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Memori

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Memori Labs is an agent-native memory platform built for production AI systems. Unlike conversational memory wrappers or vector retrieval layers, Memori structures memory from both conversation and agent execution, turning tool calls, decisions, and workflow traces into persistent, queryable state. Memori's benchmark results reflect the approach: 87% accuracy on LoCoMo using only 721 tokens per query, 2.8% of full-context cost, saving users more than 97.2% on inference costs. The open-source project has grown to more than 15.5K GitHub stars and 550K downloads, signaling strong developer pull. Bessemer Venture Partners has identified memory and context management as a key part of the emerging AI infrastructure harness layer, citing Memori as one of the category leaders.

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