r0b0tlab

r0b0tlab / hermes-concurrent-agents

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Deploy concurrent Hermes Agent workers on unified-memory GPUs (GB10, DGX Spark) for maximum total tok/s. Profile-isolated, kanban-coordinated, crash-recovering.

16
2
100% credibility
Found May 14, 2026 at 32 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

Scripts to set up, spawn, monitor, and benchmark teams of concurrent Hermes AI agents with role-specific profiles and kanban task coordination using local GPU inference.

How It Works

1
🕵️ Discover the project

You find a handy kit for running a team of smart AI helpers that work together on big tasks like coding or research.

2
🔧 Get your computer ready

You quickly install the free Hermes tool if needed, following a simple one-line command from the guide.

3
👥 Build your specialist team

Run the setup script to create ready-to-go profiles for creative thinkers, coders, researchers, quality checkers, and a team leader.

4
🧠 Power up the shared brain

Follow easy guides to start a fast local AI service on your powerful computer chip, so all helpers can think deeply.

5
🚀 Launch your worker team

Choose how many helpers to start, and watch them come alive in separate chat windows, briefed and ready to collaborate.

6
📋 Set up the task board

Initialize a shared kanban board where you assign jobs, track progress, and let agents claim and finish tasks together.

7
👀 Keep an eye on everything

Use simple monitor tools to check system health, worker status, and board updates anytime.

🎉 Celebrate teamwork success

Your team of AI agents tackles complex projects efficiently, delivering results while you oversee with ease.

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AI-Generated Review

What is hermes-concurrent-agents?

This Shell-based toolkit deploys concurrent Hermes agents on unified-memory GPUs like GB10 and DGX Spark, squeezing out maximum total tok/s through profile-isolated workers. It solves the bottleneck of running single-threaded local AI agents by spinning up specialized roles—creative, coder, research, QA, orchestrator—in tmux sessions, coordinated via kanban boards with crash-recovering and health monitoring. Run `setup.sh` to prep profiles, `spawn.sh 4` for workers, `status.sh` to watch, and `shutdown.sh` to clean up.

Why is it gaining traction?

It stands out for kanban-coordinated agent swarms that handle real concurrency without choking your GPU, plus built-in benchmarking to prove tok/s gains over solo Hermes runs. Crash-recovering sessions and GPU/system monitors keep things stable during long hauls, and vLLM/SGLang backends are tuned for NVFP4 models. Devs dig the easy local deploy for agent testing, no cloud needed.

Who should use this?

AI engineers building multi-agent workflows on GB10 or DGX Spark who need concurrent processing for tasks like code gen or research pipelines. Ideal for local dev teams prototyping kanban-driven agent teams before scaling to prod. Skip if you're on consumer GPUs or prefer managed services.

Verdict

Early alpha with 16 stars and 1.0% credibility score—docs are solid but tests are smoke-level, no broad validation yet. Grab it for GPU-heavy Hermes experiments if you're on supported hardware; otherwise, watch for maturity.

(178 words)

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