LobeHub
Agent operations hub that hires, schedules and reports on a fleet of AI agents around the clock
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lobehub.comAbout LobeHub
LobeHub positions itself as a chief agent operator rather than another chat interface: the product organises a fleet of agents into continuous operation, hiring them, scheduling them and reporting back, so the human sets strategy while the agents run around the clock. The headline use cases published on the site are long-horizon rather than conversational — dispatching fifty agents across five hundred open GitHub issues overnight, running a Shopify and Amazon storefront, moderating a Discord community — which is a different job from the single-session assistant most chat products sell. The surface area reflects that: an IM gateway puts agents in the chat tools people already use, an agent marketplace and agent builder cover creation, and a community layer shares agents and skills. Model access is broad and metered in credits rather than requests, spanning DeepSeek V4 Pro, Claude Sonnet 5, Claude Opus 5 and Grok 4.6, with the pricing page publishing approximate message counts per model at each tier — a useful honesty, since a credit allowance means very different things depending on which model you point it at. Paid tiers also carry file and vector storage allowances, agent memory and early access to state-of-the-art models. A desktop download is available alongside the hosted product, and the project has an open-source lineage.
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Key Features
LobeHub Pros & Cons
✅ Pros
- +Published per-model message estimates make the credit system legible
- +Free tier is usable for real work, not a demo
- +Fleet framing suits unattended, long-running work better than chat UIs
⚠️ Cons
- −Frontier models are unavailable on the free tier
- −Credit pricing still obscures true cost when switching models mid-task
- −Storage allowances are small — 10 MB free, 4 GB at the top tier
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