swarms-examples: A vast array of examples for the enterprise-grade and production-ready swarms framework

by The-Swarm-Corporation · Agent Tool · ★ 83

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 60/100

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.

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Quick Facts

Stars83
Forks10
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score60/100
Open Issues11
Last Updated2026-07-13
Created2024-10-14
Platformspython
Est. Tokens~2739k

About swarms-examples

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.

Compatible Skills

These tools work well together with swarms-examples for enhanced workflows:

  • pydantic-deepagents — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • subagents-pydantic-ai — semantic(0.17)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • pydantic-ai-backend — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • airbyte-agent-connectors — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)

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How this security grade is produced

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:

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