graphify

Building a knowledge graph from a codebase involves extracting entities and relationships from source files, then clustering nodes into communities to map repository architecture. This produces a queryable graph.json output for downstream analysis.

You can discover code dependencies and identify architectural god nodes by analyzing the extracted knowledge graph. The analysis surfaces highly connected abstractions and non-obvious couplings across different modules and file types.

You can export the repository knowledge graph into interactive HTML, JSON, and other documentation-friendly formats. These outputs translate dependency discovery and clustering analysis into stakeholder-friendly architecture reports.

Generating a code knowledge graph requires deterministic AST extraction to parse source files, while semantic LLM extraction is optional. This combination supports architecture discovery and answers how specific modules connect across the codebase.

Clustering helps code navigation by grouping related entities into communities within the knowledge graph, revealing low-cohesion areas and bridges. This allows you to answer how and where specific components connect across large repositories.