# Spring AI Published articles for Spring AI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Prompt Caching Support in Spring AI with Anthropic Claude DevFeed: [Prompt Caching Support in Spring AI with Anthropic Claude]() Original publisher: [Read original article]() Author: Stelios Anastasakis Published: 2026-09-16T07:49:56Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Spring AI](), [Anthropic Claude](), [Caching]() Tags: [anthropic](), [anthropic-claude](), [artificial-intelligence](), [artificial-intelligence-anthropic-spring-ai-chatclient](), [caching](), [how-to](), [spring-ai](), [spring-ai-chatclient]() ### AI overview This tutorial explains how prompt caching works in Spring AI with Anthropic Claude. It covers dependencies, model-specific requirements and limitations, configuration options, caching hierarchy, and practical considerations. Prompt caching can reduce latency and input-token costs when prompt prefixes are reused. ### Source excerpt Learn how prompt caching works, the limitations for different Claude models, and how to use it in Spring AI. The post Prompt Caching Support in Spring AI with Anthropic Claude first appeared on Baeldung. ## This Week in Spring - September 15th, 2026 DevFeed: [This Week in Spring - September 15th, 2026]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Spring AI](), [Java](), [Security](), [OAuth 2.0](), [Spring Boot](), [JavaFX](), [IntelliJ IDEA](), [debugging]() Tags: [batch](), [boot](), [cloud](), [debugging](), [event-driven](), [intellij-idea](), [java](), [learn](), [microservices](), [oauth-2-0](), [reactive](), [security](), [serverless](), [spring](), [spring-ai](), [web-applications]() ### AI overview A September 15, 2026 roundup of Spring-related developer content, covering Java memory management, Spring AI chat-memory summarization, JavaFX application security with Spring Security and OAuth 2.0, OAuth 2.1 with Spring Authorization Server, Spring and Gemini video understanding, Java AI libraries, Netflix's Java and Spring story, Spring Batch, and Spring Boot debugging. ### Source excerpt Hi, Spring fans! Welcome to another rip-roarin' installment of This Week in Spring! I'm writing this to you from sun-kissed San Francisco, CA, sipping some coffee and watching the bay from my breakfast nook. What a wonderful day! A wonderful day in which to learn about the latest and greatest in Spring, even! Let's dive right in! I really loved this presentation by Oracle's Ron Pressler on the principles of memory management in Java More Craig Walls Spring AI goodness! Here's a nice recipe on summarizing chat memory Over the last few weeks, I've done some content on securing JavaFX applications with Spring Security and OAuth 2.0. That work has landed, and our friends at JFX-central.com have taken that video and the resulting code and transcribed the content into this lovely tutorial - check it out! I loved this post on securing modern applications with OAuth 2.1 and Spring Authorization Server This is a really cool video on understanding video with Spring and Gemini I just learned about this amazing new Java library called Quixotic.ai (what a name! LOL) that provides all sorts of cool stuff that might make your Java-based AI workloads even better. I wonder if there are amazing integration possibilities for Spring AI, too... In last week's A Bootiful Podcast, I was delighted to sit down and chat with Netflix's Paul Bakker on their Java and Spring story, scaling the system, and more. Huh! There's a new Spring Batch IntelliJ IDEA plugin, but search me for what's new! No release notes. Either way, get it while it's hot! Speaking of Spring Batch, there's a nice post here on scaling to millions of rows with Spring Batch that just dropped Speaking of Spring and IntelliJ, there's a nice article over on Baeldung on debugging Spring Boot-based workloads with the Spring debugger ## This Week in Spring - September 8th, 2026 DevFeed: [This Week in Spring - September 8th, 2026]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Artificial Intelligence](), [Large Language Model](), [Electron](), [App](), [IntelliJ IDEA]() Tags: [ai](), [applications](), [article](), [batch](), [boot](), [cloud](), [event-driven](), [idea](), [intellij](), [intellij-idea](), [java](), [linux](), [llm](), [llms](), [microservices](), [oauth](), [reactive](), [serverless](), [spring](), [spring-ai](), [spring-boot](), [video](), [web-applications](), [windows]() ### AI overview This weekly Spring roundup highlights Spring AI content about running LLMs in the JVM and locally, building a Spring AI starter for agents.md, and improving tool use. It also covers Spring Boot and JavaFX desktop applications, native images, Spring Security OAuth clients, secure application images, external configuration, Spring Cloud AWS, and a Spring Boot Analyzer. ### Source excerpt Bonjour a tout le monde! Welcome to another rip-roarin' installment of This Week in Spring! It's a fabulous and fun day here in Paris, France, as I wait to board the train to Amsterdam for the IntelliJ IDEA conference! It's going to be amazing. We've got another incredible week's roundup to dive into, so let's do it! I feel like some of the best content over the past several months in this weekly roundup has been Craig Walls' Spring AI Recipes section. Fantastic stuff! This latest one looks at running an LLM in-JVM This is a nice article on using vertical slices in Spring Boot on Solodev.sk, by Dominik. Well done! Another amazing installment from Craig Walls, this one looking at running against local LLMs My friend and colleague DaShaun Carter talks about his Spring AI starter for agents.md Craig also has this lovely post on efficient tool use in Spring AI Last week, I did two videos on using Spring Boot, JavaFX, GraalVM native images, and Spring Security (and PKCE) to build native, lightning-fast, dynamic, efficient, reusable desktop applications that run well on Mac, Windows, and Linux, and look amazing, while taking small fractions of the RAM of a similar Electron-based application. Here's the first one, showing how to use Spring Boot and JavaFX together, so that you get the component model, event dispatch subsystem, internationalization, lifecycle management, and, of course, the entire and very rich ecosystem of Spring components and can use them to manage JavaFX components, too. We also look at native image compilation. Here's the second video, which looks at using Spring Security's OAuth client in the context of a desktop application, which can not, by definition, hold a client secret. In last week's installment of A Bootiful Podcast, I was delighted to chat with BellSoft's Catherine Edelveis about trusted and secure images for your Spring Boot applications This is a nice post on managing external configurations with Spring Cloud Config A nice recap of some of ## MCP Logging in Spring AI DevFeed: [MCP Logging in Spring AI]() Original publisher: [Read original article]() Author: Burak Gökmen Published: 2026-09-06T23:19:21Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Model Context Protocol](), [log management](), [AI Development](), [Remote Procedure Call (RPC)](), [telemetry]() Tags: [logging](), [mcp](), [mcp-server](), [opentelemetry](), [popular](), [rpc](), [spring](), [spring-ai](), [spring-ai-mcp-popular](), [spring-boot](), [tutorial]() ### AI overview A tutorial on MCP logging in Spring AI, covering server-to-client log notifications, client-controlled severity levels, and migration guidance following the feature's deprecation. ### Source excerpt Learn about MCP logging in Spring AI, both in an MCP server and a client. The post MCP Logging in Spring AI first appeared on Baeldung. ## LLM Streaming in the Embabel Agentic AI Framework DevFeed: [LLM Streaming in the Embabel Agentic AI Framework]() Original publisher: [Read original article]() Author: Igor Dayen Published: 2026-08-26T22:02:05Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Language models]() Tags: [embabel](), [java](), [json](), [llm](), [popular](), [reasoning](), [spring-ai](), [spring-ai-embabel](), [streaming](), [tool](), [tutorial]() ### AI overview A tutorial on using Embabel Agentic AI to stream LLM output into typed Java objects and reasoning blocks incrementally. It contrasts this capability with the limited structured-streaming support described for Spring AI and LangChain4j. ### Source excerpt Learn to how to use Embabel AI to automatically parse objects as the data arrive in a stream from the large language model you use in your application. The post LLM Streaming in the Embabel Agentic AI Framework first appeared on Baeldung. ## A2A in Java: hands on Embabel DevFeed: [A2A in Java: hands on Embabel]() Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-06-21T07:01:03Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Embabel](), [A2A protocol](), [Java](), [MCP](), [Spring AI](), [Maven](), [bedrock](), [Large Language Model]() Tags: [a2a](), [agent](), [ai](), [embabel](), [java](), [maven](), [mcp-server](), [software-engineering](), [spring-ai]() ### AI overview A hands-on tutorial for building a Java agent with Embabel. It demonstrates LLM-based story generation and review, Amazon Bedrock integration, a local Spring AI MCP server for word counts, Maven dependency management, and A2A-compliant endpoints. ### Source excerpt Part 2: Hands on with Embabel! In Part 1, we discussed how Embabel works internally. In Part 2, we'll now try to get an application started that has an LLM interaction, talks to local MCP server (for tools), and helps us achieve a goal. This time, we'll also make sure we use A2A protocol for our agent (something Embabel makes easier to do allowing us to focus on core business logic and offloading boilerplate code to Embabel). We'll use the project creator to create a skeleton project for us with some Embabel dependencies and code. uvx --from git+https://github.com/embabel/project-creator.git project-creator Please note we can use Java or Kotlin as our preferred language, I would be leveraging Java. This is a simple story teller agent who writes a story and gets it reviewed (both story generation and review comes from LLM). We'd be using Bedrock for connection to our LLM model. For fetching the word count for a topic, it uses an MCP server which is a Spring AI MCP server running locally. Dependency management is handled using Maven, and the POM file looks like this: 4.0.0 org.springframework.boot spring-boot-starter-parent 3.5.9 com.example.demo-city-agent Demo-city-agent 0.1.0-SNAPSHOT jar My first agent Generated agent project 21 0.3.1 com.embabel.agent ) Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-06-16T08:44:44Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Embabel](), [A2A protocol](), [Java](), [Spring AI](), [Agent Framework](), [interoperability](), [Artificial Intelligence](), [Large Language Model]() Tags: [a2a](), [a2a-protocol](), [agent-framework](), [ai](), [embabel](), [interoperability](), [java](), [large-language-models-llms](), [software-engineering](), [spring-ai]() ### AI overview This hands-on article introduces A2A in Java and explores Embabel, a Java agent framework built on Spring AI. It explains how A2A enables interoperability among agents across languages, frameworks, vendors, and platforms, and describes Embabel's use of goal-oriented action planning with existing Java domain logic and structured code. ### Source excerpt Part 1: What is Embabel? A2A, or Agent-to-Agent Protocol, is an open standard (now under the Linux Foundation) that provides a common language for diverse AI agents to communicate, collaborate, and share tasks, enabling interoperability across different frameworks, vendors, and platforms. You can read more about A2A here. A2A makes agents framework and tech stack agnostic, which means agents developed in Python will be able to connect to other agents in Java or any other programming language of choice (given we have A2A protocol compliance). We currently have ADK (Python) and other Python specific frameworks that simplify the development of A2A compliant agents (as you'll read in this blog). This initiative explores creating a comparable Java-specific framework to enable building A2A-compliant agentic applications in Java. Brief about Spring AI Before learning about Embabel, it's important to discuss and know about Spring AI which provides the foundation for Embabel framework. The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. This project draws inspiration from notable Python projects, such as LangChain and LlamaIndex, but Spring AI isn't a direct port of those projects. The project was founded with the belief that the next wave of generative AI applications will not only be for Python developers but ubiquitous across many programming languages. Spring AI can be used without Embabel (or any other high level framework like ADK java) to create agents and agentic applications. For more info around how to use Spring AI, check out Part 1 and Part 2. What is Embabel? Embabel is an agent framework built on Spring AI, designed to integrate Large Language Models (LLMs) with existing domain logic and structured code in Java applications. It utilizes a Goal-Oriented Action Planning (GOAP) mechanism to allow agents to dynamically determine the sequence of actions needed ## What is an Agentic Application? DevFeed: [What is an Agentic Application?]() Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-05-06T04:01:01Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Spring AI](), [Artificial Intelligence](), [Model Context Protocol](), [Amazon Bedrock](), [Spring Boot](), [Java](), [Maven](), [Large Language Model](), [Streaming]() Tags: [agentic](), [agentic-ai](), [ai](), [aws](), [developer](), [java](), [llm](), [maven](), [mcp](), [mcp-server](), [software-engineering](), [spring-ai](), [streaming]() ### AI overview A hands-on tutorial for building an agentic application with Spring AI. It demonstrates connecting an application to AWS Bedrock, exposing weather and latitude-longitude tools through a stateless MCP server over HTTP streaming, and combining MCP clients, memory, tools, and a system prompt. ### Source excerpt Part 2: Using spring AI to build a simple agentic application In Part 1 we discussed about core capabilities of Spring AI that helps it position itself as a strong framework to build Agentic applications. Taking things forward from there, let's now build something to bring things into action!! Scenario We want to build an application that connects with AWS bedrock for LLM integration. Has set of tools exposed from an MCP server running on HTTP streaming protocol in STATELESS mode(MCP is also built using Spring AI). For memory we use chat memory with inbuilt InMemory repository and MessageWindowChatMemory. This application has the ability to provide weather info, some lat long info for a particular city. MCP Server Setup Maven pom for dependency management 4.0.0 org.springframework.boot spring-boot-starter-parent 3.5.6 com.example mcp-server-demo 0.0.1-SNAPSHOT mcp-server-demo Demo project for Spring Boot 17 org.springframework.boot spring-boot-starter-web org.springframework.ai spring-ai-starter-mcp-server-webmvc 1.1.2 org.springframework.boot sprin ## Getting Started with SpringAI DevFeed: [Getting Started with SpringAI]() Original publisher: [Read original article]() Author: Samuel Lissner Published: 2025-12-19T03:03:39Z Content type: tutorial Language: en Sources: [Java Advent Calendar]() Topics: [Spring AI](), [Spring Boot](), [Java](), [Large Language Model](), [Claude](), [Retrieval Augmented Generation (RAG)]() Tags: [2025](), [apis](), [claude](), [guide](), [java](), [language-models](), [rag](), [spring](), [spring-ai]() ### AI overview A hands-on tutorial for building a Spring AI application that summarizes Wikipedia articles with large language models. It uses Spring Boot, Java 21, Anthropic's Claude models, Apache Tika for PDF text extraction, and Retrieval-Augmented Generation concepts. ### Source excerpt A Hands-On Guide to Text Summarization Over the past year, the Java ecosystem has made significant strides in making Generative AI development enterprise-ready. For Spring developers, SpringAI has emerged as the go-to toolkit for seamlessly integrating enterprise data and APIs with AI models. Are you curious in developing enterprise grade AI applications with Spring AI? [...] The post Getting Started with SpringAI appeared first on JVM Advent.