Entity & Summary Memory Strategies
Go beyond raw buffers by tracking named entities and rolling summaries so agents remember the important facts across long conversations without exploding the token budget.
Why Buffers Are Not Enough
A plain buffer stores every message verbatim. In long chats this quickly exceeds the model context window and wastes tokens on irrelevant chatter.
Two smarter strategies fix this: summary memory compresses the past, and entity memory tracks specific facts about people and things.
What Is Summary Memory?
Summary memory uses an LLM to keep a running, condensed summary of the conversation. Instead of replaying 50 turns, the agent reads a few sentences capturing the gist.
- Keeps token usage roughly constant
- Preserves long-term context
- Loses fine-grained wording
All lessons in this course
- Agent Memory Concepts
- Conversation Buffer Memory
- Advanced Memory Solutions
- Entity & Summary Memory Strategies