langgraph4j

🚀 LangGraph for Java. A library for building stateful, multi-actor applications with LLMs, built for work jointly with langchain4j

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LangGraph for Java is a library designed for building stateful, multi-agent applications with LLMs. It is a porting of the original LangGraph from the LangChain AI project to Java. The library allows users to define agent states, nodes, and edges in a graph structure to create complex workflows. It integrates with LangChain4j and provides tools for executing actions based on agent decisions. LangGraph for Java enables users to create asynchronous node actions, conditional edges, and normal edges to model decision-making processes in applications.

README:

🦜🕸️ LangGraph for Java

LangGraph for Java. A library for building stateful, multi-agents applications with LLMs, built for work with langchain4j

It is a porting of original LangGraph from LangChain AI project in Java fashion

Features

[x] StateGraph
[x] Nodes
[x] Edges
[x] Conditional Edges
[x] Entry Points
[x] Conditional Entry Points
[x] State
- [x] Schema (a series of Channels)
  - [x] Reducer (how apply updates to the state attributes)
  - [x] Default provider
  - [x] AppenderChannel (values accumulator)
[x] Compiling graph
[x] Async support (throught CompletableFuture)
[x] Streaming support (throught java-async-generator)
[x] Checkpoints (save and replay feature)
[x] Graph visualization
- [x] PlantUML
- [x] Mermaid
[x] Playground (Embeddable Webapp that plays with LangGraph4j)
[x] Threads (checkpointing of multiple different runs)
[x] Update state (interact with the state directly and update it)
[x] Breakpoints (pause and resume feature)
[x] Studio (Playground Webapp)
[X] Streaming response from LLM results
[X] Child Graphs
[ ] Parallel Node Execution

Releases

Note: ‼️

From release 1.2.x the miminum supported Java version is the Java 17 and the artifact langgraph4j-core-jdk8 is replaced by langgraph4j-core

Date	Release	info
Jan 08, 2025	`1.2.1`	official release

Samples

Project	Integrated With
Agent Executor	SpringAI
Agent Executor	Langchain4j
Image To PlantUML Diagram	Langchain4j
Adaptive RAG	Langchain4j

How To(s)

Quick Start

Adding LangGraph dependency

Last stable version

Maven

<dependency>
    <groupId>org.bsc.langgraph4j</groupId>
    <artifactId>langgraph4j-core</artifactId>
    <version>1.2.1</version>
</dependency>

Development Version

Maven

<dependency>
    <groupId>org.bsc.langgraph4j</groupId>
    <artifactId>langgraph4j-core</artifactId>
    <version>1.2-SNAPSHOT</version>
</dependency>

Define the agent state

The main type of graph in langgraph is the StatefulGraph. This graph is parameterized by a state object that it passes around to each node. Each node then returns operations to update that state. These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute. Whether to set or add is described in the state's schema provided to the graph. The schema is a Map of Channels, each Channel represent an attribute in the state. If an attribute is described with an AppendeChannel it will be a List and each element referring the attribute will be automaically added by graph during processing. The State must inherit from AgentState base class (that essentially is a Map wrapper).

public class AgentState {

   public AgentState( Map<String,Object> initData ) { ... }
   
   public final java.util.Map<String,Object> data() { ... }

   public final <T> Optional<T> value(String key) { ... }
   public final <T> T value(String key, T defaultValue ) { ... }
   public final <T> T value(String key, Supplier<T>  defaultProvider ) { ... }
    

}

Define the nodes

We now need to define a few different nodes in our graph. In langgraph, a node is an async/sync function that accept an AgentState as argument and returns a (partial) state update. There are two main nodes we need for this:

The agent: responsible for deciding what (if any) actions to take.
A function to invoke tools: if the agent decides to take an action, this node will then execute that action.

/**
 * Represents an asynchronous node action that operates on an agent state and returns state update.
 *
 * @param <S> the type of the agent state
 */
@FunctionalInterface
public interface AsyncNodeAction<S extends AgentState> extends Function<S, CompletableFuture<Map<String, Object>>> {

    CompletableFuture<Map<String, Object>> apply(S t);

    /**
     * Creates an asynchronous node action from a synchronous node action.
     */
    static <S extends AgentState> AsyncNodeAction<S> node_async(NodeAction<S> syncAction) { ... }
}

Define Edges

We will also need to define some edges. Some of these edges may be conditional. The reason they are conditional is that based on the output of a node, one of several paths may be taken. The path that is taken is not known until that node is run (the LLM decides).

Conditional Edge: after the agent is called, we should either:
- If the agent said to take an action, then the function to invoke tools should be called
- If the agent said that it was finished, then it should finish
Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next

/**
 * Represents an asynchronous edge action that operates on an agent state and returns a new route.
 *
 * @param <S> the type of the agent state
 */
public interface AsyncEdgeAction<S extends AgentState> extends Function<S, CompletableFuture<String>> {

    CompletableFuture<String> apply(S t);

    /**
     * Creates an asynchronous edge action from a synchronous edge action.
     */
    static <S extends AgentState> AsyncEdgeAction<S> edge_async(EdgeAction<S> syncAction ) { ... }
}

Define the graph

We can now put it all together and define the graph! (see example below)

Integrate with LangChain4j

Like default use case proposed in LangGraph blog, We have converted AgentExecutor implementation from langchain using LangGraph4j. In the agent-executor project's sample, there is the complete working code with tests. Feel free to checkout and use it as a reference. Below you can find a piece of code of the AgentExecutor to give you an idea of how is has built in langgraph style.

public static class State implements AgentState {

    // the state's (partial) schema 
    static Map<String, Channel<?>> SCHEMA = mapOf(
        "intermediate_steps", AppenderChannel.<IntermediateStep>of(ArrayList::new)
    );

    public State(Map<String, Object> initData) {
        super(initData);
    }

    Optional<String> input() {
        return value("input");
    }
    Optional<AgentOutcome> agentOutcome() {
        return value("agent_outcome");
    }
    List<IntermediateStep> intermediateSteps() {
        return this.<List<IntermediateStep>>value("intermediate_steps").orElseGet(emptyList());
    }
   
}

var toolInfo = ToolNode.builder()
                        .specification( objectsWithTools )
                        .build();

var agentRunnable = Agent.builder()
                        .chatLanguageModel( chatLanguageModel )
                        .tools( toolInfo.specifications() )
                        .build();

// Fluent Interface
var app = new StateGraph<>(State.SCHEMA,State::new)
                .addEdge(START,"agent")
                .addNode( "agent", node_async( state ->
                    runAgent(agentRunnable, state))
                )
                .addNode( "action", node_async( state ->
                    executeTools(toolInfoList, state))
                )
                .addConditionalEdges(
                        "agent",
                        edge_async( state -> {
                            if (state.agentOutcome().map(AgentOutcome::finish).isPresent()) {
                                return "end";
                            }
                            return "continue";
                        }),
                        mapOf("continue", "action", "end", END)
                )
                .addEdge("action", "agent")
                .compile();

return  app.stream( inputs );

References

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