farion1231/cc-switch

Registry

Summary

CC Switch is a cross-platform desktop All-in-One assistant that unifies Claude Code, Codex, OpenCode, OpenClaw, Gemini CLI, and Hermes Agent into a single interface. Built with Rust and Tauri, it offers provider management, skill management, and WSL support.

Key features

• All-in-One integration for multiple AI coding assistants
• Cross-platform desktop app built with Rust & Tauri
• Provider management for Claude Code, Codex, OpenCode, OpenClaw, Gemini CLI & Hermes Agent
• Skills management with built-in skill support
• WSL (Windows Subsystem for Linux) support
• Open-source with TypeScript and Rust codebase

Use cases

• Switch between different AI coding assistants seamlessly
• Manage multiple AI provider accounts from one desktop app
• Organize and reuse custom skills across different assistants
• Develop and test AI-assisted code on Windows with WSL
• Centralize AI tool workflows for developers and teams

README excerpt

<div align="center"> # CC Switch ### The All-in-One Manager for Claude Code, Codex, Gemini CLI, OpenCode, OpenClaw & Hermes Agent [](https://github.com/farion1231/cc-switch/releases) [](https://github.com/farion1231/cc-switch/releases) [](https://tauri.app/) [](https://github.com/farion1231/cc-switch/releases/latest) <a href="https://trendshift.io/repositories/15372" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15372" alt="farion1231%2Fcc-switch | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> ### 🌐 The Only Official Website: **[ccswitch.io](https://ccswitch.io)** English | [中文](README_ZH.md) | [日本語](README_JA.md) | [Deutsch](README_DE.md) | [Changelog](CHANGELOG.md) </div> ## ❤️Sponsor > [Want to appear here?](mailto:farion1231@gmail.com) <details open> <summary>Click to collapse</summary> [](https://platform.minimax.io/subscribe/coding-plan?code=ClLhgxr2je&source=link) MiniMax-M2.7 is a next-generation large language model designed for autonomous evolution and real-world productivity. Unlike traditional models, M2.7 actively participates in its own improvement through agent teams, dynamic tool use, and reinforcement learning loops. It delivers strong performance in software engineering (56.22% on SWE-Pro, 55.6% on VIBE-Pro, 57.0% on Terminal Bench 2) and excels in complex office workflows, achievi

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