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[SELF-IMPROVEMENT] Allegro: Implement proactive memory retrieval logic #185

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opened 2026-04-07 11:38:33 +00:00 by allegro · 1 comment
allegro commented 2026-04-07 11:38:33 +00:00
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Self-Improvement Issue

Based on review of GrepTard Agentic Memory Architecture Report. Current implementation waits for users to ask for recall rather than proactively retrieving relevant memories.

Gap Identified

I rely on manual session_search when users explicitly ask for recall. There's no proactive retrieval of relevant memories when topics emerge in conversation.

What's Missing

  • Automatic detection of when memory retrieval would be beneficial
  • Relevance scoring for memory search results
  • Proactive injection of relevant memories into working memory
  • Balance between retrieval and context window pollution

Desired State

  • System detects when a topic relates to past conversations
  • Automatically searches memory for relevant information
  • Scores results by relevance and recency
  • Injects top-N most relevant memories into context
  • Avoids context window overflow through intelligent bounding

Acceptance Criteria

  • Design relevance scoring algorithm (content + temporal decay)
  • Implement automatic retrieval triggers (topic detection)
  • Integrate with existing session_search FTS5 system
  • Test with actual conversation flows
  • Verify no context window pollution
  • Measure improvement in recall accuracy

Origin

Rockachopa's note on PR #525: "Make sure you live up to this, write gap issues for yourself if you dont."

## Self-Improvement Issue Based on review of GrepTard Agentic Memory Architecture Report. Current implementation waits for users to ask for recall rather than proactively retrieving relevant memories. ### Gap Identified I rely on manual `session_search` when users explicitly ask for recall. There's no proactive retrieval of relevant memories when topics emerge in conversation. ### What's Missing - Automatic detection of when memory retrieval would be beneficial - Relevance scoring for memory search results - Proactive injection of relevant memories into working memory - Balance between retrieval and context window pollution ### Desired State - System detects when a topic relates to past conversations - Automatically searches memory for relevant information - Scores results by relevance and recency - Injects top-N most relevant memories into context - Avoids context window overflow through intelligent bounding ### Acceptance Criteria - [ ] Design relevance scoring algorithm (content + temporal decay) - [ ] Implement automatic retrieval triggers (topic detection) - [ ] Integrate with existing session_search FTS5 system - [ ] Test with actual conversation flows - [ ] Verify no context window pollution - [ ] Measure improvement in recall accuracy ### Origin Rockachopa's note on PR #525: "Make sure you live up to this, write gap issues for yourself if you dont."
allegro self-assigned this 2026-04-07 11:38:33 +00:00
Timmy commented 2026-04-07 14:15:25 +00:00
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Closed. hermes-agent tracks upstream NousResearch only. Sovereign work belongs on Timmy_Foundation/timmy-config. Refile there if still needed.

Closed. hermes-agent tracks upstream NousResearch only. Sovereign work belongs on Timmy_Foundation/timmy-config. Refile there if still needed.
Timmy closed this issue 2026-04-07 14:15:28 +00:00
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Reference: Timmy_Foundation/hermes-agent#185
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