5 articles tagged with "Node.js"

A small SaaS team bumps a dependency on Monday morning and 14 tests go red. Nothing is seriously broken. A helper now rounds a little differently, and someone gives up the morning to fixes that are dull, careful and easy to get slightly wrong.

That morning is a good job for an AI coding agent that fixes failing tests. It takes the first pass while the team does something else, but only if "done" means the tests really pass and a person still approves the merge. An agent that says it's finished, or quietly edits the tests until they agree, costs more time than it saves.

By the end of this guide you'll have a short Node.js script that fixes a failing test, refuses to stop early, can't rewrite your tests, and opens a pull request for someone to review. It runs on a cloud model or on your own machine.

The first demo usually takes an afternoon. Someone wires a chatbot into the support tool, it answers a billing question nicely, and everyone is impressed. Six weeks later that feature has to look up real invoices, return answers a database can store, and survive a busy Monday. That's when your Node.js LLM library, the code your team uses to talk to large language models, starts to matter.

Almost any option gets you through the demo. What separates them is what comes after, and your team will live with the choice for about two years, so the real question is which trade-offs you can carry for that long.

Below we walk through the realistic options one at a time, with real numbers, working code and where each falls short, then end with a checklist.

Picture an online store with a small support team. Much of the inbox is some version of "where is my order", and every answer means opening the order system and copying a status into a reply. You've used ChatGPT, maybe called an AI API once, and now the team wants an agent to take that first pass.

This guide shows you how to build AI agents in Node.js by building that one, a piece at a time. By the end it looks orders up by itself, asks a person before it gives money back, and keeps answering when an AI service goes down. You start on your laptop with no API key and move to a hosted model when you're ready.

We ran every example on a deliberately tiny model and show you where it got things wrong. Those mistakes are much cheaper to meet here than in front of a customer.

Updated in October 2026. An earlier version of this article walked through the IntelliCloud app, which has since been retired. Everything it did now runs in the open source libraries, Intelli for Python and IntelliNode for Node.js.

Most teams don't start by looking for an AI framework. They start with a job that eats hours every week: answering the same customer questions, reading invoices, writing product copy, or arguing about which of three models is the best.

This article goes through six of those jobs. For each one you'll see what IntelliNode does and where to find working code, so you can pick the one closest to your own problem and start there.

Updated in October 2026 for IntelliNode 3.1 and today's models.

Every few weeks a new model takes the top spot on some leaderboard. Your team still has a plainer question to answer: which model should our product use for this job? Public benchmarks test someone else's tasks. What you want is to ask your own questions to a few models and see whose answers come closest to an answer you would accept.

This guide builds that check as one Node.js script. You write a question and one or two good answers. IntelliNode sends the question to each model and scores every reply against your answers, so the comparison takes minutes instead of a week of reading.