How to build a retrieval system for agents

Memelord is a local memory system for coding agents designed to address the issue of statelessness in AI coding sessions, ensuring agents remember project-specific details across sessions. Built with Turso and utilizing SQLite databases, Memelord allows for efficient retrieval of relevant memories using vector similarity search at the start of each task. This system not only enhances the agents' ability to recall useful information but also adapts over time by adjusting the weight of memories based on their utility, demoting irrelevant ones and promoting consistently helpful ones. Turso, a rewrite of SQLite, supports native vector search, enabling Memelord to perform semantic retrieval effectively without external dependencies. Memelord is easy to set up and integrate, offering SDK support for those developing custom agents, and it emphasizes the importance of persistent context in improving coding agents' performance beyond just model enhancements.