[Updated] Best Javascript Frameworks from 2018-2019

Published on Sep 25, 2018

Updated on Apr 17, 2020

With the wave of digitization, web development is rapidly evolving. Every year we see many new frameworks and platforms for application development, each with their unique benefits. However, in order to build a better audience and improve inbound sales, you need to identify which framework is best as per your requirements.

Based on our onsite and offsite web development experiences for B2B clients, I’ll list down the best Javascript framework from 2019, which you should know to stay ahead of the competition.

Best Front-end Javascript Frameworks

1. Vue.js

It is relatively new but gaining a lot of traction among the community of developers. Vue.js works with the MVVM design paradigm and has a very simple API. It demonstrates minimalism to the extreme and allows you to use selective modules as per your requirements. Inspired by AngularJS, ReactJS, knockoutJS and RivetsJS, Vue.js updates model and view via two-way data binding.

While Vue is not in the league of Angular or Ember, but conceptually it has all the potential to be the JavaScript framework of the future and hold respectable market share. It is holding the ground firmly after 10 months of its release and getting regular updates by the creator, Evan You. Vue.js has been declared as the most popular front-end JavaScript framework on GitHub with 40K stars.

Details – vuejs.org

2. AngularJS

AngularJS, the baby of Google, is the big daddy of JavaScript frameworks. It currently boasts the biggest community of developers and is also the most used JavaScript framework for developing Single Page Web Applications.

AngularJS framework gives superpowers to HTML by adding all the necessary features required to build dynamic views (interactive user interface).

It gives the option to extend HTML attributes by the use of Angular directives. Two-way data binding is at the core of AngularJS. This essentially takes away all the pain of writing manual code for DOM manipulation.

Angular 2’s final version was released in 2016. It was completely re-written, also focused on mobile apps development and was a big success. From 2016 to 2018 it has graduated further to Angular 6 as a great improvement over old. After the release of Angular 7 in October 2018, it became the most preferred framework for enterprise-level apps.

On May 28, 2019, Angular 8 was released featuring differential load for all application code, dynamic imports for lazy routes, and Typescript support. Now, Angular is integral to the MEAN stack for web application development.

Details – angularjs.org

3. React

Developers might have noticed that React is the user interface behind Facebook and Instagram. This gives us a quick idea about how powerful React is for building large-scale applications of extremely dynamic nature.

React framework is best at rendering complex user interfaces with high performance. It deploys the concept of virtual DOM (Document Object Model). React utilizes a virtual DOM, which you can render at the client-side or server-side and communicate back and forth.

The community is growing rapidly ever since its release and I must say it is the fastest-growing JavaScript framework as of today. One can find tons of resources, tutorials and React component libraries to get going within no time.

Details – reactjs.org

Also read — the basics of React Native.

4. Ember.js

Ember.js is another powerful MVC Javascript framework. Many popular websites like Discourse, Groupon, LinkedIn, Vine, Live Nation, Nordstrom, Twitch.tv, and Chipotle use Ember. Although primarily considered a framework for the web, it is also possible to build desktop and mobile applications in Ember. The most notable example of an Ember desktop application is Apple Music.

EmberJS competes with the likes of AngularJS and React when it comes to building interactive frontend user interfaces. It also has a very active community of developers.

Ember rides on the principle of two-way data binding like AngularJS. It targets the best of both AngularJS (two-way data binding) and React (server-side rendering).

Details – emberjs.com

best frontend javascript frameworks and the brands using them

Best Javascript Frameworks for Backend development

1. Express

When you google for Node.js frameworks, surely you might have come across Express very often. Express.js is among the top Node.js frameworks. It is an efficient platform for building robust applications and APIs. Express is also one of the four components of the popular MEAN stack (MongoDB, Express, AngularJS, and Node.js) for web application development. Here, Express serves as a middleware between the front-end, database, and back-end of an application.

Express is very flexible and lightweight and offers excellent possibilities in terms of using plugins, extensions and third-party databases. People often refer to Express as the server framework for Node.js as it creates an HTTP server for Node.js and supports the middleware functionality to respond to the HTTP requests. It has a superb set of documentation and enjoys great community support.

Details – expressjs.com

2. Sails.js

To build custom, enterprise-grade Node.js apps, Sails.js is a great fit. It mimics the MVC pattern of frameworks like Ruby on Rails but with support for the requirements of modern apps (i.e. data-driven APIs with scalable & service-oriented architecture). It is especially suitable for building chat, real-time dashboards, or multiplayer games.

Details – sailsjs.com

Last but not the least:

Meteor

Meteor is the magical isomorphic full stack platform for building an end to end mobile and web applications completely in JavaScript at a lightning fast speed. Isomorphic Javascript is also known as Universal Javascript defines the JS applications which run on both client and server. It is the power player, equipped with all the features you need for frontend rendering, backend development, business logic and database management.

The best thing about MeteorJS is that you use only JavaScript for an end to end application development, no need to invest time learning anything else. It is modular and you can use packages and libraries on demand basis. The server-side packages run in the Node.js and you do not need anything else but Meteor packages to access the database, all in JavaScript! This makes Meteor applications real-time web applications.

Details – meteor.com

Best Javascript Frameworks for 2018-2019: A Quick Recap

In recent years, Javascript emerges as the most preferred language because of the simplicity it brings to web application development. From new releases to typescript integration, the best Javascript frameworks are-

Front end: Vue.js, AngularJS, React, and Ember.js
Back end: Express and Sails.js

For queries and suggestions, please feel free to drop your comments or reach us at hello@mantralabsglobal.com

Happy Scripting!

Related:

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

[Updated] Best Javascript Frameworks from 2018-2019