This repository offers a practical guide to Context Engineering, a systematic methodology for optimizing Large Language Model (LLM) applications. It addresses maximizing LLM performance within limited context windows by focusing on the intelligent selection, organization, and injection of highly relevant information. Targeted at developers and researchers, it provides a framework for building robust AI agents and applications, treating context composition as the core development principle to enhance LLM reasoning and execution.

How It Works

Context Engineering prioritizes filling the LLM's context window with information most pertinent to the user's input or task. This approach is positioned as a superset of RAG and distinct from Prompt Engineering. The methodology emphasizes designing systems that dynamically identify and inject "relevant context"—encompassing background knowledge, user memory, tool definitions, session history, and structured outputs—alongside system prompts and user input. This systematic composition enables LLMs to achieve optimal performance.

Quick Start & Requirements

This repository functions as a methodology guide, not a deployable codebase with direct installation instructions. It focuses on theoretical foundations and practical implementation strategies for Context Engineering. Specific setup commands, prerequisites, or resource footprints are not detailed. Readers are directed to "在线阅读" (read online) for the description.

Highlighted Details

• Core methodology centers on seven context types: System Prompt, Problem-Relevant Context, User Memory, Tool Definitions/Outputs, Session History, Structured Output, and User Input.
• Covers foundational technologies (data persistence, vector storage, embeddings, knowledge graphs, RAG) and practical implementation modules (System Prompt design, Tool