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.
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.
This is Part 3 of the humor-inspired saga on the journey from monoliths to microservices, to serverless, and now incorporating AI agents. If you haven’t read Part 1: Mono’s Journey from Monolith to Microservices and Part 2: Mikro’s Serverless Saga, please do so first.