Spring AI vs LangChain4j: Best Choice for Java (2026)

If you’re comparing Spring AI vs LangChain4j, this guide will help you choose the right stack for your Java backend in 2026.

If you’re building AI features in a Java backend in 2026, you’ll quickly hit the same question: Spring AI or LangChain4j?
Both can work. The real difference is how they fit your architecture, your team, and your path to production.

In this guide, you’ll get:

A clear “pick this if…” decision
A practical comparison focused on production realities
Two real-world architecture examples
A simple decision matrix you can share with your team

Before you merge your next AI-related PR: grab the Java AI PR Review Pack (2026) (checklist + prompts + PR template).
👉 https://prodevaihub.com/java-ai-pr-review-pack/

Below, we’ll break down Spring AI vs LangChain4j by use case, RAG needs, and ops constraints.

TL;DR: What should you pick?

Choose Spring AI if…

Your backend is already Spring Boot-first
You want a Spring-native developer experience (configuration, conventions, integration patterns)
Your team prefers “Spring-style” abstractions and wiring
You care most about smooth integration into existing Spring architecture (security, configuration, lifecycle)

Choose LangChain4j if…

You want a framework-agnostic Java library (works beyond Spring)
You want a strong library-style API for RAG, tool calling, agents/workflows without being tied to Spring idioms
You may run on Quarkus/Micronaut, or want a clean core module independent of the web framework
You want to build AI components as reusable Java modules across services

Simple rule:

Spring-first app → Spring AI
Reusable Java AI layer across frameworks/services → LangChain4j

What these frameworks actually help you build

Ignore hype. In backend reality, you usually need these building blocks:

LLM calls (chat/completions)
Embeddings (turn text into vectors)
Vector store retrieval (search your docs/knowledge)
RAG pipeline (retrieve → inject context → generate answer)
Tool calling (let the model call your APIs safely)
Production controls (timeouts, retries, caching, logging, guardrails)

Spring AI and LangChain4j both aim to make these tasks less painful—but with different philosophies.

Two quick “Hello AI” code examples

Here’s a minimal example to illustrate Spring AI vs LangChain4j in a real backend flow.

Spring AI (Spring Boot): a simple endpoint with `ChatClient`

This is the “Spring-native” style: builder injection, straightforward controller wiring, and a clean path to production. (Spring AI ChatClient API reference)
https://docs.spring.io/spring-ai/reference/api/chatclient.html

import org.springframework.web.bind.annotation.*;
import org.springframework.ai.chat.client.ChatClient;

@RestController
@RequestMapping("/ai")
public class AiController {

    private final ChatClient chatClient;

    public AiController(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }

    @GetMapping("/ask")
    public String ask(@RequestParam String q) {
        return chatClient
                .prompt()
                .user(q)
                .call()
                .content();
    }
}
Code language: JavaScript (javascript)

LangChain4j (framework-agnostic): a minimal Java example

This is the “library-first” style: you can use it in Spring, Quarkus, a CLI tool, a batch job—anywhere Java runs. (LangChain4j project)
https://github.com/langchain4j/langchain4j

import dev.langchain4j.model.openai.OpenAiChatModel;

public class HelloLangChain4j {

    public static void main(String[] args) {
        OpenAiChatModel model = OpenAiChatModel.builder()
                .apiKey(System.getenv("OPENAI_API_KEY"))
                .build();

        String answer = model.generate("Give me 3 bullet points on when to use RAG in Java backends.");
        System.out.println(answer);
    }
}Code language: JavaScript (javascript)

LangChain4j can also generate a typed “assistant” from an interface using AiServices.
https://github.com/langchain4j/langchain4j-examples

import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.service.AiServices;

public class AiServiceExample {

    interface Assistant {
        String answer(String userMessage);
    }

    public static void main(String[] args) {
        var model = OpenAiChatModel.builder()
                .apiKey(System.getenv("OPENAI_API_KEY"))
                .build();

        Assistant assistant = AiServices.create(Assistant.class, model);

        System.out.println(assistant.answer("Explain Spring AI vs LangChain4j in 2 sentences."));
    }
}
Code language: JavaScript (javascript)

Spring AI vs LangChain4j: the real differences

1) Philosophy: “Spring-native” vs “Java library”

Spring AI feels like “AI support inside Spring.” You wire it using familiar Spring Boot patterns and configuration.
LangChain4j feels like a Java library you embed into your application logic, regardless of framework—then optionally integrate with Spring if you want.

If your team already moves fast inside Spring Boot conventions, Spring AI often feels “native.”
If you want a reusable Java module approach (domain/core module separated from web), LangChain4j often fits better.

2) Architecture fit (this matters more than features)

Ask yourself:

Do you want AI logic to live as “Spring beans & configuration”?
→ Spring AI is usually more straightforward.

Do you want AI logic to live as a clean Java service layer, independent from web framework details?
→ LangChain4j often gives you a cleaner separation.

This is the difference between:

“AI is a Spring integration concern”
vs
“AI is a domain/service capability we can reuse”

3) RAG: “It’s not the library, it’s your pipeline”

RAG is not just “connect a vector DB.” A production RAG backend needs:

chunking strategy
retrieval quality (filters, metadata, reranking if needed)
prompt template discipline
caching
guardrails (avoid leakage)
evaluation

Both frameworks can do RAG. The better choice depends on:

how you structure the pipeline
how testable and maintainable you keep it

Most teams fail RAG because of architecture and discipline—not because they picked the wrong framework

4) Tool calling: safety > cool demos

Tool calling is powerful and dangerous if you treat it like magic.

A safe approach requires:

explicit tool definitions
strict input validation
allowlists and permission boundaries
timeouts and rate limits
audit logs

Both frameworks can support tool calling. Your success comes from:

how you constrain tools
how you validate arguments
how you prevent prompt injection

5) Testing & maintainability (where “senior engineering” shows)

This is where your choice becomes real.

Ask:

Can we easily mock AI components?
Can we run deterministic tests on prompt templates?
Can we isolate retrieval logic from generation?
Can we enforce PR standards on AI changes?

If your AI layer is “entangled” with your controllers and prompt strings, you’ll suffer—no matter what library you pick.

✅ That’s why I created this: Java AI PR Review Pack (2026)
(checklist + prompts + PR template)
👉 https://prodevaihub.com/java-ai-pr-review-pack/

Two real-world scenarios (with a recommendation)

Scenario A: Spring Boot enterprise API (B2B, internal users)

You already have:

Spring Security
config management
observability patterns
standardized Spring architecture

Recommendation: start with Spring AI
Why: lower friction, fits existing architecture patterns, faster “first production endpoint.”

Scenario B: Multiple Java services, mixed stack, reusable AI core

You want:

one clean AI module reused across services
framework-independent logic
ability to run in different environments (Spring / Quarkus / batch jobs)

Recommendation: start with LangChain4j
Why: a library-centric approach often keeps your AI logic modular and reusable.

Decision Matrix: Spring AI vs LangChain4j

This Spring AI vs LangChain4j matrix is designed for Java teams shipping to production.

Criteria	Spring AI	LangChain4j
You’re fully Spring Boot	✅ Strong fit	✅ Works (via integration)
Framework-agnostic reuse	⚠️ Possible, but less natural	✅ Core strength
Fast adoption for Spring teams	✅ Very smooth	✅ Smooth if team likes library APIs
Clean AI core module	⚠️ Depends on discipline	✅ Often easier
RAG / tool calling capability	✅ Yes	✅ Yes
Long-term maintainability	✅ if architecture is clean	✅ if architecture is clean

The honest truth: the matrix often ends in “what architecture do you want?” not “what features exist?”

Common mistakes (that kill production AI projects)

Injecting AI everywhere (“LLM in every service”)
No timeouts, retries, or fallback strategies
No caching (latency + cost explode)
Prompts mixed with business logic
No security boundaries (data leakage risk)
No PR standards for AI-related changes

If you want a practical PR workflow that catches these issues before production:
👉 https://prodevaihub.com/java-ai-pr-review-pack/

Conclusion: the best default choice

If you’re a Spring Boot team shipping an AI feature soon: start with Spring AI.
If you’re building a reusable AI layer across services or frameworks: start with LangChain4j.

Either way, don’t treat the library choice as your “strategy.”
Your strategy is: architecture, constraints, testing, and safety.

Next steps (resources)

Free PR Review Pack (Checklist + Prompts + PR Template):
https://prodevaihub.com/java-ai-pr-review-pack/
My book:
https://prodevaihub.com/book/
Newsletter:
https://prodevaihub.com/newsletter
YouTube channel (video version + weekly backend content):
https://www.youtube.com/@ProDevAIHub

FAQ

1) Is Spring AI production-ready in 2026?

It’s best treated as a production option when you design proper timeouts, retries, caching, and security boundaries—just like any external dependency.

2) Can I use LangChain4j with Spring Boot?

Yes. Many teams use LangChain4j inside Spring apps when they want a library-centric AI layer rather than a Spring-first abstraction.

3) Which is better for RAG in Java?

Neither “wins” automatically. RAG quality comes from your pipeline design (chunking, retrieval, prompt discipline, evaluation). Pick the one that matches your architecture and testing strategy.

4) How do I avoid vendor lock-in?

Keep your domain logic independent, isolate providers behind interfaces, and avoid leaking provider-specific concepts across your codebase.

5) What’s the #1 thing that breaks AI backends in production?

Missing engineering controls: timeouts, rate limits, input validation, prompt injection defenses, and PR review discipline.

Sources

Spring AI (project) — https://spring.io/projects/spring-ai
Spring AI (GitHub) — https://github.com/spring-projects/spring-ai
LangChain4j (GitHub) — https://github.com/langchain4j/langchain4j
LangChain4j Spring integration — https://github.com/langchain4j/langchain4j-spring
LangChain4j examples — https://github.com/langchain4j/langchain4j-examples

Spring AI vs LangChain4j (2026): Which One Should Java Developers Use?

TL;DR: What should you pick?

Choose Spring AI if…

Choose LangChain4j if…

What these frameworks actually help you build

Two quick “Hello AI” code examples

Spring AI (Spring Boot): a simple endpoint with `ChatClient`

LangChain4j (framework-agnostic): a minimal Java example

Spring AI vs LangChain4j: the real differences

1) Philosophy: “Spring-native” vs “Java library”

2) Architecture fit (this matters more than features)

3) RAG: “It’s not the library, it’s your pipeline”

4) Tool calling: safety > cool demos

5) Testing & maintainability (where “senior engineering” shows)

Two real-world scenarios (with a recommendation)

Scenario A: Spring Boot enterprise API (B2B, internal users)

Scenario B: Multiple Java services, mixed stack, reusable AI core

Decision Matrix: Spring AI vs LangChain4j

Common mistakes (that kill production AI projects)

Conclusion: the best default choice

Next steps (resources)

FAQ

1) Is Spring AI production-ready in 2026?

2) Can I use LangChain4j with Spring Boot?

3) Which is better for RAG in Java?

4) How do I avoid vendor lock-in?

5) What’s the #1 thing that breaks AI backends in production?

Sources

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TL;DR: What should you pick?

Choose Spring AI if…

Choose LangChain4j if…

What these frameworks actually help you build

Two quick “Hello AI” code examples

Spring AI (Spring Boot): a simple endpoint with ChatClient

LangChain4j (framework-agnostic): a minimal Java example

Spring AI vs LangChain4j: the real differences

1) Philosophy: “Spring-native” vs “Java library”

2) Architecture fit (this matters more than features)

3) RAG: “It’s not the library, it’s your pipeline”

4) Tool calling: safety > cool demos

5) Testing & maintainability (where “senior engineering” shows)

Two real-world scenarios (with a recommendation)

Scenario A: Spring Boot enterprise API (B2B, internal users)

Scenario B: Multiple Java services, mixed stack, reusable AI core

Decision Matrix: Spring AI vs LangChain4j

Common mistakes (that kill production AI projects)

Conclusion: the best default choice

Next steps (resources)

FAQ

1) Is Spring AI production-ready in 2026?

2) Can I use LangChain4j with Spring Boot?

3) Which is better for RAG in Java?

4) How do I avoid vendor lock-in?

5) What’s the #1 thing that breaks AI backends in production?

Sources

Leave a Comment Cancel Reply

Spring AI (Spring Boot): a simple endpoint with `ChatClient`