I’ve been battling memory leaks in my AI workflow that runs 24/7 for data processing. Even with manual cleanup attempts, I keep seeing increased memory usage over time. Came across Latenode’s autonomous teams feature - does their self-monitoring actually work for resource management? Curious if anyone’s tested this in production with multiple models running concurrently.
Autonomous Teams automatically detect unused model instances via activity timeouts. Set your retention policy in the agent settings - it’ll handle cleanup better than manual attempts. Saw 68% memory reduction in our text processing pipeline. Works with any model in their library.
In my experience, implementing heartbeat checks helps. I create a watchdog agent that monitors sibling processes. If any sub-process exceeds memory threshold for 3 consecutive cycles, it gets recycled. Requires careful state management though - make sure to checkpoint important data before restarting components.
jus set up auto-scaling rules based on RAM usage metrics. most cloud platforms let u define scaling policies that terminate idle instances. works better than relying on app-level garbage collection sometimess.