What is LangChain4j?
LangChain4j is a powerful open-source Java library designed to simplify the integration of Large Language Models (LLMs) into Java applications. While AI frameworks like LangChain have gained massive popularity in the Python ecosystem, Java developers have long felt left behind. LangChain4j bridges this gap by providing a native, idiomatic Java experience for building AI-powered applications.
Unlike simple API wrappers that merely send requests to OpenAI or other providers, LangChain4j offers a comprehensive toolkit for building sophisticated AI applications. It provides abstractions for chat models, embeddings, vector stores, retrieval-augmented generation (RAG), agents, and much more. The library is designed with enterprise Java development in mind, offering type safety, modularity, and seamless integration with existing Java ecosystems like Spring Boot.
Key Insight
LangChain4j is not a port of Python's LangChain. It was built from scratch specifically for Java, embracing Java conventions, patterns, and the strengths of the JVM ecosystem.
Why Java Needed Its Own LangChain
When the generative AI revolution began, Python dominated the landscape. Tools like LangChain, LlamaIndex, and Hugging Face Transformers became the de facto standards for AI development. However, enterprise software development tells a different story — Java remains one of the most widely used programming languages, powering countless mission-critical applications worldwide.
Organizations with existing Java infrastructure faced a dilemma: should they introduce Python into their stack just for AI capabilities, or wait for Java-native solutions? Introducing Python means dealing with different deployment pipelines, new skill requirements, and additional complexity in maintaining polyglot systems. LangChain4j emerged as the answer to this challenge.
Benefits of Java for AI
- • Type safety catches errors at compile time
- • Excellent tooling and IDE support
- • Mature ecosystem and libraries
- • Superior concurrency with virtual threads
- • Enterprise-grade performance
Challenges Without LangChain4j
- • Writing boilerplate HTTP clients
- • Manual prompt management
- • No standardized memory handling
- • Complex RAG implementation
- • Limited agent capabilities
LangChain vs LangChain4j vs Spring AI
Understanding the differences between these frameworks is crucial for making the right choice for your project. Each has its strengths and ideal use cases.
| Feature | LangChain (Python) | LangChain4j | Spring AI |
|---|---|---|---|
| Language | Python | Java | Java (Spring) |
| AI Services | No | Yes (killer feature!) | No |
| Agents | Advanced | Good | Basic |
| RAG Support | Excellent | Excellent | Good |
| Spring Integration | None | Module available | Native |
Choose LangChain4j if you want maximum flexibility, advanced AI Services, and powerful agent capabilities. Choose Spring AI if you're building a Spring Boot application and want tight integration with the Spring ecosystem. Both are excellent choices — and they can even be used together!
Core Concepts
Before diving into code, let's understand the fundamental building blocks of LangChain4j. These concepts form the foundation for everything you'll build with the library.
Large Language Models (LLMs)
LLMs are AI models trained on massive amounts of text data. They can understand context, generate human-like text, answer questions, and follow instructions. LangChain4j provides unified interfaces to work with various LLM providers including OpenAI, Anthropic, Google Gemini, Azure OpenAI, and local models through Ollama.
Prompts
Prompts are the instructions you send to an LLM. Effective prompt engineering is crucial for getting quality responses. LangChain4j offers prompt templates with variable substitution, system messages for setting context, and user messages for specific queries.
Chains
Chains allow you to combine multiple LLM calls or other components into a single workflow. For example, you might chain a summarization step with a translation step to summarize a document and then translate it to another language.
Tools (Function Calling)
Tools extend LLM capabilities by allowing them to call external functions. When an LLM needs real-time data, calculations, or database access, it can invoke tools you define. This is how you build truly powerful AI applications that interact with the real world.
Memory
LLMs are inherently stateless — each request is independent. Memory implementations in LangChain4j maintain conversation history, allowing for coherent multi-turn conversations. Options include in-memory storage, Redis, and database persistence.
Embeddings
Embeddings convert text into numerical vectors that capture semantic meaning. Two pieces of text with similar meanings will have similar embeddings. This is the foundation for semantic search and RAG applications.
Project Setup
Getting started with LangChain4j is straightforward. Choose your preferred build tool and add the necessary dependencies.
Maven Setup
<dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j</artifactId><version>0.36.2</version></dependency><!-- For OpenAI integration --><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-open-ai</artifactId><version>0.36.2</version></dependency>Gradle Setup
implementation 'dev.langchain4j:langchain4j:0.36.2'
implementation 'dev.langchain4j:langchain4j-open-ai:0.36.2'API Key Configuration
Store your API keys securely. Never commit them to version control. Use environment variables or a secrets manager.
# Set environment variableexportOPENAI_API_KEY=sk-your-key-here
# Or in application.properties for Spring Boot
langchain4j.open-ai.api-key=${OPENAI_API_KEY}Hello World Example
Here's your first LangChain4j application - a simple chat with OpenAI:
importdev.langchain4j.model.chat.ChatLanguageModel;importdev.langchain4j.model.openai.OpenAiChatModel;publicclassHelloLangChain4j{publicstaticvoidmain(String[] args){// Create the chat modelChatLanguageModel model =OpenAiChatModel.builder().apiKey(System.getenv("OPENAI_API_KEY")).modelName("gpt-4o-mini").build();// Send a message and get a responseString response = model.generate("What is LangChain4j?");System.out.println(response);}}🎉 Congratulations!
You've completed Level 1! You now understand what LangChain4j is, why it was created, and its core concepts. In the next level, we'll write our first LangChain4j application and explore chat models and prompt engineering.