245 related articles

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Build an enterprise RAG knowledge base Q&A system using Spring AI 2.0, Cursor AI programming, Ollama local deployment, and Redis vector storage. Runs on just 4GB VRAM.

A detailed guide on building a full-process HR recruitment Workflow Agent with Spring AI Alibaba Graph, covering resume parsing, multi-dimensional screening, tiered questions, human-in-the-loop, and state rollback.

Spring AI 2.0 brings five core updates: mandatory upgrade to Spring Boot 4, Tools parsing moving up, a built-in Agentic mechanism, MCP switching to Streamable HTTP, and an on-demand tool Advisor.

Spring AI 2.0 brings five core updates: mandatory Spring Boot 4 upgrade, Tools parsing moved up, built-in Agentic mechanism, MCP switch to Streamable HTTP, and on-demand tool-loading Advisor.

Spring AI 2.0 brings five core updates: mandatory upgrade to Spring Boot 4, lifted Tools parsing, built-in Agentic mechanism, MCP switch to Streamable HTTP, and an on-demand tool-loading Advisor.

Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.

Spring AI Alibaba Admin is a visual AI workflow platform for Java, comparable to Dify. It supports Dify-to-Graph migration, multi-model integration, and code export. This article covers core features and local deployment tips.

Build a full HR recruitment Workflow Agent with Spring AI Alibaba Graph: résumé scoring, interview generation, Human-in-the-Loop, and state rollback across 20 technical concepts.

Spring AI is Java's answer to LangChain — offering unified multi-model APIs, structured output, RAG, Tool Calling, and MCP protocol support for enterprise LLM development.

A complete Spring AI guide for Java developers covering ChatModel, EmbeddingModel, ChatMemory, Tool Calling, MCP protocol, and RAG with Milvus. Build LLM apps in Spring Boot.

A deep dive into Spring AI 2.0: provider-agnostic APIs, RAG with vector databases, and how Java developers can build LLM apps using the Spring ecosystem.

Build an HR recruitment workflow Agent with Spring AI Alibaba Graph, covering resume parsing, job matching, tiered question generation, HITL checkpointing, and time travel state rollback across 20 core technical points.

Spring AI Alibaba is an enterprise-grade Java AI framework that bridges microservices and LLMs — like JDBC for databases — enabling seamless AI integration with minimal migration cost.

A complete Spring AI 2.0 guide for Java developers covering unified API abstraction, RAG, tool calling, MCP protocol, and enterprise projects to build AI Agents.

In-depth comparison of four Java AI frameworks — Spring AI, LangChain4J, DJL, and JBot AI — covering features, use cases, and ecosystem compatibility to guide your selection.

Deep dive into Spring AI Alibaba Agent framework covering core architecture, tool calling, RAG integration, multi-agent collaboration, and production deployment for Java developers.

A practical guide for Java developers to build AI apps without switching languages — covering LLM APIs, prompt engineering, RAG, Spring AI, and Langchain4j.

In-depth comparison of Spring AI and LangChain4j covering ecosystem integration, features, usability, RAG, Tools, MCP, and Agents to help Java developers choose the right AI framework.