outlines

Enforce JSON, XML, and code output formats using Python.

3|Updated Feb 21, 2026

Search Tags:#pydantic #python #llm #json #outlines #structured generation

Ready to run

npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill outlines-ihatesea69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/16-prompt-engineering/outlines
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill outlines-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill guarantees valid JSON, XML, or code structure during generation, enabling type-safe outputs and maximizing inference speed with Outlines, a library for structured generation.

Core Features & Use Cases

Guaranteed Valid Outputs: Ensure generated text strictly adheres to specified formats like JSON or XML.
Type-Safe Generation: Use Pydantic models for robust, type-checked data extraction and generation.
Local Model Support: Seamlessly integrate with local LLMs via Transformers, vLLM, or llama.cpp.
Use Case: Extracting structured user data from unstructured text into a Pydantic model for immediate use in your application.

Quick Start

Use the outlines skill to generate a JSON object representing a user with name and age from the provided text.

Frequently Asked Questions about outlines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I force an LLM to generate valid JSON from unstructured text?▼

To force valid JSON generation, you can use constrained token sampling with Python to enforce strict output formats during inference. This approach guarantees type-safe outputs by applying grammar-based generation rules to the LLM's decoding process.

Can I use Pydantic models for structured text generation with local LLMs?▼

Yes, you can use Pydantic models for structured text generation with local LLMs to ensure type-safe data extraction. This integration allows you to define Python data schemas that strictly guide and validate the model's output formatting.

Does structured generation with Python support vLLM and Transformers backends?▼

Yes, structured generation with Python supports local LLM backends including vLLM and Transformers. This compatibility allows you to enforce output formats like JSON or XML while maximizing inference speed across your preferred local model environments.

What is the best way to extract structured data from unstructured text using a local model?▼

The best way to extract structured data is by using Pydantic models to define the target schema and applying grammar-based generation. This method ensures the local LLM output strictly adheres to your specified JSON or XML formats for immediate downstream application use.

Why does constrained token sampling improve inference speed for structured outputs?▼

Constrained token sampling improves inference speed by restricting the LLM's token selection space to only valid grammar structures. This optimization reduces invalid generation attempts and ensures strict adherence to formats like JSON or XML during the decoding phase.

When should I not use grammar-based generation for LLM outputs?▼

You should avoid grammar-based generation when your task requires highly creative, unstructured, or free-form text without strict formatting constraints. This method is specifically designed for enforcing structures like JSON, XML, or code, which inherently limits the model's generative flexibility.