memo
Your coding agent starts every session with amnesia. memo fixes that — 100% on your own machine.
Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
uvx mlx-memo==4.16.0Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add io.github.jagoff-memo -- uvx mlx-memo==4.16.0รันในเทอร์มินัล
{ "mcpServers": { "io.github.jagoff-memo": { "command": "uvx", "args": [ "mlx-memo==4.16.0" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
Your coding agent starts every session with amnesia. memo fixes that — 100% on your own machine.
Memory for AI agents — MLX (Apple Silicon) or CPU (Linux), sqlite-vec + BM25, zero cloud.
Persistent, searchable memory for Claude Code, Codex, Cursor, Cline, Devin, and OpenCode. No cloud, no API keys, no Ollama, no vector DB to run. And it spends fewer tokens, not more.
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.16.0/install.sh | bash
Prefer a package manager? uv tool install mlx-memo · pipx install mlx-memo · brew tap jagoff/memo && brew install mlx-memo
Then:
memo doctor # self-check
memo save 'we use Postgres, not Mongo' # save a decision
memo search 'what database did we pick?' # search by meaning
That's it. Your agents pick it up over MCP automatically — the installer wires every client it finds.
New Mac:
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.16.0/install.sh | bash
memo sync bootstrap [email protected]:yourname/memo-sync.git
Agent-managed setup:
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.16.0/install.sh | bash
memo doctor --strict-runtime
On Linux or just want to look around first?
docker run --rm ghcr.io/jagoff/memo:latest memo doctor
Most memory servers add context. memo is built to remove it.
The default MCP surface is 43 tools, not 165 — 74% fewer tools, and about 68% less schema context: 43 tools / ~9.7k schema tokens versus 165 tools / ~30.6k tokens on the full surface — overhead paid every session, in every client.
Ambient recall injects one relevant memory before the model answers. The bundled Claude Code hook caps that injection at ~160 tokens. memo roi reports the real grounding and re-ask counts — the estimated-savings figure it used to print was removed in 4.14.0, because multiplying those counts by hardcoded constants was a savings claim memo could not support. For measured savings, memo tokens reads the provider's own usage counters through the context-compression proxy.
memo roi # value from grounded recalls and avoided re-asks
memo tokens # usage-savings ledger
memo as-of ask "what was the deploy strategy?" --date 2026-02-01
memo diff --from 2026-01-01 --to 2026-03-01
Full historical reconstruction by reverse-replaying history.db. Useful when you need to know why past-you made a call, not just what past-you decided.
memo contradict scan # find conflicting facts corpus-wide
memo contradict triage # resolve: fuse / newer-wins / dismiss
Change a decision and memo flags the now-stale version, so the agent stops reintroducing what you already threw out.
memo dream run
A 7-phase nightly pipeline: inventory → mine signals → resolve conflicts → prune stale → synthesize cross-cluster insights → optimize → pre-warm the top-100 query embeddings so tomorrow's recall stays under 200 ms. Every run writes a receipt you can audit. Zero intervention.
Hybrid retrieval. A vector leg (MLX on Apple Silicon, sentence-transformers on CPU) and a BM25 leg (FTS5, diacritic-folding for Spanish) run in parallel, fuse via Reciprocal Rank Fusion, then go through an optional MLX cross-encoder rerank.
Markdown is the source of truth. Every memory is a plain .md file you can read, grep, and version-control. SQLite is a derived index that rebuilds from the files at any time — hand-edit in Obsidian and your edit wins on the next memo reindex. Nothing is locked in a database you can't open.
Prompts and memories stay on your machine. Embedder, reranker, and LLM all run in-process. No telemetry. Memory travels only if you point memo sync at a git remote you own. Normal startup is fully offline; remote update checks and auto-update require an explicit opt-in. → Privacy and network policy
Also in the box: cross-agent memo resume (reopen any session from any agent), cross-Mac git sync, a knowledge graph with optional codegraph symbol edges, encrypted secret storage, OCR/audio ingestion, evidence packs, outcome learning, signed federation, and a local chat UI over your memory (memo chat serve). → Full feature reference
Verified July 2026 against each project's own docs. Corrections welcome — open an issue and I'll fix the table.
✅ first-class · ⚠️ partial, config-gated, or add-on · ❌ absent
Closest comparators are basic-memory (local-first + Obsidian + MCP — same thesis) and cipher (memory for coding agents).
Python ≥ 3.13 (the installer handles this via uv if you don't have it). First install pulls ~8 GB of models, 5–15 min. Optional: an Obsidian vault — without one, memo uses ~/Documents/memo/.
Core: save search ask get edit rename delete list
Recall & Hooks: recall recall-hook context briefing continuity prewarm capture-tick capture-stop interject ask-gaps guard digest
Session & History: history as-of diff record-history session chat-session resume reflect mine-history episodes chronicle
Maintenance: reindex maintain review dream consolidate synthesize dedupe cross-dedup retier contradict coordinate terminal invalidate temporal compress-context ops
Analysis & Quality: health stats doctor journey-check lint drift analytics eval roi tokens token-savings usefulness gaps outcome profile confidence graduation hype definitive evidence
Knowledge Graph: graph entities entity extract-entities links version related
Advanced Search: embed rerank contextual retrieve context-pack chat chat-ask repo
Import / Export / Sync: import export backup restore sync ingest federation
Visualization: tui dashboard map logs hook-log
Setup & Config: init setup config install-mcp install-watcher uninstall-watcher install-slash install-statusline install-recall-hook install-shell-wrapper install-shims startup-banner migrate migrate-vault migrate-independence update upgrade self-update watch release onboard
Daemons: daemons recall-daemon ingest-daemon maint-daemon embed-daemon idle-daemon
Other: backend-native collaborative events feedback query mandate drift sleep-cycle operational ocr-image provenance secret verbatim mcp-command codex-badge debug-recall http-api proxy mine-git token-gate fix undo code-facts code-nudge code-health
git clone https://github.com/jagoff/memo && cd memo
uv pip install -e '.[dev]'
Issues and PRs welcome — see CONTRIBUTING.md. If memo is useful to you, a ⭐ genuinely helps other people find it.
MIT licensed. Built on Apple MLX, sqlite-vec, and codegraph.
Memory for AI agents — MLX (Apple Silicon) or CPU (Linux), sqlite-vec + BM25, zero cloud.
{ "mcpServers": { "io.github.jagoff-memo": { "command": "uvx", "args": [ "mlx-memo==4.16.0" ] } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Memo ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-13 Memory for AI agents — MLX (Apple Silicon) or CPU (Linux), sqlite-vec + BM25, zero cloud.