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Basic Information

LangGraph4j is a Java library and framework for building stateful, multi-agent applications that orchestrate Large Language Models and custom logic. It provides primitives to define cyclical graphs where nodes represent agents, tools, or business logic and edges control execution flow including conditional transitions. The library manages a shared AgentState schema with reducers and channels so nodes can read and update persistent context. LangGraph4j is inspired by a Python counterpart and integrates natively with Java LLM ecosystems such as Langchain4j and Spring AI. The project includes a studio web UI for visual experimentation, example how-tos, and prebuilt modules like an AgentExecutor to implement ReACT-style agents. The repo also publishes Maven artifacts and targets Java 17 or later.

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Features
LangGraph4j offers stateful execution with a schema driven AgentState and reducers, support for cyclical graphs that allow loops and handoffs, explicit control flow via normal and conditional edges, and modular node abstractions that can be synchronous or asynchronous. It supports streaming responses from LLMs, asynchronous operations with CompletableFuture, checkpoints and persistence for debugging and resuming executions, graph visualization via PlantUML or Mermaid, a web based Studio playground, child graphs and subgraph composition, parallel branches and multi-threaded conversations, and built integrations demonstrating Langchain4j and Spring AI usage. The project is organized into modules and provides Maven coordinates for easy inclusion.
Use Cases
The library helps Java developers build complex, long‚Äërunning agentic workflows that require memory, context and coordination between multiple components. Checkpoints and state persistence enable debugging, time travel and resuming interrupted processes. Visualization and the Studio UI make graph structure and execution easier to inspect and iterate on. Built integrations and examples reduce integration effort with LLM providers and frameworks like Langchain4j and Spring AI. Features such as streaming, asynchronous nodes, parallel branches and subgraph composition improve responsiveness and modularity. Overall LangGraph4j accelerates development of agent executors, tool orchestration and multi‚Äëturn conversational or task workflows in Java by providing a structured, reusable runtime and tooling.

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