Outlines

// Overview

Outlines is a Python library for structured generation. Instead of cleaning up an LLM's text after the fact with parsing and regex, it constrains the model's token output during generation so the result always matches the structure you asked for, whether that's a JSON schema, a Pydantic model, a regular expression, or a plain Python type.

It is aimed at developers who need reliable, machine-readable output from language models, for example to extract fields from text, classify inputs, or feed an LLM's result into downstream code without it breaking on malformed JSON. You pass the prompt and a desired output type, and Outlines guarantees the result conforms to it.

As a structured-output tool in the LLM orchestration space, Outlines runs the same code across several backends, including OpenAI, Ollama, vLLM, and Transformers, so you can switch models without rewriting your generation logic.

// What it does

• Guarantees valid structure during generation, not by post-hoc parsing or regex cleanup
• Works across many model backends (OpenAI, Ollama, vLLM, Transformers, and more) with the same code
• Accepts plain Python types as output specs: Literal for classification, int for numbers, Pydantic models for complex objects
• Returns JSON you can validate directly with a Pydantic model's model_validate_json
• Provider independence so you can change the underlying model without changing your code
• Used in production by teams including NVIDIA, Cohere, Hugging Face, and vLLM

// Getting started

Install Outlines, connect it to a model, then pass the prompt plus the output type you want.

pip install outlines

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)
)

from typing import Literal

sentiment = model(
    "Analyze: 'This product completely changed my life!'",
    Literal["Positive", "Negative", "Neutral"]
)
print(sentiment)  # "Positive"

temperature = model("What's the boiling point of water in Celsius?", int)
print(temperature)  # 100

from pydantic import BaseModel

class ProductReview(BaseModel):
    pros: list[str]
    cons: list[str]
    summary: str

review = model(
    "Review: The XPS 13 has great battery life and a stunning display, but it runs hot.",
    ProductReview,
    max_new_tokens=200,
)
review = ProductReview.model_validate_json(review)
print(review.summary)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Turn free-form customer emails into structured support tickets with priority, category, and action items for automated routing
• Classify text inputs into a fixed set of labels using a Literal type
• Extract typed fields (numbers, lists, nested objects) from documents into a Pydantic model you can pass straight to downstream code
• Get reliable JSON from a local or hosted LLM without writing defensive parsing for malformed output

// How Outlines compares

Outlines alongside other open-source structured output tools AI/TLDR tracks, ranked by GitHub stars.