MCP-Mem0: Long-Term Memory for AI Agents

Name: MCP-Mem0: Long-Term Memory for AI Agents
Author: coleam00

@coleam00

关于 MCP-Mem0: Long-Term Memory for AI Agents

MCP server for long term agent memory with Mem0. Also useful as a template to get you started building your own MCP server with Python!

#mcp-mem0 #long-term-memory #ai-agents

基本信息

分类

记忆与知识

许可证

MIT license

运行时

python

传输方式

stdio

发布者

coleam00

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-mem0": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

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 MCP-Mem0?

MCP-Mem0 is an implementation of the Model Context Protocol (MCP) server integrated with Mem0, providing AI agents with persistent memory capabilities. It enables agents to store, retrieve, and search memories using semantic search, serving as a practical template for building custom MCP servers.

How to use MCP-Mem0?

Install dependencies with uv pip install -e . or build a Docker image. Configure a .env file with transport (sse or stdio), LLM provider (OpenAI, OpenRouter, or Ollama), embedding model, and a PostgreSQL database URL. Run the server directly with uv run src/main.py or via Docker. Then integrate with any MCP-compatible client (e.g., Claude Desktop, Windsurf) by adding the server configuration JSON with either SSE or stdio transport.

Key features of MCP-Mem0

Three memory management tools: save, retrieve, and search memories
Semantic search for finding relevant memories
Supports OpenAI, OpenRouter, and Ollama LLM providers
Deployable via Docker (recommended) or with uv
Works with SSE or stdio transport protocols

Use cases of MCP-Mem0

Give AI agents long-term memory that persists across conversations
Retrieve comprehensive context from all stored memories
Find semantically relevant memories for dynamic recall
Build custom MCP servers using this as a reference template

FAQ from MCP-Mem0

What are the prerequisites for running MCP-Mem0?

Python 3.12+, a Supabase or PostgreSQL database for vector storage, API keys for your chosen LLM provider, and optionally Docker.

What memory tools does MCP-Mem0 provide?

It provides three tools: save_memory to store information with semantic indexing, get_all_memories to retrieve all stored memories, and search_memories to find relevant memories via semantic search.

Which transport protocols are supported?

MCP-Mem0 supports both SSE (Server-Sent Events) and stdio transport, configurable via the TRANSPORT environment variable.

Where are the memories stored?

Memories are stored as vectors in a PostgreSQL database (Supabase recommended), configured through the DATABASE_URL environment variable.

How do I integrate MCP-Mem0 with Windsurf or n8n?

For Windsurf use serverUrl instead of url in the MCP configuration. For n8n, replace localhost with host.docker.internal in the SSE URL.

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