LangChain4j vs LangGraph4j: Which Java AI Framework Should You Choose?

Introduction

AI agents have become a hot topic, and in the Java ecosystem two frameworks dominate the space: LangChain4j and LangGraph4j . Many developers ask which one to use.

What They Are

LangChain4j

LangChain4j is the Java port of LangChain. It is a Java LLM application framework that makes it easy to integrate large language models and AI capabilities into Java applications.

Unified model‑calling interface (supports 20+ models such as OpenAI, Vertex AI, Ollama, DeepSeek, etc.)

Embeddings and vector retrieval (30+ vector‑db adapters)

RAG (retrieval‑augmented generation)

Tool Calling (expose Java methods to LLMs)

AiServices high‑level API (declare AI services as Java interfaces)

Think of LangChain4j as the “AI capability layer” – it answers the question “can we use this model?”.

LangGraph4j

LangGraph4j is the Java port of LangGraph (Python). It provides a state‑graph workflow engine for building multi‑agent, stateful, and conditional flows.

StateGraph – executable flow charts

Conditional and loop control

Multi‑agent collaboration (Supervisor pattern, Fan‑out)

Checkpointing – automatic state serialization for crash recovery

Human‑in‑the‑Loop support

It acts as the “graph paper” that arranges complex workflows.

Deep Comparison

LangChain4j – Capability Layer

The core problem it solves is how to connect LLM capabilities to a Java app . Its design philosophy is modular, low‑coupling, and declarative.

Key modules (illustrated in a table in the original article) include:

ChatModel : unified chat interface with streaming output.

EmbeddingModel : text vectorisation for similarity search.

Tool Calling : expose annotated Java methods as tools.

AiServices : declare an AI service as a Java interface; a single line of code creates the implementation.

Example of AiServices:

// Define an interface
public interface Assistant {
    String chat(@UserMessage String message);
}

Assistant assistant = AiServices.builder(Assistant.class)
        .chatLanguageModel(OpenAiChatModel.withApiKey("sk‑xxx"))
        .build();
String reply = assistant.chat("What is ConcurrentHashMap in Java?");
System.out.println(reply);

This demonstrates the “declarative” power: a single annotation and a few lines turn a Java interface into an AI service.

LangGraph4j – Workflow Layer

LangGraph4j focuses on how to orchestrate multiple AI agents, decisions, and actions as a stateful graph . Its design treats the graph itself as code, state as a shared notebook, and nodes as agent behaviours.

Key concepts:

State (AgentState) : a type‑safe, versioned data object shared by all nodes.

StateGraph : define nodes, edges, and conditional routing.

Checkpointing : after each state update the framework serialises the full context (tokens, timestamps, node IDs) to persistent storage, enabling exact recovery after crashes.

Example of a simple three‑step graph:

StateGraph<MyState> graph = new StateGraph<>(MyState.class)
        .addNode("think", this::thinkNode)
        .addNode("act", this::actNode)
        .addNode("answer", this::answerNode)
        .addConditionalEdges("think", this::routeAfterThink)
        .addEdge("act", "answer")
        .addEdge("answer", END)
        .build();

The routing function decides the next node based on the current state, illustrating the dynamic “conditional edge” that is the core of LangGraph4j.

Side‑by‑Side Pros & Cons

LangChain4j Advantages

Comprehensive out‑of‑the‑box features (chat, RAG, tool calling, 20+ model adapters).

Strong Spring Boot integration.

Declarative API makes it easy for Java developers.

Rich ecosystem and mature version (1.11.0 as of 2026‑02‑04).

LangChain4j Limitations

Limited workflow orchestration – no built‑in state management.

Complex multi‑step flows require manual handling.

Vector retrieval is single‑level only.

LangGraph4j Advantages

Powerful state‑graph orchestration (loops, conditionals, parallel nodes, sub‑graph nesting).

Robust multi‑agent collaboration patterns (Supervisor, Fan‑out, HITL).

Checkpointing + time‑travel debugging for production‑grade reliability.

Framework‑agnostic – can be combined with LangChain4j or Spring AI.

LangGraph4j Limitations

Does not provide model‑calling capabilities; must rely on LangChain4j or another provider.

Steeper learning curve due to graph concepts.

Newer version (1.7.10 / 1.8‑beta) with a less mature ecosystem.

Version Status (2026 H1)

Both frameworks are actively maintained.

LangChain4j 1.11.0 – released 2026‑02‑04, adds more model support, RAG optimisations, and MCP module improvements.

LangGraph4j 1.7.10 (stable) and 1.8‑beta – released 2026‑01, introduces hooks, async nodes, parallel execution optimisation, and enhanced checkpointing.

Typical Use Cases

When LangChain4j Is Enough

Simple chatbots, FAQ systems, single‑agent tool calling.

RAG‑based knowledge‑base queries.

Rapid prototyping of AI ideas.

Scenarios requiring fine‑grained control with low coupling.

When LangGraph4j Is Needed

Complex multi‑step decision flows with branching and loops.

Multi‑agent collaboration such as code‑review pipelines or supply‑chain scheduling.

Long‑running processes that need checkpoint recovery (e.g., sentiment‑monitoring agents running for hours).

Workflows that require parallel execution and result aggregation.

Best‑Practice Combination

In real projects the two are often used together: LangChain4j handles model calls, RAG, and tool definitions, while LangGraph4j orchestrates the overall stateful workflow.

Use AiServices from LangChain4j to expose model‑driven services.

Wrap those services as nodes inside a LangGraph4j StateGraph to gain conditional routing, checkpointing, and multi‑agent coordination.

Getting Started

LangChain4j

GitHub: https://github.com/langchain4j/langchain4j

Docs: https://docs.langchain4j.dev

Maven dependency:

<dependency>
  <groupId>dev.langchain4j</groupId>
  <artifactId>langchain4j</artifactId>
  <version>1.11.0</version>
</dependency>

LangGraph4j

GitHub: https://github.com/langgraph4j/langgraph4j

Maven dependency:

<dependency>
  <groupId>org.bsc.langgraph4j</groupId>
  <artifactId>langgraph4j-core</artifactId>
  <version>1.7.10</version>
</dependency>

<!-- Integration with LangChain4j -->
<dependency>
  <groupId>org.bsc.langgraph4j</groupId>
  <artifactId>langgraph4j-langchain4j</artifactId>
  <version>1.7.10</version>
</dependency>

Conclusion

LangChain4j lets you connect large models to Java ; LangGraph4j lets you orchestrate multiple AI agents elegantly . They are not alternatives but complementary layers. Choose based on the complexity of your workflow: simple single‑step AI calls → LangChain4j; multi‑step, stateful, fault‑tolerant pipelines → add LangGraph4j.