Context-Engineering-for-Multi-Agent-Systems

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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.

★ 283103 forksJupyter NotebookMITDenis2054

Book companion repo (Jupyter notebooks) that builds a domain-agnostic, glass-box Context Engine for multi-agent systems: MCP orchestration, dual RAG with citations, injection defenses, and token analytics, runnable on OpenAI, LangChain, Nemotron, or DeepSeek-R1.

Use Cases

Build domain-agnostic multi-agent systems via semantic blueprintsOrchestrate specialized agents with MCPDual high-fidelity RAG with verifiable citationsLegal compliance and risk management agentsMarketing assistant agentsNASA research assistantDefend against prompt injection and data poisoningTrack token and cost analytics per agent stepComputer-use agents for interactive environmentsDeploy Gradio web UI for context engineRun sovereign AI with local DeepSeek-R1Cross-session agent memory

Built With

Language: Jupyter Notebook
Frameworks: LangChain · LangGraph · Model Context Protocol · Gradio · Pinecone · OpenAI SDK · NVIDIA NIM · Docker · Railway

Context-Engineering-for-Multi-Agent-Systems

Use Cases

Built With

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