A powerful LangGraph-based AI agent that analyzes business data, provides actionable insights, and generates comprehensive reports with visual dashboards.

• 📊 Daily Profit Analysis: Real-time profit/loss calculations with trend comparisons
• 💰 Customer Acquisition Cost (CAC) Monitoring: Smart alerts when CAC increases >20%
• 📈 Revenue & Cost Trend Analysis: Monitors percentage changes with intelligent thresholds
• 🎯 Automated Recommendations: AI-powered actionable business advice
• ⚠️ Smart Alerts: Proactive warnings for concerning business metrics
• 📄 JSON Export: Structured data output for integration with other systems
• 🌐 Interactive Dashboard: Beautiful HTML visualization of your business metrics

pip install -r requirements.txt

# Simple execution - generates JSON output
python agent.py

This command will:

• ✅ Analyze your business data
• 📁 Generate structured JSON output files
• 🎨 Create an interactive HTML dashboard
• 📊 Display comprehensive business insights

• JSON Output: Check generated .json files for structured data
• Visual Dashboard: Open business_dashboard.html in your browser for interactive charts
• Console Output: View summary directly in terminal

from agent import run_business_analysis

# Your business data
data = {
    "daily_revenue": 5000,
    "daily_cost": 3000,
    "number_of_customers": 50,
    "previous_day_revenue": 4500,
    "previous_day_cost": 2500,
    "previous_day_customers": 45
}

result = run_business_analysis(data)
print(result)

python test_agent.py

The agent automatically generates structured JSON files containing:

• Detailed profit/loss analysis
• CAC calculations and trends
• Revenue and cost metrics
• Automated recommendations
• Business alerts and warnings

View your business metrics through our beautiful dashboard:

• 📈 Real-time charts and graphs
• 🎯 Key performance indicators
• 📊 Trend analysis visualizations
• 🚨 Alert notifications
• 💡 Recommendation highlights

This project is fully configured for LangGraph Studio:

1. Import the entire project folder
2. Test the agent interactively
3. Visualize the workflow graph
4. Debug and optimize performance

Configuration file: langgraph.json

{
  "daily_revenue": 5000,
  "daily_cost": 3000,
  "number_of_customers": 50,
  "previous_day_revenue": 4500,
  "previous_day_cost": 2500,
  "previous_day_customers": 45
}

{
  "profit_loss_status": {
    "daily_profit": 2000,
    "status": "positive",
    "revenue_change_percent": 11.11,
    "cost_change_percent": 20.0
  },
  "customer_acquisition": {
    "current_cac": 60.0,
    "cac_change_percent": 8.0,
    "cac_alert": false
  },
  "alerts": [
    "💰 Costs increased significantly"
  ],
  "recommendations": [
    "✅ Maintain current profitable operations",
    "📈 Strong revenue growth detected",
    "🎯 Consider scaling operations"
  ],
  "summary": {
    "total_alerts": 1,
    "total_recommendations": 4,
    "analysis_date": "2024-01-01"
  }
}

• Daily Profit: daily_revenue - daily_cost
• CAC: daily_cost / number_of_customers
• Revenue Change: (current - previous) / previous × 100
• Cost Change: (current - previous) / previous × 100

• 💡 Cost optimization strategies
• 📈 Marketing budget adjustments
• 🎯 Scaling recommendations
• ⚠️ Risk mitigation advice

Input → Processing → Analysis → Recommendations → Output
  ↓         ↓          ↓            ↓            ↓
Validate   Calculate   Generate     Format      Export
Data       Metrics     Insights     Report      Files

• 🧠 LangGraph: Workflow orchestration and state management
• 🐍 Python: Business logic and calculations
• 📊 JSON: Data serialization and API integration
• 🌐 HTML/CSS/JS: Interactive dashboard visualization
• 🧪 unittest: Comprehensive testing framework

Comprehensive test coverage includes:

• ✅ Profitable business scenarios
• ❌ Loss scenarios and edge cases
• 📊 High CAC alert testing
• 🔢 Metric calculation accuracy
• 💡 Recommendation logic validation
• 📥 Input validation and error handling
• 📤 Output format compliance

# Quick daily analysis
python agent.py

# Check dashboard
open business_dashboard.html

# API integration example
import requests
from agent import run_business_analysis

# Fetch data from your business system
business_data = fetch_daily_metrics()

# Analyze with AI agent
insights = run_business_analysis(business_data)

# Send to dashboard or notification system
update_business_dashboard(insights)

• Trend Analysis: Multi-day trend detection
• Predictive Insights: Future performance indicators
• Custom Thresholds: Configurable alert levels
• Export Options: Multiple output formats
• Real-time Updates: Live dashboard refresh

1. Fork the repository
2. Create a feature branch
3. Add comprehensive tests
4. Submit a pull request

MIT License - Open source and free to use for commercial projects.

1. python agent.py - Run analysis
2. Open business_dashboard.html - View results
3. Check JSON files - Integrate with your systems

Ready to transform your business intelligence? Start analyzing now! 🚀