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 CheMiguel23 · MCP Server · ★ 353
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MemoryMesh safe to install? View the security audit →
MemoryMesh MemoryMesh is a knowledge graph server designed for AI models, with a focus on text-based RPGs and interactive storytelling. It helps AI maintain consistent, structured memory across conversations, enabling richer and more dynamic interactions. The project is based on the Knowledge Graph Memory Server from the MCP servers repository and retains its core functionality. IMPORTANT v0.3.0 Update: The MCP SDK has been updated from v1.0.4 to v1.25.2 to comply with the current Model Context Protocol specification (2025-11-25). This is a major update that brings compatibility with the latest MCP clients including Claude Desktop, ChatGPT, Cursor, Gemini, and
| Stars | 353 |
| Forks | 49 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.1792143180615/100 |
| Open Issues | 8 |
| Last Updated | 2026-03-01 |
| Created | 2024-12-06 |
| Platforms | mcp, node |
| Est. Tokens | ~38k |
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MemoryMesh is A knowledge graph server that uses the Model Context Protocol (MCP) to provide structured memory persistence for AI models.. It is categorized as a MCP Server with 353 GitHub stars.
MemoryMesh is primarily written in TypeScript. It covers topics such as ai, mcp.
You can find installation instructions and usage details in the MemoryMesh GitHub repository at github.com/CheMiguel23/MemoryMesh. The project has 353 stars and 49 forks, indicating an active community.
MemoryMesh is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to MemoryMesh on Agent Skills Hub include zenfeed, rulego, mcp-language-server. 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.
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