zeph: A memory-first AI agent that remembers why decisions were made — not just the last message

by bug-ops · MCP Server · ★ 66

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install zeph

cargo install zeph
cargo install zeph --features desktop   # with the TUI dashboard

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars66
Forks7
LanguageRust
CategoryMCP Server
LicenseMIT
Quality Score64/100
Open Issues105
Last Updated2026-11-03
Created2026-02-05
Platformsclaude-code, cli, gemini, mcp, rust
Est. Tokens~27k

About zeph

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.

Compatible Skills

These tools work well together with zeph for enhanced workflows:

  • aichat — semantic(0.21)+complementary+shared_fw(ollama,openai)+same_lang+shared_platform (58%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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