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