# Meirtz/Awesome-Context-Engineering

🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and  implementation guides for LLMs and AI agents.

Repository: https://github.com/Meirtz/Awesome-Context-Engineering
Canonical: https://ross.abutalabs.com/products/awesome-context-engineering
License: MIT
License Family: permissive
Topics: agent, agentic-ai, agi, awesome-list, cognitive-science, context-engineering, llm, rag
Last push: 2026-05-28T05:40:26+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 35, longevity 30
- inputs: {"age_days": 427, "days_push": 97, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3283, forks 280 (observed 2026-08-28T04:07:53.639039+00:00)

## What it is
A curated awesome-list and survey of context engineering for LLMs and AI agents, collecting hundreds of papers, frameworks, and implementation guides. It accompanies an academic survey paper and covers topics from prompt engineering to agent runtimes, memory systems, and observability.

## Use cases
- learn about context engineering for llms
- find papers on prompt engineering and rag
- research agent memory systems
- study how to build production-grade ai agents
- find frameworks for llm context management
- prepare a literature review on context engineering

## When to choose
- you want a curated, paper-backed reading list on context engineering and agent architectures
- you are researching LLM context, memory, or RAG techniques
- you want links to frameworks and implementation guides for AI agents

## When to avoid
- you need runnable production software rather than a resource collection
- you want a hands-on tutorial with code exercises instead of a survey
- you need a specific tool rather than an overview of the field

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, prompt-engineering, agent-framework, llm-inference, documentation
- domain: large-language-models, artificial-intelligence, tutorials, awesome-lists
- platform: -
- tags: context-engineering, survey, awesome-list, llm, memory-systems, agent-runtimes, ai-agents, retrieval-augmented-generation, web-server

## Member repositories
- Meirtz/Awesome-Context-Engineering (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:53.639039+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:43:18.019736+00:00, confidence not recorded.
  - readme: https://github.com/Meirtz/Awesome-Context-Engineering (fetched 2026-08-28T04:07:53.639039+00:00, sha 96edbb380316)
- Data as of 2026-08-30T08:39:29.467469+00:00.