I'm making the stronger claim. I don't think memory (at least, what people call "memory", even though it isn't...the memories LLMs have are baked in at training, everything else is context), no matter how fancy, improves outcomes, at least for the work I do on the software I work on. I just don't think the agent needs what people are calling memory.

I think the base truth is the code, which can be loaded into context at no greater cost than whatever "memory" system you're using, probably lower cost, actually. A few hints in documentation fills out the rest of the picture.

You can't realistically give an LLM memory, as current technology doesn't allow retraining the model on the fly. You can only give it more data to ingest into its context. Unless that data is directly relevant to the task at hand, it's probably detrimental. At best, it is just burning tokens for no benefit.

Useful comment. Thanks.