Memo

Name: Memo
Author: jagoff

@jagoff

About Memo

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

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "memo": {
      "command": "memo-mcp"
    }
  }
}

Tools

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

FAQ from Memo

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

Comments

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