When I first started experimenting with LLMs, I used LangChain. It helped me get started, but over time I found that: (1) Things were often more complicated th

When I first started experimenting with LLMs, I used LangChain. It helped me get started, but over time I found that: (1) Things were often more complicated than they had to be. For example, creating prompt template objects rather than just using a simple format call to replace variables in a string. (2) Too much of the underlying mechanics got hidden away. For example, creating agents hides away the prompting to get them to use functions, so it’s hard to know why things are going wrong. (3) Different LLMs aren’t interchangeable, so adding an abstraction for the purpose of enabling plug-and-play with different providers doesn’t really help. For example, OpenAI has function calls and streaming output, while Anthropic has a 100K context window. Those differences make abstraction less compelling, because not all LLMs are interchangeable. (4) It limited my creativity. I found I was trying to make my code conform to LangChain’s implementations. (5) It made things heavier than they needed to be. Instead of just installing, say, the OpenAI library, I would install LangChain with all of its dependencies. (6) It made it harder for people to see how my code was working. They had to understand how LangChain was doing what it was doing, in addition to what my code was doing. (7) The OpenAI API (which I use primarily) is already pretty easy to work with. Other LLM-related libraries, like Chroma, or Pinecone, are also easy to work with. So why do I need LangChain? My two cents, for what it’s worth.