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outlines

Generate structured JSON, regex, and Pydantic-constrained outputs from local LLMs.

Updated Jun 5, 2026
Search Tags:#pydantic#llm#json schema#outlines#structured generation#vllm#constrained decoding
One-click install
npx skills add https://github.com/xu1713/openhorse --skill outlines-xu1713
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/xu1713/openhorse/tree/main/openhorse/openhorse/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/xu1713/openhorse --skill outlines-xu1713

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? LLMs often produce malformed JSON or free-form text that breaks downstream parsing. This Skill guarantees structurally valid outputs by constraining token generation at the logit level using finite state machines, eliminating retry loops and post-generation validation. ## Core Features & Use Cases - Schema-Constrained Generation: Generate JSON matching Pydantic models or JSON schemas with guaranteed validity, including nested models, enums, literals, and field constraints. - Multiple Generator Types: Use choice, regex, integer, float, and JSON generators for classification, data extraction, and pattern-matched output. - Local Model Backends: Run with Transformers, llama.cpp, or vLLM for high-throughput production serving, with quantization and multi-GPU support. - Use Case: Extract structured company information (name, founded year, industry, employees) from hundreds of unstructured text documents into validated Pydantic objects for a database pipeline. ## Quick Start Ask the AI to use Outlines with a Pydantic model to extract structured fields like name, age, and email from a block of unstructured text using a local Transformers model.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I generate structured JSON from an LLM with Pydantic?▼

Define a Pydantic BaseModel with your fields, load a model with outlines.models.transformers, then create a generator with outlines.generate.json(model, YourModel). The output is guaranteed to match the schema because invalid tokens are filtered during generation.

Outlines vs Instructor for structured LLM output?▼

Outlines excels with local models (Transformers, llama.cpp, vLLM) and offers zero-overhead generation via FSM token filtering. Instructor is better for API-based models like OpenAI and provides automatic retrying on validation failures.

Does Outlines work with OpenAI API models?▼

Outlines has limited OpenAI support through outlines.models.openai with models like gpt-4o-mini. However, some advanced features are unavailable with API models since FSM-based token filtering requires local model access.

Can Outlines generate text matching a regex pattern?▼

Yes, use outlines.generate.regex with a pattern like a phone number format. The generator constrains token sampling so the output is guaranteed to match the regex, useful for formatted identifiers and codes.

Which backend should I use for production Outlines deployment?▼

Use vLLM for production since it delivers 100-1000+ tokens per second with batch processing, tensor parallelism across GPUs, and quantization support. Transformers suits development, while llama.cpp fits CPU or Apple Silicon deployment.

Why use enums instead of strings in Outlines Pydantic models?▼

Enums restrict generation to a fixed set of valid values, guaranteeing outputs like status fields are always one of the defined options. Free-form strings allow arbitrary values that may break downstream logic.

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