LLMs are stateless by default, forgetting past conversations. This guide demonstrates how to add long-term memory to a Gemini 2.5 chatbot using the Gemini API and Mem0, an open-source memory layer for AI agents. Mem0 extracts salient information from conversations, stores it as vector embeddings in Qdrant, and retrieves relevant memories to personalize future responses. The tutorial covers configuring Gemini 2.5 Flash as the LLM, text-embedding-004 as the embedding model, and building an interactive chatbot that persists context across sessions.
