MCP Memory with Redis Graph MCP Server: Enabling Seamless AI Application Integration

Overview: What is MCP Memory with Redis Graph MCP Server?

MCP Memory with Redis Graph is a server implementation that leverages Redis Graph for storing and retrieving memories related to conversations with language models (LLMs). It integrates seamlessly with various AI applications, enabling them to connect to specific data sources through standardized Model Context Protocol (MCP) methods. This solution ensures efficient memory management and retrieval, which is critical for enhancing the contextual understanding and utility of AI applications in real-world scenarios.

🔧 Core Features & MCP Capabilities

Data Storage and Management

MCP Memory with Redis Graph stores different types of memories such as conversations, projects, tasks, issues, configurations, finance-related information, and to-do items. Each memory type is managed through a comprehensive set of CRUD (Create, Read, Update, Delete) operations facilitated by the MCP protocol.

Relationship Building

This server allows for the creation of complex relationships between pieces of information via Redis Graph. Relationships are crucial for building a knowledge graph that enhances context and enables sophisticated queries across data points. For instance, it can link a project with specific issues or tasks associated with it.

Comprehensive Memory Types

The system supports multiple memory types:

Conversations: General conversations about various topics.
Projects: Detailed information on various projects.
Tasks: Specific tasks to be executed.
Issues: Bugs and incidents that need addressing.
Configurations: Configurations for different systems or services.
Finance: Financial advice and data.
Todos: Lists of things to do with varying priorities.

Scalability & Performance

Redis Graph provides high-performance querying capabilities, making it ideal for handling large-scale memory stores. This reliability ensures that the system can scale as more memories are added over time without degrading performance.

⚙️ MCP Architecture & Protocol Implementation

Data Storage Backend

MCP Memory with Redis Graph uses Redis as its primary data storage backend. The RedisGraph module enables graph-based querying, which is essential for modeling relationships and connections between different memory nodes.

Application Integration

The server provides several APIs and tools for integrating with MCP clients like Claude Desktop, Continue, Cursor, and others. These clients can leverage the MCP protocol to interact with Redis via the provided services, thereby facilitating seamless data exchange and memory management across various applications.

🚀 Getting Started with Installation

Prerequisites

Before getting started, ensure that you have the following pre-installed:

Docker and Docker Compose
Node.js (version 16 or higher)

Setting Up Redis with Graph Module

Start by running the Redis container with the RedisGraph module loaded:

docker-compose up -d

Once the container is up, verify its operation by executing a session in the Redis CLI from within a Docker terminal. Here’s how you can do it:

docker exec -it mcp-memory-redis-1 redis-cli

Check if the RedisGraph module is loaded:

127.0.0.1:6379> MODULE LIST

Connecting to Redis from Your Application

The application can connect to Redis using configurations specified in src/index.ts. By default, it connects to a local Redis instance at localhost:6379.

To customize the connection string, update the Redis client configuration within this file:

// Example of updating Redis client settings
const redisConfig = {
  host: 'your-host',
  port: 6380,
};

💡 Key Use Cases in AI Workflows

Real-World Use Case 1: Project Management and Issue Tracking for LLMs

Suppose an organization uses an LLM to manage project-related tasks and issues. In this context, the MCP Memory with Redis Graph server can be used as follows:

Create Memories: Log conversations and tasks related to specific projects.
Search Memories: Use advanced queries to filter out projects involving particular issues or tasks.
Update Memories: Track progress and update status of ongoing project-related work.

Real-World Use Case 2: Financial Analysis and Risk Management

For financial analysis, this server can store historical financial data, regulatory advice, and risk assessments in a structured manner:

Store Data: Log relevant financial events, risk profiles, and investment strategies.
Analyze Memory Relationships: Establish relationships between different financial products or risk factors.
Query Data: Retrieve specific memories relevant to current projects or ongoing research.

🔌 Integration with MCP Clients

To integrate the Redis Graph server with various MCP clients, follow these steps:

Install dependencies via npm: npm install.
Start the application by running: npm start.

MCP Memory supports a matrix of compatible MCP clients that ensure seamless integration and data exchange:

AI Application	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌ (only tools support)	✅	❌ (unavailable)	Tools Only

📊 Performance & Compatibility Matrix

MCP Performance Metrics

Scalability: High, supporting thousands of memories.
Query Speed: Optimized for fast, complex memory queries.

Compatibility with MCP Clients

Redundant Data Types Support: Compliant with LLMs' needs across different data types.

🛠️ Advanced Configuration & Security

MCP Memory can be configured to enhance security and performance:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This sample configuration ensures secure communication between the client and server.

❓ Frequently Asked Questions (FAQ)

Q1: Is this MCP server compatible with all models from Claude Desktop, Continue, Cursor, etc.?

A1: Yes, this server is designed to be fully compatible with these and other MCP clients.

Q2: How can I customize the Redis connection settings for my project?

A2: You can update the src/index.ts file's configuration section as needed.

Q3: Can multiple projects share memories via this server?

A3: Yes, by using unique identifiers or tags, you can categorize and manage shared or private memories effectively.

Q4: How do I manage memory relationships within the system?

A4: Use the create_relation, get_related_memories APIs to build and query complex relationships between different pieces of data.

Q5: Can I integrate this server with other Redis databases?

A5: Yes, while RedisGraph is primarily used, MCP servers can be extended or integrated with other databases as needed through custom scripts.

👨‍💻 Development & Contribution Guidelines

If you wish to contribute to the development of this project:

Fork the repository.
Clone it locally: git clone https://github.com/your-repo.
Set up a development environment.
Implement features or fix bugs and submit pull requests.

🌐 MCP Ecosystem & Resources

Relevant Links

By integrating with this MCP Memory server, AI applications can achieve enhanced contextual understanding and data management capabilities, making the process of handling complex tasks significantly more efficient.

MCP Memory with Redis Graph