lobehub
by lobehub🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
About lobehub
lobehub is a TypeScript open-source ai apps project by lobehub. 🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team. With 78,967 GitHub stars and 15,474forks, it's one of the AI Apps tools worth knowing. You can download the source, browse it on GitHub, or read the full setup guide in the README below.
lobehub — guide
Managing agents, not chatting with one
LobeHub started as a polished self-hosted chat interface and has grown into something broader: a place to run a standing team of agents rather than a single conversation. Agents are given roles, put on schedules, and report back — the framing is operational, closer to staffing than to prompting. If you have ever wanted a task to run every morning without you opening a chat window to start it, that is the gap this fills.
What you supply
The platform is free and self-hostable; the intelligence is not. You bring your own API keys, and every agent action bills to your provider account. This is the part worth thinking through before you scale up: agents running on a schedule generate calls whether or not anyone reads the output, so an idle team still costs money. Set spend limits at the provider before you set schedules here.
What it costs to run
Deployment is deliberately easy — a container, or a one-click deploy to a hosting platform, plus a database for persistence. It is TypeScript throughout, so the operational profile is a normal Node application rather than a Python ML stack, which makes it markedly simpler to host than most things in this category.
When to pick something else
If you only want a good self-hosted chat interface over your own API keys, the simpler chat-focused build is a lighter fit and there is no reason to run the orchestration layer. And if the goal is calling many providers cleanly from your own code rather than running agents, LiteLLM is the tool for that.
From the project README
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LobeHub
LobeHub organizes your agents into 7×24 operation.
It hires, schedules, reports on your entire AI team.
You stay in charge — without staying online.
English · 简体中文 · [Official Site][official-site] · [Changelog][changelog] · [Documents][docs] · [Blog][blog] · [Feedback][github-issues-link]
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<sup>Your Chief Agent Operator</sup>
This is an excerpt from the project's own README, quoted here for reference. Read the full README on GitHub →
lobehub — FAQ
Is LobeHub free?
The platform is open-source and free to self-host. Model usage is not — you connect your own provider API keys and pay that provider for every call your agents make.
Do scheduled agents cost money when nobody is watching?
Yes. An agent on a schedule makes API calls whether or not anyone reads the result, so an idle team still bills. Set spend limits at the provider before you configure schedules, not after.
How hard is LobeHub to self-host?
Easier than most tools in this category. It is a TypeScript application, so hosting it is a normal Node deployment with a database rather than a Python ML environment with GPU dependencies. Container and one-click deploy paths both exist.
Do I need this if I just want a chat interface?
Probably not. The agent scheduling and reporting layer is the reason to choose it. For a straightforward self-hosted chat window over your own API keys, a simpler chat-focused build is less to run and maintain.
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