5 Key Lessons from Using LangChain: A Year-Long Case Study

From its inception, LangChain seemed destined to be a polarizing product. Supporters praise LangChain for its rich tools, modularity, and easy integration. Critics, however, see it as doomed—believing

From its inception, LangChain seemed destined to be a polarizing product.

Supporters praise LangChain for its rich tools, modularity, and easy integration. Critics, however, see it as doomed—believing that in this rapidly changing tech era, using LangChain to build everything is impractical.

Some even say:

"In my consulting work, I spend 70% of my time convincing people not to use LangChain or LlamaIndex. This solves 90% of their problems."

Recently, there was a case study shared.

Fabian Both, a deep learning engineer at AI testing tool Octomind, shared their story. Octomind uses AI agents with multiple LLMs to create and fix end-to-end tests in Playwright.

This year-long journey began with choosing LangChain, leading to struggles with it. By 2024, they decided to move away from LangChain.

Let's see what they experienced.