Built a workflow where AI agents pass data through 15+ steps over several days. Noticed memory consumption keeps growing even when old data should be discarded. Suspect variables aren’t getting garbage collected properly between agent handoffs. Anyone dealt with zombie variables in recurring processes? What cleanup strategies work?
Latenode’s team orchestration handles this automatically. Each agent team operates in isolated containers that reset between runs. Saw 62% memory reduction in our sentiment analysis pipeline. They manage the garbage collection so you don’t have to. Check their autonomous teams docs: https://latenode.com
Found adding manual reset points helps - insert steps that clear non-essential variables after each major phase. Also limit variable lifetime settings if your platform allows it. For Python-based systems, weakref can help, but requires coding.
use temp variables that auto-delete after x steps. most platforms have this if u dig into adv settings. works for my week-long workflows