engram: Persistent memory system for AI coding agents

by Gentleman-Programming · MCP Server · ★ 7.8k

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

How to install engram

brew install gentleman-programming/tap/engram
git clone https://github.com/Gentleman-Programming/engram.git engram-beta-repo

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

gentle-shell-package

Quick Facts

Stars7,805
Forks818
LanguageGo
CategoryMCP Server
LicenseMIT
Quality Score70/100
Open Issues82
Last Updated2026-11-05
Created2026-02-16
Platformscli, go, mcp
Est. Tokens~21k

About engram

Persistent memory for AI coding agents One brain. Local or cloud. Agent-agnostic, single binary, zero dependencies. Installation • Engram Cloud • Agent Setup • Codebase Guide • Architecture • Plugins • Team Usage • Contributing • Full Docs engram — neuroscience: the physical trace of a memory in the brain.

Compatible Skills

These tools work well together with engram for enhanced workflows:

  • graymatter — semantic(0.24)+complementary+same_lang+shared_platform (48%)
  • mnemon — semantic(0.21)+complementary+same_lang+shared_platform (48%)
  • obsidian-mind — semantic(0.18)+complementary+similar_pop+shared_platform (46%)

engram alternative? Top 6 similar tools

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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