AI-Powered GitHub Repository Summarizer with CrewAI: Automating Codebase Analysis

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Abstract

Manually reviewing large and complicated codes is time-consuming and labor-intensive. It takes a lot of time and effort to read huge and complex code projects and understand them. Such an effort is a time-consuming and not efficient process. Modern software contains thousands of files; hence, it is very difficult to understand its architecture, features, and key components easily. To address these challenges, there is a need for an automated system that can summarize, clone, and analyze GitHub repositories. Such a system should be capable of handling large and complex repositories while reducing the amount of human effort required. This paper presents an automated solution using a multi-agent system powered by Crew AI to summarize and analyze GitHub repositories. This system employs specialized agents that collaboratively clone repositories, parse key files (e.g., README, source code), and extract relevant functional and architectural insights. The backend is implemented in Python, using GitPython for repository management and large language models via OpenAI for semantic analysis. These agents operate in parallel to perform source code inspection, document analysis, and report generation. The output is human-readable, easily understandable, and structured, presented in multiple formats, such as PDF, Markdown, or JSON. This framework significantly reduces manual efforts and enables the creation of a scalable codebase understanding, making it a practical tool for developers, researchers, and automated documentation pipelines.

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Keywords

Code summarization; Crew AI; GitHub analysis; multi-agent systems; repository mining; automation; software architecture