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I have verified this feature I'm about to request hasn't been suggested before.
Describe the enhancement you want to request
Currently prune is a basic remove after X amount of tokens have been reached, with a very basic filter of the latest operations. This is not enough when interleave thinking models are debugging complex/deep problems on their big context windows. Removing outputs/results from important steps is making them feel dumb or force them to hallucinate. By implementing a summary and smarter mechanism of that, keeping the feature of reducing unnecessary tokens, we can improve the interaction with the LLM
Soft compaction / AI global workspace metabolism #3032: Soft compaction / AI global workspace metabolism - suggests a softer approach to pruning that preserves context better by allowing the model to intelligently select what to remove
These issues discuss various approaches to improving context management, pruning strategies, and handling of thinking/reasoning content in long-running sessions. Feel free to ignore if your specific use case differs from these.
Activity
github-actions commentedon Jan 9, 2026
This issue might be a duplicate of or closely related to existing issues. Please check:
These issues discuss various approaches to improving context management, pruning strategies, and handling of thinking/reasoning content in long-running sessions. Feel free to ignore if your specific use case differs from these.
thdxr commentedon Mar 25, 2026
To stay organized issues are automatically closed after 90 days of no activity. If the issue is still relevant please open a new one.