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 agentic-box · MCP Server · ★ 713
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memora safe to install? View the security audit →
Memora "You never truly know the value of a moment until it becomes a memory." Give your AI agents persistent memory An MCP memory layer for agents: structured storage, semantic retrieval, graph relations, and source-backed cross-session context. Absorb agent work into durable graph memory, then use memorydigest(topic) to retrieve relevant memories, TODOs/issues, related edges, and source IDs. Features · Preview · Install · Usage · Config · Live Graph · C
| Stars | 713 |
| Forks | 74 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.5437486288773/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-02 |
| Created | 2025-09-19 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~18k |
These tools work well together with memora for enhanced workflows:
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memora is Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.. It is categorized as a MCP Server with 713 GitHub stars.
memora is primarily written in Python. It covers topics such as agent-memory, ai-agent, claude.
You can find installation instructions and usage details in the memora GitHub repository at github.com/agentic-box/memora. The project has 713 stars and 74 forks, indicating an active community.
memora is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to memora on Agent Skills Hub include mcp-memory-service, Ori-Mnemos, omega-memory. 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: