CrabRAG: מדוע עוזרים אוטומטיים זקוקים לזיכרון גרפי, לא ליותר טוקנים

תקציר מקורי באנגליתStephen Chin gave two copies of the same agent the same facts about his home network, one storing them as a vector database, the other as a graph. He asked both what was running end of life software exposed to the internet. The vector agent said it could not find specific details. The graph agent traced the connections and flagged his daughter's Minecraft server running an out of date OS, then found real open management ports he quietly patched after the demo. Same data, and only one of them could actually answer. That gap is the whole talk. Most assistants, OpenClaw included, keep their memory as markdown files, which is why Chin's agents burn over 100,000 tokens a round loading everything in case some of it matters. It holds at small scale and breaks at large scale, because similarity in