#7373rd of 88 in Learning and cookbooks
context-engineering
Handbook and code examples for designing what goes into an LLM's context
- Stars
- 9.3k
- License
- MIT
- Last commit
- Feb 2026
- Language
- Python
8,636 on 21 Apr 20269,279 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A course-style repository on context engineering: markdown theory, Python walkthroughs, YAML templates and example projects, ordered from single prompts to memory, multi-agent setups and field-theory ideas. It also ships command files for coding agents such as Claude Code and Gemini CLI. The README summarizes recent papers (MEM1, IBM cognitive tools) and is not an installable library.
Who it is for: Engineers learning to design prompts, memory and retrieval for LLM apps
Strengths
- Progressive path: foundations, zero-to-hero guides, templates, examples, reference
- Runnable Python walkthrough and copy-paste YAML templates
- Links primary sources, including the 1400-paper context engineering survey
- Includes agent command files for Claude Code, Codex, Gemini CLI and others
Weaknesses
- Course is described as under construction; some sections may be incomplete
- Not a library or tool; nothing to install or import
- Speculative framing (quantum semantics, neural fields) mixed with practical material
- No tagged releases and no stated dependencies or tested model list
Quick answers
Is context-engineering open source?
Yes. Its code is on GitHub under the MIT license.
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