spring-ai-examples/agentic-patterns/orchestrator-workers/README.md

Orchestrator-Workers Workflow Pattern

This project demonstrates the Orchestrator-Workers workflow pattern for building effective LLM-based systems, as described in Anthropic's research on building effective agents.

Overview

The Orchestrator-Workers pattern is a flexible approach for handling complex tasks that require dynamic task decomposition and specialized processing. It consists of three main components:

• Orchestrator: A central LLM that analyzes tasks and determines required subtasks
• Workers: Specialized LLMs that execute specific subtasks
• Synthesizer: Component that combines worker outputs into a final result

When to Use

This pattern is particularly effective for:

• Complex tasks where subtasks can't be predicted upfront
• Tasks requiring different approaches or perspectives
• Situations needing adaptive problem-solving
• Tasks benefiting from specialized processing

Implementation

The implementation uses Spring AI's ChatClient for LLM interactions and consists of:

public class OrchestratorWorkers {
    public WorkerResponse process(String taskDescription) {
        // 1. Orchestrator analyzes task and determines subtasks
        OrchestratorResponse orchestratorResponse = // ...

        // 2. Workers process subtasks in parallel
        List<String> workerResponses = // ...

        // 3. Results are combined into final response
        return new WorkerResponse(/*...*/);
    }
}

Usage Example

ChatClient chatClient = // ... initialize chat client
OrchestratorWorkers agent = new OrchestratorWorkers(chatClient);

// Process a task
WorkerResponse response = agent.process(
    "Generate both technical and user-friendly documentation for a REST API endpoint"
);

// Access results
System.out.println("Analysis: " + response.analysis());
System.out.println("Worker Outputs: " + response.workerResponses());

Customization

The pattern can be customized through:

1. Custom Prompts: Provide specialized prompts for orchestrator and workers

agent = new OrchestratorWorkers(
    chatClient,
    customOrchestratorPrompt,
    customWorkerPrompt
);

2. Default Templates: Modify the default prompts for common use cases
   • DEFAULT_ORCHESTRATOR_PROMPT: Template for task analysis
   • DEFAULT_WORKER_PROMPT: Template for worker processing

Dependencies

• Spring AI
• Spring Boot
• Java 17 or later

References

• Building Effective Agents (Anthropic Research)