How do I use Vector Memory MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Vector Memory MCP Server What did we decide about the database schema in our last session?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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.

License: MIT npm version

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 CHANGELOG.md for release history and ROADMAP.md for planned features.