This article provides a comprehensive guide to the complete deployment process of OpenMemory MCP, covering project cloning, configuration file modifications (including docker-compose.yml and .env), database setup (PostgreSQL), embedding model selection (supporting OpenAI, Gemini, Ollama), and performance tier configuration (FAST/SMART/DEEP). The author shares practical deployment experience, addressing common issues such as Valkey activation failures and missing user_id, and successfully implements long-term memory functionality for large models. This technical note offers AI developers practical details on performance optimization, API key management, and frontend-backend connection debugging, serving as a valuable reference for enhancing memory capabilities in AI applications.
