memtomem-automation

memtomem

Markdown-first long-term memory for AI coding agents — your files stay yours, and core usage is hook-free by default.

🚧 Alpha — APIs, defaults, and on-disk config surfaces may still change between 0.x releases. Feedback and issue reports are especially welcome: Issues · Discussions.

memtomem turns your markdown notes, documents, and code into a searchable knowledge base that any AI coding agent can use. Write notes as plain .md files — memtomem indexes them and makes them searchable by both keywords and meaning.

flowchart LR
    A["Your files\n.md .json .py"] -->|Index| B["memtomem"]
    B -->|Search| C["AI agent\n(Claude Code, Cursor, etc.)"]

First time here? Follow the Getting Started guide — you'll have a working setup in under 5 minutes. Claude Code or Codex CLI user? See the Korean vibe-coding quickstart.

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Why memtomem?

Problem	How memtomem solves it
AI forgets everything between sessions	Index your notes once, search them in every session
Keyword search misses related content	Hybrid search: exact keywords + meaning-based similarity
Notes scattered across tools	One searchable index for markdown, JSON, YAML, Python, JS/TS
Vendor lock-in	Your .md files are the source of truth. The DB is a rebuildable cache
Hidden automation is hard to reason about	Core memory operations run only when you call them; optional client hooks are explicit, removable integrations

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Quick Start

1. Install

uv tool install 'memtomem[all]'       # or: pipx install 'memtomem[all]'
mm --version                          # verify install

[all] bundles the features the sections below describe — ONNX dense embeddings, Korean tokenizer, Ollama / OpenAI providers, code chunker, and the Web UI. For a BM25-only install without those downloads (~40 MB vs ~250 MB), see the minimal install option in the Getting Started guide.

If mm --version shows an older version than the latest release, uv is likely serving cached PyPI metadata — re-run with uv tool install 'memtomem[all]' --refresh, or clear the cache first: uv cache clean memtomem.

mm: command not found? uv tool install drops the shim into ~/.local/bin, which isn't on $PATH in fresh shells on macOS/Linux. Run uv tool update-shell, then open a new shell and re-run mm --version.

2. Setup

mm init                               # preset picker, then memory_dir + MCP

The interactive picker starts with three presets — Minimal (BM25, no downloads), English (Recommended) (ONNX bge-small-en-v1.5 + English reranker + auto-discover providers), Korean-optimized (ONNX bge-m3 + kiwipiepy tokenizer + multilingual 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; rerun mm init later when you are ready to add semantic search.

Indexing vs. discovery (Claude Code): provider memory folders that setup auto-discovers (e.g. ~/.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, Custom Commands, and Subagents — see Configuration → Context Gateway for the distinction and the lossy-slug caveats.

For automation / CI:

mm init --non-interactive                   # minimal preset, no prompts
mm init --preset korean --non-interactive   # Korean-optimized bundle, no prompts
mm init --advanced                          # force the full 10-step wizard