outlines

Generate JSON/XML outputs conforming to Pydantic schemas with local backends.

Updated Mar 22, 2026

Search Tags:#pydantic #json #xml #fsm #backends #local-models #structured-generation

Ready to run

npx skills add https://github.com/ChimeraFoundationa/Agentx --skill outlines-chimerafoundationa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/ChimeraFoundationa/Agentx/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/ChimeraFoundationa/Agentx --skill outlines-chimerafoundationa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines solves the challenge of generating strict, machine-parseable outputs (JSON/XML) by enforcing Pydantic-based schemas and enabling zero-overhead, grammar-driven generation on local backends.

Core Features & Use Cases

Guarantees JSON/XML/structured outputs that validate against Pydantic models.
Supports local backends (Transformers, llama.cpp, vLLM) for fast, offline inference.
Enables high-throughput, schema-driven generation with FSM-based constrained sampling.
Use cases include data extraction, form processing, structured data generation, and automated report generation.

Quick Start

Provide a JSON output that conforms to a given Pydantic model for the input data.

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 outputs from a local model conform to a Pydantic schema?▼

To guarantee structured JSON outputs from a local model, enforce Pydantic-based schemas using grammar-driven generation. This applies FSM-based constrained sampling to ensure the output strictly validates against your defined model.

What is FSM-based constrained sampling for structured generation?▼

FSM-based constrained sampling is a mechanism that enforces grammar constraints during text generation. It ensures zero-overhead, schema-driven outputs by guiding the model's token selection to perfectly match a defined Pydantic schema.

Can I use local models with Transformers or vLLM for schema-driven data extraction?▼

Yes, you can use local models with Transformers or vLLM for schema-driven data extraction. The system supports local backends for fast, offline inference while guaranteeing outputs conform to your Pydantic schemas.

What is the best way to generate XML formatted data from local models?▼

The best way to generate XML formatted data from local models is using grammar-driven constrained sampling. By enforcing a schema, it guarantees machine-parseable XML outputs without relying on post-generation parsing or validation.

Does constrained generation with Pydantic schemas add inference overhead?▼

No, constrained generation with Pydantic schemas does not add inference overhead. It uses caching and fast-forward optimizations to provide zero-overhead, high-throughput structured generation across supported backends.

When do I need grammar-based FSM constraints for local model generation?▼

You need grammar-based FSM constraints when you require strict, machine-parseable outputs for tasks like data extraction, form processing, or contract-driven data generation, ensuring outputs validate perfectly against Pydantic models.