Langchain is great because it provides you an easy method to filter people out when hiring. Candidates who talk about langchain are, more often than not, low quality candidates.
Would you say the same for Mastra? If so, what would you say indicates a high quality candidate when they are discussing agent harnessing and orchestration?
I somewhat take issue as a LangChain hater + Mastra lover with 20+ years of coding experience and coding awards to my name (which I don't care about, I only mention it for context).
Langchain is `left-pad` -- a big waste of your time, and Mastra is Next.js -- mostly saving you infrastructure boilerplate if you use it right.
But I think the primary difference is that Python is a very bad language for agent/LLM stuff (e.g. static typesystem, streaming, isomorphic code, strong package management ecosystem is what you want, all of which Python is bad with). And if for some ungodly reason you had to do it in Python, you'd avoid LangChain anyway so you could bolt on strong shim layers to fix Python's shortcomings in a way that won't break when you upgrade packages.
Yes, I know there's LangChain.js. But at that point you might as well use something that isn't a port from Python.
> what would you say indicates a high quality candidate when they are discussing agent harnessing and orchestration?
Anything that shows they understand exactly how data flows through the system (because at some point you're gonna be debugging it). You can even do that with LangChain, but then all you'd be doing is complaining about LangChain.
> And if for some ungodly reason you had to do it in Python
I literally invoke sglang and vllm in Python. You are supposed to (if not using them over-the-network) use the two fastest inference engines there is via Python.
Python being a very bad language for LLM stuff is a hot take I haven’t heard before. Your arguments sound mostly like personal preferences that apply to any problem, not just agentic / LLM.
If we’re going to throw experience around, after 30+ years of coding experience, I really don’t care too much anymore as long as it gets the job done and it doesn’t get in the way.
LangChain is ok, LangGraph et al I try to avoid like the plague as it’s too “framework”-ish and doesn’t compose well with other things.
I used to write web apps in C++, so I totally understand not caring if it gets the job done.
I guess the difference where I draw the line is that LLMs are inherently random I/O so you have to treat them like UI, or the network, where you really have no idea what garbage is gonna come in and you have to be defensive if you're going to build something complex -- otherwise, you as a programmer will not be able to understand or trust it and you will get hit by Murphy's law when you take off your blinders. (if it's simple or a prototype nobody is counting on, obviously none of this matters)
To me insisting that stochastic inputs be handled in a framework that provides strong typing guarantees is not too different from insisting your untrusted sandbox be written in a memory safe language.
What does static type systems provide you with that, say, using structured input / output using pydantic doesn’t?
I just don’t follow your logic of “LLMs are inherently random IO” (ok, I can somehow get behind that, but structured output is a thing) -> “you have to treat them like UI / network” (ok, yes, it’s untrusted) -> static typing solves everything (how exactly?)
This just seems like another “static typing is better than dynamic typing” debate which really doesn’t have a lot to do with LLMs.
he says its bad for agents, nit 'LLM stuff'. python is fine to throw task to the GPU. it is absolutely dreadful at any real programming. so if you want to write an agent that _uses_ LLMs etc like an agent, there are much better languages, for performance, safety and your sanity.
I'm not familiar with it. My first question would be: Are there any prominent projects that use it?
A lot of these frameworks are lauded, but if they were as good as they claim you would run into them in all sorts of apps. The only agents that i ever end up using are coding agents, i think they're obviously the most popular implementations of agents. Do they use langchain? No, i don't think so. They probably use in house logic cus it's just as easy and gives them more flexibility and less dependencies
Ok I agree with that, I think they had some weird idea of managing templates in LangSmith and then being able to load them dynamically from LangChain.
LangSmith’s prompt engineering workflow is okay-ish but a lot of work and gets quite expensive quite fast, and only works for a specific set of prompts (ie one-turn prompts, multi-turn never works).
PydanticAI seems more lightweight and gets out of the way.
I found the general premise of the tools (in particular LangGraph) to be solid. I was never in the position to use it (not my current area of work), but had I been I may have suggested building some prototypes with it.
I think there are probably lots of thorough critiques, but for me it boiled down to this:
Langchain claimed to be an abstraction on top of LLMs, but in fact, added additional unecessary complexity.
Prompt management was such a buzzword when langchain came out, but 99% of LLM use cases don't need prompt management - GitHub and strings works just fine.
