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 christopherkarani · Agent Tool · ★ 581
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Swarm safe to install? View the security audit →
Swarm Build production AI agents in Swift. Swarm is a Swift-native agent runtime for type-safe tools, on-device Apple Foundation Models, composable workflows, memory, guardrails, and streaming. Build for iOS, macOS, and Linux with the same agent loop. Get started · Browse examples · ⭐ Star on GitHub The example uses Apple Foundation Models on supported devices. Use an OpenAI-compatible provider, Ollama, a mock provider, or the deterministic examples when Foundation Models is unavailable. Install Add Swarm wi
| Stars | 581 |
| Forks | 43 |
| Language | Swift |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.0769595581239/100 |
| Last Updated | 2026-09-21 |
| Created | 2025-12-12 |
| Est. Tokens | ~17k |
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Swarm is Type-safe tools, on-device inference, multi-agent workflows, memory, and guardrails, in one Swift-native runtime. It is categorized as a Agent Tool with 581 GitHub stars.
Swarm is primarily written in Swift. It covers topics such as agent, agentic-ai, agentic-systems.
You can find installation instructions and usage details in the Swarm GitHub repository at github.com/christopherkarani/Swarm. The project has 581 stars and 43 forks, indicating an active community.
Swarm is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Swarm on Agent Skills Hub include agentic-context-engine, DeepMCPAgent, claude-code-apple-skills. 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: