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 dosco · MCP Server · ★ 3.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is graphjin safe to install? View the security audit →
GraphJin — One Governed Graph for Your AI Agents GraphJin is a compiler and runtime that gives AI agents one governed graph over the systems a real company already has: databases, warehouses, files, source code, workflows, metadata, and security policy. Instead of handing an agent raw credentials and hoping it guesses correctly, GraphJin exposes that graph through GraphQL + MCP: the agent discovers before acting, validates queries, runs approved work, observes runtime status — and every answer is checked against an execution ledger before it leaves the server. It is not only for agents. GraphJin is still a high-performance GraphQL-to-databa
| Stars | 3,167 |
| Forks | 195 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 68.1833786165692/100 |
| Open Issues | 22 |
| Last Updated | 2026-09-11 |
| Created | 2019-03-24 |
| Platforms | go, mcp |
| Est. Tokens | ~25k |
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graphjin is One governed graph for AI agents — GraphQL + MCP over your databases, files, APIs, and code. It is categorized as a MCP Server with 3.2k GitHub stars.
graphjin is primarily written in Go. It covers topics such as agentic-ai, ai-agents, cloud-native.
You can find installation instructions and usage details in the graphjin GitHub repository at github.com/dosco/graphjin. The project has 3.2k stars and 195 forks, indicating an active community.
graphjin is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to graphjin on Agent Skills Hub include dbhub, tabularis, anyquery. 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: