outlines

Constrain AI generation to Pydantic JSON schemas with FSM validation.

Updated Apr 30, 2026

Search Tags:#pydantic #json #outlines #local-models #transformers #structured-generation #vllm

Ready to run

npx skills add https://github.com/photonics-dhl/Hermes --skill outlines-photonics-dhl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/photonics-dhl/Hermes/tree/main/hermes-home/skills/mlops/inference/outlines
Command: npx skills add https://github.com/photonics-dhl/Hermes --skill outlines-photonics-dhl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic.

What problem does it solve?

Outlines ensures outputs are structurally valid and type-safe by constraining generation to predefined data schemas, reducing post-processing errors and data inconsistencies.

Core Features & Use Cases

Constrained generation with CFG/FSM based on JSON schemas and Pydantic models to guarantee validity.
Local-model support (Transformers, llama.cpp, vLLM) for offline, high-throughput inference.
Use cases include data extraction, code generation, API contracts, and structured data validation at scale.

Quick Start

Provide a prompt that asks the AI to output a JSON object strictly conforming to your Pydantic model.

Frequently Asked Questions about outlines

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

FAQPage Schema

How do I guarantee structured JSON output from a local model generation?▼

Constrain local model generation using predefined Pydantic models and JSON schemas to guarantee structured JSON output. FSM-based validation ensures structural validity and type safety, eliminating post-processing errors and data inconsistencies during generation.

Does constrained generation work with vLLM and Transformers for offline inference?▼

Constrained generation works directly with vLLM and Transformers for offline inference. Local-model support enables high-throughput generation without external API calls, ensuring structured data validation at scale.

What is the best way to enforce type-safe outputs for data extraction tasks?▼

Enforce type-safe outputs for data extraction by constraining generation to predefined data schemas. FSM-based validation guarantees structural validity, reducing post-processing errors and data inconsistencies during extraction.

Can I use Pydantic models to define JSON schemas for local language models?▼

Use Pydantic models to define JSON schemas constraining local language model generation. FSM-based validation maps these schemas to guarantee that outputs strictly conform to the specified structure.

Why does my local model output invalid JSON when generating API contracts?▼

Local models output invalid JSON for API contracts due to unconstrained generation. Applying FSM-based validation forces generation to follow predefined JSON schemas, guaranteeing structurally valid and type-safe outputs.