Denis2054/Context-Engineering-for-Multi-Agent-Systems
View on GitHub ↗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 the Agentic Era, allowing you to replace rigid, hard-coded workflows with a dynamic transparent Context Engine that provides 100% transparency.
What you need to know
Context Engineering for Multi-Agent Systems is the companion repository to Denis Rothman's book — a production blueprint for building a transparent Glass-Box 'Context Engine': a domain-agnostic dual-RAG multi-agent system orchestrated via MCP with semantic blueprints, token-analytics, trace dashboards, and a DeepSeek-R1 sovereign (offline) path.
Install
No local install required — launch the Jupyter notebooks in Google Colab (badges per chapter) or a local Python 3.10+ env with openai, pinecone-client, tiktoken, tenacity, fastapi Get OpenAI and Pinecone API keys (Colab Secrets Manager)
Usage
- •Work through chapters 1-10: semantic blueprints, building MAS with MCP, context-aware MAS, assembling the Context Engine, hardening, summarizer agent, high-fidelity RAG, moderation, and production blueprint
- •Run the Universal Context Engine (Chapter 10) notebooks, incl. the Gradio web app, for cross-domain (legal + marketing) use cases
- •Follow the Sovereign Path with DeepSeek-R1 to run fully offline on your own hardware
Key features
- ✓Glass-Box architecture with interactive trace dashboards and 100% observability into agent reasoning
- ✓Universal, domain-agnostic Context Engine core that runs cross-domain use cases (legal, marketing) without code changes
- ✓Dual high-fidelity RAG (instructions + facts agents) with verifiable citations and sanitization/security safeguards
- ✓Token & Cost analytics (tokenomics) per step; MCP-driven multi-agent orchestration; moderation and policy control
- ✓Sovereign AI path: DeepSeek-R1 on local infra (~9.75s benchmarked H100) with no external LLM APIs
Best for
Architects and engineers learning/implementing production 'context engineering' multi-agent systems who want 100% observability and domain-agnostic design.
Caveats
- ⚠This is a book companion/course repository, not a standpoint MCP server — MCP is used inside its orchestrator architecture
- ⚠Requires API keys (OpenAI + Pinecone) and multi-step reasoning latency; local execution incurs token/api costs
Reviewed 2026-08-11
Topics
- Stars
- 278★
- Forks
- 90
- Language
- Jupyter Notebook
- License
- MIT
- Created
- 2025-09-01
- Last push
- 2026-08-17