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<h2>wmux: Windows terminal multiplexer for AI agent orchestration</h2>
Experience wmux by openwong2kim, a native Windows terminal multiplexer built to coordinate command-line AI agents and multi-session workflows. The app combines pane-based terminal splitting with a Chromium-based browsing panel controllable via the Chrome DevTools Protocol, plus a zero-configuration MCP server to link AI tools. It targets developers and AI researchers who need persistent sessions, real-time agent activity signals, and quick access to multiple shells on a single desktop.
wmux is designed to let multiple AI coding agents operate inside one window, offering horizontal and vertical pane splitting and an embedded Chromium-based browser panel. The browser accepts CDP commands, so CLI agents can perform web actions and interact with live DOM elements. The app also supports image pasting into terminal sessions and a unified sidebar that reports agent activity, which helps coordinate parallel tasks from different agent instances.
Built on Electron with ConPTY for terminal emulation, the app runs natively on Windows and avoids WSL path-translation overhead. The developer notes native implementation to reduce cross-environment costs, and community feedback cites the project's lightweight nature despite Electron. The app supports Command Prompt, PowerShell, and WSL shells side-by-side, so users can balance workload across shells without adding extra translation layers.
wmux includes a zero-configuration Model Context Protocol server that automates agent registration and a browser panel controllable via CDP, which gives agents direct web access. Visual notification rings and Windows toast alerts surface background activity, and session persistence preserves layouts and directories across restarts. Installation paths listed include WinGet, Chocolatey, or GitHub releases, enabling straightforward deployment in development environments.
wmux is a practical choice for developers and AI researchers coordinating multiple command-line agents on Windows, because it centralizes agent activity and preserves session state across restarts. The main trade-off is its specialist focus on AI orchestration, which limits appeal for general terminal tasks. Practical tip: run large agent jobs during idle hours to reduce competition for system resources.
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