The All in One Framework to build Awesome Scrapers.

The web has evolved. Finally, web scraping has too.

A new version has been released, with performance boost. To update please run python -m pip install bota botasaurus botasaurus-api botasaurus-requests botasaurus-driver bota botasaurus-proxy-authentication botasaurus-server --upgrade.

🐿️ Botasaurus In a Nutshell

How wonderful that of all the web scraping tools out there, you chose to learn about Botasaurus. Congratulations!

And now that you are here, you are in for an exciting, unusual and rewarding journey that will make your web scraping life a lot, lot easier.

Now, let me tell you in bullet points about Botasaurus. (Because as per the marketing gurus, YOU as a member of Developer Tribe have a VERY short attention span.)

So, what is Botasaurus?

Botasaurus is an all-in-one web scraping framework that enables you to build awesome scrapers in less time, less code, and with more fun.

A Web Scraping Magician has put all his web scraping experience and best practices into Botasaurus to save you hundreds of hours of Development Time!

Now, for the magical powers awaiting you after learning Botasaurus:

• Convert any Web Scraper to a UI-based Scraper in minutes, which will make your Customer sing praises of you.

• In terms of humaneness, what Superman is to Man, Botasaurus is to Selenium and Playwright. Easily pass every (Yes E-V-E-R-Y) bot test, no need to spend time finding ways to access a website.

• Save up to 97%, yes 97% on browser proxy costs by using browser-based fetch requests.

• Easily save hours of Development Time with easy parallelization, profiles, extensions, and proxy configuration. Botasaurus makes asynchronous, parallel scraping a child's play.

• Use Caching, Sitemap, Data cleaning, and other utilities to save hours of time spent in writing and debugging code.

• Easily scale your scraper to multiple machines with Kubernetes, and get your data faster than ever.

And those are just the highlights. I Mean!

There is so much more to Botasaurus, that you will be amazed at how much time you will save with it.

🚀 Getting Started with Botasaurus

Let's dive right in with a straightforward example to understand Botasaurus.

In this example, we will go through the steps to scrape the heading text from https://www.omkar.cloud/.

Step 1: Install Botasaurus

First things first, you need to install Botasaurus. Run the following command in your terminal:

python -m pip install botasaurus

Step 2: Set Up Your Botasaurus Project

Next, let's set up the project:

1. Create a directory for your Botasaurus project and navigate into it:

mkdir my-botasaurus-project
cd my-botasaurus-project
code .  # This will open the project in VSCode if you have it installed

Step 3: Write the Scraping Code

Now, create a Python script named main.py in your project directory and paste the following code:

from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, data):
    # Visit the Omkar Cloud website
    driver.get("https://www.omkar.cloud/")
    
    # Retrieve the heading element's text
    heading = driver.get_text("h1")

    # Save the data as a JSON file in output/scrape_heading_task.json
    return {
        "heading": heading
    }
     
# Initiate the web scraping task
scrape_heading_task()

Let's understand this code:

• We define a custom scraping task, scrape_heading_task, decorated with @browser:

@browser
def scrape_heading_task(driver: Driver, data):

• Botasaurus automatically provides an Humane Driver to our function:

def scrape_heading_task(driver: Driver, data):

• Inside the function, we:
  • Visit Omkar Cloud
  • Extract the heading text
  • Return the data to be automatically saved as scrape_heading_task.json by Botasaurus:

    driver.get("https://www.omkar.cloud/")
    heading = driver.get_text("h1")
    return {"heading": heading}

• Finally, we initiate the scraping task:

# Initiate the web scraping task
scrape_heading_task()

Step 4: Run the Scraping Task

Time to run it:

python main.py

After executing the script, it will:

• Launch Google Chrome
• Visit omkar.cloud
• Extract the heading text
• Save it automatically as output/scrape_heading_task.json.

Now, let's explore another way to scrape the heading using the request module. Replace the previous code in main.py with the following:

from botasaurus.request import request, Request
from botasaurus.soupify import soupify

@request
def scrape_heading_task(request: Request, data):
    # Visit the Omkar Cloud website
    response = request.get("https://www.omkar.cloud/")

    # Create a BeautifulSoup object    
    soup = soupify(response)
    
    # Retrieve the heading element's text
    heading = soup.find('h1').get_text()

    # Save the data as a JSON file in output/scrape_heading_task.json
    return {
        "heading": heading
    }     
# Initiate the web scraping task
scrape_heading_task()

In this code:

• We scrape the HTML using request, which is specifically designed for making browser-like humane requests.
• Next, we parse the HTML into a BeautifulSoup object using soupify() and extract the heading.

Step 5: Run the Scraping Task (which makes Humane HTTP Requests)

Finally, run it again:

python main.py

This time, you will observe the exact same result as before, but instead of opening a whole Browser, we are making browser-like humane HTTP requests.

💡 Understanding Botasaurus

What is Botasaurus Driver, And Why should I use it over Selenium and Playwright?

Botasaurus Driver is a web automation driver like Selenium, and the single most important reason to use it is because it is truly humane, and you will not, and I repeat NOT, have any issues with accessing any website.

Plus, it is super fast to launch and use, and the API is designed by and for web scrapers, and you will love it.

How do I access Cloudflare-protected pages using Botasaurus?

Cloudflare is the most popular protection system on the web. So, let's see how Botasaurus can help you solve various Cloudflare challenges.

Connection Challenge

This is the single most popular challenge and requires making a browser-like connection with appropriate headers. It's commonly used for:

• Product Pages
• Blog Pages
• Search Result Pages

Example Page: https://www.g2.com/products/github/reviews

What Works?

• Visiting the website via Google Referrer (which makes is seems as if the user has arrived from google search).

from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, data):
    # Visit the website via Google Referrer
    driver.google_get("https://www.g2.com/products/github/reviews")
    driver.prompt()
    heading = driver.get_text('.product-head__title [itemprop="name"]')
    return heading

scrape_heading_task()

• Use the request module. The Request Object is smart and, by default, visits any link with a Google Referrer. Although it works, you will need to use retries.

from botasaurus.request import request, Request

@request(max_retry=10)
def scrape_heading_task(request: Request, data):
    response = request.get('https://www.g2.com/products/github/reviews')
    print(response.status_code)
    response.raise_for_status()
    return response.text

scrape_heading_task()

JS with Captcha Challenge

This challenge requires performing JS computations that differentiate a Chrome controlled by Selenium/Puppeteer/Playwright from a real Chrome. It also involves solving a Captcha. It's used to for pages which are rarely but sometimes visited by people, like:

• 5th Review page
• Auth pages

Example Page: https://www.g2.com/products/github/reviews.html?page=5&product_id=github

What Does Not Work?

Using @request does not work because although it can make browser-like HTTP requests, it cannot run JavaScript to solve the challenge.

What Works?

Pass the bypass_cloudflare=True argument to the google_get method.

from botasaurus.browser import browser, Driver

@browser
def scrape_heading_task(driver: Driver, data):
    driver.google_get("https://www.g2.com/products/github/reviews.html?page=5&product_id=github", bypass_cloudflare=True)
    driver.prompt()
    heading = driver.get_text('.product-head__title [itemprop="name"]')
    return heading

scrape_heading_task()

What are the benefits of a UI Scraper?

Here are some benefits of creating a scraper with a user interface:

• Simplify your scraper usage for customers, eliminating the need to teach them how to modify and run your code.
• Protect your code by hosting the scraper on the web and offering a monthly subscription, rather than providing full access to your code. This approach:
  • Safeguards your Python code from being copied and reused, increasing your customer's lifetime value.
  • Generate monthly recurring revenue via subscription from your customers, surpassing a one-time payment.
• Enable sorting, filtering, and downloading of data in various formats (JSON, Excel, CSV, etc.).
• Provide access via a REST API for seamless integration.
• Create a polished frontend, backend, and API integration with minimal code.

How to run a UI-based scraper?

Let's run the Botasaurus Starter Template (the recommended template for greenfield Botasaurus projects), which scrapes the heading of the provided link by following these steps:

1. Clone the Starter Template:

   git clone https://github.com/omkarcloud/botasaurus-starter my-botasaurus-project
   cd my-botasaurus-project
   

2. Install dependencies (will take a few minutes):

   python -m pip install -r requirements.txt
   python run.py install
   

3. Run the scraper:

   python run.py
   

Your browser will automatically open up at http://localhost:3000/. Then, enter the link you want to scrape (e.g., https://www.omkar.cloud/) and click on the Run Button.

After some seconds, the data will be scraped.

Visit http://localhost:3000/output to see all the tasks you have started.

Go to http://localhost:3000/about to see the rendered README.md file of the project.

Finally, visit http://localhost:3000/api-integration to see how to access the Scraper via API.

The API Documentation is generated dynamically based on your Scraper's Inputs, Sorts, Filters, etc., and is unique to your Scraper.

So, whenever you need to run the Scraper via API, visit this tab and copy the code specific to your Scraper.

How to create a UI Scraper using Botasaurus?

Creating a UI Scraper with Botasaurus is a simple 3-step process:

1. Create your Scraper function
2. Add the Scraper to the Server using 1 line of code
3. Define the input controls for the Scraper

To understand these steps, let's go through the code of the Botasaurus Starter Template that you just ran.

Step 1: Create the Scraper Function

In src/scrape_heading_task.py, we define a scraping function which basically does the following:

1. Receives a data object and extracts the "link".
2. Retrieves the HTML content of the webpage using the "link".
3. Converts the HTML into a BeautifulSoup object.
4. Locates the heading element, extracts its text content and returns it.

from botasaurus.request import request, Request
from botasaurus.soupify import soupify

@request
def scrape_heading_task(request: Request, data):
    # Visit the Link
    response = request.get(data["link"])

    # Create a BeautifulSoup object    
    soup = soupify(response)
    
    # Retrieve the heading element's text
    heading = soup.find('h1').get_text()

    # Save the data as a JSON file in output/scrape_heading_task.json
    return {
        "heading": heading
    }

Step 2: Add the Scraper to the Server

In backend/scrapers.py, we:

• Import our scraping function
• Use Server.add_scraper() to register the scraper

from botasaurus_server.server import Server
from src.scrape_heading_task import scrape_heading_task

# Add the scraper to the server
Server.add_scraper(scrape_heading_task)

Step 3: Define the Input Controls

In backend/inputs/scrape_heading_task.js we:

• Define a getInput function that takes the controls parameter
• Add a link input control to it
• Use comments to enable intellisense in VSCode (Very Very Important)

/**
 * @typedef {import('../../frontend/node_modules/botasaurus-controls/dist/index').Controls} Controls
 */

/**
 * @param {Controls} controls
 */
function getInput(controls) {
    controls
        // Render a Link Input, which is required, defaults to "https://www.omkar.cloud/". 
        .link('link', { isRequired: true, defaultValue: "https://www.omkar.cloud/" })
}

Above was a simple example; below is a real-world example with multi-text, number, switch, select, section, and other controls.

/**
 * @typedef {import('../../frontend/node_modules/botasaurus-controls/dist/index').Controls} Controls
 */


/**
 * @param {Controls} controls
 */
function getInput(controls) {
    controls
        .listOfTexts('queries', {
            defaultValue: ["Web Developers in Bangalore"],
            placeholder: "Web Developers in Bangalore",
            label: 'Search Queries',
            isRequired: true
        })
        .section("Email and Social Links Extraction", (section) => {
            section.text('api_key', {
                placeholder: "2e5d346ap4db8mce4fj7fc112s9h26s61e1192b6a526af51n9",
                label: 'Email and Social Links Extraction API Key',
                helpText: 'Enter your API key to extract email addresses and social media links.',
            })
        })
        .section("Reviews Extraction", (section) => {
            section
                .switch('enable_reviews_extraction', {
                    label: "Enable Reviews Extraction"
                })
                .numberGreaterThanOrEqualToZero('max_reviews', {
                    label: 'Max Reviews per Place (Leave empty to extract all reviews)',
                    placeholder: 20,
                    isShown: (data) => data['enable_reviews_extraction'], defaultValue: 20,
                })
                .choose('reviews_sort', {
                    label: "Sort Reviews By",
                    isRequired: true, isShown: (data) => data['enable_reviews_extraction'], defaultValue: 'newest', options: [{ value: 'newest', label: 'Newest' }, { value: 'most_relevant', label: 'Most Relevant' }, { value: 'highest_rating', label: 'Highest Rating' }, { value: 'lowest_rating', label: 'Lowest Rating' }]
                })
        })
        .section("Language and Max Results", (section) => {
            section
                .addLangSelect()
                .numberGreaterThanOrEqualToOne('max_results', {
                    placeholder: 100,
                    label: 'Max Results per Search Query (Leave empty to extract all places)'
                })
        })
        .section("Geo Location", (section) => {
            section
                .text('coordinates', {
                    placeholder: