Vector databases are typically used for storing embeddings and are great for tasks like similarity search. However, they are generally read-only and don't natively support the concept of time or state transitions. Let's take an example of tracking state of a tasks from your todo list in a vector database:

You might store the task's states like:

Task 1 in backlog Task 1 in progress Task 1 in canceled

But there's no concept of "latest state" or memory of how the task evolved over time. You'd have to store multiple versions and manually track changes.

With a memory-enabled system like Mem0, you could track: Task 1 (current state: in progress) with a memory of previous states (backlog, canceled, etc). This gives your AI app more stateful understanding of the world, allowing it to update and reflect the current context automatically.

Traditional databases, on the other hand, are designed for structured, relational data with fixed schemas, like customer information in a table. These are great for handling transactional data but aren't optimal for cases where the data is unstructured.

As mentioned in the post, we use a hybrid datastore approach that handles these cases effectively and that's where the graph aspect comes into picture.