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 · ★ 371
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AgentRecall-X safe to install? View the security audit →
English · 中文 AgentRecall Claude Code memory that learns from corrections. The only learning loop that measures whether your agent actually stops repeating a mistake. Corrections ledger + session lifecycle + honest measurement. MCP · SDK · CLI · Skill. <img src="https://img.shields.io/badge/cloud-z
| Stars | 371 |
| Forks | 58 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.4346207591649/100 |
| Open Issues | 24 |
| Last Updated | 2026-09-17 |
| Created | 2026-03-24 |
| Platforms | claude-code, cli, mcp, node |
| Est. Tokens | ~17k |
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AgentRecall-X is Correction-first persistent memory for AI agents. MCP server + SDK + CLI. Compounds across sessions.. It is categorized as a MCP Server with 371 GitHub stars.
AgentRecall-X 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-X GitHub repository at github.com/Goldentrii/AgentRecall-X. The project has 371 stars and 58 forks, indicating an active community.
AgentRecall-X is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AgentRecall-X on Agent Skills Hub include memorix, Ori-Mnemos, arcade-mcp. 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: