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Kivgraph
Semantic code navigation for AI coding agents
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.
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.
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.
On the analytics side, Kivgraph competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Kivgraph performed against the three products that launched closest to it on the same day.
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.
For a complete overview of Kivgraph including community comment highlights and product details, visit the product overview.
Adrià Cabrera