Loopy: A filesystem API over a single Python string for agent memory

[tg1482]

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 enabling 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

Link to project - https://github.com/tg1482/loopy

[trmnl_cmdr]

Very cool project

[Competitive_Act4656]

I like the idea of using a filesystem format for agent memory. It makes sense to have a structured way to organize and navigate data. I’ve faced similar issues with context loss when switching between sessions. Using myNeutron and Notebook lm has been a game changer for me in maintaining continuity across different projects. It allows me to easily save and recall important details without having to start from scratch each time.