There are 19 repositories under context-engineering topic.
Memory for AI Agents in 6 lines of code
Data transformation framework for AI. Ultra performant, with incremental processing. 🌟 Star if you like it!
🔥 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.
ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment
Ultimate Context Engineering Infrastructure, starting from MCPs and Integrations
Transform data into AI-ready context. Deploy knowledge graphs, private LLMs, and intelligent agents with complete data sovereignty. From data silos to actionable AI insights.
🧠 Context Engineering Research - Not just another agent collection, but using research and context engineering to function as a collective. Hub-and-spoke coordination through Claude Code.
🧠🔗 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
A minimalist MVP demonstrating a simple yet profound insight: aligning AI memory with human episodic memory granularity. Shows how this single principle enables simple methods to rival complex memory frameworks for conversational tasks.
In the midst of all the tools out there that you can possibly use to keep track of them. Here's a "shovel" that just works to try them all out.
An agentic workflow tool that provides context engineering support for opencode
This GitHub repository contains the complete code for building Business-Ready Generative AI Systems (GenAISys) from scratch. It guides you through architecting and implementing advanced AI controllers, intelligent agents, and dynamic RAG frameworks. The projects demonstrate practical applications across various domains.
Submodular optimization for context engineering: query fan-out, text selection, passage reranking
The Keystone Framework for AI-Driven Code ! Turn any AI coding assistant into a disciplined, project-aware engineering partner that respects your architecture and coding standards
A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).
🚀 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.
practical claude code commands and subagents
Implementation of contextual engineering pipeline with LangChain and LangGraph Agents
ContextX: Context-driven AI development framework powered by Claude Code. Transform documents into complete projects with intelligent agent workflows.
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.
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.
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!
Local AI-powered code review agents for Claude Code
Master AI prompting for business innovation. O'Reilly Live Learning course by Tim Warner covering ChatGPT, Claude, Copilot, and enterprise prompt engineering with MCP implementation.
An LLM-powered platform for software development, featuring precise context engineering, traceable code generation, and AI-assisted search and data analysis.
Systematic workflows for AI-assisted development - Task-oriented framework with quality gates
Prompt Assembly Language - A framework for developing LLM prompts as versioned, composable software artifacts
Lightweight, model-agnostic chat history compression (trim + summarize) for AI assistants.
Automatos AI: Open-source platform for advanced context engineering and multi-agent orchestration in enterprise automation. Built on frontier research in RAG, vector embeddings, cognitive tools, emergent symbols, and neural field theory—powered by FastAPI, Next.js, and PostgreSQL.
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.
Context Engineering For Writers (with Claude Swarm)
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.
🚀 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).