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Multi-Agent Example V2: Simple Genesis Demo

Overview

This example demonstrates the core Genesis framework capabilities using a minimal multi-agent setup. It shows how to build agents that automatically discover each other and call external services.

System Architecture

🏗️ Actual Components

  1. PersonalAssistant Agent - OpenAI-powered assistant that can call other services
  2. Calculator Service - Mathematical computation service (from existing Genesis services)
  3. CLI Interface - Simple command-line interface for testing
  4. Launch Script - Automated startup of all components

🔄 Communication Flow

CLI Interface (MonitoredInterface)
    ↓ discovers & connects
PersonalAssistant (OpenAIGenesisAgent)
    ↓ function calls
Calculator Service (via Genesis function discovery)

Example Interactions

💬 Scenario 1: Simple Conversation

User: "Tell me a joke"
CLI → PersonalAssistant → OpenAI API → Response

🧮 Scenario 2: Mathematical Calculation

User: "What is 127 + 384?"
CLI → PersonalAssistant → Calculator Service → "511"

Technical Implementation

🔧 Genesis Framework Usage

PersonalAssistant:

  • Inherits from OpenAIGenesisAgent
  • Uses enable_agent_communication=True
  • Automatically discovers calculator service via Genesis
  • Calls await self.run() to start RPC service

CLI Interface:

  • Inherits from MonitoredInterface
  • Uses built-in available_agents for discovery
  • Uses built-in connect_to_agent() and send_request()
  • No custom discovery or connection logic

Calculator Service:

  • Uses existing /services/calculator_service.py
  • No modifications needed - Genesis handles discovery

📁 File Structure

examples/MultiAgentV2/
├── agents/
│   └── personal_assistant.py     # OpenAIGenesisAgent implementation
├── test_cli.py                   # MonitoredInterface test client
├── run_multi_agent_demo.sh       # Launch script
├── README.md                     # Usage instructions
├── IMPLEMENTATION_GUIDE.md       # Technical patterns
└── IMPLEMENTATION_CHECKLIST.md   # Task tracking

Success Criteria

✅ Working Demo Requirements

  1. One-Command Launch: ./run_multi_agent_demo.sh starts everything
  2. Automatic Discovery: CLI finds PersonalAssistant without configuration
  3. LLM Integration: PersonalAssistant responds to conversational queries
  4. Service Integration: PersonalAssistant calls calculator for math
  5. Real APIs Only: No mock data or simulated responses

🎯 Demo Scenarios

  1. Start demo script
  2. CLI discovers PersonalAssistant
  3. Test: "Tell me a joke" → OpenAI response
  4. Test: "What is 127 + 384?" → Calculator service → "511"
  5. All communication automatic (no manual configuration)

Implementation Notes

✅ What Genesis Provides

  • Agent Discovery: Automatic via DDS topics
  • Function Discovery: Automatic service detection
  • RPC Communication: Built-in request/response
  • Monitoring: Comprehensive event tracking
  • Error Handling: Built-in timeouts and recovery

❌ What NOT to Build

  • Custom discovery systems
  • Connection managers
  • Conversation managers
  • Mock data or simulation
  • Manual DDS topic management

Educational Value

This example teaches:

  1. Correct Genesis Patterns - How to use the framework properly
  2. Agent Communication - Real agent-to-agent and agent-to-service calls
  3. Automatic Discovery - No configuration required
  4. LLM Integration - OpenAI + function calling
  5. Service Integration - Using existing Genesis services

Target Audience

  • Genesis developers learning the framework
  • New users wanting to see Genesis capabilities
  • System integrators needing multi-agent examples
  • Anyone who wants to see "it just works" without complex setup

Implementation Time

Estimated: 2-4 hours total

  • PersonalAssistant: 30 minutes (copy existing pattern)
  • CLI Interface: 30 minutes (copy existing pattern)
  • Launch Script: 15 minutes (standard bash script)
  • Testing: 1-2 hours (verify real agent communication)
  • Documentation: 30 minutes

This is a simple, working example that demonstrates Genesis capabilities without overengineering.