One agent is changing code. Another is reviewing the implementation. Test results and a set of requirements are open nearby. Splitting the work gives each task somewhere to go, but someone still has to keep track of progress and decide when the next step can begin.
MadoHub brings that work onto a canvas. Coding sessions, code, and notes sit alongside one another, with room to arrange them around the task. Claude Code, Codex, Amp, and OpenCode can run in the same workspace, with visible states for work in progress, requests for input, and completed tasks.
Give related work a place together
A terminal is a useful place to execute commands. Several terminal tabs are less helpful when you need to understand how parallel tasks relate. You still have to remember which session owns a change and which output belongs to the next decision.
On the canvas, related agent sessions, code windows, and notes can stay together. Requirements and review comments remain alongside a change, making it easier to see what the next task depends on.
Make handoffs explicit
A change may need implementation, review, and another pass based on the findings. MadoAgent lets you arrange agent work in natural language. Current Flows pass objectives, role responsibilities, and completion criteria to a durable Supervisor for coordination. Canvas connections no longer act as a sequential pipeline forwarding agent output.
For example, one agent can implement a change while another checks edge cases. Defining each assignment and its risks gives the review a clear scope. The developer can then assess the code and test results before accepting the change.
Keep the decisions with the developer
The developer remains responsible for key decisions: which requests to approve, which changes need more tests, and when to pause and reassess. Automatic handoffs reduce repetitive actions while code review, testing, and permission checks remain part of the workflow. Less time switching windows leaves more attention for those decisions.
Local workspace, chosen AI service
MadoHub's macOS desktop app uses Electron and an isolated Rust Host. React presents the canvas and sessions; native capabilities such as terminals, files, and persistence are accessed through restricted interfaces. AI requests use the selected model service, with your own provider keys or MadoHub Cloud. Review that service's data handling before choosing a setup.
MadoHub is currently in Early Access. Start with a small, well-scoped task to explore the canvas, review, and handoff tools before building a larger workflow around them. The website and documentation describe current capabilities and requirements.
