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 oceanbase · Codex Skill · ★ 816
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is powermem safe to install? View the security audit →
PowerMem Persistent, self-evolving memory for AI agents and applications. English · 中文 · 日本語 PowerMem combines vector, full-text, and graph retrieval with LLM-driven memory extraction and Ebbinghaus-style time decay. It ships two-layer Experience + Skill distillation for self-evolving memory, multi-agent isolation, user profiles, and multimodal signals (text, image, audio). Why PowerMem AI agents need more than chat history. Context windows are finite, and naive "save everything" memory quickly becomes noisy, expensive, and hard to retrieve from. PowerMem turns conversations, actions, and feedback into structured long-term memory that can be searched, updated, decayed, and shared across agents. What makes PowerMem different: Intelligent memory lifecycle — LLM-driven extraction
| Stars | 816 |
| Forks | 141 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 62.579158719703/100 |
| Open Issues | 49 |
| Last Updated | 2026-08-05 |
| Created | 2025-11-10 |
| Platforms | python |
| Est. Tokens | ~18k |
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powermem is PowerMem: AI Memory Plugin— Accurate, Agile, Affordable. Make AI Agent smarter.. It is categorized as a Codex Skill with 816 GitHub stars.
powermem is primarily written in Python. It covers topics such as agentic, agents, ai.
You can find installation instructions and usage details in the powermem GitHub repository at github.com/oceanbase/powermem. The project has 816 stars and 141 forks, indicating an active community.
The top alternatives to powermem on Agent Skills Hub include agentic-context-engine, Mysti, mira-OSS. 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: