daidocs

<p align="center"> <b>English</b> · <a href="README.zh-CN.md">简体中文</a> </p> <div align="center"> <img src="assets/brand/dai-file.svg" alt="" width="88" height="88"> <h1>.dai</h1> <p><b>An open plain-text format for AI memory (launched Sep 2026).</b><br> Your assistant's memory becomes files on your disk that you can open, grep and keep.</p> <p><b>Second on the LongMemEval-S leaderboard</b> among memory systems anyone can re-run, <b>22.40 points above the same model with no memory</b>, reading <b>10x fewer tokens</b> per question.<br> Every number here ships with its per-question judge verdicts and a sha256 manifest.</p> <p> <a href="QUICKSTART.md"><b>Quickstart</b></a> · <a href="docs/GUIDE.md"><b>The guide</b></a> · <a href="docs/RESULTS.md"><b>Benchmark</b></a> · <a href="spec/DAIDOCS-STANDARD.md"><b>The format</b></a> · <a href="docs/REPLICATION.md"><b>Reproduce it</b></a> </p> <p><b>Launch release V4.4n32, 12 September 2026.</b> <a href="CHANGELOG.md">What is in it.</a></p> <p> <a href="https://github.com/Kerneta/daidocs/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/Kerneta/daidocs?style=social"></a> <a href="https://discord.gg/DHDtfPx7jw"><img alt="Discord" src="https://img.shields.io/badge/Discord-join-5865F2?logo=discord&logoColor=white"></a> <a href="LICENSE"><img alt="licence: Apache-2.0" src="assets/badges/licence.svg"></a> <a href="package.json"><img alt="node: 18+" src="assets/badges/node.svg"></a> <a href="docs/INTEGRATION.md"><img alt="MCP: server included" src="assets/badges/mcp.svg"></a> <a href="docs/RESULTS.md"><img alt="LongMemEval-S: 83.00% gpt-4o" src="assets/badges/lme-gpt4o.svg"></a> <a href="RESULTS-ACTORS.md"><img alt="LongMemEval-S: 92.00% Claude Fable 5" src="assets/badges/lme-fable.svg"></a> <a href=".github/workflows/ci.yml"><img alt="checks: offline, no API key" src="assets/badges/checks.svg"></a> </p> <p>_{If <code>.dai</code> is useful to you, a <a href="https://github.com/Kerneta/daidocs/stargazers">star</a> helps other people find it.}</p> </div> <div align="center"> <img src="assets/demo/product-demo.gif" alt="The product in one loop: a one-line install, Claude Code building a site while the files appear in the folder, the memory map, the same six questions answered from a 612k-token history and from DaiDocs reading 3,674,880 against 392,120 tokens, and one .dai file saved by ChatGPT then read along the wires by Gemini and a local model" width="900"> <p>_{<b>The whole product, in one loop.</b> Install, build, remember, recall: one store, every model, about a tenth of the tokens. The recording uses invented data.}</p> <p>_{<a href="https://daidocs.com/demo.html">&#9654; For higher quality, watch the demo live in your browser</a>}</p> </div>

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Language independent, model independent

A .dai file is three plain-text zones: a YAML header, a fenced JSON block, and the text. No binary, no database, no SDK required to read it.

• Any programming language. The reference engine is Node. A reader in Python, Rust or Go is an afternoon's work, and the spec is normative, written so that two independent implementations agree.
• Any model. The store is written once by a cheap observer model and read by whichever model answers. The same store measured with five answering models: 78% to 92%. Change the model, keep the memory.
• Any tool. grep, git log, diff, your editor, a shell script. Memory that answers to ordinary tools.

Build a reader in another language and open a PR: that is the contribution that matters most.

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What is in this repository: the Kerneta Engine V4.4n that reads and writes .dai files, the MCP server that connects it to your assistants, and the complete evidence for every number quoted below: the benchmark run, the judge's verdict on each of the 500 questions, and the five-model comparison. Each evidence file is hashed in MANIFEST.sha256 so you can check that what is described is what was measured; how to do that is in docs/PROVENANCE.md.

<div align="center"> <img src="assets/charts/ranking.png" alt="Strict same-setup ranking on LongMemEval-S: Mastra OM 84.80%, .dai v4.4n 83.00%, Supermemory 81.60%, Mastra RAG topK 20 81.20%, EmergenceMem Simple Fast 79.00%, TiMem 76.88%, Zep 71.20%, Feather 69.30%, GPT-4o with Chain-of-Note 64.00%, GPT-4o with no memory system 60.60%" width="820"> <p>_{<b>Second on the leaderboard.</b> One setup for every row: LongMemEval-S, GPT-4o answering, all 500 questions, micro-averaged, and only configurations somebody who does not work for the vendor could re-run. The bottom row is that same GPT-4o with no memory system, reading the whole history pasted into its context: <b>22.40 points below us</b>. Every figure here carries a caveat and the caveat travels with it, in <a href="docs/RESULTS.md">docs/RESULTS.md</a>.}</p> </div> <p align="center"><b>Not in that table?</b> Graphify, Hindsight, Mem0 and the others publish figures measured on a different answering model, a different denominator or a different benchmark, so they cannot be set beside a GPT-4o 500/500 row in either direction. Every one of them is at <a href="https://daidocs.com/results.html"><b>daidocs.com/results.html</b></a>, with what its number actually measures and where ours sits against it.</p> <div align="center"> <img src="assets/charts/actors.png" alt="Same stores, same prompts, same judge: five answering models on LongMemEval-S" width="820"> <p>_{One memory layer, five answering models, 500 questions each. Retrieval identical for every row (proved by a byte-identical diagnostics file). Details and caveats in <a href="RESULTS-ACTORS.md">RESULTS-ACTORS.md</a>.}</p> </div>

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Why this exists

Every memory product on the market keeps your history inside its own service and hands it back through its own API. .dai takes the opposite bet: memory is a file format, the way a photo is a JPEG. Three plain-text zones per conversation, a small derived index beside them, and any model, any tool, or grep can read it.

| | memory as a service | memory as a format (.dai) | |---|---|---| | where your history lives | their database | your disk, plain text | | who can read it | their SDK | Claude, GPT, Gemini, Cursor, local models, grep, git | | when the vendor disappears | so does the memory | the files stay readable in any editor | | how you inspect a recall | logs, if any | open the file the answer cites | | what a benchmark number means | one product's pipeline | one store, measured per answering model, so you can pick the model |

The store is built once by a cheap observer model and read by any actor model. Convert with a good model, then answer with whatever is cheapest, fastest or local. Numbers below.

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Works with

One store, connected over MCP, read and written by the tools you already use. node setup.js detects and configures each of these and backs up what it touches; Install has the per-tool commands.

| assistants | editors and IDEs | CLI and any MCP client | |---|---|---| | Claude Desktop, Claude Code, any model over MCP | Cursor, Windsurf, Zed, Cline, Continue | Codex CLI, plus any MCP client via --client generic --config <file> |

Any MCP-capable runtime, too. The server is a plain stdio MCP server, so frameworks that speak MCP call save_memory and recall_memory with no adapter to write: the OpenAI Agents SDK, the Vercel AI SDK, LangGraph, LangChain, CrewAI and LlamaIndex all consume an MCP server as a tool source. Point them at node mcp_server.mjs.

Bring your history. Claude Code sessions on this machine convert automatically. From any other tool, export a folder of .txt, .md or .jsonl and run node daidocs.js convert. Native history import from more tools is on the roadmap.

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Browser extension

DaiDocs also ships a browser extension that captures your AI chats (ChatGPT, Claude, Gemini) and, opt-in, your X timeline and the websites you choose, straight into a local DaiDocs store. It is local only: nothing leaves your machine, capture is off by default, and sensitive sites (banking, health, webmail, password managers) are never touched.

It lives in browser-extension/, and it is not installed automatically: you load it once in your browser. The full step-by-step is in browser-extension/GETTING-STARTED.md. In short:

1. Start the local capture server from browser-extension/: node capture_server.mjs (no extra install needed).
2. In Chrome or Edge, open the extensions page, turn on Developer mode, then Load unpacked the browser-extension/extension folder. In Firefox, open about:debugging and load browser-extension/extension/manifest.json.
3. Accept the one-time consent, then use the on-page pill to turn capture on for a site.

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Install

Node 18 or newer.

npx daidocs setup

One command. It detects Claude Desktop, Claude Code, Cursor, Windsurf, Codex, Cline, Continue and Zed, configures all of them, installs the session hooks, the reading protocol and the .dai icon, and backs up every file it touches. On a Claude subscription there is no API key and nothing to pay.

Want the source and the benchmark artifacts too? Clone it and run setup from there instead:

git clone https://github.com/Kerneta/daidocs daidocs-app
cd daidocs-app
node setup.js

The clone is named daidocs-app on purpose. git clone would otherwise make a folder called daidocs, and the default memory store is DaiDocs: on Windows and macOS those are the same folder, so a clone made from your home directory would land on top of your own memory. Setup refuses to run from inside the store if it ever happens.

Python (pip)

Prefer Python? Read your .dai stores from code, and drive the engine from a daidocs command:

pip install daidocs

from daidocs import Store

store = Store("~/DaiDocs")          # your memory store
for entry in store.manifest():      # every document
    print(entry["id"], entry["title"])

doc = store.read(store.ids()[0])    # one document, fully parsed
print(doc["understanding"]["summary"])
print(store.search("deploy"))       # find documents by keyword

Two things in one install:

• Reader (pure Python, no Node): from daidocs import Store reads the manifest, any document, and the facts / events / profile indexes.
• daidocs command: drives the Node engine, so daidocs setup and daidocs convert behave like npx daidocs. This needs Node 18+; if Node is missing it says so and offers to install it. Full guide: readers/python/.

setup.js installs the dependencies on its first run and then configures everything. npm run setup does the same thing, but node setup.js is the one to reach for on Windows: PowerShell refuses to run npm at all until you change its execution policy, and node is not affected by that. Each line above is its own command, because Windows PowerShell 5.1 has no &&.

Setup asks nothing. It detects what you have and configures all of it: Claude Desktop, Claude Code, the session hooks, Cursor, Windsurf, Codex, Cline, Continue, Zed, the reading protocol and the .dai file icon. It backs up every file it touches.

node setup.js --status     what is on, and the command that changes each one
node setup.js --ask        choose each surface yourself instead
node setup.js --restore    put the machine back exactly as it was

The one thing it never does on its own is convert the history you already have, because that can run for a while and, with an API key, it spends money. It is one command when you want it, and it is worth wanting: see [Bring what you already have](#