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 sinaptik-ai · Agent Tool · ★ 76
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is starpod safe to install? View the security audit →
Starpod Personal AI agents. Built in Rust. Website · Docs · Console · Discord Starpod is an open-source AI agent runtime built in Rust. Bootstrap an agent in any directory with , then run it locally or deploy to the cloud. Each agent gets its own memory, vault, filesystem, and sessions — all self-contained in a directory. Install
| Stars | 76 |
| Forks | 13 |
| Language | Rust |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 75.3795483356358/100 |
| Last Updated | 2026-04-08 |
| Created | 2026-03-18 |
| Platforms | claude-code, rust |
| Est. Tokens | ~297k |
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starpod is Open-source AI agent runtime built in Rust. Define once, deploy isolated instances per tenant with built-in memory, encrypted vault, self-extending skills, cron, and multi-channel support.. It is categorized as a Agent Tool with 76 GitHub stars.
starpod is primarily written in Rust. It covers topics such as agent, agent-runtime, agentic-ai.
You can find installation instructions and usage details in the starpod GitHub repository at github.com/sinaptik-ai/starpod. The project has 76 stars and 13 forks, indicating an active community.
starpod is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to starpod on Agent Skills Hub include AIMAXXING, oreilly-ai-agents, os-moda. 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: