# SpreadSheetSwarm *Structured approach to data management and operations in spreadsheet-like format* **Swarm Type**: `SpreadSheetSwarm` ## Overview The SpreadSheetSwarm provides a structured approach to data management and operations, ideal for tasks involving data analysis, transformation, and systematic processing in a spreadsheet-like structure. This architecture organizes agents to work on data in a tabular format with clear rows, columns, and processing workflows. Key features: - **Structured Data Processing**: Organizes work in spreadsheet-like rows and columns - **Systematic Operations**: Sequential and methodical data handling - **Data Transformation**: Efficient processing of structured datasets - **Collaborative Analysis**: Multiple agents working on different data aspects ## Use Cases - Financial data analysis and reporting - Customer data processing and segmentation - Inventory management and tracking - Research data compilation and analysis ## API Usage ### Basic SpreadSheetSwarm Example === "Shell (curl)" ```bash curl -X POST "https://api.swarms.world/v1/swarm/completions" \ -H "x-api-key: $SWARMS_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "name": "Financial Analysis Spreadsheet", "description": "Systematic financial data analysis using spreadsheet structure", "swarm_type": "SpreadSheetSwarm", "task": "Analyze quarterly financial performance data for a retail company with multiple product lines and create comprehensive insights", "agents": [ { "agent_name": "Data Validator", "description": "Validates and cleans financial data", "system_prompt": "You are a data validation specialist. Clean, validate, and structure financial data ensuring accuracy and consistency.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.2 }, { "agent_name": "Revenue Analyst", "description": "Analyzes revenue trends and patterns", "system_prompt": "You are a revenue analyst. Focus on revenue trends, growth patterns, and seasonal variations across product lines.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.3 }, { "agent_name": "Cost Analyst", "description": "Analyzes cost structures and margins", "system_prompt": "You are a cost analyst. Examine cost structures, margin analysis, and expense categorization.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.3 }, { "agent_name": "Performance Calculator", "description": "Calculates KPIs and financial metrics", "system_prompt": "You are a financial metrics specialist. Calculate KPIs, ratios, and performance indicators from the analyzed data.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.1 }, { "agent_name": "Report Generator", "description": "Creates structured financial reports", "system_prompt": "You are a report generator. Create comprehensive, well-structured financial reports with insights and recommendations.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.4 } ], "max_loops": 1 }' ``` === "Python (requests)" ```python import requests import json API_BASE_URL = "https://api.swarms.world" API_KEY = "your_api_key_here" headers = { "x-api-key": API_KEY, "Content-Type": "application/json" } swarm_config = { "name": "Financial Analysis Spreadsheet", "description": "Systematic financial data analysis using spreadsheet structure", "swarm_type": "SpreadSheetSwarm", "task": "Analyze quarterly financial performance data for a retail company with multiple product lines and create comprehensive insights", "agents": [ { "agent_name": "Data Validator", "description": "Validates and cleans financial data", "system_prompt": "You are a data validation specialist. Clean, validate, and structure financial data ensuring accuracy and consistency.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.2 }, { "agent_name": "Revenue Analyst", "description": "Analyzes revenue trends and patterns", "system_prompt": "You are a revenue analyst. Focus on revenue trends, growth patterns, and seasonal variations across product lines.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.3 }, { "agent_name": "Cost Analyst", "description": "Analyzes cost structures and margins", "system_prompt": "You are a cost analyst. Examine cost structures, margin analysis, and expense categorization.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.3 }, { "agent_name": "Performance Calculator", "description": "Calculates KPIs and financial metrics", "system_prompt": "You are a financial metrics specialist. Calculate KPIs, ratios, and performance indicators from the analyzed data.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.1 }, { "agent_name": "Report Generator", "description": "Creates structured financial reports", "system_prompt": "You are a report generator. Create comprehensive, well-structured financial reports with insights and recommendations.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.4 } ], "max_loops": 1 } response = requests.post( f"{API_BASE_URL}/v1/swarm/completions", headers=headers, json=swarm_config ) if response.status_code == 200: result = response.json() print("SpreadSheetSwarm completed successfully!") print(f"Cost: ${result['metadata']['billing_info']['total_cost']}") print(f"Execution time: {result['metadata']['execution_time_seconds']} seconds") print(f"Structured analysis: {result['output']}") else: print(f"Error: {response.status_code} - {response.text}") ``` **Example Response**: ```json { "status": "success", "swarm_name": "financial-analysis-spreadsheet", "swarm_type": "SpreadSheetSwarm", "task": "Analyze quarterly financial performance data for a retail company with multiple product lines and create comprehensive insights", "output": { "data_validation": { "data_quality": "95% accuracy after cleaning", "missing_values": "Identified and filled 3% missing entries", "data_structure": "Standardized format across all product lines" }, "revenue_analysis": { "q4_revenue": "$2.4M total revenue", "growth_rate": "12% quarter-over-quarter growth", "top_performers": ["Product Line A: +18%", "Product Line C: +15%"], "seasonal_trends": "Strong holiday season performance" }, "cost_analysis": { "total_costs": "$1.8M operational costs", "cost_breakdown": "60% COGS, 25% Marketing, 15% Operations", "margin_analysis": "25% gross margin, 15% net margin", "cost_optimization": "Identified 8% potential savings in supply chain" }, "performance_metrics": { "roi": "22% return on investment", "customer_acquisition_cost": "$45 per customer", "lifetime_value": "$320 average CLV", "inventory_turnover": "6.2x annual turnover" }, "comprehensive_report": { "executive_summary": "Strong Q4 performance with 12% growth...", "recommendations": ["Expand Product Line A", "Optimize supply chain", "Increase marketing for underperformers"], "forecast": "Projected 15% growth for Q1 based on trends" } }, "metadata": { "processing_structure": { "rows_processed": 1250, "columns_analyzed": 18, "calculations_performed": 47 }, "data_pipeline": [ "Data Validation", "Revenue Analysis", "Cost Analysis", "Performance Calculation", "Report Generation" ], "execution_time_seconds": 34.2, "billing_info": { "total_cost": 0.078 } } } ``` ## Best Practices - Structure data in clear, logical formats before processing - Use systematic, step-by-step analysis approaches - Ideal for quantitative analysis and reporting tasks - Ensure data validation before proceeding with analysis ## Related Swarm Types - [SequentialWorkflow](sequential_workflow.md) - For ordered data processing - [ConcurrentWorkflow](concurrent_workflow.md) - For parallel data analysis - [HierarchicalSwarm](hierarchical_swarm.md) - For complex data projects