by ilanbenb · MCP Server · ★ 169
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wa_llm safe to install? View the security audit →
📱 WhatsApp Group Summary Bot AI-powered WhatsApp bot that joins any group, tracks conversations, and generates intelligent summaries. Features 🤖 Automated group chat responses (when mentioned) 📝 Smart LLM-based conversation summaries 📚 Knowledge base integration for context-aware answers 📂 Persistent message history with PostgreSQL + 🔗 Support for multiple message types (text, media, links) 👥 Group management & customizable settings 🔕 Opt-out feature: Users can opt-out of being tagged in summaries/answers via DM. ⚡ REST API with Swagger docs () 🐳 Docker Compose Configurations This project includes multiple Docker Compose files for different environments:
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 →
| Stars | 169 |
| Forks | 59 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 75/100 |
| Open Issues | 16 |
| Last Updated | 2026-09-15 |
| Created | 2024-12-31 |
| Platforms | docker, mcp, python |
| Est. Tokens | ~15k |
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wa_llm is A WhatsApp bot that can participate in group conversations, powered by AI. The bot monitors group messages and responds when mentioned.. It is categorized as a MCP Server with 169 GitHub stars.
wa_llm is primarily written in Python. It covers topics such as communities, mcp, mcp-server.
You can find installation instructions and usage details in the wa_llm GitHub repository at github.com/ilanbenb/wa_llm. The project has 169 stars and 59 forks, indicating an active community.
wa_llm is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to wa_llm on Agent Skills Hub include whatsapp-claude-plugin, whatsapp-mcp, open-bsp-api. 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: