LobeHub introduces Chief Agent Operator that orchestrates multiple AI agents from a 273,000-skill marketplace and schedules parallel execution across models in the cloud
It integrates with Claude Code and Cursor for developer workflows.
#321Robert Scoble@SCOBLEIZERThis open source multi agent system is great for planning out your viewing of Google IO, starting tomorrow.
It planned out my whole week and added all the sessions I cared about to my calendar.
Thanks to LobeHub for sponsoring my newsletter: https://unaligned.io that ships every Tuesday.
#1032Rohan Paul@ROHANPAUL_AIManaging AI agents by hand is yesterday’s job. hire a Chief Agent Operator and move on.
LobeHub launched just that. It turns AI agents from one-off chat helpers into a cloud-run operations team.
The old model asks you to open separate agents, repeat context, assign tasks, check progress, move results between apps, and decide when to escalate work.
LobeHub has an operator layer that hires agents from a 273K-skill marketplace, schedules them in the cloud 24/7, and sends reports through the IM apps where teams already work.
So their "Task" turns an agent into a background worker: you assign the job once, the agent keeps running, shares progress, moves finished work to Pending Review, and updates its work when you leave comments.
#1496Chubby♨️@KIMMONISMUSThe scaling bottleneck in AI right now has completely shifted from model intelligence to architecture orchestration.
Most tools still force you to sit there and babysit individual prompt windows. LobeHub introduces a complete backend management layer called a Chief Agent Operator.
You give it a high-level strategy and your CAO auto-assembles specialized teams on the fly, routing tasks across multiple models in parallel.
It plugs directly into heavy developer environments like Claude Code or Cursor as an orchestration system rather than trying to compete with them, tapping into an ecosystem of 292K skills and 55K MCP servers.
The entire platform runs 24/7 cloud-side with zero local server or Docker maintenance, which cuts operational costs by roughly 50% compared to closed alternative stacks. You literally close your device and the execution keeps scaling.