How do I start the server?
Clone the repo, install `uv`, create a virtual environment, run `uv sync`, then execute `fastapi run mcp_server.py --port 8001`. The server will listen on port 8001 (default localhost only).
@Gelembjuk
基本信息
This is the MCP server with memory interface. It can be used with an AI Chat tool as a memory service
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"cleverchatty-memory": {
"command": "uv",
"args": [
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
It is an example memory server for AI chat that follows the MCP (Model Context Protocol) interface supported by the tool CleverChatty. It exposes two tools — remember (store a chat message with a role) and recall (return a summary of previous conversations) — and is designed to be used alongside the CleverChatty CLI for chat applications with persistent memory.
Clone the repository, install uv, create a virtual environment, run uv sync, then start the server with fastapi run mcp_server.py --port 8001. The server uses SSE transport and is accessible at http://localhost:8001/mcp. A CLI (python manager.py) is provided for testing and debugging, supporting commands like remember, recall, clear-memory, patch-memories, and history-dump.
remember and recall MCP tools.Clone the repo, install `uv`, create a virtual environment, run `uv sync`, then execute `fastapi run mcp_server.py --port 8001`. The server will listen on port 8001 (default localhost only).
Two tools: `remember` (requires `role` and `message` strings) and `recall` (no input parameters). `remember` stores a message, `recall` returns a summary of previous conversations.
Use the provided CLI: `python manager.py COMMAND`, for example `python manager.py remember user "Hello"` or `python manager.py recall`.
Yes. Start the server with `--host 0.0.0.0` to make it accessible from any IP address. By default it only listens on localhost.
It uses SSE (Server-Sent Events) transport. The MCP endpoint is `http://localhost:8001/mcp` (or the configured host/port).

Mess in, magic out: pour in your D&D and TTRPG notes and LoreKeeper keeps them as a private store for your world. Ask Claude or ChatGPT about it, or build a sourcebook.

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.

Orcha is a unified context layer for AI tools and agents. It stores and indexes and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and age

Model Context Protocol Servers
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