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Constrain LLM decoding with grammar/FSM to generate valid JSON, XML, or code.

1|Updated Apr 14, 2026
Search Tags:#pydantic#json schema#structured generation#vllm#grammar-based decoding#fomrs extraction#local llm backends
One-click install
npx skills add https://github.com/eddielueng/hermes-agent-zh --skill outlines-eddielueng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/eddielueng/hermes-agent-zh/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/eddielueng/hermes-agent-zh --skill outlines-eddielueng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic, and includes references (resource) components.

What problem does it solve?

You avoid brittle, post-validated generations that produce malformed JSON, XML, or code by constraining the model to emit only tokens that keep the output structurally valid.

Core Features & Use Cases

  • Guaranteed structured generation: Enforce valid JSON/XML/code structure during decoding so invalid outputs are impossible.
  • Type-safe outputs with Pydantic: Define schemas via Pydantic models and generate directly into strongly-typed objects.
  • Fast, grammar-level constraints: Compile schemas to grammar/FSM and filter tokens at generation time for speed and correctness.
  • Flexible backend support: Run with local models (Transformers, llama.cpp, vLLM) or limited API backends (OpenAI) depending on your deployment needs.
  • Common use cases: data extraction, classification, form processing, API spec/code generation, and other workflows where downstream systems require strict structure.

Quick Start

Use outlines with a local Transformer model to generate a Pydantic-defined JSON object from a prompt while enforcing the schema automatically.

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?▼

You can guarantee valid JSON generation by compiling Pydantic or JSON schemas into grammar/FSM constraints that filter tokens at generation time, making invalid outputs impossible. This eliminates the need for brittle post-generation validation.

What is the best way to enforce strict schema-driven extraction with LLMs?▼

The best way to enforce strict schema-driven extraction is to use grammar-based decoding with Pydantic models, which compiles schemas to finite state machines. This ensures type-safe, structurally valid outputs directly matching your defined schema.

Does structured generation work with vLLM and Transformers backends?▼

Yes, structured generation works with both vLLM and Transformers local backends. It compiles your Pydantic or JSON schemas into grammar constraints that operate across these supported local model backends for fast, type-safe generation.

How do I generate Pydantic objects directly from LLM outputs?▼

Generate Pydantic objects directly by defining your schema via Pydantic models and passing them to the constrained decoding process. The model is restricted to emitting tokens that conform to the schema, yielding strongly-typed objects.

Why does my LLM output malformed JSON and how can I prevent it?▼

LLMs output malformed JSON because standard decoding allows invalid token sequences. Prevent this by applying grammar-based constrained decoding, which restricts the model to only emit tokens that maintain structural validity according to your schema.

Can I use grammar-based decoding for XML and code generation tasks?▼

Yes, you can use grammar-based decoding for XML and code generation tasks. By compiling structural rules into finite state machines, the decoding process enforces valid syntax across JSON, XML, and code formats during generation.

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