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 ![Contextwaise](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_f204acb85c6a581d4fbf9149e940ce40/contextwaise.png "Contextwaise")
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# Contextwaise Discussions

Contextwaise cuts LLM costs by removing the context your model does not need before you send the prompt. Apps and agents retrieve far more than the model uses: extra search results, raw tool output, logs, web pages and old messages, all billed on every call. Contextwaise scores each item against the current task with an AI relevance model in about 100 ms, keeps what matters, and drops what is irrelevant, duplicated or noise. It works for RAG pipelines, web search and AI agents, with any LLM provider, through a REST API and SDKs for TypeScript and Python. Kept items come back in your order, so your prompt cache stays valid. Every removal is logged with its reason, and the model can call expand() to get a trimmed part back, so nothing is lost. A report on each request shows tokens before and after and the cost avoided. On our own example requests it removed 56% to 71% of context tokens. There is no new model to adopt and no migration. Start free with 1M tokens.

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