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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[Up-to-date] Large Language Model Agent: A Survey on Methodology, Applications and Challenges
| Date | Stars |
|---|---|
| 2026-07-31 | 2817 |
| 2026-08-01 | 2817 |
| 2026-08-03 | 2817 |
| 2026-08-06 | 2817 |
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# 🤖 Comprehensive LLM Agent Research Collection <div align="center">  [](https://github.com/luo-junyu/Awesome-Agent-Papers/commits/main) [](https://github.com/luo-junyu/Awesome-Agent-Papers/pulls) </div> <p align="center"> <img src="./figs/fig-overview-agent-survey.png" width="90%" alt="LLM Agent Research Overview"> </p> ## 🌟 Overview This repository contains a **comprehensive collection** of research papers on Large Language Model (LLM) agents. We organize papers across key categories including agent construction, collaboration mechanisms, evolution, tools, security, benchmarks, and applications. Our taxonomy provides a structured framework for understanding the rapidly evolving field of LLM agents, from architectural foundations to practical implementations. The repository bridges fragmented research threads by highlighting connections between agent design principles and emergent behaviors. 📄 **[Read our survey paper here](https://arxiv.org/abs/2503.21460)** Our survey covers the rapidly evolving field of LLM agents, with a significant increase in research publications since 2023. ## 📑 Table of Contents - [🌟 Overview](#-overview) - [📊 Statistics & Trends](#-statistics--trends) - [🔍 Key Categories](#-key-categories) - [📚 Resource List](#-resource-list) - [Agent Collaboration](#agent-collaboration) - [Agent Construction](#agent-construction) - [Agent Evolution](#agent-evolution) - [Applications](#applications) - [Datasets & Benchmarks](#datasets--benchmarks) - [Ethics](#ethics) - [Security](#security) - [Survey](#survey) - [Tools](#tools) - [🤝 Contributing](#-contributing) ## 🔍 Key Categories - **🏗️ Agent Construction**: Methodologies and architectures for building LLM agents - **👥 Agent Collaboration**: Frameworks for multi-agent interaction and cooperation - **🌱 Agent Evolution**: Self-improvement and learning capabilities of agents - **🔧 Tools**: Integration of external tools and APIs with LLM agents - **🛡️ Security**: Security concerns and protections for LLM agent systems - **📊 Benchmarks**: Evaluation frameworks and datasets for testing agent capabilities - **💡 Applications**: Real-world implementations and use cases ## 📚 Resource List ### Agent Collaboration - **[Foam-Agent: Towards Automated Intelligent CFD Workflows](https://arxiv.org/abs/2505.04997)** (*2025*) `Arxiv` > The paper presents Foam - Agent, a multi - agent framework automating CFD workflows from natural language. It features unique retrieval, file - generation and error - correction systems, lowering expertise barriers. - **[Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657)** (*2025*) `Arxiv` > Presents MAST, a taxonomy for MAS failures. Develops an LLM-as-a-Judge pipeline, and opensources data to guide MAS development. - **[Linear formation control of multi-agent systems](https://www.sciencedirect.com/science/article/pii/S0005109824004291)** (*2025*) > A new distributed leader–follower control architecture (linear formation control) is proposed for formation variations, with new concepts and estimation methods. - **[MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://arxiv.org/abs/2503.01935)** (*2025*) `Arxiv` > Introduces MultiAgentBench to evaluate LLM - based multi - agent systems. Assesses collaboration and competition, evaluates protocols and strategies, code & data open - sourced. - **[A Survey of AI Agent Protocols](https://arxiv.org/abs/2504.16736)** (*2025*) `Arxiv` > Paper analyzes existing LLM agent protocols, proposes a classification, explores future directions for next - gen protocols. - **[C^2: Scalable Auto-Feedback for LLM-based Chart Generation](https://aclan
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3
Shuai Liu · Nanyang Technological University · Singapore
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Boxuan Li · Microsoft · United States
1
Ling Yue · Rensselaer Polytechnic Institute
1
1
Haoyang Liu · University of Illinois at Urbana–Champaign
1
Shuhang Xu
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a32df42945f95d69, topic:llm
matched fp:a32df42945f95d69, topic:awesome-list