by agentic-box · MCP Server · ★ 742
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install git+https://github.com/agentic-box/memora.git
pip install "memora[local]" @ git+https://github.com/agentic-box/memora.gitTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 742 |
| Forks | 80 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-17 |
| Created | 2025-09-19 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~18k |
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.
These tools work well together with memora for enhanced workflows:
Looking for a memora alternative? If you're comparing memora 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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Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
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MCP server that lets Claude Code and other AI agents read and search large PDFs, one file or a whole folder: a
A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown a
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: