How do I install Memory MCP?
Install using `uv pip install memory-mcp`. If you don't have `uv`, follow the official installation instructions at https://github.com/astral-sh/uv#installation.
@drdee
基本信息
An mcp server that you can use to store and retrieve ideas, prompt templates, personal preferences to use with you favourite AI tool that supports the modelcontextprovider protocol.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"memory-mcp": {
"command": "uv",
"args": [
"pip",
"install",
"memory-mcp"
]
}
}
}Store a new memory with a title and content
Retrieve a specific memory by ID or title
List all stored memories
Update an existing memory
Delete a memory
Memory MCP is a Model Context Protocol server for storing and retrieving memories using a low-level server implementation and SQLite storage. It is designed for developers who want to add persistent memory capabilities to MCP-compatible agents or applications.
Install the package via uv pip install memory-mcp (or from source with uv pip install .). Run the server by executing the memory-mcp command. For debugging, use the MCP Inspect CLI tool to list, call, and inspect tools.
mcp inspect with debug modeuv for fast, reliable resolutionInstall using `uv pip install memory-mcp`. If you don't have `uv`, follow the official installation instructions at https://github.com/astral-sh/uv#installation.
Run `memory-mcp` in your terminal. This starts the MCP server that exposes the five memory tools.
The server provides `remember`, `get_memory`, `list_memories`, `update_memory`, and `delete_memory`. Each has specific parameters; use `mcp inspect` to see their schemas.
Use the MCP Inspect CLI tool (`mcp inspect`) after installing `mcp[cli]` via `uv pip install mcp[cli]`. Connect to the running server and use commands like `tools`, `call`, and `debug on`.
This project uses a `uv.lock` file. Run `uv pip compile pyproject.toml -o uv.lock` to regenerate it after changing dependencies.
Person-owned, portable AI memory served over MCP. Your knowledge, preferences, and decisions — captured in one assistant, available in all of them.

Search saved employee handbooks from your AI assistant. Four read-only tools list authorized handbooks, browse policies, search content, and read source pages with citation links. Uses scoped bearer tokens and Streamable

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference

One permissioned folder your team and your AI agents both log into.
Groundwork: company memory for your AI tools. Docs public. Source closed. Public proof needs no account.
评论