2. Create a Vectorize index:

npx wrangler vectorize create mcp-memory-vectorize --dimensions 1024 --metric cosine

3. Install Wrangler:

npm run dev

4. Deploy the worker:

npm run deploy

🧠 How It Works

1. Storing Memories:

   • Your text is processed by Cloudflare Workers AI using the open-source @cf/baai/bge-m3 model to generate embeddings
   • The text and its vector embedding are stored in two places:
     • Cloudflare Vectorize: Stores the vector embeddings for similarity search
     • Cloudflare D1: Stores the original text and metadata for persistence
   • A Durable Object (MyMCP) manages the state and ensures consistency
   • The Agents framework handles the MCP protocol communication

2. Retrieving Memories:

   • Your query is converted to a vector using Workers AI with the same @cf/baai/bge-m3 model
   • Vectorize performs similarity search to find relevant memories
   • Results are ranked by similarity score
   • The D1 database provides the original text for matched vectors
   • The Durable Object coordinates the retrieval process

This architecture enables:

• Fast vector similarity search through Vectorize
• Persistent storage with D1
• Stateful operations via Durable Objects
• Standardized AI interactions through Workers AI
• Protocol compliance via the Agents framework

The system finds conceptually related information even when the exact words don't match.

🔒 Security

MCP Memory implements several security measures to protect user data:

• Each user's memories are stored in isolated namespaces within Vectorize for data separation
• Built-in rate limiting prevents abuse (100 req/min - you can change it in wrangler.jsonc)
• Authentication is based on userId only
  • While this is sufficient for basic protection due to rate limiting
  • Additional authentication layers (like API keys or OAuth) can be easily added if needed
• All data is stored in Cloudflare's secure infrastructure
• All communications are secured with industry-standard TLS encryption (automatically provided by Cloudflare's SSL/TLS certification)

💰 Cost Information - FREE for Most Users

MCP Memory is free to use for normal usage levels:

• Free tier allows 1,000 memories with ~28,000 queries per month
• Uses Cloudflare's free quota for Workers, Vectorize, Worker AI and D1 database

For more details on Cloudflare pricing, see:

• Vectorize Pricing
• Workers AI Pricing
• Workers Pricing
• Durable Objects Pricing
• Database D1 Pricing

❓ FAQ

1. Can I use memory.mcpgenerator.com to store my memories?

   • Yes, you can use memory.mcpgenerator.com to store and retrieve your memories
   • The service is free
   • Your memories are securely stored and accessible only to you
   • I cannot guarantee that the service will always be available

2. Can I host it?

   • Yes, you can host your own instance of MCP Memory for free on Cloudflare
   • You'll need a Cloudflare account and the following services:
     • Workers
     • Vectorize
     • D1 Database
     • Workers AI

3. Can I run it locally?

   • Yes, you can run MCP Memory locally for development
   • Use wrangler dev to run the worker locally
   • You'll need to set up local development credentials for Cloudflare services
   • Note that some features like vector search or workers AI requires a connection to Cloudflare's services

4. Can I use different hosting?

   • No, MCP Memory is specifically designed for Cloudflare's infrastructure

5. Why did you build it?

   • I wanted an open-source solution
   • Control over my own data was important to me

6. Can I use it for more than one person?

   • Yes, MCP Memory can be integrated into your app to serve all your users
   • Each user gets their own isolated memory space

7. Can I use it to store things other than memories?

   • Yes, MCP Memory can store any type of text-based information
   • Some practical examples:
     • Knowledge Base: Store technical documentation, procedures, and troubleshooting guides
     • User Behaviors: Track how users interact with features and common usage patterns
     • Project Notes: decisions and project updates
   • The vector search will help find related items regardless of content type

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