Memos

Name: Memos
Author: MemTensor

@MemTensor

代码仓库

关于 Memos

暂无概览

#memos #memory-os #ai-agents

基本信息

分类

其他

传输方式

stdio

发布者

MemTensor

提交者

Wenqiang Wei

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "memos-api-mcp": {
      "timeout": 60,
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@memtensor/memos-api-mcp"
      ],
      "env": {
        "MEMOS_API_KEY": "<YOUR-TOKEN>",
        "MEMOS_USER_ID": "<YOUR-USER-ID>"
      }
    }
  }
}

工具

未检测到工具

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。

概览

What is MemOS?

MemOS is an open-source Agent Memory framework that equips AI agents with long-term memory, personality consistency, and contextual recall across sessions. It provides a unified API for memory representation, retrieval, and update, and is designed for AI companions, role-playing NPCs, and multi-agent systems.

How to use MemOS?

You can use MemOS via the free online API (sign up on the MemOS dashboard), by self-hosting a server with git clone and uvicorn memos.api.server_api:app, or by installing the local SDK with pip install MemoryOS. Example code demonstrates creating a MemCube or using the higher-level MOS orchestrator to store and retrieve memories for a user.

Key features of MemOS?

Memory-Augmented Generation (MAG) unified API
Modular MemCube architecture with multiple memory types
Textual, activation (KV cache), and parametric memory
Extensible with custom modules and data sources
Millisecond-level async memory addition (v1.1.3)

Use cases of MemOS?

AI companions that remember past conversations and user preferences

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