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user-agents
Advanced tools
The 'user-agents' npm package is designed to generate random user-agent strings that can be used for web scraping, testing, or any other scenario where you need to simulate different browsers and devices.
Generate a random user-agent
This feature allows you to generate a random user-agent string. The generated user-agent string can be used to simulate different browsers and devices.
const UserAgent = require('user-agents');
const userAgent = new UserAgent();
console.log(userAgent.toString());
Generate a user-agent with specific options
This feature allows you to generate a user-agent string with specific options, such as targeting mobile devices. This can be useful for testing how your application behaves on different types of devices.
const UserAgent = require('user-agents');
const userAgent = new UserAgent({ deviceCategory: 'mobile' });
console.log(userAgent.toString());
Access user-agent properties
This feature allows you to access detailed properties of the generated user-agent, such as browser name, version, platform, and more. This can be useful for logging or debugging purposes.
const UserAgent = require('user-agents');
const userAgent = new UserAgent();
console.log(userAgent.data);
The 'random-useragent' package also generates random user-agent strings. It provides a simpler API compared to 'user-agents' and is easier to use for basic use cases. However, it lacks the advanced customization options available in 'user-agents'.
The 'useragent' package is primarily focused on parsing and identifying user-agent strings rather than generating them. It can be used to analyze user-agent strings and extract information about the browser, version, and platform. It complements 'user-agents' by providing analysis capabilities.
The 'ua-parser-js' package is a JavaScript library for parsing user-agent strings and extracting detailed information about the browser, engine, OS, CPU, and device. While it does not generate user-agent strings, it is useful for analyzing and understanding user-agent data.
User-Agents is a JavaScript package for generating random User Agents based on how frequently they're used in the wild. A new version of the package is automatically released every day, so the data is always up to date. The generated data includes hard to find browser-fingerprint properties, and powerful filtering capabilities allow you to restrict the generated user agents to fit your exact needs.
Web scraping often involves creating realistic traffic patterns, and doing so generally requires a good source of data. The User-Agents package provides a comprehensive dataset of real-world user agents and other browser properties which are commonly used for browser finerprinting and blocking automated web browsers. Unlike other random user agent generation libraries, the User-Agents package is updated automatically on a daily basis. This means that you can use it without worrying about whether the data will be stale in a matter of months.
Generating a realistic random user agent is as simple as running new UserAgent()
, but you can also easily generate user agents which correspond to a specific platform, device category, or even operating system version.
The fastest way to get started is to hop down to the Examples section where you can see it in action!
The User Agents package is available on npm with the package name user-agents. You can install it using your favorite JavaScript package manager in the usual way.
# With npm: npm install user-agents
# With pnpm: pnpm install user-agents
# With yarn:
yarn add user-agents
The User-Agents library offers a very flexible interface for generating user agents. These examples illustrate some common use cases, and show how the filtering API can be used in practice.
The most basic usage involves simply instantiating a UserAgent
instance.
It will be automatically populated with a random user agent and browser fingerprint.
import UserAgent from 'user-agents';
const userAgent = new UserAgent();
console.log(userAgent.toString());
console.log(JSON.stringify(userAgent.data, null, 2));
In this example, we've generated a random user agent and then logged out stringified versions both the userAgent.data
object and userAgent
itself to the console.
An example output might look something like this.
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36
{
"appName": "Netscape",
"connection": {
"downlink": 10,
"effectiveType": "4g",
"rtt": 0
},
"platform": "Win32",
"pluginsLength": 3,
"vendor": "Google Inc.",
"userAgent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36",
"viewportHeight": 660,
"viewportWidth": 1260,
"deviceCategory": "desktop",
"screenHeight": 800,
"screenWidth": 1280
}
The userAgent.toString()
call converts the user agent into a string which corresponds to the actual user agent.
The data
property includes a randomly generated browser fingerprint that can be used for more detailed emulation.
By passing an object as a filter, each corresponding user agent property will be restricted based on its values.
import UserAgent from 'user-agents';
const userAgent = new UserAgent({ deviceCategory: 'mobile' })
This code will generate a user agent with a deviceCategory
of mobile
.
If you replace mobile
with either desktop
or tablet
, then the user agent will correspond to one of those device types instead.
There is some computational overhead involved with applying a set of filters, so it's far more efficient to reuse the filter initialization when you need to generate many user agents with the same configuration.
You can call any initialized UserAgent
instance like a function, and it will generate a new random instance with the same filters (you can also call userAgent.random()
if you're not a fan of the shorthand).
import UserAgent from 'user-agents';
const userAgent = new UserAgent({ platform: 'Win32' });
const userAgents = Array(1000).fill().map(() => userAgent());
This code example initializes a single user agent with a filter that limits the platform to Win32
, and then uses that instance to generate 1000 more user agents with the same filter.
You can pass a regular expression as a filter and the generated user agent will be guaranteed to match that regular expression.
import UserAgent from 'user-agents';
const userAgent = new UserAgent(/Safari/);
This example will generate a user agent that contains a Safari
substring.
It's also possible to implement completely custom logic by using a filter as a function.
The raw userAgent.data
object will be passed into your function, and it will be included as a possible candidate only if your function returns true
.
In this example, we'll use the useragent package to parse the user agent string and then restrict the generated user agents to iOS devices with an operating system version of 11 or greater.
import UserAgent from 'user-agents';
import { parse } from 'useragent';
const userAgent = new UserAgent((data) => {
const os = parse(data.userAgent).os;
return os.family === 'iOS' && parseInt(os.major, 10) > 11;
});
The filtering that you apply here is completely up to you, so there's really no limit to how specific it can be.
You can also use arrays to specify collections of filters that will all be applied.
This example combines a regular expression filter with an object filter to generate a user agent with a connection type of wifi
, a platform of MacIntel
, and a user agent that includes a Safari
substring.
import UserAgent from 'user-agents';
const userAgent = new UserAgent([
/Safari/,
{
connection: {
type: 'wifi',
},
platform: 'MacIntel',
},
]);
This example also shows that you can specify both multiple and nested properties on object filters.
filters
<Array
, Function
, Number
, Object
, RegExp
, or String
> - A set of filters to apply to the generated user agents.
The filter specification is extremely flexible, and reading through the Examples section is the best way to familiarize yourself with what sort of filtering is possible.UserAgent
is an object that contains the details of a randomly generated user agent and corresponding browser fingerprint.
Each time the class is instantiated, it will randomly populate the instance with a new user agent based on the specified filters.
The instantiated class can be cast to a user agent string by explicitly calling toString()
, accessing the userAgent
property, or implicitly converting the type to a primitive or string in the standard JavaScript ways (e.g. `${userAgent}`
).
Other properties can be accessed as outlined below.
UserAgent
>This method generates a new UserAgent
instance using the same filters that were used to construct userAgent
.
The following examples both generate two user agents based on the same filters.
// Explicitly use the constructor twice.
const firstUserAgent = new UserAgent(filters);
const secondUserAgent = new UserAgent(filters);
// Use the `random()` method to construct a second user agent.
const firstUserAgent = new UserAgent(filters);
const secondUserAgent = firstUserAgent.random();
The reason to prefer the second pattern is that it reuses the filter processing and preparation of the data for random selection. Subsequent random generations can easily be over 100x faster than the initial construction.
UserAgent
>As a bit of syntactic sugar, you can call a UserAgent
instance like userAgent()
as a shorthand for userAgent.random()
.
This allows you to think of the instance as a generator, and lends itself to writing code like this.
const generateUserAgent = new UserAgent(filters);
const userAgents = Array(100).fill().map(() => generateUserAgent());
String
>Casts the UserAgent
instance to a string which corresponds to the user agent header.
Equivalent to accessing the userAgent.userAgent
property.
Object
>
appName
<String
> - The value of navigator.appName.connection
<Object
> - The value of navigator.connection.cpuClass
<String
> - The value of navigator.cpuClass.deviceCategory
<String
> - One of desktop
, mobile
, or tablet
depending on the type of device.oscpu
<String
> - The value of navigator.oscpu.platform
<String
> - The value of navigator.platform.pluginsLength
<Number
> - The value of navigator.plugins.length.screenHeight
<Number
> - The value of screen.height.screenWidth
<Number
> - The value of screen.width.vendor
<String
> - The value of navigator.vendor.userAgent
<String
> - The value of navigator.userAgent.viewportHeight
<Number
> - The value of window.innerHeight.viewportWidth
<Number
> - The value of window.innerWidth.The userAgent.data
contains the randomly generated fingerprint for the UserAgent
instance.
Note that each property of data
is also accessible directly on userAgent
.
For example, userAgent.appName
is equivalent to userAgent.data.appName
.
The project follows the Semantic Versioning guidelines. The automated deployments will always correspond to patch versions, and minor versions should not introduce breaking changes. It's likely that the structure of user agent data will change in the future, and this will correspond to a new major version.
Please keep in mind that older major versions will cease to be updated after a new major version is released. You can continue to use older versions of the software, but you'll need to upgrade to get access to the latest data.
The user agent frequency data used in this library is generously provided by Intoli, the premier residential and smart proxy provider for web scraping. The details of how the data is updated can be found in the blog post User-Agents — A random user agent generation library that's always up to date.
If you have a high-traffic website and would like to contribute data to the project, then send us an email at contact@intoli.com. Additional data sources will help make the library more useful, and we'll be happy to add a link to your site in the acknowledgements.
Contributions are welcome, but please follow these contributor guidelines outlined in CONTRIBUTING.md.
User-Agents is licensed under a BSD 2-Clause License and is copyright Intoli, LLC.
FAQs
A JavaScript library for generating random user agents.
The npm package user-agents receives a total of 90,514 weekly downloads. As such, user-agents popularity was classified as popular.
We found that user-agents demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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