Weaviate 的 Engram 解决了 AI 应用中最头疼的记忆混乱问题——做智能体或对话系统的开发者，别再让模型背锅了，试试把记忆当基础设施来管。
Weaviate 发布了名为 Engram 的托管记忆服务，旨在解决 AI 应用中因不良记忆系统导致的问题。Engram 不再简单地将聊天历史塞入上下文，而是通过异步管道提取关键事实、与已有知识协调、去重并保留更新，最终在数据库中维护干净的记忆状态。这使得 AI 代理能可靠地回忆用户偏好、学习先前任务、更新过时信息，而不会因上下文膨胀导致延迟、成本增加和混乱。对于需要长期记忆的 AI 应用开发者来说，Engram 提供了一种更严格、更高效的记忆基础设施。
We keep blaming the model for problems caused by bad memory systems. Shoving more chat history into...
We keep blaming the model for problems caused by bad memory systems. Shoving more chat history into context is not memory. It’s an invitation to delay, cost, contradiction, and eventually confusion. Modern apps need something stricter: - Extract what facts matter - Reconcile them with what’s already known - Remove duplicates and preserve updates - Retrieve everything reliably later That’s the idea behind Engram - our managed memory service built on Weaviate. Instead of treating memory as an ever-growing prompt, Engram treats it as infrastructure. You send raw inputs that could be conversations, strings, or pre-extracted facts, and Engram runs an async pipeline to extract signals, transform them against existing memory, and commit a clean state into the database. So when an agent needs to recall something later, it’s not searching a pile of noisy transcripts. It’s retrieving maintained memory. This matters most when you need agents to: - Remember user preferences without bloating context - Learn from prior tasks - Update stale facts instead of just duplicating them - Keep memory scoped correctly across users, teams, or workflows Learn more about Engram: weaviate.io/blog/engram-de… Get started: console.weaviate.cloud/signin?utm_sou… 💬 1 🔄 2 ❤️ 10 👀 414 📊 4 ⚡