chat-mcp

A Desktop Chat App that leverages MCP(Model Context Protocol) to interface with other LLMs.

Stars: 67

Visit

A Cross-Platform Interface for Large Language Models (LLMs) utilizing the Model Context Protocol (MCP) to connect and interact with various LLMs. The desktop app, built on Electron, ensures compatibility across Linux, macOS, and Windows. It simplifies understanding MCP principles, facilitates testing of multiple servers and LLMs, and supports dynamic LLM configuration and multi-client management. The UI can be extracted for web use, ensuring consistency across web and desktop versions.

README:

MCP Chat Desktop App

A Cross-Platform Interface for LLMs

This desktop application utilizes the MCP (Model Context Protocol) to seamlessly connect and interact with various Large Language Models (LLMs). Built on Electron, the app ensures full cross-platform compatibility, enabling smooth operation across different operating systems.

The primary objective of this project is to deliver a clean, minimalistic codebase that simplifies understanding the core principles of MCP. Additionally, it provides a quick and efficient way to test multiple servers and LLMs, making it an ideal tool for developers and researchers alike.

Features

Cross-Platform Compatibility: Supports Linux, macOS, and Windows.
Flexible Apache-2.0 License: Allows easy modification and building of your own desktop applications.
Dynamic LLM Configuration: Compatible with all OpenAI SDK-supported LLMs, enabling quick testing of multiple backends through manual or preset configurations.
Multi-Client Management: Configure and manage multiple clients to connect to multiple servers using MCP config.
UI Adaptability: The UI can be directly extracted for web use, ensuring consistent ecosystem and interaction logic across web and desktop versions.

Architecture

Adopted a straightforward architecture consistent with the MCP documentation to facilitate a clear understanding of MCP principles.

erDiagram
    Renderer ||--o{ APP : IPC
    APP ||--|{ Client : contains
    Client }|..|{ Server : Stdio

Only three key files need to be understood: main.ts, client.ts, and preload.ts, to grasp the essence of the project.

How to use

After cloning or downloading this repository:

Please modify the config.json file located in src/main.
Ensure that the command and path specified in the args are valid.
Please ensure that Node.js is installed on your system.
You can verify this by running node -v and npm -v in your terminal to check their respective versions.
npm install
npm start

Configuration

Create a .json file and paste the following content into it. This file can then be provided as the interface configuration for the Chat UI.

gtp-api.json

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://api.aiql.com",
        "path": "/v1/chat/completions",
        "model": "gpt-4o-mini",
        "max_tokens_value": "",
        "mcp": true
    },
    "defaultChoiceStore": {
        "model": [
            "gpt-4o-mini",
            "gpt-4o",
            "gpt-4",
            "gpt-4-turbo"
        ]
    }
}

You can replace the 'url' if you have direct access to the OpenAI API.

Alternatively, you can also use another API endpoint that supports function calls:

qwen-api.json

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://dashscope.aliyuncs.com/compatible-mode",
        "path": "/v1/chat/completions",
        "model": "qwen-turbo",
        "max_tokens_value": "",
        "mcp": true
    },
    "defaultChoiceStore": {
        "model": [
            "qwen-turbo",
            "qwen-plus",
            "qwen-max"
        ]
    }
}

deepinfra.json

{
    "chatbotStore": {
        "apiKey": "",
        "url": "https://api.deepinfra.com",
        "path": "/v1/openai/chat/completions",
        "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
        "max_tokens_value": "32000",
        "mcp": true
    },
    "defaultChoiceStore": {
        "model": [
            "meta-llama/Meta-Llama-3.1-70B-Instruct",
            "meta-llama/Meta-Llama-3.1-405B-Instruct",
            "meta-llama/Meta-Llama-3.1-8B-Instruct"
        ]
    }
}

Build Application

You can build your own desktop application by:

npm run build-app

This CLI helps you build and package your application for your current OS, with artifacts stored in the /artifacts directory.

For Debian/Ubuntu users experiencing RPM build issues, try one of the following solutions:

Edit package.json to skip the RPM build step. Or
Install rpm using sudo apt-get install rpm (You may need to run sudo apt update to ensure your package list is up-to-date)

Troubleshooting

Error: spawn npx ENOENT - ISSUE 40

Modify the config.json in src/main

On windows, npx may not work, please refer my workaround: ISSUE 101

Or you can use node in config.json:

{
    "mcpServers": {
        "filesystem": {
        "command": "node",
        "args": [
            "node_modules/@modelcontextprotocol/server-filesystem/dist/index.js",
            "D:/Github/mcp-test"
        ]
        }
    }
}

Please ensure that the provided path is valid, especially if you are using a relative path. It is highly recommended to provide an absolute path for better clarity and accuracy.

By default, I will install server-everything, server-filesystem, and server-puppeteer for test purposes. However, you can install additional server libraries or use npx to utilize other server libraries as needed.

Installation timeout

Generally, after executing npm install for the entire project, the total size of files in the node_modules directory typically exceeds 500MB.

If the installation process stalls at less than 300MB and the progress bar remains static, it is likely due to a timeout during the installation of the latter part, specifically Electron.

This issue often arises because the download speed from Electron's default server is excessively slow or even inaccessible in certain regions. To resolve this, you can modify the environment or global variable ELECTRON_MIRROR to switch to an Electron mirror site that is accessible from your location.

Electron builder timeout

When using electron-builder to package files, it automatically downloads several large release packages from GitHub. If the network connection is unstable, this process may be interrupted or timeout.

On Windows, you may need to clear the cache located under the electron and electron-builder directories within C:\Users\YOURUSERNAME\AppData\Local before attempting to retry.

Due to potential terminal permission issues, it is recommended to use the default shell terminal instead of VSCode's built-in terminal.

Demo

Multimodal Support

Reasoning and Latex Support

MCP Tools Visualization

MCP Toolcall Process Overview

MCP Prompts Template

Dynamic LLM Config

DevTool Troubleshooting

For Tasks:

Click tags to check more tools for each tasks

test servers manage clients build applications troubleshoot errors configure llms

For Jobs:

software developer researcher data scientist ai engineer web developer

Alternative AI tools for chat-mcp

Similar Open Source Tools

chat-mcp

github

: 67

langchain-extract

LangChain Extract is a simple web server that allows you to extract information from text and files using LLMs. It is built using FastAPI, LangChain, and Postgresql. The backend closely follows the extraction use-case documentation and provides a reference implementation of an app that helps to do extraction over data using LLMs. This repository is meant to be a starting point for building your own extraction application which may have slightly different requirements or use cases.

github

: 916

WebRL

WebRL is a self-evolving online curriculum learning framework designed for training web agents in the WebArena environment. It provides model checkpoints, training instructions, and evaluation processes for training the actor and critic models. The tool enables users to generate new instructions and interact with WebArena to configure tasks for training and evaluation.

github

: 270

motorhead

Motorhead is a memory and information retrieval server for LLMs. It provides three simple APIs to assist with memory handling in chat applications using LLMs. The first API, GET /sessions/:id/memory, returns messages up to a maximum window size. The second API, POST /sessions/:id/memory, allows you to send an array of messages to Motorhead for storage. The third API, DELETE /sessions/:id/memory, deletes the session's message list. Motorhead also features incremental summarization, where it processes half of the maximum window size of messages and summarizes them when the maximum is reached. Additionally, it supports searching by text query using vector search. Motorhead is configurable through environment variables, including the maximum window size, whether to enable long-term memory, the model used for incremental summarization, the server port, your OpenAI API key, and the Redis URL.

github

: 840

LLamaWorker

LLamaWorker is a HTTP API server developed to provide an OpenAI-compatible API for integrating Large Language Models (LLM) into applications. It supports multi-model configuration, streaming responses, text embedding, chat templates, automatic model release, function calls, API key authentication, and test UI. Users can switch models, complete chats and prompts, manage chat history, and generate tokens through the test UI. Additionally, LLamaWorker offers a Vulkan compiled version for download and provides function call templates for testing. The tool supports various backends and provides API endpoints for chat completion, prompt completion, embeddings, model information, model configuration, and model switching. A Gradio UI demo is also available for testing.

github

: 59

bot-on-anything

The 'bot-on-anything' repository allows developers to integrate various AI models into messaging applications, enabling the creation of intelligent chatbots. By configuring the connections between models and applications, developers can easily switch between multiple channels within a project. The architecture is highly scalable, allowing the reuse of algorithmic capabilities for each new application and model integration. Supported models include ChatGPT, GPT-3.0, New Bing, and Google Bard, while supported applications range from terminals and web platforms to messaging apps like WeChat, Telegram, QQ, and more. The repository provides detailed instructions for setting up the environment, configuring the models and channels, and running the chatbot for various tasks across different messaging platforms.

github

: 4.0k

sdfx

SDFX is the ultimate no-code platform for building and sharing AI apps with beautiful UI. It enables the creation of user-friendly interfaces for complex workflows by combining Comfy workflow with a UI. The tool is designed to merge the benefits of form-based UI and graph-node based UI, allowing users to create intricate graphs with a high-level UI overlay. SDFX is fully compatible with ComfyUI, abstracting the need for installing ComfyUI. It offers features like animated graph navigation, node bookmarks, UI debugger, custom nodes manager, app and template export, image and mask editor, and more. The tool compiles as a native app or web app, making it easy to maintain and add new features.

github

: 213

promptwright

Promptwright is a Python library designed for generating large synthetic datasets using local LLM and various LLM service providers. It offers flexible interfaces for generating prompt-led synthetic datasets. The library supports multiple providers, configurable instructions and prompts, YAML configuration, command line interface, push to Hugging Face Hub, and system message control. Users can define generation tasks using YAML configuration files or programmatically using Python code. Promptwright integrates with LiteLLM for LLM providers and supports automatic dataset upload to Hugging Face Hub. The library is not responsible for the content generated by models and advises users to review the data before using it in production environments.

github

: 382

promptwright

Promptwright is a Python library designed for generating large synthetic datasets using a local LLM and various LLM service providers. It offers flexible interfaces for generating prompt-led synthetic datasets. The library supports multiple providers, configurable instructions and prompts, YAML configuration for tasks, command line interface for running tasks, push to Hugging Face Hub for dataset upload, and system message control. Users can define generation tasks using YAML configuration or Python code. Promptwright integrates with LiteLLM to interface with LLM providers and supports automatic dataset upload to Hugging Face Hub.

github

: 367

CredSweeper

CredSweeper is a tool designed to detect credentials like tokens, passwords, and API keys in directories or files. It helps users identify potential exposure of sensitive information by scanning lines, filtering, and utilizing an AI model. The tool reports lines containing possible credentials, their location, and the expected type of credential.

github

: 117

aiocsv

aiocsv is a Python module that provides asynchronous CSV reading and writing. It is designed to be a drop-in replacement for the Python's builtin csv module, but with the added benefit of being able to read and write CSV files asynchronously. This makes it ideal for use in applications that need to process large CSV files efficiently.

github

: 59

elia

Elia is a powerful terminal user interface designed for interacting with large language models. It allows users to chat with models like Claude 3, ChatGPT, Llama 3, Phi 3, Mistral, and Gemma. Conversations are stored locally in a SQLite database, ensuring privacy. Users can run local models through 'ollama' without data leaving their machine. Elia offers easy installation with pipx and supports various environment variables for different models. It provides a quick start to launch chats and manage local models. Configuration options are available to customize default models, system prompts, and add new models. Users can import conversations from ChatGPT and wipe the database when needed. Elia aims to enhance user experience in interacting with language models through a user-friendly interface.

github

: 1.8k

CoPilot

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

github

: 55

otto-m8

otto-m8 is a flowchart based automation platform designed to run deep learning workloads with minimal to no code. It provides a user-friendly interface to spin up a wide range of AI models, including traditional deep learning models and large language models. The tool deploys Docker containers of workflows as APIs for integration with existing workflows, building AI chatbots, or standalone applications. Otto-m8 operates on an Input, Process, Output paradigm, simplifying the process of running AI models into a flowchart-like UI.

github

: 63

agent-mimir

Agent Mimir is a command line and Discord chat client 'agent' manager for LLM's like Chat-GPT that provides the models with access to tooling and a framework with which accomplish multi-step tasks. It is easy to configure your own agent with a custom personality or profession as well as enabling access to all tools that are compatible with LangchainJS. Agent Mimir is based on LangchainJS, every tool or LLM that works on Langchain should also work with Mimir. The tasking system is based on Auto-GPT and BabyAGI where the agent needs to come up with a plan, iterate over its steps and review as it completes the task.

github

: 103

redis-vl-python

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

github

: 253

For similar tasks

python-tutorial-notebooks

This repository contains Jupyter-based tutorials for NLP, ML, AI in Python for classes in Computational Linguistics, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) at Indiana University.

github

: 121

open-parse

Open Parse is a Python library for visually discerning document layouts and chunking them effectively. It is designed to fill the gap in open-source libraries for handling complex documents. Unlike text splitting, which converts a file to raw text and slices it up, Open Parse visually analyzes documents for superior LLM input. It also supports basic markdown for parsing headings, bold, and italics, and has high-precision table support, extracting tables into clean Markdown formats with accuracy that surpasses traditional tools. Open Parse is extensible, allowing users to easily implement their own post-processing steps. It is also intuitive, with great editor support and completion everywhere, making it easy to use and learn.

github

: 2.4k

MoonshotAI-Cookbook

The MoonshotAI-Cookbook provides example code and guides for accomplishing common tasks with the MoonshotAI API. To run these examples, you'll need an MoonshotAI account and associated API key. Most code examples are written in Python, though the concepts can be applied in any language.

github

: 184

AHU-AI-Repository

This repository is dedicated to the learning and exchange of resources for the School of Artificial Intelligence at Anhui University. Notes will be published on this website first: https://www.aoaoaoao.cn and will be synchronized to the repository regularly. You can also contact me at [email protected].

github

: 111

modern_ai_for_beginners

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

github

: 101

Building-AI-Applications-with-ChatGPT-APIs

This repository is for the book 'Building AI Applications with ChatGPT APIs' published by Packt. It provides code examples and instructions for mastering ChatGPT, Whisper, and DALL-E APIs through building innovative AI projects. Readers will learn to develop AI applications using ChatGPT APIs, integrate them with frameworks like Flask and Django, create AI-generated art with DALL-E APIs, and optimize ChatGPT models through fine-tuning.

github

: 54

examples

This repository contains a collection of sample applications and Jupyter Notebooks for hands-on experience with Pinecone vector databases and common AI patterns, tools, and algorithms. It includes production-ready examples for review and support, as well as learning-optimized examples for exploring AI techniques and building applications. Users can contribute, provide feedback, and collaborate to improve the resource.

github

: 2.8k

lingoose

LinGoose is a modular Go framework designed for building AI/LLM applications. It offers the flexibility to import only the necessary modules, abstracts features for customization, and provides a comprehensive solution for developing AI/LLM applications from scratch. The framework simplifies the process of creating intelligent applications by allowing users to choose preferred implementations or create their own. LinGoose empowers developers to leverage its capabilities to streamline the development of cutting-edge AI and LLM projects.

github

: 512

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 604

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k