Building Type-Safe LLM Agents With Pydantic AI

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.

What’s Included:

• 12 Lessons
• Video Subtitles and Full Transcripts
• 2 Downloadable Resources
• Accompanying Text-Based Tutorial
• Interactive Quiz to Check Your Progress
• 2 Hands-On Coding Exercises
• Q&A With Python Experts: Ask a Question
• Certificate of Completion

Downloadable Resources:

• Course Slides (.pdf)
• Sample Code (.zip)

Related Learning Paths:

• LLM Application Development With Python