Klee provides a secure, local AI desktop application with integrated Retrieval-Augmented Generation (RAG) and Markdown note-taking capabilities. It targets users prioritizing privacy and offline AI functionality, offering a desktop experience powered by local LLMs.

How It Works

Klee leverages Ollama for efficient local LLM execution and LlamaIndex as its data framework for RAG. This combination allows users to interact with AI models and their personal knowledge base without relying on external cloud services, ensuring data privacy and enabling offline use.

Quick Start & Requirements

• Installation: Clone the klee-client repository, run yarn install, configure .env variables, and set up the klee-service backend. Start with yarn dev.
• Prerequisites: Node.js 20.x+, Yarn 1.22.19+. Requires a running klee-service backend (defaulting to http://localhost:6190).
• Resources: Local LLM inference will require significant CPU/GPU resources depending on the model.
• Links: Website (implied), Discord

Highlighted Details

• Built on Electron, React, TypeScript, Vite, and Tailwind CSS for a modern desktop UI.
• Supports local-only mode for enhanced privacy.
• Includes RAG capabilities via LlamaIndex integration.
• Offers optional remote mode configuration using Supabase.

Maintenance & Community

• Active community engagement encouraged via Discord.
• Contribution guidelines provided for code, bug fixes, design, and documentation.
• Links: Discord

Licensing & Compatibility

• The README does not explicitly state a license. The repository is hosted on GitHub, implying standard GitHub terms unless otherwise specified. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

• The project requires setting up a separate backend service (klee-service), adding complexity to the initial setup.
• macOS app signing and notarization require Apple Developer credentials.
• License information is missing, which is a significant caveat for adoption decisions.