chore: consolidate Syslog Solution code into unified repository structure

- Moved scattered scripts, templates, and documentation into organized directories (applications/, scripts/, assets/).
- Updated .gitignore to strictly exclude secrets, state files, and IDE configs.
- Added comprehensive README.md outlining repository structure and best practices.
- Preserved all existing documentation and technical architecture files.
- Prepared infrastructure/ for AWS Org and Proxmox Terraform management.
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2026-05-07 11:40:02 +00:00
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#!/bin/bash
# OpenMAIC Education Setup Script
# Purpose: Deploy OpenMAIC (Open Multi-Agent AI Collaboration) educational environment
# Version: 1.0.0
set -e
echo "=== OpenMAIC Educational Environment Setup ==="
# Configuration
OPENMAIC_DIR="${HOME}/projects/openmaic"
EDUCATION_DIR="${OPENMAIC_DIR}/education"
# Create directory structure
echo "Setting up OpenMAIC directory structure..."
mkdir -p "$OPENMAIC_DIR"
mkdir -p "$EDUCATION_DIR/curriculum"
mkdir -p "$EDUCATION_DIR/demos"
mkdir -p "$EDUCATION_DIR/tutorials"
# Install OpenMAIC core
echo "Installing OpenMAIC framework..."
pip install openmaic-core openmaic-editor openmaic-visualizer
# Create sample agent definitions for education
echo "Creating educational agent examples..."
cat > "$EDUCATION_DIR/curriculum/agent1_basic_researcher.json" << 'EOF'
{
"name": "researcher",
"role": "Research Assistant",
"capabilities": ["web_search", "data_analysis", "summarization"],
"context_window": 8192,
"model": "qwen3.5:35b"
}
EOF
cat > "$EDUCATION_DIR/curriculum/agent2_data_analyst.json" << 'EOF'
{
"name": "data_analyst",
"role": "Data Analysis Specialist",
"capabilities": ["pandas", "numpy", "visualization", "statistical_testing"],
"context_window": 8192,
"model": "qwen3.5:35b"
}
EOF
cat > "$EDUCATION_DIR/curriculum/agent3_content_writer.json" << 'EOF'
{
"name": "content_writer",
"role": "Content Generator",
"capabilities": ["writing", "editing", "style_transfer"],
"context_window": 8192,
"model": "qwen3.5:35b"
}
EOF
# Create educational demos
echo "Creating educational demo workflows..."
cat > "$EDUCATION_DIR/demos/agent_collaboration_example.py" << 'EOF'
#!/usr/bin/env python3
"""
OpenMAIC Educational Demo: Multi-Agent Collaboration
This demonstrates how agents coordinate to solve complex problems
"""
from openmaic.core import Agent, Orchestrator
from openmaic.visualizer import CollaborationVisualizer
def demonstrate_agent_collaboration():
"""Show how agents collaborate to complete a task."""
# Initialize orchestrator
orchestrator = Orchestrator(
max_agents=3,
collaboration_mode="sequential",
output_format="markdown"
)
# Create agents from definitions
researcher = Agent.load("$EDUCATION_DIR/curriculum/agent1_basic_researcher.json")
analyst = Agent.load("$EDUCATION_DIR/curriculum/agent2_data_analyst.json")
writer = Agent.load("$EDUCATION_DIR/curriculum/agent3_content_writer.json")
# Define collaboration workflow
workflow = [
{"agent": researcher, "task": "Research AI trends in SMB market"},
{"agent": analyst, "task": "Analyze market data and identify opportunities"},
{"agent": writer, "task": "Write comprehensive market analysis report"}
]
# Execute and visualize
orchestrator.run_workflow(workflow)
# Display results
visualizer = CollaborationVisualizer(orchestrator)
print(visualizer.render_collaboration_flow())
print(orchestrator.get_final_output())
return orchestrator.get_results()
if __name__ == "__main__":
demonstrate_agent_collaboration()
EOF
chmod +x "$EDUCATION_DIR/demos/agent_collaboration_example.py"
# Create curriculum documentation
echo "Creating curriculum documentation..."
cat > "$EDUCATION_DIR/curriculum/README.md" << 'EOF'
# OpenMAIC Educational Curriculum
This curriculum introduces multi-agent AI systems through hands-on projects.
## Module 1: Fundamentals
- Understanding agent roles and capabilities
- Basic Orchestrator configuration
- Simple agent collaboration patterns
## Module 2: Advanced Collaboration
- Multi-agent workflow design
- Context sharing between agents
- Error handling and recovery
## Module 3: Production Patterns
- Scaling to 10+ agents
- Performance optimization
- Monitoring and diagnostics
## Module 4: Real-World Applications
- Market analysis automation
- Customer support orchestration
- Content pipeline automation
## Getting Started
1. Install OpenMAIC: `pip install openmaic-core openmaic-visualizer`
2. Review curriculum guides in `/curriculum/`
3. Run demo workflows in `/demos/`
4. Participate in hands-on exercises
## Prerequisites
- Python 3.10+
- Ollama or similar LLM service
- Basic understanding of AI/ML concepts
EOF
# Set up virtual environment
echo "Creating virtual environment..."
python3 -m venv "$OPENMAIC_DIR/venv"
source "$OPENMAIC_DIR/venv/bin/activate"
pip install openmaic-core openmaic-editor openmaic-visualizer pandas numpy matplotlib
# Create environment variables
cat > "$OPENMAIC_DIR/.env" << 'EOF'
# OpenMAIC Configuration
OPENMAIC_LOG_LEVEL=INFO
OPENMAIC_MAX_CONCURRENT_AGENTS=5
OPENMAIC_COLLABORATION_MODE=sequential
# Model Setup
OLLAMA_BASE_URL=http://localhost:11434
QWEN35_ENDPOINT=192.168.68.8:8080
# Collaboration Settings
COLLABORATION_CONTEXT_WINDOW=8192
COLLABORATION_TIMEOUT=3600
EOF
# Display summary
echo ""
echo "=== OpenMAIC Education Environment Ready ==="
echo "Directory: $OPENMAIC_DIR"
echo "Curriculum: $EDUCATION_DIR/curriculum/"
echo "Demos: $EDUCATION_DIR/demos/"
echo ""
echo "To start learning:"
echo "1. cd $OPENMAIC_DIR"
echo "2. source venv/bin/activate"
echo "3. Run demos: python $EDUCATION_DIR/demos/agent_collaboration_example.py"
echo "4. Study curriculum guides in $EDUCATION_DIR/curriculum/"
echo ""
echo "Perfect for Jerome and Theodore's strategy sessions on AI agent frameworks!"