Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
For developers and maintainers of large codebases, Agent4cs provides a more effective summarization method that leverages hierarchical structure, though the gains are incremental over existing prompting techniques.
Agent4cs introduces a multi-agent framework for summarizing large hierarchical codebases, improving semantic consistency by 8% and keyword coverage by up to 38% over structured prompting baselines.
Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Existing code summarization solutions often rely on a single language model or coding assistant like Claude Code, and treat source code as flat text, underutilizing the rich interdependencies and hierarchical information within a repository. To address these shortcomings, we propose Agent4cs - a multi-agent framework that summarizes large codebases in a bottom-up fashion, where a summarization agent focuses on producing robust summaries; a keyword-extraction agent proactively identifies critical information from subfolders; and a quality-assurance agent iteratively refines the outputs for readability, coherence, and completeness. Evaluated on 7 frontier models, Agent4cs improves semantic consistency across all folder levels by average 8% compared to two structured prompting baselines with code segments. Furthermore, extensive evaluation on real-world datasets demonstrates up to 38% gains in normalized keyword coverage rate over the same baselines.