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Overview

For scenarios requiring more precise control than autonomous agents, we offer a fully functional web browser interface for LLM agents. This allows you to observe website states and execute actions using intuitive natural language commands, giving you granular control while maintaining the simplicity of natural language interaction:
  • Observe a page: Use the observe endpoint to get the current state of a page and its available actions.
  • Step through a page: Use the step endpoint to take actions on a page.
  • Scrape (structured) data from a page: Use the scrape endpoint to extract structured data from a page.
These operations offer more granular control over what’s actually executed in a browser session compared to the agent operations.

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Executing actions

Notte has a step function that can be used to execute actions on a page using natural language. Here’s an example of how to find jobs on LinkedIn: 
from notte_sdk import NotteClient

notte = NotteClient()

with notte.Session() as page:
    url="https://www.linkedin.com/"

    # observe page and take a step
    page.execute({"type": "goto", "url": url})
    obs = page.observe(instructions="click 'jobs'")
    res = page.execute(obs.space.first())
    print(res.message)

    # another one
    obs = page.observe(instructions="dismiss the sign in check")
    res = page.execute(obs.space.first())
    print(res.message)

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Scrape (structured) data from the page

Notte provides a scraping endpoint that allows you to scrape any website with a single API call (markdown or structured JSON format supported). Here’s an example of how to extract the job title from the job posting:
scrape.py
from pydantic import BaseModel
from notte_sdk import NotteClient

class JobPosting(BaseModel):
  jobTitle: str

notte = NotteClient()
job_title =  notte.scrape(
  url="https://linkedin.com",
  instructions="Extract the job title from the job posting",
  response_format=JobPosting,
)
Notte uses Pydantic to help you define the schema of the data to be extracted.