timothywarner-org/context-engineering
š§ Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here.
Welcome to the training hub for mastering Context Engineering with Model Context Protocol (MCP). This course teaches you to implement production-ready semantic memory systems for AI assistants using Python, FastAPI, FastMCP, and LangGraph.
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git clone https://github.com/timothywarner-org/context-engineering.git
cd context-engineering/labs/lab-01-hello-mcp/starter
npm install && npm start
# Test with MCP Inspector (in another terminal)
npx @modelcontextprotocol/inspector node src/index.jscd src/warnerco/backend
uv sync
uv run uvicorn app.main:app --reload # HTTP server at http://localhost:8000
uv run warnerco-mcp # MCP stdio server for Claude Desktop---
| Segment | Topic | Focus | | ------- | ---------------------- | --------------------------------------------------------------- | | 1 | All About Context | Token economics, context loss types, why RAG isn't enough | | 2 | All About MCP | FastMCP, FastAPI, tools, resources, prompts, elicitations | | 3 | Semantic Memory Stores | JSON, ChromaDB, Azure AI Search, Graph Memory, Scratchpad | | 4 | MCP in Production | Claude Desktop, Claude Code, VS Code, GitHub Copilot, LangGraph |
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The flagship teaching application demonstrates producti
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