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.
Provides intelligent context management for AI development sessions, allowing users to track token usage, manage conversation context, and seamlessly restore context when reaching token limits.
Provides context compression via the tokenslim engine, enabling MCP hosts to reduce token usage while preserving key information. Offers compress, retrieve, and stats tools for managing compressed content.
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.