I built an open-source "approval gate" for AI agents — here's why

AI agents are increasingly given real permissions — sending payments, emails, deleting files — often with little to no governance layer in between. I wanted a small, self-hosted tool that sits in front of any agent and decides whether a proposed action should go through, get blocked, or wait for a human to approve it.

Picture this: an AI agent in your n8n workflow proposes a $100,000 payment. Without anything in the way, the workflow might just submit it. Most "agent security" tools today either work at the network level (VPN tunneling, protocol parsing — powerful, but heavy to set up) or don't exist at all for smaller, workflow-level automations.

I wanted something in between: simple enough to self-host in minutes, specific enough to actually stop a bad action before it happens.

InGravity evaluates an action against rules you configure, and returns a decision — before the action runs.

\ bash curl -X POST http://localhost:8080/check-action \ -H 'Content-Type: application/json' \ -d '{"action_type":"payment","amount":100000,"metadata":{"order_id":"order-123"}}' \ \

Response: blocked\, risk score 100\. The caller decides what to do next — InGravity doesn't execute, cancel, or intercept anything itself; it just gives you a clear, auditable answer.

I'd rather tell you this upfront than have you discover it the hard way.

I want to prove this is actually useful before thinking about anything commercial. If you're building with AI agents and this solves a real problem for you, I'd genuinely like to know — and if it doesn't, I'd like to know that too.

GitHub: https://github.com/Tchesseludovic17-cloud/ingravity