- [LLM] line per call: stage, model, prompt size, output size, cost, parsed item counts
- LLM_RAW_DUMP: full prompt/response JSON per call under outputs/<job>/llm_raw/
- Cost block at tail of job.log (per-stage, per-model, cached vs live)
- Agent mode: reset llm cost counters per job; review finalization now teed into job.log + dumps
- llm.py: set_model_overrides(vision, text) replaces the single job override;
UI picks still beat per-call agent model args, but never name the hybrid
local model (avoids main's hybrid footgun); local->cloud fallback uses the
text pick.
- jobs.py: timestamped line-split tee (job_log.py), in-memory log + log_tail
polls, full log on terminal states (done/error/needs_review/finalization_error),
log-only disk recovery, error email links to the run log, and failed runs now
append the full traceback to job.log. Keeps pipeline_mode, job.json, and the
review gate.
- models.py: vision/text split via architecture modalities, pricing kept;
/models returns {vision, text, defaults}; /check takes vision_model/text_model
(replacing model); /health adds text_model. models_catalog.py dropped.
- UI: two priced dropdowns (OpenRouter compute only) + live run-log panel.
- Tests updated for dual overrides and the /models shape; new coverage for
traceback capture and local-model immunity.
Capture pipeline stdout into job.log + API/UI so failed runs can be reviewed, and let users pick OpenRouter vision vs text models independently.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Job logs: each job's stdout/stderr is teed into outputs/<id>/job.log
(survives restarts) and served at GET /jobs/{id}/log as text/plain, so
full run logs can be shared for debugging and refinement.
- Model picker: GET /models proxies OpenRouter's public model list with
per-1M-token pricing (1h cache, 502 on failure); the UI shows a model
dropdown with costs when OpenRouter compute is selected, and the pick
overrides vision+text models for that job (Classic and Agent modes).
- Conflicts in the report view are grouped by discipline pair
(collapsible sections, severity-ordered within groups) instead of one
flat severity-only list.
APP_BASE_URL default (config, .env.example, both compose files) is now
https://conchecker.scoutitsystems.com with no port, so review-required
and final-report email links use the public site. CI bakes the short
commit SHA into the image as APP_BUILD via a Docker build-arg; /health
returns version+build and the site header shows the build so it's easy
to confirm which image is deployed. Local runs default to 'dev'.
Agent web jobs now stop after Brain consolidation and enter needs_review
with a persisted review queue (blocking: high-severity, low-confidence,
sensitive-category findings; audit sample of clean clusters). Humans
decide confirm/reject/unsure/needs_clarification via new review API and
frontend queue; a finalizer applies decisions (rejections suppressed with
reason codes), performs bounded targeted reruns for clarifications,
drafts RFIs only for kept issues, and only then marks the job done and
sends the final email. Two-phase email (review-required, then final
report), per-decision feedback labels with redacted aggregate metrics,
restart recovery from job artifacts, and CLI --no-review bypass.
Classic pipeline unchanged. 65 non-LLM tests.
Publish agent-mode pushes as :agent-mode (and :sha-<commit>) so the experimental fork is available from the registry without overwriting the Classic :latest image.
Co-authored-by: Cursor <cursoragent@cursor.com>
Wire specialist waves, Brain consolidation, and Classic-compatible reports so Agent mode can run end-to-end via OpenRouter without changing the default Classic path.
Co-authored-by: Cursor <cursoragent@cursor.com>
Containerize the app for local and production deploys, and publish images
to the Gitea container registry on main pushes and version tags.
Co-authored-by: Cursor <cursoragent@cursor.com>
Cross-discipline design-contradiction checker for construction drawing
sets. Standalone tool broken out from Iron_Bid; a pipeline stage may
later fold back into Iron_Bid.
Pipeline: PDF->images -> per-sheet assertion extraction -> deterministic
clustering by location -> per-cluster reasoning -> report.
Includes CLI (cli/run_check.py) and web UI (backend/main.py).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
woogi
