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 FreePeak · MCP Server · ★ 220
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LeanKG safe to install? View the security audit →
LeanKG Lightweight Knowledge Graph for AI-Assisted Development LeanKG is a local-first knowledge graph that gives AI coding tools accurate codebase context. It indexes your code, builds dependency graphs, and exposes an MCP server so tools like Cursor, OpenCode, and Claude Code can query the knowledge graph directly. No cloud services, no external databases. Visualize your knowledge graph with force-directed layout, WebGL rendering, and community clustering. See docs/web-ui.md for more features. Live Demo Try LeanKG without installing: https://leankg.onrender.com Installation One-Line Install (Recommended) bash curl -fsSL https://raw.githubusercontent.com/FreePeak/LeanKG/main/scripts/install.sh | bash -s -- <
| Stars | 220 |
| Forks | 28 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 70.5421300773452/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-30 |
| Created | 2026-04-13 |
| Platforms | go, mcp |
| Est. Tokens | ~19k |
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LeanKG is LeanKG: Stop Burning Tokens. Start Coding Lean.. It is categorized as a MCP Server with 220 GitHub stars.
LeanKG is primarily written in Go. It covers topics such as ai-memory, codebase-memory, codebase-memory-mcp.
You can find installation instructions and usage details in the LeanKG GitHub repository at github.com/FreePeak/LeanKG. The project has 220 stars and 28 forks, indicating an active community.
LeanKG is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to LeanKG on Agent Skills Hub include omega-memory, mem0-mcp-selfhosted, brain-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: