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 jgouviergmail · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LIA-Assistant safe to install? View the security audit →
LIA A self-hosted, multi-agent conversational assistant — LangGraph orchestration, Human-in-the-Loop before every mutation, transparency registers, enterprise observability, six languages. If you find this project valuable, a star on GitHub is the best way to say so. Thank you! <img src="https://
| Stars | 51 |
| Forks | 10 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 58.6866349913119/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-20 |
| Created | 2026-03-12 |
| Platforms | claude-code, docker, mcp, python |
| Est. Tokens | ~25k |
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LIA-Assistant is Smart multi-agent conversational assistant with LangGraph orchestration, Human-in-the-Loop, enterprise-grade observability, and full i18n support (6 languages). It is categorized as a MCP Server with 51 GitHub stars.
LIA-Assistant is primarily written in Python. It covers topics such as ai, ai-assistant, apple.
You can find installation instructions and usage details in the LIA-Assistant GitHub repository at github.com/jgouviergmail/LIA-Assistant. The project has 51 stars and 10 forks, indicating an active community.
The top alternatives to LIA-Assistant on Agent Skills Hub include c4-genai-suite, lobu, weam. 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: