AgentOS
A free, open-source dashboard for running several AI agents like a small company — giving each one a role, a task list, and a way for a human to check in on what they're actually doing.
🔗 Visit AgentOS
Description
Running one AI agent for a task is simple enough — you give it a prompt and watch what happens. Running many agents on real, ongoing work quickly turns messy: who's doing what, has anyone approved the risky steps, and how do you know what actually happened. AgentOS is an open-source control layer built on top of OpenClaw that addresses exactly that: it lets you create workspaces, assign digital workers defined roles and policies, organize their work into missions and scheduled tasks, and inspect live activity down to full transcripts and token usage.
It supports multiple AI models and providers, integrates with Telegram, Discord, and Slack so agents can be reached where a team already communicates, and includes human approval workflows for anything that needs a person's sign-off before it happens. It runs locally and is designed for a trusted operator's own machine rather than a shared multi-tenant cloud. Built with Next.js, React, and TypeScript, it requires Node.js 24+ and a recent version of OpenClaw.
💬 Our review
The short version: as more real work gets delegated to AI agents, the operational question stops being "can the agent do the task" and becomes "can a human still see and control what's happening across many agents at once" — that's the gap AgentOS is built to fill, and being free and open source removes any cost barrier to trying it.
It's squarely aimed at technical operators already using or building on OpenClaw, not a general audience — you'll need Node.js and some comfort with self-hosted tools to get value from it. Compared to a broader, backed platform like Infobip's AgentOS (a different, unrelated commercial product with a similar name) or vendor-specific agent consoles, this project's appeal is being transparent, self-hosted, and free — you can read exactly what it does and run it on your own machine rather than trusting a black-box SaaS with visibility into your agents' actions.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Entièrement gratuit, licence MIT, auto-hébergé sur votre propre machine.
Pros
Gratuit et open source (licence MIT)
Supervision centralisée : transcripts, usage de tokens, approbations humaines
Intégrations Telegram, Discord, Slack pour joindre les agents où l'équipe travaille déjà
Cons
Nécessite Node.js 24+ et OpenClaw — barrière technique réelle
Local-first, pas pensé pour du multi-tenant partagé
Projet jeune (81 étoiles GitHub), écosystème encore restreint