mem0ai/mem0

Mem0 is a memory layer for AI assistants and agents. The core problem it solves is that most AI chatbots and agents start every conversation with a blank slate, forgetting everything from previous interactions. Mem0 provides a system that stores facts learned during conversations and retrieves the relevant ones when needed, allowing an AI to remember user preferences, past decisions, and interaction history across multiple sessions. The system works by extracting facts from conversations using a language model, storing them in a vector database (which allows searching by meaning rather than exact keywords), and then retrieving relevant memories using a combination of semantic search, keyword matching, and entity linking when a new question comes in. Memories are organized at multiple levels: per-user, per-session, or per-agent. A recent update introduced a new algorithm that improved accuracy substantially on standard benchmarks, using single-pass extraction and entity linking to boost retrieval quality while keeping latency low. You would use Mem0 if you are building a customer support chatbot that should remember a user's history, a personal AI assistant that learns your preferences over time, a healthcare application that tracks patient details, or any AI system where continuity across conversations matters. It is available as a Python or JavaScript library, as a self-hosted Docker server with a web dashboard, or as a managed cloud service. The Python library installs via pip, and the project also provides CLI tools and integrations with AI coding assistants. The core tech stack uses Python, a vector database for memory storage, and supports multiple LLM providers for extraction and retrieval.