agent-memory-systems

Build agent memory retrieval by implementing short-term, episodic, and long-term stores using vector stores and metadata filters. This ensures stateful agent responses by retrieving relevant past interactions and documents to maintain coherent behavior across sessions.

Rank retrieved memory chunks by applying temporal scoring alongside vector similarity. This approach prioritizes recent and relevant contextual information, enabling coherent, stateful agent responses without re-asking users for background.

Temporal scoring improves vector store memory retrieval by factoring recency into relevance rankings. This prevents outdated information from overshadowing recent interactions, ensuring agents retrieve the most contextually appropriate memories for stateful responses.

Use different chunking strategies based on whether you need short-term context window data, episodic logs, or long-term semantic knowledge. Proper chunking manages embedding quality and ensures the agent retrieves coherent, stateful information from vector stores.

Yes, you can use metadata filtering alongside vector store queries to detect conflicts in agent memory retrieval. This ensures the agent retrieves the most accurate contextual information and avoids contradictory past interactions.

Agents forget previous interactions in document-backed workflows due to memory retrieval errors. Implementing retrieval-first memory systems with proper embedding quality tracking and vector store selection helps agents find and prioritize the right past facts.