LobeHub

Your Chief Agent Operator for multi-agent work

Productivity

Artificial Intelligence

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I read in another comment that you control/assess quality by how successfully a task was completed... how is that success defined? Is it something that I can input with tiers, or is it a matter of job done vs not done?

That "one daily brief instead of 15 tabs" framing is honestly the exact design constraint that makes or breaks agent UX right now. And Parth's quality-vs-quantity point + CanisMinor's flywheel reply nail it: throughput without real curation is basically just noise routing.

I'm really curious about the daily brief mechanic, though. Does it actually adapt its abstraction level over time? Like, does it start surface-level for a new user and naturally learn when to dig deeper, or is the "level of detail" just a fixed manual setting?

I ask because as an indie dev building AI tools myself, I've watched users churn the second a system feels either too noisy or too aggressively curated for their personal workflow. Definitely watching this space closely!

Congrats on shipping this. The CAO framing is the right one, the interesting problem isn't running agents in parallel, it's the coordination layer deciding what reaches you and what doesn't. CanisMinor's answer on conflict resolution in the thread was genuinely good: surface, never silently pick. Curious how that holds up as the agent team scales, the volume of low-stakes autonomous calls grows fast and the question of whether a human can later audit them becomes real. Either way, nice work. Watching this one.

The concept of CAO is interesting to me!! One thing I'm curious about: when scheduling agent runs, is there a way to set conditional triggers (e.g., "only run if my inbox has unread emails") or is it strictly time-based for now? Would love to see deeper automation hooks so the team can truly react to events rather than just run on a clock.

curious to see how you continue solving the skill quality/routing problem as the ecosystem grows. The 273K skills number is impressive-but the matching and curation layer will probably be where the real defensibility lives.

The CAO framing clicked — im tired of being the human router between claude code and slack pings

LobeHub’s CAO framing genuinely impressed me. This is the first product I’ve seen that treats the orchestration layer as the actual product, not an afterthought. Describe a goal, and CAO assembles the right agents, runs tasks in parallel across models, and only surfaces decisions that actually need a human. This feels less like AI tooling and more like the early infrastructure layer for how teams will operate in the next few years. Congrats on the launch.

With 273K+ skills in the pool, how does CAO decide which agents to actually assemble for a goal — is there a ranking or filtering layer, or does it try a broader set and prune based on early results?

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:

1. 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.

2. 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.

3. 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 years

I 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

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.