When an agent processes information, it needs somewhere to put it - somewhere it can search, reorganize, and grow organically. For any type of knowledge base like agent memories, product taxonomies, etc the challenge was enabling recursive interaction without a pile of specialized tools (search, create, delete, etc.) that are added to context.

Recursive Language Models (RLMs) introduced the idea of putting the entire context into a Python variable and let the model recursively interact with it, instead of reasoning over everything in one shot. RLMs: https://alexzhang13.github.io/blog/2025/rlm/. I really liked it, but enabing a python REPL seemed like a bad tradeoff for generality.

Loopy imposes a known structure (a tree / filesystem), and replaces the REPL with composable Bash-style commands.

Why this approach:

- simple - a single string can represent the full data - known structure - stored in a file system format agents already know and love - composition - compose search commands to quickly navigate the data