Web scraping is a common method used to collect data from websites. It is helpful in many areas like price tracking, lead generation, market research, and automation. As websites grow more complex, choosing the right scraping tool has become more important.
Scraping tools come in many forms. Some are code-based and made for developers, while others use visual interfaces for non-technical users. There are also AI tools that adjust automatically to website changes.
This blog introduces 10 web scraping tools that are useful in 2025. Each one fits a different use case, so you can pick the right option based on your needs and skill level.
Octoparse – Visual Scraping for Non-Coders
Octoparse is a no-code web scraping tool designed for users who prefer a visual interface. It works by letting users point and click on elements in a webpage to build scraping workflows. This makes it suitable for people who want to collect data without writing code.
It supports scraping from websites with login forms, dropdowns, infinite scroll, and other interactive elements. Users can schedule tasks, store results in the cloud, and export data in formats like CSV or Excel.
Octoparse is often used for scraping job listings, e-commerce product details, and real estate listings. It also includes templates for popular websites, which speeds up setup for common scraping tasks.
Scrapy – Full-Featured Python Scraping Framework
Scrapy is a Python-based web scraping framework built for developers who need a flexible and scalable solution. It allows users to create crawlers that follow links, extract content, and store structured data efficiently.
Scrapy supports asynchronous requests, making it faster for scraping large websites. It also offers features like request scheduling, middleware customization, and data pipeline management, which are useful in more advanced scraping projects.
Developers use Scrapy when they need more control over the scraping logic, especially for handling pagination, login systems, or complex site structures. It integrates well with other Python libraries and supports exporting data in JSON, CSV, or XML.
Selenium – Automating Browser-Based Scraping
Selenium is a browser automation tool often used for testing web applications, but it’s also useful for web scraping. It allows scripts to interact with a website the same way a user would by clicking buttons, filling out forms, or scrolling through pages.
Selenium is ideal for scraping websites that rely heavily on JavaScript to display content. It works with browsers like Chrome, Firefox, and Edge, and can simulate real-time user actions. This makes it a good choice when static HTML parsers can’t access the data you need.
It supports multiple programming languages including Python, Java, and JavaScript. Although it is slower than tools that use direct HTTP requests, Selenium is valuable for situations where full browser behavior is needed.
Puppeteer – Headless Chrome for Developers
Puppeteer is a Node.js library that provides control over a headless version of Chrome or Chromium. It is often used for scraping websites that load content dynamically with JavaScript.
With Puppeteer, developers can script complex browser interactions like navigating through pages, waiting for content to load, and taking screenshots. It can also be used to extract structured data from pages that rely on AJAX or client-side rendering.
This tool is useful when scraping pages where the content doesn’t appear in the initial HTML and requires browser-based interaction to fully load. Developers often choose Puppeteer when they need to work with JavaScript-heavy websites or build scrapers that mimic real user behavior.
ParseHub – Low-Code Scraping for Complex Structures
ParseHub is a desktop-based scraping tool designed for users who want more control than no-code tools provide but without writing full scripts. It offers a visual interface where users can click through a website and define the data they want to extract.
This tool can handle websites with complex elements like dropdowns, maps, and login forms. It works by creating a step-by-step scraping logic, and it includes features like data previews, scheduling, and cloud storage.
ParseHub supports exporting results in formats like Excel and JSON, and it allows for API access to automate workflows. It is often used for scraping real estate platforms, travel sites, and e-commerce pages where content is nested or structured in layers.
Kadoa – AI-Powered Smart Scraper
Kadoa is a no-code scraping tool that uses artificial intelligence to detect and extract data from websites. Instead of requiring users to manually configure scraping rules, it analyzes the structure of a webpage and automatically identifies the relevant data points.
This tool is helpful when working with websites that change frequently or have complex, unpredictable layouts. Kadoa adjusts to page updates without the need for manual reconfiguration, which saves time on maintenance.
It works well for teams that need to collect data from multiple sources without building custom scrapers for each site. Common use cases include aggregating business listings, monitoring product pages, and pulling structured content from dynamic websites.
Rossum.ai – Document-Focused Data Extraction
Rossum.ai is a platform built for extracting structured data from documents like PDFs, invoices, emails, and scanned forms. It uses machine learning to identify fields and content, even when layouts vary or include unstructured sections.
Unlike traditional scrapers that target web pages, Rossum focuses on file-based data that often arrives through uploads or email. It is commonly used in finance, logistics, and enterprise workflows where large volumes of documents need to be processed automatically.
Rossum supports integration with CRMs, ERPs, and cloud storage platforms. It can extract data in real time and route it into other tools for further processing.
Cheerio – Lightweight Scraping in Node.js
Cheerio is a fast and lightweight HTML parser built for Node.js. It provides a jQuery-like syntax, making it easy to select and extract elements from static web pages.
Unlike browser automation tools, Cheerio does not render JavaScript or simulate user interactions. Instead, it loads the raw HTML and lets developers parse and manipulate it quickly. This makes it well-suited for scraping simple, server-rendered websites.
Cheerio is often used in projects where speed and efficiency are more important than handling dynamic content. It is also helpful when pairing with HTTP libraries like Axios or node-fetch for building custom scraping solutions in JavaScript.
PacketStream – Residential Proxy for Scraping
PacketStream is a residential proxy network that helps maintain stable and anonymous access to websites. It routes web scraping requests through real household IP addresses, making the traffic appear like it comes from regular users rather than bots.
This approach is useful for tasks that involve scraping sites with IP bans, request limits, or region-based restrictions. Residential proxies are harder to detect and are more reliable for accessing content without triggering automated blocks.
IP addresses in the network come from verified users, called Packeters, who share their internet connection through a transparent opt-in system. Each IP is tied to a trusted residential ISP, which helps maintain long-term access across different locations.
For teams using scraping tools like Scrapy, Puppeteer, or Kadoa, residential proxies help reduce failed requests and maintain session consistency. PacketStream offers access starting at $1 per GB, making it a flexible option for both small and large-scale scraping setups.
Conclusion
Choosing the right tool for web scraping depends on the type of content you need, the structure of the website, and your level of technical experience. Some tools are built for developers who want full control, while others are designed for users who prefer visual workflows or AI-driven automation.
The tools covered in this list offer a range of features, from browser automation and static HTML parsing to document extraction and adaptive scraping. Each has its own strengths, and selecting the right one can improve efficiency and reduce errors in your scraping process.
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