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* feat(memory): add staleness review to prune silently-outdated facts
Facts created long ago may become outdated without any future conversation
explicitly contradicting them ("Silent Staleness"). This adds a staleness
review mechanism that surfaces aged facts to the LLM during the normal
memory-update call so it can semantically judge whether each is still valid.
- New MemoryConfig fields: staleness_review_enabled, staleness_age_days,
staleness_min_candidates, staleness_max_removals_per_cycle,
staleness_protected_categories
- New STALENESS_REVIEW_PROMPT section injected into MEMORY_UPDATE_PROMPT
when enough stale candidates exist
- New staleFactsToRemove output field in the LLM response schema
- Safety cap limits max removals per cycle, keeping lowest-confidence
entries when the LLM returns more than the cap
- Correction facts (category=correction) are protected by default
- Observability via structured logging of each removal with reason
- 32 unit tests covering parsing, selection, triggers, formatting,
normalization, safety cap, and integration
* fix(memory): add deterministic guardrail for staleness removals
_apply_updates previously removed any fact id the LLM returned in
staleFactsToRemove without verifying it was in the actual staleness
candidate set. An LLM slip could silently delete protected-category
facts (e.g. correction) or fresh facts, defeating the stated guarantee.
Now intersect stale_ids_to_remove with _select_stale_candidates before
the safety cap, making the protection independent of both model behavior
and the staleness_review_enabled flag.
Add three regression tests:
- test_protected_category_fact_refused_at_apply
- test_non_aged_fact_refused_at_apply
- test_guardrail_runs_when_staleness_review_disabled
* docs(memory): sync AGENTS.md staleness config + simplify datetime parsing
Address reviewer feedback from PR #3860:
- Add staleness workflow step and 5 new config fields to backend/AGENTS.md
- Simplify _parse_fact_datetime: drop manual Z→+00:00 replace, Python 3.12+ fromisoformat handles Z natively
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Co-authored-by: Willem Jiang <willem.jiang@gmail.com>