memory-mcp

A unified, local memory for Claude — an MCP server that stores memories as 1024-dimensional embeddings in a single SQLite file (memory.db) and serves them back over the Model Context Protocol.

• Runtime: TypeScript, stdio transport
• Store: SQLite + sqlite-vec
• Embedder: pluggable — local Ollama (default) or remote Voyage AI
• Vectors: float[1024], cosine distance

Data model (two tables, one id)

One memory is stored as two rows that share the same id:

memories (normal table)	vec_memories (vec0 virtual table)
id, text, tags, source, parent_id, content_hash, created_at, updated_at	rowid, embedding float[1024]

sqlite-vec's vec0 table only holds the vector, so the readable content lives in memories and the two are joined on memories.id = vec_memories.rowid. content_hash (sha256 of the text) makes exact duplicates a no-op.

Setup

cd ~/memory-mcp
npm install
npm run build

Local embeddings (default, nothing leaves the machine)

# install & run Ollama, then pull a 1024-d model:
ollama pull bge-large
ollama serve            # if not already running

Remote embeddings (Voyage)

export EMBEDDER=voyage
export VOYAGE_API_KEY=...   # voyage-3 = 1024-d

Copy .env.example to .env to see all options.

Wire it into Claude

Add to claude_desktop_config.json (Claude Desktop) or .mcp.json (Claude Code):

{
  "mcpServers": {
    "memory": {
      "command": "node",
      "args": ["/Users/tylertabarovsky/memory-mcp/dist/server.js"],
      "env": { "EMBEDDER": "ollama" }
    }
  }
}

Tools

Tool	Args	Does
memory_write	text, tags?, source?	chunk → embed → store
memory_search	query, k?	embed query → cosine kNN → ranked hits
memory_list	limit?, tag?	recent memories, optional tag filter
memory_delete	id	remove content + vector

Capture model

This scaffold uses the explicit model: Claude calls memory_write when it decides something is worth keeping. Simplest and least noisy. A passive/auto capture layer can be added later on top of the same tools.