Lol, yeah the hidden prompts in langchain, and the bloat turned me off it pretty quickly.

Still kinda nice to get something going in less than a day

I don't mind that they're hidden. I mind that they can't be overriden.

that smells wrong. are you quite sure they cant be overridden? or subclassed?

It looks like the two instances I was thinking of have been fixed:

https://github.com/langchain-ai/langchain/blob/master/libs/l...

https://github.com/langchain-ai/langchain/blob/master/libs/l...

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.

We think that too! Agency won't have implicit prompts. Or at least without explicit and easy way to override them

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.

[1] https://github.com/langroid/langroid

From the README:

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.

[2] Docs on Task Delegation https://langroid.github.io/langroid/quick-start/multi-agent-...

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.

Think of ORM for LLMs. That's not the whole purpose of "langchain-like" frameworks but on that is easy to grasp