Multi-Agent System for Industrial Manufacturing Optimization
This project implements a multi-agent system for optimizing manufacturing operations using the OpenAI Agents SDK. The system consists of three specialized agents that work together to improve production efficiency, ensure compliance, and coordinate complex manufacturing tasks.
System Overview
The multi-agent system includes:
- Content Agent: Generates production reports and ensures compliance documentation
- Decision Agent: Analyzes sensor data and optimizes production schedules
- Hybrid Agent: Coordinates between agents and resolves conflicts
Quick Start
Prerequisites
- Node.js (v18 or higher)
- OpenAI API key
Installation
- Clone or download this project
`bash
git clone https://github.com/r123singh/agnetic-manufacturing.git
`
Install dependencies:
npm install
Set up environment variables:
cp env.example .env
Edit .env and add your OpenAI API key:
OPENAI_API_KEY=your_openai_api_key_here
Running the System
Basic System
npm start
Advanced System (with real-time monitoring)
npm run advanced
Development Mode
# Basic system with auto-restart
npm run dev
# Advanced system with auto-restart
npm run advanced-dev
Testing System Components
npm test
System Architecture
Available Systems
Basic System (index.js)
A straightforward implementation of the multi-agent system with simulated manufacturing data.
Advanced System (advanced-system.js)
Enhanced version with:
- Real-time data simulation with noise
- Advanced monitoring and alerting
- Predictive analytics capabilities
- Comprehensive health scoring
- Enhanced agent instructions with visual indicators
Agent Roles
Content Agent
- Purpose: Reporting and compliance management
- Responsibilities:
- Generate detailed production reports with visual indicators
- Ensure compliance documentation is current and accurate
- Create audit trails for quality control processes
- Document safety procedures and incident reports
- Maintain regulatory compliance records
- Provide trend analysis and historical comparisons
Decision Agent
- Purpose: Real-time production optimization
- Responsibilities:
- Analyze sensor data with trend analysis
- Optimize production schedules based on current conditions
- Identify bottlenecks and suggest improvements
- Monitor equipment performance and predict maintenance needs
- Adjust production parameters for maximum efficiency
- Provide predictive analytics for future optimization
Hybrid Agent
- Purpose: Coordination and conflict resolution
- Responsibilities:
- Coordinate between Content and Decision agents effectively
- Resolve conflicts between reporting and optimization requirements
- Escalate critical issues to human operators with clear protocols
- Ensure system-wide consistency and efficiency
- Monitor overall system performance and health
- Implement continuous improvement strategies
Manufacturing Data Simulation
The system simulates realistic manufacturing data including:
- Production metrics (units per hour, quality score, energy consumption)
- Sensor data (temperature, pressure, vibration)
- Quality metrics (defect rate, customer returns, on-time delivery)
- Compliance status (safety audit, environmental compliance, quality certification)
Configuration
Environment Variables
OPENAI_API_KEY: Your OpenAI API key (required)MANUFACTURING_PLANT_ID: Plant identifier (default: PLANT_001)PRODUCTION_LINE_ID: Production line identifier (default: LINE_A)
Customizing Agent Behavior
You can modify agent instructions in index.js to adapt the system to your specific manufacturing needs:
const contentAgent = new Agent({
name: 'ContentAgent',
instructions: `Your custom instructions here...`,
});
Output Example
When you run the system, you'll see output similar to:
Starting Multi-Agent Manufacturing Optimization System
Plant: PLANT_001
Production Line: LINE_A
Content Agent: Generating compliance reports...
✅ Content Agent completed
⚙️ Decision Agent: Analyzing sensor data and optimizing production...
✅ Decision Agent completed
Hybrid Agent: Coordinating and resolving conflicts...
✅ Hybrid Agent completed
AGENT OUTPUTS:
==================================================
CONTENT AGENT OUTPUT:
------------------------------
[Detailed compliance report with quality metrics, safety status, etc.]
⚙️ DECISION AGENT OUTPUT:
------------------------------
[Production optimization recommendations based on sensor data]
HYBRID AGENT OUTPUT:
------------------------------
[Coordination results and conflict resolution recommendations]
Multi-Agent System Execution Complete!
️ Extending the System
Adding New Agents
To add a new agent, create it following the same pattern:
const newAgent = new Agent({
name: 'NewAgent',
instructions: `Your agent instructions here...`,
});
const newResult = await run(newAgent, manufacturingTask);
Integrating with Real Manufacturing Systems
To integrate with real manufacturing systems:
- Replace the simulated
manufacturingData with real data sources
- Connect to actual sensor networks and SCADA systems
- Implement real-time data streaming
- Add database integration for persistent storage
- Implement proper error handling and logging
You can extend agents with custom tools for specific manufacturing tasks:
const agentWithTools = new Agent({
name: 'CustomAgent',
instructions: 'Your instructions...',
tools: [
// Add your custom tools here
],
});
Security Considerations
- Keep your OpenAI API key secure and never commit it to version control
- Consider implementing rate limiting for API calls
- Add proper error handling for production environments
- Implement logging and monitoring for system health
References
This implementation is inspired by:
Contributing
Feel free to submit issues and enhancement requests!
License
No License.
