Recent efforts have augmented Large Language Models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning, creating a new class of “Language Agents”. While these agents have achieved substantial empirical success, we presently lack organizing principles to measure progress or guide further development. We draw on the rich history of cognitive science and symbolic artificial intelligence to propose ‘Cognitive Architectures for Language Agents’ – CoALA. CoALA describes a language agent with modular memory components, a structured action space to interact with internal memory and external environments, and a generalized decision-making process to choose actions. We use CoALA to survey and organize a large body of recent work, then identify actionable directions towards more capable agents.