Hi HN — I built Quoroom, an open-source experiment in collective AI.
Instead of one agent, a “room” has:
- a Queen (strategy + delegation)
- Workers (specialized execution)
- Quorum voting for decisions
It runs local-first (Mac/Windows/Linux), with a web UI at localhost.
Install is simple:
npm i -g quoroom
quoroom serve
Current focus:
- persistent rooms with goals/tasks/memory
- quorum-based decision flow
- Clerk assistant to manage rooms
- local or cloud runtime options
Model support:
- Claude/Codex subscriptions
- OpenAI/Anthropic APIs
This is still experimental, and I’m trying to answer one question:
Can a coordinated AI collective outperform a solo agent on real tasks?
I’d really value feedback on:
1) swarm architecture,
2) safety/control model,
3) how to benchmark “collective vs solo” fairly.
Why not just use ChatGPT or Claude directly?
ChatGPT or Claude are great for request-response work when you actively prompt them. Quoroom is built for persistent operation: a Queen + Workers system that runs 24/7 toward an objective, not only when you send the next message.
The room keeps state, delegates tasks, votes on decisions (quorum), and continues execution over time. So the difference is not just model quality, it’s operating mode: on-demand assistant vs persistent collective workflow.
How hard is setup if I’m not technical?
Setup is installer-first. There are install files for macOS and Windows, so you don’t need to configure dev tooling or run build steps. Install, launch, and the local server/UI opens. From there, you create a room and set an objective.
Advanced configuration exists if you want it, but the default path is designed so you can start without doing manual engineering work.