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 Gentleman-Programming · MCP Server · ★ 6.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is engram safe to install? View the security audit →
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. Your AI coding agent forgets everything when the session ends. Engram gives it a brain. A Go binary with SQLite + FTS5 full-text search, exposed via CLI, HTTP API, MCP server, and an interactive TUI. Works with any agent that supports MCP — Claude Code, OpenCode, Gemini CLI, Codex, VS Code (Copilot), Antigravity, Cursor, Windsurf, or anything else. Quick Start Install bash brew install gentleman
| Stars | 6,719 |
| Forks | 696 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.1306428250765/100 |
| Open Issues | 107 |
| Last Updated | 2026-09-20 |
| Created | 2026-02-16 |
| Platforms | cli, go, mcp |
| Est. Tokens | ~21k |
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engram is Persistent memory system for AI coding agents. Agent-agnostic Go binary with SQLite + FTS5, MCP server, HTTP API, CLI, and TUI.. It is categorized as a MCP Server with 6.7k GitHub stars.
engram is primarily written in Go.
You can find installation instructions and usage details in the engram GitHub repository at github.com/Gentleman-Programming/engram. The project has 6.7k stars and 696 forks, indicating an active community.
engram is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to engram on Agent Skills Hub include github-mcp-server, serena, gpt-researcher. 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: