On the other hand it took some years into the web, for some web frameworks to emerge and make sense, like Ruby on Rails. Maybe in 3-4 years time, complicated chains of commands to different A.I. engines will be so difficult to get right that a framework might make sense, and establish a set of conventions.
Agents, another central feature of LangChain, are not proved to be very useful as well, for the moment.
Kudos to the LangChain folks for building what they built. They deserve some recognition for that. But, yes, I don’t think it’s been particularly helpful for quite some time.
I ended up calling the model myself and extracting things using a flexible json parser, I ended up doing what I needed with about 80 lines of code.
Langchain, Pinecone, it’s all the same playbook.
I was fortunate in that the person I was building the project for was able to introduce me to a few other people more experienced with the entire nascent LLM agent field and both of them strongly steered me away from LangChain.
Avoiding going down that minefield ridden path really helped me out early on, and instead I focused more on learning how to build agents "from scratch" more or less. That gave me a much better handle on how to interact with agents and has led me more into learning how to run the various models independently of the API providers and get more productive results.