Memphora
Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.
README
<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
- 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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。