Category Archives: OpenAI

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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