GateMem — security grade SAFE, quality 67/100

Security audit verdict: SAFE · quality 67/100

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 rzhub · Agent Tool · ★ 195

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is GateMem safe to install? View the security audit →

About GateMem

GateMem -- Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents GateMem evaluates whether memory-augmented LLM agents can remain useful, enforce access control, and honor deletion requests in multi-principal shared-memory environments. 📄 Paper • 🤗 Hugging Face Dataset • 🧪 Benchmark Toolkit • 🗂 Dataset Card • 🏆 Leaderboard • 🌐 Project Page ⭐ If you find GateMem useful, please consider starring the repository to help others discover the benchmark. <p alig

agent-memoryai-safetybenchmarkllm-agentnlp

Quick Facts

Stars195
Forks4
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score67.3835852042123/100
Last Updated2026-06-21
Created2026-06-16
Platformspython
Est. Tokens~15k

Compatible Skills

These tools work well together with GateMem for enhanced workflows:

  • ogham-mcp — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)
  • caura-memclaw — semantic(0.30)+complementary+same_lang+similar_pop+shared_platform (60%)
  • LycheeMem — semantic(0.44)+complementary+same_lang+similar_pop+shared_platform (60%)
  • PawBench — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)

GateMem alternative? Top 6 similar tools

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

  • Awesome-LLM-Eval by onejune2018 · ⭐ 654

    Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, ma

  • OpenRCA by microsoft · ⭐ 401

    [ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?

  • Roy by JosefAlbers · ⭐ 79

    Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language mo

  • clientai by benavlabs · ⭐ 70

    A unified client for AI providers with built-in agent support.

  • cactus by pnnl · ⭐ 52

    LLM Agent that leverages cheminformatics tools to provide informed responses.

  • awesome-openclaw by SamurAIGPT · ⭐ 957

    A curated list of OpenClaw resources, tools, skills, tutorials & articles. OpenClaw (formerly Moltbot / Clawdb

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Frequently Asked Questions

What is GateMem?

GateMem is GateMem: a benchmark and evaluation toolkit for memory governance in multi-principal shared-memory LLM agents.. It is categorized as a Agent Tool with 195 GitHub stars.

What programming language is GateMem written in?

GateMem is primarily written in Python. It covers topics such as agent-memory, ai-safety, benchmark.

How do I install or use GateMem?

You can find installation instructions and usage details in the GateMem GitHub repository at github.com/rzhub/GateMem. The project has 195 stars and 4 forks, indicating an active community.

What license does GateMem use?

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

What are the best alternatives to GateMem?

The top alternatives to GateMem on Agent Skills Hub include Awesome-LLM-Eval, OpenRCA, Roy. 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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