Graph-MIND

Turnbook

Your AI forgets everything when the session ends. This one remembers, and it never calls an LLM to do it.

No AI summaries: every conversation is kept word for word, so nothing is lost or made up on the way in, and saving costs zero model tokens. 91.8% on LongMemEval from 3.1k tokens of memory per question ([details](#does-it-work)). Runs on your own PC.

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*Decide something with one agent on Monday. Ask another on Wednesday. It knows.*

All 500 LongMemEval_S questions, official gpt-4o judge. Rewriting every conversation with an LLM buys a few points on questions that combine sessions or dates; Turnbook keeps the original words instead and spends nothing to save them. How it compares

English · 한국어

Recorded live on one PC: Claude Code (Opus 5.5, effort high) and Codex (GPT-6.1-Sol, effort high) sharing one Turnbook memory. Model thinking time is fast-forwarded.

If Turnbook saves you tokens or repeated explanations, a ⭐ helps other people find it.

Install

Windows: download Turnbook-Setup.cmd and double-click it, or in PowerShell:

irm https://raw.githubusercontent.com/goyohan0611-png/turnbook/main/scripts/install.ps1 | iex

macOS / Linux:

curl -LsSf https://raw.githubusercontent.com/goyohan0611-png/turnbook/main/scripts/install.sh | sh

No Python needed; nothing else to set up. Restart your AI apps afterwards. Already have Python? pip install turnbook && turnbook-install. Rather read the script and pin a version first? Careful install.

Only Claude Code, and uv installed? It is also a plugin:

/plugin install turnbook --marketplace goyohan0611-png/turnbook

Codex: codex plugin marketplace add goyohan0611-png/turnbook-plugin, then codex plugin add turnbook@turnbook.

Connecting an MCP client by hand

After pip install turnbook:

{

"mcpServers": { "turnbook": { "command": "python", "args": ["-m", "turnbook.mcp_server"] } } }

Claude Code: claude mcp add turnbook -- python -m turnbook.mcp_server. This gives the tools only; turnbook-install also saves every conversation automatically.

Try it

Work as usual for a while, then open a new session, another app, or another model and ask:

• *"Where did we leave off yesterday?"*
• *"Which database did we pick for the billing service, and why?"*
• *"What did you change in the auth middleware last week?"*
• *"What's my dog's name?"* (any fact you mentioned once, in passing)

Nothing to tag or save by hand: every turn is already stored, and the agent pulls back the few that answer the question.

What it does

• Every agent, one memory. Claude Code, Codex, Cursor, Copilot CLI, Gemini CLI, OpenCode and any MCP app read and write the same memory. Decide with one, ask another; switch models, the memory stays.
• The right memory in every message. In Claude Code, Codex, Copilot CLI, Gemini CLI and OpenCode, Turnbook looks up what your message needs from the past and adds it before the model answers. Nothing to ask for.
• Free to save, on your PC. Every turn is stored word for word in one SQLite file: no model call, no tokens, no account, no cloud.

Everything else

• Made for orchestration. When an orchestrator runs several agents at once, they all write to the same memory as they work, so what one agent decided is there for the next. Every memory keeps which app and session it came from.
• Holds up as memory piles up. Grow a memory tenfold and the answer is still among the first 30 found for 92.7% of questions (92.6% before); a search takes about 0.1 s. As the memory grows
• Any language, across languages. The search model reads Korean almost as well as English: ask in Korean about something said in English, or the other way round. In Korean
• Your project's documents too. turnbook docs add FOLDER keeps its Markdown and text files searchable beside the conversations, section by section, and up to date as they change.
• One memory across your PCs. Run turnbook folder share, then turnbook folder on the other PC (the AI tells you the command; it never moves your memory by itself, so a web page cannot either). Office network or Tailscale, over TLS, every line encrypted before it leaves a PC.
• Hand a memory to someone else as a file. turnbook export FILE writes all of it; --project FOLDER writes only the conversations held in that folder. They run turnbook import FILE.
• Yours. No account, no cloud of its own, no telemetry. Secrets are masked before anything is stored, and *"delete the password I typed earlier"* deletes it everywhere. docs/privacy.md
• Teams, if you want them. Share a project's decisions and progress with teammates' AIs, never your personal memory; files stay in Slack and GitHub. docs/teams.md

Works with

The installer finds these and connects them; run it again after installing a new one.

Any other MCP client works through the tools: see the hand-written config above.

How it works

flowchart LR

A["Claude Code · Codex · Cursor Gemini CLI · OpenCode · ..."] -- "transcripts, hooks, plugin" --> C["capture service (runs in the background)"] C -- "verbatim turns, secrets masked" --> S[("your memory on this PC")] S --> I["local search words + meaning + time"] A -- "a question that needs the past" --> I I -- "the few turns that answer it (a few thousand tokens)" --> A

• Saving costs nothing. Turns are stored as they were said. No model summarises or extracts anything at write time, so nothing is lost or invented on the way in, and no tokens are spent.
• Search runs on your PC. Word search, a local embedding model (Google's EmbeddingGemma, 4-bit; ONNX, no GPU) and when things were said are combined, then ordered by a small learned score; the agent gets a short packet with the source turns, not a dump of history.
• The model you already use does the thinking. Turnbook hands it evidence; it answers. When models get better, the answers get better without changing the memory.

Beyond agent memory

Underneath, Turnbook is RAG over chat history with everything before the answer built in. A typical RAG stack is assembled from parts; Turnbook runs every step up to the answer on its own and leaves only the answer to the model you already use:

The same engine could serve a chatbot that remembers each of its users, a support bot that recalls past tickets, or a team's searchable decision log. Today it ships as a personal, local memory; a Python API and multi-user server mode are not built yet. Interested? Open an issue.

Does it work?

LongMemEval is the public benchmark for long-term chat memory: 500 questions over about 50 past sessions each. Turnbook's scores use gpt-5-mini as the answer model with the benchmark's own prompt and official gpt-4o judge; a question left unanswered counts as wrong.

How it compares

Other memories publish their own LongMemEval scores, each with its own answer model, judge and setup, so they are not one ranking. The closest to like-for-like: Mastra's Observational Memory reports 94.9% with the same answer model, gpt-5-mini, which reads about 30k tokens of observations that gemini-2.5-flash wrote while the conversations were saved. It and others (Mem0, Supermemory) run an LLM over every conversation when saving it, writing dated facts and replacing outdated ones; that is where Turnbook loses points: 33 of its 41 misses are questions that combine several sessions, reason about dates, or need the newest value of something that changed. Turnbook keeps the original words, spends no model tokens saving them, and runs on your own PC.

What makes the difference

The learned re-ranker. Without it (plain rank fusion, no weight fitted on any benchmark): 87.0% of all 500, and 86.8% of the 380 questions never used for tuning, where the shipped search scores 91.1%. It helps most where answers are spread over several sessions. norerank

**The memory adde