Introduction

As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.

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Overview

This web interface is designed to facilitate the management of Data Lookup Tables via a user-friendly web page. It is particularly useful when your lookup table values are large, dynamic, and frequently changing. By granting end-users controlled access to this web interface (read, write, and delete permissions limited to this page), they can efficiently manage lookup table data according to their needs.

The data managed through this interface can be seamlessly utilized in HealthConnect rules or data transformations, eliminating the need for constant manual monitoring and management of the lookup tables and thereby saving significant time.

Note:
If the standard Data Lookup Table does not meet your mapping requirements, you can create a custom table and adapt this web interface along with its supporting class with minimal modifications. Sample class code is available upon request.

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A benefit of using Doxygenerate is that Doxygen does more than just HTML output. Tweak the Doxyfile that tells Doxygen what to do and you can easily create a PDF. Our example MARINA application yielded a 524-page PDF. Here's what page 94 looks like:

You can browse the whole file here.

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Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

  • 🧠 Python + smolagents to orchestrate the agent’s “brain”
  • 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

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I’m pleased to announce the release of tree-sitter-objectscript, a new open-source tree-sitter grammar that brings first-class ObjectScript support to modern editors. If you caught the preview at READY ’25, you’ll be glad to know it’s now up on Github:

https://github.com/intersystems/tree-sitter-objectscript

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Recently, I replaced my old laptop with a new one and had to migrate all my data. I was looking for a guide but couldn’t find anything that explained in detail how to migrate server connections from InterSystems Studio and Visual Studio Code from one PC to another. Simply reinstalling the tools is not enough, and migrating all the connections manually seemed like a waste of time. In the end, I managed to solve the problem, and this article explains how.

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Been testing out the Production Validator toolkit, just to see what we can/not do with it. Seems really interesting and there seem to be some use cases for it that can really streamline some upgrades (or at least parts of upgrades) but I was running into so many hurdles with the documentation. I am curious if anyone else has used it.

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Word documents are widely used in the market. Users frequently create contracts, memos, resumes, reports, analyses, and other documents that may require data from or captured by InterSystems IRIS. However, IRIS does not have an API, SDK, library, or adapter for this. This limitation no longer exists.

The new Open Exchange library iris4word (https://openexchange.intersystems.com/package/iris4word) delivers an ObjectScript SDK where the developer passes any %DynamicObject as a parameter, a Word file template and then receives a ready document, with the structure and formatting defined in its template.

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Here are the technology bonuses for the InterSystems Developer Tools Contest 2025 that will give you extra points in the voting:

  • IRIS Vector Search usage -3
  • Embedded Python usage -3
  • InterSystems Interoperability - 3
  • InterSystems IRIS BI - 3
  • VSCode Plugin - 3
  • FHIR Tools - 3
  • Docker container usage -2
  • ZPM Package Deployment - 2
  • Implement InterSystems Community Idea - 4
  • Find a bug in Embedded Python - 2
  • Article on Developer Community - 2
  • The second article on Developer Community - 1
  • Video on YouTube - 3
  • First Time Contribution - 3

See the details below.<--break->

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Hey everyone,

I'm diving deeper into Caché ObjectScript and would love to open a discussion around the most useful tips, tricks, and best practices you’ve learned or discovered while working with it.

Whether you're an experienced developer or just getting started, ObjectScript has its own set of quirks and powerful features—some well-documented, others hidden gems. I’m looking to compile a helpful set of ideas from the community.

Some areas I’m especially interested in:

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Have you ever needed to change an IP or port before deploying an interface to production? Needed to remove items from an export? What about modifying the value(s) in a lookup table before deploying? Have you wanted to disable an interface before deploying? What about adding a comment, category, or alert setting to an interface before deploying to production?

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Prefer not to read? Check out the demo video I created:

https://www.youtube.com/embed/-OwOAHC5b3s
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

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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.

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The InterSystems platforms have always offered dynamic documentation of the packages and classes in a namespace, a feature known informally as Documatic. But what if you need to publish this class reference information on a website without requiring the site to be connected to an IRIS server containing the actual classes?

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Monitor incremental changes in the database through scheduled tasks, display change trends through charts, set alarm thresholds, and write information to messages.log

How to use it

You can install it through Docker or ZPM

Deploying with Docker Prerequisites

Make sure you have git and Docker desktop installed.

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