@blue-lotus-org
Basic information
Integration two AI's into a modernized MCP for better performance
No standard config provided
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LOTUS-MCP is a free, open-source conceptual framework for building a custom Model Context Protocol (MCP) that integrates Mistral and Gemini into a single unified system. It provides architecture, code examples, and deployment guidance for developers who want to create their own AI coordination protocol.
Follow the step-by-step guide in the README: start by implementing the adapter layer (converting MCP requests to each model’s API), build the context management system, add tool connectors, and deploy using the recommended stack. Reference the provided example files (example.py, minimalistExample.py, asyncExample.py, finalExample.py) for concrete implementations.
It provides a structured blueprint for building your own multi-model MCP, allowing you to coordinate Mistral and Gemini (or other models) under a unified protocol with routing, context sharing, and tool integration.
No. The code examples are conceptual and may not produce real results. The README recommends starting with shadow mode (run both models but show only one output) and gradually rolling out after testing. It is a framework for understanding the architecture, not a plug‑and‑play solution.
The suggested stack includes Python 3.11, Mistral and Gemini API access, Nginx for the gateway, and Prometheus/Grafana for monitoring. The README also references token‑bucket rate limiting and JWT authentication.
The architecture includes automatic fallback between models: if one model fails, the system can route to the other model or use a dedicated error handling system (shown in the architecture diagram).
The project is released under both MIT and Apache 2.0 licenses, allowing free use, modification, and distribution.
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