loop-verify

*Have a different AI grade the work your AI just did.*

loop-verify is an independent checker for the self-verification loop — the part the loop-kit loop honestly admits it cannot do.

When an AI checks its own work, it shares its own blind spots. Free loop-kit checks Claude's work with Claude (same family → the same misses slip through). loop-verify hands the grading to a different model lineage (codex / GPT / Gemini), so defects the self-check waves through get caught. The verdict format is identical to loop-kit's validator, so it's a drop-in replacement for the same-family check.

Open source (MIT). Just a tool — no accounts, no metering, no billing.

What it does

• In: your frozen YES/NO acceptance criteria + the artifact to inspect (a diff or

file contents).

• Out: a verdict — PASS/FAIL, each criterion OK/NG, any defects outside the

criteria, and concrete fix_instructions. (Same contract as loop-kit's validator.)

• The point: the grader is a different model family from whoever wrote the work, so

it doesn't share their blind spots. That independence is the whole value — and it's measurable (see the edge bench).

Three ways to use it: as an MCP server, as a Python function, or via the edge bench.

Install

python3 -m venv ~/.venvs/loop-verify
~/.venvs/loop-verify/bin/pip install -r requirements.txt

Demo (one command, runs anywhere)

python demo/run_demo.py                  # deterministic, offline (mock backend)
python demo/run_demo.py --backend codex  # the REAL edge (costs codex quota)

Exit code 0 iff the demo's invariants held, so it doubles as a smoke test. With --backend codex it shows the independent checker catching planted defects a naive same-family check misses.

Run as an MCP server

# local (stdio), codex backend:
LOOP_VERIFY_BACKEND=codex ~/.venvs/loop-verify/bin/python -m loop_verify.server

# HTTP transport (binds 127.0.0.1:8000 by default; localhost-only Host check):
LOOP_VERIFY_BACKEND=codex ~/.venvs/loop-verify/bin/python -m loop_verify.server --transport http

# ...to serve other hosts, bind all interfaces and allow their Host header:
LOOP_VERIFY_HOST=0.0.0.0 LOOP_VERIFY_PORT=8000 LOOP_VERIFY_ALLOWED_HOSTS=myhost:8000 \
LOOP_VERIFY_BACKEND=codex ~/.venvs/loop-verify/bin/python -m loop_verify.server --transport http
# (LOOP_VERIFY_ALLOWED_HOSTS="*" disables the Host check; binding 0.0.0.0 alone also
#  opens it. Host/port are read at startup — set them via env, not flags.)

# OpenAI backend (needs OPENAI_API_KEY + `pip install openai`):
OPENAI_API_KEY=... LOOP_VERIFY_BACKEND=openai \
  ~/.venvs/loop-verify/bin/python -m loop_verify.server

# Gemini backend (needs GEMINI_API_KEY + `pip install google-genai`):
GEMINI_API_KEY=... LOOP_VERIFY_BACKEND=gemini \
  ~/.venvs/loop-verify/bin/python -m loop_verify.server

Tools: independent_verify(criteria, artifact) and info(). Backend selected by LOOP_VERIFY_BACKEND (codex default | openai | gemini | mock). For http, bind with LOOP_VERIFY_HOST / LOOP_VERIFY_PORT (read at startup).

Verify the http transport is reachable end to end (boots a server, runs a real MCP client round-trip, no key needed):

python demo/http_smoke.py

Deploy (Docker)

The codex backend needs the codex CLI (not in the image), so a container uses a key-based backend:

docker build -t loop-verify .
docker run --rm -p 8000:8000 \
  -e LOOP_VERIFY_BACKEND=openai -e OPENAI_API_KEY=sk-... \
  loop-verify
# MCP endpoint: http://localhost:8000/mcp

The image binds 0.0.0.0, so FastMCP's DNS-rebinding Host check is off by default (the container accepts any Host header). To restrict it, add -e LOOP_VERIFY_ALLOWED_HOSTS=myhost:8000.

Use it from Python

from loop_verify.service import run_independent_verify

result = run_independent_verify(criteria, artifact, backend="codex")
# -> {"verdict": "PASS"|"FAIL", "passed": bool, "criteria": [...],
#     "defects_outside": [...], "fix_instructions": str, "checker": ..., "lineage": ...}

Does independence actually help? (the edge bench)

python bench/edge_bench.py --backend codex   # independent checker -> GO/NO-GO
python bench/edge_bench.py --backend mock     # naive/blind baseline -> typically NO-GO

The gap between an independent checker (catches planted defects) and a naive one (misses them) is the whole reason to use this. Exit code = the edge verdict, so it can gate CI.

Measured on the bundled 9 fixtures (4 clean / 5 buggy, diverse bug classes): the codex backend scored recall 1.0, false-positive 0.0 → GO (every real bug flagged, every clean artifact passed), while the naive same-family baseline misses them → NO-GO.

Honest limits

• codex backend cost: the codex backend runs on the operator's personal ChatGPT

Plus quota — fine for personal/local use, not for serving many users. Use the OpenAI backend with your own key for that.

• Independent ≠ ground truth: a different lineage reduces shared blind spots; it

does not eliminate error.

• The edge is the point: if the bench ever shows the independent checker ≈ a naive

one, there is no reason to use it — that is a NO-GO, reported honestly, not buried.