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outlines

Enforce strict structural constraints on LLM text generation using Finite State Machines and Pydantic models.

Updated May 5, 2026
Search Tags:#pydantic#llm#json schema#outlines#structured generation#constrained generation
One-click install
npx skills add https://github.com/iani-kuli/harness_bro --skill outlines-iani-kuli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/iani-kuli/harness_bro/tree/main/.claude/skills/ported/outlines
Command: npx skills add https://github.com/iani-kuli/harness_bro --skill outlines-iani-kuli

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the issue of unreliable LLM output formats by enforcing strict structural constraints, ensuring that generated text always adheres to your required JSON schema, regex patterns, or Pydantic models.

Core Features & Use Cases

  • Constrained Generation: Uses Finite State Machines to filter tokens at the logit level, making invalid output impossible.
  • Pydantic Integration: Automatically translates Pydantic models into JSON schemas for type-safe data extraction.
  • Use Case: Use this to extract structured user profiles, invoice data, or code snippets from unstructured text with 100% validity, eliminating the need for complex retry loops or post-generation validation.

Quick Start

Use the outlines skill to generate a structured JSON object matching the User Pydantic model based on the provided text input.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee LLM output matches my Pydantic model?▼

Constrained generation enforces strict structural constraints on LLM output by translating Pydantic models into JSON schemas and filtering tokens at the logit level, making invalid output impossible and eliminating retry loops.

What is the best way to extract structured JSON from unstructured text using LLMs?▼

Applying Finite State Machines to constrain LLM generation ensures 100% validity for type-safe JSON extraction from unstructured text, eliminating the need for complex retry loops or post-generation validation.

Does constrained generation work with vllm and transformers backends?▼

Yes, constrained generation requires integration with transformers, vllm, or llama.cpp backends to perform token-level logit filtering and enforce strict structural constraints on LLM text generation.

How do I enforce regex pattern matching during LLM text generation?▼

You can enforce regex pattern matching during LLM text generation by using Finite State Machines to filter tokens at the logit level, guaranteeing that the generated text adheres strictly to your required regex patterns.

Why do I need Finite State Machines for structured LLM output?▼

Finite State Machines are needed for structured LLM output because they filter tokens at the logit level to enforce strict structural constraints, ensuring generated text always adheres to your JSON schema or Pydantic models.

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