A desktop and web interface for Gemini CLI and Qwen Code with visual tool confirmation, chat history search, and MCP server management.
Gemini CLI Desktop is a desktop and web interface that provides a visual, unified environment for interacting with Gemini CLI and Qwen Code. It solves the problem of managing multiple AI tools and conversations by offering tool confirmation, searchable history, and MCP server management in one multilingual interface.
Developers and technical users who work with Gemini CLI, Qwen Code, or other OpenAI-compatible AI models and want a more visual, controlled interface with better conversation management.
Developers choose Gemini CLI Desktop for its visual tool confirmation that adds safety to AI interactions, unified management of multiple AI tools, and powerful search capabilities across conversation history—all available as both desktop and web applications.
Web/desktop UI for Gemini CLI/Qwen Code. Manage projects, switch between tools, search across past conversations, and manage MCP servers, all from one multilingual interface, locally or remotely.
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The visual tool confirmation feature allows users to review and approve AI actions before execution, reducing risks of unintended commands, as highlighted in the README's safety focus.
It supports multiple AI models like Gemini and Qwen in one interface, eliminating context switching between different tools, as stated in the multi-model feature.
Automatic conversation saving with full-text search enables easy retrieval of past interactions, making it efficient for developers to reference old code or discussions.
Built with Tauri, it offers desktop and web versions for Windows, macOS, and Linux, ensuring broad accessibility, as shown in the platform badges.
Requires installing multiple tools like just, Rust, Node.js, and Linux system dependencies, making onboarding complex for teams without prior expertise.
Primarily optimized for Gemini and Qwen, with only custom OpenAI providers as an extension, lacking native support for other popular models like Claude or Llama.
The roadmap lists token/cost tracking as a future feature, so users cannot monitor AI usage expenses in real-time, which is a gap for budget-conscious projects.