In this example, we’ll explore how ControlFlow enables the creation of complex, hierarchical workflows using dependent tasks. We’ve chosen a text analysis scenario to demonstrate several powerful features of ControlFlow:

  • Organizing tasks in a logical, nested structure
  • Simplifying data flow through automatic context sharing
  • Ensuring correct task execution order with dependencies
  • Maintaining a shared context across all tasks in a workflow

As you examine the code, pay attention to how these concepts are implemented and how they contribute to creating a clear, efficient workflow structure.

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Code

import controlflow as cf

@cf.flow
def analyze_text(text: str):

    # Create a parent task to represent the entire analysis
    with cf.Task(
        "Analyze the given text", 
        instructions="Include each subtask result in your result",
        result_type=dict, 
        context={"text": text}
    ) as parent_task:
        
        # Child task 1: Identify key terms
        key_terms = cf.Task(
            "Identify up to 10 key terms in the text",
            result_type=list[str]
        )

        # Child task 2: Summarize (depends on key_terms)
        summary = cf.Task(
            "Summarize the text in one sentence",
            result_type=str,
            depends_on=[key_terms]
        )

    # Run the parent task, which will automatically run all child tasks
    result = parent_task.run()
    return result

# Execute the flow
text = """
    Agentic workflow orchestration refers to the coordination of autonomous
    agents within a structured workflow, allowing them to operate independently
    while achieving a common objective. Unlike traditional workflows that rigidly
    define tasks and dependencies, agentic workflows empower agents—typically
    AI-driven—to make decisions, prioritize tasks, and collaborate dynamically.
    Each agent in this system operates with a degree of autonomy, enabling it to
    adapt to changing conditions, handle uncertainties, and optimize its own
    actions within the broader workflow. This approach enhances flexibility and
    scalability, making it particularly effective for complex, multi-step
    processes where real-time adjustments and intelligent decision-making are
    crucial. By leveraging agents with defined roles and responsibilities, agentic
    workflows maintain structure while enabling innovation and responsiveness in
    task execution.
    """
    
result = analyze_text(text)
print(result)

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Key points

  1. Task hierarchy: The parent task encompasses the entire analysis process, with child tasks handling specific aspects. This structure allows for logical organization of complex workflows.
  2. Automatic context sharing: Child tasks have access to their parent’s context without explicit passing, streamlining data flow within the workflow.
  3. Dependencies: The depends_on parameter ensures tasks are executed in the correct order, as demonstrated by the summary task depending on the key terms task.
  4. Flow context: By wrapping tasks in a flow, ControlFlow maintains a shared context across all tasks, including visibility into prior executions and conversation history.
  5. Unified execution: Running the parent task automatically executes all child tasks in the correct order, simplifying workflow management.

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Further reading

  • For more details on creating tasks and context sharing, see the task documentation.
  • To learn more about defining dependencies between tasks, check out the dependencies guide.
  • For information on how ControlFlow manages task execution and context, refer to the running tasks guide.

By leveraging these features, you can create complex workflows that maintain a clear structure and ensure efficient information flow between tasks. This approach helps in building more maintainable and scalable AI-powered applications with minimal boilerplate code.