Structured Output for Agents
Learn how structured outputs with Pydantic make LLM responses clean, reliable, and programmatically usable.
We'll cover the following...
Imagine building a deck in any trading card game, and every time you draw a card, it is a blank page with a paragraph of text describing what the card may do. “This might be a flying creature... maybe. Or a spell. Who knows. Depends on the mood.” That’s what it’s like when an LLM responds with raw natural language and you try to programmatically use that output. Good luck parsing that on the fly. It’s like playing with your eyes covered, in a game that requires precision.
Structured output is the fix to this problem. It’s what enables us to move from intuition to real logic, from prose to parseable. Once your agent’s output is structured (think: JSON, data classes, enums), your tools and downstream systems know exactly what they’re working with. No guessing. No regex band-aids. Just clean, composable, actionable responses. And that’s why, we’re going to talk about the unsung hero of Python-based agent systems: Pydantic.
If the LLM is the brain and the tools are the hands—Pydantic is the skeleton. It holds everything together, keeps the data clean, and lets your agent speak in types your code can actually trust.
What exactly is Pydantic?
At its core, Pydantic is a data validation and parsing library for Python. In our case, when the LLM generates JSON, Pydantic acts as a reliable validator at the system’s boundary, ensuring the data conforms to the expected structure. If it is, great—your code runs smoothly. If not, Pydantic raises a helpful error before issues propagate ...