mlx-memo 0.5.1

Local MCP memory backed by Obsidian vault — MLX-native LLM + embedder, sqlite-vec store. No Ollama, no API keys.

memo

Persistent semantic memory for AI agents — 100% local, MLX-native, Apple Silicon.

memo gives any MCP-aware agent (Claude Code, Claude Desktop, Cursor, Cline, Continue, Paperclip, …) a long-term memory that runs entirely on your Mac. It stores each memory as a plain Markdown file inside an Obsidian-friendly folder, indexes embeddings in a single sqlite file, and runs the LLM + embedder + reranker in-process via Apple MLX — no Ollama, no Qdrant, no cloud API, no keys.

Your prompts and memorias never leave the machine.

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What it does

• Saves what your agent decides, learns, prefers as durable Markdown files (type, tags, title, body).
• Recalls the most relevant memorias when you ask — semantic (vec), keyword (BM25), or hybrid w/ cross-encoder rerank.
• Injects context automatically: with the optional Claude Code plugin, every prompt silently consults memory; the agent sees the top-3 memorias before answering.
• Speaks MCP over stdio so any compliant client picks it up with one line of config.
• Speaks shell too: the same API ships as a memo CLI with ~25 commands.

Why memo

Pain	What memo gives you
Cloud memory products see your private notes	Zero network in the hot path. Models run in-process.
Ollama / Qdrant / docker daemons just to remember things	One Python install. sqlite-vec is one file; MLX is in-process.
DB-only stores lock your knowledge inside an opaque blob	Markdown is the source of truth. Edit in Obsidian, vim, anything.
Cold-start latencies of 2-10s per recall	MLX prewarm hook → sub-second recalls after session start.
Hand-crafted /remember invocations every turn	Ambient recall: top-3 hits auto-injected into every prompt.
Vendor lock	MIT package, open stack (sqlite-vec Apache 2.0, MLX MIT, Qwen Apache 2.0).

How it fits in your stack

Three layers, one direction of data flow:

1. Clients (Claude Code, Cursor, …) talk to memo over MCP stdio — or you talk to it directly via the memo CLI.
2. The Memory API runs save / search / rerank / ask against the MLX models in-process: embedder for semantic, optional reranker for precision, chat (Qwen2.5-7B) for ask().
3. The .md vault is the storage of record; sqlite-vec is a rebuildable index. Delete the index any time — memo reindex rebuilds from the .md files.

With the Claude Code plugin installed, two extra hooks plug in:

• SessionStart → memo prewarm (warms MLX so the first recall is fast)
• UserPromptSubmit → memo recall-hook (5s budget, injects top-3 memorias as additionalContext)

Stack

Component	Choice	Why
LLM (chat)	Qwen2.5-7B-Instruct-4bit + 3B helper via mlx-lm	Two-tier; 7B for ask() synthesis, 3B for cheap helpers. Both 4-bit fit comfortably.
Embedder	Qwen3-Embedding-0.6B-4bit-DWQ	1024-dim, ~50ms/embed, ~600 MB on disk. Upgrade path to 4B/8B is one env var.
Reranker	Qwen3-Reranker-0.6B (optional)	Cross-encoder over top-30 from vec+BM25, then α-fusion. Bumps precision on diffuse queries.
Vector store	sqlite-vec	One file, no daemon, embedded. Reset = rm memvec.db.
Source of truth	Markdown files under <vault>/... with YAML frontmatter	Human-editable, syncs through iCloud/git/Syncthing/whatever.
MCP transport	fastmcp	Stdio out of the box.

Requirements

• macOS on Apple Silicon (M1 / M2 / M3 / M4). MLX is the load-bearing piece.
• Python ≥ 3.13.
• ~4 GB free disk for the default model set (downloaded on first use).
• Optional: an Obsidian vault. If you don't have one, memo defaults to ~/Documents/memo/ and creates the folder for you.

Install

pip install mlx-memo
# or
uv tool install mlx-memo
# or via the Homebrew tap
brew tap jagoff/memo && brew install mlx-memo
# or
pipx install mlx-memo

Any of those expose two binaries: memo (CLI) and memo-mcp (MCP server).

The PyPI distribution is mlx-memo as of 0.5.0. Earlier versions shipped as memo-mcp and the binary names haven't changed — existing MCP configs keep working.

Pre-download the MLX models so the first save/search doesn't stall on a multi-GB download:

hf download mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ
hf download mlx-community/Qwen2.5-3B-Instruct-4bit
hf download mlx-community/Qwen2.5-7B-Instruct-4bit

Dev install (contributors)

git clone https://github.com/jagoff/memo
cd memo
uv pip install -e '.[dev]'

Quick start

# Self-check (validates models, vault path, sqlite-vec)
memo doctor

# Save a memory
memo save 'Bench MLX vs Ollama: ~30% faster prefill on M3 Max' \
  --title 'MLX bench result' -t bench -t mlx

# Search by meaning (not just keywords)
memo search 'cuál fue el resultado del bench MLX'

# Recent
memo list --limit 5

# RAG — ask a question, memo cites memorias by id
memo ask 'qué cambios hice en el embedder este mes?'

MCP setup

After pip install mlx-memo, register the MCP with your client.

Claude Code

claude mcp add memo -s user $(which memo-mcp)

Or hand-edit ~/.claude.json:

{
  "mcpServers": {
    "memo": {
      "type": "stdio",
      "command": "/path/to/memo-mcp",
      "args": [],
      "env": {}
    }
  }
}

Restart Claude Code. Tools surface as mcp__memo__memory_* inside the agent.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "memo": { "command": "/path/to/memo-mcp" }
  }
}

Cursor / Cline / Continue

Each client has its own MCP config UI but the contract is the same: register a stdio server pointing at the memo-mcp binary.

Paperclip

A first-party plugin under integrations/paperclip-plugin-memo/ exposes five tools (memo_search, memo_save, memo_list, memo_get, memo_ask) to any agent running in a Paperclip company.

Tools exposed over MCP

Tool	What it does
memory_save(content, title?, type?, tags?)	Persist a new memory; returns the full record.
memory_search(query, limit?, type?, body_chars=280, mode="hybrid")	Top-k. hybrid (default) fuses vec + bm25 via RRF, then optionally re-ranks. vec is semantic only; bm25 is keyword (FTS5 unicode61, diacritic-stripping for Spanish).
memory_list(limit?, type?)	Recent by updated desc.
memory_get(id)	Full record. Accepts a unique prefix ≥4 chars (git-style); returns {"error": "ambiguous", "matches": [...]} on collision.
memory_update(id, title?, type?, tags?, content?)	Patches fields; re-embeds only if body changed.
memory_reindex()	Re-scan vault, re-embed entries whose body_hash diverged.
memory_delete(id)	Removes from vec + disk.
memory_ask(question)	RAG synthesis; cites memorias by id.
memory_stats()	Counts, paths, active models.
memory_consolidate(), memory_extract_entities(), memory_entities(), memory_history()	Post-v0 endpoints — see CHANGELOG.

Ambient memory (v0.3.0+) — recall without /memo

Install the bundled Claude Code plugin and memo silently consults your past on every prompt and injects the most relevant memorias as additionalContext — the agent sees them before answering, no manual invocation.

How it works

• SessionStart hook → memo prewarm (async) — pre-loads the MLX embedder so the first recall is fast.
• UserPromptSubmit hook → memo recall-hook (5s timeout) — embeds your prompt, runs vec-only search, returns top-3 memorias above cosine 0.6.

Both run 100% local. Your prompt never leaves the machine.

Tuning

Env var	Default	Purpose
MEMO_RECALL_DISABLE	unset	Set to 1 to skip recall entirely
MEMO_RECALL_TOP_K	3	Max memorias to inject
MEMO_RECALL_MIN_SIM	0.6	Cosine similarity floor
MEMO_RECALL_MIN_PROMPT_CHARS	12	Skip very short prompts
MEMO_RECALL_BODY_CHARS	240	Snippet length per memoria
MEMO_RECALL_SKIP_SLASH	1	Skip recall on / prompts
MEMO_RECALL_TOKEN_BUDGET	0	When > 0, pack memorias greedily until ~N tokens; truncate tail to fit
MEMO_RECALL_PROJECT_BOOST	0.15	Additive score boost for memorias whose tags match the current project tag
MEMO_RECALL_DEBUG	unset	Print failure reasons to stderr

Empirical tuning of MIN_SIM=0.6

On a 223-doc corpus:

• qué decidí sobre MLX vs Ollama → 3 hits at 0.71–0.74 (relevant ✓)
• how to bake apple pie (no food memorias) → 0 hits at 0.6 ✓ (3 noise hits at 0.51–0.56 cut by the floor)

Tune lower (0.5) on sparse corpora, higher (0.7) for high-precision only.

Slash command — /memo (Claude Code only)

A Claude Code skill ships at skills/memo/SKILL.md. Symlink it:

ln -s "$(pwd)/skills/memo/SKILL.md" ~/.claude/skills/memo/SKILL.md

Or install everything (skill + MCP config + hooks) in one shot via the bundled plugin:

/plugin install memo@jagoff/memo

The skill routes user input to the right MCP tool:

Input	Action
/memo <query>	semantic search (k=5, snippet body)
/memo	smart capture — destila el insight del turno y guarda
/memo list [n]	recent memories
/memo save <text>	save with auto-derived type/tags
/memo get <id|prefix>	full record (prefix ≥4 chars)
/memo update <id|prefix> [flags] [body]	patch metadata or body
/memo delete <id|prefix>	delete (asks confirmation)
/memo stats	totals + paths + models
/memo reindex	absorb edits made directly in Obsidian
/memo doctor [--gc] [--fix]	self-check + orphan detect

CLI reference

memo doctor                       # self-check
memo doctor --gc                  # report orphans (store ↔ disk)
memo doctor --gc --fix            # drop orphan store rows (.md never auto-deleted)
memo save 'body markdown' --title 'X' -t mlx -t local
memo search 'query' --limit 5
memo list --limit 20 --type decision
memo get <id>
memo update <id> --title 'X2' -t mlx -t local --type decision
memo update <id> --content -      # read replacement body from stdin
memo reindex                      # absorb edits made directly in Obsidian
memo delete <id> --yes
memo stats
memo init                         # re-run first-run picker
memo migrate-vault <new-path>     # move memorias to a different folder
memo backup --out memo.zip        # backup .md files + index
memo mine-history --since 30      # backfill memorias from past Claude Code chats
memo watch                        # foreground file-watcher: auto-reindex on .md edit
memo install-watcher              # background watcher via launchd plist
memo uninstall-watcher            # remove the launchd watcher job
memo tui                          # live terminal dashboard (Ctrl+C exits)

Live dashboard — memo tui

Six panels, all-colored, refresh every second by default:

• corpus — total memorias, distinct project tags, top 3 types
• runtime — MLX warm/cold flags (emb / rrk / chat), vault size, watcher state
• recent saves — last 5 entries from history.db
• recent recalls — last 4 entries from the recall log (~/.local/share/memo/recall.log)
• top tags — most-frequent corpus tags (project:* highlighted)
• activity — 14-day saves/recalls sparklines (▁▂▃▄▅▆▇█)

Reads read-only from the existing history.db (saves), a JSONL recall log written by memo recall-hook (auto-rotated at ~200 KB), and the live MLX object flags (embedder._model is not None). Watcher state comes from launchctl print. No new dependencies — Rich was already pulled in.

Quit with q, ESC, or Ctrl+C.

Backfill from past Claude Code conversations

memo mine-history walks ~/.claude/projects/<hash>/*.jsonl, runs the same prefilter + helper-LLM extract + embedding-dedup pipeline as the live capture hook, and saves what's new. Resumable per file.

memo mine-history --since 30 --limit 20     # last 30 days, 20 newest sessions
memo mine-history --dry-run --debug         # cost estimation, no writes

Auto-reindex on edit

Editing a memoria directly in Obsidian normally requires a manual memo reindex to refresh embeddings. memo watch (foreground) or memo install-watcher (background launchd job) debounces FS events and runs Memory.reindex() automatically. Logs land in ~/Library/Logs/memo/.

Project-scoped recall

memo save auto-attaches a project:<repo> tag derived from the git toplevel of your cwd (or MEMO_PROJECT_TAG). The recall hook reads cwd from the Claude Code hook payload and boosts memorias whose tags match the current project by MEMO_RECALL_PROJECT_BOOST (default 0.15). Opt out per-call: memo save --no-project-tag. Disable globally: MEMO_AUTO_PROJECT_TAG=0.

First-run setup

The first time you run any memo command in an interactive shell, an arrow-key picker asks where memorias should live:

? Where should memo store your memorias?
❯ Standard macOS path: /Users/you/Documents/memo  (recommended)
  Obsidian vault: Notes  (/Users/you/Library/Mobile Documents/iCloud~md~obsidian/Documents/Notes)
  Obsidian vault: work-notes  (...)
  Custom path…

The choice is persisted to ~/.config/memo/config.toml:

[storage]
data_dir = "/Users/you/Documents/memo"
# Optional — set when you pick an Obsidian vault. Used by `memo ingest`
# to bulk-index that vault's notes alongside your memorias.
vault_path = "/Users/you/Library/.../Notes"

Re-run the picker any time with memo init. To move memorias to a different location later:

memo migrate-vault ~/Documents/memo  # copies .md files, updates config, reindexes

Hooks (recall, prewarm, capture, session) get MEMO_NONINTERACTIVE=1 prefixed in hooks/hooks.json so they never trigger the picker.

Configuration

All env vars are optional. Defaults aim at a fresh Apple Silicon Mac.

Env var	Default	What
MEMO_DATA_DIR	~/Documents/memo	Where memoria .md files live
MEMO_VAULT_PATH	(unset)	Optional Obsidian vault for memo ingest
MEMO_STATE_DIR	~/.local/share/memo	sqlite-vec DB + state
MEMO_CONFIG_FILE	~/.config/memo/config.toml	Override config-file path
MEMO_NONINTERACTIVE	unset	Set to 1 in hooks to skip the first-run picker
MEMO_LLM_MODEL	mlx-community/Qwen2.5-7B-Instruct-4bit	Chat tier
MEMO_HELPER_MODEL	mlx-community/Qwen2.5-3B-Instruct-4bit	Helper tier
MEMO_EMBEDDER_MODEL	mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ	Embedder
MEMO_EMBEDDER_DIMS	1024	Embedding dim — must match the embedder
MEMO_MAX_CONTENT_CHARS	64000	Truncate body before embed
MEMO_SEARCH_DEFAULT_LIMIT	10	Default --limit for search
MEMO_AUTO_PROJECT_TAG	1	Auto-add project:<repo> tag from git toplevel on save. Set 0 to disable.
MEMO_PROJECT_TAG	unset	Explicit project tag (overrides git-toplevel detection)

Resolution precedence (highest first): explicit kwargs → MEMO_* env vars → ~/.config/memo/config.toml → legacy MEMO_VAULT_PATH + MEMO_MEMORY_SUBDIR (back-compat) → hardcoded defaults.

Upgrading the embedder

The default 0.6B is fast (~50 ms/embed) and small (~600 MB) but recall on diffuse queries (where the doc title doesn't lexically overlap with the query) can be noisy. For the 200–2000 memorias range, swap to the 4B variant when the noise starts to bite.

Model	Dims	Disk	Recall	Per-embed
Qwen3-Embedding-0.6B-4bit-DWQ (default)	1024	~600 MB	OK	~50 ms
Qwen3-Embedding-4B-4bit-DWQ	2560	~3 GB	better	~200 ms
Qwen3-Embedding-8B-4bit-DWQ	4096	~5 GB	best	~400 ms

To upgrade (example: 0.6B → 4B):

# 1) Pre-download.
hf download mlx-community/Qwen3-Embedding-4B-4bit-DWQ

# 2) Point memo at it.
export MEMO_EMBEDDER_MODEL=mlx-community/Qwen3-Embedding-4B-4bit-DWQ
export MEMO_EMBEDDER_DIMS=2560

# 3) Backup before destructive re-embed.
memo backup --out memo-pre-4b.zip

# 4) Wipe the index and rebuild.
rm ~/.local/share/memo/memvec.db
memo reindex

The dim mismatch is a hard error: MEMO_EMBEDDER_DIMS must match the new model's hidden size. memo doctor validates the dim at load.

Design notes

• One sqlite file, no Qdrant. sqlite-vec outperforms a small Qdrant snapshot for the size of corpus memo targets (a few thousand entries, single-writer). Single file makes reset trivial: rm memvec.db.
• Embed title + body together. Titles carry the highest-density retrieval signal for memos with terse titles + long bodies. Prepending also protects the title from head-truncation when the body is long. Pure retag/type changes still skip the embedder.
• .md is the storage of record. Edit memories in Obsidian; the next memo reindex picks them up via body_hash mismatch.
• Head-truncate long inputs + append EOS. The embedder caps at 512 tokens; we head-truncate (preserves the title-like header) and explicitly append <|im_end|> so Qwen3-Embedding's last-token pool lands on the EOS hidden state it was fine-tuned for.
• Asymmetric retrieval. Queries get a Instruct: …\nQuery: … prefix; documents go raw. Without the prefix, cosine collapses toward 0.
• Cosine distance metric. The vec0 schema declares distance_metric=cosine so vec.distance is true cosine distance (1 − dot for unit vectors); score = 1 − distance is interpretable in [0, 1].
• No Ollama dep, anywhere. pyproject.toml does not declare it; doctor does not probe :11434. Anyone running memo with Ollama installed is just ignoring it.

How memo differs from other agent-memory projects

A handful of projects sit in the same neighbourhood. They diverge on the things that actually matter day-to-day: where the model runs, where the data lives, how recall is wired, and whether you can read your own memory in plain text.

Side-by-side comparison

	memo	mem0	letta (ex-MemGPT)	cognee	supermemory	mem-vault	MCP memory reference	engram
Runtime	MLX, in-process	Cloud API or Ollama	Postgres + LLM API	Cloud or Ollama	Cloud SaaS	Ollama daemon	Node, in-process	Python, in-process
LLM/embed location	local Mac (MLX)	OpenAI/Anthropic/Ollama	Anthropic/OpenAI/Ollama	OpenAI/Ollama/other	hosted	Ollama (:11434)	provider-supplied	provider-supplied
Network in hot path	0	yes (cloud) or :11434	yes (LLM API)	yes (LLM API)	always	:11434 + :6333	yes (LLM API)	0
Vector store	sqlite-vec (one file)	Qdrant / pgvector	Postgres + pgvector	LanceDB / Qdrant / pgvector	hosted	Qdrant (server)	in-memory JSON	SQLite
External daemons	none	Ollama + Qdrant	Postgres	Postgres / vector DB	none (SaaS)	Ollama + Qdrant	none	none
Storage of record	markdown files	DB blob	DB rows	DB rows + graph	hosted DB	markdown files	JSON entity graph	DB rows
Human-readable / editable	✅ open in Obsidian/vim	❌	❌	❌	❌	✅	partial (JSON dump)	❌
MCP server (stdio)	✅ 13 tools	❌	❌	❌	❌	✅ (unregistered)	✅ (official ref)	✅
Hybrid retrieval	vec + BM25 + RRF	vec	vec	vec + graph	vec	vec	n/a (entity-based)	vec
Cross-encoder reranker	✅ MLX Qwen3-Reranker	❌	❌	❌	❌	❌	❌	❌
Ambient recall (zero invoke)	✅ Claude Code hooks	❌	n/a	❌	❌	❌	❌	❌
Apple Silicon optimisation	✅ first-class (MLX)	runs, no opt	runs, no opt	runs, no opt	n/a	works	n/a	works
License	MIT	Apache-2.0	Apache-2.0	Apache-2.0	proprietary (SaaS)	MIT	MIT	MIT
Privacy posture	data never leaves Mac	depends on provider	depends on provider	depends on provider	hosted	local + cloud-ollama opt	depends on LLM	local

Notes on the table — projects move fast. The cells above reflect the public state of each repo at the time of writing. PR a correction if any is stale.

The differentiators in plain terms

1. 100 % local hot path, no Ollama. memo runs the LLM, embedder, and reranker in-process via MLX. No localhost:11434 round-trip per call, no Docker for Qdrant, no provider key. mem0 / cognee / letta all rely on either a cloud API or a local Ollama daemon; supermemory is hosted; mem-vault needs both Ollama and Qdrant running. memo just imports MLX into the same Python process and goes.

2. Markdown is the storage of record, not a DB blob. Your memorias are plain .md files with frontmatter that you can open in Obsidian, edit in vim, sync via iCloud/git/Syncthing, and grep from a shell. The sqlite-vec index is rebuildable — rm memvec.db && memo reindex. Almost every alternative locks your knowledge inside an opaque database.

3. Hybrid retrieval + cross-encoder reranker out of the box. memo fuses semantic (vec) and keyword (BM25 over FTS5 with unicode61 + diacritic stripping for Spanish/Portuguese) via RRF, then optionally reranks the top-30 with a Qwen3-Reranker cross-encoder and fuses scores α-weighted. mem0 / letta / supermemory ship vec-only. cognee adds a graph but no cross-encoder. This is the single biggest precision lift for noisy or short queries.

4. Ambient recall as a first-class feature. With the bundled Claude Code plugin, SessionStart prewarms MLX and UserPromptSubmit consults memory on every prompt (5 s budget, top-3 above cosine 0.6, injected as additionalContext). The agent sees the right memorias before it answers — no /memo call from you. No alternative ships this as a turnkey hook bundle.

5. MCP is a primary interface, not an afterthought. memo exposes 13 tools over stdio so Claude Code, Cursor, Cline, Continue, Paperclip, and any future MCP client get the same contract on day one. mem0 and letta have no MCP server; mem-vault has one but isn't published in the registry; the official MCP memory reference is entity-graph-only and stores in JSON.

6. Apple Silicon is a target, not a footnote. Embedder, reranker, and chat are 4-bit MLX builds tuned for unified memory: ~50 ms/embed on 0.6B, sub-second first recall after prewarm, ~4 GB RAM ceiling for the default 7B chat tier. Other projects "work" on M-series Macs because Python runs there — they aren't tuned for it.

7. No vendor lock and no telemetry. MIT package on top of MIT/Apache-2.0 dependencies (MLX MIT, sqlite-vec Apache-2.0, Qwen weights Apache-2.0). Nothing phones home; doctor literally does not probe :11434.

Other projects called "memo" or "memo-mcp"

A handful of unrelated repos share the name. Quick disambiguation in case you're searching:

Project	What it is	Overlap with us
upstash/memo	MCP server for handing off conversation state between agents (goals / pending tasks / decisions). State lives in Upstash Redis (managed cloud or self-hosted on Vercel). No embeddings, no RAG.	Different problem entirely — agent handoff, not a memory archive. We're local-first markdown + vector search; they're cloud-state with structured handoff objects.
milasd/memo-mcp	Local Python MCP for RAG over personal journal entries. Pluggable vector backend (ChromaDB default / FAISS / in-memory), Apple-Silicon GPU embedder, no bundled LLM.	Closest competitor. Both local RAG. We diverge on: MLX-only runtime, markdown source-of-record (Obsidian-readable), sqlite-vec + FTS5 hybrid w/ RRF, cross-encoder reranker, history.db / graph.db split, ambient recall hook bundle. PyPI name collision avoided — we ship as mlx-memo from 0.5.0.
doggybee/mcp-server-memo	Node.js MCP for append-only versioned session summaries. Plain filesystem JSON, no DB, no vector store, no embedder.	Different category — flat-file versioned summaries, no semantic search.

When you should not pick memo

Pick something else when:

• You're not on Apple Silicon. MLX is the load-bearing piece — memo will not run on Linux / Windows / Intel Macs.
• You need a hosted, multi-tenant memory service across many users — supermemory or mem0 cloud is what you want.
• You want a long-horizon agent runtime with explicit "core memory" vs "archival memory" tiers and an event loop around it — that's letta's sweet spot.
• You want a knowledge-graph + ontology layer rather than a doc store — cognee is the right pick.

memo's bet is the opposite: a single user, one machine, plain markdown, MLX, and a contract small enough to remember.

Roadmap

Ship-ready today:

• Memory API: save / search / list / get / update / delete / reindex / consolidate / ask / stats
• CLI: ~28 commands including doctor, migrate-vault, backup, ingest, mine-history, watch
• MCP server (13 tools + memo://recent / memo://memory/{id} resources)
• Hybrid search (vec + BM25 + RRF + cross-encoder rerank)
• Prefix-ID lookup (git-style, ≥4 chars)
• Ambient recall (Claude Code plugin)
• Project-scoped recall (auto-tag + cwd-based boost)
• Token-budget-aware recall packing
• Transcript miner (memo mine-history over ~/.claude/projects/)
• File-watcher daemon (memo watch / install-watcher launchd plist)
• First-run picker + migration tooling
• Paperclip plugin (5 tools)

Post-v0:

• Entity graph queries over graph.db
• LLM-driven consolidation / dedup using the 3B helper tier
• Multi-hop ask() over [[wikilinks]]

Provenance

Forked from mem-vault philosophically (storage layout + frontmatter schema), not literally — the codebase is new. The MLX backend pieces (embedder pooling, chat template handling) are direct ports from obsidian-rag Phase 1+2 of the MLX migration.

License

MIT — see LICENSE.