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Start with a direct ChatModel call to verify that your Java application can reach a model provider; then move to an AI Service interface if you want a typed application-facing API. Add conversation memory, tools, or retrieval only when the feature needs them. LangChain4j’s getting-started guide requires JDK 17 or later, and its dependency versions and example model names can change, so check the current documentation before copying them.
What LangChain4j does—and which API to start with
LangChain4j is a Java library for connecting applications to language models and related components. Its current introduction lists integrations with 20+ LLM providers and 30+ embedding stores, along with features such as AI Services, prompt templates, chat memory, streaming, output parsing, tool calling, and retrieval-augmented generation (RAG). These are project documentation claims and can change over time. See the LangChain4j introduction.
You can assemble low-level components yourself or use AI Services to reduce orchestration code. For a first connection check, the low-level ChatModel API makes the request path visible. For application code that benefits from a declarative, typed interface, AI Services can handle common input formatting and output parsing and can optionally add memory, tools, or RAG. LangChain4j implements an AI Service interface through a proxy. The project describes Chains as legacy and does not plan to expand them at this time, so AI Services are the better starting point for new higher-level examples. See Chat and Language Models and AI Services.
Make a minimal hosted-model call
The official getting-started example uses Maven, the OpenAI integration, an API key in an environment variable, and a direct chat call. The artifact version and model name below are documentation examples, not permanent recommendations. Verify the current dependency and provider instructions before using them.
- Check the project runtime. LangChain4j’s getting-started page states a minimum supported JDK version of 17. Confirm the JDK used to compile and run your application.
- Add the provider integration. For the guide’s OpenAI example, add this Maven dependency; check the current Get Started page for the version to use:
<dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> <version>1.21.0</version> </dependency> - Configure credentials outside source code. Set
OPENAI_API_KEYin the environment that launches the application. Read it withSystem.getenv("OPENAI_API_KEY"); do not hard-code a secret into a Java class or commit it to source control. LangChain4j recommends environment variables to reduce the risk of public exposure. - Construct a model and send a prompt. The following follows the documentation’s basic pattern. Use a model name supported by your provider account and the current integration:
import dev.langchain4j.model.openai.OpenAiChatModel; public class LlmExample { public static void main(String[] args) { String apiKey = System.getenv("OPENAI_API_KEY"); if (apiKey == null || apiKey.isBlank()) { throw new IllegalStateException("Set OPENAI_API_KEY before running this application"); } OpenAiChatModel model = OpenAiChatModel.builder() .apiKey(apiKey) .modelName("gpt-4o-mini") .build(); String answer = model.chat("Explain what an API is in one sentence."); System.out.println(answer); } }The example model name is time-sensitive; confirm the provider’s current model identifiers and the LangChain4j integration documentation. A successful response confirms that this basic application path works; it does not add conversation history or retrieve your application’s private data.
This example focuses on provider-specific setup. The dependency, credentials, and model identifier vary by provider; the concepts of sending chat input and receiving a response are the framework-level part. The LangChain4j documentation says the main langchain4j dependency is also needed when using the higher-level AI Services API.
Rank #2
Move from a direct call to an AI Service
A direct ChatModel call is useful when you want explicit control over model requests and their surrounding logic. As an application grows, you may prefer to expose a task as a Java interface rather than repeat prompt construction and response handling throughout the code. AI Services provide that declarative layer.
| Approach | Best fit | Trade-off |
|---|---|---|
Low-level ChatModel |
A small integration, or a flow where you want to compose model and message operations directly. | More orchestration and input/output handling remains in application code. |
| AI Services | A task that can be represented as an application-facing Java interface method. | Less boilerplate for common formatting and parsing, with behavior expressed through the AI Service abstraction. |
When you introduce AI Services, add the core langchain4j dependency as well as the provider integration, following the versions and setup shown in the current documentation. Define methods around product tasks—such as drafting a reply or classifying a support request—and use the documented AI Service configuration to bind the interface to a model. Consult the AI Services tutorial for current syntax and supported options; provider selection remains a separate configuration concern.
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Add memory only when a feature needs prior turns
A model call is not automatically a continuing conversation. Chat memory is the context LangChain4j supplies so the model can respond as though it remembers earlier turns. Conversation history is the complete exchange your application stores or displays to the user. These are related but different responsibilities.
A memory strategy can evict messages, summarize earlier turns, remove details, or add information or instructions to the context. A bounded memory window therefore defines what the model can see, not how much transcript your product retains. If users need a complete chat transcript, store it separately according to your product’s retention and privacy requirements. See Chat Memory for the framework’s memory options and behavior.
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Give the model access to application knowledge with RAG
Retrieval-augmented generation (RAG) retrieves relevant material from your data and inserts it into the model prompt before the response is generated. LangChain4j describes two broad stages:
- Indexing: load and prepare source documents, represent them for search, and place them in a store.
- Retrieval: find material relevant to a user’s request and provide it as context for the model.
Retrieval may use keyword or full-text search, vector or semantic search, or a hybrid of both. The current RAG documentation says full-text and hybrid search are supported only by the Azure AI Search and Elasticsearch integrations; verify that limitation against the current RAG tutorial before choosing an integration.
Best Value
Easy RAG for a proof of concept
LangChain4j’s Easy RAG path is intended to get a proof of concept running with less setup. It combines document ingestion, an embedding store, and a chat model, with bounded memory available when needed. The documentation cautions that this simpler configuration can produce lower quality than a tailored pipeline. Treat it as a way to validate the product flow, not as evidence that a vector store by itself guarantees correct answers.
Customize retrieval when the use case demands it
For a production feature, you may need control over document loading, segmentation, embeddings, storage, retrieval, and reranking. The right choices depend on the shape and freshness of your content and on what counts as a useful result for your users. Measure retrieval quality against representative questions and source material; a fluent answer can still be unsupported if the relevant content was not indexed or retrieved.
Choose hosted inference or an optional local route
A hosted provider integration is the simplest path shown in the getting-started guide: configure that provider’s dependency and credentials, then call its chat model. If you need local inference, LangChain4j documents a Jlama integration, but it is not the same minimal setup. Its example requires the Jlama integration and a native dependency, and the documentation states that Jlama uses Java 21 preview features. That brings additional runtime and build configuration. The Jlama page does not establish a hardware recommendation or benchmark, so evaluate compatibility and performance for your own deployment rather than assuming a particular machine will be suitable. See Jlama integration documentation.
Quick Recap
A practical implementation sequence
- Prove provider connectivity with one direct chat call before adding abstractions.
- Move task-specific behavior behind an AI Service interface when it reduces repeated application logic.
- Add memory when answers genuinely depend on earlier turns, and keep full transcript storage separate if the product requires it.
- Add RAG when the model needs your application’s private or domain-specific material; validate what is retrieved, not only how the answer reads.
- Recheck artifact versions, provider model names, and integration support in the current LangChain4j documentation before deployment.
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