by InternLM · Agent Tool · ★ 415
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install agentlego
Taken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 415 |
| Forks | 35 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 70/100 |
| Open Issues | 7 |
| Last Updated | 2024-09-13 |
| Created | 2023-05-18 |
| Platforms | python |
| Est. Tokens | ~302k |
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...
Looking for a agentlego alternative? If you're comparing agentlego with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
One-stop shop for building AI-powered products and businesses with Stripe.
A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.
🦙 Integrating LLMs into structured NLP pipelines
LLM-based ontological extraction tools, including SPIRES
Ship RAG based LLM web apps in seconds.
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: