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Kivgraph

Semantic code navigation for AI coding agents

Developer Tools
Artificial Intelligence
GitHub
Visit WebsiteSee on Product HuntGithub

Hunted byAdrià CabreraAdrià Cabrera

Kivgraph gives Claude Code, Codex, and other AI coding agents a local semantic graph of symbols, dependencies, and cross-repository relationships. It answers structural code questions with less context than grep and file reading.

Top comment

I built Kivgraph after watching my coding agent spend dozens of searches and file reads answering questions like “where is this used?” and “what breaks if I change it?” The benchmark that convinced me to keep working on it covered 37 repositories and 29 questions. Kivgraph and grep + reading both got 28/29 exact answers, but the graph used 36k tokens versus 268k. That is about 7.4× less context overall. The tradeoff is important: grep was cheaper on 5 questions, so Kivgraph is not a replacement for ordinary search. It is for structural, cross-repository questions where reconstructing relationships is expensive. Kivgraph is Apache-2.0, runs locally over stdio, needs no API key, and currently supports Go, TypeScript, Rust, Python, and Dart. I’m excited to hear where this approach is useful and where it is not.

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About Kivgraph on Product Hunt

Semantic code navigation for AI coding agents

Kivgraph was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #80 on the daily leaderboard. Kivgraph gives Claude Code, Codex, and other AI coding agents a local semantic graph of symbols, dependencies, and cross-repository relationships. It answers structural code questions with less context than grep and file reading.

Kivgraph was featured in Developer Tools (519k followers), Artificial Intelligence (478.1k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 231k products, making this a competitive space to launch in.

Who hunted Kivgraph?

Kivgraph was hunted by Adrià Cabrera. 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.

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