Summarization / Compression

Summarization or compression lets an AI agent keep the gist of past chats without saving every line. After a talk, the agent runs a small model or rule set that pulls out key facts, goals, and feelings and writes them in a short note. This note goes into long-term memory, while the full chat can be dropped or stored elsewhere. Because the note is short, the agent spends fewer tokens when it loads memory into the next prompt, so costs stay low and speed stays high. Good summaries leave out side jokes and filler but keep names, dates, open tasks, and user preferences. The agent can update the note after each session, overwriting old points that are no longer true. This process lets the agent remember what matters even after hundreds of turns.

Visit the following resources to learn more:

• @article@Evaluating LLMs for Text Summarization
• @article@The Ultimate Guide to AI Document Summarization