by getArbor-dev · MCP Server · ★ 158
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Arbor Graph-native intelligence for codebases. Know what breaks before you break it. <img src="docs/assets/arbor-demo.gif" alt="Side-by-side: an agent navigating tokio with grep-and-read (47 tool calls, still searching) vs the same agent with arbor's code graph (4 graph ca
| Stars | 158 |
| Forks | 22 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 54.0431458849492/100 |
| Open Issues | 16 |
| Last Updated | 2026-09-06 |
| Created | 2026-01-04 |
| Platforms | mcp, rust |
| Est. Tokens | ~22k |
These tools work well together with arbor for enhanced workflows:
Looking for a arbor alternative? If you're comparing arbor 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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arbor is Graph-native code intelligence that replaces embedding-based RAG with deterministic program understanding.. It is categorized as a MCP Server with 158 GitHub stars.
arbor is primarily written in Rust. It covers topics such as ai-tools, ast, code-analysis.
You can find installation instructions and usage details in the arbor GitHub repository at github.com/getArbor-dev/arbor. The project has 158 stars and 22 forks, indicating an active community.
arbor is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to arbor on Agent Skills Hub include arbor, fossil-mcp, roam-code. 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: