What problem does it solve? Understanding a large codebase or mixed document corpus requires reading hundreds of files manually. graphify turns any folder into a persistent knowledge graph with community detection and an honest audit trail (EXTRACTED/INFERRED/AMBIGUOUS), so questions about architecture, file relationships, and cross-document connections can be answered by graph traversal instead of re-reading source files. ## Core Features & Use Cases - Knowledge graph construction: Extracts entities and relationships from code (AST, no LLM needed), docs, papers, images, and transcribed video, then clusters them into labeled communities with god-node and surprising-connection analysis. - Query, path, and explain tools: Answers natural-language questions via BFS/DFS traversal, finds shortest paths between concepts, and explains individual nodes with source citations. - Multiple outputs and integrations: Generates interactive HTML visualization, GraphRAG-ready JSON, GRAPH_REPORT.md, Obsidian vaults, wikis, and exports to Neo4j, FalkorDB, SVG, GraphML, or an MCP server. - Use Case: Point it at a GitHub repo URL to clone, extract, and merge multiple repositories into one cross-repo graph, then ask "How does the auth module reach the database?" and get a cited traversal answer. ## Quick Start Run /graphify on the current directory to build the knowledge graph, then ask any codebase question and it will be answered via graphify query against the existing graph.
