InterSystems IRIS is a Complete Data Platform
InterSystems IRIS gives you everything you need to capture, share, understand, and act upon your organization’s most valuable asset – your data.
As a complete platform, InterSystems IRIS eliminates the need to integrate multiple development technologies. Applications require less code, fewer system resources, and less maintenance.
I may have mentioned this before: I believe the Visual Traces, these sequence diagrams with full content of each step, are a fantastic feature of the IRIS Data platform! Detailed information about how the API works internally, as a visual trace, can be very useful for projects on the IRIS platform. Of course, this applies when we are not developing a high-load solution, in which case we simply don't have time for saving/reading messages. For all other cases, welcome to this tutorial!
If you have ever dug through a large IRIS namespace looking for where a particular string, method call, or pattern was used, you know the pain: there was no built-in way to do a grep-style search across your server-side ObjectScript code from VS Code — at least not without jumping through some hoops.
That is what ObjectScript Search fixes.
Try it today with a simple install from the VS Code Marketplace. If you don't like it, uninstalling is just as easy. But I think you will like it — it is a huge quality-of-life improvement for anyone doing ObjectScript development in VS Code.
There are nfs disk commands (including nfsiostat) included with SystemPerformance, but disabled by default. Enable them by running:
$$Enablenfs^SystemPerformance()
Doing so will add the following nfs commands, for example, on Linux:
/usr/sbin/nfsstat -cn/usr/sbin/nfsiostat [interval] [count]Ensure the commands are installed and runnable from the OS :)
This can be subsequently disabled via $$Disablenfs^SystemPerformance()
Adding an arbitrary OS tool creates a "user" command under ^IRIS.
InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.
But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.
1 Introduction
In environments that use InterSystems IRIS, globals are the physical foundation of data storage. Although system queries and administrative tools exist for metric inspection, global growth analysis is usually reactive: the problem is generally only noticed when there is disk pressure or performance impact.
The facial recognition has become the most popular method for validating people's identities, thus enabling access to systems, confirmation of personal and documentary data, and approval of actions and documents.
The challenges are related to performance when the database is very large, accuracy, and especially the privacy of biometric facial data. For these challenges, nothing is better than using InterSystems Vector Search, as it allows:
Inspired by @Ashok Kumar T's post on the ideas portal here as well as my own wishes for a solution to this problem, I have come up with a simple way to allow more complete and consistent JSON queries without having to specify every desired field. I have created a class that extends the built-in %JSON.Adaptor class and makes its %JSONExportToString and %JSONExportToStream methods accessible through SQL with just a couple of simple SqlProc Methods.
Class DH.JSONAdaptor Extends %JSON.Adaptor [ Abstract ]
{
ClassMethod jsonstring(id, map = "") [ SqlProc ]
{
try{
set myobj = .I created iris-budget app for the InterSystems Full Stack Contest in 2026. By full stack, we mean a frontend web or mobile application that inserts, updates, or deletes data in InterSystems IRIS via REST API, Native API, ODBC/JDBC, or Embedded Python.
My app uses multiple REST APIs to add a new category or retrieve a list of categories of expenses and income.
I inherited /csp/coffee from module.xml in iris-fullstack-template.
For this project, I created a swagger file called "budget.json.
Hi Community,
In this article, I will introduce my application iris-CliniNote .
CliniNote is a full-featured clinical notes application that combines classic CRUD operations with **real-time AI-assisted notes matching** powered by **InterSystems IRIS native vector search**. The standout feature: while a doctor is writing or editing a clinical note, a side panel shows the **top 5 most similar notes** based on the semantic content of the note being written — **excluding the current patient** to avoid trivial matches. This gives clinicians immediate access to "patients like this one" — helping with differential diagnosis, treatment pattern recognition, and rare presentation detection.
https://irisclininote.sandbox.developer.intersystems.com/csp/clininote/login.html
Keywords: IRIS, Agents, Agentic AI, Smart Apps
Transformer based LLMs appear to be a pretty good "universal logical–symbolic abstractor". They started to bridge up the previous abyss among human languages and machine languages, which in essence are all logic symbols that could be mapped into the same vector space.
Wondering for 3 years we might be able to just use English (etc human natural languages) to do IRIS implementations as well, one day.
In the modern healthcare landscape, finding clinically similar patients often feels like looking for a needle in a haystack. Traditional keyword searches often fail because medical language is highly nuanced; a search for "Heart Failure" might miss a record containing "Congestive Cardiac Failure."
I am excited to share iris-medmatch, an AI-powered patient matching engine built on InterSystems IRIS for Health. By leveraging Vector Search, this tool understands clinical intent rather than just matching literal strings.
There are always those who say that their child looks more like their mother or their father. How about using math and Artificial Intelligence to be sure? The facial-matching application can answer you.
See these results:
The standard %Net.HttpRequest library in InterSystems IRIS is powerful and comprehensive, but it can be verbose for simple operations. Writing an HTTP request often requires several lines of code to instantiate the class, configure the server, the port, HTTPS, add headers, and finally send the request.
When testing in the terminal, this configuration quickly becomes too heavy, and usually ends up with the creation of temporary methods...
FastHTTP was designed to address this need. This utility class provides a fluent and concise interface to perform HTTP calls in a single line, while automatically handling the underlying complexity (SSL/TLS, URL parsing, JSON encoding, headers, etc.).
One objective of vectorization is to render unstructured text more machine-usable. Vector embeddings accomplish this by encoding the semantics of text as high-dimensional numeric vectors, which can be employed by advanced search algorithms (normally an approximate nearest neighbor algorithm like Hierarchical Navigable Small World). This not only improves our ability to interact with unstructured text programmatically but makes it searchable by context and by meaning beyond what is captured literally by keyword.
In this article I will walk through a simple vector search implementation that Kwabena Ayim-Aboagye and I fleshed out using embedded python in InterSystems IRIS for Health. I'll also dive a bit into how to use embedded python and dynamic SQL generally, and how to take advantage of vector search features offered natively through IRIS.
This is an excellent candidate for a developer community post (like Dev.to, Medium, or the InterSystems Community). It bridges the gap between high-level architecture and hands-on implementation.
Here is the summarized article format.
As applications scale, handling heavy computational tasks synchronously becomes a bottleneck. Whether it's processing large data sets, sending high-volume emails, or managing API integrations, a decoupled architecture is essential.
In the previous article, we examined how we can use the %CSP.Request and %CSP.Response classes to test a REST API without having the API fully set up and accessible across a network with an authentication mechanism. In this article, we will build on that foundation to perform some simple unit testing of one of our REST API methods.
The unit testing framework requires a couple of setup steps before we can use it. First, we have to ensure that the unit testing portion of the management portal is enabled so we can review the results of our tests.
Intersystems IRIS Productions provide a powerful framework for connecting disparate systems across various protocols and message formats in a reliable, observable, and scalable manner. intersystems_pyprod, short for InterSystems Python Productions, is a Python library that enables developers to build these interoperability components entirely in Python. Designed for flexibility, it supports a hybrid approach: you can seamlessly mix new Python-based components with existing ObjectScript-based ones, leveraging your established IRIS infrastructure.
Welcome to the finale of our journey in building MAIS.
Now, the stage is set. Our agents are ready, defined, and eager to work. However, they remain frozen in time. They require a mechanism to drive the conversation, execute their requested tools, and pass the baton to one another.
Today, we will assemble the Nervous System.
I wanted to try vibecoding a real backend + frontend setup on InterSystems IRIS, ideally using something realistic rather than a toy example. The goal was simple: take an existing, well-known persistent package in IRIS and quickly build a usable UI and API around it — letting AI handle as much of the boilerplate as possible. Here is the result of the experiments.
How to set up RAG for OpenAI agents using IRIS Vector DB in Python
In this article, I’ll walk you through an example of using InterSystems IRIS Vector DB to store embeddings and integrate them with an OpenAI agent.
To demonstrate this, we’ll create an OpenAI agent with knowledge of InterSystems technology. We’ll achieve this by storing embeddings of some InterSystems documentation in IRIS and then using IRIS vector search to retrieve relevant content—enabling a Retrieval-Augmented Generation (RAG) workflow.
Note: Section 1 details how process text into embeddings.