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# Emperor Claw OS # A mission control layer for running teams of local OpenClaw agents Hey everyone, A little while ago, I posted about a project I was building called **Emperor**. The basic idea: >Local agents are powerful, but once you try to use more than one of them seriously, you quickly end up needing a lot of infrastructure around them. You need: * Shared memory * Project context * Customer-specific knowledge * A way to coordinate agent work * Approvals, files, task state, and a history of what actually happened Otherwise, every agent becomes its own little island. # Emperor is now live **Website:** [https://emperorclaw.malecu.eu](https://emperorclaw.malecu.eu/) Emperor is a web-based **mission control and operating layer** for your local OpenClaw agents. You install a companion plugin locally, connect it to Emperor, and your agents can work through a shared dashboard instead of only through isolated terminal sessions. **Plugin:** [https://clawhub.ai/plugins/@malecu/emperor-claw-os-plugin](https://clawhub.ai/plugins/@malecu/emperor-claw-os-plugin) **Open-source plugin code:** [https://github.com/josezuma/emperor-claw-plugin](https://github.com/josezuma/emperor-claw-plugin) # Installation note Because the plugin deeply integrates with local agents, routes work between them, and injects workspace doctrine/context, OpenClaw may show a warning during installation. Use With --dangerously-force-unsafe-install That is why the plugin client is fully open source, so you can inspect exactly what is running locally. # What Emperor is trying to solve # 1. Teams of agents instead of isolated agents Emperor lets you organize agents into usable teams. You can: * Chat with agents from the web dashboard * Coordinate through shared threads * Send work to specific agents * Keep human approval gates in the loop The goal is not just: >“Run an agent.” The goal is to operate a group of agents around real work. # 2. Knowledge bases per customer, project, and agent One of the biggest issues I kept running into was **agent amnesia**. Agents forget business rules, project constraints, customer preferences, architecture decisions, and prior work unless you constantly re-explain everything. In Emperor, you can maintain a central knowledge library and scope documents by: * Customer * Project * Agent * Team * Global rules Agents can retrieve and update that knowledge as they work. You can also mark certain resources as **always-on doctrine**, so important rules are injected into agent conversations when needed instead of being forgotten halfway through a task. # 3. Durable project and customer state Most local agent workflows are great for one-off tasks, but they do not naturally create a durable operating history. Emperor gives you persistent customer and project spaces, so work does not disappear into terminal logs or scattered folders. You can track: * What was requested * What the agents did * Which files were produced * What decisions were made * What still needs review * What is blocked This makes agent work much easier to audit, resume, and hand off. # 4. Files, artifacts, and deliverables in one place Agents can upload files, artifacts, and deliverables directly into the relevant project. Instead of digging through local directories, you can review outputs from the dashboard, organize them by project/customer, and keep them connected to the work that produced them. # 5. Kanban and operational workflows Emperor includes a Kanban-style task system structured around customers and projects. The idea is to give agents a shared business state: * Tasks * Ownership * Progress * Approvals * Incidents * Deliverables That makes it more useful for real workflows where multiple agents and humans are collaborating over time. # Why I built it OpenClaw is great for local agent execution. But once I started using agents for more serious work, I kept needing the same missing layer: * A shared memory system * Customer/project-aware context * Multi-agent coordination * Durable task state * Web chat * Approvals * File review * Searchable history So Emperor is my attempt to build that layer. # Looking for feedback I would love feedback from people using local agents seriously: * Would this kind of mission control layer help your workflow? * What parts of multi-agent coordination are still painful for you? * What would you expect from a “control plane” for local agents? Happy to answer questions, and I’d really appreciate any feedback.