outlines

Enforce JSON, XML, or code output formats using Pydantic models and grammars.

1|Updated Feb 21, 2026

Search Tags:#pydantic #llm #json #structured generation #local models #inference speed

Ready to run

npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill outlines-tianhao909

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/16-prompt-engineering/outlines
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill outlines-tianhao909

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, supports local models, and maximizes inference speed for structured outputs.

Core Features & Use Cases

Guaranteed Valid Outputs: Ensures generated text strictly adheres to specified formats like JSON or Pydantic models.
Local Model Support: Works seamlessly with Hugging Face Transformers, vLLM, and llama.cpp.
High Performance: Achieves maximum inference speed with zero-overhead structured generation.
Use Case: Extracting structured user data from free-form text into a Pydantic model for downstream processing.

Quick Start

Use the outlines skill to generate a JSON object representing a user with name, age, and email.

Frequently Asked Questions about outlines

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

FAQPage Schema

How do I guarantee valid JSON output from a local LLM during text generation?▼

To guarantee valid JSON output during text generation, you can use this approach to enforce output formats via Pydantic models and grammars, ensuring local LLMs strictly adhere to your specified structure.

Does structured generation work with vLLM and Hugging Face Transformers for high-speed inference?▼

Structured generation works seamlessly with vLLM and Hugging Face Transformers, enabling high-speed inference with zero overhead while enforcing type-safe outputs directly from local models.

What is the best way to extract structured user data from free-form text into a Pydantic model?▼

The best way to extract structured user data into a Pydantic model is by enforcing generation grammars, which map free-form text directly into type-safe structured data for downstream processing.

Can I enforce XML and code formatting constraints on local models?▼

You can enforce XML and code formatting constraints on local models by applying specific generation grammars, guaranteeing that the output strictly adheres to your required format.

Do I need Pydantic to define schemas for type-safe structured generation?▼

You need Pydantic to define schemas for type-safe structured generation, as it provides the models that enforce the generated text to conform strictly to your desired JSON structure.

Why does my LLM generate malformed JSON when extracting structured data?▼

An LLM generates malformed JSON when extracting structured data because it lacks enforced grammars; applying structured generation constraints forces the model to produce valid, type-safe outputs.