What problem does it solve? Understanding a large mixed corpus of code, documents, papers, and images requires reading everything manually, and connections across files stay hidden. graphify turns any folder into a persistent, queryable knowledge graph with an honest audit trail showing which relationships were extracted versus inferred. ## Core Features & Use Cases - Knowledge Graph Extraction: Combines deterministic AST parsing for code with parallel LLM subagents for docs, papers, and images, tagging every edge as EXTRACTED, INFERRED, or AMBIGUOUS with confidence scores. - Community Detection & Analysis: Clusters the graph into labeled communities, surfaces god nodes and surprising cross-document connections, and generates a plain-language GRAPH_REPORT.md. - Multiple Export Formats: Produces interactive HTML, GraphRAG-ready JSON, Obsidian vaults, SVG, GraphML, and Neo4j Cypher or direct push, plus an MCP server for agent access. - Use Case: Point it at an unfamiliar codebase or a research reading list to get a navigable graph showing architecture, concept clusters, and unexpected connections before touching anything. ## Quick Start Run /graphify on the current directory to build a knowledge graph and open the generated graph.html report.
