memphora
Add persistent memory to AI assistants. Store and recall info across conversations.
MCP 服务配置
复制以下 JSON 到 OPClaw 或其他 MCP 客户端的配置文件中即可使用
{
"mcpServers": {
"memphora-mcp": {
"args": [
"memphora-mcp@0.1.3"
],
"command": "uvx"
}
}
}
可用工具 (5 个)
该服务在 MCP 协议中暴露的工具,AI 可按需调用
tavily_search 14 个参数 需填 1 项
Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.
必填参数:query
tavily_extract 6 个参数 需填 1 项
Extract content from URLs. Returns raw page content in markdown or text format.
必填参数:urls
tavily_crawl 11 个参数 需填 1 项
Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.
必填参数:url
tavily_map 8 个参数 需填 1 项
Map a website's structure. Returns a list of URLs found starting from the base URL.
必填参数:url
tavily_research 2 个参数 需填 1 项
Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.
必填参数:input
服务介绍
<!- - mcp-name: io.github.Memphora/memphora - ->
# 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.