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.