Best AI Agent Framework for Java in 2026 (Beginner Friendly)

The best AI agent framework for Java is LangChain4j for most beginners, and Spring AI if your project already runs on Spring Boot. Both are free, open source and work on Java 17 or newer.

You do not need to learn Python to build agents. Java has strong options now. Below you get a pick for each situation, the Java version each one needs, and a few lines of code so you can see how they feel.

What you need before you start

Every framework here is free. You only pay for the AI model calls, unless you run a local model with Ollama, which costs nothing per call.

1. Install JDK 17 or newer

   LangChain4j, Spring AI 2.x, Google ADK and Koog all need 17 or higher. Embabel needs 21. If you can, install 21 and use it for everything.

2. Use Maven or Gradle

   Each framework is a normal dependency from Maven Central. Google ADK asks for Maven 3.9 or newer.

3. Get a model API key

   Create a key with OpenAI, Anthropic, Google or another provider. Put it in an environment variable, never in your source code.

4. Set a spending cap

   Set a monthly limit in your model account first. An agent that loops by mistake can burn money fast.

Do you need an agent framework at all?

Not always. An agent is a loop: the model picks an action, a tool runs, and the result goes back to the model. With one or two tools, a plain HTTP call to the model API is enough.

Use a framework when you need memory, many tools, several agents, safety checks, or a way to switch model providers. To understand the idea first, read what an AI agent framework is. For tools outside Java, see the full list of the best AI agent frameworks.

How each framework is judged

• Beginner friendliness: how fast a Java developer gets a working agent.
• Java fit: real Java code, not a port that feels foreign.
• Agent power: tools, memory, multi-agent flows, protocols like MCP.
• Stability: is it GA, beta or experimental.
• Lock-in: does it force a model provider or a cloud.

Comparison table

The best AI agent frameworks for Java

LangChain4j example

This agent has one tool. The model can call it when the user asks for the time.

interface Assistant {
    @SystemMessage("You are a helpful assistant.")
    String chat(String message);
}

class TimeTools {
    @Tool("Returns the current time")
    String now() { return java.time.LocalTime.now().toString(); }
}

var model = OpenAiChatModel.builder()
    .apiKey(System.getenv("OPENAI_API_KEY"))
    .modelName("gpt-4o-mini")
    .build();

Assistant agent = AiServices.builder(Assistant.class)
    .chatModel(model)
    .tools(new TimeTools())
    .build();

System.out.println(agent.chat("What time is it?"));

Spring AI example

class TimeTools {
    @Tool(description = "Returns the current time")
    String now() { return java.time.LocalTime.now().toString(); }
}

String answer = ChatClient.builder(chatModel).build()
    .prompt()
    .user("What time is it?")
    .tools(new TimeTools())
    .call()
    .content();

How to choose

• You are new to AI agents: pick LangChain4j. It has the gentlest start.
• Your app is Spring Boot: pick Spring AI, and match the version to your Boot version.
• You use Quarkus or Micronaut: pick LangChain4j.
• You want predictable, testable flows: try Embabel on a side project first.
• You need workflows that resume after a crash: look at Koog.
• Your models are Gemini on Google Cloud: use Google ADK.
• Your company standard is Azure: use Semantic Kernel.

If you also work in Python, the Python framework guide shows how the same ideas look there. For a wider view, see the frameworks for AI development list.

Free vs paid: what you really pay

The frameworks cost nothing. Your bill is the model. Small models are cheap for learning. To pay nothing per call, run a model locally with Ollama, which Spring AI and Koog support.

Mistakes to avoid

• Mixing versions. Spring AI 2.x on Spring Boot 3 will not work. Match them.
• Trusting experimental modules in production. Pin exact versions and read the release notes before upgrading.
• Hiding the prompt. Layers can hide the exact text sent to the model. Turn on logging so you can see it.
• Giving tools too much power. A tool that deletes data needs a human approval step.
• Starting with many agents. Build one agent with two tools first.

FAQ