[jacky2wong]

Congrats on launching!

I personally think the target audience for this is a little hard to find when compared to products like langchain that do something similar already (i wouldn't be surprised if you guys built on top of this).

As a developer, I wouldn't have much difficulty spinning a Colab instance and running Langchain (takes a few minutes) and get it up and running compared to a solution like yours. Would be awesome to get a pros/cons table of a solution like yours compared to Langchain so developers can best figure out how to dedicate their time without having to try both tools.

[inconceivable]

i have nearly 20 years of experience in operations and development (including information retrieval / search) and it took me a good 30 solid days of staring at this stuff with a dumb look on my face before i could wrap my head around the basics of how to do something even remotely useful in this ecosystem (torch/tf, huggingface, langchain, openai, vector dbs for embeddings, all of the ancillary python modules, CUDA, basic linear algebra, etc).

in addition to pure dev and ops people, i can see how a tool like this could be useful for prompt engineers, product people, prototype / spec / doc authors, and others who aren't necessarily going to be involved directly in the nitty gritty of things writing production code or MLops.

[noaflaherty]

Appreciate the feedback! A comparison table is a great idea and something we'll look into.

We fully anticipate having tighter integrations with Langchain in the near future. We view them as complimentary frameworks in many ways. For example, we might subclass the `BaseLLM` class such that you can interact with Vellum deployments and get all the monitoring/observability that Vellum provides, but invoke them via your Lanchain chain.