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 Denis2054 · MCP Server · ★ 281
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Context-Engineering-for-Multi-Agent-Systems safe to install? View the security audit →
Context Engineering for Multi-Agent Systems Move beyond prompting to build a Context Engine in a transparent architecture of context and reasoning 🎞️▶️ In 21st‑century Agentic AI, Natural‑Language‑Programmed LLMs are the execution agents, and the domain‑agnostic dual‑RAG MAS is the environment they operate in. This repository provides a production-ready blueprint for the Agentic Era, allowing you to replace rigid, hard-coded workflows with a dynamic, transparent, observable, and sovereign Context Engine. By building universal, domain-agnostic Multi-Agent Systems through high-level semantic orchestration, you can save thousands of lines of code while maintaining 100% observability. Copyright 2025-2026, Denis Rothman. Last updated: March 14, 2026 See the Changelog for updates, fixes, and upgrades(past, present, coming). Save thousands of lines of code by building universal, domain-agnostic Multi-Agent Systems (MAS) using the ultimate new programming language: [🛰️ View S
| Stars | 281 |
| Forks | 100 |
| Language | Jupyter Notebook |
| Category | MCP Server |
| License | MIT |
| Quality Score | 56.2232638233709/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-17 |
| Created | 2025-09-01 |
| Platforms | mcp |
| Est. Tokens | ~24k |
These tools work well together with Context-Engineering-for-Multi-Agent-Systems for enhanced workflows:
Looking for a Context-Engineering-for-Multi-Agent-Systems alternative? If you're comparing Context-Engineering-for-Multi-Agent-Systems with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
An introduction to the world of AI Agents
🔥🔥🔥 Enterprise AI middleware, alternative to unifyapps, n8n, lyzr
Turbo Rig private beta HQ + devcontainer seed — the rig era starts here. Request access: turbo-rig-beta.vercel
The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular,
Chat with any codebase in under two minutes | Fully local or via third-party APIs
RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in differen
Explore other popular mcp server tools:
Context-Engineering-for-Multi-Agent-Systems is Save thousands of lines of code by building universal, domain-agnostic Multi-Agent Systems (MAS) through high-level semantic orchestration. This repository provides a production-ready blueprint for th. It is categorized as a MCP Server with 281 GitHub stars.
Context-Engineering-for-Multi-Agent-Systems is primarily written in Jupyter Notebook. It covers topics such as agentic-ai, agentic-rag, context-engineering.
You can find installation instructions and usage details in the Context-Engineering-for-Multi-Agent-Systems GitHub repository at github.com/Denis2054/Context-Engineering-for-Multi-Agent-Systems. The project has 281 stars and 100 forks, indicating an active community.
Context-Engineering-for-Multi-Agent-Systems is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Context-Engineering-for-Multi-Agent-Systems on Agent Skills Hub include oreilly-ai-agents, wavefront, turbo-flow. 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: