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.

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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

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.

Full technical detail → docs/features.md

The 19 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

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