Mastering Intelligent Multi-Agent Systems with the PEER Pattern and Google Gemini

Introduction to the PEER Pattern

This tutorial demonstrates building an intelligent multi-agent system based on the PEER pattern: Plan, Execute, Express, and Review. The workflow operates in Google Colab/Notebook, integrating specialized agents and using Google’s Gemini 1.5 Flash model via a free API key.

Setting Up the Environment

We begin by installing essential libraries such as agentUniverse and google-generativeai, then importing necessary modules and configuring the Gemini API key for AI-powered content generation.

!pip install agentUniverse google-generativeai python-dotenv pydantic
 
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)

Defining Agent Roles and Core Classes

Four agent roles are defined using an Enum: Planner, Executor, Expresser, and Reviewer. The Task dataclass holds task metadata, while the BaseAgent class implements core functionality including processing tasks via Gemini API calls, maintaining memory, and fallback mechanisms.

class AgentRole(Enum):
   PLANNER = "planner"
   EXECUTOR = "executor"
   EXPRESSER = "expresser"
   REVIEWER = "reviewer"
 
@dataclass
class Task:
   id: str
   description: str
   context: Dict[str, Any]
   status: str = "pending"
   result: Optional[str] = None
   feedback: Optional[str] = None
 
class BaseAgent:
   ...  # As defined in the source

Implementing the PEERAgent

The PEERAgent class encapsulates the PEER pattern by coordinating the four agents through iterative cycles of planning, execution, expression, and review. Up to three iterations refine task outputs, stopping early if quality criteria are met.

class PEERAgent:
   ...  # Full implementation as provided

MultiAgentOrchestrator and Domain Expertise

The MultiAgentOrchestrator manages the PEER system alongside domain-specific agents (financial, technical, creative), enhancing task processing with specialized knowledge. A simple KnowledgeBase supports domain reasoning.

class MultiAgentOrchestrator:
   ...
 
class KnowledgeBase:
   ...

Demonstrations and Usage

The run_advanced_demo() function runs three example tasks across financial analysis, technical design, and creative strategy, showcasing the system’s versatility and performance.

async def run_advanced_demo():
   ...

System Initialization and Key Insights

The system initialization checks for a valid Gemini API key, explains the PEER pattern and architecture, runs the demo, and provides performance summaries and takeaways.

if __name__ == "__main__":
   ...

Summary of Benefits

The PEER pattern enables systematic task decomposition and iterative refinement. Multi-agent collaboration improves output quality, while domain expertise integration ensures specialized insights. The approach leverages Google Gemini's free AI capabilities, making it accessible and powerful for various complex problem-solving scenarios.

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