Building a Local AI Assistant with Memory, PostgreSQL, and Multi-Model Support Update

Most local AI assistants forget everything once the conversation ends.\ While experimenting with locally hosted LLMs, I wanted to solve that problem by giving my assistant persistent memory . On 16 March 2026 , I worked on improving the architecture and reliability of my local AI assistant project. The main focus was: Adding persistent memory Integrating PostgreSQL Improving project structure Running multiple models locally This article walks through what I built and what I learned. The Problem: Local AI Assistants Have No Memory When you run models locally using tools like Ollama , they respond based only on the current prompt. They don't remember: Your preferences\ Previous conversations\ Important user information To solve this, I implemented a memory system backed by PostgreSQL . Designing a Memory Storage System The idea was simple: If the user explicitly asks the assistant to remember something, the system should store that information. Instead of storing entire conversations, I