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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:

{
  "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

SequentialWorkflow - For ordered data processing
ConcurrentWorkflow - For parallel data analysis
HierarchicalSwarm - For complex data projects

9.1 KiB Raw Blame History