You basically don't use langchain for anything besides 30 minute demos that you copied from someone else's github. It has a completely spaghettified API, is not performant, and forces you into excessive mental contortions to reason about otherwise simple tasks.
Yea discovered this with Langchain last week. Was great for a demo then started to push it harder and spent ages trawling Reddit, discord, GitHub trying to find solutions to issues only to discover what was supposed to be supported was deprecated. Got a massive headache for what should have been a simple change. Moved on now.
We originally started out building features with LangChain (loading chains from YAML sounded good—it felt like it would be easy to get non-engineers to help with prompt development) but in practice it’s just way too complicated. Nice idea, but the execution feels lacking.
It also doesn’t help that LangChain is evolving so rapidly. When we first started using it a lot of code samples on the internet couldn’t be copy/pasted because of import paths changing, and at one point we had to bump by ~60 patch versions to get a bug fix, which was painful because it broke all kinds of stuff
Echoing others’ sentiments, I was frustrated with the bloat and obscurity of existing tools. This led me to start building Langroid with an agent-oriented paradigm 8 months ago https://github.com/langroid/langroid
We have companies using it in production for various use-cases. They especially like our RAG and multi-agent orchestration.
See my other comment for details.
Besides the other comments in this thread, I'd really recommending looking at least first to the (relatively new) "Managed index" in LlamaIndex: https://docs.llamaindex.ai/en/stable/community/integrations/... . These handle combining the retrieval with the generative side. I've seen a lot of users both get frustrated and get bad results by trying to write their own glue to string together various components of retrieval and generation and these are much easier to get started with
