AgentRecall-X — security grade SAFE, quality 69/100

Security audit verdict: SAFE · quality 69/100

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 →

About AgentRecall-X

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

agent-learningagent-memoryai-memory-systemsclaude-codeclaude-mcpcorrection-trackingmcp-servermemory-palacepersistent-memorytypescript

Quick Facts

Stars371
Forks58
LanguageJavaScript
CategoryMCP Server
LicenseMIT
Quality Score69.4346207591649/100
Open Issues24
Last Updated2026-09-17
Created2026-03-24
Platformsclaude-code, cli, mcp, node
Est. Tokens~17k

Compatible Skills

These tools work well together with AgentRecall-X for enhanced workflows:

  • claude-memory-engine — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (64%)
  • hipocampus — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (61%)
  • brain.md — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • Orb — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)

AgentRecall-X alternative? Top 6 similar tools

Looking for a AgentRecall-X alternative? If you're comparing AgentRecall-X with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • memorix by AVIDS2 · ⭐ 791

    Open-source cross-agent memory layer for coding agents via MCP. Compatible with Claude Code, Codex, Cursor, Wi

  • Ori-Mnemos by aayoawoyemi · ⭐ 324

    Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.

  • arcade-mcp by ArcadeAI · ⭐ 1.0k

    MCP Server Framework and Tool Development library for building custom capabilities into agents.

  • Overture by SixHq · ⭐ 630

    Overture is an open-source, locally running web interface delivered as an MCP (Model Context Protocol) server

  • vestige by samvallad33 · ⭐ 628

    Cognitive Deterministic Memory Security OS for Agentic AI. Deterministic root-cause retrieval that reaches bac

  • omega-memory by omega-memory · ⭐ 218

    Persistent memory for AI coding agents

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Frequently Asked Questions

What is AgentRecall-X?

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.

What programming language is AgentRecall-X written in?

AgentRecall-X is primarily written in JavaScript. It covers topics such as agent-learning, agent-memory, ai-memory-systems.

How do I install or use AgentRecall-X?

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.

What license does AgentRecall-X use?

AgentRecall-X is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to AgentRecall-X?

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.

How this security grade is produced

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:

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