Here is the short version: use CodeGraph when your bottleneck is AI agents burning tokens on source-code discovery during edits. Use Graphify when you need a shareable project memory graph spanning code, docs, schemas, PDFs, and diagrams, especially for a team.

Everything below is what I learned running both on real monorepos, including this blog’s agent pipeline.

CodeGraph vs Graphify: The Decision in One Table

Why AI Coding Agents Need a Code Graph in 2026

Here’s what happens when Claude Code or Codex CLI edits a large repository without a pre-indexed graph. The agent starts a session with no memory of your codebase. It reads CLAUDE.md (if you have one), then starts exploring. To find a function definition, it calls grep. To understand a call chain, it reads files one at a time. To figure out which routes a change touches, it searches for route registrations across 50 files.

On a medium-sized monorepo — say 500 TypeScript files — this discovery phase can burn 30 to 60 tool calls and several thousand input tokens before the agent makes its first edit. Every session repeats this work because the agent’s context resets.

A code knowledge graph shortcuts that. It pre-builds a local index of symbols, relationships, call paths, and dependencies so the agent can query “who calls this function” or “what routes does this handler touch” in one MCP tool call instead of ten grep-and-read cycles.

What CodeGraph Is

CodeGraph, by Colby McHenry (colbymchenry/codegraph on GitHub), is a local-first MCP server that builds a pre-indexed code knowledge graph for AI coding agents. It installs as a global npm package and registers itself as an MCP tool with supported agents.

The architecture is straightforward:

• Tree-sitter AST parsing extracts functions, classes, methods, calls, imports, extends, and implements relationships from your source files.
• SQLite with FTS5 stores the graph locally in .codegraph/codegraph.db.
• A file watcher monitors your project for changes, debounces with a 2-second quiet window, and incrementally syncs the graph as you edit.
• An MCP server exposes tools like codegraph_explore and codegraph_lookup that agents call instead of grepping.

The install flow is:

npm install -g @colbymchenry/codegraph
codegraph install    # wires into Claude Code, Cursor, Codex CLI, etc.
codegraph init       # builds the index in the current project

The key design choice: CodeGraph is a service for agents. You do not read its output directly. The agent does. The .codegraph/ directory is opaque infrastructure — you commit it to .gitignore and forget it.

What Graphify Is

Graphify, by Safi Shamsi (safishamsi/graphify on GitHub), is a Python-based AI coding assistant skill that maps code and project artifacts into a queryable knowledge graph. It is broader in scope and more visible in output.

The install flow is:

uv tool install graphifyy
graphify install
# Then in your assistant: /graphify . or $graphify

Graphify creates visible artifacts in graphify-out/:

• graph.json — the complete graph data, suitable for programmatic querying and diffing.
• GRAPH_REPORT.md — a human-readable project map.
• graph.html — an interactive visualization you can open in a browser.

This makes Graphify inherently more shareable. You can commit graph.json to the repo, diff it across PRs, and discuss the project architecture through a document rather than asking each team member to run an agent session.

Beyond code, Graphify supports optional plugins for SQL schemas, Terraform configurations, PDF documents, Office files, Google Workspace exports, images, and video. Code extraction runs locally via tree-sitter, but non-code extraction may call the configured model API unless you route it through a local or enterprise backend.

The Artifact Difference Is Important

This is the clearest distinction between the two tools, and most comparisons gloss over it.

CodeGraph treats the graph as agent infrastructure. The .codegraph/ directory is a cached index the agent queries at runtime. You do not review it. You do not commit it. You do not diff it in code review. It is invisible operational plumbing.

Graphify treats the graph as a project artifact. The graphify-out/ directory contains files a human can open, inspect, and share. When a new engineer joins the team, they can read GRAPH_REPORT.md to understand the module structure. When you make a cross-cutting change, you can diff graph.json to verify the impact.

If you are a solo developer using Claude Code for daily edits, CodeGraph’s invisible index is the right abstraction — you do not want to manage an artifact. If you lead a team of five and want the knowledge graph to be part of your team workflow, Graphify’s visible artifacts are a real differentiator.

Benchmarks: CodeGraph’s Numbers vs Graphify’s Claims

CodeGraph publishes concrete benchmark results across seven open-source repositories. The median improvements are:

• 58% fewer tool calls
• 22% faster answers
• File reads cut near zero for the CodeGraph arm

These are reproducible claims. You can run the same repos with and without CodeGraph and verify the numbers.

Graphify’s numbers come from third-party articles. The most striking claim — 71.5× token reduction — comes from Emelia’s Graphify guide, which attributes it to Graphify’s three-pass analysis architecture. I have not been able to reproduce that exact number, and Graphify’s own README does not publish controlled benchmarks in the same style.

The honest take: both tools reduce context-discovery overhead. CodeGraph’s published benchmarks are more specific to the coding-agent use case. Graphify’s broader scope means its savings depend heavily on what artifacts you index.

Freshness and Team Workflow

CodeGraph’s auto-sync is the stronger choice during active development. The file watcher detects saves, debounces to 2 seconds, and updates the index incrementally. If you are in a 40-minute edit-compile-test loop, the graph stays current without manual intervention.

Graphify’s freshness depends on process. You can run graphify update manually, wire it to a git pre-commit hook, or schedule periodic rebuilds. The committed graph.json is a snapshot — it gets stale between commits unless your team enforces the update discipline.

For a solo developer, CodeGraph’s auto-sync wins. For a team where the graph artifact is part of the review process, Graphify’s snapshot model is a feature, not a bug — you can discuss what the graph looked like at the point of the PR.

Security and Privacy Considerations

Both tools run indexing locally, which is good. But the similarity ends there.

CodeGraph has a narrower security surface. It extracts only source code via tree-sitter, stores everything in a local SQLite file, and serves the agent through a local MCP server. No data leaves your machine. The main risk to review is trusting the npm installer and the MCP configuration.

Graphify has a broader surface because of the optional extras. Code extraction runs locally, but PDF, image, and video plugins may call configured model APIs (Ollama, OpenAI, Gemini, Anthropic, Bedrock, or Azure). If your compliance team cares about which data paths touch external APIs — and they should — Graphify’s configuration needs a documented review.

For compliance-sensitive codebases, CodeGraph’s narrower surface is easier to approve.

When to Choose CodeGraph

Use CodeGraph when:

• Your AI coding agent (Claude Code, Cursor, Codex CLI) spends too many tool calls on code discovery
• You work alone or in a small team where the graph is infrastructure, not a review artifact
• Your repo is a medium-to-large source-code project (500+ files, multiple entry points)
• You want auto-sync during active editing
• You need to minimize the security review surface

I switched this blog’s agent pipeline to use CodeGraph for exactly these reasons. The tool-call reduction is measurable — I documented a 52% drop in grep and file-read operations on our first production attempt.

When to Choose Graphify

Use Graphify when:

• Your project spans code, documentation, SQL schemas, and architecture diagrams
• You want a shareable, inspectable knowledge graph artifact your team can read and discuss
• You need to onboard new engineers to a polyglot or documentation-heavy codebase
• You want to visualize dependencies, routes, and module relationships in a browser
• You prefer Python (uv tool install graphifyy) over Node.js

When to Use Neither

Both tools are context accelerators, not correctness engines. If your agent’s failures come from unclear requirements, missing tests, incorrect assumptions about runtime state, or poor verification of its own output, a code graph will not fix those problems.

The Reddit discussion on r/codex makes this point well: “Codegraph/Graphify are solving the wrong problem for coding agents” — the post argues that if the agent still needs to validate its output against what the code actually does at runtime, a symbol graph is a modest improvement. I disagree that it is the wrong problem, but the warning is fair: measure your agent’s failure modes before assuming a graph tool is the answer.

Also skip these if your repo is small (under 50 files), you are writing throwaway scripts, or your team cannot maintain the agent configuration an MCP server or skill file requires.

Evaluation Checklist: Test Both on Your Own Repo

Run the same task — say, “add a new API endpoint that follows the existing pattern” — three times:

1. No graph — baseline with standard agent config
2. CodeGraph — after codegraph init
3. Graphify — after graphify install and /graphify .

Measure:

• Tool calls before the first edit
• File reads before the first edit
• Time to first edit
• Wrong-file edits (the agent changed a file it should not have)
• Missed-impact review comments (the agent missed a caller, route, or dependency)
• Staleness incidents (the agent relied on stale data)

This takes an afternoon but tells you exactly which tool works for your codebase.

Final Verdict

CodeGraph and Graphify are not direct competitors despite sharing the “code knowledge graph” tag. CodeGraph is a focused agent context accelerator for source-code navigation. Graphify is a multimodal project knowledge graph that happens to support coding agents.

For editing code, I reach for CodeGraph. For understanding the full project — code plus docs plus schemas — I use Graphify. If I had to pick one for this blog’s AI pipeline, which is code-heavy and solo-operated, CodeGraph is the cleaner choice.

Try both with the evaluation checklist above. Your repo will tell you which one fits.

Internal links: CodeGraph for Claude Code and Cursor Guide · Agent Skills Marketplace Guide 2026 · Best MCP Servers for Developers 2026

FAQ

Can I use CodeGraph and Graphify together?

Yes, but the overlap is small. CodeGraph handles live code navigation during editing; Graphify handles the broader project knowledge graph across docs, schemas, and media. I run both on larger repos — CodeGraph for the agent’s coding loop, Graphify for onboarding documentation and architecture visualization. The main cost is maintaining two config files and two tool registrations.

Does CodeGraph support non-TypeScript languages?

CodeGraph uses tree-sitter parsers, so it supports any language with a tree-sitter grammar. The README explicitly mentions TypeScript, JavaScript, Python, Rust, Go, and Java. Graphify covers more languages through its broader plugin system, but both tools handle the major languages well. For niche languages, check the tree-sitter grammar availability first.

Is Graphify free to use?

Yes, Graphify is MIT-licensed and free. The graphifyy PyPI package has no paywall. The cost to consider is infrastructure: if you use the optional plugins for PDF, image, or video extraction with model APIs (OpenAI, Gemini, Anthropic), those API calls incur their own billing. Local-only extraction via tree-sitter is free.

Which tool has better MCP integration?

CodeGraph is MCP-native — its entire architecture is an MCP server. Graphify offers MCP as an optional mode (graphify mcp), but its primary workflow is a skill/command executed inside the assistant. If your workflow depends on MCP tools (like mine with Hermes Agent), CodeGraph’s MCP-first design is more reliable. If you use a mix of MCP and non-MCP assistants, Graphify’s skill-based approach covers more ground.

What happens when the code graph gets stale?

CodeGraph watches your filesystem and auto-syncs with a 2-second debounce — staleness is rare during active editing. Graphify’s graph.json is a snapshot that stays stale until you run graphify update or wire a git hook. For solo development, CodeGraph’s auto-sync is better. For team review workflows, a committed snapshot at PR time is actually desirable because it lets you diff what the graph looked before and after the change.