langgraph4j

️ Welcome to LangGraph4j

Enabling Graph Engineering for Agentic AI Workflows in Java

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LangGraph for Java. A library for building stateful, multi-agents applications with LLMs, built for work with [langchain4j] and [Spring AI]

It is inspired by [LangGraph] solution, part of [LangChain AI project].

Releases

‼️ the repo has been updated to 1.9, the version 1.8 has been moved to the branch support/1.8.x.. Take a look to What's new in release 1.9

Star History

Getting Started

Welcome to LangGraph4j! This guide will help you understand the core concepts of LangGraph4j, install it, and build your first application.

Introduction

LangGraph4j is a Java library for building stateful, multi-agent applications with Large Language Models (LLMs). It is inspired by the Python library LangGraph and is designed to work seamlessly with popular Java LLM frameworks like Langchain4j and Spring AI.

At its core, LangGraph4j allows you to define cyclical graphs where different components (agents, tools, or custom logic) can interact in a stateful manner. This is crucial for building complex applications that require memory, context, and the ability for different "agents" to collaborate or hand off tasks.

Core Features & Benefits

LangGraph4j offers several features and benefits:

• Stateful Execution: Manage and update a shared state across graph nodes, enabling memory and context awareness.
• Cyclical Graphs: Unlike traditional DAGs, LangGraph4j supports cycles, essential for agent-based architectures where control flow can loop back (e.g., an agent retrying a task or asking for clarification).
• Explicit Control Flow: Clearly define the paths and conditions for transitions between nodes in your graph.
• Modularity: Build complex systems from smaller, reusable components (nodes).
• Flexibility: Integrate with various LLM providers and custom Java logic.
• Observability & Debugging:
  • Checkpoints: Save the state of your graph at any point and replay or inspect it later. This is invaluable for debugging and understanding complex interactions.
  • Graph Visualization: Generate visual representations of your graph using PlantUML or Mermaid to understand its structure.
• Asynchronous & Streaming Support: Build responsive applications with non-blocking operations and stream results from LLMs.
• Playground & Studio: A web UI to visually inspect, run, and debug your graphs.

Pattern Matrix

Core Concepts Explained

Understanding these concepts is key to using LangGraph4j effectively:

StateGraph<S extends AgentState>

The StateGraph is the primary class you'll use to define the structure of your application. It's where you add nodes and edges to create your graph. It is parameterized by an AgentState.

AgentState

The AgentState (or a class extending it) represents the shared state of your graph. It's essentially a map (Map<String, Object>) that gets passed from node to node. Each node can read from this state and return updates to it.

• Schema: The structure of the state is defined by a "schema," which is a Map<String, Channel.Reducer>. Each key in the map corresponds to an attribute in the state.
• Channel.Reducer: A reducer defines how updates to a state attribute are handled. For example, a new value might overwrite the old one, or it might be added to a list of existing values.
• Channel.Default<T>: Provides a default value for a state attribute if it's not already set.
• Channel.Appender<T> / MessageChannel.Appender<M>: A common type of reducer that appends the new value to a list associated with the state attribute. This is useful for accumulating messages, tool calls, or other sequences of data. MessageChannel.Appender is specifically designed for chat messages and can also handle message deletion by ID.

Nodes

Nodes are the building blocks of your graph that perform actions. A node is typically a function (or a class implementing NodeAction<S> or AsyncNodeAction<S>) that:

1. Receives the current AgentState as input.
2. Performs some computation (e.g., calls an LLM, executes a tool, runs custom business logic).
3. Returns a Map<String, Object> representing updates to the state. These updates are then applied to the AgentState according to the schema's reducers.

Nodes can be synchronous or asynchronous (CompletableFuture).

Edges

Edges define the flow of control between nodes.

• Normal Edges: An unconditional transition from one node to another. After node A completes, control always passes to node B. You define these with addEdge(sourceNodeName, destinationNodeName).
• Conditional Edges: The next node is determined dynamically based on the current AgentState. After a source node completes, an EdgeAction<S> (or AsyncEdgeAction<S>) function is executed. This function receives the current state and returns the name of the next node to execute. This allows for branching logic (e.g., if an agent decided to use a tool, go to the "execute_tool" node; otherwise, go to the "respond_to_user" node). Conditional edges are defined with addConditionalEdges(...).
• Entry Points: You can also define conditional entry points to your graph using addConditionalEntryPoint(...).

Compilation

Once you've defined all your nodes and edges in a StateGraph, you compile() it into a CompiledGraph<S extends AgentState>. This compiled graph is an immutable, runnable representation of your logic. Compilation validates the graph structure (e.g., checks for orphaned nodes).

Checkpoints (Persistence)

LangGraph4j allows you to save (Checkpoint) the state of your graph at any step. This is extremely useful for:

• Debugging: Inspect the state at various points to understand what happened.
• Resuming: Restore a graph to a previous state and continue execution.
• Long-running processes: Persist the state of long-running agent interactions. You'll typically use a CheckpointSaver implementation (e.g., MemorySaver for in-memory storage, or you can implement your own for persistent storage).

Useful starting points for persistence:

• langgraph4j-mysql-saver/README.md for MySQL-backed checkpoints
• langgraph4j-postgres-saver/README.md for PostgreSQL-backed checkpoints
• langgraph4j-redis-saver/README.md for Redis-backed checkpoints

If you only want to see the minimal integration point first, look for CompileConfig.builder().checkpointSaver(...) in the saver module examples before diving into the full storage details.

Project Structure

…

Installation

To use LangGraph4j in your project, you need to add it as a dependency.

Maven:

Make sure you are using Java 17 or later.

Latest Stable Version (Recommended):

<properties>
    <langgraph4j.version>1.8.13</langgraph4j.version> 
</properties>

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.bsc.langgraph4j</groupId>
      <artifactId>langgraph4j-bom</artifactId>
      <version>${langgraph4j.version}</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

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

(Note: Always check the Maven Central Repository for the latest version number.)

Development Snapshot Version: If you want to use the latest unreleased features, you can use a snapshot version.

<dependency>
    <groupId>org.bsc.langgraph4j</groupId>
    <artifactId>langgraph4j-core</artifactId>
    <version>1.8.13</version> 
</dependency>

You might need to configure your settings.xml or pom.xml to include the Sonatype OSS snapshots repository:

<repositories>
    <repository>
        <id>sonatype-oss-snapshots</id>
        <url>https://central.sonatype.com/repository/maven-snapshots</url>
        <snapshots>
            <enabled>true</enabled>
        </snapshots>
    </repository>
</repositories>

Your First Graph - A Simple Example

Let's create a very simple graph that has two nodes: greeter and responder. The greeter node will add a greeting message to the state. The responder node will add a response message based on the greeting.

Before you start, the shortest path is:

1. Make sure you are on Java 17+ and have added langgraph4j-core to your project.
2. Copy the example below exactly once to understand the basic StateGraph + shared-state flow.
3. Run the graph locally and confirm you can see the state evolve across the two nodes.
4. After that first success, jump to the built-in integrations or checkpointing sections depending on whether you want framework integration or persistence next.

1. Define the State: Our state will hold a list of messages.

…

2. Define the Nodes:

…

3. Define and Compile the Graph:

…

Explanation:

• We defined SimpleState with a MESSAGES_KEY that uses AppenderChannel to accumulate strings.
• GreeterNode adds a "Hello" message.
• ResponderNode checks for the greeting and adds an acknowledgment.
• The StateGraph is defined, nodes are added, and edges specify the flow: START -> greeter -> responder -> END.
• stateGraph.compile() creates the runnable CompiledGraph.
• compiledGraph.stream(initialState) executes the graph. We iterate through the stream to get the final state. Each