11 best open-source web crawlers and scrapers in 2024

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To effectively gain insights into organizational or company market data, structured data in a machine-readable format is essential. This is where web crawlers and scrapers come into play. Learn how this process works and discover the top free software libraries, frameworks, and SDKs for web crawling.

What are open-source web crawlers and web scrapers? 🤔

Online data extraction can be defined as web crawling or web scraping:

🕸️ Web crawlers are often used by search engines, which crawl websites, look for links and pages, and extract their content relatively indiscriminately.

🌐 Web scrapers, on the other hand, extract information from a website based on a certain script, which is often tailored to a specific website and its corresponding elements.

When software or an API is open-source, its code is available to the general public for free, and it can be modified and optimized to suit your needs. The same goes for website scrapers and open-source web crawlers: you can download or use them without paying and fine-tune them based on your use case.

Top 11 open-source web crawlers and scrapers in 2024

1. Scrapy

Language: Python 🐍 | GitHub: 51K stars | link

Scrapy is the most complete and popular web scraping framework within the Python ecosystem. It is written using Twisted, an event-driven networking framework, giving Scrapy asynchronous capabilities.

As a comprehensive web crawling framework designed specifically for data extraction, Scrapy provides built-in support for handling requests, processing responses, and exporting data in multiple formats, including CSV, JSON, and XML.

Its main drawback is that it cannot natively handle dynamic websites. However, you can configure Scrapy with a browser automation tool like Playwright or Selenium to unlock these capabilities.

Pros:

• Significant performance boost due to its asynchronous nature.
• Specifically designed for web scraping, providing a robust foundation for such tasks.
• Extensible middleware architecture makes adjusting Scrapy’s capabilities to fit various scraping scenarios easy.
• Supported by a well-established community with a wealth of resources available online.

Cons:

• Steep learning curve, which can be challenging for less experienced web scraping developers.
• Lacks the ability to handle content generated by JavaScript natively, requiring integration with tools like Selenium or Playwright to scrape dynamic pages.
• More complex than necessary for simple and small-scale scraping tasks.

Best for: Scrapy is ideally suited for developers, data scientists, and researchers embarking on large-scale web scraping projects who require a reliable and scalable solution for extracting and processing vast amounts of data.

2. MechanicalSoup 🥣

Language: Python 🐍 | GitHub: 4.6K+ stars | link

MechanicalSoup is a Python library designed to automate website interactions. It provides a simple API to access and interact with HTML content, similar to interacting with web pages through a web browser, but programmatically. MechanicalSoup essentially combines the best features of libraries like Requests for HTTP requests and Beautiful Soup for HTML parsing.

Now, you might wonder when to use MechanicalSoup over the traditional combination of BS4+ Requests. MechanicalSoup provides some distinct features particularly useful for specific web scraping tasks. These include submitting forms, handling login authentication, navigating through pages, and extracting data from HTML.

MechanicalSoup makes it possible by creating a StatefulBrowser object in Python that can store cookies and session data and handle other aspects of a browsing session.

However, while MechanicalSoup offers some browser-like functionalities akin to what you'd expect from a browser automation tool such as Selenium, it does so without launching an actual browser. This approach has its advantages but also comes with certain limitations, which we'll explore next:

Pros:

• Great choice for simple automation tasks such as filling out forms and scraping data from pages that do not require JavaScript rendering.
• Lightweight tool that interacts with web pages through requests without a graphical browser interface. This makes it faster and less demanding on system resources.
• Directly integrates Beautiful Soup, offering all the benefits you would expect from BS4, plus some extra features.

Cons:

• Unlike real browser automation tools like Playwright and Selenium, MechanicalSoup cannot execute JavaScript. Many modern websites require JavaScript for dynamic content loading and user interactions, which MechanicalSoup cannot handle.
• Unlike Selenium and Playwright, MechanicalSoup does not support advanced browser interactions such as moving the mouse, dragging and dropping, or keyboard actions that might be necessary to retrieve dates from more complex websites.

Best for: MechanicalSoup is a more efficient and lightweight option for more basic scraping tasks, especially for static websites and those with straightforward interactions and navigation.

3. Node Crawler

Language: Node.js  | GitHub: 6.6K+ stars | link

Node Crawler, often referred to as 'Crawler,' is a popular web crawling library for Node.js. At its core, Crawler utilizes Cheerio as the default parser, but it can be configured to use JSDOM if needed. The library offers a wide range of customization options, including robust queue management that allows you to enqueue URLs for crawling while it manages concurrency, rate limiting, and retries.

Advantages:

• Built on Node.js, Node Crawler excels at efficiently handling multiple, simultaneous web requests, which makes it ideal for high-volume web scraping and crawling.
• Integrates directly with Cheerio (a fast, flexible, and lean implementation of core jQuery designed specifically for the server), simplifying the process of HTML parsing and data extraction.
• Provides extensive options for customization, from user-agent strings to request intervals, making it suitable for a wide range of web crawling scenarios.
• Easy to set up and use, even for those new to Node.js or web scraping.

Disadvantages:

• Does not handle JavaScript rendering natively. For dynamic JavaScript-heavy sites, you need to integrate it with something like Puppeteer or a headless browser.
• While Node Crawler simplifies many tasks, the asynchronous model and event-driven architecture of Node.js can present a learning curve for those unfamiliar with such patterns.

Best for: Node Crawler is a great choice for developers familiar with the Node.js ecosystem who need to handle large-scale or high-speed web scraping tasks. It provides a flexible solution for web crawling that leverages the strengths of Node.js's asynchronous capabilities.

crawler

Crawler is a web spider written with Nodejs. It gives you the full power of jQuery on the server to parse a big number of pages as they are downloaded, asynchronously. Latest version: 1.5.0, last published: 4 months ago. Start using crawler in your project by running `npm i crawler`. There are 122 other projects in the npm registry using crawler.

npm

4. PySpider

Language: Python 🐍 | GitHub: 16.3K+ stars | link

PySpider is a powerful and flexible web-crawling system written in Python. It's designed to handle a wide range of web crawling and scraping tasks, offering a web-based UI that allows you to monitor and manage your crawls easily. PySpider supports both basic and advanced web scraping capabilities, making it suitable for everything from simple scraping tasks to complex, large-scale web crawling projects.

Advantages:

• Web-based dashboard offers a visual and interactive way to monitor and control crawls, which is not as developed as in other libraries like Scrapy.
• Interface and scripting are relatively easy to set up and use, especially for those familiar with It. The web UI adds an additional layer of user-friendliness.
• Ability to handle JavaScript-heavy sites out of the box gives it an edge over other tools requiring additional setup or integration for such features.

Disadvantages:

• Feature-rich and capable of running a web server for its dashboard, PySpider might consume more resources than simpler, script-based crawlers.
• Setting up and managing complex crawls might require a deeper understanding of both PySpider's system and web crawling principles in general.
• Depending on the community and development activity, there may be periods where PySpider does not receive updates or support as actively as some other tools like Scrapy.

5. Crawlee

Language: Node.js | GitHub: 12.3K+ stars | link

Crawlee is an open-source web scraping and browser automation library designed for quickly and efficiently building reliable crawlers. With built-in anti-blocking features, it makes your bots look like real human users, reducing the likelihood of getting blocked.

Running on Node.js, it offers a unified interface that supports HTTP and headless browser crawling, making it versatile for various scraping tasks. It integrates with libraries like Cheerio for efficient HTML parsing and headless browsers like Puppeteer and Playwright for JavaScript rendering.

The library excels in scalability, automatically managing concurrency based on system resources, rotating proxies to enhance efficiency, and employing human-like browser fingerprints to avoid detection. Crawlee also ensures robust data handling through persistent URL queuing and pluggable storage for data and files.

Pros:

• Easy switching between simple HTTP request/response handling and complex JavaScript-heavy pages by changing just a few lines of code.
• Built-in sophisticated anti-blocking features like proxy rotation and generation of human-like fingerprints.
• Integrating tools for common tasks like link extraction, infinite scrolling, and blocking unwanted assets, along with support for both Cheerio and JSDOM, provides a comprehensive scraping toolkit right out of the box.

Cons:

• Built on Node.js and currently supports only JavaScript and TypeScript.
• Its comprehensive feature set and the requirement to understand HTTP and browser-based scraping can create a steep learning curve.

Best for: Crawlee is ideal for developers and teams seeking to manage simple and complex web scraping and automation tasks within a JavaScript/Node.js ecosystem. It is particularly effective for scraping web applications that combine static and dynamic pages, as it allows easy switching between different types of crawlers to handle each scenario.

6. Heritrix

Language: Java | GitHub: 2.7K+ stars | link

Heritrix is open-source web crawling software developed by the Internet Archive. It is primarily used for web archiving. It is designed to collect information from the web to build a digital library and support the Internet Archive's preservation efforts.

Advantages:

• Optimized for large-scale web archiving, making it ideal for institutions like libraries and archives needing to preserve digital content systematically.
• Detailed configuration options that allow users to customize crawl behavior deeply, including deciding which URLs to crawl, how to treat them, and how to manage the data collected.
• Able to handle large datasets, which is essential for archiving significant web portions.

Disadvantages:

• As it is written in Java, running Heritrix might require more substantial system resources than lighter, script-based crawlers, and it might limit usability for those unfamiliar with Java.
• Optimized for capturing and preserving web content rather than extracting data for immediate analysis or use.
• Does not render JavaScript, which means it cannot capture content from websites that rely heavily on JavaScript for dynamic content generation.

Best for: Heritrix is best suited for organizations and projects that aim to archive and preserve digital content on a large scale, such as libraries, archives, and other cultural heritage institutions. Its specialized nature makes it an excellent tool for its intended purpose but less adaptable for more general web scraping needs.

7. Apache Nutch

Language: Java | GitHub: 2.8K+ stars | link

Apache Nutch is an extensible open-source web crawler often used in fields like data analysis. It can fetch content through protocols such as HTTPS, HTTP, or FTP and extract textual information from document formats like HTML, PDF, RSS, and ATOM.

Advantages:

• Highly reliable for continuous, extensive crawling operations given its maturity and focus on enterprise-level crawling.
• Being part of the Apache project, Nutch benefits from strong community support, continuous updates, and improvements.
• Seamless integration with Apache Solr and other Lucene-based search technologies, making it a robust backbone for building search engines.
• Leveraging Hadoop allows Nutch to efficiently process large volumes of data, which is crucial for processing the web at scale.

Disadvantages:

• Setting up Nutch and integrating it with Hadoop can be complex and daunting, especially for those new to these technologies.
• Overly complicated for simple or small-scale crawling tasks, whereas lighter, more straightforward tools could be more effective.
• Since Nutch is written in Java, it requires a Java environment, which might not be ideal for environments focused on other technologies.

Best for: Apache Nutch is ideal for organizations building large-scale search engines or collecting and processing vast amounts of web data. Its capabilities are especially useful in scenarios where scalability, robustness, and integration with enterprise-level search technologies are required.

8. Webmagic

Language: Java | GitHub: 11.3K+ stars | link

Webmagic is an open-source, simple, and flexible Java framework dedicated to web scraping. Unlike large-scale data crawling frameworks like Apache Nutch, WebMagic is designed for more specific, targeted scraping tasks, which makes it suitable for individual and enterprise users who need to extract data from various web sources efficiently.

Advantages:

• Easier to set up and use than more complex systems like Apache Nutch, designed for broader web indexing and requires more setup.
• Designed to be efficient for small to medium-scale scraping tasks, providing enough power without the overhead of larger frameworks.
• For projects already within the Java ecosystem, integrating WebMagic can be more seamless than integrating a tool from a different language or platform.

Disadvantages:

• Being Java-based, it might not appeal to developers working with other programming languages who prefer libraries available in their chosen languages.
• WebMagic does not handle JavaScript rendering natively. For dynamic content loaded by JavaScript, you might need to integrate with headless browsers, which can complicate the setup.
• While it has good documentation, the community around WebMagic might not be as large or active as those surrounding more popular frameworks like Scrapy, potentially affecting the future availability of third-party extensions and support.

Best for: WebMagic is a suitable choice for developers looking for a straightforward, flexible Java-based web scraping framework that balances ease of use with sufficient power for most web scraping tasks. It's particularly beneficial for users within the Java ecosystem who need a tool that integrates smoothly into larger Java applications.

9. Nokogiri

Language: Ruby | GitHub: 6.1K+ stars | link

Like Beautiful Soup, Nokogiri is also great at parsing HTML and XML documents via the programming language Ruby. Nokogiri relies on native parsers such as the libxml2 libxml2, libgumbo, and xerces. If you want to read or edit an XML document using Ruby programmatically, Nokogiri is the way to go.

Advantages:

• Due to its underlying implementation in C (libxml2 and libxslt), Nokogiri is extremely fast, especially compared to pure Ruby libraries.
• Able to handle both HTML and XML with equal proficiency, making it suitable for a wide range of tasks, from web scraping to RSS feed parsing.
• Straightforward and intuitive API for performing complex parsing and querying tasks.
• Strong, well-maintained community ensures regular updates and good support through forums and documentation.

Disadvantages:

• Specific to Ruby, which might not be suitable for those working in other programming environments.
• Installation can sometimes be problematic due to its dependencies on native C libraries.
• Can be relatively heavy regarding memory usage, especially when dealing with large documents.

Best for: Nokogiri is particularly well-suited for developers already working within the Ruby ecosystem and needs a robust, efficient tool for parsing and manipulating HTML and XML data. Its speed, flexibility, and Ruby-native design make it an excellent choice for a wide range of web data extraction and transformation tasks.

10. Crawler4j

Language: Java | GitHub: 4.5K+ stars | link

Crawler4j is an open-source web crawling library for Java, which provides a simple and convenient API for implementing multi-threaded web crawlers. Its design focuses on simplicity and ease of use while providing essential features needed for effective web crawling.

Advantages:

• API is designed for simplicity, allowing developers to get up and running with minimal setup and configuration.
• Multi-threaded capability enables it to handle large-scale crawls efficiently, making the most available computing resources.
• Offers hooks and configurations that can be adjusted for more complex crawling scenarios.

Disadvantages:

• Does not process JavaScript natively.
• Non-Java developers might find it less appealing as it requires integration into Java applications, which might not be suitable for projects developed in other programming languages.
• While suitable for straightforward web crawling tasks, handling more complex requirements or newer web technologies might require additional tools or custom development.
• Compared to more widely used frameworks like Scrapy (Python) or Nutch (also Java), the community around Crawler4j might be smaller, affecting the future availability of third-party resources, extensions, and support.

Best for: Crawler4j is a good choice for Java developers who need a straightforward, efficient tool for web crawling that can be easily integrated into Java applications. Its ease of use and performance capabilities make it suitable for a wide range of crawling tasks, particularly where large-scale operations are not required.

11. Web Harvest

Language: Multiple-languages | GitHub: n/a stars | link

Web-Harvest is an open-source web data extraction tool designed to efficiently scrape data from web pages. It leverages XML configuration to define scraping tasks and parse data, making it a powerful option for those familiar with XML syntax.

Advantages:

• Able to handle various data manipulation tasks during extraction, offering greater flexibility than simpler scraping tools, with support from multiple scripting languages.
• Being open-source, it is freely available for modification and use, which can reduce costs and increase adaptability for personal or commercial projects.

Disadvantages:

• As a less widely used tool than others like Scrapy or Beautiful Soup, Web-Harvest has a significantly smaller community and fewer updates, which can impact the availability of support and resources.
• Does not natively handle JavaScript-rendered content, which could limit its effectiveness on modern web applications that rely heavily on JavaScript.

Best for: While it may be an option for users comfortable with XML, the tool's niche appeal is limited by its small and shrinking community, as well as a lack of consistent updates. For more reliable and updated options, consider using other tools mentioned in this article, such as Scrapy (Python) or Crawlee (Node.js), which can more effectively meet your web crawling and scraping needs.

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➡️ To learn more about how the law sees online data extraction, read our article on the legality of web scraping.