# Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs

A clean solution for reliable LLM applications in Python.

- **Source:** [Towards Data Science](https://towardsdatascience.com)
- **Published:** 2026-07-14
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://towardsdatascience.com/pydantic-openai-the-cleanest-way-to-get-structured-outputs-from-llms/>
- **On Brifing:** https://brifing.si/s/a2f4f653-bca5-4594-a231-3481391092f5

## The Importance of Structured Outputs

Obtaining machine-readable outputs from LLMs is a critical need for modern applications. These outputs ensure data is processed accurately and consistently.

## OpenAI's Structured Outputs

OpenAI offers structured outputs through methods like JSON Mode and Function Calling. However, processing these outputs on the Python side requires additional steps.

## The Role of Pydantic

Pydantic is a Python library used for data validation. It defines data types and raises errors for data that doesn't conform to these types.

## Defining Models with Pydantic

Pydantic allows you to define data schemas as Python classes. This ensures automatic data validation and type safety.

## Integration with OpenAI

Integration of OpenAI's API with Pydantic automatically generates JSON schemas and converts outputs into Python objects.

## Advantages of Pydantic

Pydantic makes code cleaner, safer, and easier to maintain. Data validation and type safety are handled automatically.

## Example Application

Using Pydantic, you can define a person info model and automatically validate this information with the OpenAI API.

## Nested Models and Lists

Pydantic makes it easy to define nested data structures. This transforms complex JSON schemas into simple Python classes.

## Features of OpenAI SDK

The OpenAI SDK offers specific methods for working with Pydantic models. This ensures fully automated processing of structured outputs.

## Reliable Applications with Pydantic

The integration of Pydantic and OpenAI ensures LLM-based applications are more reliable and error-free. This is a significant advantage for developers.

## Conclusion and Recommendations

The combination of Pydantic and OpenAI is one of the cleanest and most effective solutions for developing LLM-based applications in Python. Adopting this method will enhance the quality of your projects.

## Sources covering this story

- https://towardsdatascience.com/pydantic-openai-the-cleanest-way-to-get-structured-outputs-from-llms/ — 

---

_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._