Creating an AI Agent in Python: A Complete Beginner's Guide

Introduction to AI Agents

AI agents are sophisticated programs capable of much more than simply answering questions, as chatbots do. They can observe, think, decide, and act to accomplish tasks with minimal human intervention. For example, if you want to buy a new laptop for intensive programming, a chatbot will suggest suitable models and answer your questions one by one. These chatbots have limited memory and primarily function as text generators. In contrast, an AI agent will take your goals into account and execute tasks automatically. It will research, compare, plan, and analyze requirements to make decisions based on thorough research. For your question about the laptop, the AI agent will provide a detailed comparison table with products, their prices, as well as their advantages and disadvantages.

How Does an AI Agent Work?

An AI agent is an intelligent program designed to achieve a specific goal. When a task is assigned to it, the AI agent begins by receiving the request, breaking it down into smaller problems to solve, and may ask the user for additional information to fully understand the requirements. It then uses appropriate tools such as web searches, calculators, and its own memory to gather additional information. The AI agent carefully analyzes this information, compares different options, and formulates a response tailored to the user's needs.

Creating an Educational AI Agent in Python

Installing Python

To get started, ensure that Python is installed on your system. This project relies on Python, so its installation is crucial. Visit the official Python website and follow the installation steps. Be sure to check the "Add Python to PATH" box before clicking "Install Now" to ensure Python is correctly set up on your system.

Installing and Configuring PyCharm

When coding, it's essential to have an integrated development environment (IDE) that allows you to write, run, and debug your code. While several IDEs are available for coding in Python, such as Spyder, Jupyter Notebooks, and Visual Studio, we will use PyCharm in this tutorial. PyCharm offers an integrated terminal and simplifies library installation, making it ideal for beginners. Download the Community Edition of PyCharm from the official website and choose the download option that corresponds to your operating system.

Setting Up the Project and Creating the Python File

Once PyCharm is installed, create a new project. A project in PyCharm is like a folder that can contain various files, including Python code files, libraries, and environment files. Create a Python file named main.py, which will contain the main code. To test your installation, write a simple code and run it:

print("Welcome to my new project on AI agents")

If you reach this step, it means everything is working correctly!

Creating the Environment File

We will now create an environment file, typically named .env, which will securely store sensitive information for the project. These files are used to save keys, passwords, and configuration settings, making the project more secure and professional. In this project, we will create an environment file and store our API key in it.

Creating the API Key

For our program to connect to an AI model, we need an API key. But what is an API? An API, or Application Programming Interface, is a set of rules or protocols that allows two distinct software systems to communicate with each other. For example, weather applications use APIs to obtain live weather data from relevant weather servers. In our project, we will use OpenRouter, which is a unified interface for LLMs and AI models. OpenRouter allows us to connect our AI agent to existing models and utilize their features in our program.

To obtain an API key, create an account on OpenRouter, access the dashboard, and click on "Get API Key." Then click on the "+ New Key" icon to create your API key. Specify the project, then copy the key and paste it into the API_KEY variable in your .env file.

Installing Relevant Dependencies

Now that our API key is created and secured in the .env file, let's return to our main.py file and start coding. The first thing to do is to install and import the necessary dependencies. In this project, we need Python to communicate with pre-built AI models, send requests, and process responses. Since these features are not available in Python's standard library, we will install the [openai](/dossier/openai) Python library and import it into our code.

To install, go to the terminal icon in your PyCharm IDE and type:

pip install openai

Once the OpenAI library is installed, we will import it into our main.py file:

from openai import OpenAI

Next, to access the API in our .env file, we will install and import the dotenv Python library, which is designed to read information from .env files.