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 ScottRBK · MCP Server · ★ 300
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is forgetful safe to install? View the security audit →
Forgetful Forgetful is a storage and retrieval tool for AI Agents. Designed as a Model Context Protocol (MCP) server built using the FastMCP framework. Once connected to this service, MCP clients such as Coding Agents, Chat Bots or your own custom built Agents can store and retrieve information from the same knowledge base. Table of Contents Overview Features Quick Start Some Examples How It Works Configuration Documentation Contributing License Overview A lot of us are using AI Agents now, especially in the realm of software development. The pace at which work and decisions are made can make it difficult for you to keep up from a notes and context persistence perspective. So if you are following something like the [BMAD Method](https://github.com/bmad-
| Stars | 300 |
| Forks | 27 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.55872433423/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-22 |
| Created | 2025-10-20 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~17k |
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forgetful is Opensource Memory for Agents. It is categorized as a MCP Server with 300 GitHub stars.
forgetful is primarily written in Python. It covers topics such as ai-agents, ai-memory, claude-code.
You can find installation instructions and usage details in the forgetful GitHub repository at github.com/ScottRBK/forgetful. The project has 300 stars and 27 forks, indicating an active community.
forgetful is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to forgetful on Agent Skills Hub include vestige, omega-memory, mem0-mcp-selfhosted. 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: