How to Effectively Use User Agents for Web Scraping?

User agents are one of the most important tools for successful large-scale web scraping. Intelligently rotating user agents help distribute scraper traffic across diverse identifiers, making it much harder for websites to detect and block automated scraping bots.

In this comprehensive guide, we'll cover everything from understanding what exactly user agents are to techniques for compiling robust user agent lists and how to implement intelligent randomized user agent rotation within your web scrapers.

What is a User Agent?

The user agent string is an HTTP header that identifies the software and device making the request to a website. It contains information about the operating system, browser, and other attributes. A typical user agent string looks something like this:

Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36

This tells the server that the request is coming from a Windows 10 device running Chrome browser version 74.

When you browse the web as a regular user, your browser automatically sends a standard user agent unique to your system. However, scrapers have the ability to spoof user agents and mimic real browsers to avoid detection.

The Critical Role of User Agents

Among the array of evasion techniques used by scrapers, intelligently rotating user agents is one of the most effective and important. User agents allow scrapers to disguise their automated traffic as organic human visitors by spoofing browser identifiers. Let's first understand what exactly user agents are.

Anatomy of a User Agent String

The user agent is an HTTP request header that identifies details about the client application and device making the request. A typical user agent string looks like:

Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36

This reveals that the request originated from:

  • Mozilla/5.0 – The legacy token that identifies the user agent string format.
  • Windows NT 10.0 – The operating system is Windows 10.
  • Win64; x64 – A 64-bit Windows architecture.
  • AppleWebKit/537.36 – The WebKit browser engine version 537.36.
  • Chrome/74.0.3729.169 – The Chrome browser version 74.0.3729.169.
  • Safari/537.36 – Reference to the Safari browser using WebKit 537.36.

By parsing the user agent, servers can identify these key attributes of the client. There are libraries like ua-parser for extracting user agent data. Now that we understand the composition of user agent strings let's see why they are so crucial for web scraping without getting blocked.

Why User Agents Matter for Web Scraping

Websites routinely analyze user agent values of incoming requests to differentiate between organic human visitors vs. automated scrapers. Tell-tale signs they look for include:

  • Suspicious headers – Unrecognized or unusual user agent strings can trigger blocks.
  • Repeating headers – The same user agent on all requests signifies a bot.
  • Mobile or niche browsers – Less popular browsers are more suspicious.
  • Old versions – Outdated Chrome on an obscure Linux distro seems robotic.
  • Missing headers – Blank or absent user agents also raise red flags.

Here are some common blocking behaviors websites exhibit when detecting scraper user agents:

  • CAPTCHAs – The classic challenge to prove you are human and not a bot.
  • IP bans – Blocking all traffic from the scraper's originating IP address.
  • 404 errors – Making pages inaccessible that were reachable earlier.
  • Block pages – Serving outright warnings about scraping detection.

A study by a leading web scraping solutions company Bright Data found that over 66% of websites block scrapers with suspicious user agents. Another 8% serve CAPTCHAs to confirm humanity. Therefore, setting the right user agent is mission-critical for any web scraper to avoid getting flagged and blocked. This brings us to user agent rotation techniques.

Techniques for Compiling Robust User Agent Lists

The first step for any web scraper employing user agent rotations is compiling a diverse pool of valid, up-to-date user agent strings. The larger the pool, the more options you have for distributing traffic. Here are some proven techniques for building a comprehensive user agent list:

Scraping Public User Agent Databases

There are a number of handy online databases that aggregate curated lists of known user agents, including:

These can be programmatically scraped using a simple script like:

import requests
from bs4 import BeautifulSoup

urls = [

user_agents = []

for url in urls:
  response = requests.get(url)
  soup = BeautifulSoup(response.text, 'html.parser')
  for ua in'td.useragent'):

This aggregates user agents from multiple sources, giving us a diverse starting point. However, public databases tend to have limited breadth. Next we'll look at some other creative ways to expand our list.

Extracting from Real Browsers

An alternative source for authentic user agents are actual browsers used by people daily. We can extract user agents from our own devices like:

  • Chrome, Firefox and Edge on Windows and MacOS
  • Safari on iOS
  • Samsung Internet on Android

Most browsers provide options to view and copy the user agent through Developer Tools. We can also use services like Browserling and BrowserStack to emulate mobile, desktop and niche browsers, then extract their default user agents.

This approach provides current, genuine user agents, albeit limited to our own devices. Let's look at some more creative ideas next.

Mining GitHub Repositories

Developers working on user agent related projects will often share their compiled lists on GitHub. For example: tamimibrahim17/List-of-user-agents – 7000+ user agents. We can directly pull and merge these GitHub repository files into our list programmatically using the GitHub API:

import requests

urls = [

user_agents = []

for url in urls:
  response = requests.get(url)

Contributed lists on GitHub can provide unique user agents not found elsewhere. Next, let's look at integrating user agent APIs.

Leveraging User Agent APIs

Several paid web scraping and proxy services like BrightData and Smartproxy expose APIs for retrieving frequently updated, high-quality user agent lists. For example, we can use BrightData's Python library to access over 40,000 user agents:

from brightdata import BrightData

bd = BrightData('YOUR_API_KEY')
user_agents = bd.user_agents()


The benefit of professional user agent APIs is having access to large, current lists without any scraping overhead. However, these come at a monetary cost. By combining user agents from all of the above sources – databases, browsers, GitHub, and APIs – we can compile a robust list with maximum diversity. Next, we'll look at how to implement smart user agent rotation.

Implementing Intelligent User Agent Rotation

Armed with a large pool of diverse user agents, the next critical step is implementing randomized user agent rotation. The key ideas are:

  • Rotate user agents on every request to distribute traffic
  • Introduce calculated randomness to appear more humanFavor user agents that are more likely to succeed


ever, simple uniform random selection is not optimal, as some user agents are known to work better than others. The key is intelligently weighted random selection.

Criteria for Rating User Agents

Here are some heuristics we can use for weighting user agents likelihood of evading blocking:

  • Browser popularity – Chrome, Firefox, Safari are less suspicious than fringe browsers.
  • Operating system – iOS, Windows, Linux and MacOS are more common than say Playstation OS.
  • Version – Higher version numbers appear more updated and human.
  • Language – English and other popular languages are less suspicious.
  • Success history – User agents known to work well should get preference.

By programmatically assigning weights to each user agent based on these criteria, we can bias random selection towards more successful ones.

Implementing Weighted Random Selection

Here is sample Python code to demonstrate a weighted random user agent rotator:

import random

class UserAgent:

  def __init__(self, ua_string):
    self.string = ua_string
    self.os = parse_os(ua_string) 
    self.browser = parse_browser(ua_string)
    self.version = parse_version(ua_string)
    self.language = parse_language(ua_string)
    # etc
  def weight(self):
    weight = 0
    # Browser popularity
    if self.browser in ['Chrome', 'Firefox']:
      weight += 100
    # Operating System 
    if self.os in ['Windows', 'iOS', 'Android', 'Linux', 'MacOS']: 
      weight += 50
    # Version
    if self.version > 100:
      weight += 25
    # Language
    if self.language in ['en', 'es', 'hi', 'ar', 'zh']:
      weight += 25
    return weight
class UserAgentRotator:

  def __init__(self, user_agents):
    self.user_agents = user_agents
  def get_random_agent(self):
    weights = []
    for ua in self.user_agents:
    return random.choices(self.user_agents, weights)[0]

This allows us to integrate intelligent weighted selection into the user agent rotation process for any scraper. Next let's look at how to put it into action.

Integrating User Agent Rotation into Web Scrapers

Now that we have a pooled list of user agents and selection logic, we can integrate randomized rotation into our web scrapers. Here are examples of adding dynamic user agents to popular scraping libraries:


import requests
from useragent_rotator import UserAgentRotator 

rotator = UserAgentRotator(['ua1', 'ua2', ...])

def scrape(url):

  headers = {
    'User-Agent': rotator.get_random_agent() 
  resp = requests.get(url, headers=headers)
  # Scrape page


from scrapy import signals
from useragent_rotator import UserAgentRotator

rotator = UserAgentRotator(['ua1', 'ua2', ...])

def set_user_agent(spider):
  spider.user_agent = rotator.get_random_agent() 
# Rest of spider code...

We can add similar hooks for user agent rotation in Selenium, Playwright, BeautifulSoup, or any scraping library. Proper implementation requires calculating new user agents on every request, while caching for performance. Next we'll compare DIY integration vs. leveraging scraping APIs.

Web Scraping APIs Handle User Agent Rotation for You

While the methods outlined above allow you to build your own user agent rotation system, it requires significant engineering effort. An alternative is leveraging web scraping APIs. API providers like BrightData, and ScraperAPI handle the heavy lifting around user agents and proxy rotation for you.

For example, BrightData's Python SDK lets you focus on writing scraper business logic:

from brightdata import BrightDataScraper

scraper = BrightDataScraper(api_key)

resp = scraper.get(url) # User agent automatically rotated

The benefits of web scraping APIs include:

  • No user agent management – Agents handled automatically behind the scenes
  • Additional evasions – Proxies, browsers, CAPTCHAs etc. also covered
  • Reliability – Run scrapers 24/7 without worrying about blocks
  • Scalability – Deploy across thousands of servers out of the box
  • Performance – Multi-threaded scraping with automatic retries

Of course, the tradeoff is the monetary cost involved in using commercial APIs versus building your own bot mitigation systems. Let's recap some best practices around user agents for robust web scraping.


User agents play a pivotal role in the world of large-scale web scraping, serving as a critical component for eluding detection. By smartly rotating user agents, scrapers can disperse their digital footprint, presenting as a myriad of unique visitors, which significantly complicates the task of websites trying to pinpoint and thwart bot activity.

This guide offers strategies for assembling extensive user agent rosters and explains how to integrate sophisticated user agent rotation tactics into your scraping tools. I hope this guide provided a comprehensive overview of how to leverage user agents in your web scraping projects effectively.


John Rooney

John Rooney

John Watson Rooney, a self-taught Python developer and content creator with a focus on web scraping, APIs, and automation. I love sharing my knowledge and expertise through my YouTube channel, My channel caters to all levels of developers, from beginners looking to get started in web scraping to experienced programmers seeking to advance their skills with modern techniques. I have worked in the e-commerce sector for many years, gaining extensive real-world experience in data handling, API integrations, and project management. I am passionate about teaching others and simplifying complex concepts to make them more accessible to a wider audience. In addition to my YouTube channel, I also maintain a personal website where I share my coding projects and other related content.

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