LangChain is a framework for building applications with large language models (LLMs). To build practical LLM products, you need to combine them with user- or domain-specific data which wasn’t part of the training. LangChain fills this niche with features like prompt management, chaining, agents and document loaders. The benefit of components like prompt templates and document loaders is that they can speed up your time to market. Although it's a popular choice for implementing Retrieval-Augmented Generation applications and the ReAct prompting pattern, LangChain has been criticized for being hard to use and overcomplicated. When choosing a tech stack for your LLM application, you may want to keep looking for similar frameworks — like Semantic Kernel — in this fast-evolving space.
LangChain is a framework for building applications with large language models (LLMs). These models have triggered a race to incorporate generative AI in several use cases. However, using these LLMs in isolation may not be enough — you have to combine them with your differentiated assets to build an impactful product. LangChain fills this niche with some neat features, including prompt management, chaining, data augmented generation and a rich set of agents to determine which actions to take and in what order. We expect more tooling and frameworks to evolve with LLMs, and we recommend assessing LangChain.