in Paris?')
print(result.data)
# WeatherResponse(location='Paris', temperature=22.5, conditions='sunny')

CLI Usage

The clai command-line tool provides instant access to language models:

# Interactive chat
clai

# Single query
clai "Explain quantum computing in simple terms"

# With specific model
clai --model claude "Write a Python function to sort a list"

This framework is particularly valuable for teams building production AI applications who want the reliability and developer experience that Pydantic provides for traditional data validation.