/AI2h ago

Engram Launches GA Memory Service For Production AI Agents

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Original postConnor Shorten#793

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: ๐—˜๐—ป๐—ด๐—ฟ๐—ฎ๐—บ ๐—ถ๐˜€ ๐—ป๐—ผ๐˜„ ๐—š๐—”.

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.

๐—˜๐—ป๐—ด๐—ฟ๐—ฎ๐—บ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ๐˜€ ๐˜๐—ต๐—ถ๐˜€.

It's a managed memory service built on Weaviate that ๐˜ข๐˜ค๐˜ต๐˜ช๐˜ท๐˜ฆ๐˜ญ๐˜บ ๐˜ฎ๐˜ข๐˜ช๐˜ฏ๐˜ต๐˜ข๐˜ช๐˜ฏ๐˜ด 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.

๐—ž๐—ฒ๐˜† ๐—ฐ๐—ฎ๐—ฝ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐—ถ๐—ฒ๐˜€:

๐—™๐—ถ๐—ฟ๐—ฒ-๐—ฎ๐—ป๐—ฑ-๐—ณ๐—ผ๐—ฟ๐—ด๐—ฒ๐˜ ๐—”๐—ฃ๐—œ โ†’ Add raw data and continue working. Pipelines run asynchronously in the background with durable execution.

๐—ง๐—ผ๐—ฝ๐—ถ๐—ฐ๐˜€ ๐—ฎ๐˜€ ๐—บ๐—ฒ๐—บ๐—ผ๐—ฟ๐˜† ๐—บ๐—ฎ๐—ด๐—ป๐—ฒ๐˜๐˜€ โ†’ Natural language descriptions that pull matching information from raw data. You control what's worth remembering.

๐—ฆ๐—ฐ๐—ผ๐—ฝ๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—ถ๐˜€๐—ผ๐—น๐—ฎ๐˜๐—ถ๐—ผ๐—ป โ†’ Project-wide, user-scoped, or property-scoped memories with hard and soft isolation enforced at the platform level.

๐—–๐—ผ๐—บ๐—ฝ๐—ผ๐˜€๐—ฎ๐—ฏ๐—น๐—ฒ ๐—ฝ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ๐˜€ โ†’ Extract, transform, buffer, and commit steps that manage memories dynamically based on data type and preferences.

๐—•๐˜‚๐—ถ๐—น๐˜ ๐—ผ๐—ป ๐—ช๐—ฒ๐—ฎ๐˜ƒ๐—ถ๐—ฎ๐˜๐—ฒ โ†’ 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, weโ€™re giving $75 in credits for your first three months of Engram! Sign up before July 15th to claim it.

Read the blog: https://weaviate.io/blog/engram-generally-available?utm_source=socials&utm_medium=w_social&utm_campaign=engram&utm_content=268035970

Get started: https://docs.weaviate.io/engram?utm_source=socials&utm_medium=w_social&utm_campaign=engram&utm_content=268069934

8:16 AM ยท Jun 3, 2026 ยท 736 Views
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