github's Original Sin. we could have so much more software training data if they just defaulted (defaulted! you can opt out instead of opt in) to an oss license. so much software is meant to be open source but pple just forgot/didnt bother/didnt know to slap a license on it
I disagree with opting people into a license, because the choice of _which_ license is a very personal and important choice. Defaulting to a very permissive license would make folks think that more restrictive (but user-benefitting) licenses like the GPL are Bad ™ when they're not, whereas defaulting to a GPL flavour would make a load of things more restrictive than they may want to be. The only person who can make that decision is the author.
However, I think one thing GitHub / other source forges can do is make it _clearer_ when no license is present and that it defaults to All-Rights-Reserved
1) your 30 lines could become 10 lines or something
2) you can write abstract code without explicit dependency on openai and easely switch to e.g. anthropic in future without rewriting the whole thing (think of ORM for LLMs)
3) ready to use abstraction like Process that allows to chain operations (more complex stuff is much easier than writing by hand)
Langchain is really bad. You’re so much better off just doing whatever you need to do yourself, especially when it comes to interfacing with OpenAI APIs. It has all of the baggage of a big system without any of the benefits.
Indeed, after being frustrated with existing frameworks, 7 months ago, we started buidling Langroid[1] Multi-Agent LLM framework. It has an elegant Inter-agent orchestration mechanism[2], among many other things. We’ve taken a measured approach to avoid bloat and excess abstractions. We have a company using it in production to assist contact-center/customer-support agents.
Langroid is an intuitive, lightweight, extensible and principled Python framework to easily build LLM-powered applications, from ex-CMU and UW-Madison researchers. You set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages.
I mind. Langchain is full of shitty, leaky abstractions and shallow function calls. It's a pile of bullshit that somehow managed to raise millions of dollars due to hype. It's everything that's wrong with the state of development in the AI space.
I think the intention behind langchain is good, but the implementation suffers from bad design. We tried to fix that by having small core with as less abstractions as possible.
