Best 12+ AI Web Scraping Tools You Should Know in 2025

We’ve categorized AI web scraping tools into the three main groups based on their technical complexity and intended audience. Click each category to jump to the relevant section:

• AI-powered platforms: Focused on using AI for structured extraction.
• No-code tools: Visual interfaces for scraping data without writing any code, with limited AI usage.
• Open-source AI tools: Ideal for developers who prefer full control over their scraping.

Here’s the table of top AI web scraping tools:

Vendor		For
1.	Bright Data	Enterprise-grade large-scale scraping
2.	Oxylabs	Adaptive scraping and parsing capabilities
3.	Diffbot	AI-based structured data extraction via Knowledge Graph
4.	Firecrawl	Headless AI-powered scraping with adaptive DOM parsing
5.	ScrapeGraphAI	Prompt-driven data extraction using schema-based HTML parsing
Show More (10)
6.	Kadoa	Beginner-friendly structured scraping
7.	Crawl4AI	AI-powered open-source scraping agents
8.	Scrapeghost	LLM-powered HTML-to-JSON extraction via schema-guided prompts
9.	LangChain	Building LLM-based data workflows
10.	Octoparse	Non-coders who want quick extraction
11.	ParseHub	No-code visual scraper that supports AJAX
12.	Browse AI	Trainable no-code bots with AI-powered data monitoring and extraction
13.	Clay	AI-enhanced scraping integrated into sales automation
14.	Import.io	Enterprise-scale data extraction
15.	Bardeen	Browser-based workflow automation with scraping capabilities

1.

Bright Data

Enterprise-grade large-scale scraping

2.

Oxylabs

Adaptive scraping and parsing capabilities

3.

Diffbot

AI-based structured data extraction via Knowledge Graph

4.

Firecrawl

Headless AI-powered scraping with adaptive DOM parsing

5.

ScrapeGraphAI

Prompt-driven data extraction using schema-based HTML parsing

Show More (10)

6.

Kadoa

Beginner-friendly structured scraping

7.

Crawl4AI

AI-powered open-source scraping agents

8.

Scrapeghost

LLM-powered HTML-to-JSON extraction via schema-guided prompts

9.

LangChain

Building LLM-based data workflows

10.

Octoparse

Non-coders who want quick extraction

11.

ParseHub

No-code visual scraper that supports AJAX

12.

Browse AI

Trainable no-code bots with AI-powered data monitoring and extraction

13.

Clay

AI-enhanced scraping integrated into sales automation

14.

Import.io

Enterprise-scale data extraction

15.

Bardeen

Browser-based workflow automation with scraping capabilities

Best AI web scraping tools

How we made this list

We intentionally excluded general-purpose data scraping tools and automation libraries that lack built-in AI capabilities (such as Scrapy or Playwright), even if they’re commonly used in web scraping and may complement AI tools in hybrid workflows.

We curated this list using the following criteria:

• Focus on AI-powered capabilities: We only included tools that use artificial intelligence, like LLMs or NLP, to understand page structure without hardcoded rules, and prompt-driven data extraction.
• Accessibility for users: We categorized tools based on technical level, such as no-code vs. developer tools.

What is AI web scraping?

AI web scraping is the process of using artificial intelligence algorithms with traditional web scraping processes to automate and refine data extraction activities. AI-powered web scraping tools are especially beneficial when you:

• Intend to scrape data from dynamic websites (design and structural changes)
• Need to categorize or analyze the scraped data
• Extract data from websites employing anti-bot measures.

AI web scraping tool types

1. AI-powered platforms

These solutions use LLMs, computer vision, or NLP for parsing, extracting, or interpreting content from web pages. For instance, Diffbot’s adaptive scraping dynamically adapts to DOM changes or inconsistent markup across pages. Many tools in this category support schema (structured format) or prompt-based extraction.

You give the tool a natural language instruction, for example, “Extract all job titles and company names from this URL”

2. No-code tools

No-code scrapers provide visual interfaces, enabling users to define the data to capture using point-and-click functionality or prebuilt templates. You can define data extraction rules visually.

However, these tools offer limited AI usage compared to AI-powered platforms, which utilize AI for pattern detection or intelligent field suggestions.

3. Open-source AI tools

This category includes libraries or frameworks that use LLMs or AI agents to extract data from web pages. They provide programmatic control; you need to define extraction schemas or AI prompts.

Techniques and technologies involved in AI-powered web scraping

AI-powered web scraping approach automatically adapts to website redesigns and extracts data loaded dynamically via JavaScript. It is important to employ these methods with consideration for the website’s terms and ethical considerations.

1. Adaptive scraping

Traditional web scraping methods rely on the specific structure or layout of a web page. When websites update their designs and structures, traditional scrapers can easily break. AI-based data collection methods, such as adaptive scraping, allow web scraping tools to adjust themselves to changes that are implied by websites, such as designs and structures.

Adaptive scrapers use machine learning and AI to adjust themselves dynamically based on the web page’s structure. They autonomously identify the structure of the target web page by analyzing the document object model (DOM) or by following specific patterns. In order to identify certain patterns or anticipate changes, the tool can be trained using scraped historical data.

For instance, AI models like convolutional neural networks (CNNs) can be used to recognize and analyze visual elements of a web page such as buttons. Typically, traditional data scraping techniques rely on the underlying code of a web page, such as HTML elements, to extract data.

Adaptive scraping examines the rendered version of the target website as it appears in a web browser. Visual elements such as buttons, banners or pop-ups disrupt the data extraction process.

A CNN can be trained with different visual representations of the pagination buttons to locate and interact with these buttons on a web page.

Sponsored

Oxylabs provides an ML-based custom parser builder, called OxyCopilot, that enhances Oxylab’s Web Scraper API, allowing users to refine and organize the data they collect using prompts. This streamlines the process by eliminating the need to sort through irrelevant data fields or perform manual data cleaning.

2. Generating human-like browsing patterns

Most websites employ anti-scraping measures, like CAPTCHAs, to prevent web scrapers from accessing and scraping their content. AI-powered web scraping tools can simulate human-like behavior like speed, mouse movements, and click patterns.

3. Generative AI models

Generative AI can be applied in different stages of the data collection process to enhance its adaptability. Pre-trained language models, such as ChatGPT, can assist developers in generating code for extracting data from websites and provide step-by-step instructions for web scraping in various programming languages.

Once you have scraped the desired web data, ChatGPT can be fine-tuned on the scraped data to generate more conversational content.

4. Natural language processing (NLP)

NLP, a subset of ML, enables you to conduct various tasks, including sentiment analysis, content summarization, and entity recognition. It is necessary to derive insights from the scraped data.

For instance, if you have extracted a significant amount of product review data, you need to determine the emotional tone behind each word, such as positive, negative, or neutral. Sentiment analysis enables you to categorize the extracted data as either positive or negative. This helps businesses to address customer concerns and improve their offerings.