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outlines

Generate schema-constrained JSON, XML, and code with grammar-based token filtering.

Updated May 15, 2026
Search Tags:#pydantic#json schema#llama-cpp#structured generation#vllm#constrained decoding#regex constraints
One-click install
npx skills add https://github.com/cabezno/bmb-encover-agent --skill outlines-cabezno
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/cabezno/bmb-encover-agent/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/cabezno/bmb-encover-agent --skill outlines-cabezno

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Outlines eliminates the problem of unreliable, malformed, or schema-breaking LLM outputs by generating constrained text that must match a target JSON/XML/code structure during generation.

Core Features & Use Cases

  • Schema-constrained generation: Produce outputs that are guaranteed to be valid against JSON schemas, regex patterns, or Pydantic models.
  • Type-safe Pydantic integration: Translate Pydantic models into structured generation constraints for safer data extraction and validation.
  • Multi-backend local inference: Use Transformers, llama.cpp (GGUF), or vLLM to keep structured generation fast and deployable on your infrastructure.
  • When to use: Extract entities, classify content into fixed categories, generate consistent forms, or produce API specs and other machine-consumable payloads.

Quick Start

Use the outlines skill to generate valid Pydantic-typed JSON from a prompt by constraining the model to your output schema.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I force LLMs to generate guaranteed-valid JSON schema outputs?▼

To generate guaranteed-valid JSON schema outputs, use constrained LLM generation to apply grammar and FSM-based token filtering during inference. This enforces schema validity, ensuring the model only produces tokens that match your target JSON structure.

Can I use Pydantic models for structured LLM data extraction?▼

Yes, you can use Pydantic models for structured LLM data extraction by translating them into generation constraints. This type-safe integration filters tokens to match your model's schema, guaranteeing valid and validated data extraction from LLM outputs.

Does constrained LLM generation work with vLLM and llama.cpp backends?▼

Yes, constrained LLM generation works with vLLM, llama.cpp, and Transformers backends. It applies FSM-based token filtering across these local and production inference environments to maintain fast, deployable structured text generation on your infrastructure.

What is the best way to classify LLM content into fixed categories?▼

The best way to classify LLM content into fixed categories is regex or choice-constrained generation. By filtering tokens to match specific choices, the model is forced to output valid classifications, eliminating malformed or unexpected categorical responses.

Why do my LLM outputs break downstream automation with invalid JSON?▼

LLM outputs break downstream automation with invalid JSON because standard generation lacks schema constraints. Applying FSM-based token filtering during decoding guarantees the output matches your JSON schema, ensuring machine-consumable payloads for reliable automation.

When do I need regex constraints for local LLM inference?▼

You need regex constraints for local LLM inference when generating consistent forms, API specs, or code payloads requiring exact structural patterns. FSM-based token filtering enforces these regex patterns across Transformers, llama.cpp, and vLLM backends during generation.

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