Context compression techniques can significantly reduce token consumption in LLM applications by filtering out noise from logs, tool outputs, and RAG chunks before processing, achieving substantial cost savings (up to 95% fewer tokens) while maintaining answer quality.
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Your agent burns tokens on noise #LLM #AI #github #opensource #tokens本站添加:
Your agent burns tokens on noise, logs, tool outputs, rag chunks, most of it useless. Headroom compresses context before it hits the LLM. 95% fewer tokens, same answers. Library, proxy, MCP server, six compression algorithms including AST aware. Cache aligner restructures prompts so KV caches save you money. 8,000 stars, pays for itself.
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