This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
GrapeRoot
Cut your AI Bill by 60–70% with a local codebase graph!
AI coding tools re-read your entire codebase every turn. GrapeRoot fixes this. It builds an AST + dependency graph of your project locally, then injects only the relevant files (~4,000 tokens) before the AI starts thinking. No cloud, no tool-call overhead, no code leaves your machine. 60–70% token savings, measurable on real codebases. Works with Claude Code, Cursor, Codex, Gemini CLI, and GitHub Copilot. Open source, zero config, 150ms injection.
Hey PH!
I built GrapeRoot after getting frustrated watching Claude Code re-read the same file on every single turn of a conversation.
The fix wasn't another chat wrapper, it was fixing context handling upstream. GrapeRoot builds an AST + dependency graph of your codebase and injects only the files relevant to your current query, before the model even starts thinking.
On a real 2000+ file production codebase: 84.7/100 quality score,60% cost reduction on complex tasks. Everything runs locally, your code never leaves your machine.
Works as an MCP server with Claude Code, Cursor, Codex, Gemini CLI, Copilot.
Would love to hear how much your Claude Code bill is eating through your budget and whether this helps. Ask me anything 🙌
Re-reading the whole repo every session is one of those hidden Ai coding costs people notice only after usage grows. Does GrapeRoot work best for large monorepos, or do smaller projects also see meaningful token savings?
About GrapeRoot on Product Hunt
“Cut your AI Bill by 60–70% with a local codebase graph!”
GrapeRoot was submitted on Product Hunt and earned 10 upvotes and 2 comments, placing #35 on the daily leaderboard. AI coding tools re-read your entire codebase every turn. GrapeRoot fixes this. It builds an AST + dependency graph of your project locally, then injects only the relevant files (~4,000 tokens) before the AI starts thinking. No cloud, no tool-call overhead, no code leaves your machine. 60–70% token savings, measurable on real codebases. Works with Claude Code, Cursor, Codex, Gemini CLI, and GitHub Copilot. Open source, zero config, 150ms injection.
GrapeRoot was featured in Open Source (68.5k followers), Developer Tools (514k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 207.8k products, making this a competitive space to launch in.
Who hunted GrapeRoot?
GrapeRoot was hunted by Krishnakant .. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how GrapeRoot stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Krishnakant .