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 infrastructure. As a promotional offer, we’re giving $75 in credits for your first three months of Engram! Sign up before July 15th to claim it. Read the blog: weaviate.io/blog/engram-gene… Get started: docs.weaviate.io/engram?utm_…

A few weeks ago, @philipvollet, @victorialslocum and @aestheticedwar1 joined the 𝗕𝗶𝗴 𝗕𝗲𝗿𝗹𝗶𝗻 𝗛𝗮𝗰𝗸 with a mission: build an epic project with Weaviate, and win the top prize. They only had 36 hours start to finish, and the concept was ambitious - combine trend-dectection on Twitter with persona-based AI content generation. 𝗧𝗵𝗲 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: • Weaviate for storing and semantically searching Twitter data • Gemini models for content generation • Tavily for additional context retrieval • A custom scoring model built with Pioneer to rank trends The system monitors Twitter to identify emerging trends using semantic clustering. When it finds something relevant, it analyzes your writing style and previous content to build a persona model. Then it generates new posts about those trends - but in 𝘺𝘰𝘶𝘳 voice, not generic AI-speak. The question is: did their sleep-deprived hackathon project manage to take home the 10k top prize? Watch our new video to find out 👀 youtu.be/i1cYfxB8-mw?si=Z-HT…

Big update from the Weaviate Cloud team! We’ve rolled out a 𝗻𝗲𝘄 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 to make it easier to understand what you’re paying for and ensure pricing scales with how you actually use the platform - no matter if you're testing ideas or running production workloads: Here's what's new: - New names: Serverless Cloud → 𝗦𝗵𝗮𝗿𝗲𝗱 𝗖𝗹𝗼𝘂𝗱, Enterprise Cloud → 𝗗𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗖𝗹𝗼𝘂𝗱 - New pricing dimensions: 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀, 𝘀𝘁𝗼𝗿𝗮𝗴𝗲, 𝗯𝗮𝗰𝗸𝘂𝗽𝘀 - New plans: 𝗙𝗹𝗲𝘅, 𝗣𝗹𝘂𝘀, and 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 - Real-time cost visibility with a new pricing calculator The result: transparent, predictable pricing that scales with your actual usage 💚 🔬 Read the full breakdown in our launch blog: weaviate.io/blog/weaviate-cl… 🔖 See the details on our pricing page: weaviate.io/pricing?utm_sour…