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 roampal-ai · MCP Server · ★ 125
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is roampal safe to install? View the security audit →
Roampal Memory that learns what works. So you can do more of it. Say it worked. Say it didn't. The AI remembers. Stop re-explaining yourself every conversation. Roampal remembers outcomes, learns from feedback, and gets smarter over time—all 100% private and local. 85.8% non-adversarial on LoCoMo (1,986 questions). +23 pts over raw ingestion. Absorbs 1,135 poison memories losing only 4 pts. (Paper) Benchmark Results LoCoMo dataset (1,986 questions, 5 categories, corrected ground truths). Evaluated with roampal-labs. Dual-graded by local 20B + MiniMax M2.7. Re
| Stars | 125 |
| Forks | 21 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 61.8102667174116/100 |
| Last Updated | 2026-05-12 |
| Created | 2025-10-26 |
| Platforms | mcp, python |
| Est. Tokens | ~551k |
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roampal is Memory that learns what works.. It is categorized as a MCP Server with 125 GitHub stars.
roampal is primarily written in Python. It covers topics such as ai-assistant, ai-memory, chromadb.
You can find installation instructions and usage details in the roampal GitHub repository at github.com/roampal-ai/roampal. The project has 125 stars and 21 forks, indicating an active community.
The top alternatives to roampal on Agent Skills Hub include lilbee, Ori-Mnemos, guaardvark. 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: