We mentioned some of the emerging criticisms about LangChain in the previous Radar. Since then, we’ve become even more wary of it. While the framework offers a powerful set of features for building applications with large language models (LLMs), we’ve found it to be hard to use and overcomplicated. LangChain gained early popularity and attention in the space, which turned it into a default for many developers. However, as LangChain is trying to evolve and keep up with the fast pace of change, it has become harder and harder to navigate those changes of concepts and patterns as a developer. We’ve also found the API design to be inconsistent and verbose. As such, it often obscures what is actually going on under the hood, making it hard for developers to understand and control how LLMs and the various patterns around them actually work. We’re moving LangChain to the Hold ring to reflect this. In many of our use cases, we’ve found that an implementation with minimum use of specialized frameworks is sufficient. Depending on your use case, you may also want to consider other frameworks such as Semantic Kernel, Haystack or LiteLLM.
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.
