Awesome-Agent-Papers and LLM_MultiAgents_Survey_Papers
These are complementary survey resources that together provide comprehensive coverage of LLM agents—one focusing on single-agent methodology and applications broadly, while the other specializes specifically in multi-agent systems and their architectural challenges.
About Awesome-Agent-Papers
luo-junyu/Awesome-Agent-Papers
[Up-to-date] Large Language Model Agent: A Survey on Methodology, Applications and Challenges
This collection helps AI researchers and practitioners stay current with the rapidly evolving field of Large Language Model (LLM) agents. It provides a structured list of research papers, categorized by aspects like agent construction, collaboration, tools, and applications. If you're building or studying LLM agents, this resource offers a comprehensive overview of the latest methodologies and implementations.
About LLM_MultiAgents_Survey_Papers
taichengguo/LLM_MultiAgents_Survey_Papers
Large Language Model based Multi-Agents: A Survey of Progress and Challenges (In IJCAI 2024)
This resource provides a comprehensive overview and curated list of research papers on Large Language Model (LLM) based multi-agent systems, including frameworks, orchestration, and applications across various fields. It takes in academic papers and research findings related to LLM multi-agents and organizes them by categories like problem-solving and world simulation. Researchers, AI engineers, and academics interested in advanced AI systems and their applications would find this beneficial.
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