outlines

Generate schema-valid JSON outputs using FSM-constrained token sampling.

52|6|Updated Nov 24, 2025

Search Tags:#grammar #Pydantic #local-models #structured-generation #JSON-schema #FSM

Ready to run

npx skills add https://github.com/ovachiever/droid-tings --skill outlines

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/ovachiever/droid-tings/tree/main/skills/outlines
Command: npx skills add https://github.com/ovachiever/droid-tings --skill outlines

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Outlines guarantees valid JSON/XML/code outputs by using FSM-constrained token sampling from local models, with optional JSON schema and Pydantic integration for type-safe results.

Core Features & Use Cases

FSM-based token filtering for guaranteed validity
JSON schema and Pydantic-backed outputs
Local model backends (Transformers/llama.cpp/vLLM)
Grammar-based generation with fast-forwarding

Quick Start

Generate a JSON payload matching a Pydantic model from a prompt.

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 a local language model?▼

Outlines uses FSM-constrained token sampling to enforce valid JSON structure directly during generation. It accepts a JSON schema or Pydantic model, then filters tokens at each step so the model can only produce outputs matching your schema, eliminating post-generation validation.

Can I use Pydantic models to constrain structured generation?▼

Yes. Outlines automatically converts Pydantic models into JSON schemas and applies FSM-based constraints during sampling. This ensures generated outputs are type-safe and conform to your model's field definitions without manual schema writing.

What backends does constrained token sampling work with?▼

Outlines supports Transformers, vLLM, llama.cpp, and OpenAI. You can deploy the same structured generation logic across local and remote backends, letting you choose based on latency, cost, and infrastructure requirements.

How does grammar-based generation control token output?▼

Outlines uses context-free grammars (CFGs) and finite-state machines to define valid token sequences. At each generation step, it filters the model's next-token distribution to only allow tokens that keep the output on a valid path, guaranteeing grammar conformance.

When should I use FSM-constrained sampling instead of post-processing validation?▼

Use FSM constraints when you need guaranteed validity without re-sampling rejected outputs, want faster inference by avoiding wasted tokens, or require production-grade reliability. Post-processing works for non-critical tasks; constraints are better for schema-dependent workflows at scale.

Does Outlines work with XML and code structure generation?▼

Yes. Beyond JSON, Outlines supports constrained generation for XML, code, and custom formats using grammar-based rules. You define the structure via CFG or FSM, and it enforces validity during sampling across all supported backends.