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 The-Swarm-Corporation · Agent Tool · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Swarm Examples The Swarms Framework is a production-grade framework designed for building and deploying multi-agent systems. It provides robust tools and libraries to facilitate the development of complex, distributed, and scalable llm-agent-based applications. Features Scalability: Easily scale your agent systems to handle large numbers of agents. Flexibility: Supports a wide range of agent architectures and communication protocols. Integration: Seamlessly integrates with existing systems and technologies. Performance: Optimized for high performance and low latency. Swarms Examples Index Single Agent Examples Core Agents [Easy Example](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/easye
| Stars | 83 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 59.5802146525025/100 |
| Open Issues | 11 |
| Last Updated | 2026-07-13 |
| Created | 2024-10-14 |
| Platforms | python |
| Est. Tokens | ~2739k |
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swarms-examples is A vast array of examples for the enterprise-grade and production-ready swarms framework.. It is categorized as a Agent Tool with 83 GitHub stars.
swarms-examples is primarily written in Python. It covers topics such as agens, ai, anthropic.
You can find installation instructions and usage details in the swarms-examples GitHub repository at github.com/The-Swarm-Corporation/swarms-examples. The project has 83 stars and 10 forks, indicating an active community.
swarms-examples is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to swarms-examples on Agent Skills Hub include palico-ai, Agent-Wiz, oreilly-ai-agents. 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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