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

2026-06-30 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

This study empirically evaluates Retrieval-Augmented Generation (RAG) dependent architectures for generating README files for GitHub repositories. It systematically compares a Single-Agent pipeline, a specialized Multi-Agent System (MAS), and a developer-guided planning (Dev-Plan) variant against the LARCH baseline and original ground truth. The Single-Agent pipeline achieves lexical quality comparable to MAS while significantly reducing token consumption by 86% (7,840 tokens vs. 56,242 tokens for MAS) and operating at twice the speed (40 seconds vs. 78 seconds). However, MAS demonstrates high structural consistency (98% precision), effectively resolving formatting issues observed in single-agent approaches. The Dev-Plan configuration, which integrates human-authored plans, produces the highest overall documentation quality (ROUGE-L F1: 0.2323, BERTScore F1: 0.8230) but incurs higher computational costs (79,196 tokens, 148 seconds). The research utilized gpt-5.1 and a dataset of 180 GitHub repositories.

Key takeaway

For software engineering teams automating README generation, you should critically evaluate the trade-off between cost and structural quality. While single-agent RAG offers significant cost savings (86% fewer tokens, twice the speed) with comparable lexical quality, multi-agent systems provide superior structural consistency. To achieve the highest documentation quality, integrate human-authored planning into your multi-agent workflows, recognizing this will increase computational costs. Prioritize structural evaluation over purely lexical metrics.

Key insights

Agentic complexity in README generation offers structural benefits, but human-guided planning yields superior quality and efficiency trade-offs.

Principles

• Multi-agent systems enhance structural consistency.
• Autonomous LLM planning is a key bottleneck.
• Lexical metrics are insufficient for documentation quality.

Method

The study designed a comparative framework for README generation, involving repository preparation, semantic indexing, and generation pipelines (Single-Agent, MAS, Dev-Plan), evaluated using ROUGE, BERTScore, and manual taxonomy analysis.

In practice

• Use single-agent RAG for cost-efficient README generation.
• Implement human-authored plans for high-quality documentation.
• Prioritize structural evaluation beyond lexical scores.

Topics

• README Generation
• Multi-Agent Systems
• Retrieval-Augmented Generation
• LLM Evaluation
• Software Documentation
• Human-AI Collaboration

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Software Engineer

Related on AIssential

• Multi-Agent Architecture with RAG and Dynamic Context Windows for Text-to-SQL Optimization — A multi-agent Text-to-SQL system uses RAG and dynamic context to reduce token consumption.
• Agentic Authoring of Interactive Multiview Visualizations in Genomics — Agentic LLM iteration significantly enhances genomics visualization quality, but complex agent architectures offer no further benefit.
• Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows — BenchAgent reveals multi-agent LLM workflows often underperform single-agent setups under controlled conditions, except for specific runtime-generated designs.
• Our AIE keynote is live! Watch it now — Persistent AI agent memory can be built with simple file structures and iterative refinement, avoiding complex databases.
• Article: Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery — Hierarchical multi-agent RAG with autonomous error recovery significantly improves accuracy and reduces hallucinations across structured and unstructured enterprise data.
• 40 RAG Interview Questions and Answers — RAG enhances LLM accuracy and reduces hallucinations by providing real-time, verifiable external knowledge.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.