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 Patdolitse · MCP Server · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is engram safe to install? View the security audit →
Piia Engram Local-first AI memory you can see, edit, and override — portable across your MCP coding tools. Tell AI once who you are, how you work, and what "good" means. Claude Code, Codex, Cursor, Windsurf, and other MCP-compatible tools can start from the same approved context — local files you own, no cloud account, no hidden memory you cannot inspect. Install · See It in Action · Supported Tools · MCP Tools · FAQ ENGLISH | 中文 [](https://gith
| Stars | 58 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65.6396715074984/100 |
| Last Updated | 2026-05-22 |
| Created | 2026-05-18 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~87k |
These tools work well together with engram for enhanced workflows:
Looking for a engram alternative? If you're comparing engram 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.
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engram is AI identity layer for Claude Code, Codex and Cursor — stores who you are, not just what you did. Local-first, MCP-compatible.. It is categorized as a MCP Server with 58 GitHub stars.
engram is primarily written in Python. It covers topics such as ai-agent, ai-identity, ai-memory.
You can find installation instructions and usage details in the engram GitHub repository at github.com/Patdolitse/engram. The project has 58 stars and 1 forks, indicating an active community.
engram is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to engram on Agent Skills Hub include omega-memory, roampal-core, mcp-fusion. 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: