Building Type-Safe LLM Agents With Pydantic AI (Overview)

Rodrigo Girão Serrão
Building Type-Safe LLM Agents With Pydantic AI Rodrigo Girão Serrão 01:31
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Pydantic AI is a Python framework for building LLM agents that return validated, structured outputs using Pydantic models. Instead of parsing raw strings from LLMs, you get type-safe objects with automatic validation.

If you’ve used FastAPI or Pydantic before, then you’ll recognize the familiar pattern of defining schemas with type hints and letting the framework handle the type validation for you.

By the end of this video course, you’ll understand that:

  • Pydantic AI uses BaseModel classes to define structured outputs that guarantee type safety and automatic validation.
  • The @agent.tool decorator registers Python functions that LLMs can invoke based on user queries and docstrings.
  • Dependency injection with deps_type provides type-safe runtime context like database connections without using global state.
  • Validation retries automatically rerun queries when the LLM returns invalid data, which increases reliability but also API costs.
  • Google Gemini, OpenAI, and Anthropic models support structured outputs best, while other providers have varying capabilities.
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00:00 Hey there, and welcome. My name is Rodrigo, and it’s my pleasure to welcome you to this video course on Building Type-Safe LLM Agents With Pydantic AI. Now, the whole course is going to hinge on a very simple idea.

00:13 When you interact with an LLM, you send text, sometimes other media as well, but you send it text, and then the LLM replies with text. And this is the typical loop of interaction.

00:23 If you open ChatGPT or Gemini or any other interface, this is how it works. But if you put Pydantic AI in the middle, which is the tool you’ll learn about, then you can leverage Pydantic AI to get structured output out of LLMs. So that’s exactly what you’re going to learn how to do.

00:42 You’re going to use Pydantic AI to interact with an LLM, and by doing that, you’ll get models as responses, which essentially just means you’ll be getting structured outputs.

00:55 So overall, what you’re going to do in this course is set up Pydantic AI. It’s a regular Python package that you can just pip install. You’re going to define models for structured outputs.

01:06 So you’re going to define the structure you want for your outputs, and you’re going to see how to do that in code. You’re going to augment Pydantic AI with tools, and you’re going to decouple dependencies in your code for easier testing.

01:21 So these are the four main things you’re going to learn throughout this video course, and once you’re ready to get everything set up, you can move on to the next lesson.

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