This repository demonstrates the Spring AI Orchestrator-Workers Workflow Pattern, a dynamic task orchestration system that enables complex problem decomposition and parallel processing in AI applications. The pattern uses a "manager" LLM to analyze requests, break them into specialized subtasks, and coordinate multiple "worker" LLMs for optimal results.
📖 Dive Deeper: For a complete walkthrough, detailed explanations of the Orchestrator-Workers Workflow pattern, and step-by-step instructions for building the example application, read our comprehensive blog post.
👉 Spring AI Orchestrator-Workers Workflow: Dynamic Task Orchestration for LLM Applications
🎥 Visual Learning: Prefer video tutorials? Watch our step-by-step implementation guide on YouTube.
👉 Spring AI Orchestrator-Workers Workflow: Dynamic Task Orchestration for LLM Applications
Make sure to provide these Java environment variables when running the application:
GEMINI_API_KEY: Your Google Gemini API key.
This project implements an AI-Powered Travel Itinerary Planner as a real-world example of the Spring AI Orchestrator-Workers Workflow. It showcases how to:
- Set up a Spring Boot application with Spring AI and Google Gemini integration.
- Implement the
TravelOrchestratorWorkflowto dynamically analyze travel requests and decompose them into specialized planning tasks. - Execute multiple specialized Worker LLMs in parallel to handle different travel aspects (accommodations, activities, dining, transportation).
- Implement a Synthesizer to combine all worker outputs into a cohesive, day-by-day travel itinerary.
Learn More: This is part of our Spring AI Agentic Workflow series. Check out our other workflow patterns:
