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 ilanbenb · MCP Server · ★ 165
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:
| Stars | 165 |
| Forks | 60 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 75.2902119786482/100 |
| Open Issues | 15 |
| Last Updated | 2026-08-19 |
| 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 165 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 165 stars and 60 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-mcp, whatsapp-claude-plugin, DeskcommCRM. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.