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Constrain token generation to produce valid JSON, XML, or code structures.

Updated Apr 10, 2026
Search Tags:#pydantic#json#fsm#outlines#local-models#structured-generation#grammar-based-generation
One-click install
npx skills add https://github.com/VYRE-Studios/Windows-Agentic-Framework --skill outlines-vyre-studios
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/VYRE-Studios/Windows-Agentic-Framework/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/VYRE-Studios/Windows-Agentic-Framework --skill outlines-vyre-studios

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Promotes reliable structured generation by enforcing schema-level constraints, delivering deterministic outputs in JSON/XML/code.

Core Features & Use Cases

  • FSM-driven validity: Constrains token generation to always produce structurally valid JSON, XML, or code.
  • Type-safe outputs: Integrates with Pydantic models and JSON schemas to guarantee correct data shapes.
  • Local-model support: Works with Transformers, llama.cpp, and vLLM for zero-dependency, high-throughput inference.
  • Use cases: Data extraction, data-to-text translation, form processing, and code scaffolding with guaranteed schemas.

Quick Start

Prompt the model to return a structured, type-safe output that matches a Pydantic model or JSON 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 generation from local LLMs?▼

To guarantee valid JSON generation from local LLMs, you constrain token generation using FSM-based constraints mapped to your Pydantic models or JSON schemas, ensuring zero-overhead structural validity during inference.

Can I enforce Pydantic schema adherence with local models like vLLM and llama.cpp?▼

Yes, you can enforce Pydantic schema adherence with local models like vLLM and llama.cpp by applying grammar-based generation constraints that restrict token outputs to match your defined data shapes natively.

What is FSM-based structured generation for language models?▼

FSM-based structured generation is a mechanism that builds a finite state machine from a JSON schema or Pydantic model, constraining the model's token generation to guarantee structurally valid outputs without post-generation validation overhead.

Why do my local model JSON outputs fail schema validation during data extraction?▼

Local model JSON outputs fail schema validation during data extraction due to unconstrained generation; applying finite state machine constraints forces token selection to match your schema, eliminating invalid structural formatting.

Does structured generation work for XML and code scaffolding as well as JSON?▼

Yes, structured generation works for XML and code scaffolding alongside JSON by applying FSM-driven constraints that restrict the model's token generation to produce structurally valid markup or code syntax on demand.

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