context-compression

Context compression minimizes token usage in long-running agent sessions by applying anchored iterative summarization to preserve critical information like file changes, decisions, and session intent while reducing history size.

Retaining conversation history across large codebases is achieved through artifact tracking and trigger-based compression strategies, which mitigate re-fetch costs and ensure essential signals are preserved for continuation.

Anchored iterative summarization works by merging new information into existing anchored memory using structured sections, including session intent, file changes, decisions, and next steps, to maintain a compressed yet complete record.

You need context compression for debugging when long-running sessions accumulate huge context histories, causing token limits to bite and slow decisions, requiring history size reduction while preserving essential signals.

Yes, opaque compression and regenerative summaries are production-ready approaches for managing token limits, providing alternative strategies alongside anchored iterative summarization to reduce history size and preserve critical information.

Limitations of context compression involve the potential loss of granular detail during summarization, requiring mandatory structured sections and artifact tracking to mitigate re-fetch costs and ensure critical decisions are not lost.