A minimal AI code-review bot for Gitea, running as a CI step on the existing act-runner. This is the pilot — a small, self-contained reviewer that predates the full pragent framework (whose design lives in docs/plans/2026-08-04-pragent-design.md). The framework will later absorb this; until then, this is what runs.

How it works

You add pragent-bot to a repo and commit .gitea/workflows/ai-review.yml.
On a PR, you add the AI-REVIEW label.
Gitea Actions runs the workflow on the act-runner; it fetches the PR diff, asks glm-5.2:cloud (on-network via the headroom proxy) to review it, and posts the findings back as a PR review authored by pragent-bot.
Remove the label to stop re-reviews on further pushes.

Fail-open: the job always exits 0 and never blocks CI. Errors become a short "review failed" comment.

Onboard a repo (3 steps)

1. Add `pragent-bot` as collaborator

Repo → Settings → Collaborators → Add → pragent-bot → permission Write. (Write is required to post reviews/comments.)

Or via API (with an admin/owner token):

curl -X PUT -H "Authorization: token $OWNER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"permission":"write"}' \
  "http://<gitea-host>:3000/api/v1/repos/OWNER/REPO/collaborators/pragent-bot"

2. Add the `PRAGENT_BOT_TOKEN` secret

Repo → Settings → Actions → Secrets → New secret → name PRAGENT_BOT_TOKEN, value = the bot's access token (ask the platform admin; stored mode-600 at ~/.claude/.pragent-bot-token on the admin host).

3. Commit the workflow

Copy pilot/workflow-template.yml into the target repo as .gitea/workflows/ai-review.yml and commit it. That's it.

Use it

Open a PR (or push to an open one), add the AI-REVIEW label. The review appears within ~30–90s depending on diff size and model latency.

What's intentionally NOT in the pilot

Deferred to the full framework (by design, see the design doc):

Attention tiering (trivial/lite/full/oversized) and per-tier cost control.
Multiple analyzer fan-out over a shared cached prompt prefix.
Prior-comment synthesis (so each push re-posts; the latest review is tagged with the head SHA so it's easy to spot).
Inline line comments and status checks.
pragent explain / replay / analytics JSONL.
A second forge (GitLab) and the provider matrix.

Pieces

File	Role
`pilot/ai_review.py`	The reviewer script (stdlib only). Single source of truth — fetched at runtime by each repo's workflow.
`pilot/workflow-template.yml`	The Gitea Action consumers copy into `.gitea/workflows/ai-review.yml`.
`tests/pilot/test_ai_review.py`	Unit tests for the pure helpers (no network).

Run the tests

cd ~/Projects/pragent
PYTHONPATH=pilot python3 -m pytest tests/pilot/    # if pytest available
# or, without pytest:
python3 - <<'PY'
import os, sys, importlib.util
sys.path.insert(0, os.path.abspath("pilot"))
import ai_review  # noqa: F401
spec = importlib.util.spec_from_file_location("t", "tests/pilot/test_ai_review.py")
m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m)
fails = 0
for n in sorted(x for x in dir(m) if x.startswith("test_")):
    try: getattr(m, n)(); print("PASS", n)
    except Exception as e: fails += 1; print("FAIL", n, e)
print("failed:", fails)
PY

Configuration knobs (env in the workflow)

Env	Default	Purpose
`OLLAMA_MODEL`	`glm-5.2:cloud`	Model id passed to the headroom proxy.
`OLLAMA_MAX_TOKENS`	`6000`	Output token cap.
`DIFF_MAX_CHARS`	`150000`	Diff truncation cap (with a noted truncation marker).
`OLLAMA_URL`	`http://<model-proxy-host>:8789`	headroom proxy (tailnet). If the act-runner can't reach the tailnet IP, expose 8789 as an in-cluster Service+Endpoints and set this to the cluster DNS name.

README.md Unescape Escape

pragent pilot — AI Review bot