🔥 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.

🧠🔗 From idea to production in just few lines: Graph-Based Programmable Neuro-Symbolic LM Framework - a production-first LM framework built with decade old Deep Learning best practices

Submodular optimization for context engineering: query fan-out, text selection, passage reranking

ApeRAG: Best choice for building your own Knowledge Graph and for Context Engineering

Context engineering is the new vibe coding – it’s how to actually make AI coding assistants work. Gemini CLI is the best for this, and this repo is based on the coleam00 template made for Claude Code!

🚀 A framework for Context Engineering using Google Gemini. Move beyond simple prompting and learn to systematically provide context to your AI coding assistant for more reliable, consistent, and complex software development.

Give Copilot a memory! MemoriPilot provides seamless, persistent context management that makes Copilot aware of your project decisions, progress, and architectural patterns - dramatically improving the relevance and quality of AI assistance.

practical claude code commands, prompts and sub-agents

🧬 KIP is a Knowledge-memory Interaction Protocol designed for LLMs, aiming to build a sustainable learning and self-evolving memory system for AI Agents.

AI-powered file organization and context engineering that understands content, not patterns. Automatically sorts temporary scripts from permanent docs while preserving your workflow. Works seamlessly with Claude Code hooks. Protects README, LICENSE, configs.

Implementation of contextual engineering pipeline with LangChain and LangGraph Agents

Kairos: An autonomous development supervisor powered by Context Engineering. It provides a living memory and a contextual constitution for your projects to prevent AI context loss.

Context Engineering For Writers (with Claude Swarm)

A comprehensive collection of prompts, strategies, and best practices for effectively utilizing large language models (LLMs).

🚀 Browser-based tool for creating reusable sets of context for LLM. Improve response quality & time and reduce token usage. Privacy-first, works with any LLM (Claude, GPT-4, Gemini). Stop re-explaining your codebase to AI (and your team members).

eLLM provides million-token inference on CPUs

Angular TypeScript template demonstrating context engineering for AI-assisted development. Features Domain-Driven Design (DDD), SOLID principles, and PRP workflow for generating high-quality code with structured AI guidance.

Yudai is a context-engineered coding agent that connects to your GitHub repo and turns curated chat summaries, file-dependency insights, and analytics into smart context cards. One click spins those cards into complexity-scored issues, auto-tested code patches, and a labeled pull request with an inline diff viewer so you can merge with confidence.

Context Engineering - The art of providing all the context for the task to be plausibly solvable by the LLM.

Direct AI agents to build production apps at unprecedented speed with this edge-first Next.js + Convex + Cloudflare starter template designed for agentic development workflows.