A curated GitHub repository, "awesome-context-engineering," that systematically compiles papers, articles and tools on the increasingly prominent practice of "Context Engineering" for AI agents and LLMs has been published and is spreading among developers.
AI Engineering · The 2025 Shift
From Prompt Engineering to Context Engineering
A curated index maps an emerging discipline: the art and science of filling an LLM's context window with the right information payload at each step. The new consensus — agent reliability hinges on context design, not model choice.
1,400+
Papers analyzed in the survey behind the formal taxonomy
5
Core areas: Retrieval, Processing, Management, Compression, Isolation
4
Strategy framing: Write · Select · Compress · Isolate
What the context window now holds
Prompt Engineering optimized one instruction. Context Engineering designs every token fed to the model.
→
Conversation history
Long-term memory
RAG retrieval results
Tool state
Output format & instructions
Context Engineering — every token, systematically managed
An umbrella that integrates existing ideas
RAG
Memory Systems
Tool Integration
Context Compression
KV-Cache optimization
Context Isolation
Supported environments span Claude Code , LangGraph , and MCP (Model Context Protocol) .
Why it matters
Static prompts fall short of production quality. Dynamic, systematic context design is directly tied to improved agent reliability.
The caveats
Not enough alone — pair it with Harness Engineering (tools, state, errors) and Loop Engineering. Terminology stays fluid; the bar is high for beginners.
Featured across the field
Anthropic
Effective context engineering · Claude Code best practices
LangChain
Write / Select / Compress / Isolate
Survey Taxonomy
1,400+ papers, formal classification
Cognition & others
Don't Build Multi-Agents · SWE-grep
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