Building a custom LLM memory layer from scratch involves creating a system that enables persistent, context-aware conversations by storing and retrieving user-specific information across sessions. Since LLMs are stateless by design, they don’t retain memory between interactions unless explicitly programmed to do so. This article walks through building a memory system inspired by the Mem0 architecture, focusing on four core components: extraction, embedding, retrieval, and maintenance. The process begins with memory extraction, where raw conversation transcripts are transformed into atomic, structured factoids. Using DSPy, a signature is defined to guide the LLM in identifying key user facts—such as preferences, habits, or self-descriptions—from the message history. The model outputs a list of concise, standalone statements like "User likes tea" or "User dislikes coffee." These factoids serve as the building blocks of the memory store. Next, the extracted factoids are embedded into numerical vectors using a model like text-embedding-3-small with a fixed dimension of 64. This ensures efficient storage and fast similarity search. The embeddings are stored in a vector database such as QDrant, which supports filtering by metadata—like user_id—allowing per-user memory isolation and fast retrieval. For retrieval, the system uses a ReAct (Reasoning and Acting) agent powered by DSPy. At each turn, the agent evaluates whether the current conversation requires context from past interactions. If so, it generates a query based on the latest message and searches the vector database for similar memories. The agent can call a tool to fetch relevant memories only when needed, avoiding unnecessary overhead. The retrieved memories are then injected into the prompt to inform the response. The maintenance phase ensures the memory remains accurate and up-to-date. When the agent decides to save a new memory, a secondary ReAct agent evaluates how to update the database. It can add, update, delete, or ignore the new information based on consistency with existing facts. For example, if a user previously said they liked tea but later says they hate it, the system detects the contradiction and replaces the old memory instead of storing both. This approach turns memory into a dynamic, self-correcting component of the AI system. It leverages core context engineering techniques such as structured extraction, summarization, vector similarity search, and agentic decision-making—all essential skills for advanced LLM applications. The system is fully modular and extensible. Future enhancements include graph-based memory for relational data, metadata tagging for semantic filtering, file-based storage for simpler setups, or personalized system prompts that evolve with the user. By building memory from the ground up, developers gain deep insight into how context shapes LLM behavior and learn how to create more personalized, intelligent, and adaptive AI experiences.