Build Intelligent Multi-Agent Systems with the PEER Pattern: A Comprehensive Coding Guide

Introduction to Multi-Agent Systems

Multi-agent systems (MAS) are becoming increasingly important in various fields, from finance to technology and creative industries. These systems consist of multiple agents that work together to solve complex problems. This article will guide you through building an intelligent multi-agent system using the PEER pattern: Plan, Execute, Express, and Review. By the end, you will understand how to set up and implement a collaborative framework that enhances task management and output quality.

Installation and Configuration

To get started, you need to install the necessary libraries. This includes agentUniverse and google-generativeai. You can easily set this up in Google Colab or Jupyter Notebook:

!pip install agentUniverse google-generativeai python-dotenv pydantic

Next, configure the Gemini API using your free API key. This step is crucial for enabling AI-powered content generation:

import os
import asyncio
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from enum import Enum
import json
import time
import google.generativeai as genai

GEMINI_API_KEY = 'Use Your API Key Here' 
genai.configure(api_key=GEMINI_API_KEY)

Agent Roles and Task Management

In our multi-agent system, we define four specific roles:

• Planner: Breaks down complex tasks into actionable steps.
• Executor: Completes tasks efficiently using available tools and knowledge.
• Expresser: Presents results clearly and professionally.
• Reviewer: Reviews outputs and provides improvement feedback.

Each agent operates within a structured framework, allowing for effective communication and task management. The Task dataclass helps manage task metadata, including status and results.

Implementing the PEER Pattern

The PEER pattern is implemented through the PEERAgent class, which coordinates the four specialized agents. This class allows for iterative refinement of outputs through structured processes. Each task goes through the phases of Planning, Execution, Expression, and Review, with up to three iterations to enhance efficiency:

class PEERAgent:
    def __init__(self):
        self.planner = BaseAgent("Strategic Planner", AgentRole.PLANNER, "You are a strategic planning agent. Break down complex tasks into actionable steps.")
        self.executor = BaseAgent("Task Executor", AgentRole.EXECUTOR, "You are an execution agent. Complete tasks efficiently using available tools and knowledge.")
        self.expresser = BaseAgent("Result Expresser", AgentRole.EXPRESSER, "You are a communication agent. Present results clearly and professionally.")
        self.reviewer = BaseAgent("Quality Reviewer", AgentRole.REVIEWER, "You are a quality assurance agent. Review outputs and provide improvement feedback.")
        self.iteration_count = 0
        self.max_iterations = 3

Orchestrating Multi-Agent Collaboration

The MultiAgentOrchestrator manages the entire system, processing complex tasks using the PEER pattern. It can also enhance results with domain-specific agents when necessary:

class MultiAgentOrchestrator:
    def __init__(self):
        self.agents = {}
        self.peer_system = PEERAgent()
        self.task_queue = []

    def register_agent(self, agent: BaseAgent):
        self.agents[agent.name] = agent

This orchestrator allows for seamless collaboration among agents, ensuring that tasks are completed efficiently and effectively.

Running the Demo

To showcase the capabilities of the multi-agent system, we can run a demo that tests the pipeline with various tasks:

async def run_advanced_demo():
    orchestrator = MultiAgentOrchestrator()
    financial_task = "Analyze the potential impact of rising interest rates on tech stocks portfolio"
    result1 = await orchestrator.process_complex_task(financial_task, "financial")
    technical_task = "Design a scalable microservices architecture for a high-traffic e-commerce platform"
    result2 = await orchestrator.process_complex_task(technical_task, "technical")
    creative_task = "Create a comprehensive brand strategy for a sustainable fashion startup"
    result3 = await orchestrator.process_complex_task(creative_task, "creative")
    return {
        "demo_results": [result1, result2, result3],
        "agent_stats": {
            "total_tasks": 3,
            "success_rate": "100%",
            "avg_iterations": sum(len(r['peer_results']['iterations']) for r in [result1, result2, result3]) / 3
        }
    }

Conclusion

This tutorial illustrates how a multi-agent system can effectively tackle complex problems by leveraging domain-specific reasoning and structured communication. The PEER framework enhances collaboration among agents, leading to improved task outcomes. As we continue to explore the potential of modular AI systems, the insights gained from this approach can pave the way for scalable and intelligent applications across various industries.

FAQs

1. What is a multi-agent system?

A multi-agent system is a system composed of multiple interacting intelligent agents that can work together to solve complex problems.

2. How does the PEER pattern work?

The PEER pattern involves four phases: Planning, Execution, Expression, and Review, allowing agents to iteratively refine their outputs.

3. What are the roles of agents in this system?

The roles include Planner, Executor, Expresser, and Reviewer, each responsible for different aspects of task management.

4. Can this system be applied to different industries?

Yes, the multi-agent system can be adapted for various sectors, including finance, technology, and creative industries.

5. How can I get started with building my own multi-agent system?

You can start by following the installation and configuration steps outlined in this article, then implement the PEER pattern using the provided code examples.