PydanticAI

Overview

PydanticAI is a Python agent framework designed to make it less painful to build production-grade applications with Generative AI. Created by the team behind Pydantic (the validation layer used by OpenAI SDK, Anthropic SDK, LangChain, and many other AI tools), PydanticAI brings Pydantic’s robust type safety and validation principles to AI agent development.

The framework is model-agnostic, supporting major LLM providers like OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral. Its key advantages include structured response validation, type safety through Pydantic, a dependency injection system for testing and production, and the ability to use standard Python control flow for agent composition.

System Requirements

• CPU: 2+ cores
• RAM: 4GB+
• GPU:
• Storage: 1GB+

Installation Guide

Prerequisites

• Basic knowledge of command line interfaces
• Git installed on your system

Installation

1. Install using pip:

   pip install pydantic-ai
   

2. Or install from source:

   git clone https://github.com/pydantic/pydantic-ai.git
   cd pydantic-ai
   pip install -e .
   

3. Set up your API keys (for OpenAI, Anthropic, etc.) as environment variables or in your code

Note: For detailed installation instructions specific to your operating system and environment, please refer to the official documentation on the project’s GitHub repository.

Practical Exercise: Creating Your First PydanticAI Agent

Let’s create a simple AI agent using PydanticAI to see how it works.

Step 1: Create a Basic Agent

from pydantic_ai import Agent, OpenAIModel
from pydantic import BaseModel, Field
from typing import List

# Define the result model with Pydantic
class BookRecommendation(BaseModel):
    title: str = Field(description="The title of the book")
    author: str = Field(description="The author of the book")
    genre: str = Field(description="The genre of the book")
    reason: str = Field(description="Why this book is recommended")

class BookRecommendations(BaseModel):
    recommendations: List[BookRecommendation] = Field(description="List of book recommendations")

# Create an agent
agent = Agent(model=OpenAIModel(model_name="gpt-4o"))

# Define a function for structured output
@agent.result_type(BookRecommendations)
async def get_book_recommendations(topic: str, audience: str):
    """
    Recommend books on a given topic for a specific audience.

    Args:
        topic: What the books should be about
        audience: Who the books are for (e.g., "beginners", "teenagers")

    Returns:
        BookRecommendations: A list of recommended books with details
    """
    pass  # The LLM will implement this function

Step 2: Using Your Agent

import asyncio

async def main():
    # Get book recommendations
    results = await get_book_recommendations(
        topic="space exploration",
        audience="teenagers"
    )

    # Print the structured results
    for i, book in enumerate(results.recommendations, 1):
        print(f"Recommendation {i}:")
        print(f"Title: {book.title}")
        print(f"Author: {book.author}")
        print(f"Genre: {book.genre}")
        print(f"Reason: {book.reason}")
        print()

if __name__ == "__main__":
    asyncio.run(main())

Step 3: Advanced Features

PydanticAI offers many advanced features you can explore:

• Tools and Dependencies: Add custom tools with the @agent.tool decorator and leverage dependency injection
• System Prompts: Customize system prompts with @agent.system_prompt to control agent behavior
• Streaming Responses: Get token-by-token results with streaming capabilities
• Model Switching: Easily swap between different LLM providers
• Error Handling: Gracefully handle validation failures and runtime errors

Resources

Official Documentation

The official documentation provides comprehensive guides, examples, and API reference.

Read the Documentation

GitHub Repository

Find the source code, contribute, and report issues on GitHub.

GitHub Repository

Example Projects

Explore example projects and implementations to learn more about PydanticAI.

Example Code

Community Support

Join the Pydantic community on Discord to discuss PydanticAI.

Pydantic Discord