dmux-workflows

You can run parallel AI agent sessions by using dmux to orchestrate multiple panes across Claude Code, Codex, and OpenCode. The dmux workflow manager coordinates simultaneous execution and merges pane outputs back to the main session for integrated results.

The best way to coordinate these tasks is using a dmux-based workflow manager to orchestrate parallel panes across multiple harnesses. It provides pattern-based guidance and cross-pane merging to synchronize research, implementation, testing, and review workflows.

Yes, you can use dmux with tmux to manage parallel AI agent panes for Claude Code and Codex simultaneously. Dmux specifies pane management rules and cross-harness coordination to ensure reliable, scalable parallel execution across different environments.

Cross-pane context sharing works by merging individual pane outputs back to the main session. This dmux workflow pattern allows integrated results from multiple parallel AI agent sessions to be consolidated, ensuring coordinated execution across different harnesses.

No specific dependencies or components are required to start a dmux session. The dmux workflow manager operates independently, allowing you to quickly create multiple agent panes and run parallel tasks across supported harnesses without additional setup.

You should avoid parallel AI agent panes if your workflow requires strict sequential execution or lacks clear task isolation. Dmux is designed for reliable, scalable parallel execution, so single-threaded or highly interdependent tasks may not benefit from multi-pane coordination.