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 Goldentrii · MCP Server · ★ 308
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AgentRecall-MCP safe to install? View the security audit →
AgentRecall Your agent doesn't just remember. It learns how you think. Every correction saved is a mistake never repeated. Every insight compounded is tokens never wasted rebuilding context. Persistent, compounding memory + automatic correction capture. MCP server + SDK + CLI. <img src="https://img.shields.io/badge/Obsidian-compatible-7
| Stars | 308 |
| Forks | 52 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.6190990497022/100 |
| Open Issues | 26 |
| Last Updated | 2026-07-15 |
| Created | 2026-03-24 |
| Platforms | claude-code, cli, mcp, node |
| Est. Tokens | ~25k |
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AgentRecall-MCP is Correction-first persistent memory for AI agents. MCP server + SDK + CLI. Compounds across sessions.. It is categorized as a MCP Server with 308 GitHub stars.
AgentRecall-MCP is primarily written in JavaScript. It covers topics such as agent-learning, agent-memory, ai-memory-systems.
You can find installation instructions and usage details in the AgentRecall-MCP GitHub repository at github.com/Goldentrii/AgentRecall-MCP. The project has 308 stars and 52 forks, indicating an active community.
AgentRecall-MCP is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AgentRecall-MCP on Agent Skills Hub include memorix, Ori-Mnemos, Zikkaron. 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: