Functions

Functions are serverless deployments of your browser automations that can be invoked via API, scheduled to run automatically, or triggered by events.

What are Functions?

Functions turn your automation scripts into:

API endpoints you can call with HTTP requests
Scheduled jobs that run on a cron schedule
Reusable workflows accessible from anywhere
Shareable automations for your team

Unlike running scripts locally, Functions:

✅ Run on Notte’s infrastructure (no servers to manage)
✅ Scale automatically based on demand
✅ Provide built-in logging and monitoring
✅ Can be invoked from any platform (Python, JavaScript, cURL, etc.)
✅ Support scheduling and automation

How Functions Work

1. Write Your Script

Create a Python file with a run() function:

basic_function.py

from notte_sdk import NotteClient

client = NotteClient()

def run(url: str, search_query: str):
    """Search a website url and extract results. """

    with client.Session() as session:
        # Navigate to site
        session.execute(type="goto", url=url)

        # Search
        session.execute(type="fill", placeholder="Search", value=search_query)
        session.execute(type="press_key", key="Enter")

        # Extract results
        results = session.scrape(
            instructions="Extract search results"
        )

        return results

2. Deploy to Notte

Upload your script to create a Function:

deploy_function.py

from notte_sdk import NotteClient

client = NotteClient()

# Deploy function
function = client.Function(
    workflow_path="my_automation.py",
    name="Search Automation",
    description="Searches a website and extracts results"
)

print(f"Function deployed: {function.function_id}")
print(f"Version: {function.response.latest_version}")

3. Invoke the Function

Call your Function as an API:

# Via SDK
result = function.run(
    url="https://example.com",
    search_query="laptop"
)

print(result.result)

# Via cURL
curl -X POST https://api.notte.cc/functions/{function_id}/runs/start \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "function_id": "workflow_123",
    "variables": {
      "url": "https://example.com",
      "search_query": "laptop"
    }
  }'

Function Structure

The Handler Function

Functions must have a run() function that serves as the entry point:

def run(param1: str, param2: int = 10):
    """
    Function docstring explains what it does.

    Args:
        param1: Description of param1
        param2: Description of param2 (optional)

    Returns:
        Description of return value
    """
    # Your automation code
    result = perform_automation(param1, param2)
    return result

Key points:

Named run() - this is the entry point
Can accept parameters (passed as variables when invoked)
Should have type hints for clarity
Should include docstring documentation
Returns a value (any JSON-serializable type)

Parameters

Define parameters as arguments to the run function:

def run(email: str, password: str, product_id: str):
    client = NotteClient()

    with client.Session() as session:
        session.execute(type="fill", id="email", value=email)
        session.execute(type="fill", id="password", value=password)
        session.execute(type="goto", url=f"https://example.com/product/{product_id}")

Pass values when invoking the function:

function.run(
    email="user@example.com",
    password="secret",
    product_id="123"
)

Return Values

Functions can return any JSON-serializable data:

# Return string
def run():
    return "Task completed successfully"

# Return dict
def run():
    return {
        "status": "success",
        "data": extracted_data,
        "count": len(extracted_data)
    }

# Return list
def run():
    return ["item1", "item2", "item3"]

# Return Pydantic model (serialized)
from pydantic import BaseModel

class Result(BaseModel):
    success: bool
    data: list[str]

def run():
    return Result(success=True, data=["a", "b"]).model_dump()

Use Cases

1. Scheduled Scraping

Extract data on a schedule:

# price_monitor.py
def run(product_urls: list[str]):
    client = NotteClient()
    prices = []

    for url in product_urls:
        with client.Session() as session:
            session.execute(type="goto", url=url)
            price = session.scrape(
                instructions="Extract product price"
            )
            prices.append({"url": url, "price": price})

    return prices

# Deploy and schedule to run daily
function = client.Function(
    workflow_path="price_monitor.py",
    name="Daily Price Monitor"
)

# Schedule via console: every day at 9 AM

2. API Endpoints

Expose automation as an API:

# contact_extractor.py
def run(company_url: str):
    client = NotteClient()

    with client.Session() as session:
        session.execute(type="goto", url=company_url)

        contact_info = session.scrape(
            instructions="Extract contact email and phone"
        )

        return contact_info

# Now callable from any service
# GET /functions/{id}/runs/start?variables={"company_url": "..."}

3. Webhooks

Trigger automations from external events:

# order_processor.py
def run(order_id: str, action: str):
    client = NotteClient()

    with client.Session() as session:
        # Login to admin panel
        session.execute(type="goto", url="https://admin.example.com")

        # Process order
        if action == "fulfill":
            session.execute(
                type="click",
                selector=f"button[data-order='{order_id}'].fulfill"
            )
        elif action == "refund":
            session.execute(
                type="click",
                selector=f"button[data-order='{order_id}'].refund"
            )

    return {"order_id": order_id, "action": action, "status": "completed"}

# Called via webhook from your e-commerce platform

4. Batch Processing

Process multiple items in parallel:

# bulk_data_extract.py
def run(urls: list[str], max_workers: int = 5):
    from concurrent.futures import ThreadPoolExecutor

    client = NotteClient()

    def extract_from_url(url):
        with client.Session() as session:
            session.execute(type="goto", url=url)
            data = session.scrape()
            return {"url": url, "data": data}

    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = list(executor.map(extract_from_url, urls))

    return results

How Functions Fit In

Functions are a deployment layer - they turn any automation into a reusable API.

┌─────────────────────────────────────────────────────┐
│                     Function                        │
│            (Deployment & Scheduling)                │
├─────────────────────────────────────────────────────┤
│                                                     │
│   ┌─────────────────┐    ┌─────────────────┐       │
│   │ Scripted        │ or │ Agent           │       │
│   │ Automation      │    │ (AI-driven)     │       │
│   └────────┬────────┘    └────────┬────────┘       │
│            │                      │                 │
│            └──────────┬───────────┘                 │
│                       │                             │
│              ┌────────▼────────┐                    │
│              │    Session      │                    │
│              │ (Cloud Browser) │                    │
│              └─────────────────┘                    │
│                                                     │
└─────────────────────────────────────────────────────┘

Session - The cloud browser that runs everything
Scripted Automation vs Agent - How you control the session
Function - Deploys your automation as an API with scheduling, versioning, and sharing

Use Functions when:

You need to run automation repeatedly
You want to expose automation as an API
You need scheduling capabilities
You want to share automation with team or customers

Functions can contain either scripted automation or agents - they’re not mutually exclusive.

Function Lifecycle

Write - Create Python script with run() function
Deploy - Upload to Notte (creates function ID)
Version - Notte tracks versions automatically
Invoke - Call via API, schedule, or webhook
Execute - Runs on Notte infrastructure
Monitor - View logs, replays, and results

Best Practices

1. Use Clear Parameters

Define parameters with type hints and descriptions:

def run(
    target_url: str,
    max_results: int = 10,
    timeout_seconds: int = 30
):
    """
    Extract data from a website.

    Args:
        target_url: The URL to scrape
        max_results: Maximum number of results to extract
        timeout_seconds: Session timeout
    """
    pass

2. Return Structured Data

Always return JSON-serializable data:

# Good
return {
    "success": True,
    "data": results,
    "count": len(results),
    "timestamp": datetime.now().isoformat()
}

# Bad - not JSON serializable
return datetime.now()  # Can't serialize datetime

3. Handle Errors Gracefully

Catch exceptions and return meaningful errors:

def run(url: str):
    try:
        client = NotteClient()
        with client.Session() as session:
            session.execute(type="goto", url=url)
            data = session.scrape()
            return {"success": True, "data": data}
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "error_type": type(e).__name__
        }

4. Add Logging

Log important steps for debugging:

from loguru import logger

def run(url: str):
    logger.info(f"Starting automation for {url}")

    client = NotteClient()
    with client.Session() as session:
        logger.info("Session started")
        session.execute(type="goto", url=url)
        logger.info("Navigation complete")

        data = session.scrape()
        logger.info(f"Extracted {len(data)} items")

    return data

Next Steps

Creating Functions

Learn how to write and deploy Functions

Invocations

Call Functions via API, SDK, or cURL

Schedules

Schedule Functions with cron

Management

Update, version, and monitor Functions

Getting Started

Sessions

Agents

Agent Tools

Scraping

Guides

Integrations

Functions

What are Functions?

How Functions Work

1. Write Your Script

2. Deploy to Notte

3. Invoke the Function

Function Structure

The Handler Function

Parameters

Return Values

Use Cases

1. Scheduled Scraping

2. API Endpoints

3. Webhooks

4. Batch Processing

How Functions Fit In

Function Lifecycle

Best Practices

1. Use Clear Parameters

2. Return Structured Data

3. Handle Errors Gracefully

4. Add Logging

Next Steps

Creating Functions

Invocations

Schedules

Management

Getting Started

Sessions

Agents

Functions

Agent Tools

Scraping

Guides

Integrations

​What are Functions?

​How Functions Work

​1. Write Your Script

​2. Deploy to Notte

​3. Invoke the Function

​Function Structure

​The Handler Function

​Parameters

​Return Values

​Use Cases

​1. Scheduled Scraping

​2. API Endpoints

​3. Webhooks

​4. Batch Processing

​How Functions Fit In

​Function Lifecycle

​Best Practices

​1. Use Clear Parameters

​2. Return Structured Data

​3. Handle Errors Gracefully

​4. Add Logging

​Next Steps

Creating Functions

Invocations

Schedules

Management

What are Functions?

How Functions Work

1. Write Your Script

2. Deploy to Notte

3. Invoke the Function

Function Structure

The Handler Function

Parameters

Return Values

Use Cases

1. Scheduled Scraping

2. API Endpoints

3. Webhooks

4. Batch Processing

How Functions Fit In

Function Lifecycle

Best Practices

1. Use Clear Parameters

2. Return Structured Data

3. Handle Errors Gracefully

4. Add Logging

Next Steps