Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History

In three linesEngram, an open-source dual-process memory engine for LLM agents, uses a bi-temporal knowledge graph to outperform full-context baselines. On LongMemEval_S (500 questions), the lean configuration retrieves ~9.6k tokens and achieves 83.6% vs 73.2% for full history (+10.4 points, p<10^-6), using 8x fewer tokens.

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