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[FEATURE]:Session compaction includes already-summarized messages, wasting tokens #8714

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opened 2026-02-16 18:10:38 -05:00 by yindo · 1 comment
yindo commented 2026-02-16 18:10:38 -05:00
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Originally created by @Liuhanlin23 on GitHub (Feb 6, 2026).

Originally assigned to: @thdxr on GitHub.

Feature hasn't been suggested before.

  • I have verified this feature I'm about to request hasn't been suggested before.

Describe the enhancement you want to request

Description:
The session compaction logic in compaction.ts currently includes all historical messages every time it performs compaction, even when previous compaction summaries already exist. This causes unnecessary processing and token waste.
I noticed that the prune() method already implements this optimization, recognizing that messages before a summary point don't need reprocessing.
If you agree this optimization is needed, I would like to be assigned to handle and fix this issue.

Originally created by @Liuhanlin23 on GitHub (Feb 6, 2026). Originally assigned to: @thdxr on GitHub. ### Feature hasn't been suggested before. - [x] I have verified this feature I'm about to request hasn't been suggested before. ### Describe the enhancement you want to request Description: The session compaction logic in compaction.ts currently includes all historical messages every time it performs compaction, even when previous compaction summaries already exist. This causes unnecessary processing and token waste. I noticed that the prune() method already implements this optimization, recognizing that messages before a summary point don't need reprocessing. If you agree this optimization is needed, I would like to be assigned to handle and fix this issue.
yindo added the discussion label 2026-02-16 18:10:38 -05:00
yindo commented 2026-02-16 18:10:39 -05:00
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@github-actions[bot] commented on GitHub (Feb 6, 2026):

This issue might be a duplicate of existing issues. Please check:

  • #11142: Bug about watchdog reminder causing excessive token amplification (O(N) growth) - similar reprocessing of already-included messages causing token waste
  • #4659: [FEATURE] Sliding window context management for long-running sessions - proposes inception messages to avoid reprocessing historical context
  • #11829: [FEATURE] Recursive Language Model (RLM) Context Management - suggests model should query context programmatically rather than include all messages to eliminate redundant reprocessing
  • #8932: [FEATURE] Allow pinning important instructions/messages per session to keep them after compaction - addresses context loss and provides mechanism to preserve critical context
  • #8455: [FEATURE] Recursive Language Model as alternative to Compaction - proposes external context storage model

These issues all converge on the same root cause: compaction logic reprocessing messages that have already been summarized, causing token waste and context degradation.

Feel free to ignore if none of these address your specific case.

@github-actions[bot] commented on GitHub (Feb 6, 2026): This issue might be a duplicate of existing issues. Please check: - #11142: Bug about watchdog reminder causing excessive token amplification (O(N) growth) - similar reprocessing of already-included messages causing token waste - #4659: [FEATURE] Sliding window context management for long-running sessions - proposes inception messages to avoid reprocessing historical context - #11829: [FEATURE] Recursive Language Model (RLM) Context Management - suggests model should query context programmatically rather than include all messages to eliminate redundant reprocessing - #8932: [FEATURE] Allow pinning important instructions/messages per session to keep them after compaction - addresses context loss and provides mechanism to preserve critical context - #8455: [FEATURE] Recursive Language Model as alternative to Compaction - proposes external context storage model These issues all converge on the same root cause: compaction logic reprocessing messages that have already been summarized, causing token waste and context degradation. Feel free to ignore if none of these address your specific case.
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Reference: anomalyco/opencode#8714
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