Category Archives: Artificial Intelligence

Building Agents with OpenAI Agent SDK

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You’ve probably noticed by now that we have a lot of AI Agent/Agentic Python frameworks. To list a few – n8n, Pydantic AI, Crewai, OpenAI SDK, Microsoft’s AutoGen, LangChain, LangGraph, Google’s Agent Development Kit (ADK), and Amazon’s Strands, among others. Having played with a few of them, I can assure you all are good frameworks and might come down to your own taste, their fan base in the open source community, and, on occasion, some key feature one of them does better. Let’s dig into OpenAI Agent SDK in this blog.

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Retrieval-Augmented Generation (RAG) with Spring AI

Retrieval-Augmented Generation (RAG) is a powerful pattern that enhances Large Language Models (LLMs) by grounding their responses in your specific documents and data. While GPT-4 is incredibly capable, it doesn’t know about your proprietary documents, internal knowledge bases, or recent updates that occurred after its training cutoff date. RAG solves this problem by retrieving relevant context from your documents before generating responses.

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Building Multi-Agent Systems with LangGraph

A practical guide to creating modular, reusable agent architectures that can be shared across projects. LangGraph is a robust framework for building stateful, multi-agent applications using Large Language Models (LLMs). Think of it as a way to create conversation flows where different AI agents can work together, each with their own specialized role.

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Building Java Applications with LangChain4j & Spring

AI is changing how we build software. Large Language Models (LLMs) like GPT, Claude, and others have transformed from research curiosities into practical tools that can understand natural language, write code, and solve complex problems. However, while Python developers have enjoyed rich AI ecosystems, such as LangChain, Java developers, who power most enterprise applications, have been left behind.

Enter LangChain4j, a comprehensive Java library that brings the full power of modern AI to the enterprise Java ecosystem. It’s not just a wrapper around API calls; it’s a comprehensive framework that leverages Java’s strengths and addresses enterprise requirements.

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Unlocking the Power of Multi-Agent AI with CrewAI

Artificial Intelligence (AI) has evolved rapidly over the last few years. From single-task large language models (LLMs) to entire systems of autonomous agents, the AI ecosystem is now enabling new classes of intelligent workflows. In this blog post, we’ll build a multi-agent AI assistant that takes in a resume profile, a resume document, and a job description link, then produces a tailored resume and interview questions. We’ll explore how to do this using CrewAI, a Python-based multi-agent framework, and run it against both local models via OLLAMA and remote LLMs like OpenAI’s API.

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The AI Revolution in Software Engineering: How Senior Leaders Can Drive Strategic Gains

As AI rapidly transforms industries worldwide, software engineering is no exception. Once experimental, AI-driven development tools have become mainstream, promising productivity gains, enhanced quality, and a shift in the role of developers. Senior engineering leaders who leverage AI strategically can expect profound impacts, from shortened development cycles to improved developer experience and better product-market alignment.

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Integrating Spring AI Framework in Your Java Application

This blog post will integrate the Spring AI framework into a Java application. We’ll use a simple project that includes a ChatService and a ChatController to demonstrate using the Spring AI framework to generate text & image responses and horoscopes based on user input.

Update 12/28/2025 – Updated from M1 release to Spring AI 1.1.0 GA

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Unlocking the Power of LLMs with LangChain

As an AI and software professional, you’ve likely heard the buzz around large language models (LLMs) like GPT-3, ChatGPT, and their growing capabilities. These powerful models can handle a wide range of natural language tasks, from text generation to question answering. However, effectively leveraging LLMs in your own applications can be a complex challenge. That’s where LangChain comes in.

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Building an OpenAI-Powered Chatbot using Python and Jupyter Notebooks

Welcome to a step-by-step guide on creating an intelligent chatbot powered by OpenAI using Python and Jupyter Notebooks. In this tutorial, we’ll cover the fundamental concepts and guide you through the process of building a simple yet effective chatbot that leverages the power of OpenAI’s language model.

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