@VincentLogic: AI coding assistants scan the entire project every time they modify code, and the token consumption breaks my heart. After installing CodeGraph, it no longer fumbles around like a headless fly using grep to search files. It first builds a local index graph, organizing function definitions, variable references, and call relationships. When AI needs to work, it directly queries…

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Summary

CodeGraph reduces the number of times an AI coding assistant scans the entire project by building a local index graph, significantly lowering token consumption and improving speed, compatible with VS Code, Claude Code, and Cursor.

AI coding assistants scan the entire project every time they modify code, and the token consumption breaks my heart. After installing CodeGraph, it no longer fumbles around like a headless fly using grep to search files. It first builds a local index graph, organizing function definitions, variable references, and call relationships. When AI needs to work, it directly queries the graph to get data, without having to repeatedly read files. In real-world testing on large projects like VS Code with tens of thousands of files, the number of tool calls dropped from 52 to 3, speed increased by 40%, and tokens were saved by nearly 60%. It runs entirely locally, code is not uploaded, compatible with Claude Code and Cursor. From now on, when asking AI to modify large projects, you won't have to tremble while looking at the bill. When you usually ask AI to write code, do you throw in the entire repository or just give it the current file?
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Cached at: 05/24/26, 10:28 AM

Every time my AI coding assistant makes a change, it scans the entire project — burning through tokens like crazy.

After installing CodeGraph, it no longer flails around using grep to search through files blindly. It first builds a local index graph that maps out function definitions, variable references, and call relationships. When the AI needs to work, it just queries the graph for data instead of repeatedly reading files.

In real-world testing with large projects in VS Code (tens of thousands of files), tool calls dropped from 52 to just 3, speed improved by 40%, and tokens were reduced by nearly 60%. Everything runs locally — no code is uploaded — and it’s compatible with Claude Code and Cursor.

Now I can finally let AI refactor big projects without wincing at the bill.

When you have AI write code, do you usually toss in the whole repo, or just give it the current file?

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