Imagine you want to see what InterSystems can give you in terms of data analytics. You studied the theory and now you want some practice. Fortunately, InterSystems provides a project that contains some good examples: Samples BI. Start with the README file, skipping anything associated with Docker, and go straight to the step-by-step installation. Launch a virtual instance, install IRIS there, follow the instructions for installing Samples BI, and then impress the boss with beautiful charts and tables. So far so good.

Inevitably, though, you’ll need to make changes.

1. Fhir-client-java

This is a simple fhir client in java to practice with fhir resources and CRUD requests to a fhir server.

Note that for the most part auto-completion is activated.

GitHub

Introduction

In the first article, a simple tutorial helped you to set up your FHIRaaS deployment.

Now, let's move forward and introduce a JS library to access the FHIR resource.

In the end, two examples of usage of this library will be presented, exploring the Appointment FHIR resource type.

SMART on FHIR JavaScript Library

FHIR is a REST API, so you can use any HTTP client in order to use it. But, it’s always a good idea to have help.

Hi Developers!

Those who use Dockerfile to work with InterSystems IRIS often need to execute several lines of ObjectScript. For me, this was a game of "escaping this and that" every time just to shoot a few commands on ObjectScript to IRIS. Ideally, I'd prefer to code ObjectScript without any quotes and escaping.

Recently I found a nice "hack" on how this could be improved to exactly this state. I got this from @Dmitry.Maslennikov's repo and this lets you use Objectscript in a way as you would type it in IRIS terminal.

Here is what you have in dockerfile:

///
COPY irissession.sh /
SHELL ["/irissession.sh"]
RUN \
  do $SYSTEM.OBJ.Load("Installer.cls", "ck") \
  set sc = ##class(App.Installer).setup()
# bringing the standard shell back
SHELL ["/bin/bash", "-c"]
CMD [ "-l", "/usr/irissys/mgr/messages.log" ]
///

Scenario

You all know Open Exchange (OEX) and the is no need for a detailed explanation.

It consists of a directory with various filters and detail pages for packages.

The latest "Bringing Ideas to Reality" InterSystems competition saw me trawling through the ideas portal for UI problems to have a go at.

https://www.youtube.com/embed/zw51X1JQhQ0
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The latest release of Serenji by George James Software introduces zero configuration debugging, so it's ready to go in just one click - no matter where you are or what you're doing, your on-the-spot debugger is ready to go. 

We have introduced a host of new features to enhance the debugging experience so you can focus on identifying and fixing errors, without losing focus by spending time setting up a launch configuration. 

Check out the new features in our release notes. 

If you've already started using Serenji 3.2.0 let us know how you're getting on! 

Hi Community,

Hi Community,
In this article, I will introduce my application iris-mlm-explainer

Last time we deployed a simple IRIS application to the Google Cloud. Now we’re going to deploy the same project to Amazon Web Services using its Elastic Kubernetes Service (EKS).

We assume you’ve already forked the IRIS project to your own private repository. It’s called <username>/my-objectscript-rest-docker-template in this article. <root_repo_dir> is its root directory.

Before getting started, install the AWS command-line interface and, for Kubernetes cluster creation, eksctl, a simple CLI utility. For AWS you can try to use aws2, but you’ll need to set aws2 usage in kube config file as described here.

Hi Community,

In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Messages.log file contains lots of useful information about IRIS. Sometimes the file gets big, and it is not easy to get to the data I am interested to review. It would be nice to jump to a specific date and time we suspect a problem may have started.

Hi Community,

Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.

This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.

Implementation Steps:

1. Create Agent Tools
   • Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
   • Implement Vector Search Functionality
2. Create Vector Search Agent
3. Handoff to Triage (Main Agent)
4. Run The Agent