agent-memory-systems

Agent memory is built using structured short-term and long-term memory layers. Short-term memory manages active context windows, while long-term memory uses vector stores and retrieval strategies to retain knowledge across sessions.

Long-term memory retrieval is implemented by chunking data, generating embeddings, and storing them in a vector store. This enables scalable retrieval architectures so agents can accurately recall relevant past interactions.

Chunking splits data into structured segments before generating embeddings for the vector store. In multi-agent workflows, this modular memory architecture ensures agents retrieve specific historical context without exceeding active limits.

Yes, single-agent systems use both layers simultaneously. Short-term memory manages the active planning context, while long-term memory retrieves past decisions to reconcile with new data during execution.

A vector store is required for long-term persistent memory. It indexes embeddings created from chunked data, allowing the agent to perform scalable retrieval of historical context accurately across multiple sessions.

Agents lose context when lacking structured memory management across interactions. Without short-term window management and long-term vector store retrieval, past decisions are lost and cannot be reconciled with new data.