doloopio 0.1.0

The doloop resistance machine: deterministic checks for AI output, from the CLI.

doloop machine

An external, deterministic check you put between your AI and your users. Send it an output, get back an objective verdict — the same verdict every time. You bring your own model; the machine never touches it.

Docs only. No product code lives here — just how to call the machine and copy-paste examples.

Base URL: https://api.doloop.io

Use doloop when…

Your problem	Call	What you get
Your agent loops or repeats itself	POST /v1/check	findings flag the repetition; verdict: fail (a model in a loop can't see its own loop — doloop can)
Output drifts or slides back to a bug it just fixed	POST /v1/check	the drift caught from outside
The model pads with slop, hedges, jargon	POST /v1/check	each tell flagged with a line
A number isn't in the source, a total won't reconcile	documents donkey	tie-out failure
You need the same verdict every time (audit)	POST /v1/check	input_sha256 — reproducible, provable
You want a human to review only the failures	gate on verdict	triage, not every line

The four donkeys

A donkey is a deterministic check for one kind of output.

Donkey	Catches	Surface
writing	de-slop: dead prose, jargon, self-management tics, flat cadence	https://writing.doloop.io
conversations	de-sycophant, de-loop: agreement drift, dialogue going in circles	https://conversations.doloop.io
presentations	land-the-finding: slides that bury the point or don't match the data	https://presentations.doloop.io
documents	tie-out: a number not in the source, a total that won't reconcile	https://documents.doloop.io

curl https://api.doloop.io/v1/donkeys

Quickstart

curl https://api.doloop.io/v1/check \
  -H 'content-type: application/json' \
  -d '{"text": "the answer your model just produced"}'

{ "verdict": "pass", "finding_count": 0, "findings": [], "input_sha256": "..." }

Same text in, same verdict out, every time. input_sha256 proves it: identical input gives an identical hash gives an identical verdict. Objective, not subjective — unlike an AI judge that scores the same answer 77% one run and 63% the next.

How people use it

1. Gate before ship — check the output; if verdict: fail, don't ship it.
2. Self-heal loop (the "doloop") — check → on fail, feed findings back to your model → re-check → ship on pass. See examples/loop.py.
3. In-agent self-check — the agent checks its own step output to catch the loop/drift it can't see itself. See examples/agent_check.py.
4. BYOL proxy — point an OpenAI-compatible base_url at the machine; the verdict rides back on every response. See examples/proxy.py.
5. CI gate — run a corpus through the donkeys; fail the build on a regression. See examples/ci_check.sh.

Endpoints

Method	Path	Does
POST	/v1/check	deterministic verdict on text
GET	/v1/donkeys	list the four donkeys
POST	/v1/chat/completions	OpenAI-compatible BYOL proxy with the verdict attached
GET	/health	liveness

Machine-readable spec: openapi.json. LLM index: llms.txt. Pricing and the loop calculator: PRICING.md.

What it is

doloop is the Deterministic Objective Loop: the external checker a model can't be for itself, because a model in a loop can't see its own loop. Bring your model; doloop returns the verdict it can't give itself.