🛠 Context Warp Drive has been released — a tool for deterministic LLM context compression without the use of the models themselves.
It utilizes a "folding" mechanism to transform dialogue history into compact structural skeletons while preserving precise identifiers in a "Coordinate Closet" block. This enables the maintenance of long agent sessions, saving up to 72% in costs and 63% in context volume.
🌍 It solves the problem of context "clutter" in agentic systems by replacing expensive LLM summarization with a fast CPU algorithm featuring a cacheable prefix.
👤 It allows for the creation of stable and inexpensive AI agents that do not forget important details (such as file paths or IDs) during prolonged operation.
Source 1: https://github.com/dogtorjonah/context-warp-drive