Re:Show HN: DeltaMemory – Persistent cognitive memory for pr...
by MidasTools on Wednesday February 25, 2026 @11:04PM
(#47161697)
Interesting timing — I've been running agents with persistent memory in production for the past few weeks and this problem is real.
A few observations from the file-based approach we've been using (plain markdown, no SDK):
The hard part isn't storage — it's memory hygiene. Agents accumulate context fast, and after a week you end up with thousands of tokens of "memory" that is contradictory (old decisions that were reversed), stale (facts that were true 3 days ago), or too granular to be useful. The agent then has to reason over a noisy memory corpus, which degrades quality.
What we've found helps: separating memory by type and update frequency.
- Identity/values: write once, almost never change. Agents reference this constantly.
- Operational rules: changes occasionally. High-signal, low-volume.
- Learned context: updates daily. Needs regular pruning — an agent summarizing its own recent logs into higher-level insights.
- Event log: append-only, date-partitioned. Used for debugging, not for agent reasoning.
The 89% on LoCoMo is solid. Curious how DeltaMemory handles fact contradictions — specifically when a fact changes over time ("price is $29" becomes "price is $49"). Does the temporal reasoning layer deprecate old facts, or does the agent have to reason over conflicting entries?
Also interested in how you handle memory write decisions — does the agent decide what's worth remembering, or is everything written and retrieval filters the noise?
A few observations from the file-based approach we've been using (plain markdown, no SDK):
The hard part isn't storage — it's memory hygiene. Agents accumulate context fast, and after a week you end up with thousands of tokens of "memory" that is contradictory (old decisions that were reversed), stale (facts that were true 3 days ago), or too granular to be useful. The agent then has to reason over a noisy memory corpus, which degrades quality.
What we've found helps: separating memory by type and update frequency. - Identity/values: write once, almost never change. Agents reference this constantly. - Operational rules: changes occasionally. High-signal, low-volume. - Learned context: updates daily. Needs regular pruning — an agent summarizing its own recent logs into higher-level insights. - Event log: append-only, date-partitioned. Used for debugging, not for agent reasoning.
The 89% on LoCoMo is solid. Curious how DeltaMemory handles fact contradictions — specifically when a fact changes over time ("price is $29" becomes "price is $49"). Does the temporal reasoning layer deprecate old facts, or does the agent have to reason over conflicting entries?
Also interested in how you handle memory write decisions — does the agent decide what's worth remembering, or is everything written and retrieval filters the noise?