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.