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 rush-db · MCP Server · ★ 323
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is rushdb safe to install? View the security audit →
RushDB The memory layer for AI agents and apps. Push any JSON. Your agent gets graph relationships, semantic search, and a live queryable schema — automatically. No pipeline. No separate stores. No schema planning. 🌐 Website • 📖 Documentation • ☁️ Cloud • 🔍 Examples The problem Your agent needs memory. The standard answer is three databases: Redis for key-value, a vector store for semantic search, a graph DB for relationships — plus glue code to keep them in sync. RushDB replaces all three. Push JSON once. Query it with graph traversal,
| Stars | 323 |
| Forks | 25 |
| Language | TypeScript |
| Category | MCP Server |
| Quality Score | 64.5717209054946/100 |
| Open Issues | 19 |
| Last Updated | 2026-08-31 |
| Created | 2024-12-15 |
| Platforms | docker, mcp, node |
| Est. Tokens | ~17k |
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rushdb is RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j.. It is categorized as a MCP Server with 323 GitHub stars.
rushdb is primarily written in TypeScript. It covers topics such as ai, ai-agents, ai-memory.
You can find installation instructions and usage details in the rushdb GitHub repository at github.com/rush-db/rushdb. The project has 323 stars and 25 forks, indicating an active community.
The top alternatives to rushdb on Agent Skills Hub include octocode, octocode-mcp, mira-OSS. 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: