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 pi22by7 · MCP Server · ★ 170
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is In-Memoria safe to install? View the security audit →
In Memoria Giving AI coding assistants a memory that actually persists. Quick Demo Watch In Memoria in action: learning a codebase, providing instant context, and routing features to files. The Problem: Session Amnesia You know the drill. You fire up Claude, Copilot, or Cursor to help with your codebase. You explain your architecture. You describe your patterns. You outline your conventions. The AI gets it, helps you out, and everything's great. Then you close the window. Next session? Complete amnesia. You're explaining the same architectural decisions again. The same naming conventions. The same "no, we don't use classes here, we use functional composition" for the fifteenth time. Every AI coding session starts from scratch. This isn't just annoying, it's inefficient. These tools re-analyze your codebase on every interaction, burning tokens and time. They give
| Stars | 170 |
| Forks | 30 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.910014578617/100 |
| Open Issues | 2 |
| Last Updated | 2025-12-23 |
| Created | 2025-08-16 |
| Platforms | mcp, rust |
| Est. Tokens | ~138k |
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In-Memoria is Persistent Intelligence Infrastructure for AI Agents. It is categorized as a MCP Server with 170 GitHub stars.
In-Memoria is primarily written in Rust. It covers topics such as ai-agents, ai-tools, codebase-intelligence.
You can find installation instructions and usage details in the In-Memoria GitHub repository at github.com/pi22by7/In-Memoria. The project has 170 stars and 30 forks, indicating an active community.
In-Memoria is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to In-Memoria on Agent Skills Hub include roam-code, mcpproxy-go, Ori-Mnemos. 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: