Hacker News Reader

Comment by loveparade | Hacker News Reader

Am I the only one who is not convinced by the value proposition of langchain? 99% of it are interface definitions and implementations for external tools, most of which are super straightforward. I can write integrations for what my app needs in less than an hour myself, why bring in a heavily opinionated external framework? It kind of feels like the npm "left-pad" to me. Everyone just uses it because it seems popular, not because they need it.

View on HN

For us LangChain actually caused more problems than it solved. We had a system in production which after working fine a few weeks suddenly started experiencing frequent failures (more than 30% of requests). On digging it seems that LangChain sets a default timeout of 60 seconds for every requests. And this behaviour isn't documented! Such spurious decisions made by LangChain are everywhere, and will all eventually come back to bite. In the end we replaced everything with vanilla request clients. Definitely not recommended to build a system on a library that provides very limited value while hiding a huge amount of details and decisions from you.

Langchain is absolutely perfect though, it's bad enough that you'll be driven to write something better out of pure frustration but gives you enough good ideas and breadcrumbs to actually do it.

It's probably the best on-ramp for "practical uses of llms" because it scratches just the right developer itch.

This is the big take-away

I am slowly coming around to the same conclusion. It isn't always clear how some agent types are different from others. Sometimes the prompts expect JSON blobs and sometimes they expect something else. I tried it out because I could see the potential, but I dont think it's architect-ed in a way that is suitable for things beyond simple PoCs.

It would probably be much better to start with the basic OpenAI API and then build on top of it.

What I find particularly frustrating is the difficulty in easily interfacing with my existing python tools (not like add two numbers, but somewhat complex analytics on top of structured data). If anybody has any success with interfacing with existing tools/scripts, would love to know how people are going about doing it.

It's brilliant for experimentation and prototyping though. Granted I've not deployed anything llm related yet so I have not thought about it yet, but I don't want to just start writing every integration I think I need by hand just to experiment with it.

> I can write integrations for what my app needs in less than an hour myself,

Or just ask ChatGPT to do it...

Joking aside, I think 'npm "left-pad"' describes it perfectly.

Most of the code my OpenAI API experiments run on was written by ChatGPT.

…which implies that a library of pre written shims is not needed…

Absolutely. Wasn't disagreeing with that premise; just confirming that you're right - ChatGPT can do this just fine.

Reply on news.ycombinator.com