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 InternLM · Agent Tool · ★ 413
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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English | 简体中文 Introduction Quick Starts Installation Use tools directly Integrated into agent frameworks Supported Tools Licence Introduction AgentLego is an open-source library of versatile tool APIs to extend and enhance large language model (LLM) based agents, with the following highlight features: Rich set of tools for multimodal extensions of LLM agents including visual perception, image generation and editing, speech processing and visual-language reasoning, etc. Flexible tool interface that allows users to easily extend custom tools with arbitrary types of arguments and outputs.
| Stars | 413 |
| Forks | 35 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 70.0942760109685/100 |
| Open Issues | 7 |
| Last Updated | 2024-09-13 |
| Created | 2023-05-18 |
| Platforms | python |
| Est. Tokens | ~302k |
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agentlego is Enhance LLM agents with rich tool APIs. It is categorized as a Agent Tool with 413 GitHub stars.
agentlego is primarily written in Python. It covers topics such as large-language-models, llm, llm-agents.
You can find installation instructions and usage details in the agentlego GitHub repository at github.com/InternLM/agentlego. The project has 413 stars and 35 forks, indicating an active community.
agentlego is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to agentlego on Agent Skills Hub include LLMCompiler, ai, awesome-LangGraph. 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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