CodeGraph vs Graphify: Comparing Two Open‑Source Code Knowledge Graph Tools

CodeGraph: Adding Semantic Awareness to AI Coding Tools

CodeGraph implements a knowledge‑graph server (MCP) that sits in front of AI coding assistants such as Claude Code, Codex, and Cursor. When a user asks a question like “who calls this function?”, the assistant queries the graph instead of grepping files, returning the answer with a single call.

Installation is a three‑step process:

curl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh
codegraph install
cd your-project && codegraph init

After initialization the graph synchronizes automatically with file changes, requiring no manual updates.

Benchmarking on seven real open‑source repositories shows consistent gains: the proxy reduces tool‑call counts by 50%‑80% and almost eliminates file reads. In the VS Code repository (≈10 k files) tool calls drop by 81% and file reads go from nine to zero. The demo GIF illustrates a single query returning precise code context.

The performance boost stems from eliminating repeated greedy searches; the graph provides all relevant information in one lookup. However, CodeGraph only understands code—Markdown, PDFs, images, and other documentation are ignored.

Graphify: Extending Knowledge Graphs Beyond Code

Graphify takes a broader approach, ingesting not only source code but also PDFs, screenshots, whiteboard photos, academic papers, and Markdown notes. It runs as a Skill inside Claude Code; invoking /graphify . scans the current directory.

Technical details: code files are parsed with tree‑sitter to extract AST symbols; images and PDFs are processed via Claude’s visual capabilities; Markdown is parsed for key terms and references. All entities are clustered using Leiden community detection to produce an interactive knowledge graph.

The output includes an interactive HTML graph (clickable nodes, search, filter), an Obsidian‑compatible knowledge‑base folder, an analysis report highlighting “god nodes” with many connections, and a graph.json for future queries.

Graphify also offers a handy command: graphify add <url> which pulls papers from arXiv or tweets from X and merges them into the existing graph—useful for researchers.

Token consumption is dramatically lower: in tests on Karpathy’s repository plus five papers and four images, each query uses only 1/71.5 of the tokens required to read the raw files.

Key Differences

Both projects build code knowledge graphs but target different problems. CodeGraph focuses on daily development efficiency, running continuously in the background to provide precise context and cut token usage, especially in large codebases. Graphify targets knowledge management, designed for one‑off deep dives into a project’s full knowledge structure, including non‑code artifacts.

In short, CodeGraph acts as a resident “semantic sensor” for routine coding, while Graphify is a “knowledge flashlight” used when a comprehensive understanding is needed.

Limitations

CodeGraph’s scope is limited to code; it cannot process documentation, images, or PDFs, and its token‑saving benefits diminish for small projects (< 500 files).

Graphify depends on Claude Code for visual analysis and its --watch mode only updates code files automatically; documentation and images require manual --update runs.

Quick Start Commands

CodeGraph:

curl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh
codegraph install
cd your-project && codegraph init

Graphify:

pip install graphifyy graphify install
/graphify .

Who Should Use Which Tool

If you regularly use AI coding assistants and want fewer file scans per query, CodeGraph is the better fit.

If you frequently take over large projects, conduct code reviews, or need cross‑document research, Graphify’s one‑time comprehensive graph is more suitable.

The two tools are complementary: CodeGraph can run continuously in your toolchain, while Graphify can be invoked for deep, occasional analysis.

Stars: codegraph 57k+ · graphify 77k+ Languages: codegraph TypeScript · graphify Python License: MIT (both)