Weaviate 的 Engram 让多智能体系统能自动从经验中学习，合并分散的反馈，不用手动处理重复和冲突。
Weaviate 推出 Engram，一种专为多智能体管道设计的“发射后不管”记忆系统。它通过三个步骤（提取、转换、提交）在后台异步处理记忆，避免阻塞和重复。在多智能体 RAG 示例中，搜索代理错误地使用文本搜索而不是类型过滤，Engram 将分散在不同代理和上下文窗口中的任务目标、动作和反馈合并为一条可操作记忆。记忆支持项目范围或用户范围，通过多租户保持隔离。系统使得智能体能从经验中持续学习，无需手动干预。
𝗙𝗶𝗿𝗲-𝗮𝗻𝗱-𝗳𝗼𝗿𝗴𝗲𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀? No block...
𝗙𝗶𝗿𝗲-𝗮𝗻𝗱-𝗳𝗼𝗿𝗴𝗲𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀? No blocking. No duplicates. Just continuous learning that works. When you add data to Engram, pipelines run in the background to: 1️⃣ 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 relevant memories matching your configured topics 2️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 by retrieving existing memories and reconciling them (deduplication, handling preference changes, updating facts) 3️⃣ 𝗖𝗼𝗺𝗺𝗶𝘁 clean, structured memories to Weaviate But how do you deal with a mutli-agent pipeline where 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘢𝘨𝘦𝘯𝘵𝘴 are adding context to the memory system? Take a multi-agent RAG system where a main agent handles conversations and a search agent uses specialized tools. A user asks about comedy movies, but the search agent does a text search on "comedy" instead of filtering by genre. The agent then sends back feedback: "Comedy is a genre, you should filter on the 'genres' property." The information needed to learn is spread across multiple agents and context windows: • Task goal: "User asked for comedy movies" (main agent) • Actions taken: "Called search with near-text query 'comedy'" (search agent) • Feedback: "Should filter on genres property" (main agent) Engram's pipeline: 1. Extracts each piece individually using different 𝘁𝗼𝗽𝗶𝗰𝘀 (task_goal, actions_taken, feedback) 2. 𝗕𝘂𝗳𝗳𝗲𝗿𝘀 collect these memories until all pieces arrive 3. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 step combines them into a single actionable memory: "𝘞𝘩𝘦𝘯 𝘢𝘴𝘬𝘦𝘥 𝘵𝘰 𝘧𝘪𝘯𝘥 𝘮𝘰𝘷𝘪𝘦𝘴 𝘰𝘧 𝘢 𝘱𝘢𝘳𝘵𝘪𝘤𝘶𝘭𝘢𝘳 𝘨𝘦𝘯𝘳𝘦 (𝘦.𝘨., 𝘤𝘰𝘮𝘦𝘥𝘺), 𝘺𝘰𝘶 𝘴𝘩𝘰𝘶𝘭𝘥 𝘧𝘪𝘭𝘵𝘦𝘳 𝘰𝘯 𝘵𝘩𝘦 𝘨𝘦𝘯𝘳𝘦𝘴 𝘱𝘳𝘰𝘱𝘦𝘳𝘵𝘺, 𝘯𝘰𝘵 𝘥𝘰 𝘢 𝘯𝘦𝘢𝘳-𝘵𝘦𝘹𝘵 𝘲𝘶𝘦𝘳𝘺." This memory is now stored in Weaviate and retrieved when starting similar tasks. The agent has actually learned from experience 🔥 Because pipelines run asynchronously, agents can: • Learn from feedback over time without blocking • Combine information spread across multiple conversations • Build up experience that improves future performance • Maintain clean, reconciled memories instead of noisy duplicates With Engram, you can configure memories to be: • 𝗣𝗿𝗼𝗷𝗲𝗰𝘁-𝘄𝗶𝗱𝗲 𝘀𝗰𝗼𝗽𝗲: Agent learns from all users' feedback (trusted teams) • 𝗨𝘀𝗲𝗿-𝘀𝗰𝗼𝗽𝗲𝗱: Each user gets their own person docs.weaviate.io/engram?utm_sou… s from their specific usage Agents can then 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 and 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲 both individually and across teams, all kept separate and secure through multi-tenancy. Find more in the docs: https://t.co/pNyS1JJy0R 💬 0 🔄 0 ❤️ 1 👀 99 ⚡