SimpleChatJS

SimpleChatJS is an MCP server that connects desktop chat workflows to the Model Context Protocol ecosystem and diverse language model providers. Built with vanilla JavaScript, Node.js, Express, and Electron, the application provides an open desktop environment where developers and researchers can run conversational AI sessions enhanced by external MCP tools. SimpleChatJS links directly to inference backends such as Ollama, OpenAI, Anthropic Claude, Google Gemini, LM Studio, and vLLM. Users interact with language models through streaming text generation, multimodal uploads including images and documents, and branchable turn-based dialogue trees stored in a local SQLite database. By implementing the Model Context Protocol SDK, the application allows users to register, manage, and trigger contextual tools directly from chat conversations. Built-in developer debug panels make it possible to monitor raw API requests, review tool invocations, and inspect incoming server responses in real time. SimpleChatJS operates locally on Windows, macOS, and Linux, serving as a transparent client and server environment for testing MCP integrations alongside diverse LLM endpoints without heavy frontend framework dependencies.

Category: AI & LLM Tooling

Tags: chat, framework, javascript, nodejs

Visit SimpleChatJS

How to install and configure SimpleChatJS

1. Clone the repository from https://github.com/Tomobobo710/SimpleChatJS to your local machine. 2. Ensure Node.js is installed. On Linux systems, verify that FUSE is available to run AppImage packages. 3. Open a terminal in the root directory and install dependencies by running npm install. 4. Start the application in development mode by running npm run dev. 5. To build standalone desktop binaries, execute npx electron-builder install-app-deps and then run npm run build. The generated packages will be placed in the dist-electron/ directory. 6. Configure your AI endpoints and register desired MCP servers via the built-in settings and tools panels.

What you can do with SimpleChatJS

• Executing external developer tools and scripts directly within AI chat sessions using Model Context Protocol server integrations. * Interfacing with local inference servers like Ollama and LM Studio to run private, offline AI chats with tool execution. * Inspecting real-time API payloads, streaming responses, and MCP tool execution logs through the embedded developer debug panel. * Managing branching conversation trees and persistent multi-session chat histories backed by a local SQLite database engine. * Analyzing images and attached documents using multimodal vision models from OpenAI, Anthropic, or Google Gemini.

Key facts

• Open Source
• https://github.com/Tomobobo710/SimpleChatJS
• AI & LLM Tooling
• chat, framework, javascript, nodejs

Part of MCP Servers

What is SimpleChatJS?

SimpleChatJS is an open-source Electron desktop application that provides an AI chat interface with Model Context Protocol support. Built with vanilla JavaScript and Node.js, it connects to local inference tools like Ollama and remote APIs like OpenAI and Anthropic, allowing users to run conversational AI with tool integration.

How do I install SimpleChatJS?

To install SimpleChatJS, clone the repository from GitHub and ensure Node.js is installed. Run npm install inside the project folder to install dependencies. You can then run npm run dev to start the application in development mode or use npm run build to compile production binaries for your desktop platform.

Which AI providers are compatible with SimpleChatJS?

SimpleChatJS supports any OpenAI-compatible API endpoint. This includes local model engines such as Ollama, LM Studio, and vLLM, as well as cloud-hosted APIs from OpenAI, Anthropic Claude, and Google Gemini. You can switch between providers using customizable settings profiles inside the application.

Is SimpleChatJS open source?

Yes, SimpleChatJS is an open-source project hosted on GitHub. Users can inspect the vanilla JavaScript and Node.js codebase, contribute modifications, build custom desktop executables for Windows, macOS, or Linux, and adapt the internal Model Context Protocol implementation to their needs.