graphify: a knowledge graph generator for codebases and documentation that replaces grepping with path-tracing and semantic queries

What it solves

Graphify transforms a codebase and its associated documentation (PDFs, images, videos) into a structured knowledge graph. This allows developers to query the relationships between concepts, trace paths between different parts of the code, and understand the architecture without manually grepping through files or relying solely on vector-based search.

How it works

Graphify uses a hybrid approach to build its knowledge graph:

• Deterministic Code Parsing: It uses tree-sitter AST to parse code across approximately 40 languages locally. This ensures that connections like calls, imports, and inheritance are mapped deterministically without using an LLM, keeping the code analysis local-first.
• Semantic Pass: For non-code assets like PDFs, images, and videos, it performs a semantic pass using a configured AI model or API key to extract information.
• Graph Structure: Instead of a vector index, it creates a real graph where every edge is tagged as either EXTRACTED (explicitly found in source) or INFERRED (resolved by Graphify).
• AI Assistant Integration: It integrates as a "skill" for various AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot), allowing the assistant to query the graph via commands like /graphify.

Who it’s for

Software engineers and architects who need to navigate complex codebases, understand the flow of logic across multiple files, and identify key architectural "god nodes" and surprising connections.

Highlights

• Local-first code mapping: Code is parsed locally via tree-sitter AST, meaning no code leaves the machine for the analysis phase.
• Multi-modal support: Maps code, documentation, PDFs, images, and video/audio into a single unified graph.
• Queryable interface: Provides tools to explain concepts, find the shortest path between two nodes, and perform plain-language queries.
• Broad assistant compatibility: Supports over 20 AI coding assistants through custom skill installations.
• Visual output: Generates an interactive graph.html for visual exploration of the project structure.