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.
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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.