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Comment by moonchrome | Hacker News Reader

I'm in the exact same spot as the author just a few days in instead of months.

Frankly I could see langchain is garbage software just by looking at the code.

It still helps me get shit done fast to figure out how things are supposed to work. Sort of a cookbook of AI recepies. Once I have an approach narrowed down I'll rewrite everything on top of stuff langchain is supposedly wrapping. For now it's faster than tracking down individual libraries and learning the apis. It will stay in notebooks basically.

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Yes! This is why I started working on AIPL. The scripts are much more like recipes (linear, contained in a single-file, self-evident even to people who don't know the language). For instance, here's a multi-level summarizer of a webpage: https://github.com/saulpw/aipl/blob/develop/examples/summari...

The goal is to capture all that knowledge that langchain has, into consistent legos that you can combine and parameterize with the prompts, without all the complexity and boilerplate of langchain, nor having to learn all the Python libraries and their APIs. Perfect for prototypes and experiments (like a notebook, as you suggest), and then if you find something that really works, you can hand-off a single text file to an engineer and they can make it work in a production environment.

This looks pretty rad. It is very expressive.

After scoffing at your shameless plug I read some of the code and was surprised to find it was not written in perl. Maybe some other options for the acronym could be:

* LASI (Language Artificial Specific Intelligence) * LLMML (Large Language Model Markup Language) * LLMMA (Large Language Model Metalinguistic Abstraction [this one is a stretch])

You don't need to look at the code: I looked at the release notes and the garbage fire of unrelated nonsense getting added and got to skip even installing it. https://github.com/hwchase17/langchain/releases

At this point Langchain is almost required to accept any PR. They raised money, and now their growth metric is Github stars.

A simple wrapper around the APIs (I used llamaflow, which is now llm-api https://github.com/dzhng/llm-api) and a templating engine is most of what you need.

AI is not at a point where generalist prompts to do agent/memory/search things is a good idea for a real product. You need to integrate procedural guidance unless you want your UX to be awful.

Pfft. You looked at the release notes? I knew it was garbage so fast that I got warped back in time to 2021, and stopped using LangChain before it even existed.

+1, but i figured it out in an hour. Langchain seemed like a ridiculous overcomplication of what would otherwise be basic python. Has been off to the races since I decided not to use it.

LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.

Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.

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