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outlines

Generate schema-constrained JSON, XML, regex, and Pydantic outputs via grammar token constraints.

Updated May 5, 2026
Search Tags:#pydantic#llm#json schema#information extraction#structured generation#vllm#grammar-constrained decoding
One-click install
npx skills add https://github.com/Z43L/zeus-agent --skill outlines-z43l
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/Z43L/zeus-agent/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/Z43L/zeus-agent --skill outlines-z43l

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Outlines removes the guesswork of getting LLM outputs in the exact JSON/XML/schema shape you need, preventing malformed responses and schema violations during generation.

Core Features & Use Cases

  • Schema-constrained generation: Produce outputs that conform to JSON, Pydantic models, or regex patterns via grammar/FSM token constraints.
  • Type-safe extraction with Pydantic: Convert your Pydantic model into a generation schema and get validated, structured results.
  • Backend flexibility: Run with local backends like Transformers, llama.cpp, and vLLM, or use supported API backends when needed.
  • Use case: Extract entities or fields from messy text into a strict Pydantic model (e.g., customer records, invoice fields, classification labels) without retry loops for invalid structure.

Quick Start

Use the outlines skill to generate valid JSON matching a Pydantic model schema from an input prompt for your chosen local model backend.

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 model?▼

Schema-constrained generation compiles your Pydantic model into token-level grammars, guaranteeing the LLM generates valid, type-safe JSON outputs without retry loops for malformed responses.

What is the best way to extract structured information from messy text without validation errors?▼

Schema-constrained generation prevents validation errors during information extraction by compiling schemas into finite state machines, ensuring LLM outputs strictly conform to your defined JSON, XML, or regex patterns.

Does structured generation work with vLLM and local Transformers backends?▼

Yes, structured generation works with local Transformers, llama.cpp, and vLLM backends, applying grammar and FSM token constraints across different environments to ensure output validity.

How do I prevent malformed LLM responses when generating XML or regex outputs?▼

You prevent malformed LLM responses by applying grammar-constrained decoding, which restricts token generation to valid XML or regex patterns at the token level, guaranteeing structural validity.

Can I use Pydantic for type-safe deserialization of LLM information extraction results?▼

Yes, you can convert a Pydantic model into a generation schema to receive validated, type-safe structured results, automatically deserializing extracted entities into your defined data models.

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