RepoReviewer: A Local-First Multi-Agent Architecture for Repository-Level Code Review
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 6 references that are in the map. First posted on arXiv 17 Mar 2026.
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References · 6
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2024CodeMirage: Hallucinations in Code Generated by Large Language Modelscited by 23
- 2024A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedbackcited by 22
- 2024Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Examplecited by 15
- 2026ResearchPilot: A Local-First Multi-Agent System for Literature Synthesis and Related Work Draftingcited by 2
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