Introduction
In this tutorial, you'll learn how to create a simple AI agent marketplace simulation using Python. This project demonstrates the core concepts behind the AI agent commerce experiments mentioned in the TechCrunch article about Anthropic's marketplace. You'll build a basic system where AI agents can buy and sell items, mimicking real-world commerce interactions.
Prerequisites
Before starting this tutorial, you'll need:
- A computer with Python 3.7 or higher installed
- Basic understanding of Python programming concepts
- Some familiarity with object-oriented programming
- Optional: A text editor or IDE like VS Code or PyCharm
Step-by-Step Instructions
Step 1: Set Up Your Development Environment
Install Python (if not already installed)
First, ensure you have Python installed on your system. You can verify this by opening a terminal or command prompt and typing:
python --versionIf Python isn't installed, download it from python.org and follow the installation instructions.
Step 2: Create Your Project Structure
Create a new directory for your project
Open your terminal and create a new folder for this project:
mkdir agent_marketplace
cd agent_marketplaceInside this directory, create a file called marketplace.py where we'll write our code.
Step 3: Define Your Agent Classes
Create the base Agent class
Open marketplace.py in your text editor and start by defining the basic agent structure:
import random
import time
class Agent:
def __init__(self, name, budget=100):
self.name = name
self.budget = budget
self.inventory = []
def __str__(self):
return f"{self.name} (Budget: ${self.budget})"
def add_item(self, item):
self.inventory.append(item)
def remove_item(self, item):
if item in self.inventory:
self.inventory.remove(item)
return True
return False
def can_afford(self, price):
return self.budget >= price
def spend(self, amount):
if self.can_afford(amount):
self.budget -= amount
return True
return FalseThis creates a basic agent with name, budget, and inventory. The agent can buy and sell items, track their money, and check if they can afford purchases.
Step 4: Create the Marketplace Class
Define the marketplace structure
class Marketplace:
def __init__(self):
self.agents = []
self.items_for_sale = []
self.transactions = []
def add_agent(self, agent):
self.agents.append(agent)
def add_item_for_sale(self, item, seller):
item_dict = {
'item': item,
'seller': seller,
'price': random.randint(10, 50) # Random price for demo
}
self.items_for_sale.append(item_dict)
def list_items(self):
print("\n--- Items for Sale ---")
for i, item_dict in enumerate(self.items_for_sale):
print(f"{i+1}. {item_dict['item']} - ${item_dict['price']} (Seller: {item_dict['seller'].name})")
def buy_item(self, buyer, item_index):
if item_index < 0 or item_index >= len(self.items_for_sale):
print("Invalid item selection!")
return False
item_dict = self.items_for_sale[item_index]
item = item_dict['item']
seller = item_dict['seller']
price = item_dict['price']
if buyer.can_afford(price):
# Process the transaction
buyer.spend(price)
seller.budget += price
buyer.add_item(item)
seller.remove_item(item)
# Record transaction
transaction = {
'buyer': buyer.name,
'seller': seller.name,
'item': item,
'price': price,
'timestamp': time.time()
}
self.transactions.append(transaction)
# Remove item from sale
self.items_for_sale.remove(item_dict)
print(f"Transaction successful! {buyer.name} bought {item} from {seller.name} for ${price}")
return True
else:
print(f"{buyer.name} cannot afford {item} for ${price}")
return FalseThis marketplace class manages agents, items for sale, and handles transactions between buyers and sellers.
Step 5: Create Sample Agents and Items
Set up your demo environment
def main():
# Create marketplace
marketplace = Marketplace()
# Create sample agents
alice = Agent("Alice", budget=150)
bob = Agent("Bob", budget=120)
charlie = Agent("Charlie", budget=200)
# Add agents to marketplace
marketplace.add_agent(alice)
marketplace.add_agent(bob)
marketplace.add_agent(charlie)
# Create sample items
items = ["Laptop", "Smartphone", "Headphones", "Tablet", "Book"]
# Add items for sale
for i, item in enumerate(items):
seller = random.choice([alice, bob, charlie])
marketplace.add_item_for_sale(item, seller)
print("Welcome to the AI Agent Marketplace!")
print("Agents: Alice, Bob, Charlie")
print("Starting inventory:")
# Display initial state
marketplace.list_items()
# Simulate some transactions
print("\n--- Simulating Transactions ---")
# Alice tries to buy something
alice_buy_index = 0 # First item
marketplace.buy_item(alice, alice_buy_index)
# Bob tries to buy something
bob_buy_index = 1 # Second item
marketplace.buy_item(bob, bob_buy_index)
# Display final state
print("\n--- Final Marketplace State ---")
marketplace.list_items()
print("\n--- Transaction History ---")
for transaction in marketplace.transactions:
print(f"{transaction['buyer']} bought {transaction['item']} from {transaction['seller']} for ${transaction['price']}")
if __name__ == "__main__":
main()This code sets up a demo marketplace with three agents and several items. It simulates buying transactions between agents.
Step 6: Run Your Marketplace Simulation
Execute your Python script
Save your marketplace.py file and run it from the terminal:
python marketplace.pyYou should see output showing agents, items for sale, transactions, and the final marketplace state.
Step 7: Enhance Your Marketplace (Optional)
Make it more realistic
For a more advanced version, you could add:
- Agent personalities that affect buying behavior
- Item categories and quality ratings
- Price negotiation features
- Market trends that affect pricing
These enhancements would make your simulation closer to real AI agent commerce systems.
Summary
In this tutorial, you've built a basic AI agent marketplace simulation that demonstrates the core concepts behind Anthropic's agent-on-agent commerce experiment. You created agents that can buy and sell items, managed a marketplace where transactions occur, and tracked the flow of goods and money between participants.
This simple simulation shows how AI agents can interact in economic systems, which is the foundation for more complex autonomous commerce systems. While this example is basic, it illustrates the fundamental principles of how AI agents might negotiate, trade, and make decisions in real-world marketplace environments.
