How to Build AI Agents in Java: Spring AI vs LangChain4j vs Google ADK

For a long time, "building AI agents" was something you did in Python. If you were a Java engineer, the advice was basically: learn Python, or wait. As of 2026, that's over. Java now has three serious, production-ready agent frameworks — Spring AI, LangChain4j, and Google's Agent Development Kit (ADK) — and you can build a real agent in any of them without leaving the JVM.

This guide does two things. First, it clears up what an "agent" actually is, because the word gets thrown around loosely. Then it compares the three frameworks honestly — what each is good at, where each hurts — so you can pick the right one instead of the most-hyped one.

First: what is an "AI agent," really?

An LLM on its own only generates text. An agent is an LLM that can also take actions — call your code, query a database, hit an API — and decide which actions to take. The jump from "chatbot" to "agent" is the ability to do things, not just say things.

It helps to think of agents as a ladder, not a single thing. Each rung gives the model more autonomy:

1. Tool calling. You give the model a set of functions ("tools"). It reads the user's request and decides which function to call, with what arguments. You stay in control of what's available. This is the foundation of every agent.
2. Workflows. You chain several steps in a fixed order — retrieve, then summarize, then classify. The model does the thinking at each step, but you decide the sequence. Predictable and easy to debug.
3. Autonomous agents. You give the model a goal and a set of tools, and it loops on its own: plan an action, execute it, look at the result, decide whether it's done or needs another step. The model owns the sequence.
4. Multi-agent systems. Several specialized agents collaborate — one researches, one writes, one reviews — coordinated by a supervisor or by agent-to-agent messaging.

Most "I want to build an agent" goals are solved at rung 1 or 2. The autonomous and multi-agent rungs are powerful but harder to keep reliable, because the more control you hand the model, the harder it is to predict. A good rule: climb only as high as your problem actually needs.

The skill in agent engineering isn't making the model maximally autonomous. It's giving it exactly enough autonomy to solve the problem and no more.

If the foundation — tool calling on a single ChatClient — is new to you, start with our two earlier posts: the Spring AI basics and RAG with Spring AI. This post sits one level above them.

The three frameworks at a glance

All three reached production-grade maturity by 2026. All three support tool calling, multi-agent patterns, and MCP (Model Context Protocol — the emerging standard for connecting models to tools). The differences are about philosophy and ecosystem fit, not raw capability.

Spring AI

Spring AI brings agents into the Spring ecosystem using the conventions you already know: dependency injection, auto-configuration, starters. You define tools as annotated methods, inject a ChatClient, and the framework wires the rest. If you've read our earlier posts, this is the natural next step.

Pros:

• Zero learning curve if you're on Spring Boot. The agent code looks like ordinary Spring code — same beans, same config, same mental model.
• Clean tool calling. Annotate a method with @Tool and the model can call it. Provider portability stays intact, just like with chat.
• Strong MCP and structured-output support. Typed Java objects back from the model, and a standard way to expose tools.
• First-party Spring project — versioned with the ecosystem, well-documented, predictable upgrades.

Cons:

• Tied to the Spring worldview. Outside a Spring app, it's an awkward fit.
• Higher-level agent orchestration is younger than the core. The autonomous/multi-agent tooling is maturing through community modules rather than being fully baked into core.

Use it when: your service is already a Spring Boot app. The integration tax is essentially zero, and that matters more than any feature checkbox.

LangChain4j

LangChain4j is a framework-agnostic Java library inspired by Python's LangChain. It works with plain Java, Spring Boot, Quarkus, or Micronaut. Its signature feature is AiServices: you declare a Java interface, and the library generates a working implementation backed by the model.

Pros:

• Works everywhere. Not tied to any web framework — ideal for polyglot shops or non-Spring stacks.
• Widest provider support of the three (20+ model providers and 30+ vector stores).
• The @AiService abstraction is elegant. Define an interface, get a typed assistant — very little boilerplate.
• Dedicated agentic modules for multi-agent and agent-to-agent patterns, backed by Red Hat and Microsoft.

Cons:

• More explicit wiring. The builder-heavy style means you write more setup code than Spring AI's auto-config — more control, more verbosity.
• In a Spring app, it's a second integration layer rather than native plumbing.

Use it when: you're not on Spring, you run Quarkus/Micronaut, or you want the broadest provider choice and a reusable library you can drop into any service.

Google ADK for Java

Google's Agent Development Kit is the newest of the three — Java 1.0 landed in March 2026 — and it's the most agent-first by design. Where the others started as LLM-integration libraries and grew agent features, ADK was built around agents, multi-agent orchestration, and the deploy-and-evaluate lifecycle from day one. It's the same framework Google uses internally for its own agent products.

Pros:

• Multi-agent is native, not bolted on. Sequential, parallel, loop, and hierarchical agent composition are first-class.
• Built-in evaluation framework and a local dev UI for tracing tool calls and replaying agent runs — genuinely useful for debugging agents, which is otherwise painful.
• One-command deploy to Google Cloud (Vertex AI Agent Engine, Cloud Run, GKE) with managed infrastructure, auth, and observability inherited automatically.
• Strong on A2A (agent-to-agent) as the primary multi-agent mechanism, plus features like human-in-the-loop confirmation.

Cons:

• Gemini-optimized. Other providers work via adapters, but the smoothest path assumes Google's models.
• Pulls you toward Google Cloud. The best deploy experience is on GCP — great if you're there, friction if you're not.
• Newest and least battle-tested in Java of the three; the Python version is more mature and has more samples.

Use it when: you're on Google Cloud / Gemini, or your problem is genuinely multi-agent and you want orchestration, evaluation, and deployment handled by the framework.

So which should you actually pick?

Ignore GitHub-star contests. The honest decision tree is short:

• Already on Spring Boot? → Spring AI. The near-zero integration cost beats every feature comparison.
• Not on Spring, or want maximum portability? → LangChain4j. Widest reach, cleanest library-style API.
• On Google Cloud, or building a real multi-agent system? → Google ADK. The eval and deploy tooling earns its keep.

And a reassuring fact: these aren't permanent marriages. Because they all speak MCP and all wrap the same underlying models, you can mix them in a microservices architecture, and switching later is a contained cost — the same portability lesson that runs through everything we've written about AI in Java.

Where this fits in the series

This is the agent layer of our Spring AI track, built on the foundation from the earlier posts:

• Spring AI basics — your first ChatClient call
• RAG — answering from your own data
• You are here: Building agents and choosing a framework
• Next: Tools & MCP in depth, and then guardrails & LLM security before production

Knowing what an agent is and which framework to reach for is the map. Building one that's reliable enough to put in front of real users — with proper tool design, memory, evaluation, and guardrails — is the territory. That's what we go deep on in the course.