How To Reduce Context Cost With Smart Context Construction

LLM API costs are often driven more by input tokens than by model outputs because production agents may repeatedly send complete conversation histories or large documents with every request. The passage argues that this naive approach causes per-call costs to grow linearly as histories accumulate, while selective semantic retrieval can keep context budgets largely fixed by injecting only a few relevant memories or document chunks. It illustrates that storing all interactions in a vector-indexed memory system while retrieving only pertinent distilled facts can reduce token use, improve contextual relevance, and limit context-window overflow. Prompt caching can further lower costs for repeated system instructions, and the piece presents Mem0 as a service that provides embedding, memory storage, distillation, and retrieval to help implement this architecture.

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