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LM Format Enforcer

AGENT

Enforce output format (JSON Schema, Regex, etc) of language models by filtering allowed tokens at each generation step. Compatible with Hugging Face, llama-cpp-python, and vLLM. MIT licensed

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Overview

Free AI agent — LM Format Enforcer. Enforce output format (JSON Schema, Regex, etc) of language models by filtering allowed tokens at each generation step. Compatible with Hugging Face, llama-cpp-python, and vLL

What this agent does

Looking for a dependable AI agent? "LM Format Enforcer" gives you a tested starting point instead of a blank prompt box. Enforce output format (JSON Schema, Regex, etc) of language models by filtering allowed tokens at each generation step. Compatible with Hugging Face, llama-cpp-python, and vLLM. MIT licensed It is catalogued next to similar resources on OpenRuna, so the rest of the toolkit you need is close by. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.

Use cases

  • Fork it as a baseline and layer in your own project context, constraints, and examples.
  • Keep it in a shared library as the canonical version of this AI agent for your organisation.
  • Use "LM Format Enforcer" when you need a repeatable AI agent for professional work without rewriting instructions every time.
  • Hand "LM Format Enforcer" to a new teammate so their AI agent output matches your team's quality bar from day one.

Example output

Ask the model to apply "LM Format Enforcer" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. Add one example of your own and the output quality jumps noticeably.

Tips by platform

Claude

In Claude, paste the full AI agent as your first message or add it to Project instructions, then ask Claude to confirm assumptions before it executes. For longer AI agents, iterate inside the artifact panel.

ChatGPT

For ChatGPT, save this AI agent as a Custom Instruction or a saved prompt so it is one click away. Add your specifics in a follow-up rather than editing the original.

Cursor

In Cursor, lift the key instructions from this AI agent into .cursorrules or a SKILL.md file, then reference it in Agent mode with @ mentions. Keep the title in a comment so teammates can find it on OpenRuna.

Frequently asked questions

What is "LM Format Enforcer"?
It is a AI agent listed on OpenRuna — Enforce output format (JSON Schema, Regex, etc) of language models by filtering allowed tokens at each generation step. Compatible with Hugging Face, llama-cpp-python, and vLLM. MIT licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "LM Format Enforcer" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this AI agent?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "LM Format Enforcer" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "LM Format Enforcer"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

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