---
title: "Conversation memory, durable memory, and context engineering"
chapter: "14"
---

# Conversation memory, durable memory, and context engineering

Models are stateless unless the provider or application carries context.
Spring AI chat memory stores message history; it is not automatically a
durable user-memory system.

## Memory types

- working context: current request, retrieved evidence, tool results;
- conversation history: recent user/assistant turns;
- summary memory: compressed older history;
- durable profile: explicit facts/preferences with provenance and consent;
- episodic memory: past outcomes retrieved for a similar task.

## Spring AI

`MessageChatMemoryAdvisor` adds conversation history. Supply
`ChatMemory.CONVERSATION_ID` on each call. Choose a repository, bounded window,
retention, and deletion design.

Spring AI 2.0 tool calling maintains intermediate tool messages inside the
loop; default memory advisors load once and store the final exchange. Advisor
order changes semantics.

## Memory safety

Never allow one tenant or user to retrieve another's history. Do not infer and
store sensitive traits casually. Show users what is remembered, why, and how
to correct/delete it.

## Summarization risk

A summary is generated data and can distort commitments. Preserve immutable
source turns for regulated decisions or attach summary provenance. Rebuild
summaries when the summarizer changes.

## Context budgeting

Rank memory by relevance, recency, importance, and authority. Current system
policy outranks old conversation content. Drop low-value history before
retrieved evidence required for the answer.

## Feynman check

Conversation memory is yesterday's chat transcript; durable memory is a
carefully maintained profile. Neither should be a mystery notebook the user
cannot inspect.