https://www.llamaindex.ai/ is much better IMO, but it's definitely a case of boilerplate-y, well-supported incumbent vs smaller, better, less supported (e.g. Java vs Python in the 00s or something like that). Depends on your team and your needs.

Also Autogen seems popular and well-ish liked https://microsoft.github.io/autogen/

LangChain definitely has the most market-/mind- share. For example, GCP has a blog post on supporting it: https://cloud.google.com/blog/products/ai-machine-learning/d...

Have a fairly thin layer than wraps the underlying LLM behind a common API (e.g., Ollama as being discussed here, Oobabooga, etc.) and leaves the application-level stuff for the application rather than a framework like LangChain.

(Better for certain use cases, that is, I’m not saying LangChain doesn't have uses.)

Not using an abstraction at all and avoiding the technical debt it causes.

Haystack is much better option and way alot flexible, scalable

Don't use langchain, just make the calls?

Its what I ended up doing.