Memory Best Practices

As you build memory-enhanced agents, keep these best practices in mind:

1. Be selective about what goes into working memory

   • Focus on information that will be relevant across multiple conversations
   • Don't overload working memory with transient details

2. Use clear instructions

   • Give your agent explicit guidance on when and how to update working memory
   • Instruct it to check memory before asking for information the user has already provided

3. Choose appropriate memory parameters

   • Adjust lastMessages, topK, and messageRange based on your use case
   • More isn't always better - larger context windows can dilute focus

4. Consider privacy implications

   • Be transparent with users about what information is being stored
   • Implement appropriate security measures for sensitive information

5. Test thoroughly

   • Verify that your agent correctly recalls information across different scenarios
   • Test edge cases like conflicting information or corrections

6. Design thoughtful templates

   • Structure your working memory templates based on your agent's specific needs
   • Include clear sections and organization to make information easy to find

7. Balance memory types

   • Use conversation history for recent context
   • Use semantic recall for finding relevant past information
   • Use working memory for persistent user details and state

By following these best practices, you can create memory-enhanced agents that provide truly personalized and contextual experiences while avoiding common pitfalls like information overload, privacy concerns, and inconsistent behavior.