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 AGI-Edgerunners · Agent Tool · ★ 2.3k
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🔒 Is LLM-Agents-Papers safe to install? View the security audit →
LLM-Agents-Papers :writinghand: Description Last Updated Time: 2025/7/12 A repo lists papers related to LLM based agent. Includes Survey Technique For Enhancement Planning Memory Mechanism Feedback&Reflection RAG Search Interaction Role Playing Conversation Game Playing Human-Agent Interaction Tool Usage Simulation Application Math Chemistry Biology Physics Geography Art Medicine Finance Software Engineering Research Automation Workflow Automatic Evaluation Training Fine tuning RL DPO Scaling Single-Agent Framework Multi-Agent System Stability Safety Bias Hallucination Infrastructure Benchmark&Evaluation Environment&Platform Dataset [Others](#Others
| Stars | 2,344 |
| Forks | 155 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 51.7534500171735/100 |
| Open Issues | 17 |
| Last Updated | 2025-07-12 |
| Created | 2023-05-31 |
| Platforms | python |
| Est. Tokens | ~259k |
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LLM-Agents-Papers is A repo lists papers related to LLM based agent. It is categorized as a Agent Tool with 2.3k GitHub stars.
LLM-Agents-Papers is primarily written in Python. It covers topics such as agents, large-language-models, llm-agent.
You can find installation instructions and usage details in the LLM-Agents-Papers GitHub repository at github.com/AGI-Edgerunners/LLM-Agents-Papers. The project has 2.3k stars and 155 forks, indicating an active community.
The top alternatives to LLM-Agents-Papers on Agent Skills Hub include PocketFlow, gptme, ruby_llm. 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.
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