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MCP-Mem0: Long-Term Memory for AI Agents

@coleam00

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT license

トランスポート

stdio

作者

coleam00

MCP-Mem0: Long-Term Memory for AI Agents について

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

MCP-Mem0: Long-Term Memory for AI Agents の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

MCP-Mem0: Long-Term Memory for AI Agents のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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.

2

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.

3

Which transport protocols are supported?

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

4

Where are the memories stored?

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

5

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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