Setting up the Environment
Overview
AI applications can be built using many programming languages and frameworks. However, Python is the most commonly used language for AI, machine learning, AI workflows, and AI agents.
- Popular in AI development
- Large ecosystem
- Strong AI library support
For these reasons, the examples in this section uses Python. The concepts remain the same regardless of the programming language, but Python is often the easiest place to start when building AI-powered applications.
Using UV for Python Projects
Before building AI workflows and agents, you need a Python project. One simple way to do this is by using UV.
UV is a Python project and package manager. It automatically creates a virtual environment for each project so that installed packages do not affect your global Python installation.
See UV Installation.
Once installed, verify that UV is available.
In the example below, the uv command is used to check the installed version.
uv --version
Output:
uv 0.11.21 (x86_64-unknown-linux-gnu)
Create a New Project
After installing UV, create a directory for your project. Inside the new directory, initialize the project.
uv init
Output:
Initialized project
After running the command, your folder may look similar to this:
project/
├── README.md
├── main.py
└── pyproject.toml
The important file for now is main.py, which will contain your Python code.
Run the Project
Open the project folder in your code editor.
Many developers use Visual Studio Code, but any code editor will work.
Once opened, locate the main.py file.
A new main.py file is usually very simple.
def main():
print("Hello from building-ai-workflows!")
if __name__ == "__main__":
main()
Run the Python file using UV.
uv run main.py
Output:
Using CPython 3.12.12
Creating virtual environment at: .venv
Hello from building-ai-workflows!
Once the project is ready, the next step is to add code that interacts with AI models and external APIs.