9f8cb92aa9
- Remove redundant workflow suffix from agentic pattern names and related classes - Update documentation to reflect pattern name changes
86 lines
2.8 KiB
Markdown
86 lines
2.8 KiB
Markdown
# Orchestrator-Workers Workflow Pattern
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This project demonstrates the Orchestrator-Workers workflow pattern for building effective LLM-based systems, as described in [Anthropic's research on building effective agents](https://www.anthropic.com/research/building-effective-agents).
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## Overview
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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:
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- **Orchestrator**: A central LLM that analyzes tasks and determines required subtasks
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- **Workers**: Specialized LLMs that execute specific subtasks
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- **Synthesizer**: Component that combines worker outputs into a final result
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## When to Use
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This pattern is particularly effective for:
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- Complex tasks where subtasks can't be predicted upfront
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- Tasks requiring different approaches or perspectives
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- Situations needing adaptive problem-solving
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- Tasks benefiting from specialized processing
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## Implementation
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The implementation uses Spring AI's ChatClient for LLM interactions and consists of:
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```java
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public class OrchestratorWorkers {
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public WorkerResponse process(String taskDescription) {
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// 1. Orchestrator analyzes task and determines subtasks
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OrchestratorResponse orchestratorResponse = // ...
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// 2. Workers process subtasks in parallel
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List<String> workerResponses = // ...
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// 3. Results are combined into final response
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return new WorkerResponse(/*...*/);
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}
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}
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```
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### Usage Example
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```java
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ChatClient chatClient = // ... initialize chat client
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OrchestratorWorkers agent = new OrchestratorWorkers(chatClient);
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// Process a task
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WorkerResponse response = agent.process(
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"Generate both technical and user-friendly documentation for a REST API endpoint"
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);
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// Access results
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System.out.println("Analysis: " + response.analysis());
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System.out.println("Worker Outputs: " + response.workerResponses());
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```
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## Customization
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The pattern can be customized through:
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1. **Custom Prompts**: Provide specialized prompts for orchestrator and workers
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```java
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agent = new OrchestratorWorkers(
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chatClient,
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customOrchestratorPrompt,
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customWorkerPrompt
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);
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```
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2. **Default Templates**: Modify the default prompts for common use cases
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- `DEFAULT_ORCHESTRATOR_PROMPT`: Template for task analysis
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- `DEFAULT_WORKER_PROMPT`: Template for worker processing
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## Dependencies
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- Spring AI
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- Spring Boot
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- Java 17 or later
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## References
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- [Building Effective Agents (Anthropic Research)](https://www.anthropic.com/research/building-effective-agents)
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