🧠 memory-eternal — a second brain for your AI

记忆核心（memory-eternal） —— 给 AI 一个不会忘、可审核、可量化的长期记忆。本地优先 · 审核闸门 · 记忆账本 · 一个库接入 DSH / Claude Code / Codex / Cursor 与任意 MCP 客户端。

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Your AI forgets every session. Give it a memory it can trust.

Local-first, review-gated, measurable — one SQLite vault shared by DSH, Claude Code, Codex, Cursor and any MCP client.

• 🔍 Review before it counts — new cards wait in a review queue; approve or reject from the Web UI or terminal; every change is written to an immutable audit log.
• ✏️ Editable & revisable — fix a card by hand in the reader, or let an agent submit a revised copy (memory_revise); the queue labels it a revision, and an approved card is only overwritten when you approve it.
• 🧾 Token ledger — see how much recall injected, how many distillation calls ran, and how often recall was actually useful.
• 🏠 Local-first, no cloud — SQLite + Markdown on your own disk. No third-party memory framework, no account. Memory data never leaves the machine; only the News page fetches public RSS / Hacker News on demand (titles, excerpts and outbound links only).
• ⭐ Core memory — promote approved cards to always-on memory; every session gets them automatically, and the review queue still decides what qualifies.
• 🧭 Reader & layout — clickable table of contents in the card reader, plus a compact / normal / large layout setting for the whole memory UI.

⚡ Quick start

DeepSeek Harness (desktop / web)

dsh plugin --profile desktop add memory-eternal
# reopen DSH → sidebar "Memory"

Claude Code, Codex or Cursor — same vault via MCP

npm i -g memory-eternal
dsh-memory connect claude    # or: codex / cursor

Any MCP client（MengBot / OpenClaw / Hermes 见 docs/INTEGRATIONS.md）

npx -y -p memory-eternal dsh-memory mcp

Browser or terminal

dsh-memory open              # http://127.0.0.1:7999
npx -y -p memory-eternal dsh-memory tui   # live queue in the terminal

🧬 How memory actually flows

One SQLite vault, one write gate, two read paths. Nothing enters recall without passing the gate.

flowchart TD
  subgraph S1["① Input"]
    A1["Conversation + tool events<br/>captured when a turn ends"]
    A2["Installed skills<br/>scanned from the host"]
    A3["News follows<br/>RSS · GitHub · HN · arXiv · DSH plugins"]
    A4["Manual cards / imports"]
  end
  subgraph S2["② Distill — the only LLM step"]
    B1["LLM distills the session<br/>on failure: raw card, nothing lost"]
    B2["Dedup: lexical Jaccard<br/>optional LLM merge decision"]
    B3["Kind classification"]
  end
  subgraph S3["③ Review gate — human only"]
    C1[("Quarantine<br/>pending / rejected")]
    C2{"approve / reject"}
    C3[("Main store<br/>approved only")]
  end
  subgraph S4["④ Storage"]
    D1[("SQLite: cards + FTS5 index")]
    D2["Markdown mirror<br/>git-able"]
    D3["audit_log + token ledger"]
  end
  subgraph S5["⑤ Recall — every session"]
    E1["Core memory → system prompt"]
    E2["memory_recall → FTS5 + BM25"]
    E3["memory_revise → pending revision"]
  end
  A1 --> B1 --> B2 --> B3 --> C1
  A2 --> B2
  A3 --> C1
  A4 --> B2
  C1 --> C2
  C2 -->|approve| C3
  C2 -->|reject| C1
  C3 --> D1
  D1 --> D2
  C3 --> D3
  C3 --> E1
  C3 --> E2
  E2 -->|hits + chars| D3
  E3 --> C1

The diagram is only true because three invariants are enforced in code, not by convention:

🔌 How agents talk to the memory

🧾 Does it actually save tokens?

Most memory products say they save tokens. We think you should check. The plugin ships a built-in memory ledger (Runtime status → Memory ledger) that records:

The honest math:

net benefit ≈ avoided context cost − (injected characters + distillation calls × price)

Illustrative numbers, not a benchmark — measure your own with the ledger. If recall usefulness stays below ~60%, tune it down (fewer results, shorter summaries) or turn memory off.

📊 How it compares — measured on a real vault

Snapshot 2026-11-07. Our figures are measured on a real 2,321-card vault on Windows (not a toy corpus); competitor facts come from their public README / docs / license page on the same date. — means not advertised or not verifiable, not "absent".

We are not the biggest here. mem0 has a funded team, a research paper and a managed service; agentmemory's automatic capture covers more coding agents than we do; Obsidian is where many people already keep their notes. What we do differently is the write path: a human gate, a three-store separation, and an audit trail you can point at — plus a token ledger so you can check whether recall is worth its cost.

Reproduce our numbers on your own vault:

node scripts/bench-search.mjs --rebuild   # rebuild time + p50/p95/m