Incremind

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Incremind

A second brain that remembers what is new.

Give it the material you choose. It organizes and archives it, extracts what adds to what you already know, and turns proposed insights into long-term memory only after you confirm them.

Quick start · Features · Evaluation · Usage · Architecture · Privacy · Development

Watch the introduction

A 2:45 walkthrough of adding material, confirming new insights, and asking a follow-up question. Chinese narration with Chinese and English subtitles · 1080p60.

https://github.com/user-attachments/assets/8f51499d-bbfb-47a2-9ab0-91193313f1f0

Download video · English subtitles (SRT) · English subtitles (VTT)

Why I am building it

I want a second brain that can work alongside me over time.

The idea comes from everyday research. Whenever I ask AI a question or give it a task about the same project, I have to explain the background, my existing judgments, and the material I have already found all over again. Knowledge accumulates, but conversations often start from scratch.

Incremind is designed around two goals:

1. Turn things I share into lasting knowledge. Accept articles, personal judgments, recordings, videos, and ideas captured on the go; organize them by project and scene; and turn them into knowledge I can revisit and insights I can confirm.
2. Use that knowledge to understand questions and complete tasks. Identify what I am asking, find the relevant projects and scenes, and give the model their existing insights, sources, and context so it can answer everyday questions and handle routine tasks with less repeated explanation.

Saving articles and videos does not automatically make their contents useful or memorable. Incremind therefore focuses on what each new source adds:

• Compare new material with existing insights. Extract three kinds of changes: additions such as new conditions, examples, or practices; differences from an existing claim; and new methods. Material that repeats what you already know adds a supporting source to the existing insight rather than creating another entry.
• Store methods with their conditions of use. Each insight says when it applies, so relevant methods can be recalled when you ask a question or work on a task.
• Let you decide what becomes established knowledge. Extracted insights start as pending confirmation. A single click on the receipt activates them. Confirmation, edits, and forgetting take effect immediately; further inference runs in the background.
• Remember and forget in a human-like way. Frequently used knowledge becomes stronger, while unused knowledge gradually leaves automatic recall. Originals remain in the Library and can be found and restored at any time.

For example, an introductory article on spaced repetition might produce an insight about reviewing on days 1, 2, 4, 7, and 15. A later article on active recall and adaptive intervals should add only the relevant differences and methods: adjust intervals to difficulty, and try answering before revealing the answer.

Features

✅ Basic usage paths are implemented. 🧪 Optional dependencies or further real-environment validation are required. The table below states the actual validation scope; the icons do not establish coverage of every scenario.

Evaluation and verification

The same frozen 48 questions were run twice on a server with 4 logical CPUs and 7.35 GiB memory, using real DeepSeek Flash and CPU EmbeddingGemma 2 through the normal Workbench ask workflow. The dataset contains four conversations, 116 sessions, and 2,536 messages; it has no conversation or question-text overlap with the development set.

Both batches used compose@30 / ladder@15 / rerank@2. The repair makes original material reachable when upper memory layers are empty; retrieve@8 is now the default. Two independent agents reviewed frozen answers against the complete original conversations. Recall measures evidence in the last dispatched answer input, with one invalid source annotation retained as null and excluded from its 47-question denominator. Time covers each complete server-side Workbench request, including failures and retries. The repair token count is a known subtotal; its complete total remains null because one transport failure has unknown usage. All 347 model attempts have terminal records. The eight Workbench 502 responses and all quality failures are retained.

The evaluation guide (Chinese) records grading, source annotation issues, CPU encoding, failures, and reproduction commands. The aggregate JSON and tools/rag_report.py reproduce this chart without model calls. Presentation follows the OpenViking RAG report. Monetary cost was not recorded. tools/rag_dataset.py freezes the original inputs; tools/rag_holdout.py runs the normal application workflow on the server.

Quick start

Requirements:

• Windows 10 or 11.
• Python 3.12. Select Add python.exe to PATH during installation.
• Node.js: 20.x ≥ 20.19.0, 22.x ≥ 22.13.0, or ≥ 24.0.0. Version 22.x ≥ 22.13.0 is recommended.
• A model API key. DeepSeek is the suggested provider; OpenAI-compatible endpoints are also supported.

Three steps:

1. Clone the repository into a short path, such as D:\incremind. Deep directory paths can encounter the Windows 260-character path limit.

   git clone https://github.com/qingmingyiyang/Incremind.git incremind
   

2. Double-click start.bat in the repository. The first run downloads and installs dependencies and takes several minutes. Later launches take about a minute. Your browser opens at http://127.0.0.1:4173. Closing the terminal window stops the application.

3. Open Settings → Models and enter the endpoint URL, model name, and API key. The application interface currently uses Chinese; the names in this guide are English translations of its controls.

Send the example files in samples/ to the Workbench in order to try organization, project assignment, and incremental insights. The sample material is in Chinese.

py -3.12 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements-web.txt -r requirements-dev.txt
cd src/frontend
npm ci
cd ../..

.\run-web.ps1

.\run-ui.ps1

$env:CHRIPTMAS_APP_ROOT = "$PWD\work\try-root"
$env:PYTHONPATH = "$PWD\src"
.\.venv\Scripts\python.exe -m uvicorn backend.memory_app.app:app --host 127.0.0.1 --port 8001

One-click startup is currently available for Windows. For Linux server deployment, see the server guide (Chinese).

Recommended synchronization: use local development commits as the code source, and publish filtered code changes along the public repository's existing clean AGPL history. Update servers to a fixed release, record deployment-version.json, back up code before restarting, check health, and retain a rollback path. Do not push private development history, runtime/, credentials, model configuration, or personal material from the server. Handle data backups and migration separately.

Load or unload the vector model on your computer, or use a standalone private server for vector computation while keeping the library on your computer. The standalone vector service and full application deployment are separate options. See Private server vectors (Chinese) for configuration and Linux templates. Self-hosted mode still follows the vector egress switch and privacy rules; it fills the cache on demand during questions. Real-server integration remains unverified.

Full server deployment keeps both the application and its data on the server. Users share the server's local vector model while retaining separate libraries and caches. The model starts unloaded; an administrator controls loading and unloading. See Server deployment (Chinese) for CPU/CUDA configuration, model installation, and device pairing. Installation, device pairing, CPU vector loading, encoding, unloading, and reloading, trusted HTTPS for the public IP address, and multi-user API isolation have been verified on an Ubuntu 22.04 instance. GPU and standalone vector-service integration remain unverified.

Usage

The Workbench provides a single input box.

While Ask or built-in Do is running, use the square Stop button on its receipt. Once it shows Stopped, Continue resumes the same task and keeps completed work. Model costs already incurred remain recorded.

After a note used in a clarification is edited or forgotten, new input uses the current materials. The original clarification and receipt remain available in the conversation.

Scene entry points and cross-project question suggestions preserve saved project and scene names containing spaces, slashes, or #.

If a file was accepted before a connection loss, resend the attachment still shown on the page to recover its original receipt. Once accepted, selecting the file again starts a new submission.

File uploads support UTF-8 TXT, Markdown (.md), and CSV, with or without a BOM. CSV keeps its original quotes, commas, and line breaks. PDF and DOCX files are also supported. DOCX extraction follows the order of paragraphs and tables, including nested tables. After a file is accepted, the receipt's More menu opens its full original text and download while organization is unfinished. Image groups list every original for download. Upload errors identify password-protected, text-free, or damaged documents and retain the text and attachment so you can replace the file and retry.

Unsaved Workbench note edits recover in the same browser after opening another original, navigating to the Library, refreshing, or reopening the browser. Saving checks the original revision; conflicts let you choose the content before saving again. Background task updates keep the selected source quotation highlighted.

Use the editor's download button to export a saved note or output as Markdown, HTML, DOCX, or PPTX. Save edits and resolve conflicts first; each file uses the exact saved revision.

Forgotten organized notes remain readable. Restore a note to edit or move it; its existing local draft is available again after restoration.

On a receipt, use ✓ to confirm an insight or ✕ to dismiss it. The project assignment receipt also offers an undo action.

Splitting or merging handwritten insights from the same scene keeps their scene for browsing and questions.

The Library groups insights, summaries, organized notes, and originals by project. Follow each layer down to its source evidence, or confirm, edit, forget, and restore insights here. Keyboard focus stays in the detail panel as you move between layers; Escape closes it and returns to the original list entry. Nested panels close one level at a time. If another client changes an insight during a split or merge, reload its current revision while keeping your text and selections, then submit explicitly.

Opening an insight's versions returns focus to its menu button. Tab enters the history, and Escape closes the detail panel.

Moving an organized note preserves its original source. If insight extraction fails, use Retry beside its assignment. After restoring the model configuration, it extracts insights from the same material and updates the original conversation and task list. Undo restores the previous project assignment.

Library and memo dates use your browser's local time zone. Date-only facts retain their recorded calendar date.

Administrators can edit a user's quota while disabling or restoring that account; unsaved quota fields remain available until explicitly saved or the account form is closed.

Sharing a result from a multipart turn includes that part's original question, answer, and selected citations.

When a method's sources change, you can refresh them, check the handwritten content, and save a new version without a model call. Review that version explicitly before exporting its ZIP. Refreshing restores focus to its button when focus has been lost, while preserving any control you selected during the request.

Inbox project suggestions file the complete suggested group, including members hidden by search. Retry in the same panel continues the accepted batch after a lost response.

Insight actions in multipart turns use the part's project. Confirming or discarding an insight immediately updates that part's receipt.

Opening an output's version list keeps keyboard focus in its panel. Escape closes the output and returns to its entry point.

Switching an editor between Markdown source and the organized view returns focus to its editor menu. Tab and Escape continue within the current panel.

Originals accepted through an external agent appear in the Library's Originals layer. You can open th