memory-systems

To persist agent memory across sessions, you can configure an integrated memory system using file-system storage, vector RAG with metadata, and knowledge graphs to maintain state and retrieve accumulated knowledge.

A temporal knowledge graph structures facts and relationships with temporal validity, enabling time-aware queries and reasoning over historical data to support robust recall for agents.

You track entities across conversations by implementing entity memory layers that preserve identity and relationships, ensuring the agent maintains context without losing prior interaction details.

Yes, you can use numpy alongside file-system memory and vector RAG with metadata to build scalable memory integration patterns that persist and retrieve agent knowledge efficiently.

You prevent uncontrolled memory growth through memory consolidation, which performs periodic cleanup and pruning to remove redundant data while maintaining useful historical context.

Reasoning over accumulated knowledge requires multiple memory layers: working memory, short-term memory, long-term memory, and temporal knowledge graphs to support robust recall.