Does LangChain Suck? What to Use Instead - Mirascope

LangChain might’ve been one of the first tools on the scene for building with LLMs for the first time (and rode an early wave of hype), but for many developers, it’s become more of a headache than a help. Here’s why: * You have to learn a bunch of custom classes and abstractions, even for things that could be done with plain Python or JavaScript. That means more complexity, less clarity, and harder debugging. * Its design doesn’t generally follow software developer best practices. Users point out that code gets messy fast, things aren’t modular, and it’s tough to scale or maintain as your project grows. Because of this, a lot of devs see LangChain as fine for prototyping, **but not something you'd want to take to production**. … For example, it doesn’t: * Automatically version both your prompt and the code around it, which makes reproducibility harder. * Evaluate multiple prompts together as a unit, so devs have to manually track and assess the behavior of interconnected prompts. In this article, we’ll walk through the biggest pain points in LangChain, and show how [Mirascope](https://github.com/mirascope/mirascope), our Python LLM toolkit, fixes them with a cleaner, more developer-friendly approach. … That makes it harder to maintain, scale, or adapt your code, something developers often struggle with across overly complex [LLM frameworks](/blog/llm-frameworks/). That said, LangChain can be a solid learning tool for people exploring how LLM apps are built. But when it comes to real production use, that’s where things start to break down.