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by quailyquaily · Agent Tool · ★ 257
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🔒 Is aqua safe to install? View the security audit →
Aqua Aqua is a message tool for AI Agents. Aqua is short for AQUA Queries & Unifies Agents. It's a protocol, a CLI, comes from . Features 🤝 Peer-to-peer agent communication with identity verification. 🤝 Mutual contact requests with explicit accept/reject flow. 🔐 End-to-end encrypted messaging. 💾 Durable message storage with inbox/outbox. 🌐 Circuit Relay v2 support for cross-network connectivity. 🔔 Optional inbound webhook delivery for external automation. 🛠️ Simple CLI for node management and messaging. Next Steps [ ] group E2EE [ ] durable retransmission queue [ ] online directory service Install Option A: download a prebuilt binary from GitHub Releases (recommended for production use): Option B: install from source with Go: Quick Start | aqua send
| Stars | 257 |
| Forks | 16 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 71.9216451333349/100 |
| Last Updated | 2026-03-24 |
| Created | 2026-02-21 |
| Platforms | cli, go |
| Est. Tokens | ~25k |
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aqua is Aqua, a cli message tool for AI agents. It is categorized as a Agent Tool with 257 GitHub stars.
aqua is primarily written in Go. It covers topics such as agent-skills, agent-tools, ai-agents.
You can find installation instructions and usage details in the aqua GitHub repository at github.com/quailyquaily/aqua. The project has 257 stars and 16 forks, indicating an active community.
aqua is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to aqua on Agent Skills Hub include ultimate_bug_scanner, claudepro-directory, ai-agent-tools-catalog. 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: