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 orneryd · MCP Server · ★ 893
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is NornicDB safe to install? View the security audit →
NornicDB Graph, vector, and historical truth in one database Neo4j-compatible • Hybrid graph + vector retrieval • Historical reads via MVCC Achieving Psygnosis for AI Multi-arch support: CPU Vulkan <img src="https://goreportcard.com/badge/github.com/orneryd/nornicdb" alt=
| Stars | 893 |
| Forks | 54 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 61.7445557103375/100 |
| Open Issues | 11 |
| Last Updated | 2026-10-02 |
| Created | 2025-12-06 |
| Platforms | go, mcp |
| Est. Tokens | ~23k |
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NornicDB is Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no chan. It is categorized as a MCP Server with 893 GitHub stars.
NornicDB is primarily written in Go. It covers topics such as bolt, cypher, database.
You can find installation instructions and usage details in the NornicDB GitHub repository at github.com/orneryd/NornicDB. The project has 893 stars and 54 forks, indicating an active community.
NornicDB is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to NornicDB on Agent Skills Hub include data-api-builder, mcp-neo4j, Wax. 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: