BatchedGridWorkflow: Complete Guide

A comprehensive guide to using BatchedGridWorkflow for parallel multi-agent task processing.

Overview

BatchedGridWorkflow enables parallel execution of multiple tasks across multiple agents, creating a grid-like execution pattern where each agent can process different tasks simultaneously.

Feature	Description
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

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Quick Start

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)

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Basic Examples

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)

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Advanced Examples

Custom Conversation Configuration

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

# Create agents with specific roles
researcher = Agent(
    model="gpt-4",
    system_prompt="You are a research specialist who conducts thorough investigations."
)

writer = Agent(
    model="gpt-4",
    system_prompt="You are a technical writer who creates clear, comprehensive documentation."
)

reviewer = Agent(
    model="gpt-4",
    system_prompt="You are a quality reviewer who ensures accuracy and completeness."
)

# Create workflow with custom conversation settings
workflow = BatchedGridWorkflow(
    id="custom-research-workflow",
    name="Custom Research Pipeline",
    description="Research, writing, and review pipeline with custom conversation tracking",
    agents=[researcher, writer, reviewer],
    conversation_args={
        "message_id_on": True,
        "conversation_id": "research-pipeline-001"
    },
    max_loops=2
)

# Research and documentation tasks
tasks = [
    "Research the latest developments in artificial intelligence safety",
    "Write comprehensive documentation for a new API endpoint",
    "Review and validate the technical specifications document"
]

# Execute with custom configuration
result = workflow.run(tasks)

Iterative Refinement Workflow

# Create refinement agents
initial_creator = Agent(
    model="gpt-4",
    system_prompt="You are a creative content creator who generates initial ideas and drafts."
)

detail_enhancer = Agent(
    model="gpt-4",
    system_prompt="You are a detail specialist who adds depth and specificity to content."
)

polish_expert = Agent(
    model="gpt-4",
    system_prompt="You are a polish expert who refines content for maximum impact and clarity."
)

# Create workflow with multiple refinement loops
workflow = BatchedGridWorkflow(
    name="Iterative Content Refinement",
    description="Multi-stage content creation with iterative improvement",
    agents=[initial_creator, detail_enhancer, polish_expert],
    max_loops=4
)

# Content creation tasks
tasks = [
    "Create an initial draft for a product launch announcement",
    "Add detailed specifications and technical details to the content",
    "Polish and refine the content for maximum engagement"
]

# Execute iterative refinement
result = workflow.run(tasks)

Specialized Domain Workflow

# Create domain-specific agents
medical_expert = Agent(
    model="gpt-4",
    system_prompt="You are a medical expert specializing in diagnostic procedures and treatment protocols."
)

legal_advisor = Agent(
    model="gpt-4",
    system_prompt="You are a legal advisor specializing in healthcare regulations and compliance."
)

technology_architect = Agent(
    model="gpt-4",
    system_prompt="You are a technology architect specializing in healthcare IT systems and security."
)

# Create specialized workflow
workflow = BatchedGridWorkflow(
    name="Healthcare Technology Assessment",
    description="Multi-domain assessment of healthcare technology solutions",
    agents=[medical_expert, legal_advisor, technology_architect],
    max_loops=1
)

# Domain-specific assessment tasks
tasks = [
    "Evaluate the medical efficacy and safety of a new diagnostic AI system",
    "Assess the legal compliance and regulatory requirements for the system",
    "Analyze the technical architecture and security implications of implementation"
]

# Execute specialized assessment
result = workflow.run(tasks)

Parallel Analysis Workflow

# Create analysis agents
market_analyst = Agent(
    model="gpt-4",
    system_prompt="You are a market analyst who evaluates business opportunities and market trends."
)

financial_analyst = Agent(
    model="gpt-4",
    system_prompt="You are a financial analyst who assesses investment potential and financial viability."
)

risk_assessor = Agent(
    model="gpt-4",
    system_prompt="You are a risk assessor who identifies potential threats and mitigation strategies."
)

# Create parallel analysis workflow
workflow = BatchedGridWorkflow(
    name="Comprehensive Business Analysis",
    description="Parallel analysis of market, financial, and risk factors",
    agents=[market_analyst, financial_analyst, risk_assessor],
    max_loops=2
)

# Analysis tasks
tasks = [
    "Analyze the market opportunity for a new fintech product",
    "Evaluate the financial projections and investment requirements",
    "Assess the regulatory and operational risks associated with the venture"
]

# Execute parallel analysis
result = workflow.run(tasks)

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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
• Quality Assurance: Comprehensive testing across multiple domains
• Domain Specialization: Multi-domain assessment and evaluation

Best Practices

1. Agent Specialization: Create agents with specific roles and expertise
2. Task Alignment: Ensure tasks match agent capabilities
3. Loop Configuration: Use multiple loops for iterative processes
4. Error Monitoring: Monitor logs for execution issues
5. Resource Management: Consider computational requirements for multiple agents