NirDiamant/Agent_Memory_Techniques — GitHub trend

FINDING #8307 · UNIT ID 1230280405

NirDiamant/Agent_Memory_Techniques

Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.

[ JUPYTER NOTEBOOK ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.000137

ENGAGEMENT0.43

FRESHNESS1.00

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

1% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

2.00

ACCEL

-0.11

RETENTION

40.0%

PEAK 2026-08-25 · FORK-RETENTION 0.0% · 14 STARS / WINDOW

Author Audience

AUDIENCE

14,563

FOLLOWERS

5,997

OWNER ★

85,695

Engagement Signals

FORKS

122

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 14 / 14 (DIVERSITY 1.00)

Why This Is A Finding

NirDiamant/Agent_Memory_Techniques собрал 14 звёзд за окно, тогда как у автора всего 5,997 подписчиков — эффективная аудитория ≈ 14,563. Это даёт surprise-индекс 0.000137 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

Related Findings

RANKS ABOVE 15% OF 9755 FINDINGS

#249Nicolepcx/ai-agents-the-definitive-guide[ JUPYTER NOTEBOOK ]0.0676 #252gmonce/scikit-learn-book[ JUPYTER NOTEBOOK ]0.066 #291xbsheng/atguigu-note[ JUPYTER NOTEBOOK ]0.047 #409gothmania/pytutor[ JUPYTER NOTEBOOK ]0.0381 #433raiyanyahya/how-to-train-your-gpt[ JUPYTER NOTEBOOK ]0.0362

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9755 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.000.00-0.00ABOVE 15%

VELOCITY2.003.71-1.71ABOVE 21%

RETENTION40.0%37.5%+2.5 PPABOVE 54%

FORKS12290+32ABOVE 57%

SURPRISE0.000.01-0.01ABOVE 5%