by FIND-Lab · Codex Skill · ★ 60
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AgentWard · 玄甲OS AgentWard (玄甲) is a full-stack security operating system purpose-built for trustworthy, scalable AI agent deployment, with native code adaptation to OpenClaw. AgentWard unifies agent onboarding, secure reasoning, and trusted execution in one cohesive security architecture, with upcoming native support for other leading mainstream agent frameworks. Its heterogeneous defense-in-depth design rearchitects the agent workflow into five coordinated security layers across startup, perception, memory, decision-making, and execution, with dynamic cross-stage protections that verify foundation integrity, block adversarial deception, stop memory tampering, and validate every autonomous decision and high-risk command — a complete, end-to-end closed security loop that delivers on the promise of "trustworthy at inception, controllable throughout the process, and reliable in outcomes". If you find this repo useful, please cite our paper on the design of AgentWard architecture: Why AgentWard 🛡️ Comprehensive Risk Coverage — Heterogeneous Defense-in-Depth (DiD) a
| Stars | 60 |
| Forks | 8 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 38.85/100 |
| Last Updated | 2026-06-04 |
| Created | 2026-03-20 |
| Platforms | node |
| Est. Tokens | ~167k |
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AgentWard is AgentWard – Built for all, hardened for OpenClaw.. It is categorized as a Codex Skill with 60 GitHub stars.
AgentWard is primarily written in TypeScript. It covers topics such as agent-security, defense-in-depth, llm-agent.
You can find installation instructions and usage details in the AgentWard GitHub repository at github.com/FIND-Lab/AgentWard. The project has 60 stars and 8 forks, indicating an active community.
AgentWard is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to AgentWard on Agent Skills Hub include claworc, agent-security-scanner-mcp, clawshield-public. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.