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 zycaskevin · MCP Server · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Vault-Agent-Memory safe to install? View the security audit →
Vault Agent Memory English 简体中文 Local-first memory governance for AI agents. Vault Agent Memory gives Codex, Claude Code, Hermes, OpenClaw, n8n, Coze, and other agents one governed memory vault to share. It is not trying to be another notes app or vector database. It helps agents decide what should be remembered, who can use it, whether it is still current, and how to roll it back when it is wrong. The Python package and existing install path remain . Vault is for people already building or working with agents. The main interface should still not be a long CLI manual: ask an agent to install Vault, answer a few setup questions, then read a short daily memory report. New here? Start with the visual demo: .
| Stars | 50 |
| Forks | 10 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.7613849064359/100 |
| Open Issues | 20 |
| Last Updated | 2026-08-27 |
| Created | 2026-04-16 |
| Platforms | mcp, python |
| Est. Tokens | ~18k |
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Vault-Agent-Memory is Local-first memory governance for AI agents: shared, reviewable, auditable memory via SQLite and MCP.. It is categorized as a MCP Server with 50 GitHub stars.
Vault-Agent-Memory is primarily written in Python. It covers topics such as agent-memory, ai-agents, knowledge-base.
You can find installation instructions and usage details in the Vault-Agent-Memory GitHub repository at github.com/zycaskevin/Vault-Agent-Memory. The project has 50 stars and 10 forks, indicating an active community.
Vault-Agent-Memory is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Vault-Agent-Memory on Agent Skills Hub include gno, caura-long-run-fleet, caura-cross-fleet-gov. 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: