Large-scale Repository Engineering via Agent-Native Reusable Code Primitives

Large language models (LLMs) equipped with development environments have moved code generation toward repository-scale construction, yet building complete repositories remains difficult because interacting modules, interfaces, configurations, tests, and dependencies must work together. We introduce Code Primitives, agent-native reusable executable components that carry their own interface contracts, dependency closures, validation tests, and provenance. Each primitive uses a resident LLM as a natural-language interface to assess relevance and adapt its implementation, interfaces, and dependencies to the target repository, and we organize $1,424$ validated primitives in CodeFace, a searchable library for repository construction. Each primitive adapts the component it owns. We introduce LEGO (Large-scale repository Engineering via aGent-native reusable cOde primitives), which activates the primitives a task requires, integrates their adapted implementations with task-specific code while resolving constraints that cross component boundaries, and revises the result against executed tests. To measure construction end to end we build LEGO-REPO, a benchmark of $522$ executable reconstruction tasks spanning seven software domains, $22$ capability tracks, and five difficulty levels, each scored against the package's native test suite between an empty-package floor and an original-source ceiling. The strongest of $13$ evaluated backbones reaches a delivery score of $0.318$ and scores zero on 41.0% of tasks; LEGO improves all $13$ by $0.1474$ on average and raises GPT-5.6-terra from $0.3180$ to $0.5134$ (+61.4%). In controlled comparisons, adapted primitives achieve higher delivery scores than retrieved code supplied as context or vendored unchanged. The effect persists against independent repository agents, across three external benchmarks, and with a disjointly re-mined CodeFace, and using GPT-OSS-20B for adaptation and diagnosis retains 95.1% of the homogeneous score at 24.0% lower cost.