Comparing Three Leading Java AI Frameworks: Feat AI, Spring AI, and LangChain4j

Code Samples

Three minimal examples demonstrate how each framework calls the same large model (Qwen3‑235B‑A22B) and prints the response.

Feat AI: Lambda Chain

ChatModel chatModel = FeatAI.chatModel(opts ->
    opts.model("Qwen3-235B-A22B")
        .system("你是一个乐于助人的助手。"));
chatModel.chat("你好，请介绍一下 Feat AI 的特点。");

Feature: Lambda configuration + CompletableFuture, high code density.

Spring AI: Spring Style

OpenAiApi openAiApi = new OpenAiApi("https://ai.gitee.com/", apiKey);
OpenAiChatOptions options = OpenAiChatOptions.builder()
    .withModel("Qwen3-235B-A22B")
    .build();
ChatModel chatModel = new OpenAiChatModel(openAiApi, options);
Prompt prompt = new Prompt("你好，请介绍一下 Spring AI 的特点。");
ChatResponse response = chatModel.call(prompt);

Feature: Explicit component assembly, clear responsibility separation.

LangChain4j: Builder Pattern

ChatLanguageModel model = OpenAiChatModel.builder()
    .apiKey(apiKey)
    .modelName("Qwen3-235B-A22B")
    .baseUrl("https://ai.gitee.com/v1/")
    .build();
String response = model.generate("你好，请介绍一下 LangChain4j 的特点。");

Feature: Flexible builder, simple synchronous API.

Fat‑Jar Size Comparison

Feat AI – 2.9 MB (core deps: feat‑core + fastjson)

LangChain4j – 11 MB (core lib + OpenAI adapter)

Spring AI – 31 MB (Spring Framework + Reactor + Jackson)

Core Differences Overview

Code lines: Feat AI minimal, Spring AI medium, LangChain4j fewer

JDK requirement: Feat AI JDK 8+, Spring AI JDK 17+, LangChain4j JDK 8+

Spring dependency: Feat AI none, Spring AI required, LangChain4j none

Multi‑turn conversation: Feat AI auto‑maintains context, Spring AI manual management, LangChain4j manual message list

Streaming output: Feat AI callback listener, Spring AI Reactor Flux, LangChain4j generic callback handler

Streaming Output Comparison

Feat AI: Consumer Callback

chatModel.chatStream("请写一首关于春天的诗", chunk -> {
    System.out.print(chunk.getContent()); // real‑time output
});

Feature: Simple direct callback API, fits Java 8 conventions, no extra learning cost.

Spring AI: Flux Reactive Stream

Flux<String> stream = chatModel.stream(prompt)
    .map(response -> response.getResult().getOutput().getContent());
stream.subscribe(System.out::print); // reactive subscription

Feature: Based on Reactor, suitable for projects already using WebFlux, but adds a learning curve.

LangChain4j: StreamingResponseHandler

model.generate("请写一首关于春天的诗", new StreamingResponseHandler<>() {
    @Override
    public void onNext(String token) {
        System.out.print(token); // handle each token
    }
    @Override
    public void onComplete(Response<AiMessage> response) {
        System.out.println("
生成完成");
    }
    @Override
    public void onError(Throwable error) {
        error.printStackTrace();
    }
});

Feature: Interface callback with full lifecycle hooks, code is relatively verbose.

Implementation Comparison

API style: Feat AI – Consumer functional; Spring AI – Reactor Flux; LangChain4j – Interface callback

Code amount: Feat AI – 1 line; Spring AI – 2–3 lines; LangChain4j – more (requires implementing callbacks)

Learning cost: Feat AI – low (standard Java 8); Spring AI – high (reactive programming); LangChain4j – medium (framework‑specific interfaces)

Error handling: Feat AI – exception; Spring AI – reactive error stream; LangChain4j – dedicated onError callback

JDK compatibility: Feat AI – JDK 8+; Spring AI – JDK 17+; LangChain4j – JDK 8+

How to Choose?

Minimalist JDK 8 projects – Feat AI

Spring Boot with JDK 17+ – Spring AI

Spring projects on JDK 8/11 – Feat AI (Spring AI needs JDK 17+)

Need a full AI toolchain – LangChain4j

Serverless / edge computing – Feat AI (small package, fast start)

Reflections

Writing the three examples revealed that all frameworks share a "configuration‑and‑execution separation" design, yet their implementation paths differ markedly. Feat AI pursues code density, Spring AI emphasizes explicit design for maintainability, and LangChain4j balances flexibility with readability. No framework is universally superior; the choice reflects the developer's programming philosophy.