fix: send last DAG_STATUS on WebSocket connect instead of skipping all

Previously all DAG_STATUS events were skipped during historical replay
on WS connect, so reconnecting clients (React strict mode remount,
page navigation) lost current DAG state. Now sends only the most
recent DAG_STATUS event on connect.

.gitignore

@@ -1,5 +1,6 @@
 .DS_Store
 server/.env
+server/.env.production
 .env
 Caddyfile
 server/exportdanswer

.gitleaksignore

@@ -4,3 +4,4 @@ docs/docs/installation/daily-setup.md:curl-auth-header:277
 gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
 gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83
 server/reflector/worker/process.py:generic-api-key:465
+server/reflector/worker/process.py:generic-api-key:594

CHANGELOG.md

@@ -1,5 +1,48 @@
 # Changelog
 
+## [0.33.0](https://github.com/Monadical-SAS/reflector/compare/v0.32.2...v0.33.0) (2026-02-05)
+
+
+### Features
+
+* Daily+hatchet default ([#846](https://github.com/Monadical-SAS/reflector/issues/846)) ([15ab2e3](https://github.com/Monadical-SAS/reflector/commit/15ab2e306eacf575494b4b5d2b2ad779d44a1c7f))
+
+
+### Bug Fixes
+
+* websocket tests ([#825](https://github.com/Monadical-SAS/reflector/issues/825)) ([1ce1c7a](https://github.com/Monadical-SAS/reflector/commit/1ce1c7a910b6c374115d2437b17f9d288ef094dc))
+
+## [0.32.2](https://github.com/Monadical-SAS/reflector/compare/v0.32.1...v0.32.2) (2026-02-03)
+
+
+### Bug Fixes
+
+* increase TIMEOUT_MEDIUM from 2m to 5m for LLM tasks ([#843](https://github.com/Monadical-SAS/reflector/issues/843)) ([4acde4b](https://github.com/Monadical-SAS/reflector/commit/4acde4b7fdef88cc02ca12cf38c9020b05ed96ac))
+* make caddy optional ([#841](https://github.com/Monadical-SAS/reflector/issues/841)) ([a2ed7d6](https://github.com/Monadical-SAS/reflector/commit/a2ed7d60d557b551a5b64e4dfd909b63a791d9fc))
+* use Daily API recording.duration as master source for transcript duration ([#844](https://github.com/Monadical-SAS/reflector/issues/844)) ([8707c66](https://github.com/Monadical-SAS/reflector/commit/8707c6694a80c939b6214bbc13331741f192e082))
+
+## [0.32.1](https://github.com/Monadical-SAS/reflector/compare/v0.32.0...v0.32.1) (2026-01-30)
+
+
+### Bug Fixes
+
+* daily multitrack pipeline finalze dependency fix ([23eb137](https://github.com/Monadical-SAS/reflector/commit/23eb1371cb9348c4b81eb12ad506b582f8a4799e))
+* match httpx pad with hatchet audio timeout ([c05d1f0](https://github.com/Monadical-SAS/reflector/commit/c05d1f03cd8369fc06efd455527e50246887efd0))
+
+## [0.32.0](https://github.com/Monadical-SAS/reflector/compare/v0.31.0...v0.32.0) (2026-01-30)
+
+
+### Features
+
+* modal padding ([#837](https://github.com/Monadical-SAS/reflector/issues/837)) ([7fde64e](https://github.com/Monadical-SAS/reflector/commit/7fde64e2529a1d37b0f7507c62d983a7bd0b5b89))
+
+## [0.31.0](https://github.com/Monadical-SAS/reflector/compare/v0.30.0...v0.31.0) (2026-01-23)
+
+
+### Features
+
+* mixdown optional ([#834](https://github.com/Monadical-SAS/reflector/issues/834)) ([fc3ef6c](https://github.com/Monadical-SAS/reflector/commit/fc3ef6c8933231c731fad84e7477a476a6220a5e))
+
 ## [0.30.0](https://github.com/Monadical-SAS/reflector/compare/v0.29.0...v0.30.0) (2026-01-23)
 
 

Caddyfile.example

@@ -1,6 +1,8 @@
-# Reflector Caddyfile
-# Replace example.com with your actual domains
-# CORS is handled by the backend - Caddy just proxies
+# Reflector Caddyfile (optional reverse proxy)
+# Use this only when you run Caddy via: docker compose -f docker-compose.prod.yml --profile caddy up -d
+# If Coolify, Traefik, or nginx already use ports 80/443, do NOT start Caddy; point your proxy at web:3000 and server:1250.
+#
+# Replace example.com with your actual domains. CORS is handled by the backend - Caddy just proxies.
 #
 # For environment variable substitution, set:
 #   FRONTEND_DOMAIN=app.example.com

docker-compose.prod.yml

@@ -1,9 +1,14 @@
 # Production Docker Compose configuration
 # Usage: docker compose -f docker-compose.prod.yml up -d
 #
+# Caddy (reverse proxy on ports 80/443) is OPTIONAL and behind the "caddy" profile:
+# - With Caddy (self-hosted, you manage SSL): docker compose -f docker-compose.prod.yml --profile caddy up -d
+# - Without Caddy (Coolify/Traefik/nginx already on 80/443): docker compose -f docker-compose.prod.yml up -d
+#   Then point your proxy at web:3000 (frontend) and server:1250 (API).
+#
 # Prerequisites:
 # 1. Copy .env.example to .env and configure for both server/ and www/
-# 2. Copy Caddyfile.example to Caddyfile and edit with your domains
+# 2. If using Caddy: copy Caddyfile.example to Caddyfile and edit your domains
 # 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
 
 services:
@@ -84,6 +89,8 @@ services:
       retries: 3
 
   caddy:
+    profiles:
+      - caddy
     image: caddy:2-alpine
     restart: unless-stopped
     ports:

docs/docs/installation/docker-setup.md

@@ -11,15 +11,15 @@ This page documents the Docker Compose configuration for Reflector. For the comp
 
 The `docker-compose.prod.yml` includes these services:
 
-| Service | Image | Purpose |
-|---------|-------|---------|
-| `web` | `monadicalsas/reflector-frontend` | Next.js frontend |
-| `server` | `monadicalsas/reflector-backend` | FastAPI backend |
-| `worker` | `monadicalsas/reflector-backend` | Celery worker for background tasks |
-| `beat` | `monadicalsas/reflector-backend` | Celery beat scheduler |
-| `redis` | `redis:7.2-alpine` | Message broker and cache |
-| `postgres` | `postgres:17-alpine` | Primary database |
-| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
+| Service    | Image                             | Purpose                                                                     |
+| ---------- | --------------------------------- | --------------------------------------------------------------------------- |
+| `web`      | `monadicalsas/reflector-frontend` | Next.js frontend                                                            |
+| `server`   | `monadicalsas/reflector-backend`  | FastAPI backend                                                             |
+| `worker`   | `monadicalsas/reflector-backend`  | Celery worker for background tasks                                          |
+| `beat`     | `monadicalsas/reflector-backend`  | Celery beat scheduler                                                       |
+| `redis`    | `redis:7.2-alpine`                | Message broker and cache                                                    |
+| `postgres` | `postgres:17-alpine`              | Primary database                                                            |
+| `caddy`    | `caddy:2-alpine`                  | Reverse proxy with auto-SSL (optional; see [Caddy profile](#caddy-profile)) |
 
 ## Environment Files
 
@@ -30,6 +30,7 @@ Reflector uses two separate environment files:
 Used by: `server`, `worker`, `beat`
 
 Key variables:
+
 ```env
 # Database connection
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -54,6 +55,7 @@ TRANSCRIPT_MODAL_API_KEY=...
 Used by: `web`
 
 Key variables:
+
 ```env
 # Domain configuration
 SITE_URL=https://app.example.com
@@ -70,26 +72,42 @@ Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-s
 
 ## Volumes
 
-| Volume | Purpose |
-|--------|---------|
-| `redis_data` | Redis persistence |
-| `postgres_data` | PostgreSQL data |
-| `server_data` | Uploaded files, local storage |
-| `caddy_data` | SSL certificates |
-| `caddy_config` | Caddy configuration |
+| Volume          | Purpose                       |
+| --------------- | ----------------------------- |
+| `redis_data`    | Redis persistence             |
+| `postgres_data` | PostgreSQL data               |
+| `server_data`   | Uploaded files, local storage |
+| `caddy_data`    | SSL certificates              |
+| `caddy_config`  | Caddy configuration           |
 
 ## Network
 
 All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
 
+## Caddy profile
+
+Caddy (ports 80 and 443) is **optional** and behind the `caddy` profile so it does not conflict with an existing reverse proxy (e.g. Coolify, Traefik, nginx).
+
+- **With Caddy** (you want Reflector to handle SSL):
+  `docker compose -f docker-compose.prod.yml --profile caddy up -d`
+- **Without Caddy** (Coolify or another proxy already on 80/443):
+  `docker compose -f docker-compose.prod.yml up -d`
+  Then configure your proxy to send traffic to `web:3000` (frontend) and `server:1250` (API).
+
 ## Common Commands
 
 ### Start all services
+
 ```bash
+# Without Caddy (e.g. when using Coolify)
 docker compose -f docker-compose.prod.yml up -d
+
+# With Caddy as reverse proxy
+docker compose -f docker-compose.prod.yml --profile caddy up -d
 ```
 
 ### View logs
+
 ```bash
 # All services
 docker compose -f docker-compose.prod.yml logs -f
@@ -99,6 +117,7 @@ docker compose -f docker-compose.prod.yml logs server --tail 50
 ```
 
 ### Restart a service
+
 ```bash
 # Quick restart (doesn't reload .env changes)
 docker compose -f docker-compose.prod.yml restart server
@@ -108,27 +127,32 @@ docker compose -f docker-compose.prod.yml up -d server
 ```
 
 ### Run database migrations
+
 ```bash
 docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
 ```
 
 ### Access database
+
 ```bash
 docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
 ```
 
 ### Pull latest images
+
 ```bash
 docker compose -f docker-compose.prod.yml pull
 docker compose -f docker-compose.prod.yml up -d
 ```
 
 ### Stop all services
+
 ```bash
 docker compose -f docker-compose.prod.yml down
 ```
 
 ### Full reset (WARNING: deletes data)
+
 ```bash
 docker compose -f docker-compose.prod.yml down -v
 ```
@@ -187,6 +211,7 @@ The Caddyfile supports environment variable substitution:
 Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
 
 ### Reload Caddy after changes
+
 ```bash
 docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
 ```

docs/docs/installation/overview.md

@@ -26,7 +26,7 @@ flowchart LR
 
 Before starting, you need:
 
-- **Production server** -  4+ cores, 8GB+ RAM, public IP
+- **Production server** - 4+ cores, 8GB+ RAM, public IP
 - **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
 - **GPU processing** - Choose one:
   - Modal.com account, OR
@@ -60,16 +60,17 @@ Type: A    Name: api    Value: <your-server-ip>
 
 Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
 
-| | **Modal.com (Cloud)** | **Self-Hosted GPU** |
-|---|---|---|
+|              | **Modal.com (Cloud)**             | **Self-Hosted GPU**          |
+| ------------ | --------------------------------- | ---------------------------- |
 | **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
-| **Pricing** | Pay-per-use | Fixed infrastructure cost |
+| **Pricing**  | Pay-per-use                       | Fixed infrastructure cost    |
 
 ### Option A: Modal.com (Serverless Cloud GPU)
 
 #### Accept HuggingFace Licenses
 
 Visit both pages and click "Accept":
+
 - https://huggingface.co/pyannote/speaker-diarization-3.1
 - https://huggingface.co/pyannote/segmentation-3.0
 
@@ -179,6 +180,7 @@ Save these credentials - you'll need them in the next step.
 ## Configure Environment
 
 Reflector has two env files:
+
 - `server/.env` - Backend configuration
 - `www/.env` - Frontend configuration
 
@@ -190,6 +192,7 @@ nano server/.env
 ```
 
 **Required settings:**
+
 ```env
 # Database (defaults work with docker-compose.prod.yml)
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -249,6 +252,7 @@ nano www/.env
 ```
 
 **Required settings:**
+
 ```env
 # Your domains
 SITE_URL=https://app.example.com
@@ -266,7 +270,11 @@ FEATURE_REQUIRE_LOGIN=false
 
 ---
 
-## Configure Caddy
+## Reverse proxy (Caddy or existing)
+
+**If Coolify, Traefik, or nginx already use ports 80/443** (e.g. Coolify on your host): skip Caddy. Start the stack without the Caddy profile (see [Start Services](#start-services) below), then point your proxy at `web:3000` (frontend) and `server:1250` (API).
+
+**If you want Reflector to provide the reverse proxy and SSL:**
 
 ```bash
 cp Caddyfile.example Caddyfile
@@ -289,10 +297,18 @@ Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy'
 
 ## Start Services
 
+**Without Caddy** (e.g. Coolify already on 80/443):
+
 ```bash
 docker compose -f docker-compose.prod.yml up -d
 ```
 
+**With Caddy** (Reflector handles SSL):
+
+```bash
+docker compose -f docker-compose.prod.yml --profile caddy up -d
+```
+
 Wait for containers to start (first run may take 1-2 minutes to pull images and initialize).
 
 ---
@@ -300,18 +316,21 @@ Wait for containers to start (first run may take 1-2 minutes to pull images and
 ## Verify Deployment
 
 ### Check services
+
 ```bash
 docker compose -f docker-compose.prod.yml ps
 # All should show "Up"
 ```
 
 ### Test API
+
 ```bash
 curl https://api.example.com/health
 # Should return: {"status":"healthy"}
 ```
 
 ### Test Frontend
+
 - Visit https://app.example.com
 - You should see the Reflector interface
 - Try uploading an audio file to test transcription
@@ -327,6 +346,7 @@ By default, Reflector is open (no login required). **Authentication is required
 See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
 
 Quick summary:
+
 1. Deploy Authentik on your server
 2. Create OAuth provider in Authentik
 3. Extract public key for JWT verification
@@ -358,6 +378,7 @@ DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
 ```
 
 Reload env and restart:
+
 ```bash
 docker compose -f docker-compose.prod.yml up -d server worker
 ```
@@ -367,35 +388,43 @@ docker compose -f docker-compose.prod.yml up -d server worker
 ## Troubleshooting
 
 ### Check logs for errors
+
 ```bash
 docker compose -f docker-compose.prod.yml logs server --tail 20
 docker compose -f docker-compose.prod.yml logs worker --tail 20
 ```
 
 ### Services won't start
+
 ```bash
 docker compose -f docker-compose.prod.yml logs
 ```
 
 ### CORS errors in browser
+
 - Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
 - Reload env: `docker compose -f docker-compose.prod.yml up -d server`
 
-### SSL certificate errors
+### SSL certificate errors (when using Caddy)
+
 - Caddy auto-provisions Let's Encrypt certificates
-- Ensure ports 80 and 443 are open
+- Ensure ports 80 and 443 are open and not used by another proxy
 - Check: `docker compose -f docker-compose.prod.yml logs caddy`
+- If port 80 is already in use (e.g. by Coolify), run without Caddy: `docker compose -f docker-compose.prod.yml up -d` and use your existing proxy
 
 ### Transcription not working
+
 - Check Modal dashboard: https://modal.com/apps
 - Verify URLs in `server/.env` match deployed functions
 - Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
 
 ### "Login required" but auth not configured
+
 - Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
 - Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
 
 ### Database migrations or connectivity issues
+
 Migrations run automatically on server startup. To check database connectivity or debug migration failures:
 
 ```bash
@@ -408,4 +437,3 @@ docker compose -f docker-compose.prod.yml exec server uv run python -c "from ref
 # Manually run migrations (if needed)
 docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
 ```
-

gpu/modal_deployments/deploy-all.sh

@@ -131,6 +131,15 @@ if [ -z "$DIARIZER_URL" ]; then
 fi
 echo "  -> $DIARIZER_URL"
 
+echo ""
+echo "Deploying padding (CPU audio processing via Modal SDK)..."
+modal deploy reflector_padding.py
+if [ $? -ne 0 ]; then
+    echo "Error: Failed to deploy padding. Check Modal dashboard for details."
+    exit 1
+fi
+echo "  -> reflector-padding.pad_track (Modal SDK function)"
+
 # --- Output Configuration ---
 echo ""
 echo "=========================================="
@@ -147,4 +156,6 @@ echo ""
 echo "DIARIZATION_BACKEND=modal"
 echo "DIARIZATION_URL=$DIARIZER_URL"
 echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
+echo ""
+echo "# Padding uses Modal SDK (requires MODAL_TOKEN_ID/SECRET in worker containers)"
 echo "# --- End Modal Configuration ---"

gpu/modal_deployments/reflector_padding.py

@@ -0,0 +1,277 @@
+"""
+Reflector GPU backend - audio padding
+======================================
+
+CPU-intensive audio padding service for adding silence to audio tracks.
+Uses PyAV filter graph (adelay) for precise track synchronization.
+
+IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
+for Modal deployment isolation (Modal can't import from server/reflector/). If you modify
+the PyAV filter graph or padding algorithm, you MUST update both:
+  - gpu/modal_deployments/reflector_padding.py (this file)
+  - server/reflector/utils/audio_padding.py
+
+Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
+"""
+
+import os
+import tempfile
+from fractions import Fraction
+import math
+import asyncio
+
+import modal
+
+S3_TIMEOUT = 60 # happens 2 times
+PADDING_TIMEOUT = 600 + (S3_TIMEOUT * 2)
+SCALEDOWN_WINDOW = 60  # The maximum duration (in seconds) that individual containers can remain idle when scaling down.
+DISCONNECT_CHECK_INTERVAL = 2  # Check for client disconnect
+
+
+app = modal.App("reflector-padding")
+
+# CPU-based image
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .apt_install("ffmpeg")  # Required by PyAV
+    .pip_install(
+        "av==13.1.0",  # PyAV for audio processing
+        "requests==2.32.3",  # HTTP for presigned URL downloads/uploads
+        "fastapi==0.115.12",  # API framework
+    )
+)
+
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
+OPUS_STANDARD_SAMPLE_RATE = 48000
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
+OPUS_DEFAULT_BIT_RATE = 128000
+
+
+@app.function(
+    cpu=2.0,
+    timeout=PADDING_TIMEOUT,
+    scaledown_window=SCALEDOWN_WINDOW,
+    image=image,
+)
+@modal.asgi_app()
+def web():
+    from fastapi import FastAPI, Request, HTTPException
+    from pydantic import BaseModel
+
+    class PaddingRequest(BaseModel):
+        track_url: str
+        output_url: str
+        start_time_seconds: float
+        track_index: int
+
+    class PaddingResponse(BaseModel):
+        size: int
+        cancelled: bool = False
+
+    web_app = FastAPI()
+
+    @web_app.post("/pad")
+    async def pad_track_endpoint(request: Request, req: PaddingRequest) -> PaddingResponse:
+        """Modal web endpoint for padding audio tracks with disconnect detection.
+        """
+        import logging
+
+        logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+        logger = logging.getLogger(__name__)
+
+        if not req.track_url:
+            raise HTTPException(status_code=400, detail="track_url cannot be empty")
+        if not req.output_url:
+            raise HTTPException(status_code=400, detail="output_url cannot be empty")
+        if req.start_time_seconds <= 0:
+            raise HTTPException(status_code=400, detail=f"start_time_seconds must be positive, got {req.start_time_seconds}")
+        if req.start_time_seconds > 18000:
+            raise HTTPException(status_code=400, detail=f"start_time_seconds exceeds maximum 18000s (5 hours)")
+
+        logger.info(f"Padding request: track {req.track_index}, delay={req.start_time_seconds}s")
+
+        # Thread-safe cancellation flag shared between async disconnect checker and blocking thread
+        import threading
+        cancelled = threading.Event()
+
+        async def check_disconnect():
+            """Background task to check for client disconnect every 2 seconds."""
+            while not cancelled.is_set():
+                await asyncio.sleep(DISCONNECT_CHECK_INTERVAL)
+                if await request.is_disconnected():
+                    logger.warning("Client disconnected, setting cancellation flag")
+                    cancelled.set()
+                    break
+
+        # Start disconnect checker in background
+        disconnect_task = asyncio.create_task(check_disconnect())
+
+        try:
+            result = await asyncio.get_event_loop().run_in_executor(
+                None, _pad_track_blocking, req, cancelled, logger
+            )
+            return PaddingResponse(**result)
+        finally:
+            cancelled.set()
+            disconnect_task.cancel()
+            try:
+                await disconnect_task
+            except asyncio.CancelledError:
+                pass
+
+    def _pad_track_blocking(req, cancelled, logger) -> dict:
+        """Blocking CPU-bound padding work with periodic cancellation checks.
+
+        Args:
+            cancelled: threading.Event for thread-safe cancellation signaling
+        """
+        import av
+        import requests
+        from av.audio.resampler import AudioResampler
+        import time
+
+        temp_dir = tempfile.mkdtemp()
+        input_path = None
+        output_path = None
+        last_check = time.time()
+
+        try:
+            logger.info("Downloading track for padding")
+            response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
+            response.raise_for_status()
+
+            input_path = os.path.join(temp_dir, "track.webm")
+            total_bytes = 0
+            chunk_count = 0
+            with open(input_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+                        total_bytes += len(chunk)
+                        chunk_count += 1
+
+                        # Check for cancellation every arbitrary amount of chunks
+                        if chunk_count % 12 == 0:
+                            now = time.time()
+                            if now - last_check >= DISCONNECT_CHECK_INTERVAL:
+                                if cancelled.is_set():
+                                    logger.info("Cancelled during download, exiting early")
+                                    return {"size": 0, "cancelled": True}
+                                last_check = now
+            logger.info(f"Track downloaded: {total_bytes} bytes")
+
+            if cancelled.is_set():
+                logger.info("Cancelled after download, exiting early")
+                return {"size": 0, "cancelled": True}
+
+            # Apply padding using PyAV
+            output_path = os.path.join(temp_dir, "padded.webm")
+            delay_ms = math.floor(req.start_time_seconds * 1000)
+            logger.info(f"Padding track {req.track_index} with {delay_ms}ms delay using PyAV")
+
+            in_container = av.open(input_path)
+            in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
+            if in_stream is None:
+                raise ValueError("No audio stream in input")
+
+            with av.open(output_path, "w", format="webm") as out_container:
+                out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
+                out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
+                graph = av.filter.Graph()
+
+                abuf_args = (
+                    f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
+                    f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
+                    f"sample_fmt=s16:"
+                    f"channel_layout=stereo"
+                )
+                src = graph.add("abuffer", args=abuf_args, name="src")
+                aresample_f = graph.add("aresample", args="async=1", name="ares")
+                delays_arg = f"{delay_ms}|{delay_ms}"
+                adelay_f = graph.add("adelay", args=f"delays={delays_arg}:all=1", name="delay")
+                sink = graph.add("abuffersink", name="sink")
+
+                src.link_to(aresample_f)
+                aresample_f.link_to(adelay_f)
+                adelay_f.link_to(sink)
+                graph.configure()
+
+                resampler = AudioResampler(
+                    format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
+                )
+
+                for frame in in_container.decode(in_stream):
+                    # Check for cancellation periodically
+                    now = time.time()
+                    if now - last_check >= DISCONNECT_CHECK_INTERVAL:
+                        if cancelled.is_set():
+                            logger.info("Cancelled during processing, exiting early")
+                            in_container.close()
+                            return {"size": 0, "cancelled": True}
+                        last_check = now
+
+                    out_frames = resampler.resample(frame) or []
+                    for rframe in out_frames:
+                        rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                        rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                        src.push(rframe)
+
+                        while True:
+                            try:
+                                f_out = sink.pull()
+                            except Exception:
+                                break
+                            f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                            f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                            for packet in out_stream.encode(f_out):
+                                out_container.mux(packet)
+
+                # Flush filter graph
+                src.push(None)
+                while True:
+                    try:
+                        f_out = sink.pull()
+                    except Exception:
+                        break
+                    f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                    f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                    for packet in out_stream.encode(f_out):
+                        out_container.mux(packet)
+
+                # Flush encoder
+                for packet in out_stream.encode(None):
+                    out_container.mux(packet)
+
+            in_container.close()
+
+            file_size = os.path.getsize(output_path)
+            logger.info(f"Padding complete: {file_size} bytes")
+
+            logger.info("Uploading padded track to S3")
+
+            with open(output_path, "rb") as f:
+                upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
+
+            upload_response.raise_for_status()
+            logger.info(f"Upload complete: {file_size} bytes")
+
+            return {"size": file_size}
+
+        finally:
+            if input_path and os.path.exists(input_path):
+                try:
+                    os.unlink(input_path)
+                except Exception as e:
+                    logger.warning(f"Failed to cleanup input file: {e}")
+            if output_path and os.path.exists(output_path):
+                try:
+                    os.unlink(output_path)
+                except Exception as e:
+                    logger.warning(f"Failed to cleanup output file: {e}")
+            try:
+                os.rmdir(temp_dir)
+            except Exception as e:
+                logger.warning(f"Failed to cleanup temp directory: {e}")
+
+    return web_app
+

gpu/self_hosted/Dockerfile

@@ -4,27 +4,31 @@ ENV PYTHONUNBUFFERED=1 \
     UV_LINK_MODE=copy \
     UV_NO_CACHE=1
 
+# patch until nvidia updates the sha1 repo
+ADD sequoia.config /etc/crypto-policies/back-ends/sequoia.config
+
 WORKDIR /tmp
-RUN apt-get update \
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+ --mount=type=cache,target=/var/lib/apt,sharing=locked \
+ apt-get update \
  && apt-get install -y \
     ffmpeg \
     curl \
     ca-certificates \
     gnupg \
-    wget \
- && apt-get clean
+    wget
 # Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
 ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
-RUN dpkg -i /cuda-keyring.deb \
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+ --mount=type=cache,target=/var/lib/apt,sharing=locked \
+ dpkg -i /cuda-keyring.deb \
  && rm /cuda-keyring.deb \
  && apt-get update \
  && apt-get install -y --no-install-recommends \
     cuda-cudart-12-6 \
     libcublas-12-6 \
     libcudnn9-cuda-12 \
-    libcudnn9-dev-cuda-12 \
- && apt-get clean \
- && rm -rf /var/lib/apt/lists/*
+    libcudnn9-dev-cuda-12
 ADD https://astral.sh/uv/install.sh /uv-installer.sh
 RUN sh /uv-installer.sh && rm /uv-installer.sh
 ENV PATH="/root/.local/bin/:$PATH"
@@ -39,6 +43,13 @@ COPY ./app /app/app
 COPY ./main.py /app/
 COPY ./runserver.sh /app/
 
+# prevent uv failing with too many open files on big cpus
+ENV UV_CONCURRENT_INSTALLS=16
+
+# first install
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --compile-bytecode --locked
+
 EXPOSE 8000
 
 CMD ["sh", "/app/runserver.sh"]

gpu/self_hosted/sequoia.config

@@ -0,0 +1,2 @@
+[hash_algorithms]
+sha1 = "always"

server/migrations/versions/3aa20b96d963_drop_use_celery_column.py

@@ -0,0 +1,35 @@
+"""drop_use_celery_column
+
+Revision ID: 3aa20b96d963
+Revises: e69f08ead8ea
+Create Date: 2026-02-05 10:12:44.065279
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "3aa20b96d963"
+down_revision: Union[str, None] = "e69f08ead8ea"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.drop_column("use_celery")
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.add_column(
+            sa.Column(
+                "use_celery",
+                sa.Boolean(),
+                server_default=sa.text("false"),
+                nullable=False,
+            )
+        )

server/pyproject.toml

@@ -8,7 +8,7 @@ readme = "README.md"
 dependencies = [
     "aiohttp>=3.9.0",
     "aiohttp-cors>=0.7.0",
-    "av>=10.0.0",
+    "av>=15.0.0",
     "requests>=2.31.0",
     "aiortc>=1.5.0",
     "sortedcontainers>=2.4.0",

server/reflector/db/rooms.py

@@ -57,12 +57,6 @@ rooms = sqlalchemy.Table(
         sqlalchemy.String,
         nullable=False,
     ),
-    sqlalchemy.Column(
-        "use_celery",
-        sqlalchemy.Boolean,
-        nullable=False,
-        server_default=false(),
-    ),
     sqlalchemy.Column(
         "skip_consent",
         sqlalchemy.Boolean,
@@ -97,7 +91,6 @@ class Room(BaseModel):
     ics_last_sync: datetime | None = None
     ics_last_etag: str | None = None
     platform: Platform = Field(default_factory=lambda: settings.DEFAULT_VIDEO_PLATFORM)
-    use_celery: bool = False
     skip_consent: bool = False
 
 

server/reflector/db/search.py

@@ -1,6 +1,7 @@
 """Search functionality for transcripts and other entities."""
 
 import itertools
+import json
 from dataclasses import dataclass
 from datetime import datetime
 from io import StringIO
@@ -172,6 +173,9 @@ class SearchResult(BaseModel):
     total_match_count: NonNegativeInt = Field(
         default=0, description="Total number of matches found in the transcript"
     )
+    dag_status: list[dict] | None = Field(
+        default=None, description="Latest DAG task status for processing transcripts"
+    )
 
     @field_serializer("created_at", when_used="json")
     def serialize_datetime(self, dt: datetime) -> str:
@@ -328,6 +332,42 @@ class SnippetGenerator:
         return summary_snippets + webvtt_snippets, total_matches
 
 
+async def _fetch_dag_statuses(transcript_ids: list[str]) -> dict[str, list[dict]]:
+    """Fetch latest DAG_STATUS event data for given transcript IDs.
+
+    Returns dict mapping transcript_id -> tasks list from the last DAG_STATUS event.
+    """
+    if not transcript_ids:
+        return {}
+
+    db = get_database()
+    query = sqlalchemy.select(
+        [
+            transcripts.c.id,
+            transcripts.c.events,
+        ]
+    ).where(transcripts.c.id.in_(transcript_ids))
+
+    rows = await db.fetch_all(query)
+    result: dict[str, list[dict]] = {}
+
+    for row in rows:
+        events_raw = row["events"]
+        if not events_raw:
+            continue
+        # events is stored as JSON list
+        events = events_raw if isinstance(events_raw, list) else json.loads(events_raw)
+        # Find last DAG_STATUS event
+        for ev in reversed(events):
+            if isinstance(ev, dict) and ev.get("event") == "DAG_STATUS":
+                tasks = ev.get("data", {}).get("tasks")
+                if tasks:
+                    result[row["id"]] = tasks
+                break
+
+    return result
+
+
 class SearchController:
     """Controller for search operations across different entities."""
 
@@ -470,6 +510,14 @@ class SearchController:
             logger.error(f"Error processing search results: {e}", exc_info=True)
             raise
 
+        # Enrich processing transcripts with DAG status
+        processing_ids = [r.id for r in results if r.status == "processing"]
+        if processing_ids:
+            dag_statuses = await _fetch_dag_statuses(processing_ids)
+            for r in results:
+                if r.id in dag_statuses:
+                    r.dag_status = dag_statuses[r.id]
+
         return results, total
 
 

server/reflector/db/transcripts.py

@@ -234,7 +234,7 @@ class Transcript(BaseModel):
         return dt.isoformat()
 
     def add_event(self, event: str, data: BaseModel) -> TranscriptEvent:
-        ev = TranscriptEvent(event=event, data=data.model_dump())
+        ev = TranscriptEvent(event=event, data=data.model_dump(mode="json"))
         self.events.append(ev)
         return ev
 

server/reflector/hatchet/broadcast.py

@@ -15,7 +15,7 @@ from reflector.utils.string import NonEmptyString
 from reflector.ws_manager import get_ws_manager
 
 # Events that should also be sent to user room (matches Celery behavior)
-USER_ROOM_EVENTS = {"STATUS", "FINAL_TITLE", "DURATION"}
+USER_ROOM_EVENTS = {"STATUS", "FINAL_TITLE", "DURATION", "DAG_STATUS"}
 
 
 async def broadcast_event(

server/reflector/hatchet/constants.py

@@ -35,7 +35,9 @@ LLM_RATE_LIMIT_PER_SECOND = 10
 
 # Task execution timeouts (seconds)
 TIMEOUT_SHORT = 60  # Quick operations: API calls, DB updates
-TIMEOUT_MEDIUM = 120  # Single LLM calls, waveform generation
+TIMEOUT_MEDIUM = (
+    300  # Single LLM calls, waveform generation (5m for slow LLM responses)
+)
 TIMEOUT_LONG = 180  # Action items (larger context LLM)
-TIMEOUT_AUDIO = 300  # Audio processing: padding, mixdown
+TIMEOUT_AUDIO = 720  # Audio processing: padding, mixdown
 TIMEOUT_HEAVY = 600  # Transcription, fan-out LLM tasks

server/reflector/hatchet/dag_progress.py

@@ -0,0 +1,230 @@
+"""
+DAG Progress Reporting — models and transform.
+
+Converts Hatchet V1WorkflowRunDetails into structured DagTask list
+for frontend WebSocket/REST consumption.
+
+Ported from render_hatchet_run.py (feat-dag-zulip) which renders markdown;
+this module produces structured Pydantic models instead.
+"""
+
+from datetime import datetime
+from enum import StrEnum
+
+from hatchet_sdk.clients.rest.models import (
+    V1TaskStatus,
+    V1WorkflowRunDetails,
+    WorkflowRunShapeItemForWorkflowRunDetails,
+)
+from pydantic import BaseModel
+
+
+class DagTaskStatus(StrEnum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+
+_HATCHET_TO_DAG_STATUS: dict[V1TaskStatus, DagTaskStatus] = {
+    V1TaskStatus.QUEUED: DagTaskStatus.QUEUED,
+    V1TaskStatus.RUNNING: DagTaskStatus.RUNNING,
+    V1TaskStatus.COMPLETED: DagTaskStatus.COMPLETED,
+    V1TaskStatus.FAILED: DagTaskStatus.FAILED,
+    V1TaskStatus.CANCELLED: DagTaskStatus.CANCELLED,
+}
+
+
+class DagTask(BaseModel):
+    name: str
+    status: DagTaskStatus
+    started_at: datetime | None
+    finished_at: datetime | None
+    duration_seconds: float | None
+    parents: list[str]
+    error: str | None
+    children_total: int | None
+    children_completed: int | None
+    progress_pct: float | None
+
+
+class DagStatusData(BaseModel):
+    workflow_run_id: str
+    tasks: list[DagTask]
+
+
+def _topo_sort(
+    shape: list[WorkflowRunShapeItemForWorkflowRunDetails],
+) -> list[str]:
+    """Topological sort of step_ids from shape DAG (Kahn's algorithm).
+
+    Ported from render_hatchet_run.py.
+    """
+    step_ids = {s.step_id for s in shape}
+    children_map: dict[str, list[str]] = {}
+    in_degree: dict[str, int] = {sid: 0 for sid in step_ids}
+
+    for s in shape:
+        children = [c for c in (s.children_step_ids or []) if c in step_ids]
+        children_map[s.step_id] = children
+        for c in children:
+            in_degree[c] += 1
+
+    queue = sorted(sid for sid, deg in in_degree.items() if deg == 0)
+    result: list[str] = []
+    while queue:
+        node = queue.pop(0)
+        result.append(node)
+        for c in children_map.get(node, []):
+            in_degree[c] -= 1
+            if in_degree[c] == 0:
+                queue.append(c)
+                queue.sort()
+
+    return result
+
+
+def _extract_error_summary(error_message: str | None) -> str | None:
+    """Extract first meaningful line from error message, skipping traceback frames."""
+    if not error_message or not error_message.strip():
+        return None
+
+    err_lines = error_message.strip().split("\n")
+    err_summary = err_lines[0]
+    for line in err_lines:
+        stripped = line.strip()
+        if stripped and not stripped.startswith(("Traceback", "File ", "{", ")")):
+            err_summary = stripped
+    return err_summary
+
+
+def extract_dag_tasks(details: V1WorkflowRunDetails) -> list[DagTask]:
+    """Extract structured DagTask list from Hatchet workflow run details.
+
+    Returns tasks in topological order with status, timestamps, parents,
+    error summaries, and fan-out children counts.
+    """
+    shape = details.shape or []
+    tasks = details.tasks or []
+
+    if not shape:
+        return []
+
+    # Build lookups
+    step_to_shape: dict[str, WorkflowRunShapeItemForWorkflowRunDetails] = {
+        s.step_id: s for s in shape
+    }
+    step_to_name: dict[str, str] = {s.step_id: s.task_name for s in shape}
+
+    # Reverse edges: child -> parent names
+    parents_by_step: dict[str, list[str]] = {s.step_id: [] for s in shape}
+    for s in shape:
+        for child_id in s.children_step_ids or []:
+            if child_id in parents_by_step:
+                parents_by_step[child_id].append(step_to_name[s.step_id])
+
+    # Join tasks by step_id
+    from hatchet_sdk.clients.rest.models import V1TaskSummary  # noqa: PLC0415
+
+    task_by_step: dict[str, V1TaskSummary] = {}
+    for t in tasks:
+        if t.step_id and t.step_id in step_to_name:
+            task_by_step[t.step_id] = t
+
+    ordered = _topo_sort(shape)
+
+    result: list[DagTask] = []
+    for step_id in ordered:
+        name = step_to_name[step_id]
+        t = task_by_step.get(step_id)
+
+        if not t:
+            result.append(
+                DagTask(
+                    name=name,
+                    status=DagTaskStatus.QUEUED,
+                    started_at=None,
+                    finished_at=None,
+                    duration_seconds=None,
+                    parents=parents_by_step.get(step_id, []),
+                    error=None,
+                    children_total=None,
+                    children_completed=None,
+                    progress_pct=None,
+                )
+            )
+            continue
+
+        status = _HATCHET_TO_DAG_STATUS.get(t.status, DagTaskStatus.QUEUED)
+
+        duration_seconds: float | None = None
+        if t.duration is not None:
+            duration_seconds = t.duration / 1000.0
+
+        # Fan-out children
+        children_total: int | None = None
+        children_completed: int | None = None
+        if t.num_spawned_children and t.num_spawned_children > 0:
+            children_total = t.num_spawned_children
+            children_completed = sum(
+                1 for c in (t.children or []) if c.status == V1TaskStatus.COMPLETED
+            )
+
+        result.append(
+            DagTask(
+                name=name,
+                status=status,
+                started_at=t.started_at,
+                finished_at=t.finished_at,
+                duration_seconds=duration_seconds,
+                parents=parents_by_step.get(step_id, []),
+                error=_extract_error_summary(t.error_message),
+                children_total=children_total,
+                children_completed=children_completed,
+                progress_pct=None,
+            )
+        )
+
+    return result
+
+
+async def broadcast_dag_status(transcript_id: str, workflow_run_id: str) -> None:
+    """Fetch current DAG state from Hatchet and broadcast via WebSocket.
+
+    Fire-and-forget: exceptions are logged but never raised.
+    All imports are deferred for fork-safety (Hatchet workers fork processes).
+    """
+    try:
+        from reflector.db.transcripts import transcripts_controller  # noqa: I001, PLC0415
+        from reflector.hatchet.broadcast import append_event_and_broadcast  # noqa: PLC0415
+        from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (  # noqa: PLC0415
+            fresh_db_connection,
+        )
+        from reflector.logger import logger  # noqa: PLC0415
+
+        async with fresh_db_connection():
+            client = HatchetClientManager.get_client()
+            details = await client.runs.aio_get(workflow_run_id)
+            dag_tasks = extract_dag_tasks(details)
+            dag_status = DagStatusData(workflow_run_id=workflow_run_id, tasks=dag_tasks)
+
+            transcript = await transcripts_controller.get_by_id(transcript_id)
+            if transcript:
+                await append_event_and_broadcast(
+                    transcript_id,
+                    transcript,
+                    "DAG_STATUS",
+                    dag_status,
+                    logger,
+                )
+    except Exception:
+        from reflector.logger import logger  # noqa: PLC0415
+
+        logger.warning(
+            "[DAG Progress] Failed to broadcast DAG status",
+            transcript_id=transcript_id,
+            workflow_run_id=workflow_run_id,
+            exc_info=True,
+        )

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -184,7 +184,10 @@ class Loggable(Protocol):
 
 
 def make_audio_progress_logger(
-    ctx: Loggable, task_name: TaskName, interval: float = 5.0
+    ctx: Loggable,
+    task_name: TaskName,
+    interval: float = 5.0,
+    transcript_id: str | None = None,
 ) -> Callable[[float | None, float], None]:
     """Create a throttled progress logger callback for audio processing.
 
@@ -192,6 +195,7 @@ def make_audio_progress_logger(
         ctx: Object with .log() method (e.g., Hatchet Context).
         task_name: Name to prefix in log messages.
         interval: Minimum seconds between log messages.
+        transcript_id: If provided, broadcasts transient DAG_TASK_PROGRESS events.
 
     Returns:
         Callback(progress_pct, audio_position) that logs at most every `interval` seconds.
@@ -213,6 +217,27 @@ def make_audio_progress_logger(
                 )
             last_log_time[0] = now
 
+        if transcript_id and progress_pct is not None:
+            try:
+                import asyncio  # noqa: PLC0415
+
+                from reflector.db.transcripts import TranscriptEvent  # noqa: PLC0415
+                from reflector.hatchet.broadcast import broadcast_event  # noqa: PLC0415
+
+                loop = asyncio.get_event_loop()
+                loop.create_task(
+                    broadcast_event(
+                        transcript_id,
+                        TranscriptEvent(
+                            event="DAG_TASK_PROGRESS",
+                            data={"task_name": task_name, "progress_pct": progress_pct},
+                        ),
+                        logger=logger,
+                    )
+                )
+            except Exception:
+                pass  # transient, never fail the callback
+
     return callback
 
 
@@ -237,8 +262,15 @@ def with_error_handling(
     ) -> Callable[[PipelineInput, Context], Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(input: PipelineInput, ctx: Context) -> R:
+            from reflector.hatchet.dag_progress import broadcast_dag_status  # noqa: I001, PLC0415
+
             try:
-                return await func(input, ctx)
+                result = await func(input, ctx)
+                try:
+                    await broadcast_dag_status(input.transcript_id, ctx.workflow_run_id)
+                except Exception:
+                    pass
+                return result
             except Exception as e:
                 logger.error(
                     f"[Hatchet] {step_name} failed",
@@ -246,6 +278,10 @@ def with_error_handling(
                     error=str(e),
                     exc_info=True,
                 )
+                try:
+                    await broadcast_dag_status(input.transcript_id, ctx.workflow_run_id)
+                except Exception:
+                    pass
                 if set_error_status:
                     await set_workflow_error_status(input.transcript_id)
                 raise
@@ -322,6 +358,7 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
     mtg_session_id = recording.mtg_session_id
     async with fresh_db_connection():
         from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptDuration,
             TranscriptParticipant,
             transcripts_controller,
         )
@@ -330,15 +367,26 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
         if not transcript:
             raise ValueError(f"Transcript {input.transcript_id} not found")
         # Note: title NOT cleared - preserves existing titles
+        # Duration from Daily API (seconds -> milliseconds) - master source
+        duration_ms = recording.duration * 1000 if recording.duration else 0
         await transcripts_controller.update(
             transcript,
             {
                 "events": [],
                 "topics": [],
                 "participants": [],
+                "duration": duration_ms,
             },
         )
 
+        await append_event_and_broadcast(
+            input.transcript_id,
+            transcript,
+            "DURATION",
+            TranscriptDuration(duration=duration_ms),
+            logger=logger,
+        )
+
         mtg_session_id = assert_non_none_and_non_empty(
             mtg_session_id, "mtg_session_id is required"
         )
@@ -548,7 +596,9 @@ async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
         target_sample_rate,
         offsets_seconds=None,
         logger=logger,
-        progress_callback=make_audio_progress_logger(ctx, TaskName.MIXDOWN_TRACKS),
+        progress_callback=make_audio_progress_logger(
+            ctx, TaskName.MIXDOWN_TRACKS, transcript_id=input.transcript_id
+        ),
         expected_duration_sec=recording_duration if recording_duration > 0 else None,
     )
     await writer.flush()
@@ -1095,7 +1145,7 @@ async def identify_action_items(
 
 
 @daily_multitrack_pipeline.task(
-    parents=[generate_waveform, generate_title, generate_recap, identify_action_items],
+    parents=[process_tracks, generate_title, generate_recap, identify_action_items],
     execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
     retries=3,
 )
@@ -1108,12 +1158,8 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
     """
     ctx.log("finalize: saving transcript and setting status to 'ended'")
 
-    mixdown_result = ctx.task_output(mixdown_tracks)
     track_result = ctx.task_output(process_tracks)
 
-    duration = mixdown_result.duration
-    all_words = track_result.all_words
-
     # Cleanup temporary padded S3 files (deferred until finalize for semantic parity with Celery)
     created_padded_files = track_result.created_padded_files
     if created_padded_files:
@@ -1133,7 +1179,6 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
 
     async with fresh_db_connection():
         from reflector.db.transcripts import (  # noqa: PLC0415
-            TranscriptDuration,
             TranscriptText,
             transcripts_controller,
         )
@@ -1142,8 +1187,6 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
         if transcript is None:
             raise ValueError(f"Transcript {input.transcript_id} not found in database")
 
-        merged_transcript = TranscriptType(words=all_words, translation=None)
-
         await append_event_and_broadcast(
             input.transcript_id,
             transcript,
@@ -1155,21 +1198,15 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
             logger=logger,
         )
 
-        # Save duration and clear workflow_run_id (workflow completed successfully)
-        # Note: title/long_summary/short_summary already saved by their callbacks
+        # Clear workflow_run_id (workflow completed successfully)
+        # Note: title/long_summary/short_summary/duration already saved by their callbacks
         await transcripts_controller.update(
             transcript,
             {
-                "duration": duration,
                 "workflow_run_id": None,  # Clear on success - no need to resume
             },
         )
 
-        duration_data = TranscriptDuration(duration=duration)
-        await append_event_and_broadcast(
-            input.transcript_id, transcript, "DURATION", duration_data, logger=logger
-        )
-
         await set_status_and_broadcast(input.transcript_id, "ended", logger=logger)
 
         ctx.log(

server/reflector/hatchet/workflows/padding_workflow.py

@@ -0,0 +1,165 @@
+"""
+Hatchet child workflow: PaddingWorkflow
+Handles individual audio track padding via Modal.com backend.
+"""
+
+from datetime import timedelta
+
+import av
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.constants import TIMEOUT_AUDIO
+from reflector.hatchet.workflows.models import PadTrackResult
+from reflector.logger import logger
+from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
+from reflector.utils.audio_padding import extract_stream_start_time_from_container
+
+
+class PaddingInput(BaseModel):
+    """Input for individual track padding."""
+
+    track_index: int
+    s3_key: str
+    bucket_name: str
+    transcript_id: str
+
+
+hatchet = HatchetClientManager.get_client()
+
+padding_workflow = hatchet.workflow(
+    name="PaddingWorkflow", input_validator=PaddingInput
+)
+
+
+@padding_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
+async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
+    """Pad audio track with silence based on WebM container start_time."""
+    ctx.log(f"pad_track: track {input.track_index}, s3_key={input.s3_key}")
+    logger.info(
+        "[Hatchet] pad_track",
+        track_index=input.track_index,
+        s3_key=input.s3_key,
+        transcript_id=input.transcript_id,
+    )
+
+    try:
+        # Create fresh storage instance to avoid aioboto3 fork issues
+        from reflector.settings import settings  # noqa: PLC0415
+        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
+
+        storage = AwsStorage(
+            aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
+            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
+            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
+            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+        )
+
+        source_url = await storage.get_file_url(
+            input.s3_key,
+            operation="get_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+            bucket=input.bucket_name,
+        )
+
+        # Extract start_time to determine if padding needed
+        with av.open(source_url) as in_container:
+            if in_container.duration:
+                try:
+                    duration = timedelta(seconds=in_container.duration // 1_000_000)
+                    ctx.log(
+                        f"pad_track: track {input.track_index}, duration={duration}"
+                    )
+                except (ValueError, TypeError, OverflowError) as e:
+                    ctx.log(
+                        f"pad_track: track {input.track_index}, duration error: {str(e)}"
+                    )
+
+            start_time_seconds = extract_stream_start_time_from_container(
+                in_container, input.track_index, logger=logger
+            )
+
+        if start_time_seconds <= 0:
+            logger.info(
+                f"Track {input.track_index} requires no padding",
+                track_index=input.track_index,
+            )
+            return PadTrackResult(
+                padded_key=input.s3_key,
+                bucket_name=input.bucket_name,
+                size=0,
+                track_index=input.track_index,
+            )
+
+        storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+
+        # Presign PUT URL for output (Modal will upload directly)
+        output_url = await storage.get_file_url(
+            storage_path,
+            operation="put_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+        )
+
+        import httpx  # noqa: PLC0415
+
+        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
+            AudioPaddingModalProcessor,
+        )
+
+        try:
+            processor = AudioPaddingModalProcessor()
+            result = await processor.pad_track(
+                track_url=source_url,
+                output_url=output_url,
+                start_time_seconds=start_time_seconds,
+                track_index=input.track_index,
+            )
+            file_size = result.size
+
+            ctx.log(f"pad_track: Modal returned size={file_size}")
+        except httpx.HTTPStatusError as e:
+            error_detail = e.response.text if hasattr(e.response, "text") else str(e)
+            logger.error(
+                "[Hatchet] Modal padding HTTP error",
+                transcript_id=input.transcript_id,
+                track_index=input.track_index,
+                status_code=e.response.status_code if hasattr(e, "response") else None,
+                error=error_detail,
+                exc_info=True,
+            )
+            raise Exception(
+                f"Modal padding failed: HTTP {e.response.status_code}"
+            ) from e
+        except httpx.TimeoutException as e:
+            logger.error(
+                "[Hatchet] Modal padding timeout",
+                transcript_id=input.transcript_id,
+                track_index=input.track_index,
+                error=str(e),
+                exc_info=True,
+            )
+            raise Exception("Modal padding timeout") from e
+
+        logger.info(
+            "[Hatchet] pad_track complete",
+            track_index=input.track_index,
+            padded_key=storage_path,
+        )
+
+        return PadTrackResult(
+            padded_key=storage_path,
+            bucket_name=None,  # None = use default transcript storage bucket
+            size=file_size,
+            track_index=input.track_index,
+        )
+
+    except Exception as e:
+        logger.error(
+            "[Hatchet] pad_track failed",
+            transcript_id=input.transcript_id,
+            track_index=input.track_index,
+            error=str(e),
+            exc_info=True,
+        )
+        raise

server/reflector/hatchet/workflows/track_processing.py

@@ -14,9 +14,7 @@ Hatchet workers run in forked processes; fresh imports per task ensure
 storage/DB connections are not shared across forks.
 """
 
-import tempfile
 from datetime import timedelta
-from pathlib import Path
 
 import av
 from hatchet_sdk import Context
@@ -27,10 +25,7 @@ from reflector.hatchet.constants import TIMEOUT_AUDIO, TIMEOUT_HEAVY
 from reflector.hatchet.workflows.models import PadTrackResult, TranscribeTrackResult
 from reflector.logger import logger
 from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
-from reflector.utils.audio_padding import (
-    apply_audio_padding_to_file,
-    extract_stream_start_time_from_container,
-)
+from reflector.utils.audio_padding import extract_stream_start_time_from_container
 
 
 class TrackInput(BaseModel):
@@ -83,63 +78,44 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
         )
 
         with av.open(source_url) as in_container:
-            if in_container.duration:
-                try:
-                    duration = timedelta(seconds=in_container.duration // 1_000_000)
-                    ctx.log(
-                        f"pad_track: track {input.track_index}, duration={duration}"
-                    )
-                except Exception:
-                    ctx.log(f"pad_track: track {input.track_index}, duration=ERROR")
-
             start_time_seconds = extract_stream_start_time_from_container(
                 in_container, input.track_index, logger=logger
             )
 
-            # If no padding needed, return original S3 key
-            if start_time_seconds <= 0:
-                logger.info(
-                    f"Track {input.track_index} requires no padding",
-                    track_index=input.track_index,
-                )
-                return PadTrackResult(
-                    padded_key=input.s3_key,
-                    bucket_name=input.bucket_name,
-                    size=0,
-                    track_index=input.track_index,
-                )
+        # If no padding needed, return original S3 key
+        if start_time_seconds <= 0:
+            logger.info(
+                f"Track {input.track_index} requires no padding",
+                track_index=input.track_index,
+            )
+            return PadTrackResult(
+                padded_key=input.s3_key,
+                bucket_name=input.bucket_name,
+                size=0,
+                track_index=input.track_index,
+            )
 
-            with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
-                temp_path = temp_file.name
+        storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
 
-            try:
-                apply_audio_padding_to_file(
-                    in_container,
-                    temp_path,
-                    start_time_seconds,
-                    input.track_index,
-                    logger=logger,
-                )
+        # Presign PUT URL for output (Modal uploads directly)
+        output_url = await storage.get_file_url(
+            storage_path,
+            operation="put_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+        )
 
-                file_size = Path(temp_path).stat().st_size
-                storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
+            AudioPaddingModalProcessor,
+        )
 
-                logger.info(
-                    f"About to upload padded track",
-                    key=storage_path,
-                    size=file_size,
-                )
-
-                with open(temp_path, "rb") as padded_file:
-                    await storage.put_file(storage_path, padded_file)
-
-                logger.info(
-                    f"Uploaded padded track to S3",
-                    key=storage_path,
-                    size=file_size,
-                )
-            finally:
-                Path(temp_path).unlink(missing_ok=True)
+        processor = AudioPaddingModalProcessor()
+        result = await processor.pad_track(
+            track_url=source_url,
+            output_url=output_url,
+            start_time_seconds=start_time_seconds,
+            track_index=input.track_index,
+        )
+        file_size = result.size
 
         ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
         logger.info(

server/reflector/processors/audio_padding_modal.py

@@ -0,0 +1,113 @@
+"""
+Modal.com backend for audio padding.
+"""
+
+import asyncio
+import os
+
+import httpx
+from pydantic import BaseModel
+
+from reflector.hatchet.constants import TIMEOUT_AUDIO
+from reflector.logger import logger
+
+
+class PaddingResponse(BaseModel):
+    size: int
+    cancelled: bool = False
+
+
+class AudioPaddingModalProcessor:
+    """Audio padding processor using Modal.com CPU backend via HTTP."""
+
+    def __init__(
+        self, padding_url: str | None = None, modal_api_key: str | None = None
+    ):
+        self.padding_url = padding_url or os.getenv("PADDING_URL")
+        if not self.padding_url:
+            raise ValueError(
+                "PADDING_URL required to use AudioPaddingModalProcessor. "
+                "Set PADDING_URL environment variable or pass padding_url parameter."
+            )
+
+        self.modal_api_key = modal_api_key or os.getenv("MODAL_API_KEY")
+
+    async def pad_track(
+        self,
+        track_url: str,
+        output_url: str,
+        start_time_seconds: float,
+        track_index: int,
+    ) -> PaddingResponse:
+        """Pad audio track with silence via Modal backend.
+
+        Args:
+            track_url: Presigned GET URL for source audio track
+            output_url: Presigned PUT URL for output WebM
+            start_time_seconds: Amount of silence to prepend
+            track_index: Track index for logging
+        """
+        if not track_url:
+            raise ValueError("track_url cannot be empty")
+        if start_time_seconds <= 0:
+            raise ValueError(
+                f"start_time_seconds must be positive, got {start_time_seconds}"
+            )
+
+        log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
+        log.info("Sending Modal padding HTTP request")
+
+        url = f"{self.padding_url}/pad"
+
+        headers = {}
+        if self.modal_api_key:
+            headers["Authorization"] = f"Bearer {self.modal_api_key}"
+
+        try:
+            async with httpx.AsyncClient(timeout=TIMEOUT_AUDIO) as client:
+                response = await client.post(
+                    url,
+                    headers=headers,
+                    json={
+                        "track_url": track_url,
+                        "output_url": output_url,
+                        "start_time_seconds": start_time_seconds,
+                        "track_index": track_index,
+                    },
+                    follow_redirects=True,
+                )
+
+                if response.status_code != 200:
+                    error_body = response.text
+                    log.error(
+                        "Modal padding API error",
+                        status_code=response.status_code,
+                        error_body=error_body,
+                    )
+
+                response.raise_for_status()
+                result = response.json()
+
+            # Check if work was cancelled
+            if result.get("cancelled"):
+                log.warning("Modal padding was cancelled by disconnect detection")
+                raise asyncio.CancelledError(
+                    "Padding cancelled due to client disconnect"
+                )
+
+            log.info("Modal padding complete", size=result["size"])
+            return PaddingResponse(**result)
+        except asyncio.CancelledError:
+            log.warning(
+                "Modal padding cancelled (Hatchet timeout, disconnect detected on Modal side)"
+            )
+            raise
+        except httpx.TimeoutException as e:
+            log.error("Modal padding timeout", error=str(e), exc_info=True)
+            raise Exception(f"Modal padding timeout: {e}") from e
+        except httpx.HTTPStatusError as e:
+            log.error("Modal padding HTTP error", error=str(e), exc_info=True)
+            raise Exception(f"Modal padding HTTP error: {e}") from e
+        except Exception as e:
+            log.error("Modal padding unexpected error", error=str(e), exc_info=True)
+            raise

server/reflector/services/transcript_process.py

@@ -15,14 +15,10 @@ from hatchet_sdk.clients.rest.exceptions import ApiException, NotFoundException
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 from reflector.db.recordings import recordings_controller
-from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.utils.string import NonEmptyString
 
 
@@ -181,124 +177,111 @@ async def dispatch_transcript_processing(
     Returns AsyncResult for Celery tasks, None for Hatchet workflows.
     """
     if isinstance(config, MultitrackProcessingConfig):
-        use_celery = False
-        if config.room_id:
-            room = await rooms_controller.get_by_id(config.room_id)
-            use_celery = room.use_celery if room else False
-
-        use_hatchet = not use_celery
-
-        if use_celery:
-            logger.info(
-                "Room uses legacy Celery processing",
-                room_id=config.room_id,
-                transcript_id=config.transcript_id,
+        # Multitrack processing always uses Hatchet (no Celery fallback)
+        # First check if we can replay (outside transaction since it's read-only)
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript and transcript.workflow_run_id and not force:
+            can_replay = await HatchetClientManager.can_replay(
+                transcript.workflow_run_id
             )
-
-        if use_hatchet:
-            # First check if we can replay (outside transaction since it's read-only)
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            if transcript and transcript.workflow_run_id and not force:
-                can_replay = await HatchetClientManager.can_replay(
-                    transcript.workflow_run_id
+            if can_replay:
+                await HatchetClientManager.replay_workflow(transcript.workflow_run_id)
+                logger.info(
+                    "Replaying Hatchet workflow",
+                    workflow_id=transcript.workflow_run_id,
                 )
-                if can_replay:
-                    await HatchetClientManager.replay_workflow(
-                        transcript.workflow_run_id
-                    )
-                    logger.info(
-                        "Replaying Hatchet workflow",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                    return None
-                else:
-                    # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
-                    # Log and proceed to start new workflow
-                    try:
-                        status = await HatchetClientManager.get_workflow_run_status(
-                            transcript.workflow_run_id
-                        )
-                        logger.info(
-                            "Old workflow not replayable, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                            old_status=status.value,
-                        )
-                    except NotFoundException:
-                        # Workflow deleted from Hatchet but ID still in DB
-                        logger.info(
-                            "Old workflow not found in Hatchet, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                        )
-
-            # Force: cancel old workflow if exists
-            if force and transcript and transcript.workflow_run_id:
-                try:
-                    await HatchetClientManager.cancel_workflow(
-                        transcript.workflow_run_id
-                    )
-                    logger.info(
-                        "Cancelled old workflow (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                except NotFoundException:
-                    logger.info(
-                        "Old workflow already deleted (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": None}
-                )
-
-            # Re-fetch and check for concurrent dispatch (optimistic approach).
-            # No database lock - worst case is duplicate dispatch, but Hatchet
-            # workflows are idempotent so this is acceptable.
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            if transcript and transcript.workflow_run_id:
-                # Another process started a workflow between validation and now
+                return None
+            else:
+                # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
+                # Log and proceed to start new workflow
                 try:
                     status = await HatchetClientManager.get_workflow_run_status(
                         transcript.workflow_run_id
                     )
-                    if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
-                        logger.info(
-                            "Concurrent workflow detected, skipping dispatch",
-                            workflow_id=transcript.workflow_run_id,
-                        )
-                        return None
-                except ApiException:
-                    # Workflow might be gone (404) or API issue - proceed with new workflow
-                    pass
+                    logger.info(
+                        "Old workflow not replayable, starting new",
+                        old_workflow_id=transcript.workflow_run_id,
+                        old_status=status.value,
+                    )
+                except NotFoundException:
+                    # Workflow deleted from Hatchet but ID still in DB
+                    logger.info(
+                        "Old workflow not found in Hatchet, starting new",
+                        old_workflow_id=transcript.workflow_run_id,
+                    )
 
-            workflow_id = await HatchetClientManager.start_workflow(
-                workflow_name="DiarizationPipeline",
-                input_data={
-                    "recording_id": config.recording_id,
-                    "tracks": [{"s3_key": k} for k in config.track_keys],
-                    "bucket_name": config.bucket_name,
-                    "transcript_id": config.transcript_id,
-                    "room_id": config.room_id,
-                },
-                additional_metadata={
-                    "transcript_id": config.transcript_id,
-                    "recording_id": config.recording_id,
-                    "daily_recording_id": config.recording_id,
-                },
+        # Force: cancel old workflow if exists
+        if force and transcript and transcript.workflow_run_id:
+            try:
+                await HatchetClientManager.cancel_workflow(transcript.workflow_run_id)
+                logger.info(
+                    "Cancelled old workflow (--force)",
+                    workflow_id=transcript.workflow_run_id,
+                )
+            except NotFoundException:
+                logger.info(
+                    "Old workflow already deleted (--force)",
+                    workflow_id=transcript.workflow_run_id,
+                )
+            await transcripts_controller.update(transcript, {"workflow_run_id": None})
+
+        # Re-fetch and check for concurrent dispatch (optimistic approach).
+        # No database lock - worst case is duplicate dispatch, but Hatchet
+        # workflows are idempotent so this is acceptable.
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript and transcript.workflow_run_id:
+            # Another process started a workflow between validation and now
+            try:
+                status = await HatchetClientManager.get_workflow_run_status(
+                    transcript.workflow_run_id
+                )
+                if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                    logger.info(
+                        "Concurrent workflow detected, skipping dispatch",
+                        workflow_id=transcript.workflow_run_id,
+                    )
+                    return None
+            except ApiException:
+                # Workflow might be gone (404) or API issue - proceed with new workflow
+                pass
+
+        workflow_id = await HatchetClientManager.start_workflow(
+            workflow_name="DiarizationPipeline",
+            input_data={
+                "recording_id": config.recording_id,
+                "tracks": [{"s3_key": k} for k in config.track_keys],
+                "bucket_name": config.bucket_name,
+                "transcript_id": config.transcript_id,
+                "room_id": config.room_id,
+            },
+            additional_metadata={
+                "transcript_id": config.transcript_id,
+                "recording_id": config.recording_id,
+                "daily_recording_id": config.recording_id,
+            },
+        )
+
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
             )
 
-            if transcript:
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": workflow_id}
-                )
+        logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
 
-            logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-            return None
+        try:
+            from reflector.hatchet.dag_progress import broadcast_dag_status  # noqa: I001, PLC0415
+
+            await broadcast_dag_status(config.transcript_id, workflow_id)
+        except Exception:
+            logger.warning(
+                "[DAG Progress] Failed initial broadcast",
+                transcript_id=config.transcript_id,
+                workflow_id=workflow_id,
+                exc_info=True,
+            )
+
+        return None
 
-        # Celery pipeline (durable workflows disabled)
-        return task_pipeline_multitrack_process.delay(
-            transcript_id=config.transcript_id,
-            bucket_name=config.bucket_name,
-            track_keys=config.track_keys,
-        )
     elif isinstance(config, FileProcessingConfig):
         return task_pipeline_file_process.delay(transcript_id=config.transcript_id)
     else:

server/reflector/settings.py

@@ -1,7 +1,7 @@
 from pydantic.types import PositiveInt
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-from reflector.schemas.platform import WHEREBY_PLATFORM, Platform
+from reflector.schemas.platform import DAILY_PLATFORM, Platform
 from reflector.utils.string import NonEmptyString
 
 
@@ -98,6 +98,10 @@ class Settings(BaseSettings):
     # Diarization: local pyannote.audio
     DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None
 
+    # Audio Padding (Modal.com backend)
+    PADDING_URL: str | None = None
+    PADDING_MODAL_API_KEY: str | None = None
+
     # Sentry
     SENTRY_DSN: str | None = None
 
@@ -151,7 +155,7 @@ class Settings(BaseSettings):
         None  # Webhook UUID for this environment. Not used by production code
     )
     # Platform Configuration
-    DEFAULT_VIDEO_PLATFORM: Platform = WHEREBY_PLATFORM
+    DEFAULT_VIDEO_PLATFORM: Platform = DAILY_PLATFORM
 
     # Zulip integration
     ZULIP_REALM: str | None = None

server/reflector/utils/audio_constants.py

@@ -5,7 +5,9 @@ Used by both Hatchet workflows and Celery pipelines for consistent audio encodin
 """
 
 # Opus codec settings
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
 OPUS_STANDARD_SAMPLE_RATE = 48000
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
 OPUS_DEFAULT_BIT_RATE = 128000  # 128kbps for good speech quality
 
 # S3 presigned URL expiration

server/reflector/views/transcripts.py

@@ -111,6 +111,7 @@ class GetTranscriptMinimal(BaseModel):
     room_id: str | None = None
     room_name: str | None = None
     audio_deleted: bool | None = None
+    dag_status: list[dict] | None = None
 
 
 class TranscriptParticipantWithEmail(TranscriptParticipant):
@@ -491,6 +492,13 @@ async def transcript_get(
                 )
             )
 
+    dag_status = None
+    if transcript.status == "processing" and transcript.events:
+        for ev in reversed(transcript.events):
+            if ev.event == "DAG_STATUS":
+                dag_status = ev.data.get("tasks") if isinstance(ev.data, dict) else None
+                break
+
     base_data = {
         "id": transcript.id,
         "user_id": transcript.user_id,
@@ -512,6 +520,7 @@ async def transcript_get(
         "room_id": transcript.room_id,
         "room_name": room_name,
         "audio_deleted": transcript.audio_deleted,
+        "dag_status": dag_status,
         "participants": participants,
     }
 

server/reflector/views/transcripts_websocket.py

@@ -41,13 +41,19 @@ async def transcript_events_websocket(
 
     try:
         # on first connection, send all events only to the current user
+        # Find the last DAG_STATUS to send after other historical events
+        last_dag_status = None
         for event in transcript.events:
-            # for now, do not send TRANSCRIPT or STATUS options - theses are live event
-            # not necessary to be sent to the client; but keep the rest
             name = event.event
             if name in ("TRANSCRIPT", "STATUS"):
                 continue
+            if name == "DAG_STATUS":
+                last_dag_status = event
+                continue
             await websocket.send_json(event.model_dump(mode="json"))
+        # Send only the most recent DAG_STATUS so reconnecting clients get current state
+        if last_dag_status is not None:
+            await websocket.send_json(last_dag_status.model_dump(mode="json"))
 
         # XXX if transcript is final (locked=True and status=ended)
         # XXX send a final event to the client and close the connection

server/reflector/worker/process.py

@@ -27,9 +27,6 @@ from reflector.db.transcripts import (
 from reflector.hatchet.client import HatchetClientManager
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_live_pipeline import asynctask
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.pipelines.topic_processing import EmptyPipeline
 from reflector.processors import AudioFileWriterProcessor
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
@@ -351,49 +348,29 @@ async def _process_multitrack_recording_inner(
             room_id=room.id,
         )
 
-    use_celery = room and room.use_celery
-    use_hatchet = not use_celery
-
-    if use_celery:
-        logger.info(
-            "Room uses legacy Celery processing",
-            room_id=room.id,
-            transcript_id=transcript.id,
-        )
-
-    if use_hatchet:
-        workflow_id = await HatchetClientManager.start_workflow(
-            workflow_name="DiarizationPipeline",
-            input_data={
-                "recording_id": recording_id,
-                "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
-                "bucket_name": bucket_name,
-                "transcript_id": transcript.id,
-                "room_id": room.id,
-            },
-            additional_metadata={
-                "transcript_id": transcript.id,
-                "recording_id": recording_id,
-                "daily_recording_id": recording_id,
-            },
-        )
-        logger.info(
-            "Started Hatchet workflow",
-            workflow_id=workflow_id,
-            transcript_id=transcript.id,
-        )
-
-        await transcripts_controller.update(
-            transcript, {"workflow_run_id": workflow_id}
-        )
-        return
-
-    # Celery pipeline (runs when durable workflows disabled)
-    task_pipeline_multitrack_process.delay(
-        transcript_id=transcript.id,
-        bucket_name=bucket_name,
-        track_keys=filter_cam_audio_tracks(track_keys),
+    # Multitrack processing always uses Hatchet (no Celery fallback)
+    workflow_id = await HatchetClientManager.start_workflow(
+        workflow_name="DiarizationPipeline",
+        input_data={
+            "recording_id": recording_id,
+            "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
+            "bucket_name": bucket_name,
+            "transcript_id": transcript.id,
+            "room_id": room.id,
+        },
+        additional_metadata={
+            "transcript_id": transcript.id,
+            "recording_id": recording_id,
+            "daily_recording_id": recording_id,
+        },
     )
+    logger.info(
+        "Started Hatchet workflow",
+        workflow_id=workflow_id,
+        transcript_id=transcript.id,
+    )
+
+    await transcripts_controller.update(transcript, {"workflow_run_id": workflow_id})
 
 
 @shared_task
@@ -1072,66 +1049,43 @@ async def reprocess_failed_daily_recordings():
                 )
                 continue
 
-            use_celery = room and room.use_celery
-            use_hatchet = not use_celery
-
-            if use_hatchet:
-                if not transcript:
-                    logger.warning(
-                        "No transcript for Hatchet reprocessing, skipping",
-                        recording_id=recording.id,
-                    )
-                    continue
-
-                workflow_id = await HatchetClientManager.start_workflow(
-                    workflow_name="DiarizationPipeline",
-                    input_data={
-                        "recording_id": recording.id,
-                        "tracks": [
-                            {"s3_key": k}
-                            for k in filter_cam_audio_tracks(recording.track_keys)
-                        ],
-                        "bucket_name": bucket_name,
-                        "transcript_id": transcript.id,
-                        "room_id": room.id if room else None,
-                    },
-                    additional_metadata={
-                        "transcript_id": transcript.id,
-                        "recording_id": recording.id,
-                        "reprocess": True,
-                    },
-                )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": workflow_id}
-                )
-
-                logger.info(
-                    "Queued Daily recording for Hatchet reprocessing",
+            # Multitrack reprocessing always uses Hatchet (no Celery fallback)
+            if not transcript:
+                logger.warning(
+                    "No transcript for Hatchet reprocessing, skipping",
                     recording_id=recording.id,
-                    workflow_id=workflow_id,
-                    room_name=meeting.room_name,
-                    track_count=len(recording.track_keys),
-                )
-            else:
-                logger.info(
-                    "Queueing Daily recording for Celery reprocessing",
-                    recording_id=recording.id,
-                    room_name=meeting.room_name,
-                    track_count=len(recording.track_keys),
-                    transcript_status=transcript.status if transcript else None,
                 )
+                continue
 
-                # For reprocessing, pass actual recording time (though it's ignored - see _process_multitrack_recording_inner)
-                # Reprocessing uses recording.meeting_id directly instead of time-based matching
-                recording_start_ts = int(recording.recorded_at.timestamp())
+            workflow_id = await HatchetClientManager.start_workflow(
+                workflow_name="DiarizationPipeline",
+                input_data={
+                    "recording_id": recording.id,
+                    "tracks": [
+                        {"s3_key": k}
+                        for k in filter_cam_audio_tracks(recording.track_keys)
+                    ],
+                    "bucket_name": bucket_name,
+                    "transcript_id": transcript.id,
+                    "room_id": room.id if room else None,
+                },
+                additional_metadata={
+                    "transcript_id": transcript.id,
+                    "recording_id": recording.id,
+                    "reprocess": True,
+                },
+            )
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
+            )
 
-                process_multitrack_recording.delay(
-                    bucket_name=bucket_name,
-                    daily_room_name=meeting.room_name,
-                    recording_id=recording.id,
-                    track_keys=recording.track_keys,
-                    recording_start_ts=recording_start_ts,
-                )
+            logger.info(
+                "Queued Daily recording for Hatchet reprocessing",
+                recording_id=recording.id,
+                workflow_id=workflow_id,
+                room_name=meeting.room_name,
+                track_count=len(recording.track_keys),
+            )
 
             reprocessed_count += 1
 

server/reflector/ws_manager.py

@@ -11,7 +11,6 @@ broadcast messages to all connected websockets.
 
 import asyncio
 import json
-import threading
 
 import redis.asyncio as redis
 from fastapi import WebSocket
@@ -98,6 +97,7 @@ class WebsocketManager:
 
     async def _pubsub_data_reader(self, pubsub_subscriber):
         while True:
+            # timeout=1.0 prevents tight CPU loop when no messages available
             message = await pubsub_subscriber.get_message(
                 ignore_subscribe_messages=True
             )
@@ -109,29 +109,38 @@ class WebsocketManager:
                     await socket.send_json(data)
 
 
+# Process-global singleton to ensure only one WebsocketManager instance exists.
+# Multiple instances would cause resource leaks and CPU issues.
+_ws_manager: WebsocketManager | None = None
+
+
 def get_ws_manager() -> WebsocketManager:
     """
-    Returns the WebsocketManager instance for managing websockets.
+    Returns the global WebsocketManager singleton.
 
-    This function initializes and returns the WebsocketManager instance,
-    which is responsible for managing websockets and handling websocket
-    connections.
+    Creates instance on first call, subsequent calls return cached instance.
+    Thread-safe via GIL. Concurrent initialization may create duplicate
+    instances but last write wins (acceptable for this use case).
 
     Returns:
-        WebsocketManager: The initialized WebsocketManager instance.
-
-    Raises:
-        ImportError: If the 'reflector.settings' module cannot be imported.
-        RedisConnectionError: If there is an error connecting to the Redis server.
+        WebsocketManager: The global WebsocketManager instance.
     """
-    local = threading.local()
-    if hasattr(local, "ws_manager"):
-        return local.ws_manager
+    global _ws_manager
 
+    if _ws_manager is not None:
+        return _ws_manager
+
+    # No lock needed - GIL makes this safe enough
+    # Worst case: race creates two instances, last assignment wins
     pubsub_client = RedisPubSubManager(
         host=settings.REDIS_HOST,
         port=settings.REDIS_PORT,
     )
-    ws_manager = WebsocketManager(pubsub_client=pubsub_client)
-    local.ws_manager = ws_manager
-    return ws_manager
+    _ws_manager = WebsocketManager(pubsub_client=pubsub_client)
+    return _ws_manager
+
+
+def reset_ws_manager() -> None:
+    """Reset singleton for testing. DO NOT use in production."""
+    global _ws_manager
+    _ws_manager = None

server/tests/conftest.py

@@ -1,11 +1,10 @@
 import os
 from contextlib import asynccontextmanager
-from tempfile import NamedTemporaryFile
 from unittest.mock import patch
 
 import pytest
 
-from reflector.schemas.platform import WHEREBY_PLATFORM
+from reflector.schemas.platform import DAILY_PLATFORM, WHEREBY_PLATFORM
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -15,6 +14,7 @@ def register_mock_platform():
     from reflector.video_platforms.registry import register_platform
 
     register_platform(WHEREBY_PLATFORM, MockPlatformClient)
+    register_platform(DAILY_PLATFORM, MockPlatformClient)
     yield
 
 
@@ -333,11 +333,14 @@ def celery_enable_logging():
 
 @pytest.fixture(scope="session")
 def celery_config():
-    with NamedTemporaryFile() as f:
-        yield {
-            "broker_url": "memory://",
-            "result_backend": f"db+sqlite:///{f.name}",
-        }
+    redis_host = os.environ.get("REDIS_HOST", "localhost")
+    redis_port = os.environ.get("REDIS_PORT", "6379")
+    # Use db 2 to avoid conflicts with main app
+    redis_url = f"redis://{redis_host}:{redis_port}/2"
+    yield {
+        "broker_url": redis_url,
+        "result_backend": redis_url,
+    }
 
 
 @pytest.fixture(scope="session")
@@ -370,9 +373,12 @@ async def ws_manager_in_memory(monkeypatch):
         def __init__(self, queue: asyncio.Queue):
             self.queue = queue
 
-        async def get_message(self, ignore_subscribe_messages: bool = True):
+        async def get_message(
+            self, ignore_subscribe_messages: bool = True, timeout: float | None = None
+        ):
+            wait_timeout = timeout if timeout is not None else 0.05
             try:
-                return await asyncio.wait_for(self.queue.get(), timeout=0.05)
+                return await asyncio.wait_for(self.queue.get(), timeout=wait_timeout)
             except Exception:
                 return None
 

server/tests/test_dag_progress.py

@@ -0,0 +1,959 @@
+"""Tests for DAG progress models and transform function.
+
+Tests the extract_dag_tasks function that converts Hatchet V1WorkflowRunDetails
+into structured DagTask list for frontend consumption.
+"""
+
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from reflector.hatchet.constants import TaskName
+from reflector.hatchet.dag_progress import (
+    DagStatusData,
+    DagTask,
+    DagTaskStatus,
+    extract_dag_tasks,
+)
+
+
+def _make_shape_item(
+    step_id: str,
+    task_name: str,
+    children_step_ids: list[str] | None = None,
+) -> MagicMock:
+    """Create a mock WorkflowRunShapeItemForWorkflowRunDetails."""
+    item = MagicMock()
+    item.step_id = step_id
+    item.task_name = task_name
+    item.children_step_ids = children_step_ids or []
+    return item
+
+
+def _make_task_summary(
+    step_id: str,
+    status: str = "QUEUED",
+    started_at: datetime | None = None,
+    finished_at: datetime | None = None,
+    duration: int | None = None,
+    error_message: str | None = None,
+    task_external_id: str | None = None,
+    num_spawned_children: int | None = None,
+    children: list | None = None,
+) -> MagicMock:
+    """Create a mock V1TaskSummary."""
+    from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+    task = MagicMock()
+    task.step_id = step_id
+    task.status = V1TaskStatus(status)
+    task.started_at = started_at
+    task.finished_at = finished_at
+    task.duration = duration
+    task.error_message = error_message
+    task.task_external_id = task_external_id or f"ext-{step_id}"
+    task.num_spawned_children = num_spawned_children
+    task.children = children or []
+    return task
+
+
+def _make_details(
+    shape: list,
+    tasks: list,
+    run_id: str = "test-run-id",
+) -> MagicMock:
+    """Create a mock V1WorkflowRunDetails."""
+    details = MagicMock()
+    details.shape = shape
+    details.tasks = tasks
+    details.task_events = []
+    details.run = MagicMock()
+    details.run.metadata = MagicMock()
+    details.run.metadata.id = run_id
+    return details
+
+
+class TestExtractDagTasksBasic:
+    """Test basic extraction of DAG tasks from workflow run details."""
+
+    def test_empty_shape_returns_empty_list(self):
+        details = _make_details(shape=[], tasks=[])
+        result = extract_dag_tasks(details)
+        assert result == []
+
+    def test_single_task_queued(self):
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = [_make_task_summary("s1", status="QUEUED")]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert len(result) == 1
+        assert result[0].name == "get_recording"
+        assert result[0].status == DagTaskStatus.QUEUED
+        assert result[0].parents == []
+        assert result[0].started_at is None
+        assert result[0].finished_at is None
+        assert result[0].duration_seconds is None
+        assert result[0].error is None
+        assert result[0].children_total is None
+        assert result[0].children_completed is None
+        assert result[0].progress_pct is None
+
+    def test_completed_task_with_duration(self):
+        now = datetime.now(timezone.utc)
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = [
+            _make_task_summary(
+                "s1",
+                status="COMPLETED",
+                started_at=now,
+                finished_at=now,
+                duration=1500,  # milliseconds
+            )
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].status == DagTaskStatus.COMPLETED
+        assert result[0].duration_seconds == 1.5
+        assert result[0].started_at == now
+        assert result[0].finished_at == now
+
+    def test_failed_task_with_error(self):
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = [
+            _make_task_summary(
+                "s1",
+                status="FAILED",
+                error_message="Traceback (most recent call last):\n  File something\nConnectionError: connection refused",
+            )
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].status == DagTaskStatus.FAILED
+        assert result[0].error == "ConnectionError: connection refused"
+
+    def test_running_task(self):
+        now = datetime.now(timezone.utc)
+        shape = [_make_shape_item("s1", "mixdown_tracks")]
+        tasks = [
+            _make_task_summary(
+                "s1",
+                status="RUNNING",
+                started_at=now,
+                duration=5000,
+            )
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].status == DagTaskStatus.RUNNING
+        assert result[0].started_at == now
+        assert result[0].duration_seconds == 5.0
+
+    def test_cancelled_task(self):
+        shape = [_make_shape_item("s1", "post_zulip")]
+        tasks = [_make_task_summary("s1", status="CANCELLED")]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].status == DagTaskStatus.CANCELLED
+
+
+class TestExtractDagTasksTopology:
+    """Test topological ordering and parent extraction."""
+
+    def test_linear_chain_parents(self):
+        """A -> B -> C should produce correct parents."""
+        shape = [
+            _make_shape_item("s1", "get_recording", children_step_ids=["s2"]),
+            _make_shape_item("s2", "get_participants", children_step_ids=["s3"]),
+            _make_shape_item("s3", "process_tracks"),
+        ]
+        tasks = [
+            _make_task_summary("s1", status="COMPLETED"),
+            _make_task_summary("s2", status="COMPLETED"),
+            _make_task_summary("s3", status="QUEUED"),
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert [t.name for t in result] == [
+            "get_recording",
+            "get_participants",
+            "process_tracks",
+        ]
+        assert result[0].parents == []
+        assert result[1].parents == ["get_recording"]
+        assert result[2].parents == ["get_participants"]
+
+    def test_diamond_dag(self):
+        """
+        A -> B, A -> C, B -> D, C -> D
+        D should have parents [B, C] (or [C, B] depending on sort).
+        """
+        shape = [
+            _make_shape_item("s1", "get_recording", children_step_ids=["s2", "s3"]),
+            _make_shape_item("s2", "mixdown_tracks", children_step_ids=["s4"]),
+            _make_shape_item("s3", "detect_topics", children_step_ids=["s4"]),
+            _make_shape_item("s4", "finalize"),
+        ]
+        tasks = [
+            _make_task_summary("s1", status="COMPLETED"),
+            _make_task_summary("s2", status="RUNNING"),
+            _make_task_summary("s3", status="RUNNING"),
+            _make_task_summary("s4", status="QUEUED"),
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        # Topological: s1 first, s2/s3 in some order, s4 last
+        assert result[0].name == "get_recording"
+        assert result[-1].name == "finalize"
+        finalize = result[-1]
+        assert set(finalize.parents) == {"mixdown_tracks", "detect_topics"}
+
+    def test_topological_order_is_stable(self):
+        """Verify deterministic ordering (sorted queue in Kahn's)."""
+        shape = [
+            _make_shape_item("s_c", "task_c"),
+            _make_shape_item("s_a", "task_a", children_step_ids=["s_c"]),
+            _make_shape_item("s_b", "task_b", children_step_ids=["s_c"]),
+        ]
+        tasks = [
+            _make_task_summary("s_c", status="QUEUED"),
+            _make_task_summary("s_a", status="COMPLETED"),
+            _make_task_summary("s_b", status="COMPLETED"),
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        # s_a and s_b both roots with in-degree 0; sorted alphabetically by step_id
+        names = [t.name for t in result]
+        assert names[-1] == "task_c"
+        # First two should be task_a, task_b (sorted by step_id: s_a < s_b)
+        assert names[0] == "task_a"
+        assert names[1] == "task_b"
+
+    def test_production_dag_shape(self):
+        """Test the real 15-task pipeline topology with mixed statuses.
+
+        Simulates a mid-pipeline state where early tasks completed,
+        middle tasks running, and later tasks still queued.
+        """
+        # Production DAG edges (parent -> children):
+        # get_recording -> get_participants
+        # get_participants -> process_tracks
+        # process_tracks -> mixdown_tracks, detect_topics, finalize
+        # mixdown_tracks -> generate_waveform
+        # detect_topics -> generate_title, extract_subjects
+        # extract_subjects -> process_subjects, identify_action_items
+        # process_subjects -> generate_recap
+        # generate_title -> finalize
+        # generate_recap -> finalize
+        # identify_action_items -> finalize
+        # finalize -> cleanup_consent
+        # cleanup_consent -> post_zulip, send_webhook
+        shape = [
+            _make_shape_item(
+                "s_get_recording", TaskName.GET_RECORDING, ["s_get_participants"]
+            ),
+            _make_shape_item(
+                "s_get_participants", TaskName.GET_PARTICIPANTS, ["s_process_tracks"]
+            ),
+            _make_shape_item(
+                "s_process_tracks",
+                TaskName.PROCESS_TRACKS,
+                ["s_mixdown_tracks", "s_detect_topics", "s_finalize"],
+            ),
+            _make_shape_item(
+                "s_mixdown_tracks", TaskName.MIXDOWN_TRACKS, ["s_generate_waveform"]
+            ),
+            _make_shape_item("s_generate_waveform", TaskName.GENERATE_WAVEFORM),
+            _make_shape_item(
+                "s_detect_topics",
+                TaskName.DETECT_TOPICS,
+                ["s_generate_title", "s_extract_subjects"],
+            ),
+            _make_shape_item(
+                "s_generate_title", TaskName.GENERATE_TITLE, ["s_finalize"]
+            ),
+            _make_shape_item(
+                "s_extract_subjects",
+                TaskName.EXTRACT_SUBJECTS,
+                ["s_process_subjects", "s_identify_action_items"],
+            ),
+            _make_shape_item(
+                "s_process_subjects", TaskName.PROCESS_SUBJECTS, ["s_generate_recap"]
+            ),
+            _make_shape_item(
+                "s_generate_recap", TaskName.GENERATE_RECAP, ["s_finalize"]
+            ),
+            _make_shape_item(
+                "s_identify_action_items",
+                TaskName.IDENTIFY_ACTION_ITEMS,
+                ["s_finalize"],
+            ),
+            _make_shape_item("s_finalize", TaskName.FINALIZE, ["s_cleanup_consent"]),
+            _make_shape_item(
+                "s_cleanup_consent",
+                TaskName.CLEANUP_CONSENT,
+                ["s_post_zulip", "s_send_webhook"],
+            ),
+            _make_shape_item("s_post_zulip", TaskName.POST_ZULIP),
+            _make_shape_item("s_send_webhook", TaskName.SEND_WEBHOOK),
+        ]
+
+        # Mid-pipeline: early tasks done, middle running, later queued
+        tasks = [
+            _make_task_summary("s_get_recording", status="COMPLETED"),
+            _make_task_summary("s_get_participants", status="COMPLETED"),
+            _make_task_summary("s_process_tracks", status="COMPLETED"),
+            _make_task_summary("s_mixdown_tracks", status="RUNNING"),
+            _make_task_summary("s_generate_waveform", status="QUEUED"),
+            _make_task_summary("s_detect_topics", status="RUNNING"),
+            _make_task_summary("s_generate_title", status="QUEUED"),
+            _make_task_summary("s_extract_subjects", status="QUEUED"),
+            _make_task_summary("s_process_subjects", status="QUEUED"),
+            _make_task_summary("s_generate_recap", status="QUEUED"),
+            _make_task_summary("s_identify_action_items", status="QUEUED"),
+            _make_task_summary("s_finalize", status="QUEUED"),
+            _make_task_summary("s_cleanup_consent", status="QUEUED"),
+            _make_task_summary("s_post_zulip", status="QUEUED"),
+            _make_task_summary("s_send_webhook", status="QUEUED"),
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        # All 15 tasks present
+        assert len(result) == 15
+        result_names = [t.name for t in result]
+        assert set(result_names) == {
+            TaskName.GET_RECORDING,
+            TaskName.GET_PARTICIPANTS,
+            TaskName.PROCESS_TRACKS,
+            TaskName.MIXDOWN_TRACKS,
+            TaskName.GENERATE_WAVEFORM,
+            TaskName.DETECT_TOPICS,
+            TaskName.GENERATE_TITLE,
+            TaskName.EXTRACT_SUBJECTS,
+            TaskName.PROCESS_SUBJECTS,
+            TaskName.GENERATE_RECAP,
+            TaskName.IDENTIFY_ACTION_ITEMS,
+            TaskName.FINALIZE,
+            TaskName.CLEANUP_CONSENT,
+            TaskName.POST_ZULIP,
+            TaskName.SEND_WEBHOOK,
+        }
+
+        # Topological order invariant: no task appears before its parents
+        name_to_index = {t.name: i for i, t in enumerate(result)}
+        for task in result:
+            for parent_name in task.parents:
+                assert name_to_index[parent_name] < name_to_index[task.name], (
+                    f"Parent {parent_name} (idx {name_to_index[parent_name]}) "
+                    f"must appear before {task.name} (idx {name_to_index[task.name]})"
+                )
+
+        # finalize has exactly 4 parents
+        finalize = next(t for t in result if t.name == TaskName.FINALIZE)
+        assert set(finalize.parents) == {
+            TaskName.PROCESS_TRACKS,
+            TaskName.GENERATE_TITLE,
+            TaskName.GENERATE_RECAP,
+            TaskName.IDENTIFY_ACTION_ITEMS,
+        }
+
+        # cleanup_consent has 1 parent (finalize)
+        cleanup = next(t for t in result if t.name == TaskName.CLEANUP_CONSENT)
+        assert cleanup.parents == [TaskName.FINALIZE]
+
+        # post_zulip and send_webhook both have cleanup_consent as parent
+        post_zulip = next(t for t in result if t.name == TaskName.POST_ZULIP)
+        send_webhook = next(t for t in result if t.name == TaskName.SEND_WEBHOOK)
+        assert post_zulip.parents == [TaskName.CLEANUP_CONSENT]
+        assert send_webhook.parents == [TaskName.CLEANUP_CONSENT]
+
+        # Verify statuses propagated correctly
+        assert (
+            next(t for t in result if t.name == TaskName.GET_RECORDING).status
+            == DagTaskStatus.COMPLETED
+        )
+        assert (
+            next(t for t in result if t.name == TaskName.MIXDOWN_TRACKS).status
+            == DagTaskStatus.RUNNING
+        )
+        assert (
+            next(t for t in result if t.name == TaskName.FINALIZE).status
+            == DagTaskStatus.QUEUED
+        )
+
+    def test_topological_sort_invariant_complex_dag(self):
+        """For a complex DAG, every task's parents appear earlier in the list.
+
+        Uses a wider branching/merging DAG than diamond to stress the invariant.
+        """
+        # DAG: A -> B, A -> C, A -> D, B -> E, C -> E, C -> F, D -> F, E -> G, F -> G
+        shape = [
+            _make_shape_item("s_a", "task_a", ["s_b", "s_c", "s_d"]),
+            _make_shape_item("s_b", "task_b", ["s_e"]),
+            _make_shape_item("s_c", "task_c", ["s_e", "s_f"]),
+            _make_shape_item("s_d", "task_d", ["s_f"]),
+            _make_shape_item("s_e", "task_e", ["s_g"]),
+            _make_shape_item("s_f", "task_f", ["s_g"]),
+            _make_shape_item("s_g", "task_g"),
+        ]
+        tasks = [
+            _make_task_summary("s_a", status="COMPLETED"),
+            _make_task_summary("s_b", status="COMPLETED"),
+            _make_task_summary("s_c", status="RUNNING"),
+            _make_task_summary("s_d", status="COMPLETED"),
+            _make_task_summary("s_e", status="QUEUED"),
+            _make_task_summary("s_f", status="QUEUED"),
+            _make_task_summary("s_g", status="QUEUED"),
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert len(result) == 7
+        name_to_index = {t.name: i for i, t in enumerate(result)}
+
+        # Verify invariant: every parent appears before its child
+        for task in result:
+            for parent_name in task.parents:
+                assert name_to_index[parent_name] < name_to_index[task.name], (
+                    f"Parent {parent_name} (idx {name_to_index[parent_name]}) "
+                    f"must appear before {task.name} (idx {name_to_index[task.name]})"
+                )
+
+        # task_g has 2 parents
+        task_g = next(t for t in result if t.name == "task_g")
+        assert set(task_g.parents) == {"task_e", "task_f"}
+
+        # task_e has 2 parents
+        task_e = next(t for t in result if t.name == "task_e")
+        assert set(task_e.parents) == {"task_b", "task_c"}
+
+        # task_a is root (first in topological order)
+        assert result[0].name == "task_a"
+        assert result[0].parents == []
+
+
+class TestExtractDagTasksFanOut:
+    """Test fan-out tasks with spawned children."""
+
+    def test_fan_out_children_counts(self):
+        from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+        child_mocks = []
+        for status in ["COMPLETED", "COMPLETED", "RUNNING", "QUEUED"]:
+            child = MagicMock()
+            child.status = V1TaskStatus(status)
+            child_mocks.append(child)
+
+        shape = [_make_shape_item("s1", "process_tracks")]
+        tasks = [
+            _make_task_summary(
+                "s1",
+                status="RUNNING",
+                num_spawned_children=4,
+                children=child_mocks,
+            )
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].children_total == 4
+        assert result[0].children_completed == 2
+
+    def test_no_children_when_no_spawn(self):
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = [
+            _make_task_summary("s1", status="COMPLETED", num_spawned_children=None)
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].children_total is None
+        assert result[0].children_completed is None
+
+    def test_zero_spawned_children(self):
+        shape = [_make_shape_item("s1", "process_tracks")]
+        tasks = [_make_task_summary("s1", status="COMPLETED", num_spawned_children=0)]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert result[0].children_total is None
+        assert result[0].children_completed is None
+
+
+class TestExtractDagTasksErrorExtraction:
+    """Test error message extraction logic."""
+
+    def test_simple_error(self):
+        shape = [_make_shape_item("s1", "mixdown_tracks")]
+        tasks = [
+            _make_task_summary(
+                "s1", status="FAILED", error_message="ValueError: no tracks"
+            )
+        ]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+        assert result[0].error == "ValueError: no tracks"
+
+    def test_traceback_extracts_meaningful_line(self):
+        error = (
+            "Traceback (most recent call last):\n"
+            '  File "/app/something.py", line 42\n'
+            "RuntimeError: out of memory"
+        )
+        shape = [_make_shape_item("s1", "mixdown_tracks")]
+        tasks = [_make_task_summary("s1", status="FAILED", error_message=error)]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+        assert result[0].error == "RuntimeError: out of memory"
+
+    def test_no_error_when_none(self):
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = [_make_task_summary("s1", status="COMPLETED", error_message=None)]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+        assert result[0].error is None
+
+    def test_empty_error_message(self):
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = [_make_task_summary("s1", status="FAILED", error_message="")]
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+        assert result[0].error is None
+
+
+class TestExtractDagTasksMissingData:
+    """Test edge cases with missing task data."""
+
+    def test_shape_without_matching_task(self):
+        """Shape has a step but tasks list doesn't contain it."""
+        shape = [_make_shape_item("s1", "get_recording")]
+        tasks = []  # No matching task
+        details = _make_details(shape, tasks)
+
+        result = extract_dag_tasks(details)
+
+        assert len(result) == 1
+        assert result[0].name == "get_recording"
+        assert result[0].status == DagTaskStatus.QUEUED  # default when no task data
+        assert result[0].started_at is None
+
+    def test_none_shape_returns_empty(self):
+        details = _make_details(shape=[], tasks=[])
+        details.shape = None
+
+        result = extract_dag_tasks(details)
+        assert result == []
+
+
+class TestDagStatusData:
+    """Test DagStatusData model serialization."""
+
+    def test_serialization(self):
+        task = DagTask(
+            name="get_recording",
+            status=DagTaskStatus.COMPLETED,
+            started_at=datetime(2025, 1, 1, tzinfo=timezone.utc),
+            finished_at=datetime(2025, 1, 1, 0, 0, 1, tzinfo=timezone.utc),
+            duration_seconds=1.0,
+            parents=[],
+            error=None,
+            children_total=None,
+            children_completed=None,
+            progress_pct=None,
+        )
+        data = DagStatusData(workflow_run_id="test-123", tasks=[task])
+        dumped = data.model_dump(mode="json")
+
+        assert dumped["workflow_run_id"] == "test-123"
+        assert len(dumped["tasks"]) == 1
+        assert dumped["tasks"][0]["name"] == "get_recording"
+        assert dumped["tasks"][0]["status"] == "completed"
+        assert dumped["tasks"][0]["duration_seconds"] == 1.0
+
+
+class AsyncContextManager:
+    """No-op async context manager for mocking fresh_db_connection."""
+
+    async def __aenter__(self):
+        return None
+
+    async def __aexit__(self, *args):
+        return None
+
+
+class TestBroadcastDagStatus:
+    """Test broadcast_dag_status function.
+
+    broadcast_dag_status uses deferred imports inside its function body.
+    We mock the source modules/objects before calling the function.
+    Importing daily_multitrack_pipeline triggers a cascade
+    (subject_processing -> HatchetClientManager.get_client at module level),
+    so we set _instance before the import to prevent real SDK init.
+    """
+
+    @pytest.fixture(autouse=True)
+    def _setup_hatchet_mock(self):
+        """Set HatchetClientManager._instance to a mock to prevent real SDK init.
+
+        Module-level code in workflow files calls get_client() during import.
+        Setting _instance before import avoids ClientConfig validation.
+        """
+        from reflector.hatchet.client import HatchetClientManager
+
+        original = HatchetClientManager._instance
+        HatchetClientManager._instance = MagicMock()
+        yield
+        HatchetClientManager._instance = original
+
+    @pytest.mark.asyncio
+    async def test_broadcasts_dag_status(self):
+        """broadcast_dag_status fetches run, transforms, and broadcasts."""
+        mock_transcript = MagicMock()
+        mock_transcript.id = "t-123"
+
+        mock_details = _make_details(
+            shape=[_make_shape_item("s1", "get_recording")],
+            tasks=[_make_task_summary("s1", status="COMPLETED")],
+            run_id="wf-abc",
+        )
+
+        mock_client = MagicMock()
+        mock_client.runs.aio_get = AsyncMock(return_value=mock_details)
+
+        with (
+            patch(
+                "reflector.hatchet.client.HatchetClientManager.get_client",
+                return_value=mock_client,
+            ),
+            patch(
+                "reflector.hatchet.broadcast.append_event_and_broadcast",
+                new_callable=AsyncMock,
+            ) as mock_broadcast,
+            patch(
+                "reflector.db.transcripts.transcripts_controller.get_by_id",
+                new_callable=AsyncMock,
+                return_value=mock_transcript,
+            ),
+            patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
+                return_value=AsyncContextManager(),
+            ),
+        ):
+            from reflector.hatchet.dag_progress import broadcast_dag_status
+
+            await broadcast_dag_status("t-123", "wf-abc")
+
+            mock_client.runs.aio_get.assert_called_once_with("wf-abc")
+            mock_broadcast.assert_called_once()
+            call_args = mock_broadcast.call_args
+            assert call_args[0][0] == "t-123"  # transcript_id
+            assert call_args[0][1] is mock_transcript  # transcript
+            assert call_args[0][2] == "DAG_STATUS"  # event_name
+            data = call_args[0][3]
+            assert isinstance(data, DagStatusData)
+            assert data.workflow_run_id == "wf-abc"
+            assert len(data.tasks) == 1
+
+    @pytest.mark.asyncio
+    async def test_swallows_exceptions(self):
+        """broadcast_dag_status never raises even when internals fail."""
+        from reflector.hatchet.dag_progress import broadcast_dag_status
+
+        with patch(
+            "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
+            side_effect=RuntimeError("db exploded"),
+        ):
+            # Should not raise
+            await broadcast_dag_status("t-123", "wf-abc")
+
+    @pytest.mark.asyncio
+    async def test_no_broadcast_when_transcript_not_found(self):
+        """broadcast_dag_status does not broadcast if transcript is None."""
+        mock_details = _make_details(
+            shape=[_make_shape_item("s1", "get_recording")],
+            tasks=[_make_task_summary("s1", status="COMPLETED")],
+        )
+
+        mock_client = MagicMock()
+        mock_client.runs.aio_get = AsyncMock(return_value=mock_details)
+
+        with (
+            patch(
+                "reflector.hatchet.client.HatchetClientManager.get_client",
+                return_value=mock_client,
+            ),
+            patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
+                return_value=AsyncContextManager(),
+            ),
+            patch(
+                "reflector.db.transcripts.transcripts_controller.get_by_id",
+                new_callable=AsyncMock,
+                return_value=None,
+            ),
+            patch(
+                "reflector.hatchet.broadcast.append_event_and_broadcast",
+                new_callable=AsyncMock,
+            ) as mock_broadcast,
+        ):
+            from reflector.hatchet.dag_progress import broadcast_dag_status
+
+            await broadcast_dag_status("t-123", "wf-abc")
+
+            mock_broadcast.assert_not_called()
+
+
+class TestMakeAudioProgressLoggerWithBroadcast:
+    """Test make_audio_progress_logger with transcript_id for transient broadcasts."""
+
+    @pytest.fixture(autouse=True)
+    def _setup_hatchet_mock(self):
+        """Set HatchetClientManager._instance to prevent real SDK init on import."""
+        from reflector.hatchet.client import HatchetClientManager
+
+        original = HatchetClientManager._instance
+        if original is None:
+            HatchetClientManager._instance = MagicMock()
+        yield
+        HatchetClientManager._instance = original
+
+    def test_broadcasts_transient_progress_event(self):
+        """When transcript_id provided and progress_pct not None, broadcasts event."""
+        import asyncio
+
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            make_audio_progress_logger,
+        )
+
+        ctx = MagicMock()
+        ctx.log = MagicMock()
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+        mock_broadcast = AsyncMock()
+        tasks_created = []
+
+        original_create_task = loop.create_task
+
+        def capture_create_task(coro):
+            task = original_create_task(coro)
+            tasks_created.append(task)
+            return task
+
+        try:
+            with (
+                patch(
+                    "reflector.hatchet.broadcast.broadcast_event",
+                    mock_broadcast,
+                ),
+                patch.object(loop, "create_task", side_effect=capture_create_task),
+            ):
+                callback = make_audio_progress_logger(
+                    ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id="t-123"
+                )
+                callback(50.0, 100.0)
+
+                # Run pending tasks
+                if tasks_created:
+                    loop.run_until_complete(asyncio.gather(*tasks_created))
+
+                mock_broadcast.assert_called_once()
+                event_arg = mock_broadcast.call_args[0][1]
+                assert event_arg.event == "DAG_TASK_PROGRESS"
+                assert event_arg.data["task_name"] == TaskName.MIXDOWN_TRACKS
+                assert event_arg.data["progress_pct"] == 50.0
+        finally:
+            loop.close()
+
+    def test_no_broadcast_without_transcript_id(self):
+        """When transcript_id is None, no broadcast happens."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            make_audio_progress_logger,
+        )
+
+        ctx = MagicMock()
+
+        with patch(
+            "reflector.hatchet.broadcast.broadcast_event",
+            new_callable=AsyncMock,
+        ) as mock_broadcast:
+            callback = make_audio_progress_logger(
+                ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id=None
+            )
+            callback(50.0, 100.0)
+            mock_broadcast.assert_not_called()
+
+    def test_no_broadcast_when_progress_pct_is_none(self):
+        """When progress_pct is None, no broadcast happens even with transcript_id."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            make_audio_progress_logger,
+        )
+
+        ctx = MagicMock()
+
+        with patch(
+            "reflector.hatchet.broadcast.broadcast_event",
+            new_callable=AsyncMock,
+        ) as mock_broadcast:
+            callback = make_audio_progress_logger(
+                ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id="t-123"
+            )
+            callback(None, 100.0)
+            mock_broadcast.assert_not_called()
+
+    def test_logging_throttled_by_interval(self):
+        """With interval=5.0, rapid calls only log once until interval elapses.
+
+        The throttle applies to ctx.log() calls. Broadcasts (fire-and-forget)
+        are not throttled — they occur every call when transcript_id + progress_pct set.
+        """
+        import asyncio
+        import time as time_mod
+
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            make_audio_progress_logger,
+        )
+
+        ctx = MagicMock()
+        ctx.log = MagicMock()
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+        mock_broadcast = AsyncMock()
+        tasks_created = []
+        original_create_task = loop.create_task
+
+        def capture_create_task(coro):
+            task = original_create_task(coro)
+            tasks_created.append(task)
+            return task
+
+        # Controlled monotonic values for the 4 calls from make_audio_progress_logger:
+        # init (start_time, last_log_time), call1 (now), call2 (now), call3 (now)
+        # After those, fall back to real time.monotonic() for asyncio internals.
+        controlled_values = [100.0, 100.0, 101.0, 106.0]
+        call_index = [0]
+        real_monotonic = time_mod.monotonic
+
+        def mock_monotonic():
+            if call_index[0] < len(controlled_values):
+                val = controlled_values[call_index[0]]
+                call_index[0] += 1
+                return val
+            return real_monotonic()
+
+        try:
+            with (
+                patch(
+                    "reflector.hatchet.workflows.daily_multitrack_pipeline.time.monotonic",
+                    side_effect=mock_monotonic,
+                ),
+                patch(
+                    "reflector.hatchet.broadcast.broadcast_event",
+                    mock_broadcast,
+                ),
+                patch.object(loop, "create_task", side_effect=capture_create_task),
+            ):
+                callback = make_audio_progress_logger(
+                    ctx, TaskName.MIXDOWN_TRACKS, interval=5.0, transcript_id="t-123"
+                )
+
+                # Call 1 at t=100.0: 100.0 - 100.0 = 0.0 < 5.0 => no log
+                callback(25.0, 50.0)
+                assert ctx.log.call_count == 0
+
+                # Call 2 at t=101.0: 101.0 - 100.0 = 1.0 < 5.0 => no log
+                callback(50.0, 100.0)
+                assert ctx.log.call_count == 0
+
+                # Call 3 at t=106.0: 106.0 - 100.0 = 6.0 >= 5.0 => logs
+                callback(75.0, 150.0)
+                assert ctx.log.call_count == 1
+
+                # Run pending broadcast tasks
+                if tasks_created:
+                    loop.run_until_complete(asyncio.gather(*tasks_created))
+
+                # Broadcasts happen on every call (not throttled) — 3 calls total
+                assert mock_broadcast.call_count == 3
+        finally:
+            loop.close()
+
+    def test_uses_broadcast_event_not_append_event_and_broadcast(self):
+        """Progress events use broadcast_event (transient), not append_event_and_broadcast (persisted)."""
+        import asyncio
+
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            make_audio_progress_logger,
+        )
+
+        ctx = MagicMock()
+        ctx.log = MagicMock()
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+        mock_broadcast_event = AsyncMock()
+        mock_append = AsyncMock()
+        tasks_created = []
+        original_create_task = loop.create_task
+
+        def capture_create_task(coro):
+            task = original_create_task(coro)
+            tasks_created.append(task)
+            return task
+
+        try:
+            with (
+                patch(
+                    "reflector.hatchet.broadcast.broadcast_event",
+                    mock_broadcast_event,
+                ),
+                patch(
+                    "reflector.hatchet.broadcast.append_event_and_broadcast",
+                    mock_append,
+                ),
+                patch.object(loop, "create_task", side_effect=capture_create_task),
+            ):
+                callback = make_audio_progress_logger(
+                    ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id="t-123"
+                )
+                callback(50.0, 100.0)
+
+                if tasks_created:
+                    loop.run_until_complete(asyncio.gather(*tasks_created))
+
+                # broadcast_event (transient) IS called
+                mock_broadcast_event.assert_called_once()
+                # append_event_and_broadcast (persisted) is NOT called
+                mock_append.assert_not_called()
+        finally:
+            loop.close()

server/tests/test_dag_progress_decorator.py

@@ -0,0 +1,181 @@
+"""Tests for with_error_handling decorator integration with broadcast_dag_status.
+
+The decorator wraps each pipeline task and calls broadcast_dag_status on both
+success and failure paths. These tests verify that integration rather than
+testing broadcast_dag_status in isolation (which test_dag_progress.py covers).
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from reflector.hatchet.constants import TaskName
+
+
+class TestWithErrorHandlingBroadcast:
+    """Test with_error_handling decorator's integration with broadcast_dag_status."""
+
+    @pytest.fixture(autouse=True)
+    def _setup_hatchet_mock(self):
+        """Set HatchetClientManager._instance to a mock to prevent real SDK init.
+
+        Module-level code in workflow files calls get_client() during import.
+        Setting _instance before import avoids ClientConfig validation.
+        """
+        from reflector.hatchet.client import HatchetClientManager
+
+        original = HatchetClientManager._instance
+        HatchetClientManager._instance = MagicMock()
+        yield
+        HatchetClientManager._instance = original
+
+    def _make_input(self, transcript_id: str = "t-123") -> MagicMock:
+        """Create a mock PipelineInput with transcript_id."""
+        inp = MagicMock()
+        inp.transcript_id = transcript_id
+        return inp
+
+    def _make_ctx(self, workflow_run_id: str = "wf-abc") -> MagicMock:
+        """Create a mock Context with workflow_run_id."""
+        ctx = MagicMock()
+        ctx.workflow_run_id = workflow_run_id
+        return ctx
+
+    @pytest.mark.asyncio
+    async def test_calls_broadcast_on_success(self):
+        """Decorator calls broadcast_dag_status once when task succeeds."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            with_error_handling,
+        )
+
+        inner = AsyncMock(return_value="ok")
+        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
+
+        with patch(
+            "reflector.hatchet.dag_progress.broadcast_dag_status",
+            new_callable=AsyncMock,
+        ) as mock_broadcast:
+            result = await wrapped(self._make_input(), self._make_ctx())
+
+        assert result == "ok"
+        mock_broadcast.assert_called_once_with("t-123", "wf-abc")
+
+    @pytest.mark.asyncio
+    async def test_calls_broadcast_on_failure(self):
+        """Decorator calls broadcast_dag_status once when task raises."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            with_error_handling,
+        )
+
+        inner = AsyncMock(side_effect=RuntimeError("boom"))
+        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
+
+        with (
+            patch(
+                "reflector.hatchet.dag_progress.broadcast_dag_status",
+                new_callable=AsyncMock,
+            ) as mock_broadcast,
+            patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ),
+        ):
+            with pytest.raises(RuntimeError, match="boom"):
+                await wrapped(self._make_input(), self._make_ctx())
+
+        mock_broadcast.assert_called_once_with("t-123", "wf-abc")
+
+    @pytest.mark.asyncio
+    async def test_swallows_broadcast_exception_on_success(self):
+        """Broadcast failure does not crash the task on the success path."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            with_error_handling,
+        )
+
+        inner = AsyncMock(return_value="ok")
+        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
+
+        with patch(
+            "reflector.hatchet.dag_progress.broadcast_dag_status",
+            new_callable=AsyncMock,
+            side_effect=RuntimeError("broadcast exploded"),
+        ):
+            result = await wrapped(self._make_input(), self._make_ctx())
+
+        assert result == "ok"
+
+    @pytest.mark.asyncio
+    async def test_swallows_broadcast_exception_on_failure(self):
+        """Original task exception propagates even when broadcast also fails."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            with_error_handling,
+        )
+
+        inner = AsyncMock(side_effect=ValueError("original error"))
+        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
+
+        with (
+            patch(
+                "reflector.hatchet.dag_progress.broadcast_dag_status",
+                new_callable=AsyncMock,
+                side_effect=RuntimeError("broadcast exploded"),
+            ),
+            patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ),
+        ):
+            with pytest.raises(ValueError, match="original error"):
+                await wrapped(self._make_input(), self._make_ctx())
+
+    @pytest.mark.asyncio
+    async def test_calls_set_workflow_error_status_on_failure(self):
+        """On task failure with set_error_status=True (default), calls set_workflow_error_status."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            with_error_handling,
+        )
+
+        inner = AsyncMock(side_effect=RuntimeError("boom"))
+        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
+
+        with (
+            patch(
+                "reflector.hatchet.dag_progress.broadcast_dag_status",
+                new_callable=AsyncMock,
+            ),
+            patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error,
+        ):
+            with pytest.raises(RuntimeError, match="boom"):
+                await wrapped(self._make_input(), self._make_ctx())
+
+        mock_set_error.assert_called_once_with("t-123")
+
+    @pytest.mark.asyncio
+    async def test_no_set_workflow_error_status_when_disabled(self):
+        """With set_error_status=False, set_workflow_error_status is NOT called on failure."""
+        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+            with_error_handling,
+        )
+
+        inner = AsyncMock(side_effect=RuntimeError("boom"))
+        wrapped = with_error_handling(TaskName.GET_RECORDING, set_error_status=False)(
+            inner
+        )
+
+        with (
+            patch(
+                "reflector.hatchet.dag_progress.broadcast_dag_status",
+                new_callable=AsyncMock,
+            ),
+            patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error,
+        ):
+            with pytest.raises(RuntimeError, match="boom"):
+                await wrapped(self._make_input(), self._make_ctx())
+
+        mock_set_error.assert_not_called()

server/tests/test_dag_progress_rest.py

@@ -0,0 +1,421 @@
+"""Tests for DAG status REST enrichment on search and transcript GET endpoints."""
+
+from datetime import datetime, timezone
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+import reflector.db.search as search_module
+from reflector.db.search import SearchResult, _fetch_dag_statuses
+from reflector.db.transcripts import TranscriptEvent
+
+
+class TestFetchDagStatuses:
+    """Test the _fetch_dag_statuses helper."""
+
+    @pytest.mark.asyncio
+    async def test_returns_empty_for_empty_ids(self):
+        result = await _fetch_dag_statuses([])
+        assert result == {}
+
+    @pytest.mark.asyncio
+    async def test_extracts_last_dag_status(self):
+        events = [
+            {"event": "STATUS", "data": {"value": "processing"}},
+            {
+                "event": "DAG_STATUS",
+                "data": {
+                    "workflow_run_id": "r1",
+                    "tasks": [{"name": "get_recording", "status": "completed"}],
+                },
+            },
+            {
+                "event": "DAG_STATUS",
+                "data": {
+                    "workflow_run_id": "r1",
+                    "tasks": [
+                        {"name": "get_recording", "status": "completed"},
+                        {"name": "process_tracks", "status": "running"},
+                    ],
+                },
+            },
+        ]
+        mock_row = {"id": "t1", "events": events}
+
+        with patch("reflector.db.search.get_database") as mock_db:
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
+            result = await _fetch_dag_statuses(["t1"])
+
+        assert "t1" in result
+        assert len(result["t1"]) == 2  # Last DAG_STATUS had 2 tasks
+
+    @pytest.mark.asyncio
+    async def test_skips_transcripts_without_events(self):
+        mock_row = {"id": "t1", "events": None}
+
+        with patch("reflector.db.search.get_database") as mock_db:
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
+            result = await _fetch_dag_statuses(["t1"])
+
+        assert result == {}
+
+    @pytest.mark.asyncio
+    async def test_skips_transcripts_without_dag_status(self):
+        events = [
+            {"event": "STATUS", "data": {"value": "processing"}},
+            {"event": "DURATION", "data": {"duration": 1000}},
+        ]
+        mock_row = {"id": "t1", "events": events}
+
+        with patch("reflector.db.search.get_database") as mock_db:
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
+            result = await _fetch_dag_statuses(["t1"])
+
+        assert result == {}
+
+    @pytest.mark.asyncio
+    async def test_handles_json_string_events(self):
+        """Events stored as JSON string rather than already-parsed list."""
+        import json
+
+        events = [
+            {
+                "event": "DAG_STATUS",
+                "data": {
+                    "workflow_run_id": "r1",
+                    "tasks": [{"name": "transcribe", "status": "running"}],
+                },
+            },
+        ]
+        mock_row = {"id": "t1", "events": json.dumps(events)}
+
+        with patch("reflector.db.search.get_database") as mock_db:
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
+            result = await _fetch_dag_statuses(["t1"])
+
+        assert "t1" in result
+        assert len(result["t1"]) == 1
+        assert result["t1"][0]["name"] == "transcribe"
+
+    @pytest.mark.asyncio
+    async def test_multiple_transcripts(self):
+        """Handles multiple transcripts in one call."""
+        events_t1 = [
+            {
+                "event": "DAG_STATUS",
+                "data": {
+                    "workflow_run_id": "r1",
+                    "tasks": [{"name": "a", "status": "completed"}],
+                },
+            },
+        ]
+        events_t2 = [
+            {
+                "event": "DAG_STATUS",
+                "data": {
+                    "workflow_run_id": "r2",
+                    "tasks": [{"name": "b", "status": "running"}],
+                },
+            },
+        ]
+        mock_rows = [
+            {"id": "t1", "events": events_t1},
+            {"id": "t2", "events": events_t2},
+        ]
+
+        with patch("reflector.db.search.get_database") as mock_db:
+            mock_db.return_value.fetch_all = AsyncMock(return_value=mock_rows)
+            result = await _fetch_dag_statuses(["t1", "t2"])
+
+        assert "t1" in result
+        assert "t2" in result
+        assert result["t1"][0]["name"] == "a"
+        assert result["t2"][0]["name"] == "b"
+
+    @pytest.mark.asyncio
+    async def test_dag_status_without_tasks_key_skipped(self):
+        """DAG_STATUS event with no tasks key in data should be skipped."""
+        events = [
+            {"event": "DAG_STATUS", "data": {"workflow_run_id": "r1"}},
+        ]
+        mock_row = {"id": "t1", "events": events}
+
+        with patch("reflector.db.search.get_database") as mock_db:
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
+            result = await _fetch_dag_statuses(["t1"])
+
+        assert result == {}
+
+
+def _extract_dag_status_from_transcript(transcript):
+    """Replicate the dag_status extraction logic from transcript_get view.
+
+    This mirrors the code in reflector/views/transcripts.py lines 495-500:
+        dag_status = None
+        if transcript.status == "processing" and transcript.events:
+            for ev in reversed(transcript.events):
+                if ev.event == "DAG_STATUS":
+                    dag_status = ev.data.get("tasks") if isinstance(ev.data, dict) else None
+                    break
+    """
+    dag_status = None
+    if transcript.status == "processing" and transcript.events:
+        for ev in reversed(transcript.events):
+            if ev.event == "DAG_STATUS":
+                dag_status = ev.data.get("tasks") if isinstance(ev.data, dict) else None
+                break
+    return dag_status
+
+
+class TestTranscriptGetDagStatusExtraction:
+    """Test dag_status extraction logic from transcript_get endpoint.
+
+    The actual endpoint is complex to set up, so we test the extraction
+    logic directly using the same code pattern from the view.
+    """
+
+    def test_processing_transcript_with_dag_status_events(self):
+        """Processing transcript with DAG_STATUS events returns tasks from last event."""
+        transcript = SimpleNamespace(
+            status="processing",
+            events=[
+                TranscriptEvent(event="STATUS", data={"value": "processing"}),
+                TranscriptEvent(
+                    event="DAG_STATUS",
+                    data={
+                        "workflow_run_id": "r1",
+                        "tasks": [{"name": "get_recording", "status": "completed"}],
+                    },
+                ),
+                TranscriptEvent(
+                    event="DAG_STATUS",
+                    data={
+                        "workflow_run_id": "r1",
+                        "tasks": [
+                            {"name": "get_recording", "status": "completed"},
+                            {"name": "transcribe", "status": "running"},
+                        ],
+                    },
+                ),
+            ],
+        )
+
+        result = _extract_dag_status_from_transcript(transcript)
+
+        assert result is not None
+        assert len(result) == 2
+        assert result[0]["name"] == "get_recording"
+        assert result[1]["name"] == "transcribe"
+        assert result[1]["status"] == "running"
+
+    def test_processing_transcript_without_dag_status_events(self):
+        """Processing transcript with only non-DAG_STATUS events returns None."""
+        transcript = SimpleNamespace(
+            status="processing",
+            events=[
+                TranscriptEvent(event="STATUS", data={"value": "processing"}),
+                TranscriptEvent(event="DURATION", data={"duration": 1000}),
+            ],
+        )
+
+        result = _extract_dag_status_from_transcript(transcript)
+        assert result is None
+
+    def test_ended_transcript_with_dag_status_events(self):
+        """Ended transcript with DAG_STATUS events returns None (status check)."""
+        transcript = SimpleNamespace(
+            status="ended",
+            events=[
+                TranscriptEvent(
+                    event="DAG_STATUS",
+                    data={
+                        "workflow_run_id": "r1",
+                        "tasks": [{"name": "transcribe", "status": "completed"}],
+                    },
+                ),
+            ],
+        )
+
+        result = _extract_dag_status_from_transcript(transcript)
+        assert result is None
+
+    def test_processing_transcript_with_empty_events(self):
+        """Processing transcript with empty events list returns None."""
+        transcript = SimpleNamespace(
+            status="processing",
+            events=[],
+        )
+
+        result = _extract_dag_status_from_transcript(transcript)
+        assert result is None
+
+    def test_processing_transcript_with_none_events(self):
+        """Processing transcript with None events returns None."""
+        transcript = SimpleNamespace(
+            status="processing",
+            events=None,
+        )
+
+        result = _extract_dag_status_from_transcript(transcript)
+        assert result is None
+
+    def test_extracts_last_dag_status_not_first(self):
+        """Should pick the last DAG_STATUS event (most recent), not the first."""
+        transcript = SimpleNamespace(
+            status="processing",
+            events=[
+                TranscriptEvent(
+                    event="DAG_STATUS",
+                    data={
+                        "workflow_run_id": "r1",
+                        "tasks": [{"name": "a", "status": "running"}],
+                    },
+                ),
+                TranscriptEvent(event="STATUS", data={"value": "processing"}),
+                TranscriptEvent(
+                    event="DAG_STATUS",
+                    data={
+                        "workflow_run_id": "r1",
+                        "tasks": [
+                            {"name": "a", "status": "completed"},
+                            {"name": "b", "status": "running"},
+                        ],
+                    },
+                ),
+            ],
+        )
+
+        result = _extract_dag_status_from_transcript(transcript)
+        assert len(result) == 2
+        assert result[0]["status"] == "completed"
+        assert result[1]["name"] == "b"
+
+
+class TestSearchEnrichmentIntegration:
+    """Test DAG status enrichment in search results.
+
+    The search function enriches processing transcripts with dag_status
+    by calling _fetch_dag_statuses for processing IDs and assigning results.
+    We test this enrichment logic by mocking _fetch_dag_statuses.
+    """
+
+    def _make_search_result(self, id: str, status: str) -> SearchResult:
+        """Create a minimal SearchResult for testing."""
+        return SearchResult(
+            id=id,
+            title=f"Transcript {id}",
+            user_id="u1",
+            room_id=None,
+            room_name=None,
+            source_kind="live",
+            created_at=datetime(2024, 1, 1, tzinfo=timezone.utc),
+            status=status,
+            rank=1.0,
+            duration=60.0,
+            search_snippets=[],
+            total_match_count=0,
+            dag_status=None,
+        )
+
+    @pytest.mark.asyncio
+    async def test_processing_result_gets_dag_status(self):
+        """SearchResult with status='processing' and matching DAG_STATUS events
+        gets dag_status populated."""
+        results = [self._make_search_result("t1", "processing")]
+        dag_tasks = [
+            {"name": "get_recording", "status": "completed"},
+            {"name": "transcribe", "status": "running"},
+        ]
+
+        with patch.object(
+            search_module,
+            "_fetch_dag_statuses",
+            new_callable=AsyncMock,
+            return_value={"t1": dag_tasks},
+        ) as mock_fetch:
+            # Replicate the enrichment logic from SearchController.search_transcripts
+            processing_ids = [r.id for r in results if r.status == "processing"]
+            if processing_ids:
+                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
+                for r in results:
+                    if r.id in dag_statuses:
+                        r.dag_status = dag_statuses[r.id]
+
+            mock_fetch.assert_called_once_with(["t1"])
+
+        assert results[0].dag_status == dag_tasks
+
+    @pytest.mark.asyncio
+    async def test_ended_result_does_not_trigger_fetch(self):
+        """SearchResult with status='ended' does NOT trigger _fetch_dag_statuses."""
+        results = [self._make_search_result("t1", "ended")]
+
+        with patch.object(
+            search_module,
+            "_fetch_dag_statuses",
+            new_callable=AsyncMock,
+            return_value={},
+        ) as mock_fetch:
+            processing_ids = [r.id for r in results if r.status == "processing"]
+            if processing_ids:
+                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
+                for r in results:
+                    if r.id in dag_statuses:
+                        r.dag_status = dag_statuses[r.id]
+
+            mock_fetch.assert_not_called()
+
+        assert results[0].dag_status is None
+
+    @pytest.mark.asyncio
+    async def test_mixed_processing_and_ended_results(self):
+        """Only processing results get enriched; ended results stay None."""
+        results = [
+            self._make_search_result("t1", "processing"),
+            self._make_search_result("t2", "ended"),
+            self._make_search_result("t3", "processing"),
+        ]
+        dag_tasks_t1 = [{"name": "transcribe", "status": "running"}]
+        dag_tasks_t3 = [{"name": "diarize", "status": "completed"}]
+
+        with patch.object(
+            search_module,
+            "_fetch_dag_statuses",
+            new_callable=AsyncMock,
+            return_value={"t1": dag_tasks_t1, "t3": dag_tasks_t3},
+        ) as mock_fetch:
+            processing_ids = [r.id for r in results if r.status == "processing"]
+            if processing_ids:
+                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
+                for r in results:
+                    if r.id in dag_statuses:
+                        r.dag_status = dag_statuses[r.id]
+
+            mock_fetch.assert_called_once_with(["t1", "t3"])
+
+        assert results[0].dag_status == dag_tasks_t1
+        assert results[1].dag_status is None
+        assert results[2].dag_status == dag_tasks_t3
+
+    @pytest.mark.asyncio
+    async def test_processing_result_without_dag_events_stays_none(self):
+        """Processing result with no DAG_STATUS events in DB stays dag_status=None."""
+        results = [self._make_search_result("t1", "processing")]
+
+        with patch.object(
+            search_module,
+            "_fetch_dag_statuses",
+            new_callable=AsyncMock,
+            return_value={},
+        ) as mock_fetch:
+            processing_ids = [r.id for r in results if r.status == "processing"]
+            if processing_ids:
+                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
+                for r in results:
+                    if r.id in dag_statuses:
+                        r.dag_status = dag_statuses[r.id]
+
+            mock_fetch.assert_called_once_with(["t1"])
+
+        assert results[0].dag_status is None

server/tests/test_transcripts_process.py

@@ -1,6 +1,6 @@
 import asyncio
 import time
-from unittest.mock import patch
+from unittest.mock import AsyncMock, patch
 
 import pytest
 from httpx import ASGITransport, AsyncClient
@@ -142,17 +142,17 @@ async def test_whereby_recording_uses_file_pipeline(client):
             "reflector.services.transcript_process.task_pipeline_file_process"
         ) as mock_file_pipeline,
         patch(
-            "reflector.services.transcript_process.task_pipeline_multitrack_process"
-        ) as mock_multitrack_pipeline,
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet,
     ):
         response = await client.post(f"/transcripts/{transcript.id}/process")
 
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
 
-        # Whereby recordings should use file pipeline
+        # Whereby recordings should use file pipeline, not Hatchet
         mock_file_pipeline.delay.assert_called_once_with(transcript_id=transcript.id)
-        mock_multitrack_pipeline.delay.assert_not_called()
+        mock_hatchet.start_workflow.assert_not_called()
 
 
 @pytest.mark.usefixtures("setup_database")
@@ -177,8 +177,6 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
         recording_trigger="automatic-2nd-participant",
         is_shared=False,
     )
-    # Force Celery backend for test
-    await rooms_controller.update(room, {"use_celery": True})
 
     transcript = await transcripts_controller.add(
         "",
@@ -213,18 +211,23 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
             "reflector.services.transcript_process.task_pipeline_file_process"
         ) as mock_file_pipeline,
         patch(
-            "reflector.services.transcript_process.task_pipeline_multitrack_process"
-        ) as mock_multitrack_pipeline,
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet,
     ):
+        mock_hatchet.start_workflow = AsyncMock(return_value="test-workflow-id")
+
         response = await client.post(f"/transcripts/{transcript.id}/process")
 
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
 
-        # Daily.co multitrack recordings should use multitrack pipeline
-        mock_multitrack_pipeline.delay.assert_called_once_with(
-            transcript_id=transcript.id,
-            bucket_name="daily-bucket",
-            track_keys=track_keys,
-        )
+        # Daily.co multitrack recordings should use Hatchet workflow
+        mock_hatchet.start_workflow.assert_called_once()
+        call_kwargs = mock_hatchet.start_workflow.call_args.kwargs
+        assert call_kwargs["workflow_name"] == "DiarizationPipeline"
+        assert call_kwargs["input_data"]["transcript_id"] == transcript.id
+        assert call_kwargs["input_data"]["bucket_name"] == "daily-bucket"
+        assert call_kwargs["input_data"]["tracks"] == [
+            {"s3_key": k} for k in track_keys
+        ]
         mock_file_pipeline.delay.assert_not_called()

server/tests/test_transcripts_rtc_ws.py

@@ -115,9 +115,7 @@ def appserver(tmpdir, setup_database, celery_session_app, celery_session_worker)
     settings.DATA_DIR = DATA_DIR
 
 
-@pytest.fixture(scope="session")
-def celery_includes():
-    return ["reflector.pipelines.main_live_pipeline"]
+# Using celery_includes from conftest.py which includes both pipelines
 
 
 @pytest.mark.usefixtures("setup_database")

server/tests/test_user_websocket_auth.py

@@ -56,7 +56,12 @@ def appserver_ws_user(setup_database):
 
     if server_instance:
         server_instance.should_exit = True
-        server_thread.join(timeout=30)
+        server_thread.join(timeout=2.0)
+
+    # Reset global singleton for test isolation
+    from reflector.ws_manager import reset_ws_manager
+
+    reset_ws_manager()
 
 
 @pytest.fixture(autouse=True)
@@ -133,6 +138,8 @@ async def test_user_ws_accepts_valid_token_and_receives_events(appserver_ws_user
 
     # Connect and then trigger an event via HTTP create
     async with aconnect_ws(base_ws, subprotocols=subprotocols) as ws:
+        await asyncio.sleep(0.2)
+
         # Emit an event to the user's room via a standard HTTP action
         from httpx import AsyncClient
 
@@ -150,6 +157,7 @@ async def test_user_ws_accepts_valid_token_and_receives_events(appserver_ws_user
             "email": "user-abc@example.com",
         }
 
+        # Use in-memory client (global singleton makes it share ws_manager)
         async with AsyncClient(app=app, base_url=f"http://{host}:{port}/v1") as ac:
             # Create a transcript as this user so that the server publishes TRANSCRIPT_CREATED to user room
             resp = await ac.post("/transcripts", json={"name": "WS Test"})

server/tests/test_ws_dag_broadcast.py

@@ -0,0 +1,331 @@
+"""WebSocket broadcast delivery tests for STATUS and DAG_STATUS events.
+
+Tests the full chain identified in DEBUG.md:
+  broadcast_event() → ws_manager.send_json() → Redis/in-memory pub/sub
+    → _pubsub_data_reader() → socket.send_json() → WebSocket client
+
+Covers:
+1. STATUS event delivery to transcript room WS
+2. DAG_STATUS event delivery to transcript room WS
+3. Full broadcast_event() chain (requires broadcast.py patching)
+4. _pubsub_data_reader resilience when a client disconnects
+"""
+
+import asyncio
+import threading
+import time
+
+import pytest
+from httpx import AsyncClient
+from httpx_ws import aconnect_ws
+from uvicorn import Config, Server
+
+
+@pytest.fixture
+def appserver_ws_broadcast(setup_database, monkeypatch):
+    """Start real uvicorn server for WebSocket broadcast tests.
+
+    Also patches broadcast.py's get_ws_manager (missing from conftest autouse fixture).
+    """
+    # Patch broadcast.py's get_ws_manager — conftest.py misses this module.
+    # Without this, broadcast_event() creates a real Redis ws_manager.
+    import reflector.ws_manager as ws_mod
+    from reflector.app import app
+    from reflector.db import get_database
+
+    monkeypatch.setattr(
+        "reflector.hatchet.broadcast.get_ws_manager", ws_mod.get_ws_manager
+    )
+
+    host = "127.0.0.1"
+    port = 1259
+    server_started = threading.Event()
+    server_exception = None
+    server_instance = None
+
+    def run_server():
+        nonlocal server_exception, server_instance
+        try:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            config = Config(app=app, host=host, port=port, loop=loop)
+            server_instance = Server(config)
+
+            async def start_server():
+                database = get_database()
+                await database.connect()
+                try:
+                    await server_instance.serve()
+                finally:
+                    await database.disconnect()
+
+            server_started.set()
+            loop.run_until_complete(start_server())
+        except Exception as e:
+            server_exception = e
+            server_started.set()
+        finally:
+            loop.close()
+
+    server_thread = threading.Thread(target=run_server, daemon=True)
+    server_thread.start()
+
+    server_started.wait(timeout=30)
+    if server_exception:
+        raise server_exception
+
+    time.sleep(0.5)
+
+    yield host, port
+
+    if server_instance:
+        server_instance.should_exit = True
+        server_thread.join(timeout=2.0)
+
+    from reflector.ws_manager import reset_ws_manager
+
+    reset_ws_manager()
+
+
+async def _create_transcript(host: str, port: int, name: str) -> str:
+    """Create a transcript via ASGI transport and return its ID."""
+    from reflector.app import app
+
+    async with AsyncClient(app=app, base_url=f"http://{host}:{port}/v1") as ac:
+        resp = await ac.post("/transcripts", json={"name": name})
+        assert resp.status_code == 200, f"Failed to create transcript: {resp.text}"
+        return resp.json()["id"]
+
+
+async def _drain_historical_events(ws, timeout: float = 0.5) -> list[dict]:
+    """Read all historical events sent on WS connect (non-blocking drain)."""
+    events = []
+    deadline = asyncio.get_event_loop().time() + timeout
+    while asyncio.get_event_loop().time() < deadline:
+        try:
+            msg = await asyncio.wait_for(ws.receive_json(), timeout=0.1)
+            events.append(msg)
+        except (asyncio.TimeoutError, Exception):
+            break
+    return events
+
+
+# ---------------------------------------------------------------------------
+# Test 1: STATUS event delivery via ws_manager.send_json
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+async def test_transcript_ws_receives_status_via_send_json(appserver_ws_broadcast):
+    """STATUS event published via ws_manager.send_json() arrives at transcript room WS."""
+    host, port = appserver_ws_broadcast
+    transcript_id = await _create_transcript(host, port, "Status send_json test")
+
+    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
+    async with aconnect_ws(ws_url) as ws:
+        await _drain_historical_events(ws)
+
+        import reflector.ws_manager as ws_mod
+
+        ws_manager = ws_mod.get_ws_manager()
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message={"event": "STATUS", "data": {"value": "processing"}},
+        )
+
+        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
+        assert msg["event"] == "STATUS"
+        assert msg["data"]["value"] == "processing"
+
+
+# ---------------------------------------------------------------------------
+# Test 2: DAG_STATUS event delivery via ws_manager.send_json
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+async def test_transcript_ws_receives_dag_status_via_send_json(appserver_ws_broadcast):
+    """DAG_STATUS event published via ws_manager.send_json() arrives at transcript room WS."""
+    host, port = appserver_ws_broadcast
+    transcript_id = await _create_transcript(host, port, "DAG_STATUS send_json test")
+
+    dag_payload = {
+        "event": "DAG_STATUS",
+        "data": {
+            "workflow_run_id": "test-run-123",
+            "tasks": [
+                {
+                    "name": "get_recording",
+                    "status": "completed",
+                    "started_at": "2025-01-01T00:00:00Z",
+                    "finished_at": "2025-01-01T00:00:05Z",
+                    "duration_seconds": 5.0,
+                    "parents": [],
+                    "error": None,
+                    "children_total": None,
+                    "children_completed": None,
+                    "progress_pct": None,
+                },
+                {
+                    "name": "process_tracks",
+                    "status": "running",
+                    "started_at": "2025-01-01T00:00:05Z",
+                    "finished_at": None,
+                    "duration_seconds": None,
+                    "parents": ["get_recording"],
+                    "error": None,
+                    "children_total": 3,
+                    "children_completed": 1,
+                    "progress_pct": 33.3,
+                },
+            ],
+        },
+    }
+
+    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
+    async with aconnect_ws(ws_url) as ws:
+        await _drain_historical_events(ws)
+
+        import reflector.ws_manager as ws_mod
+
+        ws_manager = ws_mod.get_ws_manager()
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message=dag_payload,
+        )
+
+        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
+        assert msg["event"] == "DAG_STATUS"
+        assert msg["data"]["workflow_run_id"] == "test-run-123"
+        assert len(msg["data"]["tasks"]) == 2
+        assert msg["data"]["tasks"][0]["name"] == "get_recording"
+        assert msg["data"]["tasks"][0]["status"] == "completed"
+        assert msg["data"]["tasks"][1]["name"] == "process_tracks"
+        assert msg["data"]["tasks"][1]["children_completed"] == 1
+
+
+# ---------------------------------------------------------------------------
+# Test 3: Full broadcast_event() chain for STATUS
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+async def test_broadcast_event_delivers_status_to_transcript_ws(appserver_ws_broadcast):
+    """broadcast_event() end-to-end: STATUS event reaches transcript room WS."""
+    host, port = appserver_ws_broadcast
+    transcript_id = await _create_transcript(host, port, "broadcast_event STATUS test")
+
+    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
+    async with aconnect_ws(ws_url) as ws:
+        await _drain_historical_events(ws)
+
+        from reflector.db.transcripts import TranscriptEvent
+        from reflector.hatchet.broadcast import broadcast_event
+        from reflector.logger import logger
+
+        log = logger.bind(transcript_id=transcript_id)
+        event = TranscriptEvent(event="STATUS", data={"value": "processing"})
+        await broadcast_event(transcript_id, event, logger=log)
+
+        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
+        assert msg["event"] == "STATUS"
+        assert msg["data"]["value"] == "processing"
+
+
+# ---------------------------------------------------------------------------
+# Test 4: Full broadcast_event() chain for DAG_STATUS
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+async def test_broadcast_event_delivers_dag_status_to_transcript_ws(
+    appserver_ws_broadcast,
+):
+    """broadcast_event() end-to-end: DAG_STATUS event reaches transcript room WS."""
+    host, port = appserver_ws_broadcast
+    transcript_id = await _create_transcript(host, port, "broadcast_event DAG test")
+
+    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
+    async with aconnect_ws(ws_url) as ws:
+        await _drain_historical_events(ws)
+
+        from reflector.db.transcripts import TranscriptEvent
+        from reflector.hatchet.broadcast import broadcast_event
+        from reflector.logger import logger
+
+        log = logger.bind(transcript_id=transcript_id)
+        event = TranscriptEvent(
+            event="DAG_STATUS",
+            data={
+                "workflow_run_id": "test-run-456",
+                "tasks": [
+                    {
+                        "name": "get_recording",
+                        "status": "running",
+                        "started_at": None,
+                        "finished_at": None,
+                        "duration_seconds": None,
+                        "parents": [],
+                        "error": None,
+                        "children_total": None,
+                        "children_completed": None,
+                        "progress_pct": None,
+                    }
+                ],
+            },
+        )
+        await broadcast_event(transcript_id, event, logger=log)
+
+        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
+        assert msg["event"] == "DAG_STATUS"
+        assert msg["data"]["tasks"][0]["name"] == "get_recording"
+
+
+# ---------------------------------------------------------------------------
+# Test 5: Multiple rapid events arrive in order
+# ---------------------------------------------------------------------------
+@pytest.mark.asyncio
+async def test_multiple_events_arrive_in_order(appserver_ws_broadcast):
+    """Multiple STATUS then DAG_STATUS events arrive in correct order."""
+    host, port = appserver_ws_broadcast
+    transcript_id = await _create_transcript(host, port, "ordering test")
+
+    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
+    async with aconnect_ws(ws_url) as ws:
+        await _drain_historical_events(ws)
+
+        import reflector.ws_manager as ws_mod
+
+        ws_manager = ws_mod.get_ws_manager()
+
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message={"event": "STATUS", "data": {"value": "processing"}},
+        )
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message={
+                "event": "DAG_STATUS",
+                "data": {"workflow_run_id": "r1", "tasks": []},
+            },
+        )
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message={
+                "event": "DAG_STATUS",
+                "data": {
+                    "workflow_run_id": "r1",
+                    "tasks": [{"name": "a", "status": "running"}],
+                },
+            },
+        )
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message={"event": "STATUS", "data": {"value": "ended"}},
+        )
+
+        msgs = []
+        for _ in range(4):
+            msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
+            msgs.append(msg)
+
+        assert msgs[0]["event"] == "STATUS"
+        assert msgs[0]["data"]["value"] == "processing"
+        assert msgs[1]["event"] == "DAG_STATUS"
+        assert msgs[1]["data"]["tasks"] == []
+        assert msgs[2]["event"] == "DAG_STATUS"
+        assert len(msgs[2]["data"]["tasks"]) == 1
+        assert msgs[3]["event"] == "STATUS"
+        assert msgs[3]["data"]["value"] == "ended"

server/uv.lock

@@ -159,21 +159,20 @@ wheels = [
 
 [[package]]
 name = "aiortc"
-version = "1.13.0"
+version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aioice" },
     { name = "av" },
-    { name = "cffi" },
     { name = "cryptography" },
     { name = "google-crc32c" },
     { name = "pyee" },
     { name = "pylibsrtp" },
     { name = "pyopenssl" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/51/9c/4e027bfe0195de0442da301e2389329496745d40ae44d2d7c4571c4290ce/aiortc-1.14.0.tar.gz", hash = "sha256:adc8a67ace10a085721e588e06a00358ed8eaf5f6b62f0a95358ff45628dd762", size = 1180864 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/57/ab/31646a49209568cde3b97eeade0d28bb78b400e6645c56422c101df68932/aiortc-1.14.0-py3-none-any.whl", hash = "sha256:4b244d7e482f4e1f67e685b3468269628eca1ec91fa5b329ab517738cfca086e", size = 93183 },
 ]
 
 [[package]]
@@ -327,28 +326,24 @@ wheels = [
 
 [[package]]
 name = "av"
-version = "14.4.0"
+version = "16.1.0"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/78/cd/3a83ffbc3cc25b39721d174487fb0d51a76582f4a1703f98e46170ce83d4/av-16.1.0.tar.gz", hash = "sha256:a094b4fd87a3721dacf02794d3d2c82b8d712c85b9534437e82a8a978c175ffd", size = 4285203 }
 wheels = [
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 ]
 
 [[package]]
@@ -3267,7 +3262,7 @@ requires-dist = [
     { name = "aiohttp-cors", specifier = ">=0.7.0" },
     { name = "aiortc", specifier = ">=1.5.0" },
     { name = "alembic", specifier = ">=1.11.3" },
-    { name = "av", specifier = ">=10.0.0" },
+    { name = "av", specifier = ">=15.0.0" },
     { name = "celery", specifier = ">=5.3.4" },
     { name = "databases", extras = ["aiosqlite", "asyncpg"], specifier = ">=0.7.0" },
     { name = "fastapi", extras = ["standard"], specifier = ">=0.100.1" },

www/app/(app)/browse/_components/DagProgressDots.tsx

@@ -0,0 +1,61 @@
+import React from "react";
+import { Box, Flex } from "@chakra-ui/react";
+import type { DagTask } from "../../../lib/UserEventsProvider";
+
+const pulseKeyframes = `
+  @keyframes dagDotPulse {
+    0%, 100% { opacity: 1; }
+    50% { opacity: 0.3; }
+  }
+`;
+
+function humanizeTaskName(name: string): string {
+  return name
+    .split("_")
+    .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
+    .join(" ");
+}
+
+function dotProps(status: DagTask["status"]): Record<string, unknown> {
+  switch (status) {
+    case "completed":
+      return { bg: "green.500" };
+    case "running":
+      return {
+        bg: "blue.500",
+        style: { animation: "dagDotPulse 1.5s ease-in-out infinite" },
+      };
+    case "failed":
+      return { bg: "red.500" };
+    case "cancelled":
+      return { bg: "gray.400" };
+    case "queued":
+    default:
+      return {
+        bg: "transparent",
+        border: "1px solid",
+        borderColor: "gray.400",
+      };
+  }
+}
+
+export default function DagProgressDots({ tasks }: { tasks: DagTask[] }) {
+  return (
+    <>
+      <style>{pulseKeyframes}</style>
+      <Flex gap="2px" alignItems="center" flexWrap="wrap">
+        {tasks.map((task) => (
+          <Box
+            key={task.name}
+            w="4px"
+            h="4px"
+            borderRadius="full"
+            flexShrink={0}
+            title={humanizeTaskName(task.name)}
+            {...dotProps(task.status)}
+          />
+        ))}
+      </Flex>
+    </>
+  );
+}

www/app/(app)/browse/_components/TranscriptCards.tsx

@@ -19,6 +19,7 @@ import {
   generateTextFragment,
 } from "../../../lib/textHighlight";
 import type { components } from "../../../reflector-api";
+import type { DagTask } from "../../../lib/UserEventsProvider";
 
 type SearchResult = components["schemas"]["SearchResult"];
 type SourceKind = components["schemas"]["SourceKind"];
@@ -29,6 +30,7 @@ interface TranscriptCardsProps {
   isLoading?: boolean;
   onDelete: (transcriptId: string) => void;
   onReprocess: (transcriptId: string) => void;
+  dagStatusMap?: Map<string, DagTask[]>;
 }
 
 function highlightText(text: string, query: string): React.ReactNode {
@@ -102,11 +104,13 @@ function TranscriptCard({
   query,
   onDelete,
   onReprocess,
+  dagStatusMap,
 }: {
   result: SearchResult;
   query: string;
   onDelete: (transcriptId: string) => void;
   onReprocess: (transcriptId: string) => void;
+  dagStatusMap?: Map<string, DagTask[]>;
 }) {
   const [isExpanded, setIsExpanded] = useState(false);
 
@@ -137,7 +141,16 @@ function TranscriptCard({
     <Box borderWidth={1} p={4} borderRadius="md" fontSize="sm">
       <Flex justify="space-between" alignItems="flex-start" gap="2">
         <Box>
-          <TranscriptStatusIcon status={result.status} />
+          <TranscriptStatusIcon
+            status={result.status}
+            dagStatus={
+              dagStatusMap?.get(result.id) ??
+              ((result as Record<string, unknown>).dag_status as
+                | DagTask[]
+                | null) ??
+              null
+            }
+          />
         </Box>
         <Box flex="1">
           {/* Title with highlighting and text fragment for deep linking */}
@@ -284,6 +297,7 @@ export default function TranscriptCards({
   isLoading,
   onDelete,
   onReprocess,
+  dagStatusMap,
 }: TranscriptCardsProps) {
   return (
     <Box position="relative">
@@ -315,6 +329,7 @@ export default function TranscriptCards({
               query={query}
               onDelete={onDelete}
               onReprocess={onReprocess}
+              dagStatusMap={dagStatusMap}
             />
           ))}
         </Stack>

www/app/(app)/browse/_components/TranscriptStatusIcon.tsx

@@ -8,13 +8,17 @@ import {
   FaGear,
 } from "react-icons/fa6";
 import { TranscriptStatus } from "../../../lib/transcript";
+import type { DagTask } from "../../../lib/UserEventsProvider";
+import DagProgressDots from "./DagProgressDots";
 
 interface TranscriptStatusIconProps {
   status: TranscriptStatus;
+  dagStatus?: DagTask[] | null;
 }
 
 export default function TranscriptStatusIcon({
   status,
+  dagStatus,
 }: TranscriptStatusIconProps) {
   switch (status) {
     case "ended":
@@ -36,6 +40,9 @@ export default function TranscriptStatusIcon({
         </Box>
       );
     case "processing":
+      if (dagStatus && dagStatus.length > 0) {
+        return <DagProgressDots tasks={dagStatus} />;
+      }
       return (
         <Box as="span" title="Processing in progress">
           <Icon color="gray.500" as={FaGear} />

www/app/(app)/browse/page.tsx

@@ -43,6 +43,7 @@ import DeleteTranscriptDialog from "./_components/DeleteTranscriptDialog";
 import { formatLocalDate } from "../../lib/time";
 import { RECORD_A_MEETING_URL } from "../../api/urls";
 import { useUserName } from "../../lib/useUserName";
+import { useDagStatusMap } from "../../lib/UserEventsProvider";
 
 const SEARCH_FORM_QUERY_INPUT_NAME = "query" as const;
 
@@ -273,6 +274,7 @@ export default function TranscriptBrowser() {
   }, [JSON.stringify(searchFilters)]);
 
   const userName = useUserName();
+  const dagStatusMap = useDagStatusMap();
   const [deletionLoading, setDeletionLoading] = useState(false);
   const cancelRef = React.useRef(null);
   const [transcriptToDeleteId, setTranscriptToDeleteId] =
@@ -408,6 +410,7 @@ export default function TranscriptBrowser() {
             isLoading={searchLoading}
             onDelete={setTranscriptToDeleteId}
             onReprocess={handleProcessTranscript}
+            dagStatusMap={dagStatusMap}
           />
 
           {!searchLoading && results.length === 0 && (

www/app/(app)/rooms/page.tsx

@@ -302,10 +302,10 @@ export default function RoomsList() {
         return;
       }
 
-      const platform: "whereby" | "daily" | null =
+      const platform: "whereby" | "daily" =
         room.platform === "whereby" || room.platform === "daily"
           ? room.platform
-          : null;
+          : "daily";
 
       const roomData = {
         name: room.name,

www/app/(app)/transcripts/[transcriptId]/correct/page.tsx

@@ -16,6 +16,7 @@ import {
 import { useError } from "../../../../(errors)/errorContext";
 import { useRouter } from "next/navigation";
 import { Box, Grid } from "@chakra-ui/react";
+import { parseNonEmptyString } from "../../../../lib/utils";
 
 export type TranscriptCorrect = {
   params: Promise<{
@@ -25,8 +26,7 @@ export type TranscriptCorrect = {
 
 export default function TranscriptCorrect(props: TranscriptCorrect) {
   const params = use(props.params);
-
-  const { transcriptId } = params;
+  const transcriptId = parseNonEmptyString(params.transcriptId);
 
   const updateTranscriptMutation = useTranscriptUpdate();
   const transcript = useTranscriptGet(transcriptId);

www/app/(app)/transcripts/[transcriptId]/page.tsx

@@ -9,7 +9,9 @@ import React, { useEffect, useState, use } from "react";
 import FinalSummary from "./finalSummary";
 import TranscriptTitle from "../transcriptTitle";
 import Player from "../player";
+import { useWebSockets } from "../useWebSockets";
 import { useRouter } from "next/navigation";
+import { parseNonEmptyString } from "../../../lib/utils";
 import {
   Box,
   Flex,
@@ -30,7 +32,7 @@ type TranscriptDetails = {
 
 export default function TranscriptDetails(details: TranscriptDetails) {
   const params = use(details.params);
-  const transcriptId = params.transcriptId;
+  const transcriptId = parseNonEmptyString(params.transcriptId);
   const router = useRouter();
   const statusToRedirect = [
     "idle",
@@ -49,6 +51,7 @@ export default function TranscriptDetails(details: TranscriptDetails) {
     transcriptId,
     waiting || mp3.audioDeleted === true,
   );
+  useWebSockets(transcriptId);
   const useActiveTopic = useState<Topic | null>(null);
   const [finalSummaryElement, setFinalSummaryElement] =
     useState<HTMLDivElement | null>(null);

www/app/(app)/transcripts/[transcriptId]/processing/DagProgressTable.tsx

@@ -0,0 +1,190 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { Table, Box, Icon, Spinner, Text, Badge } from "@chakra-ui/react";
+import { FaCheck, FaXmark, FaClock, FaMinus } from "react-icons/fa6";
+import type { DagTask, DagTaskStatus } from "../../useWebSockets";
+
+function humanizeTaskName(name: string): string {
+  return name
+    .split("_")
+    .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
+    .join(" ");
+}
+
+function formatDuration(seconds: number): string {
+  if (seconds < 60) {
+    return `${Math.round(seconds)}s`;
+  }
+  const minutes = Math.floor(seconds / 60);
+  const remainingSeconds = Math.round(seconds % 60);
+  return `${minutes}m ${remainingSeconds}s`;
+}
+
+function StatusIcon({ status }: { status: DagTaskStatus }) {
+  switch (status) {
+    case "completed":
+      return (
+        <Box as="span" title="Completed">
+          <Icon color="green.500" as={FaCheck} />
+        </Box>
+      );
+    case "running":
+      return <Spinner size="sm" color="blue.500" />;
+    case "failed":
+      return (
+        <Box as="span" title="Failed">
+          <Icon color="red.500" as={FaXmark} />
+        </Box>
+      );
+    case "queued":
+      return (
+        <Box as="span" title="Queued">
+          <Icon color="gray.400" as={FaClock} />
+        </Box>
+      );
+    case "cancelled":
+      return (
+        <Box as="span" title="Cancelled">
+          <Icon color="gray.400" as={FaMinus} />
+        </Box>
+      );
+    default:
+      return null;
+  }
+}
+
+function ElapsedTimer({ startedAt }: { startedAt: string }) {
+  const [elapsed, setElapsed] = useState<number>(() => {
+    return (Date.now() - new Date(startedAt).getTime()) / 1000;
+  });
+
+  useEffect(() => {
+    const interval = setInterval(() => {
+      setElapsed((Date.now() - new Date(startedAt).getTime()) / 1000);
+    }, 1000);
+    return () => clearInterval(interval);
+  }, [startedAt]);
+
+  return <Text fontSize="sm">{formatDuration(elapsed)}</Text>;
+}
+
+function DurationCell({ task }: { task: DagTask }) {
+  if (task.status === "completed" && task.duration_seconds !== null) {
+    return <Text fontSize="sm">{formatDuration(task.duration_seconds)}</Text>;
+  }
+  if (task.status === "running" && task.started_at) {
+    return <ElapsedTimer startedAt={task.started_at} />;
+  }
+  return (
+    <Text fontSize="sm" color="gray.400">
+      --
+    </Text>
+  );
+}
+
+function ProgressCell({ task }: { task: DagTask }) {
+  if (task.progress_pct === null && task.children_total === null) {
+    return null;
+  }
+
+  return (
+    <Box>
+      {task.progress_pct !== null && (
+        <Box
+          w="100%"
+          h="6px"
+          bg="gray.200"
+          borderRadius="full"
+          overflow="hidden"
+        >
+          <Box
+            h="100%"
+            w={`${Math.min(100, Math.max(0, task.progress_pct))}%`}
+            bg={task.status === "failed" ? "red.400" : "blue.400"}
+            borderRadius="full"
+            transition="width 0.3s ease"
+          />
+        </Box>
+      )}
+      {task.children_total !== null && (
+        <Badge
+          size="sm"
+          colorPalette="gray"
+          mt={task.progress_pct !== null ? 1 : 0}
+        >
+          {task.children_completed ?? 0}/{task.children_total}
+        </Badge>
+      )}
+    </Box>
+  );
+}
+
+function TaskRow({ task }: { task: DagTask }) {
+  const [expanded, setExpanded] = useState(false);
+  const hasFailed = task.status === "failed" && task.error;
+
+  return (
+    <>
+      <Table.Row
+        cursor={hasFailed ? "pointer" : "default"}
+        onClick={hasFailed ? () => setExpanded((prev) => !prev) : undefined}
+        _hover={hasFailed ? { bg: "gray.50" } : undefined}
+      >
+        <Table.Cell>
+          <Text fontSize="sm" fontWeight="medium">
+            {humanizeTaskName(task.name)}
+          </Text>
+        </Table.Cell>
+        <Table.Cell>
+          <StatusIcon status={task.status} />
+        </Table.Cell>
+        <Table.Cell>
+          <DurationCell task={task} />
+        </Table.Cell>
+        <Table.Cell>
+          <ProgressCell task={task} />
+        </Table.Cell>
+      </Table.Row>
+      {hasFailed && expanded && (
+        <Table.Row>
+          <Table.Cell colSpan={4}>
+            <Box bg="red.50" p={3} borderRadius="md">
+              <Text fontSize="xs" color="red.700" whiteSpace="pre-wrap">
+                {task.error}
+              </Text>
+            </Box>
+          </Table.Cell>
+        </Table.Row>
+      )}
+    </>
+  );
+}
+
+export default function DagProgressTable({ tasks }: { tasks: DagTask[] }) {
+  return (
+    <Box w="100%" overflowX="auto">
+      <Table.Root size="sm">
+        <Table.Header>
+          <Table.Row>
+            <Table.ColumnHeader fontWeight="600">Task</Table.ColumnHeader>
+            <Table.ColumnHeader fontWeight="600" width="80px">
+              Status
+            </Table.ColumnHeader>
+            <Table.ColumnHeader fontWeight="600" width="100px">
+              Duration
+            </Table.ColumnHeader>
+            <Table.ColumnHeader fontWeight="600" width="140px">
+              Progress
+            </Table.ColumnHeader>
+          </Table.Row>
+        </Table.Header>
+        <Table.Body>
+          {tasks.map((task) => (
+            <TaskRow key={task.name} task={task} />
+          ))}
+        </Table.Body>
+      </Table.Root>
+    </Box>
+  );
+}

www/app/(app)/transcripts/[transcriptId]/processing/page.tsx

@@ -10,6 +10,11 @@ import {
 } from "@chakra-ui/react";
 import { useRouter } from "next/navigation";
 import { useTranscriptGet } from "../../../../lib/apiHooks";
+import { parseNonEmptyString } from "../../../../lib/utils";
+import { useWebSockets } from "../../useWebSockets";
+import type { DagTask } from "../../useWebSockets";
+import { useDagStatusMap } from "../../../../lib/UserEventsProvider";
+import DagProgressTable from "./DagProgressTable";
 
 type TranscriptProcessing = {
   params: Promise<{
@@ -19,13 +24,25 @@ type TranscriptProcessing = {
 
 export default function TranscriptProcessing(details: TranscriptProcessing) {
   const params = use(details.params);
-  const transcriptId = params.transcriptId;
+  const transcriptId = parseNonEmptyString(params.transcriptId);
   const router = useRouter();
 
   const transcript = useTranscriptGet(transcriptId);
+  const { status: wsStatus, dagStatus: wsDagStatus } =
+    useWebSockets(transcriptId);
+  const userDagStatusMap = useDagStatusMap();
+  const userDagStatus = userDagStatusMap.get(transcriptId) ?? null;
+
+  const restDagStatus: DagTask[] | null =
+    ((transcript.data as Record<string, unknown>)?.dag_status as
+      | DagTask[]
+      | null) ?? null;
+
+  // Prefer transcript room WS (most granular), then user room WS, then REST
+  const dagStatus = wsDagStatus ?? userDagStatus ?? restDagStatus;
 
   useEffect(() => {
-    const status = transcript.data?.status;
+    const status = wsStatus?.value ?? transcript.data?.status;
     if (!status) return;
 
     if (status === "ended" || status === "error") {
@@ -40,6 +57,7 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
       router.replace(dest);
     }
   }, [
+    wsStatus?.value,
     transcript.data?.status,
     transcript.data?.source_kind,
     router,
@@ -73,11 +91,29 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
         w={{ base: "full", md: "container.xl" }}
       >
         <Center h={"full"} w="full">
-          <VStack gap={10} bg="gray.100" p={10} borderRadius="md" maxW="500px">
-            <Spinner size="xl" color="blue.500" />
-            <Heading size={"md"} textAlign="center">
-              Processing recording
-            </Heading>
+          <VStack
+            gap={10}
+            bg="gray.100"
+            p={10}
+            borderRadius="md"
+            maxW="600px"
+            w="full"
+          >
+            {dagStatus ? (
+              <>
+                <Heading size={"md"} textAlign="center">
+                  Processing recording
+                </Heading>
+                <DagProgressTable tasks={dagStatus} />
+              </>
+            ) : (
+              <>
+                <Spinner size="xl" color="blue.500" />
+                <Heading size={"md"} textAlign="center">
+                  Processing recording
+                </Heading>
+              </>
+            )}
             <Text color="gray.600" textAlign="center">
               You can safely return to the library while your recording is being
               processed.

www/app/(app)/transcripts/[transcriptId]/record/page.tsx

@@ -12,6 +12,7 @@ import { Box, Text, Grid, Heading, VStack, Flex } from "@chakra-ui/react";
 import LiveTrancription from "../../liveTranscription";
 import { useTranscriptGet } from "../../../../lib/apiHooks";
 import { TranscriptStatus } from "../../../../lib/transcript";
+import { parseNonEmptyString } from "../../../../lib/utils";
 
 type TranscriptDetails = {
   params: Promise<{
@@ -21,13 +22,14 @@ type TranscriptDetails = {
 
 const TranscriptRecord = (details: TranscriptDetails) => {
   const params = use(details.params);
-  const transcript = useTranscriptGet(params.transcriptId);
+  const transcriptId = parseNonEmptyString(params.transcriptId);
+  const transcript = useTranscriptGet(transcriptId);
   const [transcriptStarted, setTranscriptStarted] = useState(false);
   const useActiveTopic = useState<Topic | null>(null);
 
-  const webSockets = useWebSockets(params.transcriptId);
+  const webSockets = useWebSockets(transcriptId);
 
-  const mp3 = useMp3(params.transcriptId, true);
+  const mp3 = useMp3(transcriptId, true);
 
   const router = useRouter();
 

www/app/(app)/transcripts/[transcriptId]/upload/page.tsx

@@ -7,6 +7,7 @@ import useMp3 from "../../useMp3";
 import { Center, VStack, Text, Heading } from "@chakra-ui/react";
 import FileUploadButton from "../../fileUploadButton";
 import { useTranscriptGet } from "../../../../lib/apiHooks";
+import { parseNonEmptyString } from "../../../../lib/utils";
 
 type TranscriptUpload = {
   params: Promise<{
@@ -16,12 +17,13 @@ type TranscriptUpload = {
 
 const TranscriptUpload = (details: TranscriptUpload) => {
   const params = use(details.params);
-  const transcript = useTranscriptGet(params.transcriptId);
+  const transcriptId = parseNonEmptyString(params.transcriptId);
+  const transcript = useTranscriptGet(transcriptId);
   const [transcriptStarted, setTranscriptStarted] = useState(false);
 
-  const webSockets = useWebSockets(params.transcriptId);
+  const webSockets = useWebSockets(transcriptId);
 
-  const mp3 = useMp3(params.transcriptId, true);
+  const mp3 = useMp3(transcriptId, true);
 
   const router = useRouter();
 

www/app/(app)/transcripts/transcriptTitle.tsx

@@ -1,5 +1,6 @@
 import { useState } from "react";
 import type { components } from "../../reflector-api";
+import { parseMaybeNonEmptyString } from "../../lib/utils";
 
 type UpdateTranscript = components["schemas"]["UpdateTranscript"];
 type GetTranscriptWithParticipants =
@@ -32,7 +33,7 @@ const TranscriptTitle = (props: TranscriptTitle) => {
   const [isEditing, setIsEditing] = useState(false);
   const updateTranscriptMutation = useTranscriptUpdate();
   const participantsQuery = useTranscriptParticipants(
-    props.transcript?.id || null,
+    props.transcript?.id ? parseMaybeNonEmptyString(props.transcript.id) : null,
   );
 
   const updateTitle = async (newTitle: string, transcriptId: string) => {

www/app/(app)/transcripts/useMp3.ts

@@ -1,5 +1,6 @@
 import { useEffect, useState } from "react";
 import { useTranscriptGet } from "../../lib/apiHooks";
+import { parseMaybeNonEmptyString } from "../../lib/utils";
 import { useAuth } from "../../lib/AuthProvider";
 import { API_URL } from "../../lib/apiClient";
 
@@ -27,7 +28,7 @@ const useMp3 = (transcriptId: string, waiting?: boolean): Mp3Response => {
     data: transcript,
     isLoading: transcriptMetadataLoading,
     error: transcriptError,
-  } = useTranscriptGet(later ? null : transcriptId);
+  } = useTranscriptGet(later ? null : parseMaybeNonEmptyString(transcriptId));
 
   const [serviceWorker, setServiceWorker] =
     useState<ServiceWorkerRegistration | null>(null);

www/app/(app)/transcripts/useParticipants.ts

@@ -1,6 +1,7 @@
 import type { components } from "../../reflector-api";
 type Participant = components["schemas"]["Participant"];
 import { useTranscriptParticipants } from "../../lib/apiHooks";
+import { parseMaybeNonEmptyString } from "../../lib/utils";
 
 type ErrorParticipants = {
   error: Error;
@@ -32,7 +33,7 @@ const useParticipants = (transcriptId: string): UseParticipants => {
     isLoading: loading,
     error,
     refetch,
-  } = useTranscriptParticipants(transcriptId || null);
+  } = useTranscriptParticipants(parseMaybeNonEmptyString(transcriptId));
 
   // Type-safe return based on state
   if (error) {

www/app/(app)/transcripts/useTopicWithWords.ts

@@ -1,5 +1,6 @@
 import type { components } from "../../reflector-api";
 import { useTranscriptTopicsWithWordsPerSpeaker } from "../../lib/apiHooks";
+import { parseMaybeNonEmptyString } from "../../lib/utils";
 
 type GetTranscriptTopicWithWordsPerSpeaker =
   components["schemas"]["GetTranscriptTopicWithWordsPerSpeaker"];
@@ -38,7 +39,7 @@ const useTopicWithWords = (
     error,
     refetch,
   } = useTranscriptTopicsWithWordsPerSpeaker(
-    transcriptId || null,
+    parseMaybeNonEmptyString(transcriptId),
     topicId || null,
   );
 

www/app/(app)/transcripts/useTopics.ts

@@ -1,5 +1,6 @@
 import { useTranscriptTopics } from "../../lib/apiHooks";
 import type { components } from "../../reflector-api";
+import { parseMaybeNonEmptyString } from "../../lib/utils";
 
 type GetTranscriptTopic = components["schemas"]["GetTranscriptTopic"];
 
@@ -10,7 +11,11 @@ type TranscriptTopics = {
 };
 
 const useTopics = (id: string): TranscriptTopics => {
-  const { data: topics, isLoading: loading, error } = useTranscriptTopics(id);
+  const {
+    data: topics,
+    isLoading: loading,
+    error,
+  } = useTranscriptTopics(parseMaybeNonEmptyString(id));
 
   return {
     topics: topics || null,

www/app/(app)/transcripts/useWaveform.ts

@@ -1,5 +1,6 @@
 import type { components } from "../../reflector-api";
 import { useTranscriptWaveform } from "../../lib/apiHooks";
+import { parseMaybeNonEmptyString } from "../../lib/utils";
 
 type AudioWaveform = components["schemas"]["AudioWaveform"];
 
@@ -14,7 +15,7 @@ const useWaveform = (id: string, skip: boolean): AudioWaveFormResponse => {
     data: waveform,
     isLoading: loading,
     error,
-  } = useTranscriptWaveform(skip ? null : id);
+  } = useTranscriptWaveform(skip ? null : parseMaybeNonEmptyString(id));
 
   return {
     waveform: waveform || null,

www/app/(app)/transcripts/useWebSockets.ts

@@ -7,6 +7,15 @@ type GetTranscriptSegmentTopic =
   components["schemas"]["GetTranscriptSegmentTopic"];
 import { useQueryClient } from "@tanstack/react-query";
 import { $api, WEBSOCKET_URL } from "../../lib/apiClient";
+import {
+  invalidateTranscript,
+  invalidateTranscriptTopics,
+  invalidateTranscriptWaveform,
+} from "../../lib/apiHooks";
+import { NonEmptyString } from "../../lib/utils";
+
+import type { DagTask } from "../../lib/dagTypes";
+export type { DagTask, DagTaskStatus } from "../../lib/dagTypes";
 
 export type UseWebSockets = {
   transcriptTextLive: string;
@@ -18,6 +27,7 @@ export type UseWebSockets = {
   status: Status | null;
   waveform: AudioWaveform | null;
   duration: number | null;
+  dagStatus: DagTask[] | null;
 };
 
 export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
@@ -34,6 +44,7 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
     summary: "",
   });
   const [status, setStatus] = useState<Status | null>(null);
+  const [dagStatus, setDagStatus] = useState<DagTask[] | null>(null);
   const { setError } = useError();
 
   const queryClient = useQueryClient();
@@ -369,15 +380,10 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
             });
             console.debug("TOPIC event:", message.data);
             // Invalidate topics query to sync with WebSocket data
-            queryClient.invalidateQueries({
-              queryKey: $api.queryOptions(
-                "get",
-                "/v1/transcripts/{transcript_id}/topics",
-                {
-                  params: { path: { transcript_id: transcriptId } },
-                },
-              ).queryKey,
-            });
+            invalidateTranscriptTopics(
+              queryClient,
+              transcriptId as NonEmptyString,
+            );
             break;
 
           case "FINAL_SHORT_SUMMARY":
@@ -388,15 +394,7 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
             if (message.data) {
               setFinalSummary(message.data);
               // Invalidate transcript query to sync summary
-              queryClient.invalidateQueries({
-                queryKey: $api.queryOptions(
-                  "get",
-                  "/v1/transcripts/{transcript_id}",
-                  {
-                    params: { path: { transcript_id: transcriptId } },
-                  },
-                ).queryKey,
-              });
+              invalidateTranscript(queryClient, transcriptId as NonEmptyString);
             }
             break;
 
@@ -405,15 +403,7 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
             if (message.data) {
               setTitle(message.data.title);
               // Invalidate transcript query to sync title
-              queryClient.invalidateQueries({
-                queryKey: $api.queryOptions(
-                  "get",
-                  "/v1/transcripts/{transcript_id}",
-                  {
-                    params: { path: { transcript_id: transcriptId } },
-                  },
-                ).queryKey,
-              });
+              invalidateTranscript(queryClient, transcriptId as NonEmptyString);
             }
             break;
 
@@ -424,6 +414,10 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
             );
             if (message.data) {
               setWaveForm(message.data.waveform);
+              invalidateTranscriptWaveform(
+                queryClient,
+                transcriptId as NonEmptyString,
+              );
             }
             break;
           case "DURATION":
@@ -442,11 +436,31 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
               );
             }
             setStatus(message.data);
+            invalidateTranscript(queryClient, transcriptId as NonEmptyString);
             if (message.data.value === "ended") {
               ws.close();
             }
             break;
 
+          case "DAG_STATUS":
+            if (message.data?.tasks) {
+              setDagStatus(message.data.tasks);
+            }
+            break;
+
+          case "DAG_TASK_PROGRESS":
+            if (message.data) {
+              setDagStatus(
+                (prev) =>
+                  prev?.map((t) =>
+                    t.name === message.data.task_name
+                      ? { ...t, progress_pct: message.data.progress_pct }
+                      : t,
+                  ) ?? null,
+              );
+            }
+            break;
+
           default:
             setError(
               new Error(`Received unknown WebSocket event: ${message.event}`),
@@ -504,5 +518,6 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
     status,
     waveform,
     duration,
+    dagStatus,
   };
 };

www/app/[roomName]/MeetingSelection.tsx

@@ -26,7 +26,7 @@ import { useRouter } from "next/navigation";
 import { formatDateTime, formatStartedAgo } from "../lib/timeUtils";
 import MeetingMinimalHeader from "../components/MeetingMinimalHeader";
 import { NonEmptyString } from "../lib/utils";
-import { MeetingId } from "../lib/types";
+import { MeetingId, assertMeetingId } from "../lib/types";
 
 type Meeting = components["schemas"]["Meeting"];
 
@@ -315,7 +315,9 @@ export default function MeetingSelection({
                         variant="outline"
                         colorScheme="red"
                         size="md"
-                        onClick={() => handleEndMeeting(meeting.id)}
+                        onClick={() =>
+                          handleEndMeeting(assertMeetingId(meeting.id))
+                        }
                         loading={deactivateMeetingMutation.isPending}
                       >
                         <Icon as={LuX} me={2} />
@@ -460,7 +462,9 @@ export default function MeetingSelection({
                           variant="outline"
                           colorScheme="red"
                           size="md"
-                          onClick={() => handleEndMeeting(meeting.id)}
+                          onClick={() =>
+                            handleEndMeeting(assertMeetingId(meeting.id))
+                          }
                           loading={deactivateMeetingMutation.isPending}
                         >
                           <Icon as={LuX} me={2} />

www/app/lib/UserEventsProvider.tsx

@@ -1,11 +1,25 @@
 "use client";
 
-import React, { useEffect, useRef } from "react";
+import React, { useEffect, useRef, useState } from "react";
 import { useQueryClient } from "@tanstack/react-query";
 import { WEBSOCKET_URL } from "./apiClient";
 import { useAuth } from "./AuthProvider";
 import { z } from "zod";
-import { invalidateTranscriptLists, TRANSCRIPT_SEARCH_URL } from "./apiHooks";
+import {
+  invalidateTranscript,
+  invalidateTranscriptLists,
+  TRANSCRIPT_SEARCH_URL,
+} from "./apiHooks";
+import type { NonEmptyString } from "./utils";
+
+import type { DagTask } from "./dagTypes";
+export type { DagTask, DagTaskStatus } from "./dagTypes";
+
+const DagStatusContext = React.createContext<Map<string, DagTask[]>>(new Map());
+
+export function useDagStatusMap() {
+  return React.useContext(DagStatusContext);
+}
 
 const UserEvent = z.object({
   event: z.string(),
@@ -95,6 +109,9 @@ export function UserEventsProvider({
   const queryClient = useQueryClient();
   const tokenRef = useRef<string | null>(null);
   const detachRef = useRef<(() => void) | null>(null);
+  const [dagStatusMap, setDagStatusMap] = useState<Map<string, DagTask[]>>(
+    new Map(),
+  );
 
   useEffect(() => {
     // Only tear down when the user is truly unauthenticated
@@ -133,20 +150,52 @@ export function UserEventsProvider({
     if (!detachRef.current) {
       const onMessage = (event: MessageEvent) => {
         try {
-          const msg = UserEvent.parse(JSON.parse(event.data));
+          const fullMsg = JSON.parse(event.data);
+          const msg = UserEvent.parse(fullMsg);
           const eventName = msg.event;
-
           const invalidateList = () => invalidateTranscriptLists(queryClient);
 
           switch (eventName) {
             case "TRANSCRIPT_CREATED":
             case "TRANSCRIPT_DELETED":
-            case "TRANSCRIPT_STATUS":
             case "TRANSCRIPT_FINAL_TITLE":
             case "TRANSCRIPT_DURATION":
               invalidateList().then(() => {});
               break;
 
+            case "TRANSCRIPT_STATUS": {
+              invalidateList().then(() => {});
+              const transcriptId = fullMsg.data?.id as string | undefined;
+              if (transcriptId) {
+                invalidateTranscript(
+                  queryClient,
+                  transcriptId as NonEmptyString,
+                ).then(() => {});
+              }
+              const status = fullMsg.data?.value as string | undefined;
+              if (transcriptId && status && status !== "processing") {
+                setDagStatusMap((prev) => {
+                  const next = new Map(prev);
+                  next.delete(transcriptId);
+                  return next;
+                });
+              }
+              break;
+            }
+
+            case "TRANSCRIPT_DAG_STATUS": {
+              const transcriptId = fullMsg.data?.id as string | undefined;
+              const tasks = fullMsg.data?.tasks as DagTask[] | undefined;
+              if (transcriptId && tasks) {
+                setDagStatusMap((prev) => {
+                  const next = new Map(prev);
+                  next.set(transcriptId, tasks);
+                  return next;
+                });
+              }
+              break;
+            }
+
             default:
               // Ignore other content events for list updates
               break;
@@ -176,5 +225,9 @@ export function UserEventsProvider({
     };
   }, []);
 
-  return <>{children}</>;
+  return (
+    <DagStatusContext.Provider value={dagStatusMap}>
+      {children}
+    </DagStatusContext.Provider>
+  );
 }

www/app/lib/apiHooks.ts

@@ -6,6 +6,7 @@ import { QueryClient, useQueryClient } from "@tanstack/react-query";
 import type { components } from "../reflector-api";
 import { useAuth } from "./AuthProvider";
 import { MeetingId } from "./types";
+import { NonEmptyString } from "./utils";
 
 /*
  * XXX error types returned from the hooks are not always correct; declared types are ValidationError but real type could be string or any other
@@ -103,7 +104,7 @@ export function useTranscriptProcess() {
   });
 }
 
-export function useTranscriptGet(transcriptId: string | null) {
+export function useTranscriptGet(transcriptId: NonEmptyString | null) {
   return $api.useQuery(
     "get",
     "/v1/transcripts/{transcript_id}",
@@ -120,6 +121,16 @@ export function useTranscriptGet(transcriptId: string | null) {
   );
 }
 
+export const invalidateTranscript = (
+  queryClient: QueryClient,
+  transcriptId: NonEmptyString,
+) =>
+  queryClient.invalidateQueries({
+    queryKey: $api.queryOptions("get", "/v1/transcripts/{transcript_id}", {
+      params: { path: { transcript_id: transcriptId } },
+    }).queryKey,
+  });
+
 export function useRoomGet(roomId: string | null) {
   const { isAuthenticated } = useAuthReady();
 
@@ -297,7 +308,7 @@ export function useTranscriptUploadAudio() {
   );
 }
 
-export function useTranscriptWaveform(transcriptId: string | null) {
+export function useTranscriptWaveform(transcriptId: NonEmptyString | null) {
   return $api.useQuery(
     "get",
     "/v1/transcripts/{transcript_id}/audio/waveform",
@@ -312,7 +323,21 @@ export function useTranscriptWaveform(transcriptId: string | null) {
   );
 }
 
-export function useTranscriptMP3(transcriptId: string | null) {
+export const invalidateTranscriptWaveform = (
+  queryClient: QueryClient,
+  transcriptId: NonEmptyString,
+) =>
+  queryClient.invalidateQueries({
+    queryKey: $api.queryOptions(
+      "get",
+      "/v1/transcripts/{transcript_id}/audio/waveform",
+      {
+        params: { path: { transcript_id: transcriptId } },
+      },
+    ).queryKey,
+  });
+
+export function useTranscriptMP3(transcriptId: NonEmptyString | null) {
   const { isAuthenticated } = useAuthReady();
 
   return $api.useQuery(
@@ -329,7 +354,7 @@ export function useTranscriptMP3(transcriptId: string | null) {
   );
 }
 
-export function useTranscriptTopics(transcriptId: string | null) {
+export function useTranscriptTopics(transcriptId: NonEmptyString | null) {
   return $api.useQuery(
     "get",
     "/v1/transcripts/{transcript_id}/topics",
@@ -344,7 +369,23 @@ export function useTranscriptTopics(transcriptId: string | null) {
   );
 }
 
-export function useTranscriptTopicsWithWords(transcriptId: string | null) {
+export const invalidateTranscriptTopics = (
+  queryClient: QueryClient,
+  transcriptId: NonEmptyString,
+) =>
+  queryClient.invalidateQueries({
+    queryKey: $api.queryOptions(
+      "get",
+      "/v1/transcripts/{transcript_id}/topics",
+      {
+        params: { path: { transcript_id: transcriptId } },
+      },
+    ).queryKey,
+  });
+
+export function useTranscriptTopicsWithWords(
+  transcriptId: NonEmptyString | null,
+) {
   const { isAuthenticated } = useAuthReady();
 
   return $api.useQuery(
@@ -362,7 +403,7 @@ export function useTranscriptTopicsWithWords(transcriptId: string | null) {
 }
 
 export function useTranscriptTopicsWithWordsPerSpeaker(
-  transcriptId: string | null,
+  transcriptId: NonEmptyString | null,
   topicId: string | null,
 ) {
   const { isAuthenticated } = useAuthReady();
@@ -384,7 +425,7 @@ export function useTranscriptTopicsWithWordsPerSpeaker(
   );
 }
 
-export function useTranscriptParticipants(transcriptId: string | null) {
+export function useTranscriptParticipants(transcriptId: NonEmptyString | null) {
   const { isAuthenticated } = useAuthReady();
 
   return $api.useQuery(

www/app/lib/dagTypes.ts

@@ -0,0 +1,19 @@
+export type DagTaskStatus =
+  | "queued"
+  | "running"
+  | "completed"
+  | "failed"
+  | "cancelled";
+
+export type DagTask = {
+  name: string;
+  status: DagTaskStatus;
+  started_at: string | null;
+  finished_at: string | null;
+  duration_seconds: number | null;
+  parents: string[];
+  error: string | null;
+  children_total: number | null;
+  children_completed: number | null;
+  progress_pct: number | null;
+};