Memory Types in LangChain

Ever felt like your LLM needs a memory? LangChain felt the same thing. From full chat transcripts to summaries, entities, and vector backed recall, it gives you several ways to make a stateless model feel like it actually remembers what matters. Large Language Models are inherently stateless. Every request you send arrives as a blank slate with no recollection of what was discussed five minutes ago. To create a coherent conversation, the system must manually feed previous messages back into the model. LangChain provides several distinct patterns for managing this history. Choosing the right one is a balance between providing perfect context and managing the cost of every token. LangChain Memory Types Use the Transcript Pattern for quick, high precision support tasks. Use the Window Pattern for predictable, task oriented interactions. Use the Summary Pattern for long, creative, or collaborative sessions. Use the Entity Pattern for personal assistants that track user preferences. Use the