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 syncable-dev · MCP Server · ★ 487
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memtrace-public safe to install? View the security audit →
Featured in: Agent Memory
Your agents deserve structural memory. 📖 Docs · ⭐ Star us · memtrace.io · npm · Discord Memtrace turns your codebase into a live knowledge graph that AI coding agents can query in milliseconds — every function, class, call edge, and version, across every session, without re-reading files or breaking things they can't see. Get your fleet on shared structural memory in under 90 seconds. Structural · zero LLM calls · Bi-temporal · time-travel queries · Replay-aware · zero blind refactors <img src="https://img.shields.io/npm/v/memtrace?style=flat-square&color=00d4b8&log
| Stars | 487 |
| Forks | 46 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 62.1365798559643/100 |
| Open Issues | 5 |
| Last Updated | 2026-10-01 |
| Created | 2026-04-11 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~18k |
These tools work well together with memtrace-public for enhanced workflows:
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memtrace-public is Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · DeepSeek Harness · Hermes · VS Code · Windsurf.. It is categorized as a MCP Server with 487 GitHub stars.
memtrace-public is primarily written in Python. It covers topics such as agent-memory, ai-agents, bi-temporal-graph.
You can find installation instructions and usage details in the memtrace-public GitHub repository at github.com/syncable-dev/memtrace-public. The project has 487 stars and 46 forks, indicating an active community.
The top alternatives to memtrace-public on Agent Skills Hub include octocode, antigravity-workspace-template, memorix. 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: