Verified project record

Graphify-Labs/graphify

Graphify converts local project directories into queryable knowledge graphs using deterministic tree-sitter AST parsing across approximately 40 programming languages without requiring LLM calls. It outputs an interactive HTML visualization and a graph.json file, which can be exposed to AI assistants via an MCP server or direct skill registration.

Project overview

Project type: MCP · AI Coding · RAG
Use cases: Knowledge Q&A · Coding & Development
Deployment: Refer to project documentation
License: MIT

Best for

Developers and data teams who want to query, trace, and visualize code relationships locally without incurring LLM costs for the code parsing phase.
AI engineers seeking to expose structural code context, nodes, and pathfinding tools to AI assistants via MCP or direct skill registration.

Key capabilities

Parses code locally using tree-sitter AST across ~40 languages to deterministically build a knowledge graph without LLM calls.
Provides query, path, and explain commands to ask questions, trace connections between two things, or explain concepts against the generated graph.json.
Exposes the graph as an MCP stdio or HTTP server with structured access to query, node, neighbors, and path tools.
Generates an interactive graph.html file to explore nodes, filter communities, and visualize project structure in any browser.
Installs and registers the graphify skill into 20+ AI coding assistants via hooks or instruction files.
Extracts concepts from docs, PDFs, images, and videos by routing to configured LLM backends like Gemini, Claude, OpenAI, or Ollama.

Limitations and risks

Fully local and free operation is limited to code files; semantic extraction over docs or media requires a configured LLM backend.
Semantic extraction of docs, PDFs, and images sends data to a configured external AI provider.

Getting started

Setup is documented as easy, requiring an install via uv tool or pipx. Users can achieve the first success path by installing the package, registering the skill, and running the graphify command in their AI assistant.

Evidence and sources

GitHub project description: Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in on…
README: uv tool install graphifyy # install the CLI (or: pipx install graphifyy) graphify install # register the skill with your AI assistant
README: graphify-out/ ├── graph.html open in any browser — click nodes, filter, search ├── GRAPH_REPORT.md the highlights: key concepts, surprising connections, suggested questions └── gr…
README: Type `/graphify` in your AI coding assistant and it maps your entire project (code, docs, PDFs, images, videos) into a **knowledge graph** you can **query instead of grepping** th…
README: Not a vector index. No embeddings, no vector store: a real graph you traverse. Ask a question, trace the path between two things, or explain one concept.

Reviewed at: 2026-07-17T04:45:52Z