# Teamwork makes the dream work: LLMs-Based Agents for GitHub README.MD Summarization

arXiv: https://arxiv.org/abs/2503.10876 · HTML: https://mlsanity.com/paper/2503.10876/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 11 of 11)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [Understanding the Factors that Impact the Popularity of GitHub Repositories](https://mlsanity.com/paper/1606.04984/) (2016)
- [Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization](https://mlsanity.com/paper/2312.15475/) (2023)
- [Generating Question Titles for Stack Overflow from Mined Code Snippets](https://mlsanity.com/paper/2005.10157/) (2020)
- [iTiger: An Automatic Issue Title Generation Tool](https://mlsanity.com/paper/2206.10811/) (2022)
- [Good things come in three: Generating SO Post Titles with Pre-Trained Models, Self Improvement and Post Ranking](https://mlsanity.com/paper/2406.15633/) (2024)

## Built on by (most cited first, 4 of 4)

- [Many Hands Make Light Work: An LLM-based Multi-Agent System for Detecting Malicious PyPI Packages](https://mlsanity.com/paper/2601.12148/) (2026)
- [Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach](https://mlsanity.com/paper/2606.24689/) (2026)
- [Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report](https://mlsanity.com/paper/2608.11965/) (2026)
- [The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems](https://mlsanity.com/paper/2606.30524/) (2026)

## Abstract (arXiv)

The proliferation of Large Language Models (LLMs) in recent years has realized many applications in various domains. Being trained with a huge of amount of data coming from various sources, LLMs can be deployed to solve different tasks, including those in Software Engineering (SE). Though they have been widely adopted, the potential of using LLMs cooperatively has not been thoroughly investigated. In this paper, we proposed Metagente as a novel approach to amplify the synergy of various LLMs. Metagente is a Multi-Agent framework based on a series of LLMs to self-optimize the system through evaluation, feedback, and cooperation among specialized agents. Such a framework creates an environment where multiple agents iteratively refine and optimize prompts from various perspectives. The results of these explorations are then reviewed and aggregated by a teacher agent. To study its performance, we evaluated Metagente with an SE task, i.e., summarization of README.MD files, and compared it with three well-established baselines, i.e., GitSum, LLaMA-2, and GPT-4o. The results show that our proposed approach works efficiently and effectively, consuming a small amount of data for fine-tuning but still getting a high accuracy, thus substantially outperforming the baselines. The performance gain compared to GitSum, the most relevant benchmark, ranges from 27.63% to 60.43%. More importantly, compared to using only one LLM, Metagente boots up the accuracy to multiple folds.