by timothywarner-org · MCP 服务器 · ★ 17
Context Engineering with MCP: Build AI Systems That Actually Remember 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.
| Stars | 17 |
| Forks | 17 |
| Language | Python |
| Category | MCP 服务器 |
| License | MIT |
| Quality Score | 36.8/100 |
| Open Issues | 3 |
| Last Updated | 2026-04-27 |
| Created | 2025-07-08 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~13497k |
These tools work well together with context-engineering for enhanced workflows:
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context-engineering is 🧠 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 st. It is categorized as a MCP 服务器 with 17 GitHub stars.
context-engineering is primarily written in Python. It covers topics such as ai-agents, ai-memory, anthropic.
You can find installation instructions and usage details in the context-engineering GitHub repository at github.com/timothywarner-org/context-engineering. The project has 17 stars and 17 forks, indicating an active community.
context-engineering is released under the MIT license, making it free to use and modify according to the license terms.