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code-review-graph maps your entire codebase—every file, every function, every…

2026-08-02 · 18:37 UTC ·@dr_cintas ·1 min read

Brief

code-review-graph maps an entire repository (every file, function, and connection) and traces the impact of a single edit to callers, dependent files, and tests so your LLM (e.g., Claude Code) reads only affected code. Setup reportedly takes ~2 minutes (pip install + install + build), and @dr_cintas claims it cuts a ~100,000-token/$1 run to about one cent.

Why it matters

code-review-graph maps your entire codebase—every file, every function, every connection—and traces changes to every caller, dependent file, and test so the AI only reads affected code.

Key details

  • Setup is advertised as ~2 minutes: pip install code-review-graph; code-review-graph install (auto-configures Claude Code, Cursor, Codex, Gemini CLI and more); then code-review-graph build.
  • Author @dr_cintas claims the tool reduces context from ~100,000 tokens (about $1) per task to roughly one cent, with the post published 2026-08-02.
Source evidence

if you use claude code, this will save you real money.

the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had.

this repo fixes it.

it’s called code review graph, and it just maps your entire codebase:

every file, every function, every connection laid out so you can see the actual shape of your project

how to set up (2 min):

  1. pip install code-review-graph
  2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more
  3. code-review-graph build

change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo.

and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

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