Rift CLI: Manage Git Worktrees for Parallel AI Agent Workflows

Rift is a Git worktree manager built specifically for parallel AI agent workflows. It addresses the friction of having a single repository occupied by one agent while you want to start another task, eliminating issues with branch switching, stashing, and dependency conflicts.
How It Works
The basic workflow involves three commands:
rift init- Initialize in your projectrift open- Creates an isolated branch + worktree and launches Claude Code (or any agent) inside itrift close- Cleans up the worktree and branch when done
Each worktree is a full, independent copy of your repository on its own branch, allowing you to work on as many features simultaneously as you want.
Key Features for Multi-Agent Workflows
- Lifecycle hooks that let you auto-install dependencies, run migrations, and assign ports per worktree
- Deterministic port mapping - every worktree gets unique ports so you can run multiple development servers without collisions
rift codeopens all active worktrees in one VS Code/Cursor/Windsurf workspace- Works with any CLI agent - Claude Code, Copilot, Codex, Aider, or whatever you prefer
Technical Details
The project was built with Claude Code itself and is open source under the MIT license. It's built with Bun and available on npm:
npm install -g @priyashpatil/riftGitHub repository: https://github.com/priyashpatil/rift
Documentation: https://rift.priyashpatil.com
📖 Read the full source: r/ClaudeAI
👀 See Also

Developer Builds Open Source AI Skill to Validate Startup Ideas, Kills Own Idea in 10 Minutes
A developer built an open source AI skill called startup-design that walks through 8 phases of startup validation from brainstorming to financial projections. When testing it on his own startup idea, the skill asked hard questions that revealed he wasn't the right founder for that particular concept.

Red Queen: A Deterministic Orchestrator That Runs Claude Code as a Worker Pool
Red Queen uses a state machine to orchestrate Claude Code subprocesses, eliminating LLM routing errors and token waste from mega-prompts.

llm-use – An Open-Source Framework for Routing and Orchestrating Multi-LLM Agent Workflows
llm-use is revolutionizing automation with its open-source framework designed to efficiently route and orchestrate multi-LLM agent workflows. Explore its impact on AI operations.

Ktx: An Executable Context Layer to Fix Data Agent Accuracy
Ktx is an open-source executable context layer that makes agents reliable on your data stack by combining Markdown wiki ingestion with YAML semantic definitions.