Sequential Workflow Example

A SequentialWorkflow executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one. This architecture is ideal for processes that have clear, ordered steps and ensures that tasks with dependencies are handled correctly.

How Sequential Workflow Works

In a sequential workflow:

Linear Execution: Agents execute tasks one after another in a defined order
Output Chaining: The output of one agent becomes the input for the next agent
Dependency Management: Ensures tasks with dependencies are handled correctly
Sequential Processing: Each agent must complete before the next one starts

Basic Example: Researcher to Writer Pipeline

This example demonstrates a two-agent workflow for researching and writing a blog post:

from swarms import Agent, SequentialWorkflow

# Agent 1: The Researcher
researcher = Agent(
    agent_name="Researcher",
    system_prompt="Your job is to research the provided topic and provide a detailed summary.",
    model_name="gpt-5.4",
)

# Agent 2: The Writer
writer = Agent(
    agent_name="Writer",
    system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
    model_name="gpt-5.4",
)

# Create a sequential workflow where the researcher's output feeds into the writer's input
workflow = SequentialWorkflow(agents=[researcher, writer])

# Run the workflow on a task
final_post = workflow.run("The history and future of artificial intelligence")
print(final_post)

How This Example Works

Task Assignment: The initial task “The history and future of artificial intelligence” is sent to the first agent (Researcher)
Research Phase: The Researcher agent processes the task and produces a detailed summary
Handoff: The Researcher’s output automatically becomes the input for the Writer agent
Writing Phase: The Writer agent takes the research summary and creates an engaging blog post
Final Output: The workflow returns the final blog post from the Writer agent

Common Use Cases

SequentialWorkflow is ideal for:

Content Creation Pipelines: Research → Writing → Editing → Publishing
Data Processing: Collection → Cleaning → Analysis → Reporting
Software Development: Design → Implementation → Testing → Deployment
Document Processing: Extraction → Transformation → Validation → Storage
Report Generation: Data gathering → Analysis → Visualization → Summary

Variations

Three-Stage Content Pipeline

Add an editor to review and polish the content:

from swarms import Agent, SequentialWorkflow

# Define three agents for a complete content pipeline
researcher = Agent(
    agent_name="Researcher",
    system_prompt="Research the topic thoroughly and provide comprehensive findings.",
    model_name="gpt-5.4",
)

writer = Agent(
    agent_name="Writer",
    system_prompt="Transform research into an engaging, well-structured article.",
    model_name="gpt-5.4",
)

editor = Agent(
    agent_name="Editor",
    system_prompt="Review the article for clarity, grammar, and style. Polish the final content.",
    model_name="gpt-5.4",
)

# Create sequential workflow with three stages
workflow = SequentialWorkflow(agents=[researcher, writer, editor])

# Run the complete pipeline
polished_article = workflow.run("The impact of quantum computing on cybersecurity")
print(polished_article)

Multi-Step Data Processing

Process data through multiple transformation stages:

from swarms import Agent, SequentialWorkflow

# Define agents for data processing pipeline
data_collector = Agent(
    agent_name="DataCollector",
    system_prompt="Gather and collect data from various sources on the given topic.",
    model_name="gpt-5.4",
)

data_cleaner = Agent(
    agent_name="DataCleaner",
    system_prompt="Clean and normalize the collected data, removing inconsistencies.",
    model_name="gpt-5.4",
)

data_analyzer = Agent(
    agent_name="DataAnalyzer",
    system_prompt="Analyze the cleaned data and extract meaningful insights.",
    model_name="gpt-5.4",
)

report_generator = Agent(
    agent_name="ReportGenerator",
    system_prompt="Generate a comprehensive report based on the analysis.",
    model_name="gpt-5.4",
)

# Create the data processing workflow
workflow = SequentialWorkflow(
    agents=[data_collector, data_cleaner, data_analyzer, report_generator]
)

# Process data through the pipeline
final_report = workflow.run("Analyze customer feedback trends from Q1 2024")
print(final_report)

Code Development Pipeline

Build a complete software development workflow:

from swarms import Agent, SequentialWorkflow

# Define agents for software development
designer = Agent(
    agent_name="Designer",
    system_prompt="Design the architecture and structure for the requested feature.",
    model_name="gpt-5.4",
)

developer = Agent(
    agent_name="Developer",
    system_prompt="Implement the feature based on the design specifications.",
    model_name="gpt-5.4",
)

tester = Agent(
    agent_name="Tester",
    system_prompt="Test the implementation and identify any bugs or issues.",
    model_name="gpt-5.4",
)

deployer = Agent(
    agent_name="Deployer",
    system_prompt="Create deployment instructions and documentation for the feature.",
    model_name="gpt-5.4",
)

# Create the development workflow
workflow = SequentialWorkflow(agents=[designer, developer, tester, deployer])

# Run the development pipeline
deployment_package = workflow.run("Create a user authentication system with JWT tokens")
print(deployment_package)

Best Practices

Clear Agent Roles: Define specific, focused responsibilities for each agent
Appropriate Ordering: Arrange agents in logical sequence based on task dependencies
Detailed System Prompts: Provide clear instructions about input expectations and output format
Error Handling: Consider what happens if an intermediate agent fails
Pipeline Length: Keep workflows manageable; very long pipelines may benefit from breaking into sub-workflows

Key Benefits

Maintainability: Easy to understand and modify the workflow sequence
Reliability: Deterministic execution order ensures consistent results
Debugging: Simple to identify and fix issues at specific stages
Reusability: Individual agents can be reused in different workflows
Scalability: Easy to add or remove stages from the pipeline

ConcurrentWorkflow: Run agents in parallel instead of sequence
HierarchicalSwarm: Use a director to coordinate multiple workers
AgentRearrange: Define complex agent relationships

Index

Basic Examples

Model Providers

Single Agent

Multi-Agent Examples

Applications

Research

Technical Analysis

Use Cases

Finance

Voice Agents

Integrations

Deployment

CLI

Sequential Workflow Example

How Sequential Workflow Works

Basic Example: Researcher to Writer Pipeline

How This Example Works

Common Use Cases

Variations

Three-Stage Content Pipeline

Multi-Step Data Processing

Code Development Pipeline

Best Practices

Key Benefits

Learn More

​How Sequential Workflow Works

​Basic Example: Researcher to Writer Pipeline

​How This Example Works

​Common Use Cases

​Variations

​Three-Stage Content Pipeline

​Multi-Step Data Processing

​Code Development Pipeline

​Best Practices

​Key Benefits

​Related Architectures

​Learn More

How Sequential Workflow Works

Basic Example: Researcher to Writer Pipeline

How This Example Works

Common Use Cases

Variations

Three-Stage Content Pipeline

Multi-Step Data Processing

Code Development Pipeline

Best Practices

Key Benefits

Related Architectures

Learn More