I m building Kivgraph, a local semantic code graph for AI coding agents. It maps symbols, dependencies, and cross-repository relationships so an agent can answer structural questions like where is this used? or what breaks if I change it? without repeatedly reading the same files.
In my benchmark, Kivgraph and grep + reading reached 28/29 exact answers, while the graph used 36k tokens versus 268k. Grep was still cheaper for some simple searches, so I see this as a complement rather than a replacement.
The launch is scheduled for tomorrow. I d love to hear: what codebase questions make your agent burn the most context, and what would you want a tool like this to expose?
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.
