Maestro is a free desktop app that allows you to run multiple AI programming assistants like Claude Code, OpenAI Codex, OpenCode, and more in parallel in your projects. It supports quick operation of shortcuts, branch isolation with Git worktrees, 24-hour unattended tasks through Auto Run, coordination of multiple agents in group chat mode, and remote monitoring via mobile terminal. This tool enables true parallel and conflict-free development, greatly improving development efficiency, reducing the time spent on switching between multiple projects, and keeping you focused and fluent, completing coding and automation faster.
Over the past year, the number of AI programming assistants has increased. Some people are used to using Claude Code, some are using OpenAI Codex, and some are trying OpenCode or other CLI proxy tools. They are strong in each, but as projects gradually increase and tasks start to go hand in hand, a new question quickly arises: how can you manage multiple AI programming agents at the same time instead of switching back and forth between different windows?
Maestro is a tool that was born for this problem.
It’s an open-source desktop app with a simple goal: to bring different AI programming assistants together in one place so they can work simultaneously. Instead of switching between multiple terminals and multiple editor windows, you can launch multiple agents directly in Maestro, allowing them to handle different tasks.
This way of working is actually a bit like teamwork with traditional development, except that the team members have changed from “human developers” to “AI agents”. You can have one agent fix bugs, another agent write new features, and one agent builds test code. They operate in their own environments without interfering with each other.
A key design of Maestro is the use of Git worktrees for task isolation. Each AI agent runs in a separate worktree branch, so it doesn’t pollute the main branch and doesn’t conflict with other agents. In other words, you can really do parallel development: multiple AIs change code at the same time, but completely isolated from each other.
This mechanism makes it possible to develop things that were difficult to achieve in the past. For example, you can have several AI agents try different implementations at the same time before picking the best results and merging them back into the main project. This “multi-path attempt” development model, which is expensive in traditional manual development, has become very natural in the AI agent environment.
Maestro also offers a mode called Auto Run . When turned on, the AI agent can continue to run on a set task without requiring you to keep your eyes glued to the screen. The author mentioned in the project description that he once had the system run continuously for nearly 24 hours, allowing AI to automatically complete a series of development tasks. For many developers, this “unattended development” may become a new way of working.
In addition to parallel tasks, Maestro also tries to solve another problem: multi-agent collaboration. In some complex tasks, one AI may not be enough, so multiple agents can coordinate their work in the same task environment, similar to a small “AI development team”. This model is still in the exploratory stage, but it already shows potential in the future.
The operation of the entire application is also clearly biased towards efficient users. Maestro’s interactive design emphasizes keyboard interactions and quick switching, many of which can be done with shortcuts. This design will be very convenient for developers who are used to working at high speeds in terminals and IDEs.
Another interesting detail is that Maestro is not a replacement for the AI tools you already have. It’s more like a scheduling layer. If you’ve already configured Claude Code, Codex, or OpenCode, the tools will still work the way they did in Maestro. Your MCP tools, permission configurations, and API settings can be reused directly.
In a sense, Maestro is doing things a bit like a command center for an AI programming agent. It doesn’t try to be a new AI model, but rather allows different AIs to work together more efficiently.
As AI programming tools become more and more powerful, the future development process may gradually change from “humans write code, AI assists” to “AI writes code, and people are responsible for scheduling”. In this trend, tools like Maestro may become increasingly important, as real efficiency gains don’t just come from stronger models, but also from better workflows.
If you’re already using AI programming assistants a lot in your daily development, Maestro offers a new way to think about it: instead of using just one AI, you have multiple AIs working for you at the same time.
Github：https://github.com/pedramamini/Maestro
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