LLM applications often require maintaining context over extended interactions, exceeding the capabilities of simple memory buffers. Effectively managing conversational history is essential for building coherent and stateful applications.
This chapter provides techniques for implementing advanced memory management in LangChain. You will learn to compare and select appropriate memory types, such as VectorStore-backed or Entity memory, configure persistent storage for long-term recall, and apply strategies for managing limited context windows. We will also cover creating custom memory modules tailored to specific needs and integrating these memory systems with complex chains and agents, addressing challenges in asynchronous environments. By the end of this chapter, you will be equipped to handle sophisticated context requirements in your LangChain projects.
3.1 Comparing Advanced Memory Types
3.2 Implementing Persistent Memory Stores
3.3 Context Window Management Strategies
3.4 Custom Memory Module Development
3.5 Integrating Memory with Agents and Chains
3.6 Handling Memory in Asynchronous Applications
3.7 Hands-on Practical: Implementing Vector Store Memory