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 Edlineas · MCP Server · ★ 91
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is aivectormemory safe to install? View the security audit →
🌐 简体中文 日本語 AIVectorMemory Give your AI coding assistant a memory — Cross-session persistent memory MCP Server Still using CLAUDE.md / MEMORY.md as memory? This Markdown-file memory approach has fatal flaws: the file keeps growing, injecting everything into every session and burning massive tokens; content only supports keyword matching — search "database timeout" and you won't find "MySQL connection pool pitfall"; sharing one file across projects causes c
| Stars | 91 |
| Forks | 24 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65.445455316167/100 |
| Open Issues | 2 |
| Last Updated | 2026-04-19 |
| Created | 2026-02-14 |
| Platforms | claude-code, mcp, python, vscode |
| Est. Tokens | ~583k |
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Sync and store memories, shared identity files (AGENTS.md, CLAUDE.md), session transcripts, institutional know
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aivectormemory is aivectormemory 是一款基于 Model Context Protocol (MCP) 开发的OpenClaw、OpenCode、ClaudeCodeAI记忆管理工具。它专门为 Claude、OpenCode、Cursor 和 主流IDE 编程工具设计，通过向量数据库技术解决 AI 在不同对话会话中「健忘」的问题。aivectormemory: A lightweight MCP Se. It is categorized as a MCP Server with 91 GitHub stars.
aivectormemory is primarily written in Python. It covers topics such as claudecode, claudecode-hooks, cursor.
You can find installation instructions and usage details in the aivectormemory GitHub repository at github.com/Edlineas/aivectormemory. The project has 91 stars and 24 forks, indicating an active community.
aivectormemory is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to aivectormemory on Agent Skills Hub include Adnify, kasetto, skills. 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: