AI
best practice
Modularize Memory Stores for Faster Agent Retrieval
Optimize agent performance by using modular, small-scale memory files.
Break down large datasets into small, focused files capped at 100KB to ensure your AI agents can retrieve information quickly and stay within system limits.
Claude
The Scenario
You need to feed an agent an entire company handbook but want to ensure it remains fast and doesn't exceed the memory file limits.
Before & after
The old way
Users try to upload massive 10MB context files which hit token limits and slow down agent response times by 5–10 minutes.
With AI
By splitting files into 100KB chunks, the agent can search and read relevant documents in under 1 minute.
The Prompt
Analyze this large documentation set [PASTE_DOC_TEXT] and suggest how to split it into 100KB files for a Claude Memory Store.
Individual memory files are capped at 100KB. Using many small, focused files (e.g., 'user_style.txt', 'project_archive.txt') is more efficient than one large file.
Source
Claude Platform - Claude Platform Docs"Individual memory files are capped at 100KB (~25K tokens). Store many small focused files."