Context Compression

Context compression reduces the token footprint of information that must stay in the prompt without losing its essential meaning. Techniques include extractive summarization of prior turns, LLM-driven distillation into bullet points, lossy removal of irrelevant exchanges, and selective retention of salient facts. Compression trades fidelity for capacity, allowing agents to maintain longer effective memories within a fixed context window budget.