memory-systems

Persistent agent memory is designed using layered architectures with working, short-term, long-term, and entity/temporal graphs. This enables cross-session continuity and reasoning over accumulated knowledge, supporting planning and multi-agent coordination with hybrid retrieval strategies.

The best way to structure agent memory retrieval is through hybrid architectures combining vector stores and knowledge graphs. This supports robust reasoning by implementing consolidation, invalidation, and retrieval-quality guardrails alongside temporal knowledge management.

Yes, you can prototype filesystem memory for simple, transparent persistence and quick validation. This approach enables basic cross-session recall before scaling to more complex vector-backed stores or graph memories depending on your requirements.

Yes, numpy is required as a dependency to implement cross-session memory systems. It supports the numerical operations underlying hybrid retrieval, consolidation logic, and retrieval-quality guardrails across working, short-term, and long-term memory layers.

Temporal knowledge in agent memory handles consolidation and invalidation through structured entity and temporal graphs. These track knowledge evolution over time, ensuring retrieval quality by invalidating stale facts and consolidating redundant information across sessions.

Filesystem memory offers simple, transparent persistence suited for quick validation but lacks the robust retrieval capabilities of vector-backed stores. Vector stores and graph memories better support hybrid retrieval, large-scale reasoning, and complex multi-agent coordination across extended sessions.