Hey everyone,

Here we are again. New year, new contest, new project, old reasons.

Triple Slash is in the house!

Hey Developers,

New "Coding Talk" video was specially recorded by @Maks Atygaev for the IRIS Programming Contest:

⏯ Declarative ObjectScript Promo

https://www.youtube.com/embed/EXSXF1-q1LU
[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]

Hey community!

It's time to elevate your unit tests to the next level with iris-TripleSlash new solo feature!

We are bring to the game Setup and Tear down events!

Just add description on class, with special tags like:

• beforeAll
• beforeOne
• afterAll
• afterOne

The tripleSlash will turn it to:

Learn how to design scalable, autonomous AI agents that combine reasoning, vector search, and tool integration using LangGraph.

Description

This is a template for a Flask application that can be deployed in IRIS as an native Web Application.

Description

This is a template for a FastApi application that can be deployed in IRIS as an native Web Application.

Attention all developers!

Get ready to revolutionize your testing process with iris-tripleSlash, the ultimate unit test solution.

Say goodbye to boring and repetitive unit testing and hello to effortless and efficient testing.

With iris-tripleSlash, you can easily generate unit test cases using the power of automatic documentation.

We're back with more AI, more ML and more presenters!

We are pleased to invite you to the upcoming webinar in English: AI Robotization (Python, R, Interoperability) for InterSystems IRIS on November 7 at 11:00 EST!

Artificial intelligence is not limited only to generating images through text with instructions or creating narratives with simple directions.

You can also make variations of a picture or include a special background to an already existing one.

Additionally, you can obtain the transcription of audio regardless of its language and the speed of the speaker.

So, let's analyze how the file management works.

For a long time I have wanted to learn the Django framework, but another more pressing project has always taken priority. Like many developers, I use python when it comes to machine learning, but when I first learned web programming PHP was still enjoying primacy, and so when it was time for me to pick up a new complicated framework for creating web applications to publish my machine learning work, I still turned to PHP.

Hi, Community!

In the previous article, we introduced the Streamlit web framework, a powerful tool that enables data scientists and machine learning engineers to build interactive web applications with minimal effort. First, we explored how to install Streamlit and run a basic Streamlit app. Then, we incorporated some of Streamlit's basic commands, e.g., adding titles, headers, markdown, and displaying such multimedia as images, audio, and videos.

Later, we covered Streamlit widgets, which allow users to interact with the app through buttons, sliders, checkboxes, and more. Additionally, we examined how to display progress bars and status messages and organize the app with sidebars and containers. We also highlighted data visualization, using charts and Matplotlib figures to present data interactively.

In this article, we will cover the following topics:

How can IRIS productions be deployed more quickly and with greater peace of mind?

The aim of interoperability productions is to enable you to connect systems in order to transform and route messages between them. To connect systems, you develop, configure, deploy and manage productions that integrate several software systems.

This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.

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.