Building Stateful Multi AI Agents -LangGraph4J & Spring AI
Introduction
In the evolving landscape of AI, retrieval-augmented generation (RAG) systems have become go-to solutions for addressing straightforward queries and delivering contextually relevant responses. However, as the complexity of AI applications continues to grow, the need for systems that extend beyond simple retrieval becomes more apparent. This is where AI agents come into play — autonomous entities capable of executing complex, multi-step tasks, maintaining state across interactions, and adapting dynamically to new information.
What is LangGraph4J?
LangGraph4J is a library designed to elevate your Large Language Model (LLM) applications by introducing cyclic computational capabilities. While LangChain4J supports the creation of Directed Acyclic Graphs (DAGs) for linear workflows, LangGraph4J extends this functionality by enabling the inclusion of cycles, which are crucial for building sophisticated, agent-like behaviors. These behaviors allow LLMs to continuously iterate through processes, dynamically determining the next action to take based on evolving conditions.
LangGraph4j, an excellent library by bsorrentino, serves as a robust orchestration framework for AI workflows. Pairing it with Spring AI makes it possible to build intelligent solutions right in the Java ecosystem. If you’re searching for an LLM orchestration framework in Java, this approach could be a practical alternative to Python-based frameworks like LangChain, LangGraph, and Crew AI.
At the heart of LangGraph is the concept of a stateful graph:
Our team recently explored a powerful combination of tools for Java developers looking to work with AI — LangGraph4j and Spring AI. While experimenting, I created a simple conceptual demo featuring a Weather Agent to showcase the potential of these tools.
I hope this demo helps those who are exploring tools to simplify AI-driven workflow orchestration in Java.
Concept Overview
This system comprises three intelligent agents:
- Weather Agent: Provides weather updates based on the user’s location.
- Travel Agent: Suggests travel destinations and plans based on weather conditions.
- Food Agent: Recommends food options tailored to the user’s preferences and current weather.
These agents interact with each other in a seamless workflow orchestrated by LangGraph4J, ensuring that user prompts are processed and routed intelligently.
How It Works
The system follows a structured flow:
- User Prompt: The journey begins with the user inputting their requirements (e.g., “Current weather in Atlanta?”).
- Weather Agent: The first node evaluates the weather at the user’s location. Based on the conditions, it determines the next course of action.
- Travel Node: If weather conditions are favorable, the system moves to the travel node, where the travel agent generates suggestions for destinations and itineraries.
- Food Node: Finally, the system activates the food agent to recommend local cuisines based on weather conditions.
- End Node: The application compiles the responses into a cohesive output for the user.
Agent Collaboration with LangGraph4J
LangGraph4J acts as the backbone of the system. It provides a graph-based framework to design and implement workflows. Each agent represents a node, while the edges define transitions between nodes based on conditions.
For instance:
- The Weather Node has conditional edges determining whether to proceed to the Travel Node or terminate the flow.
- The Travel Node connects to the Food Node, enabling a seamless experience.
This design ensures that every user prompt triggers a dynamic and context-aware workflow.
Agent State Management
State management is a critical component of this app. Here’s how it is handled:
- Each agent maintains a record of its responses in a shared Agent State Object.
- The state object evolves as the workflow progresses, storing:
- User input
- Weather agent response
- Travel suggestions
- Food recommendations
By the end of the workflow, this state object forms a complete, user-friendly response.
Why Spring AI and LangGraph4J?
- Spring AI: A robust framework for integrating AI-powered services. It provides seamless interaction with OpenAI APIs, ensuring accurate and real-time responses.
- LangGraph4J: A powerful library for building graph-based workflows. It simplifies the design and execution of multi-agent systems, making the code modular and scalable.
Check Out Our GitHub Repository
We’ve packaged this demo into a starter template to make it easier for you to start. You can find the repository here: Dynamo Multi-AI Agent LangGraph4j Starter.
This repository is part of our broader Dynamo ecosystem, which includes various tools and products designed to simplify development. Dynamo offers production-ready templates to accelerate your journey in building scalable and intelligent applications.
Conclusion
With the combination of LangGraph4j and Spring AI. This demo is a starting point to inspire developers who want to integrate LLMs into their applications while maintaining the robust structure of the Java ecosystem.
We encourage you to check out our other Dynamo products that are built to simplify development workflows and provide ready-to-deploy templates.
