A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought

Effective onboarding in software engineering is crucial but difficult due to the fast-paced evolution of technologies. Traditional methods, like exploration and workshops, are costly, time-consuming, and quickly outdated in large projects. We propose the Onboarding Buddy system, which leverages large language models, retrieval augmented generation, and an automated chain-of-thought approach to improve onboarding. It integrates dynamic, context-specific support within the development environment, offering natural language explanations, code insights, and project guidance. Our solution is agent-based and provides customized assistance with minimal human intervention. Our study results among the eight participants show an average helpfulness rating of (M=3.26, SD=0.86) and ease of onboarding at (M=3.0, SD=0.96) out of four. While similar to tools like GitHub Copilot, Onboarding Buddy uniquely integrates a chain-of-thought reasoning mechanism with retrieval-augmented generation, tailored specifically for dynamic onboarding contexts. While our initial evaluation is based on eight participants within one project, we will explore larger teams and multiple real-world codebases in the company to demonstrate broader applicability. Overall, Onboarding Buddy holds great potential for enhancing developer productivity and satisfaction. Our tool, source code [7], and demonstration video [6] are publicly available.