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

Auto-Manage Your Personal Task Context with AI

The agentic AI platform for enterprise. Built for availability, scalability, and security. Complete end-to-end context engineering and LLM orchestration infrastructure. Run anywhere - local, cloud, or bare metal.

🧠🔗 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

CTX: a tool that solves the context management gap when working with LLMs like ChatGPT or Claude. It helps developers organize and automatically collect information from their codebase into structured documents that can be easily shared with AI assistants.

🧠 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.

An agentic workflow tool that provides context engineering support for opencode

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.

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 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.

A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).

practical claude code commands and subagents

Implementation of contextual engineering pipeline with LangChain and LangGraph Agents