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What is Linksee Memory?

Linksee Memory is a local-first MCP server that gives every AI agent on your machine persistent, structured memory — and ties that memory to a product map, so it can also catch when your README, docs, and code quietly drift apart. Sessions end. Agents forget. You’re juggling several projects, and so is your agent. Linksee Memory fixes the forgetting — then uses the same memory + map to catch drift, with file:line evidence.

Cross-agent

Claude Code, Cursor, Windsurf, OpenAI Codex, Gemini CLI — one memory, all agents.

Product map

where_am_i + the linksee-memory map CLI locate any file on your map and report its blast radius.

Drift detection

Reconcile what you decided against the actual code — convergence / divergence with evidence.

Local-first

No cloud. No account. No network calls. One SQLite file on your machine.

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Quick Start

One command sets up the MCP server, the skill, and the auto-capture hook: 
npx -y linksee-memory setup
Restart your agent. Done. (Prefer manual config? See Installation.)
Works with any MCP-compatible client — Claude Desktop, Claude Code, Cursor, Windsurf, Cline, and more.

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Key Features

  • 11 MCP tools — memory (remember, recall, read_smart), drift (drift_status, declare_anchor, check_decision, resolve_drift, flag_proposals, resolve_proposal, dream), and where_am_i — locate the current file on your product map + its blast radius.
  • 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. Bilingual via --lang ja.
  • 6-layer structured memory — goal / context / emotion / implementation / caveat / learning, with an Ebbinghaus forgetting curve (caveats and goals protected forever).
  • AST-aware token-saving reads — read_smart returns only changed chunks (50–99% fewer tokens on re-reads).
  • Local-first & bilingual — one SQLite file, no network, full Japanese + English (trigram FTS5).

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How It Works

  1. During a session, the agent calls remember to store decisions, caveats, and context.
  2. On the next session, recall retrieves relevant memories ranked by relevance, heat, and importance.
  3. Across the product, a map.yaml records how those decisions reach the user; reconcile checks the map against the actual code and flags drift with file:line evidence.
  4. Anytime, where_am_i tells the agent where it is on the map and what else a change would touch.

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System Requirements

  • Node.js 20+
  • Any MCP-compatible client
  • ~10 MB disk space for the SQLite database

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Next Steps

Installation

Detailed setup for every MCP client

Quick Start

Your first remember → recall flow in 2 minutes

Product map & drift

where_am_i, the map CLI, and how drift detection works

Memory Layers

Understand the 6-layer structure