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Features

This guide introduces LangGraph4j’s key features in a beginner-friendly way. These are the mainly features for LangGraph4j:

StateGraph: Workflow as a Graph

LangGraph4j lets you define your AI workflow as a graph with:

  • Nodes: Each node represents a unit of logic (e.g., calling an LLM, fetching data).
  • Edges: Control the flow between nodes.
  • Normal edges: Move to the next step.
  • Conditional edges: Choose the next step based on conditions (like an if/else).
  • Entry Points: Where the graph starts.
  • State Schema: Defines what data is passed and updated through the graph.

📌 Think of it like drawing a flowchart for your AI application’s logic.

Async & Streaming Support

LangGraph4j uses:

  • CompletableFuture: Allows non-blocking, asynchronous operations.
  • Java async generators: Enable streaming responses from LLMs and other sources.

✅ Your app stays responsive and handles large outputs smoothly.

Checkpoints and Breakpoints

  • Checkpoints: Save the state of the graph so you can resume or replay later.
  • Breakpoints: Pause execution at certain points and resume when ready.

🛠️ Great for debugging or long-running workflows that need manual review or delay.

Embedded Playground Web App (Studio)

LangGraph4j comes with an embeddable web UI (Studio) that allows you to visualize, run, and interact with your graphs in real-time. This is excellent for development and debugging.

👉 LangGraph4j Studio

  • Visualize and test your graph
  • Simulate inputs
  • Step through execution

🎨 No need to write code to understand how the graph works!

Graph Visualization

LangGraph4j supports two popular diagram tools:

You can export and view your workflow as a diagram.

🧭 Easily share and review your application’s logic visually.

Multi-Agent & Parallel Execution

LangGraph4j supports:

  • Multiple agents: Each with its own logic or behavior
  • Threads and sub-graphs: Reuse workflows inside others
  • Parallel node execution: Run parts of your graph at the same time

🤖 Useful for building advanced systems where multiple AI agents collaborate.

Framework Integrations

LangGraph4j works well with popular Java AI tools:

🔌 Smooth integration into your existing Java tech stack.

Visual Builder Tool

Build your LangGraph4j applications visually using the official tool: 👉 LangGraph4j Builder

🧱 No need to hand-code everything — click and build!

These are 2 examples of workflow generate from LangGraph4j Builder:

Banking agent workflow

An AI Agentic workflow that provide a Banking Assistant that include the Human-in-the-Loop to confirm, if any, required payments:

Banking agent

Supervisor agent workflow

A classical ReACT agent plus RAG integration developed using a controllable and well defined workflow

Supervisor agent