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Convert successful agent runs into deterministic function code that can be executed faster and cheaper than agents.

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Overview

After an agent completes a task, you can extract the steps as function code: 
from notte_sdk import NotteClient

client = NotteClient()

with client.Session() as session:
    agent = client.Agent(session=session)
    result = agent.run(task="Navigate to pricing page and extract plans")

    if result.success:
        # Convert to function code
        function_code = agent.workflow.code()
        print(function_code.python_script)

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Why Convert to Functions?

AspectAgentFunction
ExecutionAI decides each stepPredefined steps
SpeedSlower (LLM reasoning)Faster (direct execution)
CostHigher (LLM tokens)Lower (actions only)
ReliabilityCan varyDeterministic
AdaptabilityHandles changesBreaks if page changes
Use functions when:
  • The task is well-defined and repeatable
  • Page structure is stable
  • Speed and cost matter
  • You need predictable execution

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Getting Function Code

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As Python Script

Get executable Python code:
get-python-script.py
agent = client.Agent(session=session)
result = agent.run(task="Login and navigate to dashboard")

if result.success:
    # Get function code
    code = agent.workflow.code(as_workflow=True)

    print(code.python_script)
Example output: 
from notte_sdk import NotteClient

client = NotteClient()

with client.Session() as session:
    # Step 1: Navigate to login page
    session.execute(type="goto", url="https://example.com/login")

    # Step 2: Fill email field
    session.execute(type="fill", selector="input[name='email']", value="user@example.com")

    # Step 3: Fill password field
    session.execute(type="fill", selector="input[name='password']", value="********")

    # Step 4: Click login button
    session.execute(type="click", selector="button[type='submit']")

    # Step 5: Wait for dashboard
    session.execute(type="goto", url="https://example.com/dashboard")

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As Function Object

Create a reusable function:
create-function-object.py
result = agent.run(task="Extract product data")

if result.success:
    # Create function from agent
    function = agent.workflow.create_function()

    print(f"Created function: {function.function_id}")

    # Run function later
    function_result = function.run()

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Running Functions

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Execute Generated Code

Copy and run the generated Python: 
# Generated from agent
from notte_sdk import NotteClient

client = NotteClient()

with client.Session() as session:
    session.execute(type="goto", url="https://example.com")
    session.execute(type="click", selector="button.search")
    session.execute(type="fill", selector="input[name='query']", value="laptop")
    data = session.scrape(instructions="Extract product names")

print(data)

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Use Function API

Run via the function endpoint:
use-function-api.py
agent.run(task="Complete task")

# Create function from successful agent
function = agent.workflow.create_function()

# Run multiple times
for query in ["laptop", "phone", "tablet"]:

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Use Cases

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1. Prototyping with Agents

Use agents to figure out the automation, then convert:
prototyping-with-agents.py
# Phase 1: Prototype with agent
agent = client.Agent(session=session)
result = agent.run(task="Find all products under $100")

if result.success:
    # Phase 2: Convert to function for production
    code = agent.workflow.code()

    # Save for production use
    with open("production_function.py", "w") as f:
        f.write(code.python_script)

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2. Scheduled Tasks

Convert one-time agent runs to scheduled functions:
scheduled-tasks.py
agent = client.Agent(session=session)
result = agent.run(task="Extract daily price changes")

# Convert to function
function = agent.workflow.create_function()

# Schedule to run daily (via API or console)
# function.schedule(cron="0 9 * * *")

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3. Cost Optimization

Run agents once, then use cheaper functions:
cost-optimization.py
agent = client.Agent(session=session)
result = agent.run(task="Complex data extraction")

# Recurring: Use function ($0.05 per run)
function = agent.workflow.create_function()

# Run 100 times - save $15 vs running agent each time
for i in range(100):
    function.run()

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4. Testing Variations

Create function templates from agents:
testing-variations.py
# Base workflow from agent
agent = client.Agent(session=session)
result = agent.run(task="Search and extract results")

code = agent.workflow.code()

# Modify code for variations
# - Different search queries
# - Different extraction logic
# - Different URLs

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Customizing Generated Code

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Add Parameters

Make functions reusable with parameters:
add-parameters.py
    # Agent-generated base code
    code = agent.workflow.code()

    # Customize with parameters
    customized_code = f"""
def extract_products(search_query: str, max_results: int = 10):
    {code.python_script}
"""

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Add Error Handling

Enhance with production-ready error handling:
add-error-handling.py
    # Agent-generated code
    base_code = agent.workflow.code()

    enhanced_code = f"""
from notte_sdk import NotteClient
from loguru import logger

def run_function():
    client = NotteClient()
    try:
        with client.Session() as session:
            {base_code.python_script}
            return {{"success": True, "data": result}}
    except Exception as e:
        logger.error(f"Function failed: {{e}}")

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Optimize Selectors

Review and improve generated selectors:
optimize-selectors.py
# Generated code might use:
session.execute(type="click", selector="div.container > button:nth-child(3)")

# Optimize to:
session.execute(type="click", selector="button[data-testid='submit']")

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Best Practices

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1. Test Generated Functions

Always test before production:
test-generated-functions.py
    agent.run(task="Complete task")

    # Generate function code
    code = agent.workflow.code()

# Test in fresh session
with client.Session() as session:
    exec(code.python_script)

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2. Document the Source

Track which agent generated the function:
document-the-source.py
result = agent.run(task="Extract product data")

function = agent.workflow.create_function()

# Add metadata
metadata = {
    "agent_id": agent.agent_id,
    "original_task": "Extract product data",
    "created_at": datetime.now().isoformat(),
    "success_rate": result.success,
}

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3. Version Control

Store functions in git:
version-control.py
agent = client.Agent(session=session)
agent.run(task="Extract products")

# Save function code
code = agent.workflow.code()

function_path = Path("functions/extract_products.py")
function_path.parent.mkdir(exist_ok=True)

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4. Monitor Function Success

Track if functions continue to work: 
from datetime import datetime


def run_monitored_function():
    try:
        function = client.Function(function_id="func_abc123")
        result = function.run()

        if result.status == "closed":
            log_success(datetime.now())
        else:
            alert_failure(result.result)

    except Exception as e:
        # Function broken - maybe page changed?
        # Time to re-run agent and regenerate
        alert_function_broken(e)

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5. Regenerate When Pages Change

When functions break, use agents to update:
regenerate-when-pages-change.py
# Old function failing
try:
    old_function.run()
except Exception:
    print("Function broken, regenerating...")

    with client.Session() as session:
        # Use agent to figure out new function
        agent = client.Agent(session=session)
        result = agent.run(task=original_task_description)

        if result.success:
            # Generate new function

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Limitations

Functions work best for:
  • ✅ Stable, unchanging pages
  • ✅ Deterministic tasks
  • ✅ Known sequences of actions
Functions are not suitable for:
  • ❌ Pages that change frequently
  • ❌ Tasks requiring adaptation
  • ❌ Complex decision-making
When pages change or tasks become complex, revert to agents.

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Next Steps

Functions

Deploy automations as serverless APIs

Actions

Available session actions

Agent Configuration

Optimize agent runs

Functions

Deploy and manage serverless functions