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Introduction to LangGraph4j

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⚠️ This page is just unchecked AI-generated sample data to demonstrate the resource structure for flatmap-docs-kit.

LangGraph4j is the Java implementation of LangGraph, a powerful framework for building stateful, multi-step AI applications. It enables Java developers to create sophisticated workflows that can handle complex reasoning, maintain context, and orchestrate multiple AI operations.

🎯 What is LangGraph4j?

LangGraph4j is designed for building AI applications that require:

  • Stateful Operations - Maintain context across multiple steps
  • Complex Workflows - Orchestrate multiple AI operations
  • Decision Making - Implement conditional logic and routing
  • Tool Integration - Connect to external services and APIs
  • Memory Management - Remember and learn from interactions

🏗️ Core Concepts

Graphs and Nodes

LangGraph4j uses a graph-based architecture where:

  • Nodes represent individual operations or steps
  • Edges define the flow between nodes
  • State is passed between nodes to maintain context
Graph<MyState> graph = Graph.builder()
.addNode("step1", this::operation1)
.addNode("step2", this::operation2)
.addEdge("step1", "step2")
.build();

State Management

State objects carry data through the workflow:

public class MyState {
private String input;
private String processedData;
private String result;

// Constructor and builder methods...
}

Node Types

  • Function Nodes - Execute custom logic
  • Conditional Nodes - Make decisions based on state
  • Tool Nodes - Integrate with external services
  • LLM Nodes - Generate AI responses

🚀 Key Features

1. Workflow Orchestration

Build complex multi-step processes with clear flow control and error handling.

2. State Persistence

Maintain context across workflow executions, enabling sophisticated conversation flows.

3. Tool Integration

Easily connect to external APIs, databases, and services through a unified interface.

4. Conditional Logic

Implement decision trees and branching logic based on workflow state.

5. Memory Management

Built-in support for conversation memory and context management.

6. Error Handling

Robust error handling and recovery mechanisms for production applications.

🔧 Getting Started

Prerequisites

  • Java 17 or higher
  • Maven or Gradle build tool
  • Basic understanding of Java development
  • Access to an LLM provider (OpenAI, Anthropic, etc.)

Quick Setup

<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langgraph4j</artifactId>
<version>0.27.1</version>
</dependency>

Your First Workflow

public class SimpleWorkflow {

private final Graph<WorkflowState> graph;

public SimpleWorkflow() {
this.graph = Graph.builder()
.addNode("process", this::processInput)
.addNode("respond", this::generateResponse)
.addEdge("process", "respond")
.build();
}

public String execute(String input) {
WorkflowState state = new WorkflowState(input);
WorkflowState result = graph.execute(state);
return result.getResponse();
}
}

🎯 Use Cases

Customer Service Bots

Build intelligent customer service agents that can:

  • Understand customer intent
  • Route to appropriate departments
  • Maintain conversation context
  • Integrate with CRM systems

Data Processing Pipelines

Create workflows for:

  • Data validation and cleaning
  • Multi-step analysis
  • Report generation
  • Automated decision making

Content Generation

Develop systems for:

  • Multi-step content creation
  • Review and approval workflows
  • Content optimization
  • Automated publishing

🔄 Integration Ecosystem

LangGraph4j integrates seamlessly with:

  • Spring Boot - Native Spring integration
  • LangChain4J - Leverage existing LangChain4J components
  • Various LLM Providers - OpenAI, Anthropic, local models
  • External APIs - REST, GraphQL, gRPC services
  • Databases - SQL, NoSQL, vector databases

📊 Performance & Scalability

  • High Performance - Optimized for production workloads
  • Memory Efficient - Minimal memory footprint
  • Scalable - Support for distributed execution
  • Observable - Built-in monitoring and tracing

🎓 Learning Path

  1. Start with Basics - Understand graphs, nodes, and state
  2. Build Simple Workflows - Create your first multi-step process
  3. Add Conditional Logic - Implement decision-making capabilities
  4. Integrate Tools - Connect to external services
  5. Optimize Performance - Scale your applications
  6. Deploy to Production - Production-ready applications

🤝 Community & Support

Ready to start building with LangGraph4j? Check out our Getting Started Guide for your first workflow!