aggregator-workflow

Multi-Agent Aggregator Workflow

Demonstrates multi-agent collaboration using the Aggregator agent to synthesize insights from specialized AI agents.

Quick Start

cd examples/aggregator-workflow
export OPENAI_API_KEY="your-key"
go run main.go

Overview

This example showcases a Research Synthesis System with:

• 6 specialized expert agents - Technical, Data Science, Business, Security, Ethics, Domain experts
• 3 aggregation strategies - Consensus, Semantic, and Weighted synthesis
• Conflict resolution - LLM-powered reasoning to resolve expert disagreements
• Semantic clustering - Group related insights thematically
• Final synthesis - Comprehensive analysis with recommendations

Comprehensive Guide: See Classifier & Aggregator Examples for strategy selection, configuration options, and advanced patterns.

Aggregation Strategies

Files

• main.go - Complete multi-agent workflow
• config.yaml - Expert definitions, aggregation settings
• research_synthesis_output.json - Detailed results (generated)

Example Output

=== CONSENSUS AGGREGATION ===
Consensus Level: 0.83
Content: Based on expert consensus, LLMs are transforming software
development with 83% agreement on key impacts...
Conflicts Resolved: 2

=== FINAL SYNTHESIS ===
Key Insights:
1. Technical Impact (Confidence: 0.92) - 40-60% acceleration
2. Security Considerations (Priority: 0.95) - New vulnerability classes
3. Business Implications (Confidence: 0.75) - Positive ROI at scale

Related

• Classifier Workflow - Intelligent classification
• Patterns Guide - All orchestration patterns