How to operate multiple AI coding assistants safely using Git worktrees
This technique allows developers to run several AI coding assistants on the same repository simultaneously without risking file overwrites or merge collisions. Isolating each assistant into its own working tree preserves local repository history while keeping uncommitted changes separate.
Try it yourself
- 1Create a separate git worktree and branch for each AI coding assistant instance.
- 2Assign distinct files or components to each assistant to prevent direct code overlap.
- 3Review pull requests individually and resolve any file conflicts before combining branches.
Copy this prompt
Use the gh CLI to display all open pull requests in this repository. Provide a summary of modified files for each pull request. Recommend a safe sequence for merging, highlight potential conflicts, and fix conflicts as merges occur. Execute test suites following each merge and halt the process if test failures occur.
The Blend
As artificial intelligence tools become standard fixtures in software development, programmers increasingly face a practical hurdle. Running multiple AI helpers on the same codebase at once can cause them to edit over each other's work. To address this friction, developers are turning to a built in feature of the version control system Git known as worktrees. Documentation published for Anthropic's coding assistant, Claude Code, highlights this method as a clear way to manage concurrent automated tasks without creating file conflicts.
For everyday software creators, this approach changes how fast new features can be built. Normally, an AI agent working inside a project folder locks up that space or risks writing over unsaved files if a human or another bot is also making edits. By placing each AI assistant inside its own isolated working tree, the underlying code history remains shared while the active draft files stay completely separate. This setup enables a developer to assign research, bug fixes, and feature design to different automated agents simultaneously.
While this strategy solves the immediate problem of file overwrites on a local computer, it introduces new challenges for overall project hygiene. Merging half a dozen branches generated by AI back into a main project still requires careful human review to ensure the pieces fit together cleanly. It remains unclear whether software teams will adopt automated review systems to handle the massive volume of parallel code these multi agent workflows produce, or if human bottlenecks will simply shift from writing code to reviewing it.
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Ingredients
- Common workflows - Claude Code Docs
Anthropic recommends using Git worktrees to run simultaneous Claude Code sessions without conflicting file edits.