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TokenKeeper acts as a local Retrieval Augmented Generation (RAG) memory system for AI coding agents, specifically designed to manage large project contexts without consuming excessive prompt tokens. It intelligently indexes project documents and code into a local vector database, enabling AI agents to query only the most relevant information rather than loading entire files. This process significantly reduces prompt token usage by up to 80%, allowing agents to maintain focus on the task, prevent context window overflow, and ultimately improve the quality of their reasoning and code generation.
