The best web crawler tools in 2025

With the rapid development of big data and artificial intelligence technology, web crawlers have become an indispensable tool in the field of data collection and analysis. In 2025, efficient, stable and secure crawler tools have become the mainstream of the market. This article will introduce several best web crawler tools combined with 98IP proxy services, and provide some practical code examples to help you be more handy in the data crawling process.

I. Basic principles for choosing crawler tools

• Efficiency: Ability to quickly and accurately crawl data from the target website.
• Stability: Ability to run continuously to avoid frequent interruptions due to anti-crawler mechanisms.
• Security: Protect user privacy and avoid burdening or legal risks on the target website.
• Scalability: Supports custom configuration and is easy to integrate into other data processing systems.

II. Recommendation of the best web crawler tools in 2025

1.Scrapy + 98IP proxy
Features: Scrapy is an open source, collaborative web crawling framework that supports multi-threaded crawling and is very suitable for large-scale data collection. Combined with the stable proxy service provided by 98IP, it can effectively bypass the access restrictions of the target website.

Code example:

import scrapy
from scrapy.downloadermiddlewares.httpproxy import HttpProxyMiddleware
import random

# Deployment agent IP Pond
PROXY_LIST = [
    'http://proxy1.98ip.com:port',
    'http://proxy2.98ip.com:port',
    # More proxy IPs...
]

class MySpider(scrapy.Spider):
    name = 'my_spider'
    start_urls = ['https://example.com']

    custom_settings = {
        'DOWNLOADER_MIDDLEWARES': {
            HttpProxyMiddleware.name: 410,  # Setting Proxy Middleware Priority
        },
        'HTTP_PROXY': random.choice(PROXY_LIST),  # Randomly select a proxy IP
    }

    def parse(self, response):
        # Parsing page content
        pass

2.BeautifulSoup + Requests + 98IP proxy
Features: For small-scale, simple-structured websites, BeautifulSoup combined with the Requests library can quickly implement page parsing and data crawling. By adding 98IP proxy, the flexibility and success rate of crawling can be further improved.

Code example:

import requests
from bs4 import BeautifulSoup
import random

# Deployment agent IP Pond
PROXY_LIST = [
    'http://proxy1.98ip.com:port',
    'http://proxy2.98ip.com:port',
    # More proxy IPs...
]

def fetch_page(url):
    proxy = random.choice(PROXY_LIST)
    try:
        response = requests.get(url, proxies={'http': proxy, 'https': proxy})
        response.raise_for_status()  # Check if the request was successful
        return response.text
    except requests.RequestException as e:
        print(f"Error fetching {url}: {e}")
        return None

def parse_page(html):
    soup = BeautifulSoup(html, 'html.parser')
    # Parsing data according to page structure
    pass

if __name__ == "__main__":
    url = 'https://example.com'
    html = fetch_page(url)
    if html:
        parse_page(html)

3.Selenium + 98IP Proxy
Features: Selenium is a tool for automated testing of Web applications, but it is also suitable for data crawling. It can simulate user browser behaviors, such as clicking, inputting, etc., and is suitable for websites that require login or complex interactions. Combined with 98IP proxy, some anti-crawler mechanisms based on user behavior can be bypassed.

Code example:

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.proxy import Proxy, ProxyType
import random

# Deployment agent IP Pond
PROXY_LIST = [
    'http://proxy1.98ip.com:port',
    'http://proxy2.98ip.com:port',
    # 更多代理IP...
]

chrome_options = Options()
chrome_options.add_argument("--headless")  # headless mode

# Setting up a proxy
proxy = Proxy({
    'proxyType': ProxyType.MANUAL,
    'httpProxy': random.choice(PROXY_LIST),
    'sslProxy': random.choice(PROXY_LIST),
})

chrome_options.add_argument("--proxy-server={}".format(proxy.proxy_str))

service = Service(executable_path='/path/to/chromedriver')  # Specify the chromedriver path
driver = webdriver.Chrome(service=service, options=chrome_options)

driver.get('https://example.com')
# Perform page manipulation and data crawling
# ...

driver.quit()

4.Pyppeteer + 98IP proxy
Features: Pyppeteer is a Python library that provides encapsulation of Puppeteer, allowing Puppeteer's functions to be used in a Python environment. Puppeteer is a Node library used to automate Chrome or Chromium browsers, suitable for scenarios where user behavior needs to be simulated.

Code example:

import asyncio
from pyppeteer import launch

async def fetch_page(url, proxy):
    browser = await launch(headless=True, args=[f'--proxy-server={proxy}'])
    page = await browser.newPage()
    await page.goto(url)
    content = await page.content()
    await browser.close()
    return content

async def main():
    # Deployment agent IP Pond
    PROXY_LIST = [
        'http://proxy1.98ip.com:port',
        'http://proxy2.98ip.com:port',
        # More proxy IPs...
    ]
    url = 'https://example.com'
    proxy = random.choice(PROXY_LIST)
    html = await fetch_page(url, proxy)
    # Parsing page content
    # ...

if __name__ == "__main__":
    asyncio.run(main())

III. Summary

Web crawler tools in 2025 have significantly improved in efficiency, stability, security, and scalability. Combined with 98IP proxy service, the flexibility and success rate of crawler tools can be effectively improved. Whether it is Scrapy, BeautifulSoup + Requests, Selenium or Pyppeteer, they can meet the data collection needs in different scenarios. In practical applications, it is recommended to select appropriate crawler tools according to the characteristics and crawling requirements of the target website, and reasonably configure the proxy IP to achieve efficient and secure data crawling.