跪拜 Guibai
← Back to the summary

Java's AI Toolchain Is Production-Ready in 2026 — No Python Required

This article is based on Spring AI 2.0.0 / LangChain4j 1.19.0 (as of 2026-08-19). This field iterates extremely fast, please refer to the official documentation.

Introduction

"AI is all about Python" — this statement might have been true in 2023, but it's no longer accurate in 2026.

Over the past two years, three things have happened in the Java ecosystem regarding AI:

  1. Mainstream frameworks have all reached GA: Spring AI 2.0 (2026-06), LangChain4j 1.19 (2026-08), Spring AI Alibaba 1.1.2, and AgentScope Java 2.0 (2026-07) are advancing together;
  2. The protocol layer has matured: Three major open standards—MCP (tool access), A2A (Agent interconnection), and Agent Skills (capability reuse)—all have official Java implementations;
  3. Inference engines have caught up: Jlama allows you to run Llama directly in a pure JVM process, no longer dependent on a Python runtime.

The result: AI application development today ≈ calling APIs + orchestration + engineering — which is exactly what Java engineers have been doing for over a decade.

Insert image description here

Current State: What the Java Ecosystem Has in 2026

Here is a quick reference table of noteworthy AI projects in the Java ecosystem (data as of 2026-08-19):

Project Version One-Liner Description
Spring AI 2.0.0 GA Spring ecosystem's AI-native runtime, analogous to "JDBC for the AI world"
LangChain4j 1.19.0 Full-featured LLM application framework, starting from Java 8+
Spring AI Alibaba 1.1.2.0 Agent suite, Graph orchestration + Alibaba Cloud ecosystem
AgentScope Java 2.0.0 GA Alibaba's standalone multi-Agent framework, ReAct paradigm, focused on production engineering
Google ADK Java Continuously iterating Code-first Agent toolkit, bound to Vertex AI + A2A
spring-ai-agent-utils v0.10.0 Java/Spring port of Claude Code
MCP Java SDK 2.0.0 GA Official MCP implementation maintained by the Spring team
Solon AI 3.5.x+ Framework-agnostic lightweight solution, supports Java 8~26
Jlama Continuously iterating Pure Java LLM inference engine (GGUF)

A supplementary market observation (unofficial data, for reference only): According to recent informal statistics from recruitment platforms, nearly 80% of Java backend JDs at top domestic cloud vendors have already included AI capabilities in their job requirements. The numbers might not be exact, but the direction is very clear—AI is becoming a default skill for Java backend engineers, not a separate career path.

Five Major Frameworks: How to Choose

Not every project needs AgentScope. Before choosing a framework, ask two questions:

  1. Are you already in the Spring ecosystem? Yes → Spring AI 2.0 is the lowest-friction choice.
  2. What type of application are you building? Single/multi-turn conversations use the base layer; complex multi-Agent scenarios use an Agent framework.
Framework Suitable For Strengths Caveats
Spring AI 2.0 Spring shops ChatClient + Advisor chain; tool-calling loops become pluggable components Baseline jumps to Boot 4 + Framework 7, upgrade has breaking changes
LangChain4j 1.19 Non-Spring / Java 8+ starters AiServices declarative interfaces; first to support MCP 2026-07-28 spec Three parallel maintenance lines, pay attention to version selection
Spring AI Alibaba 1.1.2 Alibaba Cloud ecosystem Graph Core (partially analogous to LangGraph) + Agent suite Based on Spring AI 1.1.2 + Boot 3.5.x, has not yet kept up with Spring AI 2.0 / Boot 4
AgentScope Java 2.0 Multi-Agent production scenarios Model fault tolerance, event streams, multi-tenancy, Workspace isolation Same team as SAA but two product lines; the roadmap debate is worth a separate article
Solon AI Teams that dislike Spring's weight Framework-agnostic, supports Java 8~26 Relatively smaller community size

If you are still hesitating: Start with Spring AI 2.0 to run a minimal demo, then decide whether to add SAA Graph or AgentScope based on whether you need graph workflows / multi-Agent collaboration.

Java's Three Cards for AI

Card 1: Enterprise-Grade Engineering Capabilities

This is the heritage the Java community has accumulated over decades. Combining it with AI precisely covers the hardest parts of production environments:

Python frameworks are catching up in the engineering direction, but a maturity gap of over a decade cannot be closed in a year.

Card 2: JVM Performance and Deployment

The JVM is not the fastest runtime, but it is one of the most stable:

Card 3: Legacy System Integration

This is the biggest "home-field advantage" for Java engineers:

The Python ecosystem has an overwhelming advantage in research and model training, but on the enterprise application integration side, Java remains the de facto standard. The last-mile engineering problems of AI applications mostly fall into the hands of Java engineers.

10-Minute Example: Spring AI 2.0 + DeepSeek

DeepSeek is chosen here instead of OpenAI because it remains one of the most accessible OpenAI-compatible APIs domestically in 2026. OpenAI compatibility means you can switch to any domestic model like Qwen, Moonshot, Zhipu, or Yi by changing a base-url, without modifying business code.

The complete project is ready to run: Maven + Spring Boot 4.0 + Spring AI 2.0 + Java 17.

pom.xml

<parent>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-parent</artifactId>
    <version>4.0.0</version>
</parent>

<properties>
    <java.version>17</java.version>
</properties>

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>2.0.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-openai</artifactId>
    </dependency>
</dependencies>

src/main/resources/application.yml

spring:
  ai:
    openai:
      api-key: ${DEEPSEEK_API_KEY}
      base-url: https://api.deepseek.com
      chat:
        model: deepseek-chat

Spring AI 2.0 removed the .options section from the configuration; spring.ai.openai.chat.options.model in 1.x is directly written as spring.ai.openai.chat.model in 2.0. This is a frequent pitfall when upgrading from 1.x to 2.0, which will be expanded upon in a subsequent upgrade article.

src/main/java/com/example/ChatController.java

package com.example;

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

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

    private final ChatClient chatClient;

    public ChatController(ChatClient.Builder builder) {
        this.chatClient = builder
            .defaultSystem("你是一名资深 Java 架构师，回复简洁有力。")
            .build();
    }

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

Run it:

export DEEPSEEK_API_KEY=sk-xxx
./mvnw spring-boot:run

# Another terminal
curl 'http://localhost:8080/ai/chat?message=用一句话介绍%20Spring%20AI%202.0'

At this point, you have a production-ready minimal AI service running—in under 10 minutes.

Next, want to give the model tools? Just add a method with the @Tool annotation on a Spring Bean and pass it into chatClient.prompt().tools(bean)—one annotation turns an existing Java service into an AI-callable tool. This is a unique "annotation-driven AI integration" experience for Java programmers; Python frameworks lack this seamless integration with the existing Spring ecosystem. See the next article in this series for a complete example.

What Scenarios Still Require Python

No sugar-coating—Python is still the better choice for the following scenarios:

But note: These are matters on the "model production" side, not the "model consumption/application" side. As an application developer, your job is to build products on top of models, and Java is already sufficient for this part.

More precisely:

Final Words: Don't Switch Languages, Switch Your Mindset

If you are a Java engineer hesitating about whether to switch to Python, my advice is:

  1. Don't rush to change languages. Spend a week getting Spring AI 2.0 running and build a small demo. You'll find calling a model's API is simpler than calling a database.
  2. Focus your energy on concepts: Prompt, Tool, Memory, RAG, Agent—these concepts are completely identical in Java and Python; learning them today will be useful in any ecosystem.
  3. Return to your main engineering battleground: The second half of the AI game will definitely be about production engineering—fault tolerance, observability, security, multi-tenancy, cost control—these are the core skills Java engineers have accumulated over a decade.
  4. The Java ecosystem in 2026 is already "sufficient and good to use". Spring AI 2.0's Advisor chain, SAA's Graph Core, AgentScope's engineering, Jlama's pure JVM inference—any single one of these is worth studying for half a year.

Anxiety usually comes from information gaps, not capability gaps.