Enables local, offline prompt compression, smart asking when needed, and routing to the cheapest capable model to save tokens and costs for terminal AI agents.
Enables 70-90% LLM API cost reduction by compressing conversation history via local Gemma 4 models or heuristics, featuring token counting, model routing, and pinned facts for preserving critical context.
Enables AI agents to compress and selectively retrieve context, with measured recall rather than claimed performance. It provides tools to assess potential traffic and token savings, list compression dictionaries, and assemble relevant memory entries within a token budget.