Top 5 Java AI Frameworks You Should Know

Preface

During the rapid AI expansion of 2022‑2024, Java developers lacked a unified ecosystem comparable to Python’s LangChain or JavaScript’s LangChain.js. The release of Spring AI 1.0 GA (May 2025), the continued evolution of LangChain4j , the open‑source Spring AI Alibaba project, AgentScope‑Java from Alibaba Tongyi Lab, and Microsoft Semantic Kernel finally filled this gap.

Why Java AI frameworks matter

A production‑grade AI application typically requires:

Multi‑turn conversation memory – preserve context across user turns.

Retrieval‑Augmented Generation (RAG) – fetch relevant documents from a vector store before answering.

Tool calling – invoke external APIs (weather, order lookup, email, etc.).

Agent orchestration – coordinate multiple AI agents to solve complex tasks.

Observability – monitor latency, success rate, token usage.

Model switching – run the same code against OpenAI, Anthropic, Azure, Google Gemini, Amazon Bedrock, etc.

Security sandbox – restrict unsafe operations in high‑security environments.

Implementing all of these from scratch is a massive effort; AI frameworks encapsulate the complexity.

The five major Java AI frameworks

Spring AI – the official Spring solution

Project overview : Open‑source repository https://github.com/spring-projects/spring-ai. Released 2025‑05‑20, maintained by the Spring team.

Core architecture : POJO‑first design; AI capabilities are injected via Spring’s dependency‑injection.

@Configuration
public class SpringAIConfig {
    @Bean
    public ChatClient chatClient(ChatClient.Builder builder) {
        return builder
                .defaultSystem("You are a professional customer‑service assistant")
                .defaultOptions(ChatOptions.builder()
                        .temperature(0.7)
                        .maxTokens(500)
                        .build())
                .build();
    }
}

Core features

Unified model abstraction (OpenAI, Anthropic, Azure, Gemini, Bedrock, etc.).

Full Model Context Protocol (MCP) support.

Built‑in RAG support.

Spring Boot auto‑configuration and starter modules.

Pros

Seamless integration with the Spring ecosystem (auto‑configuration, DI).

Model switching via simple configuration changes.

Observability through Micrometer.

Official support guarantees stable releases.

Cons

Requires Java 17+, not compatible with Java 8.

Agent capabilities are limited; multi‑agent orchestration must be built manually.

Feature iteration is slower than LangChain4j.

Applicable scenarios

Teams already using Spring Boot.

Projects that need quick integration of basic AI features.

Lightweight AI services.

LangChain4j – the most flexible pure‑Java AI toolkit

Project overview : Open‑source repository https://github.com/langchain4j/langchain4j. Started early 2023 to provide a Java‑level LLM library comparable to Python’s LangChain.

Core architecture : Two‑layer API.

High‑level API : Declarative interfaces annotated with @AiService, similar to MyBatis or Spring Data JPA.

Low‑level API : Core abstractions such as ChatModel and EmbeddingStore.

Key feature – Declarative AI service

interface Assistant {
    @SystemMessage("You are a professional customer‑service assistant")
    String chat(@UserMessage String userMessage);

    @MemoryId
    String chatWithMemory(@MemoryId String userId, @UserMessage String message);
}

Assistant assistant = AiServices.builder(Assistant.class)
        .chatModel(chatModel)
        .chatMemory(chatMemory)
        .build();

String response = assistant.chat("Hello, I want to return a product");

Tool calling example

@Component
public class WeatherService {
    @Tool(description = "Query weather for a specific city")
    public String getWeather(@P("city name") String city) {
        return weatherApi.get(city);
    }
}

Core features

Supports >20 LLM providers and >30 vector databases – the most comprehensive integration in Java.

Rich RAG and agent capabilities.

Modular design; dependencies can be added on demand.

Pros

Richest feature set; strongest RAG and agent support.

Modular; works in Spring, Quarkus, or plain Java SE.

Active community; far more GitHub stars than Spring AI.

Cons

Steeper learning curve due to many concepts.

No official commercial backing; enterprises must maintain themselves.

Documentation still catching up.

Applicable scenarios

Complex AI applications that need strong agent and RAG capabilities.

Projects not tied to the Spring stack.

Teams that value functional richness and flexible architecture.

Spring AI Alibaba – Spring AI + Alibaba Cloud ecosystem

Project overview : Open‑source repository https://github.com/alibaba/spring-ai-alibaba. Released September 2024 by the same team behind Apache Dubbo and Spring Cloud Alibaba.

Core architecture : Adds a Graph multi‑agent framework for workflow‑style orchestration.

Core features

Graph workflow – define nodes (e.g., analyze, search, confirm) and conditional edges to build state‑machine‑like processes.

MCP Gateway – based on Nacos MCP server registry; converts existing services to MCP protocol without code changes.

Enterprise integrations: native DashScope model support, Nacos MCP Registry for load‑balanced deployment, Higress AI gateway for model proxy and traffic control, ARMS observability with OpenTelemetry compatibility.

Pros

Deep integration with Alibaba Cloud services; ideal for domestic developers.

Powerful Graph workflow for enterprise‑level business scenarios.

MCP Gateway enables zero‑code migration of legacy applications.

Seamless Spring ecosystem integration.

Cons

Tightly coupled with Alibaba Cloud services.

Requires Java 17+.

Community size is relatively small.

Applicable scenarios

Enterprises already using Alibaba Cloud.

Complex business systems that need workflow orchestration.

Situations demanding data security and private deployment.

AgentScope‑Java – enterprise‑grade multi‑agent framework

Project overview : Open‑source repository https://github.com/modelscope/agentscope-java. Version 1.0 released December 2025; brings the ReAct (Reason‑Act) paradigm to Java.

Core architecture : Implements ReAct – a loop that alternates between reasoning and acting.

Key features

Native ReAct support via @ReasonStep and @ActStep annotations.

Security sandbox : file‑system isolation ( /tmp/agentscope/{agentId}/), network whitelist, high‑risk operations executed inside Docker containers.

Multi‑agent collaboration (A2A) – agents discover and invoke each other through AgentClient.

@AgentComponent
public class OrderRefundAgent {
    @ReasonStep
    public Plan reason(UserRequest request) {
        // LLM generates plan: check order → risk assessment → execute refund
    }

    @ActStep
    public ActionResult act(Plan plan) {
        // Call tools to perform the plan
    }
}

Pros

Native multi‑agent collaboration and A2A protocol.

Built‑in security sandbox suitable for finance, government, and other high‑security domains.

Strong observability: breakpoint debugging, state replay, human‑in‑the‑loop.

Official support from Alibaba Tongyi Lab.

Cons

Requires Java 17+.

Community is still growing.

Documentation is relatively sparse.

Applicable scenarios

Financial, governmental, or any domain with strict security compliance.

Complex business processes that need multiple agents to cooperate.

Enterprise‑grade production AI deployments.

Semantic Kernel – Microsoft’s AI orchestration framework

Project overview : Open‑source repository https://github.com/microsoft/semantic-kernel. Supports .NET, Python, and Java (released 2024).

Core architecture : Central Kernel object orchestrates models, plugins, tools, and memory stores.

public class MathPlugin implements SKPlugin {
    @DefineSKFunction(description = "Adds two numbers")
    public int add(int a, int b) {
        return a + b;
    }
}

Kernel kernel = Kernel.builder()
        .withAIService(OpenAIChatCompletion.class, chatService)
        .withPlugin(plugin)
        .withMemoryStorage(memoryStore)
        .build();

KernelFunction<String> prompt = KernelFunction.fromPrompt("Some prompt...").build();
FunctionResult<String> result = prompt.invokeAsync(kernel)
        .withToolCallBehavior(ToolCallBehavior.allowAllKernelFunctions(true))
        .block();

Core features

Kernel‑centric orchestration of AI components.

Plugin‑style tools via @DefineSKFunction.

Pluggable vector‑database memory backends.

Deep Azure integration: Azure Cognitive Search, Azure OpenAI, Azure AD.

Pros

Official Microsoft backing; tight Azure service integration.

Kernel design is ideal for complex AI workflows.

Built‑in support for Project Reactor (reactive applications).

Cons

Java edition lacks some features available in .NET/Python (e.g., image generation, speech).

Smaller community (~30 contributors) and slower feature rollout.

No Spring Boot starter; manual configuration required.

Strong reliance on Project Reactor adds complexity for non‑reactive projects.

Applicable scenarios

Projects built on Microsoft Azure.

Use cases that need sophisticated AI workflow orchestration.

Reactive programming environments.

Comparison summary

Design philosophy

Spring AI – lightweight Spring integration.

LangChain4j – modular, library‑style assembly.

Spring AI Alibaba – workflow‑oriented Graph engine.

AgentScope‑Java – agent‑first, ReAct paradigm.

Semantic Kernel – kernel‑centric orchestration.

Key differentiators

Spring AI : Seamless Spring Boot auto‑configuration, Micrometer observability, but no native multi‑agent support.

LangChain4j : Most extensive feature set, declarative @AiService, supports >20 LLM providers and >30 vector stores.

Spring AI Alibaba : Graph workflow, MCP Gateway for zero‑code migration, deep Alibaba Cloud service integration.

AgentScope‑Java : Built‑in security sandbox, native A2A multi‑agent protocol, strong observability for production.

Semantic Kernel : Official Microsoft support, Azure‑deep integration, kernel‑based plugin system, reactive‑first design.

How to choose a framework

Existing technology stack (Spring Boot vs. non‑Spring).

Need for advanced agent or RAG capabilities.

Security and compliance requirements.

Cloud‑provider lock‑in (Alibaba Cloud, Azure, etc.).

Team familiarity with reactive programming.

Scenario‑based recommendations

// Scenario 1: Standard Spring Boot enterprise application
Recommendation: Spring AI
Reason: Consistent with Spring ecosystem, simple configuration, quick integration of basic AI features.

// Scenario 2: Need complex agents and RAG
Recommendation: LangChain4j
Reason: Richest feature set, declarative @AiService, flexible component composition.

// Scenario 3: Alibaba Cloud‑centric enterprise
Recommendation: Spring AI Alibaba
Reason: Deep Alibaba Cloud integration, zero‑code MCP gateway, powerful Graph workflow.

// Scenario 4: High‑security (finance, government)
Recommendation: AgentScope‑Java
Reason: Built‑in security sandbox, multi‑agent collaboration, strong observability for production.

// Scenario 5: Microsoft Azure ecosystem project
Recommendation: Semantic Kernel
Reason: Official Microsoft support, seamless Azure service integration.

Conclusion

The Java AI framework landscape has converged into a clear hierarchy:

Spring AI : Official Spring choice; elegant integration but still maturing in agent capabilities.

LangChain4j : Most feature‑rich modular toolkit; de‑facto standard for Java AI development.

Spring AI Alibaba : Extends Spring AI with Alibaba Cloud services and Graph workflow.

AgentScope‑Java : Enterprise‑grade multi‑agent framework with a built‑in security sandbox.

Semantic Kernel : Microsoft’s AI orchestration framework, tightly coupled with Azure.

Many teams adopt a hybrid approach—using Spring Boot for the base application while importing LangChain4j’s @AiService for advanced RAG and agent features—demonstrating that these frameworks complement rather than replace each other.