Today’s agents are one-off, task-driven tools — isolated, slow, costly, and hard to build — failing to unlock the full potential of AI models.LobeHub changes that. We build long-term agent teammates that grow with you. Anyone can easily create and collaborate with agent teams to deliver complex, end-to-end work. With multi-model support, LobeHub is faster, more cost-effective, and goes beyond single-agent systems.
I noticed the LobeChat project shortly after it was open-sourced. I'm not a programmer, but this was indeed the first open-source application I deployed on a server.
Initially, it was to take advantage of the free quotas offered by various platforms, as many large language model (LLM) providers offer generous free credits upon registration. I used all of these through LobeChat.
Later, I found that LobeChat's design philosophy greatly helped me in understanding "how to interact with AI" and "how to use AI" in the early stages. I even shared my deployed LobeChat with colleagues and friends, which was truly a wonderful memory.
Although I rarely use it now, I'm delighted to see them introduce a new generation of AI interaction methods!
Really impressed by LobeHub’s vision of agents as evolving collaborators rather than disposable tools. The idea of agent teams with persistent, editable memory and multi-model support could be a game changer for workflows that require continuity and context. Looking forward to exploring how this redefines AI-driven productivity!
I tried the stock trading agent group and it provided clear, structured reports with risk analysis. Well done guys!
Congrats on the launch.
Agent teammates that grow with you is a strong concept. What’s the biggest problem LobeHub solves compared to single-agent setups?
Congrats on the launch 🚀 Curious to see how teams evolve over time once memory and co-creation really kick in, feels like this could redefine what “working with AI” actually means.
What kinds of work become possible with agent teams that are fundamentally impossible with today’s single-agent or prompt-based tools?
The co-evolving human–agent model is interesting. Curious how you think about trust and control as agents accumulate long-term memory.
Collaboration among multiple agents sounds like a great way to boost efficiency in solving complex projects. When a task is actually running, are the mechanisms for their communication, task handover and conflict resolution sufficiently intelligent and smooth? Or will I instead end up spending a lot of time coordinating and managing them?
How do LobeHub agents build long-term memory and context without becoming stale, biased, or overly confident over time?
Amazing product, congrats! LobeHub is a wonderful open-source agent project that can help people collaborate with an agent team. That's an interesting idea!
I see LobeHub becoming an essential part of remote team workflows; asynchronous AI collaboration is powerful.
I love how I can clone and adapt existing agents: great for learning, customizing, and experimenting. Congratulations on the launch!
This doesn’t feel like “another AI chat app.” Lobehub is clearly aiming to be infrastructure, and that’s way more interesting long-term!
The ability to annotate and refine agent outputs inline speeds up my review process.
Watching agents collaborate in parallel on complex problems is pretty cool!!!
LobeHub's clear documentation makes self-hosting straightforward,even for non-experts.
Most “agents” today are chat sessions pretending to be coworkers: stateless loops, siloed context, and brittle hand-offs. You end up doing the real orchestration yourself—copy/paste between tabs, re-explaining intent, paying tokens to reconstruct state, and losing the thread of why the work mattered in the first place.
The human part matters as much as the system part. Memory should not be a black box that quietly profiles people; it should be legible and editable. LobeHub’s approach is “white-box” memory: keep what’s useful, discard what isn’t, and let agents adapt to how someone works without taking away agency. The goal isn’t more AI output—it’s less cognitive load, fewer context switches, and work that stays coherent over time.
I see that the landing page directly takes to the sign-up page. Is there a home page I can read more about the product? Or you recommend, I signup and explore? :)
I noticed the LobeChat project shortly after it was open-sourced. I'm not a programmer, but this was indeed the first open-source application I deployed on a server.
Initially, it was to take advantage of the free quotas offered by various platforms, as many large language model (LLM) providers offer generous free credits upon registration. I used all of these through LobeChat.
Later, I found that LobeChat's design philosophy greatly helped me in understanding "how to interact with AI" and "how to use AI" in the early stages. I even shared my deployed LobeChat with colleagues and friends, which was truly a wonderful memory.
Although I rarely use it now, I'm delighted to see them introduce a new generation of AI interaction methods!