In our research paper we present the approach that is aimed at effectively expanding the context through integrating a database of associative memory into the pipeline. In order to improve long-term memory and personalization we have utilized methods close to Retrieval-Augmented Generation (RAG). Our method uses a multi-agent pipeline with a cold-start agent for initial interactions, a fact extraction agent to process user inputs, an associative memory agent for storing and retrieving context, and a generation agent for replying to user’s queries.Evaluation results show promising results: a 41% accuracy improvement over the base Gemma3 model (from 16% to 57%). Hence, with our approach, we demonstrate that personalized chatbots can bypass LLM memory limitations while increasing information reliability under the conditions of limited context and memory.