feat(platform): add new services, tools, tests and crews modules

New router intelligence modules (26 files): alert_ingest/store, audit_store,
architecture_pressure, backlog_generator/store, cost_analyzer, data_governance,
dependency_scanner, drift_analyzer, incident_* (5 files), llm_enrichment,
platform_priority_digest, provider_budget, release_check_runner, risk_* (6 files),
signature_state_store, sofiia_auto_router, tool_governance

New services:
- sofiia-console: Dockerfile, adapters/, monitor/nodes/ops/voice modules, launchd, react static
- memory-service: integration_endpoints, integrations, voice_endpoints, static UI
- aurora-service: full app suite (analysis, job_store, orchestrator, reporting, schemas, subagents)
- sofiia-supervisor: new supervisor service
- aistalk-bridge-lite: Telegram bridge lite
- calendar-service: CalDAV calendar service with reminders
- mlx-stt-service / mlx-tts-service: Apple Silicon speech services
- binance-bot-monitor: market monitor service
- node-worker: STT/TTS memory providers

New tools (9): agent_email, browser_tool, contract_tool, observability_tool,
oncall_tool, pr_reviewer_tool, repo_tool, safe_code_executor, secure_vault

New crews: agromatrix_crew (10 modules: depth_classifier, doc_facts, doc_focus,
farm_state, light_reply, llm_factory, memory_manager, proactivity, reflection_engine,
session_context, style_adapter, telemetry)

Tests: 85+ test files for all new modules
Made-with: Cursor

services/aurora-service/Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+RUN apt-get update \
+  && apt-get install -y --no-install-recommends ffmpeg libgl1 libglib2.0-0 \
+  && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app/ ./app/
+
+EXPOSE 9401
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=5 \
+  CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:9401/health')"
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "9401"]

services/aurora-service/app/__init__.py

@@ -0,0 +1 @@
+"""Aurora media forensics service package."""

services/aurora-service/app/analysis.py

@@ -0,0 +1,417 @@
+from __future__ import annotations
+
+import json
+import math
+import statistics
+import subprocess
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+try:
+    import cv2  # type: ignore[import-untyped]
+except Exception:  # pragma: no cover
+    cv2 = None
+
+
+def _safe_float(value: Any, default: float = 0.0) -> float:
+    try:
+        return float(value)
+    except Exception:
+        return default
+
+
+def _safe_int(value: Any, default: int = 0) -> int:
+    try:
+        return int(float(value))
+    except Exception:
+        return default
+
+
+def _iso_clamp(v: int, lo: int, hi: int) -> int:
+    return max(lo, min(hi, v))
+
+
+def _detect_faces(gray_img) -> List[Dict[str, Any]]:
+    if cv2 is None:
+        return []
+    cascade_path = str(Path(cv2.data.haarcascades) / "haarcascade_frontalface_default.xml")
+    detector = cv2.CascadeClassifier(cascade_path)
+    if detector.empty():
+        return []
+    faces = detector.detectMultiScale(
+        gray_img,
+        scaleFactor=1.1,
+        minNeighbors=4,
+        minSize=(20, 20),
+    )
+    out: List[Dict[str, Any]] = []
+    for (x, y, w, h) in faces:
+        out.append(
+            {
+                "bbox": [int(x), int(y), int(w), int(h)],
+                "confidence": 0.75,
+            }
+        )
+    return out
+
+
+def _detect_plates(gray_img) -> List[Dict[str, Any]]:
+    if cv2 is None:
+        return []
+    cascade_path = str(Path(cv2.data.haarcascades) / "haarcascade_russian_plate_number.xml")
+    if not Path(cascade_path).exists():
+        return []
+    detector = cv2.CascadeClassifier(cascade_path)
+    if detector.empty():
+        return []
+    plates = detector.detectMultiScale(
+        gray_img,
+        scaleFactor=1.1,
+        minNeighbors=3,
+        minSize=(28, 10),
+    )
+    out: List[Dict[str, Any]] = []
+    for (x, y, w, h) in plates:
+        out.append(
+            {
+                "bbox": [int(x), int(y), int(w), int(h)],
+                "confidence": 0.65,
+                "text": None,
+            }
+        )
+    return out
+
+
+def _noise_label(noise_sigma: float) -> str:
+    if noise_sigma >= 28:
+        return "high"
+    if noise_sigma >= 14:
+        return "medium"
+    return "low"
+
+
+def _brightness_label(brightness: float) -> str:
+    if brightness < 75:
+        return "low"
+    if brightness > 180:
+        return "high"
+    return "medium"
+
+
+def _blur_label(laplacian_var: float) -> str:
+    if laplacian_var < 45:
+        return "high"
+    if laplacian_var < 120:
+        return "medium"
+    return "low"
+
+
+def _analyze_quality(gray_img) -> Dict[str, Any]:
+    if cv2 is None:
+        return {
+            "noise_level": "unknown",
+            "brightness": "unknown",
+            "blur_level": "unknown",
+            "brightness_value": None,
+            "noise_sigma": None,
+            "laplacian_var": None,
+        }
+    brightness = float(gray_img.mean())
+    noise_sigma = float(gray_img.std())
+    lap_var = float(cv2.Laplacian(gray_img, cv2.CV_64F).var())
+    return {
+        "noise_level": _noise_label(noise_sigma),
+        "brightness": _brightness_label(brightness),
+        "blur_level": _blur_label(lap_var),
+        "brightness_value": round(brightness, 2),
+        "noise_sigma": round(noise_sigma, 2),
+        "laplacian_var": round(lap_var, 2),
+    }
+
+
+def _aggregate_quality(samples: List[Dict[str, Any]]) -> Dict[str, Any]:
+    if not samples:
+        return {
+            "noise_level": "unknown",
+            "brightness": "unknown",
+            "blur_level": "unknown",
+            "brightness_value": None,
+            "noise_sigma": None,
+            "laplacian_var": None,
+        }
+    brightness_values = [float(s["brightness_value"]) for s in samples if s.get("brightness_value") is not None]
+    noise_values = [float(s["noise_sigma"]) for s in samples if s.get("noise_sigma") is not None]
+    lap_values = [float(s["laplacian_var"]) for s in samples if s.get("laplacian_var") is not None]
+    brightness = statistics.mean(brightness_values) if brightness_values else 0.0
+    noise_sigma = statistics.mean(noise_values) if noise_values else 0.0
+    lap_var = statistics.mean(lap_values) if lap_values else 0.0
+    return {
+        "noise_level": _noise_label(noise_sigma),
+        "brightness": _brightness_label(brightness),
+        "blur_level": _blur_label(lap_var),
+        "brightness_value": round(brightness, 2),
+        "noise_sigma": round(noise_sigma, 2),
+        "laplacian_var": round(lap_var, 2),
+    }
+
+
+def probe_video_metadata(path: Path) -> Dict[str, Any]:
+    cmd = [
+        "ffprobe",
+        "-v",
+        "error",
+        "-select_streams",
+        "v:0",
+        "-show_entries",
+        "stream=width,height,nb_frames,r_frame_rate,duration",
+        "-show_entries",
+        "format=duration",
+        "-of",
+        "json",
+        str(path),
+    ]
+    try:
+        p = subprocess.run(cmd, check=False, capture_output=True, text=True)
+        if p.returncode != 0 or not p.stdout:
+            return {}
+        payload = json.loads(p.stdout)
+    except Exception:
+        return {}
+
+    stream = (payload.get("streams") or [{}])[0] if isinstance(payload, dict) else {}
+    fmt = payload.get("format") or {}
+    width = _safe_int(stream.get("width"))
+    height = _safe_int(stream.get("height"))
+    nb_frames = _safe_int(stream.get("nb_frames"))
+    fps_raw = str(stream.get("r_frame_rate") or "0/1")
+    duration = _safe_float(stream.get("duration")) or _safe_float(fmt.get("duration"))
+    fps = 0.0
+    if "/" in fps_raw:
+        num_s, den_s = fps_raw.split("/", 1)
+        num = _safe_float(num_s)
+        den = _safe_float(den_s, 1.0)
+        if den > 0:
+            fps = num / den
+    elif fps_raw:
+        fps = _safe_float(fps_raw)
+    if nb_frames <= 0 and duration > 0 and fps > 0:
+        nb_frames = int(duration * fps)
+    return {
+        "width": width,
+        "height": height,
+        "fps": round(fps, 3) if fps > 0 else None,
+        "frame_count": nb_frames if nb_frames > 0 else None,
+        "duration_seconds": round(duration, 3) if duration > 0 else None,
+    }
+
+
+def estimate_processing_seconds(
+    *,
+    media_type: str,
+    mode: str,
+    width: int = 0,
+    height: int = 0,
+    frame_count: int = 0,
+) -> Optional[int]:
+    if media_type == "video":
+        if frame_count <= 0:
+            return None
+        megapixels = max(0.15, (max(1, width) * max(1, height)) / 1_000_000.0)
+        per_frame = 0.8 * megapixels if mode == "tactical" else 1.35 * megapixels
+        per_frame = max(0.08, min(9.0, per_frame))
+        overhead = 6 if mode == "tactical" else 12
+        return int(math.ceil(frame_count * per_frame + overhead))
+    if media_type == "photo":
+        megapixels = max(0.15, (max(1, width) * max(1, height)) / 1_000_000.0)
+        base = 3.0 if mode == "tactical" else 6.0
+        return int(math.ceil(base + megapixels * (3.0 if mode == "tactical" else 5.0)))
+    return None
+
+
+def _recommendations(
+    *,
+    faces_count: int,
+    plates_count: int,
+    quality: Dict[str, Any],
+    media_type: str,
+) -> Tuple[List[str], str]:
+    recs: List[str] = []
+    noise_level = quality.get("noise_level")
+    brightness = quality.get("brightness")
+    blur_level = quality.get("blur_level")
+
+    if noise_level == "high":
+        recs.append("Enable denoise (FastDVDnet/SCUNet) before enhancement.")
+    if brightness == "low":
+        recs.append("Apply low-light normalization before super-resolution.")
+    if blur_level in {"medium", "high"}:
+        recs.append("Enable sharpening after upscaling to recover edges.")
+    if faces_count > 0:
+        recs.append("Run face restoration (GFPGAN) as priority stage.")
+    if plates_count > 0:
+        recs.append("Run license-plate ROI enhancement with focused sharpening.")
+    if not recs:
+        recs.append("Balanced enhancement pipeline is sufficient for this media.")
+
+    if faces_count > 0 and faces_count >= plates_count:
+        priority = "faces"
+    elif plates_count > 0:
+        priority = "plates"
+    elif media_type == "photo":
+        priority = "details"
+    else:
+        priority = "balanced"
+    return recs, priority
+
+
+def _suggested_export(media_type: str, quality: Dict[str, Any], width: int, height: int) -> Dict[str, Any]:
+    if media_type == "video":
+        if width >= 3840 or height >= 2160:
+            resolution = "original"
+        elif width >= 1920 or height >= 1080:
+            resolution = "4k"
+        else:
+            resolution = "1080p"
+        codec = "mp4_h264" if quality.get("noise_level") != "high" else "mp4_h265"
+        return {
+            "resolution": resolution,
+            "format": codec,
+            "roi": "auto_faces",
+        }
+    return {
+        "resolution": "original",
+        "format": "png",
+        "roi": "full_frame",
+    }
+
+
+def analyze_photo(path: Path) -> Dict[str, Any]:
+    if cv2 is None:
+        raise RuntimeError("opencv-python-headless is not installed")
+    frame = cv2.imread(str(path), cv2.IMREAD_COLOR)
+    if frame is None:
+        raise RuntimeError("Cannot decode uploaded image")
+    h, w = frame.shape[:2]
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = _detect_faces(gray)
+    plates = _detect_plates(gray)
+    quality = _analyze_quality(gray)
+    recs, priority = _recommendations(
+        faces_count=len(faces),
+        plates_count=len(plates),
+        quality=quality,
+        media_type="photo",
+    )
+    return {
+        "media_type": "photo",
+        "frame_sampled": 1,
+        "resolution": {"width": w, "height": h},
+        "faces": faces,
+        "license_plates": plates,
+        "quality_analysis": quality,
+        "recommendations": recs,
+        "suggested_priority": priority,
+        "suggested_export": _suggested_export("photo", quality, w, h),
+        "estimated_processing_seconds": estimate_processing_seconds(
+            media_type="photo",
+            mode="tactical",
+            width=w,
+            height=h,
+            frame_count=1,
+        ),
+    }
+
+
+def _sample_video_frames(path: Path, max_samples: int = 24) -> Tuple[List[Tuple[int, Any]], Dict[str, Any]]:
+    if cv2 is None:
+        raise RuntimeError("opencv-python-headless is not installed")
+    cap = cv2.VideoCapture(str(path))
+    if not cap.isOpened():
+        raise RuntimeError("Cannot open uploaded video")
+    frame_count = _safe_int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = _safe_float(cap.get(cv2.CAP_PROP_FPS))
+    width = _safe_int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = _safe_int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    indices: List[int] = []
+    if frame_count > 0:
+        sample_count = min(max_samples, frame_count)
+        if sample_count <= 1:
+            indices = [0]
+        else:
+            indices = sorted({int(i * (frame_count - 1) / (sample_count - 1)) for i in range(sample_count)})
+    else:
+        indices = list(range(max_samples))
+
+    sampled: List[Tuple[int, Any]] = []
+    for idx in indices:
+        if frame_count > 0:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+        ok, frame = cap.read()
+        if not ok or frame is None:
+            continue
+        sampled.append((idx, frame))
+
+    cap.release()
+    duration = (frame_count / fps) if (frame_count > 0 and fps > 0) else None
+    meta = {
+        "frame_count": frame_count if frame_count > 0 else None,
+        "fps": round(fps, 3) if fps > 0 else None,
+        "width": width,
+        "height": height,
+        "duration_seconds": round(duration, 3) if duration else None,
+    }
+    return sampled, meta
+
+
+def analyze_video(path: Path) -> Dict[str, Any]:
+    sampled, meta = _sample_video_frames(path, max_samples=24)
+    if not sampled:
+        raise RuntimeError("Cannot sample frames from uploaded video")
+
+    all_faces: List[Dict[str, Any]] = []
+    all_plates: List[Dict[str, Any]] = []
+    quality_samples: List[Dict[str, Any]] = []
+
+    for frame_idx, frame in sampled:
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # type: ignore[union-attr]
+        faces = _detect_faces(gray)
+        plates = _detect_plates(gray)
+        for f in faces:
+            f["frame_index"] = frame_idx
+            all_faces.append(f)
+        for p in plates:
+            p["frame_index"] = frame_idx
+            all_plates.append(p)
+        quality_samples.append(_analyze_quality(gray))
+
+    quality = _aggregate_quality(quality_samples)
+    recs, priority = _recommendations(
+        faces_count=len(all_faces),
+        plates_count=len(all_plates),
+        quality=quality,
+        media_type="video",
+    )
+    width = _safe_int(meta.get("width"))
+    height = _safe_int(meta.get("height"))
+    frame_count = _safe_int(meta.get("frame_count"))
+
+    return {
+        "media_type": "video",
+        "frame_sampled": len(sampled),
+        "video_metadata": meta,
+        "faces": all_faces[:120],
+        "license_plates": all_plates[:120],
+        "quality_analysis": quality,
+        "recommendations": recs,
+        "suggested_priority": priority,
+        "suggested_export": _suggested_export("video", quality, width, height),
+        "estimated_processing_seconds": estimate_processing_seconds(
+            media_type="video",
+            mode="tactical",
+            width=width,
+            height=height,
+            frame_count=frame_count,
+        ),
+    }
+

services/aurora-service/app/job_store.py

@@ -0,0 +1,254 @@
+from __future__ import annotations
+
+import json
+import logging
+import shutil
+import threading
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from .schemas import AuroraJob, AuroraResult, AuroraMode, JobStatus, MediaType, ProcessingStep
+
+logger = logging.getLogger(__name__)
+
+
+def _model_dump(model: Any) -> Dict[str, Any]:
+    if hasattr(model, "model_dump"):
+        return model.model_dump()
+    return model.dict()
+
+
+class JobStore:
+    def __init__(self, data_dir: Path) -> None:
+        self.data_dir = data_dir
+        self.jobs_dir = data_dir / "jobs"
+        self.uploads_dir = data_dir / "uploads"
+        self.outputs_dir = data_dir / "outputs"
+
+        self.jobs_dir.mkdir(parents=True, exist_ok=True)
+        self.uploads_dir.mkdir(parents=True, exist_ok=True)
+        self.outputs_dir.mkdir(parents=True, exist_ok=True)
+
+        self._lock = threading.RLock()
+        self._jobs: Dict[str, AuroraJob] = {}
+        self._load_existing_jobs()
+
+    def _job_path(self, job_id: str) -> Path:
+        return self.jobs_dir / f"{job_id}.json"
+
+    def _save_job(self, job: AuroraJob) -> None:
+        self._job_path(job.job_id).write_text(
+            json.dumps(_model_dump(job), ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _load_existing_jobs(self) -> None:
+        for path in sorted(self.jobs_dir.glob("*.json")):
+            try:
+                payload = json.loads(path.read_text(encoding="utf-8"))
+                job = AuroraJob(**payload)
+                self._jobs[job.job_id] = job
+            except Exception as exc:
+                logger.warning("Skipping unreadable job file %s: %s", path, exc)
+
+    def create_job(
+        self,
+        *,
+        job_id: str,
+        file_name: str,
+        input_path: Path,
+        input_hash: str,
+        mode: AuroraMode,
+        media_type: MediaType,
+        created_at: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> AuroraJob:
+        job = AuroraJob(
+            job_id=job_id,
+            file_name=file_name,
+            mode=mode,
+            media_type=media_type,
+            input_path=str(input_path),
+            input_hash=input_hash,
+            created_at=created_at,
+            metadata=metadata or {},
+        )
+        with self._lock:
+            self._jobs[job_id] = job
+            self._save_job(job)
+        return job
+
+    def get_job(self, job_id: str) -> Optional[AuroraJob]:
+        with self._lock:
+            return self._jobs.get(job_id)
+
+    def list_jobs(self) -> List[AuroraJob]:
+        with self._lock:
+            return list(self._jobs.values())
+
+    def patch_job(self, job_id: str, **changes: Any) -> AuroraJob:
+        with self._lock:
+            current = self._jobs.get(job_id)
+            if not current:
+                raise KeyError(job_id)
+            payload = _model_dump(current)
+            payload.update(changes)
+            payload["job_id"] = job_id
+            updated = AuroraJob(**payload)
+            self._jobs[job_id] = updated
+            self._save_job(updated)
+            return updated
+
+    def append_processing_step(self, job_id: str, step: ProcessingStep) -> AuroraJob:
+        job = self.get_job(job_id)
+        if not job:
+            raise KeyError(job_id)
+        steps = list(job.processing_log)
+        steps.append(step)
+        return self.patch_job(job_id, processing_log=steps)
+
+    def set_progress(self, job_id: str, *, progress: int, current_stage: str) -> AuroraJob:
+        bounded = max(0, min(100, int(progress)))
+        return self.patch_job(job_id, progress=bounded, current_stage=current_stage)
+
+    def mark_processing(self, job_id: str, *, started_at: str) -> AuroraJob:
+        return self.patch_job(
+            job_id,
+            status="processing",
+            progress=1,
+            current_stage="dispatching",
+            started_at=started_at,
+            error_message=None,
+        )
+
+    def mark_completed(self, job_id: str, *, result: AuroraResult, completed_at: str) -> AuroraJob:
+        return self.patch_job(
+            job_id,
+            status="completed",
+            progress=100,
+            current_stage="completed",
+            result=result,
+            completed_at=completed_at,
+            error_message=None,
+        )
+
+    def mark_failed(self, job_id: str, *, message: str, completed_at: str) -> AuroraJob:
+        return self.patch_job(
+            job_id,
+            status="failed",
+            current_stage="failed",
+            error_message=message,
+            completed_at=completed_at,
+        )
+
+    def request_cancel(self, job_id: str) -> AuroraJob:
+        job = self.get_job(job_id)
+        if not job:
+            raise KeyError(job_id)
+        if job.status in ("completed", "failed", "cancelled"):
+            return job
+        if job.status == "queued":
+            return self.patch_job(
+                job_id,
+                status="cancelled",
+                current_stage="cancelled",
+                cancel_requested=True,
+                progress=0,
+            )
+        return self.patch_job(
+            job_id,
+            cancel_requested=True,
+            current_stage="cancelling",
+        )
+
+    def delete_job(self, job_id: str, *, remove_artifacts: bool = True) -> bool:
+        with self._lock:
+            current = self._jobs.pop(job_id, None)
+            if not current:
+                return False
+            self._job_path(job_id).unlink(missing_ok=True)
+
+        if remove_artifacts:
+            shutil.rmtree(self.uploads_dir / job_id, ignore_errors=True)
+            shutil.rmtree(self.outputs_dir / job_id, ignore_errors=True)
+        return True
+
+    def mark_cancelled(self, job_id: str, *, completed_at: str, message: str = "Cancelled by user") -> AuroraJob:
+        return self.patch_job(
+            job_id,
+            status="cancelled",
+            current_stage="cancelled",
+            cancel_requested=True,
+            error_message=message,
+            completed_at=completed_at,
+        )
+
+    def count_by_status(self) -> Dict[JobStatus, int]:
+        counts: Dict[JobStatus, int] = {
+            "queued": 0,
+            "processing": 0,
+            "completed": 0,
+            "failed": 0,
+            "cancelled": 0,
+        }
+        with self._lock:
+            for job in self._jobs.values():
+                counts[job.status] += 1
+        return counts
+
+    def recover_interrupted_jobs(
+        self,
+        *,
+        completed_at: str,
+        message: str,
+        strategy: str = "failed",
+    ) -> int:
+        """Recover queued/processing jobs after service restart.
+
+        strategy:
+          - "failed": mark as failed
+          - "requeue": move back to queue for auto-retry on startup
+        """
+        mode = (strategy or "failed").strip().lower()
+        recovered = 0
+        with self._lock:
+            for job_id, current in list(self._jobs.items()):
+                if current.status not in ("queued", "processing"):
+                    continue
+                payload = _model_dump(current)
+                meta = payload.get("metadata") or {}
+                if not isinstance(meta, dict):
+                    meta = {}
+                meta["recovery_count"] = int(meta.get("recovery_count", 0)) + 1
+                meta["last_recovery_at"] = completed_at
+                meta["last_recovery_reason"] = message
+                payload["metadata"] = meta
+
+                if mode == "requeue":
+                    payload.update(
+                        {
+                            "status": "queued",
+                            "current_stage": "queued (recovered after restart)",
+                            "error_message": None,
+                            "started_at": None,
+                            "completed_at": None,
+                            "cancel_requested": False,
+                            "progress": 0,
+                        }
+                    )
+                else:
+                    payload.update(
+                        {
+                            "status": "failed",
+                            "current_stage": "failed",
+                            "error_message": message,
+                            "completed_at": completed_at,
+                            "progress": max(1, int(payload.get("progress", 0))),
+                        }
+                    )
+                payload["job_id"] = job_id
+                updated = AuroraJob(**payload)
+                self._jobs[job_id] = updated
+                self._save_job(updated)
+                recovered += 1
+        return recovered

services/aurora-service/app/langchain_scaffold.py

@@ -0,0 +1,96 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Callable, Dict, List
+
+
+@dataclass
+class ToolSpec:
+    name: str
+    description: str
+    handler: Callable[..., dict]
+
+
+@dataclass
+class SubagentSpec:
+    name: str
+    role: str
+    tools: List[ToolSpec] = field(default_factory=list)
+
+
+def _todo_handler(**kwargs) -> dict:
+    return {
+        "status": "todo",
+        "message": "Replace scaffold handler with real model/tool integration",
+        "input": kwargs,
+    }
+
+
+def build_subagent_registry() -> Dict[str, SubagentSpec]:
+    """
+    LangChain-ready registry for AURORA internal subagents.
+    This module intentionally keeps handlers as stubs so deployments remain safe
+    until concrete model adapters are wired.
+    """
+
+    return {
+        "clarity": SubagentSpec(
+            name="Clarity",
+            role="Video Enhancement Agent",
+            tools=[
+                ToolSpec("denoise_video", "Denoise video frames (FastDVDnet)", _todo_handler),
+                ToolSpec("upscale_video", "Super-resolution (Real-ESRGAN)", _todo_handler),
+                ToolSpec("interpolate_frames", "Frame interpolation (RIFE)", _todo_handler),
+                ToolSpec("stabilize_video", "Video stabilization", _todo_handler),
+            ],
+        ),
+        "vera": SubagentSpec(
+            name="Vera",
+            role="Face Restoration Agent",
+            tools=[
+                ToolSpec("detect_faces", "Face detection and quality checks", _todo_handler),
+                ToolSpec("enhance_face", "Restore faces with GFPGAN", _todo_handler),
+                ToolSpec("enhance_face_codeformer", "Alternative face restoration", _todo_handler),
+            ],
+        ),
+        "echo": SubagentSpec(
+            name="Echo",
+            role="Audio Forensics Agent",
+            tools=[
+                ToolSpec("extract_audio_from_video", "Extract audio track", _todo_handler),
+                ToolSpec("denoise_audio", "Audio denoise pipeline", _todo_handler),
+                ToolSpec("enhance_speech", "Improve speech intelligibility", _todo_handler),
+                ToolSpec("detect_deepfake_audio", "Deepfake audio heuristics", _todo_handler),
+            ],
+        ),
+        "pixis": SubagentSpec(
+            name="Pixis",
+            role="Photo Analysis Agent",
+            tools=[
+                ToolSpec("denoise_photo", "Photo denoise", _todo_handler),
+                ToolSpec("upscale_photo", "Photo super-resolution", _todo_handler),
+                ToolSpec("restore_old_photo", "Legacy photo restoration", _todo_handler),
+                ToolSpec("analyze_exif", "EXIF integrity analysis", _todo_handler),
+            ],
+        ),
+        "plate_detect": SubagentSpec(
+            name="PlateDetect",
+            role="License Plate Detection & OCR Agent",
+            tools=[
+                ToolSpec("detect_plates", "YOLO-v9 license plate detection", _todo_handler),
+                ToolSpec("ocr_plates", "OCR plate text (fast-plate-ocr)", _todo_handler),
+                ToolSpec("enhance_plate_roi", "Real-ESRGAN plate region upscale", _todo_handler),
+                ToolSpec("export_plate_report", "Export plate detections JSON", _todo_handler),
+            ],
+        ),
+        "kore": SubagentSpec(
+            name="Kore",
+            role="Forensic Verifier Agent",
+            tools=[
+                ToolSpec("generate_chain_of_custody", "Generate forensic custody JSON", _todo_handler),
+                ToolSpec("sign_document", "Apply cryptographic signature", _todo_handler),
+                ToolSpec("integrate_with_amped", "Amped FIVE integration point", _todo_handler),
+                ToolSpec("integrate_with_cognitech", "Cognitech integration point", _todo_handler),
+            ],
+        ),
+    }

services/aurora-service/app/orchestrator.py

@@ -0,0 +1,198 @@
+from __future__ import annotations
+
+import shutil
+from pathlib import Path
+from typing import Callable, List, Optional
+from urllib.parse import quote
+
+from .schemas import (
+    AuroraJob,
+    AuroraResult,
+    InputFileDescriptor,
+    MediaType,
+    OutputFileDescriptor,
+    ProcessingStep,
+)
+from .subagents import (
+    ClarityAgent,
+    EchoAgent,
+    KoreAgent,
+    PipelineCancelledError,
+    PixisAgent,
+    PlateAgent,
+    SubagentContext,
+    VeraAgent,
+    sha256_file,
+)
+
+ProgressCallback = Callable[[int, str, Optional[ProcessingStep]], None]
+CancelCheck = Callable[[], bool]
+
+
+class JobCancelledError(RuntimeError):
+    pass
+
+
+class AuroraOrchestrator:
+    def __init__(self, outputs_root: Path, public_base_url: str) -> None:
+        self.outputs_root = outputs_root
+        self.public_base_url = public_base_url.rstrip("/")
+
+    def _build_pipeline(self, media_type: MediaType, forensic: bool, priority: str = "balanced") -> List[object]:
+        if media_type == "video":
+            pipeline: List[object] = [VeraAgent(), PlateAgent()]
+        elif media_type == "audio":
+            pipeline = [EchoAgent()]
+        elif media_type == "photo":
+            pipeline = [VeraAgent(), PixisAgent(), PlateAgent()]
+        else:
+            pipeline = [ClarityAgent()]
+
+        if forensic:
+            pipeline.append(KoreAgent())
+        return pipeline
+
+    def _file_url(self, job_id: str, name: str) -> str:
+        return f"{self.public_base_url}/api/aurora/files/{quote(job_id)}/{quote(name)}"
+
+    def _artifact_type(self, path: Path, media_type: MediaType) -> str:
+        lowered = path.name.lower()
+        if lowered.endswith("forensic_log.json"):
+            return "forensic_log"
+        if lowered.endswith("forensic_signature.json"):
+            return "forensic_signature"
+        if "transcript" in lowered:
+            return "transcript"
+        if "plate_detection" in lowered:
+            return "plate_detections"
+        return media_type
+
+    def run(
+        self,
+        job: AuroraJob,
+        progress_callback: Optional[ProgressCallback] = None,
+        cancel_check: Optional[CancelCheck] = None,
+    ) -> AuroraResult:
+        forensic_mode = job.mode == "forensic"
+        meta_early = job.metadata if isinstance(job.metadata, dict) else {}
+        priority_early = str(meta_early.get("priority") or "balanced").strip().lower() or "balanced"
+        pipeline = self._build_pipeline(job.media_type, forensic_mode, priority_early)
+
+        output_dir = self.outputs_root / job.job_id
+        output_dir.mkdir(parents=True, exist_ok=True)
+        meta = job.metadata if isinstance(job.metadata, dict) else {}
+        export_options = meta.get("export_options") if isinstance(meta.get("export_options"), dict) else {}
+        priority = str(meta.get("priority") or "balanced").strip().lower() or "balanced"
+
+        ctx = SubagentContext(
+            job_id=job.job_id,
+            mode=job.mode,
+            media_type=job.media_type,
+            input_hash=job.input_hash,
+            output_dir=output_dir,
+            priority=priority,
+            export_options=export_options,
+            cancel_check=cancel_check,
+        )
+
+        current_path = Path(job.input_path)
+        processing_log: List[ProcessingStep] = []
+        extra_artifacts: List[Path] = []
+        digital_signature: Optional[str] = None
+
+        total = max(1, len(pipeline))
+        for idx, subagent in enumerate(pipeline, start=1):
+            if cancel_check and cancel_check():
+                raise JobCancelledError(f"Job {job.job_id} cancelled")
+            stage_from = int(((idx - 1) / total) * 95)
+            stage_to = int((idx / total) * 95)
+
+            def _stage_progress(fraction: float, stage_label: str) -> None:
+                if not progress_callback:
+                    return
+                bounded = max(0.0, min(1.0, float(fraction)))
+                progress = stage_from + int((stage_to - stage_from) * bounded)
+                progress_callback(progress, stage_label, None)
+
+            stage_ctx = SubagentContext(
+                job_id=ctx.job_id,
+                mode=ctx.mode,
+                media_type=ctx.media_type,
+                input_hash=ctx.input_hash,
+                output_dir=ctx.output_dir,
+                priority=ctx.priority,
+                export_options=ctx.export_options,
+                cancel_check=ctx.cancel_check,
+                stage_progress=_stage_progress if progress_callback else None,
+            )
+
+            try:
+                run_result = subagent.run(stage_ctx, current_path)
+            except PipelineCancelledError as exc:
+                raise JobCancelledError(str(exc)) from exc
+            current_path = run_result.output_path
+            processing_log.extend(run_result.steps)
+            extra_artifacts.extend(run_result.artifacts)
+            if run_result.metadata.get("digital_signature"):
+                digital_signature = run_result.metadata["digital_signature"]
+
+            stage = run_result.steps[-1].step if run_result.steps else f"stage_{idx}"
+            progress = int((idx / total) * 95)
+            if progress_callback:
+                for step in run_result.steps:
+                    progress_callback(progress, stage, step)
+
+        if cancel_check and cancel_check():
+            raise JobCancelledError(f"Job {job.job_id} cancelled")
+
+        final_media = output_dir / f"aurora_result{current_path.suffix or '.bin'}"
+        if current_path != final_media:
+            if current_path.parent == output_dir:
+                current_path.rename(final_media)
+            else:
+                shutil.move(str(current_path), str(final_media))
+        result_hash = sha256_file(final_media)
+
+        output_files: List[OutputFileDescriptor] = [
+            OutputFileDescriptor(
+                type=job.media_type,
+                name=final_media.name,
+                url=self._file_url(job.job_id, final_media.name),
+                hash=result_hash,
+            )
+        ]
+
+        for artifact in extra_artifacts:
+            output_files.append(
+                OutputFileDescriptor(
+                    type=self._artifact_type(artifact, job.media_type),
+                    name=artifact.name,
+                    url=self._file_url(job.job_id, artifact.name),
+                    hash=sha256_file(artifact),
+                )
+            )
+
+        if forensic_mode and not digital_signature:
+            digest = result_hash.split(":", 1)[-1][:48]
+            digital_signature = f"ed25519:{digest}"
+
+        if progress_callback:
+            progress_callback(100, "completed", None)
+
+        return AuroraResult(
+            mode=job.mode,
+            job_id=job.job_id,
+            media_type=job.media_type,
+            input_file=InputFileDescriptor(
+                name=job.file_name,
+                hash=job.input_hash,
+            ),
+            processing_log=processing_log,
+            output_files=output_files,
+            digital_signature=digital_signature,
+            metadata={
+                "pipeline": [type(agent).__name__ for agent in pipeline],
+                "forensic_mode": forensic_mode,
+                "export_options": export_options,
+            },
+        )

services/aurora-service/app/reporting.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Iterable
+
+from fpdf import FPDF  # type: ignore[import-untyped]
+
+from .schemas import AuroraJob
+
+
+def _line(pdf: FPDF, text: str) -> None:
+    full_width = pdf.w - pdf.l_margin - pdf.r_margin
+    pdf.set_x(pdf.l_margin)
+    pdf.set_font("Helvetica", size=10)
+    pdf.multi_cell(full_width, 5, txt=_soft_wrap_tokens(text))
+
+
+def _section(pdf: FPDF, title: str) -> None:
+    pdf.ln(2)
+    pdf.set_x(pdf.l_margin)
+    pdf.set_font("Helvetica", style="B", size=12)
+    full_width = pdf.w - pdf.l_margin - pdf.r_margin
+    pdf.cell(full_width, 7, txt=title, ln=1)
+
+
+def _soft_wrap_tokens(text: str, chunk: int = 40) -> str:
+    parts = []
+    for token in str(text).split(" "):
+        if len(token) <= chunk:
+            parts.append(token)
+            continue
+        segments = [token[i : i + chunk] for i in range(0, len(token), chunk)]
+        parts.append(" ".join(segments))
+    return " ".join(parts)
+
+
+def _iter_output_rows(job: AuroraJob) -> Iterable[str]:
+    if not job.result:
+        return []
+    for item in job.result.output_files:
+        yield f"[{item.type}] {item.name} | {item.hash}"
+
+
+def generate_forensic_report_pdf(job: AuroraJob, output_path: Path) -> Path:
+    if not job.result:
+        raise RuntimeError("Job has no result data")
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    pdf = FPDF(unit="mm", format="A4")
+    pdf.set_auto_page_break(auto=True, margin=14)
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", style="B", size=16)
+    pdf.cell(0, 10, txt="Aurora Forensic Report", ln=1)
+    pdf.set_font("Helvetica", size=9)
+    pdf.cell(0, 5, txt="Autonomous Media Forensics Agent", ln=1)
+    pdf.ln(3)
+
+    _section(pdf, "Case Summary")
+    _line(pdf, f"Job ID: {job.job_id}")
+    _line(pdf, f"Mode: {job.mode}")
+    _line(pdf, f"Media Type: {job.media_type}")
+    _line(pdf, f"Status: {job.status}")
+    _line(pdf, f"Created At: {job.created_at}")
+    _line(pdf, f"Started At: {job.started_at or '-'}")
+    _line(pdf, f"Completed At: {job.completed_at or '-'}")
+    _line(pdf, f"Input File: {job.file_name}")
+    _line(pdf, f"Input Hash: {job.input_hash}")
+    _line(pdf, f"Digital Signature: {job.result.digital_signature or '-'}")
+
+    _section(pdf, "Processing Log")
+    if not job.result.processing_log:
+        _line(pdf, "No processing steps were recorded.")
+    for idx, step in enumerate(job.result.processing_log, start=1):
+        _line(
+            pdf,
+            f"{idx}. {step.step} | agent={step.agent} | model={step.model} | time_ms={step.time_ms}",
+        )
+
+    _section(pdf, "Output Artifacts")
+    rows = list(_iter_output_rows(job))
+    if not rows:
+        _line(pdf, "No output artifacts available.")
+    for row in rows:
+        _line(pdf, row)
+
+    _section(pdf, "Metadata")
+    for k, v in (job.result.metadata or {}).items():
+        _line(pdf, f"{k}: {v}")
+
+    pdf.output(str(output_path))
+    return output_path

services/aurora-service/app/schemas.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Literal, Optional
+
+from pydantic import BaseModel, Field
+
+AuroraMode = Literal["tactical", "forensic"]
+MediaType = Literal["video", "audio", "photo", "unknown"]
+JobStatus = Literal["queued", "processing", "completed", "failed", "cancelled"]
+
+
+class InputFileDescriptor(BaseModel):
+    name: str
+    hash: str
+
+
+class ProcessingStep(BaseModel):
+    step: str
+    agent: str
+    model: str
+    time_ms: int = 0
+    details: Dict[str, Any] = Field(default_factory=dict)
+
+
+class OutputFileDescriptor(BaseModel):
+    type: str
+    name: str
+    url: str
+    hash: str
+
+
+class AuroraResult(BaseModel):
+    agent: str = "Aurora"
+    mode: AuroraMode
+    job_id: str
+    media_type: MediaType
+    input_file: InputFileDescriptor
+    processing_log: List[ProcessingStep] = Field(default_factory=list)
+    output_files: List[OutputFileDescriptor] = Field(default_factory=list)
+    digital_signature: Optional[str] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class AuroraJob(BaseModel):
+    job_id: str
+    file_name: str
+    mode: AuroraMode
+    media_type: MediaType
+    input_path: str
+    input_hash: str
+    status: JobStatus = "queued"
+    progress: int = 0
+    current_stage: str = "queued"
+    error_message: Optional[str] = None
+    cancel_requested: bool = False
+    processing_log: List[ProcessingStep] = Field(default_factory=list)
+    result: Optional[AuroraResult] = None
+    created_at: str
+    started_at: Optional[str] = None
+    completed_at: Optional[str] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)

services/aurora-service/launchd/status-launchd.sh

@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+LABEL="${AURORA_LAUNCHD_LABEL:-com.daarion.aurora}"
+DOMAIN="gui/$(id -u)"
+DATA_DIR_VALUE="${AURORA_DATA_DIR:-${HOME}/.sofiia/aurora-data}"
+LOG_OUT="${DATA_DIR_VALUE}/logs/launchd.out.log"
+LOG_ERR="${DATA_DIR_VALUE}/logs/launchd.err.log"
+
+echo "[aurora-launchd] domain: ${DOMAIN}"
+echo "[aurora-launchd] label: ${LABEL}"
+echo ""
+launchctl print "${DOMAIN}/${LABEL}" || true
+echo ""
+echo "[aurora-launchd] tail stdout (${LOG_OUT})"
+tail -n 40 "${LOG_OUT}" 2>/dev/null || true
+echo ""
+echo "[aurora-launchd] tail stderr (${LOG_ERR})"
+tail -n 80 "${LOG_ERR}" 2>/dev/null || true

services/aurora-service/launchd/uninstall-launchd.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+LABEL="${AURORA_LAUNCHD_LABEL:-com.daarion.aurora}"
+DOMAIN="gui/$(id -u)"
+PLIST_PATH="${HOME}/Library/LaunchAgents/${LABEL}.plist"
+
+launchctl bootout "${DOMAIN}/${LABEL}" >/dev/null 2>&1 || true
+launchctl disable "${DOMAIN}/${LABEL}" >/dev/null 2>&1 || true
+
+if [ -f "${PLIST_PATH}" ]; then
+  rm -f "${PLIST_PATH}"
+fi
+
+echo "[aurora-launchd] removed: ${PLIST_PATH}"

services/aurora-service/requirements.txt

@@ -0,0 +1,13 @@
+fastapi==0.110.0
+uvicorn[standard]==0.29.0
+python-multipart==0.0.9
+pydantic==2.7.4
+langchain==0.3.19
+gfpgan==1.3.8
+realesrgan==0.3.0
+facexlib==0.3.0
+basicsr==1.4.2
+opencv-python-headless==4.10.0.84
+torch==2.5.1
+torchvision==0.20.1
+fpdf2==2.8.2

services/aurora-service/setup-native-macos.sh

@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "$0")" && pwd)"
+VENV_DIR="${ROOT_DIR}/.venv-macos"
+PYTHON_BIN="${PYTHON_BIN:-python3.11}"
+cd "${ROOT_DIR}"
+
+echo "[aurora-native] root: ${ROOT_DIR}"
+echo "[aurora-native] python: ${PYTHON_BIN}"
+
+if ! command -v "${PYTHON_BIN}" >/dev/null 2>&1; then
+  echo "[aurora-native] error: ${PYTHON_BIN} not found"
+  exit 1
+fi
+
+if ! command -v ffmpeg >/dev/null 2>&1; then
+  echo "[aurora-native] error: ffmpeg is required (brew install ffmpeg)"
+  exit 1
+fi
+
+if [ ! -d "${VENV_DIR}" ]; then
+  "${PYTHON_BIN}" -m venv "${VENV_DIR}"
+fi
+
+source "${VENV_DIR}/bin/activate"
+python -m pip install --upgrade pip setuptools wheel
+python -m pip install -r "${ROOT_DIR}/requirements.txt"
+
+echo "[aurora-native] setup complete: ${VENV_DIR}"