CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents

Abstract

An incremental code index can complete every update without error and still return the wrong graph half the time. Coding agents depend on such reused views for search and navigation, yet the systems that supply them never measure where a precomputed answer may stand in for the trusted route: localization agents rebuild a graph per task, tool servers query a live language server per request, and code-intelligence databases serve developers, not agents. CodeNib is a multi-view data system that makes that measurement possible. It is the first to compile lexical, dense, and structural views of one commit behind a single manifest and source-address contract, so that a lexical hit, a graph occurrence, and a dense block can be checked against each other and against a rebuild or a live server, and it serves search, navigation, and bounded context to agents through one cost-visible runtime. The comparisons overturn three assumptions. Execution status is not correctness: symbol-level repair completes on every held-out change but reproduces the rebuild on 13 of 24, so the runtime admits it per repository, while vector reuse is exact on 27 of 30 held-out changes at 40$\times$ and static definitions match the live server on 86\% of requests at a 1.2\,s ready point. Shorter answers finish more tasks: behind LocAgent, CodeNib's views let the agent finish 78 of 90 runs against 65 at lower cost, and selected context policies use 50--87\% fewer tokens than grep/read across five models. And context pays most when it is taken back: on 200 SWE-bench Verified tasks, handing the agent CodeNib's candidates and retiring them after its first source read resolves 79 issues, the most of five context policies, against 66 for on-demand exploration and 56 for Aider's repository map.