---
slug: "graphify"
source_type: "readme"
source_url: "https://cdn.jsdelivr.net/gh/Graphify-Labs/graphify@main/README.md"
repo: "https://github.com/Graphify-Labs/graphify"
source_file: "README.md"
branch: "main"
---
# graphify

[![CI](https://github.com/safishamsi/graphify/actions/workflows/ci.yml/badge.svg?branch=v1)](https://github.com/safishamsi/graphify/actions/workflows/ci.yml)

**A Claude Code skill.** Type `/graphify` in Claude Code - it reads your files, builds a knowledge graph, and gives you back structure you didn't know was there.

Fully multimodal. Drop in code, PDFs, markdown, screenshots, diagrams, whiteboard photos, even images in other languages - graphify uses Claude vision to extract concepts and relationships from all of it and connects them into one graph.

> Andrej Karpathy keeps a `/raw` folder where he drops papers, tweets, screenshots, and notes. graphify is the answer to that problem - 71.5x fewer tokens per query vs reading the raw files, persistent across sessions, honest about what it found vs guessed.

```
/graphify .                        # works on any folder - your codebase, notes, papers, anything
```

```
graphify-out/
├── graph.html       interactive graph - click nodes, search, filter by community
├── obsidian/        open as Obsidian vault
├── wiki/            Wikipedia-style articles for agent navigation (--wiki)
├── GRAPH_REPORT.md  god nodes, surprising connections, suggested questions
├── graph.json       persistent graph - query weeks later without re-reading
└── cache/           SHA256 cache - re-runs only process changed files
```

## Install

**Requires:** [Claude Code](https://claude.ai/code) and Python 3.10+

```bash
pip install graphifyy && graphify install
```

> The PyPI package is temporarily named `graphifyy` while the `graphify` name is being reclaimed. The CLI and skill command are still `graphify`.

> **Windows:** If `graphify` is not recognized after install, add the Python Scripts folder to your PATH: `%APPDATA%\Python\Python3xx\Scripts` (replace `3xx` with your Python version, e.g. `313`). Or use `pipx install graphifyy` which handles PATH automatically.
> **macOS (externally managed):** Use `pipx install graphifyy` if `pip install` fails with an "externally-managed-environment" error.

Then open Claude Code in any directory and type:

```
/graphify .
```

<details>
<summary>Manual install (curl)</summary>

```bash
mkdir -p ~/.claude/skills/graphify
curl -fsSL https://raw.githubusercontent.com/safishamsi/graphify/v1/skills/graphify/skill.md \
  > ~/.claude/skills/graphify/SKILL.md
```

Add to `~/.claude/CLAUDE.md`:

```
- **graphify** (`~/.claude/skills/graphify/SKILL.md`) - any input to knowledge graph. Trigger: `/graphify`
When the user types `/graphify`, invoke the Skill tool with `skill: "graphify"` before doing anything else.
```

</details>

## Usage

```
/graphify                          # run on current directory
/graphify ./raw                    # run on a specific folder
/graphify ./raw --mode deep        # more aggressive INFERRED edge extraction
/graphify ./raw --update           # re-extract only changed files, merge into existing graph

/graphify add https://arxiv.org/abs/1706.03762        # fetch a paper, save, update graph
/graphify add https://x.com/karpathy/status/...       # fetch a tweet

/graphify query "what connects attention to the optimizer?"
/graphify path "DigestAuth" "Response"
/graphify explain "SwinTransformer"

/graphify ./raw --watch            # auto-sync graph as files change (code: instant, docs: notifies you)
/graphify ./raw --wiki             # build agent-crawlable wiki (index.md + article per community)
/graphify ./raw --svg              # export graph.svg
/graphify ./raw --graphml          # export graph.graphml (Gephi, yEd)
/graphify ./raw --neo4j            # generate cypher.txt for Neo4j
/graphify ./raw --mcp              # start MCP stdio server

graphify hook install              # post-commit git hook - rebuilds graph on every commit automatically
```

Works with any mix of file types:

| Type | Extensions | Extraction |
|------|-----------|------------|
| Code | `.py .ts .js .go .rs .java .c .cpp .rb .cs .kt .scala .php` | AST via tree-sitter + call-graph pass |
| Docs | `.md .txt .rst` | Concepts + relationships via Claude |
| Papers | `.pdf` | Citation mining + concept extraction |
| Images | `.png .jpg .webp .gif` | Claude vision - screenshots, diagrams, any language |

## What you get

**God nodes** - highest-degree concepts (what everything connects through)

**Surprising connections** - ranked by composite score. Code-paper edges rank higher than code-code. Each result includes a plain-English why.

**Suggested questions** - 4-5 questions the graph is uniquely positioned to answer

**Token benchmark** - printed automatically after every run. On a mixed corpus (Karpathy repos + papers + images): **71.5x** fewer tokens per query vs reading raw files.

**Auto-sync** (`--watch`) - run in a background terminal and the graph updates itself as your codebase changes. Code file saves trigger an instant rebuild (AST only, no LLM). Doc/image changes notify you to run `--update` for the LLM re-pass. Useful for agentic workflows where multiple agents are writing code in parallel - the graph stays current between waves automatically.

**Git commit hook** (`graphify hook install`) - installs a post-commit hook that rebuilds the graph after every commit. No background process needed. Triggers once per commit, works with any editor, safe to add alongside existing hooks.

**Wiki** (`--wiki`) - Wikipedia-style markdown articles per community and god node, with an `index.md` entry point. Point any agent at `index.md` and it can navigate the knowledge base by reading files instead of parsing JSON.

Every edge is tagged `EXTRACTED`, `INFERRED`, or `AMBIGUOUS` - you always know what was found vs guessed.

## Worked examples

| Corpus | Files | Reduction | Output |
|--------|-------|-----------|--------|
| Karpathy repos + 5 papers + 4 images | 52 | **71.5x** | [`worked/karpathy-repos/`](https://github.com/Graphify-Labs/graphify/tree/HEAD/worked/karpathy-repos/) |
| graphify source + Transformer paper | 4 | **5.4x** | [`worked/mixed-corpus/`](https://github.com/Graphify-Labs/graphify/tree/HEAD/worked/mixed-corpus/) |
| httpx (synthetic Python library) | 6 | ~1x | [`worked/httpx/`](https://github.com/Graphify-Labs/graphify/tree/HEAD/worked/httpx/) |

Token reduction scales with corpus size. 6 files fits in a context window anyway, so graph value there is structural clarity, not compression. At 52 files (code + papers + images) you get 71x+. Each `worked/` folder has the raw input files and the actual output (`GRAPH_REPORT.md`, `graph.json`) so you can run it yourself and verify the numbers.

## Tech stack

NetworkX + Leiden (graspologic) + tree-sitter + Claude + vis.js. No Neo4j required, no server, runs entirely locally.

<details>
<summary>Contributing</summary>

**Worked examples** are the most trust-building contribution. Run `/graphify` on a real corpus, save output to `worked/{slug}/`, write an honest `review.md` evaluating what the graph got right and wrong, submit a PR.

**Extraction bugs** - open an issue with the input file, the cache entry (`graphify-out/cache/`), and what was missed or invented.

See [ARCHITECTURE.md](https://github.com/Graphify-Labs/graphify/blob/HEAD/ARCHITECTURE.md) for module responsibilities and how to add a language.

</details>