memphora-mcp

<p align="center"> <img src="logo.png" alt="Memphora Logo" width="120" height="120"> </p>

<h1 align="center">Memphora MCP Server</h1>

<!-- mcp-name: io.github.Memphora/memphora -->

<p align="center"> <strong>Add persistent memory to Claude, Cursor, Windsurf, and other AI assistants using the Model Context Protocol (MCP).</strong> </p>

<p align="center"> <a href="https://pypi.org/project/memphora-mcp/"><img src="https://img.shields.io/pypi/v/memphora-mcp.svg" alt="PyPI"></a> <a href="https://github.com/Memphora/memphora-mcp/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a> <a href="https://memphora.ai"><img src="https://img.shields.io/badge/website-memphora.ai-orange.svg" alt="Website"></a> </p>

What is this?

This MCP server connects your AI assistant to Memphora, giving it the ability to:

• Remember information across conversations
• Search your personal knowledge base
• Extract insights from conversations automatically
• Recall your preferences, facts, and context

Quick Start

1. Install

# Using pip
pip install memphora-mcp

# Or using uvx (recommended for Claude Desktop)
uvx memphora-mcp

2. Get Your API Key

1. Go to memphora.ai/dashboard
2. Create an account or sign in
3. Copy your API key from the dashboard

3. Configure Claude Desktop

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "memphora": {
      "command": "uvx",
      "args": ["memphora-mcp"],
      "env": {
        "MEMPHORA_API_KEY": "your_api_key_here",
        "MEMPHORA_USER_ID": "your_unique_user_id"
      }
    }
  }
}

4. Restart Claude Desktop

Close and reopen Claude Desktop. You should see the Memphora tools available!

Usage Examples

Storing Memories

Just tell Claude something about yourself:

You: "I work at Google as a software engineer"
Claude: [stores memory] "Got it! I'll remember that you work at Google as a software engineer."

You: "My favorite programming language is Python"
Claude: [stores memory] "Noted! I'll remember that Python is your favorite programming language."

Recalling Memories

Ask Claude about things you've told it before:

You: "Where do I work?"
Claude: [searches memories] "You work at Google as a software engineer."

You: "What programming languages do I like?"
Claude: [searches memories] "Your favorite programming language is Python."

Automatic Context

Claude will automatically search your memories when relevant:

You: "Can you help me with some code?"
Claude: [searches memories for context]
        "Sure! Since you prefer Python and work at Google, I'll write this in Python 
         following Google's style guide..."

Available Tools

| Tool | Description | |------|-------------| | memphora_search | Search memories for relevant information | | memphora_store | Store new information for future recall | | memphora_extract_conversation | Extract memories from a conversation | | memphora_list_memories | List all stored memories | | memphora_delete | Delete a specific memory |

Configuration Options

| Environment Variable | Description | Default | |---------------------|-------------|---------| | MEMPHORA_API_KEY | Your Memphora API key | Required | | MEMPHORA_USER_ID | Unique identifier for your memories | mcp_default_user |

Using with Other MCP Clients

Cursor

Add to your Cursor settings:

{
  "mcp": {
    "servers": {
      "memphora": {
        "command": "uvx",
        "args": ["memphora-mcp"],
        "env": {
          "MEMPHORA_API_KEY": "your_api_key_here"
        }
      }
    }
  }
}

Windsurf

Add to your Windsurf MCP configuration:

{
  "mcpServers": {
    "memphora": {
      "command": "python",
      "args": ["-m", "memphora_mcp"],
      "env": {
        "MEMPHORA_API_KEY": "your_api_key_here"
      }
    }
  }
}

Development

Running Locally

# Clone the repo
git clone https://github.com/Memphora/memphora-mcp.git
cd memphora-mcp

# Install dependencies
pip install -e ".[dev]"

# Set your API key
export MEMPHORA_API_KEY="your_key"

# Run the server
python -m memphora_mcp

Testing

pytest tests/

Privacy & Security

• Your memories are stored securely in Memphora's cloud
• Each user has isolated memory storage
• API keys are stored locally on your machine
• All communication is encrypted via HTTPS

Support

• Documentation: memphora.ai/docs
• Issues: GitHub Issues
• Email: support@memphora.ai

License

MIT License - see LICENSE for details.