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outlines

Generate structured JSON, XML, or code outputs using finite-state machines.

1|Updated Apr 13, 2026
Search Tags:#pydantic#schema#json#type-safety#fsm#local-models#structured-generation
One-click install
npx skills add https://github.com/tangzheng202202/hermes-skills --skill outlines-tangzheng202202
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/tangzheng202202/hermes-skills/tree/main/03-mlops/mlops/inference/outlines
Command: npx skills add https://github.com/tangzheng202202/hermes-skills --skill outlines-tangzheng202202

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Schema-driven generation enforces structure and safety for LLM outputs, enabling reliable JSON, XML, and code generation with local backends.

Core Features & Use Cases

  • Structured generation powered by finite-state machines to guarantee valid outputs.
  • Local-model backends with Transformers, llama.cpp, and vLLM for fast, private inference.
  • Pydantic integration for type-safe outputs and automatic schema validation.
  • Typical use cases include data extraction, content generation, data validation, and automation workflows.

Quick Start

Install outlines, load a local model, and start generating structured outputs from prompts.

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 structured generation from local models?▼

Schema-driven generation uses finite-state machines to guarantee structurally valid JSON, XML, or code outputs from local models by constraining the generation process to match a predefined schema.

Can I use Pydantic for type-safe LLM outputs with vLLM?▼

Yes, Pydantic integration provides type-safe outputs and automatic schema validation, allowing you to enforce strict type constraints when generating structured data with local backends like vLLM.

Does structured generation work with Transformers and llama.cpp backends?▼

Structured generation works with multiple local-model backends including Transformers and llama.cpp, enabling fast and private inference while maintaining zero-overhead schema validation for data extraction tasks.

What is the best way to enforce a specific schema during LLM data extraction?▼

The best way to enforce a schema during LLM data extraction is applying finite-state machine driven generation, which constrains the model output to match Pydantic schemas or JSON structures automatically.

When do I need finite-state machines for local model structured generation?▼

You need finite-state machines for local model structured generation when your automation workflows require guaranteed valid JSON or XML formatting, preventing malformed outputs without relying on post-generation validation.

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