Vector Memory Mcp Server

by Xsaven

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GitHub

About

Vector Memory MCP Server is a secure, vector-based memory server for Claude Desktop that uses sqlite-vec and sentence-transformers. It provides persistent semantic memory capabilities to enhance AI coding assistants by remembering and retrieving relevant coding experiences…

Explore

- 🔍 Semantic Search: Vector-based similarity search using 384-dimensional embeddings
- 💾 Persistent Storage: SQLite database with vector indexing via sqlite-vec
- 🏷️ Smart Organization: Categories and tags for better memory organization
- 🔒 Security First: Input validation, path sanitization, and resource limits
- ⚡ High Performance: Fast embedding generation with sentence-transformers
- 🧹 Auto-Cleanup: Intelligent memory management and cleanup tools
- 📊 Rich Statistics: Comprehensive memory database analytics
- 🔄 Automatic Deduplication: SHA-256 content hashing prevents storing duplicate memories
- 📈 Access Tracking: Monitors memory usage with access counts and timestamps for optimization
- 🧠 Smart Cleanup Algorithm: Prioritizes memory retention based on recency, access patterns, and importance

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Vector Memory Mcp Server

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

From the repository

- Python 3.10 or higher (recommended: 3.11)
- uv package manager
- Claude Desktop app

Installing uv (if not already installed):

macOS and Linux:
``bash curl -LsSf https://astral.sh/uv/install.sh | sh`Verify installation:`bash uv --version



The easiest way to use this MCP server - no cloning or setup required!



Once published to PyPI, you can use it directly:

bash

uvx vector-memory-mcp --working-dir /path/to/your/project`Claude Desktop Configuration (using uvx):`json { "mcpServers": { "vector-memory": { "command": "uvx", "args": [ "vector-memory-mcp", "--working-dir", "/absolute/path/to/your/project" ] } } }`> Note: Publishing to PyPI is in progress. See PUBLISHING.md for details.

1. Clone the project:`bash git clone <repository-url> cd vector-memory-mcp`2. Install dependencies (automatic with uv): Dependencies are automatically managed via inline metadata in main.py. No manual installation needed. To verify dependencies:`bash uv pip list``

3. Test the server:

bash

pipx install vector-memory-mcp

"Production deployment requires running migration scripts before code deploy"

uv --version

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "vector memory mcp server": {
            "vector-memory": {
                "command": "uvx",
                "args": [
                    "vector-memory-mcp",
                    "--working-dir",
                    "/absolute/path/to/your/project"
                ]
            }
        }
    }
}

McpServers

{
    "vector-memory": {
        "command": "uvx",
        "args": [
            "vector-memory-mcp",
            "--working-dir",
            "/absolute/path/to/your/project"
        ]
    }
}

A secure, vector-based memory server for Claude Desktop using sqlite-vec and sentence-transformers. This MCP server provides persistent semantic memory capabilities that enhance AI coding assistants by remembering and retrieving relevant coding experiences, solutions, and knowledge.

✨ Features

- 🔍 Semantic Search: Vector-based similarity search using 384-dimensional embeddings - 💾 Persistent Storage: SQLite database with vector indexing via sqlite-vec - 🏷️ Smart Organization: Categories and tags for better memory organization - 🔒 Security First: Input validation, path sanitization, and resource limits - ⚡ High Performance: Fast embedding generation with sentence-transformers - 🧹 Auto-Cleanup: Intelligent memory management and cleanup tools - 📊 Rich Statistics: Comprehensive memory database analytics - 🔄 Automatic Deduplication: SHA-256 content hashing prevents storing duplicate memories - 📈 Access Tracking: Monitors memory usage with access counts and timestamps for optimization - 🧠 Smart Cleanup Algorithm: Prioritizes memory retention based on recency, access patterns, and importance

🛠️ Technical Stack

| Component | Technology | Purpose | |-----------|------------|---------| | Vector DB | sqlite-vec | Vector storage and similarity search | | Embeddings | sentence-transformers/all-MiniLM-L6-v2 | 384D text embeddings | | MCP Framework | FastMCP | High-level tools-only server | | Dependencies | uv script headers | Self-contained deployment | | Security | Custom validation | Path/input sanitization | | Testing | pytest + coverage | Comprehensive test suite |

📁 Project Structure


vector-memory-mcp/
├── main.py                              # Main MCP server entry point
├── README.md                            # This documentation
├── requirements.txt                     # Python dependencies
├── pyproject.toml                       # Modern Python project config
├── .python-version                      # Python version specification
├── claude-desktop-config.example.json  # Claude Desktop config example
│
├── src/                                # Core package modules
│   ├── __init__.py                    # Package initialization
│   ├── models.py                      # Data models & configuration
│   ├── security.py                    # Security validation & sanitization
│   ├── embeddings.py                  # Sentence-transformers wrapper
│   └── memory_store.py                # SQLite-vec operations
│
└── .gitignore                         # Git exclusions



🗂️ Organization Guide


This project is organized for clarity and ease of use:

-

main.py

 - Start here! Main server entry point
-

src/

 - Core implementation (security, embeddings, memory store)
-

claude-desktop-config.example.json

 - Configuration template

New here? Start with

main.py and claude-desktop-config.example.json



🚀 Quick Start


Prerequisites


- Python 3.10 or higher (recommended: 3.11)
- uv package manager
- Claude Desktop app

Installing uv (if not already installed):

macOS and Linux:

bash
curl -LsSf https://astral.sh/uv/install.sh | sh



Verify installation:

bash
uv --version



Installation


Option 1: Quick Install via uvx (Recommended)


The easiest way to use this MCP server - no cloning or setup required!

Once published to PyPI, you can use it directly:

``bash

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About

Explore

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

✨ Features

🛠️ Technical Stack

📁 Project Structure

🗂️ Organization Guide

🚀 Quick Start

Prerequisites

Installation

Option 1: Quick Install via uvx (Recommended)

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