Self-policing: audit a call with your agent's own API

A single-file copy of the tool at https://aryafayyazi.github.io/GreyBox/selfpolice.html. Opened from your disk, it can call a model server on your own machine (for example vLLM at http://127.0.0.1:8000/v1, or Ollama at http://127.0.0.1:11434/v1).

1. Audit a call with your agent's own API

Provider Model API key

Calling a model on your own machine (vLLM, Ollama)? Browsers do not let this https page call http://localhost. Use the single-file copy of this tool (open it from your disk), or the Python package (pip install greybox-monitor, graybox.selfaudit).

Question Answers Hold when this share says "no"

The agent's instructions and policy (its system prompt) The conversation so far: OpenAI-format messages (JSON), or plain text

Proposed tool Arguments (JSON)

Remember my key in this browser

Your key is used only for calls from this tab to the provider you chose, and is not stored unless you tick the box. Anthropic calls use the header for direct browser access. The prompts are the ones used in our preregistered tests, verbatim (graybox.selfaudit in Python). "Check each rule in turn" also checks the user's task constraints and takes about ten seconds per answer; "plain" takes about a second.

4. Predict the effect before you deploy

For a gate that holds a call when the agent's own audit flags it, the share of violations prevented is about Σ_k w_k τ_k: for each kind of violation k, its share w_k of your agent's violations times the share τ_k of such calls that the audit flags. Measure τ_k by auditing some of your agent's known violations (section 1).

Kind of violation	Share of violations w	Flag rate τ (0–1)

Shares w are normalized, so counts work too.

Predicted share of violations prevented: –