Graphify-Labs / graphify

2026-04-03 · Source: Github Trending: All languages · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, extended

Summary

Graphify is an open-source tool that transforms an entire project's content, including code, documentation, PDFs, images, and videos, into a queryable knowledge graph. It operates by parsing code locally using tree-sitter AST for deterministic, LLM-free analysis, ensuring nothing leaves the machine for code-related data. For other content types, it utilizes an AI assistant's model or a configured API key for semantic extraction. The system generates an interactive graph.html visualization, a GRAPH_REPORT.md with key insights, and a graph.json for direct querying. Edges in the graph are explicitly tagged as EXTRACTED or INFERRED, providing transparency on data provenance. Graphify is not a vector index but a traversable graph, enabling queries, path tracing, and concept explanations. It supports over 20 AI coding assistants and offers various optional extras for specific file types like PDFs and Office documents. Benchmarks show a LOCOMO recall@10 of 0.497 and LongMemEval-S QA accuracy of 76%.

Key takeaway

For AI Engineers and Software Architects navigating complex codebases or integrating AI assistants, Graphify offers a powerful alternative to traditional file searching. You should integrate this tool to transform your project's code and documentation into a queryable knowledge graph, enabling faster comprehension of system architecture and cross-file dependencies. This approach allows you to directly query relationships, trace concept paths, and identify critical components, significantly accelerating debugging, refactoring, and onboarding processes. Consider using "graphify hook install" for automated graph updates.

Key insights

Graphify transforms diverse project content into a queryable knowledge graph, enabling precise understanding and navigation without traditional file searching.

Principles

• Code parsing must be local and deterministic.
• Explicitly tag graph edges as EXTRACTED or INFERRED.
• Utilize graph traversal over vector similarity for context.

Method

Install graphifyy and register the skill, then execute "/graphify ." in an AI assistant to generate an interactive HTML graph, a report, and a queryable JSON graph.

In practice

• Query codebases for specific concepts or relationships.
• Trace shortest paths between any two entities.
• Identify critical "God nodes" in project architecture.

Topics

• Knowledge Graphs
• Code Analysis
• AI Coding Assistants
• Project Documentation
• Tree-sitter AST
• Software Architecture
• Graph Querying

Code references

• Graphify-Labs/graphify
• anthropics/skills
• googleworkspace/cli
• karpathy/nanoGPT
• safishamsi/graphify

Best for: Machine Learning Engineer, NLP Engineer, AI Engineer, Software Engineer, AI Architect

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• From Relational Database to Knowledge Graph: A Three-Pass Migration That Actually Works — Existing relational databases and data catalogs are foundational drafts for knowledge graph development.
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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.