No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by js-lee-AI · Agent Tool · ★ 87
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-llm-agent-papers safe to install? View the security audit →
🤖 Awesome LLM Agent Papers 200+ must-read papers, continuously growing: the annotated reading list for building LLM agents that plan, remember, use tools, and cooperate. Companion to the survey “LLM Agents: A Survey.” -- <img src="https://img.shields.io/github/stars/js-lee-AI/awesome-llm-agent-papers?style=flat-square&labelColor=2b2b2b&color=8B2635" alt="GitHub
| Stars | 87 |
| Forks | 27 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 57.1198901604346/100 |
| Last Updated | 2026-10-03 |
| Created | 2026-07-08 |
| Platforms | python |
| Est. Tokens | ~25k |
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awesome-llm-agent-papers is A curated, continuously updated reading list of 500+ papers on LLM agents: planning, memory, tool use, multi-agent, evaluation & safety. Companion to the survey 'LLM Agents: A Survey'.. It is categorized as a Agent Tool with 87 GitHub stars.
awesome-llm-agent-papers is primarily written in Python. It covers topics such as agent-benchmark, agent-survey, agents.
You can find installation instructions and usage details in the awesome-llm-agent-papers GitHub repository at github.com/js-lee-AI/awesome-llm-agent-papers. The project has 87 stars and 27 forks, indicating an active community.
awesome-llm-agent-papers is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-llm-agent-papers on Agent Skills Hub include awesome-ai-agents, agentic-ai, Awesome-AI-For-Security. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: