# Graphify – Open-Source Knowledge Graph Skill for AI Coding Assistants > Source: > Published: 2026-06-27 22:26:37+00:00 # Graphify — Knowledge Graphs for AI Coding Assistants Graphify is an open-source skill that helps AI coding assistants understand multi-modal codebases by building a queryable knowledge graph from code, docs, papers and diagrams. `pip install graphifyy` ## What is Graphify? Graphify is a multi-modal knowledge graph builder created for AI coding assistants such as Claude Code, OpenAI Codex and OpenCode. By combining Tree-sitter static analysis with LLM-driven semantic extraction, Graphify turns an entire repository — including source code, documentation, research papers and diagrams — into an interactive graph that explains both *what* the code does and *why* it was designed that way. The project is maintained by Safi Shamsi, released under the permissive MIT license, and built on widely-trusted libraries including NetworkX and Tree-sitter. **3.7k+** GitHub Stars **MIT** License **71.5×** Token Reduction **Python 3.10+** Runtime ## Core Capabilities Graphify unifies static analysis, semantic extraction and graph clustering into a single skill that any AI coding assistant can invoke. ### Multi-Modal Extraction Parses code (.py, .js, .go, .java, …), Markdown, PDFs and images. Tree-sitter extracts ASTs, call graphs and docstrings; LLMs extract concepts from prose; vision models read diagrams. ### Knowledge Graph Build Merges all extracted nodes and edges into a NetworkX graph and applies the Leiden algorithm for semantic community detection — no vector embeddings required. ### God Nodes & Surprises Identifies the highest-degree "god nodes" at the heart of the system and flags unexpected cross-file or cross-domain connections worth investigating. ### Interactive Outputs Exports an interactive `graph.html` , a queryable `graph.json` , and a human-readable `GRAPH_REPORT.md` audit report. ### Assistant Integration Ships with `/graphify` , `/graphify query` , `/graphify path` and `/graphify explain` commands for Claude Code, Codex, OpenCode and more. ### Secure by Design Strict input validation: only http/https URLs, size and timeout limits, path containment, HTML-escaped node labels — defending against SSRF, injection and XSS. ## Architecture & Pipeline Graphify is a multi-stage pipeline. Each stage is an isolated module so contributors can extend any step independently. **detect**— collect files **extract**— AST + LLM nodes/edges **build**— NetworkX graph **cluster**— Leiden communities **analyze**— god nodes & surprises **report**— GRAPH_REPORT.md **export**— HTML / JSON / Obsidian Supporting modules include `ingest.py` for URL fetching, `cache.py` for semantic caching, `security.py` for input validation, `watch.py` for live updates and `serve.py` for MCP-protocol service. ## Install & Run Graphify is distributed on PyPI. The package name is `graphifyy` ; the CLI command remains `graphify` . ``` # Requires Python 3.10+ pip install graphifyy && graphify install # Build a knowledge graph for any project folder /graphify ./raw # Outputs land in graphify-out/ graphify-out/ ├── graph.html # interactive visualization ├── GRAPH_REPORT.md # core nodes, surprises, suggested questions ├── graph.json # persistent, queryable graph └── cache/ # incremental cache ``` Graphify does not bundle an LLM. It uses the model API key already configured by your AI coding assistant (Claude, Codex, etc.) and only sends semantic content — never raw source code — to the upstream model. ## Worked Examples The repository ships with reproducible corpora demonstrating Graphify on both small libraries and large mixed code-and-paper collections. ### httpx (small) 6 Python files modeling an HTTP transport layer. Result: **144 nodes, 330 edges, 6 communities**. God nodes: `Client` , `AsyncClient` , `Response` , `Request` . Surprise edge: `DigestAuth → Response` . ### Karpathy mixed corpus 3 GPT framework repos + 5 attention papers + 4 diagrams (~52 files, ~92k words). Result: **285 nodes, 340 edges, 53 communities**. Average query cost ~1.7k tokens vs ~123k naive — a **71.5×** reduction. ## Comparison How Graphify relates to adjacent open-source projects in the code-intelligence space. | Project | Focus | Strength | Limitation vs Graphify | |---|---|---|---| | Sourcegraph | Cross-repo code search | Enterprise-grade navigation | Not a knowledge graph; limited design semantics | | Code2Vec | Function-level embeddings | Vector retrieval & classification | No graph structure, no multi-modal input | | Neo4j | General graph database | Powerful Cypher queries | Does not generate graphs from code itself | ## Security, Licensing & Trust Graphify is released under the **MIT License**. Its core dependencies — NetworkX (BSD) and Tree-sitter (MIT) — are all permissive open-source licenses with no conflicts. The project performs no telemetry. The only outbound network call is the semantic-extraction step, which uses your own configured AI model API key; only semantic descriptions of documents are transmitted, never raw source code. URLs are restricted to http/https, downloads are size- and time-bounded, output paths are containment-checked, and node labels are HTML-escaped to prevent SSRF, Cypher injection and XSS. ## Learn more about Graphify Deeper guides on how Graphify builds, clusters and serves knowledge graphs to AI coding assistants. ### Knowledge Graphs for AI Coding Assistants Why structural graphs beat vector RAG for code understanding. ### Tree-sitter AST Extraction How Graphify parses 19 languages locally, with no LLM calls on source. ### Leiden Community Detection Clustering on graph topology alone — no embeddings, no vector store. ### Claude Code Integration CLAUDE.md directives and the PreToolUse hook, step by step. ### CLI Command Reference Every `/graphify` and `graphify` command in one place. ### Graphify vs Alternatives Honest comparison against Sourcegraph, Code2Vec and Neo4j. ## Frequently Asked Questions ## Does Graphify send my code to a third-party model? No. Graphify only sends semantic descriptions of documents and diagrams to the AI model you have already configured in your assistant — never raw source files. ## Which AI coding assistants are supported? Claude Code, OpenAI Codex and OpenCode are supported out of the box via dedicated `skill-*.md` manifests. Any assistant that can call shell commands can invoke `graphify` . ## How large a codebase can Graphify handle? Tree-sitter parsing and NetworkX construction scale linearly with code size. On a ~500k-word corpus, BFS subgraph queries stay around ~2k tokens versus ~670k naive — preserving compression at scale. ## Is Graphify free for commercial use? Yes. Graphify is MIT-licensed and free for both personal and commercial use.