SpreadSheetSwarm Example

The SpreadSheetSwarm is a multi-agent architecture that processes tasks concurrently using multiple agents and saves the results to a CSV file. This is perfect for batch processing, data collection, and tracking agent outputs over time.

How It Works

1. Concurrent Execution: Multiple agents work on tasks simultaneously
2. Task Distribution: Each agent can have its own task or all agents work on the same task
3. CSV Tracking: All results are automatically saved to a CSV file with metadata
4. Batch Processing: Supports running multiple loops for repeated tasks

This architecture is ideal for: - Data collection and analysis - Batch processing multiple tasks - Tracking agent performance over time - Generating reports from multiple agents

Installation

Install the swarms package using pip:

pip install -U swarms

Basic Setup

1. First, set up your environment variables:

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY="your-api-key"

Step-by-Step Example

Step 1: Import Required Modules

from swarms import Agent, SpreadSheetSwarm

Step 2: Create Specialized Agents

# Agent 1: Market Researcher
market_researcher = Agent(
    agent_name="Market-Researcher",
    system_prompt="You are a market research analyst. Analyze market trends, competitors, and opportunities.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Agent 2: Financial Analyst
financial_analyst = Agent(
    agent_name="Financial-Analyst",
    system_prompt="You are a financial analyst. Analyze financial data, calculate metrics, and provide insights.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Agent 3: Risk Assessor
risk_assessor = Agent(
    agent_name="Risk-Assessor",
    system_prompt="You are a risk assessment specialist. Identify and evaluate potential risks.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

Step 3: Create the SpreadSheetSwarm

swarm = SpreadSheetSwarm(
    name="Market-Analysis-Swarm",
    description="A swarm for comprehensive market analysis",
    agents=[
        market_researcher,
        financial_analyst,
        risk_assessor,
    ],
    max_loops=1,
    autosave=True,
    verbose=True,
)

Step 4: Run the Swarm

# All agents work on the same task concurrently
task = "Analyze the current state of the AI market and provide key insights"

result = swarm.run(task=task)

print(result)

Step 5: Access Results

The results include: - Run ID and metadata - Number of tasks completed - All agent outputs - Timestamps for each task

The result dictionary contains all this information, which you can access programmatically.

Advanced: Different Tasks for Each Agent

You can also configure each agent with a different task using a CSV file or by setting tasks programmatically:

# Create swarm with agent-specific tasks
swarm = SpreadSheetSwarm(
    name="Multi-Task-Swarm",
    agents=[market_researcher, financial_analyst, risk_assessor],
    max_loops=1,
)

# Set different tasks for each agent
swarm.agent_tasks = {
    "Market-Researcher": "Research AI market trends",
    "Financial-Analyst": "Calculate market size and growth rate",
    "Risk-Assessor": "Identify market risks and challenges",
}

# Run from configuration
result = swarm.run_from_config()

CSV Output

The swarm automatically saves results to a CSV file in your workspace directory. The CSV includes: - Run ID - Agent Name - Task - Result - Timestamp

You can find the CSV file in your WORKSPACE_DIR with a filename like: spreadsheet_swarm_run_id_{uuid}.csv

Support and Community

If you're facing issues or want to learn more, check out the following resources:

Platform	Link	Description
📚 Documentation	docs.swarms.world	Official documentation and guides
💬 Discord	Join Discord	Live chat and community support
🐦 Twitter	@swarms_corp	Latest news and announcements