OpenClaw-mission-control is a focused TypeScript project built to provide a centralized dashboard for monitoring and managing AI agents running inside the OpenClaw framework. If you work with OpenClaw AI agents, this project offers a convenient mission control interface that hooks directly into OpenClaw’s event system, enabling real-time visibility and operational control of your AI agents and tasks.
what openclaw-mission-control does and how it integrates with OpenClaw
At its core, openclaw-mission-control acts as a bridge between your local OpenClaw AI environment and a web-based dashboard. The project provides a frontend dashboard UI served via a development server (using Bun.js) and a backend powered by Convex, a serverless platform designed for real-time applications.
The dashboard is designed to display a roster of agents and their associated tasks, giving you a clear view of ongoing operations within your OpenClaw deployment. This is particularly useful during development and testing phases, where understanding agent behavior and task flow is critical.
Integration happens through a mission control hook that you install into your OpenClaw hooks directory. This hook forwards OpenClaw events to the Convex backend via a webhook URL, which you configure in your OpenClaw config file or as an environment variable. This tight coupling allows the dashboard to reflect live agent activity without polling or manual refresh.
The technology stack primarily includes TypeScript for both frontend and backend code, Bun.js as a fast runtime and bundler for development, and Convex as the backend database and API layer. The project uses npm scripts and npx commands for seeding initial data, adding to a smooth developer experience.
technical strengths and tradeoffs
What stands out technically is the seamless integration with OpenClaw’s hook system and the use of Convex to handle real-time data storage and queries. Convex’s serverless model means you don’t need to manage a separate backend server, which simplifies deployment and scaling during development.
Using Bun.js for installation and running the development environment adds a modern touch, speeding up startup times compared to traditional Node.js setups.
The codebase focuses on clarity and developer experience, with straightforward commands to install dependencies, start the server, and seed data. This simplicity is a strength for developers who want to get up and running quickly.
The tradeoff is the strong dependency on the OpenClaw ecosystem and Convex, which means this dashboard is not a generic AI agent management tool but a specialized interface for OpenClaw users. If your AI agents are not based on OpenClaw, this project offers limited value.
Another limitation is that the backend relies on Convex, which is a third-party service. This introduces potential limitations around vendor lock-in and data privacy depending on your deployment needs.
quick start
The README provides a minimal but complete quick start to get the dashboard running locally:
bun install
bun dev
This installs dependencies and starts the development server.
To populate the dashboard with initial agents and tasks, run:
npx convex run seed:run
After that, open the app in your browser (usually at http://localhost:5173), sign up to create commander credentials, and start monitoring operations.
The README also details how to install the mission control hook into your OpenClaw hooks directory and configure the webhook URL in OpenClaw’s config file or environment.
cp -r ~/.openclaw/hooks/mission-control ~/.openclaw/hooks/
Configure the webhook URL as shown:
{
"hooks": {
"internal": {
"enabled": true,
"entries": {
"mission-control": {
"enabled": true,
"env": {
"MISSION_CONTROL_URL": "https://your-project.convex.site/openclaw/event"
}
}
}
}
}
}
Finally, restart the OpenClaw gateway:
openclaw gateway restart
This setup gets the dashboard fully integrated and capable of receiving live event data.
verdict
openclaw-mission-control is a practical, well-scoped dashboard solution for developers working within the OpenClaw AI framework. It trades generality for deep integration, providing a live, centralized view of agents and tasks through a modern TypeScript and Bun.js-based stack.
Its strengths lie in its clear developer experience, real-time data handling via Convex, and straightforward hook integration for event streaming. The tradeoffs are its tight coupling to OpenClaw and reliance on a third-party backend service, which might not fit all production or privacy-sensitive environments.
If you are building or testing AI workflows with OpenClaw and want an easy way to monitor agent activity and seed controlled test data, this repo is a solid choice. For broader or more generic AI agent monitoring, you’ll likely need something more extensible or standalone.
Overall, openclaw-mission-control delivers on its mission: to be a mission control center for OpenClaw AI agents, with a developer-friendly setup and a clean, real-time dashboard experience.
→ GitHub Repo: manish-raana/openclaw-mission-control ⭐ 269 · TypeScript