Turbo Quant Memory

Name: Turbo Quant Memory
Author: Lexus2016

by Lexus2016version 0.34.0i★ –iRuns on your computer

Registry nameio.github.Lexus2016/turbo-quant-memory

Install

Overview

Description from the maker (original English)

Local-first memory and knowledge graph for coding agents. Compact retrieval, no network.

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
UsageiNot measured yet★ –

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
You need to fill in: TQMEMORY_SECRETS_PASSPHRASE

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: TQMEMORY_FTS_LANGUAGE, TQMEMORY_EMBEDDING_BACKEND, TQMEMORY_MIGRATE_ON_STARTUP, TQMEMORY_SECRETS_PASSPHRASE

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "turbo-quant-memory": {
      "command": "uvx",
      "args": [
        "turbo-quant-memory",
        "serve"
      ],
      "env": {
        "TQMEMORY_FTS_LANGUAGE": "your-value",
        "TQMEMORY_EMBEDDING_BACKEND": "your-value",
        "TQMEMORY_MIGRATE_ON_STARTUP": "your-value",
        "TQMEMORY_SECRETS_PASSPHRASE": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add turbo-quant-memory --env TQMEMORY_FTS_LANGUAGE=your-value --env TQMEMORY_EMBEDDING_BACKEND=your-value --env TQMEMORY_MIGRATE_ON_STARTUP=your-value --env TQMEMORY_SECRETS_PASSPHRASE=your-value -- uvx turbo-quant-memory serve

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "turbo-quant-memory": {
      "command": "uvx",
      "args": [
        "turbo-quant-memory",
        "serve"
      ],
      "env": {
        "TQMEMORY_FTS_LANGUAGE": "your-value",
        "TQMEMORY_EMBEDDING_BACKEND": "your-value",
        "TQMEMORY_MIGRATE_ON_STARTUP": "your-value",
        "TQMEMORY_SECRETS_PASSPHRASE": "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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "turbo-quant-memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "turbo-quant-memory",
        "serve"
      ],
      "env": {
        "TQMEMORY_FTS_LANGUAGE": "your-value",
        "TQMEMORY_EMBEDDING_BACKEND": "your-value",
        "TQMEMORY_MIGRATE_ON_STARTUP": "your-value",
        "TQMEMORY_SECRETS_PASSPHRASE": "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

TQMEMORY_FTS_LANGUAGE env
Snowball stemmer for the BM25 lane. Use Russian for Cyrillic-dominant corpora.
TQMEMORY_EMBEDDING_BACKEND env
Embedder backend. Default fastembed (ONNX); sentence-transformers needs the [torch] extra.
TQMEMORY_MIGRATE_ON_STARTUP env
Set to 1 to apply pending schema migrations automatically on startup.
TQMEMORY_SECRETS_PASSPHRASE secret env
Argon2id passphrase for the secrets vault on headless hosts. Omit to use the OS keyring.

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Lexus2016, 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 7249 servers in this category
Published to the registry more recently than 71% of the other 7249 servers in this category
Basic checks equal to or better than 100% of the other 7249 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 Turbo Quant Memory 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?

You need to fill in: TQMEMORY_SECRETS_PASSPHRASE

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 (0.34.0) reached the registry 16 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

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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★ 64,224MITUpdated today

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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

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.

Local-first memory and knowledge graph for AI coding agents

Your agent stops re-reading files and re-deriving the same conclusions.

Your notes, code and secrets never leave your machine.

Demo

Watch the 30-second demo

The problem

A long session accumulates hard-won detail about why the code is the way it is. Then the context compacts and it is gone. Next session the agent re-reads the same files, re-derives the same conclusions, and bills you for the same tokens again.

CLAUDE.md does not scale past a few dozen lines, and it cannot answer *"what did we decide about X, and why?"*.

Turbo Quant Memory is an MCP server that gives the agent a persistent, searchable store it writes to while it works — decisions, lessons, patterns, session handoffs — plus a compact index of your Markdown. Retrieval returns ~220-character result cards rather than whole documents; the agent loads full content only when a card is not enough.

Why this one

	Turbo Quant Memory	mem0 / OpenMemory	MCP `memory` server
Where your data lives	your disk, always	vendor cloud or self-host	your disk
Your data leaves the host	never	yes, unless self-hosted	never
Retrieval	hybrid BM25 + dense vector, RRF-fused	dense vector	exact graph lookup
What a search returns	compact cards, hydrate on demand	full memories	full nodes
Knowledge graph	yes — with lifecycle + linting	no	yes
Non-English content	Cyrillic exact-match out of the box	varies	n/a
Measures its own savings	yes — `server_info()`	no	no
Price	free, MIT	paid tiers	free

No HTTP client, no telemetry, no phone-home. Verify it yourself — this returns nothing:

grep -rnE '^[[:space:]]*(import|from)[[:space:]]+(requests|httpx|aiohttp|urllib3)\b' src/

To be precise about the one exception: on first run fastembed downloads the embedding model (~0.22 GB) from Hugging Face. After that the server runs fully offline. Your notes, code and secrets are never transmitted anywhere — there is nothing in the package that could send them.

Install

Let your agent install it

Paste this into Claude Code, Codex, Gemini CLI, Cursor or Antigravity:

Install and configure the Turbo Quant Memory MCP server for this workspace from

https://github.com/Lexus2016/turbo_quant_memory — follow the README, register the

tqmemory server, run turbo-memory-mcp skill install, run the health check, and

index this project.

skill install copies an operating manual into every agent skill directory on the machine, so every future session already knows how to use the memory without being told.

Or install it yourself

uv tool install turbo-quant-memory

Upgrading from 0.27.x or earlier? The distribution was renamed in 0.28.0, so uv tool upgrade turbo-memory-mcp no longer resolves — run uv tool install --force turbo-quant-memory once, and uv tool upgrade turbo-quant-memory afterwards. The turbo-memory-mcp command itself is unchanged, so client configs keep working.

Then register the server with your client:

claude mcp add --scope project tqmemory -- turbo-memory-mcp serve   # Claude Code

codex mcp add tqmemory -- turbo-memory-mcp serve # Codex gemini mcp add tqmemory turbo-memory-mcp serve # Gemini CLI

Cursor, OpenCode, Antigravity and other clients → CLIENT_INTEGRATIONS.md. Hermes runs MCP through a systemd gateway → docs/hermes.md.

📈 It measures its own savings — see for yourself

Turbo Quant Memory doesn't just *claim* to save tokens — every install keeps a running tally you can read anytime with server_info() (field usage_stats.headline). The savings are yours to verify, not ours to promise.

Live snapshot from a real developer instance (v0.28.2):

What the memory did	Number
🔢 Input tokens saved (cumulative)	≈ 2,640,000 and counting
🔁 Retrievals served	2,280 searches + 280 deep hydrations
📉 Average saved per retrieval	≈ 1,200 tokens
📚 Knowledge under management	237 active notes + 763 indexed code blocks
🛡️ Integrity	0 corrupted records · 0 pending migrations

These are one machine's cumulative numbers, not a synthetic benchmark — your own counter starts at zero and grows as your agent works. Run server_info() on your install to see your real figure.

What it does

Typed notes. decision, lesson, pattern, handoff — each stored with tags, provenance and a knowledge-graph link to the file or issue it is about.
Tiered memory. durable (decisions, patterns) and reference (indexed docs) are searched by default; episodic (session handoffs) stays out of the way until you ask for it, so yesterday's noise never buries an architectural decision.
Hybrid retrieval. A dense vector lane leads; a BM25 lane rescues exact terms — function names, file paths, IDs — fused with Reciprocal Rank Fusion. Cyrillic and other non-English terms match exactly, case- and accent-insensitive, with no configuration.
Knowledge graph. Directed, timestamped relations between notes, files and issues. Search results carry the linked context inline, so the agent does not need a second lookup.
Human notes outrank agent notes. Anything you explicitly asked to remember is flagged human-explicit and ranks above the agent's own observations at equal relevance.
Encrypted secrets vault. Project-scoped, AES-256-GCM, structurally unreachable from search. → docs/secrets-vault.md
Runs on a small machine. The default embedder is ONNX via fastembed — no PyTorch, ~0.22 GB model, comfortable on 2 GB of RAM.
Several agents, one project — automatically. Agents working in the same git working tree see each other's sessions, announced work and recent file changes; while two or more of them share the tree, changes to text files are recorded so they can be restored. No setup.

Full technical detail → docs/features.md

Several agents in one project

If you run more than one agent in the same working tree, tqmemory connects them automatically. There is nothing to configure.

Agents see each other. workspace_status lists the active sessions, what each one announced it is working on (announce_work, finish_work) and which files changed recently. A session that made no tqmemory call for 30 minutes is listed under idle_sessions instead: it is not an active peer and its announced areas are left out of overlaps. Other tqmemory responses carry a short workspace note only when something new has happened, and never for a lone session. health and server_info always include a workspace diagnostics block.
Changes are recorded. While two or more sessions share a git working tree, the watcher records new versions of text files it sees change (up to 1 MiB each, skipping anything matched by .gitignore or .tqmemoryignore). Several quick writes in a row can be merged into one version. file_history lists the versions and restore_file brings one back; the restore is itself undoable. The same is available from a terminal: turbo-memory-mcp files log and turbo-memory-mcp files restore.
Limits. Versions are recorded only while two or more sessions share a git working tree. The journal never runs outside a git working tree, in your home directory or at a filesystem root. Defaults: 1 MiB per file, a 256 MiB per-project size target and 14 days of retention. Recorded versions stay recoverable for 14 days; past that, or when the project is over its size target, the oldest are removed. The newest version of each file changed in the last 24 hours is never dropped for size. These defaults are set by TQMEMORY_JOURNAL_MAX_FILE_BYTES, TQMEMORY_JOURNAL_MAX_BYTES and TQMEMORY_JOURNAL_RETENTION_DAYS; the size limit is a target, not a hard maximum.

This is partial protection, not a guarantee that every change is saved. It does not block writes: two agents can still overwrite each other's work, and tqmemory can only show it and help you recover. Agents that never call tqmemory see nothing. It works on one computer only. To turn it off, set TQMEMORY_WORKSPACE_DISABLE=1.

The 24 MCP tools

Group	Tools
Write	`remember_note` · `deprecate_note` · `promote_note` · `index_paths`
Read	`semantic_search` · `hydrate` · `recent_context` · `list_scopes`
Graph	`link_entities` · `unlink_entities` · `get_related_entities`
Hygiene	`lint_knowledge_base` · `health` · `self_test` · `server_info`
Vault	`set_secret` · `get_secret` · `list_secrets` · `delete_secret`
Workspace	`announce_work` · `finish_work` · `workspace_status` · `file_history` · `restore_file`

Documentation


MEMORY_STRATEGY.md	How to actually use the memory day to day
CLIENT_INTEGRATIONS.md	Per-client setup: Cursor, OpenCode, Antigravity, …
TECHNICAL_SPEC.md	Architecture and storage format

| [docs/featur

Only the beginning is shown; read the rest in the repo

Turbo Quant Memory

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Demo

The problem

Why this one

Install

Let your agent install it

Or install it yourself

📈 It measures its own savings — see for yourself

What it does

Several agents in one project

The 24 MCP tools

Documentation