Configure AI outputs
The Outlines library enables the responses from large language models to be presented as structured outputs according to predefined schemas. With this Python-based tool, developers protect data integrity by restricting model outputs with regular expressions or context-free grammar rules.
Updates
- August 7, 2026: Stars 15,477 → 15,525, latest release 1.3.3 (August 6, 2026).
- August 2, 2026: Stars 14,917 → 15,477, latest release 1.3.2 (July 20, 2026).
What you get
- Constrains model outputs according to predefined schemas
- Fully compatible with JSON or Python data types
- Eliminates the need to debug erroneous outputs
Installation
pip install outlinesRunning it
import outlines
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
model = outlines.from_transformers(
AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto"),
AutoTokenizer.from_pretrained(MODEL_NAME)
)If you don't write code
I want to restrict the response from an AI model to a specific Pydantic data structure or Python type (e.g. int or Literal) using the Outlines library. How can I use the model(request, output_type) function after defining the model object to ensure that the model's output always conforms to the schema I want? Please explain with example how to define the Pydantic model for complex objects and apply this structure to the model output.
Repository: https://github.com/dottxt-ai/outlines. Follow the official instructions in the README of this repository for setup and execution.
Before starting, check which OS we are on and if the necessary tools (e.g., Python, Node.js, Git, Docker) are installed. Install any missing ones step-by-step according to this system.
Create a new, empty folder to work in. Do not touch any files outside this folder and do not delete anything. Tell me what you are going to do in one sentence before running each command.
Tell me what I should see on the screen at the end of each step. If an error occurs, I will paste the error message to you exactly as it is, and we will fix it together.
Related dictionary terms
Links
TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-07-22: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. The introductory text was prepared with AI from the repository's own documentation, and this page was machine-translated from the Turkish original · the Turkish version prevails.