Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Why we no longer use LangChain for building our AI agents

octomind.dev

Post reply on HN
Print view View original

51–60 of 307 posts

Re: Why we no longer use LangChain for building our AI agents

#51

Post by __cayenne__ »

refulgentis wrote: post #37
riwsky wrote: post #25

Earlier quoted context omitted.

The point is that you don’t need a framework for that; the APIs are already similar enough that it should be obvious how to abstract over them using whatever approach is natural in your programming language of choice.

I have a consumer app that swaps between the 5 bigs and wholeheartedly agree, except, God help you if you're doing Gemini. I somewhat regret hacking it into the same concepts as everyone else. I should have built stronger separation boundaries with more general abstractions. It works fine, I haven't had any critical bugs / mistakes, but it's really nasty once you get to the actual JSON you'll send. Google's was 100%…

luckily Google now support's using the OpenAI lib https://cloud.google.com/vertex-ai/generative-ai/docs/multim...

Re: Why we no longer use LangChain for building our AI agents

#52

Post by nosefurhairdo »

danielmarkbruce wrote: post #4

Yup. The problem with frameworks is they assume (historically mostly but not always correctly) that layers of abstraction mean one can forget about the layers below. This just doesn't work with LLMs. The systems are closer to biology or something.

Very much depends on the framework. I'm currently building a GitHub App with the Probot framework, which mostly just handles authentication boilerplate and some testing niceties, then just gives you an authenticated GitHub API client (no facade/abstraction).

Then of course there's the many web application frameworks, because nobody in their right mind would want to implement http request parsing themselves (outside of academic exercises).

In fact, I would argue that most popular frameworks exist precisely because it's often more time efficient to forget about underlying details. All computer software is built on abstraction. The key is picking the right level of abstraction for your use case.

Re: Why we no longer use LangChain for building our AI agents

#53

Post by laborcontract »

elijahbenizzy wrote: post #39

I really like the idea of "good" and "bad" abstractions. I have absolutely built both. This sentiment is echoed in this comment in reddit comment as well: https://www.reddit.com/r/LocalLLaMA/comments/1d4p1t6/comment... . Similarly to this post, I think that the "good" abstractions handle application logic (telemetry, state management, common complexity), and the "bad" abstractions make things abstract away tasks that…

    > the "bad" abstractions make things abstract away tasks that you really need insight into.
Yup. People say to use langchain to prototype stuff before it goes into production but I find it falls flat there. The documentation is horrible and they explain absolutely zero about the methods they use, so the only way to “learn” is by reading their spaghetti code.

Re: Why we no longer use LangChain for building our AI agents

#54

Post by manquer »

newzisforsukas wrote: post #27
andybak wrote: post #22

Earlier quoted context omitted.

Surely SQL is an API? The line between language and API is fairly blurry.

Can you elaborate?

What GP means is it is a Programmable Interface, any interface you can interact against is an API. That means any programing complete language is an API, so are sign languages or human languages.

While nobody does it , SQL implementations have network API, authentication, authorization, ACL/RBAC, serialization, Business logic all the things you use in RESTful apis can all be done with just db servers.

You can expose in theory a direct SQL API to clients to consume without any other language or other components to the stack .

Most SQL servers use some layer on top of TCP/IP to connect their backends to frontend .libpq is the client which does this in postgreSQL for example .

You could either wrap that in Backend SQL server with an extension and talk to browser and other clients in HTTP[1], or you can write a wasm client in browsers to directly talk to TCP/IP port on the SQL server

Perhaps if you are oracle , that makes sense, but for no one else, they do build and push products that basically do parts of this .

[1] projects like postgREST basically do this .

Re: Why we no longer use LangChain for building our AI agents

#56

Post by dongobread »

chatmasta wrote: post #49
infecto wrote: post #33

LangChain itself blows my mind as one of the most useless libraries to exist. I hope this does not come off the wrong way but so many people told me they were using it so it was easy to move been models. I just did not understand it, these are simple API calls that felt like Web Dev 101 when starting a new product. Maybe its that so many new people were coming into the field using LLM but it surprised me as even what…

It was the first pass at solving the common problems when building with LLMs. People jumped on it because it was trendy and popular. But it quickly became obvious that LangChain would be better named LangSpaghetti. That’s nothing against the authors. What are the chances the first attempt at solving a problem is successful? They should be commended for shipping quickly and raising money on top of it to keep iterating…

Langchain feels very much like shovelware that was created for the sole purpose of parting VCs of their money. At one point the codebase had a "prompt template" class that literally just called Python's f-string.

Re: Why we no longer use LangChain for building our AI agents

#57

Post by pveierland »

Treesrule14 wrote: post #20

Has anyone else found a good way to swap out models between companies, Langchain has made it very easy for us to swap between openai/anthropic etc

Using Llama Index for this via the `llama_index.core.base.llms.base.BaseLLM` interface. Using config files to describe the args to different models makes swapping models literally as easy as:

  chat_model:
    cls: llama_index.llms.openai.OpenAI
    kwargs:
      model: gpt-4

  chat_model:
    cls: llama_index.llms.gemini.Gemini
    kwargs:
      model_name: models/gemini-pro

Re: Why we no longer use LangChain for building our AI agents

#58

Post by elijahbenizzy »

laborcontract wrote: post #53
elijahbenizzy wrote: post #39

I really like the idea of "good" and "bad" abstractions. I have absolutely built both. This sentiment is echoed in this comment in reddit comment as well: https://www.reddit.com/r/LocalLLaMA/comments/1d4p1t6/comment... . Similarly to this post, I think that the "good" abstractions handle application logic (telemetry, state management, common complexity), and the "bad" abstractions make things abstract away tasks that…

> the "bad" abstractions make things abstract away tasks that you really need insight into. Yup. People say to use langchain to prototype stuff before it goes into production but I find it falls flat there. The documentation is horrible and they explain absolutely zero about the methods they use, so the only way to “learn” is by reading their spaghetti code.

Agreed — also I’m generally against prototyping stuff and then entirely rewriting it for production as the default approach. It’s a nice idea but nobody ever actually rewrites it (or they do and it’s exceedingly painful). In true research it makes sense, but very little of what engineers do falls under that category.

Instead, it’s either “welp, pushed this to prod and got promoted and it’s someone else’s problem” or “sorry, this valuable thing is too complex to do right but this cool demo got me promoted...”

Re: Why we no longer use LangChain for building our AI agents

#60

Post by choilive »

infecto wrote: post #33

LangChain itself blows my mind as one of the most useless libraries to exist. I hope this does not come off the wrong way but so many people told me they were using it so it was easy to move been models. I just did not understand it, these are simple API calls that felt like Web Dev 101 when starting a new product. Maybe its that so many new people were coming into the field using LLM but it surprised me as even what…

This seems to be a universal sentiment.. we took a short look at langchain and determined it was doing really trivial string manipulation/string templating stuff but inside really rigid and unnecessary abstractions. It was all stuff that could be implemented by any competent programmer in hours in any language without all the crap, so that's what we did. shrug
Post reply on HN