LOTUS-MCP
About
Integration two AI's into a modernized MCP for better performance
Details
- Author
- blue-lotus-org
- GitHub stars
- 1
- Downloads
- 423
- Categories
- Other, AI
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- Routing and fallback strategies between Mistral and Gemini
- Consensus engine to compare and merge model outputs
- Context-aware processing across sessions and interactions
- Extensible tool integration for external APIs and databases
- Rate limiting and security for production stability
- Unified interface for both models via a single 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.
LOTUS-MCP
FOSS solutionThe LOTUS-MCP protocol outlined here is an impressive approach to model coordination and processing, integrating Mistral and Gemini with a structured architecture that allows for:
- Routing & fallback strategies between models.
- Consensus engine to compare outputs.
- Context-aware processing, improving coherence across interactions.
- Tool integration, making it extensible for external APIs.
- Rate limiting & security for production stability.
The Model Context Protocol (MCP) developed by Anthropic for Claude is a groundbreaking open standard that enables AI assistants to connect with external data sources and tools.\
As a developer or business maybe you like to have your own protocol. This guide made for you.
First looking into MCP exist by claude:
+-------------+ +-------------+ +-------------+
| | | | | |
| User | | AI | | External |
| Interface |<--->| Model |<--->| Tools |
| | |(e.g. Claude)| | & Data |
| | | | | |
+-------------+ +-------------+ +-------------+
^ ^ ^
| | |
| | |
v v v
+--------------------------------------------------+
| |
| Model Context Protocol |
| (MCP) |
| |
+--------------------------------------------------+
^ ^ ^
| | |
| | |
v v v
+-------------+ +-------------+ +-------------+
| | | | | |
| Development | | Business | | Content |
| Environment | | Tools | | Repositories|
| | | | | |
+-------------+ +-------------+ +-------------+
---
Statement
Then implement a new modernized structure for MCP. So first thing first is the cost:| Metric | Mistral Target | Gemini Target |
|-----------------|----------------|---------------|
| Latency | <800ms | <1200ms |
| Accuracy | 95% | 92% |
| Cost/1k tokens | $0.15 | $0.25 |
So to build it we need an architecture design, something like this:
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ │ │ Decision │ │ │
│ User ├────►│ Router ├────►│ Mistral │
│ Interface │ │ (Task Type │ │ (Code/ │
│ │◄────┤ Analysis) │◄────┤ Text) │
└─────────────┘ └─────────────┘ └─────────────┘
▲ │ ▲ │
│ └───────┐ │ └────┐
▼ ▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ Gemini │ │Fallback │ │Error │
│(Multi- │ │Model │ │Handling │
│ modal) │ │ │ │System │
└─────────┘ └─────────┘ └─────────┘
This is
User Input → Mistral (code/text processing) → Gemini (multimodal enhancement) → Final Output at the final of our journey we can to build. So go to start:Sign in to leave a review
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