Burning tokens on AI code review? 🔥 This tool turns your codebase into a structural graph so the AI reads only relevant files — cutting tokens by a median of 82x. Title: tirth8205/code-review-graph URL: 🔥 Overview A local-first code intelligence tool that persists a structural map (graph) of your codebase via Tree-sitter parsing. It lets AI assistants review by reading only contextually relevant files instead of the whole repository. ❓ Challenges Solved AI code review tools waste tokens by re-reading large parts of the codebase on every review. ・In large monorepos, context bloats and both cost and latency worsen ・Analyzing change impact required scanning the entire project 💡 Methodology & How It Works A three-stage pipeline. ・Parsing: Tree-sitter builds ASTs, extracting functions, classes, imports, and call relationships ・Graph storage: nodes and edges persist in SQLite (no external database) ・Analysis: on changes, blast-radius analysis traces affected callers, dependents, and tests, returning minimal context It supports many languages, incremental updates under 2 seconds, MCP integration (30 tools), a GitHub Action, and D3.js visualization. 📊 Experimental Results ・Token efficiency: 38x-528x reduction (median ~82x across 6 repos) ・Impact prediction F1 score: 0.71 average ・CLI example: full context 12,921 tokens → graph context 762 tokens (~94% saved) #CodeReview# #AIAgents#