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projectedanx
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outlines

Enforce grammar-based constraints on LLM token sampling using Pydantic models or JSON schemas.

1|Updated May 12, 2026
Search Tags:#pydantic#json schema#inference#outlines#structured generation#constrained sampling
One-click install
npx skills add https://github.com/projectedanx/hermes-agent --skill outlines-projectedanx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/projectedanx/hermes-agent/tree/main/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/projectedanx/hermes-agent --skill outlines-projectedanx

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the challenge of unreliable LLM output formats by enforcing strict structural constraints, ensuring that generated text always adheres to your required JSON, regex, or Pydantic schemas.

Core Features & Use Cases

  • Constrained Generation: Uses Finite State Machines to guarantee valid JSON, XML, or code structure at the token level.
  • Type-Safe Pydantic Integration: Automatically translates Pydantic models into generation constraints for seamless data extraction.
  • Use Case: If you need to extract user profiles from unstructured emails, this Skill ensures the output is always a valid JSON object matching your specific User Pydantic model, eliminating the need for complex post-generation validation or retry loops.

Quick Start

Use the outlines skill to generate a JSON object matching the User Pydantic model based on the provided text input.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee LLM output matches my Pydantic model?▼

Guaranteeing LLM output matches a Pydantic model requires enforcing structured text generation. This Skill translates Pydantic models into grammar-based constraints, filtering invalid tokens during inference to ensure valid JSON without post-generation validation.

What is structured generation and how does it work with LLMs?▼

Structured generation enforces strict output formats like JSON or XML during LLM inference. It uses Finite State Machines to apply grammar-based constraints to token sampling, guaranteeing valid syntax before text is generated.

Does this approach work with both local and API-based LLM backends?▼

Yes, constrained sampling works with both local and API-based backends. It operates with zero-overhead by filtering invalid tokens during the inference process, supporting environments running transformers, vllm, or API models.

How do I extract structured data from unstructured text without retry loops?▼

Extract structured data without retry loops by applying constrained generation. This Skill uses Pydantic models to enforce schema constraints during sampling, ensuring the LLM output is always a valid JSON object matching your specific data model.

What's the best way to ensure valid JSON schema compliance during LLM inference?▼

The best way to ensure valid JSON schema compliance is grammar-based constrained sampling. This Skill applies Finite State Machines to filter invalid tokens during inference, guaranteeing strict structural adherence to your JSON schema.

Why does LLM structured generation fail to produce valid JSON?▼

LLM structured generation fails to produce valid JSON due to unconstrained token sampling. This Skill solves unreliable output formats by applying grammar-based constraints, ensuring generated text always adheres to required JSON or Pydantic schemas.

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