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Introduction

The Dendrite SDK is designed to be synchronous by default, providing a simple, easy-to-use interface for most scenarios. However, it also includes asynchronous support for tasks that would benefit from concurrency, such as handling high-volume I/O operations or multiple API calls at once.

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When to Use Async

Asynchronous programming allows you to handle multiple tasks concurrently without blocking the execution of other code. While synchronous programming works well in most cases, async can offer significant performance improvements in specific scenarios. The main use case for the asynchronous Dendrite browser is for cases with parallell execution, some examples are:
  • Performing actions on multiple pages that do not need to happen in order
  • Extracting data from multiple pages with the same structured output
  • Long tasks where you want to let your agent continue with other tasks while waiting for execution

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How to Use Async

To use the asynchronous Dendrite browser, just import AsyncDendrite from dendrite and use it just like you would use the Dendrite class. It has all the same methods and all classes that stem from it will also be Async, like AsyncPage.

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Simple Example

import asyncio
from dendrite import AsyncDendrite

async def main():
  # Initiate async Dendrite browser
  browser = AsyncDendrite()

  # Navigate to page click on the described button
  await browser.goto("https://example.com")
  await browser.click("The button for getting started")s

asyncio.run(main())

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Example With Concurrency

In this example we will go to the YC startup directory, extract the urls of all AI agent companies, and extract data from all those companies. 
import asyncio
from pydantic import BaseModel
from dendrite import AsyncDendrite

# Define Pydantic Models for data extraction
class Founder(BaseModel):
  name: str
  linkedin: str | None
  twitter: str | None

class Company(BaseModel):
  name: str
  team_size: int
  location: str
  website_url: str
  founders: list[Founder]


# Extract company info function
async def extract_info(browser, url):
  # Open the URL in a new tab and extract company info
  page = await browser.new_tab(url)
  company_info = await browser.extract("The company info", type_spec=Company)

  # Close tab and return info
  await page.close()
  return company_info


# Main function (asynchronous)
async def main():
  # Initiate async Dendrite browser
  browser = AsyncDendrite()

  # Go to YC and search for AI agent companies
  await browser.goto("https://ycombinator.com/companies")
  await browser.fill("Search field", value="AI agent")

  # Extract the urls of the resulting list
  urls = await browser.extract(
      "The URLs of each listed startup. Return this format: list[str]"
  )

  # Go to each URL concurrently in different tabs and extract company info
  tasks = [extract_info(browser, url) for url in urls]
  companies = await asyncio.gather(*tasks)

  # Do something with the extracted company info
  print(companies)


asyncio.run(main())
This example is a perfect use of async, since execution would take much longer if the data extraction happened one page at a time.

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Considerations

  • Event Loop: Asynchronous code must run within an event loop, which manages the execution of tasks. The built-in asyncio library makes this straightforward in Python.
  • Mixing Sync and Async: Be cautious when mixing synchronous and asynchronous code. It can add complexity and require proper structuring to avoid potential deadlocks or performance degradation.