witrium 0.4.2

Python client for Witrium cloud browser automation API

Witrium Client

A Python client library for interacting with the Witrium API. Witrium is a cloud-based browser automation platform that allows you to create and execute web automations through a visual interface and control them programmatically via this client.

How Witrium Works

Witrium operates by spinning up browser instances in the cloud to execute predefined automations that you create through the Witrium UI. Here's the typical workflow:

1. Create Automations via UI: You use the Witrium web interface to record and define your automations (workflows)
2. Execute via API: You use this Python client to trigger those automations programmatically
3. Cloud Execution: Witrium runs your automation in a real browser instance in the cloud
4. Retrieve Results: You poll for results and handle the automation outcomes

Each workflow is identified by a unique workflow_id and can accept arguments to customize its execution.

Installation

pip install witrium

Quick Start

The snippet below shows the minimum you need to get up-and-running:

from witrium import SyncWitriumClient, WorkflowRunStatus

# 1. Provide your API endpoint & token (export these as env-vars in production)
api_token = "YOUR_WITRIUM_API_TOKEN"  # Obtain from dashboard

with SyncWitriumClient(api_token=api_token) as client:
    # 2. Kick-off the **login** workflow and keep the browser alive
    login = client.run_workflow(
        workflow_id="login-workflow-id",  # This workflow performs the sign-in steps
        args={"username": "[e-mail]", "password": "secretPass!"},
        keep_session_alive=True  # 🔑 keep the browser running after login
    )

    # 3. Block until the browser is *ready for reuse*
    client.wait_until_state(
        run_id=login.run_id,
        target_status=WorkflowRunStatus.RUNNING,  # Wait until browser is alive
        all_instructions_executed=True  # …and the last login step finished
    )

    # 4. Re-use that **same** browser session in a follow-up workflow
    scrape = client.run_workflow(
        workflow_id="dashboard-scrape-workflow-id",
        args={"section": "sales"},
        use_existing_session=login.run_id  # 👈 same browser instance
    )

    # 5. Wait for the scrape to finish and collect the results
    results = client.wait_until_state(
        run_id=scrape.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )

print("Sales data:", results.result)

---

Workflow Lifecycle & Polling Essentials

client.run_workflow(...) only submits a job – the real browser work happens asynchronously in the cloud. Keep these steps in mind whenever you design multi-step automations:

1. Submit – your call returns instantly with a run_id.
2. Poll / Wait – use wait_until_state() (or run_workflow_and_wait()) to block until the run reaches: • WorkflowRunStatus.RUNNING – the browser has spun-up and is ready (handy when you enabled keep_session_alive).
   • WorkflowRunStatus.COMPLETED – the workflow has finished executing.
3. Chain or Fetch Results – once the target state is reached you can either run another workflow (chaining sessions) or read the data via get_workflow_results().

When to wait for which state?

Scenario	Recommended target_status	Extra flags
You saved state using preserve_state	COMPLETED	–
You kept the session alive using keep_session_alive and intend to reuse it	RUNNING	all_instructions_executed=True

⏳ Tip: For very long login flows (e.g. multi-factor auth) combine min_wait_time with polling_interval to reduce server load.

Concurrency vs. Serial Execution

• State Preservation (preserve_state) – Each follow-up workflow spins up its own browser. Scale horizontally & run many in parallel.
• Session Persistence (keep_session_alive) – All follow-up workflows share one browser instance. Run them serially (until multi-tab support lands).

---

Common Use Cases and Session Management

The Authentication Challenge

A common pattern in web automation involves authentication: you need to log into a service first, then perform actions in the authenticated session. Witrium provides two powerful approaches to handle this:

Approach 1: State Preservation (Concurrent-Friendly)

• Best for: Running multiple post-login automations concurrently
• How it works: Save browser state after login, then restore it in new browser instances

Approach 2: Session Persistence (Resource-Efficient)

• Best for: Sequential automations that need to share the exact same browser session
• How it works: Keep the browser alive after login, run subsequent automations in the same instance

Session Management Patterns

Pattern 1: Disconnected Sessions with State Preservation

This approach allows you to save the browser state (cookies, localStorage, etc.) after a login workflow and then restore that state in new browser instances for subsequent workflows.

Advantages:

• Multiple post-login workflows can run concurrently
• Each workflow gets its own browser instance
• Horizontal scaling of browser instances
• Robust isolation between concurrent executions

Use Case Example:

from witrium import SyncWitriumClient, WorkflowRunStatus

with SyncWitriumClient(api_token="your-api-token") as client:
    # Step 1: Run login workflow and preserve the authenticated state
    login_response = client.run_workflow(
        workflow_id="login-workflow-id",
        args={"username": "[e-mail]", "password": "secure123"},
        preserve_state="authenticated-session"  # Save state with this name
    )

    # Step 2: Wait for login to complete
    login_results = client.wait_until_state(
        run_id=login_response.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )

# Step 3: Run multiple post-login workflows concurrently
# Each will spawn a new browser but restore the authenticated state

# Workflow A: Extract data from dashboard
dashboard_response = client.run_workflow(
    workflow_id="dashboard-scraping-workflow-id",
    args={"report_type": "monthly"},
    use_states=["authenticated-session"]  # Restore the saved state
)

# Workflow B: Update user profile (can run concurrently)
profile_response = client.run_workflow(
    workflow_id="profile-update-workflow-id",
    args={"new_email": "[e-mail]"},
    use_states=["authenticated-session"]  # Same state, different browser instance
)

# Both workflows are now running concurrently in separate browser instances
# but both have access to the authenticated session

Pattern 2: Persistent Session with Keep-Alive

This approach keeps the browser instance alive after the login workflow completes, allowing subsequent workflows to run in the same browser session.

Advantages:

• More resource-efficient (reuses same browser instance)
• Maintains exact session continuity
• No need to restore state (session never ends)
• Faster execution for subsequent workflows

Limitations:

• Subsequent workflows must run serially (one after another)
• Cannot run multiple post-login workflows concurrently in the same session

Use Case Example:

from witrium import SyncWitriumClient, WorkflowRunStatus

with SyncWitriumClient(api_token="your-api-token") as client:
    # Step 1: Run login workflow and keep the browser session alive
    login_response = client.run_workflow(
        workflow_id="login-workflow-id",
        args={"username": "[e-mail]", "password": "secure123"},
        keep_session_alive=True  # Keep browser instance running
    )

    # Step 2: Wait for login to complete and start running
    # We wait for RUNNING status because the browser is kept alive
    login_results = client.wait_until_state(
        run_id=login_response.run_id,
        target_status=WorkflowRunStatus.RUNNING,
        all_instructions_executed=True  # Ensure login steps are done
    )

    # Step 3: Run subsequent workflows in the same browser session
    # These must run serially, not concurrently

    # Workflow A: Extract data from dashboard
    dashboard_response = client.run_workflow(
        workflow_id="dashboard-scraping-workflow-id",
        args={"report_type": "monthly"},
        use_existing_session=login_response.run_id  # Use the live session
    )

    # Wait for dashboard workflow to complete before next one
    dashboard_results = client.wait_until_state(
        run_id=dashboard_response.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )

    # Workflow B: Update user profile (must wait for previous to complete)
    profile_response = client.run_workflow(
        workflow_id="profile-update-workflow-id",
        args={"new_email": "[e-mail]"},
        use_existing_session=login_response.run_id  # Same live session
    )

Choosing the Right Pattern

Factor	State Preservation	Session Persistence
Concurrency	✅ Multiple workflows can run simultaneously	❌ Must run serially
Resource Usage	Higher (multiple browser instances)	✅ Lower (single browser instance)
Isolation	✅ Complete isolation between workflows	❌ Shared session state
Setup Complexity	Medium (manage state names)	✅ Simple (just workflow run IDs)
Use Case	Bulk data processing, parallel operations	Sequential workflows, state-dependent operations

Complete Examples

Example 1: E-commerce Data Extraction (State Preservation)

from witrium import SyncWitriumClient, WorkflowRunStatus
import concurrent.futures
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def extract_category_data(client, category, state_name):
    """Extract data for a specific product category."""
    try:
        response = client.run_workflow(
            workflow_id="category-scraper-workflow",
            args={"category": category},
            use_states=[state_name]
        )

        results = client.wait_until_state(
            run_id=response.run_id,
            target_status=WorkflowRunStatus.COMPLETED
        )

        return {"category": category, "data": results.result}
    except Exception as e:
        logger.error(f"Failed to extract {category}: {e}")
        return {"category": category, "error": str(e)}


with SyncWitriumClient(api_token="your-api-token") as client:
    # Step 1: Login and save state
    logger.info("Logging into e-commerce platform...")
    login_response = client.run_workflow(
        workflow_id="ecommerce-login-workflow",
        args={"email": "[e-mail]", "password": "secure123"},
        preserve_state="ecommerce-authenticated"
    )

    # Wait for login completion
    client.wait_until_state(
        run_id=login_response.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )
    logger.info("Login completed, state preserved")

    # Step 2: Extract data from multiple categories concurrently
    categories = ["electronics", "clothing", "home-garden", "books", "sports"]

    with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
        # Submit all category extraction tasks concurrently
        future_to_category = {
            executor.submit(extract_category_data, client, category,
                            "ecommerce-authenticated"): category
            for category in categories
        }

        results = []
        for future in concurrent.futures.as_completed(future_to_category):
            result = future.result()
            results.append(result)
            logger.info(f"Completed extraction for {result['category']}")

    logger.info(f"Extracted data from {len(results)} categories")
    for result in results:
        if "error" in result:
            logger.error(f"Error in {result['category']}: {result['error']}")
        else:
            logger.info(f"{result['category']}: {len(result['data'])} items extracted")

Example 2: Banking Workflow (Session Persistence)

from witrium import SyncWitriumClient, WorkflowRunStatus
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

with SyncWitriumClient(api_token="your-api-token") as client:
    # Step 1: Secure login with 2FA
    logger.info("Initiating secure banking login...")
    login_response = client.run_workflow(
        workflow_id="bank-login-with-2fa-workflow",
        args={
            "username": "customer123",
            "password": "secure456",
            "phone_number": "[phone]"  # For 2FA
        },
        keep_session_alive=True  # Keep session for subsequent operations
    )

    # Wait for login and 2FA to complete
    logger.info("Waiting for login and 2FA completion...")
    login_results = client.wait_until_state(
        run_id=login_response.run_id,
        target_status=WorkflowRunStatus.RUNNING,
        all_instructions_executed=True,
        min_wait_time=30  # 2FA usually takes some time
    )
    logger.info("Secure login completed")

    # Step 2: Check account balances
    logger.info("Checking account balances...")
    balance_response = client.run_workflow(
        workflow_id="check-balances-workflow",
        args={"account_types": ["checking", "savings", "credit"]},
        use_existing_session=login_response.run_id
    )

    balance_results = client.wait_until_state(
        run_id=balance_response.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )
    logger.info(f"Account balances retrieved: {balance_results.result}")

    # Step 3: Download transaction history
    logger.info("Downloading transaction history...")
    transaction_response = client.run_workflow(
        workflow_id="download-transactions-workflow",
        args={
            "date_range": "last_30_days",
            "format": "csv",
            "accounts": ["checking", "savings"]
        },
        use_existing_session=login_response.run_id
    )

    transaction_results = client.wait_until_state(
        run_id=transaction_response.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )
    logger.info("Transaction history downloaded")

    # Step 4: Generate financial report
    logger.info("Generating financial report...")
    report_response = client.run_workflow(
        workflow_id="generate-financial-report-workflow",
        args={
            "report_type": "monthly_summary",
            "include_charts": True
        },
        use_existing_session=login_response.run_id
    )

    report_results = client.wait_until_state(
        run_id=report_response.run_id,
        target_status=WorkflowRunStatus.COMPLETED
    )

    logger.info("Financial report generated successfully")
    logger.info("All banking operations completed in the same secure session")

Running Talents

In addition to workflows, you can execute "Talents" directly. Talents are pre-defined capabilities or simpler automation units that can be executed with specific arguments.

from witrium import SyncWitriumClient
from witrium.types import TalentExecuteSchema

with SyncWitriumClient(api_token="your-api-token") as client:
    # Run a talent by ID with its execution schema
    result = client.run_talent(
        talent_id="talent-uuid",
        execute_schema=TalentExecuteSchema(
            args={"key": "value"},
            # Optional parameters:
            # files=[...],
            # use_states=["state-id"],
            # preserve_state="new-state-name"
        )
    )

    # The result is a TalentResultSchema object
    print(f"Status: {result.status}")
    print(f"Result data: {result.result}")
    if result.error_message:
        print(f"Error: {result.error_message}")

Basic Usage

Synchronous Client

from witrium import SyncWitriumClient

# Using with context manager (recommended)
with SyncWitriumClient(api_token="your-api-token") as client:
    # Run a workflow and wait for results
    results = client.run_workflow_and_wait(
        workflow_id="workflow-uuid",
        args={"key1": "value1", "key2": 42},
        polling_interval=5,
        timeout=300
    )
    print(f"Workflow completed with status: {results.status}")
    print(f"Results: {results.result}")

    # Or run a workflow without waiting
    response = client.run_workflow(
        workflow_id="workflow-uuid",
        args={"key1": "value1"}
    )
    print(f"Workflow run started: {response.run_id}")

    # Get results later
    results = client.get_workflow_results(
        run_id=response.run_id
    )

    # Wait for workflow to start running
    results = client.wait_until_state(
        run_id=response.run_id,
        target_status=WorkflowRunStatus.RUNNING
    )
    print(f"Workflow is now running: {results.status}")

Asynchronous Client

import asyncio
from witrium import AsyncWitriumClient, WorkflowRunStatus


async def run_workflow():
    # Using with async context manager (recommended)
    async with AsyncWitriumClient(api_token="your-api-token") as client:
        # Run a workflow and wait for results
        results = await client.run_workflow_and_wait(
            workflow_id="workflow-uuid",
            args={"key1": "value1", "key2": 42},
            polling_interval=5,
            timeout=300
        )
        print(f"Workflow completed with status: {results.status}")
        print(f"Results: {results.result}")

        # Or start a workflow and wait for it to begin running
        response = await client.run_workflow(
            workflow_id="workflow-uuid",
            args={"key1": "value1"}
        )

        # Wait until workflow starts running
        results = await client.wait_until_state(
            run_id=response.run_id,
            target_status=WorkflowRunStatus.RUNNING
        )
        print(f"Workflow is now running: {results.status}")


# Run the async function
asyncio.run(run_workflow())

Progress Tracking and Monitoring

Real-time Progress Tracking

import time
from tqdm import tqdm
from witrium import SyncWitriumClient, WorkflowRunStatus


def create_progress_tracker():
    """Create a progress tracking function."""
    pbar = tqdm(total=100, desc="Workflow Progress")
    last_execution_count = 0

    def update_progress(result):
        nonlocal last_execution_count
        # Get execution count
        executions = result.executions or []
        execution_count = len(executions)

        # Update progress bar only if we have new executions
        if execution_count > last_execution_count:
            pbar.update(execution_count - last_execution_count)
            last_execution_count = execution_count

        # Update description based on status
        pbar.set_description(f"Status: {result.status}")

        # Show individual execution details
        for execution in executions:
            if execution.status == "C":  # Completed
                tqdm.write(f"✅ {execution.instruction}")
            elif execution.status == "F":  # Failed
                tqdm.write(f"❌ {execution.instruction}: {execution.error_message}")

    return update_progress, pbar


with SyncWitriumClient(api_token="your-api-token") as client:
    progress_callback, progress_bar = create_progress_tracker()

    try:
        # Run workflow with progress tracking
        result = client.run_workflow_and_wait(
            workflow_id="workflow-uuid",
            args={"key1": "value1"},
            on_progress=progress_callback
        )
        progress_bar.close()
        print("Workflow completed!")
    except Exception as e:
        progress_bar.close()
        print(f"Workflow failed: {e}")

Using Callbacks for Custom Monitoring

# Define a custom progress callback
def monitor_workflow_progress(result):
    """Custom monitoring function."""
    status = result.status
    executions = result.executions or []
    
    print(f"📊 Status: {status}, Executions: {len(executions)}")
    
    # Log each execution step
    for i, execution in enumerate(executions):
        status_emoji = {
            "P": "⏳",  # Pending
            "R": "🔄",  # Running
            "C": "✅",  # Completed
            "F": "❌",  # Failed
        }.get(execution.status, "❓")
        
        print(f"  {status_emoji} Step {i+1}: {execution.instruction}")
        
        if execution.error_message:
            print(f"    ⚠️  Error: {execution.error_message}")

# Use the callback
with SyncWitriumClient(api_token="your-api-token") as client:
    results = client.run_workflow_and_wait(
        workflow_id="workflow-uuid",
        args={"key1": "value1"},
        on_progress=monitor_workflow_progress
    )

API Reference

SyncWitriumClient / AsyncWitriumClient

Initialization

SyncWitriumClient(
    api_token: str,           # API token for authentication
    timeout: int = 60,        # Request timeout in seconds
    verify_ssl: bool = True   # Whether to verify SSL certificates
)

Core Methods

run_workflow()

Execute a workflow in the Witrium platform.

run_workflow(
    workflow_id: str,                                    # Required: ID of the workflow to run
    args: Optional[Dict[str, Union[str, int, float]]] = None,  # Arguments to pass to the workflow
    use_states: Optional[List[str]] = None,              # List of saved state names to restore
    preserve_state: Optional[str] = None,                # Name to save the browser state as
    no_intelligence: bool = False,                       # Disable AI assistance
    record_session: Optional[bool] = False,              # Record the browser session
    keep_session_alive: bool = False,                    # Keep browser alive after completion
    use_existing_session: Optional[str] = None           # Workflow run ID of existing session to use
) -> WorkflowRunSubmittedSchema

Session Management Parameters:

• preserve_state: Save the browser state with this name after workflow completion. Other workflows can then restore this state using use_states.
• use_states: List of previously saved state names to restore at the start of this workflow.
• keep_session_alive: If True, keeps the browser instance running after workflow completion.
• use_existing_session: Run this workflow in an existing browser session (identified by workflow run ID).

run_talent()

Run a talent by ID.

run_talent(
    talent_id: str,                      # Required: ID of the talent to run
    execute_schema: TalentExecuteSchema  # Required: Schema containing args, files, etc.
) -> TalentResultSchema

wait_until_state()

Wait for a workflow run to reach a specific status.

wait_until_state(
    run_id: str,                         # The workflow run ID to wait for
    target_status: str,                  # Target status (e.g., WorkflowRunStatus.RUNNING)
    all_instructions_executed: bool = False,  # Also wait for all executions to complete
    min_wait_time: int = 0,              # Minimum seconds to wait before polling starts
    polling_interval: int = 2,           # Seconds between polling attempts
    timeout: int = 60                    # Maximum seconds to wait
) -> WorkflowRunResultsSchema

Key Parameters:

• target_status: Use WorkflowRunStatus constants (PENDING, RUNNING, COMPLETED, FAILED, CANCELLED)
• all_instructions_executed: When True, also waits for all individual execution steps to complete
• min_wait_time: Useful for long-running workflows to reduce unnecessary polling

Other Methods

• get_workflow_results(run_id): Get current results of a workflow run
• run_workflow_and_wait(...): Run a workflow and poll until completion
• cancel_run(run_id): Cancel a workflow run and clean up associated resources
• close(): Close the HTTP client (called automatically with context manager)

Status Constants

WorkflowRunStatus

WorkflowRunStatus.PENDING      # "P" - Workflow is queued
WorkflowRunStatus.RUNNING      # "R" - Workflow is executing
WorkflowRunStatus.COMPLETED    # "C" - Workflow finished successfully
WorkflowRunStatus.FAILED       # "F" - Workflow failed
WorkflowRunStatus.CANCELLED    # "X" - Workflow was cancelled

# Helper lists
WorkflowRunStatus.TERMINAL_STATUSES  # [COMPLETED, FAILED, CANCELLED]

AgentExecutionStatus

AgentExecutionStatus.PENDING      # "P" - Execution step is queued
AgentExecutionStatus.RUNNING      # "R" - Execution step is running
AgentExecutionStatus.COMPLETED    # "C" - Execution step completed
AgentExecutionStatus.FAILED       # "F" - Execution step failed
AgentExecutionStatus.CANCELLED    # "X" - Execution step cancelled

Response Schemas

WorkflowRunSubmittedSchema

{
    "workflow_id": str,
    "run_id": str,  # Use this for polling and session management
    "status": str
}

WorkflowRunResultsSchema

{
    "workflow_id": str,
    "run_id": str,
    "status": str,
    "started_at": Optional[str],
    "completed_at": Optional[str],
    "message": Optional[str],
    "executions": List[AgentExecutionSchema],  # Individual execution steps
    "result": Optional[dict | list],           # Final workflow result
    "result_format": Optional[str],
    "error_message": Optional[str]
}

AgentExecutionSchema

{
    "status": str,
    "instruction_order": int,
    "instruction": str,
    "result": Optional[dict | list],
    "result_format": Optional[str],
    "error_message": Optional[str]
}

TalentExecuteSchema

{
    "args": Optional[dict[str, Any]],
    "files": Optional[List[FileUpload]],
    "use_states": Optional[List[str]],
    "preserve_state": Optional[str]
}

Exception Handling

from witrium import WitriumClientException

try:
    result = client.run_workflow_and_wait(
        workflow_id="my-workflow",
        args={"key": "value"}
    )
except WitriumClientException as e:
    print(f"Witrium API error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Cancelling Workflow Runs

You can cancel a workflow run that is in progress:

from witrium import SyncWitriumClient

with SyncWitriumClient(api_token="your-api-token") as client:
    # Start a workflow
    response = client.run_workflow(
        workflow_id="long-running-workflow",
        args={"parameter": "value"}
    )

    # Later, decide to cancel it
    cancel_result = client.cancel_run(run_id=response.run_id)
    print(f"Workflow cancelled with status: {cancel_result.status}")

This is particularly useful for:

• Long-running workflows that are no longer needed
• Error recovery scenarios
• Resource management (freeing up browser sessions)
• User-initiated cancellations in interactive applications

Best Practices

1. Always Use Context Managers

# ✅ Good - Automatically closes connections
with SyncWitriumClient(api_token=token) as client:
    results = client.run_workflow(...)

# ❌ Bad - Manual cleanup required
client = SyncWitriumClient(api_token=token)
results = client.run_workflow(...)
client.close()  # Easy to forget!

2. Choose the Right Session Management Pattern

# ✅ For concurrent operations - use state preservation
for category in categories:
    client.run_workflow(
        workflow_id="scraper",
        args={"category": category},
        use_states=["logged-in-state"]  # Each runs in new browser
    )

# ✅ For sequential operations - use session persistence
login_run_id = client.run_workflow(..., keep_session_alive=True).run_id
client.wait_until_state(..., target_status=WorkflowRunStatus.RUNNING)
client.run_workflow(..., use_existing_session=login_run_id)  # Same browser

3. Implement Proper Error Handling

def run_workflow_with_retry(client, workflow_id, args, max_retries=3):
    """Run workflow with retry logic."""
    for attempt in range(max_retries):
        try:
            return client.run_workflow_and_wait(
                workflow_id=workflow_id,
                args=args,
                timeout=300
            )
        except WitriumClientException as e:
            if attempt == max_retries - 1:
                raise
            logger.warning(f"Attempt {attempt + 1} failed: {e}. Retrying...")
            time.sleep(2 ** attempt)  # Exponential backoff

4. Use Appropriate Timeouts

# ✅ Adjust timeouts based on workflow complexity
quick_results = client.run_workflow_and_wait(
    workflow_id="simple-data-extraction",
    timeout=60  # Simple workflows
)

complex_results = client.run_workflow_and_wait(
    workflow_id="complex-multi-page-workflow", 
    timeout=600,  # Complex workflows need more time
    min_wait_time=30  # Don't start polling immediately
)

5. Monitor Progress for Long-Running Workflows

# ✅ Use callbacks for visibility into long-running processes
def log_progress(result):
    completed_steps = sum(1 for ex in result.executions if ex.status == "C")
    total_steps = len(result.executions)
    logger.info(f"Progress: {completed_steps}/{total_steps} steps completed")

client.run_workflow_and_wait(
    workflow_id="long-running-workflow",
    on_progress=log_progress,
    polling_interval=10  # Poll less frequently for long workflows
)