Memo MCP Server

@jagoff

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

作者

jagoff

投稿者

Fer F.

Memo MCP Server について

memo is a local-first, long-term semantic memory for AI coding agents (Claude Code, Cursor, Cline, Codex). Runs fully offline with in-process embeddings — MLX on Apple Silicon or CPU sentence-transformers on Linux/Docker — and a sqlite-vec + BM25 hybrid retrieval pipeline with re

#memory #rag #mcp#mlx#local-first #embeddings#sqlite-vec#obsidian #agents #knowledge-graph

Memo MCP Server 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": {
    "memo": {
      "command": "memo-mcp"
    }
  }
}

Memo MCP Server 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 Memo?

Memo is a local-first, long-term semantic memory server for AI coding agents (Claude Code, Cursor, Cline, Codex). It runs fully offline with in-process embeddings and uses a sqlite-vec + BM25 hybrid retrieval pipeline with reranking.

How to use Memo?

Install via pipx install mlx-memo. Use the memo CLI for command-line interaction, or run the memo-mcp MCP server to connect with compatible AI coding tools.

Key features of Memo

Local-first and fully offline, no cloud or API keys required.
Uses MLX (Apple Silicon) or CPU sentence-transformers (Linux/Docker) for embeddings.
Hybrid retrieval with sqlite-vec + BM25 and reranking.
Markdown files on disk are the source of truth; SQLite index is rebuildable.
Recall hook auto-injects relevant memories into every prompt.
Includes a knowledge graph, temporal/time-machine queries, contradiction detection, and nightly synthesis.

Use cases of Memo

Provide persistent semantic memory for AI coding agents across sessions.
Enable offline retrieval of past context, decisions, and code snippets.
Detect contradictions in project documentation or agent memory.
Build a searchable knowledge graph from markdown notes.

よくある質問

What makes Memo local-first?

All processing happens on your machine. Embeddings are computed in-process using MLX or sentence-transformers, and the SQLite index is stored locally. No data leaves your environment.

What are the runtime requirements?

On Apple Silicon, Memo uses ML

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