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 Lyellr88 · MCP Server · ★ 305
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MARM-Systems safe to install? View the security audit →
MARM: The AI That Remembers Your Conversations Memory Accurate Response Mode v2.2.6 - The intelligent persistent memory system for AI agents (supports HTTP and STDIO), stop fighting your memory and control it. Experience long-term recall, session continuity, and reliable conversation history, so your LLMs never lose track of what matters. [ server implementation
A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and
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MARM-Systems is Stop re-explaining yourself to AI. MARM offers persistent memory, cross-agent context sharing, write queues, swarm-ready presets, and compaction for clean recall. Includes a live web dashboard for man. It is categorized as a MCP Server with 305 GitHub stars.
MARM-Systems is primarily written in Python. It covers topics such as agent-swarms, claude, developer-tools.
You can find installation instructions and usage details in the MARM-Systems GitHub repository at github.com/Lyellr88/MARM-Systems. The project has 305 stars and 57 forks, indicating an active community.
MARM-Systems is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to MARM-Systems on Agent Skills Hub include rust-docs-mcp-server, octocode, mychatarchive. 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.
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