Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases

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[Submitted on 1 Jul 2026]

Title:Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases

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Abstract: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.

Comments: Accepted to the main track of the 23rd European Conference on Multi-Agent Systems (EUMAS 2026)

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.01425 [cs.AI]

(or arXiv:2607.01425v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.01425

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yongjian Tang [view email] [v1] Wed, 1 Jul 2026 19:41:38 UTC (1,880 KB)

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