Your Complete Guide to the Best AI Web Scrapers in 2025

Sergey Ermakovich

2 Sept 2025

When you need to extract data like prices and trends from popular websites, AI scrapers let you do that without having to deal with broken parsers or proxy management. Instead of writing manual CSS selectors and maintaining infrastructure, you just describe the data you want and get back structured JSON.

Choosing the right scraper depends on what you need. For example, if you’re a developer, HasData’s API delivers reliable JSON output. If you prefer no-code, Browse AI handles simple visual scraping.  This article reviews each AI web scraping tool in depth, giving you the clarity to pick the right scraper for your project.

Top AI Web Scrapers

1. HasData

HasData provides a high-performance web scraping API that manages infrastructure like proxies, CAPTCHA handling, and browser rendering.

The core of HasData’s AI is aiExtractRules, which allows you to define a JSON schema directly within the API call. Instead of using CSS selectors, you just specify the data fields, and the AI model parses the page to return clean, structured JSON.

Hint for low-code users: You don’t need to be an experienced developer to use HasData’s API. The extraction rules are written in plain JSON and can be generated automatically by an AI assistant. For example:

Based on the HasData Web Scraping API LLM Extraction documentation, write a JSON schema for aiExtractRules for this page: https://b2bdemoexperience.myshopify.com/collections/furniture. Extract the following product fields and infer the correct data type for each: name, url, price. Return only valid JSON.

Example request:

curl --request POST \
	--url https://api.hasdata.com/scrape/web \
	--header 'Content-Type: application/json' \
	--header 'x-api-key: YOUR-API-KEY' \
	--data '{"url":"https://b2bdemoexperience.myshopify.com/collections/furniture","proxyType":"datacenter","proxyCountry":"US","screenshot":false,"jsRendering":false,"aiExtractRules":{"products":{"type":"list","description":"List of products on the furniture collection page","output":{"name":{"type":"string","description":"Product name as displayed on the product card"},"url":{"type":"string","description":"Absolute URL to the product detail page"},"price":{"type":"number","description":"Price value as a number"}}}}}'

Example output:

// Truncated for brevity
{
  "products": [
    {
      "name": "Bluff Nightstand",
      "url": "https://b2bdemoexperience.myshopify.com/products/bluff-oval-nightstand",
      "price": 399
    },
    {
      "name": "Butte Coffee Table",
      "url": "https://b2bdemoexperience.myshopify.com/products/butte-coffee-table",
      "price": 1099
    },
    {
      "name": "Canyon Bed Frame",
      "url": "https://b2bdemoexperience.myshopify.com/products/canyon-bed-frame-with-footboard",
      "price": 2900
    }
  ]
}

Before analysis, we clean the document by removing inline scripts, SVG icons, base64 blobs, and heavy styling tags. This reduces the amount of junk the LLM must process, keeping the input lean and content-focused while lowering the risk of hallucinations.

Pros:

• Schema-driven AI extraction: Define your desired output structure for clean and predictable JSON.
• Eliminates parser maintenance: The AI adapts to website layout changes. You don’t need to constantly update selectors.
• All-in-one solution: Combines scraping infrastructure with AI parsing in a single API.
• Built for modern pipelines: Clean output formats suitable for data pipelines, including RAG systems.
• Scalable and reliable: HasData’s Web Scraping API is built to handle millions of pages, with auto-retry logic.
• Anti-bot handling: HasData manages proxies, CAPTCHAs, and bypasses services like Cloudflare and Akamai.

Cons:

• API-Focused: No visual point-and-click interface.

Pricing: Free plan with 1,000 credits. Paid plans start at $49/month for 200,000 credits. An aiExtractRules request costs 10 credits, which is approximately $2.45 per 1,000 extractions on the starter plan.

Best for: Enterprise teams and developers who need clean, structured JSON and precise control over data extraction for production systems.

2. Crawl4AI

Crawl4AI is an open-source Python library for building AI-ready crawlers. It uses Playwright for JavaScript rendering and can extract structured data with LLMs or CSS/XPath. It’s a library, not a managed service, so you are responsible for the entire environment, including dependencies, proxies, and LLM API costs.

To extract data, configure an LLMExtractionStrategy with your LLM provider and an instruction, then run the crawler.

Example setup:

# Define the extraction strategy
llm_strategy = LLMExtractionStrategy(
    llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token="..."),
    instruction="Extract a list of 'products' with 'name', 'URL', and numeric 'price'"
)

# Run the crawler
result = await crawler.arun(
    url="https://b2bdemoexperience.myshopify.com/collections/furniture",
    config=CrawlerRunConfig(extraction_strategy=llm_strategy)
)

Example output: The process returns a JSON object. However, in our test, the model hallucinated incorrect prices.

// Truncated for brevity
[
  {
    "name": "Grove Side Table",
    "URL": "https://b2bdemoexperience.myshopify.com/products/grove-side-table",
    "price": 304.23 // Incorrect, actual price is $349
  },
  {
    "name": "Horizon Bed Frame",
    "URL": "https://b2bdemoexperience.myshopify.com/products/horizon-bed-frame",
    "price": 2179.31 // Incorrect, actual price is $2500
  }
]

Pros:

• Open-source and highly customizable: Complete flexibility to tailor the scraping process.
• Granular control: Advanced options for managing sessions, hooks, and crawling strategies.
• No vendor lock-in: Full ownership of your scraping infrastructure.

Cons:

• Requires significant setup and maintenance: You are responsible for the complete technical setup and anti-bot updates.
• Hidden operational costs: Total cost includes developer time, servers, proxies, and third-party LLM API calls.
• Potential for inaccurate data: Prompt-based extraction can be unreliable for precise fields like price.

Pricing: The library is free. Infrastructure and API costs, however, are your responsibility.

Best for: Developers who need maximum control to build a custom solution and are prepared to manage the associated infrastructure and potential data inconsistencies.

3. Scrapy-LLM

Scrapy-LLM is open-source middleware that integrates LLMs into the Scrapy framework. It is an extension for developers already building spiders with Scrapy, not a standalone tool. It intercepts scraped HTML and sends it to an LLM for data extraction based on a predefined schema.

To use it, define your desired data structure with Pydantic models, which serve as a guide for the LLM’s output.

Example schema definition:

from pydantic import BaseModel, Field
from typing import List, Optional

class Product(BaseModel):
    name: str = Field(description="Product name")
    url: str = Field(description="Absolute URL to the product page")
    price: float = Field(description="Price as a number")

class ProductCollection(BaseModel):
    products: Optional[List[Product]] = Field(description="List of products")

Example output: The middleware produces JSON matching this structure. However, as with the other tools using general-purpose LLMs, our test showed significant data quality issues, with the model consistently hallucinating incorrect prices.

// Truncated for brevity
{
  "products": [
    {
      "name": "Bluff Nightstand",
      "url": "https://b2bdemoexperience.myshopify.com/products/bluff-nightstand",
      "price": 347.84 // Incorrect, actual is $399
    },
    {
      "name": "Butte Coffee Table",
      "url": "https://b2bdemoexperience.myshopify.com/products/butte-coffee-table",
      "price": 958.1 // Incorrect, actual is $1099
    }
  ]
}

Pros:

• Native Scrapy integration: Fits directly into your existing Scrapy workflows.
• Schema-based extraction: Uses Pydantic models to define the data structure.
• Open-source flexibility: Allows developers to customize the middleware.

Cons:

• Niche application: Only useful for developers working within the Scrapy framework.
• Unreliable for critical data: Even with a schema, the LLM can hallucinate incorrect values for precise fields like price.
• Dependent on Scrapy architecture: Inherits the complexities of Scrapy setup and deployment.

Pricing: The middleware is free, but costs depend on the LLM provider and your infrastructure.

Best for: Teams who are already using Scrapy and want to experiment with AI-based parsing, but not for production systems, where data accuracy is critical.

4. Parsera

Parsera is an AI parsing tool that extracts structured JSON from a URL. It operates as a standalone service or as an “Actor” on the Apify Store, which allows for scheduling, proxy use, and data storage within the Apify platform. Parsera infers the data structure automatically.

Pros:

• Automatic selector inference: Automatically determines the data to extract without CSS selectors.
• Simplified data extraction: Efficient for simple to moderately complex pages.

Cons:

• Platform dependency: Full features require operating within the Apify platform.
• Higher cost at scale: Cost per extraction is higher than with other API-based solutions.

Pricing: Free plan with 200 credits. Paid plans start at $29/month. An extraction costs 5 credits, or $72.50 per 1,000 extractions on the starter plan.

Best for: Users with low-volume needs or those already in the Apify ecosystem.

5. Bardeen.AI

Bardeen.AI is an AI automation agent in a Chrome extension, designed for go-to-market teams. Web scraping is one of its features, allowing users to build “Playbooks” to scrape data and send it to applications like Google Sheets or a CRM.

Pros:

• In-browser automation: Excellent for automating tasks like saving LinkedIn profiles.
• Broad integrations: Connects with hundreds of popular applications.
• Pre-built playbooks: Offers a library of templates for common tasks.

Cons:

• Browser-dependent: Not designed for backend scraping pipelines.
• Costly for data extraction: Pricing is optimized for workflows, not data volume.
• Workflow-focused: Built for automating actions, not for bulk data extraction.

Pricing: Free plan with 100 credits/month. Paid plans start at $129/month for 1,200 credits.

Best for: Sales and marketing professionals automating repetitive tasks and light data collection in their browsers.

6. ScrapeGraphAI

ScrapeGraphAI uses natural language prompts to extract structured data. It’s available as an open-source Python library and a premium API. The idea is that you simply tell the tool what you want in plain English (e.g., “Extract the name, price, and URL for each product”). In our tests, though, it failed to extract product prices, even after multiple attempts.

Pros:

• Natural language prompts: Intuitive and fast to get started.
• Dual offering: Provides an open-source library and a managed API.
• Good for unstructured content: Effective for extracting info from articles and descriptions.

Cons:

• Potential for ambiguity: Prompts can be less precise than a strict schema.
• Higher cost per request: Less cost-effective for high-volume scraping.
• Open source requires setup: The free library requires you to manage LLM keys and infrastructure.

Pricing: Free grant of 50 API credits. Paid plans start at $20/month. An extraction costs 10 credits, or $40.00 per 1,000 extractions on the starter plan.

Best for: Users who prioritize a natural language interface for quick, small-scale tasks.

7. Browse AI

Browse AI is a no-code tool designed for non-technical users. You train a “robot” by clicking on the data you want, and it learns to repeat the process.

Pros:

• Intuitive visual interface: No programming knowledge required.
• Website monitoring: Can be scheduled to run automatically and send notifications.
• Extensive integrations: Connects with over 7,000 applications via Zapier and Make.

Cons:

• Browser-based: Not suited for building scalable, server-side data pipelines.
• Less efficient for bulk extraction: Mimicking user actions is slower than a direct API call.
• Row-based pricing: Can be costly for pages with large tables or lists.

Pricing: Free plan with 50 credits/month. Paid plans start at $48/month for 2,000 credits.

Best for: Non-technical users automating targeted, low-volume data extraction.

Side-by-Side Comparison of AI Web Scrapers

Conclusion

Our tests revealed a critical flaw in some tools. Wrappers around general-purpose LLMs, like Crawl4AI and Scrapy-LLM, tend to hallucinate, failing to extract accurate data. 

HasData, with its schema-driven AI, is developed specifically to prevent this. It delivers clean, predictable JSON, eliminating guesswork. You just define the structure and get correct data. 

No-code tools like Browse AI are fine for simple, non-technical tasks. Open-source libraries give you full control if you’re willing to manage the infrastructure and constantly validate the data quality yourself.

The choice depends on your requirements. For experiments, anything will do. For production systems that require accurate, reliable data, the solution is HasData.

Sergey Ermakovich

I am a seasoned marketer with a passion for digital innovation. My expertise lies in creating innovative solutions to present data-driven insights, and I have extensive experience in the web scraping and data analysis industry.

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