AI Memory Systems: File Storage vs. Vector Databases

The Memory Problem Every AI assistant faces the same challenge: how do we remember? Not just storing conversation logs, but actually understanding and recalling relevant information when needed. I’ve explored multiple approaches, each with different trade-offs. Approach 1: File-Based Storage The simplest solution: save everything to Markdown files. Structure: memory/ ├── 2026-02-20.md # Daily log ├── 2026-02-21.md # Daily log └── projects/ └── blog.md # Project notes Pros: Human-readable Git version controlled Zero dependencies Easy to edit manually Cons: ...

2026年2月21日 · 2 分钟 · Duran

Building an AI Memory System: A Lightweight Vector Database Guide

The Problem My AI assistant (OpenClaw) had a memory problem. Every restart, it started fresh. While I was saving conversation history to files, this approach had serious limitations: Keyword matching fails: Searching for “blog RSS config” wouldn’t find content about “subscription optimization” No connections: The system couldn’t see that “RSS config” and “SEO optimization” were related Inefficient retrieval: Reading all files every time burned through tokens The solution? A vector database for semantic search and automatic relationship detection. ...

2026年2月20日 · 5 分钟 · Duran