TIL people still fall for eh I mean use Langchain. Sorry, low value comment; I don’t know how to do that differently; it is such bad garbage since day one and strangely it did not improve. Sorry anyway for the comment, at least it was not LLM generated?
It (and most other systems) abstract the wrong concepts when you want to create an agent. They promote what was likely never a great strategy but even parsimoniously what are strategies that were good ideas months or a year back.
For example how langchain or whatever else handles subagents and deep research is laughable even today.
So whats the recommendation? Just use pydantic and code everything up yourself. Maybe strands I dont know.
Which specific strategies do you think are outdated, and what would you recommend instead? This sounds more like a criticism of older versions of LangChain than its current APIs. The post also uses deepagents, a separate package in the same ecosystem. Is there something in the current implementation of either that you’re referring to?
Creating deep research multiagent systems without explicitly handing it a file system is a terrible idea. The only successful way we have found to reliably hand off context in multiagent systems is using a file system. Langchain even today doesnt make that default or even encourage it.
anonzzzies · · focus · HN ↗
bogdanoff_2 · · focus · HN ↗
ramraj07 · · focus · HN ↗
For example how langchain or whatever else handles subagents and deep research is laughable even today.
So whats the recommendation? Just use pydantic and code everything up yourself. Maybe strands I dont know.
mdrxy · · focus · HN ↗
ramraj07 · · focus · HN ↗