Memtomem

Name: Memtomem
Author: memtomem

by memtomemversion 0.7.5i★ –iRuns on your computer

Registry nameio.github.memtomem/memtomem

Install

Overview

Description from the maker (original English)

Markdown-first long-term memory for AI agents. Hybrid BM25 + vector search, runs locally.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "memtomem": {
      "command": "uvx",
      "args": [
        "memtomem",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add memtomem -- uvx memtomem serve

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "memtomem": {
      "command": "uvx",
      "args": [
        "memtomem",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "memtomem": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memtomem",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account memtomem, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7226 servers in this category
Published to the registry more recently than 98% of the other 7226 servers in this category
Basic checks equal to or better than 100% of the other 7226 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Memtomem safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.7.5) reached the registry yesterday.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

memtomem

Long-term memory for AI coding agents, kept as Markdown you own. Core usage is hook-free by default: memory changes happen only when you or your agent call memtomem.

🧪 Beta — the nine core MCP tools, the mm CLI, and the format of your memory and config files change only with an Upgrading note in the CHANGELOG. Features marked experimental can change without one; see Stability. Feedback is welcome: Issues · Discussions.

The session loop

Three plugin workflows carry a project's memory from one session to the next, and from one coding agent to another.

Illustrative output — agent wording and results vary.

# End of a session, in Claude Code

/memtomem:handoff save codex-cli

handoff_id: 3f2a9c1e-7b4d-4e8a-9c2f-1d5e6a7b8c9d scope: project_local · namespace: shared:retry-policy · 1 file written, 2 chunks indexed

Next session, in Codex CLI

$memtomem-brief

Recent updates and active handoff IDs

3f2a9c1e-7b4d-4e8a-9c2f-1d5e6a7b8c9d from claude-code → codex-cli load it with: $memtomem-handoff resume 3f2a9c1e-7b4d-4e8a-9c2f-1d5e6a7b8c9d

$memtomem-handoff resume 3f2a9c1e-7b4d-4e8a-9c2f-1d5e6a7b8c9d

git_head matches the live HEAD · worktree clean · in sync with origin refs/heads/main

After a few commits, back in Claude Code

/memtomem:update

mode: incremental · 4 of the latest 10 commits not yet reviewed saved 2 items · commit-derived: 1 · session-derived: 1

handoff save writes one Markdown record into the project's own .memtomem/memories.local; brief only reads; update saves decisions, bug fixes, and preferences as separate items. All three run only when you invoke them, and nothing runs in the background. The Project workflows guide has each one's modes, checks, and host-specific names.

Why memtomem?

Your files stay yours. Memories are plain .md files you can read, edit, and version. The search index is rebuilt from them, day-file namespaces included; tag edits and working state live only in the database (what a rebuild restores).
Explicit by default. Memory operations run on your CLI or MCP calls (mm add, mem_add, mem_index). Optional client hooks are separate, removable integrations, not a hidden runtime default.
One store, many tools. Claude Code, Codex CLI, OpenCode, Kimi Code, and Antigravity share one local store, and any MCP client can search it. memtomem-stm is a separate, optional proxy that surfaces memories proactively.

Quick Start

1. Install

uv tool install 'memtomem[all]'       # or: pipx install 'memtomem[all]'

mm --version # verify install

[all] bundles ONNX dense embeddings, the Korean tokenizer, the code chunker, and the Web UI (about 340 MB installed, before any model download). The Ollama and OpenAI embedding providers need no extra. A smaller install is under Other install options below.

2. Setup

mm init                               # preset picker, then memory_dir + MCP

The interactive picker starts with three presets — Minimal (BM25, no downloads), English (Recommended) (ONNX multilingual-e5-small + auto-discover providers), Korean-optimized (ONNX multilingual-e5-small + kiwipiepy tokenizer); no preset turns on a reranker — plus an Advanced entry that opens the full 10-step wizard. Preset paths only ask about the memory directory and MCP registration; everything else is set from the preset.

Choose Minimal for the fastest no-download first proof and rerun mm init later to add semantic search. For automation, mm init --non-interactive picks the minimal preset without prompts. See Embeddings for the model and provider matrix. The presets keep embedding on your machine; choosing OpenAI embeddings, a cloud LLM, or the Cohere reranker sends note text to that service (What leaves your machine).

3. Verify a complete memory round trip

mm status

mm add "Deployment checklist uses blue-green rollout" --tags ops mm search "blue-green"

The last command should return the sentence you just added. mm add writes to your user memory directory and indexes the entry immediately, so this works without an existing notes directory or a connected editor. To bring existing notes into the same index, run mm index /path/to/your/notes.

4. Connect your coding agent

Install the plugin for your tool — Claude Code, Codex CLI, OpenCode, Kimi Code, Antigravity, or Cursor — then, in the project you work on, register the memory tier the session loop writes to:

cd <your-project> && mm mem init --scope project_local

Start a new session and run the brief workflow: /memtomem:brief in Claude Code, and the name each other host uses is in the Project workflows guide. The Claude Code & Codex CLI plugin quickstart (한국어) walks through plugin installation and a first memory save-and-search round trip in 10–15 minutes. For Windsurf, Claude Desktop, Gemini CLI, other MCP clients, and registering Cursor by hand instead of through the plugin, see MCP Client Setup; from any client, asking it to call the mem_status tool confirms the connection.

5. Open the Web UI (optional)

mm web                # dashboard on http://127.0.0.1:8080

mm web -b # run in the background; logs go to ~/.memtomem/logs/web.log mm web stop # stop the tracked Web UI process

mm web shows the polished page set. mm web --dev (or MEMTOMEM_WEB__MODE=dev) adds maintainer pages such as Sessions, Working Memory, and Health Report.

Other install options

Minimal (no bundled model, about 50 MB):

uv tool install memtomem             # no extras — no local ONNX model, Web UI or Korean tokenizer until you add them; Ollama/OpenAI embeddings still work

Opt in later per feature: uv tool install --reinstall 'memtomem[onnx,web]' (see the extras table). List every extra you want to keep, because --reinstall replaces the whole set. A plain mm upgrade keeps the extras it detects from the current install, but an explicit mm upgrade --extras list must name the complete set too.

Project-scoped (per-project isolation):

uv add 'memtomem[all]' && uv run mm init    # all commands need `uv run` prefix

No install (uvx on demand):

claude mcp add memtomem -s user -e MEMTOMEM_EXTRA_TOOLS=mem_update_coverage -- uvx --isolated --from "memtomem[all]==0.7.5" memtomem-server

Troubleshooting the install

`mm --version` shows an older version than the [latest release](https://github.com/memtomem/memtomem/releases) right after installing: uv is likely serving cached PyPI metadata. Re-run with uv tool install 'memtomem[all]' --refresh, or clear the cache first with uv cache clean memtomem. To upgrade an existing uv tool install, use mm upgrade rather than re-running uv tool install; see the CLI reference for what it preserves and stops.
`mm: command not found`: uv tool install drops the shim into ~/.local/bin, which is not on $PATH in fresh shells on macOS/Linux. Run uv tool update-shell, then open a new shell and re-run mm --version.
Duplicate tools after upgrading the Claude plugin: re-check /mcp after reloading. The plugin launches memtomem[onnx]==0.7.5. A manual registration whose launch differs — base-only memtomem, or the ONNX launch pinned to an earlier release — no longer matches for deduplication. Confirm the manual entry's name and scope, then align its launch command with the plugin or remove the redundant registration.
Indexing vs. discovery (Claude Code): provider memory folders that setup auto-discovers (such as ~/.claude/projects/*/memory/) are added to the search *index*. That is separate from the Web UI's opt-in Context Gateway scan of ~/.claude/projects/, which discovers project *roots* for Skills, Commands, and Subagents; see Configuration → Context Gateway.

What else is inside

Your memory, your files	Operate it	Plug it in
Markdown is the source of truth; the search index is rebuilt from it (database-only state such as tag edits is not)	Web UI for search, sources, tags, timeline, and dedup (`mm web`)	Nine core MCP tools, with `mem_do` routing every other action so agents load a small tool surface
Hybrid search: BM25 keyword + dense vector + RRF fusion in one query	Namespaces, auto-derived from folder names and labelled from the Web UI	Context Gateway: keep Skills, Commands, and Subagents in one Store and push them to your AI runtimes
Semantic chunking: heading-aware Markdown, AST-based Python, tree-sitter JS/TS, structure-aware JSON/YAML/TOML	Maintenance: near-duplicate detection, time-based decay, TTL expiration, auto-tagging	Pinned Context: small user/project/agent Markdown blocks composed ahead of retrieved results
Incremental indexing: chunk-level SHA-256 diff, so only changed chunks are re-embedded	Scheduled jobs for compaction, decay, dead-link cleanup, and dedup scans	LangGraph `MemtomemBaseStore`, plus the Hybrid Store with SQLite persistence and BM25/dense/RRF retrieval
	Scriptable CLI: `--json` output on status, search, recall, and the write commands; `mm warmup` pre-loads local models	memtomem-stm: optional proxy for proactive surfacing, compression, and caching

Stability

memtomem is beta (0.x). The nine core MCP tools, the mm CLI, and the format of your memory files and config files change only with an Upgrading note in the CHANGELOG, and behind a deprecation window where one is possible. Other features are supported — maintained, with their changes recorded in the CHA

Only the beginning is shown; read the rest in the repo

Memtomem

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

memtomem

The session loop

Next session, in Codex CLI

After a few commits, back in Claude Code

Why memtomem?

Quick Start

1. Install

2. Setup

3. Verify a complete memory round trip

4. Connect your coding agent

5. Open the Web UI (optional)

What else is inside

Stability