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LLM_MultiAgents_Survey_Papers

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Basic Information

This repository is a curated, research-focused collection of papers and resources about large language model (LLM) based multi-agent systems. It hosts an annotated bibliography organized into thematic streams and links a survey paper on the authors' overview of LLM-based multi-agent architectures. The README provides an overview table, architecture figure, news about updates, and a table of contents that groups papers into categories such as frameworks, orchestration and efficiency, problem solving, world simulation, and datasets and benchmarks. The project is maintained with periodic updates and invites community contributions via pull requests and issues. Contact information for the maintainer is provided for questions and suggested additions. The repository aims to centralize literature, track trends, and present a structured entry point into the fast-growing field of LLM-driven multi-agent research.

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1064
App URL
https://github.com/taichengguo/LLM_MultiAgents_Survey_Papers
Github Repository
https://github.com/taichengguo/LLM_MultiAgents_Survey_Papers/blob/main/README.md

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App Details

Features
The README features a curated list of recent and historical papers organized by topic and date, an arXiv-linked survey paper, and illustrative overview images including an architecture diagram and summary table. It provides a detailed table of contents that segments research into Multi-Agents Framework, Orchestration and Efficiency, Problem Solving (with subtopics such as software development and embodied agents), World Simulation (society, games, psychology, economy, recommender systems, policy making, disease simulation), and Datasets and Benchmarks. The page documents news about update cadence, highlights representative papers per category, shows commit history for transparency, and includes contribution guidance plus maintainer contact details. The structure supports quick navigation to research streams and ongoing additions by the community.
Use Cases
This repository helps researchers, students, and practitioners rapidly discover and navigate the literature on LLM-based multi-agent systems by aggregating and categorizing relevant papers in one place. The curated topics and table of contents make it easier to find works on frameworks, coordination and efficiency, specific applications like software development or simulation, and benchmarks and datasets. The linked survey and architecture overview give readers a synthesized view of trends and common designs. Regular updates and an open contribution process help keep the collection current. Contact information and a clear contribution invitation allow community correction and expansion, making the resource useful for literature reviews, identifying gaps, and tracking the evolving research landscape.

Categories

Content Creation (5042)
Monetization & Business (2246)
Development Tools (1407)
Marketing & Social Media (1403)
Education & Career (1380)
Multi-Agent Platforms & Frameworks (1009)
Health & Life (957)
AI Resources (920)
Agents & Automation (778)
Data & Analytics (764)
Productivity Tools (679)
Writing & Editing (666)

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