TokenPilot：通过缓存感知压缩降低LLM Agent成本

TokenPilot reduces LLM agent costs via ingestion-aware compaction and lifecycle-aware eviction.

Achieves 61–87% cost reduction on PinchBench and Claw-Eval with competitive scores.

Argues that cheaper AI agents need stable memory, not just shorter prompts.

Older methods usually cut or summarize the history, but that can shift the text around and break the prompt cache, which is the system that reuses unchanged prompt text to save money.

TokenPilot tries to fix both sides at once by cleaning new tool results before they enter the context and by keeping the early prompt layout stable across tasks.

It also waits before deleting old task history, because finished work can still help later tasks that refer to the same files or goals.

Link – arxiv. org/abs/2606.17016v1

Title: "TokenPilot: Cache-Efficient Context Management for LLM Agents"