Multimodal memory system for lifelong learning agents capable of simultaneously understanding and remembering text, images, and video. Represents a step beyond traditional text-only memory systems toward multimodal context management for AI agents operating in diverse data environments.

Integrated database-and-inference engine that processes document data through an LLM's forward pass into tensors stored in a dedicated KV Cache system. Advocated as an approach to optimize the retrieval-context pipeline by combining storage and inference into a unified architecture.

A comprehensive curated survey on Context Engineering covering the progression from prompt engineering to production-grade AI systems. The repository contains hundreds of papers, frameworks, and implementation guides for LLMs and AI agents, serving as a centralized reference for researchers and practitioners.

Agent architecture pattern in AG2 where agents respond to events rather than polling, enabling better async execution, scalability, and resource efficiency.

Agent orchestration pattern used by Emergence AI that plans tasks, executes with specialized agents, and verifies results to achieve reliable autonomous workflow automation.

Modular agent coordination patterns in AG2 allowing developers to swap orchestration logic without changing agent code, enabling flexible multi-agent workflows.