Towards AGI: [Part 1] Agents with Memory

Agents, powered by large language models (LLMs), represent a new class of AI systems that interact dynamically with their environments by utilizing common-sense reasoning and memory storage to perform tasks. These agents, differing from traditional chatbots, have evolved to access various tools and adapt to novel tasks without pre-determined prompt chains, making them increasingly relevant for future applications. They employ both short-term and long-term memory structures, analogous to computer memory systems, to manage and utilize information effectively. Short-term memory serves as the main context available during runtime, while long-term memory, which includes episodic, semantic, and procedural types, stores information externally for future retrieval and decision-making. The design of an agent's memory system is crucial and varies according to use cases, such as role-play, customer support, or task execution. As agents become more sophisticated, they are expected to become more integral to daily life, with improvements in core reasoning capabilities and refined designs set to make 2024 a pivotal year for their widespread adoption. Future discussions will delve into the detailed aspects of how agents interact with memory, focusing on retrieval and learning as key actions within their operational framework.