MCP Memory
An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-
What is MCP Memory?
MCP Memory is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via redis stack, and temporary scratchpad storage. it also enables context compression...
An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-
This server falls under the Knowledge & Memory category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- An advanced contextual memory server for agent orchestration
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-memoryConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MCP Memory
MCP Memory is a persistent, vector-search-powered memory server for MCP clients such as Claude Desktop, Cursor, and Windsurf, allowing AI assistants to remember user preferences and past conversation context across sessions. It runs on Cloudflare Workers with Vectorize for semantic similarity search and D1 for relational storage, using the open-source bge-m3 embedding model via Workers AI. Memories are stored in isolated per-user namespaces, rate-limited to 100 requests per minute, and free to host for most personal usage levels on Cloudflare's free tier. A public hosted version is also available at memory.mcpgenerator.com for users who do not want to self-host.
Prerequisites
- A Cloudflare account (free tier sufficient for personal use)
- Node.js and npm installed for running Wrangler CLI
- Wrangler CLI installed globally (npm install -g wrangler)
- An MCP-compatible client such as Claude Desktop or Cursor
- Optional: a userId string to namespace your memories (any unique identifier works)
Use the hosted version (quickest start)
Point your MCP client at the public hosted instance at memory.mcpgenerator.com. No deployment required. Skip to the client configuration step.
Deploy your own instance to Cloudflare (one-click)
Click the 'Deploy to Cloudflare' button in the GitHub repository, or use the Cloudflare CLI to create from the template. When prompted for Vectorize settings choose Dimensions: 1024 and Metric: cosine.
npm create cloudflare@latest --git https://github.com/puliczek/mcp-memoryCreate the Vectorize index
After cloning or templating the repo, create the required vector index with the correct dimensions and distance metric.
npx wrangler vectorize create mcp-memory-vectorize --dimensions 1024 --metric cosineInstall dependencies and deploy
Install npm packages and deploy the Cloudflare Worker.
npm install
npm run deployConfigure your MCP client
Add the deployed Worker URL (or the public hosted URL) to your MCP client configuration. Use your own unique userId to namespace your memories.
MCP Memory Examples
Client configuration
Claude Desktop configuration connecting to the public hosted MCP Memory instance. Replace the URL with your own deployed Worker URL if self-hosting.
{
"mcpServers": {
"mcp-memory": {
"command": "npx",
"args": ["mcp-memory"],
"env": {
"MCP_MEMORY_URL": "https://memory.mcpgenerator.com",
"USER_ID": "your-unique-user-id"
}
}
}
}Prompts to try
Example prompts to store and retrieve memories once the server is connected.
- "Remember that I prefer TypeScript over JavaScript for all new projects"
- "What do you remember about my coding preferences?"
- "Store the fact that my timezone is UTC+5:30 and I work best in the morning"Troubleshooting MCP Memory
Rate limit errors (429) when querying memories frequently
The default rate limit is 100 requests per minute per userId. If you hit it regularly, deploy your own instance and increase the rate limit in wrangler.jsonc. Self-hosted instances let you set any value you need.
Vectorize 'dimensions mismatch' error on deploy
The index must be created with exactly 1024 dimensions and cosine metric to match the bge-m3 model output. Delete any incorrectly configured index and recreate with: npx wrangler vectorize create mcp-memory-vectorize --dimensions 1024 --metric cosine.
Memories are not persisted between Cloudflare Worker requests
Ensure the Durable Object binding (MyMCP) is correctly configured in wrangler.jsonc and the D1 database binding is set. Run 'npx wrangler dev' locally to see binding errors before deploying.
Frequently Asked Questions about MCP Memory
What is MCP Memory?
MCP Memory is a Model Context Protocol (MCP) server that advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via redis stack, and temporary scratchpad storage. it also enables context compression through history rollups and facilitates inter- It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MCP Memory?
Follow the installation instructions on the MCP Memory GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MCP Memory?
MCP Memory works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MCP Memory free to use?
Yes, MCP Memory is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
MCP Memory Alternatives — Similar Knowledge & Memory Servers
Looking for alternatives to MCP Memory? Here are other popular knowledge & memory servers you can use with Claude, Cursor, and VS Code.
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Everos
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