MineContext

Table of Contents

• What is MineContext
• Key Features
• Privacy Protection
  • Local-First
  • Local AI model
• Quick Start
  • 1. Installation
  • 2. Enter Your API Key
  • 3. Start Recording
  • 4. Forget it
  • 5. Backend Debugging
• Contribution Guide
  • Frontend Architecture
    • Core Tech Stack
    • Core Architecture
  • Frontend Usage
    • Build Backend
    • Install Dependencies
    • Development and Debugging
    • Application Packaging
  • ️ Backend Architecture
    • Core Architecture Components
    • Layer Responsibilities
  • Backend Usage
    • Installation
    • Configuration
    • Running the Server
• The Philosophy Behind the Name
• Target User
• Context-Source
• Comparison with Familiar Application
  • MineContext vs ChatGPT Pulse
  • MineContext vs Dayflow
• Community
  • Community and Support
• Star History
• License

** Related Project**: Check out OpenViking - An open-source Context Database designed for AI Agents. OpenViking unifies Memories, Resources, and Skills through a "file system paradigm", providing the infrastructure layer for sophisticated context management.

What is MineContext

MineContext is a proactive context-aware AI partner. By utilizing screenshots and content comprehension (with future support for multi-source multimodal information including documents, images, videos, code, and external application data), it can see and understand the user's digital world context. Based on an underlying contextual engineering framework, it actively delivers high-quality information such as insights, daily/weekly summaries, to-do lists, and activity records.

Key Features

MineContext focuses on four key features: effortless collection, intelligent resurfacing, proactive delivery, and a context engineering architecture.

1. Effortless Collection Capable of gathering and processing massive amounts of context. Designed storage management enables extensive collection without adding mental burden.
2. Proactive Delivery Delivers key information and insights proactively in daily use. It extracts summarized content from your context—such as daily/weekly summaries, tips, and todos—and pushes them directly to your homepage.
3. Intelligent Resurfacing Surfaces relevant and useful context intelligently during creation. Ensures assisted creativity without overwhelming you with information.
4. Context Engineering Architecture Supports the complete lifecycle of multimodal, multi-source data—from capture, processing, and storage to management, retrieval, and consumption—enabling the generation of six types of intelligent context.

Privacy Protection

Local-First

MineContext places a high priority on user privacy. By default, all data is stored locally in the following path to ensure your privacy and security.

~/Library/Application Support/MineContext/Data

Local AI model

In addition, we support custom model services based on the OpenAI API protocol. You can use fully local models in MineContext, ensuring that any data does not leave your local environment.

Quick Start

1. Installation

Click Github Latest Release to Download

Note: Starting from v0.1.5, MineContext supports Apple notarization, so you no longer need to disable the quarantine attribute. If you're using an older version, please refer to the previous documentation for instructions.

2. Enter Your API Key

After the application launches, please follow the prompts to enter your API key. (Note: On the first run, the application needs to install the backend environment, which may take about two minutes).

We currently support services from Doubao, OpenAI, and custom models. This includes any local models or third-party model services that are compatible with the OpenAI API format.

We recommend using LMStudio to run local models. It provides a simple interface and powerful features to help you quickly deploy and manage them.

Considering both cost and performance, we recommend using the Doubao model. The Doubao API Key can be generated in the API Management Interface.

After obtaining the Doubao API Key, you need to activate two models in the Model Activation Management Interface: the Visual Language Model and the Embedding Model.

• Visual Language Model: Doubao-Seed-1.6-flash

• Embedding Model: Doubao-embedding-vision

The following is the filling process after obtaining the API Key:

3. Start Recording

Enter [Screen Monitor] to enable the system permissions for screen sharing. After completing the setup, you need to restart the application for the changes to take effect.

After restarting the application, please first set your screen sharing area in [Settings], then click [Start Recording] to begin taking screenshots.

4. Forget it

After starting the recording, your context will gradually be collected. It will take some time to generate value. So, forget about it and focus on other tasks with peace of mind. MineContext will generate to-dos, prompts, summaries, and activities for you in the background. Of course, you can also engage in proactive Q&A through [Chat with AI].

5. Backend Debugging

MineContext supports backend debugging, which can be accessed at http://localhost:1733.

1.View Token Consumption and Usage

2.Configure Interval for Automated Tasks

3.Adjust System Prompt for Automated Tasks

Contribution Guide

Frontend Architecture

The MineContext frontend is a cross-platform desktop application built with Electron, React, and TypeScript, providing a modular, maintainable, and high-performance foundation for desktop development.

Core Tech Stack

Technology	Description
Electron	Allows for the development of cross-platform desktop applications using web technologies.
React	A component-based UI library for building dynamic user interfaces.
TypeScript	Provides static type checking to enhance code maintainability.
Vite	A modern frontend build tool optimized for Electron.
Tailwind CSS	A utility-first CSS framework for rapid and consistent UI styling.
pnpm	A fast and efficient package manager suitable for monorepo projects.

Core Architecture

The project follows a standard Electron architectural design, clearly separating the code for the main process, preload scripts, and renderer process to ensure security and maintainability.

…

1. Main Process (src/main/) is responsible for:

   • Managing application windows
   • Handling lifecycle events (startup, quit, activate)
   • Establishing secure IPC communication
   • Integrating with backend services (Python and system APIs)

2. Preload Script (src/preload/) is responsible for:

   • Securely exposing Node.js APIs to the renderer process
   • Handling IPC communication with the main process
   • Implementing cross-process resource access

3. Renderer Process (src/renderer/) is responsible for:

   • Implementing the user interface with React
   • Managing global state with Jotai and Redux
   • Utilizing an efficient styling system based on Tailwind CSS
   • Implementing dynamic loading and performance optimization mechanisms

4. Build and Packaging are responsible for:

   • electron-vite.config.ts — Configures the build logic for both the main and renderer processes (aliases, plugins, etc.).
   • electron-builder.yml — Defines packaging and distribution configurations for Windows, macOS, and Linux.

Frontend Usage

Build Backend

Before starting frontend development, you need to build the backend first:

uv sync
source .venv/bin/activate
./build.sh

Install Dependencies

Due to package version issues, using a domestic PyPI mirror is not currently supported. Please run the following command to ensure you are using the original PyPI source:

pip config unset global.index-url
cd frontend
pnpm install

Development and Debugging

During local development, it is normal for the screen capture area selection to be slow. Please wait, as this issue does not exist in the packaged application.

pnpm dev

Application Packaging

To build APP for macOS:

pnpm build:mac
# Data Path
# ～/Library/Application\ Support/MineContext

The executable files generated by the packaging process will be stored in the MineContext/frontend/dist directory.

️ Backend Architecture

MineContext adopts a modular, layered architecture design with clear separation of concerns and well-defined responsibilities for each component.

Core Architecture Components

opencontext/
├── server/             # Web server and API layer
├── managers/           # Business logic managers
├── context_capture/    # Context acquisition modules
├── context_processing/ # Context processing pipeline
├── context_consumption/# Context consumption and generation
├── storage/            # Multi-backend storage layer
├── llm/               # LLM integration layer
├── tools/             # Tool system
└── monitoring/        # System monitoring

Layer Responsibilities

1. Server Layer (server/)

   • FastAPI-based RESTful API
   • WebSocket support for real-time communication
   • Static file serving and template rendering

2. Manager Layer (managers/)

   • CaptureManager: Manages all context capture sources
   • ProcessorManager: Coordinates context processing pipeline
   • ConsumptionManager: Handles context consumption and generation
   • EventManager: Event-driven system coordination

3. Context Capture Layer (context_capture/)

   • Screenshot monitoring
   • Document monitoring
   • Extensible capture interface for future sources

4. Processing Layer (context_processing/)

   • Document chunking strategies
   • Entity extraction and normalization
   • Context merging and deduplication
   • Multi-modal content processing (text, images)

5. Storage Layer (storage/)

   • Multi-backend support (SQLite, ChromaDB)
   • Vector storage for similarity search
   • Unified storage interface

6. LLM Integration (llm/)

   • Support for multiple LLM providers (OpenAI, Doubao)
   • VLM (Vision-Language Model) integration
   • Embedding generation services

Backend Usage

Installation

We recommend using uv for fast and reliable package management:

# Clone r