What is a "cognitive architecture"?

The blog post explores the concept of "cognitive architecture" in the context of building applications powered by large language models (LLMs), highlighting its evolution from simple code-based systems to complex autonomous agents. Initially inspired by Flo Crivello, the term describes how a system processes user input through a series of LLM calls, ranging from basic single-call applications to intricate autonomous agents that dynamically determine their own actions. The author emphasizes the importance of selecting the appropriate cognitive architecture based on task requirements, noting that while simpler architectures are suitable for straightforward tasks, more sophisticated frameworks like state machines or autonomous agents offer greater flexibility and unpredictability. The post also discusses the development of LangChain and LangGraph, which provide low-level, customizable orchestration frameworks to support varied cognitive architectures, contrasting with the early focus on easy-to-use, pre-built chains. The author encourages experimentation with these tools to enhance the adaptability and control of LLM applications.

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