Makes sense. I wonder if it affects the model output performance (sans quotes), as I could imagine that splitting up the model output to add the quotes could cause it to lose attention on what it was saying.
So after using Mem0 a bit for a hackathon project, I have sort of two thoughts: 1. Memory is extremely useful and almost a requirement when it comes to building next level agents and Mem0 is probably the best designed/easiest way to get there.
2. I think the interface between structured and unstructured memory still needs some thinking.
What I mean by that is when I look at the memory feature of OpenAI it's obviously completely unstructured, free form text, and that makes sense when it's a general use product.
At the same time, when I'm thinking about more vertical specific use cases up until now, there are very specific things generally that we want to remember about our customers (for example, for advertising, age range, location, etc.) However, as the use of LLMs in chatbots increases, we may want to also remember less structured details.
So the killer app here would be something that can remember and synthesize both structured and unstructured information about the user in a way that's natural for a developer.
I think the graph integration is a step in this direction but still more on the unstructured side for now. Look forward to seeing how it develops.