Weaviate发了Engram demo，它主动维护记忆而不是堆上下文，让聊天机器人越用越聪明，比老方法好多了。
Weaviate发布Engram演示，解决大多数AI智能体因记忆管理不当而变笨的问题。传统方法要么将整段对话塞入上下文（导致成本高、性能下降），要么存储每条消息（产生噪音和矛盾）。Engram在后台异步运行管道：提取配置主题的信息、调和新旧记忆、处理重复和偏好变化，并将结构化记忆存储到Weaviate。这种主动维护机制让聊天机器人每次交互都实际提升性能。
Most AI agents get 𝗱𝘂𝗺𝗯𝗲𝗿 as they learn more. (There's a better way) We just released a demo...
Most AI agents get 𝗱𝘂𝗺𝗯𝗲𝗿 as they learn more. (There's a better way) We just released a demo of 𝗘𝗻𝗴𝗿𝗮𝗺 that shows exactly how intelligent memory management should work. T engram.playground.weaviate.io/?utm_source=x&… o/GwVlXXb7Bo Most agentic chatbots handle memory in one of two broken ways: • Dump the 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 every time (problems: cost, degraded performance, no consistency across conversations) • 𝗦𝘁𝗼𝗿𝗲 𝗲𝘃𝗲𝗿𝘆 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 for retrieval (problems: noise, contradictions, and facts that changed over time) 𝗘𝗻𝗴𝗿𝗮𝗺 takes a completely different approach: 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗶𝗻𝗴 𝗺𝗲𝗺𝗼𝗿𝗶𝗲𝘀 instead of just piling them up. As conversations happen, Engram runs asynchronous pipelines in the background that: 1. Extract relevant information matching your configured topics 2. Reconcile new memories with existing ones (handling duplicates, preference changes, evolving facts) 3. Store structured, scoped memories in Weaviate for semantic retrieval This solves the core problem with naive approaches. Rather than cramming ever-growing context into your LLM (causing degradation, latency, and cost issues) or storing raw conversations (which are noisy and contradictory), Engram actively maintains clean, structured memories that can update or be deleted over time. The result is agentic chatbots that 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 improve with each interaction, learning from experience without context pollution. Your browser does not support the video tag. 🔗 View on Twitter 💬 0 🔄 0 ❤️ 0 👀 99 ⚡