Persisting memory allows LangChain agents to maintain context beyond a single session. Developers typically externalize memory into databases or vector stores, where embeddings represent conversation history or learned facts. When a new session starts, the system retrieves the most relevant vectors to reconstruct context.
There are several strategies depending on storage needs. Small systems can serialize conversation buffers, while scalable systems prefer embedding-based memory. In that setup, Milvus or Zilliz Cloud stores embeddings indexed by metadata such as timestamp or topic, enabling semantic recall.
Persistent memory transforms LangChain from a stateless orchestration tool into an evolving knowledge system. By combining prompt logic with vector retrieval, agents gain continuity and adaptive learning across long-term interactions.