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This guide explains how to use the Python SDK to define, initiate, and monitor multi-step workflows. Prerequisites: Before using the methods described here, ensure you have:
  1. Followed the Python SDK Setup guide for installation and initialization.
  2. Obtained your credentials as described in Managing API Keys.
  3. Successfully initialized the agent object as shown in the SDK Setup guide.

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Creating a Workflow Definition

Define a reusable sequence of tasks that can be executed later. 
import os
from curalabs_agent import cura # Assuming 'cura' is the client object

# Assume agent is initialized as shown in the Initialization section
# agent = cura(...)

workflow_tasks = [
    {"instructions": "Navigate to gmail.com and log in."},
    {"instructions": "Find the latest email from '[email protected]'."},
    {"instructions": "Extract the subject line."}
]

try:
    workflow_def = agent.create_workflow(
        name="Check Support Email Subject", # Required: A user-friendly name
        tasks=workflow_tasks # Required: List of task steps (dictionaries with 'instructions' and optional 'target_url')
    )

    if workflow_def:
        workflow_id = workflow_def.get('workflow_id')
        print(f"Workflow definition created successfully with ID: {workflow_id}")
        # Store workflow_id for later use
    else:
        print("Failed to create workflow definition.")

except Exception as e:
    print(f"An error occurred while creating the workflow definition: {e}")
The create_workflow method requires a name for the workflow and a list of tasks. Each item in the tasks list defines a step and must contain either:
  • An instructions string (which guides the agent’s actions for that step). This may optionally be paired with a target_url (string or list of strings) to specify an exact starting page(s) for the step.
  • OR the task_id of a pre-defined task (like one generated from video).
You cannot provide both instructions and task_id for the same step. The target_url within a task step definition is optional. If provided, it specifies the starting page(s) for that specific step. If omitted for a step, the agent will begin that step based on the context from the previous step or navigate based purely on the instructions provided for the current step. For more details on using target_url (including multiple URLs) versus instruction-based navigation within steps, see Advanced SDK Patterns: Using target_url. Example using task_id: 
# Assume task_id_from_video was obtained from the video processing job
task_id_from_video = "tsk_xyz789abc123" 
standard_logout_task_id = "tsk_logout_task_def" # Assumes a predefined task for logging out

workflow_tasks_mixed = [
    {"task_id": task_id_from_video}, # Step 1: Run the task generated from video
    {"instructions": "Summarize the result obtained in the previous step and send it as an email."}, # Step 2: Use instructions
    {"task_id": standard_logout_task_id} # Step 3: Run a standard logout task
]

try:
    workflow_def_mixed = agent.create_workflow(
        name="Process Video Task and Summarize", 
        tasks=workflow_tasks_mixed 
    )
    if workflow_def_mixed:
        print(f"Workflow using task_id created: {workflow_def_mixed.get('workflow_id')}")
    else:
        print("Failed to create mixed workflow.")
except Exception as e:
    print(f"Error: {e}")
On success, create_workflow returns a dictionary containing the details of the created workflow, including its unique workflow_id.

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Starting a Workflow Run

Initiate an execution run of a previously defined workflow for a specific end-user. 
workflow_id_to_run = "wf_..." # Replace with the actual workflow_id from create_workflow
user_id = "user-unique-id-in-your-system-456" # Replace with the end-user's ID in your system

try:
    start_response = agent.start_workflow_run(
        workflow_id=workflow_id_to_run, # Required: ID of the workflow to run
        developer_user_id=user_id # Required: Your internal ID for the end-user
    )

    if start_response:
        run_id = start_response.get('run_id')
        print(f"Workflow run successfully queued with Run ID: {run_id}")
        # Store run_id to check status later
    else:
        print("Failed to queue workflow run.")

except Exception as e:
    print(f"An error occurred while starting the workflow run: {e}")
The start_workflow_run method requires the workflow_id of the definition to execute and the developer_user_id of the target end-user. On success, it returns a dictionary containing the unique run_id for this specific execution and its initial status (usually ‘queued’).

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Checking Workflow Run Status (Polling)

Monitor the progress and retrieve the results of a specific workflow run using its run_id. 
import time

run_id_to_check = "wfr_..." # Replace with the actual run_id obtained from start_workflow_run

if run_id_to_check:
    while True:
        try:
            status_info = agent.get_workflow_run_status(run_id_to_check) # Required: ID of the run

            if not status_info:
                print("Failed to get workflow run status.")
                break

            status = status_info.get('status')
            print(f"Current workflow run status: {status}")

            if status == 'completed':
                print("Workflow run completed.")
                print(f"Result: {status_info.get('result')}") # Result structure may vary
                break
            elif status == 'failed':
                print("Workflow run failed.")
                print(f"Error: {status_info.get('error')}")
                break
            elif status in ['queued', 'processing']:
                # Wait before checking again
                print("Workflow run still in progress, checking again in 10 seconds...")
                time.sleep(10)
            else:
                print(f"Unknown status: {status}")
                break

        except Exception as e:
            print(f"An error occurred while checking workflow run status: {e}")
            break
else:
    print("No Run ID provided to check status.")
The get_workflow_run_status method requires the run_id. It returns a dictionary containing the run’s current status, result (if completed, structure may vary), error (if failed), and other details like workflow_id and timestamps, or None on error. (Note: Handling results via webhooks, similar to tasks, is also recommended for workflows to avoid polling. The webhook payload structure for workflow events would need to be defined and verified using agent.verify_webhook.)