10 JavaScript-based tools and frameworks for AI and machine learning

Many developers associate machine learning and large language models with Python. That makes sense because Python was the first choice for many early adopters, and it remains popular today. But there’s plenty of room in the AI revolution, and JavaScript developers have their pick of tools for integrating AI into their software.

The tools and libraries in this article are all excellent options for unlocking the power of AI and machine learning without leaving the JavaScript sandbox. Some simplify connecting web servers and applications with major models running in large data centers. Others offer powerful routines for training and running LLMs locally. All are solid building blocks for creating AI and machine learning applications in JavaScript.

TensorFlow.js

Google gave the world a great gift when it released TensorFlow as open source. With TensorFlow.js, you can develop machine learning models in JavaScript or TypeScript and deploy them in a web browser or Node.js environment. An additional perk is tfjs-vis. One of the many interesting libraries developed for TensorFlow.js, the Vis API lets you visualize model performance right in your browser.

Also see: How to use TensorFlow in your browser.

Hugging Face Transformers.js

Another option for running models in a web browser, or any other environment that supports JavaScript, comes from HuggingFace. Transformers.js delivers the same functionality as the popular Python-based Transformers library, but the JavaScript version puts the power of WebGPU and WebAssembly in the browser. Transformers.js also offers more high-level support, like sentiment analysis and chat templating, making it easier to handle many AI chores locally. It might be a stretch to say that you won’t need any server-side code, but you can definitely offload much of the work to the user’s machine.

Brain.js

If you’ve ever dreamed of building a neural network and sending it out into the world through a web browser, consider Brain.js. It offers multiple models for implementing neural networks in JavaScript while also leveraging any GPUs that are available. The Brain.js tutorials are there to help you understand what’s happening inside a neural network as it learns from data. Find the Brain.js source code on GitHub, along with examples written in TypeScript and JavaScript.

Angular

Angular used to be just a web application framework, right? It was built by human hands to help humans build better web applications. But now all that is changing. Google has added new features to make it easier for large language models to write Angular applications themselves. They’ve included llms.txt files as a machine-friendly guide to the resources and best-practices.md to help the models write Angular code using all the best techniques. Now you can employ some of the most popular LLMs to write your Angular apps for you.

AI.JSX

The folks at Fixie.ai created AI.JSX to support conversational-style interfaces, usually inside React-centered projects. The simplest examples show how to recreate the popular OpenAI interface. More advanced users can create dynamic websites that use AI responses to construct new interfaces with different components as needed.

LlamaIndex.js

Many AI tasks require merging the power of a general LLM with some RAG (retrieval augmented generation) tool that can find the most relevant documents in a large collection. LlamaIndex.js is a framework stuffed full of tools for simplifying the work of ingesting documents, building vector representations, and indexing them for fast retrieval. Using these tools lets you combine any query with the right documents. All you need to do is integrate LlamaIndex.js, string together some workflows, and set the tool loose to find what you need in the great digital haystack.

ml5.js

The browser is where JavaScript first appeared, so it’s a natural fit for machine learning applications written in JavaScript and TypeScript. ml5.js is a library for machine learning that’s ready to be incorporated into browser-based machine learning applications. This tool is popular with educators who want to give their students the hands-on experience of building and training models. Some pair it with TeachableMachine for visual experiments. Others just hardwire it into their own personal web page.

Vercel AI SDK

The AI SDK from Vercel is a clearinghouse for accessing models from all the major LLM vendors and many smaller ones, too. Just insert your code into any of the major JavaScript frameworks (Angular, React, Vue, etc.) and let it send out prompts for answers from an LLM. There are more than a dozen different options including big-name options from Azure, Mistral, Grok, and Perplexity. The SDK worries about the differences between the respective APIs, so you don’t have to.

LangChain

As AI developers grow more sophisticated and start building more complex architectures with multiple calls to various models, tools like LangChain are invaluable. The meta tool abstracts away the process of calling different models, while simultaneously stringing together multiple calls to tackle complex tasks and unlock agency. Devops teams also can use LangChain to monitor application behavior in production.

Also see: What is LangChain? An easier way to develop LLM applications.

Vendor libraries

All the major vendors have nice libraries for accessing their APIs, and these are often all you need to unlock the power of large language models. In goes a prompt string and out comes an answer. The vendors handle the work of keeping the models trained and running, all for a fee. Some of the most prominent JavaScript libraries are these from OpenAI, Google Gemini, Vercel, IBM and Amazon. These and other libraries provide a solid foundation for venturing into AI without ever leaving the familiarity of JavaScript.