Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations
In this tutorial, we build a fully offline Graphify workflow that turns a realistic multi-module Python application into a knowledge graph. We start by installing Graphify and supporting graph libraries, then generate a small but connected sample application with configuration, database, authentication, service, API, cache, model, and SQL layers. We extract the graph locally using Graphify’s tree-sitter-based analysis, so we do not need an API key or any LLM backend. After loading the generated
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This tutorial demonstrates how to analyze the structure of a Python codebase by converting it into a knowledge graph using Graphify and NetworkX libraries. The process involves extracting code relationships locally using tree-sitter-based analysis without requiring an API key or LLM backend, then loading the resulting graph data to identify patterns like "god nodes" (heavily connected components), communities of related modules, and shortest paths between important symbols. The approach enables both static and interactive visualizations that show how modules, classes, functions, and database objects connect across a multi-layer application with configuration, database, authentication, service, API, cache, and model components.
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