About one and a half years ago I was an early adopter of DSPy and I had better results (compared to LlamaIndex) with structuring unstructured data just by putting it in DSPy models, before any optimization step whatsoever.

Also, IMO DSPy didn't take off because it requires preparing train and test datasets and that takes time and effort. Now with Gepa I expect things are getting very interesting, the optimizations can come just from descriptions.

IMO LangGraph is currently used a lot as an agent and RAG framework, DSPy doesn't have the same use case, even though there's overlap. And I think the montly numbers doesn't do justice, because what I see now is a lot of companies doing things wrongly.