Player not loading? Watch on YouTube
Mem0 adds persistent memory between an application and its language model. This tutorial explains how it extracts facts from conversations, retrieves relevant records and inserts them into later prompts. The paper's two-phase design asks the model to add, update, delete or leave a memory unchanged. Graph memory stores entities and relationships for questions about connected facts and timelines.
The local setup uses Ollama, BAAI/bge-small-en-v1.5 embeddings on CUDA and Qdrant, with an RTX 4090 demonstration. The presenter reports that this stack runs offline without cloud API calls. Installation guidance includes checking GPU visibility and installing the CUDA build of Torch first. A Gradio app pairs chat with a memory browser and shows which retrieved memories informed each answer.
The Python walkthrough covers memory creation, search filters, record history and AsyncMemory. One concrete API warning: the demonstrated search interface expects user_id inside filters; passing it at the top level raises an error. user_id, agent_id and run_id separate personal, assistant and session memory.
The presenter attributes 91% lower p95 latency and over 90% token savings versus full conversation context to the paper's benchmark, rather than to every deployment. The video also describes an April 2026 pipeline revision and compares memory retrieval with full context and RAG.
