Add persistent memory to AI assistants. Store and recall info across conversations.
This MCP server connects your AI assistant to Memphora, giving it the ability to:
# Using pip
pip install memphora-mcp
# Or using uvx (recommended for Claude Desktop)
uvx memphora-mcp
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"
}
}
}
}
Close and reopen Claude Desktop. You should see the Memphora tools available!
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."
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."
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..."
| 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 |
| Environment Variable | Description | Default |
|---|---|---|
MEMPHORA_API_KEY | Your Memphora API key | Required |
MEMPHORA_USER_ID | Unique identifier for your memories | mcp_default_user |
Add to your Cursor settings:
{
"mcp": {
"servers": {
"memphora": {
"command": "uvx",
"args": ["memphora-mcp"],
"env": {
"MEMPHORA_API_KEY": "your_api_key_here"
}
}
}
}
}
Add to your Windsurf MCP configuration:
{
"mcpServers": {
"memphora": {
"command": "python",
"args": ["-m", "memphora_mcp"],
"env": {
"MEMPHORA_API_KEY": "your_api_key_here"
}
}
}
}
# 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
pytest tests/
MIT License - see LICENSE for details.
Source-derived launch command. Check the maintainer’s required arguments and credentials before running:
uvx memphora-mcpMerge this template into ~/Library/Application Support/Claude/claude_desktop_config.json. Keep existing servers. Add any arguments, credentials, and permissions required by the maintainer; this template has not been install-tested.
{
"mcpServers": {
"io-github-memphora-memphora": {
"command": "uvx",
"args": [
"memphora-mcp"
]
}
}
}Restart Claude Desktop completely for changes to take effect. Confirm the server appears connected in the client’s tool list, then try a read-only example from its documentation.
Claude Desktop setup referencememphora-mcppypiMemphora works with any MCP-compatible client. Copy the config snippet from the Configuration section above and add it to the file shown for your client, then restart the application.
~/Library/Application Support/Claude/claude_desktop_config.jsonRestart Claude Desktop completely for changes to take effect.~/.cursor/mcp.jsonRestart Cursor for changes to take effect..vscode/mcp.jsonReload VS Code window for changes to take effect.~/.codeium/windsurf/mcp_config.jsonRestart Windsurf for changes to take effect..mcp.jsonSave at the project root, then start Claude Code in that project and review the MCP server approval prompt. Keep real credentials out of shared files.