MemOS
MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.
README
MCP Server for MemOS API
A Model Context Protocol (MCP) implementation for the MemOS API service. This package provides a command-line interface to interact with MemOS API using MCP standards.
MCP Configuration
To use this package In MCP Client, use the following configuration:
{
"mcpServers": {
"memos-api-mcp": {
"command": "npx",
"args": ["-y", "@memtensor/memos-api-mcp"],
"env": {
"MEMOS_API_KEY": "your-api-key",
"MEMOS_USER_ID": "your-user-id",
"MEMOS_CHANNEL": "the-site-where-you-are-seeing-this-document"
}
}
}
}
Configuration Explanation
command: Usesnpxto run the packageargs: Arguments passed to npx to run the packageenv: Environment variablesMEMOS_API_KEY: Your Memos API key for authentication (Get your API Key in Dashboard[https://memos-dashboard-pre.openmem.net/cn/quickstart/])MEMOS_USER_ID: Stable per-human identifier. MUST be deterministic and non-PII, and MUST remain the same for the same person across devices/sessions. NEVER reuse across different people. DO NOT use random values, device IDs, or model/chat session IDs. Recommended: SHA-256(lowercase(trim(email))) or your SSO subject/employee ID.MEMOS_CHANNEL: The site where you are seeing this document. Candidate values:MODELSCOPE,MCPSO,MCPMARKETCN,MCPMARKETCOM,GLAMA,PULSEMCP,MCPSERVERS,MEMOS(meaning the MemOS official website),GITHUB(meaning this README file)
Available MCP Tools
This package provides the following MCP tools:
-
add_message- Adds a new message to a conversation
- Parameters:
conversation_id: Conversation ID to identify which conversation the message belongs tomessages: Array of messages containing role and content informationrole: Role of the message sender, e.g., user, assistantcontent: Message content
-
search_memory- Searches for memories in a conversation
- Parameters:
query: Search query to find relevant content in conversation historyconversation_id: Conversation ID to define the search scopememory_limit_number: Maximum number of results to return, defaults to 6
-
get_message- Retrieves messages from a conversation
- Parameters:
conversation_id: Conversation ID to identify which conversation's messages to retrieve
All tools use the same configuration and require the MEMOS_API_KEY environment variable.
Features
- MCP-compliant API interface
- Command-line tool for easy interaction
- Built with TypeScript for type safety
- Express.js server implementation
- Zod schema validation
Prerequisites
- Node.js >= 18
- npm or pnpm (recommended)
Installation
You can install the package globally using npm:
npm install -g @memtensor/memos-api-mcp
Or using pnpm:
pnpm add -g @memtensor/memos-api-mcp
Usage
After installation, you can run the CLI tool using:
npx @memtensor/memos-api-mcp
Or if installed globally:
memos-api-mcp
Development
- Clone the repository:
git clone <repository-url>
cd memos-api-mcp
- Install dependencies:
pnpm install
- Start development server:
pnpm dev
- Build the project:
pnpm build
Available Scripts
pnpm build- Build the projectpnpm dev- Start development server using tsxpnpm start- Run the built versionpnpm inspect- Inspect the MCP implementation using @modelcontextprotocol/inspector
Project Structure
memos-mcp/
├── src/ # Source code
├── build/ # Compiled JavaScript files
├── package.json # Project configuration
└── tsconfig.json # TypeScript configuration
Dependencies
@modelcontextprotocol/sdk: ^1.0.0express: ^4.19.2zod: ^3.23.8ts-md5: ^2.0.0
Version
Current version: 1.0.0-beta.2
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