outlines

Name: outlines
Availability: InStock
Author: Aayushsoam

Generate structured JSON, XML, or Pydantic data using LLMs with schema support.

1|1|Updated May 25, 2026

Search Tags:#pydantic #json schema #local models #grammar-based generation #structured text generation

Ready to run

npx skills add https://github.com/aayushsoam/clawbot-agent --skill outlines-aayushsoam

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/aayushsoam/clawbot-agent/tree/main/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/aayushsoam/clawbot-agent --skill outlines-aayushsoam

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables the creation of structured text outputs, ensuring valid JSON/XML/code structure during generation, and supports local models for efficient inference.

Core Features & Use Cases

Structured Text Generation: Generate valid JSON/XML/Pydantic data based on given schemas.
Use Cases: Ideal for creating structured data from text inputs, such as extracting information from forms, converting text to structured data, and generating code.

Quick Start

Run the 'outlines' skill with the model and schema to generate structured data:

generate structured data with outlines --model "microsoft/Phi-3-mini-4k-instruct" --schema "UserSchema.json"

Frequently Asked Questions about outlines

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

FAQPage Schema

How do I generate valid JSON from a local LLM using a Pydantic schema?▼

To generate valid JSON from a local LLM, this Skill uses the outlines library with Pydantic schemas to constrain model outputs. It applies grammar-based generation to ensure the structured text strictly matches your defined schema during inference.

Can I use local models for structured text generation without getting invalid JSON outputs?▼

Yes, you can use local models for structured text generation without invalid outputs. The Skill leverages grammar-based generation with transformers and vllm to enforce JSON or regex constraints, preventing malformed syntax during inference.

What's the best way to extract structured data from unstructured text using local models?▼

The best way to extract structured data from unstructured text is using grammar-based generation with local models. This Skill applies Pydantic or JSON schemas to constrain outputs, converting raw text into validated structured data efficiently.

Does structured text generation with outlines work with vllm and transformers?▼

Yes, structured text generation with outlines works directly with vllm and transformers. These libraries are listed as required dependencies to run local models and apply grammar-based constraints for generating valid JSON or regex outputs.

Why do I need Pydantic and outlines for LLM structured output generation?▼

You need Pydantic and outlines for LLM structured output generation to enforce schema compliance. They apply grammar-based generation rules to local models, ensuring the generated text matches exact JSON or Pydantic structures without parsing errors.

What are the limitations of using grammar-based generation for structured text?▼

Limitations of grammar-based generation for structured text include dependency on specific libraries like outlines, transformers, and vllm. Complex schemas may also increase inference latency when constraining local models to strict JSON or regex formats.