Is io.github.michielinksee/linksee-memory safe to use?

io.github.michielinksee/linksee-memory has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.michielinksee/linksee-memory?

io.github.michielinksee/linksee-memory can be installed by cloning its GitHub repository and following the setup instructions in the README.

Is io.github.michielinksee/linksee-memory actively maintained?

io.github.michielinksee/linksee-memory is actively maintained — last commit was 0 days ago.

io.github.michielinksee/linksee-memory

Local-first agent memory MCP. 6-layer brain + token-saving file diff cache (86% savings).

ActiveNot tested

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GitHub

27DNo CVEsactive

Quick Install

{
  "mcpServers": {
    "io-github-michielinksee-linksee-memory": {
      "command": "<see-readme>",
      "args": []
    }
  }
}

Setup guide

No install config available. Check the server's README for setup instructions.

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[![MCPpedia Score](https://mcppedia.org/api/badge/io-github-michielinksee-linksee-memory)](https://mcppedia.org/s/io-github-michielinksee-linksee-memory)

Should you use this server?

Local-first agent memory MCP. 6-layer brain + token-saving file diff cache (86% savings).

✓

Is it safe?

No package registry to scan.

No authentication — any process on your machine can connect.

License not specified.

✓

Is it maintained?

Last commit 0 days ago.

Will it work with my client?

Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.

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Test This Server

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Score Breakdown

MCPpedia Score

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27D

Last scored 13h ago

How we score →

Security

2/30

No known vulnerabilities.

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Known CVEs — No package registry to scan

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Tool safety — No tools to analyze

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Tool poisoning — No tools to analyze

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Injection vectors — No tools to analyze

○

Tool stability — First scan — baseline recorded

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Dependency health — No package to analyze

✗

License — No license specified

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Authentication — No authentication required

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Repository — active repo

CVEs checked daily via OSV.dev. Score algorithm is open source.

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Frequently Asked Questions

Is io.github.michielinksee/linksee-memory safe to use?: io.github.michielinksee/linksee-memory has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.michielinksee/linksee-memory?: io.github.michielinksee/linksee-memory can be installed by cloning its GitHub repository and following the setup instructions in the README.
What AI clients work with io.github.michielinksee/linksee-memory?: io.github.michielinksee/linksee-memory is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.
Is io.github.michielinksee/linksee-memory actively maintained?: io.github.michielinksee/linksee-memory is actively maintained — last commit was 0 days ago.

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Discussion

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Has anyone used this with Cursor?How do you handle auth?Any alternatives?

linksee-memory

Local-first agent memory MCP. A cross-agent brain for Claude Code, Cursor, and ChatGPT Desktop — with a token-saving file diff cache that nobody else does.

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. linksee-memory keeps the WHY.

It is a Model Context Protocol (MCP) server that gives any AI agent four superpowers:

	Mem0 / Letta / Zep	Claude Code auto-memory	linksee-memory
Cross-agent	△ (cloud)	❌ Claude only	✅ single SQLite file
6-layer WHY structure	❌ flat	❌ flat markdown	✅ goal / context / emotion / impl / caveat / learning
File diff cache	❌	❌	✅ AST-aware, 50-99% token savings on re-reads
Active forgetting	△	❌	✅ Ebbinghaus curve, caveat layer protected
Local-first / private	❌	✅	✅

Three pillars

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.
Cross-agent portability — single SQLite file at ~/.linksee-memory/memory.db. Same brain for Claude Code, Cursor, ChatGPT Desktop.
WHY-first structured memory — six explicit layers (goal / context / emotion / implementation / caveat / learning). Solves "flat fact memory is useless without goals".

Install

npm install -g linksee-memory
linksee-memory-import --help   # bundled importer for Claude Code session history

Or use npx ad hoc:

npx linksee-memory             # starts the MCP server on stdio

The default database lives at ~/.linksee-memory/memory.db. Override with the LINKSEE_MEMORY_DIR environment variable.

Register with Claude Code

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

Restart Claude Code. Tools appear as mcp__linksee__remember, mcp__linksee__recall, mcp__linksee__recall_file, mcp__linksee__read_smart, mcp__linksee__forget, mcp__linksee__consolidate.

Recommended: install the skill (auto-invocation)

Installing the MCP alone doesn't teach Claude Code when to call recall / remember. The bundled skill fixes that:

npx -y linksee-memory-install-skill

This copies a SKILL.md to ~/.claude/skills/linksee-memory/. Claude Code auto-discovers it and fires the skill on phrases like "前に…", "また同じエラー", "覚えておいて", new task starts, file edits, and so on — no need to say "use linksee-memory".

Flags: --dry-run, --force, --help.

Optional: auto-capture every session (Stop hook)

Add to ~/.claude/settings.json to record every Claude Code session to your local brain automatically:

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

Each turn end takes ~100 ms. Failures are silent (Claude Code never blocks). Logs at ~/.linksee-memory/hook.log.

Tools

Tool	Purpose
`remember`	Store memory in 1 of 6 layers for an entity
`recall`	FTS5 + heat-score + momentum composite ranking, JP/EN trigram search
`recall_file`	Get the COMPLETE edit history of a file across all sessions, with per-edit user-intent context
`read_smart`	Diff-only file read. Returns full content on first read, ~50 tokens on unchanged re-reads, only changed chunks on real edits
`forget`	Explicit delete OR auto-sweep based on forgettingRisk (importance × heat × age)
`consolidate`	Sleep-mode compression: cluster cold low-importance memories → protected learning-layer summary

The 6 memory layers

Each entity (person / company / project / file / concept) can have me

... View full README on GitHub