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 iikarus · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Dragon-Brain safe to install? View the security audit →
Dragon Brain English Français Memory infrastructure for AI agents — that fails loud, by design. []() []() []() [-blue)]() []() 100% LongMemEval R@5 · 34 MCP tools · sub-200ms hybrid search · CI-gated fail-loud contracts · No LLM req
| Stars | 51 |
| Forks | 8 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.4802071418517/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-22 |
| Created | 2026-02-23 |
| Platforms | claude-code, cli, codex, docker, gemini, mcp, python |
| Est. Tokens | ~20k |
These tools work well together with Dragon-Brain for enhanced workflows:
Looking for a Dragon-Brain alternative? If you're comparing Dragon-Brain 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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Dragon-Brain is Dragon Brain — persistent long-term memory for AI agents via MCP (Model Context Protocol). Knowledge graph (FalkorDB) + vector search (Qdrant) + CUDA GPU embeddings. Works with Claude, Gemini CLI, Cur. It is categorized as a MCP Server with 51 GitHub stars.
Dragon-Brain is primarily written in Python. It covers topics such as ai-memory, claude, codex-cli.
You can find installation instructions and usage details in the Dragon-Brain GitHub repository at github.com/iikarus/Dragon-Brain. The project has 51 stars and 8 forks, indicating an active community.
Dragon-Brain is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Dragon-Brain on Agent Skills Hub include omega-memory, claude-self-reflect, 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: