Instructor and Pydantic AI: Type-Safe Outputs as the Contract
LLMs answer in prose; your code needs data. Instructor wraps any LLM client so the model must fill out a Pydantic form, and failed checks trigger automatic repair retries until you get typed Python objects. Pydantic AI (1.0, September 2025) is the same team's agent framework built on one principle: typed, verifiable outputs are the interface between models and software.
01.The Problem: Your Model Answers in Prose, Your Code Needs Data
Imagine you build one small feature.
An email arrives. You want the name, age, and city of the person who wrote it, stored in a database.
So you ask an LLM:
"Extract the name, age, and city from this email."
Sometimes it behaves and gives you exactly that.
Other days it gives you:
- a full sentence: "The person is Ravi, he is twenty years old and lives in Pune."
- JSON with a missing comma.
- an age written as the word
twentyinstead of the number 20. - a field called
"Name"with a capital N, when your code expects"name".
Your code then does something like int(data["age"]) — and crashes at 2 a.m. on a word.
You can beg in the prompt. "Please return valid JSON. Do not add text." It helps. It never guarantees anything. A language model is, at heart, a very creative text guesser.
So the question becomes
How do you get a creative text guesser to reliably hand you data your program can use?
Teams first tried glue: regex-scrape the model's text, patch missing keys, retry by hand. That works until it doesn't — and every production team eventually pays for it.
The better answer: stop asking for prose. Demand a form.
Validate-or-retry: the structured-output loop
Validate-or-retry: the structured-output loop
Both libraries make a Pydantic schema the boundary contract: responses are parsed and validated, validation errors feed automatic repair retries, and your code receives typed objects instead of strings.
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