Overview
The SpreadSheetSwarm processes tasks concurrently across multiple agents and automatically saves execution metadata to CSV files. It supports loading agent configurations from CSV files and running tasks either from configuration or on-demand.
Installation
Attributes
name
str
default:"Spreadsheet-Swarm"
The name of the swarm
description
str
default:"A swarm that processes tasks concurrently..."
The description of the swarm
agents
List[Agent]
default:"None"
The agents participating in the swarm. If None, agents will be loaded from load_path
Whether to enable autosave of swarm metadata
The file path to save the swarm metadata as a CSV file (auto-generated if None)
The number of times to repeat the swarm tasks
Path to CSV file containing agent configurations. Required if agents is None
Methods
run()
Run the swarm with the specified task.
def run(self, task: str = None, *args, **kwargs) -> dict
task (str): The task to be executed by the swarm. If None, uses tasks from config
Returns: Dictionary containing run summary with outputs and metadata
run_from_config()
Run all agents with their configured tasks concurrently.
def run_from_config(self) -> dict
load_from_csv()
Load agent configurations from a CSV file.
Expected CSV format:
agent_name,description,system_prompt,task,model_name,max_loops
export_to_json()
Export the swarm outputs to JSON.
def export_to_json(self) -> str
Usage Examples
Basic Usage with Agents
from swarms import Agent, SpreadSheetSwarm
# Create agents
agents = [
Agent(
agent_name="Research-Agent",
system_prompt="You are a research agent.",
model_name="openai/gpt-4o",
),
Agent(
agent_name="Analysis-Agent",
system_prompt="You are an analysis agent.",
model_name="openai/gpt-4o",
),
]
# Create swarm
swarm = SpreadSheetSwarm(
name="My-Swarm",
agents=agents,
max_loops=1,
autosave=True
)
# Run with a task
result = swarm.run("Analyze the latest AI trends")
print(result)
Load from CSV Configuration
Create a CSV file (agents_config.csv):
agent_name,description,system_prompt,task,model_name,max_loops
Research-Agent,Research specialist,You are a research agent,Research quantum computing,openai/gpt-4o,1
Analysis-Agent,Data analyst,You are an analysis agent,Analyze the research findings,openai/gpt-4o,1
swarm = SpreadSheetSwarm(
name="CSV-Swarm",
load_path="agents_config.csv",
max_loops=1
)
# Run agents with their configured tasks
result = swarm.run_from_config()
Multiple Loops
# Run each agent multiple times
swarm = SpreadSheetSwarm(
agents=agents,
max_loops=3 # Each agent runs 3 times
)
result = swarm.run("Process this task multiple times")
Custom Save Path
swarm = SpreadSheetSwarm(
name="Custom-Swarm",
agents=agents,
save_file_path="./results/swarm_outputs.csv",
autosave=True
)
result = swarm.run("Custom task")
Export Results
# Run swarm
result = swarm.run("Some task")
# Export to JSON
json_output = swarm.export_to_json()
print(json_output)
# Results are automatically saved to CSV if autosave=True
run() method returns a dictionary:
{
"run_id": "spreadsheet_swarm_run_abc123",
"name": "My-Swarm",
"description": "A swarm that processes tasks...",
"start_time": "2024-01-01T12:00:00",
"end_time": "2024-01-01T12:05:00",
"tasks_completed": 6,
"number_of_agents": 3,
"outputs": [
{
"agent_name": "Research-Agent",
"task": "Analyze the latest AI trends",
"result": "...",
"timestamp": "2024-01-01T12:01:00"
},
# ... more outputs
]
}
Run ID,Agent Name,Task,Result,Timestamp
abc-123,Research-Agent,Analyze trends,...,2024-01-01T12:00:00
abc-123,Analysis-Agent,Analyze trends,...,2024-01-01T12:00:05
Features
- Concurrent Execution: All agents run tasks in parallel for maximum performance
- Automatic CSV Logging: All executions are logged to CSV files automatically
- CSV Configuration: Load agent configurations from CSV files
- Multiple Loops: Run each agent multiple times with
max_loops
- Workspace Integration: Automatically uses workspace directory from environment
- JSON Export: Export results to JSON format
- Metadata Tracking: Tracks timestamps, run IDs, and execution metadata