Node.js bindings to OpenCV 3 and OpenCV 4

Node.js bindings to OpenCV 3 and OpenCV 4

The ultimate goal of this article is to provide a comprehensive collection of nodejs bindings to the API of OpenCV and the OpenCV-contrib modules.

The ultimate goal of this article is to provide a comprehensive collection of nodejs bindings to the API of OpenCV and the OpenCV-contrib modules.

opencv4nodejs allows you to use the native OpenCV library in Nodejs. Besides a synchronous API the package provides an asynchronous API, which allows you to build non-blocking and multithreaded computer vision tasks. **opencv4nodejs **supports OpenCV 3 and OpenCV 4.

To get an overview of the currently implemented bindings, have a look at the type declarations of this package. Furthermore, contribution is highly appreciated. If you want to add missing bindings check out the contribution guide.

Table content

  • How to install
  • Usage with Docker
  • Usage with Electron
  • Usage with NW.js
  • Quick Start
  • Async API
  • With TypeScript
  • External Memory Tracking (v4.0.0)
How to install
npm install --save opencv4nodejs

Native node modules are built via node-gyp, which already comes with npm by default. However, node-gyp requires you to have python installed. If you are running into node-gyp specific issues have a look at known issues with node-gyp first.

Important note: node-gyp won't handle whitespaces properly, thus make sure, that the path to your project directory does not contain any whitespaces. Installing opencv4nodejs under "C:\Program Files\some_dir" or similar will not work and will fail with: "fatal error C1083: Cannot open include file: 'opencv2/core.hpp'"!**

On Windows you will furthermore need Windows Build Tools to compile OpenCV and opencv4nodejs. If you don't have Visual Studio or Windows Build Tools installed, you can easily install the VS2015 build tools:

npm install --global windows-build-tools

Installing OpenCV Manually

Setting up OpenCV on your own will require you to set an environment variable to prevent the auto build script to run:

# linux and osx:
# on windows:


You can install any of the OpenCV 3 or OpenCV 4 releases manually or via the Chocolatey package manager:

# to install OpenCV 4.1.0
choco install OpenCV -y -version 4.1.0

Note, this will come without contrib modules. To install OpenCV under windows with contrib modules you have to build the library from source or you can use the auto build script.

Before installing opencv4nodejs with an own installation of OpenCV you need to expose the following environment variables:

  • OPENCV_INCLUDE_DIR pointing to the directory with the subfolder opencv2 containing the header files
  • OPENCV_LIB_DIR pointing to the lib directory containing the OpenCV .lib files

Also you will need to add the OpenCV binaries to your system path:

  • add an environment variable OPENCV_BIN_DIR pointing to the binary directory containing the OpenCV .dll files
  • append ;%OPENCV_BIN_DIR%; to your system path variable

Note: Restart your current console session after making changes to your environment.


Under OSX we can simply install OpenCV via brew:

brew update
brew install [email protected]
brew link --force [email protected]


Under Linux we have to build OpenCV from source manually or using the auto build script.

Installing OpenCV via Auto Build Script

The auto build script comes in form of the opencv-build npm package, which will run by default when installing opencv4nodejs. The script requires you to have git and a recent version of cmake installed.

Auto Build Flags

You can customize the autobuild flags using OPENCV4NODEJS_AUTOBUILD_FLAGS=. Flags must be space-separated.

This is an advanced customization and you should have knowledge regarding the **OpenCV **compilation flags. Flags added by default are listed here.

Installing a Specific Version of OpenCV

You can specify the Version of **OpenCV **you want to install via the script by setting an environment variable: export OPENCV4NODEJS_AUTOBUILD_OPENCV_VERSION=4.1.0

Installing only a Subset of OpenCV modules

If you only want to build a subset of the OpenCV modules you can pass the -DBUILD_LIST cmake flag via the OPENCV4NODEJS_AUTOBUILD_FLAGS environment variable. For example export OPENCV4NODEJS_AUTOBUILD_FLAGS=-DBUILD_LIST=dnn will build only modules required for dnn and reduces the size and compilation time of the OpenCV package.

Usage with Docker

opencv-express - example for opencv4nodejs with express.js and docker

Or simply pull from justadudewhohacks/opencv-nodejs for opencv-3.2 + contrib-3.2 with opencv4nodejs globally installed:

FROM justadudewhohacks/opencv-nodejs 

Note: The aforementioned Docker image already has opencv4nodejs installed globally. In order to prevent build errors during an npm install, your package.json should not include opencv4nodejs, and instead should include/require the global package either by requiring it by absolute path or setting the NODE_PATH environment variable to /usr/lib/node_modules in your Dockerfile and requiring the package as you normally would.

Different OpenCV 3.x base images can be found here:

Usage with Electron

opencv-electron - example for opencv4nodejs with electron

Add the following script to your package.json:

"electron-rebuild": "electron-rebuild -w opencv4nodejs"

Run the script:

$ npm run electron-rebuild

Require it in the application:

const cv = require('opencv4nodejs');

Usage with NW.js

Any native modules, including opencv4nodejs, must be recompiled to be used with NW.js. Instructions on how to do this are available in the Use Native Modules section of the the NW.js documentation.

Once recompiled, the module can be installed and required as usual:

const cv = require('opencv4nodejs');

Quick Start
const cv = require('opencv4nodejs');

Initializing Mat (image matrix), Vec, Point

const rows = 100; // height
const cols = 100; // width

// empty Mat
const emptyMat = new cv.Mat(rows, cols, cv.CV_8UC3);

// fill the Mat with default value
const whiteMat = new cv.Mat(rows, cols, cv.CV_8UC1, 255);
const blueMat = new cv.Mat(rows, cols, cv.CV_8UC3, [255, 0, 0]);

// from array (3x3 Matrix, 3 channels)
const matData = [
  [[255, 0, 0], [255, 0, 0], [255, 0, 0]],
  [[0, 0, 0], [0, 0, 0], [0, 0, 0]],
  [[255, 0, 0], [255, 0, 0], [255, 0, 0]]
const matFromArray = new cv.Mat(matData, cv.CV_8UC3);

// from node buffer
const charData = [255, 0, ...];
const matFromArray = new cv.Mat(Buffer.from(charData), rows, cols, cv.CV_8UC3);

// Point
const pt2 = new cv.Point(100, 100);
const pt3 = new cv.Point(100, 100, 0.5);

// Vector
const vec2 = new cv.Vec(100, 100);
const vec3 = new cv.Vec(100, 100, 0.5);
const vec4 = new cv.Vec(100, 100, 0.5, 0.5);

Mat and Vec operations

const mat0 = new cv.Mat(...);
const mat1 = new cv.Mat(...);

// arithmetic operations for Mats and Vecs
const matMultipliedByScalar = mat0.mul(0.5);  // scalar multiplication
const matDividedByScalar = mat0.div(2);       // scalar division
const mat0PlusMat1 = mat0.add(mat1);          // addition
const mat0MinusMat1 = mat0.sub(mat1);         // subtraction
const mat0MulMat1 = mat0.hMul(mat1);          // elementwise multiplication
const mat0DivMat1 = mat0.hDiv(mat1);          // elementwise division

// logical operations Mat only
const mat0AndMat1 = mat0.and(mat1);
const mat0OrMat1 = mat0.or(mat1);
const mat0bwAndMat1 = mat0.bitwiseAnd(mat1);
const mat0bwOrMat1 = mat0.bitwiseOr(mat1);
const mat0bwXorMat1 = mat0.bitwiseXor(mat1);
const mat0bwNot = mat0.bitwiseNot();

Accessing Mat data

const matBGR = new cv.Mat(..., cv.CV_8UC3);
const matGray = new cv.Mat(..., cv.CV_8UC1);

// get pixel value as vector or number value
const vec3 =, 100);
const grayVal =, 100);

// get raw pixel value as array
const [b, g, r] = matBGR.atRaw(200, 100);

// set single pixel values
matBGR.set(50, 50, [255, 0, 0]);
matBGR.set(50, 50, new Vec(255, 0, 0));
matGray.set(50, 50, 255);

// get a 25x25 sub region of the Mat at offset (50, 50)
const width = 25;
const height = 25;
const region = matBGR.getRegion(new cv.Rect(50, 50, width, height));

// get a node buffer with raw Mat data
const matAsBuffer = matBGR.getData();

// get entire Mat data as JS array
const matAsArray = matBGR.getDataAsArray();


// load image from file
const mat = cv.imread('./path/img.jpg');
cv.imreadAsync('./path/img.jpg', (err, mat) => {

// save image
cv.imwrite('./path/img.png', mat);
cv.imwriteAsync('./path/img.jpg', mat,(err) => {

// show image
cv.imshow('a window name', mat);

// load base64 encoded image
const base64text='data:image/png;base64,R0lGO..';//Base64 encoded string
const base64data =base64text.replace('data:image/jpeg;base64','')
                            .replace('data:image/png;base64','');//Strip image type prefix
const buffer = Buffer.from(base64data,'base64');
const image = cv.imdecode(buffer); //Image is now represented as Mat

// convert Mat to base64 encoded jpg image
const outBase64 =  cv.imencode('.jpg', croppedImage).toString('base64'); // Perform base64 encoding
const htmlImg='<img src=data:image/jpeg;base64,'+outBase64 + '>'; //Create insert into HTML compatible <img> tag

// open capture from webcam
const devicePort = 0;
const wCap = new cv.VideoCapture(devicePort);

// open video capture
const vCap = new cv.VideoCapture('./path/video.mp4');

// read frames from capture
const frame =;
vCap.readAsync((err, frame) => {

// loop through the capture
const delay = 10;
let done = false;
while (!done) {
  let frame =;
  // loop back to start on end of stream reached
  if (frame.empty) {
    frame =;

  // ...

  const key = cv.waitKey(delay);
  done = key !== 255;

Useful Mat methods

const matBGR = new cv.Mat(..., cv.CV_8UC3);

// convert types
const matSignedInt = matBGR.convertTo(cv.CV_32SC3);
const matDoublePrecision = matBGR.convertTo(cv.CV_64FC3);

// convert color space
const matGray = matBGR.bgrToGray();
const matHSV = matBGR.cvtColor(cv.COLOR_BGR2HSV);
const matLab = matBGR.cvtColor(cv.COLOR_BGR2Lab);

// resize
const matHalfSize = matBGR.rescale(0.5);
const mat100x100 = matBGR.resize(100, 100);
const matMaxDimIs100 = matBGR.resizeToMax(100);

// extract channels and create Mat from channels
const [matB, matG, matR] = matBGR.splitChannels();
const matRGB = new cv.Mat([matR, matB, matG]);

Drawing a Mat into HTML Canvas

const img = ...

// convert your image to rgba color space
const matRGBA = img.channels === 1
  ? img.cvtColor(cv.COLOR_GRAY2RGBA)
  : img.cvtColor(cv.COLOR_BGR2RGBA);

// create new ImageData from raw mat data
const imgData = new ImageData(
  new Uint8ClampedArray(matRGBA.getData()),

// set canvas dimensions
const canvas = document.getElementById('myCanvas');
canvas.height = img.rows;
canvas.width = img.cols;

// set image data
const ctx = canvas.getContext('2d');
ctx.putImageData(imgData, 0, 0);

Method Interface

OpenCV method interface from official docs or src:

void GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT);

translates to:

const src = new cv.Mat(...);
// invoke with required arguments
const dst0 = src.gaussianBlur(new cv.Size(5, 5), 1.2);
// with optional paramaters
const dst2 = src.gaussianBlur(new cv.Size(5, 5), 1.2, 0.8, cv.BORDER_REFLECT);
// or pass specific optional parameters
const optionalArgs = {
  borderType: cv.BORDER_CONSTANT
const dst2 = src.gaussianBlur(new cv.Size(5, 5), 1.2, optionalArgs);

Async API

The async API can be consumed by passing a callback as the last argument of the function call. By default, if an async method is called without passing a callback, the function call will yield a Promise.

Async Face Detection

const classifier = new cv.CascadeClassifier(cv.HAAR_FRONTALFACE_ALT2);

// by nesting callbacks
cv.imreadAsync('./faceimg.jpg', (err, img) => {
  if (err) { return console.error(err); }

  const grayImg = img.bgrToGray();
  classifier.detectMultiScaleAsync(grayImg, (err, res) => {
    if (err) { return console.error(err); }

    const { objects, numDetections } = res;

// via Promise
  .then(img =>
      .then(grayImg => classifier.detectMultiScaleAsync(grayImg))
      .then((res) => {
        const { objects, numDetections } = res;
  .catch(err => console.error(err));

// using async await
try {
  const img = await cv.imreadAsync('./faceimg.jpg');
  const grayImg = await img.bgrToGrayAsync();
  const { objects, numDetections } = await classifier.detectMultiScaleAsync(grayImg);
} catch (err) {

With TypeScript
import * as cv from 'opencv4nodejs'

Check out the TypeScript examples.

External Memory Tracking (v4.0.0)

Since version 4.0.0 was released, external memory tracking has been enabled by default. Simply put, the memory allocated for Matrices (cv.Mat) will be manually reported to the node process. This solves the issue of inconsistent Garbage Collection, which could have resulted in spiking memory usage of the node process eventually leading to overflowing the RAM of your system, prior to version 4.0.0.

Note, that in doubt this feature can be disabled by setting an environment variable OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING before requiring the module:


Or directly in your code:

const cv = require('opencv4nodejs')

How to Use Express.js, Node.js and MongoDB.js

How to Use Express.js, Node.js and MongoDB.js

In this post, I will show you how to use Express.js, Node.js and MongoDB.js. We will be creating a very simple Node application, that will allow users to input data that they want to store in a MongoDB database. It will also show all items that have been entered into the database.

In this post, I will show you how to use Express.js, Node.js and MongoDB.js. We will be creating a very simple Node application, that will allow users to input data that they want to store in a MongoDB database. It will also show all items that have been entered into the database.

Creating a Node Application

To get started I would recommend creating a new database that will contain our application. For this demo I am creating a directory called node-demo. After creating the directory you will need to change into that directory.

mkdir node-demo
cd node-demo

Once we are in the directory we will need to create an application and we can do this by running the command
npm init

This will ask you a series of questions. Here are the answers I gave to the prompts.

The first step is to create a file that will contain our code for our Node.js server.

touch app.js

In our app.js we are going to add the following code to build a very simple Node.js Application.

var express = require("express");
var app = express();
var port = 3000;
app.get("/", (req, res) => {
&nbsp;&nbsp;res.send("Hello World");
app.listen(port, () => {
  console.log("Server listening on port " + port);

What the code does is require the express.js application. It then creates app by calling express. We define our port to be 3000.

The app.use line will listen to requests from the browser and will return the text “Hello World” back to the browser.

The last line actually starts the server and tells it to listen on port 3000.

Installing Express

Our app.js required the Express.js module. We need to install express in order for this to work properly. Go to your terminal and enter this command.

npm install express --save

This command will install the express module into our package.json. The module is installed as a dependency in our package.json as shown below.

To test our application you can go to the terminal and enter the command

node app.js

Open up a browser and navigate to the url http://localhost:3000

You will see the following in your browser

Creating Website to Save Data to MongoDB Database

Instead of showing the text “Hello World” when people view your application, what we want to do is to show a place for user to save data to the database.

We are going to allow users to enter a first name and a last name that we will be saving in the database.

To do this we will need to create a basic HTML file. In your terminal enter the following command to create an index.html file.

touch index.html

In our index.html file we will be creating an input filed where users can input data that they want to have stored in the database. We will also need a button for users to click on that will add the data to the database.

Here is what our index.html file looks like.

<!DOCTYPE html>
    <title>Intro to Node and MongoDB<title>

    <h1>Into to Node and MongoDB<&#47;h1>
    <form method="post" action="/addname">
      <label>Enter Your Name<&#47;label><br>
      <input type="text" name="firstName" placeholder="Enter first name..." required>
      <input type="text" name="lastName" placeholder="Enter last name..." required>
      <input type="submit" value="Add Name">

If you are familiar with HTML, you will not find anything unusual in our code for our index.html file. We are creating a form where users can input their first name and last name and then click an “Add Name” button.

The form will do a post call to the /addname endpoint. We will be talking about endpoints and post later in this tutorial.

Displaying our Website to Users

We were previously displaying the text “Hello World” to users when they visited our website. Now we want to display our html file that we created. To do this we will need to change the app.use line our our app.js file.

We will be using the sendFile command to show the index.html file. We will need to tell the server exactly where to find the index.html file. We can do that by using a node global call __dirname. The __dirname will provide the current directly where the command was run. We will then append the path to our index.html file.

The app.use lines will need to be changed to
app.use("/", (req, res) => {   res.sendFile(__dirname + "/index.html"); });

Once you have saved your app.js file, we can test it by going to terminal and running node app.js

Open your browser and navigate to “http://localhost:3000”. You will see the following

Connecting to the Database

Now we need to add our database to the application. We will be connecting to a MongoDB database. I am assuming that you already have MongoDB installed and running on your computer.

To connect to the MongoDB database we are going to use a module called Mongoose. We will need to install mongoose module just like we did with express. Go to your terminal and enter the following command.
npm install mongoose --save

This will install the mongoose model and add it as a dependency in our package.json.

Connecting to the Database

Now that we have the mongoose module installed, we need to connect to the database in our app.js file. MongoDB, by default, runs on port 27017. You connect to the database by telling it the location of the database and the name of the database.

In our app.js file after the line for the port and before the app.use line, enter the following two lines to get access to mongoose and to connect to the database. For the database, I am going to use “node-demo”.

var mongoose = require("mongoose"); mongoose.Promise = global.Promise; mongoose.connect("mongodb://localhost:27017/node-demo");

Creating a Database Schema

Once the user enters data in the input field and clicks the add button, we want the contents of the input field to be stored in the database. In order to know the format of the data in the database, we need to have a Schema.

For this tutorial, we will need a very simple Schema that has only two fields. I am going to call the field firstName and lastName. The data stored in both fields will be a String.

After connecting to the database in our app.js we need to define our Schema. Here are the lines you need to add to the app.js.
var nameSchema = new mongoose.Schema({   firstName: String,   lastNameName: String });

Once we have built our Schema, we need to create a model from it. I am going to call my model “DataInput”. Here is the line you will add next to create our mode.
var User = mongoose.model("User", nameSchema);

Creating RESTful API

Now that we have a connection to our database, we need to create the mechanism by which data will be added to the database. This is done through our REST API. We will need to create an endpoint that will be used to send data to our server. Once the server receives this data then it will store the data in the database.

An endpoint is a route that our server will be listening to to get data from the browser. We already have one route that we have created already in the application and that is the route that is listening at the endpoint “/” which is the homepage of our application.

HTTP Verbs in a REST API

The communication between the client(the browser) and the server is done through an HTTP verb. The most common HTTP verbs are

The following table explains what each HTTP verb does.

HTTP Verb Operation
GET Read
POST Create
PUT Update

As you can see from these verbs, they form the basis of CRUD operations that I talked about previously.

Building a CRUD endpoint

If you remember, the form in our index.html file used a post method to call this endpoint. We will now create this endpoint.

In our previous endpoint we used a “GET” http verb to display the index.html file. We are going to do something very similar but instead of using “GET”, we are going to use “POST”. To get started this is what the framework of our endpoint will look like."/addname", (req, res) => {
Express Middleware

To fill out the contents of our endpoint, we want to store the firstName and lastName entered by the user into the database. The values for firstName and lastName are in the body of the request that we send to the server. We want to capture that data, convert it to JSON and store it into the database.

Express.js version 4 removed all middleware. To parse the data in the body we will need to add middleware into our application to provide this functionality. We will be using the body-parser module. We need to install it, so in your terminal window enter the following command.

npm install body-parser --save

Once it is installed, we will need to require this module and configure it. The configuration will allow us to pass the data for firstName and lastName in the body to the server. It can also convert that data into JSON format. This will be handy because we can take this formatted data and save it directly into our database.

To add the body-parser middleware to our application and configure it, we can add the following lines directly after the line that sets our port.

var bodyParser = require('body-parser');
app.use(bodyParser.urlencoded({ extended: true }));
Saving data to database

Mongoose provides a save function that will take a JSON object and store it in the database. Our body-parser middleware, will convert the user’s input into the JSON format for us.

To save the data into the database, we need to create a new instance of our model that we created early. We will pass into this instance the user’s input. Once we have it then we just need to enter the command “save”.

Mongoose will return a promise on a save to the database. A promise is what is returned when the save to the database completes. This save will either finish successfully or it will fail. A promise provides two methods that will handle both of these scenarios.

If this save to the database was successful it will return to the .then segment of the promise. In this case we want to send text back the user to let them know the data was saved to the database.

If it fails it will return to the .catch segment of the promise. In this case, we want to send text back to the user telling them the data was not saved to the database. It is best practice to also change the statusCode that is returned from the default 200 to a 400. A 400 statusCode signifies that the operation failed.

Now putting all of this together here is what our final endpoint will look like."/addname", (req, res) => {
  var myData = new User(req.body);
    .then(item => {
      res.send("item saved to database");
    .catch(err => {
      res.status(400).send("unable to save to database");
Testing our code

Save your code. Go to your terminal and enter the command node app.js to start our server. Open up your browser and navigate to the URL “http://localhost:3000”. You will see our index.html file displayed to you.

Make sure you have mongo running.

Enter your first name and last name in the input fields and then click the “Add Name” button. You should get back text that says the name has been saved to the database like below.

Access to Code

The final version of the code is available in my Github repo. To access the code click here. Thank you for reading !

Node.js for Beginners - Learn Node.js from Scratch (Step by Step)

Node.js for Beginners - Learn Node.js from Scratch (Step by Step)

Node.js for Beginners - Learn Node.js from Scratch (Step by Step) - Learn the basics of Node.js. This Node.js tutorial will guide you step by step so that you will learn basics and theory of every part. Learn to use Node.js like a professional. You’ll learn: Basic Of Node, Modules, NPM In Node, Event, Email, Uploading File, Advance Of Node.

Node.js for Beginners

Learn Node.js from Scratch (Step by Step)

Welcome to my course "Node.js for Beginners - Learn Node.js from Scratch". This course will guide you step by step so that you will learn basics and theory of every part. This course contain hands on example so that you can understand coding in Node.js better. If you have no previous knowledge or experience in Node.js, you will like that the course begins with Node.js basics. otherwise if you have few experience in programming in Node.js, this course can help you learn some new information . This course contain hands on practical examples without neglecting theory and basics. Learn to use Node.js like a professional. This comprehensive course will allow to work on the real world as an expert!
What you’ll learn:

  • Basic Of Node
  • Modules
  • NPM In Node
  • Event
  • Email
  • Uploading File
  • Advance Of Node

Top 7 Most Popular Node.js Frameworks You Should Know

Top 7 Most Popular Node.js Frameworks You Should Know

Node.js is an open-source, cross-platform, runtime environment that allows developers to run JavaScript outside of a browser. In this post, you'll see top 7 of the most popular Node frameworks at this point in time (ranked from high to low by GitHub stars).

Node.js is an open-source, cross-platform, runtime environment that allows developers to run JavaScript outside of a browser.

One of the main advantages of Node is that it enables developers to use JavaScript on both the front-end and the back-end of an application. This not only makes the source code of any app cleaner and more consistent, but it significantly speeds up app development too, as developers only need to use one language.

Node is fast, scalable, and easy to get started with. Its default package manager is npm, which means it also sports the largest ecosystem of open-source libraries. Node is used by companies such as NASA, Uber, Netflix, and Walmart.

But Node doesn't come alone. It comes with a plethora of frameworks. A Node framework can be pictured as the external scaffolding that you can build your app in. These frameworks are built on top of Node and extend the technology's functionality, mostly by making apps easier to prototype and develop, while also making them faster and more scalable.

Below are 7of the most popular Node frameworks at this point in time (ranked from high to low by GitHub stars).


With over 43,000 GitHub stars, Express is the most popular Node framework. It brands itself as a fast, unopinionated, and minimalist framework. Express acts as middleware: it helps set up and configure routes to send and receive requests between the front-end and the database of an app.

Express provides lightweight, powerful tools for HTTP servers. It's a great framework for single-page apps, websites, hybrids, or public HTTP APIs. It supports over fourteen different template engines, so developers aren't forced into any specific ORM.


Meteor is a full-stack JavaScript platform. It allows developers to build real-time web apps, i.e. apps where code changes are pushed to all browsers and devices in real-time. Additionally, servers send data over the wire, instead of HTML. The client renders the data.

The project has over 41,000 GitHub stars and is built to power large projects. Meteor is used by companies such as Mazda, Honeywell, Qualcomm, and IKEA. It has excellent documentation and a strong community behind it.


Koa is built by the same team that built Express. It uses ES6 methods that allow developers to work without callbacks. Developers also have more control over error-handling. Koa has no middleware within its core, which means that developers have more control over configuration, but which means that traditional Node middleware (e.g. req, res, next) won't work with Koa.

Koa already has over 26,000 GitHub stars. The Express developers built Koa because they wanted a lighter framework that was more expressive and more robust than Express. You can find out more about the differences between Koa and Express here.


Sails is a real-time, MVC framework for Node that's built on Express. It supports auto-generated REST APIs and comes with an easy WebSocket integration.

The project has over 20,000 stars on GitHub and is compatible with almost all databases (MySQL, MongoDB, PostgreSQL, Redis). It's also compatible with most front-end technologies (Angular, iOS, Android, React, and even Windows Phone).


Nest has over 15,000 GitHub stars. It uses progressive JavaScript and is built with TypeScript, which means it comes with strong typing. It combines elements of object-oriented programming, functional programming, and functional reactive programming.

Nest is packaged in such a way it serves as a complete development kit for writing enterprise-level apps. The framework uses Express, but is compatible with a wide range of other libraries.


LoopBack is a framework that allows developers to quickly create REST APIs. It has an easy-to-use CLI wizard and allows developers to create models either on their schema or dynamically. It also has a built-in API explorer.

LoopBack has over 12,000 GitHub stars and is used by companies such as GoDaddy, Symantec, and the Bank of America. It's compatible with many REST services and a wide variety of databases (MongoDB, Oracle, MySQL, PostgreSQL).


Similar to Express, hapi serves data by intermediating between server-side and client-side. As such, it's can serve as a substitute for Express. Hapi allows developers to focus on writing reusable app logic in a modular and prescriptive fashion.

The project has over 11,000 GitHub stars. It has built-in support for input validation, caching, authentication, and more. Hapi was originally developed to handle all of Walmart's mobile traffic during Black Friday.