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Memo

@jagoff

Repository

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

Category

Memory & Knowledge

Transports

stdio

Publisher

jagoff

Submitted by

Fer F.

Config

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

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

Tools

No tools detected

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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