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outlines

Constrain generated outputs to valid JSON, XML, or code using finite-state machines.

Updated Apr 3, 2026
Search Tags:#pydantic#validation#json#xml#fsm#local-models#structured-generation
One-click install
npx skills add https://github.com/handsomelong922/my-codex-skills --skill outlines-handsomelong922
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/outlines
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill outlines-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines enables zero-overhead structured generation for JSON, XML, and code by enforcing schema-adherent outputs using finite-state machine constraints.

Core Features & Use Cases

  • Local backends support (Transformers, vLLM, llama.cpp) with Pydantic type safety
  • Deterministic generation that guarantees valid outputs and easy integration into data pipelines
  • Use cases include data extraction, document processing, form automation, and structured code generation

Quick Start

Provide a sample input and request a structured JSON output that conforms to a defined schema.

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 outputs from local language models?▼

Structured generation constrains local model outputs to be structurally valid JSON or XML using finite-state machine constraints. It enforces strict schema adherence by filtering tokens during generation, ensuring deterministic and type-safe results.

Can I use Pydantic models for structured generation with vLLM and Transformers?▼

Yes, you can use Pydantic models with local backends like vLLM, Transformers, and llama.cpp for structured generation. This enforces strict schema adherence and provides type-safe, deterministic outputs for local models.

What is the best way to extract structured data from documents using local models?▼

The best way to extract structured data is using finite-state machine guided generation with local backends. This enforces strict JSON or Pydantic schema adherence, guaranteeing valid structured outputs for document processing and form automation.

How does finite-state machine guided generation work for local models?▼

Finite-state machine guided generation applies zero-overhead, fast-path token filtering during generation. It constrains the model to only generate tokens that strictly conform to the defined JSON schema or Pydantic model, guaranteeing valid outputs.

Does structured generation add overhead to local model inference?▼

No, structured generation adds zero overhead to local model inference. It uses fast-path token filtering to enforce schema adherence during the generation process without impacting performance or adding inference latency.

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