做智能体应用的团队终于有了正经的记忆基础设施——Engram 解决了智能体随时间变笨的核心痛点，做聊天机器人、经验学习或多智能体系统的开发者值得立即试用。
Weaviate 宣布 Engram 正式 GA，这是一个专为智能体应用设计的托管记忆服务。传统记忆系统只是扩展上下文窗口，导致智能体随时间推移性能停滞、重复解决问题、浪费 token。Engram 通过异步管道主动维护记忆，支持去重、偏好变化和时间演化事实的处理。它提供“发后即忘”API、自然语言主题记忆磁铁、多级隔离和可组合管道，基于 Weaviate 的向量+关键词+元数据搜索。适用于聊天机器人、经验学习智能体和多智能体系统，前三个月免费至7月15日。
Most agent memory systems are just glorified context windows. And this is exactly why production ag...
Most agent memory systems are just glorified context windows. And this is exactly why production agents fail at scale. We've been working on this for months, and it's finally here: 𝗘𝗻𝗴𝗿𝗮𝗺 𝗶𝘀 𝗻𝗼𝘄 𝗚𝗔. If you've been building agentic applications, you know the problem. Agents that should get smarter over time stay flat instead. They forget user preferences, re-solve the same problems repeatedly, and waste tokens on work that can't be reused. Long context windows help, but cramming them full degrades accuracy, inflates costs, and increases latency. 𝗘𝗻𝗴𝗿𝗮𝗺 𝘀𝗼𝗹𝘃𝗲𝘀 𝘁𝗵𝗶𝘀. It's a managed memory service built on Weaviate that 𝘢𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘮𝘢𝘪𝘯𝘵𝘢𝘪𝘯𝘴 memory instead of just storing it. Asynchronous pipelines extract relevant information from raw data, reconcile it with existing memories (handling deduplication, preference changes, time-evolving facts), and persist clean, structured memory state ready for retrieval. 𝗞𝗲𝘆 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: 𝗙𝗶𝗿𝗲-𝗮𝗻𝗱-𝗳𝗼𝗿𝗴𝗲𝘁 𝗔𝗣𝗜 → Add raw data and continue working. Pipelines run asynchronously in the background with durable execution. 𝗧𝗼𝗽𝗶𝗰𝘀 𝗮𝘀 𝗺𝗲𝗺𝗼𝗿𝘆 𝗺𝗮𝗴𝗻𝗲𝘁𝘀 → Natural language descriptions that pull matching information from raw data. You control what's worth remembering. 𝗦𝗰𝗼𝗽𝗲𝘀 𝗳𝗼𝗿 𝗶𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻 → Project-wide, user-scoped, or property-scoped memories with hard and soft isolation enforced at the platform level. 𝗖𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗹𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 → Extract, transform, buffer, and commit steps that manage memories dynamically based on data type and preferences. 𝗕𝘂𝗶𝗹𝘁 𝗼𝗻 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 → Memory retrieval inherits Weaviate's vector + keyword + metadata search on the same production stack you already trust, using native multi-tenancy to isolate instances. Whether you're building chatbots that remember user preferences, agents that learn from experience, or multi-agent systems that need shared context, Engram gives you memory as infrastructur weaviate.io/blog/engram-ge… fer, we’re givin docs.weaviate.io/engram?utm_sou… ur first three months of Engram! Sign up before July 15th to claim it. Read the blog: https://t.co/24koipQIjO Get started: https://t.co/v9fMGMNCej Your browser does not support the video tag. 🔗 View on Twitter 💬 1 🔄 5 ❤️ 22 👀 771 📊 7 ⚡