outlines

Constrain language model outputs to schema-validated JSON, XML, and code formats.

Updated Apr 24, 2026
One-click install
npx skills add https://github.com/Harries/hermes-agent --skill outlines-harries
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/Harries/hermes-agent/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/Harries/hermes-agent --skill outlines-harries

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines solves the problem of producing strictly structured outputs by constraining language model generations to valid JSON, XML, or code formats.

Core Features & Use Cases

  • Structured generation using JSON, XML, or code formats with Pydantic models, JSON schemas, and grammar-based constraints.
  • Local-model support (Transformers, vLLM, llama.cpp) for zero-overhead inference and type-safe pipelines.
  • Production workflows like data extraction, form processing, and contract validation with deterministic outputs.

Quick Start

Generate a structured JSON output from a free-form prompt using a local model.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I force a local model to generate valid JSON output?

You can constrain local model outputs to valid JSON by applying grammar-based constraints and FSM-backed guarantees during generation. This enforces schema-validated structured formats, ensuring reliable data extraction without overhead.

Can I use Pydantic models to define structured generation schemas for local models?

Yes, Pydantic models define type-safe schemas for structured generation with local backends like Transformers, vLLM, and llama.cpp. This produces deterministic, schema-validated outputs for reliable data extraction and contract validation.

Does structured generation with local models add inference overhead?

Grammar-based structured generation provides zero-overhead inference for local models. By constraining outputs to valid JSON or XML formats using FSM-backed guarantees, it maintains production speed without adding latency.

What is the best way to extract structured data from free-form text using local models?

The best way to extract structured data is using grammar-based constraints to map free-form text into schema-validated formats like JSON. This enables reliable form processing and contract validation with deterministic outputs.

What local backends are supported for schema-validated text generation?

Supported local backends for schema-validated text generation include Transformers, vLLM, and llama.cpp. These platforms enable zero-overhead inference and type-safe pipelines for structured outputs.