
4/2/2026
What this post added
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.