java-ai-in-action/framework-compare

Spring AI vs LangChain4j vs SAA 三框架同台对比（配套篇2）

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Java

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created 2026-09-16

on radar since 2026-09-22

agentic-ai ai ai-agentchatclientdeepseek javajava-ailangchain4jllm mcp model-context-protocol openai pgvector qdrant qwen ragspring-aispring-ai-alibabaspring-boot vector-database

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About framework-compare

配套文章：篇2《Spring AI vs LangChain4j vs Spring AI Alibaba：2026 终极选型指南》

一个仓库跑通三个框架的最小可运行示例，用同一套场景对比代码量、配置方式与运行表现。

Step 能力文件 --------- 1 最小可跑 ChatClient DemoApplication.java 2 ChatMemory 多轮对话 config/ChatConfig.java 3 Function Calling tools/OrderTools.java 4 MCP Client application.yml（注释段） 5 流式输出 SSE controller/AdvancedController.java

维度最强说明 --------- 模型集成数量 LangChain4j 30+ 模型开箱即用向量库支持 LangChain4j 覆盖最广，差异 <10% MCP 支持 Spring AI 2.0 @McpTool 注解 47 行暴露 10 个方法多 Agent 编排 Spring AI Alibaba Graph + Mermaid 可视化可观测 Spring AI / SAA Micrometer + OTel 原生集成中文社区 SAA 官方中文文档 + 国内生态最全

1. 别追 RC 版本——锁定 GA，Milestone/RC 的 API 可能突变 2. Spring AI 2.0 强制 JDK 21 + Spring Boot 4——老项目升级前先看本仓库 spring-ai-quickstart 的 pom 3. MCP 包名迁移：io.modelcontextprotocol. → org.springframework.ai.mcp. 4. LangChain4j 中文资料滞后官方 1–2 个月——以 GitHub README + Javadoc 为准 5. 国内合规：生产环境优先通义 / DeepSeek / 豆包，开源模型走 Ollama / vLLM

- [x] v0.1 三框架 hello world + docker-compose - [x] v0.2 spring-ai-quickstart 补齐 Step 2-5（记忆/工具/SSE） - [ ] v0.3 RAG 对比（同文档、同问题、三框架召回效果） - [ ] v0.4 多 Agent 编排对比 - [ ] v1.0 基准测试报告（压测 + Token 成本）

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