CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents
Abstract
Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, language servers, and task-local histories force repeated discovery and obscure lifecycle costs. CodeNib builds reusable lexical, dense, and structural views per repository commit, maps outputs to repository-relative source ranges, maintains selected views across edits, and serves ranked search, symbol navigation, and bounded context through one runtime. Across 100 snapshots, we map quality-cost frontiers across the repository-context lifecycle. When outputs match an independent rebuild, graph and vector updates are 8.7times and 25.4times faster at the median. On the static-navigation subset matching normalized live-server locations (63% of 1,000 requests), the median per-request live/static latency ratio is 4.7times. Across five models, selected context policies preserve localization with 50--87% fewer trajectory tokens than paired grep/read. Together, these results support multi-view repository-context serving with explicit, operation-specific validity boundaries.
Community
Coding agents keep rediscovering the same repository. CodeNib treats repo context as a data-systems problem: build lexical, dense, and structural views once per commit, maintain each through its own incremental path, and serve them as bounded agent context.
š 8.7Ć/25.4Ć faster graph/vector updates than rebuilding (only counted where outputs match an independent rebuild)
š§ 4.7Ć lower median latency than a live LSP on compatible navigation requests
šŖ 50ā87% fewer trajectory tokens than grep/read at matched localization quality, across 5 agent models
Code & artifacts: github.com/sysevol-ai/CodeNib
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