As a developer who frequently uses AI coding assistants like GitHub Copilot, I've often noticed that these tools tend to re-read large portions of the codebase repeatedly. This not only wastes processing tokens but also slows down the review process. That's why I found the Code-Review-Graph (CRG) project incredibly useful. It effectively builds a structural map of the code, using an abstract syntax tree (AST) and graphs to track function calls, inheritance, and test coverage. What really sets CRG apart is how it incrementally updates this graph whenever code changes occur, providing AI assistants with the precise context they need without flooding them with unnecessary data. This approach dramatically reduces the number of tokens AI tools consume — by up to hundreds of times in some cases — leading to faster, more efficient reviews. For projects with hundreds of files, the initial setup takes only about 10 seconds, which is reasonable compared to the time saved during subsequent reviews. I especially appreciate the flexible configuration options CRG offers. It supports multiple AI platforms, including Claude Code, Cursor, Copilot, Gemini, and Kiro, automatically detecting and configuring native hooks specific to each tool. This reduces integration friction regardless of whether you install it via pip or pipx. Additionally, for those using continuous integration (CI) pipelines, CRG provides a GitHub Action that performs risk-scored pull request reviews by analyzing the affected functions and flows based on changes. This feature helps maintain code quality by flagging risky code paths before merge. Overall, incorporating Code-Review-Graph into my workflow has not only lowered operational costs related to token usage but also increased my confidence in AI-assisted code reviews. If you're a developer leveraging AI tools, adopting CRG can be a game-changer in optimizing both performance and accuracy.