this post was submitted on 20 Jun 2026
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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.

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[–] nexttech@lemmy.world 2 points 16 hours ago

This sounds like chatgpt