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

# 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

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

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, [email protected]"
generator = outlines.generate.json(model, User)
user = generator(prompt)

print(user.name)   # "John Doe"
print(user.age)    # 30
print(user.email)  # "[email protected]"

Core Concepts

1. Constrained Token Sampling

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

How it works:

1. Convert schema (JSON/Pydantic/regex) to context-free grammar (CFG)
2. Transform CFG into Finite State Machine (FSM)
3. Filter invalid tokens at each step during generation
4. Fast-forward when only one valid token exists

Benefits:

• Zero overhead: Filtering happens at token level
• Speed improvement: Fast-forward through deterministic paths
• Guaranteed validity: Invalid outputs impossible

import outlines

# Pydantic model -> JSON schema -> CFG -> FSM
class Person(BaseModel):
    name: str
    age: int

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

# Behind the scenes:
# 1. Person -> JSON schema
# 2. JSON schema -> CFG
# 3. CFG -> FSM
# 4. FSM filters tokens during generation

generator = outlines.generate.json(model, Person)
result = generator("Generate person: Alice, 25")

2. Structured Generators

Outlines provides specialized generators for different output types.

Choice Generator

# Multiple choice selection
generator = outlines.generate.choice(
    model,
    ["positive", "negative", "neutral"]
)

sentiment = generator("Review: This is great!")
# Result: One of the three choices

JSON Generator

from pydantic import BaseModel

class Product(BaseModel):
    name: str
    price: float
    in_stock: bool

# Generate valid JSON matching schema
generator = outlines.generate.json(model, Product)
product = generator("Extract: iPhone 15, $999, available")

# Guaranteed valid Product instance
print(type(product))  # <class '__main__.Product'>

Regex Generator

# Generate text matching regex
generator = outlines.generate.regex(
    model,
    r"[0-9]{3}-[0-9]{3}-[0-9]{4}"  # Phone number pattern
)

phone = generator("Generate phone number:")
# Result: "555-123-4567" (guaranteed to match pattern)

Integer/Float Generators

# Generate specific numeric types
int_generator = outlines.generate.integer(model)
age = int_generator("Person's age:")  # Guaranteed integer

float_generator = outlines.generate.float(model)
price = float_generator("Product price:")  # Guaranteed float

3. Model Backends

Outlines supports multiple local and API-based backends.

Transformers (Hugging Face)

import outlines

# Load from Hugging Face
model = outlines.models.transformers(
    "microsoft/Phi-3-mini-4k-instruct",
    device="cuda"  # Or "cpu"
)

# Use with any generator
generator = outlines.generate.json(model, YourModel)

llama.cpp

# Load GGUF model
model = outlines.models.llamacpp(
    "./models/llama-3.1-8b-instruct.Q4_K_M.gguf",
    n_gpu_layers=35
)

generator = outlines.generate.json(model, YourModel)

vLLM (High Throughput)

# For production deployments
model = outlines.models.vllm(
    "meta-llama/Llama-3.1-8B-Instruct",
    tensor_parallel_size=2  # Multi-GPU
)

generator = outlines.generate.json(model, YourModel)

OpenAI (Limited Support)

# Basic OpenAI support
model = outlines.models.openai(
    "gpt-4o-mini",
    api_key="your-api-key"
)

# Note: Some features limited with API models
generator = outlines.generate.json(model, YourModel)

4. Pydantic Integration

Outlines has first-class Pydantic support with automatic schema translation.

Basic Models

from pydantic import BaseModel, Field

class Article(BaseModel):
    title: str = Field(description="Article title")
    author: str = Field(description="Author name")
    word_count: int = Field(description="Number of words", gt=0)
    tags: list[str] = Field(description="List of tags")

model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, Article)

article = generator("Generate article about AI")
print(article.title)
print(article.word_count
how to use outlines
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use outlines on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add outlines
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill outlinescopy
The skills CLI fetches outlines from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/outlines
Reload or restart Cursor to activate outlines. Access the skill through slash commands (e.g., /outlines) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?