Loop Library turns AI agent work into repeatable steps
Loop Library is an of s for AI agents. Each loop tells an agent what goal to work toward, how to check whether the latest attempt worked, what to try next, and when to stop or ask a person for help. Instead of giving a broad instruction such as making a website faster, a loop can guide the agent to find the slowest page, make one focused improvement, measure again, keep only changes that help, and repeat until the target is met or further attempts stop helping.
This is meant for work where the first answer is unlikely to be final, such as fixing production errors, improving tests, reviewing a product, or keeping current. The library can be installed for Codex, Cursor, and Claude Code. It can help find a published loop, review and repair an existing loop, adapt a loop to specific tools and limits, or design a new one through a short plain-language exchange.
Choosing a loop does not automatically deploy code, delete data, send messages, or start schedules; those actions still need normal and approval.
Key points
- Loop Library provides s for AI agents.
- Each loop defines the goal, success check, next step, and stopping point.
- It supports Codex, Cursor, and Claude Code.
- It can find, audit, adapt, or help workflows.
- It may reduce wasted agent work, but it is not a direct token cost tracker.