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
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 🙏
Metix AI (formerly 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.
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.
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!
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?
The 'one daily brief instead of 15 tabs' framing is the most honest description of what agentic work actually looks like right now — most orchestration tools make you babysit them constantly which defeats the purpose. The conflict resolution design is what I'm most curious about: when two agents running in parallel produce contradictory outputs for the same goal, does CAO surface both with their reasoning and ask you to decide, or does it try to auto-merge? Silent auto-merging is exactly where trust collapses in multi-agent systems.
The 'Chief Agent Operator' framing is interesting — routing work across a team of specialized agents rather than one monolithic one makes sense for complex workflows. The real question is memory: most multi-agent systems still share a flat global context. Curious how LobeHub handles per-agent vs shared context without collisions when agents work in parallel.