LangGraph4j is the Java port of LangGraph — a library for building stateful, multi-agent AI applications using cyclical graph workflows. At v1.8.10 (March 2026), it integrates natively with both Spring AI and LangChain4j, and adds the crucial capability that DAG-based frameworks lack: loops that allow agents to reason, retry, and revise.
The Gap It Fills
Most Java AI frameworks (including LangChain4j's AiServices and Spring AI's ChatClient) model AI interactions as linear pipelines or directed acyclic graphs (DAGs): input → processing → output. This is sufficient for RAG and simple tool-calling.
Agentic loops require something different: the ability to go back. An agent that writes code, runs tests, sees failures, and tries a different approach is running a cycle — forbidden in a DAG. LangGraph4j's StateGraph enables exactly this.
Why It's in Trial
LangGraph4j is mature enough for production use on real projects, with active development (version 1.8.10 as of March 2026), Spring AI integration, and a growing community of Java practitioners building production agentic systems with it.
It's in Trial rather than Adopt because:
- Compared to Spring AI and LangChain4j, the Java community's familiarity with graph-based agent orchestration is still growing
- Documentation and examples, while good, are less comprehensive than the Python LangGraph ecosystem
- Best practices for production observability and debugging of graph-based agents in Java are still emerging
Core Concepts
StateGraph
The central abstraction. Define nodes (agents, tools, custom logic), edges (transitions), and shared state (AgentState — a Map<String, Object> that flows through the graph).
StateGraph<AgentState> graph = new StateGraph<>(AgentState.class)
.addNode("agent", this::callAgent)
.addNode("tools", this::executeTool)
.addEdge(START, "agent")
.addConditionalEdges("agent", this::shouldContinue,
Map.of("continue", "tools", "end", END))
.addEdge("tools", "agent"); // ← the cycle: tools feed back to agent
Checkpoints Save graph state at any step. Use for:
- Debugging (inspect state at each node)
- Human-in-the-loop (pause, await approval, resume)
- Long-running processes (persist across JVM restarts)
Visual Debugging (Embedded Studio) LangGraph4j ships with a visual debugger — a browser-based graph viewer that shows the current node, state values, and execution history in real time. This is one of its standout features for teams debugging complex agent flows.
Streaming Support
Nodes can stream partial results using CompletableFuture — important for long-running steps where the caller needs progress feedback.
Integration with Spring AI & LangChain4j
LangGraph4j is framework-agnostic at the orchestration layer and integrates with both major Java AI frameworks for model calls and tool execution:
// With Spring AI
@Autowired ChatClient chatClient;
private Map<String, Object> callAgent(AgentState state) {
String response = chatClient.prompt()
.user(state.input())
.call().content();
return Map.of("lastResponse", response);
}
Real-World Pattern: Agentic Code Review
START → Analyse PR → Check Security → Check Style → [loop back if issues found]
↓ (no issues)
END (approved)
Each node is a specialised sub-agent. The conditional edge after each check loops back to the analysis step if violations are found, allowing the agent to incorporate feedback from previous checks.
Key Characteristics
| Property | Value |
|---|---|
| Version | 1.8.10 (March 2026) |
| Minimum Java | Java 17 |
| Build tools | Maven, Gradle |
| Integrations | Spring AI, LangChain4j |
| Licence | MIT |
| GitHub | langgraph4j/langgraph4j |
| Docs | langgraph4j.github.io/langgraph4j |