Option 3: Create with CloudFlare CLI

npm create cloudflare@latest --git https://github.com/puliczek/mcp-memory

🔧 Setup (Only Option 2 & 3)

1. Install dependencies:

npm install

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
• Persiste...