Introduction

Java has always been a serious language for production systems, and in 2026, the Generative AI ecosystem has finally caught up. For years, Java developers watched from the sidelines as Python and TypeScript accumulated framework after framework for building LLM-powered applications. Today, the picture is very different. Java has multiple mature, actively maintained AI frameworks, each with its own philosophy and trade-offs.

This article covers the four frameworks I have personally used to ship Java AI applications: Genkit Java, Spring AI, LangChain4j, and Google ADK Java. Each one represents a meaningfully different bet on what a Java AI framework should be, and understanding those differences will save you from picking the wrong tool.

Genkit Java

History and Direction

Genkit started life as a TypeScript-first framework launched by Google at I/O 2024. The Java SDK arrived as a community-maintained effort, built and maintained by developers within the Google ecosystem who wanted to bring the same developer experience to Java that Genkit had established in TypeScript. As of 2026, Genkit Java is unofficial, it is not an official Google product, but it is actively maintained, follows the core Genkit design closely, and ships its own plugin ecosystem.

The framework's first stable release landed in early 2026 after months of preview use. Its ambition mirrors the TypeScript SDK's: bring Genkit's multi-level abstractions (vanilla generation, typed flows, agents), its broad provider-neutral plugin model, and, crucially, the Genkit Developer UI to Java developers. The Java SDK ships with Spring Boot and Jetty server plugins, making it a natural fit for teams that already run Java services in production. The Javadoc and architecture are clean and idiomatic Java, this does not feel like a port; it feels designed for the language.

The direction is clear: maintain parity with the TypeScript Genkit SDK's abstractions while embracing Java idioms (builder patterns, typed schemas via Java classes, annotation-free configuration). Support for evaluation, MCP (Model Context Protocol), RAG with pgvector and Pinecone, and multi-agent patterns is already in place.

What Makes Genkit Java Stand Out

Like its TypeScript counterpart, Genkit Java provides three levels of abstraction in a single SDK: direct model calls, typed flows (observable pipelines), and agents. This is unique in the Java AI space, no other Java framework gives you all three in one coherent API.

Supported languages: Java 21+ (primary). Deploys to Spring Boot, Jetty, or Firebase Cloud Functions.

Vanilla Generation

import com.google.genkit.Genkit;
import com.google.genkit.ai.GenerateOptions;
import com.google.genkit.plugins.googlegenai.GoogleGenAIPlugin;

Genkit genkit = Genkit.builder()
    .plugin(GoogleGenAIPlugin.create())
    .build();

String text = genkit.generate(GenerateOptions.builder()
    .model("googleai/gemini-flash-latest")
    .prompt("Explain the CAP theorem in two sentences.")
    .build()).getText();

System.out.println(text);

Typed Flows — Observable Pipelines

Flows are the heart of Genkit Java. They wrap your AI logic in a named, typed, traceable unit that is automatically exposed as an HTTP endpoint and visible in the Dev UI.

import com.google.genkit.Genkit;
import com.google.genkit.flow.FlowOptions;
import com.google.genkit.plugins.googlegenai.GoogleGenAIPlugin;
import com.google.genkit.plugins.jetty.JettyPlugin;
import com.google.genkit.plugins.jetty.JettyPluginOptions;

record TranslateRequest(String text, String targetLanguage) {}
record TranslateResponse(String translation, String detectedLanguage) {}

JettyPlugin jetty = new JettyPlugin(JettyPluginOptions.builder().port(8080).build());

Genkit genkit = Genkit.builder()
    .plugin(GoogleGenAIPlugin.create())
    .plugin(jetty)
    .build();

genkit.defineFlow(
    FlowOptions.<TranslateRequest, TranslateResponse>builder()
        .name("translateText")
        .inputClass(TranslateRequest.class)
        .outputClass(TranslateResponse.class)
        .build(),
    (ctx, request) -> {
        var response = genkit.generate(GenerateOptions.builder()
            .model("googleai/gemini-flash-latest")
            .prompt("Translate '%s' to %s. Return JSON with 'translation' and 'detectedLanguage'."
                .formatted(request.text(), request.targetLanguage()))
            .outputClass(TranslateResponse.class)
            .build());
        return response.getOutput(TranslateResponse.class);
    }
);

jetty.start();

Tools and Agents

import com.google.genkit.ai.tool.ToolDefinition;

var weatherTool = genkit.defineTool(
    ToolDefinition.<String, String>builder()
        .name("getWeather")
        .description("Returns current weather for a city.")
        .inputClass(String.class)
        .outputClass(String.class)
        .build(),
    (ctx, city) -> "Sunny, 24°C in " + city
);

// Use the tool inside a flow or agent
var result = genkit.generate(GenerateOptions.builder()
    .model("googleai/gemini-flash-latest")
    .prompt("What's the weather like in Tokyo?")
    .tools(List.of(weatherTool))
    .build());

The Dev UI — Same Power as TypeScript

One of Genkit Java's most compelling features is that the same Genkit Developer UI used by the TypeScript SDK works directly with Java applications. You install the Genkit CLI (Node.js-based) and start your Java app through it:

npm install -g genkit
genkit start -- mvn exec:java

The Dev UI opens at http://localhost:4000 and gives you:

• Flow runner — execute any flow interactively with custom inputs and inspect typed outputs.
• Trace explorer — full OpenTelemetry traces for every generate and flow call, showing latency, token counts, and exact prompts.
• Model playground — test any registered model directly.
• Tool testing — stub and test tools in isolation.
• Dotprompt editor — edit .prompt files live with variable injection.

This is the single biggest advantage Genkit Java has over every other Java AI framework: a zero-config, local developer UI that replaces the need for LangSmith or Grafana during development.

Provider Support

Genkit Java ships plugins for: Google GenAI (Gemini), OpenAI, Anthropic (Claude), AWS Bedrock, Azure AI Foundry, Ollama, xAI (Grok), DeepSeek, Cohere, Mistral, and Groq. All accessed through the same genkit.generate() interface.

Vector store plugins cover: Firebase Firestore, Weaviate, PostgreSQL (pgvector), Pinecone, and a local in-memory store.

Pros and Cons

Spring AI

History and Direction

Spring AI was announced by the Spring team (Broadcom) in mid-2023 and reached its 1.0 GA release in mid-2024. It is the most enterprise-grade option in this comparison, built by the same team that maintains Spring Framework, Spring Boot, and Spring Data, which together underpin a vast proportion of the world's Java server-side applications.

The founding premise of Spring AI is that AI integration in Java applications should feel like every other Spring integration: auto-configured, testable, portable, and production-ready out of the box. The project draws inspiration from LangChain and LlamaIndex but explicitly avoids being a port, it is designed from the ground up to be idiomatic Spring. If you have written Spring applications, Spring AI will feel immediately familiar: @Autowired AI clients, Spring Boot starters, application.properties configuration, and Advisor patterns that mirror Spring's existing interception model.

Spring AI's direction through 2025 and into 2026 has been to deepen its observability story (Micrometer-native metrics and traces), expand its ChatClient fluent API, and ship more vector store integrations. The framework is now the de facto standard for teams that are already invested in the Spring ecosystem and want to add AI capabilities without introducing a foreign dependency philosophy.

What Makes Spring AI Stand Out

Spring AI's killer feature is Spring Boot integration depth. There is no framework on this list, in any language, that integrates AI capabilities as seamlessly into an existing application framework as Spring AI does with Spring Boot. Auto-configuration, conditional beans, health indicators, Actuator endpoints for AI metrics, everything a Spring developer expects, applied to AI.

Supported languages: Java (primary). Also supports Kotlin (via Spring's Kotlin DSL). Runs anywhere Spring Boot runs: embedded Tomcat, Jetty, Undertow, GraalVM native images.

// application.properties
// spring.ai.openai.api-key=${OPENAI_API_KEY}
// spring.ai.openai.chat.options.model=gpt-4o

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

@RestController
public class ChatController {

    private final ChatClient chatClient;

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

    @GetMapping("/chat")
    public String chat(@RequestParam String message) {
        return chatClient.prompt()
            .user(message)
            .call()
            .content();
    }
}

Structured Output

Spring AI's BeanOutputConverter maps model responses directly to Java POJOs, using the class schema to generate format instructions automatically.

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.converter.BeanOutputConverter;

record MovieReview(String title, int rating, String summary, List<String> pros) {}

BeanOutputConverter<MovieReview> converter = new BeanOutputConverter<>(MovieReview.class);

MovieReview review = chatClient.prompt()
    .user(u -> u.text("Review the movie Inception. {format}")
        .param("format", converter.getFormat()))
    .call()
    .entity(MovieReview.class);

System.out.println(review.title() + " — " + review.rating() + "/10");

RAG with Advisors

Spring AI's Advisors API is one of its most elegant features. Advisors wrap ChatClient calls with cross-cutting concerns, RAG retrieval, chat memory, logging, guardrails, in a declarative, composable way.

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.vectorstore.VectorStore;

@Service
public class DocumentQAService {

    private final ChatClient chatClient;

    public DocumentQAService(ChatClient.Builder builder, VectorStore vectorStore) {
        this.chatClient = builder
            .defaultAdvisors(new QuestionAnswerAdvisor(vectorStore))
            .build();
    }

    public String answerQuestion(String question) {
        return chatClient.prompt()
            .user(question)
            .call()
            .content();
    }
}

Observability

Spring AI ships with Micrometer integration out of the box. Every chat call generates spans (Spring Boot tracing) and metrics (prompt token count, completion token count, model latency) visible in any Micrometer-compatible backend: Prometheus, Grafana, Zipkin, or Datadog. There is no separate Dev UI, observability is handled by your existing Spring Boot infrastructure.

Broad Vector Store and Model Support

Spring AI supports 10+ model providers (OpenAI, Anthropic, Google Vertex AI, Amazon Bedrock, Azure OpenAI, Mistral, Ollama, Groq, and more) and 20+ vector stores (PGVector, Pinecone, Weaviate, Redis, Elasticsearch, MongoDB Atlas, Chroma, and more), the broadest integration coverage of any Java AI framework.

Pros and Cons

LangChain4j

History and Direction

LangChain4j was started in early 2023 by a small community of Java developers who noticed that the LLM framework explosion happening in Python had no Java equivalent. Despite the name, the project is not a mechanical port of LangChain Python, it is a fusion of ideas from LangChain, Haystack, LlamaIndex, and original innovation, packaged in a way that makes sense for Java.

It grew quickly through 2023 and 2024, driven by its comprehensive integration list (20+ LLM providers, 30+ vector stores) and its clean two-level abstraction model: low-level primitives for maximum control and high-level AI Services for rapid development. The AI Services pattern, where you define an interface with annotations and LangChain4j implements it for you at runtime, became the framework's signature feature and arguably the most Java-idiomatic approach to LLM integration in the ecosystem.

By 2025, LangChain4j had formal integrations with Quarkus, Spring Boot, Micronaut, and Helidon, covering every major Java application framework. The team's direction in 2026 is focused on deepening agentic capabilities (multi-step tools, planning loops, MCP support) and improving the observability story, which has historically been a weaker point compared to Spring AI's Micrometer integration or Genkit's Dev UI.

What Makes LangChain4j Stand Out

LangChain4j's AI Services pattern is its defining feature. Instead of writing imperative LLM call code, you declare an interface, annotate it with @SystemMessage, @UserMessage, and memory annotations, and LangChain4j generates the implementation. The result is AI code that reads like a Java service contract, clean, testable, and completely familiar to Java developers.

Supported languages: Java (primary). Kotlin extensions available (coroutine-based async support). Integrates with Spring Boot, Quarkus, Micronaut, Helidon.

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

interface TranslationAssistant {
    @SystemMessage("You are a professional translator. Translate text accurately and naturally.")
    String translate(@UserMessage String text, @V("language") String targetLanguage);
}

var model = OpenAiChatModel.withApiKey(System.getenv("OPENAI_API_KEY"));

TranslationAssistant assistant = AiServices.builder(TranslationAssistant.class)
    .chatLanguageModel(model)
    .build();

String result = assistant.translate("The quick brown fox jumps over the lazy dog", "Spanish");
System.out.println(result);

Memory and Streaming

import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.service.MemoryId;

interface ConversationalAssistant {
    @SystemMessage("You are a helpful assistant.")
    String chat(@MemoryId String userId, @UserMessage String message);
}

ConversationalAssistant assistant = AiServices.builder(ConversationalAssistant.class)
    .chatLanguageModel(model)
    .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(20))
    .build();

// Each userId gets its own isolated memory
assistant.chat("user-42", "My name is Alice.");
String response = assistant.chat("user-42", "What's my name?");
// Returns: "Your name is Alice."

RAG Pipeline

import dev.langchain4j.data.document.loader.UrlDocumentLoader;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;

// Ingest documents
var documents = UrlDocumentLoader.load("https://example.com/docs");
var splitter = DocumentSplitters.recursive(500, 50);
var segments = splitter.splitAll(documents);

var embeddingModel = OpenAiEmbeddingModel.withApiKey(apiKey);
var embeddingStore = new InMemoryEmbeddingStore<TextSegment>();
EmbeddingStoreIngestor.ingest(segments, embeddingStore, embeddingModel);

// Build RAG-enabled assistant
interface DocsAssistant {
    String answer(@UserMessage String question);
}

var retriever = EmbeddingStoreContentRetriever.builder()
    .embeddingStore(embeddingStore)
    .embeddingModel(embeddingModel)
    .maxResults(3)
    .build();

DocsAssistant assistant = AiServices.builder(DocsAssistant.class)
    .chatLanguageModel(model)
    .contentRetriever(retriever)
    .build();

Two Abstraction Levels

LangChain4j explicitly offers two levels:

• Low level — ChatModel, UserMessage, AiMessage, EmbeddingStore: full control, more code.
• High level — AiServices: declarative interfaces, minimal boilerplate.

This mirrors what Genkit Java achieves differently. Where Genkit gives you flows and agents as pipeline concepts, LangChain4j uses interface-based AI Services as its high-level abstraction, very idiomatic in Java terms.

Pros and Cons

Google ADK Java

History and Direction

Google ADK (Agent Development Kit) launched in 2024 as a Python-first agent framework targeting enterprise deployments on Google Cloud. Java was a late addition to the multi-language roadmap, with ADK Java 1.0 shipping in early 2026 alongside ADK Go 1.0. The Java SDK arrival was significant: it signaled that Google views ADK as a serious enterprise runtime, not just a Python scripting tool.

ADK Java follows the same design philosophy as the Python SDK: everything is an agent, workflow, or tool. The framework is optimized for building reliable, evaluatable, production-grade multi-agent systems and deploying them to Google Cloud infrastructure, primarily Vertex AI Agent Engine, Cloud Run, and GKE. Like its Python counterpart, ADK Java carries the weight of Google Cloud gravity. The best developer experience, the smoothest deployment path, and the most mature observability story all assume you are running on GCP.

ADK Java 1.0 includes the full agent runtime (LLM agents, sequential/loop/parallel workflow agents), tool calling, MCP support, A2A (Agent-to-Agent) protocol, session/memory management, and streaming. The Java API closely mirrors the Python API in structure, which means the mental model transfers well, but also means the Java SDK carries a style that reflects Python-first design decisions.

ADK Java's Position: Agent-Only, Enterprise-Grade

Like its Python counterpart, ADK Java is an agent framework, it has no vanilla generation primitive or flow abstraction outside the agent model. Its raison d'être is spinning up reliable, evaluatable agents and deploying them at enterprise scale. If you are building a multi-agent system on Google Cloud and Java is your language of choice, ADK Java 1.0 is Google's recommended path.

Supported languages: Java (with ADK Java 1.0). Also: Python (primary), TypeScript, Go.

import com.google.adk.agents.LlmAgent;
import com.google.adk.tools.GoogleSearchTool;
import com.google.adk.runner.InMemoryRunner;
import com.google.genai.types.Content;

var researchAgent = LlmAgent.builder()
    .name("researcher")
    .model("gemini-flash-latest")
    .instruction("You help users research topics thoroughly and accurately.")
    .tools(List.of(new GoogleSearchTool()))
    .build();

var runner = new InMemoryRunner(researchAgent);

var session = runner.sessionService().createSession(
    researchAgent.name(), "user-1"
).blockingGet();

var userMessage = Content.fromParts(Part.fromText(
    "What are the latest developments in fusion energy?"
));

runner.runAsync(researchAgent.name(), session.id(), userMessage)
    .blockingForEach(event -> {
        if (event.finalResponse()) {
            System.out.println(event.stringifyContent());
        }
    });

Multi-Agent Orchestration

ADK Java's multi-agent capabilities match the Python SDK's, including sequential, parallel, and loop orchestration.

import com.google.adk.agents.SequentialAgent;

var researcher = LlmAgent.builder()
    .name("researcher")
    .model("gemini-flash-latest")
    .instruction("Research the given topic and provide key facts.")
    .build();

var writer = LlmAgent.builder()
    .name("writer")
    .model("gemini-flash-latest")
    .instruction("Write a clear, well-structured article from the research provided.")
    .build();

var editor = LlmAgent.builder()
    .name("editor")
    .model("gemini-flash-latest")
    .instruction("Polish and format the article for publication.")
    .build();

var pipeline = SequentialAgent.builder()
    .name("contentPipeline")
    .subAgents(List.of(researcher, writer, editor))
    .build();

Vertex AI Lock-In

ADK Java's production deployment story is built around Vertex AI Agent Engine and Google Cloud. While you can run ADK Java locally (via the ADK CLI or directly) and deploy to Cloud Run or GKE independently, the managed evaluation tools, performance dashboards, and enterprise support all assume GCP. This is the clearest example in the Java AI space of a framework built to serve a platform rather than being platform-neutral.

Pros and Cons

Head-to-Head Comparison

Developer Experience

Abstraction Levels

Genkit Java is the only Java AI framework that provides all three levels: vanilla generation, typed flows (pipelines), and agents. Spring AI covers generation and a basic agent model via tools, but lacks a flow abstraction. LangChain4j provides two levels (low-level primitives and high-level AI Services) but is agent/service focused. ADK Java is agent-only.

Observability

Framework Neutrality

Genkit Java and LangChain4j are built to be provider-neutral: they support every major model and deploy to any infrastructure. Spring AI is similarly neutral on model providers, though it carries Spring's opinionated application framework as a dependency, a worthwhile trade for most Java shops. ADK Java carries the heaviest platform dependency: its full value is unlocked on Google Cloud.

Java Ecosystem Fit

Which Framework Should You Choose?

Choose Genkit Java if:

• You want to iterate on your AI fast and get feedback with less back and forth — Genkit was built from the ground up for powerful local tooling and observability, and the Dev UI is genuinely transformative.
• You need multiple abstraction levels (vanilla calls, typed flows, and agents) in one SDK.
• Provider neutrality matters: you need to swap or mix Gemini, Claude, OpenAI, and Bedrock.
• Your team also writes TypeScript and wants a consistent framework story across both stacks.

Choose Spring AI if:

• You are already running Spring Boot and want AI to feel like any other Spring integration.
• Micrometer-native metrics and traces plugging into your existing Prometheus/Grafana stack are a priority.
• You need the broadest model and vector store coverage with production-grade auto-configuration.
• GraalVM native images are a requirement for your deployment targets.

Choose LangChain4j if:

• You want the most Java-idiomatic high-level AI abstraction: interface-based AI Services with annotations.
• You need the largest integration ecosystem and don't want to be tied to any application framework.
• Your team works across Spring Boot, Quarkus, Micronaut, and Helidon, LangChain4j is the most framework-agnostic.
• RAG pipelines with rich document ingestion and retrieval are a core use case.

Choose ADK Java if:

• You are building enterprise-grade multi-agent systems and Google Cloud is your runtime.
• You need official Google support and SLA-backed infrastructure for agent deployment.
• Multi-agent orchestration (sequential, parallel, loop) and the A2A interoperability protocol matter.
• Your team is already using the ADK Python SDK and wants to extend to Java services.

Conclusion

Java's AI framework landscape in 2026 is surprisingly rich. The four frameworks covered here serve genuinely different needs, and unlike in the JavaScript world where Genkit, Vercel, Mastra, LangChain, and ADK overlap significantly, the Java options each occupy a clearer niche.

For enterprise Spring Boot teams, Spring AI is the obvious choice, zero friction, production-ready observability via Micrometer, and the broadest integration matrix. For teams that value developer experience above all, Genkit Java's Dev UI is a category apart and worth the unofficial status trade-off. For framework-agnostic Java developers who want the most idiomatic Java AI service abstraction, LangChain4j's AI Services pattern is hard to beat. And for Google Cloud enterprise workloads that need reliable multi-agent orchestration at scale, ADK Java 1.0 is where Google is putting its weight.

The most important thing is that you no longer have an excuse to reach for Python just because it has better AI tooling. Java's time in generative AI has arrived.

Last updated: April 2026. Framework versions referenced: Genkit Java 1.0.0-SNAPSHOT, Spring AI 1.x, LangChain4j 0.36.x, Google ADK Java 1.0.