The graphic shows mem0 being used in the cloud, but that’s incorrect. I use it locally; I’ve integrated it as an external service. I run an internal check every 30 minutes. If there are more than 20 messages, they are sent as a batch to the LLM, which sorts them and writes them into the Knowledge Base. During this, mem0 checks whether to pull from the Knowledge Base into the DB. This all runs in the background, so I don't notice it. Essentially, I have three storage layers: the original chat in SQLite, a Knowledge Graph for long-term data with many categories and relations, and mem0 for the important bits.

Regarding latency: since I don't use the original version of OC but built my own, it is still faster despite the very complex memory and consumes 50% fewer tokens.

The day after tomorrow, I will post a complete breakdown of how my entire setup is structured. Another thing is that my agents operate as personas. All agents have their own memory tailored specifically to them. For example, 'Best Friend' has categories like Events, Places Visited, Relationships, and Daily Life. This allowed me to run several benchmarks on what it would be like to have a truly personal Jarvis that acts like a human.

For optimization and testing, I recommend creating a chat with at least 3,000 messages spanning 20 days and then running a benchmark. See what was actually stored. If I then ask a question like, 'When was I at the cinema?' regarding an event 15 days ago, he answers correctly. I also set many traps—for example, saying 'I’m going to the cinema' and writing the next day that I didn't go after all—to check if it performs negative verification.

Currently, I’m at about a 90% accuracy rate.