Scrapy vs Beautifulsoup: What’s the Difference?

Web scraping is an essential skill for any data scientist or engineer working with online data. When it comes to scraping the web in Python, there are two extremely popular packages that every scraper needs to know: Scrapy and BeautifulSoup.

At first glance, Scrapy and BeautifulSoup may seem like interchangeable options for parsing HTML pages. But they actually serve quite different purposes when it comes to building scrapers. In this guide, I'll explain:

  • How Scrapy and BeautifulSoup work under the hood
  • Key differences in their scope and capabilities
  • When to use each library based on your scraping needs

By the end, you'll understand the unique strengths of Scrapy and BeautifulSoup so you can determine which approach is best for your next web scraping project.

How Scrapy Works

Before we dive into comparisons, let's build a solid understanding of how Scrapy operates. Scrapy is a powerful web crawling and scraping framework written in Python. Beyond simple request and parsing capabilities, Scrapy gives you everything you need to build complex web scrapers that scale.

Some key things Scrapy provides:

  • Built-in asynchronous HTTP client and HTML/XML parser.
  • Flexible selector engine to extract data (XPath and CSS).
  • Spider classes to define scraping logic and rules.
  • Link extractors to follow links across domains.
  • Middleware and pipeline architecture to customize scraping behavior.
  • Management of throttling, retries, queues for large crawls.
  • Handling of cookies, authentication, proxies, robots.txt.
  • Wide variety of ready-made extensions.

In other words, Scrapy takes care of all the difficult infrastructure and orchestration required for industrial-strength scraping. This frees you up to just focus on writing the custom extraction logic.


When you define a Spider in Scrapy, it kicks off a systematic crawling and scraping process:

  1. The Spider's start_urls are requested using Scrapy's built-in asynchronous HTTP client.
  2. The response is fed through Scrapy's parsing engine to extract text and data.
  3. The extracted data is passed to item pipelines for processing and storage.
  4. Link extractors identify any links to follow from the page.
  5. Any links are added to Scrapy's scheduling system to be crawled recursively.
  6. The process repeats for each webpage, following links across domains.
  7. Scrapy handles throttling, retries, filtering, and other coordination of the crawl.

So in summary, Scrapy provides all the capabilities you need to:

  • Make massive amounts of concurrent HTTP requests.
  • Parse HTML and XML documents.
  • Follow links recursively across domains.
  • Clean and store extracted data.
  • Avoid getting rate limited or banned.

Giving you the tools to build complex crawlers at scale with Python.

How BeautifulSoup Works

Now let's examine how BeautifulSoup operates to see where it differs from Scrapy. BeautifulSoup is a very popular Python library that makes it easy to parse and extract information from HTML and XML documents. The key thing BeautifulSoup provides is a simple, Pythonic API for navigating and searching an HTML document after it has been parsed.

Some primary uses of BeautifulSoup include:

  • Extracting text, attributes, or tag content from elements.
  • Isolating and modifying elements in the parse tree.
  • Cleaning up, prettifyting, and reformatting HTML.
  • Handling malformed HTML gracefully.

So BeautifulSoup focuses on what happens after a page has been downloaded and parsed – making it easy to search and manipulate the resulting document structure.


The typical workflow when using BeautifulSoup is:

  1. Make a request to download an HTML page using the requests library or a similar HTTP client.
  2. Pass the raw HTML content to a BeautifulSoup object.
  3. BeautifulSoup parses the document and creates a nested Python object representing the elements.
  4. Use BeautifulSoup's API and methods to search and modify this parse tree.
  5. Extract any data you need from BeautifulSoup's representation of the HTML elements.
  6. Repeat the process for any additional pages you need to scrape.

So BeautifulSoup is focused on parsing and extracting from individual pages after making HTTP requests. It does not provide tools for managing large scale crawling across multiple links or domains.

Key Differences Between Scrapy and BeautifulSoup

Now that we've covered the basics of how each library works let's dig into the key differences developers should understand when it comes to picking Scrapy or BeautifulSoup for web scraping.

1. Scraping Scope and Scale

The core difference between Scrapy and BeautifulSoup comes down to the scope and scale of scraping you need to do:

Scrapy is designed for large scale web crawling across multiple pages and websites. It is meant for scraping projects where you need to extract data from thousands or millions of pages in an automated way. For example, some common use cases where Scrapy shines:

  • Crawling an entire website domain by following links.
  • Scraping product listings from an ecommerce site.
  • Extracting company profile data from a business directory.
  • Building a local search engine by crawling web pages in a region.

BeautifulSoup is designed for parsing and extracting information from individual HTML documents. It is meant for smaller scripts where you just need to scrape a few pages or process an HTML file. Some common use cases where BeautifulSoup excels:

  • Cleaning and processing HTML content from an API response.
  • Extracting article content from a handful of webpage URLs.
  • Parsing and manipulating HTML/XML feeds.
  • Exploring and prototyping a scrape of a new website.

So in summary:

  • Scrapy is great for large scale, automated multi-page/domain scraping.
  • BeautifulSoup is great for use cases focused just on parsing HTML documents.

2. Built-in HTTP Capabilities

One of Scrapy's biggest advantages is its built-in asynchronous HTTP client. This allows you to make thousands of concurrent requests and handle responses asynchronously. BeautifulSoup has no HTTP capabilities on its own. It only works on data after a request has been made using some other library like requests.

This means Scrapy is well-suited for high performance scraping where you need:

  • Very high throughput and concurrency.
  • Asynchronous request handling.
  • Retries, timeouts, and error handling.

Meanwhile, BeautifulSoup just operates on each document once downloaded. So performance depends on the external HTTP library you use it with.

3. Link Crawling Logic

Scrapy includes link extractors and crawling logic to automatically follow links across pages and domains as you define rules. BeautifulSoup has no functionality for automatically crawling links or navigating pages. You would need to handle any link following logic yourself.

So Scrapy is great if you need to:

  • Crawl across an entire website by following links.
  • Scrape data from linked pages recursively.
  • Extract links to queue for future scraping.

BeautifulSoup focuses on parsing individual pages one by one.

4. Parsing and Selectors

Both Scrapy and BeautifulSoup have HTML parsing capabilities but work in different ways:

  • Scrapy uses its own fast HTML parser called Parsel under the hood. Parsel supports using XPath or CSS selectors to extract data very efficiently.
  • BeautifulSoup creates a tree representation of HTML/XML and provides a rich Python API for navigating and searching the tree. You can use CSS selectors or search by tags/attributes/text.

In terms of parsers:

  • Parsel tends to provide better functionality for advanced scraping tasks.
  • BeautifulSoup makes simple parsing very straightforward.

So Scrapy gives you a very flexible selector engine, while BeautifulSoup makes basic parsing easy through its API.

5. Handling Invalid HTML

One area where BeautifulSoup excels is in handling malformed and invalid HTML gracefully. Its original purpose was to parse “tag soup” HTML found in the wild, so BeautifulSoup is very forgiving when encountering poor markup. Meanwhile, Scrapy uses the general-purpose Parsel parser, which does not focus specifically on malformed HTML.

So if you need to scrape sites with very broken HTML, BeautifulSoup may perform better in some cases.

6. Extensibility and Customization

Scrapy provides a very flexible architecture for extending and customizing scraping behavior through:

  • Middleware hooks to inject logic at different stages.
  • A pipeline system for post-processing data.
  • Over 500 ready-made extensions.
  • Built-in caching and storage mechanisms.
  • Robust configuration system.

BeautifulSoup primarily focuses on parsing. So out of the box, Scrapy provides far more extensibility for complex scraping logic. But BeautifulSoup's simple and clean API also makes writing custom extraction code straightforward.

When Should You Use Each Library?

Based on their different strengths, here is a breakdown of when to use Scrapy vs BeautifulSoup:

Use Scrapy when:

  • Crawling across an entire website by following links.
  • Scraping 1000s of product pages from an ecommerce site.
  • Extracting data across multple sites or API endpoints.
  • High performance asynchronous scraping is needed.
  • Customizing complex scraping pipelines and workflows.
  • Following links recursively to build a local search index.
  • Managing throttling, proxies, retries for large crawls.

Use BeautifulSoup when:

  • Parsing and extracting text/data from a small # of pages.
  • Cleaning HTML content from an API response.
  • Processing HTML/XML feeds or exports.
  • Exploring pages interactively as you prototype a scraper.
  • Scraper involves navigating complex page structures.
  • Gracefully handling extremely malformed HTML.

Use Both Together

The best practice is to use Scrapy and BeautifulSoup together in your projects.

A common pattern is:

  • Use Scrapy to crawl target sites and download all HTML.
  • Pass each page into BeautifulSoup to help parse and extract data.

This takes advantage of Scrapy's robust crawling abilities and BeautifulSoup's simple parsing tools.


Scrapy and BeautifulSoup are extremely useful packages for web scraping in Python that serve different purposes.

  • Scrapy is a full framework geared towards large-scale, robust scraping operations across sites. BeautifulSoup is focused on simplifying parsing and extraction from individual HTML/XML documents.
  • For small scraping tasks, BeautifulSoup is easier to use out of the box. For large crawling projects, Scrapy has the performance and flexibility needed to build advanced scrapers.

Ideally, take advantage of both Scrapy and BeautifulSoup together in your projects. Use Scrapy to crawl and download pages, then utilize BeautifulSoup to help clean and parse the extracted HTML.

The most important thing is choosing the right tool for your web scraping needs. Evaluate if you need to crawl multiple pages across a site or are just focused on parsing a few documents. Understanding the strengths of both Scrapy and BeautifulSoup will ensure you use the best approach.

John Rooney

John Rooney

John Watson Rooney, a self-taught Python developer and content creator with a focus on web scraping, APIs, and automation. I love sharing my knowledge and expertise through my YouTube channel, My channel caters to all levels of developers, from beginners looking to get started in web scraping to experienced programmers seeking to advance their skills with modern techniques. I have worked in the e-commerce sector for many years, gaining extensive real-world experience in data handling, API integrations, and project management. I am passionate about teaching others and simplifying complex concepts to make them more accessible to a wider audience. In addition to my YouTube channel, I also maintain a personal website where I share my coding projects and other related content.

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