LobeHub: AI Agent Teammates
LobeHub Team · Other · Chatbot
LobeHub is an AI agent collaboration platform where people build persistent agent teammates and work with them in shared group chats.
About LobeHub: AI Agent Teammates
What Is LobeHub
LobeHub is a platform for building AI agent teammates and working alongside them the way you would with coworkers. Instead of one chat window that forgets everything the moment you close it, you assemble agents that keep their context, hold a role, and show up in the same shared space as the rest of your team. The project began as LobeChat, a widely used open-source chat interface, and grew into a full platform for agent collaboration built around persistent teammates that remember what your team is working on.
The pitch is simple. You stop re-explaining your project to a fresh chatbot every morning. Agents remember what you told them, you can put several of them in one group conversation, and everyone sees the same thread. That shared context is the point. It turns a pile of separate AI chats into something closer to a small team that keeps working while you step away, and that shift is what separates it from a plain multi-model chat client with no memory worth the name.
The biggest limit is that quality depends on credits. LobeHub Cloud measures model usage in credits, and heavier models eat through them faster. The free plan covers 500,000 credits a month, which is fine for light use but runs dry quickly once you lean on top-tier models that cost several times more per token. Yes, really. If you need private deployment or custom work, that sits behind the Enterprise tier, so budget for a conversation with sales before you commit your team to the platform.
Getting Started
- Create a free account at lobehub.com or sign in to LobeHub Cloud at app.lobehub.com.
- Pick a model or set of models to power your agents, or connect your own provider key.
- Add an agent teammate, give it a name and a role, then seed it with the context you want it to keep.
- Start a chat or open a group where several agents share one conversation.
- Adjust your plan or buy extra credits once your monthly allowance gets tight.
Product Information
A quick look at LobeHub: AI Agent Teammates's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Small product and research teams that want shared context across several AI helpers instead of scattered chats.
- Solo builders juggling recurring tasks, since persistent agent memory means less setup each session.
- Developers who prefer open-source foundations and want to self-host rather than rent everything.
Tasks
- Running a multi-agent group chat where different roles review the same draft or plan.
- Keeping a long project brief alive so agents answer with the right background every time.
- Comparing output across models like GPT, Claude, Gemini, and GLM without leaving one workspace.
Scenarios
- Daily standups where you hand off a summary to an agent teammate overnight.
- Early project research, when you want several angles explored in one shared thread.
- Ongoing content or support work that repeats weekly and benefits from agents that remember past decisions.
Key features
Persistent Agent Teammates
Agents in LobeHub keep their memory between sessions, so they hold onto your project context instead of starting blank. You give each one a role and a name, and it answers with that background intact. For anyone tired of re-pasting the same instructions every day, this alone changes the routine.
Group Collaboration with Shared Context
Several agents can sit in one conversation and see the same thread. That means a writer, a reviewer, and a researcher can all work on the same task without you copying messages between windows, which saves a surprising amount of tedious work once the habit sticks. It's closer to a team channel than to a single chatbot. Big difference.
Multi-Model AI Workspace
This multi-model AI workspace gives you a wide set of text models in one place, from lightweight options like GLM-5.3-Flash to heavier flagships from OpenAI, Anthropic, and Google. You switch models per task rather than committing to a single vendor. The trade-off? Credit cost, and it varies sharply by model. That's it.
Agent Memory and Knowledge Base
Agents store what matters in vector storage, so they can recall earlier decisions and reference your files later. The free plan includes 15 MB of file storage. Paid tiers scale this up. Expect several gigabytes with thousands or tens of thousands of vector entries. That's the engine behind the "they remember me" feeling.
Open Source with a Cloud Option
The project's roots are open source, which matters if you want to inspect the code, self-host, or avoid lock-in. LobeHub Cloud handles hosting and billing for people who would rather not run servers themselves. Same core idea, either way. That flexibility is rare in this category.
Desktop and Mobile Apps
There are desktop and mobile apps in addition to the web version, so your agents are reachable away from a laptop. The Android app is on Google Play. Why does that matter? Portability decides whether you use it daily or forget about it, and a tool you forget about is a subscription you cancel.
Pros and cons
Pros
- Persistent agent memory cuts the repetitive setup that plagues plain chatbots.
- Shared group context lets several agents work one task without manual copy-paste.
- Access to many models in a single workspace, so you're not tied to one provider.
- Open-source roots give privacy-minded users a self-hosting path.
- Apps for desktop and mobile keep agents within reach throughout the day.
Cons
- Credit-based pricing means top-tier models burn through the free 500,000 monthly credits fast.
- The free plan's 15 MB file storage fills up quickly if you feed it documents.
- Private deployment and customization sit behind the Enterprise tier, which requires contacting sales.
Frequently asked questions
It's used to build AI agent teammates that keep their context and work together in shared conversations. People use it for project research, recurring content work, and running several agents on one task at the same time. Think of it as a workspace, not a chat box.
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