GoKawiil4.7M DSPy monthly downloads 222M LangChain monthly downloads
For a framework that promises to solve the biggest challenges in AI engineering, this gap is suspicious. Still, companies using Dspy consistently report the same benefits.
They can test a new model quickly, even if their current prompt doesn't transfer well. Their systems are more maintainable. They are focusing on the context more than the plumbing.
So why aren’t more people using it?
DSPy’s problem isn’t that it’s wrong. It’s that it’s hard. The abstractions are unfamiliar and force you to think a litle bit differently. And what you want right now is not to think differently; you just want the pain to go away.
But I keep watching the same thing happen: people end up implementing a worse version of Dspy. I like to jokingly say there’s a Khattab’s Law now (based off of Greenspun’s Law about Common Lisp):
Any sufficiently complicated AI system contains an ad hoc, informally-specified, bug-ridden implementation of half of DSPy.
You’re going to build these patterns anyway. You’ll just do it worse, after a lot of time, and through a lot of pain.
Let’s walk through how virtually every team ends up implementing their own “Dspy at home”. We’ll use a simple structured extraction task as an example throughout. Don’t let the simplicity of the example fool you though; these patterns only become more important as the system becomes more complex.
Let’s say you need to extract company names from some text, you might start out with the OpenAI API:
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