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 bug-ops · MCP Server · ★ 59
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is zeph safe to install? View the security audit →
Zeph A memory-first AI agent for long-running work — local, cloud, or decentralized. Most AI assistants forget everything the moment you close the window. Zeph is built the other way around: it remembers. Point it at your code, your documents, or your team chat, and it keeps working across days and sessions — recalling not just what was said, but why a decision was made. It runs on your laptop with free local models, reaches for the cloud (or a decentralized network) only when a task is genuinely hard, and keeps your API keys encrypted and your tools sandboxed the entire time. It's a single 12 MB Rust binary. No Python, no Node, no database server to b
| Stars | 59 |
| Forks | 4 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.2009691475435/100 |
| Open Issues | 102 |
| Last Updated | 2026-09-18 |
| Created | 2026-02-05 |
| Platforms | claude-code, cli, gemini, mcp, rust |
| Est. Tokens | ~27k |
These tools work well together with zeph for enhanced workflows:
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zeph is A memory-first AI agent that remembers why decisions were made — not just the last message. Runs local (Ollama), cloud (Claude · OpenAI · Gemini), or decentralized TEE. Graph memory, self-learning ski. It is categorized as a MCP Server with 59 GitHub stars.
zeph is primarily written in Rust. It covers topics such as a2a, acp, ai-agent.
You can find installation instructions and usage details in the zeph GitHub repository at github.com/bug-ops/zeph. The project has 59 stars and 4 forks, indicating an active community.
zeph is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to zeph on Agent Skills Hub include c4-genai-suite, captain-claw, cognithor. 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: