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 recallium-ai · MCP Server · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is recallium safe to install? View the security audit →
Recallium The memory OS for AI coding agents. Your digital developer twin — because "explain our setup again" gets old fast. It's Wednesday. Your agent just asked you to re-explain your auth architecture. You explained it on Monday. Recallium fixes this — because it was built for developers, not for generic chat. Built for developers. Not chat agents. Memory only works when it understands context. A doctor's memory looks nothing like a customer service agent's. And neither looks like a developer's. Developers work in projects, make reversible decisions, debug recurring patterns, and build on choices made mont
| Stars | 50 |
| Forks | 5 |
| Language | Batchfile |
| Category | MCP Server |
| Quality Score | 62.4686723774413/100 |
| Last Updated | 2026-05-15 |
| Created | 2025-11-27 |
| Platforms | claude-code, mcp |
| Est. Tokens | ~688k |
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recallium is Recallium is a local, self-hosted universal AI memory system providing a persistent knowledge layer for developer tools (Copilot, Cursor, Claude Desktop). It eliminates "AI amnesia" by automatically c. It is categorized as a MCP Server with 50 GitHub stars.
recallium is primarily written in Batchfile. It covers topics such as ai-amnesia-fix, ai-coding-agent, ai-memory-system.
You can find installation instructions and usage details in the recallium GitHub repository at github.com/recallium-ai/recallium. The project has 50 stars and 5 forks, indicating an active community.
The top alternatives to recallium on Agent Skills Hub include ue5-mcp-bridge, BifrostMCP, ollama-mcp. 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: