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Basic Information

LangGraph4j is a Java library and framework for building stateful, multi-agent applications that orchestrate Large Language Models (LLMs) and custom Java logic. It lets developers define cyclical graphs of nodes and edges where a shared AgentState flows through nodes that perform actions such as LLM calls, tool execution, or business logic. The project targets integration with existing Java LLM ecosystems, notably langchain4j and Spring AI, and provides abstractions for state schemas, reducers, channels, synchronous and asynchronous node actions, and conditional transitions. LangGraph4j includes tools for compiling graphs into runnable artifacts, running graphs in streaming or invoked modes, and persisting execution checkpoints. It also ships an embeddable Studio web UI for visual inspection and debugging of graphs.

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App Details

Features
LangGraph4j supports stateful execution with a typed AgentState and a schema of channels and reducers to manage shared data. It allows cyclical graphs and explicit control flow including conditional edges and entry points, enabling agent handoffs and retries. Nodes may be synchronous or asynchronous and return state updates that are applied by reducers. The library provides checkpointing and persistence to save and restore graph state, graph compilation which validates structure, streaming execution and non-blocking asynchronous operations, and visualization generation for PlantUML or Mermaid. Additional features include a Studio playground UI, nested child graphs for modular composition, parallel branch execution, multi-threaded conversation support, and built-in integrations and example AgentExecutor implementations for langchain4j and Spring AI.
Use Cases
LangGraph4j helps developers build complex, memoryful agent workflows in Java by providing a structured, reusable graph model where nodes, edges, and a shared state are first-class concepts. Checkpoints and replay enable debugging, resuming long-running processes, and time-travel inspection of intermediate states. Streaming and asynchronous node support make real-time responses and non-blocking LLM calls easier to implement. Graph compilation enforces correctness and simplifies deployment of runnable graphs. Visualization and the Studio UI improve observability and development speed. Built-in integrations, examples, and an AgentExecutor reference for langchain4j and Spring AI reduce integration effort and provide practical starting points for LLM-based agents.

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