Manage AI agents, tasks, workspaces from one control layer
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Kazim Akgül

AgentOS - Manage AI agents, tasks, workspaces from one control layer

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Run AI agents like a company. AgentOS helps you coordinate workspaces, agents, tasks, jobs, approvals, and runtime visibility from one local-first control surface built on OpenClaw.

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Kazim Akgül
Maker
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Hey Product Hunt 👋 I built AgentOS because running one AI agent is easy — but operating many agents across real projects gets messy fast. AgentOS lets you run AI agents like a company: organize workspaces, agents, tasks, models, sessions, approvals, onboarding, and runtime visibility from one local-first control surface. It is built on top of OpenClaw, which handles the agent runtime and orchestration. AgentOS focuses on the human operating layer: structure, visibility, control, and daily execution. My goal is to make agent teams operationally useful for builders, solo founders, and one-person companies. This is still early, so I’d love your feedback: What feels useful? What feels confusing? What would make this part of your workflow? AgentOS is open source — stars, forks, issues, PRs, and contributors are very welcome. Thanks for checking it out 🙏
Tina Chhabra

the "run agents like a company" framing makes sense because that's where things are heading. one agent is easy, five agents doing different things across different workflows gets chaotic fast. curious how you handle cost visibility though... like if one agent starts looping and burning through tokens, is there a way to set per-agent spending limits or kill switches before the bill surprises you

Kazim Akgül
Maker

@tina_chhabra Exactly — this is one of the core problems we’re trying to solve with AgentOS.

OpenClaw is powerful, and our goal is to make it more manageable, transparent, and accountable for real operations.

Today, every agent already has visibility around which model it uses, task/session activity, token usage, and task-level execution details. From there, the natural next layer is operator controls: per-agent budgets, task-level cost tracking, spend alerts, loop detection, and kill switches before anything gets out of hand.

So yes — the goal is not just “run more agents.”

It’s to make agent work visible, measurable, and controllable.