How do I install Turbo Quant Memory?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Turbo Quant Memory need an API key?

The publisher lists these environment variables: TQMEMORY_FTS_LANGUAGE, TQMEMORY_EMBEDDING_BACKEND, TQMEMORY_MIGRATE_ON_STARTUP, TQMEMORY_SECRETS_PASSPHRASE.

What transport does Turbo Quant Memory use?

Turbo Quant Memory runs locally over stdio.

When was Turbo Quant Memory last checked?

Last checked 2026-11-10. Local-first memory and knowledge graph for coding agents. Compact retrieval, no network.

Turbo Quant Memory MCP server

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx turbo-quant-memory==0.34.0

Credentials from the publisher

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

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1TQMEMORY_FTS_LANGUAGE=<value> TQMEMORY_EMBEDDING_BACKEND=<value> TQMEMORY_MIGRATE_ON_STARTUP=<value> TQMEMORY_SECRETS_PASSPHRASE=<secret> claude mcp add io.github.lexus2016-turbo-quant-memory -- uvx turbo-quant-memory==0.34.0

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.lexus2016-turbo-quant-memory": {4      "command": "uvx",5      "args": [6        "turbo-quant-memory==0.34.0"7      ],8      "env": {9        "TQMEMORY_FTS_LANGUAGE": "<value>",10        "TQMEMORY_EMBEDDING_BACKEND": "<value>",11        "TQMEMORY_MIGRATE_ON_STARTUP": "<value>",12        "TQMEMORY_SECRETS_PASSPHRASE": "<secret>"13      }14    }15  }16}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

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/features.md	Retrieval, graph, tiers, embedder, FTS language
docs/secrets-vault.md	Vault setup, threat model, FAQ
docs/hermes.md	Hermes gateway setup and troubleshooting
CHANGELOG.md	Release history

License

MIT. Copy it, modify it, fork it, ship it inside a closed-source product, sell it. Attribution is the only condition.

Languages

🇺🇸 English · 🇺🇦 Українська · 🇷🇺 Русский