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Engram: Managed Memory and Context Service

Engram: Managed Memory and Context Service

4
posts
2026

Engram is Weaviate's managed memory and context service, designed to help agents orchestrate workflows, learn from experience, and anchor decisions to trusted knowledge. It transforms raw agent events into structured, durable, and scoped memories, serving them via Weaviate's hybrid semantic and keyword retrieval. Engram features asynchronous pipelines for extraction, reconciliation, and persistence of clean memory states, with templates for personalization, continual learning, and multi-agent sy. This post introduces Weaviate Agent Skills, a repository that acts as a bridge between coding agents and Weaviate's infrastructure, providing Weaviate-specific scripts for cluster management, data lifecycle, agentic search, and advanced retrieval, as well as end-to-end project blueprints (cookbooks) for building applications with Weaviate and modern frameworks.

2026

Engram is now Generally Available | Weaviate

6/3/2026

This post announces the general availability of Engram, Weaviate's managed memory and context service. It details the problems Engram solves, such as long-context degradation, messy raw data, and multi-agent context fragmentation, by treating memory as infrastructure. Key features highlighted include actively maintained memory through pipelines, fire-and-forget asynchronous operations with Temporal-grade durability, templates for common use cases with primitives for customization, built-in scopes for isolation, and unified retrieval on Weaviate. The post also provides guidance on who should use Engram and how to get started with it in Weaviate Cloud.

Engram: Memory by Weaviate | Weaviate

4/21/2026

This post provides a deep dive into the architecture and implementation of Engram, Weaviate's managed memory and context service. It explains the core concepts of topics, scopes, and groups for organizing and isolating memories. The post details the asynchronous pipeline architecture built on Temporal workflows, outlining the different types of extract steps (conversation data, string data, pre-extracted memories) used to process raw data into structured memories. It also elaborates on the different scope types (project-wide, user-scoped, property-scoped) and the concept of bounded topics for managing memory constraints.

Oh Memories, Where'd You Go | Weaviate

4/2/2026

This post details the practical challenges and solutions for integrating Engram with LLM agents. It describes the initial problem of LLMs defaulting to local context (`MEMORY.md`) over external tools, leading to the development of deterministic, infrastructure-level retrieval hooks that inject relevant memories at specific session lifecycle points (session start, before user prompt). It also addresses save performance issues by moving to an "eventually consistent" fire-and-forget model for saves, and proposes a more robust memory capture pipeline that buffers all messages. The post also introduces the concept of collaboration scope for managing personal versus shared memories.

Introducing Weaviate Agent Skills | Weaviate

2/18/2026

Introduces Weaviate Agent Skills, a repository of scripts and cookbooks designed to integrate coding agents with Weaviate. The repository is divided into Weaviate Skills (for granular Weaviate operations like schema management, data ingestion, and search) and Cookbooks (for end-to-end application blueprints like chatbots and RAG systems). It also details six commands for agents to interact with Weaviate: Ask, Collections, Explore, Fetch, Query, and Search.