Optimization of LLM Systems with DSPy and LangChain/LangSmith 

It does beautiful caching across the system, so it only calls the backend LLM, if you haven't done so, until then!

IMO and from an application-building perspective, LangChain is necessary since you cant give the final user of your application a DSPy code as frontend. In the video they mention the idea of using DSPy with LangGraph, which I think resonates with what I said preivously ~ rather than seing them as a replacement for each other, they can be used together to build your apps.

I think you need both! The small discussion at the end about Keras which is on top of TensorFlow illustrates that the same idea can be integrated. We need 'glue' and we also need higher-level abstraction building blocks for 95% of applications out there. The mariage of LangChain and DSPy fall into this category, in my opinion. Therefore, I am looking forward to learning more about this and am excited to use these higher-level building blocks in a LangChain application. It will speed up development and testing, and at the end of the day, that is what matters most!

I'm wondering the same thing. I only briefly used LangChain, but have bounced around all the different frameworks since then and have been using DSPy for a while now. While I see what Miguel and Vandarvas are saying here, I don't understand what Langchain can do that DSPy can't. The DSPy abstractions are much more peaceful to work with and allow great flexibility. "LangChain is necessary since you cant give the final user of your application a DSPy code as frontend" - not sure if I'm misunderstanding, but when would you give LLM app code as a frontend? Interested to hear more on this. It sounded like Harrison was really pushing the idea of going between LangChain and DSPy, and of course pushing LangSmith as the product which looks very nice I have to say.