Langchain has long solved (we can argue on if it's done it well, opinions vary) the problem of needing to orchestrate LLM calls into a coherent workflow. Plus it had a first mover advantage.

MCP solves a data and API integration problem.

Both are concrete things that people need to do today. AI agents talking to one another is not a concrete problem that organizations building features that integrate AI have today.

Langchain is one of the most hilarious libraries I've ever had the displeasure of looking through. Many of the abstractions look like they were written by a college student who took clean code way too literally. Many of the methods are so trivial and shallow that I'm shocked people use it in any serious capacity.

This. I was amazed opening something like PagagraphLineReaderFactory, thinking it somehow deals smartly with paragraph boundaries and too long paragraphs etc., and finding a trivial single line regex wrapped in two screens of OOP boilerplate.

> the problem of needing to orchestrate LLM calls into a coherent workflow.

I didn't feel the need to use Langchain, chaining LLM calls is usually just a few lines of code (I think even fewer than when using Langchain).

I am still simply doing plain fetch requests to LLMs APIs, and it works great, 10/10 would recommend.