outlines

Generate grammar-constrained JSON, XML, regex, and code outputs.

Updated Feb 15, 2026

Search Tags:#pydantic #validation #json schema #transformers #structured generation #vllm

Ready to run

npx skills add https://github.com/ricable/mcai --skill outlines-ricable

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/ricable/mcai/tree/main/.agents/skills/outlines
Command: npx skills add https://github.com/ricable/mcai --skill outlines-ricable

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines removes the risk and overhead of manually validating model outputs when you need reliable structured text for downstream systems.

Core Features & Use Cases

Structured generation: Produce valid JSON, XML, regex-matched text, and code-like output with schema-aware constraints.
Type-safe extraction: Use Pydantic models or JSON schemas to extract fields from documents, messages, and records without brittle post-processing.
Backend flexibility: Apply the same structured workflow across local and production model backends such as Transformers, llama.cpp, and vLLM.
Production scenarios: Build classification, form processing, document metadata extraction, SQL drafting, and API spec generation workflows that need dependable output shape and validation.

Quick Start

Ask Outlines to generate a type-safe JSON response for your chosen task using a compatible model backend and a Pydantic schema.

Frequently Asked Questions about outlines

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

FAQPage Schema

How do I ensure valid JSON schema generation from LLMs without manual validation?▼

Structured generation with grammar-constrained sampling applies schema-aware constraints to force valid LLM outputs, eliminating the risk and overhead of manually validating model outputs for downstream systems.

Can I use Pydantic models for type-safe data extraction from documents?▼

Yes, Pydantic models enable type-safe extraction, allowing you to extract fields from documents, messages, and records without relying on brittle post-processing or manual validation.

Does structured generation work with vLLM and local Transformers backends?▼

Structured generation works with vLLM and Transformers backends, allowing you to apply the same schema-aware Pydantic workflow flexibly across both local and production model environments.

What is the best way to generate XML and regex-matched text from language models?▼

Grammar-constrained sampling is the best way to generate XML and regex-matched text, enforcing schema-aware constraints to reliably produce valid structured outputs for specific code patterns.

How do I generate type-safe JSON responses for classification tasks?▼

Type-safe JSON responses for classification tasks are generated by applying grammar-constrained sampling with a Pydantic schema and a compatible model backend to ensure dependable output shape and validation.