Convo's long-term memory is personalized through a "user dimension data persistence" mechanism that consists of three main technical layers:
- data storage layer: Stores user characteristics in the form of key-value pairs (e.g.
agent.store_memory(user_id, key, value)) - session association layer: automatically correlate interactions of the same user at different times
- real time search layer: By
get_memoryInterface instantly calls historical data
Typical application scenarios include:
- E-commerce customer service: Memorize users' purchasing preferences (e.g., "Prefer organic food") and prioritize related products in subsequent inquiries.
- Education Assistant: Record students' progress (e.g., "mastered basic calculus") and dynamically adjust the difficulty of the exercises.
- Medical Consultation: store key information such as the patient's allergy history, avoiding repetitive questioning of sensitive issues
This feature provides visual memory management through the official website dashboard and supports batch operation and data export.
This answer comes from the articleConvo: Logging and Debugging Tools for Building Intelligent AI AgentsThe
May not be reproduced without permission:AI productivity tools " How does Convo's long-term memory feature personalize interactions? What are the specific application scenarios?
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