Build an AI Agent Simulator with PyQt6 | Step-by-Step Tutorial for Students and Developers

In this video, we'll walk you through the development of a simple AI Agent Simulator built with Python and PyQt6. This project introduces you to core concepts in GUI development and rule-based AI decision-making. By the end of this tutorial, you will have built a desktop application that simulates a basic environment with price fluctuations and uses a simple AI agent to make decisions like Buy, Sell, or Wait. 🧠 What You'll Learn - How to build an interactive GUI using PyQt6 - Create a simple rule-based AI agent - Handle user input and trigger both manual and automated decisions - Structure and organize a PyQt6-based application 📦 Prerequisites To follow along, you should have: - Basic Python knowledge PyQt6 installed: pip install PyQt6 💻 Step-by-Step Breakdown 🔹 Step 1: Define a Simple AI Agent class SimpleAgent: def decide(self, environment): price = environment["price"] if price (less sign) 30: return "Buy" elif price (great sign) 70: return "Sell" else: return "Wait" This AI agent uses basic logic to determine what action to take based on a price value. 🔹 Step 2: Create the Main Application Window The AIAgentSimulator class inherits from QWidget, creating the main interface: class AIAgentSimulator(QWidget): def __init__(self): super().__init__() self.setWindowTitle("AI Agent Simulator") self.setGeometry(100, 100, 400, 300) self.environment = {"price": 50} self.agent = SimpleAgent() self.create_widgets() self.layout_widgets() We set an initial price of 50 and instantiate the SimpleAgent. 🔹 Step 3: Build Interactive Widgets We add a price slider, buttons for manual actions, and a text box to show the agent’s responses. self.price_slider = QSlider(Qt.Orientation.Horizontal) self.price_slider.setRange(0, 100) self.price_slider.setValue(self.environment['price']) self.price_slider.valueChanged.connect(self.update_price) Manual action buttons (Buy, Sell, Wait) are linked to their respective handlers. 🔹 Step 4: Layout the Components Use QVBoxLayout and QHBoxLayout to neatly organize all widgets: layout = QVBoxLayout() layout.addWidget(QLabel("Environment Controls")) layout.addWidget(self.price_label) layout.addWidget(self.price_slider) # Manual action group h_layout = QHBoxLayout() h_layout.addWidget(self.buy_button) h_layout.addWidget(self.sell_button) h_layout.addWidget(self.wait_button) self.action_group.setLayout(h_layout) layout.addWidget(self.action_group) layout.addWidget(self.auto_decide_button) layout.addWidget(QLabel("Agent Output")) layout.addWidget(self.result_box) 🔹 Step 5: Add Functionality Whenever the price is updated or a button is clicked, we reflect the changes accordingly: def update_price(self, value): self.environment["price"] = value self.price_label.setText(f"Price: {value}") def manual_action(self, action): self.result_box.append(f"🔧 Manual action triggered: {action}") def agent_decision(self): decision = self.agent.decide(self.environment) self.result_box.append(f"🤖 Agent decision based on price {self.environment['price']}: {decision}") 🔹 Step 6: Run the Application if __name__ == "__main__": app = QApplication(sys.argv) simulator = AIAgentSimulator() simulator.show() sys.exit(app.exec()) 📺 What to Include in the Video - Introduction to PyQt6 and AI Agent concepts - Live-coding or walkthrough of each step - Real-time interaction with sliders and buttons - Explanation of the rule-based decision logic - Encourage viewers to extend the project (e.g., add more rules, change environments) 💡 Suggested Extensions - Use a real-time data feed (e.g., stock API) - Replace rule-based logic with machine learning - Add logging, charts, or export features - Support multi-agent simulations 🧑‍🏫 Ideal For - Students learning AI and Python GUI programming - Developers exploring rule-based agents - Educators seeking simple, interactive coding examples 📢 Final Thoughts This is a powerful starter project to understand how AI agents can interact with environments and be visualized in a desktop application. Once you’re comfortable, try replacing the agent with more advanced logic or integrate real-world data. 👉 Don’t forget to like, subscribe, and share if this helped you learn something new!