agent-framework/python/samples/03-workflows/declarative/human_in_loop

Human-in-Loop Workflow Sample

This sample demonstrates how to build interactive workflows that request user input during execution using the Question and RequestExternalInput actions.

What This Sample Shows

• Using Question to prompt for user responses
• Using RequestExternalInput to request external data
• Processing user responses to drive workflow decisions
• Interactive conversation patterns

Files

• workflow.yaml - The declarative workflow definition
• main.py - Python script that loads and runs the workflow with simulated user interaction

Running the Sample

1. Ensure you have the package installed:

   cd python
   pip install -e packages/agent-framework-declarative
   

2. Run the sample:

   python main.py
   

How It Works

The workflow demonstrates a simple survey/questionnaire pattern:

1. Greeting: Sends a welcome message
2. Question 1: Asks for the user's name
3. Question 2: Asks how they're feeling today
4. Processing: Stores responses and provides personalized feedback
5. Summary: Summarizes the collected information

The main.py script shows how to handle ExternalInputRequest to provide responses during workflow execution.

Key Concepts

ExternalInputRequest

When a human-in-loop action is executed, the workflow yields an ExternalInputRequest containing:

• variable: The variable path where the response should be stored
• prompt: The question or prompt text for the user

The workflow runner should:

1. Detect ExternalInputRequest in the event stream
2. Display the prompt to the user
3. Collect the response
4. Resume the workflow (in a real implementation, using external loop patterns)

ExternalLoopEvent

For more complex scenarios where external processing is needed, the workflow can yield an ExternalLoopEvent that signals the runner to pause and wait for external input.