用多智能体分解代码仓库审查，提升准确性和可复现性。
RepoReviewer: A Local-First Multi-Agent Architecture for Repository-Level Code Review
- 分步拆解审查流程：获取仓库、合成上下文、文件分析等
- 支持本地优先，通过CLI与API实现可复用的审查流水线
- 适合研究自动化审查系统或构建开发工具链的开发者
代码仓库级别的审查需综合项目结构、仓库上下文与文件级实现细节。现有自动化审查流程常将这些任务合并为单次处理，导致相关性降低、重复增多、优先级模糊。我们提出RepoReviewer，一种基于本地优先的多智能体系统，用于自动化GitHub仓库审查，包含Python命令行接口、FastAPI API、LangGraph编排层和Next.js用户界面。该系统将审查过程分解为仓库获取、上下文合成、文件级分析、问题优先级排序与摘要生成五个阶段。本文阐述系统设计、实现权衡、面向开发者的接口及实际故障模式。不同于宣称基准性能领先，我们将RepoReviewer定位为技术系统贡献：提供一个务实的仓库级自动化审查架构，并附带可复用的评估与报告基础设施，以支持未来实证研究。
原文摘要 · Abstract (English)
Repository-level code review requires reasoning over project structure, repository context, and file-level implementation details. Existing automated review workflows often collapse these tasks into a single pass, which can reduce relevance, increase duplication, and weaken prioritization. We present RepoReviewer, a local-first multi-agent system for automated GitHub repository review with a Python CLI, FastAPI API, LangGraph orchestration layer, and Next.js user interface. RepoReviewer decomposes review into repository acquisition, context synthesis, file-level analysis, finding prioritization, and summary generation. We describe the system design, implementation tradeoffs, developer-facing interfaces, and practical failure modes. Rather than claiming benchmark superiority, we frame RepoReviewer as a technical systems contribution: a pragmatic architecture for repository-level automated review, accompanied by reusable evaluation and reporting infrastructure for future empirical study.
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