by lobu-ai · Codex Skill · ★ 173
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Lobu — Multi-tenant OpenClaw for Organizations Lobu is an open-source multi-tenant gateway for OpenClaw. One sandbox and filesystem per user/channel. Shared memory across contexts. Agents never see secrets. OpenClaw is a full agent runtime (800k LOC) but it's single-tenant by design — every user shares the same filesystem and bash session. Lobu rewrites only the gateway layer (40k LOC) to be multi-tenant and keeps OpenClaw's Pi harness untouched inside each worker. Embedded mode uses just-bash + Nix for reproducible packages. Each user gets an isolated virtual filesystem and bash session at 50MB per instance — tested at 300 concurrent instances on a single machine, no Docker needed. Embed OpenClaw-powered agents into your product, or give your team agents without managing a separate instance per person. https://github.com/user-attachments/assets/d72a9286-0325-4b8b-afc0-c1efe9c96f4e Channels & API REST API — programmatic agent creation, control, and state. Slack — multi-channel/DM agents with rich interactivity. Telegram — long-polling bot with interactive workflows. WhatsApp — WhatsApp Business Cloud API. Discord — channel + DM bot support.
| Stars | 173 |
| Forks | 23 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 70.1729581498719/100 |
| Open Issues | 18 |
| Last Updated | 2026-07-03 |
| Created | 2025-07-27 |
| Platforms | node |
| Est. Tokens | ~16k |
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lobu is Build AI teammates programmatically. It is categorized as a Codex Skill with 173 GitHub stars.
lobu is primarily written in TypeScript. It covers topics such as agent, chatbot, clawdbot.
You can find installation instructions and usage details in the lobu GitHub repository at github.com/lobu-ai/lobu. The project has 173 stars and 23 forks, indicating an active community.
lobu is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to lobu on Agent Skills Hub include nuwax, ai-maestro, Wegent. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.