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 MemoriLabs · Codex Skill · ★ 17.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Memori safe to install? View the security audit →
Memory from what agents do, not just what they say. Memori plugs into the software and infrastructure you already use. It is LLM, datastore and framework agnostic and seamlessly integrates into the architecture you've already designed. → Memori Cloud — Zero config. Get an API key and start building in minutes. <img src="https://img.shields.io/discord/1
| Stars | 17,058 |
| Forks | 3,644 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 64.5091856251664/100 |
| Open Issues | 36 |
| Last Updated | 2026-10-03 |
| Created | 2025-07-24 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
These tools work well together with Memori for enhanced workflows:
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Memori is Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori wo. It is categorized as a Codex Skill with 17.1k GitHub stars.
Memori is primarily written in Python. It covers topics such as agent, agent-memory, agenticai.
You can find installation instructions and usage details in the Memori GitHub repository at github.com/MemoriLabs/Memori. The project has 17.1k stars and 3644 forks, indicating an active community.
The top alternatives to Memori on Agent Skills Hub include honcho, MemOS, mem0. 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: