Documentation Index
Fetch the complete documentation index at: https://docs.smooth.sh/llms.txt
Use this file to discover all available pages before exploring further.
Run an agent task within the current browser session. The agent will perform the task autonomously, starting from the current page state.
Usage
from smooth import SmoothClient
client = SmoothClient()
with client.session() as session:
result = session.run_task(
task="Fill out the contact form with test data",
url="https://example.com/contact"
)
print(f"Output: {result.output}")
print(f"Credits used: {result.credits_used}")
Request
The task for the agent to execute.Example: Fill out the contact form and submit it
The upper limit on the number of steps the agent can take during task execution. The range is from 2 to 128. Default: 32.Example: 64
If provided, enforces a structured output schema. It should be a dictionary describing a JSON schema.
Default: None.Example:{
"type": "object",
"properties": {
"confirmation_number": {
"type": "string",
"description": "The confirmation number after form submission"
}
}
}
Navigate to this URL before executing the task. If not provided, the task starts from the current page.Example: https://example.com/contact
A dictionary containing variables or parameters that will be passed to the agent.Example: {"username": "test_user", "email": "test@example.com"}
Response
Returns an object with the following attributes.
The task output returned by the agent. If response_model was provided, this will conform to the specified schema.
The number of credits used for this task. 1 credit corresponds to $0.01.
The duration in seconds taken to perform the task.
Examples
Basic task:
with client.session() as session:
result = session.run_task(
task="Find the top story and tell me its title",
url="https://news.ycombinator.com"
)
print(result.output)
with client.session() as session:
result = session.run_task(
task="Search for one-way flights from NYC to LA on March 15, then return the 3 cheapest options",
url="https://www.google.com/travel/flights",
response_model={
"type": "array",
"items": {
"type": "object",
"properties": {
"airline": {"type": "string"},
"price": {"type": "number"},
"departure_time": {"type": "string"},
"duration": {"type": "string"}
}
}
}
)
for flight in result.output:
print(f"{flight['airline']}: ${flight['price']} - {flight['departure_time']} ({flight['duration']})")
with client.session() as session:
result = session.run_task(
task="Fill out the shipping form with the provided address",
url="https://shop.example.com/checkout",
metadata={
"name": "John Smith",
"address": "123 Main Street",
"city": "New York",
"zip": "10001"
}
)
status_schema = {
"type": "object",
"properties": {
"success": {"type": "boolean", "description": "Whether the task was completed successfully"},
"message": {"type": "string", "description": "Details about what was done or why it failed"}
}
}
with client.session(url="https://shop.example.com") as session:
# Task 1: Search for a product
result = session.run_task(
task="Search for 'wireless headphones'",
response_model=status_schema
)
if not result.output["success"]:
raise Exception(f"Search failed: {result.output['message']}")
# Task 2: Apply filters
result = session.run_task(
task="Filter by price under $100 and rating 4+ stars",
response_model=status_schema
)
if not result.output["success"]:
raise Exception(f"Filter failed: {result.output['message']}")
# Extract the results
products = session.extract(
schema={
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"price": {"type": "number"}
}
}
},
prompt="Extract the first 3 products from the search results"
)
print(products.output)
Pro Tip: Use response_model to have the agent report whether each task succeeded before moving to the next one. This prevents your workflow from continuing blindly when something went wrong.
