@Suryanshti777: This is wild Somebody finally realized AI coding agents spend half their time searching your codebase instead of actual…
Summary
CodeGraph builds a local knowledge graph for AI coding agents to index codebase relationships, cutting token usage by ~59% and execution time by ~49% compared to traditional search methods.
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Cached at: 05/22/26, 11:51 AM
This is wild
Somebody finally realized AI coding agents spend half their time searching your codebase instead of actually understanding it.
So they built a local knowledge graph for Claude Code, Cursor, Codex CLI, OpenCode, and Hermes Agent.
Not another wrapper Not another “AI devtool” landing page
An actual semantic layer that indexes your entire repo and lets agents query relationships, call graphs, routes, symbols, and dependencies instantly.
The wild part?
On real repos like VS Code, Django, Excalidraw, Tokio, and OkHttp, CodeGraph cut:
→ ~59% tokens → ~70% tool calls → ~49% execution time → ~35% cost
Instead of Claude Code or Codex endlessly grepping files and spawning exploration agents, they query a pre-built graph and move straight to the relevant context.
That changes the feel of AI coding completely.
Especially on larger codebases where Cursor, Claude Code, and Codex usually start drowning in file reads.
And the setup is absurdly simple:
npx @colbymchenry/codegraph
No external APIs No cloud dependency No weird config hell
Just local semantic intelligence for your codebase.
This is one of those repos where you instantly understand why it blew up to 14k+ stars so fast.
100% open source
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