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": "[email protected]"} 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.
