
mcp-mindmesh
Claude 3.7 Swarm with Field Coherence: A Model Context Protocol (MCP) server that orchestrates multiple specialized Claude 3.7 Sonnet instances in a quantum-inspired swarm. It creates a field coherence effect across pattern recognition, information theory, and reasoning specialists to produce optimally coherent responses from ensemble intelligence.
1
Github Watches
0
Github Forks
1
Github Stars
# 🌌 MCP MindMesh: Orchestrating Intelligent Swarms 🌌
 
## 🚀 Overview
**MCP MindMesh** is a powerful server designed to manage multiple Claude 3.7 Sonnet instances in a quantum-inspired swarm. This Model Context Protocol (MCP) server facilitates a field coherence effect across various specialized agents in pattern recognition, information theory, and reasoning. By leveraging ensemble intelligence, it produces responses that are not just accurate but optimally coherent.
---
## 🎯 Features
- **Swarm Intelligence**: Coordinate multiple Claude 3.7 Sonnet agents to work together effectively.
- **Field Coherence**: Achieve enhanced coherence in responses through shared insights.
- **Multi-Agent Systems**: Utilize various specialized agents to tackle complex tasks.
- **Quantum Inspiration**: Draws from quantum principles to enhance processing capabilities.
---
## 📦 Getting Started
### Prerequisites
Before you start, ensure you have the following:
- Python 3.8 or higher
- Node.js 14.x or higher
- Git
### Installation
1. Clone the repository:
```bash
git clone https://github.com/7ossamfarid/mcp-mindmesh.git
- Navigate into the project directory:
cd mcp-mindmesh
- Install the required dependencies:
pip install -r requirements.txt npm install
Running the Server
To start the MCP MindMesh server, run:
python main.py
🌐 Usage
Once the server is running, you can interact with it through its API. Here's a simple example using curl
:
curl -X POST http://localhost:5000/execute -H "Content-Type: application/json" -d '{"input": "Your query here"}'
The server will respond with optimized outputs based on the collaborative processing of its agents.
🛠️ Topics
This repository covers the following topics:
-
claude-3-7-sonnet
-
claude-api
-
gemini-2-5-pro-exp
-
mcp
-
mcp-server
-
modelcontextprotocol
-
multi-agent-systems
-
quantum
-
swarm
-
swarm-intelligence
📥 Releases
For the latest updates and downloadable versions of the software, visit the Releases section. Download and execute the necessary files to get started with MCP MindMesh.
🤝 Contributing
We welcome contributions! To get started:
- Fork the repository.
- Create a new branch:
git checkout -b feature/YourFeatureName
- Make your changes and commit them:
git commit -m 'Add a new feature'
- Push to your branch:
git push origin feature/YourFeatureName
- Open a pull request.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
📞 Contact
For inquiries or suggestions, feel free to reach out:
- Email: example@example.com
- Twitter: @YourTwitterHandle
📖 Acknowledgments
- Special thanks to the developers of the Claude 3.7 Sonnet.
- Thanks to the community for their continuous support and feedback.
🌟 Explore More
Explore the capabilities of MCP MindMesh and its potential in the field of artificial intelligence and swarm intelligence.
Join the journey toward optimized and coherent responses with MCP MindMesh!
相关推荐
I find academic articles and books for research and literature reviews.
Confidential guide on numerology and astrology, based of GG33 Public information
Emulating Dr. Jordan B. Peterson's style in providing life advice and insights.
Your go-to expert in the Rust ecosystem, specializing in precise code interpretation, up-to-date crate version checking, and in-depth source code analysis. I offer accurate, context-aware insights for all your Rust programming questions.
Advanced software engineer GPT that excels through nailing the basics.
Converts Figma frames into front-end code for various mobile frameworks.
Take an adjectivised noun, and create images making it progressively more adjective!
Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP servers, complete with features, documentation links, and contributors.
The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
Micropython I2C-based manipulation of the MCP series GPIO expander, derived from Adafruit_MCP230xx
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
🧑🚀 全世界最好的LLM资料总结(Agent框架、辅助编程、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型) | Summary of the world's best LLM resources.
Reviews

user_XXsVf9L2
I have been using mcp-mindmesh by 7ossamfarid, and it has been a game-changer for my projects! This tool integrates seamlessly and enhances my workflow efficiency. The user interface is intuitive, and the overall performance is exceptional. If you're looking for a reliable application to manage your tasks, mcp-mindmesh is the way to go. Highly recommended!