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 keta1930 · MCP Server · ★ 192
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-agent-graph safe to install? View the security audit →
English | 中文 Agent-Graph is a Multi-Agent System built on the principles of Context Engineering. It integrates Sub-agent, Long-term Memory, MCP, Agent-based Workflow, and other capabilities. By integrating Context Engineering best practices into a visual development experience, Agent-Graph enables developers to rapidly build, test, and deploy complex multi-agent applications. Table of Contents Framework Deployment Guide Clone Project Configure and Start Docker Services Deploy Backend Access Application Core Features Future Roadmap Coming Soon Future Plans Frontend Feature Showcase Citation WeChat Group Framework System Architecture User Journey Deployment Guide 📖 Detailed Installation Documentation: docs/first-steps/install.md System Requirements Lin
| Stars | 192 |
| Forks | 34 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 67.4912685155845/100 |
| Open Issues | 4 |
| Last Updated | 2026-02-07 |
| Created | 2025-04-24 |
| Platforms | mcp, python |
| Est. Tokens | ~5498k |
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mcp-agent-graph is MCP Agent Graph is a Multi-Agent System built on the principles of Context Engineering. It is categorized as a MCP Server with 192 GitHub stars.
mcp-agent-graph is primarily written in Python. It covers topics such as agent, agentic-framework, agentic-workflow.
You can find installation instructions and usage details in the mcp-agent-graph GitHub repository at github.com/keta1930/mcp-agent-graph. The project has 192 stars and 34 forks, indicating an active community.
mcp-agent-graph is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to mcp-agent-graph on Agent Skills Hub include evo-ai, browser-operator-core, entroly. 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: