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#Python

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Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace

Official site.

InterSystems Python Binding Documentation.

Introduction

In InterSystems IRIS 2024.3 and subsequent IRIS versions, the AutoML component is now delivered as a separate Python package that is installed after installation. Unfortunately, some recent versions of Python packages that AutoML relies on have introduced incompatibilities, and can cause failures when training models (TRAIN MODEL statement). If you see an error mentioning "TypeError" and the keyword argument "fit_params" or "sklearn_tags", read on for a quick fix.

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I'm exploring this right now: given a bunch of types defined as Pydantic models, how can I come up with an equivalent %RegisteredObject/%SerialObject and convert to/from (e.g., to support persistence and match validation as much as possible)?

People who know Python better than I do (e.g., your average undergraduate from this decade): is this a stupid idea or a cool idea? Has anyone else done this before?

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image

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.
  • 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.

Open Exchange app
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You can learn a lot from a first impression; we want to hear about yours.

As a continuation of our User Insights Interview program (see this post for more details), we’re expanding our scope to include Python developers, particularly those of you who are new to working with InterSystems technology. We’re looking to conduct one-on-one interviews to hear your honest thoughts about getting started: What made sense, what didn’t, and where we could improve.

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