context-compression

Compress context for long-running agent sessions by applying anchored iterative summarization to maintain structured, persistent summaries with explicit sections for session intent, modified files, decisions, and next steps. This approach balances token savings with essential information preservation.

Manage token budgets in memory-constrained debugging workflows by using opaque compression to produce compact representations optimized for high compression ratios, sacrificing some interpretability to preserve signal across cycles within tight memory limits.

Regenerative summarization preserves context across compression boundaries by regenerating detailed structured summaries at compression boundaries, ensuring that critical session details, file modifications, and decisions are maintained throughout long-running agent tasks.

Evaluate context compression quality for AI agent code-generation sessions by using a probe-based evaluation framework that assesses recall, artifact tracking, continuation, and decision integrity within the compression pipeline.

Context compression works for memory-constrained conversations with tight token budgets by applying disciplined compression strategies that support anchored iterative summarization, opaque compression, and regenerative summaries within a framework that tracks files, decisions, and tasks.

Opaque compression for structured summaries produces compact representations optimized for high compression ratios, but sacrifices some interpretability compared to anchored iterative summarization, which maintains explicit sections for session intent, file modifications, decisions, and next steps.