Context Engineering Multi-Agent
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
What is Context Engineering Multi-Agent?
Context Engineering Multi-Agent is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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...
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
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Save thousands of lines of code by building universal, domai
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx context-engineering-for-multi-agent-systemsConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Context Engineering Multi-Agent
Context Engineering for Multi-Agent Systems is a companion repository to the book of the same name by Denis Rothman, providing production-ready Jupyter notebook blueprints for building transparent, domain-agnostic Multi-Agent Systems (MAS) orchestrated with the Model Context Protocol. Rather than shipping a standalone MCP server binary, the repo delivers a Universal Context Engine implemented as a glass-box architecture with dual-RAG pipelines, token analytics, moderation, and MCP-based agent orchestration across legal, marketing, and other domains — all observable through interactive trace dashboards. AI engineers and architects studying or building enterprise-grade agentic systems use this codebase as their reference implementation.
Prerequisites
- Python 3.10 or higher with Jupyter notebook or JupyterLab installed
- An OpenAI API key (notebooks target GPT-4.1 / GPT-5.x) or a compatible API endpoint
- A Pinecone account and API key for the dual-RAG vector store components
- git to clone the repository
- Optionally: a Hugging Face or Google Colab account for cloud notebook execution
Clone the repository
Clone the repository to get all notebook chapters, the engine module, and example configurations.
git clone https://github.com/Denis2054/Context-Engineering-for-Multi-Agent-Systems.git
cd Context-Engineering-for-Multi-Agent-SystemsInstall Python dependencies
Each chapter folder contains its own requirements. Install the root requirements first, then chapter-specific ones as you work through the material.
pip install -r requirements.txtConfigure API keys
Set your OpenAI and Pinecone API keys as environment variables or in a .env file. Notebooks reference these at runtime.
export OPENAI_API_KEY="sk-your-openai-key"
export PINECONE_API_KEY="your-pinecone-key"Start with Chapter 1 to understand the semantic blueprint
Open Chapter01/SLR.ipynb in Jupyter. This notebook introduces context design and the Semantic Layer Representation (SLR) that underpins the entire architecture.
jupyter notebook Chapter01/SLR.ipynbRun the Universal Context Engine
The fully assembled engine is in Chapter10/Universal_Context_Engine.ipynb. It demonstrates the glass-box architecture running cross-domain use cases (legal and marketing) on the same core without code changes.
jupyter notebook Chapter10/Universal_Context_Engine.ipynbDeploy the Gradio UI (optional)
Chapter10/Universal_Context_Engine_Gradio_UI.ipynb provides a deployable Gradio web app with a live public URL in Colab and one-command deploy to Hugging Face Spaces.
Context Engineering Multi-Agent Examples
MCP server config for Chapter 2 MAS notebook
The MCP-based orchestration used in Chapter 2 runs via Python. To expose it as an MCP tool in Claude Desktop, use a stdio config pointing to the server module.
{
"mcpServers": {
"context-engine-mas": {
"command": "python",
"args": ["-m", "context_engine.server"],
"cwd": "/path/to/Context-Engineering-for-Multi-Agent-Systems/Chapter02"
}
}
}Prompts to try
Example prompts that reflect the cross-domain, RAG-backed, MCP-orchestrated capabilities demonstrated in the notebooks.
- "Analyze this legal compliance document for GDPR risk using the context engine"
- "Run a cross-domain marketing strategy analysis using the dual-RAG pipeline"
- "Show me the token usage breakdown for the last agent reasoning trace"
- "Switch the context engine to the legal domain and summarize the uploaded contract"
- "What agents were invoked and in what order for the last MCP-orchestrated task?"Troubleshooting Context Engineering Multi-Agent
OpenAI API errors referencing model names that don't exist
The notebooks have been updated to GPT-5.1 / GPT-4.1. If your API access does not include these models, change the model name in the notebook's config cell to a model you have access to (e.g., gpt-4o). See the CHANGELOG.md for the list of affected notebooks.
Pinecone connection failures during dual-RAG notebook execution
Ensure your PINECONE_API_KEY environment variable is set and that you have created an index with the correct dimension (varies by embedding model used). Check Pinecone's dashboard to confirm the index is in a 'Ready' state before running the notebook.
Notebook kernels die silently on large context windows
Some chapters process large documents. If the kernel crashes, increase your Jupyter server memory limit or run the notebook on Colab/Kaggle which provides more RAM. The Token Analytics dashboard in Chapter 8-9 can help identify which steps consume the most tokens.
Frequently Asked Questions about Context Engineering Multi-Agent
What is Context Engineering Multi-Agent?
Context Engineering Multi-Agent is a Model Context Protocol (MCP) server that 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 It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Context Engineering Multi-Agent?
Follow the installation instructions on the Context Engineering Multi-Agent GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Context Engineering Multi-Agent?
Context Engineering Multi-Agent works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Context Engineering Multi-Agent free to use?
Yes, Context Engineering Multi-Agent is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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