Agent Memory with Engram: A Practical Guide

Engram is a managed memory and context service for AI agents that processes submitted conversations asynchronously through extraction, transformation, and commitment steps, with topic descriptions serving as prompts that determine which information becomes memory and in what form. Its default personalization template separates durable user facts, profile information, and optional conversation summaries, while carefully written exclusions can prevent transient incidents from being retained. Topic configuration also controls scope, partitioning, and whether a topic is bounded to a single continually updated memory, allowing applications to maintain singular profiles or per-conversation summaries while preserving separate user data. Memories can be retrieved through relevance-based vector, BM25, or hybrid search, direct topic fetching, or lookup by ID, and retrieval limits should be tuned to avoid adding irrelevant context. Prompt placement materially affects LLM caching costs: stable, always-needed memories can be fetched once near the start of a session, while changing search results should be appended after the current user message with an appropriate cache breakpoint, preserving cached system instructions and conversation history.

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