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 SALT-NLP · Agent Tool · ★ 211
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DyLAN Official Implementation of Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization Authors: Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, Diyi Yang Overview Abstract Large language model (LLM) agents have been shown effective on a wide range of tasks, and by ensembling multiple LLM agents, their performances could be further improved. Existing approaches employ a fixed set of agents to interact with each other in a static architecture, which limits their generalizability to various tasks and requires strong human prior in designing these agents. In this work, we propose to construct a strategic team of agents communicating in a dynamic interaction architecture based on the task query. Specifically, we build a framework named Dynamic LLM-Agent Network (DyLAN) for LLM-agent collaboration on complicated ta
| Stars | 211 |
| Forks | 29 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.9908535097003/100 |
| Open Issues | 9 |
| Last Updated | 2024-05-16 |
| Created | 2023-10-03 |
| Platforms | python |
| Est. Tokens | ~1603k |
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DyLAN is Official Implementation of Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization. It is categorized as a Agent Tool with 211 GitHub stars.
DyLAN is primarily written in Python. It covers topics such as chatgpt, gpt4, llm.
You can find installation instructions and usage details in the DyLAN GitHub repository at github.com/SALT-NLP/DyLAN. The project has 211 stars and 29 forks, indicating an active community.
DyLAN is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to DyLAN on Agent Skills Hub include wcgw, llama-cpp-agent, edsl. 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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