langgraph4j

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

Stars: 365

Visit

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] Appender channel (values accumulator)
    - [x] delete messages
[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] Spring Boot
- [x] Jetty
- [x] Quarkus
[X] Streaming response from LLM results
[X] Child Graphs
[X] Parallel Node Execution
- With some constraints
[X] Generator (Visual Builder)

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
Apr 06, 2025	`1.5.4`	official release

How To - (Java Notebook)

Samples

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

Quick Start

Adding LangGraph dependency

Last stable version

Maven

<dependency>
    <groupId>org.bsc.langgraph4j</groupId>
    <artifactId>langgraph4j-core</artifactId>
    <version>1.5.4</version>
</dependency>

Development Version

Maven

<dependency>
    <groupId>org.bsc.langgraph4j</groupId>
    <artifactId>langgraph4j-core</artifactId>
    <version>1.5-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.

/**
 * Represents the state of an agent.
 */
class State extends MessagesState<ChatMessage> {

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

    public Optional<String> finalResponse() {
        return value("agent_response");
    }

}

var toolNode = ToolNode.builder()
                    .toolSpecification( tools )
                    .build();

var agent = Agent.builder()
        .chatLanguageModel(chatLanguageModel)
        .tools(toolNode.toolSpecifications())
        .build();

var callAgent = new CallAgent(agent);
var executeTools = new ExecuteTools(agent, toolNode);
                                 
// Fluent Interface
var app = new StateGraph<>(State.SCHEMA, State::new)
                .addEdge(START,"agent")
                .addNode("agent", node_async(callAgent))
                .addNode("action", node_async(executeTools))
                .addConditionalEdges(
                        "agent",
                        edge_async( state -> 
                            state.finalResponse()
                                .map(res -> "end")
                                .orElse("continue");
                        ),
                        Map.of("continue", "action", "end", END)
                )
                .addEdge("action", "agent")
                .compile();

return  app.stream( inputs );

References

Projects

For Tasks:

Click tags to check more tools for each tasks

build stateful applications define agent states create graph nodes model decision processes integrate with langchain4j

For Jobs:

software developer data scientist ai engineer java developer machine learning engineer

Alternative AI tools for langgraph4j

Similar Open Source Tools

langgraph4j

github

: 365

MCPSharp

MCPSharp is a .NET library that helps build Model Context Protocol (MCP) servers and clients for AI assistants and models. It allows creating MCP-compliant tools, connecting to existing MCP servers, exposing .NET methods as MCP endpoints, and handling MCP protocol details seamlessly. With features like attribute-based API, JSON-RPC support, parameter validation, and type conversion, MCPSharp simplifies the development of AI capabilities in applications through standardized interfaces.

github

: 142

mcp-agent

mcp-agent is a simple, composable framework designed to build agents using the Model Context Protocol. It handles the lifecycle of MCP server connections and implements patterns for building production-ready AI agents in a composable way. The framework also includes OpenAI's Swarm pattern for multi-agent orchestration in a model-agnostic manner, making it the simplest way to build robust agent applications. It is purpose-built for the shared protocol MCP, lightweight, and closer to an agent pattern library than a framework. mcp-agent allows developers to focus on the core business logic of their AI applications by handling mechanics such as server connections, working with LLMs, and supporting external signals like human input.

github

: 7.3k

StepWise

StepWise is a code-first, event-driven workflow framework for .NET designed to help users build complex workflows in a simple and efficient way. It allows users to define workflows using C# code, visualize and execute workflows from a browser, execute steps in parallel, and resolve dependencies automatically. StepWise also features an AI assistant called `Geeno` in its WebUI to help users run and analyze workflows with ease.

github

: 111

Google_GenerativeAI

Google GenerativeAI (Gemini) is an unofficial C# .Net SDK based on REST APIs for accessing Google Gemini models. It offers a complete rewrite of the previous SDK with improved performance, flexibility, and ease of use. The SDK seamlessly integrates with LangChain.net, providing easy methods for JSON-based interactions and function calling with Google Gemini models. It includes features like enhanced JSON mode handling, function calling with code generator, multi-modal functionality, Vertex AI support, multimodal live API, image generation and captioning, retrieval-augmented generation with Vertex RAG Engine and Google AQA, easy JSON handling, Gemini tools and function calling, multimodal live API, and more.

github

: 71

gpustack

GPUStack is an open-source GPU cluster manager designed for running large language models (LLMs). It supports a wide variety of hardware, scales with GPU inventory, offers lightweight Python package with minimal dependencies, provides OpenAI-compatible APIs, simplifies user and API key management, enables GPU metrics monitoring, and facilitates token usage and rate metrics tracking. The tool is suitable for managing GPU clusters efficiently and effectively.

github

: 2.0k

claude-code.nvim

Claude Code Neovim Plugin is a seamless integration between Claude Code AI assistant and Neovim. It allows users to toggle Claude Code in a terminal window with a single key press, automatically detect and reload files modified by Claude Code, provide real-time buffer updates when files are changed externally, offer customizable window position and size, integrate with which-key, use git project root as working directory, maintain a modular code structure, provide type annotations with LuaCATS for better IDE support, offer configuration validation, and include a testing framework for reliability. The plugin creates a terminal buffer running the Claude Code CLI, sets up autocommands to detect file changes on disk, automatically reloads files modified by Claude Code, provides keymaps and commands for toggling the terminal, and detects git repositories to set the working directory to the git root.

github

: 70

pgvecto.rs

pgvecto.rs is a Postgres extension written in Rust that provides vector similarity search functions. It offers ultra-low-latency, high-precision vector search capabilities, including sparse vector search and full-text search. With complete SQL support, async indexing, and easy data management, it simplifies data handling. The extension supports various data types like FP16/INT8, binary vectors, and Matryoshka embeddings. It ensures system performance with production-ready features, high availability, and resource efficiency. Security and permissions are managed through easy access control. The tool allows users to create tables with vector columns, insert vector data, and calculate distances between vectors using different operators. It also supports half-precision floating-point numbers for better performance and memory usage optimization.

github

: 1.9k

hugging-chat-api

Unofficial HuggingChat Python API for creating chatbots, supporting features like image generation, web search, memorizing context, and changing LLMs. Users can log in, chat with the ChatBot, perform web searches, create new conversations, manage conversations, switch models, get conversation info, use assistants, and delete conversations. The API also includes a CLI mode with various commands for interacting with the tool. Users are advised not to use the application for high-stakes decisions or advice and to avoid high-frequency requests to preserve server resources.

github

: 780

HuixiangDou

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

github

: 2.3k

IntelliNode

IntelliNode is a javascript module that integrates cutting-edge AI models like ChatGPT, LLaMA, WaveNet, Gemini, and Stable diffusion into projects. It offers functions for generating text, speech, and images, as well as semantic search, multi-model evaluation, and chatbot capabilities. The module provides a wrapper layer for low-level model access, a controller layer for unified input handling, and a function layer for abstract functionality tailored to various use cases.

github

: 201

graphiti

Graphiti is a framework for building and querying temporally-aware knowledge graphs, tailored for AI agents in dynamic environments. It continuously integrates user interactions, structured and unstructured data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

github

: 17.7k

gpt-computer-assistant

GPT Computer Assistant (GCA) is an open-source framework designed to build vertical AI agents that can automate tasks on Windows, macOS, and Ubuntu systems. It leverages the Model Context Protocol (MCP) and its own modules to mimic human-like actions and achieve advanced capabilities. With GCA, users can empower themselves to accomplish more in less time by automating tasks like updating dependencies, analyzing databases, and configuring cloud security settings.

github

: 5.8k

scalene

Scalene is a high-performance CPU, GPU, and memory profiler for Python that provides detailed information and runs faster than many other profilers. It incorporates AI-powered proposed optimizations, allowing users to generate optimization suggestions by clicking on specific lines or regions of code. Scalene separates time spent in Python from native code, highlights hotspots, and identifies memory usage per line. It supports GPU profiling on NVIDIA-based systems and detects memory leaks. Users can generate reduced profiles, profile specific functions using decorators, and suspend/resume profiling for background processes. Scalene is available as a pip or conda package and works on various platforms. It offers features like profiling at the line level, memory trends, copy volume reporting, and leak detection.

github

: 12.5k

swiftide

Swiftide is a fast, streaming indexing and query library tailored for Retrieval Augmented Generation (RAG) in AI applications. It is built in Rust, utilizing parallel, asynchronous streams for blazingly fast performance. With Swiftide, users can easily build AI applications from idea to production in just a few lines of code. The tool addresses frustrations around performance, stability, and ease of use encountered while working with Python-based tooling. It offers features like fast streaming indexing pipeline, experimental query pipeline, integrations with various platforms, loaders, transformers, chunkers, embedders, and more. Swiftide aims to provide a platform for data indexing and querying to advance the development of automated Large Language Model (LLM) applications.

github

: 558

continuous-eval

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

github

: 461

For similar tasks

langgraph4j

github

: 365

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 669

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k