New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

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The article explains a new agentic memory framework called MRAgent that improves long-horizon reasoning for AI agents. It replaces passive retrieve-then-reason pipelines with an active memory reconstruction process that iteratively prunes irrelevant paths. The framework uses a Cue-Tag-Content structure to reduce noise, improve retrieval quality, and lower token usage. In benchmark tests, it outperformed several alternatives and cut prompt consumption sharply compared with LangMem and A-MEM. The article also notes that developers need to prepare the underlying memory graph and ingestion pipeline before deployment.

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