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Simple BatchedGridWorkflow Example

This example demonstrates the basic usage of BatchedGridWorkflow with minimal configuration for easy understanding.

Basic Example

from swarms import Agent
from swarms.structs.batched_grid_workflow import BatchedGridWorkflow

# Create two basic agents
agent1 = Agent(model="gpt-4")
agent2 = Agent(model="gpt-4")

# Create workflow with default settings
workflow = BatchedGridWorkflow(
    agents=[agent1, agent2]
)

# Define simple tasks
tasks = [
    "What is the capital of France?",
    "Explain photosynthesis in simple terms"
]

# Run the workflow
result = workflow.run(tasks)

Named Workflow Example

# Create agents
writer = Agent(model="gpt-4")
analyst = Agent(model="gpt-4")

# Create named workflow
workflow = BatchedGridWorkflow(
    name="Content Analysis Workflow",
    description="Analyze and write content in parallel",
    agents=[writer, analyst]
)

# Content tasks
tasks = [
    "Write a short paragraph about renewable energy",
    "Analyze the benefits of solar power"
]

# Execute workflow
result = workflow.run(tasks)

Multi-Loop Example

# Create agents
agent1 = Agent(model="gpt-4")
agent2 = Agent(model="gpt-4")

# Create workflow with multiple loops
workflow = BatchedGridWorkflow(
    agents=[agent1, agent2],
    max_loops=3
)

# Tasks for iterative processing
tasks = [
    "Generate ideas for a mobile app",
    "Evaluate the feasibility of each idea"
]

# Run with multiple loops
result = workflow.run(tasks)

Three Agent Example

# Create three agents
researcher = Agent(model="gpt-4")
writer = Agent(model="gpt-4")
editor = Agent(model="gpt-4")

# Create workflow
workflow = BatchedGridWorkflow(
    name="Research and Writing Pipeline",
    agents=[researcher, writer, editor]
)

# Three different tasks
tasks = [
    "Research the history of artificial intelligence",
    "Write a summary of the research findings",
    "Review and edit the summary for clarity"
]

# Execute workflow
result = workflow.run(tasks)

Key Points

  • Simple Setup: Minimal configuration required for basic usage
  • Parallel Execution: Tasks run simultaneously across agents
  • Flexible Configuration: Easy to customize names, descriptions, and loop counts
  • Error Handling: Built-in error handling and logging
  • Scalable: Works with any number of agents and tasks

Use Cases

  • Content Creation: Multiple writers working on different topics
  • Research Tasks: Different researchers investigating various aspects
  • Analysis Work: Multiple analysts processing different datasets
  • Educational Content: Different instructors creating materials for various subjects