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 swarmauri · Agent Tool · ★ 103
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is swarmauri-sdk safe to install? View the security audit →
Swarmauri SDK Swarmauri is a composable intelligence infrastructure SDK for building typed, pluggable Python systems. The repository contains the public namespace package, interface contracts, reusable base classes, standard components, community integrations, plugin packages, and package-level documentation f
| Stars | 103 |
| Forks | 50 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 72.67865376276/100 |
| Open Issues | 176 |
| Last Updated | 2026-08-12 |
| Created | 2024-04-08 |
| Platforms | python |
| Est. Tokens | ~16k |
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swarmauri-sdk is Modular Python SDK and monorepo for AI agents, LLM integrations, tools, parsers, embeddings, vector stores, and extensible application workflows.. It is categorized as a Agent Tool with 103 GitHub stars.
swarmauri-sdk is primarily written in Python. It covers topics such as agent-framework, ai-agents, artificial-intelligence.
You can find installation instructions and usage details in the swarmauri-sdk GitHub repository at github.com/swarmauri/swarmauri-sdk. The project has 103 stars and 50 forks, indicating an active community.
swarmauri-sdk is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to swarmauri-sdk on Agent Skills Hub include ai-agent-tools-catalog, SimplerLLM, ai-microcore. 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: