This tutorial opens with an examination of Griffe, presenting it as the core of a sophisticated AI Code Analyzer. Griffe’s introspection features allow dynamic loading, traversal, and breakdown of Python package structures in real time. The guide covers integration with NetworkX for generating dependency graphs and Matplotlib for crafting visual dashboards that convert raw codebases into useful intelligence. Along the way, Griffe helps measure complexity, highlight missing documentation, and identify structural vulnerabilities, with an automatic shift to basic introspection if deeper parsing encounters limits.
First, the script imports Griffe together with requests, Matplotlib, and NetworkX to examine Python package internals on the fly. Once the dependencies are loaded, the setup is primed to leverage Griffe’s introspection capabilities, generate dependency mappings, and produce clear visual output free of extraneous warnings.
An AICodeAnalyzer class wraps Griffe’s in-depth inspection engine into a unified interface. This class can load any target package, walk through its modules, classes, and functions, and store computed metrics for rapid retrieval. During traversal, Griffe uncovers inheritance hierarchies and pulls in-line docstrings. The analyzer then applies custom complexity metrics, flags potential risks, and supports comparative analyses between packages, all set for comprehensive visualization. Griffe supplies the baseline structure; we convert it into targeted AI insights.
The main() function orchestrates a live demonstration of Griffe in action. It scans key modules, switches to basic introspection if deeper parsing fails, and reports immediate complexity scores. Next, these metrics drive an interactive dashboard and enable head-to-head comparisons of two packages, delivering data-driven recommendations on demand.
A standard if __name__ == "__main__": guard at the end ensures that executing the script directly triggers the Griffe-powered main() routine without additional setup.
This demonstration shows how Griffe extends analysis beyond basic static checks, equipping AI agents with detailed, metric-based insights into Python codebases. By centering the process on Griffe, complexity scores are calculated automatically, component distributions are rendered visually, and package-to-package comparisons yield concise recommendations. The workflow accelerates architectural evaluations and establishes a modular framework for future upgrades, such as extra static-analysis routines, expanded visualization options, or enhanced risk detection policies.