Introduction

The AI industry keeps reaching for the newest framework, as if intelligence begins and ends with the model. But an LLM cannot click through a workflow, inspect a stubborn interface, or reliably make sense of every visual detail on its own. To do useful work, it needs tools that can interact with the world—and Python has had some of those tools for years.

Selenium can give an AI agent controlled access to browser interfaces: opening pages, following workflows, and collecting what a user can actually see. OpenCV can process images and screen captures, extracting visual signals that help the system interpret what is happening. Connected to an LLM or transformer through a deliberate tool-and-feedback loop, they can extend what the model can do—without pretending that either library is part of the model itself.

That distinction matters. Selenium and OpenCV do not magically make an LLM smarter, and their integration is not automatic.…