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 TheLunarCompany · MCP Server · ★ 498
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is lunar safe to install? View the security audit →
Welcome to Lunar.dev Lunar.dev is an open-source platform for managing, governing and optimizing third-party API consumption across applications and AI agent workloads at scale. Consumption Management for the AI Era As AI agents and autonomous workflows increasingly rely on external APIs, there's a growing need for a mediation layer that acts as a central aggregation point between applications, agents, and the services they depend on. Lunar.dev provides that layer—serving as a unified API Gateway for AI, delivering: Live API Traffic Visibility: Get real-time metrics on latency, errors, cost, and token usage across all outbound traffic, including LLM and agent calls. AI-Aware Policy Enforcement: Control tool access, thr
| Stars | 498 |
| Forks | 51 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 54.1969396415684/100 |
| Open Issues | 14 |
| Last Updated | 2026-09-22 |
| Created | 2023-08-15 |
| Platforms | cli, mcp, node |
| Est. Tokens | ~14k |
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lunar is lunar.dev: Agent native MCP Gateway for governance and security. It is categorized as a MCP Server with 498 GitHub stars.
lunar is primarily written in TypeScript. It covers topics such as agent-skills, api-proxy, mcp.
You can find installation instructions and usage details in the lunar GitHub repository at github.com/TheLunarCompany/lunar. The project has 498 stars and 51 forks, indicating an active community.
lunar is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to lunar on Agent Skills Hub include mcp-router, mcphub.nvim, arcade-mcp. 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: