This episode explores MAGIS, a new framework that uses large language models (LLMs) and a multi-agent system to resolve complex GitHub issues. MAGIS consists of four agents: a Manager, Repository Custodian, Developer, and Quality Assurance (QA) Engineer. Together, they collaborate to identify relevant files, generate code changes, and ensure quality. Key highlights include:- The challenges of using LLMs for complex code modifications.- How MAGIS improves performance by dividing tasks, retrieving relevant files, and enhancing collaboration.- Experiments on SWE-bench showing MAGIS's effectiveness, achieving an eightfold improvement over GPT-4 in code issue resolution.- Ablation studies highlighting the robustness of the framework.The episode delves into MAGIS’s practical application for automating and improving software development, offering a glimpse into the future of AI-driven development workflows.https://arxiv.org/pdf/2403.17927v1

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