# Outlines

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill outlines -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill outlines -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill outlines -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill outlines -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Outlines: Structured Text Generation

## When to Use This Skill

Use Outlines when you need to:
- **Guarantee valid JSON/XML/code** structure during generation
- **Use Pydantic models** for type-safe outputs
- **Support local models** (Transformers, llama.cpp, vLLM)
- **Maximize inference speed** with zero-overhead structured generation
- **Generate against JSON schemas** automatically
- **Control token sampling** at the grammar level

**GitHub Stars**: 8,000+ | **From**: dottxt.ai (formerly .txt)

## Installation

```bash
# Base installation
pip install outlines

# With specific backends
pip install outlines transformers  # Hugging Face models
pip install outlines llama-cpp-python  # llama.cpp
pip install outlines vllm  # vLLM for high-throughput
```

## Quick Start

### Basic Example: Classification

```python
import outlines
from typing import Literal

# Load model
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")

# Generate with type constraint
prompt = "Sentiment of 'This product is amazing!': "
generator = outlines.generate.choice(model, ["positive", "negative", "neutral"])
sentiment = generator(prompt)

print(sentiment)  # "positive" (guaranteed one of these)
```

### With Pydantic Models

```python
from pydantic import BaseModel
import outlines

class User(BaseModel):
    name: str
    age: int
    email: str

model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")

# Generate structured output
prompt = "Extract user: John Doe, 30 years old, john@example.com"
generator = outlines.generate.json(model, User)
user = generator(prompt)

print(user.name)   # "John Doe"
print(user.age)    # 30
print(user.email)  # "john@example.com"
```

## Core Concepts

### 1. Constrained Token Sampling

Outlines uses Finite State Machines (FSM) to constrain token generation at the logit level.

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/16-prompt-engineering/outlines/SKILL.md