linksee-memory

Claude Code forgets everything when you start a new session. Your successor knows even less.

Linksee Memory lets you hand a project over — to your next session, to Cursor or Codex, to the person after you — with the reasons attached. Record a decision once (remember({ content, anchor: {} })) and it comes back before the agent acts on it: re-injected on session start and ahead of every Edit / Write / Bash that touches it. Change your mind on the record and it stops nagging; leave it and it keeps the next agent from repeating last week's mistake.

Underneath: drift detection across every project you run in parallel — which ones quietly left their goal (🔴), which were checked and hold (🔵), which nobody has looked at yet (⚫). One local SQLite file that Claude Code, Cursor, Windsurf, OpenAI Codex and Gemini CLI all read. Nothing leaves your machine.

npx -y linksee-memory setup — one command, on in every repo.

🌐 Landing page: linksee.app (includes non-developer onboarding for Claude Desktop / Cursor / Claude Code / OpenAI Codex / Gemini CLI) 📖 Docs: docs.linksee.app — full reference: the product map & drift, install, and all 11 tools

Questions this answers

These are the questions people actually type — measured, verbatim (2026-09-04). If yours is here, this is the answer.

Claude Code forgets everything when I start a new session. How do I make it remember decisions across sessions?
Run npx -y linksee-memory setup. Every new session opens with recall() — a brief of what needs attention, where you are, what's unresolved — and your locked decisions are re-injected before the agent acts. Nothing to re-explain.

Claude Codeがセッションをまたぐと過去の経緯を忘れてしまいます。記憶を持たせる方法を教えてください。
npx -y linksee-memory setup の一回で入ります。新しいセッションは recall() の brief（注意すべきこと・いまどこか・未解決）から始まり、固定した決定は行動の直前に再注入されます。説明し直す必要はありません。

What is the best MCP server for persistent memory across AI coding agents?
Honest answer: if "remember across sessions" is all you need, a CLAUDE.md is enough — we measured it. Linksee is for the three moments it isn't: right before a mistake, when you change your mind, and when you run many projects and need to see which one drifted.

AIコーディングエージェント用の記憶MCPサーバーでおすすめはありますか？
正直に言うと、「セッションを跨いで覚える」だけなら CLAUDE.md で足ります（実測しました）。Linksee が要るのは、間違いかけたとき・方針を変えたとき・並行案件のどれが外れたかを知りたいとき、の3場面です。

I want one shared memory between Claude Code, Cursor, and OpenAI Codex. What tools exist for that?
One SQLite file. Claude Code, Cursor, Windsurf, OpenAI Codex and Gemini CLI all read and write the same memory — a decision made in one shows up in the others.

Claude CodeとCursorとCodexで同じ記憶を共有したいのですが、どうすればいいですか？
SQLite ファイル1つを全員で読みます。Claude Code / Cursor / Windsurf / Codex / Gemini CLI のどこで決めた決定も、他のエージェントの行動の直前に出てきます。

My AI agent keeps re-implementing things we already decided against. How can I detect that a codebase has drifted from past decisions?
Declare the decision (remember({ content, anchor: { violation_signal: [...] } })). The guard then warns — or blocks, if you hardened it — the moment an edit contradicts it, and drift_status shows every decision that reality has quietly left.

過去に決めた設計方針とコードがずれていないかを検出できるツールはありますか？
決定を宣言しておくと（remember({ content, anchor: {...} })）、それに反する編集の直前に警告（hardened なら拒否）が出ます。drift_status は「宣言と現実がずれた決定」を証拠つきで一覧します。

Is there a local-first, self-hosted alternative to Mem0 for agent memory?
Yes. No account, no API key, no cloud — one local file, MIT licensed. npx -y linksee-memory setup and it's on.

Mem0 vs Zep vs Letta for a coding agent's long-term memory — which should I pick?
We installed them and ran one scenario across all of them. Storing and recalling a decision: everyone passes. The difference appears before a mistake and when you change your mind — Linksee is built for those two moments; the others leave them to you.

How do I stop Claude Code from repeating the same mistake it made last week?
Record it as a caveat (remember({ content, layer: 'caveat' })). Caveats are protected from forgetting and come back when the same ground is touched again — and if you anchor it, the guard stops the repeat before it lands.

開発の意思決定履歴をMCPサーバーで残しておく定番のやり方はありますか？
remember({ content, anchor: {} }) の1回で、記録と強制が同時に入ります。drift_status がその台帳で、各決定が いま守られているか（🔵）・ずれているか（🔴）・誰も確かめていないか（⚫）を示します。

Related MCP server: auxly-memory-cli

🪄 Three spells to remember

Make it automatic: add "Use Linksee Memory" to your system prompt / CLAUDE.md.

🗺️ Not just memory — a product map

Memory is the entry point. Tie it to a map.yaml of how your product fits together, and the linksee-memory map CLI catches drift with file:line evidence:

The 30-second demo above: the README says --export. The code doesn't. Linksee catches it — and shows what else a change would touch.

npx -y linksee-memory map where README.md   # this file belongs to the README node — and what it touches
npx -y linksee-memory map explain readme    # README promises --export; the code doesn't implement it — drift, with evidence
npx -y linksee-memory map affects readme    # changing the README also touches docs, the CLI help, and the npm listing

→ see The Map for the full flow.

📣 As featured on

• Zenn: あなたの Claude Code、 実は前回のセッションを完全に忘れている — 73 ♡ on Zenn, 165+ users on Hatena Bookmark, picked up by tech blogs + YouTube shorts (May 2026)

• Zenn: あなたの MCP server、 実は Tools しか使ってない (5 blocks 全実装 / v0.3.0) — the 1% of MCP servers that implement all 5 blocks

• Zenn: あなたの Claude memory、 実は Claude にしか残らない (5 LLM 横断する方法) — cross-LLM memory pattern (May 12, 2026)

• Zenn: Glama listing で 3 週間止まった話 (5 つの罠と解決策) — npm + Glama deployment retrospective

「Cordex/Cursor/Code/Gemini 全部につなげられるから、 横断的にできてる MCP ってところがこれのすごいところ」 — Hatena Bookmark, May 2026 (165+ users)

🎬 See it in action

Drift, caught. Decisions don't survive session boundaries — but their consequences do. Ask any session:

You:    What's drifting right now?
Agent:  [calls drift_status]

        28 anchors: ⚪ 1 held · 🔵 27 aligned

        Needs attention:
        ⚪ HELD — "Focus on 4 areas: Recipe layer, agent-native API,
           Japanese market, Agent Insights"
           ↻ Reopens 2026-07-04

        Everything else is aligned — no unaccounted divergence.

The memory underneath. Drift detection only works because the why survives the session boundary:

Without linksee-memory — Monday morning, new Claude session:

You:    We deployed last week but it crashed. How did we fix it?
Claude: I don't have access to previous sessions. Can you describe
        what happened and walk me through the problem?

[30 minutes of log-spelunking and re-explanation]

With linksee-memory — Same question, different outcome:

You:    We deployed last week but it crashed. How did we fix it?
Claude: Let me check my caveats...

        🧠 [caveat] NextAuth sessions invalidate when JWT_SECRET
           rotates — redeploy all affected projects in parallel.
           (from session 2026-04-13, importance: 0.9)

        Is this the deploy you're asking about? We hit it when
        we rotated secrets mid-flow.

You:    Yes, exactly. Let's not repeat that.

That single caveat memory is what separates "flat fact storage" from "the agent actually remembers the WHY". linksee-memory stores it across six explicit layers so retrieval stays explainable.

🔍 Drift Detection — "Intent Datadog"

Most teams make decisions, then forget them. The agent from last week decided "we'll use FTS5 instead of vector search" — but this week a new session installs pgvector without knowing why that was rejected. That's drift. Not a bug. Not malice. Just forgotten context.

Memory tools remember what you did. Nothing notices when you drift from what you decided — that's the layer Linksee Memory adds. Think "Datadog for product decisions": unaccounted divergences surface as drift, intentional evolution (recorded as supersede/fix) stays quiet.

How it works

1. Declare decisions as anchors: declare_anchor({ kind: "decision", statement: "We use FTS5, not vector search", violation_signal: ["pgvector", "embedding"] })

2. The engine detects when committed code reality diverges from these anchors

3. State derivation classifies each anchor:

   • 🔴 Drift — reality diverges with no recorded resolution

   • 🟡 Review — a soft signal awaits your decision

   • ⚪ Held — you acknowledged the gap, parked it with a review date

   • 🔵 Aligned — reality matches intent, or a recorded resolution explains the change

4. Resolve with fix, supersede, acknowledge, or dismiss — plus two gates: harden (PreToolUse will block) and soften (back to a warning)

The make-or-break rule: a divergence accounted for by a recorded resolution (supersede/fix/acknowledge) is NOT drift. Only unaccounted gaps are flagged. This means intentional evolution stays quiet while silent abandonment gets caught.

4-species taxonomy

Anchors are classified into four species with different display formats:

🗺️ The Map — linksee-memory map

Drift detection (above) checks individual anchors. The Map lifts it to the whole product: a map.yaml describing how value reaches your user (discover → understand → try → adopt → retain → monetize → expand), with typed dependencies between the pieces — README, npm listing, onboarding, the engine that powers them. The reconciler checks that map against your real code, and the CLI answers the question an engineer actually has:

I'm touching this file — where is it on the map, and what else must move?

1. Where am I? — locate a file (or, with no argument, infer from your recent edits):

$ npx -y linksee-memory map where README.md
"README.md" belongs to this Map node:

  readme  [understand]  convergence
    changes ripple to:
      must fix together (hard):  lp, docs-site
      should align (soft):       onboarding, client-configs
      fyi (may ripple):          telemetry-contract

The blast radius is graded — must fix together vs should align vs fyi — so a wide ripple isn't flat noise.

2. Why is it in this state? — the diagnosis, with file:line evidence:

$ npx -y linksee-memory map explain readme

STATUS
  declared: healthy (active)
  reality:  implemented / matches
  verdict:  declared and reality agree (verified)

EVIDENCE
  ✓ README's Tools section lists where_am_i
      README.md:424 — found "where_am_i" in section "Tools"

Declared state and the reality verdict are shown separately — a hand-declared suspect the scanner refutes reads as "declared suspect, refuted by reality (→ convergence)", not a confusing mix.

3. Whole-project triage: npx -y linksee-memory map status — a health %, what is fixable now in code vs external checks, and any deferral with no expiry (so "accounted-for" can't quietly become a drift graveyard).

How it works

• map.yaml (repo root) is the desired-state source of truth: a journey spine × surface/implementation layers × typed edges (must-stay-consistent-with / should-align-with / realizes).

• reconcile checks each node's declared reality against the code (signal / regex / section_contains / file checks) and overlays a verdict — reality overrides what you hand-declared, with evidence.

• where_am_i is also an MCP tool, so a coding agent can re-anchor itself mid-task.

Commands: where · affects · explain · status · next · reconcile · inspect --json · blueprint. Add --lang ja for Japanese labels.

🛡 Re-injection Guard — enforce decisions before the action

Drift detection (above) is post-hoc — it tells you reality diverged after the change lands. The re-injection guard is the pre-action half: it re-surfaces the decision you locked before the agent runs the tool that would break it.

It exists for one specific, infuriating failure mode (anthropics/claude-code#15443): "Claude read the rule, understood it, and still used cp." Having the rule in context isn't enough — so the guard runs outside the agent's volition, as a Claude Code hook:

It is fail-open by construction: any parse / DB / logic error surfaces nothing and lets the action through. The only thing that ever blocks is an explicit hard contradiction on a decision you declared.

Enable it

npx -y linksee-memory setup wires this into ~/.claude/settings.json (Step 4), so it is on in every repo — the same scope your memory already lives at. One SQLite file holds the anchors for all your projects; enforcing them per-repo meant declaring a decision once and having it enforced nowhere.

• --project-guard — this repo only, the old behaviour

• --no-guard — skip it

Anchors with affects globs fire only on matching paths; an unscoped anchor fires on its own detect_terms / violation_signal. Nothing is ever blocked unless you explicitly hardened it (resolve_drift(action:'harden')) — everything else re-injects the decision as context.

To wire it by hand instead, drop this block into .claude/settings.json (project root, or ~/.claude/settings.json for every repo) — it points at the globally-installed linksee-memory-guard bin, so no build step is needed:

{
  "hooks": {
    "SessionStart": [
      {
        "matcher": "startup|resume|compact",
        "hooks": [
          { "type": "command", "command": "npx -y linksee-memory guard", "timeout": 15 }
        ]
      }
    ],
    "PreToolUse": [
      {
        "matcher": "Edit|Write|Bash",
        "hooks": [
          { "type": "command", "command": "npx -y linksee-memory guard", "timeout": 8 }
        ]
      }
    ]
  }
}

It's project-scoped on purpose — the guard enforces this repo's decisions, and you opt in per project rather than letting it deny tool calls everywhere (the Stop hook from setup, by contrast, is user-global). Declare what it should watch with declare_anchor(...); set card_policy.gate_mode:'hard' on an anchor to make a contradiction block instead of just warn (the soft default only re-injects). Anchors that are stale (at_risk), superseded, or card-disabled never gate.

Developing linksee-memory itself? The repo dogfoods the guard via a (gitignored) .claude/settings.json that points at the local build (node ${CLAUDE_PROJECT_DIR}/dist/bin/guard-hook.js) so it runs against your uncommitted changes. End-user projects should use the published npx -y linksee-memory guard form above.

What it does

Most "agent memory" services (Mem0, Letta, Zep) save a flat list of facts. Then the agent looks at "edited file X 30 times" and has no idea why. And none of them notice when this week's work contradicts last week's decision. linksee-memory keeps the WHY — and watches the drift.

It is a Model Context Protocol (MCP) server with 11 tools that gives any AI agent structured memory + drift detection:

Four pillars

1. Drift detection — declare decisions as anchors, then the engine automatically detects when committed reality diverges from stated intent. Think "Datadog for product decisions" — unaccounted divergences surface as drift, intentional evolution (recorded as supersede/fix) stays quiet.

2. Cross-agent portability — single SQLite file at ~/.linksee-memory/memory.db. Same brain for Claude Code, Cursor, Windsurf, OpenAI Codex, Gemini CLI.

3. WHY-first structured memory — six explicit layers (goal / context / emotion / implementation / caveat / learning). Solves "flat fact memory is useless without goals".

4. Token savings via read_smart — sha256 + AST/heading/indent chunking. Re-reads return only diffs. Measured 86% saved on a typical TS file edit, 99% saved on unchanged re-reads.

🧠 The 6-layer structure

┌─────────────────────────────────────────────────────────────┐
│ 🎯 goal           ← what the user is working toward         │
├─────────────────────────────────────────────────────────────┤
│ 🧭 context        ← why this, why now — constraints, people │
├─────────────────────────────────────────────────────────────┤
│ 💗 emotion        ← user tone signals (frustration, etc.)   │
├─────────────────────────────────────────────────────────────┤
│ 🛠  implementation ← how it was done (+ what failed)         │
├─────────────────────────────────────────────────────────────┤
│ ⚠️  caveat         ← "never do this again" · auto-protected │
├─────────────────────────────────────────────────────────────┤
│ 🌱 learning       ← patterns distilled from cold memories   │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
           Ranked recall via relevance × heat × momentum × importance
                  Returns match_reasons explaining each hit

Every memory is tagged with exactly one layer. caveat-layer entries are protected from auto-forgetting. Cold low-importance memories are auto-consolidated into learning entries on server startup.

Quick Start — One Command

npx -y linksee-memory setup

This does everything:

1. Registers the MCP server with Claude Code

2. Installs the agent skill (teaches the agent when to recall/remember)

3. Configures auto-capture (every session saved to your local brain)

4. Offers to wire the re-injection guard into this project (pre-action decision enforcement)

Restart Claude Code, then just chat normally. Add "Use Linksee" to any prompt to trigger memory recall.

Manual setup (if you prefer step-by-step)

Install & register:

claude mcp add -s user linksee -- npx -y linksee-memory

Tools appear as mcp__linksee__remember, mcp__linksee__recall, mcp__linksee__read_smart.

Install the skill (auto-invocation):

npx -y linksee-memory install-skill

Copies SKILL.md to ~/.claude/skills/linksee-memory/. Agent auto-fires on phrases like "前に…", "また同じエラー", "覚えておいて", new task starts, file edits, etc.

Configure auto-capture (Stop hook):

Add to ~/.claude/settings.json:

{
  "hooks": {
    "Stop": [
      {
        "matcher": "",
        "hooks": [
          { "type": "command", "command": "npx -y linksee-memory sync" }
        ]
      }
    ]
  }
}

Each turn end takes ~100 ms. Failures are silent. Logs at ~/.linksee-memory/hook.log.

Other editors / CLIs

Linksee Memory is a standard MCP server (stdio). Any tool that speaks MCP can connect:

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "linksee": {
      "command": "npx",
      "args": ["-y", "linksee-memory"]
    }
  }
}

Restart Cursor. Memory tools appear in the agent panel.

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "linksee": {
      "command": "npx",
      "args": ["-y", "linksee-memory"]
    }
  }
}

codex mcp add linksee -- npx -y linksee-memory

Or add to ~/.codex/config.toml:

[mcp_servers.linksee]
command = "npx"
args = ["-y", "linksee-memory"]

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "linksee": {
      "command": "npx",
      "args": ["-y", "linksee-memory"]
    }
  }
}

Add the same stdio command to claude_desktop_config.json:

{
  "mcpServers": {
    "linksee": {
      "command": "npx",
      "args": ["-y", "linksee-memory"]
    }
  }
}

Config file: macOS ~/Library/Application Support/Claude/, Windows %APPDATA%\Claude\. Restart Claude Desktop.

All editors share the same ~/.linksee-memory/memory.db. A decision made in Claude Code is recalled in Cursor. A caveat recorded in Windsurf prevents the same mistake in Codex.

Database location

Default: ~/.linksee-memory/memory.db. Override with LINKSEE_MEMORY_DIR env var.

Uninstall

# 1. Remove the MCP server registration
claude mcp remove linksee

# 2. Remove the hooks from settings.json (edit the file, delete the linksee entries):
#    ~/.claude/settings.json          → the Stop hook running "npx -y linksee-memory sync"
#    <project>/.claude/settings.json  → the SessionStart/PreToolUse hooks running "npx -y linksee-memory guard"

# 3. Remove the installed skill and all local memory (optional)
rm -rf ~/.claude/skills/linksee-memory
rm -rf ~/.linksee-memory   # deletes all stored memory — nothing is kept anywhere else

Nothing ever leaves your machine, so step 3 fully erases everything Linksee stored.

What's new in v0.9

What's new in v0.8

6 Tools

Two pillars, one surface. Memory and drift each get the minimum; nothing else is exposed. Eleven tools bled model-dependent behaviour across Claude / GPT / Cursor / Codex / Gemini — six is what an agent can hold without a manual.

The five earlier names — where_am_i, check_decision, flag_proposals, dream, resolve_proposal — are folded into the six above. They are hidden from tools/list but still answer if called, so a skill or agent written against an older version keeps working. LINKSEE_LEGACY_TOOLS=1 lists them.

CLI utilities

The 6 memory layers

Each entity (person / company / project / file / concept) can have memories across six layers. Since v0.4, each memory uses the 3-axis structured format (altitude × type × state):

{
  "title": "freee OAuth token expires in 24h",
  "altitude": "implementation",
  "type": "outcome",
  "state": "done",
  "what": "freee OAuth token expires in 24 hours. Must refresh proactively.",
  "why": "freee uses short-lived tokens unlike most SaaS (usually 30-90 day expiry)",
  "affects": ["src/integrations/freee/auth.ts"],
  "next_action": null
}

• caveat memories are auto-protected from forgetting (pain lessons, never lost).

• goal memories bypass decay while the goal is active.

• state tracks lifecycle: open → decided → in_progress → done / stalled / superseded.

Architecture

A single SQLite file (better-sqlite3 + FTS5 trigram tokenizer for JP/EN) contains five layers:

• Layer 1 — entities (facts: people / companies / projects / concepts / files)

• Layer 2 — edges (associations, graph adjacency)

• Layer 3 — memories (6-layer structured meanings per entity)

• Layer 4 — events (time-series log for heat / momentum computation)

• Layer 5 — file_snapshots + session_file_edits (diff cache + conversation↔file linkage)

The conversation↔file linkage is the key. Every file edit captured by the Stop hook is stored alongside the user message that drove the edit. So recall({ path: "server.ts" }) returns "this file was edited 30 times across 3 days, and here are the actual user instructions that motivated each change".

Why the design choices

• Local-first — your conversation history is private. Nothing leaves your machine.

• Single file — memory.db is one portable artifact. Backup = file copy.

• MCP stdio — works with every agent that speaks MCP, no plugins per host.

• Reuses proven schemas — heat_score / momentum_score ported from a production sales-intelligence codebase. Rule-based, no LLM dependency in the hot path.

Roadmap

• ✅ 3-tool unified surface (remember / recall / read_smart) — v0.7.0

• ✅ Auto-consolidate on server startup — v0.7.0

• ✅ Claude Code Plugin (claude plugin add -- linksee-memory)

• ✅ Five MCP Blocks (Tools + Resources + Prompts + Sampling + Roots + Elicitation)

• ✅ Stop-hook auto-capture for Claude Code

• ✅ JP/EN trigram FTS5

• ✅ One-command setup (npx -y linksee-memory setup)

• ✅ Structured memory v2 (3-axis classification: altitude × type × state)

• ✅ Cross-LLM: Claude Code, Cursor, Windsurf, OpenAI Codex, Gemini CLI

• ✅ Landing page (linksee.app)

• ✅ Drift detection engine + 4 MCP drift tools — v0.8.0

• ✅ 4-species truth map (hypothesis/constraint/commitment/source_of_truth) — v0.8.0

• ✅ Dashboard with Decision Register visualization

• 🔮 Obsidian plugin (read truth map in your vault)

• 🔮 Vector search via sqlite-vec (already in deps, embedding backend pending)

• 🔮 Cross-device cloud sync (Pro tier)

Comparison with Claude Code auto-memory

Claude Code ships a built-in memory feature at ~/.claude/projects/<path>/memory/*.md — flat markdown notes for user preferences. linksee-memory complements it:

• auto-memory = your scrapbook of "remember I prefer X"

• linksee-memory = structured cross-agent brain with file diff cache and per-edit WHY

Use both.

Security & privacy

linksee-memory runs locally and is built to read — and send — as little as possible.

• Local-first. Memory is one SQLite file at ~/.linksee-memory/memory.db. No account, no cloud, no API key.

• Telemetry is opt-in and OFF by default. setup asks once; nothing is sent unless you agree there (or set LINKSEE_TELEMETRY=basic). Even then it never sends your source code, file contents, prompts, conversation, entity/project names, or the memory DB — only anonymous counters (details).

• No automatic repo crawling. linksee reads: memory you explicitly save, your map.yaml, the specific files a map reality-check points at, the local SQLite DB, and — when the Stop hook fires — your Claude Code session transcript (locally, to capture what happened). It does not crawl your repo, read .env/secrets/node_modules, or touch your home directory on its own.

• Clean MCP transport. The server writes only JSON-RPC to stdout; all logs go to stderr.

• Hooks are documented and removable. setup adds a Stop hook (session capture) and an optional guard hook. They make no network calls by default, are time-bounded, fail-open (a hook error never breaks your session), and are listed under Uninstall.

• No shell-injection surface. Subcommands run via spawn with array args and shell: false, from a fixed allowlist; map.yaml is parsed with the safe yaml parser (no arbitrary tag execution).

• Supply chain. MIT, published from a single owner. npx -y linksee-memory runs the published package — pin a version in CI if you need reproducibility.

Found a security issue? See SECURITY.md.

Telemetry (opt-in, off by default)

linksee-memory ships with opt-in anonymous telemetry that helps us understand which MCP servers and workflows actually work in the wild. Nothing is sent unless you explicitly enable it. No conversation content, no file content, no entity names, no project paths — ever.

Enable

export LINKSEE_TELEMETRY=basic     # opt in
export LINKSEE_TELEMETRY=off       # opt out (or just unset the variable)
# `linksee-memory setup` also asks once and records your choice in
# ~/.linksee-memory/telemetry-consent (delete that file to be asked again).

Exactly what gets sent (Level 1 contract)

After each Claude Code session ends, the Stop hook sends one POST to https://linksee-site.vercel.app/api/telemetry/linksee containing only these fields:

What is NEVER sent:

• ❌ Conversation messages (user or assistant)

• ❌ File contents

• ❌ Entity names, project names, file paths, URLs

• ❌ Memory-layer text (goal / context / emotion / impl / caveat / learning)

• ❌ Authentication tokens, API keys, secrets

• ❌ Your IP address (only a one-way hash for abuse detection)

Why we ask

Aggregated MCP-usage data helps the KanseiLink project rank which agent integrations actually work for real developers. If you're happy to contribute, LINKSEE_TELEMETRY=basic takes 1 second to set and helps the entire MCP ecosystem improve.

The full payload schema and validation logic is open-source — read src/lib/telemetry.ts if you want to verify exactly what leaves your machine.

Pricing

Free forever.

linksee-memory is local-first and runs entirely on your machine. There is no hosted component you need to pay for. The SQLite DB lives in your home directory; backup = file copy.

No account, no credit card, no API key. Just install and use.

Troubleshooting

1. Verify the skill was installed:

   ls ~/.claude/skills/linksee-memory/SKILL.md

   If absent, run npx -y linksee-memory install-skill.

2. Restart Claude Code. Skills are indexed on session start.

3. Check that the MCP is registered under the name linksee (the skill expects mcp__linksee__* tool names):

   claude mcp list | grep linksee

   If it's registered as something else, either re-register or edit ~/.claude/skills/linksee-memory/SKILL.md to match.

1. Check the hook log: cat ~/.linksee-memory/hook.log

2. Run a manual test:

   echo '{"session_id":"test","transcript_path":"/path/to/some.jsonl"}' | npx -y linksee-memory sync

3. Make sure the Stop hook in ~/.claude/settings.json points to npx -y linksee-memory sync (not the old -import).

v0.0.6+ fixed the entity detection bug that collapsed all memories into the session's starting cwd. To re-index existing history with correct project attribution, run:

npx -y linksee-memory import --all

The importer is idempotent (wipes existing session data before re-inserting). Typical runtime: a few minutes for hundreds of sessions. Expect a dramatic improvement in recall precision afterward.

Reduce max_tokens:

recall({ query: "...", max_tokens: 800 })   // default is 2000

Or narrow with entity_name and layer:

recall({ query: "...", entity_name: "my-project", layer: "caveat" })

rm -rf ~/.linksee-memory   # nuke everything; next run creates a fresh DB

Or delete individual memories via remember({ forget: true, memory_id: <id> }).

Consolidation runs automatically on server startup (7-day threshold). It clusters old cold memories into compressed learning-layer summaries. Caveat and active-goal layers are always preserved.

If you want to force a manual consolidation, restart the MCP server — auto-consolidate triggers on every startup.

FAQ

Drift = when your code reality silently diverges from what you decided. Example: Last week you decided "FTS5, not vector search" but this week a new agent session installs pgvector without knowing the history.

Linksee Memory tracks this by letting you declare decisions as "anchors" and then automatically checking committed code against them. The make-or-break rule: intentional evolution (recorded as fix/supersede) stays quiet, while unaccounted gaps get flagged. It's like Datadog but for product decisions instead of server metrics.

You don't need to use drift detection to benefit from linksee-memory — the 3 memory tools (remember/recall/read_smart) work independently. Drift tools are an additional layer for teams and solo devs managing multiple projects.

Three axes:

1. Local-first: those tools require cloud accounts and send your data to their servers. linksee-memory runs entirely on your machine — one SQLite file, no network calls by default.

2. WHY-layered: they store flat facts or knowledge-graph nodes. linksee-memory has 6 explicit layers (goal/context/emotion/implementation/caveat/learning) so retrieval returns structured reasoning, not just data.

3. File diff cache: read_smart tool saves 86–99% of tokens on file re-reads via AST-aware chunking. None of the memory services do this — it's a feature usually shipped in IDEs.

Claude Code's auto-memory is Claude-only (doesn't help if you switch to Cursor, OpenAI Codex, or Gemini CLI) and stores flat markdown with no structure. linksee-memory is the same local-first principle but:

• Works across Claude Code, Cursor, OpenAI Codex, Gemini CLI (shared SQLite)

• Structured 6-layer format makes recall explainable

• Auto-consolidation compresses cold memories on startup; caveats are permanently protected

Yes — see tools/bench-read-smart.ts in the repo. The read_smart tool:

1. Hashes file content on first read, returns full content + chunk metadata (AST/heading/indent boundaries).

2. On re-read with unchanged mtime+sha256, returns ~50 tokens of "unchanged" confirmation instead of re-sending the file.

3. On real edits, returns only the changed chunks as full content + unchanged chunks as metadata-only references.

For a typical TypeScript file edit in an agentic loop, this cuts round-trip token costs by ~86%. On pure re-reads (user navigating back to a previously-read file), savings exceed 99%.

The default is no sync — the SQLite file lives at ~/.linksee-memory/memory.db and stays there. If you want multi-machine sync, put that directory under Syncthing / iCloud Drive / Dropbox / Google Drive — it's a single file, so any file-sync tool works. (Avoid simultaneous edits from two machines while the MCP server is running on both; SQLite's WAL mode handles single-writer well but multi-writer conflicts can corrupt.)

Two mechanisms:

1. Ebbinghaus forgetting: cold low-importance memories decay naturally, eligible for auto-forget sweeps. caveat layer and memories with importance ≥ 0.9 are always protected.

2. Auto-consolidation: runs on every server startup (7-day threshold). Compresses clusters of cold low-importance memories by entity into a single learning-layer summary, then deletes the originals. No manual scheduling needed.

In practice a solo developer hits ~100MB after 6 months of heavy use. A year-old DB I tested with 80K memories still recalls in <10ms.

Yes — any MCP-compatible client works:

• Claude Code: claude mcp add -s user linksee -- npx -y linksee-memory

• Claude Desktop: add to claude_desktop_config.json (see onboarding on the LP)

• Cursor: add to MCP settings in Cursor → Settings → Features → Model Context Protocol

• OpenAI Codex: codex mcp add linksee -- npx -y linksee-memory (or ~/.codex/config.toml with [mcp_servers.linksee] block)

• Gemini CLI: add to ~/.gemini/settings.json mcpServers section

• ChatGPT (web/mobile app): stdio MCP not supported by the consumer app — requires Remote MCP server over HTTPS (not yet available).

• Custom agent: the MCP stdio protocol is documented at modelcontextprotocol.io

By default: zero network calls, zero telemetry. There's an optional Level-1 telemetry mode you can enable that sends anonymized aggregate metrics (tool call counts, error rates, latency percentiles — never memory content, never file paths, never queries). The exact payload schema is documented in the Telemetry section and you see every byte before opting in.

After install, in a new Claude session ask: "Can you remember that I prefer TypeScript over JavaScript? Use Linksee Memory." Claude should confirm it called mcp__linksee__remember and stored this. Then in a different session ask: "What languages do I prefer? Use Linksee Memory." It should recall via mcp__linksee__recall and return the preference with match_reasons showing why.

Support

• Issues & bug reports: github.com/michielinksee/linksee-memory/issues

• Feature requests: open an issue with the enhancement label

• Security concerns: see SECURITY.md if present, or file a private advisory on GitHub

• Company: Synapse Arrows PTE. LTD. (Singapore)

Changelog

v0.11.3 — Robustness + MCP hygiene (2026-06-16)

• Corrupt-database recovery: if ~/.linksee-memory/memory.db is unreadable, linksee preserves it as memory.db.corrupt-<timestamp> and starts a fresh one (with a clear message) instead of crashing with a raw SQLite error. Old memories stay recoverable in the backup.

• recall tool description no longer suggests editing your system prompt — cleaner MCP citizenship.

v0.11.2 — More cold-start hardening (2026-06-16)

• stats works on a fresh database instead of crashing with no such table — it ensures the schema exists first (it may be the first command a new user runs).

• map --help prints usage instead of trying to import a map.

v0.11.1 — Cold-start fixes (2026-06-16)

• Run any CLI through the package name: npx -y linksee-memory setup (and map, sync, guard, stats, import, install-skill). A fresh user couldn't reach the standalone bins (linksee-memory-setup, …) via npx — npx resolves package names, not sibling bin names — so the one-command install 404'd. The main bin now dispatches subcommands; the standalone bins remain as aliases.

• map exits gracefully with a next-step message when there's no map.yaml yet (was a raw stack trace — the exact state of a first-time user).

• serverInfo now reports the real package version (was pinned to an old string).

v0.11.0 — The Map: where_am_i + linksee-memory map (2026-06-15)

Memory is the entry point; the product map is the new surface. Drift detection grows up from individual anchors into a whole-product map you navigate from the CLI.

• where_am_i (11th MCP tool) — locate the current topic/file on the Current Truth Map and get its blast radius. Call it with no args to auto-locate from your recent edits.

• linksee-memory map CLI — where · affects · explain · status · next · reconcile · inspect --json · blueprint. A map.yaml (git source of truth) describes how value reaches your user; the reconciler checks it against your code with file:line evidence. Bilingual: add --lang ja.

• Graded blast radius (must fix together / should align / fyi), declared-vs-reality verdicts, and an anti-graveyard guard for accounted-for drift.

• Per-project keys so the Map handles many projects at once.

v0.8.0 — Drift Detection MCP Tools (2026-06-08)

3 tools → 7 tools. The biggest update since launch — agents can now detect, query, and resolve intent ↔ reality drift.

New tools:

• drift_status — returns the truth map with 4-species classification and per-node drift state

• check_decision — deep-dive into a single anchor: state, edges, pending candidates

• declare_anchor — record a decision/constraint/prohibition as a truth-map node (with v9 ProjectCoreNode fields)

• resolve_drift — close the feedback loop: fix / supersede / acknowledge / dismiss

New engine module:

• truth-engine.ts — state derivation logic migrated from the dashboard into the MCP engine. Any MCP client can now query drift status without a dashboard.

• Resolution priority fix: when multiple resolutions reference the same anchor, the most recent one wins (by resolved_at timestamp). Prevents a stale acknowledge from shadowing a newer fix.

• 4-species classification: nodes classified by decision_mode into hypothesis / constraint / commitment / source_of_truth with display format guidance.

No breaking changes to existing memory tools. All 3 memory tools (remember, recall, read_smart) are unchanged.

v0.7.2 — Recall ergonomics + auto-edge detection + classifier precision (2026-05-30)

Quality pass on v0.7.0 / v0.7.1 — sharper day-to-day agent UX and cleaner data for the dashboard:

• recall token discipline: drops the redundant content_raw from the response (parsed content was already there — it was a 2× duplicate), and actually enforces max_tokens by greedy assembly that measures real serialized size (was a flat ~100 tok/memory estimate). Adds approx_tokens to the response so the agent can see its budget usage. The same query that previously returned ~15,800 tokens for a 1200 budget now stays inside it.

• recall precision: near-duplicate memories — same entity + near-identical core text, e.g. the same message captured under both goal and learning — collapse to one in the result set. Composite weights adapt to query specificity: multi-term queries weight relevance higher so off-topic-but-pinned memories don't crowd narrow recalls.

• Capture dedup (write side): session-extractor now produces AT MOST one memory per user turn, with priority goal[first_intent] > caveat > decision > context. A first-intent message containing decision words (e.g. "決めた" / "これで進めよう") is no longer double-saved as both goal and learning.

• memory_edges auto-detection: the previously-empty memory_edges table is now populated during the sleep-mode consolidation sweep. detectMemoryEdges() links a later DECISION memory to the most-recent earlier same-topic decision within an entity (chain, not clique) so the dashboard can render Pivot Chains. The default relation is extends — a same-topic later decision builds on, but does NOT deactivate, the earlier one. Explicit reversal markers (やめる / revert / instead of) produce contradicts; explicit replacement markers (の代わり / replaces / deprecate) produce supersedes. Prevents silent deactivation of still-valid decisions.

• inferType / inferState precision: chitchat acknowledgements ("そうだね" / "ありがとう"), pasted terminal/git/email content, and meta-noise no longer classify as decision — they return note / open before pattern matching. The learning-layer default → decision is gated by this guard. Real decisions (採用 / 決めた, even after an acknowledgement opener) survive.

No schema migration, no breaking API changes. Existing rows keep their stored content; the classifier improvements apply to new captures going forward.

v0.7.1 — Review fixes (2026-05-29)

Based on Opus 4.7 design review of v0.7.0:

• P0 — Required params guidance: remember tool description now includes "REQUIRED PARAMS BY MODE" section so LLMs know exactly which fields are needed for create vs update vs delete.

• P0 — Migration guidance: Deprecated tool names (forget, recall_file, etc.) now return specific migration examples instead of generic errors.

• P1 — recall path+query merge: When both path and query are provided to recall, results from file history and memory search are merged into a single response.

• P2 — Auto-consolidate safety: Table existence check via sqlite_master before querying consolidations table, preventing errors on fresh databases.

v0.7.0 — 3-Tool Unified Surface (2026-05-29)

8 tools → 3 tools. Following Context7's proven pattern of fewer tools = better cross-LLM consistency.

Breaking change: The following tools are removed from the MCP surface. Calling them returns a migration guide:

New unified tools:

• remember — create + update + delete in one tool. Mode is inferred from params.

• recall — search + file history + overview in one tool. Mode is inferred from params.

• read_smart — unchanged.

Other changes:

• Auto-consolidate on server startup (non-blocking setTimeout, 7-day threshold, sqlite_master safety check)

• Claude Code Plugin bundle (claude plugin add -- linksee-memory)

• Deprecation errors include specific migration examples

All internal handler functions are preserved — this is a surface change, not a logic rewrite.

v0.2.0 — English-first launch readiness (2026-04-20)

Prepares the package for a broader (primarily English-speaking) audience on Reddit, Hacker News, and Anthropic Discord. No breaking API changes.

• Bilingualized SKILL.md (auto-invocation skill). The bundled skill that linksee-memory-install-skill copies into ~/.claude/skills/linksee-memory/SKILL.md was Japanese-first; it is now English-primary with Japanese trigger phrases preserved inline. English speakers now get the skill firing on natural English phrases ("how did we solve this before?", "same error again", "remember this") in addition to the existing JP triggers.

• Install-skill CLI output is bilingual: example test phrases shown after installation include both English and Japanese.

• Session-extractor EN coverage (linksee-memory-import): expanded regex patterns for decisions, failures, and caveats so English Claude Code session logs get auto-tagged correctly. Additions include let's go, pivot, switch to, settled on, approved, doesn't work, stuck, same error again, hit an error, debug, broke, revert.

• Clearer caveat-forget error hint: the previous message said "lower importance below 0.9 first, then forget" which was misleading — caveat-layer memories are permanently protected regardless of importance. The hint now correctly distinguishes layer-protection from pin-protection.

• README rework for launch readiness: added a "See it in action" before/after scenario, ASCII 6-layer diagram, MCP Official Registry + Glama score badges, landing-page link, and an 8-item FAQ covering questions that surface during public launches.

• Internal: SKILL.md now documents pairing with KanseiLink skill as an English workflow example.

No code changes to the MCP protocol surface; all existing MCP clients continue to work unchanged.

v0.1.1 — Pin threshold tweak (2026-04-19)

Based on real-world feedback that importance=0.95 memories were not being treated as pinned despite intent.

• Pin threshold lowered from >= 1.0 to >= 0.9. Memories with importance >= 0.9 are now exempt from the auto-forget sweep and surface pinned: true in recall and remember responses. This matches the natural mental model ("0.9 = high importance = should survive cleanup") without requiring exact 1.0.

• All existing memories with importance >= 0.9 (including older ones set to 0.9 or 0.95) become pinned automatically — no migration needed.

• Updated tool descriptions and error messages to reflect the new threshold.

v0.1.0 — Major UX update (2026-04-18)

Based on one week of dogfooding, here's what changed:

New tools

• update_memory — atomic edit with preserved memory_id. Solves the "forget+remember breaks session_file_edits links" bug.

• list_entities — fast "what do I know about?" primitive for session init. Supports kind/min_memories filters and returns layer breakdown.

• npx -y linksee-memory stats — local DB summary CLI.

recall enhancements

• match_reasons array on each memory: e.g. ["content_match_fts", "heat:hot", "pinned"].

• score_breakdown with per-dimension scores (relevance / heat / momentum / importance).

• Pagination via offset / has_more / stopped_by.

• limit parameter (hard cap, complements max_tokens budget).

• band filter to request only hot/warm/cold/frozen memories.

• mark_accessed=false for preview queries that shouldn't bump heat.

• Layer aliases: decisions → learning, warnings → caveat, how → implementation, etc.

• Fix: opportunistic refresh of stale entity momentum scores. Entities recalled >1 h after last remember() no longer return stale momentum.

remember enhancements

• Quality check: rejects pasted assistant output / CI logs / stack traces unless force=true.

• importance=1.0 now implicitly pins the memory (survives auto-forget).

• Layer aliases accepted.

forget changes

• Pinned memories (importance=1.0) now preserved alongside caveat-layer memories.

• Clear error response when attempting to delete a protected or missing memory.

• dry-run now includes sample_ids_to_drop.

consolidate changes

• dry_run: true preview mode — reports cluster count + candidates without writing.

Infra

• Fixed fresh-DB migration bug (was querying meta table before it existed).

• Bumped to Node 20+ for structured language feature usage.

All changes are backward compatible — existing integrations continue to work. Server.ts version banner now reports v0.1.0.

Older versions

See GitHub Releases.

License

MIT — Synapse Arrows PTE. LTD.