LobeHub - Your Chief Agent Operator for multi-agent work
by•
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
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.
The 'you become the human scheduler' problem is genuinely the bottleneck nobody talks about.. I've been there, juggling Claude Code, separate LangGraph agent sessions, and MCP tool calls across different terminals and it gets messy fast. CAO framing makes sense: the coordination overhead is the real work, not the individual agent tasks.
The 273K+ Skills and 51K+ MCP servers scale is wild.. curious how CAO actually selects the right skill for a given goal though. Is it embedding-based retrieval over skill descriptions, or something more structured? Because at that scale, skill selection quality basically determines whether the whole system works or collapses into noisy results. That's the hard part I'd want to understand before trusting it with anything important.
Building an extensible, open-source UI that unifies both cloud models and local inference is a massive undertaking. From a React architecture standpoint, I am highly curious how you are managing state sync and streaming latency when a user hot-swaps between different LLM APIs and active plugins mid-conversation without breaking the frontend.