Memory for AI Agents in 6 lines of code

MineContext is your proactive context-aware AI partner（Context-Engineering+ChatGPT Pulse）

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.

Auto-Manage Your Personal Task Context with AI.

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

The production agentic context stack. Data streaming + knowledge graphs + vector search + LLM orchestration. All in a single deployment for self hosting, BYOC, or cloud.

🧠 Make your agents learn from experience. Based on the Agentic Context Engineering (ACE) framework.

🧠 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

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.

An agentic workflow tool that provides context engineering support for opencode

Asking yours data in a natural language way through pre-modeling (data models and semantic models).

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.

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.

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.

RapidFire AI: Rapid Experimentation Engine for Customizing LLMs

AI Agent that researches the lives of historical figures and extracts events into structured JSON timelines using LangGraph multi-agent orchestration.

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