Databases & StorageMCP RegistrynpmA · 88

Mnemosyne MCP server

Name: Mnemosyne
Author: 28naem-del

io.github.28naem-del · Updated today

Evidence-tracked local memory MCP server: one SQLite file; corrections retire dependent memories.

At a glance

Mnemosyne is an MCP server in the Databases & Storage category. It runs locally over stdio via `npx -y mnemosy-ai --db --workspace --agent`, and declares no required credentials. It scores A (88/100) on the MCP Skills Hub health score as of Oct 28, 2026.

Source 154

One-click install

VS Code VS Code Insiders Cursor

Opens your editor and pre-fills the config. Your editor will ask before adding it.

How to install Mnemosyne in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install Mnemosyne in Claude CodeAnthropic's agentic CLI

Add the server

bash

claude mcp add mnemosyne -- npx -y mnemosy-ai --db --workspace --agent

2
Verify it's connected
bash
```
claude mcp list
```
Sharing it with your team?
Add `--scope project` to write it to .mcp.json in the repository root, which you can commit (with the secrets left as environment variables).
Use it in a session
Ask Claude to use the tools this server exposes. It discovers them automatically.

How to install Mnemosyne in Claude DesktopDesktop app (MCP config)

Open your MCP config
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this server

json

{
  "mcpServers": {
    "mnemosyne": {
      "command": "npx",
      "args": [
        "-y",
        "mnemosy-ai",
        "--db",
        "--workspace",
        "--agent"
      ]
    }
  }
}

Restart Claude Desktop
The tools appear under the 🔌 menu once the app reloads.

How to install Mnemosyne in CursorAI-first code editor

Create / edit .cursor/mcp.json in your project

json

{
  "mcpServers": {
    "mnemosyne": {
      "command": "npx",
      "args": [
        "-y",
        "mnemosy-ai",
        "--db",
        "--workspace",
        "--agent"
      ]
    }
  }
}

Enable it
Cursor Settings → MCP → toggle the server on. Tools become available to the Composer/Agent.

How to install Mnemosyne in Codex CLIOpenAI coding agent

Add to ~/.codex/config.toml

toml

[mcp_servers.mnemosyne]
command = "npx"
args = ["-y", "mnemosy-ai", "--db", "--workspace", "--agent"]

Run Codex
Codex loads MCP servers from config.toml on startup and exposes their tools to the agent.

How to install Mnemosyne in WindsurfCodeium's agentic IDE

Edit ~/.codeium/windsurf/mcp_config.json

json

{
  "mcpServers": {
    "mnemosyne": {
      "command": "npx",
      "args": [
        "-y",
        "mnemosy-ai",
        "--db",
        "--workspace",
        "--agent"
      ]
    }
  }
}

Reload
Windsurf → Cascade → refresh MCP servers. Cascade can now call the tools.

How to install Mnemosyne in ClineVS Code autonomous agent

Open Cline → MCP Servers → Configure
Adds to cline_mcp_settings.json.

Add the server

json

{
  "mcpServers": {
    "mnemosyne": {
      "command": "npx",
      "args": [
        "-y",
        "mnemosy-ai",
        "--db",
        "--workspace",
        "--agent"
      ]
    }
  }
}

How to install Mnemosyne in VS CodeCopilot / MCP support

Create .vscode/mcp.json

json

{
  "servers": {
    "mnemosyne": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "mnemosy-ai",
        "--db",
        "--workspace",
        "--agent"
      ]
    }
  }
}

Start it
VS Code shows a ▶ Start action above the server entry. Agent mode (Copilot Chat) can then use the tools.

What does Mnemosyne need from you?

Configuration only — Declares config values but no credentials.

MNEMOSYNE_INDEX_MODEoptionalEnvironment variable
Dense index used for semantic recall; defaults to auto

Pass secrets through your shell environment rather than committing them into a config file.

What tools does Mnemosyne expose?

This server runs as a local package. We only query remote servers for their tool list — asking this one would mean executing it, so its tools are listed in the documentation below.

What the code can do

Read from the published package source — never executed. Parsed 80 of 150 source files.

This server can run other programs on your machine, read and write files and make network requests.

How it was published

Published by a verified CI identity (github trusted publishing) rather than a personal token.
Has a provenance attestation tying this artifact to the commit it was built from.
1 npm maintainer — a single point of compromise · last published by GitHub Actions.

Maintainer of this package and think something here is wrong? Tell us and we'll correct it.

Before you install

Signals gathered from the source repo and package registry — not a safety verdict.

What it can reach

Runs code on your machine — Executes as a local process with your user's permissions, including filesystem access.
Downloads at launch — The package is fetched from its registry each run unless you pin a version.

Last commit today — Actively maintained.
154 GitHub stars — Community adoption signal.

Client compatibility

Claude Code: NativeClaude Desktop: NativeCursor: NativeCodex CLI: NativeWindsurf: NativeCline: NativeVS Code: Native

Health score

A · 88

Maintenance, adoption, transparency and packaging — not a safety audit. How this is calculated

Maintenance30%100%
Last commit 0 days ago.
Adoption20%66%
154 stars.
Transparency20%75%
3/4: ✓ public source repo, ✓ declared license, ✓ documentation, ✗ discoverable tool list.
Provenance15%100%
Package and registry entry point at the same repository.
Packaging15%100%
Publishes a pinned version (3.0.0-rc.3).

Documentation

Full README

Mnemosyne

The next agent starts wiser.

Mnemosyne is local-first, long-term memory for AI agents: one SQLite file that keeps what an agent learned together with the evidence it came from, served to any host over TypeScript, MCP, CLI or HTTP. When a source is corrected, the memories recorded as depending on it are retired, so the next agent starts from what current recorded evidence still supports instead of repeating a stale lesson. The local engine needs no model API key, database server or Docker.

Evidence on every memory. Each record keeps its source and trust tier, and the superseded version stays inspectable after a correction.
Corrections that reach dependents. A corrected requirement retires the procedures and skills recorded as depending on it; 3.0 can also propose dependencies nobody declared, for review.
Auditable over time. Past beliefs replay exactly. Given the records a failed retrieval should have returned, bisect finds the records that pushed them out. In the 3.0 pre-release, consolidation is outcome-gated and reversible.
Any host. MCP for Claude Code, Cursor and Codex; a TypeScript SDK; a CLI; and authenticated HTTP with a Python client and live inspector.

Stable: 2.0.0 (`npm install mnemosy-ai`) · Newest pre-release: 3.0.0-rc.3, the breakthrough release (`npm install mnemosy-ai@next`) · MIT · Node.js ≥22.16

Website · Quickstart · Documentation · Examples · Migration · Measured results · Trust evidence · Claims and evidence · Releases · npm · Contributing · Issues

[!NOTE] Which Mnemosyne is this? This repository is the TypeScript package published on npm as `mnemosy-ai`, with its site at mnemosy.ai. It is a separate project from mnemosyne-oss/mnemosyne, the Python memory library published on PyPI as mnemosyne-memory; the two share a name, not code or maintainers. Please file issues for that project in its own repository.

Try it

The first npx run downloads the package; after that, the 3.0.0-rc.3 demo runs offline on synthetic corpora, with no API key and no model, and measures each claim while it runs:

npx -y -p mnemosy-ai@next mnemosy demo

The recording above is one unedited run (`assets/demo.tape` regenerates it with vhs). The last line of each of its five moments, matching the recorded run of 28 October 2026 (`site/demo-five.json`; timings vary by machine; the pass bars printed after each line are omitted here):

text

PROOF  replay determinism: 1000/1000 identical, 24000/24000 as-of answers = ground truth   [PASS]
PROOF  downstream repair: undeclared invalidated 3/3, accepted edges true 3/3, stale after correction 0 (11 probes)   [PASS]
PROOF  blame precision / time-to-locate: precision 1.00, 0.40 s, 2000 records   [PASS]
PROOF  poisoning suite v1, live: 227 cases, 0 escapes, 0 false quarantines   [PASS]
PROOF  consolidation receipts: kept 1 (delta +2), rolled back 2 (regression, invariant violated), rollback identical hash true, chain verified 4 receipts   [PASS]

5/5 moments proved.

What each moment proves, and what it does not. Then give the memory to Claude Code (other clients: Install and connect an agent):

claude mcp add mnemosyne -- npx -y -p mnemosy-ai@next mnemosy mcp --db ./.mnemosyne/memory.sqlite --workspace my-project --agent assistant

Excerpt — the full document is on GitHub.

Alternatives to Mnemosyne

Maintain Mnemosyne? Add a badge to your README

Shows its health grade, and links readers to install steps for every client.

markdown

[![Mnemosyne on MCP Skills Hub](https://mcp-skills-hub.vercel.app/badge/io-github-28naem-del-mnemosyne)](https://mcp-skills-hub.vercel.app/mcps/io-github-28naem-del-mnemosyne)

Catalog data refreshed Oct 28, 2026. Something wrong? Report it.