One Repository, a Thousand Graphs: A Repository Context Interface for Coding Agents

Large codebases far exceed the context that large language models can effectively process. Encoding repository knowledge in model parameters is costly, lags behind changes, and is difficult to sustain through continual learning. On-demand search can provide uneven coverage or miss relevant information, while maintaining explicit code graphs outside the model for large repositories poses challenges for covering task-relevant relations and keeping them synchronized as the code evolves. We propose ORTG as an integrated repository interface that organizes entity context and a partial set of prebuilt relations by module, delivers task-relevant information on demand through a two-level structure within a limited context, and specifies a workflow for updating entity outlines and relation fields after edits during task execution. End-to-end evaluation covers SWE-bench Verified, Deep-SWE, and FeatureBench. In the evaluated configurations, complete ORTG achieves higher mean task success rates than the corresponding baselines and ablation configurations. Behavioral statistics on Deep-SWE show fewer interactions and file reads, but higher cumulative token usage including index construction and updates.