A distinctive feature of LangChain is its use of “chains ,” setting it apart from SK, which is centered around a kernel, as previously discussed. In LangChain, the output from one component serves as the input for the next, allowing elements such as prompts, models, and parsers to be connected in sequence before activation. Developers can harness LangChain to assemble new prompt chains, enabling the integration of multiple LLMs in a sequential manner, where the output from one LLM feeds into the next; hence, the term LangChain. Additionally, LangChain includes features that permit LLMs to incorporate new datasets without requiring retraining, similar to SK.