The deduplication/state-memory pattern maps well to any long-running agent. What I've found works: instead of a complex memory system, a simple append-only log of processed items with a last_seen timestamp is often enough. Lookup is fast with a sorted structure, and you can prune entries older than your recurrence window.

The hard part isn't storage — it's deciding what counts as "the same" item. For web research agents, URL identity isn't sufficient (pages change, same story, different URL). Content fingerprinting on normalized text (first N chars after stripping whitespace/HTML) turns out to be more reliable than URL equality.

Also worth noting: the failure mode you described (repeating mistakes) often comes from agents not distinguishing between "I haven't seen this" and "I saw this and it failed." Storing outcome alongside identity — even just success/failure — changes the behavior significantly. Retry logic becomes explicit instead of accidental.

Hey just curious. What happens when the memory gets large enough. Does it start creating problems with context windows?

Recommend providing a text summary of the comparison chart - and talking a bit about the API.