vector-memory-mcp by AerionDyseti - MCP Server

Vector Memory MCP Server

Semantic memory storage for AI assistants. Store decisions, patterns, and context that persists across sessions.

A local-first MCP server that provides vector-based memory storage. Uses local embeddings and LanceDB for fast, private semantic search.

Features

Local & Private - All embeddings generated locally, data stored in local LanceDB
Semantic Search - Vector similarity search with configurable scoring
Batch Operations - Store, update, delete, and retrieve multiple memories at once
Session Handoffs - Save and restore project context between sessions
MCP Native - Standard protocol, works with any MCP-compatible client

Quick Start

Prerequisites

Bun 1.0+
An MCP-compatible client (Claude Code, Claude Desktop, etc.)

Install

bun install -g @aeriondyseti/vector-memory-mcp

First install downloads ML models (~90MB). This may take a minute.

Configure

Add to your MCP client config (e.g., ~/.claude/settings.json):

{
  "mcpServers": {
    "vector-memory": {
      "type": "stdio",
      "command": "bunx",
      "args": ["--bun", "@aeriondyseti/vector-memory-mcp"]
    }
  }
}

Use

Restart your MCP client. You now have access to:

Tool	Description
`store_memories`	Save memories (accepts array)
`search_memories`	Find relevant memories semantically
`get_memories`	Retrieve memories by ID (accepts array)
`update_memories`	Update existing memories
`delete_memories`	Remove memories (accepts array)
`store_handoff`	Save session context for later
`get_handoff`	Restore session context

Usage

Store a memory:

You: "Remember that we use Drizzle ORM for database access"
Assistant: [calls store_memories]

Search memories:

You: "What did we decide about the database?"
Assistant: [calls search_memories with relevant query]

Session handoffs:

You: "Save context for next session"
Assistant: [calls store_handoff with summary, completed items, next steps]

Configuration

Environment variables:

Variable	Default	Description
`VECTOR_MEMORY_DB_PATH`	`.vector-memory/memories.db`	Database location
`VECTOR_MEMORY_MODEL`	`Xenova/all-MiniLM-L6-v2`	Embedding model
`VECTOR_MEMORY_HTTP_PORT`	`3271`	HTTP server port

Development

git clone https://github.com/AerionDyseti/vector-memory-mcp.git
cd vector-memory-mcp
bun install

bun run test      # Run all tests
bun run dev       # Watch mode
bun run typecheck # Type checking

See for release history and for planned features.

Contributing

Contributions welcome! See issues for areas we'd love help with.

License

MIT - see

Built with MCP SDK, LanceDB, and Transformers.js