[Karrot_Kream]

The docs are lacking but the underlying code is so simple that it's just a few clicks/rg searches away from figuring out what you need. It's all mostly ways to do string templating. IMO the ergonomics of LangChain need an overhaul; there's too many ways to do the same thing and there's too much type erasure that makes it hard to use strengths in a particular LLM. For example, it's still a pain to distinguish between using a Chat oriented LLM vs a regular completion one.

There also seems to be really poor observability in the code and performance seems to be an afterthought. I tell friends who ask about LangChain that it's great to experiment with but not something I'd put into production. Hopefully this funding helps them shore things up.

[SheinhardtWigCo]

Are you saying you'd use something else in production?

> For example, it's still a pain to distinguish between using a Chat oriented LLM vs a regular completion one.

Totally agree. After using it for a few weeks, this is one of the most visible weaknesses in the design.

[Karrot_Kream]

> Are you saying you'd use something else in production?

Absolutely. The Langchain interface is quite easy to build atop any old LLM, and if you're just using OpenAI then OpenAI already distirbutes clients in a bunch of languages that you can just use. Even then, you're calling a few HTTP endpoints and stuffing some data in a payload. It's really basic stuff. I'd prototype in Langchain, grab the prompts and agents I ended up using, and reimplement them in $MY_PL on $MY_PLATFORM. That's what I find so fun about these LLMs, they're just trivial to use for anyone that can ship text off to an endpoint (whether networked or local) and receive a text response.

[popcorncowboy]

This is what blows my mind. They raised a 10M seed on what is (no disrespect intended) a wafer thin abstraction that an experienced dev really could implement the core of in a few days. Obviously the raise was about the momentum and mindshare Langchain holds, but still. Wow.

[mattizzle81]

Agreed. The text parsing for feeding prompts into an LLM and parsing a response is about as simple and straightforward as it gets in programming. It is nice to have some control over that process and know what your code is doing every step along the way. It doesn’t need to do much anyway, the LLM is doing the hard work mostly. It makes no sense to me and I’m trying to understand it, but I just can’t see the value in a black box library to interface with an LLM when it’s so easy to DIY.

[Karrot_Kream]

I agree that their current implementation is what you said, something an experienced dev can do in a couple days. But they have the potential to really make a robust library here. The thing is, there's a lot of small things with stop sequences, token estimation, API call backoff, and generic structures that are just a pain to make yourself.

But you're right that their moat will probably be razor then. A few senior devs can get together and probably hackathon a library that's just like Langchain but much more robust. Thanks for an idea on what I'm gonna do this weekend lol.

[satvikpendem]

> But you're right that their moat will probably be razor then. A few senior devs can get together and probably hackathon a library that's just like Langchain but much more robust. Thanks for an idea on what I'm gonna do this weekend lol.

Did you do it? I'm doing something similar but in Rust, for my product. It'll be open source soon enough.

[thequadehunter]

I was about to rant about the documentation, but I just checked and it seems to have improved a lot.

[lebed2045]

I wonder is it already possible for an AI to write documentation from scratch based of code base?