With the unveiling of the Java version of Agent Development Kit (ADK) which lets you build AI agents in Java, I recently covered how to get started developing your first agent.

The installation and quickstart documentation also helps for the first steps, but I realized that it would be handy to provide a template project, to further accelarate your time-to-first-conversation with your Java agents! This led me to play with GitHub’s template project feature, which allows you to create a copy of the template project on your own account or organization. It comes with a ready-made project structure, a configured pom.xml file, and a first Java agent you can customize at will, and run from both the command-line or the ADK Dev UI.

At Google Cloud Next ‘25, last April, Google released Agent Development Kit (ADK) for Python, a flexible and modular framework for developing and deploying AI agents.

Now at Google I/O, a Java version of ADK has been made available! And I’m glad to have had the chance to participate in its launch, via code samples, documentation, and helping shape the API so it’s idiomatic for Java developers.

In this article, my goal is to give you the basis to get started with the ADK framework, in Java, using the Gemini model, and running your first Java agents locally.

Unlike Quarkus and Spring Boot, Micronaut doesn’t (yet?) provide a module to facilitate the implementation of MCP servers (Model Context Protocol). But being my favorite framework, I decided to see what it takes to build a quick implementation, by vibe coding it, with the help of Gemini!

In a recent article, I explored how to use the MCP reference implementation for Java to implement an MCP server, served as a servlet via Jetty, and to call that server from LangChain4j’s great MCP support. One approach with Micronaut may have been to somehow integrate the servlet I had built via Micronaut’s servlet support, but that didn’t really feel like a genuine and native way to implement a server, so I decided to do it from scratch.

MCP (Model Context Protocol) is making a buzz these days! MCP is a protocol invented last November by Anthropic, integrated in Claude Desktop and in more and more tools and frameworks, to expand LLMs capabilities by giving them access to various external tools and functions.

My colleague Philipp Schmid gave a great introduction to MCP recently, so if you want to learn more about MCP, this is the place for you.

In this article, I’d like to guide you through the implementation of an MCP server, and an MCP client, in Java. As I’m contributing to LangChain4j, I’ll be using LangChain4j’s mcp module for the client.

With Large Language Models loving to output Markdown responses, I’ve been wanting to display those Markdown snippets nicely in the console, when developing some LLM-powered apps and experiments. At first, I thought I could use a Markdown parser library, and implement some kind of output formatter to display the text nicely, taking advantage of ANSI color codes and formats. However it felt a bit over-engineered, so I thought “hey, why not just use some simple regular expressions!” (and now you’ll tell me I have a second problem with regexes)

Retrieval Augmented Generation (RAG) is a great way to expand the knowledge of Large Language Models to let them know about your own data and documents. With RAG, LLMs can ground their answers on the information your provide, which reduces the chances of hallucinations.

Implementing RAG is fairly trivial with a framework like LangChain4j. However, the results may not be on-par with your quality expectations. Often, you’ll need to further tweak different aspects of the RAG pipeline, like the document preparation phase (in particular docs chunking), or the retrieval phase to find the best information in your vector database.

This project demonstrates how to build a fully automated short story generator using Java, LangChain4j, Google Cloud’s Gemini and Imagen 3 models, and a serverless deployment on Cloud Run.

Every night at midnight UTC, a new story is created, complete with AI-generated illustrations, and published via Firebase Hosting. So if you want to read a new story every day, head over to:

→ short-ai-story.web.app ←

The code of this agent is available on Github. So don’t hesitate to check out the code:

First article of the year, so let me start by wishing you all, my dear readers, a very happy new year! And what is the subject of this new piece of content? For a while, I’ve been interested in analyzing trends and topics in social media streams. I recently joined Bluesky (you can follow me at @glaforge.dev), and contrarily to X, it’s possible to access its Firehose (the stream of all the messages sent by its users) pretty easily, and even for free. So let’s see what we can learn from the firehose!

Yesterday, Google released yet another cool Gemini model update, with Gemini 2.0 Flash thinking mode. Integrating natively and transparently some chain of thought techniques, the model is able to take some more thinking time, and automatically decomposes a complex task into smaller steps, and explores various paths in its thinking process. Thanks to this approach, Gemini 2.0 Flash is able to solve more complex problems than Gemini 1.5 Pro or the recent Gemini 2.0 Flash experiment.

Hot on the heels of the announcement of Gemini 2.0, I played with the new experimental model both from within Google AI Studio, and with LangChain4j.

Google released Gemini 2.0 Flash, with new modalities, including interleaving images, audio, text, video, both in input and output. Even a live bidirectional speech-to-speech mode, which is really exciting!

When experimenting with AI Studio, what attracted my attention was AI Studio’s new starter apps section. There are 3 examples (including links to Github projects showing how they were implemented):