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
