latest changes feature parity

* Identify action items

* Add action items to mock summary

* Add action items validator

* Remove final prefix from action items

* Make on action items callback required

* Don't mutation action items response

* Assign action items to none on error

* Use timeout constant

* Exclude action items from transcript list

.github/workflows/deploy.yml

@@ -1,90 +0,0 @@
-name: Build container/push to container registry
-
-on: [workflow_dispatch]
-
-env:
-  # 950402358378.dkr.ecr.us-east-1.amazonaws.com/reflector
-  AWS_REGION: us-east-1
-  ECR_REPOSITORY: reflector
-
-jobs:
-  build:
-    strategy:
-      matrix:
-        include:
-          - platform: linux/amd64
-            runner: linux-amd64
-            arch: amd64
-          - platform: linux/arm64
-            runner: linux-arm64
-            arch: arm64
-
-    runs-on: ${{ matrix.runner }}
-
-    permissions:
-      contents: read
-
-    outputs:
-      registry: ${{ steps.login-ecr.outputs.registry }}
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v4
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ env.AWS_REGION }}
-
-      - name: Login to Amazon ECR
-        id: login-ecr
-        uses: aws-actions/amazon-ecr-login@v2
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push ${{ matrix.arch }}
-        uses: docker/build-push-action@v5
-        with:
-          context: server
-          platforms: ${{ matrix.platform }}
-          push: true
-          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest-${{ matrix.arch }}
-          cache-from: type=gha,scope=${{ matrix.arch }}
-          cache-to: type=gha,mode=max,scope=${{ matrix.arch }}
-          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
-          provenance: false
-
-  create-manifest:
-    runs-on: ubuntu-latest
-    needs: [build]
-
-    permissions:
-      deployments: write
-      contents: read
-
-    steps:
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v4
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ env.AWS_REGION }}
-
-      - name: Login to Amazon ECR
-        uses: aws-actions/amazon-ecr-login@v2
-
-      - name: Create and push multi-arch manifest
-        run: |
-          # Get the registry URL (since we can't easily access job outputs in matrix)
-          ECR_REGISTRY=$(aws ecr describe-registry --query 'registryId' --output text).dkr.ecr.${{ env.AWS_REGION }}.amazonaws.com
-
-          docker manifest create \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-amd64 \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-arm64
-
-          docker manifest push $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest
-
-          echo "✅ Multi-arch manifest pushed: $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest"

.github/workflows/dockerhub-backend.yml

@@ -1,35 +1,31 @@
-name: Build and Push Frontend Docker Image
+name: Build and Push Backend Docker Image (Docker Hub)
 
 on:
   push:
-    branches:
+    tags:
-      - main
+      - "v*"
-    paths:
-      - 'www/**'
-      - '.github/workflows/docker-frontend.yml'
   workflow_dispatch:
 
 env:
-  REGISTRY: ghcr.io
+  REGISTRY: docker.io
-  IMAGE_NAME: ${{ github.repository }}-frontend
+  IMAGE_NAME: monadicalsas/reflector-backend
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
     permissions:
       contents: read
-      packages: write
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
-      - name: Log in to GitHub Container Registry
+      - name: Log in to Docker Hub
         uses: docker/login-action@v3
         with:
           registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
+          username: monadicalsas
-          password: ${{ secrets.GITHUB_TOKEN }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
 
       - name: Extract metadata
         id: meta
@@ -38,7 +34,7 @@ jobs:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
           tags: |
             type=ref,event=branch
-            type=sha,prefix={{branch}}-
+            type=ref,event=tag
             type=raw,value=latest,enable={{is_default_branch}}
 
       - name: Set up Docker Buildx
@@ -47,11 +43,11 @@ jobs:
       - name: Build and push Docker image
         uses: docker/build-push-action@v5
         with:
-          context: ./www
+          context: ./server
-          file: ./www/Dockerfile
+          file: ./server/Dockerfile
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: type=gha
           cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
+          platforms: linux/amd64,linux/arm64

.github/workflows/dockerhub-frontend.yml

@@ -0,0 +1,70 @@
+name: Build and Push Frontend Docker Image
+
+on:
+  push:
+    tags:
+      - "v*"
+  workflow_dispatch:
+
+env:
+  REGISTRY: docker.io
+  IMAGE_NAME: monadicalsas/reflector-frontend
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: monadicalsas
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=branch
+            type=ref,event=tag
+            type=raw,value=latest,enable={{is_default_branch}}
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: ./www
+          file: ./www/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          platforms: linux/amd64,linux/arm64
+
+  deploy:
+    needs: build-and-push
+    runs-on: ubuntu-latest
+    if: success()
+    strategy:
+      matrix:
+        environment: [reflector-monadical, reflector-media]
+    environment: ${{ matrix.environment }}
+    steps:
+      - name: Trigger Coolify deployment
+        run: |
+          curl -X POST "${{ secrets.COOLIFY_WEBHOOK_URL }}" \
+            -H "Content-Type: application/json" \
+            -H "Authorization: Bearer ${{ secrets.COOLIFY_WEBHOOK_TOKEN }}" \
+            -f || (echo "Failed to trigger Coolify deployment for ${{ matrix.environment }}" && exit 1)

.gitignore

@@ -18,3 +18,4 @@ CLAUDE.local.md
 www/.env.development
 www/.env.production
 .playwright-mcp
+.secrets

.secrets.example

@@ -0,0 +1,24 @@
+# Example secrets file for GitHub Actions workflows
+# Copy this to .secrets and fill in your values
+# These secrets should be configured in GitHub repository settings:
+# Settings > Secrets and variables > Actions
+
+# DockerHub Configuration (required for frontend and backend deployment)
+# Create a Docker Hub access token at https://hub.docker.com/settings/security
+# Username: monadicalsas
+DOCKERHUB_TOKEN=your-dockerhub-access-token
+
+# GitHub Token (required for frontend and backend deployment)
+# Used by docker/metadata-action for extracting image metadata
+# Can use the default GITHUB_TOKEN or create a personal access token
+GITHUB_TOKEN=your-github-token-or-use-default-GITHUB_TOKEN
+
+# Coolify Deployment Webhook (required for frontend deployment)
+# Used to trigger automatic deployment after image push
+# Configure these secrets in GitHub Environments:
+# Each environment should have:
+#   - COOLIFY_WEBHOOK_URL: The webhook URL for that specific deployment
+#   - COOLIFY_WEBHOOK_TOKEN: The webhook token (can be the same for both if using same token)
+
+# Optional: GitHub Actions Cache Token (for local testing with act)
+GHA_CACHE_TOKEN=your-github-token-or-empty

CHANGELOG.md

@@ -1,5 +1,64 @@
 # Changelog
 
+## [0.24.0](https://github.com/Monadical-SAS/reflector/compare/v0.23.2...v0.24.0) (2025-12-18)
+
+
+### Features
+
+* identify action items ([#790](https://github.com/Monadical-SAS/reflector/issues/790)) ([964cd78](https://github.com/Monadical-SAS/reflector/commit/964cd78bb699d83d012ae4b8c96565df25b90a5d))
+
+
+### Bug Fixes
+
+* automatically reprocess daily recordings ([#797](https://github.com/Monadical-SAS/reflector/issues/797)) ([5f458aa](https://github.com/Monadical-SAS/reflector/commit/5f458aa4a7ec3d00ca5ec49d62fcc8ad232b138e))
+* daily video optimisation ([#789](https://github.com/Monadical-SAS/reflector/issues/789)) ([16284e1](https://github.com/Monadical-SAS/reflector/commit/16284e1ac3faede2b74f0d91b50c0b5612af2c35))
+* main menu login ([#800](https://github.com/Monadical-SAS/reflector/issues/800)) ([0bc971b](https://github.com/Monadical-SAS/reflector/commit/0bc971ba966a52d719c8c240b47dc7b3bdea4391))
+* retry on workflow timeout ([#798](https://github.com/Monadical-SAS/reflector/issues/798)) ([5f7dfad](https://github.com/Monadical-SAS/reflector/commit/5f7dfadabd3e8017406ad3720ba495a59963ee34))
+
+## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
+
+
+### Bug Fixes
+
+* build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
+
+## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
+
+
+### Bug Fixes
+
+* populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
+
+## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
+
+
+### Features
+
+* dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
+* llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
+
+
+### Bug Fixes
+
+* celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
+* deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
+* hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
+* return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
+
+## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
+
+
+### Bug Fixes
+
+* Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
+
+## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
+
+
+### Bug Fixes
+
+* align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
+
 ## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
 
 

docker-compose.prod.yml

@@ -3,10 +3,8 @@
 
 services:
   web:
-    build:
+    image: monadicalsas/reflector-frontend:latest
-      context: ./www
+    pull_policy: always
-      dockerfile: Dockerfile
-    image: reflector-frontend:latest
     environment:
       - KV_URL=${KV_URL:-redis://redis:6379}
       - SITE_URL=${SITE_URL}
@@ -36,4 +34,4 @@ services:
       - redis_data:/data
 
 volumes:
-  redis_data:
+  redis_data:

docker-compose.yml

@@ -34,6 +34,20 @@ services:
     environment:
       ENTRYPOINT: beat
 
+  hatchet-worker:
+    build:
+      context: server
+    volumes:
+      - ./server/:/app/
+      - /app/.venv
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: hatchet-worker
+    depends_on:
+      hatchet:
+        condition: service_healthy
+
   redis:
     image: redis:7.2
     ports:
@@ -55,6 +69,7 @@ services:
 
   postgres:
     image: postgres:17
+    command: postgres -c 'max_connections=200'
     ports:
       - 5432:5432
     environment:
@@ -63,6 +78,42 @@ services:
       POSTGRES_DB: reflector
     volumes:
       - ./data/postgres:/var/lib/postgresql/data
+      - ./server/docker/init-hatchet-db.sql:/docker-entrypoint-initdb.d/init-hatchet-db.sql:ro
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -d reflector -U reflector"]
+      interval: 10s
+      timeout: 10s
+      retries: 5
+      start_period: 10s
+
+  hatchet:
+    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
+    ports:
+      - "8889:8888"
+      - "7078:7077"
+    depends_on:
+      postgres:
+        condition: service_healthy
+    environment:
+      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable"
+      SERVER_AUTH_COOKIE_DOMAIN: localhost
+      SERVER_AUTH_COOKIE_INSECURE: "t"
+      SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
+      SERVER_GRPC_INSECURE: "t"
+      SERVER_GRPC_BROADCAST_ADDRESS: hatchet:7077
+      SERVER_GRPC_PORT: "7077"
+      SERVER_URL: http://localhost:8889
+      SERVER_AUTH_SET_EMAIL_VERIFIED: "t"
+      # SERVER_DEFAULT_ENGINE_VERSION: "V1"  # default
+      SERVER_INTERNAL_CLIENT_INTERNAL_GRPC_BROADCAST_ADDRESS: hatchet:7077
+    volumes:
+      - ./data/hatchet-config:/config
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/api/live"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
 
 networks:
   default:

server/README.md

@@ -53,6 +53,36 @@ response = sqs.receive_message(QueueUrl=queue_url, ...)
 uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID
 ```
 
+## Hatchet Setup (Fresh DB)
+
+After resetting the Hatchet database:
+
+### Option A: Automatic (CLI)
+
+```bash
+# Get default tenant ID and create token in one command
+TENANT_ID=$(docker compose exec -T postgres psql -U reflector -d hatchet -t -c \
+  "SELECT id FROM \"Tenant\" WHERE slug = 'default';" | tr -d ' \n') && \
+TOKEN=$(docker compose exec -T hatchet /hatchet-admin token create \
+  --config /config --tenant-id "$TENANT_ID" 2>/dev/null | tr -d '\n') && \
+echo "HATCHET_CLIENT_TOKEN=$TOKEN"
+```
+
+Copy the output to `server/.env`.
+
+### Option B: Manual (UI)
+
+1. Create API token at http://localhost:8889 → Settings → API Tokens
+2. Update `server/.env`: `HATCHET_CLIENT_TOKEN=<new-token>`
+
+### Then restart workers
+
+```bash
+docker compose restart server hatchet-worker
+```
+
+Workflows register automatically when hatchet-worker starts.
+
 ## Pipeline Management
 
 ### Continue stuck pipeline from final summaries (identify_participants) step:

server/docker/init-hatchet-db.sql

@@ -0,0 +1,2 @@
+-- Create hatchet database for Hatchet workflow engine
+CREATE DATABASE hatchet;

server/migrations/versions/05f8688d6895_add_action_items.py

@@ -0,0 +1,26 @@
+"""add_action_items
+
+Revision ID: 05f8688d6895
+Revises: bbafedfa510c
+Create Date: 2025-12-12 11:57:50.209658
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "05f8688d6895"
+down_revision: Union[str, None] = "bbafedfa510c"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column("transcript", sa.Column("action_items", sa.JSON(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column("transcript", "action_items")

server/migrations/versions/0f943fede0e0_add_workflow_run_id_to_transcript.py

@@ -0,0 +1,28 @@
+"""add workflow_run_id to transcript
+
+Revision ID: 0f943fede0e0
+Revises: 05f8688d6895
+Create Date: 2025-12-16 01:54:13.855106
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "0f943fede0e0"
+down_revision: Union[str, None] = "05f8688d6895"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("transcript", schema=None) as batch_op:
+        batch_op.add_column(sa.Column("workflow_run_id", sa.String(), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("transcript", schema=None) as batch_op:
+        batch_op.drop_column("workflow_run_id")

server/migrations/versions/bd3a729bb379_add_use_hatchet_to_room.py

@@ -0,0 +1,35 @@
+"""add use_hatchet to room
+
+Revision ID: bd3a729bb379
+Revises: 0f943fede0e0
+Create Date: 2025-12-16 16:34:03.594231
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "bd3a729bb379"
+down_revision: Union[str, None] = "0f943fede0e0"
+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.add_column(
+            sa.Column(
+                "use_hatchet",
+                sa.Boolean(),
+                server_default=sa.text("false"),
+                nullable=False,
+            )
+        )
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.drop_column("use_hatchet")

server/pyproject.toml

@@ -39,6 +39,7 @@ dependencies = [
     "pytest-env>=1.1.5",
     "webvtt-py>=0.5.0",
     "icalendar>=6.0.0",
+    "hatchet-sdk>=0.47.0",
 ]
 
 [dependency-groups]
@@ -126,6 +127,7 @@ markers = [
 select = [
     "I",       # isort - import sorting
     "F401",    # unused imports
+    "E402",    # module level import not at top of file
     "PLC0415", # import-outside-top-level - detect inline imports
 ]
 

server/reflector/asynctask.py

@@ -1,13 +1,19 @@
 import asyncio
 import functools
+from uuid import uuid4
+
+from celery import current_task
 
 from reflector.db import get_database
+from reflector.llm import llm_session_id
 
 
 def asynctask(f):
     @functools.wraps(f)
     def wrapper(*args, **kwargs):
         async def run_with_db():
+            task_id = current_task.request.id if current_task else None
+            llm_session_id.set(task_id or f"random-{uuid4().hex}")
             database = get_database()
             await database.connect()
             try:

server/reflector/dailyco_api/__init__.py

@@ -18,6 +18,7 @@ from .requests import (
 
 # Response models
 from .responses import (
+    FinishedRecordingResponse,
     MeetingParticipant,
     MeetingParticipantsResponse,
     MeetingResponse,
@@ -79,6 +80,7 @@ __all__ = [
     "MeetingParticipant",
     "MeetingResponse",
     "RecordingResponse",
+    "FinishedRecordingResponse",
     "RecordingS3Info",
     "MeetingTokenResponse",
     "WebhookResponse",

server/reflector/dailyco_api/client.py

@@ -47,7 +47,7 @@ class DailyApiError(Exception):
         )
 
         super().__init__(
-            f"Daily.co API error: {operation} failed with status {self.status_code}"
+            f"Daily.co API error: {operation} failed with status {self.status_code}: {response.text}"
         )
 
 

server/reflector/dailyco_api/requests.py

@@ -40,6 +40,10 @@ class RoomProperties(BaseModel):
     )
     enable_chat: bool = Field(default=True, description="Enable in-meeting chat")
     enable_screenshare: bool = Field(default=True, description="Enable screen sharing")
+    enable_knocking: bool = Field(
+        default=False,
+        description="Enable knocking for private rooms (allows participants to request access)",
+    )
     start_video_off: bool = Field(
         default=False, description="Start with video off for all participants"
     )

server/reflector/dailyco_api/responses.py

@@ -121,7 +121,10 @@ class RecordingS3Info(BaseModel):
 
 class RecordingResponse(BaseModel):
     """
-    Response from recording retrieval endpoint.
+    Response from recording retrieval endpoint (network layer).
+
+    Duration may be None for recordings still being processed by Daily.
+    Use FinishedRecordingResponse for recordings ready for processing.
 
     Reference: https://docs.daily.co/reference/rest-api/recordings
     """
@@ -135,7 +138,9 @@ class RecordingResponse(BaseModel):
     max_participants: int | None = Field(
         None, description="Maximum participants during recording (may be missing)"
     )
-    duration: int = Field(description="Recording duration in seconds")
+    duration: int | None = Field(
+        None, description="Recording duration in seconds (None if still processing)"
+    )
     share_token: NonEmptyString | None = Field(
         None, description="Token for sharing recording"
     )
@@ -149,6 +154,25 @@ class RecordingResponse(BaseModel):
         None, description="Meeting session identifier (may be missing)"
     )
 
+    def to_finished(self) -> "FinishedRecordingResponse | None":
+        """Convert to FinishedRecordingResponse if duration is available and status is finished."""
+        if self.duration is None or self.status != "finished":
+            return None
+        return FinishedRecordingResponse(**self.model_dump())
+
+
+class FinishedRecordingResponse(RecordingResponse):
+    """
+    Recording with confirmed duration - ready for processing.
+
+    This model guarantees duration is present and status is finished.
+    """
+
+    status: Literal["finished"] = Field(
+        description="Recording status (always 'finished')"
+    )
+    duration: int = Field(description="Recording duration in seconds")
+
 
 class MeetingTokenResponse(BaseModel):
     """

server/reflector/db/recordings.py

@@ -3,6 +3,7 @@ from typing import Literal
 
 import sqlalchemy as sa
 from pydantic import BaseModel, Field
+from sqlalchemy import or_
 
 from reflector.db import get_database, metadata
 from reflector.utils import generate_uuid4
@@ -79,5 +80,35 @@ class RecordingController:
         results = await get_database().fetch_all(query)
         return [Recording(**row) for row in results]
 
+    async def get_multitrack_needing_reprocessing(
+        self, bucket_name: str
+    ) -> list[Recording]:
+        """
+        Get multitrack recordings that need reprocessing:
+        - Have track_keys (multitrack)
+        - Either have no transcript OR transcript has error status
+
+        This is more efficient than fetching all recordings and filtering in Python.
+        """
+        from reflector.db.transcripts import (
+            transcripts,  # noqa: PLC0415 cyclic import
+        )
+
+        query = (
+            recordings.select()
+            .outerjoin(transcripts, recordings.c.id == transcripts.c.recording_id)
+            .where(
+                recordings.c.bucket_name == bucket_name,
+                recordings.c.track_keys.isnot(None),
+                or_(
+                    transcripts.c.id.is_(None),
+                    transcripts.c.status == "error",
+                ),
+            )
+        )
+        results = await get_database().fetch_all(query)
+        recordings_list = [Recording(**row) for row in results]
+        return [r for r in recordings_list if r.is_multitrack]
+
 
 recordings_controller = RecordingController()

server/reflector/db/rooms.py

@@ -57,6 +57,12 @@ rooms = sqlalchemy.Table(
         sqlalchemy.String,
         nullable=False,
     ),
+    sqlalchemy.Column(
+        "use_hatchet",
+        sqlalchemy.Boolean,
+        nullable=False,
+        server_default=false(),
+    ),
     sqlalchemy.Index("idx_room_is_shared", "is_shared"),
     sqlalchemy.Index("idx_room_ics_enabled", "ics_enabled"),
 )
@@ -85,6 +91,7 @@ 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_hatchet: bool = False
 
 
 class RoomController:

server/reflector/db/transcripts.py

@@ -44,6 +44,7 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Column("title", sqlalchemy.String),
     sqlalchemy.Column("short_summary", sqlalchemy.String),
     sqlalchemy.Column("long_summary", sqlalchemy.String),
+    sqlalchemy.Column("action_items", sqlalchemy.JSON),
     sqlalchemy.Column("topics", sqlalchemy.JSON),
     sqlalchemy.Column("events", sqlalchemy.JSON),
     sqlalchemy.Column("participants", sqlalchemy.JSON),
@@ -83,6 +84,8 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Column("audio_deleted", sqlalchemy.Boolean),
     sqlalchemy.Column("room_id", sqlalchemy.String),
     sqlalchemy.Column("webvtt", sqlalchemy.Text),
+    # Hatchet workflow run ID for resumption of failed workflows
+    sqlalchemy.Column("workflow_run_id", sqlalchemy.String),
     sqlalchemy.Index("idx_transcript_recording_id", "recording_id"),
     sqlalchemy.Index("idx_transcript_user_id", "user_id"),
     sqlalchemy.Index("idx_transcript_created_at", "created_at"),
@@ -164,6 +167,10 @@ class TranscriptFinalLongSummary(BaseModel):
     long_summary: str
 
 
+class TranscriptActionItems(BaseModel):
+    action_items: dict
+
+
 class TranscriptFinalTitle(BaseModel):
     title: str
 
@@ -204,6 +211,7 @@ class Transcript(BaseModel):
     locked: bool = False
     short_summary: str | None = None
     long_summary: str | None = None
+    action_items: dict | None = None
     topics: list[TranscriptTopic] = []
     events: list[TranscriptEvent] = []
     participants: list[TranscriptParticipant] | None = []
@@ -217,6 +225,7 @@ class Transcript(BaseModel):
     zulip_message_id: int | None = None
     audio_deleted: bool | None = None
     webvtt: str | None = None
+    workflow_run_id: str | None = None  # Hatchet workflow run ID for resumption
 
     @field_serializer("created_at", when_used="json")
     def serialize_datetime(self, dt: datetime) -> str:
@@ -368,7 +377,12 @@ class TranscriptController:
         room_id: str | None = None,
         search_term: str | None = None,
         return_query: bool = False,
-        exclude_columns: list[str] = ["topics", "events", "participants"],
+        exclude_columns: list[str] = [
+            "topics",
+            "events",
+            "participants",
+            "action_items",
+        ],
     ) -> list[Transcript]:
         """
         Get all transcripts

server/reflector/db/users.py

@@ -88,5 +88,11 @@ class UserController:
         results = await get_database().fetch_all(query)
         return [User(**r) for r in results]
 
+    @staticmethod
+    async def get_by_ids(user_ids: list[NonEmptyString]) -> dict[str, User]:
+        query = users.select().where(users.c.id.in_(user_ids))
+        results = await get_database().fetch_all(query)
+        return {user.id: User(**user) for user in results}
+
 
 user_controller = UserController()

server/reflector/hatchet/__init__.py

@@ -0,0 +1,5 @@
+"""Hatchet workflow orchestration for Reflector."""
+
+from reflector.hatchet.client import HatchetClientManager
+
+__all__ = ["HatchetClientManager"]

server/reflector/hatchet/broadcast.py

@@ -0,0 +1,98 @@
+"""WebSocket broadcasting helpers for Hatchet workflows.
+
+DUPLICATION NOTE: To be kept when Celery is deprecated. Currently dupes Celery logic.
+
+Provides WebSocket broadcasting for Hatchet that matches Celery's @broadcast_to_sockets
+decorator behavior. Events are broadcast to transcript rooms and user rooms.
+"""
+
+from typing import Any
+
+import structlog
+
+from reflector.db.transcripts import Transcript, TranscriptEvent, transcripts_controller
+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"}
+
+
+async def broadcast_event(
+    transcript_id: NonEmptyString,
+    event: TranscriptEvent,
+    logger: structlog.BoundLogger,
+) -> None:
+    """Broadcast a TranscriptEvent to WebSocket subscribers.
+
+    Fire-and-forget: errors are logged but don't interrupt workflow execution.
+    """
+    logger.info(
+        "Broadcasting event",
+        transcript_id=transcript_id,
+        event_type=event.event,
+    )
+    try:
+        ws_manager = get_ws_manager()
+
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message=event.model_dump(mode="json"),
+        )
+        logger.info(
+            "Event sent to transcript room",
+            transcript_id=transcript_id,
+            event_type=event.event,
+        )
+
+        if event.event in USER_ROOM_EVENTS:
+            transcript = await transcripts_controller.get_by_id(transcript_id)
+            if transcript and transcript.user_id:
+                await ws_manager.send_json(
+                    room_id=f"user:{transcript.user_id}",
+                    message={
+                        "event": f"TRANSCRIPT_{event.event}",
+                        "data": {"id": transcript_id, **event.data},
+                    },
+                )
+    except Exception as e:
+        logger.warning(
+            "Failed to broadcast event",
+            error=str(e),
+            transcript_id=transcript_id,
+            event_type=event.event,
+        )
+
+
+async def set_status_and_broadcast(
+    transcript_id: NonEmptyString,
+    status: str,
+    logger: structlog.BoundLogger,
+) -> None:
+    """Set transcript status and broadcast to WebSocket.
+
+    Wrapper around transcripts_controller.set_status that adds WebSocket broadcasting.
+    """
+    event = await transcripts_controller.set_status(transcript_id, status)
+    if event:
+        await broadcast_event(transcript_id, event, logger=logger)
+
+
+async def append_event_and_broadcast(
+    transcript_id: NonEmptyString,
+    transcript: Transcript,
+    event_name: str,
+    data: Any,
+    logger: structlog.BoundLogger,
+) -> TranscriptEvent:
+    """Append event to transcript and broadcast to WebSocket.
+
+    Wrapper around transcripts_controller.append_event that adds WebSocket broadcasting.
+    """
+    event = await transcripts_controller.append_event(
+        transcript=transcript,
+        event=event_name,
+        data=data,
+    )
+    await broadcast_event(transcript_id, event, logger=logger)
+    return event

server/reflector/hatchet/client.py

@@ -0,0 +1,111 @@
+"""Hatchet Python client wrapper.
+
+Uses singleton pattern because:
+1. Hatchet client maintains persistent gRPC connections for workflow registration
+2. Creating multiple clients would cause registration conflicts and resource leaks
+3. The SDK is designed for a single client instance per process
+4. Tests use `HatchetClientManager.reset()` to isolate state between tests
+"""
+
+import logging
+import threading
+
+from hatchet_sdk import ClientConfig, Hatchet
+from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+from reflector.logger import logger
+from reflector.settings import settings
+
+
+class HatchetClientManager:
+    """Singleton manager for Hatchet client connections.
+
+    See module docstring for rationale. For test isolation, use `reset()`.
+    """
+
+    _instance: Hatchet | None = None
+    _lock = threading.Lock()
+
+    @classmethod
+    def get_client(cls) -> Hatchet:
+        """Get or create the Hatchet client (thread-safe singleton)."""
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    if not settings.HATCHET_CLIENT_TOKEN:
+                        raise ValueError("HATCHET_CLIENT_TOKEN must be set")
+
+                    # Pass root logger to Hatchet so workflow logs appear in dashboard
+                    root_logger = logging.getLogger()
+                    cls._instance = Hatchet(
+                        debug=settings.HATCHET_DEBUG,
+                        config=ClientConfig(logger=root_logger),
+                    )
+        return cls._instance
+
+    @classmethod
+    async def start_workflow(
+        cls,
+        workflow_name: str,
+        input_data: dict,
+        additional_metadata: dict | None = None,
+    ) -> str:
+        """Start a workflow and return the workflow run ID.
+
+        Args:
+            workflow_name: Name of the workflow to trigger.
+            input_data: Input data for the workflow run.
+            additional_metadata: Optional metadata for filtering in dashboard
+                (e.g., transcript_id, recording_id).
+        """
+        client = cls.get_client()
+        result = await client.runs.aio_create(
+            workflow_name,
+            input_data,
+            additional_metadata=additional_metadata,
+        )
+        return result.run.metadata.id
+
+    @classmethod
+    async def get_workflow_run_status(cls, workflow_run_id: str) -> V1TaskStatus:
+        client = cls.get_client()
+        return await client.runs.aio_get_status(workflow_run_id)
+
+    @classmethod
+    async def cancel_workflow(cls, workflow_run_id: str) -> None:
+        client = cls.get_client()
+        await client.runs.aio_cancel(workflow_run_id)
+        logger.info("[Hatchet] Cancelled workflow", workflow_run_id=workflow_run_id)
+
+    @classmethod
+    async def replay_workflow(cls, workflow_run_id: str) -> None:
+        client = cls.get_client()
+        await client.runs.aio_replay(workflow_run_id)
+        logger.info("[Hatchet] Replaying workflow", workflow_run_id=workflow_run_id)
+
+    @classmethod
+    async def can_replay(cls, workflow_run_id: str) -> bool:
+        """Check if workflow can be replayed (is FAILED)."""
+        try:
+            status = await cls.get_workflow_run_status(workflow_run_id)
+            return status == V1TaskStatus.FAILED or status == V1TaskStatus.CANCELLED
+        except Exception as e:
+            logger.warning(
+                "[Hatchet] Failed to check replay status",
+                workflow_run_id=workflow_run_id,
+                error=str(e),
+            )
+            return False
+
+    @classmethod
+    async def get_workflow_status(cls, workflow_run_id: str) -> dict:
+        """Get the full workflow run details as dict."""
+        client = cls.get_client()
+        run = await client.runs.aio_get(workflow_run_id)
+        return run.to_dict()
+
+    @classmethod
+    def reset(cls) -> None:
+        """Reset the client instance (for testing)."""
+        with cls._lock:
+            cls._instance = None

server/reflector/hatchet/run_workers.py

@@ -0,0 +1,63 @@
+"""
+Run Hatchet workers for the diarization pipeline.
+Runs as a separate process, just like Celery workers.
+
+Usage:
+    uv run -m reflector.hatchet.run_workers
+
+    # Or via docker:
+    docker compose exec server uv run -m reflector.hatchet.run_workers
+"""
+
+import signal
+import sys
+
+from reflector.logger import logger
+from reflector.settings import settings
+
+
+def main() -> None:
+    """Start Hatchet worker polling."""
+    if not settings.HATCHET_ENABLED:
+        logger.error("HATCHET_ENABLED is False, not starting workers")
+        sys.exit(1)
+
+    if not settings.HATCHET_CLIENT_TOKEN:
+        logger.error("HATCHET_CLIENT_TOKEN is not set")
+        sys.exit(1)
+
+    logger.info(
+        "Starting Hatchet workers",
+        debug=settings.HATCHET_DEBUG,
+    )
+
+    # Import here (not top-level) - workflow modules call HatchetClientManager.get_client()
+    # at module level because Hatchet SDK decorators (@workflow.task) bind at import time.
+    # Can't use lazy init: decorators need the client object when function is defined.
+    from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
+    from reflector.hatchet.workflows import (  # noqa: PLC0415
+        diarization_pipeline,
+        track_workflow,
+    )
+
+    hatchet = HatchetClientManager.get_client()
+
+    worker = hatchet.worker(
+        "reflector-diarization-worker",
+        workflows=[diarization_pipeline, track_workflow],
+    )
+
+    def shutdown_handler(signum: int, frame) -> None:
+        logger.info("Received shutdown signal, stopping workers...")
+        # Worker cleanup happens automatically on exit
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, shutdown_handler)
+    signal.signal(signal.SIGTERM, shutdown_handler)
+
+    logger.info("Starting Hatchet worker polling...")
+    worker.start()
+
+
+if __name__ == "__main__":
+    main()

server/reflector/hatchet/workflows/__init__.py

@@ -0,0 +1,14 @@
+"""Hatchet workflow definitions."""
+
+from reflector.hatchet.workflows.diarization_pipeline import (
+    PipelineInput,
+    diarization_pipeline,
+)
+from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+
+__all__ = [
+    "diarization_pipeline",
+    "track_workflow",
+    "PipelineInput",
+    "TrackInput",
+]

server/reflector/hatchet/workflows/diarization_pipeline.py

@@ -0,0 +1,961 @@
+"""
+Hatchet main workflow: DiarizationPipeline
+
+Multitrack diarization pipeline for Daily.co recordings.
+Orchestrates the full processing flow from recording metadata to final transcript.
+
+Note: This file uses deferred imports (inside functions/tasks) intentionally.
+Hatchet workers run in forked processes; fresh imports per task ensure DB connections
+are not shared across forks, avoiding connection pooling issues.
+"""
+
+import asyncio
+import functools
+import tempfile
+from contextlib import asynccontextmanager
+from datetime import timedelta
+from pathlib import Path
+from typing import Callable
+
+import httpx
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.dailyco_api.client import DailyApiClient
+from reflector.hatchet.broadcast import (
+    append_event_and_broadcast,
+    set_status_and_broadcast,
+)
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.workflows.models import (
+    ConsentResult,
+    FinalizeResult,
+    MixdownResult,
+    PaddedTrackInfo,
+    ParticipantsResult,
+    ProcessTracksResult,
+    RecordingResult,
+    SummaryResult,
+    TitleResult,
+    TopicsResult,
+    WaveformResult,
+    WebhookResult,
+    ZulipResult,
+)
+from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+from reflector.logger import logger
+from reflector.pipelines import topic_processing
+from reflector.processors import AudioFileWriterProcessor
+from reflector.processors.types import (
+    TitleSummary,
+    TitleSummaryWithId,
+    Word,
+)
+from reflector.processors.types import (
+    Transcript as TranscriptType,
+)
+from reflector.settings import settings
+from reflector.storage.storage_aws import AwsStorage
+from reflector.utils.audio_constants import (
+    PRESIGNED_URL_EXPIRATION_SECONDS,
+    WAVEFORM_SEGMENTS,
+)
+from reflector.utils.audio_mixdown import (
+    detect_sample_rate_from_tracks,
+    mixdown_tracks_pyav,
+)
+from reflector.utils.audio_waveform import get_audio_waveform
+from reflector.utils.daily import (
+    filter_cam_audio_tracks,
+    parse_daily_recording_filename,
+)
+from reflector.utils.string import NonEmptyString, assert_non_none_and_non_empty
+from reflector.zulip import post_transcript_notification
+
+
+class PipelineInput(BaseModel):
+    """Input to trigger the diarization pipeline."""
+
+    recording_id: NonEmptyString
+    tracks: list[dict]  # List of {"s3_key": str}
+    bucket_name: NonEmptyString
+    transcript_id: NonEmptyString
+    room_id: NonEmptyString | None = None
+
+
+hatchet = HatchetClientManager.get_client()
+
+diarization_pipeline = hatchet.workflow(
+    name="DiarizationPipeline", input_validator=PipelineInput
+)
+
+
+@asynccontextmanager
+async def fresh_db_connection():
+    """Context manager for database connections in Hatchet workers.
+    TECH DEBT: Made to make connection fork-aware without changing db code too much.
+    The real fix would be making the db module fork-aware instead of bypassing it.
+    Current pattern is acceptable given Hatchet's process model.
+    """
+    import databases  # noqa: PLC0415
+
+    from reflector.db import _database_context  # noqa: PLC0415
+
+    _database_context.set(None)
+    db = databases.Database(settings.DATABASE_URL)
+    _database_context.set(db)
+    await db.connect()
+    try:
+        yield db
+    finally:
+        await db.disconnect()
+        _database_context.set(None)
+
+
+async def set_workflow_error_status(transcript_id: NonEmptyString) -> bool:
+    """Set transcript status to 'error' on workflow failure.
+
+    Returns:
+        True if status was set successfully, False if failed.
+        Failure is logged as CRITICAL since it means transcript may be stuck.
+    """
+    try:
+        async with fresh_db_connection():
+            await set_status_and_broadcast(transcript_id, "error", logger=logger)
+            return True
+    except Exception as e:
+        logger.critical(
+            "[Hatchet] CRITICAL: Failed to set error status - transcript may be stuck in 'processing'",
+            transcript_id=transcript_id,
+            error=str(e),
+            exc_info=True,
+        )
+        return False
+
+
+def _spawn_storage():
+    """Create fresh storage instance."""
+    return 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,
+    )
+
+
+def with_error_handling(step_name: str, set_error_status: bool = True) -> Callable:
+    """Decorator that handles task failures uniformly.
+
+    Args:
+        step_name: Name of the step for logging and progress tracking.
+        set_error_status: Whether to set transcript status to 'error' on failure.
+    """
+
+    def decorator(func: Callable) -> Callable:
+        @functools.wraps(func)
+        async def wrapper(input: PipelineInput, ctx: Context):
+            try:
+                return await func(input, ctx)
+            except Exception as e:
+                logger.error(
+                    f"[Hatchet] {step_name} failed",
+                    transcript_id=input.transcript_id,
+                    error=str(e),
+                    exc_info=True,
+                )
+                if set_error_status:
+                    await set_workflow_error_status(input.transcript_id)
+                raise
+
+        return wrapper
+
+    return decorator
+
+
+@diarization_pipeline.task(execution_timeout=timedelta(seconds=60), retries=3)
+@with_error_handling("get_recording")
+async def get_recording(input: PipelineInput, ctx: Context) -> RecordingResult:
+    """Fetch recording metadata from Daily.co API."""
+    ctx.log(f"get_recording: recording_id={input.recording_id}")
+
+    # Set transcript status to "processing" at workflow start (broadcasts to WebSocket)
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            await set_status_and_broadcast(
+                input.transcript_id, "processing", logger=logger
+            )
+            ctx.log(f"Set transcript status to processing: {input.transcript_id}")
+
+    if not settings.DAILY_API_KEY:
+        raise ValueError("DAILY_API_KEY not configured")
+
+    async with DailyApiClient(api_key=settings.DAILY_API_KEY) as client:
+        recording = await client.get_recording(input.recording_id)
+
+    ctx.log(
+        f"get_recording complete: room={recording.room_name}, duration={recording.duration}s"
+    )
+
+    return RecordingResult(
+        id=recording.id,
+        mtg_session_id=recording.mtgSessionId,
+        duration=recording.duration,
+    )
+
+
+@diarization_pipeline.task(
+    parents=[get_recording], execution_timeout=timedelta(seconds=60), retries=3
+)
+@with_error_handling("get_participants")
+async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsResult:
+    """Fetch participant list from Daily.co API and update transcript in database."""
+    ctx.log(f"get_participants: transcript_id={input.transcript_id}")
+
+    recording = ctx.task_output(get_recording)
+    mtg_session_id = recording.mtg_session_id
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptParticipant,
+            transcripts_controller,
+        )
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            # Note: title NOT cleared - preserves existing titles
+            await transcripts_controller.update(
+                transcript,
+                {
+                    "events": [],
+                    "topics": [],
+                    "participants": [],
+                },
+            )
+
+        mtg_session_id = assert_non_none_and_non_empty(
+            mtg_session_id, "mtg_session_id is required"
+        )
+        daily_api_key = assert_non_none_and_non_empty(
+            settings.DAILY_API_KEY, "DAILY_API_KEY is required"
+        )
+
+        async with DailyApiClient(api_key=daily_api_key) as client:
+            participants = await client.get_meeting_participants(mtg_session_id)
+
+        id_to_name = {}
+        id_to_user_id = {}
+        for p in participants.data:
+            if p.user_name:
+                id_to_name[p.participant_id] = p.user_name
+            if p.user_id:
+                id_to_user_id[p.participant_id] = p.user_id
+
+        track_keys = [t["s3_key"] for t in input.tracks]
+        cam_audio_keys = filter_cam_audio_tracks(track_keys)
+
+        participants_list = []
+        for idx, key in enumerate(cam_audio_keys):
+            try:
+                parsed = parse_daily_recording_filename(key)
+                participant_id = parsed.participant_id
+            except ValueError as e:
+                logger.error(
+                    "Failed to parse Daily recording filename",
+                    error=str(e),
+                    key=key,
+                )
+                continue
+
+            default_name = f"Speaker {idx}"
+            name = id_to_name.get(participant_id, default_name)
+            user_id = id_to_user_id.get(participant_id)
+
+            participant = TranscriptParticipant(
+                id=participant_id, speaker=idx, name=name, user_id=user_id
+            )
+            await transcripts_controller.upsert_participant(transcript, participant)
+            participants_list.append(
+                {
+                    "participant_id": participant_id,
+                    "user_name": name,
+                    "speaker": idx,
+                }
+            )
+
+        ctx.log(f"get_participants complete: {len(participants_list)} participants")
+
+    return ParticipantsResult(
+        participants=participants_list,
+        num_tracks=len(input.tracks),
+        source_language=transcript.source_language if transcript else "en",
+        target_language=transcript.target_language if transcript else "en",
+    )
+
+
+@diarization_pipeline.task(
+    parents=[get_participants], execution_timeout=timedelta(seconds=600), retries=3
+)
+@with_error_handling("process_tracks")
+async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksResult:
+    """Spawn child workflows for each track (dynamic fan-out)."""
+    ctx.log(f"process_tracks: spawning {len(input.tracks)} track workflows")
+
+    participants_result = ctx.task_output(get_participants)
+    source_language = participants_result.source_language
+
+    child_coroutines = [
+        track_workflow.aio_run(
+            TrackInput(
+                track_index=i,
+                s3_key=track["s3_key"],
+                bucket_name=input.bucket_name,
+                transcript_id=input.transcript_id,
+                language=source_language,
+            )
+        )
+        for i, track in enumerate(input.tracks)
+    ]
+
+    results = await asyncio.gather(*child_coroutines)
+
+    target_language = participants_result.target_language
+
+    track_words = []
+    padded_tracks = []
+    created_padded_files = set()
+
+    for result in results:
+        transcribe_result = result.get("transcribe_track", {})
+        track_words.append(transcribe_result.get("words", []))
+
+        pad_result = result.get("pad_track", {})
+        padded_key = pad_result.get("padded_key")
+        bucket_name = pad_result.get("bucket_name")
+
+        # Store S3 key info (not presigned URL) - consumer tasks presign on demand
+        if padded_key:
+            padded_tracks.append(
+                PaddedTrackInfo(key=padded_key, bucket_name=bucket_name)
+            )
+
+        track_index = pad_result.get("track_index")
+        if pad_result.get("size", 0) > 0 and track_index is not None:
+            storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{track_index}.webm"
+            created_padded_files.add(storage_path)
+
+    all_words = [word for words in track_words for word in words]
+    all_words.sort(key=lambda w: w.get("start", 0))
+
+    ctx.log(
+        f"process_tracks complete: {len(all_words)} words from {len(input.tracks)} tracks"
+    )
+
+    return ProcessTracksResult(
+        all_words=all_words,
+        padded_tracks=padded_tracks,
+        word_count=len(all_words),
+        num_tracks=len(input.tracks),
+        target_language=target_language,
+        created_padded_files=list(created_padded_files),
+    )
+
+
+@diarization_pipeline.task(
+    parents=[process_tracks], execution_timeout=timedelta(seconds=300), retries=3
+)
+@with_error_handling("mixdown_tracks")
+async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
+    """Mix all padded tracks into single audio file using PyAV (same as Celery)."""
+    ctx.log("mixdown_tracks: mixing padded tracks into single audio file")
+
+    track_result = ctx.task_output(process_tracks)
+    padded_tracks = track_result.padded_tracks
+
+    # TODO think of NonEmpty type to avoid those checks, e.g. sized.NonEmpty from https://github.com/antonagestam/phantom-types/
+    if not padded_tracks:
+        raise ValueError("No padded tracks to mixdown")
+
+    storage = _spawn_storage()
+
+    # Presign URLs on demand (avoids stale URLs on workflow replay)
+    padded_urls = []
+    for track_info in padded_tracks:
+        if track_info.key:
+            url = await storage.get_file_url(
+                track_info.key,
+                operation="get_object",
+                expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+                bucket=track_info.bucket_name,
+            )
+            padded_urls.append(url)
+
+    valid_urls = [url for url in padded_urls if url]
+    if not valid_urls:
+        raise ValueError("No valid padded tracks to mixdown")
+
+    target_sample_rate = detect_sample_rate_from_tracks(valid_urls, logger=logger)
+    if not target_sample_rate:
+        logger.error("Mixdown failed - no decodable audio frames found")
+        raise ValueError("No decodable audio frames in any track")
+
+    output_path = tempfile.mktemp(suffix=".mp3")
+    duration_ms_callback_capture_container = [0.0]
+
+    async def capture_duration(d):
+        duration_ms_callback_capture_container[0] = d
+
+    writer = AudioFileWriterProcessor(path=output_path, on_duration=capture_duration)
+
+    await mixdown_tracks_pyav(
+        valid_urls,
+        writer,
+        target_sample_rate,
+        offsets_seconds=None,
+        logger=logger,
+    )
+    await writer.flush()
+
+    file_size = Path(output_path).stat().st_size
+    storage_path = f"{input.transcript_id}/audio.mp3"
+
+    with open(output_path, "rb") as mixed_file:
+        await storage.put_file(storage_path, mixed_file)
+
+    Path(output_path).unlink(missing_ok=True)
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"audio_location": "storage"}
+            )
+
+    ctx.log(f"mixdown_tracks complete: uploaded {file_size} bytes to {storage_path}")
+
+    return MixdownResult(
+        audio_key=storage_path,
+        duration=duration_ms_callback_capture_container[0],
+        tracks_mixed=len(valid_urls),
+    )
+
+
+@diarization_pipeline.task(
+    parents=[mixdown_tracks], execution_timeout=timedelta(seconds=120), retries=3
+)
+@with_error_handling("generate_waveform")
+async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResult:
+    """Generate audio waveform visualization using AudioWaveformProcessor (matches Celery)."""
+    ctx.log(f"generate_waveform: transcript_id={input.transcript_id}")
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptWaveform,
+        transcripts_controller,
+    )
+
+    mixdown_result = ctx.task_output(mixdown_tracks)
+    audio_key = mixdown_result.audio_key
+
+    storage = _spawn_storage()
+    audio_url = await storage.get_file_url(
+        audio_key,
+        operation="get_object",
+        expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+    )
+
+    # Download MP3 to temp file (AudioWaveformProcessor needs local file)
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
+        temp_path = temp_file.name
+
+    try:
+        async with httpx.AsyncClient() as client:
+            response = await client.get(audio_url, timeout=120)
+            response.raise_for_status()
+            with open(temp_path, "wb") as f:
+                f.write(response.content)
+
+        waveform = get_audio_waveform(
+            path=Path(temp_path), segments_count=WAVEFORM_SEGMENTS
+        )
+
+        async with fresh_db_connection():
+            transcript = await transcripts_controller.get_by_id(input.transcript_id)
+            if transcript:
+                waveform_data = TranscriptWaveform(waveform=waveform)
+                await append_event_and_broadcast(
+                    input.transcript_id,
+                    transcript,
+                    "WAVEFORM",
+                    waveform_data,
+                    logger=logger,
+                )
+
+    finally:
+        Path(temp_path).unlink(missing_ok=True)
+
+    ctx.log("generate_waveform complete")
+
+    return WaveformResult(waveform_generated=True)
+
+
+@diarization_pipeline.task(
+    parents=[mixdown_tracks], execution_timeout=timedelta(seconds=300), retries=3
+)
+@with_error_handling("detect_topics")
+async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
+    """Detect topics using LLM and save to database (matches Celery on_topic callback)."""
+    ctx.log("detect_topics: analyzing transcript for topics")
+
+    track_result = ctx.task_output(process_tracks)
+    words = track_result.all_words
+    target_language = track_result.target_language
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptTopic,
+        transcripts_controller,
+    )
+
+    word_objects = [Word(**w) for w in words]
+    transcript_type = TranscriptType(words=word_objects)
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        async def on_topic_callback(data):
+            topic = TranscriptTopic(
+                title=data.title,
+                summary=data.summary,
+                timestamp=data.timestamp,
+                transcript=data.transcript.text,
+                words=data.transcript.words,
+            )
+            if isinstance(
+                data, TitleSummaryWithId
+            ):  # Celery parity: main_live_pipeline.py
+                topic.id = data.id
+            await transcripts_controller.upsert_topic(transcript, topic)
+            await append_event_and_broadcast(
+                input.transcript_id, transcript, "TOPIC", topic, logger=logger
+            )
+
+        topics = await topic_processing.detect_topics(
+            transcript_type,
+            target_language,
+            on_topic_callback=on_topic_callback,
+            empty_pipeline=empty_pipeline,
+        )
+
+    topics_list = [t.model_dump() for t in topics]
+
+    ctx.log(f"detect_topics complete: found {len(topics_list)} topics")
+
+    return TopicsResult(topics=topics_list)
+
+
+@diarization_pipeline.task(
+    parents=[detect_topics], execution_timeout=timedelta(seconds=120), retries=3
+)
+@with_error_handling("generate_title")
+async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
+    """Generate meeting title using LLM and save to database (matches Celery on_title callback)."""
+    ctx.log("generate_title: generating title from topics")
+
+    topics_result = ctx.task_output(detect_topics)
+    topics = topics_result.topics
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptFinalTitle,
+        transcripts_controller,
+    )
+
+    topic_objects = [TitleSummary(**t) for t in topics]
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+    title_result = None
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        async def on_title_callback(data):
+            nonlocal title_result
+            title_result = data.title
+            final_title = TranscriptFinalTitle(title=data.title)
+            if not transcript.title:
+                await transcripts_controller.update(
+                    transcript,
+                    {"title": final_title.title},
+                )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_TITLE",
+                final_title,
+                logger=logger,
+            )
+
+        await topic_processing.generate_title(
+            topic_objects,
+            on_title_callback=on_title_callback,
+            empty_pipeline=empty_pipeline,
+            logger=logger,
+        )
+
+    ctx.log(f"generate_title complete: '{title_result}'")
+
+    return TitleResult(title=title_result)
+
+
+@diarization_pipeline.task(
+    parents=[detect_topics], execution_timeout=timedelta(seconds=300), retries=3
+)
+@with_error_handling("generate_summary")
+async def generate_summary(input: PipelineInput, ctx: Context) -> SummaryResult:
+    """Generate meeting summary using LLM and save to database (matches Celery callbacks)."""
+    ctx.log("generate_summary: generating long and short summaries")
+
+    topics_result = ctx.task_output(detect_topics)
+    topics = topics_result.topics
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptActionItems,
+        TranscriptFinalLongSummary,
+        TranscriptFinalShortSummary,
+        transcripts_controller,
+    )
+
+    topic_objects = [TitleSummary(**t) for t in topics]
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+    summary_result = None
+    short_summary_result = None
+    action_items_result = None
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        async def on_long_summary_callback(data):
+            nonlocal summary_result
+            summary_result = data.long_summary
+            final_long_summary = TranscriptFinalLongSummary(
+                long_summary=data.long_summary
+            )
+            await transcripts_controller.update(
+                transcript,
+                {"long_summary": final_long_summary.long_summary},
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_LONG_SUMMARY",
+                final_long_summary,
+                logger=logger,
+            )
+
+        async def on_short_summary_callback(data):
+            nonlocal short_summary_result
+            short_summary_result = data.short_summary
+            final_short_summary = TranscriptFinalShortSummary(
+                short_summary=data.short_summary
+            )
+            await transcripts_controller.update(
+                transcript,
+                {"short_summary": final_short_summary.short_summary},
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_SHORT_SUMMARY",
+                final_short_summary,
+                logger=logger,
+            )
+
+        async def on_action_items_callback(data):
+            nonlocal action_items_result
+            action_items_result = data.action_items
+            action_items = TranscriptActionItems(action_items=data.action_items)
+            await transcripts_controller.update(
+                transcript,
+                {"action_items": action_items.action_items},
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "ACTION_ITEMS",
+                action_items,
+                logger=logger,
+            )
+
+        await topic_processing.generate_summaries(
+            topic_objects,
+            transcript,
+            on_long_summary_callback=on_long_summary_callback,
+            on_short_summary_callback=on_short_summary_callback,
+            on_action_items_callback=on_action_items_callback,
+            empty_pipeline=empty_pipeline,
+            logger=logger,
+        )
+
+    ctx.log("generate_summary complete")
+
+    return SummaryResult(
+        summary=summary_result,
+        short_summary=short_summary_result,
+        action_items=action_items_result,
+    )
+
+
+@diarization_pipeline.task(
+    parents=[generate_waveform, generate_title, generate_summary],
+    execution_timeout=timedelta(seconds=60),
+    retries=3,
+)
+@with_error_handling("finalize")
+async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
+    """Finalize transcript: save words, emit TRANSCRIPT event, set status to 'ended'.
+
+    Matches Celery's on_transcript + set_status behavior.
+    Note: Title and summaries are already saved by their respective task callbacks.
+    """
+    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:
+        ctx.log(f"Cleaning up {len(created_padded_files)} temporary S3 files")
+        storage = _spawn_storage()
+        cleanup_results = await asyncio.gather(
+            *[storage.delete_file(path) for path in created_padded_files],
+            return_exceptions=True,
+        )
+        for storage_path, result in zip(created_padded_files, cleanup_results):
+            if isinstance(result, Exception):
+                logger.warning(
+                    "[Hatchet] Failed to cleanup temporary padded track",
+                    storage_path=storage_path,
+                    error=str(result),
+                )
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptDuration,
+            TranscriptText,
+            transcripts_controller,
+        )
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript is None:
+            raise ValueError(f"Transcript {input.transcript_id} not found in database")
+
+        word_objects = [Word(**w) for w in all_words]
+        merged_transcript = TranscriptType(words=word_objects, translation=None)
+
+        await append_event_and_broadcast(
+            input.transcript_id,
+            transcript,
+            "TRANSCRIPT",
+            TranscriptText(
+                text=merged_transcript.text,
+                translation=merged_transcript.translation,
+            ),
+            logger=logger,
+        )
+
+        # Save duration and clear workflow_run_id (workflow completed successfully)
+        # Note: title/long_summary/short_summary 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(
+            f"finalize complete: transcript {input.transcript_id} status set to 'ended'"
+        )
+
+    return FinalizeResult(status="COMPLETED")
+
+
+@diarization_pipeline.task(
+    parents=[finalize], execution_timeout=timedelta(seconds=60), retries=3
+)
+@with_error_handling("cleanup_consent", set_error_status=False)
+async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
+    """Check consent and delete audio files if any participant denied."""
+    ctx.log(f"cleanup_consent: transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.db.meetings import (  # noqa: PLC0415
+            meeting_consent_controller,
+            meetings_controller,
+        )
+        from reflector.db.recordings import recordings_controller  # noqa: PLC0415
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+        from reflector.storage import get_transcripts_storage  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            ctx.log("cleanup_consent: transcript not found")
+            return ConsentResult()
+
+        consent_denied = False
+        if transcript.meeting_id:
+            meeting = await meetings_controller.get_by_id(transcript.meeting_id)
+            if meeting:
+                consent_denied = await meeting_consent_controller.has_any_denial(
+                    meeting.id
+                )
+
+        if not consent_denied:
+            ctx.log("cleanup_consent: consent approved, keeping all files")
+            return ConsentResult()
+
+        ctx.log("cleanup_consent: consent denied, deleting audio files")
+
+        input_track_keys = set(t["s3_key"] for t in input.tracks)
+
+        # Detect if recording.track_keys was manually modified after workflow started
+        if transcript.recording_id:
+            recording = await recordings_controller.get_by_id(transcript.recording_id)
+            if recording and recording.track_keys:
+                db_track_keys = set(filter_cam_audio_tracks(recording.track_keys))
+
+                if input_track_keys != db_track_keys:
+                    added = db_track_keys - input_track_keys
+                    removed = input_track_keys - db_track_keys
+                    logger.warning(
+                        "[Hatchet] Track keys mismatch: DB changed since workflow start",
+                        transcript_id=input.transcript_id,
+                        recording_id=transcript.recording_id,
+                        input_count=len(input_track_keys),
+                        db_count=len(db_track_keys),
+                        added_in_db=list(added) if added else None,
+                        removed_from_db=list(removed) if removed else None,
+                    )
+                    ctx.log(
+                        f"WARNING: track_keys mismatch - "
+                        f"input has {len(input_track_keys)}, DB has {len(db_track_keys)}. "
+                        f"Using input tracks for deletion."
+                    )
+
+        deletion_errors = []
+
+        if input_track_keys and input.bucket_name:
+            master_storage = get_transcripts_storage()
+            for key in input_track_keys:
+                try:
+                    await master_storage.delete_file(key, bucket=input.bucket_name)
+                    ctx.log(f"Deleted recording file: {input.bucket_name}/{key}")
+                except Exception as e:
+                    error_msg = f"Failed to delete {key}: {e}"
+                    logger.error(error_msg, exc_info=True)
+                    deletion_errors.append(error_msg)
+
+        if transcript.audio_location == "storage":
+            storage = get_transcripts_storage()
+            try:
+                await storage.delete_file(transcript.storage_audio_path)
+                ctx.log(f"Deleted processed audio: {transcript.storage_audio_path}")
+            except Exception as e:
+                error_msg = f"Failed to delete processed audio: {e}"
+                logger.error(error_msg, exc_info=True)
+                deletion_errors.append(error_msg)
+
+        if deletion_errors:
+            logger.warning(
+                "[Hatchet] cleanup_consent completed with errors",
+                transcript_id=input.transcript_id,
+                error_count=len(deletion_errors),
+                errors=deletion_errors,
+            )
+            ctx.log(f"cleanup_consent completed with {len(deletion_errors)} errors")
+        else:
+            await transcripts_controller.update(transcript, {"audio_deleted": True})
+            ctx.log("cleanup_consent: all audio deleted successfully")
+
+    return ConsentResult()
+
+
+@diarization_pipeline.task(
+    parents=[cleanup_consent], execution_timeout=timedelta(seconds=60), retries=5
+)
+@with_error_handling("post_zulip", set_error_status=False)
+async def post_zulip(input: PipelineInput, ctx: Context) -> ZulipResult:
+    """Post notification to Zulip."""
+    ctx.log(f"post_zulip: transcript_id={input.transcript_id}")
+
+    if not settings.ZULIP_REALM:
+        ctx.log("post_zulip skipped (Zulip not configured)")
+        return ZulipResult(zulip_message_id=None, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            message_id = await post_transcript_notification(transcript)
+            ctx.log(f"post_zulip complete: zulip_message_id={message_id}")
+        else:
+            message_id = None
+
+    return ZulipResult(zulip_message_id=message_id)
+
+
+@diarization_pipeline.task(
+    parents=[post_zulip], execution_timeout=timedelta(seconds=120), retries=30
+)
+@with_error_handling("send_webhook", set_error_status=False)
+async def send_webhook(input: PipelineInput, ctx: Context) -> WebhookResult:
+    """Send completion webhook to external service."""
+    ctx.log(f"send_webhook: transcript_id={input.transcript_id}")
+
+    if not input.room_id:
+        ctx.log("send_webhook skipped (no room_id)")
+        return WebhookResult(webhook_sent=False, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.rooms import rooms_controller  # noqa: PLC0415
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        room = await rooms_controller.get_by_id(input.room_id)
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        if room and room.webhook_url and transcript:
+            webhook_payload = {
+                "event": "transcript.completed",
+                "transcript_id": input.transcript_id,
+                "title": transcript.title,
+                "duration": transcript.duration,
+            }
+
+            async with httpx.AsyncClient() as client:
+                response = await client.post(
+                    room.webhook_url, json=webhook_payload, timeout=30
+                )
+                response.raise_for_status()
+
+            ctx.log(f"send_webhook complete: status_code={response.status_code}")
+
+            return WebhookResult(webhook_sent=True, response_code=response.status_code)
+
+    return WebhookResult(webhook_sent=False, skipped=True)

server/reflector/hatchet/workflows/models.py

@@ -0,0 +1,124 @@
+"""
+Pydantic models for Hatchet workflow task return types.
+
+Provides static typing for all task outputs, enabling type checking
+and better IDE support.
+"""
+
+from typing import Any
+
+from pydantic import BaseModel
+
+from reflector.utils.string import NonEmptyString
+
+
+class PadTrackResult(BaseModel):
+    """Result from pad_track task."""
+
+    padded_key: NonEmptyString  # S3 key (not presigned URL) - presign on demand to avoid stale URLs on replay
+    bucket_name: (
+        NonEmptyString | None
+    )  # None means use default transcript storage bucket
+    size: int
+    track_index: int
+
+
+class TranscribeTrackResult(BaseModel):
+    """Result from transcribe_track task."""
+
+    words: list[dict[str, Any]]
+    track_index: int
+
+
+class RecordingResult(BaseModel):
+    """Result from get_recording task."""
+
+    id: NonEmptyString | None
+    mtg_session_id: NonEmptyString | None
+    duration: float
+
+
+class ParticipantsResult(BaseModel):
+    """Result from get_participants task."""
+
+    participants: list[dict[str, Any]]
+    num_tracks: int
+    source_language: NonEmptyString
+    target_language: NonEmptyString
+
+
+class PaddedTrackInfo(BaseModel):
+    """Info for a padded track - S3 key + bucket for on-demand presigning."""
+
+    key: NonEmptyString
+    bucket_name: NonEmptyString | None  # None = use default storage bucket
+
+
+class ProcessTracksResult(BaseModel):
+    """Result from process_tracks task."""
+
+    all_words: list[dict[str, Any]]
+    padded_tracks: list[PaddedTrackInfo]  # S3 keys, not presigned URLs
+    word_count: int
+    num_tracks: int
+    target_language: NonEmptyString
+    created_padded_files: list[NonEmptyString]
+
+
+class MixdownResult(BaseModel):
+    """Result from mixdown_tracks task."""
+
+    audio_key: NonEmptyString
+    duration: float
+    tracks_mixed: int
+
+
+class WaveformResult(BaseModel):
+    """Result from generate_waveform task."""
+
+    waveform_generated: bool
+
+
+class TopicsResult(BaseModel):
+    """Result from detect_topics task."""
+
+    topics: list[dict[str, Any]]
+
+
+class TitleResult(BaseModel):
+    """Result from generate_title task."""
+
+    title: str | None
+
+
+class SummaryResult(BaseModel):
+    """Result from generate_summary task."""
+
+    summary: str | None
+    short_summary: str | None
+    action_items: dict | None = None
+
+
+class FinalizeResult(BaseModel):
+    """Result from finalize task."""
+
+    status: NonEmptyString
+
+
+class ConsentResult(BaseModel):
+    """Result from cleanup_consent task."""
+
+
+class ZulipResult(BaseModel):
+    """Result from post_zulip task."""
+
+    zulip_message_id: int | None = None
+    skipped: bool = False
+
+
+class WebhookResult(BaseModel):
+    """Result from send_webhook task."""
+
+    webhook_sent: bool
+    skipped: bool = False
+    response_code: int | None = None

server/reflector/hatchet/workflows/track_processing.py

@@ -0,0 +1,222 @@
+"""
+Hatchet child workflow: TrackProcessing
+
+Handles individual audio track processing: padding and transcription.
+Spawned dynamically by the main diarization pipeline for each track.
+
+Architecture note: This is a separate workflow (not inline tasks in DiarizationPipeline)
+because Hatchet workflow DAGs are defined statically, but the number of tracks varies
+at runtime. Child workflow spawning via `aio_run()` + `asyncio.gather()` is the
+standard pattern for dynamic fan-out. See `process_tracks` in diarization_pipeline.py.
+
+Note: This file uses deferred imports (inside tasks) intentionally.
+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
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+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,
+)
+
+
+class TrackInput(BaseModel):
+    """Input for individual track processing."""
+
+    track_index: int
+    s3_key: str
+    bucket_name: str
+    transcript_id: str
+    language: str = "en"
+
+
+hatchet = HatchetClientManager.get_client()
+
+track_workflow = hatchet.workflow(name="TrackProcessing", input_validator=TrackInput)
+
+
+@track_workflow.task(execution_timeout=timedelta(seconds=300), retries=3)
+async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
+    """Pad single audio track with silence for alignment.
+
+    Extracts stream.start_time from WebM container metadata and applies
+    silence padding using PyAV filter graph (adelay).
+    """
+    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,
+        )
+
+        with av.open(source_url) as in_container:
+            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,
+                )
+
+            with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
+                temp_path = temp_file.name
+
+            try:
+                apply_audio_padding_to_file(
+                    in_container,
+                    temp_path,
+                    start_time_seconds,
+                    input.track_index,
+                    logger=logger,
+                )
+
+                file_size = Path(temp_path).stat().st_size
+                storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+
+                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)
+
+        ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
+        logger.info(
+            "[Hatchet] pad_track complete",
+            track_index=input.track_index,
+            padded_key=storage_path,
+        )
+
+        # Return S3 key (not presigned URL) - consumer tasks presign on demand
+        # This avoids stale URLs when workflow is replayed
+        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", error=str(e), exc_info=True)
+        raise
+
+
+@track_workflow.task(
+    parents=[pad_track], execution_timeout=timedelta(seconds=600), retries=3
+)
+async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackResult:
+    """Transcribe audio track using GPU (Modal.com) or local Whisper."""
+    ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
+    logger.info(
+        "[Hatchet] transcribe_track",
+        track_index=input.track_index,
+        language=input.language,
+    )
+
+    try:
+        pad_result = ctx.task_output(pad_track)
+        padded_key = pad_result.padded_key
+        bucket_name = pad_result.bucket_name
+
+        if not padded_key:
+            raise ValueError("Missing padded_key from pad_track")
+
+        # Presign URL on demand (avoids stale URLs on workflow replay)
+        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,
+        )
+
+        audio_url = await storage.get_file_url(
+            padded_key,
+            operation="get_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+            bucket=bucket_name,
+        )
+
+        from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
+            transcribe_file_with_processor,
+        )
+
+        transcript = await transcribe_file_with_processor(audio_url, input.language)
+
+        # Tag all words with speaker index
+        words = []
+        for word in transcript.words:
+            word_dict = word.model_dump()
+            word_dict["speaker"] = input.track_index
+            words.append(word_dict)
+
+        ctx.log(
+            f"transcribe_track complete: track {input.track_index}, {len(words)} words"
+        )
+        logger.info(
+            "[Hatchet] transcribe_track complete",
+            track_index=input.track_index,
+            word_count=len(words),
+        )
+
+        return TranscribeTrackResult(
+            words=words,
+            track_index=input.track_index,
+        )
+
+    except Exception as e:
+        logger.error("[Hatchet] transcribe_track failed", error=str(e), exc_info=True)
+        raise

server/reflector/llm.py

@@ -1,14 +1,32 @@
 import logging
-from typing import Type, TypeVar
+from contextvars import ContextVar
+from typing import Generic, Type, TypeVar
+from uuid import uuid4
 
 from llama_index.core import Settings
 from llama_index.core.output_parsers import PydanticOutputParser
-from llama_index.core.program import LLMTextCompletionProgram
 from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.core.workflow import (
+    Context,
+    Event,
+    StartEvent,
+    StopEvent,
+    Workflow,
+    step,
+)
 from llama_index.llms.openai_like import OpenAILike
 from pydantic import BaseModel, ValidationError
+from workflows.errors import WorkflowTimeoutError
+
+from reflector.utils.retry import retry
 
 T = TypeVar("T", bound=BaseModel)
+OutputT = TypeVar("OutputT", bound=BaseModel)
+
+# Session ID for LiteLLM request grouping - set per processing run
+llm_session_id: ContextVar[str | None] = ContextVar("llm_session_id", default=None)
+
+logger = logging.getLogger(__name__)
 
 STRUCTURED_RESPONSE_PROMPT_TEMPLATE = """
 Based on the following analysis, provide the information in the requested JSON format:
@@ -20,6 +38,158 @@ Analysis:
 """
 
 
+class LLMParseError(Exception):
+    """Raised when LLM output cannot be parsed after retries."""
+
+    def __init__(self, output_cls: Type[BaseModel], error_msg: str, attempts: int):
+        self.output_cls = output_cls
+        self.error_msg = error_msg
+        self.attempts = attempts
+        super().__init__(
+            f"Failed to parse {output_cls.__name__} after {attempts} attempts: {error_msg}"
+        )
+
+
+class ExtractionDone(Event):
+    """Event emitted when LLM JSON formatting completes."""
+
+    output: str
+
+
+class ValidationErrorEvent(Event):
+    """Event emitted when validation fails."""
+
+    error: str
+    wrong_output: str
+
+
+class StructuredOutputWorkflow(Workflow, Generic[OutputT]):
+    """Workflow for structured output extraction with validation retry.
+
+    This workflow handles parse/validation retries only. Network error retries
+    are handled internally by Settings.llm (OpenAILike max_retries=3).
+    The caller should NOT wrap this workflow in additional retry logic.
+    """
+
+    def __init__(
+        self,
+        output_cls: Type[OutputT],
+        max_retries: int = 3,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.output_cls: Type[OutputT] = output_cls
+        self.max_retries = max_retries
+        self.output_parser = PydanticOutputParser(output_cls)
+
+    @step
+    async def extract(
+        self, ctx: Context, ev: StartEvent | ValidationErrorEvent
+    ) -> StopEvent | ExtractionDone:
+        """Extract structured data from text using two-step LLM process.
+
+        Step 1 (first call only): TreeSummarize generates text analysis
+        Step 2 (every call): Settings.llm.acomplete formats analysis as JSON
+        """
+        current_retries = await ctx.store.get("retries", default=0)
+        await ctx.store.set("retries", current_retries + 1)
+
+        if current_retries >= self.max_retries:
+            last_error = await ctx.store.get("last_error", default=None)
+            logger.error(
+                f"Max retries ({self.max_retries}) reached for {self.output_cls.__name__}"
+            )
+            return StopEvent(result={"error": last_error, "attempts": current_retries})
+
+        if isinstance(ev, StartEvent):
+            # First call: run TreeSummarize to get analysis, store in context
+            prompt = ev.get("prompt")
+            texts = ev.get("texts")
+            tone_name = ev.get("tone_name")
+            if not prompt or not isinstance(texts, list):
+                raise ValueError(
+                    "StartEvent must contain 'prompt' (str) and 'texts' (list)"
+                )
+
+            summarizer = TreeSummarize(verbose=False)
+            analysis = await summarizer.aget_response(
+                prompt, texts, tone_name=tone_name
+            )
+            await ctx.store.set("analysis", str(analysis))
+            reflection = ""
+        else:
+            # Retry: reuse analysis from context
+            analysis = await ctx.store.get("analysis")
+            if not analysis:
+                raise RuntimeError("Internal error: analysis not found in context")
+
+            wrong_output = ev.wrong_output
+            if len(wrong_output) > 2000:
+                wrong_output = wrong_output[:2000] + "... [truncated]"
+            reflection = (
+                f"\n\nYour previous response could not be parsed:\n{wrong_output}\n\n"
+                f"Error:\n{ev.error}\n\n"
+                "Please try again. Return ONLY valid JSON matching the schema above, "
+                "with no markdown formatting or extra text."
+            )
+
+        # Step 2: Format analysis as JSON using LLM completion
+        format_instructions = self.output_parser.format(
+            "Please structure the above information in the following JSON format:"
+        )
+
+        json_prompt = STRUCTURED_RESPONSE_PROMPT_TEMPLATE.format(
+            analysis=analysis,
+            format_instructions=format_instructions + reflection,
+        )
+
+        # Network retries handled by OpenAILike (max_retries=3)
+        response = await Settings.llm.acomplete(json_prompt)
+        return ExtractionDone(output=response.text)
+
+    @step
+    async def validate(
+        self, ctx: Context, ev: ExtractionDone
+    ) -> StopEvent | ValidationErrorEvent:
+        """Validate extracted output against Pydantic schema."""
+        raw_output = ev.output
+        retries = await ctx.store.get("retries", default=0)
+
+        try:
+            parsed = self.output_parser.parse(raw_output)
+            if retries > 1:
+                logger.info(
+                    f"LLM parse succeeded on attempt {retries}/{self.max_retries} "
+                    f"for {self.output_cls.__name__}"
+                )
+            return StopEvent(result={"success": parsed})
+
+        except (ValidationError, ValueError) as e:
+            error_msg = self._format_error(e, raw_output)
+            await ctx.store.set("last_error", error_msg)
+
+            logger.error(
+                f"LLM parse error (attempt {retries}/{self.max_retries}): "
+                f"{type(e).__name__}: {e}\nRaw response: {raw_output[:500]}"
+            )
+
+            return ValidationErrorEvent(
+                error=error_msg,
+                wrong_output=raw_output,
+            )
+
+    def _format_error(self, error: Exception, raw_output: str) -> str:
+        """Format error for LLM feedback."""
+        if isinstance(error, ValidationError):
+            error_messages = []
+            for err in error.errors():
+                field = ".".join(str(loc) for loc in err["loc"])
+                error_messages.append(f"- {err['msg']} in field '{field}'")
+            return "Schema validation errors:\n" + "\n".join(error_messages)
+        else:
+            return f"Parse error: {str(error)}"
+
+
 class LLM:
     def __init__(self, settings, temperature: float = 0.4, max_tokens: int = 2048):
         self.settings_obj = settings
@@ -30,11 +200,12 @@ class LLM:
         self.temperature = temperature
         self.max_tokens = max_tokens
 
-        # Configure llamaindex Settings
         self._configure_llamaindex()
 
     def _configure_llamaindex(self):
         """Configure llamaindex Settings with OpenAILike LLM"""
+        session_id = llm_session_id.get() or f"fallback-{uuid4().hex}"
+
         Settings.llm = OpenAILike(
             model=self.model_name,
             api_base=self.url,
@@ -44,6 +215,7 @@ class LLM:
             is_function_calling_model=False,
             temperature=self.temperature,
             max_tokens=self.max_tokens,
+            additional_kwargs={"extra_body": {"litellm_session_id": session_id}},
         )
 
     async def get_response(
@@ -60,44 +232,38 @@ class LLM:
         texts: list[str],
         output_cls: Type[T],
         tone_name: str | None = None,
+        timeout: int | None = None,
     ) -> T:
-        """Get structured output from LLM for non-function-calling models"""
+        """Get structured output from LLM with validation retry via Workflow."""
-        logger = logging.getLogger(__name__)
+        if timeout is None:
+            timeout = self.settings_obj.LLM_STRUCTURED_RESPONSE_TIMEOUT
 
-        summarizer = TreeSummarize(verbose=True)
+        async def run_workflow():
-        response = await summarizer.aget_response(prompt, texts, tone_name=tone_name)
+            workflow = StructuredOutputWorkflow(
-
+                output_cls=output_cls,
-        output_parser = PydanticOutputParser(output_cls)
+                max_retries=self.settings_obj.LLM_PARSE_MAX_RETRIES + 1,
-
+                timeout=timeout,
-        program = LLMTextCompletionProgram.from_defaults(
-            output_parser=output_parser,
-            prompt_template_str=STRUCTURED_RESPONSE_PROMPT_TEMPLATE,
-            verbose=False,
-        )
-
-        format_instructions = output_parser.format(
-            "Please structure the above information in the following JSON format:"
-        )
-
-        try:
-            output = await program.acall(
-                analysis=str(response), format_instructions=format_instructions
             )
-        except ValidationError as e:
-            # Extract the raw JSON from the error details
-            errors = e.errors()
-            if errors and "input" in errors[0]:
-                raw_json = errors[0]["input"]
-                logger.error(
-                    f"JSON validation failed for {output_cls.__name__}. "
-                    f"Full raw JSON output:\n{raw_json}\n"
-                    f"Validation errors: {errors}"
-                )
-            else:
-                logger.error(
-                    f"JSON validation failed for {output_cls.__name__}. "
-                    f"Validation errors: {errors}"
-                )
-            raise
 
-        return output
+            result = await workflow.run(
+                prompt=prompt,
+                texts=texts,
+                tone_name=tone_name,
+            )
+
+            if "error" in result:
+                error_msg = result["error"] or "Max retries exceeded"
+                raise LLMParseError(
+                    output_cls=output_cls,
+                    error_msg=error_msg,
+                    attempts=result.get("attempts", 0),
+                )
+
+            return result["success"]
+
+        return await retry(run_workflow)(
+            retry_attempts=3,
+            retry_backoff_interval=1.0,
+            retry_backoff_max=30.0,
+            retry_ignore_exc_types=(WorkflowTimeoutError,),
+        )

server/reflector/pipelines/main_file_pipeline.py

@@ -97,13 +97,8 @@ class PipelineMainFile(PipelineMainBase):
                 },
             )
 
-        # Extract audio and write to transcript location
         audio_path = await self.extract_and_write_audio(file_path, transcript)
-
-        # Upload for processing
         audio_url = await self.upload_audio(audio_path, transcript)
-
-        # Run parallel processing
         await self.run_parallel_processing(
             audio_path,
             audio_url,
@@ -197,7 +192,6 @@ class PipelineMainFile(PipelineMainBase):
         transcript_result = results[0]
         diarization_result = results[1]
 
-        # Handle errors - raise any exception that occurred
         self._handle_gather_exceptions(results, "parallel processing")
         for result in results:
             if isinstance(result, Exception):
@@ -212,7 +206,6 @@ class PipelineMainFile(PipelineMainBase):
             transcript=transcript_result, diarization=diarization_result or []
         )
 
-        # Store result for retrieval
         diarized_transcript: Transcript | None = None
 
         async def capture_result(transcript):
@@ -309,6 +302,7 @@ class PipelineMainFile(PipelineMainBase):
             transcript,
             on_long_summary_callback=self.on_long_summary,
             on_short_summary_callback=self.on_short_summary,
+            on_action_items_callback=self.on_action_items,
             empty_pipeline=self.empty_pipeline,
             logger=self.logger,
         )
@@ -340,7 +334,6 @@ async def task_send_webhook_if_needed(*, transcript_id: str):
 @asynctask
 async def task_pipeline_file_process(*, transcript_id: str):
     """Celery task for file pipeline processing"""
-
     transcript = await transcripts_controller.get_by_id(transcript_id)
     if not transcript:
         raise Exception(f"Transcript {transcript_id} not found")
@@ -349,7 +342,6 @@ async def task_pipeline_file_process(*, transcript_id: str):
     try:
         await pipeline.set_status(transcript_id, "processing")
 
-        # Find the file to process
         audio_file = next(transcript.data_path.glob("upload.*"), None)
         if not audio_file:
             audio_file = next(transcript.data_path.glob("audio.*"), None)

server/reflector/pipelines/main_live_pipeline.py

@@ -27,6 +27,7 @@ from reflector.db.recordings import recordings_controller
 from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import (
     Transcript,
+    TranscriptActionItems,
     TranscriptDuration,
     TranscriptFinalLongSummary,
     TranscriptFinalShortSummary,
@@ -306,6 +307,23 @@ class PipelineMainBase(PipelineRunner[PipelineMessage], Generic[PipelineMessage]
                 data=final_short_summary,
             )
 
+    @broadcast_to_sockets
+    async def on_action_items(self, data):
+        action_items = TranscriptActionItems(action_items=data.action_items)
+        async with self.transaction():
+            transcript = await self.get_transcript()
+            await transcripts_controller.update(
+                transcript,
+                {
+                    "action_items": action_items.action_items,
+                },
+            )
+            return await transcripts_controller.append_event(
+                transcript=transcript,
+                event="ACTION_ITEMS",
+                data=action_items,
+            )
+
     @broadcast_to_sockets
     async def on_duration(self, data):
         async with self.transaction():
@@ -465,6 +483,7 @@ class PipelineMainFinalSummaries(PipelineMainFromTopics):
                 transcript=self._transcript,
                 callback=self.on_long_summary,
                 on_short_summary=self.on_short_summary,
+                on_action_items=self.on_action_items,
             ),
         ]
 

server/reflector/pipelines/main_multitrack_pipeline.py

@@ -1,11 +1,8 @@
 import asyncio
-import math
 import tempfile
-from fractions import Fraction
 from pathlib import Path
 
 import av
-from av.audio.resampler import AudioResampler
 from celery import chain, shared_task
 
 from reflector.asynctask import asynctask
@@ -31,8 +28,16 @@ from reflector.processors import AudioFileWriterProcessor
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.processors.types import TitleSummary
 from reflector.processors.types import Transcript as TranscriptType
-from reflector.settings import settings
 from reflector.storage import Storage, get_transcripts_storage
+from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
+from reflector.utils.audio_mixdown import (
+    detect_sample_rate_from_tracks,
+    mixdown_tracks_pyav,
+)
+from reflector.utils.audio_padding import (
+    apply_audio_padding_to_file,
+    extract_stream_start_time_from_container,
+)
 from reflector.utils.daily import (
     filter_cam_audio_tracks,
     parse_daily_recording_filename,
@@ -40,13 +45,6 @@ from reflector.utils.daily import (
 from reflector.utils.string import NonEmptyString
 from reflector.video_platforms.factory import create_platform_client
 
-# Audio encoding constants
-OPUS_STANDARD_SAMPLE_RATE = 48000
-OPUS_DEFAULT_BIT_RATE = 128000
-
-# Storage operation constants
-PRESIGNED_URL_EXPIRATION_SECONDS = 7200  # 2 hours
-
 
 class PipelineMainMultitrack(PipelineMainBase):
     def __init__(self, transcript_id: str):
@@ -126,8 +124,8 @@ class PipelineMainMultitrack(PipelineMainBase):
         try:
             # PyAV streams input from S3 URL efficiently (2-5MB fixed overhead for codec/filters)
             with av.open(track_url) as in_container:
-                start_time_seconds = self._extract_stream_start_time_from_container(
+                start_time_seconds = extract_stream_start_time_from_container(
-                    in_container, track_idx
+                    in_container, track_idx, logger=self.logger
                 )
 
                 if start_time_seconds <= 0:
@@ -145,8 +143,12 @@ class PipelineMainMultitrack(PipelineMainBase):
                     temp_path = temp_file.name
 
                 try:
-                    self._apply_audio_padding_to_file(
+                    apply_audio_padding_to_file(
-                        in_container, temp_path, start_time_seconds, track_idx
+                        in_container,
+                        temp_path,
+                        start_time_seconds,
+                        track_idx,
+                        logger=self.logger,
                     )
 
                     storage_path = (
@@ -157,7 +159,6 @@ class PipelineMainMultitrack(PipelineMainBase):
                     with open(temp_path, "rb") as padded_file:
                         await storage.put_file(storage_path, padded_file)
                 finally:
-                    # Clean up temp file
                     Path(temp_path).unlink(missing_ok=True)
 
                 padded_url = await storage.get_file_url(
@@ -187,309 +188,28 @@ class PipelineMainMultitrack(PipelineMainBase):
                 f"Track {track_idx} padding failed - transcript would have incorrect timestamps"
             ) from e
 
-    def _extract_stream_start_time_from_container(
-        self, container, track_idx: int
-    ) -> float:
-        """
-        Extract meeting-relative start time from WebM stream metadata.
-        Uses PyAV to read stream.start_time from WebM container.
-        More accurate than filename timestamps by ~209ms due to network/encoding delays.
-        """
-        start_time_seconds = 0.0
-        try:
-            audio_streams = [s for s in container.streams if s.type == "audio"]
-            stream = audio_streams[0] if audio_streams else container.streams[0]
-
-            # 1) Try stream-level start_time (most reliable for Daily.co tracks)
-            if stream.start_time is not None and stream.time_base is not None:
-                start_time_seconds = float(stream.start_time * stream.time_base)
-
-            # 2) Fallback to container-level start_time (in av.time_base units)
-            if (start_time_seconds <= 0) and (container.start_time is not None):
-                start_time_seconds = float(container.start_time * av.time_base)
-
-            # 3) Fallback to first packet DTS in stream.time_base
-            if start_time_seconds <= 0:
-                for packet in container.demux(stream):
-                    if packet.dts is not None:
-                        start_time_seconds = float(packet.dts * stream.time_base)
-                        break
-        except Exception as e:
-            self.logger.warning(
-                "PyAV metadata read failed; assuming 0 start_time",
-                track_idx=track_idx,
-                error=str(e),
-            )
-            start_time_seconds = 0.0
-
-        self.logger.info(
-            f"Track {track_idx} stream metadata: start_time={start_time_seconds:.3f}s",
-            track_idx=track_idx,
-        )
-        return start_time_seconds
-
-    def _apply_audio_padding_to_file(
-        self,
-        in_container,
-        output_path: str,
-        start_time_seconds: float,
-        track_idx: int,
-    ) -> None:
-        """Apply silence padding to audio track using PyAV filter graph, writing to file"""
-        delay_ms = math.floor(start_time_seconds * 1000)
-
-        self.logger.info(
-            f"Padding track {track_idx} with {delay_ms}ms delay using PyAV",
-            track_idx=track_idx,
-            delay_ms=delay_ms,
-        )
-
-        try:
-            with av.open(output_path, "w", format="webm") as out_container:
-                in_stream = next(
-                    (s for s in in_container.streams if s.type == "audio"), None
-                )
-                if in_stream is None:
-                    raise Exception("No audio stream in input")
-
-                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")
-                # adelay requires one delay value per channel separated by '|'
-                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
-                )
-                # Decode -> resample -> push through graph -> encode Opus
-                for frame in in_container.decode(in_stream):
-                    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)
-
-                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)
-
-                for packet in out_stream.encode(None):
-                    out_container.mux(packet)
-        except Exception as e:
-            self.logger.error(
-                "PyAV padding failed for track",
-                track_idx=track_idx,
-                delay_ms=delay_ms,
-                error=str(e),
-                exc_info=True,
-            )
-            raise
-
     async def mixdown_tracks(
         self,
         track_urls: list[str],
         writer: AudioFileWriterProcessor,
         offsets_seconds: list[float] | None = None,
     ) -> None:
-        """Multi-track mixdown using PyAV filter graph (amix), reading from S3 presigned URLs"""
+        """Multi-track mixdown using PyAV filter graph (amix), reading from S3 presigned URLs."""
-
+        target_sample_rate = detect_sample_rate_from_tracks(
-        target_sample_rate: int | None = None
+            track_urls, logger=self.logger
-        for url in track_urls:
+        )
-            if not url:
-                continue
-            container = None
-            try:
-                container = av.open(url)
-                for frame in container.decode(audio=0):
-                    target_sample_rate = frame.sample_rate
-                    break
-            except Exception:
-                continue
-            finally:
-                if container is not None:
-                    container.close()
-            if target_sample_rate:
-                break
-
         if not target_sample_rate:
             self.logger.error("Mixdown failed - no decodable audio frames found")
             raise Exception("Mixdown failed: No decodable audio frames in any track")
-        # Build PyAV filter graph:
-        # N abuffer (s32/stereo)
-        #   -> optional adelay per input (for alignment)
-        #   -> amix (s32)
-        #   -> aformat(s16)
-        #   -> sink
-        graph = av.filter.Graph()
-        inputs = []
-        valid_track_urls = [url for url in track_urls if url]
-        input_offsets_seconds = None
-        if offsets_seconds is not None:
-            input_offsets_seconds = [
-                offsets_seconds[i] for i, url in enumerate(track_urls) if url
-            ]
-        for idx, url in enumerate(valid_track_urls):
-            args = (
-                f"time_base=1/{target_sample_rate}:"
-                f"sample_rate={target_sample_rate}:"
-                f"sample_fmt=s32:"
-                f"channel_layout=stereo"
-            )
-            in_ctx = graph.add("abuffer", args=args, name=f"in{idx}")
-            inputs.append(in_ctx)
 
-        if not inputs:
+        await mixdown_tracks_pyav(
-            self.logger.error("Mixdown failed - no valid inputs for graph")
+            track_urls,
-            raise Exception("Mixdown failed: No valid inputs for filter graph")
+            writer,
-
+            target_sample_rate,
-        mixer = graph.add("amix", args=f"inputs={len(inputs)}:normalize=0", name="mix")
+            offsets_seconds=offsets_seconds,
-
+            logger=self.logger,
-        fmt = graph.add(
-            "aformat",
-            args=(
-                f"sample_fmts=s32:channel_layouts=stereo:sample_rates={target_sample_rate}"
-            ),
-            name="fmt",
         )
 
-        sink = graph.add("abuffersink", name="out")
-
-        # Optional per-input delay before mixing
-        delays_ms: list[int] = []
-        if input_offsets_seconds is not None:
-            base = min(input_offsets_seconds) if input_offsets_seconds else 0.0
-            delays_ms = [
-                max(0, int(round((o - base) * 1000))) for o in input_offsets_seconds
-            ]
-        else:
-            delays_ms = [0 for _ in inputs]
-
-        for idx, in_ctx in enumerate(inputs):
-            delay_ms = delays_ms[idx] if idx < len(delays_ms) else 0
-            if delay_ms > 0:
-                # adelay requires one value per channel; use same for stereo
-                adelay = graph.add(
-                    "adelay",
-                    args=f"delays={delay_ms}|{delay_ms}:all=1",
-                    name=f"delay{idx}",
-                )
-                in_ctx.link_to(adelay)
-                adelay.link_to(mixer, 0, idx)
-            else:
-                in_ctx.link_to(mixer, 0, idx)
-        mixer.link_to(fmt)
-        fmt.link_to(sink)
-        graph.configure()
-
-        containers = []
-        try:
-            # Open all containers with cleanup guaranteed
-            for i, url in enumerate(valid_track_urls):
-                try:
-                    c = av.open(url)
-                    containers.append(c)
-                except Exception as e:
-                    self.logger.warning(
-                        "Mixdown: failed to open container from URL",
-                        input=i,
-                        url=url,
-                        error=str(e),
-                    )
-
-            if not containers:
-                self.logger.error("Mixdown failed - no valid containers opened")
-                raise Exception("Mixdown failed: Could not open any track containers")
-
-            decoders = [c.decode(audio=0) for c in containers]
-            active = [True] * len(decoders)
-            resamplers = [
-                AudioResampler(format="s32", layout="stereo", rate=target_sample_rate)
-                for _ in decoders
-            ]
-
-            while any(active):
-                for i, (dec, is_active) in enumerate(zip(decoders, active)):
-                    if not is_active:
-                        continue
-                    try:
-                        frame = next(dec)
-                    except StopIteration:
-                        active[i] = False
-                        continue
-
-                    if frame.sample_rate != target_sample_rate:
-                        continue
-                    out_frames = resamplers[i].resample(frame) or []
-                    for rf in out_frames:
-                        rf.sample_rate = target_sample_rate
-                        rf.time_base = Fraction(1, target_sample_rate)
-                        inputs[i].push(rf)
-
-                    while True:
-                        try:
-                            mixed = sink.pull()
-                        except Exception:
-                            break
-                        mixed.sample_rate = target_sample_rate
-                        mixed.time_base = Fraction(1, target_sample_rate)
-                        await writer.push(mixed)
-
-            for in_ctx in inputs:
-                in_ctx.push(None)
-            while True:
-                try:
-                    mixed = sink.pull()
-                except Exception:
-                    break
-                mixed.sample_rate = target_sample_rate
-                mixed.time_base = Fraction(1, target_sample_rate)
-                await writer.push(mixed)
-        finally:
-            # Cleanup all containers, even if processing failed
-            for c in containers:
-                if c is not None:
-                    try:
-                        c.close()
-                    except Exception:
-                        pass  # Best effort cleanup
-
     @broadcast_to_sockets
     async def set_status(self, transcript_id: str, status: TranscriptStatus):
         async with self.lock_transaction():
@@ -632,55 +352,43 @@ class PipelineMainMultitrack(PipelineMainBase):
 
         transcript.data_path.mkdir(parents=True, exist_ok=True)
 
-        if settings.SKIP_MIXDOWN:
+        mp3_writer = AudioFileWriterProcessor(
-            self.logger.warning(
+            path=str(transcript.audio_mp3_filename),
-                "SKIP_MIXDOWN enabled: Skipping mixdown and waveform generation. "
+            on_duration=self.on_duration,
-                "UI will have no audio playback or waveform.",
+        )
-                num_tracks=len(padded_track_urls),
+        await self.mixdown_tracks(padded_track_urls, mp3_writer, offsets_seconds=None)
-                transcript_id=transcript.id,
+        await mp3_writer.flush()
-            )
-        else:
-            mp3_writer = AudioFileWriterProcessor(
-                path=str(transcript.audio_mp3_filename),
-                on_duration=self.on_duration,
-            )
-            await self.mixdown_tracks(
-                padded_track_urls, mp3_writer, offsets_seconds=None
-            )
-            await mp3_writer.flush()
 
-            if not transcript.audio_mp3_filename.exists():
+        if not transcript.audio_mp3_filename.exists():
-                raise Exception(
+            raise Exception(
-                    "Mixdown failed - no MP3 file generated. Cannot proceed without playable audio."
+                "Mixdown failed - no MP3 file generated. Cannot proceed without playable audio."
-                )
-
-            storage_path = f"{transcript.id}/audio.mp3"
-            # Use file handle streaming to avoid loading entire MP3 into memory
-            mp3_size = transcript.audio_mp3_filename.stat().st_size
-            with open(transcript.audio_mp3_filename, "rb") as mp3_file:
-                await transcript_storage.put_file(storage_path, mp3_file)
-            mp3_url = await transcript_storage.get_file_url(storage_path)
-
-            await transcripts_controller.update(
-                transcript, {"audio_location": "storage"}
             )
 
-            self.logger.info(
+        storage_path = f"{transcript.id}/audio.mp3"
-                f"Uploaded mixed audio to storage",
+        # Use file handle streaming to avoid loading entire MP3 into memory
-                storage_path=storage_path,
+        mp3_size = transcript.audio_mp3_filename.stat().st_size
-                size=mp3_size,
+        with open(transcript.audio_mp3_filename, "rb") as mp3_file:
-                url=mp3_url,
+            await transcript_storage.put_file(storage_path, mp3_file)
-            )
+        mp3_url = await transcript_storage.get_file_url(storage_path)
 
-            self.logger.info("Generating waveform from mixed audio")
+        await transcripts_controller.update(transcript, {"audio_location": "storage"})
-            waveform_processor = AudioWaveformProcessor(
+
-                audio_path=transcript.audio_mp3_filename,
+        self.logger.info(
-                waveform_path=transcript.audio_waveform_filename,
+            f"Uploaded mixed audio to storage",
-                on_waveform=self.on_waveform,
+            storage_path=storage_path,
-            )
+            size=mp3_size,
-            waveform_processor.set_pipeline(self.empty_pipeline)
+            url=mp3_url,
-            await waveform_processor.flush()
+        )
-            self.logger.info("Waveform generated successfully")
+
+        self.logger.info("Generating waveform from mixed audio")
+        waveform_processor = AudioWaveformProcessor(
+            audio_path=transcript.audio_mp3_filename,
+            waveform_path=transcript.audio_waveform_filename,
+            on_waveform=self.on_waveform,
+        )
+        waveform_processor.set_pipeline(self.empty_pipeline)
+        await waveform_processor.flush()
+        self.logger.info("Waveform generated successfully")
 
         speaker_transcripts: list[TranscriptType] = []
         for idx, padded_url in enumerate(padded_track_urls):
@@ -777,6 +485,7 @@ class PipelineMainMultitrack(PipelineMainBase):
             transcript,
             on_long_summary_callback=self.on_long_summary,
             on_short_summary_callback=self.on_short_summary,
+            on_action_items_callback=self.on_action_items,
             empty_pipeline=self.empty_pipeline,
             logger=self.logger,
         )

server/reflector/pipelines/topic_processing.py

@@ -89,6 +89,7 @@ async def generate_summaries(
     *,
     on_long_summary_callback: Callable,
     on_short_summary_callback: Callable,
+    on_action_items_callback: Callable,
     empty_pipeline: EmptyPipeline,
     logger: structlog.BoundLogger,
 ):
@@ -96,11 +97,14 @@ async def generate_summaries(
         logger.warning("No topics for summary generation")
         return
 
-    processor = TranscriptFinalSummaryProcessor(
+    processor_kwargs = {
-        transcript=transcript,
+        "transcript": transcript,
-        callback=on_long_summary_callback,
+        "callback": on_long_summary_callback,
-        on_short_summary=on_short_summary_callback,
+        "on_short_summary": on_short_summary_callback,
-    )
+        "on_action_items": on_action_items_callback,
+    }
+
+    processor = TranscriptFinalSummaryProcessor(**processor_kwargs)
     processor.set_pipeline(empty_pipeline)
 
     for topic in topics:

server/reflector/processors/summary/summary_builder.py

@@ -96,6 +96,36 @@ RECAP_PROMPT = dedent(
     """
 ).strip()
 
+ACTION_ITEMS_PROMPT = dedent(
+    """
+    Identify action items from this meeting transcript. Your goal is to identify what was decided and what needs to happen next.
+
+    Look for:
+
+    1. **Decisions Made**: Any decisions, choices, or conclusions reached during the meeting. For each decision:
+       - What was decided? (be specific)
+       - Who made the decision or was involved? (use actual participant names)
+       - Why was this decision made? (key factors, reasoning, or rationale)
+
+    2. **Next Steps / Action Items**: Any tasks, follow-ups, or actions that were mentioned or assigned. For each action item:
+       - What specific task needs to be done? (be concrete and actionable)
+       - Who is responsible? (use actual participant names if mentioned, or "team" if unclear)
+       - When is it due? (any deadlines, timeframes, or "by next meeting" type commitments)
+       - What context is needed? (any additional details that help understand the task)
+
+    Guidelines:
+    - Be thorough and identify all action items, even if they seem minor
+    - Include items that were agreed upon, assigned, or committed to
+    - Include decisions even if they seem obvious or implicit
+    - If someone says "I'll do X" or "We should do Y", that's an action item
+    - If someone says "Let's go with option A", that's a decision
+    - Use the exact participant names from the transcript
+    - If no participant name is mentioned, you can leave assigned_to/decided_by as null
+
+    Only return empty lists if the transcript contains NO decisions and NO action items whatsoever.
+    """
+).strip()
+
 STRUCTURED_RESPONSE_PROMPT_TEMPLATE = dedent(
     """
     Based on the following analysis, provide the information in the requested JSON format:
@@ -155,6 +185,53 @@ class SubjectsResponse(BaseModel):
     )
 
 
+class ActionItem(BaseModel):
+    """A single action item from the meeting"""
+
+    task: str = Field(description="The task or action item to be completed")
+    assigned_to: str | None = Field(
+        default=None, description="Person or team assigned to this task (name)"
+    )
+    assigned_to_participant_id: str | None = Field(
+        default=None, description="Participant ID if assigned_to matches a participant"
+    )
+    deadline: str | None = Field(
+        default=None, description="Deadline or timeframe mentioned for this task"
+    )
+    context: str | None = Field(
+        default=None, description="Additional context or notes about this task"
+    )
+
+
+class Decision(BaseModel):
+    """A decision made during the meeting"""
+
+    decision: str = Field(description="What was decided")
+    rationale: str | None = Field(
+        default=None,
+        description="Reasoning or key factors that influenced this decision",
+    )
+    decided_by: str | None = Field(
+        default=None, description="Person or group who made the decision (name)"
+    )
+    decided_by_participant_id: str | None = Field(
+        default=None, description="Participant ID if decided_by matches a participant"
+    )
+
+
+class ActionItemsResponse(BaseModel):
+    """Pydantic model for identified action items"""
+
+    decisions: list[Decision] = Field(
+        default_factory=list,
+        description="List of decisions made during the meeting",
+    )
+    next_steps: list[ActionItem] = Field(
+        default_factory=list,
+        description="List of action items and next steps to be taken",
+    )
+
+
 class SummaryBuilder:
     def __init__(self, llm: LLM, filename: str | None = None, logger=None) -> None:
         self.transcript: str | None = None
@@ -166,6 +243,8 @@ class SummaryBuilder:
         self.model_name: str = llm.model_name
         self.logger = logger or structlog.get_logger()
         self.participant_instructions: str | None = None
+        self.action_items: ActionItemsResponse | None = None
+        self.participant_name_to_id: dict[str, str] = {}
         if filename:
             self.read_transcript_from_file(filename)
 
@@ -189,13 +268,20 @@ class SummaryBuilder:
         self.llm = llm
 
     async def _get_structured_response(
-        self, prompt: str, output_cls: Type[T], tone_name: str | None = None
+        self,
+        prompt: str,
+        output_cls: Type[T],
+        tone_name: str | None = None,
+        timeout: int | None = None,
     ) -> T:
         """Generic function to get structured output from LLM for non-function-calling models."""
-        # Add participant instructions to the prompt if available
         enhanced_prompt = self._enhance_prompt_with_participants(prompt)
         return await self.llm.get_structured_response(
-            enhanced_prompt, [self.transcript], output_cls, tone_name=tone_name
+            enhanced_prompt,
+            [self.transcript],
+            output_cls,
+            tone_name=tone_name,
+            timeout=timeout,
         )
 
     async def _get_response(
@@ -216,11 +302,19 @@ class SummaryBuilder:
     # Participants
     # ----------------------------------------------------------------------------
 
-    def set_known_participants(self, participants: list[str]) -> None:
+    def set_known_participants(
+        self,
+        participants: list[str],
+        participant_name_to_id: dict[str, str] | None = None,
+    ) -> None:
         """
         Set known participants directly without LLM identification.
         This is used when participants are already identified and stored.
         They are appended at the end of the transcript, providing more context for the assistant.
+
+        Args:
+            participants: List of participant names
+            participant_name_to_id: Optional mapping of participant names to their IDs
         """
         if not participants:
             self.logger.warning("No participants provided")
@@ -231,10 +325,12 @@ class SummaryBuilder:
             participants=participants,
         )
 
+        if participant_name_to_id:
+            self.participant_name_to_id = participant_name_to_id
+
         participants_md = self.format_list_md(participants)
         self.transcript += f"\n\n# Participants\n\n{participants_md}"
 
-        # Set instructions that will be automatically added to all prompts
         participants_list = ", ".join(participants)
         self.participant_instructions = dedent(
             f"""
@@ -413,6 +509,92 @@ class SummaryBuilder:
         self.recap = str(recap_response)
         self.logger.info(f"Quick recap: {self.recap}")
 
+    def _map_participant_names_to_ids(
+        self, response: ActionItemsResponse
+    ) -> ActionItemsResponse:
+        """Map participant names in action items to participant IDs."""
+        if not self.participant_name_to_id:
+            return response
+
+        decisions = []
+        for decision in response.decisions:
+            new_decision = decision.model_copy()
+            if (
+                decision.decided_by
+                and decision.decided_by in self.participant_name_to_id
+            ):
+                new_decision.decided_by_participant_id = self.participant_name_to_id[
+                    decision.decided_by
+                ]
+            decisions.append(new_decision)
+
+        next_steps = []
+        for item in response.next_steps:
+            new_item = item.model_copy()
+            if item.assigned_to and item.assigned_to in self.participant_name_to_id:
+                new_item.assigned_to_participant_id = self.participant_name_to_id[
+                    item.assigned_to
+                ]
+            next_steps.append(new_item)
+
+        return ActionItemsResponse(decisions=decisions, next_steps=next_steps)
+
+    async def identify_action_items(self) -> ActionItemsResponse | None:
+        """Identify action items (decisions and next steps) from the transcript."""
+        self.logger.info("--- identify action items using TreeSummarize")
+
+        if not self.transcript:
+            self.logger.warning(
+                "No transcript available for action items identification"
+            )
+            self.action_items = None
+            return None
+
+        action_items_prompt = ACTION_ITEMS_PROMPT
+
+        try:
+            response = await self._get_structured_response(
+                action_items_prompt,
+                ActionItemsResponse,
+                tone_name="Action item identifier",
+                timeout=settings.LLM_STRUCTURED_RESPONSE_TIMEOUT,
+            )
+
+            response = self._map_participant_names_to_ids(response)
+
+            self.action_items = response
+            self.logger.info(
+                f"Identified {len(response.decisions)} decisions and {len(response.next_steps)} action items",
+                decisions_count=len(response.decisions),
+                next_steps_count=len(response.next_steps),
+            )
+
+            if response.decisions:
+                self.logger.debug(
+                    "Decisions identified",
+                    decisions=[d.decision for d in response.decisions],
+                )
+            if response.next_steps:
+                self.logger.debug(
+                    "Action items identified",
+                    tasks=[item.task for item in response.next_steps],
+                )
+            if not response.decisions and not response.next_steps:
+                self.logger.warning(
+                    "No action items identified from transcript",
+                    transcript_length=len(self.transcript),
+                )
+
+            return response
+
+        except Exception as e:
+            self.logger.error(
+                f"Error identifying action items: {e}",
+                exc_info=True,
+            )
+            self.action_items = None
+            return None
+
     async def generate_summary(self, only_subjects: bool = False) -> None:
         """
         Generate summary by extracting subjects, creating summaries for each, and generating a recap.
@@ -424,6 +606,7 @@ class SummaryBuilder:
 
         await self.generate_subject_summaries()
         await self.generate_recap()
+        await self.identify_action_items()
 
     # ----------------------------------------------------------------------------
     # Markdown
@@ -526,8 +709,6 @@ if __name__ == "__main__":
         if args.summary:
             await sm.generate_summary()
 
-        # Note: action items generation has been removed
-
         print("")
         print("-" * 80)
         print("")

server/reflector/processors/transcript_final_summary.py

@@ -1,7 +1,12 @@
 from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.summary.summary_builder import SummaryBuilder
-from reflector.processors.types import FinalLongSummary, FinalShortSummary, TitleSummary
+from reflector.processors.types import (
+    ActionItems,
+    FinalLongSummary,
+    FinalShortSummary,
+    TitleSummary,
+)
 from reflector.settings import settings
 
 
@@ -27,15 +32,20 @@ class TranscriptFinalSummaryProcessor(Processor):
         builder = SummaryBuilder(self.llm, logger=self.logger)
         builder.set_transcript(text)
 
-        # Use known participants if available, otherwise identify them
         if self.transcript and self.transcript.participants:
-            # Extract participant names from the stored participants
             participant_names = [p.name for p in self.transcript.participants if p.name]
             if participant_names:
                 self.logger.info(
                     f"Using {len(participant_names)} known participants from transcript"
                 )
-                builder.set_known_participants(participant_names)
+                participant_name_to_id = {
+                    p.name: p.id
+                    for p in self.transcript.participants
+                    if p.name and p.id
+                }
+                builder.set_known_participants(
+                    participant_names, participant_name_to_id=participant_name_to_id
+                )
             else:
                 self.logger.info(
                     "Participants field exists but is empty, identifying participants"
@@ -63,7 +73,6 @@ class TranscriptFinalSummaryProcessor(Processor):
             self.logger.warning("No summary to output")
             return
 
-        # build the speakermap from the transcript
         speakermap = {}
         if self.transcript:
             speakermap = {
@@ -76,8 +85,6 @@ class TranscriptFinalSummaryProcessor(Processor):
                 speakermap=speakermap,
             )
 
-        # build the transcript as a single string
-        # Replace speaker IDs with actual participant names if available
         text_transcript = []
         unique_speakers = set()
         for topic in self.chunks:
@@ -111,4 +118,9 @@ class TranscriptFinalSummaryProcessor(Processor):
             )
             await self.emit(final_short_summary, name="short_summary")
 
+        if self.builder and self.builder.action_items:
+            action_items = self.builder.action_items.model_dump()
+            action_items = ActionItems(action_items=action_items)
+            await self.emit(action_items, name="action_items")
+
         await self.emit(final_long_summary)

server/reflector/processors/transcript_topic_detector.py

@@ -78,7 +78,11 @@ class TranscriptTopicDetectorProcessor(Processor):
         """
         prompt = TOPIC_PROMPT.format(text=text)
         response = await self.llm.get_structured_response(
-            prompt, [text], TopicResponse, tone_name="Topic analyzer"
+            prompt,
+            [text],
+            TopicResponse,
+            tone_name="Topic analyzer",
+            timeout=settings.LLM_STRUCTURED_RESPONSE_TIMEOUT,
         )
         return response
 

server/reflector/processors/types.py

@@ -264,6 +264,10 @@ class FinalShortSummary(BaseModel):
     duration: float
 
 
+class ActionItems(BaseModel):
+    action_items: dict  # JSON-serializable dict from ActionItemsResponse
+
+
 class FinalTitle(BaseModel):
     title: str
 

server/reflector/services/transcript_process.py

@@ -11,13 +11,19 @@ from typing import Literal, Union, assert_never
 
 import celery
 from celery.result import AsyncResult
+from hatchet_sdk.clients.rest.exceptions import ApiException
+from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 from reflector.db.recordings import recordings_controller
-from reflector.db.transcripts import Transcript
+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.settings import settings
 from reflector.utils.string import NonEmptyString
 
 
@@ -37,6 +43,8 @@ class MultitrackProcessingConfig:
     transcript_id: NonEmptyString
     bucket_name: NonEmptyString
     track_keys: list[str]
+    recording_id: NonEmptyString | None = None
+    room_id: NonEmptyString | None = None
     mode: Literal["multitrack"] = "multitrack"
 
 
@@ -49,6 +57,7 @@ class ValidationOk:
     # transcript currently doesnt always have recording_id
     recording_id: NonEmptyString | None
     transcript_id: NonEmptyString
+    room_id: NonEmptyString | None = None
 
 
 @dataclass
@@ -96,6 +105,7 @@ async def validate_transcript_for_processing(
     if transcript.status == "idle":
         return ValidationNotReady(detail="Recording is not ready for processing")
 
+    # Check Celery tasks
     if task_is_scheduled_or_active(
         "reflector.pipelines.main_file_pipeline.task_pipeline_file_process",
         transcript_id=transcript.id,
@@ -105,8 +115,25 @@ async def validate_transcript_for_processing(
     ):
         return ValidationAlreadyScheduled(detail="already running")
 
+    # Check Hatchet workflows (if enabled)
+    if settings.HATCHET_ENABLED and transcript.workflow_run_id:
+        try:
+            status = await HatchetClientManager.get_workflow_run_status(
+                transcript.workflow_run_id
+            )
+            # If workflow is running or queued, don't allow new processing
+            if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                return ValidationAlreadyScheduled(
+                    detail="Hatchet workflow already running"
+                )
+        except ApiException:
+            # Workflow might be gone (404) or API issue - allow processing
+            pass
+
     return ValidationOk(
-        recording_id=transcript.recording_id, transcript_id=transcript.id
+        recording_id=transcript.recording_id,
+        transcript_id=transcript.id,
+        room_id=transcript.room_id,
     )
 
 
@@ -116,6 +143,7 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
     """
     bucket_name: str | None = None
     track_keys: list[str] | None = None
+    recording_id: str | None = validation.recording_id
 
     if validation.recording_id:
         recording = await recordings_controller.get_by_id(validation.recording_id)
@@ -137,6 +165,8 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
             bucket_name=bucket_name,  # type: ignore (validated above)
             track_keys=track_keys,
             transcript_id=validation.transcript_id,
+            recording_id=recording_id,
+            room_id=validation.room_id,
         )
 
     return FileProcessingConfig(
@@ -144,8 +174,104 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
     )
 
 
-def dispatch_transcript_processing(config: ProcessingConfig) -> AsyncResult:
+async def dispatch_transcript_processing(
+    config: ProcessingConfig, force: bool = False
+) -> AsyncResult | None:
+    """Dispatch transcript processing to appropriate backend (Hatchet or Celery).
+
+    Returns AsyncResult for Celery tasks, None for Hatchet workflows.
+    """
     if isinstance(config, MultitrackProcessingConfig):
+        # Check if room has use_hatchet=True (overrides env vars)
+        room_forces_hatchet = False
+        if config.room_id:
+            room = await rooms_controller.get_by_id(config.room_id)
+            room_forces_hatchet = room.use_hatchet if room else False
+
+        # Start durable workflow if enabled (Hatchet)
+        # or if room has use_hatchet=True
+        use_hatchet = settings.HATCHET_ENABLED or room_forces_hatchet
+
+        if room_forces_hatchet:
+            logger.info(
+                "Room forces Hatchet workflow",
+                room_id=config.room_id,
+                transcript_id=config.transcript_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,
+                    )
+                    return None
+
+            # Force: cancel old workflow if exists
+            if force and transcript and transcript.workflow_run_id:
+                await HatchetClientManager.cancel_workflow(transcript.workflow_run_id)
+                logger.info(
+                    "Cancelled old workflow (--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}
+                )
+
+            logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
+            return None
+
+        # Celery pipeline (durable workflows disabled)
         return task_pipeline_multitrack_process.delay(
             transcript_id=config.transcript_id,
             bucket_name=config.bucket_name,
@@ -160,7 +286,10 @@ def dispatch_transcript_processing(config: ProcessingConfig) -> AsyncResult:
 def task_is_scheduled_or_active(task_name: str, **kwargs):
     inspect = celery.current_app.control.inspect()
 
-    for worker, tasks in (inspect.scheduled() | inspect.active()).items():
+    scheduled = inspect.scheduled() or {}
+    active = inspect.active() or {}
+    all = scheduled | active
+    for worker, tasks in all.items():
         for task in tasks:
             if task["name"] == task_name and task["kwargs"] == kwargs:
                 return True

server/reflector/settings.py

@@ -74,6 +74,13 @@ class Settings(BaseSettings):
     LLM_API_KEY: str | None = None
     LLM_CONTEXT_WINDOW: int = 16000
 
+    LLM_PARSE_MAX_RETRIES: int = (
+        3  # Max retries for JSON/validation errors (total attempts = retries + 1)
+    )
+    LLM_STRUCTURED_RESPONSE_TIMEOUT: int = (
+        300  # Timeout in seconds for structured responses (5 minutes)
+    )
+
     # Diarization
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"
@@ -138,14 +145,6 @@ class Settings(BaseSettings):
     DAILY_WEBHOOK_UUID: str | None = (
         None  # Webhook UUID for this environment. Not used by production code
     )
-
-    # Multitrack processing
-    # SKIP_MIXDOWN: When True, skips audio mixdown and waveform generation.
-    # Transcription still works using individual tracks. Useful for:
-    # - Diagnosing OOM issues in mixdown
-    # - Fast processing when audio playback is not needed
-    # Note: UI will have no audio playback or waveform when enabled.
-    SKIP_MIXDOWN: bool = True
     # Platform Configuration
     DEFAULT_VIDEO_PLATFORM: Platform = WHEREBY_PLATFORM
 
@@ -154,5 +153,19 @@ class Settings(BaseSettings):
     ZULIP_API_KEY: str | None = None
     ZULIP_BOT_EMAIL: str | None = None
 
+    # Durable workflow orchestration
+    # Provider: "hatchet" (or "none" to disable)
+    DURABLE_WORKFLOW_PROVIDER: str = "none"
+
+    # Hatchet workflow orchestration
+    HATCHET_CLIENT_TOKEN: str | None = None
+    HATCHET_CLIENT_TLS_STRATEGY: str = "none"  # none, tls, mtls
+    HATCHET_DEBUG: bool = False
+
+    @property
+    def HATCHET_ENABLED(self) -> bool:
+        """True if Hatchet is the active provider."""
+        return self.DURABLE_WORKFLOW_PROVIDER == "hatchet"
+
 
 settings = Settings()

server/reflector/tools/process_transcript.py

@@ -15,8 +15,11 @@ import time
 from typing import Callable
 
 from celery.result import AsyncResult
+from hatchet_sdk.clients.rest.models import V1TaskStatus
 
+from reflector.db import get_database
 from reflector.db.transcripts import Transcript, transcripts_controller
+from reflector.hatchet.client import HatchetClientManager
 from reflector.services.transcript_process import (
     FileProcessingConfig,
     MultitrackProcessingConfig,
@@ -34,24 +37,26 @@ async def process_transcript_inner(
     transcript: Transcript,
     on_validation: Callable[[ValidationResult], None],
     on_preprocess: Callable[[PrepareResult], None],
-) -> AsyncResult:
+    force: bool = False,
+) -> AsyncResult | None:
     validation = await validate_transcript_for_processing(transcript)
     on_validation(validation)
     config = await prepare_transcript_processing(validation)
     on_preprocess(config)
-    return dispatch_transcript_processing(config)
+    return await dispatch_transcript_processing(config, force=force)
 
 
-async def process_transcript(transcript_id: str, sync: bool = False) -> None:
+async def process_transcript(
+    transcript_id: str, sync: bool = False, force: bool = False
+) -> None:
     """
     Process a transcript by ID, auto-detecting multitrack vs file pipeline.
 
     Args:
         transcript_id: The transcript UUID
         sync: If True, wait for task completion. If False, dispatch and exit.
+        force: If True, cancel old workflow and start new (latest code). If False, replay failed workflow.
     """
-    from reflector.db import get_database
-
     database = get_database()
     await database.connect()
 
@@ -82,10 +87,42 @@ async def process_transcript(transcript_id: str, sync: bool = False) -> None:
                 print(f"Dispatching file pipeline", file=sys.stderr)
 
         result = await process_transcript_inner(
-            transcript, on_validation=on_validation, on_preprocess=on_preprocess
+            transcript,
+            on_validation=on_validation,
+            on_preprocess=on_preprocess,
+            force=force,
         )
 
-        if sync:
+        if result is None:
+            # Hatchet workflow dispatched
+            if sync:
+                # Re-fetch transcript to get workflow_run_id
+                transcript = await transcripts_controller.get_by_id(transcript_id)
+                if not transcript or not transcript.workflow_run_id:
+                    print("Error: workflow_run_id not found", file=sys.stderr)
+                    sys.exit(1)
+
+                print("Waiting for Hatchet workflow...", file=sys.stderr)
+                while True:
+                    status = await HatchetClientManager.get_workflow_run_status(
+                        transcript.workflow_run_id
+                    )
+                    print(f"  Status: {status.value}", file=sys.stderr)
+
+                    if status == V1TaskStatus.COMPLETED:
+                        print("Workflow completed successfully", file=sys.stderr)
+                        break
+                    elif status in (V1TaskStatus.FAILED, V1TaskStatus.CANCELLED):
+                        print(f"Workflow failed: {status}", file=sys.stderr)
+                        sys.exit(1)
+
+                    await asyncio.sleep(5)
+            else:
+                print(
+                    "Task dispatched (use --sync to wait for completion)",
+                    file=sys.stderr,
+                )
+        elif sync:
             print("Waiting for task completion...", file=sys.stderr)
             while not result.ready():
                 print(f"  Status: {result.state}", file=sys.stderr)
@@ -118,9 +155,16 @@ def main():
         action="store_true",
         help="Wait for task completion instead of just dispatching",
     )
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="Cancel old workflow and start new (uses latest code instead of replaying)",
+    )
 
     args = parser.parse_args()
-    asyncio.run(process_transcript(args.transcript_id, sync=args.sync))
+    asyncio.run(
+        process_transcript(args.transcript_id, sync=args.sync, force=args.force)
+    )
 
 
 if __name__ == "__main__":

server/reflector/utils/audio_constants.py

@@ -0,0 +1,15 @@
+"""
+Shared audio processing constants.
+
+Used by both Hatchet workflows and Celery pipelines for consistent audio encoding.
+"""
+
+# Opus codec settings
+OPUS_STANDARD_SAMPLE_RATE = 48000
+OPUS_DEFAULT_BIT_RATE = 128000  # 128kbps for good speech quality
+
+# S3 presigned URL expiration
+PRESIGNED_URL_EXPIRATION_SECONDS = 7200  # 2 hours
+
+# Waveform visualization
+WAVEFORM_SEGMENTS = 255

server/reflector/utils/audio_mixdown.py

@@ -0,0 +1,227 @@
+"""
+Audio track mixdown utilities.
+
+Shared PyAV-based functions for mixing multiple audio tracks into a single output.
+Used by both Hatchet workflows and Celery pipelines.
+"""
+
+from fractions import Fraction
+
+import av
+from av.audio.resampler import AudioResampler
+
+
+def detect_sample_rate_from_tracks(track_urls: list[str], logger=None) -> int | None:
+    """Detect sample rate from first decodable audio frame.
+
+    Args:
+        track_urls: List of URLs to audio files (S3 presigned or local)
+        logger: Optional logger instance
+
+    Returns:
+        Sample rate in Hz, or None if no decodable frames found
+    """
+    for url in track_urls:
+        if not url:
+            continue
+        container = None
+        try:
+            container = av.open(url)
+            for frame in container.decode(audio=0):
+                return frame.sample_rate
+        except Exception:
+            continue
+        finally:
+            if container is not None:
+                container.close()
+    return None
+
+
+async def mixdown_tracks_pyav(
+    track_urls: list[str],
+    writer,
+    target_sample_rate: int,
+    offsets_seconds: list[float] | None = None,
+    logger=None,
+) -> None:
+    """Multi-track mixdown using PyAV filter graph (amix).
+
+    Builds a filter graph: N abuffer -> optional adelay -> amix -> aformat -> sink
+    Reads from S3 presigned URLs or local files, pushes mixed frames to writer.
+
+    Args:
+        track_urls: List of URLs to audio tracks (S3 presigned or local)
+        writer: AudioFileWriterProcessor instance with async push() method
+        target_sample_rate: Sample rate for output (Hz)
+        offsets_seconds: Optional per-track delays in seconds for alignment.
+            If provided, must have same length as track_urls. Delays are relative
+            to the minimum offset (earliest track has delay=0).
+        logger: Optional logger instance
+
+    Raises:
+        ValueError: If offsets_seconds length doesn't match track_urls,
+            no valid tracks provided, or no containers can be opened
+    """
+    if offsets_seconds is not None and len(offsets_seconds) != len(track_urls):
+        raise ValueError(
+            f"offsets_seconds length ({len(offsets_seconds)}) must match track_urls ({len(track_urls)})"
+        )
+
+    valid_track_urls = [url for url in track_urls if url]
+    if not valid_track_urls:
+        if logger:
+            logger.error("Mixdown failed - no valid track URLs provided")
+        raise ValueError("Mixdown failed: No valid track URLs")
+
+    # Calculate per-input delays if offsets provided
+    input_offsets_seconds = None
+    if offsets_seconds is not None:
+        input_offsets_seconds = [
+            offsets_seconds[i] for i, url in enumerate(track_urls) if url
+        ]
+
+    # Build PyAV filter graph:
+    # N abuffer (s32/stereo)
+    #   -> optional adelay per input (for alignment)
+    #   -> amix (s32)
+    #   -> aformat(s16)
+    #   -> sink
+    graph = av.filter.Graph()
+    inputs = []
+
+    for idx, url in enumerate(valid_track_urls):
+        args = (
+            f"time_base=1/{target_sample_rate}:"
+            f"sample_rate={target_sample_rate}:"
+            f"sample_fmt=s32:"
+            f"channel_layout=stereo"
+        )
+        in_ctx = graph.add("abuffer", args=args, name=f"in{idx}")
+        inputs.append(in_ctx)
+
+    if not inputs:
+        if logger:
+            logger.error("Mixdown failed - no valid inputs for graph")
+        raise ValueError("Mixdown failed: No valid inputs for filter graph")
+
+    mixer = graph.add("amix", args=f"inputs={len(inputs)}:normalize=0", name="mix")
+
+    fmt = graph.add(
+        "aformat",
+        args=f"sample_fmts=s32:channel_layouts=stereo:sample_rates={target_sample_rate}",
+        name="fmt",
+    )
+
+    sink = graph.add("abuffersink", name="out")
+
+    # Optional per-input delay before mixing
+    delays_ms: list[int] = []
+    if input_offsets_seconds is not None:
+        base = min(input_offsets_seconds) if input_offsets_seconds else 0.0
+        delays_ms = [
+            max(0, int(round((o - base) * 1000))) for o in input_offsets_seconds
+        ]
+    else:
+        delays_ms = [0 for _ in inputs]
+
+    for idx, in_ctx in enumerate(inputs):
+        delay_ms = delays_ms[idx] if idx < len(delays_ms) else 0
+        if delay_ms > 0:
+            # adelay requires one value per channel; use same for stereo
+            adelay = graph.add(
+                "adelay",
+                args=f"delays={delay_ms}|{delay_ms}:all=1",
+                name=f"delay{idx}",
+            )
+            in_ctx.link_to(adelay)
+            adelay.link_to(mixer, 0, idx)
+        else:
+            in_ctx.link_to(mixer, 0, idx)
+
+    mixer.link_to(fmt)
+    fmt.link_to(sink)
+    graph.configure()
+
+    containers = []
+    try:
+        # Open all containers with cleanup guaranteed
+        for i, url in enumerate(valid_track_urls):
+            try:
+                c = av.open(
+                    url,
+                    options={
+                        # S3 streaming options
+                        "reconnect": "1",
+                        "reconnect_streamed": "1",
+                        "reconnect_delay_max": "5",
+                    },
+                )
+                containers.append(c)
+            except Exception as e:
+                if logger:
+                    logger.warning(
+                        "Mixdown: failed to open container from URL",
+                        input=i,
+                        url=url,
+                        error=str(e),
+                    )
+
+        if not containers:
+            if logger:
+                logger.error("Mixdown failed - no valid containers opened")
+            raise ValueError("Mixdown failed: Could not open any track containers")
+
+        decoders = [c.decode(audio=0) for c in containers]
+        active = [True] * len(decoders)
+        resamplers = [
+            AudioResampler(format="s32", layout="stereo", rate=target_sample_rate)
+            for _ in decoders
+        ]
+
+        while any(active):
+            for i, (dec, is_active) in enumerate(zip(decoders, active)):
+                if not is_active:
+                    continue
+                try:
+                    frame = next(dec)
+                except StopIteration:
+                    active[i] = False
+                    # Signal end of stream to filter graph
+                    inputs[i].push(None)
+                    continue
+
+                if frame.sample_rate != target_sample_rate:
+                    continue
+                out_frames = resamplers[i].resample(frame) or []
+                for rf in out_frames:
+                    rf.sample_rate = target_sample_rate
+                    rf.time_base = Fraction(1, target_sample_rate)
+                    inputs[i].push(rf)
+
+                while True:
+                    try:
+                        mixed = sink.pull()
+                    except Exception:
+                        break
+                    mixed.sample_rate = target_sample_rate
+                    mixed.time_base = Fraction(1, target_sample_rate)
+                    await writer.push(mixed)
+
+        # Flush remaining frames from filter graph
+        while True:
+            try:
+                mixed = sink.pull()
+            except Exception:
+                break
+            mixed.sample_rate = target_sample_rate
+            mixed.time_base = Fraction(1, target_sample_rate)
+            await writer.push(mixed)
+
+    finally:
+        # Cleanup all containers, even if processing failed
+        for c in containers:
+            if c is not None:
+                try:
+                    c.close()
+                except Exception:
+                    pass  # Best effort cleanup

server/reflector/utils/audio_padding.py

@@ -0,0 +1,186 @@
+"""
+Audio track padding utilities.
+
+Shared PyAV-based functions for extracting stream metadata and applying
+silence padding to audio tracks. Used by both Hatchet workflows and Celery pipelines.
+"""
+
+import math
+from fractions import Fraction
+
+import av
+from av.audio.resampler import AudioResampler
+
+from reflector.utils.audio_constants import (
+    OPUS_DEFAULT_BIT_RATE,
+    OPUS_STANDARD_SAMPLE_RATE,
+)
+
+
+def extract_stream_start_time_from_container(
+    container,
+    track_idx: int,
+    logger=None,
+) -> float:
+    """Extract meeting-relative start time from WebM stream metadata.
+
+    Uses PyAV to read stream.start_time from WebM container.
+    More accurate than filename timestamps by ~209ms due to network/encoding delays.
+
+    Args:
+        container: PyAV container opened from audio file/URL
+        track_idx: Track index for logging context
+        logger: Optional logger instance (structlog or stdlib compatible)
+
+    Returns:
+        Start time in seconds (0.0 if extraction fails)
+    """
+    start_time_seconds = 0.0
+    try:
+        audio_streams = [s for s in container.streams if s.type == "audio"]
+        stream = audio_streams[0] if audio_streams else container.streams[0]
+
+        # 1) Try stream-level start_time (most reliable for Daily.co tracks)
+        if stream.start_time is not None and stream.time_base is not None:
+            start_time_seconds = float(stream.start_time * stream.time_base)
+
+        # 2) Fallback to container-level start_time (in av.time_base units)
+        if (start_time_seconds <= 0) and (container.start_time is not None):
+            start_time_seconds = float(container.start_time * av.time_base)
+
+        # 3) Fallback to first packet DTS in stream.time_base
+        if start_time_seconds <= 0:
+            for packet in container.demux(stream):
+                if packet.dts is not None:
+                    start_time_seconds = float(packet.dts * stream.time_base)
+                    break
+    except Exception as e:
+        if logger:
+            logger.warning(
+                "PyAV metadata read failed; assuming 0 start_time",
+                track_idx=track_idx,
+                error=str(e),
+            )
+        start_time_seconds = 0.0
+
+    if logger:
+        logger.info(
+            f"Track {track_idx} stream metadata: start_time={start_time_seconds:.3f}s",
+            track_idx=track_idx,
+        )
+    return start_time_seconds
+
+
+def apply_audio_padding_to_file(
+    in_container,
+    output_path: str,
+    start_time_seconds: float,
+    track_idx: int,
+    logger=None,
+) -> None:
+    """Apply silence padding to audio track using PyAV filter graph.
+
+    Uses adelay filter to prepend silence, aligning track to meeting start time.
+    Output is WebM/Opus format.
+
+    Args:
+        in_container: PyAV container opened from source audio
+        output_path: Path for output WebM file
+        start_time_seconds: Amount of silence to prepend (in seconds)
+        track_idx: Track index for logging context
+        logger: Optional logger instance (structlog or stdlib compatible)
+
+    Raises:
+        Exception: If no audio stream found or PyAV processing fails
+    """
+    delay_ms = math.floor(start_time_seconds * 1000)
+
+    if logger:
+        logger.info(
+            f"Padding track {track_idx} with {delay_ms}ms delay using PyAV",
+            track_idx=track_idx,
+            delay_ms=delay_ms,
+        )
+
+    try:
+        with av.open(output_path, "w", format="webm") as out_container:
+            in_stream = next(
+                (s for s in in_container.streams if s.type == "audio"), None
+            )
+            if in_stream is None:
+                raise Exception("No audio stream in input")
+
+            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")
+            # adelay requires one delay value per channel separated by '|'
+            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
+            )
+
+            # Decode -> resample -> push through graph -> encode Opus
+            for frame in in_container.decode(in_stream):
+                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 remaining frames from 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)
+
+    except Exception as e:
+        if logger:
+            logger.error(
+                "PyAV padding failed for track",
+                track_idx=track_idx,
+                delay_ms=delay_ms,
+                error=str(e),
+                exc_info=True,
+            )
+        raise

server/reflector/utils/common.py

@@ -0,0 +1,4 @@
+def assert_not_none[T](value: T | None, message: str = "Value is None") -> T:
+    if value is None:
+        raise ValueError(message)
+    return value

server/reflector/utils/string.py

@@ -2,6 +2,17 @@ from typing import Annotated, TypeVar
 
 from pydantic import Field, TypeAdapter, constr
 
+T_NotNone = TypeVar("T_NotNone")
+
+
+def assert_not_none(
+    value: T_NotNone | None, message: str = "Value is None"
+) -> T_NotNone:
+    if value is None:
+        raise ValueError(message)
+    return value
+
+
 NonEmptyStringBase = constr(min_length=1, strip_whitespace=False)
 NonEmptyString = Annotated[
     NonEmptyStringBase,
@@ -23,10 +34,18 @@ def try_parse_non_empty_string(s: str) -> NonEmptyString | None:
     return parse_non_empty_string(s)
 
 
-T = TypeVar("T", bound=str)
+T_Str = TypeVar("T_Str", bound=str)
 
 
-def assert_equal[T](s1: T, s2: T) -> T:
+def assert_equal(s1: T_Str, s2: T_Str) -> T_Str:
     if s1 != s2:
         raise ValueError(f"assert_equal: {s1} != {s2}")
     return s1
+
+
+def assert_non_none_and_non_empty(
+    value: str | None, error: str | None = None
+) -> NonEmptyString:
+    return parse_non_empty_string(
+        assert_not_none(value, error or "Value is None"), error
+    )

server/reflector/video_platforms/daily.py

@@ -31,6 +31,7 @@ class DailyClient(VideoPlatformClient):
     PLATFORM_NAME: Platform = "daily"
     TIMESTAMP_FORMAT = "%Y%m%d%H%M%S"
     RECORDING_NONE: RecordingType = "none"
+    RECORDING_LOCAL: RecordingType = "local"
     RECORDING_CLOUD: RecordingType = "cloud"
 
     def __init__(self, config: VideoPlatformConfig):
@@ -54,19 +55,23 @@ class DailyClient(VideoPlatformClient):
         timestamp = datetime.now().strftime(self.TIMESTAMP_FORMAT)
         room_name = f"{room_name_prefix}{ROOM_PREFIX_SEPARATOR}{timestamp}"
 
+        enable_recording = None
+        if room.recording_type == self.RECORDING_LOCAL:
+            enable_recording = "local"
+        elif room.recording_type == self.RECORDING_CLOUD:
+            enable_recording = "raw-tracks"
+
         properties = RoomProperties(
-            enable_recording="raw-tracks"
+            enable_recording=enable_recording,
-            if room.recording_type != self.RECORDING_NONE
-            else False,
             enable_chat=True,
             enable_screenshare=True,
+            enable_knocking=room.is_locked,
             start_video_off=False,
             start_audio_off=False,
             exp=int(end_date.timestamp()),
         )
 
-        # Only configure recordings_bucket if recording is enabled
+        if room.recording_type == self.RECORDING_CLOUD:
-        if room.recording_type != self.RECORDING_NONE:
             daily_storage = get_dailyco_storage()
             assert daily_storage.bucket_name, "S3 bucket must be configured"
             properties.recordings_bucket = RecordingsBucketConfig(
@@ -172,15 +177,16 @@ class DailyClient(VideoPlatformClient):
     async def create_meeting_token(
         self,
         room_name: DailyRoomName,
-        enable_recording: bool,
+        start_cloud_recording: bool,
+        enable_recording_ui: bool,
         user_id: NonEmptyString | None = None,
         is_owner: bool = False,
     ) -> NonEmptyString:
         properties = MeetingTokenProperties(
             room_name=room_name,
             user_id=user_id,
-            start_cloud_recording=enable_recording,
+            start_cloud_recording=start_cloud_recording,
-            enable_recording_ui=False,
+            enable_recording_ui=enable_recording_ui,
             is_owner=is_owner,
         )
         request = CreateMeetingTokenRequest(properties=properties)

server/reflector/views/rooms.py

@@ -89,7 +89,7 @@ class CreateRoom(BaseModel):
     ics_url: Optional[str] = None
     ics_fetch_interval: int = 300
     ics_enabled: bool = False
-    platform: Optional[Platform] = None
+    platform: Platform
 
 
 class UpdateRoom(BaseModel):
@@ -248,7 +248,7 @@ async def rooms_create(
         ics_url=room.ics_url,
         ics_fetch_interval=room.ics_fetch_interval,
         ics_enabled=room.ics_enabled,
-        platform=room.platform or settings.DEFAULT_VIDEO_PLATFORM,
+        platform=room.platform,
     )
 
 
@@ -310,6 +310,22 @@ async def rooms_create_meeting(
                     room=room, current_time=current_time
                 )
 
+                if meeting is not None:
+                    settings_match = (
+                        meeting.is_locked == room.is_locked
+                        and meeting.room_mode == room.room_mode
+                        and meeting.recording_type == room.recording_type
+                        and meeting.recording_trigger == room.recording_trigger
+                        and meeting.platform == room.platform
+                    )
+                    if not settings_match:
+                        logger.info(
+                            f"Room settings changed for {room_name}, creating new meeting",
+                            room_id=room.id,
+                            old_meeting_id=meeting.id,
+                        )
+                        meeting = None
+
             if meeting is None:
                 end_date = current_time + timedelta(hours=8)
 
@@ -549,21 +565,16 @@ async def rooms_join_meeting(
     if meeting.end_date <= current_time:
         raise HTTPException(status_code=400, detail="Meeting has ended")
 
-    if meeting.platform == "daily":
+    if meeting.platform == "daily" and user_id is not None:
         client = create_platform_client(meeting.platform)
-        enable_recording = room.recording_trigger != "none"
         token = await client.create_meeting_token(
             meeting.room_name,
-            enable_recording=enable_recording,
+            start_cloud_recording=meeting.recording_type == "cloud",
+            enable_recording_ui=meeting.recording_type == "local",
             user_id=user_id,
             is_owner=user_id == room.user_id,
         )
         meeting = meeting.model_copy()
         meeting.room_url = add_query_param(meeting.room_url, "t", token)
-        if meeting.host_room_url:
-            meeting.host_room_url = add_query_param(meeting.host_room_url, "t", token)
-
-    if user_id != room.user_id and meeting.platform == "whereby":
-        meeting.host_room_url = ""
 
     return meeting

server/reflector/views/transcripts.py

@@ -17,6 +17,7 @@ from pydantic import (
 import reflector.auth as auth
 from reflector.db import get_database
 from reflector.db.recordings import recordings_controller
+from reflector.db.rooms import rooms_controller
 from reflector.db.search import (
     DEFAULT_SEARCH_LIMIT,
     SearchLimit,
@@ -37,6 +38,7 @@ from reflector.db.transcripts import (
     TranscriptTopic,
     transcripts_controller,
 )
+from reflector.db.users import user_controller
 from reflector.processors.types import Transcript as ProcessorTranscript
 from reflector.processors.types import Word
 from reflector.schemas.transcript_formats import TranscriptFormat, TranscriptSegment
@@ -111,8 +113,12 @@ class GetTranscriptMinimal(BaseModel):
     audio_deleted: bool | None = None
 
 
+class TranscriptParticipantWithEmail(TranscriptParticipant):
+    email: str | None = None
+
+
 class GetTranscriptWithParticipants(GetTranscriptMinimal):
-    participants: list[TranscriptParticipant] | None
+    participants: list[TranscriptParticipantWithEmail] | None
 
 
 class GetTranscriptWithText(GetTranscriptWithParticipants):
@@ -468,6 +474,23 @@ async def transcript_get(
 
     is_multitrack = await _get_is_multitrack(transcript)
 
+    room_name = None
+    if transcript.room_id:
+        room = await rooms_controller.get_by_id(transcript.room_id)
+        room_name = room.name if room else None
+
+    participants = []
+    if transcript.participants:
+        user_ids = [p.user_id for p in transcript.participants if p.user_id is not None]
+        users_dict = await user_controller.get_by_ids(user_ids) if user_ids else {}
+        for p in transcript.participants:
+            user = users_dict.get(p.user_id) if p.user_id else None
+            participants.append(
+                TranscriptParticipantWithEmail(
+                    **p.model_dump(), email=user.email if user else None
+                )
+            )
+
     base_data = {
         "id": transcript.id,
         "user_id": transcript.user_id,
@@ -478,6 +501,7 @@ async def transcript_get(
         "title": transcript.title,
         "short_summary": transcript.short_summary,
         "long_summary": transcript.long_summary,
+        "action_items": transcript.action_items,
         "created_at": transcript.created_at,
         "share_mode": transcript.share_mode,
         "source_language": transcript.source_language,
@@ -486,8 +510,9 @@ async def transcript_get(
         "meeting_id": transcript.meeting_id,
         "source_kind": transcript.source_kind,
         "room_id": transcript.room_id,
+        "room_name": room_name,
         "audio_deleted": transcript.audio_deleted,
-        "participants": transcript.participants,
+        "participants": participants,
     }
 
     if transcript_format == "text":

server/reflector/views/transcripts_process.py

@@ -50,5 +50,5 @@ async def transcript_process(
     if isinstance(config, ProcessError):
         raise HTTPException(status_code=500, detail=config.detail)
     else:
-        dispatch_transcript_processing(config)
+        await dispatch_transcript_processing(config)
         return ProcessStatus(status="ok")

server/reflector/worker/app.py

@@ -38,6 +38,10 @@ else:
             "task": "reflector.worker.process.reprocess_failed_recordings",
             "schedule": crontab(hour=5, minute=0),  # Midnight EST
         },
+        "reprocess_failed_daily_recordings": {
+            "task": "reflector.worker.process.reprocess_failed_daily_recordings",
+            "schedule": crontab(hour=5, minute=0),  # Midnight EST
+        },
         "poll_daily_recordings": {
             "task": "reflector.worker.process.poll_daily_recordings",
             "schedule": 180.0,  # Every 3 minutes (configurable lookback window)

server/reflector/worker/process.py

@@ -12,7 +12,7 @@ from celery import shared_task
 from celery.utils.log import get_task_logger
 from pydantic import ValidationError
 
-from reflector.dailyco_api import RecordingResponse
+from reflector.dailyco_api import FinishedRecordingResponse, RecordingResponse
 from reflector.db.daily_participant_sessions import (
     DailyParticipantSession,
     daily_participant_sessions_controller,
@@ -24,6 +24,7 @@ from reflector.db.transcripts import (
     SourceKind,
     transcripts_controller,
 )
+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 (
@@ -286,6 +287,45 @@ async def _process_multitrack_recording_inner(
             room_id=room.id,
         )
 
+    # Start durable workflow if enabled (Hatchet) or room overrides it
+    durable_started = False
+    use_hatchet = settings.HATCHET_ENABLED or (room and room.use_hatchet)
+
+    if room and room.use_hatchet and not settings.HATCHET_ENABLED:
+        logger.info(
+            "Room forces Hatchet workflow",
+            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,
@@ -322,16 +362,38 @@ async def poll_daily_recordings():
         )
         return
 
-    recording_ids = [rec.id for rec in api_recordings]
+    finished_recordings: List[FinishedRecordingResponse] = []
+    for rec in api_recordings:
+        finished = rec.to_finished()
+        if finished is None:
+            logger.debug(
+                "Skipping unfinished recording",
+                recording_id=rec.id,
+                room_name=rec.room_name,
+                status=rec.status,
+            )
+            continue
+        finished_recordings.append(finished)
+
+    if not finished_recordings:
+        logger.debug(
+            "No finished recordings found from Daily.co API",
+            total_api_count=len(api_recordings),
+        )
+        return
+
+    recording_ids = [rec.id for rec in finished_recordings]
     existing_recordings = await recordings_controller.get_by_ids(recording_ids)
     existing_ids = {rec.id for rec in existing_recordings}
 
-    missing_recordings = [rec for rec in api_recordings if rec.id not in existing_ids]
+    missing_recordings = [
+        rec for rec in finished_recordings if rec.id not in existing_ids
+    ]
 
     if not missing_recordings:
         logger.debug(
             "All recordings already in DB",
-            api_count=len(api_recordings),
+            api_count=len(finished_recordings),
             existing_count=len(existing_recordings),
         )
         return
@@ -339,7 +401,7 @@ async def poll_daily_recordings():
     logger.info(
         "Found recordings missing from DB",
         missing_count=len(missing_recordings),
-        total_api_count=len(api_recordings),
+        total_api_count=len(finished_recordings),
         existing_count=len(existing_recordings),
     )
 
@@ -649,7 +711,7 @@ async def reprocess_failed_recordings():
     Find recordings in Whereby S3 bucket and check if they have proper transcriptions.
     If not, requeue them for processing.
 
-    Note: Daily.co recordings are processed via webhooks, not this cron job.
+    Note: Daily.co multitrack recordings are handled by reprocess_failed_daily_recordings.
     """
     logger.info("Checking Whereby recordings that need processing or reprocessing")
 
@@ -702,6 +764,150 @@ async def reprocess_failed_recordings():
     return reprocessed_count
 
 
+@shared_task
+@asynctask
+async def reprocess_failed_daily_recordings():
+    """
+    Find Daily.co multitrack recordings in the database and check if they have proper transcriptions.
+    If not, requeue them for processing.
+    """
+    logger.info(
+        "Checking Daily.co multitrack recordings that need processing or reprocessing"
+    )
+
+    if not settings.DAILYCO_STORAGE_AWS_BUCKET_NAME:
+        logger.debug(
+            "DAILYCO_STORAGE_AWS_BUCKET_NAME not configured; skipping Daily recording reprocessing"
+        )
+        return 0
+
+    bucket_name = settings.DAILYCO_STORAGE_AWS_BUCKET_NAME
+    reprocessed_count = 0
+
+    try:
+        multitrack_recordings = (
+            await recordings_controller.get_multitrack_needing_reprocessing(bucket_name)
+        )
+
+        logger.info(
+            "Found multitrack recordings needing reprocessing",
+            count=len(multitrack_recordings),
+            bucket=bucket_name,
+        )
+
+        for recording in multitrack_recordings:
+            if not recording.meeting_id:
+                logger.debug(
+                    "Skipping recording without meeting_id",
+                    recording_id=recording.id,
+                )
+                continue
+
+            meeting = await meetings_controller.get_by_id(recording.meeting_id)
+            if not meeting:
+                logger.warning(
+                    "Meeting not found for recording",
+                    recording_id=recording.id,
+                    meeting_id=recording.meeting_id,
+                )
+                continue
+
+            # Fetch room to check use_hatchet flag
+            room = None
+            if meeting.room_id:
+                room = await rooms_controller.get_by_id(meeting.room_id)
+
+            transcript = None
+            try:
+                transcript = await transcripts_controller.get_by_recording_id(
+                    recording.id
+                )
+            except ValidationError:
+                await transcripts_controller.remove_by_recording_id(recording.id)
+                logger.warning(
+                    "Removed invalid transcript for recording",
+                    recording_id=recording.id,
+                )
+
+            if not recording.track_keys:
+                logger.warning(
+                    "Recording has no track_keys, cannot reprocess",
+                    recording_id=recording.id,
+                )
+                continue
+
+            use_hatchet = settings.HATCHET_ENABLED or (room and room.use_hatchet)
+
+            if use_hatchet:
+                # Hatchet requires a transcript for workflow_run_id tracking
+                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",
+                    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,
+                )
+
+                process_multitrack_recording.delay(
+                    bucket_name=bucket_name,
+                    daily_room_name=meeting.room_name,
+                    recording_id=recording.id,
+                    track_keys=recording.track_keys,
+                )
+
+            reprocessed_count += 1
+
+    except Exception as e:
+        logger.error(
+            "Error checking Daily multitrack recordings",
+            error=str(e),
+            exc_info=True,
+        )
+
+    logger.info(
+        "Daily reprocessing complete",
+        requeued_count=reprocessed_count,
+    )
+    return reprocessed_count
+
+
 @shared_task
 @asynctask
 async def trigger_daily_reconciliation() -> None:

server/reflector/worker/webhook.py

@@ -123,6 +123,7 @@ async def send_transcript_webhook(
                 "target_language": transcript.target_language,
                 "status": transcript.status,
                 "frontend_url": frontend_url,
+                "action_items": transcript.action_items,
             },
             "room": {
                 "id": room.id,

server/reflector/ws_manager.py

@@ -16,6 +16,7 @@ import threading
 import redis.asyncio as redis
 from fastapi import WebSocket
 
+from reflector.events import subscribers_shutdown
 from reflector.settings import settings
 
 
@@ -109,29 +110,30 @@ class WebsocketManager:
                     await socket.send_json(data)
 
 
+_ws_manager_instance: WebsocketManager | None = None
+_ws_manager_lock = threading.Lock()
+
+
 def get_ws_manager() -> WebsocketManager:
-    """
+    """Returns the WebsocketManager singleton instance."""
-    Returns the WebsocketManager instance for managing websockets.
+    global _ws_manager_instance
+    if _ws_manager_instance is None:
+        with _ws_manager_lock:
+            if _ws_manager_instance is None:
+                pubsub_client = RedisPubSubManager(
+                    host=settings.REDIS_HOST,
+                    port=settings.REDIS_PORT,
+                )
+                _ws_manager_instance = WebsocketManager(pubsub_client=pubsub_client)
+    return _ws_manager_instance
 
-    This function initializes and returns the WebsocketManager instance,
-    which is responsible for managing websockets and handling websocket
-    connections.
 
-    Returns:
+async def cleanup_ws_manager(_app=None) -> None:
-        WebsocketManager: The initialized WebsocketManager instance.
+    """Cleanup WebsocketManager singleton on shutdown."""
+    global _ws_manager_instance
+    if _ws_manager_instance is not None:
+        await _ws_manager_instance.pubsub_client.disconnect()
+        _ws_manager_instance = None
 
-    Raises:
-        ImportError: If the 'reflector.settings' module cannot be imported.
-        RedisConnectionError: If there is an error connecting to the Redis server.
-    """
-    local = threading.local()
-    if hasattr(local, "ws_manager"):
-        return local.ws_manager
 
-    pubsub_client = RedisPubSubManager(
+subscribers_shutdown.append(cleanup_ws_manager)
-        host=settings.REDIS_HOST,
-        port=settings.REDIS_PORT,
-    )
-    ws_manager = WebsocketManager(pubsub_client=pubsub_client)
-    local.ws_manager = ws_manager
-    return ws_manager

server/reflector/zulip.py

@@ -3,7 +3,8 @@ from urllib.parse import urlparse
 
 import httpx
 
-from reflector.db.transcripts import Transcript
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.settings import settings
 
 
@@ -113,6 +114,49 @@ def get_zulip_message(transcript: Transcript, include_topics: bool):
     return message
 
 
+async def post_transcript_notification(transcript: Transcript) -> int | None:
+    """Post or update transcript notification in Zulip.
+
+    Uses transcript.room_id directly (Hatchet flow).
+    Celery's pipeline_post_to_zulip uses recording→meeting→room path instead.
+    DUPLICATION NOTE: This function will stay when we use Celery no more, and Celery one will be removed.
+    """
+    if not transcript.room_id:
+        return None
+
+    room = await rooms_controller.get_by_id(transcript.room_id)
+    if not room or not room.zulip_stream or not room.zulip_auto_post:
+        return None
+
+    message = get_zulip_message(transcript=transcript, include_topics=True)
+    message_updated = False
+
+    if transcript.zulip_message_id:
+        try:
+            await update_zulip_message(
+                transcript.zulip_message_id,
+                room.zulip_stream,
+                room.zulip_topic,
+                message,
+            )
+            message_updated = True
+        except Exception:
+            pass
+
+    if not message_updated:
+        response = await send_message_to_zulip(
+            room.zulip_stream, room.zulip_topic, message
+        )
+        message_id = response.get("id")
+        if message_id:
+            await transcripts_controller.update(
+                transcript, {"zulip_message_id": message_id}
+            )
+        return message_id
+
+    return transcript.zulip_message_id
+
+
 def extract_domain(url: str) -> str:
     return urlparse(url).netloc
 

server/runserver.sh

@@ -7,6 +7,8 @@ elif [ "${ENTRYPOINT}" = "worker" ]; then
     uv run celery -A reflector.worker.app worker --loglevel=info
 elif [ "${ENTRYPOINT}" = "beat" ]; then
     uv run celery -A reflector.worker.app beat --loglevel=info
+elif [ "${ENTRYPOINT}" = "hatchet-worker" ]; then
+    uv run python -m reflector.hatchet.run_workers
 else
     echo "Unknown command"
 fi

server/tests/conftest.py

@@ -318,6 +318,14 @@ async def dummy_storage():
         yield
 
 
+@pytest.fixture
+def test_settings():
+    """Provide isolated settings for tests to avoid modifying global settings"""
+    from reflector.settings import Settings
+
+    return Settings()
+
+
 @pytest.fixture(scope="session")
 def celery_enable_logging():
     return True
@@ -519,6 +527,22 @@ def fake_mp3_upload():
         yield
 
 
+@pytest.fixture(autouse=True)
+def reset_hatchet_client():
+    """Reset HatchetClientManager singleton before and after each test.
+
+    This ensures test isolation - each test starts with a fresh client state.
+    The fixture is autouse=True so it applies to all tests automatically.
+    """
+    from reflector.hatchet.client import HatchetClientManager
+
+    # Reset before test
+    HatchetClientManager.reset()
+    yield
+    # Reset after test to clean up
+    HatchetClientManager.reset()
+
+
 @pytest.fixture
 async def fake_transcript_with_topics(tmpdir, client):
     import shutil

server/tests/test_hatchet_client.py

@@ -0,0 +1,54 @@
+"""
+Tests for HatchetClientManager error handling and validation.
+
+Only tests that catch real bugs - not mock verification tests.
+
+Note: The `reset_hatchet_client` fixture (autouse=True in conftest.py)
+automatically resets the singleton before and after each test.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_hatchet_client_can_replay_handles_exception():
+    """Test can_replay returns False when status check fails.
+
+    Useful: Ensures network/API errors don't crash the system and
+    gracefully allow reprocessing when workflow state is unknown.
+    """
+    from reflector.hatchet.client import HatchetClientManager
+
+    with patch("reflector.hatchet.client.settings") as mock_settings:
+        mock_settings.HATCHET_CLIENT_TOKEN = "test-token"
+        mock_settings.HATCHET_DEBUG = False
+
+        with patch("reflector.hatchet.client.Hatchet") as mock_hatchet_class:
+            mock_client = MagicMock()
+            mock_hatchet_class.return_value = mock_client
+
+            mock_client.runs.aio_get_status = AsyncMock(
+                side_effect=Exception("Network error")
+            )
+
+            can_replay = await HatchetClientManager.can_replay("workflow-123")
+
+            # Should return False on error (workflow might be gone)
+            assert can_replay is False
+
+
+def test_hatchet_client_raises_without_token():
+    """Test that get_client raises ValueError without token.
+
+    Useful: Catches if someone removes the token validation,
+    which would cause cryptic errors later.
+    """
+    from reflector.hatchet.client import HatchetClientManager
+
+    with patch("reflector.hatchet.client.settings") as mock_settings:
+        mock_settings.HATCHET_CLIENT_TOKEN = None
+
+        with pytest.raises(ValueError, match="HATCHET_CLIENT_TOKEN must be set"):
+            HatchetClientManager.get_client()

server/tests/test_hatchet_dispatch.py

@@ -0,0 +1,398 @@
+"""
+Tests for Hatchet workflow dispatch and routing logic.
+
+These tests verify:
+1. Routing to Hatchet when HATCHET_ENABLED=True
+2. Replay logic for failed workflows
+3. Force flag to cancel and restart
+4. Validation prevents concurrent workflows
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+from hatchet_sdk.clients.rest.exceptions import ApiException
+from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+from reflector.db.transcripts import Transcript
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_blocks_running_workflow():
+    """Test that validation blocks reprocessing when workflow is running."""
+    from reflector.services.transcript_process import (
+        ValidationAlreadyScheduled,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="processing",
+        source_kind="room",
+        workflow_run_id="running-workflow-123",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.RUNNING
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationAlreadyScheduled)
+            assert "running" in result.detail.lower()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_blocks_queued_workflow():
+    """Test that validation blocks reprocessing when workflow is queued."""
+    from reflector.services.transcript_process import (
+        ValidationAlreadyScheduled,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="processing",
+        source_kind="room",
+        workflow_run_id="queued-workflow-123",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.QUEUED
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationAlreadyScheduled)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_allows_failed_workflow():
+    """Test that validation allows reprocessing when workflow has failed."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="error",
+        source_kind="room",
+        workflow_run_id="failed-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.FAILED
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationOk)
+            assert result.transcript_id == "test-transcript-id"
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_allows_completed_workflow():
+    """Test that validation allows reprocessing when workflow has completed."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="ended",
+        source_kind="room",
+        workflow_run_id="completed-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.COMPLETED
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_allows_when_status_check_fails():
+    """Test that validation allows reprocessing when status check fails (workflow might be gone)."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="error",
+        source_kind="room",
+        workflow_run_id="old-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            # Status check fails (workflow might be deleted)
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                side_effect=ApiException("Workflow not found")
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            # Should allow processing when we can't get status
+            assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_skipped_when_no_workflow_id():
+    """Test that Hatchet validation is skipped when transcript has no workflow_run_id."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="uploaded",
+        source_kind="room",
+        workflow_run_id=None,  # No workflow yet
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            # Should not be called
+            mock_hatchet.get_workflow_run_status = AsyncMock()
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            # Should not check Hatchet status
+            mock_hatchet.get_workflow_run_status.assert_not_called()
+            assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_skipped_when_disabled():
+    """Test that Hatchet validation is skipped when HATCHET_ENABLED is False."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="uploaded",
+        source_kind="room",
+        workflow_run_id="some-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = False  # Hatchet disabled
+
+        with patch(
+            "reflector.services.transcript_process.task_is_scheduled_or_active"
+        ) as mock_celery_check:
+            mock_celery_check.return_value = False
+
+            result = await validate_transcript_for_processing(mock_transcript)
+
+        # Should not check Hatchet at all
+        assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_validation_locked_transcript():
+    """Test that validation rejects locked transcripts."""
+    from reflector.services.transcript_process import (
+        ValidationLocked,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="ended",
+        source_kind="room",
+        locked=True,
+    )
+
+    result = await validate_transcript_for_processing(mock_transcript)
+
+    assert isinstance(result, ValidationLocked)
+    assert "locked" in result.detail.lower()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_validation_idle_transcript():
+    """Test that validation rejects idle transcripts (not ready)."""
+    from reflector.services.transcript_process import (
+        ValidationNotReady,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="idle",
+        source_kind="room",
+    )
+
+    result = await validate_transcript_for_processing(mock_transcript)
+
+    assert isinstance(result, ValidationNotReady)
+    assert "not ready" in result.detail.lower()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_prepare_multitrack_config():
+    """Test preparing multitrack processing config."""
+    from reflector.db.recordings import Recording
+    from reflector.services.transcript_process import (
+        MultitrackProcessingConfig,
+        ValidationOk,
+        prepare_transcript_processing,
+    )
+
+    validation = ValidationOk(
+        recording_id="test-recording-id",
+        transcript_id="test-transcript-id",
+    )
+
+    mock_recording = Recording(
+        id="test-recording-id",
+        bucket_name="test-bucket",
+        object_key="recordings/test",
+        recorded_at="2024-01-01T00:00:00Z",
+        track_keys=["track1.webm", "track2.webm"],
+    )
+
+    with patch(
+        "reflector.services.transcript_process.recordings_controller"
+    ) as mock_rc:
+        mock_rc.get_by_id = AsyncMock(return_value=mock_recording)
+
+        result = await prepare_transcript_processing(validation)
+
+    assert isinstance(result, MultitrackProcessingConfig)
+    assert result.bucket_name == "test-bucket"
+    assert result.track_keys == ["track1.webm", "track2.webm"]
+    assert result.transcript_id == "test-transcript-id"
+    assert result.room_id is None  # ValidationOk didn't specify room_id
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_prepare_file_config():
+    """Test preparing file processing config (no track keys)."""
+    from reflector.db.recordings import Recording
+    from reflector.services.transcript_process import (
+        FileProcessingConfig,
+        ValidationOk,
+        prepare_transcript_processing,
+    )
+
+    validation = ValidationOk(
+        recording_id="test-recording-id",
+        transcript_id="test-transcript-id",
+    )
+
+    mock_recording = Recording(
+        id="test-recording-id",
+        bucket_name="test-bucket",
+        object_key="recordings/test.mp4",
+        recorded_at="2024-01-01T00:00:00Z",
+        track_keys=None,  # No track keys = file pipeline
+    )
+
+    with patch(
+        "reflector.services.transcript_process.recordings_controller"
+    ) as mock_rc:
+        mock_rc.get_by_id = AsyncMock(return_value=mock_recording)
+
+        result = await prepare_transcript_processing(validation)
+
+    assert isinstance(result, FileProcessingConfig)
+    assert result.transcript_id == "test-transcript-id"

server/tests/test_llm_retry.py

@@ -0,0 +1,488 @@
+"""Tests for LLM parse error recovery using llama-index Workflow"""
+
+from time import monotonic
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from pydantic import BaseModel, Field
+from workflows.errors import WorkflowRuntimeError, WorkflowTimeoutError
+
+from reflector.llm import LLM, LLMParseError, StructuredOutputWorkflow
+from reflector.utils.retry import RetryException
+
+
+class TestResponse(BaseModel):
+    """Test response model for structured output"""
+
+    title: str = Field(description="A title")
+    summary: str = Field(description="A summary")
+    confidence: float = Field(description="Confidence score", ge=0, le=1)
+
+
+def make_completion_response(text: str):
+    """Create a mock CompletionResponse with .text attribute"""
+    response = MagicMock()
+    response.text = text
+    return response
+
+
+class TestLLMParseErrorRecovery:
+    """Test parse error recovery with Workflow feedback loop"""
+
+    @pytest.mark.asyncio
+    async def test_parse_error_recovery_with_feedback(self, test_settings):
+        """Test that parse errors trigger retry with error feedback"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            # TreeSummarize returns plain text analysis (step 1)
+            mock_summarizer.aget_response = AsyncMock(
+                return_value="The analysis shows a test with summary and high confidence."
+            )
+
+            call_count = {"count": 0}
+
+            async def acomplete_handler(prompt, *args, **kwargs):
+                call_count["count"] += 1
+                if call_count["count"] == 1:
+                    # First JSON formatting call returns invalid JSON
+                    return make_completion_response('{"title": "Test"}')
+                else:
+                    # Second call should have error feedback in prompt
+                    assert "Your previous response could not be parsed:" in prompt
+                    assert '{"title": "Test"}' in prompt
+                    assert "Error:" in prompt
+                    assert "Please try again" in prompt
+                    return make_completion_response(
+                        '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                    )
+
+            mock_settings.llm.acomplete = AsyncMock(side_effect=acomplete_handler)
+
+            result = await llm.get_structured_response(
+                prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+            )
+
+            assert result.title == "Test"
+            assert result.summary == "Summary"
+            assert result.confidence == 0.95
+            # TreeSummarize called once, Settings.llm.acomplete called twice
+            assert mock_summarizer.aget_response.call_count == 1
+            assert call_count["count"] == 2
+
+    @pytest.mark.asyncio
+    async def test_max_parse_retry_attempts(self, test_settings):
+        """Test that parse error retry stops after max attempts"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            # Always return invalid JSON from acomplete
+            mock_settings.llm.acomplete = AsyncMock(
+                return_value=make_completion_response(
+                    '{"invalid": "missing required fields"}'
+                )
+            )
+
+            with pytest.raises(LLMParseError, match="Failed to parse"):
+                await llm.get_structured_response(
+                    prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+                )
+
+            expected_attempts = test_settings.LLM_PARSE_MAX_RETRIES + 1
+            # TreeSummarize called once, acomplete called max_retries times
+            assert mock_summarizer.aget_response.call_count == 1
+            assert mock_settings.llm.acomplete.call_count == expected_attempts
+
+    @pytest.mark.asyncio
+    async def test_raw_response_logging_on_parse_error(self, test_settings, caplog):
+        """Test that raw response is logged when parse error occurs"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+            caplog.at_level("ERROR"),
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            call_count = {"count": 0}
+
+            async def acomplete_handler(*args, **kwargs):
+                call_count["count"] += 1
+                if call_count["count"] == 1:
+                    return make_completion_response('{"title": "Test"}')  # Invalid
+                return make_completion_response(
+                    '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                )
+
+            mock_settings.llm.acomplete = AsyncMock(side_effect=acomplete_handler)
+
+            result = await llm.get_structured_response(
+                prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+            )
+
+            assert result.title == "Test"
+
+            error_logs = [r for r in caplog.records if r.levelname == "ERROR"]
+            raw_response_logged = any("Raw response:" in r.message for r in error_logs)
+            assert raw_response_logged, "Raw response should be logged on parse error"
+
+    @pytest.mark.asyncio
+    async def test_multiple_validation_errors_in_feedback(self, test_settings):
+        """Test that validation errors are included in feedback"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            call_count = {"count": 0}
+
+            async def acomplete_handler(prompt, *args, **kwargs):
+                call_count["count"] += 1
+                if call_count["count"] == 1:
+                    # Missing title and summary
+                    return make_completion_response('{"confidence": 0.5}')
+                else:
+                    # Should have schema validation errors in prompt
+                    assert (
+                        "Schema validation errors" in prompt
+                        or "error" in prompt.lower()
+                    )
+                    return make_completion_response(
+                        '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                    )
+
+            mock_settings.llm.acomplete = AsyncMock(side_effect=acomplete_handler)
+
+            result = await llm.get_structured_response(
+                prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+            )
+
+            assert result.title == "Test"
+            assert call_count["count"] == 2
+
+    @pytest.mark.asyncio
+    async def test_success_on_first_attempt(self, test_settings):
+        """Test that no retry happens when first attempt succeeds"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            mock_settings.llm.acomplete = AsyncMock(
+                return_value=make_completion_response(
+                    '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                )
+            )
+
+            result = await llm.get_structured_response(
+                prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+            )
+
+            assert result.title == "Test"
+            assert result.summary == "Summary"
+            assert result.confidence == 0.95
+            assert mock_summarizer.aget_response.call_count == 1
+            assert mock_settings.llm.acomplete.call_count == 1
+
+
+class TestStructuredOutputWorkflow:
+    """Direct tests for the StructuredOutputWorkflow"""
+
+    @pytest.mark.asyncio
+    async def test_workflow_retries_on_validation_error(self):
+        """Test workflow retries when validation fails"""
+        workflow = StructuredOutputWorkflow(
+            output_cls=TestResponse,
+            max_retries=3,
+            timeout=30,
+        )
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            call_count = {"count": 0}
+
+            async def acomplete_handler(*args, **kwargs):
+                call_count["count"] += 1
+                if call_count["count"] < 2:
+                    return make_completion_response('{"title": "Only title"}')
+                return make_completion_response(
+                    '{"title": "Test", "summary": "Summary", "confidence": 0.9}'
+                )
+
+            mock_settings.llm.acomplete = AsyncMock(side_effect=acomplete_handler)
+
+            result = await workflow.run(
+                prompt="Extract data",
+                texts=["Some text"],
+                tone_name=None,
+            )
+
+            assert "success" in result
+            assert result["success"].title == "Test"
+            assert call_count["count"] == 2
+
+    @pytest.mark.asyncio
+    async def test_workflow_returns_error_after_max_retries(self):
+        """Test workflow returns error after exhausting retries"""
+        workflow = StructuredOutputWorkflow(
+            output_cls=TestResponse,
+            max_retries=2,
+            timeout=30,
+        )
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            # Always return invalid JSON
+            mock_settings.llm.acomplete = AsyncMock(
+                return_value=make_completion_response('{"invalid": true}')
+            )
+
+            result = await workflow.run(
+                prompt="Extract data",
+                texts=["Some text"],
+                tone_name=None,
+            )
+
+            assert "error" in result
+            # TreeSummarize called once, acomplete called max_retries times
+            assert mock_summarizer.aget_response.call_count == 1
+            assert mock_settings.llm.acomplete.call_count == 2
+
+
+class TestNetworkErrorRetries:
+    """Test that network error retries are handled by OpenAILike, not Workflow"""
+
+    @pytest.mark.asyncio
+    async def test_network_error_propagates_after_openai_retries(self, test_settings):
+        """Test that network errors are retried by OpenAILike and then propagate.
+
+        Network retries are handled by OpenAILike (max_retries=3), not by our
+        StructuredOutputWorkflow. This test verifies that network errors propagate
+        up after OpenAILike exhausts its retries.
+        """
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            # Simulate network error from acomplete (after OpenAILike retries exhausted)
+            network_error = ConnectionError("Connection refused")
+            mock_settings.llm.acomplete = AsyncMock(side_effect=network_error)
+
+            # Network error wrapped in WorkflowRuntimeError
+            with pytest.raises(WorkflowRuntimeError, match="Connection refused"):
+                await llm.get_structured_response(
+                    prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+                )
+
+            # acomplete called only once - network error propagates, not retried by Workflow
+            assert mock_settings.llm.acomplete.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_network_error_not_retried_by_workflow(self, test_settings):
+        """Test that Workflow does NOT retry network errors (OpenAILike handles those).
+
+        This verifies the separation of concerns:
+        - StructuredOutputWorkflow: retries parse/validation errors
+        - OpenAILike: retries network errors (internally, max_retries=3)
+        """
+        workflow = StructuredOutputWorkflow(
+            output_cls=TestResponse,
+            max_retries=3,
+            timeout=30,
+        )
+
+        with (
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+
+            # Network error should propagate immediately, not trigger Workflow retry
+            mock_settings.llm.acomplete = AsyncMock(
+                side_effect=TimeoutError("Request timed out")
+            )
+
+            # Network error wrapped in WorkflowRuntimeError
+            with pytest.raises(WorkflowRuntimeError, match="Request timed out"):
+                await workflow.run(
+                    prompt="Extract data",
+                    texts=["Some text"],
+                    tone_name=None,
+                )
+
+            # Only called once - Workflow doesn't retry network errors
+            assert mock_settings.llm.acomplete.call_count == 1
+
+
+class TestWorkflowTimeoutRetry:
+    """Test timeout retry mechanism in get_structured_response"""
+
+    @pytest.mark.asyncio
+    async def test_timeout_retry_succeeds_on_retry(self, test_settings):
+        """Test that WorkflowTimeoutError triggers retry and succeeds"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        call_count = {"count": 0}
+
+        async def workflow_run_side_effect(*args, **kwargs):
+            call_count["count"] += 1
+            if call_count["count"] == 1:
+                raise WorkflowTimeoutError("Operation timed out after 120 seconds")
+            return {
+                "success": TestResponse(
+                    title="Test", summary="Summary", confidence=0.95
+                )
+            }
+
+        with (
+            patch("reflector.llm.StructuredOutputWorkflow") as mock_workflow_class,
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_workflow = MagicMock()
+            mock_workflow.run = AsyncMock(side_effect=workflow_run_side_effect)
+            mock_workflow_class.return_value = mock_workflow
+
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+            mock_settings.llm.acomplete = AsyncMock(
+                return_value=make_completion_response(
+                    '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                )
+            )
+
+            result = await llm.get_structured_response(
+                prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+            )
+
+            assert result.title == "Test"
+            assert result.summary == "Summary"
+            assert call_count["count"] == 2
+
+    @pytest.mark.asyncio
+    async def test_timeout_retry_exhausts_after_max_attempts(self, test_settings):
+        """Test that timeout retry stops after max attempts"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        call_count = {"count": 0}
+
+        async def workflow_run_side_effect(*args, **kwargs):
+            call_count["count"] += 1
+            raise WorkflowTimeoutError("Operation timed out after 120 seconds")
+
+        with (
+            patch("reflector.llm.StructuredOutputWorkflow") as mock_workflow_class,
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_workflow = MagicMock()
+            mock_workflow.run = AsyncMock(side_effect=workflow_run_side_effect)
+            mock_workflow_class.return_value = mock_workflow
+
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+            mock_settings.llm.acomplete = AsyncMock(
+                return_value=make_completion_response(
+                    '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                )
+            )
+
+            with pytest.raises(RetryException, match="Retry attempts exceeded"):
+                await llm.get_structured_response(
+                    prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+                )
+
+            assert call_count["count"] == 3
+
+    @pytest.mark.asyncio
+    async def test_timeout_retry_with_backoff(self, test_settings):
+        """Test that exponential backoff is applied between retries"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        call_times = []
+
+        async def workflow_run_side_effect(*args, **kwargs):
+            call_times.append(monotonic())
+            if len(call_times) < 3:
+                raise WorkflowTimeoutError("Operation timed out after 120 seconds")
+            return {
+                "success": TestResponse(
+                    title="Test", summary="Summary", confidence=0.95
+                )
+            }
+
+        with (
+            patch("reflector.llm.StructuredOutputWorkflow") as mock_workflow_class,
+            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            patch("reflector.llm.Settings") as mock_settings,
+        ):
+            mock_workflow = MagicMock()
+            mock_workflow.run = AsyncMock(side_effect=workflow_run_side_effect)
+            mock_workflow_class.return_value = mock_workflow
+
+            mock_summarizer = MagicMock()
+            mock_summarize.return_value = mock_summarizer
+            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+            mock_settings.llm.acomplete = AsyncMock(
+                return_value=make_completion_response(
+                    '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                )
+            )
+
+            result = await llm.get_structured_response(
+                prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
+            )
+
+            assert result.title == "Test"
+            if len(call_times) >= 2:
+                time_between_calls = call_times[1] - call_times[0]
+                assert (
+                    time_between_calls >= 1.5
+                ), f"Expected ~2s backoff, got {time_between_calls}s"

server/tests/test_pipeline_main_file.py

@@ -266,7 +266,11 @@ async def mock_summary_processor():
         # When flush is called, simulate summary generation by calling the callbacks
         async def flush_with_callback():
             mock_summary.flush_called = True
-            from reflector.processors.types import FinalLongSummary, FinalShortSummary
+            from reflector.processors.types import (
+                ActionItems,
+                FinalLongSummary,
+                FinalShortSummary,
+            )
 
             if hasattr(mock_summary, "_callback"):
                 await mock_summary._callback(
@@ -276,12 +280,19 @@ async def mock_summary_processor():
                 await mock_summary._on_short_summary(
                     FinalShortSummary(short_summary="Test short summary", duration=10.0)
                 )
+            if hasattr(mock_summary, "_on_action_items"):
+                await mock_summary._on_action_items(
+                    ActionItems(action_items={"test": "action item"})
+                )
 
         mock_summary.flush = flush_with_callback
 
-        def init_with_callback(transcript=None, callback=None, on_short_summary=None):
+        def init_with_callback(
+            transcript=None, callback=None, on_short_summary=None, on_action_items=None
+        ):
             mock_summary._callback = callback
             mock_summary._on_short_summary = on_short_summary
+            mock_summary._on_action_items = on_action_items
             return mock_summary
 
         mock_summary_class.side_effect = init_with_callback

server/tests/test_transcripts.py

@@ -1,5 +1,8 @@
 import pytest
 
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import transcripts_controller
+
 
 @pytest.mark.asyncio
 async def test_transcript_create(client):
@@ -182,3 +185,51 @@ async def test_transcript_mark_reviewed(authenticated_client, client):
     response = await client.get(f"/transcripts/{tid}")
     assert response.status_code == 200
     assert response.json()["reviewed"] is True
+
+
+@pytest.mark.asyncio
+async def test_transcript_get_returns_room_name(authenticated_client, client):
+    """Test that getting a transcript returns its room_name when linked to a room."""
+    # Create a room
+    room = await rooms_controller.add(
+        name="test-room-for-transcript",
+        user_id="test-user",
+        zulip_auto_post=False,
+        zulip_stream="",
+        zulip_topic="",
+        is_locked=False,
+        room_mode="normal",
+        recording_type="cloud",
+        recording_trigger="automatic-2nd-participant",
+        is_shared=False,
+        webhook_url="",
+        webhook_secret="",
+    )
+
+    # Create a transcript linked to the room
+    transcript = await transcripts_controller.add(
+        name="transcript-with-room",
+        source_kind="file",
+        room_id=room.id,
+    )
+
+    # Get the transcript and verify room_name is returned
+    response = await client.get(f"/transcripts/{transcript.id}")
+    assert response.status_code == 200
+    assert response.json()["room_id"] == room.id
+    assert response.json()["room_name"] == "test-room-for-transcript"
+
+
+@pytest.mark.asyncio
+async def test_transcript_get_returns_null_room_name_when_no_room(
+    authenticated_client, client
+):
+    """Test that room_name is null when transcript has no room."""
+    response = await client.post("/transcripts", json={"name": "no-room-transcript"})
+    assert response.status_code == 200
+    tid = response.json()["id"]
+
+    response = await client.get(f"/transcripts/{tid}")
+    assert response.status_code == 200
+    assert response.json()["room_id"] is None
+    assert response.json()["room_name"] is None

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

@@ -15,9 +15,12 @@ import {
   createListCollection,
   useDisclosure,
   Tabs,
+  Popover,
+  Text,
+  HStack,
 } from "@chakra-ui/react";
 import { useEffect, useMemo, useState } from "react";
-import { LuEye, LuEyeOff } from "react-icons/lu";
+import { LuEye, LuEyeOff, LuInfo } from "react-icons/lu";
 import useRoomList from "./useRoomList";
 import type { components } from "../../reflector-api";
 import {
@@ -67,6 +70,11 @@ const recordingTypeOptions: SelectOption[] = [
   { label: "Cloud", value: "cloud" },
 ];
 
+const platformOptions: SelectOption[] = [
+  { label: "Whereby", value: "whereby" },
+  { label: "Daily", value: "daily" },
+];
+
 const roomInitialState = {
   name: "",
   zulipAutoPost: false,
@@ -82,6 +90,7 @@ const roomInitialState = {
   icsUrl: "",
   icsEnabled: false,
   icsFetchInterval: 5,
+  platform: "whereby",
 };
 
 export default function RoomsList() {
@@ -99,6 +108,11 @@ export default function RoomsList() {
   const recordingTypeCollection = createListCollection({
     items: recordingTypeOptions,
   });
+
+  const platformCollection = createListCollection({
+    items: platformOptions,
+  });
+
   const [roomInput, setRoomInput] = useState<null | typeof roomInitialState>(
     null,
   );
@@ -143,15 +157,24 @@ export default function RoomsList() {
             zulipStream: detailedEditedRoom.zulip_stream,
             zulipTopic: detailedEditedRoom.zulip_topic,
             isLocked: detailedEditedRoom.is_locked,
-            roomMode: detailedEditedRoom.room_mode,
+            roomMode:
+              detailedEditedRoom.platform === "daily"
+                ? "group"
+                : detailedEditedRoom.room_mode,
             recordingType: detailedEditedRoom.recording_type,
-            recordingTrigger: detailedEditedRoom.recording_trigger,
+            recordingTrigger:
+              detailedEditedRoom.platform === "daily"
+                ? detailedEditedRoom.recording_type === "cloud"
+                  ? "automatic-2nd-participant"
+                  : "none"
+                : detailedEditedRoom.recording_trigger,
             isShared: detailedEditedRoom.is_shared,
             webhookUrl: detailedEditedRoom.webhook_url || "",
             webhookSecret: detailedEditedRoom.webhook_secret || "",
             icsUrl: detailedEditedRoom.ics_url || "",
             icsEnabled: detailedEditedRoom.ics_enabled || false,
             icsFetchInterval: detailedEditedRoom.ics_fetch_interval || 5,
+            platform: detailedEditedRoom.platform,
           }
         : null,
     [detailedEditedRoom],
@@ -277,21 +300,32 @@ export default function RoomsList() {
         return;
       }
 
+      const platform: "whereby" | "daily" | null =
+        room.platform === "whereby" || room.platform === "daily"
+          ? room.platform
+          : null;
+
       const roomData = {
         name: room.name,
         zulip_auto_post: room.zulipAutoPost,
         zulip_stream: room.zulipStream,
         zulip_topic: room.zulipTopic,
         is_locked: room.isLocked,
-        room_mode: room.roomMode,
+        room_mode: platform === "daily" ? "group" : room.roomMode,
         recording_type: room.recordingType,
-        recording_trigger: room.recordingTrigger,
+        recording_trigger:
+          platform === "daily"
+            ? room.recordingType === "cloud"
+              ? "automatic-2nd-participant"
+              : "none"
+            : room.recordingTrigger,
         is_shared: room.isShared,
         webhook_url: room.webhookUrl,
         webhook_secret: room.webhookSecret,
         ics_url: room.icsUrl,
         ics_enabled: room.icsEnabled,
         ics_fetch_interval: room.icsFetchInterval,
+        platform,
       };
 
       if (isEditing) {
@@ -339,15 +373,21 @@ export default function RoomsList() {
       zulipStream: roomData.zulip_stream,
       zulipTopic: roomData.zulip_topic,
       isLocked: roomData.is_locked,
-      roomMode: roomData.room_mode,
+      roomMode: roomData.platform === "daily" ? "group" : roomData.room_mode, // Daily always uses 2-200
       recordingType: roomData.recording_type,
-      recordingTrigger: roomData.recording_trigger,
+      recordingTrigger:
+        roomData.platform === "daily"
+          ? roomData.recording_type === "cloud"
+            ? "automatic-2nd-participant"
+            : "none"
+          : roomData.recording_trigger,
       isShared: roomData.is_shared,
       webhookUrl: roomData.webhook_url || "",
       webhookSecret: roomData.webhook_secret || "",
       icsUrl: roomData.ics_url || "",
       icsEnabled: roomData.ics_enabled || false,
       icsFetchInterval: roomData.ics_fetch_interval || 5,
+      platform: roomData.platform,
     });
     setEditRoomId(roomId);
     setIsEditing(true);
@@ -482,6 +522,52 @@ export default function RoomsList() {
                       )}
                     </Field.Root>
 
+                    <Field.Root mt={4}>
+                      <Field.Label>Platform</Field.Label>
+                      <Select.Root
+                        value={[room.platform]}
+                        onValueChange={(e) => {
+                          const newPlatform = e.value[0] as "whereby" | "daily";
+                          const updates: Partial<typeof room> = {
+                            platform: newPlatform,
+                          };
+                          if (newPlatform === "daily") {
+                            updates.roomMode = "group";
+                            updates.recordingTrigger =
+                              room.recordingType === "cloud"
+                                ? "automatic-2nd-participant"
+                                : "none";
+                          } else {
+                            if (room.recordingType !== "cloud") {
+                              updates.recordingTrigger = "none";
+                            }
+                          }
+                          setRoomInput({ ...room, ...updates });
+                        }}
+                        collection={platformCollection}
+                      >
+                        <Select.HiddenSelect />
+                        <Select.Control>
+                          <Select.Trigger>
+                            <Select.ValueText placeholder="Select platform" />
+                          </Select.Trigger>
+                          <Select.IndicatorGroup>
+                            <Select.Indicator />
+                          </Select.IndicatorGroup>
+                        </Select.Control>
+                        <Select.Positioner>
+                          <Select.Content>
+                            {platformOptions.map((option) => (
+                              <Select.Item key={option.value} item={option}>
+                                {option.label}
+                                <Select.ItemIndicator />
+                              </Select.Item>
+                            ))}
+                          </Select.Content>
+                        </Select.Positioner>
+                      </Select.Root>
+                    </Field.Root>
+
                     <Field.Root mt={4}>
                       <Checkbox.Root
                         name="isLocked"
@@ -504,50 +590,95 @@ export default function RoomsList() {
                         <Checkbox.Label>Locked room</Checkbox.Label>
                       </Checkbox.Root>
                     </Field.Root>
+                    {room.platform !== "daily" && (
+                      <Field.Root mt={4}>
+                        <Field.Label>Room size</Field.Label>
+                        <Select.Root
+                          value={[room.roomMode]}
+                          onValueChange={(e) =>
+                            setRoomInput({ ...room, roomMode: e.value[0] })
+                          }
+                          collection={roomModeCollection}
+                        >
+                          <Select.HiddenSelect />
+                          <Select.Control>
+                            <Select.Trigger>
+                              <Select.ValueText placeholder="Select room size" />
+                            </Select.Trigger>
+                            <Select.IndicatorGroup>
+                              <Select.Indicator />
+                            </Select.IndicatorGroup>
+                          </Select.Control>
+                          <Select.Positioner>
+                            <Select.Content>
+                              {roomModeOptions.map((option) => (
+                                <Select.Item key={option.value} item={option}>
+                                  {option.label}
+                                  <Select.ItemIndicator />
+                                </Select.Item>
+                              ))}
+                            </Select.Content>
+                          </Select.Positioner>
+                        </Select.Root>
+                      </Field.Root>
+                    )}
                     <Field.Root mt={4}>
-                      <Field.Label>Room size</Field.Label>
+                      <HStack gap={2} alignItems="center">
-                      <Select.Root
+                        <Field.Label>Recording type</Field.Label>
-                        value={[room.roomMode]}
+                        <Popover.Root>
-                        onValueChange={(e) =>
+                          <Popover.Trigger asChild>
-                          setRoomInput({ ...room, roomMode: e.value[0] })
+                            <IconButton
-                        }
+                              aria-label="Recording type help"
-                        collection={roomModeCollection}
+                              variant="ghost"
-                      >
+                              size="xs"
-                        <Select.HiddenSelect />
+                              colorPalette="gray"
-                        <Select.Control>
+                            >
-                          <Select.Trigger>
+                              <LuInfo />
-                            <Select.ValueText placeholder="Select room size" />
+                            </IconButton>
-                          </Select.Trigger>
+                          </Popover.Trigger>
-                          <Select.IndicatorGroup>
+                          <Popover.Positioner>
-                            <Select.Indicator />
+                            <Popover.Content>
-                          </Select.IndicatorGroup>
+                              <Popover.Arrow />
-                        </Select.Control>
+                              <Popover.Body>
-                        <Select.Positioner>
+                                <Text fontSize="sm" lineHeight="1.6">
-                          <Select.Content>
+                                  <strong>None:</strong> No recording will be
-                            {roomModeOptions.map((option) => (
+                                  created.
-                              <Select.Item key={option.value} item={option}>
+                                  <br />
-                                {option.label}
+                                  <br />
-                                <Select.ItemIndicator />
+                                  <strong>Local:</strong> Recording happens on
-                              </Select.Item>
+                                  each participant's device. Files are saved
-                            ))}
+                                  locally.
-                          </Select.Content>
+                                  <br />
-                        </Select.Positioner>
+                                  <br />
-                      </Select.Root>
+                                  <strong>Cloud:</strong> Recording happens on
-                    </Field.Root>
+                                  the platform's servers and is available after
-                    <Field.Root mt={4}>
+                                  the meeting ends.
-                      <Field.Label>Recording type</Field.Label>
+                                </Text>
+                              </Popover.Body>
+                            </Popover.Content>
+                          </Popover.Positioner>
+                        </Popover.Root>
+                      </HStack>
                       <Select.Root
                         value={[room.recordingType]}
-                        onValueChange={(e) =>
+                        onValueChange={(e) => {
-                          setRoomInput({
+                          const newRecordingType = e.value[0];
-                            ...room,
+                          const updates: Partial<typeof room> = {
-                            recordingType: e.value[0],
+                            recordingType: newRecordingType,
-                            recordingTrigger:
+                          };
-                              e.value[0] !== "cloud"
+                          if (room.platform === "daily") {
+                            updates.recordingTrigger =
+                              newRecordingType === "cloud"
+                                ? "automatic-2nd-participant"
+                                : "none";
+                          } else {
+                            updates.recordingTrigger =
+                              newRecordingType !== "cloud"
                                 ? "none"
-                                : room.recordingTrigger,
+                                : room.recordingTrigger;
-                          })
+                          }
-                        }
+                          setRoomInput({ ...room, ...updates });
+                        }}
                         collection={recordingTypeCollection}
                       >
                         <Select.HiddenSelect />
@@ -571,40 +702,77 @@ export default function RoomsList() {
                         </Select.Positioner>
                       </Select.Root>
                     </Field.Root>
-                    <Field.Root mt={4}>
+                    {room.recordingType === "cloud" &&
-                      <Field.Label>Cloud recording start trigger</Field.Label>
+                      room.platform !== "daily" && (
-                      <Select.Root
+                        <Field.Root mt={4}>
-                        value={[room.recordingTrigger]}
+                          <HStack gap={2} alignItems="center">
-                        onValueChange={(e) =>
+                            <Field.Label>Recording start trigger</Field.Label>
-                          setRoomInput({
+                            <Popover.Root>
-                            ...room,
+                              <Popover.Trigger asChild>
-                            recordingTrigger: e.value[0],
+                                <IconButton
-                          })
+                                  aria-label="Recording start trigger help"
-                        }
+                                  variant="ghost"
-                        collection={recordingTriggerCollection}
+                                  size="xs"
-                        disabled={room.recordingType !== "cloud"}
+                                  colorPalette="gray"
-                      >
+                                >
-                        <Select.HiddenSelect />
+                                  <LuInfo />
-                        <Select.Control>
+                                </IconButton>
-                          <Select.Trigger>
+                              </Popover.Trigger>
-                            <Select.ValueText placeholder="Select trigger" />
+                              <Popover.Positioner>
-                          </Select.Trigger>
+                                <Popover.Content>
-                          <Select.IndicatorGroup>
+                                  <Popover.Arrow />
-                            <Select.Indicator />
+                                  <Popover.Body>
-                          </Select.IndicatorGroup>
+                                    <Text fontSize="sm" lineHeight="1.6">
-                        </Select.Control>
+                                      <strong>None:</strong> Recording must be
-                        <Select.Positioner>
+                                      started manually by a participant.
-                          <Select.Content>
+                                      <br />
-                            {recordingTriggerOptions.map((option) => (
+                                      <br />
-                              <Select.Item key={option.value} item={option}>
+                                      <strong>Prompt:</strong> Participants will
-                                {option.label}
+                                      be prompted to start recording when they
-                                <Select.ItemIndicator />
+                                      join.
-                              </Select.Item>
+                                      <br />
-                            ))}
+                                      <br />
-                          </Select.Content>
+                                      <strong>Automatic:</strong> Recording
-                        </Select.Positioner>
+                                      starts automatically when a second
-                      </Select.Root>
+                                      participant joins.
-                    </Field.Root>
+                                    </Text>
+                                  </Popover.Body>
+                                </Popover.Content>
+                              </Popover.Positioner>
+                            </Popover.Root>
+                          </HStack>
+                          <Select.Root
+                            value={[room.recordingTrigger]}
+                            onValueChange={(e) =>
+                              setRoomInput({
+                                ...room,
+                                recordingTrigger: e.value[0],
+                              })
+                            }
+                            collection={recordingTriggerCollection}
+                          >
+                            <Select.HiddenSelect />
+                            <Select.Control>
+                              <Select.Trigger>
+                                <Select.ValueText placeholder="Select trigger" />
+                              </Select.Trigger>
+                              <Select.IndicatorGroup>
+                                <Select.Indicator />
+                              </Select.IndicatorGroup>
+                            </Select.Control>
+                            <Select.Positioner>
+                              <Select.Content>
+                                {recordingTriggerOptions.map((option) => (
+                                  <Select.Item key={option.value} item={option}>
+                                    {option.label}
+                                    <Select.ItemIndicator />
+                                  </Select.Item>
+                                ))}
+                              </Select.Content>
+                            </Select.Positioner>
+                          </Select.Root>
+                        </Field.Root>
+                      )}
 
                     <Field.Root mt={4}>
                       <Checkbox.Root

www/app/(auth)/userInfo.tsx

@@ -2,20 +2,29 @@
 
 import { Spinner, Link } from "@chakra-ui/react";
 import { useAuth } from "../lib/AuthProvider";
+import { usePathname } from "next/navigation";
+import { getLogoutRedirectUrl } from "../lib/auth";
 
 export default function UserInfo() {
   const auth = useAuth();
+  const pathname = usePathname();
   const status = auth.status;
   const isLoading = status === "loading";
   const isAuthenticated = status === "authenticated";
   const isRefreshing = status === "refreshing";
+
+  const callbackUrl = getLogoutRedirectUrl(pathname);
+
   return isLoading ? (
     <Spinner size="xs" className="mx-3" />
   ) : !isAuthenticated && !isRefreshing ? (
     <Link
-      href="/"
+      href="#"
       className="font-light px-2"
-      onClick={() => auth.signIn("authentik")}
+      onClick={(e) => {
+        e.preventDefault();
+        auth.signIn("authentik");
+      }}
     >
       Log in
     </Link>
@@ -23,7 +32,7 @@ export default function UserInfo() {
     <Link
       href="#"
       className="font-light px-2"
-      onClick={() => auth.signOut({ callbackUrl: "/" })}
+      onClick={() => auth.signOut({ callbackUrl })}
     >
       Log out
     </Link>

www/app/[roomName]/components/DailyRoom.tsx

@@ -52,7 +52,7 @@ export default function DailyRoom({ meeting }: DailyRoomProps) {
     join();
   }, [meeting?.id, roomName, authLastUserId]);
 
-  const roomUrl = joinedMeeting?.host_room_url || joinedMeeting?.room_url;
+  const roomUrl = joinedMeeting?.room_url;
 
   const handleLeave = useCallback(() => {
     router.push("/browse");
@@ -89,20 +89,35 @@ export default function DailyRoom({ meeting }: DailyRoomProps) {
 
         frame.on("left-meeting", handleLeave);
 
-        // TODO this method must not ignore no-recording option
-        // TODO this method is here to make dev environments work in some cases (not examined which)
         frame.on("joined-meeting", async () => {
           try {
-            assertExists(
+            const frameInstance = assertExists(
               frame,
               "frame object got lost somewhere after frame.on was called",
-            ).startRecording({ type: "raw-tracks" });
+            );
+
+            if (meeting.recording_type === "cloud") {
+              console.log("Starting cloud recording");
+              await frameInstance.startRecording({ type: "raw-tracks" });
+            }
           } catch (error) {
             console.error("Failed to start recording:", error);
           }
         });
 
-        await frame.join({ url: roomUrl });
+        await frame.join({
+          url: roomUrl,
+          sendSettings: {
+            video: {
+              // Optimize bandwidth for camera video
+              // allowAdaptiveLayers automatically adjusts quality based on network conditions
+              allowAdaptiveLayers: true,
+              // Use bandwidth-optimized preset as fallback for browsers without adaptive support
+              maxQuality: "medium",
+            },
+            // Note: screenVideo intentionally not configured to preserve full quality for screen shares
+          },
+        });
       } catch (error) {
         console.error("Error creating Daily frame:", error);
       }

www/app/lib/auth.ts

@@ -18,3 +18,8 @@ export const LOGIN_REQUIRED_PAGES = [
 export const PROTECTED_PAGES = new RegExp(
   LOGIN_REQUIRED_PAGES.map((page) => `^${page}$`).join("|"),
 );
+
+export function getLogoutRedirectUrl(pathname: string): string {
+  const transcriptPagePattern = /^\/transcripts\/[^/]+$/;
+  return transcriptPagePattern.test(pathname) ? pathname : "/";
+}

www/package.json

@@ -31,7 +31,7 @@
     "ioredis": "^5.7.0",
     "jest-worker": "^29.6.2",
     "lucide-react": "^0.525.0",
-    "next": "^15.5.3",
+    "next": "^15.5.9",
     "next-auth": "^4.24.7",
     "next-themes": "^0.4.6",
     "nuqs": "^2.4.3",