This tutorial explains how to install Graphify, generate a local knowledge graph of an entire codebase—including code, docs, PDFs, images, and videos—and then query the graph via AI assistants or the CLI, eliminating the need for manual full‑text file searches.
This week’s GitHub roundup highlights OpenCut’s surge to 12,718 stars as a free, privacy‑first CapCut replacement, the rapid rise of five Skills‑related projects—including Graphify cutting token usage by 71 times and Hallmark adding 57 design‑gate checks—while AI agent toolchains deepen and major players like OpenAI and academia enter the open‑source arena.
The article examines why current AI coding assistants lack a global view of projects, introduces the open‑source CodeGraph and Graphify projects that build code knowledge graphs, compares their architectures, benchmark results, token savings, limitations, and provides quick start instructions for each.
Graphify is a Python tool and Claude Code skill that creates a persistent, queryable knowledge graph of code, documentation, and media, cutting token usage by up to 71.5× compared with raw file reads, and it does so through a three‑pass pipeline that combines deterministic AST extraction, optional local audio transcription, and AI‑driven semantic extraction.
Graphify turns scattered READMEs, design docs, code, and media into a searchable knowledge graph, letting AI programming assistants query relationships instead of repeatedly re‑scanning files, with step‑by‑step installation, command usage, and best‑practice guidance for teams.
Graphify transforms scattered code, documentation, and business knowledge into a structured knowledge graph that serves as a “second brain” for AI coding assistants, enabling them to navigate and understand complex enterprise legacy systems, reduce token costs, and improve answer quality, as demonstrated by detailed tests on the BettaFish project.
