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.

The VS Code extension development team is looking for beta testers to provide feedback on a proposed overhaul of the client-side editing workflow. The full list of changes can be found in the GitHub pull request description. Here are the highlights:

Introduction

Since InterSystems has recently announced the discontinuation of support for InterSystems Studio starting from version 2023.2 in favor of exclusive development of extensions for the Visual Studio Code (VSC) IDE, believing that the latter offers a superior experience compared to Studio, many of us developers have switched or are beginning to use VSC. Many may have wondered how to open the Terminal to perform operations, as VSC does not have an Output panel like Studio did, nor an integrated feature to open the IRIS terminal, except by downloading the plugins developed by InterSystems.

When developing a new Interoperability Production, it is quite natural that settings are initially added in the Production.

However, as soon as you want to move the Production from development to a test or staging environment, it becomes clear that some settings like HTTP Servers, IP addresses and/or ports need to be changed. In order to avoid these settings being overwritten during a redeployment later on, it is essential that you move these settings from the Production to the System Default settings.

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.

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

https://www.youtube.com/embed/-OwOAHC5b3s
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Hi Community,

OpenAPI-Client Gen has just released, this is an application to create an IRIS Interoperability Production client from Swagger 2.0 specification.

Instead of the existing tool ^%REST that creates a server-side REST application, OpenAPI-Client Gen creates a complete REST Interoperability Production client template.

Install by ZPM:

We are glad to announce that DBeaver has supported InterSystems IRIS out-of-the-box since version 7.2.4. You don't need to configure it manually anymore, just find the IRIS icon in the Connections list.

There are many applications for working with HL7 V2 messages, but the tools for working with XML in IRIS Management Portal and Cache Studio are limited.

Hi everyone,

In this article, I’m excited to introduce CodeInspector, a tool designed to simplify code validation by applying custom rules tailored to your development requirements. Whether you're managing a large codebase or working in an agile environment, CodeInspector helps ensure code quality by offering flexibility and adaptability to specific project needs.

See the new team members in action:

https://www.youtube.com/embed/PdndX1p7pjc
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Try them online for yourself:

https://gitpod.io#snapshot/b31bdf9c-4657-402a-a2d

Get it from the Extensions view inside VS Code, or here in Marketplace.

Hi, I am considering making my own swagger json generation based on my rest api implementing the %CSP.REST class.

Before I begin, I was wondering if there is already some kind of support, perhaps in IRIS already, or some tools that might help me.

I have been using embedded python for more than 2 years now on a daily basis.
May be it's time to share some feedback about this journey.

Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.

• Feedback : Using embedded python daily for 2 years
• Introduction
• Starting with Python
  • Python is not ObjectScript
    • Pep8
    • Modules
    • Dunders
  • Conclusion
• Embedded Python
  • What is Embedded Python ?
  • How to use Embedded Python ?
  • How I use Embedded Python
    • Use Python libraries and code as they were ObjectScript classes
      • With the language tag
      • Without the language tag
      • Conclusion
    • Use a python first approach
      • Example : iris-fhir-python-strategy
      • Remarks
      • Where to find the code
      • How to implement a Strategy
      • Implementation of InteractionsStrategy
      • Implementation of Interactions
      • Interactions in Python
      • Implementation of the abstract python class
      • Too long, do a summary

TL;DR

This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.