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outlines

Generate schema-constrained LLM outputs as JSON, regex matches, or Pydantic instances.

Updated May 26, 2026
Search Tags:#pydantic#json schema#outlines#structured generation#vllm#llama.cpp#constrained decoding
One-click install
npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill outlines-ruiyangruiyi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/ruiyangruiyi/hermes-agent/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill outlines-ruiyangruiyi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Outlines solves the problem of unreliable LLM outputs by guaranteeing that generations conform to a target structure such as valid JSON, regex patterns, or Pydantic-defined types—so downstream systems can consume results safely without fragile post-processing.

Core Features & Use Cases

  • Structured generation: Produce syntactically valid JSON/XML/code using schemas and grammars instead of best-effort text.
  • Type-safe outputs with Pydantic: Define a Pydantic model and generate instances that match required fields and constraints.
  • Backend flexibility: Run with local model backends including Transformers, llama.cpp (GGUF), and vLLM, and support constrained decoding to speed up inference.
  • Output controls: Constrain token sampling with grammar-based decoding (FSM), enabling strict adherence to schema/regex and reducing validation retries.

Use case examples:

  • Extract entities from a news article into a Pydantic model (e.g., people, organizations, locations).
  • Classify sentiment while enforcing a fixed label set and returning a typed confidence field.
  • Generate production-ready JSON for downstream APIs (e.g., invoice objects, job applications) with constraints like ranges, patterns, and enums.

Quick Start

Ask an AI to extract information from your text into a defined Pydantic schema using outlines structured JSON generation on your chosen local model.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee valid JSON output from an LLM using a Pydantic schema?▼

To guarantee valid JSON output from an LLM, use grammar-based constrained decoding to enforce your Pydantic schema, ensuring syntactically valid JSON without fragile post-processing. This allows downstream APIs to consume typed data safely.

Can I use constrained decoding for structured generation with vLLM or llama.cpp?▼

Yes, constrained decoding for structured generation works with vLLM and llama.cpp backends. It constrains token sampling using finite state machines to enforce strict adherence to your JSON schema or regex patterns during inference.

What is the best way to extract typed entities from text into a structured format?▼

The best way to extract typed entities is using schema-constrained LLM outputs to generate Pydantic instances. This method guarantees that extracted people, organizations, and locations match required fields and constraints directly.

How does FSM-based grammar decoding improve LLM structured outputs?▼

FSM-based grammar decoding improves LLM structured outputs by constraining token sampling to strictly adhere to defined schemas or regex patterns. This guarantees valid JSON, reduces validation retries, and speeds up inference.

Does outlines support local Transformers models for generating type-safe JSON?▼

Yes, outlines supports local Transformers models for generating type-safe JSON. You can define a Pydantic model and generate instances that match required fields across Transformers, llama.cpp, and vLLM backends.

When do I need schema-constrained generation for my LLM workflows?▼

You need schema-constrained generation when downstream systems require reliable, typed data extraction without validation retries. It ensures LLM outputs conform to target structures like valid JSON or regex patterns for production-ready workflows.

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