Revolutionizing AI Memory: Meet HAM (Hierarchical Adaptive Memory)
Most AI agents struggle with memory overload—every interaction becomes a repetitive burden. Enter HAM, designed to mimic human memory by retrieving only what’s relevant, fostering efficiency and cost-effectiveness.
Key Features:
- Memory Structure: Four sophisticated tiers:
- L0: 8 tokens for topic context
- L1: 35 tokens for key facts
- L2: 150 tokens for summaries
- L3: 500+ tokens for deep-dive details
- Efficiency Gains:
- Reduced memory costs by 82.3% compared to naive methods.
- Learning Mechanics: If the agent encounters an unknown question, it learns and retains valuable information for future queries.
Explore how HAM addresses unique AI challenges and contributes to smarter tech.
🔍 Join the conversation! Check it out on GitHub: agent-os and share your thoughts on improving AI memory!
