Pydantic AI in 10 Minutes

With new agent frameworks and services seemingly emerging weekly, deciding where to focus your attention and learning efforts can be challenging.

But of all the frameworks out there, nothing has captured my attention more than Pydantic AI:

The reasons for this are simple:

• Built on a proven foundation: Pydantic is the definitive validation library in Python. Virtually every company doing serious work with data and analytics relies on it, from tech giants to startups.
• Created by an exceptional team: The Pydantic team has demonstrated their technical excellence repeatedly. They understand the problems developers face because they've solved them at scale.
• Born from genuine need: The framework wasn't created to chase a trend. The Pydantic team developed it to solve their own challenges with agent development—they're scratching their own itch.
• Developer-friendly design: Working with Pydantic AI is genuinely enjoyable. The framework's intuitive approach significantly boosts productivity by eliminating common pain points and reducing boilerplate code.
• Positioned for enterprise adoption: While many frameworks come and go, Pydantic AI has the foundation for serious enterprise adoption. Organizations already using Pydantic for data validation will naturally gravitate toward Pydantic AI for their agent development needs.

Pydantic AI stands out by building on the strengths that made its parent library indispensable to the Python ecosystem.

The Pydantic team's mission is clear: do for generative AI what FastAPI did for web development. That means creating a framework that's intuitive to learn, accelerates your development process, and significantly reduces bugs through comprehensive type checking and data validation.

In this video, we will explore Pydantic AI by building something useful - a Shopify customer support agent that can personalize responses, access customer data, and provide product recommendations.

This will teach you how to integrate external APIs, inject dependencies for personalization, and extend your agent with context-aware tools.

I'll walk you through the core concepts step by step:

• Creating a basic agent
• Connecting to external APIs
• Injecting dependencies for personalization
• Building tools that extend your agent's capabilities
• Ensuring type safety throughout your application

By the end, you'll understand how Pydantic AI can speed up your agent development workflow while maintaining production-grade reliability.