View all releases | Size: ~150MB | Platform: Windows 10/11
Features • Quick Start • Documentation • Build • Contributing
SENTINEL is an enterprise-grade system monitoring solution that combines real-time performance analytics with artificial intelligence to deliver predictive insights and automated diagnostics. Built as a standalone Windows application, SENTINEL eliminates the complexity of traditional monitoring tools while providing professional-level capabilities through an intuitive, cyberpunk-inspired interface.
- Comprehensive Monitoring — Track CPU, memory, disk, network, and GPU metrics in real-time
- AI-Powered Diagnostics — Leverage local or cloud-based AI for intelligent system analysis
- Anomaly Detection — Machine learning algorithms identify unusual patterns before they become critical
- Predictive Analytics — Forecast resource usage trends to prevent system overload
- Automated Response — Configure intelligent alerts and process management rules
- Zero Configuration — Single executable deployment with no installation required
|
Performance Metrics
|
Data Visualization
|
|
Local AI Engine
|
Cloud AI Engine
|
ML Analytics
|
-
Multi-Channel Notifications
- Native Windows toast notifications
- Email alerts via SMTP
- Webhook integrations for third-party services
- Customizable notification templates
-
Smart Throttling
- Prevents alert fatigue through intelligent rate limiting
- Configurable cooldown periods
- Priority-based alert routing
-
Threshold Management
- Granular control over warning and critical levels
- Per-metric threshold configuration
- Dynamic threshold adjustment based on historical data
| Feature | Description |
|---|---|
| Auto-Restart | Monitor critical processes and automatically restart on failure |
| Priority Management | Dynamically adjust process priorities based on system load |
| Custom Metrics | Define and track custom system commands and scripts |
| Scheduled Actions | Time-based automation rules for routine maintenance |
- Credential Management — Windows Credential Manager integration via keyring
- Encrypted Storage — API keys and sensitive data never stored in plain text
- Access Control — Per-user configuration and data isolation
- Audit Logging — Comprehensive activity tracking for compliance
SENTINEL employs a modular, event-driven architecture designed for performance, scalability, and maintainability.
┌───────────────────────────────────────────────────────────────────────────┐
│ SENTINEL Application Layer │
├───────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │
│ │ Presentation │ │ Application │ │ Data │ │
│ │ Layer │◄──────►│ Layer │◄──────►│ Layer │ │
│ │ │ │ │ │ │ │
│ │ • Flet UI │ │ • Event Bus │ │ • SQLite │ │
│ │ • Dashboard │ │ • Scheduler │ │ • Metrics │ │
│ │ • Analytics │ │ • State Mgmt │ │ • Models │ │
│ │ • AI Chat │ │ • AsyncIO │ │ • Cache │ │
│ │ • Settings │ │ │ │ │ │
│ └─────────────────┘ └─────────────────┘ └──────────────┘ │
│ │ │ │ │
│ └──────────────────────────┼──────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ Service Layer │ │
│ │ │ │
│ │ ┌───────────────┐ │ │
│ │ │ Collectors │ │ │
│ │ │ • CPU/Memory │ │ │
│ │ │ • Disk/Net │ │ │
│ │ │ • GPU │ │ │
│ │ └───────────────┘ │ │
│ │ │ │
│ │ ┌───────────────┐ │ │
│ │ │ Intelligence │ │ │
│ │ │ • AI Engines │ │ │
│ │ │ • ML Models │ │ │
│ │ │ • Forecaster │ │ │
│ │ └───────────────┘ │ │
│ │ │ │
│ │ ┌───────────────┐ │ │
│ │ │ Automation │ │ │
│ │ │ • Alerts │ │ │
│ │ │ • Actions │ │ │
│ │ │ • Notifiers │ │ │
│ │ └───────────────┘ │ │
│ └─────────────────────┘ │
│ │
└───────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────┐
│ External Systems │
│ │
│ • NVIDIA SMI │
│ • Windows APIs │
│ • Email SMTP │
│ • Webhooks │
│ • OpenRouter API │
└─────────────────────┘
| Layer | Components | Responsibility |
|---|---|---|
| Presentation | UI Components, Pages, Theme | User interface rendering and interaction |
| Application | Event Bus, Scheduler, State Manager | Application logic and coordination |
| Service | Collectors, Intelligence, Automation | Core business logic and processing |
| Data | SQLite, Metrics Store, Model Cache | Data persistence and retrieval |
SENTINEL/
│
├── 📄 main.py # Application entry point
├── 🔧 build.ps1 # PyInstaller build automation
├── 📋 requirements.txt # Python dependencies
│
├── 🎨 assets/
│ ├── icon.ico # Windows application icon
│ ├── icon.png # PNG icon source
│ └── background.png # UI background image
│
├── 📦 app/
│ │
│ ├── ⚙️ core/ # Core Infrastructure
│ │ ├── event_bus.py # Pub/sub event system
│ │ ├── scheduler.py # Task scheduling
│ │ └── state.py # Application state management
│ │
│ ├── 📊 collectors/ # Metric Collection
│ │ ├── cpu_collector.py # CPU usage tracking
│ │ ├── memory_collector.py # Memory statistics
│ │ ├── disk_collector.py # Disk I/O and capacity
│ │ ├── network_collector.py # Network throughput
│ │ └── gpu_collector.py # NVIDIA GPU metrics
│ │
│ ├── 💾 storage/ # Data Persistence
│ │ ├── database.py # SQLite connection manager
│ │ ├── reader.py # Data retrieval queries
│ │ └── writer.py # Data insertion logic
│ │
│ ├── 🤖 ml/ # Machine Learning
│ │ ├── anomaly_detector.py # Isolation Forest implementation
│ │ ├── forecaster.py # Time-series prediction
│ │ └── normalizer.py # Data preprocessing
│ │
│ ├── 🧠 intelligence/ # AI Engines
│ │ ├── local_engine.py # GPT4All integration
│ │ ├── cloud_engine.py # OpenRouter API client
│ │ └── health_state.py # System health analyzer
│ │
│ ├── 🎯 logic/ # Business Logic
│ │ ├── decision_engine.py # Alert decision making
│ │ └── action_router.py # Action execution routing
│ │
│ ├── 🔔 notifications/ # Alert Delivery
│ │ ├── toast_notifier.py # Windows toast notifications
│ │ ├── throttle.py # Rate limiting
│ │ └── rules.py # Notification rules engine
│ │
│ ├── 🚨 alerts/ # Alert Management
│ │ └── alert_manager.py # Alert orchestration
│ │
│ ├── 🔄 automation/ # Process Automation
│ │ └── process_automation.py # Process monitoring and control
│ │
│ ├── 🤖 ai/ # AI Model Management
│ │ └── model_manager.py # GPT4All model handler
│ │
│ └── 🎨 ui/ # User Interface
│ ├── components/ # Reusable UI components
│ │ ├── metric_card.py # Metric display cards
│ │ ├── health_badge.py # Status indicators
│ │ └── charts.py # Visualization charts
│ │
│ ├── pages/ # Application pages
│ │ ├── dashboard.py # Main monitoring dashboard
│ │ ├── analytics.py # Historical analytics
│ │ ├── ai_chat.py # AI diagnostic interface
│ │ └── settings.py # Configuration panel
│ │
│ ├── app_shell.py # Main application shell
│ ├── sidebar.py # Navigation sidebar
│ └── theme.py # Cyberpunk theme colors
│
└── 📦 dist/
└── SENTINEL.exe # Standalone executable (~150MB)
System Requirements:
- Windows 10/11 (64-bit)
- 4GB RAM minimum (8GB recommended)
- 500MB free disk space
- NVIDIA GPU (optional, for GPU monitoring)
Installation Steps:
- Download the latest release from the
dist/directory - Double-click
SENTINEL.exeto launch - Application data will be stored in
%APPDATA%\SENTINEL\
No installation, configuration files, or dependencies required.
Prerequisites:
- Python 3.11 or higher
- pip package manager
- Virtual environment (recommended)
Setup Instructions:
# Clone the repository
git clone https://github.com/adidev001/SENTINEL.git
cd SENTINEL
# Create and activate virtual environment
python -m venv .venv
.\.venv\Scripts\Activate.ps1
# Install dependencies
pip install -r requirements.txt
# Launch application
python main.py- Python 3.11+
- Virtual environment with all dependencies installed
- PowerShell execution policy allowing scripts
# Ensure virtual environment is activated
.\.venv\Scripts\Activate.ps1
# Execute build script
.\build.ps1
# Output location
# dist/SENTINEL.exe (~150MB)The build process uses PyInstaller with the following optimizations:
- Bundling Mode: Single-file executable
- Compression: UPX compression enabled
- Dependencies: All Python packages and assets embedded
- Icon: Custom application icon included
- Console: Hidden for production builds
Access AI configuration through Settings → AI Configuration
| Mode | Description | Requirements | Use Case |
|---|---|---|---|
| Local | GPT4All (Orca Mini 3B) | ~4GB disk space for model download | Privacy-focused, offline diagnostics |
| Cloud | OpenRouter API | API key from openrouter.ai | Enhanced analysis, multi-model access |
| Disabled | AI features turned off | None | Minimal resource usage, metrics-only monitoring |
Customize warning and critical thresholds through Settings → Alert Configuration
| Metric | Default Warning | Default Critical | Recommended Range |
|---|---|---|---|
| CPU Usage | 75% | 90% | Warning: 60-80%, Critical: 85-95% |
| Memory Usage | 80% | 95% | Warning: 70-85%, Critical: 90-98% |
| Disk Usage | 80% | 95% | Warning: 75-85%, Critical: 90-98% |
| Network | Custom | Custom | Based on connection capacity |
Toast Notifications (Default)
- Native Windows 10/11 notifications
- No configuration required
- Appears in Action Center
Email Notifications (Optional)
- Requires SMTP server configuration
- Configure in Settings → Notifications → Email
- Supports TLS/SSL encryption
Webhook Integration (Optional)
- POST requests to custom endpoints
- JSON payload with alert details
- Configurable retry logic
| Category | Technology | Version | Purpose |
|---|---|---|---|
| UI Framework | Flet | 0.21+ | Cross-platform UI with Flutter rendering |
| Language | Python | 3.11+ | Core application logic |
| Database | SQLite | 3.x | Embedded metrics storage |
| Packaging | PyInstaller | Latest | Standalone executable creation |
| Component | Technology | Purpose |
|---|---|---|
| Local AI | GPT4All | Offline natural language diagnostics |
| Cloud AI | OpenRouter API | Enhanced cloud-based analysis |
| Anomaly Detection | Scikit-learn (Isolation Forest) | Unsupervised outlier detection |
| Time Series | Custom forecasting algorithms | Resource usage prediction |
| Component | Technology | Purpose |
|---|---|---|
| System Metrics | psutil | CPU, memory, disk, network statistics |
| GPU Monitoring | nvidia-smi | NVIDIA GPU utilization tracking |
| Notifications | win11toast | Windows toast notification API |
| Credentials | keyring | Windows Credential Manager integration |
| smtplib | Email alert delivery |
All application data is stored in the user's AppData directory:
%APPDATA%\SENTINEL\
│
├── 📁 data\
│ └── sys_sentinel.db # SQLite metrics database (auto-managed)
│
├── 📁 models\
│ └── orca-mini-3b-gguf2-q4_0.gguf # GPT4All model (~4GB, downloaded on first use)
│
└── 📁 logs\
└── debug.log # Application debug logs (rotated daily)
Metrics Table:
- Timestamp (PRIMARY KEY)
- CPU percentage
- Memory used/total
- Disk used/total
- Network sent/received
- GPU utilization (if available)
Anomalies Table:
- Detection timestamp
- Metric type
- Anomaly score
- Contextual data
Dashboard shows 0% or N/A for all metrics
Cause: Collectors haven't completed their first cycle
Solution:
- Wait 5-10 seconds after launch
- Check
%APPDATA%\SENTINEL\logs\debug.logfor errors - Verify psutil is installed:
pip show psutil
AI Chat returns errors or no response
For Local Mode:
- Verify model downloaded: Check
%APPDATA%\SENTINEL\models\ - Model download can take 5-10 minutes on first use
- Requires ~4GB free disk space
For Cloud Mode:
- Verify API key in Settings → AI Configuration
- Get API key from OpenRouter
- Check internet connectivity
GPU monitoring shows N/A
Requirements:
- NVIDIA GPU with driver version 450.80 or higher
nvidia-smimust be in system PATH
Verification:
nvidia-smiIf command fails, reinstall NVIDIA drivers or add to PATH.
Application crashes on startup
Debug Steps:
- Run from command line to see error messages:
SENTINEL.exe - Check debug log:
%APPDATA%\SENTINEL\logs\debug.log - Verify Windows version: Windows 10 1809+ required
- Disable antivirus temporarily (may block first run)
We welcome contributions from the community! Here's how to get involved:
# Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/SENTINEL.git
cd SENTINEL
# Create feature branch
git checkout -b feature/your-feature-name
# Install development dependencies
pip install -r requirements.txt
# Make your changes and test
python main.py
# Commit with descriptive message
git commit -m "feat: add new metric collector for battery status"
# Push to your fork
git push origin feature/your-feature-name- Follow PEP 8 style guidelines
- Add docstrings to all functions and classes
- Include unit tests for new features
- Update documentation as needed
- Ensure build script passes before submitting PR
- 📊 New metric collectors (battery, temperature, etc.)
- 🎨 UI/UX improvements and themes
- 🤖 Additional AI model integrations
- 🌐 Cross-platform support (Linux, macOS)
- 📝 Documentation and examples
- 🐛 Bug fixes and performance optimizations
This project is licensed under the MIT License - see the LICENSE file for details.
Copyright (c) 2024 Devansh (adidev001)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- Flet Team - For the excellent cross-platform UI framework
- GPT4All - For providing accessible local AI models
- OpenRouter - For the multi-model cloud API
- Open Source Community - For the countless libraries that make this possible
