Why Ditch Spring AI? 3 Java AI Frameworks That Supercharge Spring Boot

Introduction

Many Java developers either treat AI as a pure Python problem or rewrite their Spring Boot services in Python, losing existing authentication, transaction, logging, and configuration capabilities. The author argues that Spring Boot can host AI agents if the right third‑party frameworks are used.

LangChain4j: The Most Java‑Friendly AI Framework

LangChain4j is not a Java port of Python’s LangChain; it is designed with Java conventions such as interfaces, annotations, dependency injection, a Spring Boot starter, and type safety. The @AiService annotation works like Spring Data JPA: you define an interface and the framework generates a proxy that handles model calls, prompts, tools, and memory.

@AiService
public interface CustomerAgent {
    @SystemMessage("你是电商客服助手，只能根据工具返回的数据回答，不要瞎编订单状态")
    String chat(String userMessage);
}

@Component
public class CustomerTools {
    private final OrderService orderService;
    private final TicketService ticketService;
    public CustomerTools(OrderService orderService, TicketService ticketService) {
        this.orderService = orderService;
        this.ticketService = ticketService;
    }
    @Tool("根据订单号查询物流、支付、退款状态")
    OrderInfo queryOrder(String orderNo) {
        // 真实数据仍然走老系统 Service，模型只负责决定什么时候调用
        return orderService.queryOrderInfo(orderNo);
    }
    @Tool("订单异常时创建售后工单")
    String createAfterSaleTicket(String orderNo, String reason) {
        // 写操作必须做幂等，避免模型重复调用生成多张工单
        return ticketService.createIfAbsent(orderNo, reason);
    }
}

In a controller the agent is used without any prompt handling, keeping the business entry clean:

@RestController
public class AiCustomerController {
    private final CustomerAgent customerAgent;
    public AiCustomerController(CustomerAgent customerAgent) {
        this.customerAgent = customerAgent;
    }
    @PostMapping("/ai/customer")
    public String chat(@RequestBody String message) {
        // 这里不写 Prompt 拼接，不写模型协议，保持业务入口干净
        return customerAgent.chat(message);
    }
}

The main value of LangChain4j is not merely model invocation but enabling Java teams to write AI using familiar engineering patterns. It fits lightweight agents such as customer service assistants, knowledge‑base Q&A, or business system entry points.

Spring AI Alibaba: An Enterprise‑Grade Agent Platform

Spring AI Alibaba goes beyond simple model adapters. It adds Graph, Agent Framework, Admin, Studio, and Nacos MCP, positioning itself as a full‑stack agent platform within the Spring ecosystem.

The key feature is its graph‑based workflow, which breaks complex tasks into nodes that pass state, allowing retries, human intervention, and observability.

@Configuration
public class SalesAgentGraphConfig {
    @Bean
    public StateGraph salesAnalysisGraph(SalesService salesService, ReportService reportService) {
        StateGraph graph = new StateGraph("sales-analysis");
        graph.addNode("load_sales", state -> {
            String shopId = state.value("shopId");
            state.put("salesData", salesService.loadWeeklyData(shopId));
            return state;
        });
        graph.addNode("analyze", state -> {
            Object salesData = state.value("salesData");
            state.put("analysis", salesService.analyzeByModel(salesData));
            return state;
        });
        graph.addNode("make_report", state -> {
            Object analysis = state.value("analysis");
            state.put("reportId", reportService.createReport(analysis));
            return state;
        });
        graph.addEdge("load_sales", "analyze");
        graph.addEdge("analyze", "make_report");
        return graph;
    }
}

This approach is suitable for internal agents such as data‑analysis assistants, operation bots, ticket routing, or automated sales reporting. However, the learning curve is higher than LangChain4j, and small projects may be overwhelmed by the full suite.

LangGraph4j: State‑Machine and Flow‑Chart Oriented Agent Orchestration

LangGraph4j focuses on bringing graph‑based orchestration to the Java ecosystem. It can be combined with LangChain4j or Spring AI Alibaba to model multi‑step, stateful agents.

Example: a code‑review agent that first runs static analysis, then lets the model assess semantic risk, and finally flags high‑risk findings for human review.

public class ReviewState {
    private String diff;
    private String lintResult;
    private String aiFinding;
    private boolean needHumanReview;
}

@Component
public class CodeReviewWorkflow {
    public ReviewState run(ReviewState state) {
        // Node 1: static check
        state.setLintResult(runLint(state.getDiff()));
        // Node 2: model analysis if lint passes
        if (!state.getLintResult().contains("ERROR")) {
            state.setAiFinding(analyzeDiffByModel(state.getDiff()));
        }
        // Node 3: high‑risk requires human review
        state.setNeedHumanReview(isHighRisk(state.getAiFinding()));
        return state;
    }
    private String runLint(String diff) { return "OK"; }
    private String analyzeDiffByModel(String diff) { return "发现潜在空指针风险"; }
    private boolean isHighRisk(String finding) {
        return finding != null && finding.contains("风险");
    }
}

The author stresses that agents should have explicit state, nodes, and conditional branches rather than a single monolithic prompt.

Choosing the Right Framework

All three are AI frameworks, but their use cases differ:

If you need a quick, lightweight AI feature (chatbot, knowledge‑base, business assistant) inside an existing Spring Boot project, start with LangChain4j —the smallest footprint and most Java‑native API.

If you aim to build an internal enterprise Agent platform with long task chains, multi‑agent coordination, visual management, and integration with Spring Cloud, Nacos, or Alibaba Cloud, Spring AI Alibaba is the better fit.

If you already have a model‑calling setup but your workflows are becoming tangled, and you want to decompose them into graphs, state machines, and explicit branches, explore LangGraph4j .

In summary, the three frameworks address different layers of AI integration: LangChain4j for Java‑friendly agent coding, Spring AI Alibaba for full‑stack enterprise Agent platforms, and LangGraph4j for graph‑based orchestration of complex, stateful AI tasks.