How to Build a Dual-Engine AI Agent with GPT-4o + Gemini (Python Tutorial)

What You’ll Learn

• How to initialize modern OpenAI and Google GenAI clients
• Intelligent task routing between GPT-4o and Gemini
• Implementing lightweight memory for context retention
• Secure API key management with environment variables

What We’re Building

In this tutorial, we will build a dual-engine AI agent that can dynamically choose between GPT-4o and Gemini 2.5 Flash depending on the task.

The agent uses a simple but effective routing system to decide which model to use, while also maintaining memory of previous interactions. This pattern helps balance cost, speed, and output quality.

Setup and Installation

Install the required packages:

pip install openai google-genai python-dotenv

Create a .env file to store your API keys:

# .env
    OPENAI_API_KEY=sk-...
    GEMINI_API_KEY=AIza...

Step 1: Initializing the Dual-Engine Agent

We begin by setting up the agent class with both OpenAI and Google GenAI clients using the latest SDK patterns.

import os
from dotenv import load_dotenv
from openai import OpenAI
from google import genai
load_dotenv()
class DualEngineAgent:
def init(self):
self.memory = []
self.openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
self.gemini_client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
def query_openai(self, prompt: str) -> str:
response = self.openai_client.chat.completions.create(
...
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