GitHub - MarlBurroW/hivekeep: Hivekeep is a self-hosted platform of autonomous, persistent personal AI agents. Your AI team. At home.
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Hivekeep is a self-hosted platform of autonomous, persistent personal AI agents. Your AI team. At home. - MarlBurroW/hivekeep

I’ve been running self-hosted AI agents for a while. Tools like OpenClaw and Hermes do this well and were a big inspiration, but they’re CLI/dev-first and headless. I wanted that kind of power with a real, mobile-friendly UI my non-technical wife could actually use from her phone. I couldn’t find it, so I built it for my own household and open-sourced it. Not claiming to reinvent anything (there’s a new “AI agents platform” every other week right now), I just took the UI-first angle.

Self-hosting fundamentals:

  • Single Docker container. Bun + SQLite, no Postgres, no Redis, no external cloud. All state in one volume.
  • Light enough to run on a small home server.
  • Secrets are stored in an AES-256-GCM encrypted vault and never sent to the LLM provider.
  • Reachable over Telegram, WhatsApp, Slack, Discord, Signal and Matrix.
  • Bring your own keys: Anthropic, OpenAI, Gemini, OpenRouter (an OpenAI-compatible endpoint for llama.cpp / LM Studio / vLLM is in progress).
  • MIT, actively maintained.

The parts I focused on (where having a UI actually pays off):

  • A proper web UI that works on mobile, not a terminal.
  • Full transparency into what the model sees: you can inspect the exact context sent to the LLM and the token cost of every message. No black box.
  • Tool calls rendered visually in the chat with custom renderers (a weather call shows a weather card, not raw JSON).
  • Mini-apps embedded in the UI: small interactive apps, dashboards, even live background services.
  • Create your own tools from inside the platform, instantly reusable by any agent.
  • A Kanban board to manage projects and tickets the agents work on.
  • A plugin system (NPM) to add providers, channels, tools and more.
  • Connected accounts with triggers (e.g. an incoming email can wake an agent).
  • A workspace file browser and terminal, in the UI.
  • Conversational setup: an onboarding agent walks you through configuring everything.
  • Image generation and TTS/STT built in.

Install:

docker run -d -p 3000:3000 -v hivekeep:/app/data ghcr.io/marlburrow/hivekeep:latest

Open the web UI and the setup agent takes it from there.

GitHub: https://github.com/MarlBurroW/hivekeep Site + demo: https://hivekeep.app/

It’s young and I’m after honest feedback. Disclosure: I’m the author, happy to answer anything.

  • Mike Wooskey@lemmy.thewooskeys.comEnglish
    0·
    10 hours ago

    Yeah, my thinking was definitely biased to my environment. I selfhost llama.cpp so even if Hivekeep doesn’t require significant resources, whatever LLMs it runs will use my hardware.

    agents are activated serially per message, not all firing at once, and the persistent memory is exactly what keeps each context small

    It sounds like I need to try it!