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 kaymen99 · Agent Tool · ★ 264
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is langgraph-email-automation safe to install? View the security audit →
Featured in: Email Automation
🚀 Customer Support Email Automation with AI Agents and RAG 📩 FULL TUTORIAL: Build AI-Powered Email Automation Using AI Agents + RAG! 👉 Read Now 🎯 Introduction In today's fast-paced environment, customers demand quick, accurate, and personalized responses—expectations that can overwhelm traditional support teams. Managing large volumes of emails, categorizing them, crafting appropriate replies, and ensuring quality consumes significant time and resources, often leading to delays or errors, which can harm customer satisfaction. Customer Support Email Automation is an AI solution designed to enhance customer communication for businesses. Leveraging a Langgraph-driven workflow, multiple AI agents collabo
| Stars | 264 |
| Forks | 71 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 68.5535095009077/100 |
| Open Issues | 6 |
| Last Updated | 2025-02-13 |
| Created | 2024-06-10 |
| Platforms | python |
| Est. Tokens | ~21k |
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langgraph-email-automation is Multi AI agents for customer support email automation built with Langchain & Langgraph. It is categorized as a Agent Tool with 264 GitHub stars.
langgraph-email-automation is primarily written in Python. It covers topics such as ai-agents, ai-automation, ai-customer-service.
You can find installation instructions and usage details in the langgraph-email-automation GitHub repository at github.com/kaymen99/langgraph-email-automation. The project has 264 stars and 71 forks, indicating an active community.
The top alternatives to langgraph-email-automation on Agent Skills Hub include sre, flock, DeepMCPAgent. 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: