outlines

Constrain LLM token decoding to schema-derived grammars for valid structured outputs.

Updated Aug 23, 2026

Search Tags:#pydantic #json schema #structured generation #vllm #local llm #regex constraints #grammar-based decoding

Ready to run

npx skills add https://github.com/box755/simlens-research --skill outlines-box755

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/box755/simlens-research/tree/main/skill-packs/AI-Research-SKILLs/16-prompt-engineering/outlines
Command: npx skills add https://github.com/box755/simlens-research --skill outlines-box755

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you produce outputs that always conform to a required structure (JSON, XML, code, or a schema) instead of relying on post-generation validation that can still fail.

Core Features & Use Cases

Grammar-based structured generation that filters tokens during decoding to ensure validity at generation time.
Pydantic model support so you can generate type-safe, schema-conformant objects without manual parsing.
Local and high-throughput backends including Transformers, llama.cpp, and vLLM to run with local models and maximize inference speed.
Use Case: Automatically extract entities, build structured forms, or generate typed records (e.g., product specs or classification results) with guaranteed schema compliance.

Quick Start

Use the outlines skill to generate a JSON object that matches your Pydantic schema and never produces invalid fields.

Frequently Asked Questions about outlines

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

FAQPage Schema

How do I force an LLM to generate valid JSON matching a Pydantic schema?▼

Grammar-based decoding constrains token sampling during generation to guarantee valid JSON matching your Pydantic schema. This filters invalid tokens at inference time, ensuring type-safe structured outputs without post-generation validation failures.

What is grammar-based structured generation for local LLMs?▼

Grammar-based structured generation enforces output validity by translating schemas into finite state machines or context-free grammars that constrain token decoding. This ensures local LLMs produce syntactically valid JSON, regex-constrained strings, or typed fields during inference.

Can I use regex constraints to format specific fields during structured generation?▼

Yes, regex constraints can be applied to format specific string fields during structured generation. The grammar-based decoding mechanism filters tokens to ensure the generated output strictly matches your specified regular expression patterns at runtime.

Does this structured generation approach work with vLLM and local Transformer backends?▼

Yes, grammar-based structured generation supports vLLM, local Transformers, and llama.cpp backends. It enforces schema-derived constraints during decoding across these environments, allowing high-throughput inference and local model execution with guaranteed output validity.

Why does my LLM output fail JSON schema validation even with prompt instructions?▼

Prompt-based generation cannot guarantee schema compliance because token sampling remains unconstrained. Grammar-based decoding solves this by filtering tokens during generation using a schema-derived FSM, ensuring outputs always conform to the required JSON structure.

What are the limitations of grammar-based decoding for structured outputs?▼

Grammar-based decoding requires schema translation to FSM or CFG formats and operates during inference, adding minimal runtime overhead. It is limited to supported backends like Transformers, vLLM, and llama.cpp, and requires predefined schemas to constrain generation.