On the main langchain post (In January) that got the traction on hackernews, i left this comment: https://news.ycombinator.com/item?id=34422917 . It still remains true, a "simpler langchain"

> To offer this code-style interface on top of LLMs, I made something similar to LangChain, but scoped what i made to only focus on the bare functional interface and the concept of a "prompt function", and leave the power of the "execution flow" up to the language interpreter itself (in this case python) so the user can make anything with it.

I made a really lightweight wrapper over requests and call it lambdaprompt https://github.com/approximatelabs/lambdaprompt It has served all of my personal use-cases since making it, including powering `sketch` (copilot for pandas) https://github.com/approximatelabs/sketch

Core things it does: Uses jinja templates, does sync and async, and most importantly treats LLM completion endpoints as "function calls", which you can compose and build structures around just with simple python. I also combined it with fastapi so you can just serve up any templates you want directly as rest endpoints. It also offers callback hooks so you can log & trace execution graphs.

All together its only ~600 lines of python.

I haven't had a chance to really push all the different examples out there, so I think it hasn't seen much adoption outside of those that give it a try.

I hope to get back to it sometime in the next week to introduce local-mode (eg. all the open source smaller models are now available, I want to make those first-class)

The dust around language models needs to settle a bit, for a useful framework to emerge from it.

For our own use-cases, I built a framework from scratch, and it was the best decision we made.

https://blog.scottlogic.com/2023/05/04/langchain-mini.html

check out llama-index. its purpose-built for document indexing and retrieval and less agents and "everything else"