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
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) |
Verdict? SpringAI is awesome if you’re already knee-deep in Spring. LangChain4j? More accessible for side projects and smaller stacks.
Architectural Snapshots
LangChain4j: The DIY Toolkit
- ChatModel connects to:
- Tools
- Memory
- Chains
SpringAI: The Full-Stack AI Spring App
- ApplicationContext powers:
- 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();
}
}
LangChain4j
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);
}
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);
}
}
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;
}
}
LangChain4j: Memory, your way
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());
}
LangChain4j: SSE FTW
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();
}
}
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);
}
}
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% |
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 |
Bonus: Combine Them
Routing logic makes this possible:
- Use SpringAI for stable, audited flows
- Use LangChain4j for hackathon-style prototypes
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
LangChain4j
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.