Your AI agents are burning tokens solving the same problems over and over. Long context windows won...

Long context windows won't save you - here's what will.

Without memory, your agents are stuck in an endless loop of solving the same problems repeatedly, wasting tokens and time. Long context windows https://t.co/6WIOxWQo6F" / X

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Your AI agents are burning tokens solving the same problems over and over. Long context windows won't save you - here's what will. Without memory, your agents are stuck in an endless loop of solving the same problems repeatedly, wasting tokens and time. Long context windows seem like a solution, but they degrade accuracy, increase latency, and inflate costs with every request. Naively storing every message for retrieval creates its own mess. Raw conversations are noisy, contradictory, and full of facts that change over time. You end up with an ever-growing pile of context that degrades rather than improves. That's why we built 𝗘𝗻𝗴𝗿𝗮𝗺 - a managed memory service running on Weaviate that treats memory as robust infrastructure, not an afterthought. Here's what makes Engram different: !Image 1: 1️⃣ 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Engram doesn't just store memories - it actively maintains them. When a user tells you they've been promoted, Engram automatically: • Retrieves related memories about their work • Rewrites the existing memory to reflect the update while maintaining history • Deletes duplicate information to keep memories clean !Image 2: 2️⃣ 𝗔𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 Fire-and-forget your raw data, and let Engram remember what matters. Pipelines extract memories, reconcile new and existing information, and persist to Weaviate - all in the background, so your requests are always low-latency. !Image 3: 3️⃣ 𝗧𝗼𝗽𝗶𝗰𝘀 & 𝗦𝗰𝗼𝗽𝗲𝘀 Topics are "magnets for memories" - natural language descriptions of what information to extract. Scopes control isolation (project-wide, user-scoped, or property-scoped) with hard boundaries enforced by Weaviate's multi-tenancy. !Image 4: 4️⃣ 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗦𝘁𝗲𝗽𝘀 Extract → Transform → Commit. Engram comes with templates to make getting started simple, but the underlying system is highly configurable. Customize topics, adjust pipeline steps, or take full control of extraction while still leveraging Engram's integration and persistence logic. !Image 5: 5️⃣ 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗧𝗮𝘀𝗸𝘀 𝘄𝗶𝘁𝗵 𝗕𝘂𝗳𝗳𝗲𝗿𝘀 Use buffers to aggregate memories over time or multiple context windows. Engram can extract information from multi-agent systems (input queries, agent actions, user feedback) and combine them into a single useful memory for your agents. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁: Engram maintains your memory over time so it always stays clean. No duplicate information, historical context preserved, and every memory stays accurate as facts change. Memory isn't just a feature - it's infrastructure. And infrastructure needs to be predictable, scalable, and reliable. !Image 6: 🔥 Read the full deep dive: weaviate.io/blog/engram-de