Spring Boot で Claude API を最短ルートで動かす — anthropic-java 2.35.0 実戦ガイド A hands-on guide for Java developers on integrating the Claude API into Spring Boot using …

The overwhelming majority of LLM tutorials are written for Python developers, leaving Java engineers largely on their own when it comes to integrating services like Claude into production Spring Boot applications. This article addresses that gap head-on, offering a practical walkthrough of Anthropic's official Java SDK — java">anthropic-java 2.35.0 — wired into a working Spring Boot project.

The java">anthropic-java library is available through Maven Central, making dependency setup straightforward for any Maven or Gradle project. As of version 2.35.0, the SDK supports both synchronous and asynchronous call patterns, and integrates naturally with Spring's dependency injection model. The article walks through the full setup: adding the dependency to pom.xml, managing the API key via environment variables, defining a Spring Bean for the Anthropic client, and sending and receiving messages with minimal boilerplate. The result is a configuration developers can drop into an existing project with little friction.

The Java LLM client landscape is still maturing compared to Python's ecosystem. Spring AI, a separate Spring ecosystem project, offers a higher-level abstraction that can talk to multiple providers — including Anthropic, OpenAI, and Mistral — through a unified interface. Using java">anthropic-java directly, as this article does, bypasses that abstraction layer and gives developers finer-grained control over Claude-specific features such as system prompts, streaming responses, and tool use. Which approach suits a team best likely depends on whether multi-provider flexibility or Claude-specific capabilities take priority.

The business case for Java-native LLM integration is substantial. Enterprise backends, internal APIs, and long-running services are still predominantly built on Java and Kotlin, and the cost of maintaining a Python sidecar service just to call an LLM can be significant. Having a well-maintained official SDK removes that friction and opens the door for teams to embed AI capabilities directly into their existing Spring Boot services.

As java">anthropic-java continues to evolve, it seems likely that richer examples covering streaming and function calling will emerge, further lowering the barrier for Java teams. For now, this kind of step-by-step guide fills a genuine void in the documentation landscape and gives Java developers a reproducible starting point they can adapt to real-world use cases.