what is mind_loops_lab fr?

Agent Loop Runtime is a small, local first tool that gives coding agents and other AI powered apps a cheap way to double check themselves before doing something risky. The idea behind it is narrow and practical: right before an AI agent is about to take an action or state something with confidence, it can run a quick check that either lets it continue, tells it to flag uncertainty, tells it to ask a human first, or tells it to stop entirely. The project is explicit about what it is not: it is not a cloud service, not a monitor watching an agent from the outside, not a policy engine that judges intent, and not a general purpose lie detector. It only reads the specific text and files it is given. You install it from PyPI with a normal pip install command, or run it directly from a source checkout using Python's module runner to check the install and do a release check. The main way you use it day to day is a single check command where you describe the action the agent is about to take, and the tool responds with one of four signals: PASS to continue as planned, WARN to flag uncertainty or verify a claim first, ASK to check with a human before expanding scope or doing something like a write, external, or private action, and BLOCK to stop. The project frames itself as turning a handful of good habits, like checking authorization before acting and checking evidence before making a strong claim, into small reusable pieces of infrastructure with a consistent, compact signal format that both humans and other agents can read. The stated design goal is boring reliability: small checks, clear boundaries, minimal extra token usage, and no hidden network calls. Documentation includes a quickstart guide, a runtime manual, and a threat model document. The project is directed by one person, built with Codex, and reviewed by Claude as an independent second check. It is released under the MIT license.