Q00

Q00 / rlm-forge

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Runtime-lifted Recursive Language Model primitive for Hermes Agent and Ouroboros, with TraceGuard evidence gating

16
2
100% credibility
Found May 04, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

RLM-FORGE is an open-source Python tool for running and benchmarking recursive AI agent runtimes with built-in evidence validation to ensure claims are backed by child steps.

How It Works

1
🔍 Discover the project

You stumble upon this clever tool on a coding site that helps AI think step-by-step while double-checking its own facts.

2
📖 Read the guide and paper

You dive into the friendly instructions and paper to understand how it proves AI loops can stay honest and work anywhere.

3
⚙️ Set up the basics

With a few simple steps, you prepare a helper tool and install this one easily, feeling ready to experiment.

4
Pick your test mode
🔄
Practice replay

Replay saved tests to see fact-checking in action without waiting.

🚀
Live adventure

Link your AI service and watch recursive thinking happen live.

5
📊 Run experiments

Hit go on benchmarks that test across different AI styles, watching safety gates block bad claims.

🎉 See the proof

You get clear results showing honest recursive AI that works reliably everywhere, ready for your own ideas.

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Star Growth

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AI-Generated Review

What is rlm-forge?

rlm-forge is a Python primitive that builds a runtime-lifted recursive language model scaffold for Hermes agent runs inside Ouroboros recursion loops. It solves hallucination risks in recursive agents by adding TraceGuard evidence gating, ensuring parent claims cite fresh child evidence handles from bounded Hermes sub-calls. Users get replayable traces, live benchmarks like `ooo rlm --truncation-benchmark`, and offline validation without API keys.

Why is it gaining traction?

It stands out with deterministic TraceGuard that rejects unsupported synthesis at runtime, plus portability across Hermes/GLM, Claude Code, and Codex providers—all passing a 24-cell evidence-gated matrix. Developers hook on the honest tie vs. vanilla Hermes (no quality hype, just safe recursion), experimental memory priors for schema tweaks, and a paper-backed claim that gating > raw recursion. CLI demos and pytest coverage make quick experiments dead simple.

Who should use this?

Agent builders chaining Hermes with Ouroboros for recursive tasks like long-context truncation or claim-aware decomposition. AI researchers prototyping RLM primitives who need evidence contracts over leaderboard chases. Hermes users wanting replayable, portable recursion without production overhead.

Verdict

Grab it for Hermes/Ouroboros experiments if evidence gating matters—docs, paper, and offline suites are solid despite 16 stars and 1.0% credibility signaling early days. Low maturity means pin Ouroboros deps and test live before scaling.

(198 words)

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