A CLI tool that simplifies Git worktree management, specifically designed for running multiple AI coding agents in parallel.
Worktrunk is a CLI tool designed to simplify the management of Git worktrees. It solves the problem of Git's native worktree commands being verbose and cumbersome, especially for developers who need to manage many parallel branches simultaneously, such as when running multiple AI coding agents. It provides intuitive commands that treat worktrees like branches, along with automation features to streamline the entire workflow.
Developers and engineers who use AI coding agents (like Claude Code) extensively and need to manage multiple concurrent, autonomous coding sessions without conflicts. It's also valuable for any developer managing complex feature branches or parallel streams of work.
Developers choose Worktrunk because it dramatically reduces the command-line overhead of using Git worktrees, turning a multi-step, error-prone process into simple, memorable commands. Its unique integration of hooks, LLM features, and workflow automation is specifically tailored for the modern, agent-assisted development paradigm.
Worktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows
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Replaces verbose Git worktree commands with intuitive ones like `wt switch feat`, making management as easy as branches, as shown in the comparison table with plain git.
Automatically generates commit messages from diffs and can launch AI agents like Claude directly into new worktrees, specifically tailored for parallel agent development.
The `wt list` command displays CI status, branch divergence, and AI-generated summaries, providing a comprehensive overview beyond basic git worktree list.
Supports hooks for tasks like dependency installation and combines squashing, rebasing, merging, and cleanup into a single `wt merge` command, streamlining workflows.
On Windows, the `wt` command conflicts with Windows Terminal, requiring workarounds like using `git-wt` or disabling system aliases, as noted in the installation section.
Many features, such as LLM commit messages and AI agent launching, are optimized for AI workflows, reducing utility for traditional development without agents.
Setting up hooks, templates, and LLM integrations requires additional configuration, which might be complex for users seeking a plug-and-play solution.