Lessons from building an LLM agent with persistent memory: split recall from reflect, retain selectively

abhinavvreddy · reddit · 2026-09-30

The author built an incident-response agent (CyberMemory AI) where the core problem was continuity: every alert started from a blank context, so the model returned generic checklists. Using Hindsight as a memory layer, the flow normalizes each alert into a query (alert is evidence, not memory), then recall retrieves related past incidents via semantic, keyword, graph, and temporal retrieval; reflect reasons over the memory bank to produce a grounded report; after resolution, retain writes back root cause, actions, and outcome.

Key lessons:

Recall and reflect solve different problems: recall surfaces raw evidence, reflect does synthesis. Keeping them separate made debugging trivial—you know if a bad answer came from retrieval or reasoning.
Memory before reasoning: generating the report first and attaching memory afterward performed worse; history should shape which hypotheses get explored.
Retaining everything is the wrong default: log noise and temp IDs pollute retrieval. Store each resolved incident as a structured record (context, cause, actions, outcome) plus tracing metadata.
Similar wording isn't similar incidents: same-user/IP-range/host relationships don't show up in pure embedding similarity, so single-pass vector search felt too thin.
Write-back is the feedback loop: the resolution is more valuable than the original alert.

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Lessons from building an LLM agent with persistent memory: split recall from reflect, retain selectively

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