Memphora MCP Server

Add persistent memory to Claude, Cursor, Windsurf, and other AI assistants using the Model Context Protocol (MCP).

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

Go to memphora.ai/dashboard
Create an account or sign in
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.