Summary Chatless is a lightweight, local-first AI chat client prioritizing user privacy and model flexibility. It supports diverse cloud (OpenAI, Anthropic) and local (Ollama, LM Studio) AI providers within a single desktop application. Key benefits include local data storage, document parsing (PDF, Word, Markdown), Retrieval-Augmented Generation (RAG) for context-aware responses, and image analysis capabilities.

How It Works Built with Tauri 2.0 and Next.js 15, Chatless offers a cross-platform desktop experience using TypeScript and Rust. It unifies access to multiple AI services and local models. Data is stored locally via SQLite for enhanced privacy. Document parsing and vector retrieval (RAG) allow the AI to leverage user-provided knowledge bases, improving response accuracy. Vision model support enables image understanding, and MCP protocol integration extends tool capabilities.

Quick Start & Requirements Pre-built installers are available on GitHub Releases for Windows, macOS, and Linux. Windows users may require the Microsoft Visual C++ 2015-2022 Redistributable (x64) to resolve startup issues. Local development involves cloning the repo, running pnpm install, and starting with pnpm tauri dev. Links to detailed installation and usage guides are referenced.

Highlighted Details

• Broad AI provider support (cloud & local).
• Document parsing (PDF, Word, Markdown) with local RAG.
• Image analysis via Vision models.
• Local data storage for privacy.

Maintenance & Community The project acknowledges contributors like @ukhack and @duokebei. Support channels include GitHub Issues/Discussions and in-app feedback. A roadmap is referenced, suggesting ongoing development. The project is dated "© 2025 chatless," indicating recent activity.

Licensing & Compatibility Released under the permissive MIT License, allowing for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats Some Windows installations may require manual installation of specific Visual C++ runtime libraries to prevent startup crashes. Detailed setup time and resource footprint information are not explicitly provided.