LobeHub

The "Chief Agent Operator" concept resonates. I run 15+ automated agents (uptime monitoring, social media engagement, security audits, competitor analysis) and the coordination layer is what took the longest to build. Getting agents to read each other's outputs and prioritize actions without conflicting recommendations was months of iteration.The daily briefing approach is smart — my system does something similar with a "Manager" agent that aggregates all overnight findings into one executive summary. How does LobeHub handle conflicting recommendations from different agents?

The agent coordination layer feels futuristic. Can agents collaborate with each other dynamically during long tasks?

Multi-model routing sounds like a huge advantage. Are users able to choose preferred AI models for certain workflows?

Been waiting months to post about this one. CAO is the update I've been quietly demoing to friends since the alpha — reactions ranged from "wait, that's it?" to "wait, that's it." Both meant in a good way. Go try it.

273K+ Skills and 51K+ MCPs sounds fantastically large. Where do these skills come from? Has anyone verified them? In other words, is there any kind of quality evaluation beyond what you probably did with a vector database, which can show semantic similarity but does not guarantee that the skill or MCP actually works?

Hey Product Hunt 👋 Arvin here, founder of LobeHub.Quick question before I pitch anything: how many AI tabs do you have open right now?Claude Code in one window. Codex in another. Maybe OpenClaw or Hermes pinging you in Slack. On paper, you have an AI team. In practice, you became its operator — manually switching contexts, syncing progress across terminals, queuing up a "complex enough" task before bed because letting Claude Code idle feels like burning money.BCG calls this "AI Brain Fry" — cognitive overload, fragmented attention, decision fatigue. 14% of heavy AI users already report it. We were promised AI would make work lighter. Somehow it made us tired in a new way.We don't think the answer is a smarter agent. We think you shouldn't be the operator at all.A company with a CEO but no COO is one where the founder personally chases every deadline and debugs every fire. That's exactly what your AI workflow looks like today.So we're naming the role: CAO — Chief Agent Operator. And we're building LobeHub to be yours.Why "CAO" and not "AI agent platform"? Because "agent tools" implies you have one agent and your job is to use it. The reality in 2026 is that you already have several agents running. This category doesn't need a better single agent — it needs a layer above them. Someone (something) to run the team.Why this is possible now, and wasn't 2 years ago — three things shifted at once:Agent self-evolution moved from papers to products. OpenClaw and Hermes proved agents can learn from sessions and turn successful workflows into reusable skills. LobeHub covers their capabilities — and goes further, because we're cloud-native: memory and skills evolve across sessions, devices, and teams.MCP and Skills became the de facto standard. The LobeHub Marketplace now hosts 57k MCP servers and 270k skills. Your CAO has enough tools to actually do the job.Multi-agent left the demo stage. The future isn't a single super-agent. It's an organization of agents — and organizations need an operator.What you can do with LobeHub today:🧠 Run multi-agent teams with shared memory and skills, not isolated chat windows🔌 Plug into 57k MCP servers and 270k community skills out of the box📡 Deploy your CAO across Discord, Telegram, Slack, Lark, and iMessage WhatsApp soon— one agent team, every surface🛠️ Open source, self-hostable, and built on a runtime we've shipped to production for 3 yearsI treated agents as first-class citizens on day one of LobeChat, back when "agent" still meant "a prompt with a name." Three years later, tools, MCP, skills, memory, and runtime finally compose into something that feels qualitatively different.We're nowhere near the CAO I have in my head. Heterogeneous agent adoption, team workspaces, Agent Group 2.0 — all on the roadmap. But the direction is clear: free people from babysitting their AI, so they can spend that energy on what actually matters.I'll be here all day answering questions. Brutal feedback especially welcome — tell me what's missing, what's broken, or what you'd want your CAO to handle first. 🙏— Arvin, founder @ LobeHub

Have been using it for icons. Really cool.

273K skills sounds impressive but how many of those have actually been vetted for security. running untested skills across parallel agents is a huge attack surface especially when you're routing through real credentials on slack and gmailv

How does CAO handle failed tasks  retry, swap model, or escalate to me?

😁 273K Skills + 51K MCP servers behind one prompt feels a little unreal even to me. Let me know what you end up running through it — I want to see the weird stuff.

Is iMessage support live? That’s the first time I have seen a launch support Apple’s native chat.

A new skill with zero history can't compete with one that's been routed 10K times. Wonder how do you avoid rich-get-richer if that makes sense? All in all, solid work!

Does CAO work with custom local models via something like Ollama, or only cloud APIs?

Heterogeneous agents was the technical bet I was most nervous about. Claude Code, Codex, OpenClaw — none of them were designed to be managed by something else. Took longer than we planned. Worth it.

What happens if an agent gets stuck in a loop? Does CAO intervene or just surface it?