Spring Boot 4.0 launches alongside Spring Framework 7, Spring Data 2025.1 and Spring AI 1.1, delivering a sweeping upgrade that moves the stack to Jakarta EE 11, JDK 25, JSpecify null‑safety, build‑time optimizations, native OpenTelemetry support, and a dual‑track AI strategy.
A third‑year student recounts four distinct campus interviews—from a state‑owned big‑data firm to an AI startup—detailing test formats, technical questions, and personal growth, and concludes that most graduates ultimately join the countless small‑mid‑size companies that keep society running.
Spring Boot 4 arrives alongside Spring Framework 7, upgrading to Jakarta EE 11, JDK 25, JSpecify null‑safety, build‑time optimizations, a declarative HTTP client, full Jackson 3 support, native API versioning, built‑in resilience, OpenTelemetry integration, and a dual‑track Spring AI strategy.
Spring AI 1.1.0 arrives with a major overhaul, adding out‑of‑the‑box Model Context Protocol support, five‑mode prompt caching that can cut LLM costs by up to 90%, reasoning APIs, recursive advisors, a broadened model ecosystem, enhanced vector‑store and chat‑memory options, and richer observability integrations.
Spring AI Alibaba Admin is an Alibaba‑built platform that extends Spring AI to address three major engineering hurdles—inefficient prompt debugging, uncertain AI quality, and opaque production operations—by offering comprehensive prompt, dataset, evaluator, experiment, observability, and model‑configuration capabilities for enterprise AI agents.
This article reviews Spring AI's evolution, architecture, and key features—including unified APIs, prompt engineering, streaming, RAG, multi‑modal support, and agent workflows—showing how Java back‑ends can efficiently expose AI capabilities without handling low‑level model logic.
Spring AI Agents brings a unified Java SDK that lets developers integrate, orchestrate, and run multiple AI coding agents—such as Claude, Gemini, and OpenAI Codex—directly within GitHub workflows, transforming development from single‑assistant Copilot models to collaborative AI team‑based architectures.
The article introduces Spring AI Alibaba DeepResearch, a Java‑based system that automates end‑to‑end research by integrating multi‑agent workflows, configurable RAG pipelines, data ingestion, observability, and dynamic report generation, and provides detailed deployment and extension guidance.
This article explores how Spring AI Alibaba enables the development of autonomous AI agents that run on schedules, interact with humans when needed, and handle tasks such as periodic business automation, batch processing, emergency response, and long‑cycle data analysis, illustrated with Java code examples.
Spring AI Alibaba extends Spring AI with multi‑agent and enterprise features, but faces three engineering hurdles—inefficient prompt debugging, unguaranteed AI quality, and opaque operations—so Alibaba released Spring AI Alibaba Admin, offering prompt templating, dataset versioning, evaluator configuration, experiment management, and deep observability to streamline AI agent development and deployment.
This article outlines the industry shift toward large‑scale AI Agent deployment, identifies key engineering challenges such as prompt management, quality assessment, and observability, and presents Spring AI Alibaba Admin—a cloud‑native platform that offers prompt, dataset, evaluator, and tracing capabilities, complete with setup instructions and future roadmap.
This article explains the challenges of moving AI agents from demo to production, compares native framework and non‑intrusive observability solutions, and provides a step‑by‑step guide to integrate Spring AI Alibaba with LoongSuite's Java agent for OpenTelemetry‑based monitoring.
This tutorial demonstrates how to integrate multiple large language models from different providers into a Spring Boot application using Spring AI, configure primary, secondary, and tertiary models, and implement a fallback mechanism with Spring Retry to ensure high availability of the chatbot.
This article defines the Model Context Protocol (MCP), explains why it is needed to unify LLM and external tool interactions, details its client‑host‑server architecture, compares its communication modes, outlines Java/Spring AI support, and discusses current adoption trends and open challenges.
The article examines how rapid advances in generative AI have transformed enterprise AI Agent development, comparing evolving frameworks like LangChain, Semantic Kernel, and Spring AI with emerging low‑code platforms such as Dify and Copilot Studio, and outlines architectural challenges, integration strategies, and best‑practice design principles for Java‑centric organizations.
This article introduces Spring AI, explains its relationship to Spring Boot, outlines support for major AI model providers and capabilities, and provides step‑by‑step code examples for a chatbot, conversational memory, and prompt engineering, while highlighting version pitfalls and future extensions.
This article explains the Model Context Protocol (MCP) introduced by Anthropic, compares it with Function Call, details its architecture and communication flow, and provides a step‑by‑step Spring AI guide—including server and client development, configuration, code examples, and testing—so developers can integrate AI models with back‑end services efficiently.
This article walks through Spring AI’s three core classes—Message, Prompt, and ChatModel—explaining their roles, showing concrete code examples for constructing messages, building prompts, and invoking a large language model via a REST controller, and provides a complete demo repository.
This article explores how programming paradigms have shifted from traditional languages to AI‑driven development, detailing AI Agent concepts, workflow versus agentic modes, single versus multi‑agent strategies, prompt versus context engineering, the reference architecture with Spring AI Alibaba, Nacos, Higress and RocketMQ, and the observability solutions built on OpenTelemetry and LoongSuite.
Spring AI 1.0.1 has been officially released, introducing over 150 changes that improve stability, add new AI model provider capabilities, advanced features like vector store enhancements and MCP streaming, and outline upcoming roadmap priorities for developers and enterprises.
This guide walks through creating a Spring Boot 3 application that loads web pages into a vector database using Spring AI, configures AI models and embeddings, and exposes REST endpoints for storing documents and answering queries with retrieval‑augmented generation.
This article explains how the open‑source JManus framework enables Java developers to create, configure, and deploy multi‑agent AI applications on Alibaba Cloud's Serverless SAE and Function Compute platforms, highlighting its OpenManus compatibility, MCP protocol support, PLAN‑ACT mode, high‑availability architecture, performance advantages over low‑code solutions, and step‑by‑step deployment instructions.
This article explains the MCP framework that standardizes AI tool invocation using JSON‑RPC, outlines its client‑server architecture, details communication methods such as STDIO, SSE and streamable HTTP, and provides a Spring AI demo showing tool registration, discovery, and execution.
This article walks you through what Spring AI Alibaba 1.0 offers, highlights its major updates such as the Graph multi‑agent framework and ecosystem integrations, and provides a step‑by‑step upgrade path with Maven dependency changes, code fixes, and configuration adjustments for Java developers.
This article explains the MCP framework’s architecture, execution flow, JSON‑RPC communication, and lifecycle, showing how it standardizes AI function calling and tool integration using Spring AI, with code examples and comparisons of communication methods.
This article walks through the fundamentals of large language models, their stateless and structured-output nature, explains how Spring‑AI provides a Java‑friendly API for model integration, covers RAG architecture, the MCP protocol, and demonstrates end‑to‑end code examples for building intelligent agents.
This tutorial walks through creating a natural‑language‑to‑SQL chatbot using Spring AI, configuring a MySQL school database with Flyway, defining system prompts for a DeepSeek LLM, implementing service beans and a REST API, and interacting with the bot via curl commands.
Learn how to leverage Spring AI's prompt template feature to create flexible, variable-driven queries, and implement backend services using DeepSeek and QWEN models for dynamic travel recommendations, complete with code examples for interfaces, service implementations, and controller routing.
This step‑by‑step tutorial shows how to install Ollama, pull the DeepSeek‑R1 model, create a Spring Boot project with the Spring AI Ollama starter, code a ChatController, and test a local AI "Hello World" integration, illustrating AI‑enhanced backend development.
This article introduces essential AI concepts—including models, prompts and prompt templates, embeddings, tokens, structured output, and Retrieval‑Augmented Generation—explaining their meanings and relevance for effectively using Spring AI in real‑world applications.
This article walks through how Java developers can connect to Python‑based AI services using the Model Context Protocol (MCP), compares STDIO and SSE transports, explains why Spring AI’s MCP support is limited, and shows a complete implementation with the raw MCP Java SDK and OpenAI client to invoke tools like Blender from Java code.
The article announces the official release of Spring AI 1.0, explains the meaning of GA, provides the Maven dependency configuration, describes how Spring AI simplifies AI integration for Java applications, and outlines its key features and supported model types.
Learn how to implement Spring AI’s Model Context Protocol (MCP) in Spring Boot 3.4.2, enabling dynamic runtime tool updates, configuring MCP server and client, building sample tools with code snippets, and demonstrating add/remove operations and testing via Cline and REST endpoints.
This article explains fundamental AI concepts—including models, prompts, prompt templates, embeddings, tokens, structured output, data integration, RAG, and tool calling—and shows how Spring AI simplifies their use for Java developers building intelligent applications.
This guide walks Java developers through integrating Spring AI with large‑model services such as DeepSeek, covering setup, API key configuration, code examples for synchronous and streaming calls, reactive implementation, monitoring with Actuator, and compatibility with OpenAI‑style APIs.
The article announces the Spring AI 1.0 release, provides the Maven BOM dependency, compares Spring AI with LangChain4j for Java AI development, and outlines upcoming integration, performance, and observability improvements for both frameworks amid rapid large‑model advancements.
This article explains why observability is essential for Spring AI applications, outlines common cost‑control and performance challenges, and provides a step‑by‑step guide—including Maven setup, client configuration, service implementation, metric exposure, Zipkin tracing, and architecture insights—to create a fully observable, enterprise‑grade AI translation service.
Spring AI 1.0 GA introduces a comprehensive suite of AI capabilities for Java developers, including a ChatClient supporting 20 models, vector‑store integrations, RAG pipelines, advanced chat memory, @Tool function calling, model evaluation, observability, Model Context Protocol, and autonomous agents, with examples for major cloud providers.
Spring AI 1.0, the first production‑grade Java AI framework, introduces ready‑to‑use APIs, seamless model integration, enterprise‑level RAG engine, smart tool calling, and three development modes, empowering developers to rapidly build, customize, and fully control AI applications with major model providers like OpenAI, Anthropic, DeepSeek.
This article explains the challenges of integrating diverse tools without MCP, then demonstrates step‑by‑step how to configure Spring‑AI and the native MCP SDK to call LLMs, register tools, handle SSE and stdio services, and troubleshoot common issues, providing code snippets and best‑practice recommendations.
This article walks you through using Spring AI's MCP client starter in a Spring Boot 3 project to connect to Baidu Map's MCP server, covering dependencies, YAML and JSON configurations, communication modes, custom server setup, and sample code for invoking the service.
This article introduces Spring AI, explains its core features for integrating various AI models, and walks through creating a Spring Boot chat application that connects to the DeepSeek model using both synchronous and streaming endpoints.
JManus is an open‑source, Java‑centric AI agent platform built on Spring AI Alibaba that lets developers quickly create, configure, and run multi‑agent applications with support for major large language models, a web UI, and advanced PLAN‑ACT reasoning capabilities.
This tutorial demonstrates how to create a complete meeting‑management CRUD application using Spring Boot 3, Spring AI Tools, and a custom ChatClient, covering environment setup, entity definitions, repository, service layer, tool integration, and REST endpoints with code examples and screenshots.
The article explains Anthropic’s Model Context Protocol, outlines its architecture, and provides a step‑by‑step guide to creating a Spring AI‑based MCP server in Java—including adding the starter, defining @Tool‑annotated services, packaging the jar, configuring the Cline plugin, and demonstrating advanced tools such as Elasticsearch queries.
This article introduces Spring AI's multimodal capabilities, explains the Message API for handling text, image, audio, and video inputs, and provides step‑by‑step Spring Boot examples for image analysis, ID card extraction, and structured JSON output of car‑color counts.
This tutorial walks developers through setting up a low‑cost, containerized AI service on Windows by installing Docker, deploying Redis and Ollama containers, pulling the DeepSeek‑R1 model, and integrating everything with Spring AI to enable continuous conversation support.
This article introduces Spring AI, a Java‑based AI framework inspired by LangChain, explains its core capabilities such as model management, inference, extension and Spring ecosystem integration, and provides step‑by‑step installation, configuration, sample code, and guidance for creating MCP services.
An in‑depth guide shows how to configure Spring AI for various LLM providers, tune model parameters such as temperature and max tokens, and apply a range of prompt‑engineering patterns—including zero‑shot, few‑shot, chain‑of‑thought, self‑consistency, role‑based and automatic prompting—using concise Java code examples.
This article demonstrates how to enable conversation memory and history in Spring Boot 3 applications using Spring AI's Advisor API, covering environment setup, Maven dependencies, configuration, code examples for advisors, unique chat IDs, history retrieval, and optional database persistence.
This article explores the newly popular Quasar Alpha AI model, detailing its high-performance, multimodal capabilities, speculative origins, and provides a step‑by‑step guide to obtain an OpenRouter API key, configure a Spring Boot environment, and call the model using Java code.
This article explains the evolution from Spring AI FunctionCall to the newer ToolCalling, introduces the Model Context Protocol (MCP), and provides step‑by‑step code examples for building MCP servers and clients in Java, demonstrating how to integrate large language models with backend services.
This article explains how Spring AI's tool‑calling feature extends large language models by integrating custom Java tools for information retrieval and operations, and provides step‑by‑step Spring Boot 3 examples—including date‑time and weather tools—complete with configuration, code snippets, and execution results.
The article introduces Spring AI Alibaba OpenManus, a Java‑based multi‑agent framework that showcases complex task execution through browser automation, travel itinerary generation, and document translation, while detailing its architecture, current limitations, and future development roadmap.
The article introduces Spring AI as an emerging tool for Java developers, explains its background, goals, and core components such as data processing, model training, deployment and monitoring, showcases application scenarios like NLP, image processing, recommendation systems and predictive analytics, and also includes promotional offers for AI resources and community groups.
This article introduces Spring AI, a Spring ecosystem module that simplifies building, training, and deploying AI applications for Java developers, covering its background, goals, core components such as data processing, model training, deployment, practical code examples, use cases, advantages, challenges, and future outlook.
This article introduces Spring AI, explains its background, goals, core components such as data processing, model training, deployment and monitoring, showcases practical use cases like NLP, image processing and recommendation systems, and discusses its advantages, challenges, and future outlook for Java developers.
This article explains the Retrieval‑Augmented Generation (RAG) paradigm, walks through its four core steps, and provides a detailed Spring AI implementation—including configuration, vector storage, REST controller, multi‑query expansion, query rewriting, document joining, and error handling—plus best‑practice recommendations for production deployments.
This article demonstrates how to use Spring AI with a large language model to automatically generate and execute SELECT SQL statements in a Spring Boot 3 application, covering dependency setup, configuration files, prompt templates, controller implementation, and testing with example scripts.
AI Services, inspired by Spring Data JPA and Retrofit, offers a declarative Java API that abstracts LLM interactions, supporting input formatting, output parsing, chat memory, function calling, and RAG, with detailed examples using LangChain4j, custom Spring AI annotations, AOP aspects, and controller integration.
The QwQ-32B model, released by Alibaba Cloud, delivers DeepSeek‑R1‑level results with only 32 billion parameters, offers integrated agent capabilities, is open‑source under Apache 2.0, and can be quickly deployed locally via Ollama or integrated into Java applications using Spring AI.
This article examines three leading Java AI frameworks—LangChain4j, Spring AI, and Agent-Flex—by comparing their architectures, core capabilities, and ideal use‑cases, helping developers choose the most suitable solution for enterprise, domestic, or rapid‑prototype projects.
This tutorial walks you through integrating DeepSeek via Spring AI into a Spring Boot project, covering Spring AI basics, obtaining an API key, adding dependencies and configuration, implementing controller endpoints, testing with Postman, and accessing the full source code.
This tutorial explains what Spring AI is, walks through creating a Spring Boot project with Maven, adding the necessary dependencies, writing a simple controller that forwards user messages to a local DeepSeek model, configuring the application, and testing the AI‑powered endpoint.
This tutorial walks you through creating a Java‑based AI chat agent using Spring AI Alibaba, integrating Alibaba Cloud's large language model, adding function‑calling for weather queries, and enabling full observability with ARMS in a cloud‑native deployment.
This guide explains how to locally deploy the DeepSeek large‑language model using Ollama on Windows, macOS, and Linux, configure model storage and CORS, build personal and enterprise RAG knowledge bases with AnythingLLM and Open WebUI, and integrate the model into a Spring AI application via Docker and Docker‑Compose.
This article explains how to integrate DeepSeek's large language models into a Spring AI application, covering model selection, API key configuration, URL setup, dependency inclusion, and providing complete Java code examples for both synchronous and streaming chat interactions.
This article explains how to obtain a DeepSeek API key, configure Spring AI with the appropriate base URL and model, and provides Java code examples for both synchronous and streaming chat interactions using the DeepSeek large‑language model.
This article walks through installing Ollama, downloading the DeepSeek‑r1:1.5b model, configuring Spring AI in a Spring Boot 3 project, and exposing the model via a REST endpoint with Server‑Sent Events, providing complete code snippets and step‑by‑step screenshots for a functional AI integration.
This article explains how to integrate DeepSeek's large language models—both the chat‑oriented deepseek‑chat and the reasoning‑focused deepseek‑reasoner—into a Spring AI application, covering API key setup, base‑URL configuration, model selection, and providing full code examples for dependency, configuration, and a simple chat controller.
This guide walks Java developers through integrating DeepSeek’s large‑language model into Spring AI, covering environment setup, Maven dependencies, configuration, and sample controller code for both standard and streaming chat responses, including real‑time streaming.
This article explains how Spring AI Alibaba enables AI agents to leverage Retrieval‑Augmented Generation and Tool Calling by providing over twenty ready‑made RAG data source connectors and more than twenty function‑calling interfaces, along with practical code examples for integrating document readers and weather services.
Learn how to deploy the open‑source DeepSeek‑R1 model using Ollama on Linux or macOS, configure various model sizes, and integrate it into a Spring Boot application with Spring AI to build an API‑driven translation service, complete with code examples and testing.
This guide explains how the open‑source Model Context Protocol (MCP) enables natural‑language control of Docker containers, walks through environment setup, Spring AI integration, client project configuration, and demonstrates a full execution log showing automated image pulling, port mapping, volume mounting, and container startup.
This article introduces DeepSeek R1, a breakthrough reasoning‑focused large model that visualizes its chain‑of‑thought process, matches OpenAI O1 performance, offers open‑source advantages, and provides step‑by‑step Spring AI integration guidance, including dependency setup, configuration, and code examples.
This tutorial explains how to integrate Spring AI's Model Context Protocol (MCP) with Chat2DB to enable secure, natural‑language queries against a PostgreSQL database, covering configuration, core Java code, execution flow, and sample SQL queries.
Spring AI now supports the Model Context Protocol (MCP), a universal adapter that lets AI applications seamlessly read databases, files, and APIs, offering a plug‑and‑play alternative to function calling, with simple Maven setup and code examples to get started quickly.
This guide shows how to use Spring AI Alibaba together with Nacos to securely store API keys, dynamically update Prompt templates and model parameters, and encrypt sensitive configurations without restarting Java AI applications.
This tutorial walks through creating an intelligent airline‑ticket customer‑service agent using Spring AI Alibaba, covering requirements, architecture, RAG integration, function calling, chat memory, core capabilities, code implementation with ChatClient, and a sample running result.
Learn step‑by‑step how to integrate Spring AI Alibaba into a Spring Boot backend, configure API keys, add necessary dependencies, create a CORS‑enabled chat controller, and then develop a matching React frontend that streams AI responses, enabling a complete Java‑to‑React AI chat application.
This article introduces the open‑source Spring AI Alibaba framework, explains its background, core features such as chat model abstraction, prompt templates, structured output, function calling, RAG and chat memory, and walks through a complete smart‑ticket‑assistant example with code snippets and deployment guidance.
Spring AI Alibaba is an open‑source Java framework that integrates Alibaba Cloud's large‑model services with Spring AI, offering high‑level abstractions for chat models, prompts, function calling, RAG, and conversation memory, and includes a complete ticket‑assistant example with code snippets.
Spring AI is a Java‑centric framework that unifies access to chat, text‑to‑image, embedding and retrieval‑augmented generation models—including OpenAI, Anthropic and Alibaba’s Tongyi Qianwen—through synchronous or asynchronous APIs, POJO mapping, function calling, vector‑store integration and fluent tooling for rapid AI agent development.
Spring AI has added full support for OpenAI's structured output feature, enabling developers to define JSON schemas for AI responses, with example code showing how to configure prompts, response formats, and BeanOutputConverter for seamless JSON-to-Java bean conversion, plus Spring Boot starter configuration.
This article explains how to quickly develop an AI‑native application using Spring AI, covering core features such as chat models, prompt templates, function calling, structured output, image generation, embedding, vector stores, and Retrieval‑Augmented Generation (RAG), and provides end‑to‑end Java code examples for building a simple AI‑driven service.
This guide walks you through using Spring AI and Spring Cloud Alibaba AI to quickly create a Java chat application powered by Alibaba's Tongyi large‑model service, covering setup, configuration, core code, and verification steps for a functional generative AI chatbot.
This article introduces Spring Cloud Alibaba AI, explains its relationship to Spring AI, and provides a step‑by‑step tutorial—including Maven setup, dependency configuration, code examples, and sample calls—to integrate Alibaba's Tongyi large‑model services for text QA, image generation, and speech synthesis in a Java Spring Boot application.
This guide explains what an AI‑native application is, compares AI‑native and AI‑based approaches, and walks through Spring AI’s core features—including chat models, prompt templates, function calling, structured output, image generation, embedding, and vector stores—showing step‑by‑step code examples and how to assemble a complete AI‑native app with RAG support.
This tutorial introduces Spring AI and Spring Cloud Alibaba AI, explains their core features, shows how to set up a Maven project with the required dependencies, and provides step‑by‑step code examples for invoking text, image, and audio generation models using Spring Boot.
Spring AI brings artificial‑intelligence capabilities to the Spring Boot ecosystem, offering model support, vector‑database integration, SQL‑like filtering, and easy Maven configuration, enabling Java developers to add generative AI, semantic search, and AI‑driven image generation to their backend applications.
This tutorial walks through setting up Spring AI 0.8.1 with Spring Boot 3.1.1, configuring Alibaba Tongyi model access, and implementing chat, streaming, image, and audio generation endpoints using Java code and vector database integrations.
This guide walks you through setting up Spring AI, configuring model providers and vector stores, initializing a Spring Boot project, adding OpenAI credentials, and running a complete RAG (Retrieval‑Augmented Generation) demo with code snippets and sample API calls.
This guide introduces Spring AI, shows how to add dependencies, configure OpenAI credentials, and demonstrates chat, image, and prompt APIs with code examples, then explores advanced function calling to build business‑level large language model integrations in Java.
This tutorial walks you through adding OpenAI's generative capabilities to a Spring Boot application with Spring AI, covering project setup, API key configuration, controller creation, simple and advanced prompt usage, and how to test the resulting AI-powered endpoints.
This tutorial walks you through creating a Spring Boot project, configuring OpenAI credentials, building a controller, and using Spring AI's Prompt and PromptTemplate classes to integrate generative AI capabilities into your backend application.
This article summarizes a video tutorial that introduces Spring AI, a new project enabling generative AI in Spring Boot applications, demonstrates adding dependencies, configuring OpenAI or Azure OpenAI API keys, using prompts, templates, output parsers, and showcases converting AI responses into domain objects.
Spring Boot 3.2 introduces Java 21 virtual thread support, SSL hot‑reload for embedded servers, GraalVM native image improvements, and a preview of Spring AI, marking a major evolution for Java backend development and performance optimization.
