A comprehensive AI Agent demonstration system featuring real-time animated visualizations of swarm intelligence, BERT-based PhD-level reasoning, and interactive SwarmAgentic demonstrations.
SwarmAgentic.mp4
Experience SwarmAgentic in action with our real-time animated demonstrations:
- 🔄 Swarm Formation: Watch agents self-organize using flocking algorithms
- ⚡ PSO Optimization: See particle swarm optimization in real-time
- 🤝 Agent Collaboration: Observe multi-agent task coordination
- 🌟 Emergent Behavior: Witness complex patterns from simple rules
- Download the latest release from Releases
- Extract and run
SwarmAgentic_Demo.exe - No installation required!
git clone https://github.com/ai-in-pm/SwarmAgentic.git
cd SwarmAgentic
python -m venv ai_agent_env
ai_agent_env\Scripts\activate # Windows
# source ai_agent_env/bin/activate # Linux/macOS
pip install -r requirements.txt
python demo_launcher.py # For animated demo
# or
python main.py # For full AI system- 🔄 Swarm Formation: Live flocking behavior with separation, alignment, and cohesion
- ⚡ PSO Optimization: Real-time particle swarm optimization with convergence tracking
- 🤝 Multi-Agent Collaboration: Dynamic coordinator-worker interactions
- 🌟 Emergent Behavior: Complex collective intelligence patterns
- 🎮 Interactive Controls: Speed adjustment, mode switching, real-time parameters
- BERT-Based Reasoning: Advanced language processing with attention mechanisms
- Chain-of-Thought Processing: Visible reasoning steps with confidence scoring
- Real-Time Scratchpad: Live visualization of agent's working memory
- Dynamic Learning: Adaptive training on SwarmAgentic academic content
- Knowledge Graph: Concept relationships and understanding depth
- Output Accuracy Tracking: Real-time percentage monitoring with trend analysis
- Expected Discoveries: Probability metrics for pattern recognition
- Source Quality Assessment: Information reliability and freshness scoring
- Performance Dashboard: Comprehensive metrics with visual indicators
- Quality Trends: Historical analysis and prediction capabilities
- Animated Visualization Panel: Real-time SwarmAgentic demonstrations
- Interactive Control Panel: Start Training, Demo, Pause, Stop, Reset
- Progress Visualization: Live training and demonstration progress
- Metrics Dashboard: Real-time performance and quality indicators
- Professional Styling: Modern design with intuitive navigation
| Component | Minimum | Recommended |
|---|---|---|
| OS | Windows 10, Linux, macOS | Windows 11 |
| Python | 3.8+ | 3.11+ |
| RAM | 8GB | 16GB+ |
| Storage | 2GB free | 5GB+ |
| CPU | Multi-core (i5/Ryzen 5) | i7/Ryzen 7+ |
| GPU | Integrated | Dedicated (for faster BERT) |
| Display | 1400x900 | 1920x1080+ |
# Download from GitHub Releases
wget https://github.com/ai-in-pm/SwarmAgentic/releases/latest/download/SwarmAgentic_Demo.exe
# Or download manually from: https://github.com/ai-in-pm/SwarmAgentic/releases
# Run immediately - no installation required!
./SwarmAgentic_Demo.exe# Clone the repository
git clone https://github.com/ai-in-pm/SwarmAgentic.git
cd SwarmAgentic
# Create and activate virtual environment
python -m venv ai_agent_env
# Windows
ai_agent_env\Scripts\activate
# Linux/macOS
source ai_agent_env/bin/activate
# Install dependencies
pip install -r requirements.txt
# Validate installation
python test_installation.py
# Run the application
python demo_launcher.py # Animated demo
# OR
python main.py # Full AI system# Test the animated visualization system
python test_animated_demo.py- Launch the application
- The system will automatically initialize all components
- Wait for the "System Ready" status
- Click "Start Training" to begin training on the academic paper
- Monitor progress in the training progress bar
- Watch real-time thoughts in the scratchpad
- Training typically takes 2-5 minutes
- After training completion, "Start Demo" becomes enabled
- Click "Start Demo" to begin interactive demonstrations
- Monitor demonstration progress and agent responses
- View real-time accuracy and confidence metrics
- Scratchpad: View agent's real-time thought processes
- Working Memory: Monitor active memory contents
- Performance Metrics: Track accuracy, confidence, and processing times
- Quality Dashboard: Assess information source reliability
- Advanced language processing with BERT integration
- Chain-of-thought reasoning implementation
- Confidence scoring and attention analysis
- Real-time performance metrics
- PhD-level cognitive architecture
- Dynamic knowledge acquisition
- Working memory management
- State management and callbacks
- Adaptive learning algorithms
- Knowledge graph construction
- Concept relationship mapping
- Progress validation
- Real-time accuracy tracking
- Source quality assessment
- Expected discovery patterns
- Comprehensive monitoring dashboard
- Scenario-based demonstrations
- Performance evaluation
- Interactive response generation
- Success metrics calculation
- Modern interface design
- Real-time visualization
- Control panel integration
- Progress monitoring
The system can be configured by modifying the configuration in main.py:
config = {
"bert_model_path": "bert-base-uncased-mrpc/...",
"academic_paper_path": "academic_summary.md",
"max_sequence_length": 512,
"quality_metrics_window": 1000,
"enable_real_time_monitoring": True,
"demo_auto_start": False,
"export_results": True
}- Default: Intel optimized BERT base uncased MRPC
- Location:
bert-base-uncased-mrpc/bert-base-uncased-mrpc/huggingface_Intel_bert-base-uncased-mrpc_v1 - Supports custom BERT models with compatible interfaces
- Training: Start training → Monitor progress → Wait for completion
- Demonstration: Start demo → Observe scenarios → Review results
- Analysis: Monitor metrics → Export results → Review performance
- Custom Scenarios: Modify demonstration scenarios in the code
- Metric Export: Automatic export of session results
- Real-time Monitoring: Continuous quality assessment
- Performance Tuning: Adjust learning parameters
- Missing Dependencies: Ensure all requirements are installed
- Python Version: Verify Python 3.8+ is being used
- Virtual Environment: Use virtual environment to avoid conflicts
- BERT Model Loading: Verify model path and files exist
- Memory Issues: Ensure sufficient RAM (8GB minimum)
- GUI Problems: Check display resolution and graphics drivers
- Slow Training: Consider GPU acceleration or reduce sequence length
- High Memory Usage: Adjust batch sizes and window sizes
- Unresponsive GUI: Check for blocking operations in main thread
- "BERT model not found": Check model path configuration
- "Academic paper not found": Verify
academic_summary.mdexists - "GUI initialization failed": Check tkinter installation
- "Import errors": Verify all dependencies are installed
# Install development dependencies
pip install -r requirements.txt
# Run tests (if available)
python -m pytest tests/
# Build executable
python build_executable.pyai-agent-demo/
├── src/
│ ├── core/ # Core AI components
│ ├── gui/ # User interface
│ ├── training/ # Learning systems
│ ├── monitoring/ # Quality metrics
│ └── demonstration/ # Demo scenarios
├── bert-base-uncased-mrpc/ # BERT model
├── logs/ # Application logs
├── results/ # Exported results
├── main.py # Application entry point
├── requirements.txt # Dependencies
└── README.md # This file
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
For support and questions:
- Check the troubleshooting section above
- Review the logs in the
logs/directory - Create an issue on the project repository
- BERT model: Intel optimized BERT base uncased
- Academic content: SwarmAgentic research paper
- GUI framework: tkinter with matplotlib integration
- Machine learning: PyTorch and Transformers libraries