Java AI Framework
The End of Java AI’s ‘Warring States Period’
Fall 2025 marks a turning point: AI application development is no longer Python’s exclusive domain. The era of Java developers sitting on the sidelines is over. Particularly significant was Spring’s release of Spring AI 1.0 GA in May 2025—a watershed moment that cemented this massive shift in the Java ecosystem.
The Java AI development landscape has now consolidated into a clear “two-horse race.” On one side stands Spring AI, backed by Spring’s massive ecosystem. On the other, LangChain4j has steadily expanded its reach, armed with flexibility and rapid extensibility.
Java teams looking to implement core AI features like RAG and Function Calling now face a delightful dilemma: “So, which one should we actually use?”
This article is a practical guide to answering that exact question. We’ll dissect everything from core philosophies and architectures to key features and production deployment trade-offs. By the end, you’ll be confident in choosing the right AI stack for your team in the second half of 2025.
Core Philosophy & Architecture: ‘Integration’ vs ‘Independence’
While both frameworks share the same functional goal—building AI applications—their approaches and philosophies are fundamentally different. Think of it as the difference between a carefully curated “full-course meal” and a “salad bar” where you pick exactly what you want.
Spring AI: “Spring Ecosystem Completeness (Opinionated)”
Spring AI’s core philosophy is ‘integration’. The primary goal is enabling Spring developers to use AI features in the most familiar way possible.
If you’re comfortable with Spring Boot’s @Autowired or auto-configuration through application.properties, Spring AI will feel like it was always part of Spring. Simply inject well-abstracted interfaces like ChatClient and EmbeddingClient via DI and start using them immediately. Spring Boot handles all the complex AI model integration setup for you.
This is Spring’s traditional ‘opinionated’ approach. The framework strongly suggests and supports what it considers “the best way,” allowing developers to focus on business logic instead of infrastructure complexity.
Key Advantage: Perfect integration with existing Spring infrastructure like Spring Observability, Actuator, and Spring Cloud. Development productivity is extremely high.
Consideration: Everything becomes tightly coupled to the Spring ecosystem.
LangChain4j: “Modularity and Flexibility (Unopinionated)”
LangChain4j’s core philosophy is ‘independence’ and ‘flexibility’. It’s an independent Java library not bound to any specific framework.
LangChain4j takes a modular approach where developers select and “assemble” only the features they need. LLM integration modules, vector store modules, and prompt template modules are each provided independently. Whether your application is Spring Boot-based, Quarkus, Micronaut, or pure Java doesn’t matter—you can integrate freely.
This is the ‘unopinionated’ approach. Since the framework doesn’t force a particular way of doing things, developers gain more control but also shoulder more responsibility for architectural decisions. As of October 2025, support for over 20 LLMs and 30+ embedding stores demonstrates the extensibility this flexible philosophy enables.
Key Advantage: High degree of freedom and extensibility. No framework lock-in.
Consideration: Initial setup and structural design may require more thought than Spring AI.
Detailed Feature Comparison (Feature Battle)
Regardless of philosophy, what ultimately matters to developers is: “How well do these implement the RAG and Function Calling I actually need?” As of October 2025, both frameworks support these core features excellently. But here again, the philosophical differences of ‘integration’ vs ‘flexibility’ become crystal clear.
A. RAG (Retrieval-Augmented Generation)
RAG accounts for 90% of enterprise AI adoption—the core technology enabling AI to answer based on ‘our company’s internal documents’.
(Load data → Split → Embed → Store vectors → Search → Augment prompt → LLM response)
Spring AI (Integrated Pipeline): Spring AI 1.0 brings the entire RAG pipeline fully into the Spring ecosystem. It provides a common VectorStore interface, allowing you to swap between various vector DBs like Pinecone, Milvus, or PGVector with just application.properties changes. Particularly noteworthy is its ETL framework. Like Spring Batch or Integration, it provides a standardized way to load documents (Resource reader), parse with Tika, and split (Transformer). The RAG construction process itself feels like a well-designed Spring application.
LangChain4j (Flexible Toolbox): LangChain4j provides a ‘toolbox’ for RAG. It offers numerous implementations for each RAG stage: DocumentLoader, DocumentSplitter, EmbeddingStore, etc. (30+ embedding stores supported as of 2025). Developers assemble these tools like Lego blocks to build their desired RAG pipeline. This enables more granular control than Spring AI without framework lock-in. For example, DocumentSplitters.recursive(...) options allow highly sophisticated text splitting strategies.
B. Function Calling (Tool Use)
The core feature enabling AI to break out of the chat window and actually invoke Java code (e.g., DB queries, external API calls)—the essence of practical AI.
Spring AI (Spring Beans as Functions): Spring AI’s approach is shockingly simple for Spring developers. Methods from Spring Beans already registered as @Service or @Bean can immediately become AI-callable functions. Simply add @Description annotations to tell the AI what each method does, and Spring AI automatically handles the Model Context Protocol (MCP) compliance. Integrating existing business logic with AI feels magical.
LangChain4j (Explicit @Tool): LangChain4j uses the explicit @Tool annotation. You mark methods in specific classes (don’t even need to be Spring Beans) with @Tool to explicitly declare them as AI-usable tools. This approach is framework-independent and makes it easier to manage AI tools separately from business logic.
C. Structured Output
Solving the developer complaint: “I’m tired of receiving AI responses as strings and parsing JSON!” This feature directly maps AI text responses to desired Java objects (POJOs).
Spring AI (Client-Level Support): One of Spring AI 1.0’s powerful features. When calling ChatClient, simply pass the desired class type like ...call(prompt, MyPojo.class), and Spring AI automatically prompts the AI to respond in JSON format and deserializes the result into that POJO. Developer experience (DX) is outstanding.
LangChain4j (Control via Parser): LangChain4j implements this through the ResponseParser interface. Developers must more explicitly combine the prompt template instructing the AI on response format with the logic parsing that response string. Requires slightly more work than Spring AI, but offers more flexibility when handling non-standard or complex response formats.
Production Deployment & Ecosystem (Production Readiness)
No matter how great the features, team leads and developers both care about: “Is it hard to learn?”, “Is it stable in production?”, “Where do I ask when things break?”
A. Learning Curve
Spring AI: For existing Spring developers, the learning curve is nearly zero. Spring AI feels less like learning a new framework and more like adding another dependency like spring-boot-starter-web. Since you use familiar patterns like @Service, @Bean, and application.properties, the barrier to entry is overwhelmingly low.
LangChain4j: Low, but learning it as a ‘new library’ is necessary. Being framework-independent means you need to learn LangChain4j’s unique abstractions like AiService, @Tool, and EmbeddingStore. Fortunately, official documentation and examples are abundant, making learning straightforward—though not quite Spring AI’s ‘zero learning’ level.
B. Production Stability & Monitoring
Spring AI: The ‘1.0 GA’ designation provides the greatest sense of stability. The May 2025 official release means VMware (Broadcom) officially endorses production use. The most powerful weapon is monitoring. Spring AI integrates perfectly with Spring Boot Actuator and Micrometer (Observability). You can track AI model call counts, response times, token usage, even costs—all on the same dashboard where you track microservice metrics. This is a huge advantage in production environments.
LangChain4j: Stability has been proven through numerous real-world cases since 2024. Many companies used LangChain4j in production even before Spring AI 1.0’s release. However, monitoring is an area developers must build themselves. While you can directly integrate tools like Micrometer, it doesn’t integrate ‘automatically’ like Spring AI. This means more control but additional effort building operational infrastructure.
C. Community & Support
Spring AI: The name ‘Spring’ itself is the community. Backed by millions of Spring developers worldwide and official support from VMware (Broadcom). High trust in long-term roadmap, maintenance, and security patches. Related content is already rapidly proliferating across the vast Spring ecosystem (Stack Overflow, blogs, etc.).
LangChain4j: Strength lies in its vibrant ‘open-source’ community. GitHub stars and Discord/Gitter channel activity show its dynamism. When new AI models or cutting-edge research features emerge (e.g., novel RAG techniques), LangChain4j may adopt them faster. Problem-solving happens community-based (GitHub Issues, Discord) rather than through official support.
Selection Guide: “So, Which Should Our Team Use?”
We’ve now compared both frameworks’ philosophies, core features, and operational aspects. As of October 2025, both frameworks are fully capable of building production-level AI applications.
Therefore, the choice isn’t “which is better?” but rather “which better fits our team’s current situation and goals?”
Here’s a clear checklist to help you decide.
Strongly Recommend Spring AI If:
- Your team’s main stack is already Spring Boot / Spring Cloud (most important reason)
- You prefer Spring’s opinionated approach with DI, Auto-Configuration, and rapid development speed
- Your primary goal is ‘adding’ AI features to existing Spring applications rather than building dedicated AI services (MSA)
- You want to perfectly integrate AI call metrics (cost, response time, etc.) with existing Actuator dashboards in production
- VMware (Broadcom)’s enterprise-level official support and long-term roadmap are important for technology choices
Strongly Recommend LangChain4j If:
- You don’t want Spring framework lock-in (or already use other frameworks like Quarkus, Micronaut)
- You need granular control over AI model or vector DB parameters and complete customization of RAG pipelines at the component level
- Rapidly experimenting with/adopting the latest AI models/techniques (from research papers) via community power matters more than official corporate support roadmaps
- AI functionality is core to your application, and you’re building an independent AI service that multiple other services (possibly non-Spring) will call
Conclusion: Level-Up Complete for Java AI—Now It’s Time to Choose
Fall 2025 finds us at a massive inflection point in the Java AI ecosystem. If just 1-2 years ago we hesitated asking “Java for AI?”, we now happily ponder “which framework should we use?”
Spring AI 1.0 GA’s arrival symbolizes the perfect union of ‘Enterprise Java’ and ‘AI’. It’s a defining moment enabling Spring developers to reach the AI frontier via ‘the most familiar path’.
Simultaneously, the maturing LangChain4j has proven how powerful ‘flexibility’ and ‘rapid extensibility’ unbound by frameworks can be.
The ultimate winner of this fierce competition is clear: the Java developer.
We no longer need to learn Python for AI features or struggle with clumsy API integrations. Whether you choose ‘integrated’ Spring AI or ‘flexible’ LangChain4j, you can select the powerful tool that fits your team and start building AI applications right now. Welcome to the leveled-up world of Java AI.