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
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents (2026)
- Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads (2026)
- StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows (2026)
- The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation (2026)
- Experience Graphs: The Data Foundation for Self-Improving Agents (2026)
- Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents (2026)
- TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Get this paper in your agent:
hf papers read 2607.25431 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Datasets citing this paper 1
Spaces citing this paper 0
No Space linking this paper