The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems

• URL: http://arxiv.org/abs/2606.30524v2
• Date: Wed, 01 Jul 2026 14:28:34 GMT
• Title: The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems
• Abstract summary: This study empirically evaluates Retrieval-Augmented Generation (RAG) dependent architectures for the generation of files for GitHub repositories.<n>Results indicate a critical architectural trade-off: the Single-Agent pipeline achieves lexical quality comparable to MAS while reducing token consumption by 86% and operating at twice the speed.
• Score: 3.7149543017244238
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) are increasingly utilized to automate several software engineering tasks, including code completion, code summarization, testing, and the generation of repository-level documentation. While Multi-Agent Systems (MAS) are often adopted to support such tasks under the premise that task decomposition improves performance, the impact of architectural complexity on practical efficiency remains under-examined. This study empirically evaluates Retrieval-Augmented Generation (RAG) dependent architectures for the generation of README files for GitHub repositories. In this work, we conducted a systematic comparison between a Single-Agent pipeline, a specialized MAS, and a developer-guided planning (DevPlan) variant, benchmarked against LARCH -- a state-of-the-art baseline -- and the original ground truth. Results indicate a critical architectural trade-off: the Single-Agent pipeline achieves lexical quality comparable to MAS while reducing token consumption by 86% and operating at twice the speed. In contrast, manual taxonomy analysis demonstrates that MAS achieves high structural consistency (98%), resolving formatting issues observed in single-agent approaches. Autonomous planning is identified as the primary pipeline bottleneck; incorporating lightweight developer-guided plans produces the highest overall documentation quality, surpassing all the analyzed configurations.

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