Wah Loon Multi-Agent CAD/BIM Automation System

A sophisticated AI-powered multi-agent system for automated analysis and quality assurance of M&E (Mechanical & Electrical) CAD/BIM projects, specifically designed for Singapore’s construction standards and practices.

🎯 Overview

This system leverages LangGraph and OpenAI’s GPT models to create a collaborative multi-agent workflow that analyzes CAD/BIM projects across multiple dimensions:

Design Analysis - Technical completeness and system integration
Clash Detection - Spatial conflicts and coordination issues
Compliance Checking - Singapore standards verification (BCA, SS530, CP5, SS564)
Energy Analysis - Sustainability and efficiency optimization
Quality Assurance - Comprehensive review and approval recommendations

🏗️ Architecture

Multi-Agent System Design

The system employs a sequential workflow where specialized AI agents collaborate to analyze different aspects of M&E projects:

Project Data → Design Analyzer → Clash Detector → Compliance Checker 
             → Energy Analyzer → QA Reviewer → Final Report

Each agent is powered by an LLM (GPT-4) with specialized system prompts and domain expertise.

Key Components

1. Agents (`src/agents/`)

agent_base.py - Base class providing common functionality
design_analyzer.py - Senior M&E Design Engineer agent
clash_detector.py - BIM Coordination Specialist agent
compliance_checker.py - Singapore M&E Compliance Expert agent
energy_analyzer.py - Energy Efficiency Specialist agent
qa_reviewer.py - Quality Assurance Lead agent

2. Workflows (`src/workflows/`)

multi_agent_workflow.py - LangGraph orchestration engine
state_schema.py - State management and TypedDict schemas

3. Data Generation (`src/data/`)

generators.py - Realistic CAD/BIM project data generator

4. Utilities (`src/utils/`)

config.py - Configuration management
logger.py - Logging utilities

🚀 Getting Started

Prerequisites

Python 3.8 or higher
OpenAI API key
Virtual environment (recommended)

Installation

Clone or navigate to the project directory:

cd F:\AI_Coding\wahloon-multi-agent-cad

Create and activate virtual environment:

python -m venv .myenv
.myenv\Scripts\activate  # Windows
# source .myenv/bin/activate  # Linux/Mac

Install dependencies:

pip install -r requirements.txt

Configure environment variables:

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4
OPENAI_TEMPERATURE=0.1
LOG_LEVEL=INFO

Quick Start

Single Project Analysis

python main.py --mode single --project-id WL-2024-001 --design-intent "Sustainable M&E design for commercial tower"

Batch Analysis

python main.py --mode batch --project-count 5

Using the Python API

import asyncio
from src.workflows.multi_agent_workflow import EnhancedMEAgentWorkflow
from src.data.generators import WahLoonDataGenerator
from langchain_openai import ChatOpenAI

async def analyze_project():
    # Initialize LLM
    llm = ChatOpenAI(model="gpt-4", temperature=0.1)
    
    # Generate project data
    generator = WahLoonDataGenerator()
    project_data = generator.generate_cad_project("WL-2024-TEST-001")
    
    # Run workflow
    workflow = EnhancedMEAgentWorkflow(llm)
    results = await workflow.run_workflow(project_data)
    
    return results

# Execute
results = asyncio.run(analyze_project())

📊 Project Structure

wahloon-multi-agent-cad/
├── src/
│   ├── agents/                 # Specialized AI agents
│   │   ├── agent_base.py      # Base agent class
│   │   ├── design_analyzer.py
│   │   ├── clash_detector.py
│   │   ├── compliance_checker.py
│   │   ├── energy_analyzer.py
│   │   └── qa_reviewer.py
│   ├── workflows/              # LangGraph workflows
│   │   ├── multi_agent_workflow.py
│   │   └── state_schema.py
│   ├── data/                   # Data generation
│   │   └── generators.py
│   └── utils/                  # Utilities
│       ├── config.py
│       └── logger.py
├── examples/
│   └── basic_usage.py         # Usage examples
├── tests/                      # Unit tests
│   ├── test_agents.py
│   └── test_workflows.py
├── output/                     # Analysis results
├── main.py                     # CLI entry point
├── requirements.txt            # Dependencies
├── .env                        # Environment variables (create this)
└── README.md                   # This file

🔧 Agent Descriptions

Design Analyzer Agent

Role: Senior M&E Design Engineer
Responsibilities: - Assess design completeness (scoring 0-100) - Identify missing systems and documentation gaps - Evaluate technical risks with severity levels - Analyze system integration (electrical-mechanical, mechanical-plumbing) - Review constructability and installation sequences - Provide prioritized recommendations

Key Outputs:

{
  "completeness_assessment": {
    "score": 85,
    "missing_systems": ["Fire alarm integration"],
    "documentation_gaps": ["Cable schedules incomplete"]
  },
  "technical_risks": [
    {
      "risk": "Inadequate cooling capacity",
      "severity": "HIGH",
      "impact": "schedule",
      "recommendation": "Increase chiller capacity by 15%"
    }
  ]
}

Clash Detection Agent

Role: BIM Coordination Specialist
Responsibilities: - Detect spatial conflicts between M&E systems - Identify coordination issues - Analyze construction sequence problems - Estimate cost and schedule impacts - Propose resolution strategies

Key Outputs:

{
  "spatial_clashes": [
    {
      "clash_id": "C001",
      "elements": ["Electrical Conduit", "HVAC Duct"],
      "location": "Level 5 Ceiling Void",
      "severity": "HIGH",
      "clearance_violation_mm": 150,
      "estimated_cost_impact_sgd": 5000
    }
  ],
  "summary_metrics": {
    "total_clashes": 8,
    "critical_clashes": 2
  }
}

Compliance Checker Agent

Role: Singapore M&E Compliance Expert
Responsibilities: - Verify compliance with Singapore standards: - BCA Green Mark 2021 - SS530 (Energy Efficiency) - CP5 (Electrical Installations) - SS564 (Plumbing Standards) - Assess certification feasibility - Identify regulatory risks - Provide rectification requirements

Key Outputs:

{
  "standards_compliance": [
    {
      "standard": "SS530",
      "requirement": "Motor efficiency IE3+",
      "status": "NON_COMPLIANT",
      "severity": "HIGH",
      "rectification_required": "Replace motors with IE3 rated units"
    }
  ],
  "certification_impact": {
    "green_mark_achievable": "Gold",
    "missing_requirements": ["Energy monitoring points"]
  }
}

Energy Analyzer Agent

Role: Energy Efficiency Specialist
Responsibilities: - Calculate energy performance metrics - Identify efficiency opportunities - Assess sustainability potential - Perform cost-benefit analysis - Recommend equipment upgrades

Key Outputs:

{
  "efficiency_opportunities": [
    {
      "measure": "VSD installation for AHUs",
      "savings_percent": 25,
      "cost_savings_sgd_year": 45000,
      "payback_period_years": 2.5,
      "green_mark_points": 3
    }
  ],
  "cost_benefit_analysis": {
    "total_annual_savings_sgd": 120000,
    "simple_payback_years": 3.2
  }
}

QA Reviewer Agent

Role: Quality Assurance Lead
Responsibilities: - Synthesize all agent findings - Provide comprehensive quality scoring - Assess overall project risks - Generate executive summary - Make GO/NO-GO recommendation

Key Outputs:

{
  "quality_assessment": {
    "overall_score": 78,
    "design_quality_score": 82,
    "compliance_quality_score": 75
  },
  "recommendations": {
    "project_status": "GO_WITH_ISSUES",
    "key_decisions": ["Resolve critical clashes before construction"],
    "priority_actions": [...]
  }
}

🎨 Key Features

1. Automated Multi-Dimensional Analysis

Parallel processing of multiple analysis dimensions
Context-aware agent collaboration
Comprehensive quality assessment

2. Singapore Standards Integration

Built-in knowledge of local regulations
BCA Green Mark compliance checking
SS530, CP5, SS564 verification

3. Practical Engineering Focus

Constructability analysis
Cost and schedule impact estimation
Actionable recommendations

4. State Management with LangGraph

Type-safe state transitions
Error handling and recovery
Progress tracking and metrics

5. Flexible Data Generation

Realistic test data generation
Customizable project parameters
Support for various building types

📈 Output Analysis

The system generates comprehensive JSON reports containing:

Project Information
- ID, name, client, location
- Technical specifications
- Timeline and budget
Agent-Specific Analyses
- Detailed findings from each specialized agent
- Quantified metrics and scores
- Prioritized recommendations
Final Report
- Executive summary
- Quality scores across all dimensions
- Critical issues and risks
- GO/NO-GO recommendation
Performance Metrics
- Processing time per agent
- Total workflow duration
- Error and warning counts

Sample Output Structure

{
  "project_id": "WL-2024-001",
  "project_name": "Commercial Tower Project",
  "design_analysis": { ... },
  "clash_analysis": { ... },
  "compliance_check": { ... },
  "energy_analysis": { ... },
  "qa_report": { ... },
  "final_report": {
    "executive_summary": "...",
    "quality_scores": { ... },
    "final_recommendation": "GO_WITH_ISSUES"
  },
  "processing_times": {
    "total": 45.2
  }
}

🧪 Testing

Run the test suite:

# All tests
pytest tests/

# Specific test file
pytest tests/test_agents.py

# With coverage
pytest tests/ --cov=src --cov-report=html

🔐 Security & Best Practices

API Key Management
- Store API keys in .env file
- Never commit .env to version control
- Use environment variables in production
Error Handling
- Comprehensive try-catch blocks
- Graceful degradation on agent failures
- Detailed error logging
Data Privacy
- No persistent storage of project data
- In-memory processing only
- Configurable data retention

📝 Configuration Options

Environment Variables

Variable	Description	Default
`OPENAI_API_KEY`	OpenAI API key	Required
`OPENAI_MODEL`	Model to use	`gpt-4`
`OPENAI_TEMPERATURE`	Model temperature	`0.1`
`LOG_LEVEL`	Logging level	`INFO`
`MAX_CONCURRENT_AGENTS`	Max parallel agents	`5`
`TIMEOUT_SECONDS`	Agent timeout	`300`

Command Line Options

python main.py --help

Options:
  --mode {single,batch}       Run mode
  --project-id ID            Project identifier
  --project-count N          Number of projects (batch mode)
  --design-intent TEXT       Design intent description
  --output-dir PATH          Output directory

🤝 Contributing

This is a specialized internal tool for Wah Loon Engineering. For enhancements or bug reports, please contact the development team.

📚 Dependencies

Core frameworks and libraries:

LangChain 0.0.354 - LLM application framework
LangGraph - Multi-agent workflow orchestration
OpenAI 1.12.0 - LLM API integration
Pandas/NumPy - Data processing
Faker - Test data generation
Pydantic - Data validation
python-dotenv - Environment management

See requirements.txt for complete dependency list.

🔄 Workflow Execution Flow

graph TD
    A[Start] --> B[Initialize LLM & Agents]
    B --> C[Generate/Load Project Data]
    C --> D[Design Analyzer]
    D --> E[Clash Detector]
    E --> F[Compliance Checker]
    F --> G[Energy Analyzer]
    G --> H[QA Reviewer]
    H --> I[Finalize Report]
    I --> J[Save Results]
    J --> K[End]
    
    D -.Context.-> E
    E -.Context.-> F
    F -.Context.-> G
    G -.Context.-> H

🎓 Usage Examples

Example 1: Hospital Project Analysis

import asyncio
from src.workflows.multi_agent_workflow import EnhancedMEAgentWorkflow
from langchain_openai import ChatOpenAI

async def analyze_hospital():
    llm = ChatOpenAI(model="gpt-4", temperature=0.1)
    workflow = EnhancedMEAgentWorkflow(llm)
    
    hospital_data = {
        "project_id": "WL-2024-HOSPITAL-001",
        "project_name": "New Hospital Wing",
        "total_area_sqm": 35000,
        "green_mark_target": "Platinum",
        # ... additional specs
    }
    
    design_intent = """
    Critical healthcare facility requiring:
    - 100% backup power for critical loads
    - Medical gas systems integration
    - Infection control HVAC design
    - BCA Green Mark Platinum certification
    """
    
    results = await workflow.run_workflow(hospital_data, design_intent)
    return results

asyncio.run(analyze_hospital())

Example 2: Batch Processing

# Process multiple projects in sequence
python main.py --mode batch --project-count 10 --output-dir batch_results

🏆 Performance Metrics

Typical performance for a standard commercial project:

Design Analysis: 8-12 seconds
Clash Detection: 10-15 seconds
Compliance Check: 8-10 seconds
Energy Analysis: 10-12 seconds
QA Review: 12-15 seconds
Total Workflow: 45-65 seconds

📞 Support

For technical support or questions: - Review the documentation in Wah Loon Multi-Agent CADBIM Automation System.pdf - Check the examples/ directory for usage patterns - Consult the development team

📄 License

Proprietary software for Wah Loon Engineering Pte Ltd.

🔖 Version History

v1.0 - Initial release with core multi-agent functionality
- 5 specialized agents
- LangGraph integration
- Singapore standards compliance
- Comprehensive reporting

Built for Wah Loon Engineering Pte Ltd
Advancing M&E Excellence through AI Innovation

Wah Loon Multi-Agent CAD/BIM Automation System

🎯 Overview

🏗️ Architecture

Multi-Agent System Design

Key Components

1. Agents (src/agents/)

2. Workflows (src/workflows/)

3. Data Generation (src/data/)

4. Utilities (src/utils/)

🚀 Getting Started

Prerequisites

Installation

Quick Start

Single Project Analysis

Batch Analysis

Using the Python API

📊 Project Structure

🔧 Agent Descriptions

Design Analyzer Agent

Clash Detection Agent

Compliance Checker Agent

Energy Analyzer Agent

QA Reviewer Agent

🎨 Key Features

1. Automated Multi-Dimensional Analysis

2. Singapore Standards Integration

3. Practical Engineering Focus

4. State Management with LangGraph

5. Flexible Data Generation

📈 Output Analysis

Sample Output Structure

🧪 Testing

🔐 Security & Best Practices

📝 Configuration Options

Environment Variables

Command Line Options

🤝 Contributing

📚 Dependencies

🔄 Workflow Execution Flow

🎓 Usage Examples

Example 1: Hospital Project Analysis

Example 2: Batch Processing

🏆 Performance Metrics

📞 Support

📄 License

🔖 Version History

1. Agents (`src/agents/`)

2. Workflows (`src/workflows/`)

3. Data Generation (`src/data/`)

4. Utilities (`src/utils/`)