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outlines

Enforce JSON and Pydantic schemas on LLM outputs using CFG/FSM constraints.

150|25|Updated Apr 20, 2026
Search Tags:#pydantic#json-schema#fsm#local-models#structured-generation#data-validation#grammar-based
One-click install
npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill outlines-devsoul2026
Or copy as Structured Prompt for Agentâ–¼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/Devsoul2026/Hermes-One-Click/tree/main/hermes-agent/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill outlines-devsoul2026

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines solves the challenge of producing structured, verifiable outputs from language models by converting constraints into grammars and finite-state machines, ensuring outputs conform to JSON and Pydantic schemas.

Core Features & Use Cases

  • Constrained generation using CFG/FSM to enforce structure
  • Native JSON/Pydantic schema support for type-safe outputs
  • Local model backends (Transformers, llama.cpp, vLLM) for offline, private deployments
  • Use cases include data extraction, form-like data generation, and producing robust structured content

Quick Start

Prompt the AI to generate a structured JSON payload that conforms to a Pydantic model.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I enforce JSON schema constraints during LLM text generation?â–¼

To enforce JSON schema constraints during LLM text generation, this Skill converts schemas into context-free grammars and finite-state machines, ensuring outputs conform strictly to JSON and Pydantic schemas.

Can I generate structured Pydantic outputs using local models offline?â–¼

Yes, you can generate structured Pydantic outputs offline using local model backends like Transformers, llama.cpp, and vLLM, ensuring private deployments without external API calls.

What is the best way to extract structured data from local LLMs?â–¼

The best way to extract structured data from local LLMs is using CFG and FSM-based constrained generation, which guarantees the language model output matches your required JSON schema or Pydantic model.

Does constrained generation work with vLLM and llama.cpp backends?â–¼

Yes, constrained generation works with vLLM and llama.cpp backends, alongside Transformers, applying finite-state machine constraints to produce verifiable, type-safe structured payloads.

Why does my local model output invalid JSON when generating structured payloads?â–¼

Local models output invalid JSON because they lack native schema enforcement; applying FSM-based grammar constraints forces token generation to strictly follow your defined JSON or Pydantic schema.

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