Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud
Mnemosyne
Overview
Description from the maker (original English)
Evidence-tracked local memory MCP server: one SQLite file; corrections retire dependent memories.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 232/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
MNEMOSYNE_INDEX_MODE
You need
- npmNode.js installed (for the npx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"mnemosyne": {
"command": "npx",
"args": [
"-y",
"mnemosy-ai",
"mcp",
"--db",
"your-value",
"--workspace",
"your-value",
"--agent",
"your-value"
],
"env": {
"MNEMOSYNE_INDEX_MODE": "your-value"
}
}
}
}Run this in your terminal
claude mcp add mnemosyne --env MNEMOSYNE_INDEX_MODE=your-value -- npx -y mnemosy-ai mcp --db your-value --workspace your-value --agent your-valueFill in the empty values before running it.
This one needs values on its command line, so copy the settings and fill them in.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"mnemosyne": {
"command": "npx",
"args": [
"-y",
"mnemosy-ai",
"mcp",
"--db",
"your-value",
"--workspace",
"your-value",
"--agent",
"your-value"
],
"env": {
"MNEMOSYNE_INDEX_MODE": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one needs values on its command line, so copy the settings and fill them in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"mnemosyne": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"mnemosy-ai",
"mcp",
"--db",
"your-value",
"--workspace",
"your-value",
"--agent",
"your-value"
],
"env": {
"MNEMOSYNE_INDEX_MODE": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
MNEMOSYNE_INDEX_MODEenvDense index used for semantic recall; defaults to auto
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account 28naem-del, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 5694 servers in this category
- Published to the registry more recently than 80% of the other 5694 servers in this category
- Basic checks equal to or better than 100% of the other 5694 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Mnemosyne safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (3.0.0-rc.4) reached the registry 10 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud✓ exa.ai
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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,
bisectfinds 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.4, 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 asmnemosyne-memory; the two share a name, not code or maintainers. Please file issues for that project in its own repository.
This repository predates `mnemosyne-oss/mnemosyne`: GitHub records its creation on February 24, 2026; the separate Python repository was created on April 5, 2026.
Try it
The first npx run downloads the package; after that, the 3.0.0-rc.4 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 demoThe 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):
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 assistantFor the stable release, write mnemosy-ai@2.0.0 in place of mnemosy-ai@next; on 2.0.0, mnemosy demo runs the earlier single-scenario demo.
Contents: 3.0: the breakthrough release · Install and connect an agent · Measured results · Choose your engine · A working demonstration · Corrections in code · Tune the dense index · What you can build · Entry points · Migrate · Limits · Operational boundaries · Build with us
New in 3.0: the breakthrough release (pre-release on npm next)
3.0 is the breakthrough release. Replay and bisect already existed in 2.0.0. 3.0 adds four things: dependencies nobody declared, inferred and proposed for review, so that corrections reach them once accepted; a poisoning gate checked against a reproducible attack suite; outcome-gated, reversible consolidation with signed receipts; and opt-in precision controls. bisect also gained the hybrid retrieval path: since 3.0.0-rc.4 the mnemosy bisect CLI re-executes the store's own path (--retrieval auto|lexical|hybrid, default auto, which picks hybrid when the store has stored vectors for the bundled model; an hnsw index is refused). bisect is not a public package export, so from npm the CLI is the entry point. Each of these is documented and tested below, and each public claim is listed with its evidence in Claims and evidence. 3.0.0-rc.4 is the current release candidate on the npm next channel: npm install mnemosy-ai@next installs it, while npm install mnemosy-ai still installs the stable 2.0.0. Every number below was measured on 3.0.0-rc.2, offline, with no API key, no paid model and no LLM judge. Each one reproduces with the commands in Benchmarks and trust evidence, which also lists what is not claimed.
| Measurement | Result | Protocol |
|---|---|---|
| PrecisionMemBench | 70/77 single-turn, 11/12 session turns (rc.1: 22/77 unfiltered, 48/77 with tuned wrapper filters, session 6/12) | Public 35-belief fixture the controls were designed against, protocol-exact, retrieval mode, no wrapper-side filter, no LLM judge · protocol |
| Poisoning suite v1, consolidation on | 0/167 escapes, 0/60 false quarantines (85/167 escape without the gate) | 227 synthetic cases, lexical path, 294 gate decisions · protocol |
| Dependency suite v1 | recall 96.05 %, precision 98.06 %, 0 stale procedures with the premise guard (inference alone: 7/248 stale; guard false-hold rate 92.78 %) | Synthetic corpus, seed 20261028, 120 corrections, accept-all eval reviewer · protocol |
| Replay determinism | 1000/1000 identical states, 24,000/24,000 as-of answers | Two readers of one store at 1000 seeded cut points; answers against ground truth from the plan · protocol |
| Bisect | precision 1.00 on 2,000 records, 0.40 s in the recorded run of 2026-10-28 (site/demo-five.json; varies by machine; bar 60 s) | One seeded false record in a synthetic store; the expected record is known, and the blame is confirmed by re-running recall without the culprit · protocol |
The default retrieval path's recall did not move: LoCoMo and LongMemEval-S recall are identical before and after (regression check). Recall latency was measured only on a loaded machine, with a scratch harness that is not committed, so those figures cannot be reproduced from this repository; the known limits, including a slowdown of the non-default overlap scorer, are in the changelog.
- Precision you can switch on. The new opt-in controls are a names gate and guarded single-edit fuzzy matching. Empty answers are native abstentions, and the ledger records why. With the controls on, the kernel scores 70/77 single-turn cases and 11/12 session turns on PrecisionMemBench (session was 6/12 with the rc.1 filtered configuration). That run was protocol-exact, in retrieval mode, with no LLM judge, no wrapper-side filter and no wrapper
kindspredicate, and the lexical and hybrid paths score the same. The 12th session turn needs an open-question kind policy that the kernel is not told about. - For comparison, the kernel scored 22/77 unfiltered and 48/77 with the fixture-tuned wrapper filters of rc.1.
- The benchmark fixture is public, and the controls were designed against it, so this is not a held-out estimate.
- The 7 remaining misses are those of our metadata-based configuration; the single-turn protocol does not send explicit lifecycle fields.
- The default retrieval path is unchanged: LoCoMo and LongMemEval-S recall are
Only the beginning is shown; read the rest in the repo