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
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
E2B
Using MCP to run code via e2b.