hierarchical-memory

Hierarchical memory management structures AI agent context into short-term, long-term, and episodic layers. A Memory Router fetches information from the most relevant layer, ensuring efficient recall and coherent responses over extended conversations.

The best way to store long-term memory and episodic events is using a multi-layered system that automatically consolidates important session details into persistent semantic and event-based memory stores for later retrieval.

Yes, you can use Qdrant for persistent long-term memory. This Skill integrates directly with Qdrant to manage semantic memory stores, enabling efficient vector-based retrieval of consolidated information for your AI agent.

You can consolidate short-term session context into long-term memory automatically through the Skill's built-in consolidation feature, which seamlessly transfers important information from session-based stores to persistent semantic storage.

Yes, this AI agent memory system integrates with claude-mem and taisun-proxy. These integrations facilitate robust memory operations, allowing the hierarchical structure to efficiently manage and recall context across different layers.

You should use episodic memory for specific event-based details and long-term semantic memory for persistent learned patterns. The Memory Router automatically determines which layer is most relevant for the current context retrieval task.