dmux-workflows

Run parallel AI agent sessions by using dmux to orchestrate multiple harness panes within a single tmux-based workflow manager. It coordinates parallel tasks across Claude Code, Codex, OpenCode, Cline, Gemini, and Qwen without cross-task interference.

Yes, you can coordinate multiple AI coding agents like Claude Code and Codex through dmux. It provides a tmux-based pane manager that supports rapid switching between harnesses and collaborative experimentation with consistent context sharing across panes.

The best way to split and merge outputs from multiple AI agents is using dmux. It provides clear split-and-merge patterns for research, implementation, and testing cycles, allowing you to press 'n' to create a new pane and 'm' to merge output.

To create a new pane and merge output in a multi-agent workflow, use dmux and press 'n' to create a new pane, then press 'm' to merge output. This allows you to manage multiple harness panes and coordinate parallel tasks effectively.

Yes, dmux supports cross-harness orchestration for parallel tasks. It applies to coordinating multi-agent workflows across Claude Code, Codex, OpenCode, Cline, Gemini, and Qwen, managing multiple harness panes in a single, cohesive workflow.

You should not use a tmux-based multi-agent orchestration approach for tasks requiring sequential execution or strict isolation without context sharing. dmux is designed for parallel tasks and collaborative experimentation, so sequential workflows may face cross-task interference.