If you're a Java dev tired of watching Python get all the AI toys—this one's for you. If you're a Java dev tired of watching Python get all the AI toys—this one's for you. While Python still rules in AI research, Java isn't just alive—it's quietly powering a solid chunk of enterprise AI deployments. Two frameworks are stepping up to give Java devs access to LLMs without leaving their comfort zone: SpringAI: Think "Spring Boot meets OpenAI"
LangChain4j: The Java flavor of the LangChain ecosystem SpringAI: Think "Spring Boot meets OpenAI" SpringAI LangChain4j: The Java flavor of the LangChain ecosystem LangChain4j Both offer powerful abstractions for building LLM apps, but their philosophies, integrations, and flexibility couldn’t be more different. Framework Philosophies: Spring vs. Explicit Modularity Feature

SpringAI

LangChain4j



Design Style

Convention over configuration

Explicit composition



Dev Experience

Spring Boot magic

Java first, Spring optional



Integration Style

Autowire everything

Build what you need manually



Learning Curve

Steep (Spring skills required)

Moderate (plain Java works) Feature

SpringAI

LangChain4j



Design Style

Convention over configuration

Explicit composition



Dev Experience

Spring Boot magic

Java first, Spring optional



Integration Style

Autowire everything

Build what you need manually



Learning Curve

Steep (Spring skills required)

Moderate (plain Java works) Feature

SpringAI

LangChain4j Feature Feature SpringAI SpringAI LangChain4j LangChain4j Design Style

Convention over configuration

Explicit composition Design Style Design Style Convention over configuration Convention over configuration Explicit composition Explicit composition Dev Experience

Spring Boot magic

Java first, Spring optional Dev Experience Dev Experience Spring Boot magic Spring Boot magic Java first, Spring optional Java first, Spring optional Integration Style

Autowire everything

Build what you need manually Integration Style Integration Style Autowire everything Autowire everything Build what you need manually Build what you need manually Learning Curve

Steep (Spring skills required)

Moderate (plain Java works) Learning Curve Learning Curve Steep (Spring skills required) Steep (Spring skills required) Moderate (plain Java works) Moderate (plain Java works) Verdict? SpringAI is awesome if you’re already knee-deep in Spring. LangChain4j? More accessible for side projects and smaller stacks. Verdict? Architectural Snapshots LangChain4j: The DIY Toolkit ChatModel connects to:

Tools
Memory
Chains ChatModel connects to:

Tools
Memory
Chains Tools
Memory
Chains Tools Memory Chains SpringAI: The Full-Stack AI Spring App ApplicationContext powers:

AI Models
Repositories
Security and observability ApplicationContext powers:

AI Models
Repositories
Security and observability AI Models
Repositories
Security and observability AI Models Repositories Security and observability LangChain4j feels like JavaScript—small modules, easy to plug together. SpringAI is more like Ruby on Rails for AI—opinionated and tightly integrated. Building a Chat Feature SpringAI @RestController
public class BotController {
  @Autowired ChatClient chat;

  @PostMapping("/chat")
  public String ask(@RequestBody String input) {
    return chat.call(new Prompt(input))
              .getResult().getOutput().getContent();
  }
} @RestController
public class BotController {
  @Autowired ChatClient chat;

  @PostMapping("/chat")
  public String ask(@RequestBody String input) {
    return chat.call(new Prompt(input))
              .getResult().getOutput().getContent();
  }
} LangChain4j var model = OpenAiChatModel.builder()
  .apiKey("sk-demo")
  .modelName("gpt-4")
  .build();

String reply = model.generate("What's the meaning of life?"); var model = OpenAiChatModel.builder()
  .apiKey("sk-demo")
  .modelName("gpt-4")
  .build();

String reply = model.generate("What's the meaning of life?"); LangChain4j lets you go straight to the point. SpringAI gives you structure and safety—once you’ve set it up. Calling Functions (Tools) SpringAI's Magic Beans @AiFunction(name = "getWeather")
public String getWeather(@AiParam("city") String city) {
    return weatherService.get(city);
} @AiFunction(name = "getWeather")
public String getWeather(@AiParam("city") String city) {
    return weatherService.get(city);
} LangChain4j's Explicit Tools public class WeatherTool implements Tool {
  public String name() { return "getWeather"; }
  public String description() { return "Returns weather info"; }
  public String execute(String input) {
    return weatherService.get(input);
  }
} public class WeatherTool implements Tool {
  public String name() { return "getWeather"; }
  public String description() { return "Returns weather info"; }
  public String execute(String input) {
    return weatherService.get(input);
  }
} Want speed and control? Go LangChain4j. Want less boilerplate? SpringAI. Memory Management SpringAI: Chat sessions with annotation magic @ChatSession
public class BotMemory {
  @ChatPrompt("You're a helpful bot")
  public String chat(@UserMessage String msg) {
    return msg;
  }
} @ChatSession
public class BotMemory {
  @ChatPrompt("You're a helpful bot")
  public String chat(@UserMessage String msg) {
    return msg;
  }
} LangChain4j: Memory, your way ChatMemory memory = MessageWindowChatMemory.withMaxMessages(10);
memory.add("user123", userMessage("Hi there!"));
String response = model.generate(memory.messages("user123")); ChatMemory memory = MessageWindowChatMemory.withMaxMessages(10);
memory.add("user123", userMessage("Hi there!"));
String response = model.generate(memory.messages("user123")); LangChain4j makes you work for it—but you get total control. Streaming Responses SpringAI: Reactive-style @GetMapping("/stream")
public Flux<String> stream(@RequestParam String message) {
  return chatClient.stream(new Prompt(message))
                   .map(res -> res.getResult().getOutput().getContent());
} @GetMapping("/stream")
public Flux<String> stream(@RequestParam String message) {
  return chatClient.stream(new Prompt(message))
                   .map(res -> res.getResult().getOutput().getContent());
} LangChain4j: SSE FTW model.generate("Hey!", new StreamingResponseHandler() {
  public void onNext(String token) { emitter.send(token); }
  public void onComplete() { emitter.complete(); }
}); model.generate("Hey!", new StreamingResponseHandler() {
  public void onNext(String token) { emitter.send(token); }
  public void onComplete() { emitter.complete(); }
}); Security & Monitoring SpringAI: You’ve got Spring Security @Configuration
public class SecConfig {
  @Bean
  SecurityFilterChain configure(HttpSecurity http) throws Exception {
    return http.authorizeHttpRequests(req -> req
        .requestMatchers("/ai/**").hasRole("AI_USER"))
        .oauth2ResourceServer(oauth2 -> oauth2.jwt())
        .build();
  }
} @Configuration
public class SecConfig {
  @Bean
  SecurityFilterChain configure(HttpSecurity http) throws Exception {
    return http.authorizeHttpRequests(req -> req
        .requestMatchers("/ai/**").hasRole("AI_USER"))
        .oauth2ResourceServer(oauth2 -> oauth2.jwt())
        .build();
  }
} LangChain4j: DIY auth layer public class AuthTool implements Tool {
  public String execute(String input) {
    if (!SecurityContext.has("AI_ACCESS")) throw new SecurityException();
    return delegateTool.execute(input);
  }
} public class AuthTool implements Tool {
  public String execute(String input) {
    if (!SecurityContext.has("AI_ACCESS")) throw new SecurityException();
    return delegateTool.execute(input);
  }
} If you need enterprise-grade compliance, SpringAI wins this round. Benchmarks (AWS c5.2xlarge, Java 21) Scenario

SpringAI QPS

LangChain4j QPS

Memory Use



Basic Chat

1420

1560

+15%



Function Calls

860

920

+8%



Session Chat

350

410

-12%



Streaming

2100

2400

+5% Scenario

SpringAI QPS

LangChain4j QPS

Memory Use



Basic Chat

1420

1560

+15%



Function Calls

860

920

+8%



Session Chat

350

410

-12%



Streaming

2100

2400

+5% Scenario

SpringAI QPS

LangChain4j QPS

Memory Use Scenario Scenario SpringAI QPS SpringAI QPS LangChain4j QPS LangChain4j QPS Memory Use Memory Use Basic Chat

1420

1560

+15% Basic Chat Basic Chat 1420 1420 1560 1560 +15% +15% Function Calls

860

920

+8% Function Calls Function Calls 860 860 920 920 +8% +8% Session Chat

350

410

-12% Session Chat Session Chat 350 350 410 410 -12% -12% Streaming

2100

2400

+5% Streaming Streaming 2100 2100 2400 2400 +5% +5% LangChain4j is leaner and faster, but SpringAI isn’t far behind—especially if you already run Spring Boot. 🤔 Which One Should You Use? Use Case

Pick This

Why



Spring Boot Application

SpringAI

Seamless integration



Standalone App / CLI

LangChain4j

Less overhead, more modularity



Advanced Agent Toolchain

LangChain4j

Tool-first design



Corporate AI Integration

SpringAI

Security + observability Use Case

Pick This

Why



Spring Boot Application

SpringAI

Seamless integration



Standalone App / CLI

LangChain4j

Less overhead, more modularity



Advanced Agent Toolchain

LangChain4j

Tool-first design



Corporate AI Integration

SpringAI

Security + observability Use Case

Pick This

Why Use Case Use Case Pick This Pick This Why Why Spring Boot Application

SpringAI

Seamless integration Spring Boot Application Spring Boot Application SpringAI SpringAI Seamless integration Seamless integration Standalone App / CLI

LangChain4j

Less overhead, more modularity Standalone App / CLI Standalone App / CLI LangChain4j LangChain4j Less overhead, more modularity Less overhead, more modularity Advanced Agent Toolchain

LangChain4j

Tool-first design Advanced Agent Toolchain Advanced Agent Toolchain LangChain4j LangChain4j Tool-first design Tool-first design Corporate AI Integration

SpringAI

Security + observability Corporate AI Integration Corporate AI Integration SpringAI SpringAI Security + observability Security + observability Bonus: Combine Them Routing logic makes this possible: Use SpringAI for stable, audited flows
Use LangChain4j for hackathon-style prototypes Use SpringAI for stable, audited flows SpringAI Use LangChain4j for hackathon-style prototypes LangChain4j Build once. Route wisely. Quickstart for the Impatient SpringAI git clone https://github.com/spring-projects/spring-ai.git
cd samples/ai-spring-boot
./mvnw spring-boot:run git clone https://github.com/spring-projects/spring-ai.git
cd samples/ai-spring-boot
./mvnw spring-boot:run LangChain4j OpenAiChatModel model = OpenAiChatModel.withApiKey("sk-...");
System.out.println(model.generate("Summarize the Matrix movie in one line.")); OpenAiChatModel model = OpenAiChatModel.withApiKey("sk-...");
System.out.println(model.generate("Summarize the Matrix movie in one line.")); Final Thoughts Java isn’t going anywhere. And thanks to SpringAI and LangChain4j, neither are Java devs in the age of AI. So whether you’re building tools for the next unicorn—or just shipping AI-powered chat into your boring enterprise dashboard—you’ve got options. And they both speak fluent Java.

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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SpringAI vs LangChain4j: The Real-World LLM Battle for Java Devs

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