LobeHub - Your Chief Agent Operator for multi-agent work
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LobeHub is a Chief Agent Operator (CAO) that builds, runs, and coordinates your AI agent team. Describe a goal, and it assembles the right agents/skills, runs tasks in parallel in the cloud, routes work across models, and reports back only when decisions are needed—via your existing channels (Slack/Discord/Telegram/iMessage). Less tab-switching, more outcomes.
Replies
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?
LobeHub
@hirogure Great question — we don't load all 273K into context. The agent only sees a lightweight catalog (identifier + one-line description) of what's installed, picks what fits via an Activator call, and only hits the marketplace's ranked searchSkill API when nothing local matches. So it's rank-and-filter upfront, not broad fan-out then prune — picking wrong is much cheaper than executing wrong.
The CAO framing clicked — im tired of being the human router between claude code and slack pings
LobeHub
@novamaker01 Haha "human router" is painfully accurate — that was literally the whiteboard sketch that started this whole thing. Glad the framing landed 🫡
DeckSpeed
LobeHub
@hanzhizhang0405 Thanks for asking. We care a lot about this problem.
We're actively working on skill selection and governance to ensure quality keeps pace with scale.
On the LobeHub Skill Market, that means routing, ranking by quality signals, and safety governance with auditing as a commitment to the community.
On the LobeHub Web, it means a more personalized, long-term skill experience where skills can evolve with each user's workflows.
The 273K figure is exciting, but the real goal is to make that supply reliable, useful, discoverable, safe, and personal.
wait so its like... i just tell it what i want to do and it goes off and does it? not sure i'm brave enough to let an AI just run 5 agents at once in parallel without watching what's happening lol
LobeHub
@boyuan_deng1 It's designed to do task like this. Just give a try.
Multi-model routing sounds like a huge advantage. Are users able to choose preferred AI models for certain workflows?
LobeHub
@elijah_smith6 yes , Absolutely
LobeHub
@elijah_smith6 You can pin a preferred model per agent, per skill, or per workflow step — e.g. Claude for long-context reasoning, GPT for structured output, a cheap fast model for classification/routing. CAO respects those pins by default.
When nothing's pinned, it auto-routes based on the task profile (context length, latency budget, cost ceiling, tool-use needs). And you can always override mid-run if you want to swap models for a specific step.
The agent coordination layer feels futuristic. Can agents collaborate with each other dynamically during long tasks?
LobeHub
@ethan_thompson3 Yeah, that's basically the whole point of CAO 👇
Agents pass context and intermediate outputs to each other mid-task, and CAO can spin up new agents on the fly if a step needs a capability the current crew doesn't have. You stay in the loop only when a real decision needs you — everything else just flows.
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?
LobeHub
@ytubviral Respect — 15+ agents in production is no joke, you've clearly done the hard miles 🫡
On conflicts: CAO doesn't try to auto-resolve them. When two agents disagree on what to do next, it pauses, surfaces both recommendations with their reasoning, and asks you to call it. The bet is that conflicts usually mean the goal itself needs clarifying — not that one agent is "wrong." Silent auto-merging is where trust dies.
Sounds like your Manager agent setup is doing similar work — would genuinely love to compare notes sometime.
FunBlocks MindMax
Amazing idea! Congrats on this launch!
LobeHub
@peng_wood Thank you! 🙌 Means a lot 🙏
OpenJobs AI
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.
LobeHub
@genedai Thanks, this really means a lot 🙏
You nailed the bet we're making — orchestration is the product. Models keep getting stronger, but nobody was solving the "who runs all of them for you" part. Felt like the obvious missing layer.
Excited to see where you take it.