Random User Agent

rand-user-agent is a nodejs package that provides random generation of a real user-agent string, based on the frequency the user-agents occur.

This package was originally created as a functionality for WebScrapingAPI, but it can be integrated into any node.js scraping project. If you need a dependable and feature-rich web scraper, give the WebScrapingAPI free trial a go!

Installation

Run the following command in the main folder of your project:

npm i rand-user-agent

📝	Starting from version 2.0.0 onwards, this package is migrating to ESM.

Usage Example

import randUserAgent from "rand-user-agent";
const agent = randUserAgent("desktop");

console.log(agent);

You can also provide a browser and an operating system in the parameters of randUserAgent in order to filter out the user agents:

import randUserAgent from "rand-user-agent";
const agent = randUserAgent("desktop", "chrome", "linux");

console.log(agent);

How does it work

Using our own database with data about guests and their user-agent, we update a file called "user-agents.json" on a weekly basis with new information.

This data is saved in a json under the following format:

{
    deviceType1: {
        userAgent1: frequencyUserAgent1,
        userAgent2: frequencyUserAgent2,
        ...
    }
    ...
}

Because sometimes one user-agent might occur so many times that it will end up being returned most of the times in the result, we need to normalize the frequency values to prevent that. To do so, we sort an array with all the unique values of the frequency, and replace the frequency for each user-agent with the position where the frequency is in the sorted array. We are doing this using the JSONfrequencyNormalize function from helpers.js

To make things easier for us, when somebody uses the package we are first transforming the processed json into a indexes json, such as the one below:

{
    deviceType1: {
        userAgent1: {
            minIndex: 0,
            maxIndex: frequencyUserAgent1 - 1,
        },
        userAgent2: {
            minIndex: frequencyUserAgent1,
            maxIndex: frequencyUserAgent2 - lastMaxIndex - 1,
        },
        ...
    }
    ...
}

Using the data in this format allows us to easily retrieve a random user-agent, while also taking into account how often it occured in our data.