Turbo Quant Memory

io.github.Lexus2016Published by GitHub account Lexus2016v0.34.0Updated Oct 30, 2026

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

The publisher's own server: Memory.fit

Overview

AI-generated overview

Local-first memory and knowledge graph for coding agents: stores typed project notes, indexes Markdown, and retrieves compact context offline.

What you can do
  • Write and manage typed project notes (decision, lesson, pattern, handoff), promote or deprecate them, and index Markdown directories
  • Search memory with hybrid dense-vector plus BM25 retrieval, hydrate fuller context, and list recent notes or scopes
  • Link notes, files and issues into a knowledge graph and query or remove those relations
  • Lint the knowledge base for broken links, orphans and duplicate titles, plus health, self-test and server info
  • Store, read, list and delete encrypted project secrets in a local vault
  • Coordinate several agents in one working tree: announce and finish work, check workspace status, list file versions and restore one
When to use it
Useful when long coding sessions lose context to compaction and the agent keeps re-reading files and re-deriving decisions. It suits projects where you want decisions, lessons and handoffs searchable locally, and where several agents share one git working tree.
Requirements
Runs as a local stdio process, installed with uv tool install or run via uvx from the PyPI package. First run downloads an embedding model (about 0.22 GB) from Hugging Face; afterwards it works offline. Optional environment variables set the embedding backend, BM25 stemmer language, startup migrations, and the vault passphrase TQMEMORY_SECRETS_PASSPHRASE (otherwise the OS keyring is used).
Before you install
The secrets vault holds credentials; the master key comes from TQMEMORY_SECRETS_PASSPHRASE or the OS keyring, so protect that passphrase. Tools write notes, index directories, link entities, delete secrets and restore files over existing content. The file journal is partial protection only: it does not block writes, two agents can still overwrite each other, and versions are kept about 14 days. Only agents that call the server are visible to it.

Installation

In SourceWeft

  1. Open Turbo Quant Memory in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

[Turbo Quant Memory]

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.

[PyPI] [License: MIT] [MCP Registry] [Python 3.11+] [CI] [MCP tools] [Local-first]


Demo

[30-second demo of Turbo Quant Memory]
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 Memorymem0 / OpenMemoryMCP memory server
Where your data livesyour disk, alwaysvendor cloud or self-hostyour disk
Your data leaves the hostneveryes, unless self-hostednever
Retrievalhybrid BM25 + dense vector, RRF-fuseddense vectorexact graph lookup
What a search returnscompact cards, hydrate on demandfull memoriesfull nodes
Knowledge graphyes — with lifecycle + lintingnoyes
Non-English contentCyrillic exact-match out of the boxvariesn/a
Measures its own savingsyes — server_info()nono
Pricefree, MITpaid tiersfree

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

bash
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

bash
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:

bash
claude mcp add --scope project tqmemory -- turbo-memory-mcp serve   # Claude Codecodex  mcp add tqmemory -- turbo-memory-mcp serve                   # Codexgemini 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 didNumber
🔢 Input tokens saved (cumulative)≈ 2,640,000 and counting
🔁 Retrievals served2,280 searches + 280 deep hydrations
📉 Average saved per retrieval≈ 1,200 tokens
📚 Knowledge under management237 active notes + 763 indexed code blocks
🛡️ Integrity0 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

GroupTools
Writeremember_note · deprecate_note · promote_note · index_paths
Readsemantic_search · hydrate · recent_context · list_scopes
Graphlink_entities · unlink_entities · get_related_entities
Hygienelint_knowledge_base · health · self_test · server_info
Vaultset_secret · get_secret · list_secrets · delete_secret
Workspaceannounce_work · finish_work · workspace_status · file_history · restore_file

Documentation

MEMORY_STRATEGY.mdHow to actually use the memory day to day
CLIENT_INTEGRATIONS.mdPer-client setup: Cursor, OpenCode, Antigravity, …
TECHNICAL_SPEC.mdArchitecture and storage format
docs/features.mdRetrieval, graph, tiers, embedder, FTS language
docs/secrets-vault.mdVault setup, threat model, FAQ
docs/hermes.mdHermes gateway setup and troubleshooting
CHANGELOG.mdRelease 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

Source: README.md at commit 448b451

Tools

24

health

Takes no parameters.

server_info

Takes no parameters.

list_scopes

Takes no parameters.

self_test

Takes no parameters.

remember_note

Store a typed project note. kind must be exactly one of: lesson, decision, pattern, handoff. tags: 2-3 lowercase tags are strongly recommended — tags are the only way to FILTER notes; an untagged note is reachable by semantic_search alone. source_refs: the files / notes / issues this note is about. Pass them as entity URIs (note://<id>, file://<relative/path>, issue://KEY, https://...) and they are auto-linked into the knowledge graph as `references` relations, so get_related_entities can later surface them. provenance: who originated the note. Use "human-explicit" when the USER asked to remember something ("remember this", "save that"); "agent" for your own observations (the default); "external-tool" for API/tool data; "system" for automatic processes. tier: normally derived from `kind` (handoff -> episodic, every other kind -> durable). Override e.g. tier="durable" to keep a `handoff` in the default-searchable set, or tier="episodic" to keep a noisy lesson out of regular search. After storing, connect the note with link_entities(source_uri="note://<id>", ...) when it relates to a file, issue, or prior note — the response echoes the note's uri and hints this when the note still has no relations.

Parameterskindrequiredtagstierscopetitlerequiredcontentrequiredprovenancesource_refs

promote_note

Parametersnote_idrequired

deprecate_note

Parametersscopereasonnote_idrequiredreplacement_scopereplacement_note_id

hydrate

Parametersmodescoperequireditem_idrequired

index_paths

Register and index Markdown directories into project memory. Supports .tqmemoryignore files (placed in project root or any indexed directory) with glob patterns to exclude paths from indexing. One pattern per line, # for comments. Example patterns: ``workspace-*`` skips any directory matching the glob; ``data/reports/*.md`` skips files matching a path pattern; ``backups/`` (trailing slash, as in .gitignore) skips a directory. Agent worktrees under ``.claude/worktrees`` are skipped by default.

Parametersmodepaths

lint_knowledge_base

Parameterspathsmax_issues

set_secret

Store an encrypted secret in the active project's vault. The value is encrypted with AES-256-GCM under a per-project master key resolved from TQMEMORY_SECRETS_PASSPHRASE or the OS keyring. Secrets are NEVER indexed, embedded, or returned via ``semantic_search`` / ``hydrate``. They live in ``~/.turbo-quant-memory/projects/<project_id>/secrets/vault.tqv`` and stay on this machine.

Parametersnamerequiredvaluerequired

get_secret

Fetch a project secret by exact name. Returns the value in a dedicated ``secret_value`` field (never in descriptive text). Status is ``"ok"`` on hit, ``"missing"`` when no such name exists, or ``"error"`` with ``setup_hint`` when no master key is configured yet.

Parametersnamerequired

list_secrets

List secret names in the active project. Never returns values.

Takes no parameters.

delete_secret

Delete a project secret by exact name.

Parametersnamerequired

recent_context

Query-free session bootstrap: the most recently updated notes. Call this FIRST when starting a new session or resuming after a context compaction, when you do not yet know what to search for. Returns notes ordered by recency (newest first), NOT by relevance — including `handoff` notes (episodic tier), which a plain semantic_search hides by default. This is the reliable "where did I leave off" entry point. scope: 'project' (default), 'global', or 'hybrid'. Use 'hybrid' to also surface promoted cross-project knowledge. tier_filter: defaults to all tiers (so handoffs are included). Pass e.g. ["durable"] to exclude episodic session notes. format: 'json' (default); 'text' — compact plain text, one line per note with its hydrate handle (about 20% of the JSON size); 'hook' — the same text as Claude Code SessionStart hook output. Use 'hook' from a SessionStart mcp_tool hook: Claude Code shows the model only an mcp_tool hook's JSON additionalContext, never its plain text.

Parameterslimitscopeformattier_filter

announce_work

Tell the other agents in this working tree what you are changing. feature: short name of the work. areas: workspace-relative files or directories (globs allowed). Returns the peers whose announced areas overlap yours and recent changes inside them. Inferred writers are guesses from announced areas, never facts.

Parametersareasdetailsfeaturerequired

finish_work

Clear the work this session announced with announce_work.

Takes no parameters.

workspace_status

Overview of the sessions sharing this working tree: peers, overlaps and recent file changes. Call it at session start. Inferred writers are guesses from announced areas, never facts. Peers that made no tqmemory call for a long time are listed under `idle_sessions`, not in `sessions`, and their announced areas are left out of `overlaps`.

Takes no parameters.

file_history

Journaled versions of one file in this working tree, newest first. Versions are snapshots taken while several sessions share the tree. Use restore_file with a version id to bring one back; the overwritten content is kept as a new version.

Parameterspathrequiredlimit

restore_file

Write a journaled version of one file back into the working tree. version: a version id from file_history, or "HEAD" for the committed content. The content being overwritten is kept as a new version, so the restore can be undone.

Parameterspathrequiredversionrequired

Version history

10
  1. v0.34.0LatestOct 21, 2026
  2. v0.33.1Oct 21, 2026
  3. v0.33.0Oct 19, 2026
  4. v0.32.0Oct 17, 2026
  5. v0.31.1Oct 17, 2026
  6. v0.31.0Oct 17, 2026
  7. v0.30.0Oct 4, 2026
  8. v0.29.1Sep 20, 2026
  9. v0.28.3Sep 16, 2026
  10. v0.28.1Sep 16, 2026

Score

90% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts
  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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