chore(main): release 0.38.1 (#904)

Bumps the npm_and_yarn group with 1 update in the /docs directory: [dompurify](https://github.com/cure53/DOMPurify).


Updates `dompurify` from 3.3.1 to 3.3.2
- [Release notes](https://github.com/cure53/DOMPurify/releases)
- [Commits](https://github.com/cure53/DOMPurify/compare/3.3.1...3.3.2)

---
updated-dependencies:
- dependency-name: dompurify
  dependency-version: 3.3.2
  dependency-type: indirect
  dependency-group: npm_and_yarn
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/workflows/selfhost-script.yml

@@ -0,0 +1,36 @@
+# Validates the self-hosted setup script: runs with --cpu and --garage,
+# brings up services, runs health checks, then tears down.
+name: Selfhost script (CPU + Garage)
+
+on:
+  workflow_dispatch: {}
+  push:
+    branches:
+      - main
+  pull_request: {}
+
+jobs:
+  selfhost-cpu-garage:
+    runs-on: ubuntu-latest
+    timeout-minutes: 25
+    concurrency:
+      group: selfhost-${{ github.ref }}
+      cancel-in-progress: true
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Run setup-selfhosted.sh (CPU + Garage)
+        run: |
+          ./scripts/setup-selfhosted.sh --cpu --garage
+
+      - name: Quick health checks
+        run: |
+          curl -sf http://localhost:1250/health && echo " Server OK"
+          curl -sf http://localhost:3000 > /dev/null && echo " Frontend OK"
+          curl -sf http://localhost:3903/metrics > /dev/null && echo " Garage admin OK"
+
+      - name: Teardown
+        if: always()
+        run: |
+          docker compose -f docker-compose.selfhosted.yml --profile cpu --profile garage down -v --remove-orphans 2>/dev/null || true

.github/workflows/test_server.yml

@@ -34,7 +34,7 @@ jobs:
           uv run -m pytest -v tests
 
   docker-amd64:
-    runs-on: linux-amd64
+    runs-on: [linux-amd64]
     concurrency:
       group: docker-amd64-${{ github.ref }}
       cancel-in-progress: true
@@ -52,12 +52,14 @@ jobs:
           github-token: ${{ secrets.GHA_CACHE_TOKEN }}
 
   docker-arm64:
-    runs-on: linux-arm64
+    runs-on: [linux-arm64]
     concurrency:
       group: docker-arm64-${{ github.ref }}
       cancel-in-progress: true
     steps:
       - uses: actions/checkout@v4
+      - name: Wait for Docker daemon
+        run: while ! docker version; do sleep 1; done
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Build ARM64

.gitignore

@@ -3,6 +3,7 @@ server/.env
 server/.env.production
 .env
 Caddyfile
+.env.hatchet
 server/exportdanswer
 .vercel
 .env*.local
@@ -20,7 +21,6 @@ CLAUDE.local.md
 www/.env.development
 www/.env.production
 .playwright-mcp
-docs/pnpm-lock.yaml
 .secrets
 opencode.json
 

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
       - id: format
         name: run format
         language: system
-        entry: bash -c 'source "$HOME/.nvm/nvm.sh" && cd www && pnpm format'
+        entry: bash -c 'if [ -f "$HOME/.nvm/nvm.sh" ]; then source "$HOME/.nvm/nvm.sh"; fi; cd www && pnpm format'
         pass_filenames: false
         files: ^www/
 

CHANGELOG.md

@@ -1,5 +1,47 @@
 # Changelog
 
+## [0.38.1](https://github.com/GreyhavenHQ/reflector/compare/v0.38.0...v0.38.1) (2026-03-06)
+
+
+### Bug Fixes
+
+* pin hatchet sdk version ([#903](https://github.com/GreyhavenHQ/reflector/issues/903)) ([504ca74](https://github.com/GreyhavenHQ/reflector/commit/504ca74184211eda9020d0b38ba7bd2b55d09991))
+
+## [0.38.0](https://github.com/GreyhavenHQ/reflector/compare/v0.37.0...v0.38.0) (2026-03-06)
+
+
+### Features
+
+* 3-mode selfhosted refactoring (--gpu, --cpu, --hosted) + audio token auth fallback ([#896](https://github.com/GreyhavenHQ/reflector/issues/896)) ([a682846](https://github.com/GreyhavenHQ/reflector/commit/a6828466456407c808302e9eb8dc4b4f0614dd6f))
+
+
+### Bug Fixes
+
+* improve hatchet workflow reliability ([#900](https://github.com/GreyhavenHQ/reflector/issues/900)) ([c155f66](https://github.com/GreyhavenHQ/reflector/commit/c155f669825e8e2a6e929821a1ef0bd94237dc11))
+
+## [0.37.0](https://github.com/GreyhavenHQ/reflector/compare/v0.36.0...v0.37.0) (2026-03-03)
+
+
+### Features
+
+* enable daily co in selfhosted + only schedule tasks when necessary ([#883](https://github.com/GreyhavenHQ/reflector/issues/883)) ([045eae8](https://github.com/GreyhavenHQ/reflector/commit/045eae8ff2014a7b83061045e3c8cb25cce9d60a))
+
+
+### Bug Fixes
+
+* aws storage construction ([#895](https://github.com/GreyhavenHQ/reflector/issues/895)) ([f5ec2d2](https://github.com/GreyhavenHQ/reflector/commit/f5ec2d28cfa2de9b2b4aeec81966737b740689c2))
+* remaining dependabot security issues ([#890](https://github.com/GreyhavenHQ/reflector/issues/890)) ([0931095](https://github.com/GreyhavenHQ/reflector/commit/0931095f49e61216e651025ce92be460e6a9df9e))
+* test selfhosted script ([#892](https://github.com/GreyhavenHQ/reflector/issues/892)) ([4d915e2](https://github.com/GreyhavenHQ/reflector/commit/4d915e2a9fe9f05f31cbd0018d9c2580daf7854f))
+* upgrade to nextjs 16 ([#888](https://github.com/GreyhavenHQ/reflector/issues/888)) ([f6cc032](https://github.com/GreyhavenHQ/reflector/commit/f6cc03286baf3e3a115afd3b22ae993ad7a4b7e3))
+
+## [0.35.1](https://github.com/GreyhavenHQ/reflector/compare/v0.35.0...v0.35.1) (2026-02-25)
+
+
+### Bug Fixes
+
+* enable sentry on frontend ([#876](https://github.com/GreyhavenHQ/reflector/issues/876)) ([bc6bb63](https://github.com/GreyhavenHQ/reflector/commit/bc6bb63c32dc84be5d3b00388618d53f04f64e35))
+* switch structured output to tool-call with reflection retry ([#879](https://github.com/GreyhavenHQ/reflector/issues/879)) ([5d54758](https://github.com/GreyhavenHQ/reflector/commit/5d547586ef0f54514d1d65aacca8e57869013a82))
+
 ## [0.35.0](https://github.com/Monadical-SAS/reflector/compare/v0.34.0...v0.35.0) (2026-02-23)
 
 

CLAUDE.md

@@ -6,7 +6,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 Reflector is an AI-powered audio transcription and meeting analysis platform with real-time processing capabilities. The system consists of:
 
-- **Frontend**: Next.js 14 React application (`www/`) with Chakra UI, real-time WebSocket integration
+- **Frontend**: Next.js 16 React application (`www/`) with Chakra UI, real-time WebSocket integration
 - **Backend**: Python FastAPI server (`server/`) with async database operations and background processing
 - **Processing**: GPU-accelerated ML pipeline for transcription, diarization, summarization via Modal.com
 - **Infrastructure**: Redis, PostgreSQL/SQLite, Celery workers, WebRTC streaming

README.md

@@ -34,6 +34,8 @@ Reflector is an AI-powered audio transcription and meeting analysis platform tha
   </tr>
 </table>
 
+<p align="center" style="font-size: 1.5em; font-weight: bold;">By <a href="https://greyhaven.co">Greyhaven</a></p>
+
 ## What is Reflector?
 
 Reflector is a web application that utilizes local models to process audio content, providing:
@@ -210,7 +212,13 @@ All new contributions should be made in a separate branch, and goes through a Pu
 
 ## Future Plans
 
-- **Daily.co integration with multitrack processing**: Support for Daily.co live rooms with per-participant audio tracks for improved diarization and transcription quality.
+- **Multi-language support enhancement**: Default language selection per room/user, automatic language detection improvements, multi-language diarization, and RTL language UI support
+- **Jitsi integration**: Self-hosted video conferencing rooms with no external API keys, full control over video infrastructure, and enhanced privacy
+- **Calendar integration**: Google Calendar and Microsoft Outlook synchronization, automatic meeting room creation, and post-meeting transcript delivery
+- **Enhanced analytics**: Meeting insights dashboard, speaker participation metrics, topic trends over time, and team collaboration patterns
+- **Advanced AI features**: Real-time sentiment analysis, emotion detection, meeting quality scores, and automated coaching suggestions
+- **Integration ecosystem**: Slack/Teams notifications, CRM integration (Salesforce, HubSpot), project management tools (Jira, Asana), and knowledge bases (Notion, Confluence)
+- **Performance improvements**: WebAssembly for client-side processing, edge computing support, and network optimization
 
 ## Legacy Documentation
 

docker-compose.selfhosted.yml

@@ -1,16 +1,20 @@
 # Self-hosted production Docker Compose — single file for everything.
 #
-# Usage: ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
+# Usage: ./scripts/setup-selfhosted.sh <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--garage] [--caddy]
-#   or:  docker compose -f docker-compose.selfhosted.yml --profile gpu [--profile ollama-gpu] [--profile garage] [--profile caddy] up -d
+#   or:  docker compose -f docker-compose.selfhosted.yml [--profile gpu] [--profile ollama-gpu] [--profile garage] [--profile caddy] up -d
 #
-# Specialized models (pick ONE — required):
+# ML processing modes (pick ONE — required):
-#   --profile gpu          NVIDIA GPU for transcription/diarization/translation
+#   --gpu                  NVIDIA GPU container for transcription/diarization/translation (profile: gpu)
-#   --profile cpu          CPU-only for transcription/diarization/translation
+#   --cpu                  In-process CPU processing on server/worker (no ML container needed)
+#   --hosted               Remote GPU service URL (no ML container needed)
 #
 # Local LLM (optional — for summarization/topics):
 #   --profile ollama-gpu   Local Ollama with NVIDIA GPU
 #   --profile ollama-cpu   Local Ollama on CPU only
 #
+# Daily.co multitrack processing (auto-detected from server/.env):
+#   --profile dailyco      Hatchet workflow engine + CPU/LLM workers
+#
 # Other optional services:
 #   --profile garage       Local S3-compatible storage (Garage)
 #   --profile caddy        Reverse proxy with auto-SSL
@@ -32,7 +36,7 @@ services:
     restart: unless-stopped
     ports:
       - "127.0.0.1:1250:1250"
-      - "50000-50100:50000-50100/udp"
+      - "51000-51100:51000-51100/udp"
     env_file:
       - ./server/.env
     environment:
@@ -42,18 +46,11 @@ services:
       REDIS_HOST: redis
       CELERY_BROKER_URL: redis://redis:6379/1
       CELERY_RESULT_BACKEND: redis://redis:6379/1
-      HATCHET_CLIENT_SERVER_URL: ""
+      # ML backend config comes from env_file (server/.env), set per-mode by setup script
-      HATCHET_CLIENT_HOST_PORT: ""
+      # HF_TOKEN needed for in-process pyannote diarization (--cpu mode)
-      # Specialized models via gpu/cpu container (aliased as "transcription")
+      HF_TOKEN: ${HF_TOKEN:-}
-      TRANSCRIPT_BACKEND: modal
-      TRANSCRIPT_URL: http://transcription:8000
-      TRANSCRIPT_MODAL_API_KEY: selfhosted
-      DIARIZATION_BACKEND: modal
-      DIARIZATION_URL: http://transcription:8000
-      TRANSLATION_BACKEND: modal
-      TRANSLATE_URL: http://transcription:8000
       # WebRTC: fixed UDP port range for ICE candidates (mapped above)
-      WEBRTC_PORT_RANGE: "50000-50100"
+      WEBRTC_PORT_RANGE: "51000-51100"
     depends_on:
       postgres:
         condition: service_healthy
@@ -76,15 +73,8 @@ services:
       REDIS_HOST: redis
       CELERY_BROKER_URL: redis://redis:6379/1
       CELERY_RESULT_BACKEND: redis://redis:6379/1
-      HATCHET_CLIENT_SERVER_URL: ""
+      # ML backend config comes from env_file (server/.env), set per-mode by setup script
-      HATCHET_CLIENT_HOST_PORT: ""
+      HF_TOKEN: ${HF_TOKEN:-}
-      TRANSCRIPT_BACKEND: modal
-      TRANSCRIPT_URL: http://transcription:8000
-      TRANSCRIPT_MODAL_API_KEY: selfhosted
-      DIARIZATION_BACKEND: modal
-      DIARIZATION_URL: http://transcription:8000
-      TRANSLATION_BACKEND: modal
-      TRANSLATE_URL: http://transcription:8000
     depends_on:
       postgres:
         condition: service_healthy
@@ -147,12 +137,14 @@ services:
   postgres:
     image: postgres:17-alpine
     restart: unless-stopped
+    command: ["postgres", "-c", "max_connections=200"]
     environment:
       POSTGRES_USER: reflector
       POSTGRES_PASSWORD: reflector
       POSTGRES_DB: reflector
     volumes:
       - postgres_data:/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 -U reflector"]
       interval: 30s
@@ -161,7 +153,10 @@ services:
 
   # ===========================================================
   # Specialized model containers (transcription, diarization, translation)
-  # Both gpu and cpu get alias "transcription" so server config never changes.
+  # Only the gpu profile is activated by the setup script (--gpu mode).
+  # The cpu service definition is kept for manual/standalone use but is
+  # NOT activated by --cpu mode (which uses in-process local backends).
+  # Both services get alias "transcription" so server config never changes.
   # ===========================================================
 
   gpu:
@@ -305,6 +300,87 @@ services:
       - web
       - server
 
+  # ===========================================================
+  # Hatchet + Daily.co workers (optional — for Daily.co multitrack processing)
+  # Auto-enabled when DAILY_API_KEY is configured in server/r
+  # ===========================================================
+
+  hatchet:
+    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
+    profiles: [dailyco]
+    restart: on-failure
+    depends_on:
+      postgres:
+        condition: service_healthy
+    ports:
+      - "8888:8888"
+      - "7078:7077"
+    env_file:
+      - ./.env.hatchet
+    environment:
+      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable&connect_timeout=30"
+      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_AUTH_SET_EMAIL_VERIFIED: "t"
+      SERVER_INTERNAL_CLIENT_INTERNAL_GRPC_BROADCAST_ADDRESS: hatchet:7077
+    volumes:
+      - hatchet_config:/config
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/api/live"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+
+  hatchet-worker-cpu:
+    build:
+      context: ./server
+      dockerfile: Dockerfile
+    image: monadicalsas/reflector-backend:latest
+    profiles: [dailyco]
+    restart: unless-stopped
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: hatchet-worker-cpu
+      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
+      REDIS_HOST: redis
+      CELERY_BROKER_URL: redis://redis:6379/1
+      CELERY_RESULT_BACKEND: redis://redis:6379/1
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
+    depends_on:
+      hatchet:
+        condition: service_healthy
+    volumes:
+      - server_data:/app/data
+
+  hatchet-worker-llm:
+    build:
+      context: ./server
+      dockerfile: Dockerfile
+    image: monadicalsas/reflector-backend:latest
+    profiles: [dailyco]
+    restart: unless-stopped
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: hatchet-worker-llm
+      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
+      REDIS_HOST: redis
+      CELERY_BROKER_URL: redis://redis:6379/1
+      CELERY_RESULT_BACKEND: redis://redis:6379/1
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
+    depends_on:
+      hatchet:
+        condition: service_healthy
+    volumes:
+      - server_data:/app/data
+
 volumes:
   postgres_data:
   redis_data:
@@ -315,6 +391,7 @@ volumes:
   ollama_data:
   caddy_data:
   caddy_config:
+  hatchet_config:
 
 networks:
   default:

docker-compose.yml

@@ -83,25 +83,22 @@ services:
     ports:
       - 6379:6379
   web:
-    image: node:22-alpine
+    build:
+      context: ./www
+      dockerfile: Dockerfile
     ports:
       - "3000:3000"
-    command: sh -c "corepack enable && pnpm install && pnpm dev"
-    restart: unless-stopped
-    working_dir: /app
-    volumes:
-      - ./www:/app/
-      - /app/node_modules
-      - next_cache:/app/.next
     env_file:
       - ./www/.env.local
     environment:
-      - NODE_ENV=development
+      NODE_ENV: development
-      - SERVER_API_URL=http://host.docker.internal:1250
+      SERVER_API_URL: http://host.docker.internal:1250
+      KV_URL: redis://redis:6379
     extra_hosts:
       - "host.docker.internal:host-gateway"
     depends_on:
-      - server
+      redis:
+        condition: service_started
 
   postgres:
     image: postgres:17

docs/.dockerignore

@@ -0,0 +1,7 @@
+node_modules
+build
+.git
+.gitignore
+*.log
+.DS_Store
+.env*

docs/Dockerfile

@@ -1,14 +1,17 @@
-FROM node:18-alpine AS builder
+FROM node:20-alpine AS builder
 WORKDIR /app
 
 # Install curl for fetching OpenAPI spec
 RUN apk add --no-cache curl
 
-# Copy package files
+# Enable pnpm
-COPY package*.json ./
+RUN corepack enable && corepack prepare pnpm@latest --activate
+
+# Copy package files and lockfile
+COPY package.json pnpm-lock.yaml* ./
 
 # Install dependencies
-RUN npm ci
+RUN pnpm install --frozen-lockfile
 
 # Copy source
 COPY . .
@@ -21,7 +24,7 @@ RUN mkdir -p ./static && curl -sf "${OPENAPI_URL}" -o ./static/openapi.json || e
 RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
 
 # Build static site (skip prebuild hook by calling docusaurus directly)
-RUN npx docusaurus build
+RUN pnpm exec docusaurus build
 
 # Production image
 FROM nginx:alpine

docs/README.md

@@ -5,13 +5,13 @@ This website is built using [Docusaurus](https://docusaurus.io/), a modern stati
 ### Installation
 
 ```
-$ yarn
+$ pnpm install
 ```
 
 ### Local Development
 
 ```
-$ yarn start
+$ pnpm start
 ```
 
 This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
@@ -19,7 +19,7 @@ This command starts a local development server and opens up a browser window. Mo
 ### Build
 
 ```
-$ yarn build
+$ pnpm build
 ```
 
 This command generates static content into the `build` directory and can be served using any static contents hosting service.
@@ -29,13 +29,13 @@ This command generates static content into the `build` directory and can be serv
 Using SSH:
 
 ```
-$ USE_SSH=true yarn deploy
+$ USE_SSH=true pnpm deploy
 ```
 
 Not using SSH:
 
 ```
-$ GIT_USER=<Your GitHub username> yarn deploy
+$ GIT_USER=<Your GitHub username> pnpm deploy
 ```
 
 If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.

docs/create-docs.sh

@@ -254,14 +254,14 @@ Reflector can run completely offline:
 Control where each step happens:
 
 ```yaml
-# All local processing
+# All in-process processing
-TRANSCRIPT_BACKEND=local
+TRANSCRIPT_BACKEND=whisper
-DIARIZATION_BACKEND=local
+DIARIZATION_BACKEND=pyannote
-TRANSLATION_BACKEND=local
+TRANSLATION_BACKEND=marian
 
 # Hybrid approach
 TRANSCRIPT_BACKEND=modal    # Fast GPU processing
-DIARIZATION_BACKEND=local # Sensitive speaker data
+DIARIZATION_BACKEND=pyannote # Sensitive speaker data
 TRANSLATION_BACKEND=modal    # Non-sensitive translation
 ```
 

docs/docs/concepts/overview.md

@@ -11,7 +11,7 @@ Reflector is built as a modern, scalable, microservices-based application design
 
 ### Frontend Application
 
-The user interface is built with **Next.js 15** using the App Router pattern, providing:
+The user interface is built with **Next.js 16** using the App Router pattern, providing:
 
 - Server-side rendering for optimal performance
 - Real-time WebSocket connections for live transcription

docs/docs/installation/docs-deployment.md

@@ -36,14 +36,15 @@ This creates `docs/static/openapi.json` (should be ~70KB) which will be copied d
 The Dockerfile is already in `docs/Dockerfile`:
 
 ```dockerfile
-FROM node:18-alpine AS builder
+FROM node:20-alpine AS builder
 WORKDIR /app
 
-# Copy package files
+# Enable pnpm and copy package files + lockfile
-COPY package*.json ./
+RUN corepack enable && corepack prepare pnpm@latest --activate
+COPY package.json pnpm-lock.yaml* ./
 
-# Inshall dependencies
+# Install dependencies
-RUN npm ci
+RUN pnpm install --frozen-lockfile
 
 # Copy source (includes static/openapi.json if pre-fetched)
 COPY . .
@@ -52,7 +53,7 @@ COPY . .
 RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
 
 # Build static site
-RUN npx docusaurus build
+RUN pnpm exec docusaurus build
 
 FROM nginx:alpine
 COPY --from=builder /app/build /usr/share/nginx/html

docs/docs/intro.md

@@ -46,7 +46,7 @@ Reflector consists of three main components:
 
 Ready to deploy Reflector? Head over to our [Installation Guide](./installation/overview) to set up your own instance.
 
-For a quick overview of how Reflector processes audio, check out our [Pipeline Documentation](./pipelines/overview).
+For a quick overview of how Reflector processes audio, check out our [Pipeline Documentation](./concepts/pipeline).
 
 ## Open Source
 

docs/docusaurus.config.ts

@@ -124,11 +124,11 @@ const config: Config = {
           items: [
             {
               label: 'Architecture',
-              to: '/docs/reference/architecture/overview',
+              to: '/docs/concepts/overview',
             },
             {
               label: 'Pipelines',
-              to: '/docs/pipelines/overview',
+              to: '/docs/concepts/pipeline',
             },
             {
               label: 'Roadmap',

docs/package.json

@@ -14,26 +14,26 @@
     "write-heading-ids": "docusaurus write-heading-ids",
     "typecheck": "tsc",
     "fetch-openapi": "./scripts/fetch-openapi.sh",
-    "gen-api-docs": "npm run fetch-openapi && docusaurus gen-api-docs reflector",
+    "gen-api-docs": "pnpm run fetch-openapi && docusaurus gen-api-docs reflector",
-    "prebuild": "npm run fetch-openapi"
+    "prebuild": "pnpm run fetch-openapi"
   },
   "dependencies": {
-    "@docusaurus/core": "3.6.3",
+    "@docusaurus/core": "3.9.2",
-    "@docusaurus/preset-classic": "3.6.3",
+    "@docusaurus/preset-classic": "3.9.2",
-    "@mdx-js/react": "^3.0.0",
+    "@docusaurus/theme-mermaid": "3.9.2",
-    "clsx": "^2.0.0",
+    "@mdx-js/react": "^3.1.1",
-    "docusaurus-plugin-openapi-docs": "^4.5.1",
+    "clsx": "^2.1.1",
-    "docusaurus-theme-openapi-docs": "^4.5.1",
+    "docusaurus-plugin-openapi-docs": "^4.7.1",
-    "@docusaurus/theme-mermaid": "3.6.3",
+    "docusaurus-theme-openapi-docs": "^4.7.1",
-    "prism-react-renderer": "^2.3.0",
+    "prism-react-renderer": "^2.4.1",
-    "react": "^18.0.0",
+    "react": "^19.2.4",
-    "react-dom": "^18.0.0"
+    "react-dom": "^19.2.4"
   },
   "devDependencies": {
-    "@docusaurus/module-type-aliases": "3.6.3",
+    "@docusaurus/module-type-aliases": "3.9.2",
-    "@docusaurus/tsconfig": "3.6.3",
+    "@docusaurus/tsconfig": "3.9.2",
-    "@docusaurus/types": "3.6.3",
+    "@docusaurus/types": "3.9.2",
-    "typescript": "~5.6.2"
+    "typescript": "~5.9.3"
   },
   "browserslist": {
     "production": [
@@ -49,5 +49,16 @@
   },
   "engines": {
     "node": ">=18.0"
+  },
+  "pnpm": {
+    "overrides": {
+      "minimatch@<3.1.4": "3.1.5",
+      "minimatch@>=5.0.0 <5.1.8": "5.1.8",
+      "minimatch@>=9.0.0 <9.0.7": "9.0.7",
+      "lodash@<4.17.23": "4.17.23",
+      "js-yaml@<4.1.1": "4.1.1",
+      "gray-matter": "github:jonschlinkert/gray-matter#234163e",
+      "serialize-javascript": "7.0.4"
+    }
   }
 }

docsv2/selfhosted-production.md

@@ -39,7 +39,6 @@ Deploy Reflector on a single server with everything running in Docker. Transcrip
   - **Any S3-compatible provider** (Backblaze B2, Cloudflare R2, DigitalOcean Spaces, etc.): same fields + custom endpoint URL
 
 **Optional add-ons (configure after initial setup):**
-- **Daily.co** (live meeting rooms): Requires a Daily.co account (https://www.daily.co/), API key, subdomain, and an AWS S3 bucket + IAM Role for recording storage. See [Enabling Daily.co Live Rooms](#enabling-dailyco-live-rooms) below.
 - **Authentik** (user authentication): Requires an Authentik instance with an OAuth2/OIDC application configured for Reflector. See [Enabling Authentication](#enabling-authentication-authentik) below.
 
 ## Quick Start
@@ -54,9 +53,12 @@ cd reflector
 # Same but without a domain (self-signed cert, access via IP):
 ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
 
-# CPU-only (same, but slower):
+# CPU-only (in-process ML, no GPU container):
 ./scripts/setup-selfhosted.sh --cpu --ollama-cpu --garage --caddy
 
+# Remote GPU service (your own hosted GPU, no local ML container):
+./scripts/setup-selfhosted.sh --hosted --garage --caddy
+
 # With password authentication (single admin user):
 ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --password mysecretpass
 
@@ -66,14 +68,15 @@ cd reflector
 
 That's it. The script generates env files, secrets, starts all containers, waits for health checks, and prints the URL.
 
-## Specialized Models (Required)
+## ML Processing Modes (Required)
 
-Pick `--gpu` or `--cpu`. This determines how **transcription, diarization, and translation** run:
+Pick `--gpu`, `--cpu`, or `--hosted`. This determines how **transcription, diarization, translation, and audio padding** run:
 
 | Flag | What it does | Requires |
 |------|-------------|----------|
-| `--gpu` | NVIDIA GPU acceleration for ML models | NVIDIA GPU + drivers + `nvidia-container-toolkit` |
+| `--gpu` | NVIDIA GPU container for ML models | NVIDIA GPU + drivers + `nvidia-container-toolkit` |
-| `--cpu` | CPU-only (slower but works without GPU) | 8+ cores, 32GB+ RAM recommended |
+| `--cpu` | In-process CPU processing on server/worker (no ML container) | 8+ cores, 16GB+ RAM (32GB recommended for large files) |
+| `--hosted` | Remote GPU service URL (no local ML container) | A running GPU service instance (e.g. `gpu/self_hosted/`) |
 
 ## Local LLM (Optional)
 
@@ -86,6 +89,21 @@ Optionally add `--ollama-gpu` or `--ollama-cpu` for a **local Ollama instance**
 | `--llm-model MODEL` | Choose which Ollama model to download (default: `qwen2.5:14b`) | `--ollama-gpu` or `--ollama-cpu` |
 | *(omitted)* | User configures external LLM (OpenAI, Anthropic, etc.) | LLM API key |
 
+### macOS / Apple Silicon
+
+`--ollama-gpu` requires an NVIDIA GPU and **does not work on macOS**. Docker on macOS cannot access Apple GPU acceleration, so the containerized Ollama will run on CPU only regardless of the flag used.
+
+For the best performance on Mac, we recommend running Ollama **natively outside Docker** (install from https://ollama.com) — this gives Ollama direct access to Apple Metal GPU acceleration. Then omit `--ollama-gpu`/`--ollama-cpu` from the setup script and point the backend to your local Ollama instance:
+
+```env
+# In server/.env
+LLM_URL=http://host.docker.internal:11434/v1
+LLM_MODEL=qwen2.5:14b
+LLM_API_KEY=not-needed
+```
+
+`--ollama-cpu` does work on macOS but will be significantly slower than a native Ollama install with Metal acceleration.
+
 ### Choosing an Ollama model
 
 The default model is `qwen2.5:14b` (~9GB download, good multilingual support and summary quality). Override with `--llm-model`:
@@ -116,9 +134,11 @@ Browse all available models at https://ollama.com/library.
 
 - **`--gpu --ollama-gpu`**: Best for servers with NVIDIA GPU. Fully self-contained, no external API keys needed.
 - **`--cpu --ollama-cpu`**: No GPU available but want everything self-contained. Slower but works.
+- **`--hosted --ollama-cpu`**: Remote GPU for ML, local CPU for LLM. Great when you have a separate GPU server.
 - **`--gpu --ollama-cpu`**: GPU for transcription, CPU for LLM. Saves GPU VRAM for ML models.
 - **`--gpu`**: Have NVIDIA GPU but prefer a cloud LLM (faster/better summaries with GPT-4, Claude, etc.).
 - **`--cpu`**: No GPU, prefer cloud LLM. Slowest transcription but best summary quality.
+- **`--hosted`**: Remote GPU, cloud LLM. No local ML at all.
 
 ## Other Optional Flags
 
@@ -146,8 +166,9 @@ Without `--caddy` or `--domain`, no ports are exposed. Point your own reverse pr
 4. **Generate `www/.env`** — Auto-detects server IP, sets URLs
 5. **Storage setup** — Either initializes Garage (bucket, keys, permissions) or prompts for external S3 credentials
 6. **Caddyfile** — Generates domain-specific (Let's Encrypt) or IP-specific (self-signed) configuration
-7. **Build & start** — Always builds GPU/CPU model image from source. With `--build`, also builds backend and frontend from source; otherwise pulls prebuilt images from the registry
+7. **Build & start** — For `--gpu`, builds the GPU model image from source. For `--cpu` and `--hosted`, no ML container is built. With `--build`, also builds backend and frontend from source; otherwise pulls prebuilt images from the registry
-8. **Health checks** — Waits for each service, pulls Ollama model if needed, warns about missing LLM config
+8. **Auto-detects video platforms** — If `DAILY_API_KEY` is found in `server/.env`, generates `.env.hatchet` (dashboard URL/cookie config), starts Hatchet workflow engine, and generates an API token. If any video platform is configured, enables the Rooms feature
+9. **Health checks** — Waits for each service, pulls Ollama model if needed, warns about missing LLM config
 
 > For a deeper dive into each step, see [How the Self-Hosted Setup Works](selfhosted-architecture.md).
 
@@ -166,12 +187,23 @@ Without `--caddy` or `--domain`, no ports are exposed. Point your own reverse pr
 | `ADMIN_PASSWORD_HASH` | PBKDF2 hash for password auth | *(unset)* |
 | `WEBRTC_HOST` | IP advertised in WebRTC ICE candidates | Auto-detected (server IP) |
 | `TRANSCRIPT_URL` | Specialized model endpoint | `http://transcription:8000` |
+| `PADDING_BACKEND` | Audio padding backend (`pyav` or `modal`) | `modal` (selfhosted), `pyav` (default) |
+| `PADDING_URL` | Audio padding endpoint (when `PADDING_BACKEND=modal`) | `http://transcription:8000` |
 | `LLM_URL` | OpenAI-compatible LLM endpoint | Auto-set for Ollama modes |
 | `LLM_API_KEY` | LLM API key | `not-needed` for Ollama |
 | `LLM_MODEL` | LLM model name | `qwen2.5:14b` for Ollama (override with `--llm-model`) |
 | `CELERY_BEAT_POLL_INTERVAL` | Override all worker polling intervals (seconds). `0` = use individual defaults | `300` (selfhosted), `0` (other) |
 | `TRANSCRIPT_STORAGE_BACKEND` | Storage backend | `aws` |
 | `TRANSCRIPT_STORAGE_AWS_*` | S3 credentials | Auto-set for Garage |
+| `DAILY_API_KEY` | Daily.co API key (enables live rooms) | *(unset)* |
+| `DAILY_SUBDOMAIN` | Daily.co subdomain | *(unset)* |
+| `DAILYCO_STORAGE_AWS_ACCESS_KEY_ID` | AWS access key for reading Daily's recording bucket | *(unset)* |
+| `DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY` | AWS secret key for reading Daily's recording bucket | *(unset)* |
+| `HATCHET_CLIENT_TOKEN` | Hatchet API token (auto-generated) | *(unset)* |
+| `HATCHET_CLIENT_SERVER_URL` | Hatchet server URL | Auto-set when Daily.co configured |
+| `HATCHET_CLIENT_HOST_PORT` | Hatchet gRPC address | Auto-set when Daily.co configured |
+| `TRANSCRIPT_FILE_TIMEOUT` | HTTP timeout (seconds) for file transcription requests | `600` (`3600` in CPU mode) |
+| `DIARIZATION_FILE_TIMEOUT` | HTTP timeout (seconds) for file diarization requests | `600` (`3600` in CPU mode) |
 
 ### Frontend Environment (`www/.env`)
 
@@ -183,6 +215,7 @@ Without `--caddy` or `--domain`, no ports are exposed. Point your own reverse pr
 | `NEXTAUTH_SECRET` | Auth secret | Auto-generated |
 | `FEATURE_REQUIRE_LOGIN` | Require authentication | `false` |
 | `AUTH_PROVIDER` | Auth provider (`authentik` or `credentials`) | *(unset)* |
+| `FEATURE_ROOMS` | Enable meeting rooms UI | Auto-set when video platform configured |
 
 ## Storage Options
 
@@ -341,20 +374,84 @@ By default, authentication is disabled (`AUTH_BACKEND=none`, `FEATURE_REQUIRE_LO
 
 ## Enabling Daily.co Live Rooms
 
-Daily.co enables real-time meeting rooms with automatic recording and transcription.
+Daily.co enables real-time meeting rooms with automatic recording and per-participant
+audio tracks for improved diarization. When configured, the setup script automatically
+starts the Hatchet workflow engine for multitrack recording processing.
+
+### Prerequisites
+
+- **Daily.co account** — Sign up at https://www.daily.co/
+- **API key** — From Daily.co Dashboard → Developers → API Keys
+- **Subdomain** — The `yourname` part of `yourname.daily.co`
+- **AWS S3 bucket** — For Daily.co to store recordings. See [Daily.co recording storage docs](https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket)
+- **IAM role ARN** — An AWS IAM role that Daily.co assumes to write recordings to your bucket
+
+### Setup
+
+1. Configure Daily.co env vars in `server/.env` **before** running the setup script:
 
-1. Create a [Daily.co](https://www.daily.co/) account
-2. Add to `server/.env`:
    ```env
-   DEFAULT_VIDEO_PLATFORM=daily
    DAILY_API_KEY=your-daily-api-key
    DAILY_SUBDOMAIN=your-subdomain
-   DAILY_WEBHOOK_SECRET=your-webhook-secret
+   DEFAULT_VIDEO_PLATFORM=daily
-   DAILYCO_STORAGE_AWS_BUCKET_NAME=reflector-dailyco
+   DAILYCO_STORAGE_AWS_BUCKET_NAME=your-recordings-bucket
    DAILYCO_STORAGE_AWS_REGION=us-east-1
-   DAILYCO_STORAGE_AWS_ROLE_ARN=arn:aws:iam::role/DailyCoAccess
+   DAILYCO_STORAGE_AWS_ROLE_ARN=arn:aws:iam::123456789:role/DailyCoAccess
+   # Worker credentials for reading/deleting recordings from Daily's S3 bucket.
+   # Required when transcript storage is separate from Daily's bucket
+   # (e.g., selfhosted with Garage or a different S3 account).
+   DAILYCO_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
+   DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
    ```
-3. Restart the server: `docker compose -f docker-compose.selfhosted.yml restart server worker`
+
+   > **Important:** The `DAILYCO_STORAGE_AWS_ACCESS_KEY_ID` and `SECRET_ACCESS_KEY` are AWS IAM
+   > credentials that allow the Hatchet workers to **read and delete** recording files from Daily's
+   > S3 bucket. These are separate from the `ROLE_ARN` (which Daily's API uses to *write* recordings).
+   > Without these keys, multitrack processing will fail with 404 errors when transcript storage
+   > (e.g., Garage) uses different credentials than the Daily recording bucket.
+
+2. Run the setup script as normal:
+
+   ```bash
+   ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
+   ```
+
+   The script detects `DAILY_API_KEY` and automatically:
+   - Starts the Hatchet workflow engine (`hatchet` container)
+   - Starts Hatchet CPU and LLM workers (`hatchet-worker-cpu`, `hatchet-worker-llm`)
+   - Generates a `HATCHET_CLIENT_TOKEN` and saves it to `server/.env`
+   - Sets `HATCHET_CLIENT_SERVER_URL` and `HATCHET_CLIENT_HOST_PORT`
+   - Enables `FEATURE_ROOMS=true` in `www/.env`
+   - Registers Daily.co beat tasks (recording polling, presence reconciliation)
+
+3. (Optional) For faster recording discovery, configure a Daily.co webhook:
+   - In the Daily.co dashboard, add a webhook pointing to `https://your-domain/v1/daily/webhook`
+   - Set `DAILY_WEBHOOK_SECRET` in `server/.env` (the signing secret from Daily.co)
+   - Without webhooks, the system polls the Daily.co API every 15 seconds
+
+### What Gets Started
+
+| Service | Purpose |
+|---------|---------|
+| `hatchet` | Workflow orchestration engine (manages multitrack processing pipelines) |
+| `hatchet-worker-cpu` | CPU-heavy audio tasks (track mixdown, waveform generation) |
+| `hatchet-worker-llm` | Transcription, LLM inference (summaries, topics, titles), orchestration |
+
+### Hatchet Dashboard
+
+The Hatchet workflow engine includes a web dashboard for monitoring workflow runs and debugging. The setup script auto-generates `.env.hatchet` at the project root with the dashboard URL and cookie domain configuration. This file is git-ignored.
+
+- **With Caddy**: Accessible at `https://your-domain:8888` (TLS via Caddy)
+- **Without Caddy**: Accessible at `http://your-ip:8888` (direct port mapping)
+
+### Conditional Beat Tasks
+
+Beat tasks are registered based on which services are configured:
+
+- **Whereby tasks** (only if `WHEREBY_API_KEY` or `AWS_PROCESS_RECORDING_QUEUE_URL`): `process_messages`, `reprocess_failed_recordings`
+- **Daily.co tasks** (only if `DAILY_API_KEY`): `poll_daily_recordings`, `trigger_daily_reconciliation`, `reprocess_failed_daily_recordings`
+- **Platform tasks** (if any video platform configured): `process_meetings`, `sync_all_ics_calendars`, `create_upcoming_meetings`
+- **Always registered**: `cleanup_old_public_data` (if `PUBLIC_MODE`), `healthcheck_ping` (if `HEALTHCHECK_URL`)
 
 ## Enabling Real Domain with Let's Encrypt
 
@@ -449,6 +546,15 @@ docker compose -f docker-compose.selfhosted.yml logs server --tail 50
 For self-signed certs, your browser will warn. Click Advanced > Proceed.
 For Let's Encrypt, ensure ports 80/443 are open and DNS is pointed correctly.
 
+### File processing timeout on CPU
+CPU transcription and diarization are significantly slower than GPU. A 20-minute audio file can take 20-40 minutes to process on CPU. The setup script automatically sets `TRANSCRIPT_FILE_TIMEOUT=3600` and `DIARIZATION_FILE_TIMEOUT=3600` (1 hour) for `--cpu` mode. If you still hit timeouts with very long files, increase these values in `server/.env`:
+```bash
+# Increase to 2 hours for files over 1 hour
+TRANSCRIPT_FILE_TIMEOUT=7200
+DIARIZATION_FILE_TIMEOUT=7200
+```
+Then restart the worker: `docker compose -f docker-compose.selfhosted.yml restart worker`
+
 ### Summaries/topics not generating
 Check LLM configuration:
 ```bash
@@ -504,16 +610,29 @@ The setup script is idempotent — it won't overwrite existing secrets or env va
           │              │            │
           v              v            v
     ┌───────────┐  ┌─────────┐  ┌─────────┐
-    │transcription│  │postgres │  │  redis  │
+    │ ML models │  │postgres │  │  redis  │
-    │(gpu/cpu)  │  │ :5432   │  │ :6379   │
+    │ (varies)  │  │ :5432   │  │ :6379   │
-    │ :8000     │  └─────────┘  └─────────┘
+    └───────────┘  └─────────┘  └─────────┘
-    └───────────┘
           │
     ┌─────┴─────┐     ┌─────────┐
     │  ollama   │     │ garage  │
     │ (optional)│     │(optional│
     │ :11435    │     │ S3)     │
     └───────────┘     └─────────┘
+
+    ┌───────────────────────────────────┐
+    │  Hatchet (optional — Daily.co)   │
+    │  ┌─────────┐  ┌───────────────┐  │
+    │  │ hatchet │  │ hatchet-worker│  │
+    │  │ :8888   │──│  -cpu / -llm  │  │
+    │  └─────────┘  └───────────────┘  │
+    └───────────────────────────────────┘
+
+ML models box varies by mode:
+  --gpu:    Local GPU container (transcription:8000)
+  --cpu:    In-process on server/worker (no container)
+  --hosted: Remote GPU service (user URL)
 ```
 
-All services communicate over Docker's internal network. Only Caddy (if enabled) exposes ports to the internet.
+All services communicate over Docker's internal network. Only Caddy (if enabled) exposes ports to the internet. Hatchet services are only started when `DAILY_API_KEY` is configured.
+

gpu/self_hosted/app/factory.py

@@ -3,6 +3,7 @@ from contextlib import asynccontextmanager
 from fastapi import FastAPI
 
 from .routers.diarization import router as diarization_router
+from .routers.padding import router as padding_router
 from .routers.transcription import router as transcription_router
 from .routers.translation import router as translation_router
 from .services.transcriber import WhisperService
@@ -27,4 +28,5 @@ def create_app() -> FastAPI:
     app.include_router(transcription_router)
     app.include_router(translation_router)
     app.include_router(diarization_router)
+    app.include_router(padding_router)
     return app

gpu/self_hosted/app/routers/padding.py

@@ -0,0 +1,199 @@
+"""
+Audio padding endpoint for selfhosted GPU service.
+
+CPU-intensive audio padding service for adding silence to audio tracks.
+Uses PyAV filter graph (adelay) for precise track synchronization.
+
+IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
+for deployment isolation (self_hosted can't import from server/reflector/). If you modify
+the PyAV filter graph or padding algorithm, you MUST update both:
+  - gpu/self_hosted/app/routers/padding.py (this file)
+  - server/reflector/utils/audio_padding.py
+
+Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
+"""
+
+import logging
+import math
+import os
+import tempfile
+from fractions import Fraction
+
+import av
+import requests
+from av.audio.resampler import AudioResampler
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel
+
+from ..auth import apikey_auth
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(tags=["padding"])
+
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
+OPUS_STANDARD_SAMPLE_RATE = 48000
+OPUS_DEFAULT_BIT_RATE = 128000
+
+S3_TIMEOUT = 60
+
+
+class PaddingRequest(BaseModel):
+    track_url: str
+    output_url: str
+    start_time_seconds: float
+    track_index: int
+
+
+class PaddingResponse(BaseModel):
+    size: int
+    cancelled: bool = False
+
+
+@router.post("/pad", dependencies=[Depends(apikey_auth)], response_model=PaddingResponse)
+def pad_track(req: PaddingRequest):
+    """Pad audio track with silence using PyAV adelay filter graph."""
+    if not req.track_url:
+        raise HTTPException(status_code=400, detail="track_url cannot be empty")
+    if not req.output_url:
+        raise HTTPException(status_code=400, detail="output_url cannot be empty")
+    if req.start_time_seconds <= 0:
+        raise HTTPException(
+            status_code=400,
+            detail=f"start_time_seconds must be positive, got {req.start_time_seconds}",
+        )
+    if req.start_time_seconds > 18000:
+        raise HTTPException(
+            status_code=400,
+            detail="start_time_seconds exceeds maximum 18000s (5 hours)",
+        )
+
+    logger.info(
+        "Padding request: track %d, delay=%.3fs", req.track_index, req.start_time_seconds
+    )
+
+    temp_dir = tempfile.mkdtemp()
+    input_path = None
+    output_path = None
+
+    try:
+        # Download source audio
+        logger.info("Downloading track for padding")
+        response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
+        response.raise_for_status()
+
+        input_path = os.path.join(temp_dir, "track.webm")
+        total_bytes = 0
+        with open(input_path, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+                    total_bytes += len(chunk)
+        logger.info("Track downloaded: %d bytes", total_bytes)
+
+        # Apply padding using PyAV
+        output_path = os.path.join(temp_dir, "padded.webm")
+        delay_ms = math.floor(req.start_time_seconds * 1000)
+        logger.info("Padding track %d with %dms delay using PyAV", req.track_index, delay_ms)
+
+        in_container = av.open(input_path)
+        in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
+        if in_stream is None:
+            in_container.close()
+            raise HTTPException(status_code=400, detail="No audio stream in input")
+
+        with av.open(output_path, "w", format="webm") as out_container:
+            out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
+            out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
+            graph = av.filter.Graph()
+
+            abuf_args = (
+                f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
+                f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
+                f"sample_fmt=s16:"
+                f"channel_layout=stereo"
+            )
+            src = graph.add("abuffer", args=abuf_args, name="src")
+            aresample_f = graph.add("aresample", args="async=1", name="ares")
+            delays_arg = f"{delay_ms}|{delay_ms}"
+            adelay_f = graph.add(
+                "adelay", args=f"delays={delays_arg}:all=1", name="delay"
+            )
+            sink = graph.add("abuffersink", name="sink")
+
+            src.link_to(aresample_f)
+            aresample_f.link_to(adelay_f)
+            adelay_f.link_to(sink)
+            graph.configure()
+
+            resampler = AudioResampler(
+                format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
+            )
+
+            for frame in in_container.decode(in_stream):
+                out_frames = resampler.resample(frame) or []
+                for rframe in out_frames:
+                    rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                    rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                    src.push(rframe)
+
+                    while True:
+                        try:
+                            f_out = sink.pull()
+                        except Exception:
+                            break
+                        f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                        f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                        for packet in out_stream.encode(f_out):
+                            out_container.mux(packet)
+
+            # Flush filter graph
+            src.push(None)
+            while True:
+                try:
+                    f_out = sink.pull()
+                except Exception:
+                    break
+                f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                for packet in out_stream.encode(f_out):
+                    out_container.mux(packet)
+
+            # Flush encoder
+            for packet in out_stream.encode(None):
+                out_container.mux(packet)
+
+        in_container.close()
+
+        file_size = os.path.getsize(output_path)
+        logger.info("Padding complete: %d bytes", file_size)
+
+        # Upload padded track
+        logger.info("Uploading padded track to S3")
+        with open(output_path, "rb") as f:
+            upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
+        upload_response.raise_for_status()
+        logger.info("Upload complete: %d bytes", file_size)
+
+        return PaddingResponse(size=file_size)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error("Padding failed for track %d: %s", req.track_index, e, exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Padding failed: {e}") from e
+    finally:
+        if input_path and os.path.exists(input_path):
+            try:
+                os.unlink(input_path)
+            except Exception as e:
+                logger.warning("Failed to cleanup input file: %s", e)
+        if output_path and os.path.exists(output_path):
+            try:
+                os.unlink(output_path)
+            except Exception as e:
+                logger.warning("Failed to cleanup output file: %s", e)
+        try:
+            os.rmdir(temp_dir)
+        except Exception as e:
+            logger.warning("Failed to cleanup temp directory: %s", e)

gpu/self_hosted/pyproject.toml

@@ -11,9 +11,11 @@ dependencies = [
     "faster-whisper>=1.1.0",
     "librosa==0.10.1",
     "numpy<2",
-    "silero-vad==5.1.0",
+    "silero-vad==5.1.2",
     "transformers>=4.35.0",
     "sentencepiece",
-    "pyannote.audio==3.1.0",
+    "pyannote.audio==3.4.0",
+    "pytorch-lightning<2.6",
     "torchaudio>=2.3.0",
+    "av>=13.1.0",
 ]

gpu/self_hosted/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 
 [[package]]
@@ -13,7 +13,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.15"
+version = "3.13.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -24,42 +24,76 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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+version = "0.0.22"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f3/87/f44d7c9f274c7ee665a29b885ec97089ec5dc034c7f3fafa03da9e39a09e/python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13", size = 37158, upload-time = "2024-12-16T19:45:46.972Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/01/979e98d542a70714b0cb2b6728ed0b7c46792b695e3eaec3e20711271ca3/python_multipart-0.0.22.tar.gz", hash = "sha256:7340bef99a7e0032613f56dc36027b959fd3b30a787ed62d310e951f7c3a3a58", size = 37612, upload-time = "2026-01-25T10:15:56.219Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/45/58/38b5afbc1a800eeea951b9285d3912613f2603bdf897a4ab0f4bd7f405fc/python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104", size = 24546, upload-time = "2024-12-16T19:45:44.423Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/d0/397f9626e711ff749a95d96b7af99b9c566a9bb5129b8e4c10fc4d100304/python_multipart-0.0.22-py3-none-any.whl", hash = "sha256:2b2cd894c83d21bf49d702499531c7bafd057d730c201782048f7945d82de155", size = 24579, upload-time = "2026-01-25T10:15:54.811Z" },
 ]
 
 [[package]]
@@ -1988,11 +2069,13 @@ name = "reflector-gpu"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "av" },
     { name = "fastapi", extra = ["standard"] },
     { name = "faster-whisper" },
     { name = "librosa" },
     { name = "numpy" },
     { name = "pyannote-audio" },
+    { name = "pytorch-lightning" },
     { name = "sentencepiece" },
     { name = "silero-vad" },
     { name = "torch" },
@@ -2003,13 +2086,15 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "av", specifier = ">=13.1.0" },
     { name = "fastapi", extras = ["standard"], specifier = ">=0.116.1" },
     { name = "faster-whisper", specifier = ">=1.1.0" },
     { name = "librosa", specifier = "==0.10.1" },
     { name = "numpy", specifier = "<2" },
-    { name = "pyannote-audio", specifier = "==3.1.0" },
+    { name = "pyannote-audio", specifier = "==3.4.0" },
+    { name = "pytorch-lightning", specifier = "<2.6" },
     { name = "sentencepiece" },
-    { name = "silero-vad", specifier = "==5.1.0" },
+    { name = "silero-vad", specifier = "==5.1.2" },
     { name = "torch", specifier = ">=2.3.0" },
     { name = "torchaudio", specifier = ">=2.3.0" },
     { name = "transformers", specifier = ">=4.35.0" },
@@ -2389,16 +2474,16 @@ wheels = [
 
 [[package]]
 name = "silero-vad"
-version = "5.1"
+version = "5.1.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "onnxruntime" },
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/7c/5d/b912e45d21b8b61859a552554893222d2cdebfd0f9afa7e8ba69c7a3441a/silero_vad-5.1.tar.gz", hash = "sha256:c644275ba5df06cee596cc050ba0bd1e0f5237d1abfa44d58dd4618f6e77434d", size = 3996829, upload-time = "2024-07-09T13:19:24.181Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b1/b4/d0311b2e6220a11f8f4699f4a278cb088131573286cdfe804c87c7eb5123/silero_vad-5.1.2.tar.gz", hash = "sha256:c442971160026d2d7aa0ad83f0c7ee86c89797a65289fe625c8ea59fc6fb828d", size = 5098526, upload-time = "2024-10-09T09:50:47.019Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0e/be/0fdbc72030b93d6f55107490d5d2185ddf0dbabdc921f589649d3e92ccd5/silero_vad-5.1-py3-none-any.whl", hash = "sha256:ecb50b484f538f7a962ce5cd3c07120d9db7b9d5a0c5861ccafe459856f22c8f", size = 3939986, upload-time = "2024-07-09T13:19:21.383Z" },
+    { url = "https://files.pythonhosted.org/packages/98/f7/5ae11d13fbb733cd3bfd7ff1c3a3902e6f55437df4b72307c1f168146268/silero_vad-5.1.2-py3-none-any.whl", hash = "sha256:93b41953d7774b165407fda6b533c119c5803864e367d5034dc626c82cfdf661", size = 5026737, upload-time = "2024-10-09T09:50:44.355Z" },
 ]
 
 [[package]]
@@ -2515,15 +2600,15 @@ wheels = [
 
 [[package]]
 name = "starlette"
-version = "0.47.3"
+version = "0.49.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/15/b9/cc3017f9a9c9b6e27c5106cc10cc7904653c3eec0729793aec10479dd669/starlette-0.47.3.tar.gz", hash = "sha256:6bc94f839cc176c4858894f1f8908f0ab79dfec1a6b8402f6da9be26ebea52e9", size = 2584144, upload-time = "2025-08-24T13:36:42.122Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1b/3f/507c21db33b66fb027a332f2cb3abbbe924cc3a79ced12f01ed8645955c9/starlette-0.49.1.tar.gz", hash = "sha256:481a43b71e24ed8c43b11ea02f5353d77840e01480881b8cb5a26b8cae64a8cb", size = 2654703, upload-time = "2025-10-28T17:34:10.928Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ce/fd/901cfa59aaa5b30a99e16876f11abe38b59a1a2c51ffb3d7142bb6089069/starlette-0.47.3-py3-none-any.whl", hash = "sha256:89c0778ca62a76b826101e7c709e70680a1699ca7da6b44d38eb0a7e61fe4b51", size = 72991, upload-time = "2025-08-24T13:36:40.887Z" },
+    { url = "https://files.pythonhosted.org/packages/51/da/545b75d420bb23b5d494b0517757b351963e974e79933f01e05c929f20a6/starlette-0.49.1-py3-none-any.whl", hash = "sha256:d92ce9f07e4a3caa3ac13a79523bd18e3bc0042bb8ff2d759a8e7dd0e1859875", size = 74175, upload-time = "2025-10-28T17:34:09.13Z" },
 ]
 
 [[package]]
@@ -2777,14 +2862,14 @@ wheels = [
 
 [[package]]
 name = "typing-inspection"
-version = "0.4.1"
+version = "0.4.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f8/b1/0c11f5058406b3af7609f121aaa6b609744687f1d158b3c3a5bf4cc94238/typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28", size = 75726, upload-time = "2025-05-21T18:55:23.885Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/17/69/cd203477f944c353c31bade965f880aa1061fd6bf05ded0726ca845b6ff7/typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51", size = 14552, upload-time = "2025-05-21T18:55:22.152Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" },
 ]
 
 [[package]]
@@ -2798,11 +2883,11 @@ wheels = [
 
 [[package]]
 name = "urllib3"
-version = "2.5.0"
+version = "2.6.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185, upload-time = "2025-06-18T14:07:41.644Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c7/24/5f1b3bdffd70275f6661c76461e25f024d5a38a46f04aaca912426a2b1d3/urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed", size = 435556, upload-time = "2026-01-07T16:24:43.925Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
+    { url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" },
 ]
 
 [[package]]

scripts/setup-selfhosted.sh

@@ -4,11 +4,12 @@
 # Single script to configure and launch everything on one server.
 #
 # Usage:
-#   ./scripts/setup-selfhosted.sh <--gpu|--cpu> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASSWORD] [--build]
+#   ./scripts/setup-selfhosted.sh <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASSWORD] [--build]
 #
-# Specialized models (pick ONE — required):
+# ML processing modes (pick ONE — required):
-#   --gpu              NVIDIA GPU for transcription/diarization/translation
+#   --gpu              NVIDIA GPU container for transcription/diarization/translation
-#   --cpu              CPU-only for transcription/diarization/translation (slower)
+#   --cpu              In-process CPU processing (no ML container, slower)
+#   --hosted           Remote GPU service URL (no ML container)
 #
 # Local LLM (optional — for summarization & topic detection):
 #   --ollama-gpu       Local Ollama with NVIDIA GPU acceleration
@@ -29,11 +30,16 @@
 #   ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
 #   ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --domain reflector.example.com
 #   ./scripts/setup-selfhosted.sh --cpu --ollama-cpu --garage --caddy
+#   ./scripts/setup-selfhosted.sh --hosted --garage --caddy
 #   ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model mistral --garage --caddy
 #   ./scripts/setup-selfhosted.sh --gpu --garage --caddy --password mysecretpass
 #   ./scripts/setup-selfhosted.sh --gpu --garage --caddy
 #   ./scripts/setup-selfhosted.sh --cpu
 #
+# The script auto-detects Daily.co (DAILY_API_KEY) and Whereby (WHEREBY_API_KEY)
+# from server/.env. If Daily.co is configured, Hatchet workflow services are
+# started automatically for multitrack recording processing.
+#
 # Idempotent — safe to re-run at any time.
 #
 set -euo pipefail
@@ -179,11 +185,14 @@ for i in "${!ARGS[@]}"; do
     arg="${ARGS[$i]}"
     case "$arg" in
         --gpu)
-            [[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu and --cpu. Pick one."; exit 1; }
+            [[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu, --cpu, and --hosted. Pick one."; exit 1; }
             MODEL_MODE="gpu" ;;
         --cpu)
-            [[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu and --cpu. Pick one."; exit 1; }
+            [[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu, --cpu, and --hosted. Pick one."; exit 1; }
             MODEL_MODE="cpu" ;;
+        --hosted)
+            [[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu, --cpu, and --hosted. Pick one."; exit 1; }
+            MODEL_MODE="hosted" ;;
         --ollama-gpu)
             [[ -n "$OLLAMA_MODE" ]] && { err "Cannot combine --ollama-gpu and --ollama-cpu. Pick one."; exit 1; }
             OLLAMA_MODE="ollama-gpu" ;;
@@ -220,20 +229,21 @@ for i in "${!ARGS[@]}"; do
             SKIP_NEXT=true ;;
         *)
             err "Unknown argument: $arg"
-            err "Usage: $0 <--gpu|--cpu> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
+            err "Usage: $0 <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
             exit 1
             ;;
     esac
 done
 
 if [[ -z "$MODEL_MODE" ]]; then
-    err "No model mode specified. You must choose --gpu or --cpu."
+    err "No model mode specified. You must choose --gpu, --cpu, or --hosted."
     err ""
-    err "Usage: $0 <--gpu|--cpu> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
+    err "Usage: $0 <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
     err ""
-    err "Specialized models (required):"
+    err "ML processing modes (required):"
-    err "  --gpu              NVIDIA GPU for transcription/diarization/translation"
+    err "  --gpu              NVIDIA GPU container for transcription/diarization/translation"
-    err "  --cpu              CPU-only (slower but works without GPU)"
+    err "  --cpu              In-process CPU processing (no ML container, slower)"
+    err "  --hosted           Remote GPU service URL (no ML container)"
     err ""
     err "Local LLM (optional):"
     err "  --ollama-gpu       Local Ollama with GPU (for summarization/topics)"
@@ -251,7 +261,9 @@ if [[ -z "$MODEL_MODE" ]]; then
 fi
 
 # Build profiles list — one profile per feature
-COMPOSE_PROFILES=("$MODEL_MODE")
+# Only --gpu needs a compose profile; --cpu and --hosted use in-process/remote backends
+COMPOSE_PROFILES=()
+[[ "$MODEL_MODE" == "gpu" ]] && COMPOSE_PROFILES+=("gpu")
 [[ -n "$OLLAMA_MODE" ]] && COMPOSE_PROFILES+=("$OLLAMA_MODE")
 [[ "$USE_GARAGE" == "true" ]] && COMPOSE_PROFILES+=("garage")
 [[ "$USE_CADDY" == "true" ]] && COMPOSE_PROFILES+=("caddy")
@@ -418,18 +430,74 @@ step_server_env() {
         env_set "$SERVER_ENV" "WEBRTC_HOST" "$PRIMARY_IP"
     fi
 
-    # Specialized models (always via gpu/cpu container aliased as "transcription")
+    # Specialized models — backend configuration per mode
+    env_set "$SERVER_ENV" "DIARIZATION_ENABLED" "true"
+    case "$MODEL_MODE" in
+        gpu)
+            # GPU container aliased as "transcription" on docker network
             env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "modal"
             env_set "$SERVER_ENV" "TRANSCRIPT_URL" "http://transcription:8000"
             env_set "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY" "selfhosted"
-    env_set "$SERVER_ENV" "DIARIZATION_ENABLED" "true"
             env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "modal"
             env_set "$SERVER_ENV" "DIARIZATION_URL" "http://transcription:8000"
             env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "modal"
             env_set "$SERVER_ENV" "TRANSLATE_URL" "http://transcription:8000"
+            env_set "$SERVER_ENV" "PADDING_BACKEND" "modal"
+            env_set "$SERVER_ENV" "PADDING_URL" "http://transcription:8000"
+            ok "ML backends: GPU container (modal)"
+            ;;
+        cpu)
+            # In-process backends — no ML service container needed
+            env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "whisper"
+            env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "pyannote"
+            env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "marian"
+            env_set "$SERVER_ENV" "PADDING_BACKEND" "pyav"
+            ok "ML backends: in-process CPU (whisper/pyannote/marian/pyav)"
+            ;;
+        hosted)
+            # Remote GPU service — user provides URL
+            local gpu_url=""
+            if env_has_key "$SERVER_ENV" "TRANSCRIPT_URL"; then
+                gpu_url=$(env_get "$SERVER_ENV" "TRANSCRIPT_URL")
+            fi
+            if [[ -z "$gpu_url" ]] && [[ -t 0 ]]; then
+                echo ""
+                info "Enter the URL of your remote GPU service (e.g. https://gpu.example.com)"
+                read -rp "  GPU service URL: " gpu_url
+            fi
+            if [[ -z "$gpu_url" ]]; then
+                err "GPU service URL required for --hosted mode."
+                err "Set TRANSCRIPT_URL in server/.env or provide it interactively."
+                exit 1
+            fi
+            env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "modal"
+            env_set "$SERVER_ENV" "TRANSCRIPT_URL" "$gpu_url"
+            env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "modal"
+            env_set "$SERVER_ENV" "DIARIZATION_URL" "$gpu_url"
+            env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "modal"
+            env_set "$SERVER_ENV" "TRANSLATE_URL" "$gpu_url"
+            env_set "$SERVER_ENV" "PADDING_BACKEND" "modal"
+            env_set "$SERVER_ENV" "PADDING_URL" "$gpu_url"
+            # API key for remote service
+            local gpu_api_key=""
+            if env_has_key "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY"; then
+                gpu_api_key=$(env_get "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY")
+            fi
+            if [[ -z "$gpu_api_key" ]] && [[ -t 0 ]]; then
+                read -rp "  GPU service API key (or Enter to skip): " gpu_api_key
+            fi
+            if [[ -n "$gpu_api_key" ]]; then
+                env_set "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY" "$gpu_api_key"
+            fi
+            ok "ML backends: remote hosted ($gpu_url)"
+            ;;
+    esac
 
     # HuggingFace token for gated models (pyannote diarization)
-    # Written to root .env so docker compose picks it up for gpu/cpu containers
+    # --gpu: written to root .env (docker compose passes to GPU container)
+    # --cpu: written to both root .env and server/.env (in-process pyannote needs it)
+    # --hosted: not needed (remote service handles its own auth)
+    if [[ "$MODEL_MODE" != "hosted" ]]; then
         local root_env="$ROOT_DIR/.env"
         local current_hf_token="${HF_TOKEN:-}"
         if [[ -f "$root_env" ]] && env_has_key "$root_env" "HF_TOKEN"; then
@@ -440,18 +508,25 @@ step_server_env() {
             warn "HF_TOKEN not set. Diarization will use a public model fallback."
             warn "For best results, get a token at https://huggingface.co/settings/tokens"
             warn "and accept pyannote licenses at https://huggingface.co/pyannote/speaker-diarization-3.1"
+            if [[ -t 0 ]]; then
                 read -rp "  HuggingFace token (or press Enter to skip): " current_hf_token
             fi
+        fi
         if [[ -n "$current_hf_token" ]]; then
             touch "$root_env"
             env_set "$root_env" "HF_TOKEN" "$current_hf_token"
             export HF_TOKEN="$current_hf_token"
+            # In CPU mode, server process needs HF_TOKEN directly
+            if [[ "$MODEL_MODE" == "cpu" ]]; then
+                env_set "$SERVER_ENV" "HF_TOKEN" "$current_hf_token"
+            fi
             ok "HF_TOKEN configured"
         else
             touch "$root_env"
             env_set "$root_env" "HF_TOKEN" ""
             ok "HF_TOKEN skipped (using public model fallback)"
         fi
+    fi
 
     # LLM configuration
     if [[ "$USES_OLLAMA" == "true" ]]; then
@@ -466,7 +541,7 @@ step_server_env() {
         if env_has_key "$SERVER_ENV" "LLM_URL"; then
             current_llm_url=$(env_get "$SERVER_ENV" "LLM_URL")
         fi
-        if [[ -z "$current_llm_url" ]] || [[ "$current_llm_url" == "http://host.docker.internal"* ]]; then
+        if [[ -z "$current_llm_url" ]]; then
             warn "LLM not configured. Summarization and topic detection will NOT work."
             warn "Edit server/.env and set LLM_URL, LLM_API_KEY, LLM_MODEL"
             warn "Example: LLM_URL=https://api.openai.com/v1  LLM_MODEL=gpt-4o-mini"
@@ -475,6 +550,20 @@ step_server_env() {
         fi
     fi
 
+    # CPU mode: increase file processing timeouts (default 600s is too short for long audio on CPU)
+    if [[ "$MODEL_MODE" == "cpu" ]]; then
+        env_set "$SERVER_ENV" "TRANSCRIPT_FILE_TIMEOUT" "3600"
+        env_set "$SERVER_ENV" "DIARIZATION_FILE_TIMEOUT" "3600"
+        ok "CPU mode — file processing timeouts set to 3600s (1 hour)"
+    fi
+
+    # If Daily.co is manually configured, ensure Hatchet connectivity vars are set
+    if env_has_key "$SERVER_ENV" "DAILY_API_KEY" && [[ -n "$(env_get "$SERVER_ENV" "DAILY_API_KEY")" ]]; then
+        env_set "$SERVER_ENV" "HATCHET_CLIENT_SERVER_URL" "http://hatchet:8888"
+        env_set "$SERVER_ENV" "HATCHET_CLIENT_HOST_PORT" "hatchet:7077"
+        ok "Daily.co detected — Hatchet connectivity configured"
+    fi
+
     ok "server/.env ready"
 }
 
@@ -535,6 +624,19 @@ step_www_env() {
         fi
     fi
 
+    # Enable rooms if any video platform is configured in server/.env
+    local _daily_key="" _whereby_key=""
+    if env_has_key "$SERVER_ENV" "DAILY_API_KEY"; then
+        _daily_key=$(env_get "$SERVER_ENV" "DAILY_API_KEY")
+    fi
+    if env_has_key "$SERVER_ENV" "WHEREBY_API_KEY"; then
+        _whereby_key=$(env_get "$SERVER_ENV" "WHEREBY_API_KEY")
+    fi
+    if [[ -n "$_daily_key" ]] || [[ -n "$_whereby_key" ]]; then
+        env_set "$WWW_ENV" "FEATURE_ROOMS" "true"
+        ok "Rooms feature enabled (video platform configured)"
+    fi
+
     ok "www/.env ready (URL=$base_url)"
 }
 
@@ -739,6 +841,23 @@ CADDYEOF
     else
         ok "Caddyfile already exists"
     fi
+
+    # Add Hatchet dashboard route if Daily.co is detected
+    if [[ "$DAILY_DETECTED" == "true" ]]; then
+        if ! grep -q "hatchet" "$caddyfile" 2>/dev/null; then
+            cat >> "$caddyfile" << CADDYEOF
+
+# Hatchet workflow dashboard (Daily.co multitrack processing)
+:8888 {
+    tls internal
+    reverse_proxy hatchet:8888
+}
+CADDYEOF
+            ok "Added Hatchet dashboard route to Caddyfile (port 8888)"
+        else
+            ok "Hatchet dashboard route already in Caddyfile"
+        fi
+    fi
 }
 
 # =========================================================
@@ -747,11 +866,12 @@ CADDYEOF
 step_services() {
     info "Step 6: Starting Docker services"
 
-    # Build GPU/CPU image from source (always needed — no prebuilt image)
+    # Build GPU image from source (only for --gpu mode)
-    local build_svc="$MODEL_MODE"
+    if [[ "$MODEL_MODE" == "gpu" ]]; then
-    info "Building $build_svc image (first build downloads ML models, may take a while)..."
+        info "Building gpu image (first build downloads ML models, may take a while)..."
-    compose_cmd build "$build_svc"
+        compose_cmd build gpu
-    ok "$build_svc image built"
+        ok "gpu image built"
+    fi
 
     # Build or pull backend and frontend images
     if [[ "$BUILD_IMAGES" == "true" ]]; then
@@ -766,6 +886,37 @@ step_services() {
         compose_cmd pull server web || warn "Pull failed — using cached images"
     fi
 
+    # Build hatchet workers if Daily.co is configured (same backend image)
+    if [[ "$DAILY_DETECTED" == "true" ]] && [[ "$BUILD_IMAGES" == "true" ]]; then
+        info "Building Hatchet worker images..."
+        compose_cmd build hatchet-worker-cpu hatchet-worker-llm
+        ok "Hatchet worker images built"
+    fi
+
+    # Ensure hatchet database exists before starting hatchet (init-hatchet-db.sql only runs on fresh postgres volumes)
+    if [[ "$DAILY_DETECTED" == "true" ]]; then
+        info "Ensuring postgres is running for Hatchet database setup..."
+        compose_cmd up -d postgres
+        local pg_ready=false
+        for i in $(seq 1 30); do
+            if compose_cmd exec -T postgres pg_isready -U reflector > /dev/null 2>&1; then
+                pg_ready=true
+                break
+            fi
+            sleep 2
+        done
+        if [[ "$pg_ready" == "true" ]]; then
+            compose_cmd exec -T postgres psql -U reflector -tc \
+                "SELECT 1 FROM pg_database WHERE datname = 'hatchet'" 2>/dev/null \
+                | grep -q 1 \
+                || compose_cmd exec -T postgres psql -U reflector -c "CREATE DATABASE hatchet" 2>/dev/null \
+                || true
+            ok "Hatchet database ready"
+        else
+            warn "Postgres not ready — hatchet database may need to be created manually"
+        fi
+    fi
+
     # Start all services
     compose_cmd up -d
     ok "Containers started"
@@ -788,25 +939,29 @@ step_services() {
 step_health() {
     info "Step 7: Health checks"
 
-    # Specialized model service (gpu or cpu)
+    # Specialized model service (only for --gpu mode)
-    local model_svc="$MODEL_MODE"
+    if [[ "$MODEL_MODE" == "gpu" ]]; then
-
+        info "Waiting for gpu service (first start downloads ~1GB of models)..."
-    info "Waiting for $model_svc service (first start downloads ~1GB of models)..."
         local model_ok=false
         for i in $(seq 1 120); do
             if curl -sf http://localhost:8000/docs > /dev/null 2>&1; then
                 model_ok=true
                 break
             fi
-        echo -ne "\r  Waiting for $model_svc service... ($i/120)"
+            echo -ne "\r  Waiting for gpu service... ($i/120)"
             sleep 5
         done
         echo ""
         if [[ "$model_ok" == "true" ]]; then
-        ok "$model_svc service healthy (transcription + diarization)"
+            ok "gpu service healthy (transcription + diarization)"
         else
-        warn "$model_svc service not ready yet — it will keep loading in the background"
+            warn "gpu service not ready yet — it will keep loading in the background"
-        warn "Check with: docker compose -f docker-compose.selfhosted.yml logs $model_svc"
+            warn "Check with: docker compose -f docker-compose.selfhosted.yml logs gpu"
+        fi
+    elif [[ "$MODEL_MODE" == "cpu" ]]; then
+        ok "CPU mode — ML processing runs in-process on server/worker (no separate service)"
+    elif [[ "$MODEL_MODE" == "hosted" ]]; then
+        ok "Hosted mode — ML processing via remote GPU service (no local health check)"
     fi
 
     # Ollama (if applicable)
@@ -894,6 +1049,26 @@ step_health() {
         fi
     fi
 
+    # Hatchet (if Daily.co detected)
+    if [[ "$DAILY_DETECTED" == "true" ]]; then
+        info "Waiting for Hatchet workflow engine..."
+        local hatchet_ok=false
+        for i in $(seq 1 60); do
+            if curl -sf http://localhost:8888/api/live > /dev/null 2>&1; then
+                hatchet_ok=true
+                break
+            fi
+            echo -ne "\r  Waiting for Hatchet... ($i/60)"
+            sleep 3
+        done
+        echo ""
+        if [[ "$hatchet_ok" == "true" ]]; then
+            ok "Hatchet workflow engine healthy"
+        else
+            warn "Hatchet not ready yet. Check: docker compose logs hatchet"
+        fi
+    fi
+
     # LLM warning for non-Ollama modes
     if [[ "$USES_OLLAMA" == "false" ]]; then
         local llm_url=""
@@ -911,6 +1086,71 @@ step_health() {
     fi
 }
 
+# =========================================================
+# Step 8: Hatchet token generation (Daily.co only)
+# =========================================================
+step_hatchet_token() {
+    if [[ "$DAILY_DETECTED" != "true" ]]; then
+        return
+    fi
+
+    # Skip if token already set
+    if env_has_key "$SERVER_ENV" "HATCHET_CLIENT_TOKEN" && [[ -n "$(env_get "$SERVER_ENV" "HATCHET_CLIENT_TOKEN")" ]]; then
+        ok "HATCHET_CLIENT_TOKEN already set — skipping generation"
+        return
+    fi
+
+    info "Step 8: Generating Hatchet API token"
+
+    # Wait for hatchet to be healthy
+    local hatchet_ok=false
+    for i in $(seq 1 60); do
+        if curl -sf http://localhost:8888/api/live > /dev/null 2>&1; then
+            hatchet_ok=true
+            break
+        fi
+        echo -ne "\r  Waiting for Hatchet API... ($i/60)"
+        sleep 3
+    done
+    echo ""
+
+    if [[ "$hatchet_ok" != "true" ]]; then
+        err "Hatchet not responding — cannot generate token"
+        err "Check: docker compose logs hatchet"
+        return
+    fi
+
+    # Get tenant ID from hatchet database
+    local tenant_id
+    tenant_id=$(compose_cmd exec -T postgres psql -U reflector -d hatchet -t -c \
+        "SELECT id FROM \"Tenant\" WHERE slug = 'default';" 2>/dev/null | tr -d ' \n')
+
+    if [[ -z "$tenant_id" ]]; then
+        err "Could not find default tenant in Hatchet database"
+        err "Hatchet may still be initializing. Try re-running the script."
+        return
+    fi
+
+    # Generate token via hatchet-admin
+    local token
+    token=$(compose_cmd exec -T hatchet /hatchet-admin token create \
+        --config /config --tenant-id "$tenant_id" 2>/dev/null | tr -d '\n')
+
+    if [[ -z "$token" ]]; then
+        err "Failed to generate Hatchet token"
+        err "Try generating manually: see server/README.md"
+        return
+    fi
+
+    env_set "$SERVER_ENV" "HATCHET_CLIENT_TOKEN" "$token"
+    ok "HATCHET_CLIENT_TOKEN generated and saved to server/.env"
+
+    # Restart services that need the token
+    info "Restarting services with new Hatchet token..."
+    compose_cmd restart server worker hatchet-worker-cpu hatchet-worker-llm
+    ok "Services restarted with Hatchet token"
+}
+
 # =========================================================
 # Main
 # =========================================================
@@ -957,6 +1197,48 @@ main() {
     echo ""
     step_server_env
     echo ""
+
+    # Auto-detect video platforms from server/.env (after step_server_env so file exists)
+    DAILY_DETECTED=false
+    WHEREBY_DETECTED=false
+    if env_has_key "$SERVER_ENV" "DAILY_API_KEY" && [[ -n "$(env_get "$SERVER_ENV" "DAILY_API_KEY")" ]]; then
+        DAILY_DETECTED=true
+    fi
+    if env_has_key "$SERVER_ENV" "WHEREBY_API_KEY" && [[ -n "$(env_get "$SERVER_ENV" "WHEREBY_API_KEY")" ]]; then
+        WHEREBY_DETECTED=true
+    fi
+    ANY_PLATFORM_DETECTED=false
+    [[ "$DAILY_DETECTED" == "true" || "$WHEREBY_DETECTED" == "true" ]] && ANY_PLATFORM_DETECTED=true
+
+    # Conditional profile activation for Daily.co
+    if [[ "$DAILY_DETECTED" == "true" ]]; then
+        COMPOSE_PROFILES+=("dailyco")
+        ok "Daily.co detected — enabling Hatchet workflow services"
+    fi
+
+    # Generate .env.hatchet for hatchet dashboard config
+    if [[ "$DAILY_DETECTED" == "true" ]]; then
+        local hatchet_server_url hatchet_cookie_domain
+        if [[ -n "$CUSTOM_DOMAIN" ]]; then
+            hatchet_server_url="https://${CUSTOM_DOMAIN}:8888"
+            hatchet_cookie_domain="$CUSTOM_DOMAIN"
+        elif [[ -n "$PRIMARY_IP" ]]; then
+            hatchet_server_url="http://${PRIMARY_IP}:8888"
+            hatchet_cookie_domain="$PRIMARY_IP"
+        else
+            hatchet_server_url="http://localhost:8888"
+            hatchet_cookie_domain="localhost"
+        fi
+        cat > "$ROOT_DIR/.env.hatchet" << EOF
+SERVER_URL=$hatchet_server_url
+SERVER_AUTH_COOKIE_DOMAIN=$hatchet_cookie_domain
+EOF
+        ok "Generated .env.hatchet (dashboard URL=$hatchet_server_url)"
+    else
+        # Create empty .env.hatchet so compose doesn't fail if dailyco profile is ever activated manually
+        touch "$ROOT_DIR/.env.hatchet"
+    fi
+
     step_www_env
     echo ""
     step_storage
@@ -966,6 +1248,8 @@ main() {
     step_services
     echo ""
     step_health
+    echo ""
+    step_hatchet_token
 
     echo ""
     echo "=========================================="
@@ -995,6 +1279,9 @@ main() {
     [[ "$USE_GARAGE" != "true" ]] && echo "  Storage: External S3"
     [[ "$USES_OLLAMA" == "true" ]] && echo "  LLM:     Ollama ($OLLAMA_MODEL) for summarization/topics"
     [[ "$USES_OLLAMA" != "true" ]] && echo "  LLM:     External (configure in server/.env)"
+    [[ "$DAILY_DETECTED" == "true" ]] && echo "  Video:   Daily.co (live rooms + multitrack processing via Hatchet)"
+    [[ "$WHEREBY_DETECTED" == "true" ]] && echo "  Video:   Whereby (live rooms)"
+    [[ "$ANY_PLATFORM_DETECTED" != "true" ]] && echo "  Video:   None (rooms disabled)"
     echo ""
     echo "  To stop:   docker compose -f docker-compose.selfhosted.yml down"
     echo "  To re-run: ./scripts/setup-selfhosted.sh $*"

server/.env.example

@@ -86,11 +86,23 @@ LLM_API_KEY=not-needed
 ## Context size for summary generation (tokens)
 LLM_CONTEXT_WINDOW=16000
 
+## =======================================================
+## Audio Padding
+##
+## backends: pyav (in-process PyAV), modal (HTTP API client)
+## Default is "pyav" — no external service needed.
+## Set to "modal" when using Modal.com or self-hosted gpu/self_hosted/ container.
+## =======================================================
+#PADDING_BACKEND=pyav
+#PADDING_BACKEND=modal
+#PADDING_URL=https://xxxxx--reflector-padding-web.modal.run
+#PADDING_MODAL_API_KEY=xxxxx
+
 ## =======================================================
 ## Diarization
 ##
-## Only available on modal
+## backends: modal (HTTP API), pyannote (in-process pyannote.audio)
-## To allow diarization, you need to expose expose the files to be dowloded by the pipeline
+## To allow diarization, you need to expose expose the files to be downloaded by the pipeline
 ## =======================================================
 DIARIZATION_ENABLED=false
 DIARIZATION_BACKEND=modal
@@ -137,6 +149,10 @@ TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
 #DAILYCO_STORAGE_AWS_ROLE_ARN=...  # IAM role ARN for Daily.co S3 access
 #DAILYCO_STORAGE_AWS_BUCKET_NAME=reflector-dailyco
 #DAILYCO_STORAGE_AWS_REGION=us-west-2
+# Worker credentials for reading/deleting from Daily's recording bucket
+# Required when transcript storage is separate from Daily's bucket (e.g., selfhosted with Garage)
+#DAILYCO_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
+#DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
 
 ## Whereby (optional separate bucket)
 #WHEREBY_STORAGE_AWS_BUCKET_NAME=reflector-whereby

server/.env.selfhosted.example

@@ -32,23 +32,46 @@ AUTH_BACKEND=none
 
 # =======================================================
 # Specialized Models (Transcription, Diarization, Translation)
-# These run in the gpu/cpu container — NOT an LLM.
+# These do NOT use an LLM. Configured per mode by the setup script:
-# The "modal" backend means "HTTP API client" — it talks to
+#
-# the self-hosted container, not Modal.com cloud.
+# --gpu mode:    modal backends → GPU container (http://transcription:8000)
+# --cpu mode:    whisper/pyannote/marian/pyav → in-process ML on server/worker
+# --hosted mode: modal backends → user-provided remote GPU service URL
 # =======================================================
+
+# --- --gpu mode (default) ---
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=http://transcription:8000
 TRANSCRIPT_MODAL_API_KEY=selfhosted
-
 DIARIZATION_ENABLED=true
 DIARIZATION_BACKEND=modal
 DIARIZATION_URL=http://transcription:8000
-
 TRANSLATION_BACKEND=modal
 TRANSLATE_URL=http://transcription:8000
+PADDING_BACKEND=modal
+PADDING_URL=http://transcription:8000
 
-# HuggingFace token — optional, for gated models (e.g. pyannote).
+# --- --cpu mode (set by setup script) ---
-# Falls back to public S3 model bundle if not set.
+# TRANSCRIPT_BACKEND=whisper
+# DIARIZATION_BACKEND=pyannote
+# TRANSLATION_BACKEND=marian
+# PADDING_BACKEND=pyav
+
+# --- --hosted mode (set by setup script) ---
+# TRANSCRIPT_BACKEND=modal
+# TRANSCRIPT_URL=https://your-gpu-service.example.com
+# DIARIZATION_BACKEND=modal
+# DIARIZATION_URL=https://your-gpu-service.example.com
+# ... (all URLs point to one remote service)
+
+# Whisper model sizes for local transcription (--cpu mode)
+# Options: "tiny", "base", "small", "medium", "large-v2"
+# WHISPER_CHUNK_MODEL=tiny
+# WHISPER_FILE_MODEL=tiny
+
+# HuggingFace token — for gated models (e.g. pyannote diarization).
+# Required for --gpu and --cpu modes; falls back to public S3 bundle if not set.
+# Not needed for --hosted mode (remote service handles its own auth).
 # HF_TOKEN=hf_xxxxx
 
 # =======================================================
@@ -93,15 +116,42 @@ TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
 # =======================================================
 # Daily.co Live Rooms (Optional)
 # Enable real-time meeting rooms with Daily.co integration.
-# Requires a Daily.co account: https://www.daily.co/
+# Configure these BEFORE running setup-selfhosted.sh and the
+# script will auto-detect and start Hatchet workflow services.
+#
+# Prerequisites:
+#   1. Daily.co account: https://www.daily.co/
+#   2. API key: Dashboard → Developers → API Keys
+#   3. S3 bucket for recordings: https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket
+#   4. IAM role ARN for Daily.co to write recordings to your bucket
+#
+# After configuring, run: ./scripts/setup-selfhosted.sh <your-flags>
+# The script will detect DAILY_API_KEY and automatically:
+#   - Start Hatchet workflow engine + CPU/LLM workers
+#   - Generate a Hatchet API token
+#   - Enable FEATURE_ROOMS in the frontend
 # =======================================================
-# DEFAULT_VIDEO_PLATFORM=daily
 # DAILY_API_KEY=your-daily-api-key
 # DAILY_SUBDOMAIN=your-subdomain
-# DAILY_WEBHOOK_SECRET=your-daily-webhook-secret
+# DEFAULT_VIDEO_PLATFORM=daily
 # DAILYCO_STORAGE_AWS_BUCKET_NAME=reflector-dailyco
 # DAILYCO_STORAGE_AWS_REGION=us-east-1
 # DAILYCO_STORAGE_AWS_ROLE_ARN=arn:aws:iam::role/DailyCoAccess
+# Worker credentials for reading/deleting from Daily's recording bucket
+# Required when transcript storage is separate from Daily's bucket (e.g., selfhosted with Garage)
+# DAILYCO_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
+# DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
+# DAILY_WEBHOOK_SECRET=your-daily-webhook-secret  # optional, for faster recording discovery
+
+# =======================================================
+# Hatchet Workflow Engine (Auto-configured for Daily.co)
+# Required for Daily.co multitrack recording processing.
+# The setup script generates HATCHET_CLIENT_TOKEN automatically.
+# Do not set these manually unless you know what you're doing.
+# =======================================================
+# HATCHET_CLIENT_TOKEN=<auto-generated-by-script>
+# HATCHET_CLIENT_SERVER_URL=http://hatchet:8888
+# HATCHET_CLIENT_HOST_PORT=hatchet:7077
 
 # =======================================================
 # Feature Flags

server/Dockerfile

@@ -6,7 +6,7 @@ ENV PYTHONUNBUFFERED=1 \
 
 # builder install base dependencies
 WORKDIR /tmp
-RUN apt-get update && apt-get install -y curl && apt-get clean
+RUN apt-get update && apt-get install -y curl ffmpeg && apt-get clean
 ADD https://astral.sh/uv/install.sh /uv-installer.sh
 RUN sh /uv-installer.sh && rm /uv-installer.sh
 ENV PATH="/root/.local/bin/:$PATH"
@@ -17,9 +17,6 @@ WORKDIR /app
 COPY pyproject.toml uv.lock README.md /app/
 RUN uv sync --compile-bytecode --locked
 
-# pre-download nltk packages
-RUN uv run python -c "import nltk; nltk.download('punkt_tab'); nltk.download('averaged_perceptron_tagger_eng')"
-
 # bootstrap
 COPY alembic.ini runserver.sh /app/
 COPY images /app/images

server/pyproject.toml

@@ -18,17 +18,16 @@ dependencies = [
     "fastapi[standard]>=0.100.1",
     "sentry-sdk[fastapi]>=1.29.2",
     "httpx>=0.24.1",
-    "fastapi-pagination>=0.12.6",
+    "fastapi-pagination>=0.14.2",
     "databases[aiosqlite, asyncpg]>=0.7.0",
     "sqlalchemy<1.5",
     "alembic>=1.11.3",
-    "nltk>=3.8.1",
     "prometheus-fastapi-instrumentator>=6.1.0",
     "sentencepiece>=0.1.99",
     "protobuf>=4.24.3",
     "celery>=5.3.4",
     "redis>=5.0.1",
-    "python-jose[cryptography]>=3.3.0",
+    "pyjwt[crypto]>=2.8.0",
     "python-multipart>=0.0.6",
     "transformers>=4.36.2",
     "jsonschema>=4.23.0",
@@ -39,7 +38,8 @@ dependencies = [
     "pytest-env>=1.1.5",
     "webvtt-py>=0.5.0",
     "icalendar>=6.0.0",
-    "hatchet-sdk>=0.47.0",
+    "hatchet-sdk==1.22.16",
+    "pydantic>=2.12.5",
 ]
 
 [dependency-groups]
@@ -71,9 +71,12 @@ local = [
     "faster-whisper>=0.10.0",
 ]
 silero-vad = [
-    "silero-vad>=5.1.2",
+    "silero-vad==5.1.2",
     "torch>=2.8.0",
     "torchaudio>=2.8.0",
+    "pyannote.audio==3.4.0",
+    "pytorch-lightning<2.6",
+    "librosa==0.10.1",
 ]
 
 [tool.uv]

server/reflector/_warnings_filter.py

@@ -0,0 +1,13 @@
+"""
+Suppress known dependency warnings. Import this before any reflector/hatchet_sdk
+imports that pull in pydantic (e.g. llama_index) to hide UnsupportedFieldAttributeWarning
+about validate_default.
+"""
+
+import warnings
+
+warnings.filterwarnings(
+    "ignore",
+    message=".*validate_default.*",
+    category=UserWarning,
+)

server/reflector/auth/__init__.py

@@ -14,6 +14,7 @@ current_user = auth_module.current_user
 current_user_optional = auth_module.current_user_optional
 parse_ws_bearer_token = auth_module.parse_ws_bearer_token
 current_user_ws_optional = auth_module.current_user_ws_optional
+verify_raw_token = auth_module.verify_raw_token
 
 # Optional router (e.g. for /auth/login in password backend)
 router = getattr(auth_module, "router", None)

server/reflector/auth/auth_jwt.py

@@ -4,8 +4,8 @@ from fastapi import Depends, HTTPException
 
 if TYPE_CHECKING:
     from fastapi import WebSocket
+import jwt
 from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
-from jose import JWTError, jwt
 from pydantic import BaseModel
 
 from reflector.db.user_api_keys import user_api_keys_controller
@@ -54,7 +54,7 @@ class JWTAuth:
                 audience=jwt_audience,
             )
             return payload
-        except JWTError as e:
+        except jwt.PyJWTError as e:
             logger.error(f"JWT error: {e}")
             raise
 
@@ -94,7 +94,7 @@ async def _authenticate_user(
                 )
 
             user_infos.append(UserInfo(sub=user.id, email=email))
-        except JWTError as e:
+        except jwt.PyJWTError as e:
             logger.error(f"JWT error: {e}")
             raise HTTPException(status_code=401, detail="Invalid authentication")
 
@@ -144,3 +144,8 @@ async def current_user_ws_optional(websocket: "WebSocket") -> Optional[UserInfo]
     if not token:
         return None
     return await _authenticate_user(token, None, JWTAuth())
+
+
+async def verify_raw_token(token: str) -> Optional[UserInfo]:
+    """Verify a raw JWT token string (used for query-param auth fallback)."""
+    return await _authenticate_user(token, None, JWTAuth())

server/reflector/auth/auth_none.py

@@ -27,3 +27,8 @@ def parse_ws_bearer_token(websocket):
 
 async def current_user_ws_optional(websocket):
     return None
+
+
+async def verify_raw_token(token):
+    """Verify a raw JWT token string (used for query-param auth fallback)."""
+    return None

server/reflector/auth/auth_password.py

@@ -9,9 +9,9 @@ from collections import defaultdict
 from datetime import datetime, timedelta, timezone
 from typing import TYPE_CHECKING, Annotated, Optional
 
+import jwt
 from fastapi import APIRouter, Depends, HTTPException, Request
 from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
-from jose import JWTError, jwt
 from pydantic import BaseModel
 
 from reflector.auth.password_utils import verify_password
@@ -110,7 +110,7 @@ async def _authenticate_user(
             user_id = payload["sub"]
             email = payload.get("email")
             user_infos.append(UserInfo(sub=user_id, email=email))
-        except JWTError as e:
+        except jwt.PyJWTError as e:
             logger.error(f"JWT error: {e}")
             raise HTTPException(status_code=401, detail="Invalid authentication")
 
@@ -168,6 +168,11 @@ async def current_user_ws_optional(websocket: "WebSocket") -> Optional[UserInfo]
     return await _authenticate_user(token, None)
 
 
+async def verify_raw_token(token: str) -> Optional[UserInfo]:
+    """Verify a raw JWT token string (used for query-param auth fallback)."""
+    return await _authenticate_user(token, None)
+
+
 # --- Login router ---
 router = APIRouter(prefix="/auth", tags=["auth"])
 

server/reflector/hatchet/constants.py

@@ -39,5 +39,12 @@ TIMEOUT_MEDIUM = (
     300  # Single LLM calls, waveform generation (5m for slow LLM responses)
 )
 TIMEOUT_LONG = 180  # Action items (larger context LLM)
-TIMEOUT_AUDIO = 720  # Audio processing: padding, mixdown
+TIMEOUT_TITLE = 300  # generate_title (single LLM call; doc: reduce from 600s)
-TIMEOUT_HEAVY = 600  # Transcription, fan-out LLM tasks
+TIMEOUT_AUDIO = 720  # Audio processing: padding, mixdown (Hatchet execution_timeout)
+TIMEOUT_AUDIO_HTTP = (
+    660  # httpx timeout for pad_track — below 720 so Hatchet doesn't race
+)
+TIMEOUT_HEAVY = 600  # Transcription, fan-out LLM tasks (Hatchet execution_timeout)
+TIMEOUT_HEAVY_HTTP = (
+    540  # httpx timeout for transcribe_track — below 600 so Hatchet doesn't race
+)

server/reflector/hatchet/error_classification.py

@@ -0,0 +1,74 @@
+"""Classify exceptions as non-retryable for Hatchet workflows.
+
+When a task raises NonRetryableException (or an exception classified as
+non-retryable and re-raised as such), Hatchet stops immediately — no further
+retries. Used by with_error_handling to avoid wasting retries on config errors,
+auth failures, corrupt data, etc.
+"""
+
+# Optional dependencies: only classify if the exception type is available.
+# This avoids hard dependency on openai/av/botocore for code paths that don't use them.
+try:
+    import openai
+except ImportError:
+    openai = None  # type: ignore[assignment]
+
+try:
+    import av
+except ImportError:
+    av = None  # type: ignore[assignment]
+
+try:
+    from botocore.exceptions import ClientError as BotoClientError
+except ImportError:
+    BotoClientError = None  # type: ignore[misc, assignment]
+
+from hatchet_sdk import NonRetryableException
+from httpx import HTTPStatusError
+
+from reflector.llm import LLMParseError
+
+# HTTP status codes that won't change on retry (auth, not found, payment, payload)
+NON_RETRYABLE_HTTP_STATUSES = {401, 402, 403, 404, 413}
+NON_RETRYABLE_S3_CODES = {"AccessDenied", "NoSuchBucket", "NoSuchKey"}
+
+
+def is_non_retryable(e: BaseException) -> bool:
+    """Return True if the exception should stop Hatchet retries immediately.
+
+    Hard failures (config, auth, missing resource, corrupt data) return True.
+    Transient errors (timeouts, 5xx, 429, connection) return False.
+    """
+    if isinstance(e, NonRetryableException):
+        return True
+
+    # Config/input errors
+    if isinstance(e, (ValueError, TypeError)):
+        return True
+
+    # HTTP status codes that won't change on retry
+    if isinstance(e, HTTPStatusError):
+        return e.response.status_code in NON_RETRYABLE_HTTP_STATUSES
+
+    # OpenAI auth errors
+    if openai is not None and isinstance(e, openai.AuthenticationError):
+        return True
+
+    # LLM parse failures (already retried internally)
+    if isinstance(e, LLMParseError):
+        return True
+
+    # S3 permission/existence errors
+    if BotoClientError is not None and isinstance(e, BotoClientError):
+        code = e.response.get("Error", {}).get("Code", "")
+        return code in NON_RETRYABLE_S3_CODES
+
+    # Corrupt audio (PyAV) — AVError in some versions; fallback to InvalidDataError
+    if av is not None:
+        av_error = getattr(av, "AVError", None) or getattr(
+            getattr(av, "error", None), "InvalidDataError", None
+        )
+        if av_error is not None and isinstance(e, av_error):
+            return True
+
+    return False

server/reflector/hatchet/run_workers_cpu.py

@@ -7,6 +7,7 @@ Configuration:
 - Worker affinity: pool=cpu-heavy
 """
 
+import reflector._warnings_filter  # noqa: F401 -- side effect: suppress pydantic validate_default warning
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     daily_multitrack_pipeline,

server/reflector/hatchet/run_workers_llm.py

@@ -5,6 +5,7 @@ Handles: all tasks except mixdown_tracks (transcription, LLM inference, orchestr
 
 import asyncio
 
+import reflector._warnings_filter  # noqa: F401 -- side effect: suppress pydantic validate_default warning
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     daily_multitrack_pipeline,

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -27,6 +27,7 @@ from hatchet_sdk import (
     ConcurrencyExpression,
     ConcurrencyLimitStrategy,
     Context,
+    NonRetryableException,
 )
 from hatchet_sdk.labels import DesiredWorkerLabel
 from pydantic import BaseModel
@@ -43,8 +44,10 @@ from reflector.hatchet.constants import (
     TIMEOUT_LONG,
     TIMEOUT_MEDIUM,
     TIMEOUT_SHORT,
+    TIMEOUT_TITLE,
     TaskName,
 )
+from reflector.hatchet.error_classification import is_non_retryable
 from reflector.hatchet.workflows.models import (
     ActionItemsResult,
     ConsentResult,
@@ -90,7 +93,6 @@ from reflector.processors.summary.summary_builder import SummaryBuilder
 from reflector.processors.types import TitleSummary, 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,
@@ -117,6 +119,7 @@ class PipelineInput(BaseModel):
     bucket_name: NonEmptyString
     transcript_id: NonEmptyString
     room_id: NonEmptyString | None = None
+    source_platform: str = "daily"
 
 
 hatchet = HatchetClientManager.get_client()
@@ -170,15 +173,10 @@ async def set_workflow_error_status(transcript_id: NonEmptyString) -> bool:
 
 
 def _spawn_storage():
-    """Create fresh storage instance."""
+    """Create fresh storage instance for writing to our transcript bucket."""
-    # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
+    from reflector.storage import get_transcripts_storage  # noqa: PLC0415
-    return AwsStorage(
+
-        aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
+    return get_transcripts_storage()
-        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,
-        aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
-    )
 
 
 class Loggable(Protocol):
@@ -221,6 +219,13 @@ def make_audio_progress_logger(
 R = TypeVar("R")
 
 
+def _successful_run_results(
+    results: list[dict[str, Any] | BaseException],
+) -> list[dict[str, Any]]:
+    """Return only successful (non-exception) results from aio_run_many(return_exceptions=True)."""
+    return [r for r in results if not isinstance(r, BaseException)]
+
+
 def with_error_handling(
     step_name: TaskName, set_error_status: bool = True
 ) -> Callable[
@@ -248,8 +253,12 @@ def with_error_handling(
                     error=str(e),
                     exc_info=True,
                 )
+                if is_non_retryable(e):
+                    # Hard fail: stop retries, set error status, fail workflow
                     if set_error_status:
                         await set_workflow_error_status(input.transcript_id)
+                    raise NonRetryableException(str(e)) from e
+                # Transient: do not set error status — Hatchet will retry
                 raise
 
         return wrapper  # type: ignore[return-value]
@@ -258,7 +267,10 @@ def with_error_handling(
 
 
 @daily_multitrack_pipeline.task(
-    execution_timeout=timedelta(seconds=TIMEOUT_SHORT), retries=3
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
 )
 @with_error_handling(TaskName.GET_RECORDING)
 async def get_recording(input: PipelineInput, ctx: Context) -> RecordingResult:
@@ -314,6 +326,8 @@ async def get_recording(input: PipelineInput, ctx: Context) -> RecordingResult:
     parents=[get_recording],
     execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
 )
 @with_error_handling(TaskName.GET_PARTICIPANTS)
 async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsResult:
@@ -417,6 +431,8 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
     parents=[get_participants],
     execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
 )
 @with_error_handling(TaskName.PROCESS_TRACKS)
 async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksResult:
@@ -434,12 +450,13 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
                 bucket_name=input.bucket_name,
                 transcript_id=input.transcript_id,
                 language=source_language,
+                source_platform=input.source_platform,
             )
         )
         for i, track in enumerate(input.tracks)
     ]
 
-    results = await track_workflow.aio_run_many(bulk_runs)
+    results = await track_workflow.aio_run_many(bulk_runs, return_exceptions=True)
 
     target_language = participants_result.target_language
 
@@ -447,7 +464,18 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
     padded_tracks = []
     created_padded_files = set()
 
-    for result in results:
+    for i, result in enumerate(results):
+        if isinstance(result, BaseException):
+            logger.error(
+                "[Hatchet] process_tracks: track workflow failed, failing step",
+                transcript_id=input.transcript_id,
+                track_index=i,
+                error=str(result),
+            )
+            ctx.log(f"process_tracks: track {i} failed ({result}), failing step")
+            raise ValueError(
+                f"Track {i} workflow failed after retries: {result!s}"
+            ) from result
         transcribe_result = TranscribeTrackResult(**result[TaskName.TRANSCRIBE_TRACK])
         track_words.append(transcribe_result.words)
 
@@ -485,7 +513,9 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
 @daily_multitrack_pipeline.task(
     parents=[process_tracks],
     execution_timeout=timedelta(seconds=TIMEOUT_AUDIO),
-    retries=3,
+    retries=2,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
     desired_worker_labels={
         "pool": DesiredWorkerLabel(
             value="cpu-heavy",
@@ -597,6 +627,8 @@ async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
     parents=[mixdown_tracks],
     execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
 )
 @with_error_handling(TaskName.GENERATE_WAVEFORM)
 async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResult:
@@ -665,6 +697,8 @@ async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResul
     parents=[process_tracks],
     execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
 )
 @with_error_handling(TaskName.DETECT_TOPICS)
 async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
@@ -726,11 +760,22 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
         for chunk in chunks
     ]
 
-    results = await topic_chunk_workflow.aio_run_many(bulk_runs)
+    results = await topic_chunk_workflow.aio_run_many(bulk_runs, return_exceptions=True)
 
-    topic_chunks = [
+    topic_chunks: list[TopicChunkResult] = []
-        TopicChunkResult(**result[TaskName.DETECT_CHUNK_TOPIC]) for result in results
+    for i, result in enumerate(results):
-    ]
+        if isinstance(result, BaseException):
+            logger.error(
+                "[Hatchet] detect_topics: chunk workflow failed, failing step",
+                transcript_id=input.transcript_id,
+                chunk_index=i,
+                error=str(result),
+            )
+            ctx.log(f"detect_topics: chunk {i} failed ({result}), failing step")
+            raise ValueError(
+                f"Topic chunk {i} workflow failed after retries: {result!s}"
+            ) from result
+        topic_chunks.append(TopicChunkResult(**result[TaskName.DETECT_CHUNK_TOPIC]))
 
     async with fresh_db_connection():
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
@@ -768,8 +813,10 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
 
 @daily_multitrack_pipeline.task(
     parents=[detect_topics],
-    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    execution_timeout=timedelta(seconds=TIMEOUT_TITLE),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
 )
 @with_error_handling(TaskName.GENERATE_TITLE)
 async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
@@ -834,7 +881,9 @@ async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
 @daily_multitrack_pipeline.task(
     parents=[detect_topics],
     execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
-    retries=3,
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
 )
 @with_error_handling(TaskName.EXTRACT_SUBJECTS)
 async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult:
@@ -913,6 +962,8 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
     parents=[extract_subjects],
     execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
 )
 @with_error_handling(TaskName.PROCESS_SUBJECTS)
 async def process_subjects(input: PipelineInput, ctx: Context) -> ProcessSubjectsResult:
@@ -939,12 +990,24 @@ async def process_subjects(input: PipelineInput, ctx: Context) -> ProcessSubject
         for i, subject in enumerate(subjects)
     ]
 
-    results = await subject_workflow.aio_run_many(bulk_runs)
+    results = await subject_workflow.aio_run_many(bulk_runs, return_exceptions=True)
 
-    subject_summaries = [
+    subject_summaries: list[SubjectSummaryResult] = []
+    for i, result in enumerate(results):
+        if isinstance(result, BaseException):
+            logger.error(
+                "[Hatchet] process_subjects: subject workflow failed, failing step",
+                transcript_id=input.transcript_id,
+                subject_index=i,
+                error=str(result),
+            )
+            ctx.log(f"process_subjects: subject {i} failed ({result}), failing step")
+            raise ValueError(
+                f"Subject {i} workflow failed after retries: {result!s}"
+            ) from result
+        subject_summaries.append(
             SubjectSummaryResult(**result[TaskName.GENERATE_DETAILED_SUMMARY])
-        for result in results
+        )
-    ]
 
     ctx.log(f"process_subjects complete: {len(subject_summaries)} summaries")
 
@@ -955,6 +1018,8 @@ async def process_subjects(input: PipelineInput, ctx: Context) -> ProcessSubject
     parents=[process_subjects],
     execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
 )
 @with_error_handling(TaskName.GENERATE_RECAP)
 async def generate_recap(input: PipelineInput, ctx: Context) -> RecapResult:
@@ -1044,6 +1109,8 @@ async def generate_recap(input: PipelineInput, ctx: Context) -> RecapResult:
     parents=[extract_subjects],
     execution_timeout=timedelta(seconds=TIMEOUT_LONG),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
 )
 @with_error_handling(TaskName.IDENTIFY_ACTION_ITEMS)
 async def identify_action_items(
@@ -1112,6 +1179,8 @@ async def identify_action_items(
     parents=[process_tracks, generate_title, generate_recap, identify_action_items],
     execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
     retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=5,
 )
 @with_error_handling(TaskName.FINALIZE)
 async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
@@ -1181,7 +1250,11 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
 
 
 @daily_multitrack_pipeline.task(
-    parents=[finalize], execution_timeout=timedelta(seconds=TIMEOUT_SHORT), retries=3
+    parents=[finalize],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
 )
 @with_error_handling(TaskName.CLEANUP_CONSENT, set_error_status=False)
 async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
@@ -1195,7 +1268,10 @@ async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
         )
         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
+        from reflector.storage import (  # noqa: PLC0415
+            get_source_storage,
+            get_transcripts_storage,
+        )
 
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if not transcript:
@@ -1245,7 +1321,7 @@ async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
         deletion_errors = []
 
         if input_track_keys and input.bucket_name:
-            master_storage = get_transcripts_storage()
+            master_storage = get_source_storage(input.source_platform)
             for key in input_track_keys:
                 try:
                     await master_storage.delete_file(key, bucket=input.bucket_name)
@@ -1284,6 +1360,8 @@ async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
     parents=[cleanup_consent],
     execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
     retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
 )
 @with_error_handling(TaskName.POST_ZULIP, set_error_status=False)
 async def post_zulip(input: PipelineInput, ctx: Context) -> ZulipResult:
@@ -1311,6 +1389,8 @@ async def post_zulip(input: PipelineInput, ctx: Context) -> ZulipResult:
     parents=[cleanup_consent],
     execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
     retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
 )
 @with_error_handling(TaskName.SEND_WEBHOOK, set_error_status=False)
 async def send_webhook(input: PipelineInput, ctx: Context) -> WebhookResult:
@@ -1379,3 +1459,32 @@ async def send_webhook(input: PipelineInput, ctx: Context) -> WebhookResult:
         except Exception as e:
             ctx.log(f"send_webhook unexpected error, continuing anyway: {e}")
             return WebhookResult(webhook_sent=False)
+
+
+async def on_workflow_failure(input: PipelineInput, ctx: Context) -> None:
+    """Run when the workflow is truly dead (all retries exhausted).
+
+    Sets transcript status to 'error' only if it is not already 'ended'.
+    Post-finalize tasks (cleanup_consent, post_zulip, send_webhook) use
+    set_error_status=False; if one of them fails, we must not overwrite
+    the 'ended' status that finalize already set.
+    """
+    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 and transcript.status == "ended":
+            logger.info(
+                "[Hatchet] on_workflow_failure: transcript already ended, skipping error status (failure was post-finalize)",
+                transcript_id=input.transcript_id,
+            )
+            ctx.log(
+                "on_workflow_failure: transcript already ended, skipping error status"
+            )
+            return
+    await set_workflow_error_status(input.transcript_id)
+
+
+@daily_multitrack_pipeline.on_failure_task()
+async def _register_on_workflow_failure(input: PipelineInput, ctx: Context) -> None:
+    await on_workflow_failure(input, ctx)

server/reflector/hatchet/workflows/padding_workflow.py

@@ -24,6 +24,7 @@ class PaddingInput(BaseModel):
     s3_key: str
     bucket_name: str
     transcript_id: str
+    source_platform: str = "daily"
 
 
 hatchet = HatchetClientManager.get_client()
@@ -33,7 +34,12 @@ padding_workflow = hatchet.workflow(
 )
 
 
-@padding_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
+@padding_workflow.task(
+    execution_timeout=timedelta(seconds=TIMEOUT_AUDIO),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
 async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
     """Pad audio track with silence based on WebM container start_time."""
     ctx.log(f"pad_track: track {input.track_index}, s3_key={input.s3_key}")
@@ -45,20 +51,14 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
     )
 
     try:
-        # Create fresh storage instance to avoid aioboto3 fork issues
+        from reflector.storage import (  # noqa: PLC0415
-        from reflector.settings import settings  # noqa: PLC0415
+            get_source_storage,
-        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
+            get_transcripts_storage,
-
-        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
-        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,
-            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
         )
 
-        source_url = await storage.get_file_url(
+        # Source reads: use platform-specific credentials
+        source_storage = get_source_storage(input.source_platform)
+        source_url = await source_storage.get_file_url(
             input.s3_key,
             operation="get_object",
             expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
@@ -96,21 +96,19 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
 
         storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
 
-        # Presign PUT URL for output (Modal will upload directly)
+        # Output writes: use transcript storage (our own bucket)
-        output_url = await storage.get_file_url(
+        output_storage = get_transcripts_storage()
+        output_url = await output_storage.get_file_url(
             storage_path,
             operation="put_object",
             expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
         )
 
-        import httpx  # noqa: PLC0415
+        from reflector.processors.audio_padding_auto import (  # noqa: PLC0415
-
+            AudioPaddingAutoProcessor,
-        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
-            AudioPaddingModalProcessor,
         )
 
-        try:
+        processor = AudioPaddingAutoProcessor()
-            processor = AudioPaddingModalProcessor()
         result = await processor.pad_track(
             track_url=source_url,
             output_url=output_url,
@@ -119,29 +117,7 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
         )
         file_size = result.size
 
-            ctx.log(f"pad_track: Modal returned size={file_size}")
+        ctx.log(f"pad_track: padding returned size={file_size}")
-        except httpx.HTTPStatusError as e:
-            error_detail = e.response.text if hasattr(e.response, "text") else str(e)
-            logger.error(
-                "[Hatchet] Modal padding HTTP error",
-                transcript_id=input.transcript_id,
-                track_index=input.track_index,
-                status_code=e.response.status_code if hasattr(e, "response") else None,
-                error=error_detail,
-                exc_info=True,
-            )
-            raise Exception(
-                f"Modal padding failed: HTTP {e.response.status_code}"
-            ) from e
-        except httpx.TimeoutException as e:
-            logger.error(
-                "[Hatchet] Modal padding timeout",
-                transcript_id=input.transcript_id,
-                track_index=input.track_index,
-                error=str(e),
-                exc_info=True,
-            )
-            raise Exception("Modal padding timeout") from e
 
         logger.info(
             "[Hatchet] pad_track complete",

server/reflector/hatchet/workflows/subject_processing.py

@@ -13,7 +13,7 @@ from hatchet_sdk.rate_limit import RateLimit
 from pydantic import BaseModel
 
 from reflector.hatchet.client import HatchetClientManager
-from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_MEDIUM
+from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_HEAVY
 from reflector.hatchet.workflows.models import SubjectSummaryResult
 from reflector.logger import logger
 from reflector.processors.summary.prompts import (
@@ -41,8 +41,10 @@ subject_workflow = hatchet.workflow(
 
 
 @subject_workflow.task(
-    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
-    retries=3,
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=60,
     rate_limits=[RateLimit(static_key=LLM_RATE_LIMIT_KEY, units=2)],
 )
 async def generate_detailed_summary(

server/reflector/hatchet/workflows/topic_chunk_processing.py

@@ -50,7 +50,9 @@ topic_chunk_workflow = hatchet.workflow(
 
 @topic_chunk_workflow.task(
     execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
-    retries=3,
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=60,
     rate_limits=[RateLimit(static_key=LLM_RATE_LIMIT_KEY, units=1)],
 )
 async def detect_chunk_topic(input: TopicChunkInput, ctx: Context) -> TopicChunkResult:

server/reflector/hatchet/workflows/track_processing.py

@@ -36,6 +36,7 @@ class TrackInput(BaseModel):
     bucket_name: str
     transcript_id: str
     language: str = "en"
+    source_platform: str = "daily"
 
 
 hatchet = HatchetClientManager.get_client()
@@ -43,7 +44,12 @@ hatchet = HatchetClientManager.get_client()
 track_workflow = hatchet.workflow(name="TrackProcessing", input_validator=TrackInput)
 
 
-@track_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
+@track_workflow.task(
+    execution_timeout=timedelta(seconds=TIMEOUT_AUDIO),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
 async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
     """Pad single audio track with silence for alignment.
 
@@ -59,20 +65,14 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
     )
 
     try:
-        # Create fresh storage instance to avoid aioboto3 fork issues
+        from reflector.storage import (  # noqa: PLC0415
-        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
+            get_source_storage,
-        from reflector.settings import settings  # noqa: PLC0415
+            get_transcripts_storage,
-        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,
-            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
         )
 
-        source_url = await storage.get_file_url(
+        # Source reads: use platform-specific credentials
+        source_storage = get_source_storage(input.source_platform)
+        source_url = await source_storage.get_file_url(
             input.s3_key,
             operation="get_object",
             expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
@@ -99,18 +99,19 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
 
         storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
 
-        # Presign PUT URL for output (Modal uploads directly)
+        # Output writes: use transcript storage (our own bucket)
-        output_url = await storage.get_file_url(
+        output_storage = get_transcripts_storage()
+        output_url = await output_storage.get_file_url(
             storage_path,
             operation="put_object",
             expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
         )
 
-        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
+        from reflector.processors.audio_padding_auto import (  # noqa: PLC0415
-            AudioPaddingModalProcessor,
+            AudioPaddingAutoProcessor,
         )
 
-        processor = AudioPaddingModalProcessor()
+        processor = AudioPaddingAutoProcessor()
         result = await processor.pad_track(
             track_url=source_url,
             output_url=output_url,
@@ -141,7 +142,11 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
 
 
 @track_workflow.task(
-    parents=[pad_track], execution_timeout=timedelta(seconds=TIMEOUT_HEAVY), retries=3
+    parents=[pad_track],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
 )
 async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackResult:
     """Transcribe audio track using GPU (Modal.com) or local Whisper."""
@@ -161,18 +166,18 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
             raise ValueError("Missing padded_key from pad_track")
 
         # Presign URL on demand (avoids stale URLs on workflow replay)
-        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
+        from reflector.storage import (  # noqa: PLC0415
-        from reflector.settings import settings  # noqa: PLC0415
+            get_source_storage,
-        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
+            get_transcripts_storage,
-
-        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,
-            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
         )
 
+        # If bucket_name is set, file is still in the platform's source bucket (no padding applied).
+        # If bucket_name is None, padded file was written to our transcript storage.
+        if bucket_name:
+            storage = get_source_storage(input.source_platform)
+        else:
+            storage = get_transcripts_storage()
+
         audio_url = await storage.get_file_url(
             padded_key,
             operation="get_object",

server/reflector/llm.py

@@ -1,42 +1,23 @@
 import logging
 from contextvars import ContextVar
-from typing import Generic, Type, TypeVar
+from typing import Type, TypeVar
 from uuid import uuid4
 
 from llama_index.core import Settings
-from llama_index.core.output_parsers import PydanticOutputParser
+from llama_index.core.prompts import PromptTemplate
 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:
-
-Analysis:
-{analysis}
-
-{format_instructions}
-"""
-
 
 class LLMParseError(Exception):
     """Raised when LLM output cannot be parsed after retries."""
@@ -50,157 +31,6 @@ class LLMParseError(Exception):
         )
 
 
-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_format enables grammar-based constrained decoding on backends
-        # that support it (DMR/llama.cpp, vLLM, Ollama, OpenAI).
-        response = await Settings.llm.acomplete(
-            json_prompt,
-            response_format={
-                "type": "json_schema",
-                "json_schema": {
-                    "name": self.output_cls.__name__,
-                    "schema": self.output_cls.model_json_schema(),
-                },
-            },
-        )
-        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 | None = None
@@ -225,7 +55,7 @@ class LLM:
             api_key=self.api_key,
             context_window=self.context_window,
             is_chat_model=True,
-            is_function_calling_model=False,
+            is_function_calling_model=True,
             temperature=self.temperature,
             max_tokens=self.max_tokens,
             timeout=self.settings_obj.LLM_REQUEST_TIMEOUT,
@@ -235,11 +65,26 @@ class LLM:
     async def get_response(
         self, prompt: str, texts: list[str], tone_name: str | None = None
     ) -> str:
-        """Get a text response using TreeSummarize for non-function-calling models"""
+        """Get a text response using TreeSummarize for non-function-calling models.
+
+        Uses the same retry() wrapper as get_structured_response for transient
+        network errors (connection, timeout, OSError) with exponential backoff.
+        """
+
+        async def _call():
             summarizer = TreeSummarize(verbose=False)
-        response = await summarizer.aget_response(prompt, texts, tone_name=tone_name)
+            response = await summarizer.aget_response(
+                prompt, texts, tone_name=tone_name
+            )
             return str(response).strip()
 
+        return await retry(_call)(
+            retry_attempts=3,
+            retry_backoff_interval=1.0,
+            retry_backoff_max=30.0,
+            retry_ignore_exc_types=(ConnectionError, TimeoutError, OSError),
+        )
+
     async def get_structured_response(
         self,
         prompt: str,
@@ -248,36 +93,91 @@ class LLM:
         tone_name: str | None = None,
         timeout: int | None = None,
     ) -> T:
-        """Get structured output from LLM with validation retry via Workflow."""
+        """Get structured output from LLM using tool-call with reflection retry.
-        if timeout is None:
-            timeout = self.settings_obj.LLM_STRUCTURED_RESPONSE_TIMEOUT
 
-        async def run_workflow():
+        Uses astructured_predict (function-calling / tool-call mode) for the
-            workflow = StructuredOutputWorkflow(
+        first attempt.  On ValidationError or parse failure the wrong output
-                output_cls=output_cls,
+        and error are fed back as a reflection prompt and the call is retried
-                max_retries=self.settings_obj.LLM_PARSE_MAX_RETRIES + 1,
+        up to LLM_PARSE_MAX_RETRIES times.
-                timeout=timeout,
+
+        The outer retry() wrapper handles transient network errors with
+        exponential back-off.
+        """
+        max_retries = self.settings_obj.LLM_PARSE_MAX_RETRIES
+
+        async def _call_with_reflection():
+            # Build full prompt: instruction + source texts
+            if texts:
+                texts_block = "\n\n".join(texts)
+                full_prompt = f"{prompt}\n\n{texts_block}"
+            else:
+                full_prompt = prompt
+
+            prompt_tmpl = PromptTemplate("{user_prompt}")
+            last_error: str | None = None
+
+            for attempt in range(1, max_retries + 2):  # +2: first try + retries
+                try:
+                    if attempt == 1:
+                        result = await Settings.llm.astructured_predict(
+                            output_cls, prompt_tmpl, user_prompt=full_prompt
+                        )
+                    else:
+                        reflection_tmpl = PromptTemplate(
+                            "{user_prompt}\n\n{reflection}"
+                        )
+                        result = await Settings.llm.astructured_predict(
+                            output_cls,
+                            reflection_tmpl,
+                            user_prompt=full_prompt,
+                            reflection=reflection,
                         )
 
-            result = await workflow.run(
+                    if attempt > 1:
-                prompt=prompt,
+                        logger.info(
-                texts=texts,
+                            f"LLM structured_predict succeeded on attempt "
-                tone_name=tone_name,
+                            f"{attempt}/{max_retries + 1} for {output_cls.__name__}"
+                        )
+                    return result
+
+                except (ValidationError, ValueError) as e:
+                    wrong_output = str(e)
+                    if len(wrong_output) > 2000:
+                        wrong_output = wrong_output[:2000] + "... [truncated]"
+
+                    last_error = self._format_validation_error(e)
+                    reflection = (
+                        f"Your previous response could not be parsed.\n\n"
+                        f"Error:\n{last_error}\n\n"
+                        "Please try again and return valid data matching the schema."
+                    )
+
+                    logger.error(
+                        f"LLM parse error (attempt {attempt}/{max_retries + 1}): "
+                        f"{type(e).__name__}: {e}\n"
+                        f"Raw response: {wrong_output[:500]}"
                     )
 
-            if "error" in result:
-                error_msg = result["error"] or "Max retries exceeded"
             raise LLMParseError(
                 output_cls=output_cls,
-                    error_msg=error_msg,
+                error_msg=last_error or "Max retries exceeded",
-                    attempts=result.get("attempts", 0),
+                attempts=max_retries + 1,
             )
 
-            return result["success"]
+        return await retry(_call_with_reflection)(
-
-        return await retry(run_workflow)(
             retry_attempts=3,
             retry_backoff_interval=1.0,
             retry_backoff_max=30.0,
-            retry_ignore_exc_types=(WorkflowTimeoutError,),
+            retry_ignore_exc_types=(ConnectionError, TimeoutError, OSError),
         )
+
+    @staticmethod
+    def _format_validation_error(error: Exception) -> str:
+        """Format a validation/parse error for LLM reflection 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)
+        return f"Parse error: {str(error)}"

server/reflector/processors/__init__.py

@@ -4,6 +4,8 @@ from .audio_diarization_auto import AudioDiarizationAutoProcessor  # noqa: F401
 from .audio_downscale import AudioDownscaleProcessor  # noqa: F401
 from .audio_file_writer import AudioFileWriterProcessor  # noqa: F401
 from .audio_merge import AudioMergeProcessor  # noqa: F401
+from .audio_padding import AudioPaddingProcessor  # noqa: F401
+from .audio_padding_auto import AudioPaddingAutoProcessor  # noqa: F401
 from .audio_transcript import AudioTranscriptProcessor  # noqa: F401
 from .audio_transcript_auto import AudioTranscriptAutoProcessor  # noqa: F401
 from .base import (  # noqa: F401

server/reflector/processors/_audio_download.py

@@ -0,0 +1,86 @@
+"""
+Shared audio download utility for local processors.
+
+Downloads audio from a URL to a temporary file for in-process ML inference.
+"""
+
+import asyncio
+import os
+import tempfile
+from pathlib import Path
+
+import requests
+
+from reflector.logger import logger
+
+S3_TIMEOUT = 60
+
+
+async def download_audio_to_temp(url: str) -> Path:
+    """Download audio from URL to a temporary file.
+
+    The caller is responsible for deleting the temp file after use.
+
+    Args:
+        url: Presigned URL or public URL to download audio from.
+
+    Returns:
+        Path to the downloaded temporary file.
+    """
+    loop = asyncio.get_event_loop()
+    return await loop.run_in_executor(None, _download_blocking, url)
+
+
+def _download_blocking(url: str) -> Path:
+    """Blocking download implementation."""
+    log = logger.bind(url=url[:80])
+    log.info("Downloading audio to temp file")
+
+    response = requests.get(url, stream=True, timeout=S3_TIMEOUT)
+    response.raise_for_status()
+
+    # Determine extension from content-type or URL
+    ext = _detect_extension(url, response.headers.get("content-type", ""))
+
+    fd, tmp_path = tempfile.mkstemp(suffix=ext)
+    try:
+        total_bytes = 0
+        with os.fdopen(fd, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+                    total_bytes += len(chunk)
+        log.info("Audio downloaded", bytes=total_bytes, path=tmp_path)
+        return Path(tmp_path)
+    except Exception:
+        # Clean up on failure
+        try:
+            os.unlink(tmp_path)
+        except OSError:
+            pass
+        raise
+
+
+def _detect_extension(url: str, content_type: str) -> str:
+    """Detect audio file extension from URL or content-type."""
+    # Try URL path first
+    path = url.split("?")[0]  # Strip query params
+    for ext in (".wav", ".mp3", ".mp4", ".m4a", ".webm", ".ogg", ".flac"):
+        if path.lower().endswith(ext):
+            return ext
+
+    # Try content-type
+    ct_map = {
+        "audio/wav": ".wav",
+        "audio/x-wav": ".wav",
+        "audio/mpeg": ".mp3",
+        "audio/mp4": ".m4a",
+        "audio/webm": ".webm",
+        "audio/ogg": ".ogg",
+        "audio/flac": ".flac",
+    }
+    for ct, ext in ct_map.items():
+        if ct in content_type.lower():
+            return ext
+
+    return ".audio"

server/reflector/processors/_marian_translator_service.py

@@ -0,0 +1,76 @@
+"""
+MarianMT translation service.
+
+Singleton service that loads HuggingFace MarianMT translation models
+and reuses them across all MarianMT translator processor instances.
+
+Ported from gpu/self_hosted/app/services/translator.py for in-process use.
+"""
+
+import logging
+import threading
+
+from transformers import MarianMTModel, MarianTokenizer, pipeline
+
+logger = logging.getLogger(__name__)
+
+
+class MarianTranslatorService:
+    """MarianMT text translation service for in-process use."""
+
+    def __init__(self):
+        self._pipeline = None
+        self._current_pair = None
+        self._lock = threading.Lock()
+
+    def load(self, source_language: str = "en", target_language: str = "fr"):
+        """Load the translation model for a specific language pair."""
+        model_name = self._resolve_model_name(source_language, target_language)
+        logger.info(
+            "Loading MarianMT model: %s (%s -> %s)",
+            model_name,
+            source_language,
+            target_language,
+        )
+        tokenizer = MarianTokenizer.from_pretrained(model_name)
+        model = MarianMTModel.from_pretrained(model_name)
+        self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
+        self._current_pair = (source_language.lower(), target_language.lower())
+
+    def _resolve_model_name(self, src: str, tgt: str) -> str:
+        """Resolve language pair to MarianMT model name."""
+        pair = (src.lower(), tgt.lower())
+        mapping = {
+            ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
+            ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
+            ("en", "es"): "Helsinki-NLP/opus-mt-en-es",
+            ("es", "en"): "Helsinki-NLP/opus-mt-es-en",
+            ("en", "de"): "Helsinki-NLP/opus-mt-en-de",
+            ("de", "en"): "Helsinki-NLP/opus-mt-de-en",
+        }
+        return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
+
+    def translate(self, text: str, source_language: str, target_language: str) -> dict:
+        """Translate text between languages.
+
+        Args:
+            text: Text to translate.
+            source_language: Source language code (e.g. "en").
+            target_language: Target language code (e.g. "fr").
+
+        Returns:
+            dict with "text" key containing {source_language: original, target_language: translated}.
+        """
+        pair = (source_language.lower(), target_language.lower())
+        if self._pipeline is None or self._current_pair != pair:
+            self.load(source_language, target_language)
+        with self._lock:
+            results = self._pipeline(
+                text, src_lang=source_language, tgt_lang=target_language
+            )
+        translated = results[0]["translation_text"] if results else ""
+        return {"text": {source_language: text, target_language: translated}}
+
+
+# Module-level singleton — shared across all MarianMT translator processors
+translator_service = MarianTranslatorService()

server/reflector/processors/_pyannote_diarization_service.py

@@ -0,0 +1,133 @@
+"""
+Pyannote diarization service using pyannote.audio.
+
+Singleton service that loads the pyannote speaker diarization model once
+and reuses it across all pyannote diarization processor instances.
+
+Ported from gpu/self_hosted/app/services/diarizer.py for in-process use.
+"""
+
+import logging
+import tarfile
+import threading
+from pathlib import Path
+from urllib.request import urlopen
+
+import torch
+import torchaudio
+import yaml
+from pyannote.audio import Pipeline
+
+from reflector.settings import settings
+
+logger = logging.getLogger(__name__)
+
+S3_BUNDLE_URL = "https://reflector-public.s3.us-east-1.amazonaws.com/pyannote-speaker-diarization-3.1.tar.gz"
+BUNDLE_CACHE_DIR = Path.home() / ".cache" / "pyannote-bundle"
+
+
+def _ensure_model(cache_dir: Path) -> str:
+    """Download and extract S3 model bundle if not cached."""
+    model_dir = cache_dir / "pyannote-speaker-diarization-3.1"
+    config_path = model_dir / "config.yaml"
+
+    if config_path.exists():
+        logger.info("Using cached model bundle at %s", model_dir)
+        return str(model_dir)
+
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    tarball_path = cache_dir / "model.tar.gz"
+
+    logger.info("Downloading model bundle from %s", S3_BUNDLE_URL)
+    with urlopen(S3_BUNDLE_URL) as response, open(tarball_path, "wb") as f:
+        while chunk := response.read(8192):
+            f.write(chunk)
+
+    logger.info("Extracting model bundle")
+    with tarfile.open(tarball_path, "r:gz") as tar:
+        tar.extractall(path=cache_dir, filter="data")
+    tarball_path.unlink()
+
+    _patch_config(model_dir, cache_dir)
+    return str(model_dir)
+
+
+def _patch_config(model_dir: Path, cache_dir: Path) -> None:
+    """Rewrite config.yaml to reference local pytorch_model.bin paths."""
+    config_path = model_dir / "config.yaml"
+    with open(config_path) as f:
+        config = yaml.safe_load(f)
+
+    config["pipeline"]["params"]["segmentation"] = str(
+        cache_dir / "pyannote-segmentation-3.0" / "pytorch_model.bin"
+    )
+    config["pipeline"]["params"]["embedding"] = str(
+        cache_dir / "pyannote-wespeaker-voxceleb-resnet34-LM" / "pytorch_model.bin"
+    )
+
+    with open(config_path, "w") as f:
+        yaml.dump(config, f)
+
+    logger.info("Patched config.yaml with local model paths")
+
+
+class PyannoteDiarizationService:
+    """Pyannote speaker diarization service for in-process use."""
+
+    def __init__(self):
+        self._pipeline = None
+        self._device = "cpu"
+        self._lock = threading.Lock()
+
+    def load(self):
+        self._device = "cuda" if torch.cuda.is_available() else "cpu"
+        hf_token = settings.HF_TOKEN
+
+        if hf_token:
+            logger.info("Loading pyannote model from HuggingFace (HF_TOKEN set)")
+            self._pipeline = Pipeline.from_pretrained(
+                "pyannote/speaker-diarization-3.1",
+                use_auth_token=hf_token,
+            )
+        else:
+            logger.info("HF_TOKEN not set — loading model from S3 bundle")
+            model_path = _ensure_model(BUNDLE_CACHE_DIR)
+            config_path = Path(model_path) / "config.yaml"
+            self._pipeline = Pipeline.from_pretrained(str(config_path))
+
+        self._pipeline.to(torch.device(self._device))
+
+    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
+        """Run speaker diarization on an audio file.
+
+        Args:
+            file_path: Path to the audio file.
+            timestamp: Offset to add to all segment timestamps.
+
+        Returns:
+            dict with "diarization" key containing list of
+            {"start": float, "end": float, "speaker": int} segments.
+        """
+        if self._pipeline is None:
+            self.load()
+        waveform, sample_rate = torchaudio.load(file_path)
+        with self._lock:
+            diarization = self._pipeline(
+                {"waveform": waveform, "sample_rate": sample_rate}
+            )
+        segments = []
+        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
+            segments.append(
+                {
+                    "start": round(timestamp + diarization_segment.start, 3),
+                    "end": round(timestamp + diarization_segment.end, 3),
+                    "speaker": int(speaker[-2:])
+                    if speaker and speaker[-2:].isdigit()
+                    else 0,
+                }
+            )
+        return {"diarization": segments}
+
+
+# Module-level singleton — shared across all pyannote diarization processors
+diarization_service = PyannoteDiarizationService()

server/reflector/processors/audio_diarization_pyannote.py

@@ -0,0 +1,37 @@
+"""
+Pyannote audio diarization processor using pyannote.audio in-process.
+
+Downloads audio from URL, runs pyannote diarization locally,
+and returns speaker segments. No HTTP backend needed.
+"""
+
+import asyncio
+import os
+
+from reflector.processors._audio_download import download_audio_to_temp
+from reflector.processors._pyannote_diarization_service import diarization_service
+from reflector.processors.audio_diarization import AudioDiarizationProcessor
+from reflector.processors.audio_diarization_auto import AudioDiarizationAutoProcessor
+from reflector.processors.types import AudioDiarizationInput
+
+
+class AudioDiarizationPyannoteProcessor(AudioDiarizationProcessor):
+    INPUT_TYPE = AudioDiarizationInput
+
+    async def _diarize(self, data: AudioDiarizationInput):
+        """Run pyannote diarization on audio from URL."""
+        tmp_path = await download_audio_to_temp(data.audio_url)
+        try:
+            loop = asyncio.get_event_loop()
+            result = await loop.run_in_executor(
+                None, diarization_service.diarize_file, str(tmp_path)
+            )
+            return result["diarization"]
+        finally:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+
+AudioDiarizationAutoProcessor.register("pyannote", AudioDiarizationPyannoteProcessor)

server/reflector/processors/audio_padding.py

@@ -0,0 +1,23 @@
+"""
+Base class for audio padding processors.
+"""
+
+from pydantic import BaseModel
+
+
+class PaddingResponse(BaseModel):
+    size: int
+    cancelled: bool = False
+
+
+class AudioPaddingProcessor:
+    """Base class for audio padding processors."""
+
+    async def pad_track(
+        self,
+        track_url: str,
+        output_url: str,
+        start_time_seconds: float,
+        track_index: int,
+    ) -> PaddingResponse:
+        raise NotImplementedError

server/reflector/processors/audio_padding_auto.py

@@ -0,0 +1,32 @@
+import importlib
+
+from reflector.processors.audio_padding import AudioPaddingProcessor
+from reflector.settings import settings
+
+
+class AudioPaddingAutoProcessor(AudioPaddingProcessor):
+    _registry = {}
+
+    @classmethod
+    def register(cls, name, kclass):
+        cls._registry[name] = kclass
+
+    def __new__(cls, name: str | None = None, **kwargs):
+        if name is None:
+            name = settings.PADDING_BACKEND
+        if name not in cls._registry:
+            module_name = f"reflector.processors.audio_padding_{name}"
+            importlib.import_module(module_name)
+
+        # gather specific configuration for the processor
+        # search `PADDING_XXX_YYY`, push to constructor as `xxx_yyy`
+        config = {}
+        name_upper = name.upper()
+        settings_prefix = "PADDING_"
+        config_prefix = f"{settings_prefix}{name_upper}_"
+        for key, value in settings:
+            if key.startswith(config_prefix):
+                config_name = key[len(settings_prefix) :].lower()
+                config[config_name] = value
+
+        return cls._registry[name](**config | kwargs)

server/reflector/processors/audio_padding_modal.py

@@ -6,18 +6,14 @@ import asyncio
 import os
 
 import httpx
-from pydantic import BaseModel
 
-from reflector.hatchet.constants import TIMEOUT_AUDIO
+from reflector.hatchet.constants import TIMEOUT_AUDIO_HTTP
 from reflector.logger import logger
+from reflector.processors.audio_padding import AudioPaddingProcessor, PaddingResponse
+from reflector.processors.audio_padding_auto import AudioPaddingAutoProcessor
 
 
-class PaddingResponse(BaseModel):
+class AudioPaddingModalProcessor(AudioPaddingProcessor):
-    size: int
-    cancelled: bool = False
-
-
-class AudioPaddingModalProcessor:
     """Audio padding processor using Modal.com CPU backend via HTTP."""
 
     def __init__(
@@ -64,7 +60,7 @@ class AudioPaddingModalProcessor:
             headers["Authorization"] = f"Bearer {self.modal_api_key}"
 
         try:
-            async with httpx.AsyncClient(timeout=TIMEOUT_AUDIO) as client:
+            async with httpx.AsyncClient(timeout=TIMEOUT_AUDIO_HTTP) as client:
                 response = await client.post(
                     url,
                     headers=headers,
@@ -111,3 +107,6 @@ class AudioPaddingModalProcessor:
         except Exception as e:
             log.error("Modal padding unexpected error", error=str(e), exc_info=True)
             raise
+
+
+AudioPaddingAutoProcessor.register("modal", AudioPaddingModalProcessor)

server/reflector/processors/audio_padding_pyav.py

@@ -0,0 +1,133 @@
+"""
+PyAV audio padding processor.
+
+Pads audio tracks with silence directly in-process (no HTTP).
+Reuses the shared PyAV utilities from reflector.utils.audio_padding.
+"""
+
+import asyncio
+import os
+import tempfile
+
+import av
+
+from reflector.logger import logger
+from reflector.processors.audio_padding import AudioPaddingProcessor, PaddingResponse
+from reflector.processors.audio_padding_auto import AudioPaddingAutoProcessor
+from reflector.utils.audio_padding import apply_audio_padding_to_file
+
+S3_TIMEOUT = 60
+
+
+class AudioPaddingPyavProcessor(AudioPaddingProcessor):
+    """Audio padding processor using PyAV (no HTTP backend)."""
+
+    async def pad_track(
+        self,
+        track_url: str,
+        output_url: str,
+        start_time_seconds: float,
+        track_index: int,
+    ) -> PaddingResponse:
+        """Pad audio track with silence via PyAV.
+
+        Args:
+            track_url: Presigned GET URL for source audio track
+            output_url: Presigned PUT URL for output WebM
+            start_time_seconds: Amount of silence to prepend
+            track_index: Track index for logging
+        """
+        if not track_url:
+            raise ValueError("track_url cannot be empty")
+        if start_time_seconds <= 0:
+            raise ValueError(
+                f"start_time_seconds must be positive, got {start_time_seconds}"
+            )
+
+        log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
+        log.info("Starting local PyAV padding")
+
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None,
+            self._pad_track_blocking,
+            track_url,
+            output_url,
+            start_time_seconds,
+            track_index,
+        )
+
+    def _pad_track_blocking(
+        self,
+        track_url: str,
+        output_url: str,
+        start_time_seconds: float,
+        track_index: int,
+    ) -> PaddingResponse:
+        """Blocking padding work: download, pad with PyAV, upload."""
+        import requests
+
+        log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
+        temp_dir = tempfile.mkdtemp()
+        input_path = None
+        output_path = None
+
+        try:
+            # Download source audio
+            log.info("Downloading track for local padding")
+            response = requests.get(track_url, stream=True, timeout=S3_TIMEOUT)
+            response.raise_for_status()
+
+            input_path = os.path.join(temp_dir, "track.webm")
+            total_bytes = 0
+            with open(input_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+                        total_bytes += len(chunk)
+            log.info("Track downloaded", bytes=total_bytes)
+
+            # Apply padding using shared PyAV utility
+            output_path = os.path.join(temp_dir, "padded.webm")
+            with av.open(input_path) as in_container:
+                apply_audio_padding_to_file(
+                    in_container,
+                    output_path,
+                    start_time_seconds,
+                    track_index,
+                    logger=logger,
+                )
+
+            file_size = os.path.getsize(output_path)
+            log.info("Local padding complete", size=file_size)
+
+            # Upload padded track
+            log.info("Uploading padded track to S3")
+            with open(output_path, "rb") as f:
+                upload_response = requests.put(output_url, data=f, timeout=S3_TIMEOUT)
+            upload_response.raise_for_status()
+            log.info("Upload complete", size=file_size)
+
+            return PaddingResponse(size=file_size)
+
+        except Exception as e:
+            log.error("Local padding failed", error=str(e), exc_info=True)
+            raise
+        finally:
+            if input_path and os.path.exists(input_path):
+                try:
+                    os.unlink(input_path)
+                except Exception as e:
+                    log.warning("Failed to cleanup input file", error=str(e))
+            if output_path and os.path.exists(output_path):
+                try:
+                    os.unlink(output_path)
+                except Exception as e:
+                    log.warning("Failed to cleanup output file", error=str(e))
+            try:
+                os.rmdir(temp_dir)
+            except Exception as e:
+                log.warning("Failed to cleanup temp directory", error=str(e))
+
+
+AudioPaddingAutoProcessor.register("pyav", AudioPaddingPyavProcessor)

server/reflector/processors/audio_transcript_whisper.py

@@ -3,13 +3,17 @@ from faster_whisper import WhisperModel
 from reflector.processors.audio_transcript import AudioTranscriptProcessor
 from reflector.processors.audio_transcript_auto import AudioTranscriptAutoProcessor
 from reflector.processors.types import AudioFile, Transcript, Word
+from reflector.settings import settings
 
 
 class AudioTranscriptWhisperProcessor(AudioTranscriptProcessor):
     def __init__(self):
         super().__init__()
         self.model = WhisperModel(
-            "tiny", device="cpu", compute_type="float32", num_workers=12
+            settings.WHISPER_CHUNK_MODEL,
+            device="cpu",
+            compute_type="float32",
+            num_workers=12,
         )
 
     async def _transcript(self, data: AudioFile):

server/reflector/processors/file_diarization_pyannote.py

@@ -0,0 +1,39 @@
+"""
+Pyannote file diarization processor using pyannote.audio in-process.
+
+Downloads audio from URL, runs pyannote diarization locally,
+and returns speaker segments. No HTTP backend needed.
+"""
+
+import asyncio
+import os
+
+from reflector.processors._audio_download import download_audio_to_temp
+from reflector.processors._pyannote_diarization_service import diarization_service
+from reflector.processors.file_diarization import (
+    FileDiarizationInput,
+    FileDiarizationOutput,
+    FileDiarizationProcessor,
+)
+from reflector.processors.file_diarization_auto import FileDiarizationAutoProcessor
+
+
+class FileDiarizationPyannoteProcessor(FileDiarizationProcessor):
+    async def _diarize(self, data: FileDiarizationInput):
+        """Run pyannote diarization on file from URL."""
+        self.logger.info(f"Starting pyannote diarization from {data.audio_url}")
+        tmp_path = await download_audio_to_temp(data.audio_url)
+        try:
+            loop = asyncio.get_event_loop()
+            result = await loop.run_in_executor(
+                None, diarization_service.diarize_file, str(tmp_path)
+            )
+            return FileDiarizationOutput(diarization=result["diarization"])
+        finally:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+
+FileDiarizationAutoProcessor.register("pyannote", FileDiarizationPyannoteProcessor)

server/reflector/processors/file_transcript_whisper.py

@@ -0,0 +1,275 @@
+"""
+Local file transcription processor using faster-whisper with Silero VAD pipeline.
+
+Downloads audio from URL, segments it using Silero VAD, transcribes each
+segment with faster-whisper, and merges results. No HTTP backend needed.
+
+VAD pipeline ported from gpu/self_hosted/app/services/transcriber.py.
+"""
+
+import asyncio
+import os
+import shutil
+import subprocess
+import threading
+from typing import Generator
+
+import numpy as np
+from silero_vad import VADIterator, load_silero_vad
+
+from reflector.processors._audio_download import download_audio_to_temp
+from reflector.processors.file_transcript import (
+    FileTranscriptInput,
+    FileTranscriptProcessor,
+)
+from reflector.processors.file_transcript_auto import FileTranscriptAutoProcessor
+from reflector.processors.types import Transcript, Word
+from reflector.settings import settings
+
+SAMPLE_RATE = 16000
+
+VAD_CONFIG = {
+    "batch_max_duration": 30.0,
+    "silence_padding": 0.5,
+    "window_size": 512,
+}
+
+
+class FileTranscriptWhisperProcessor(FileTranscriptProcessor):
+    """Transcribe complete audio files using local faster-whisper with VAD."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._model = None
+        self._lock = threading.Lock()
+
+    def _ensure_model(self):
+        """Lazy-load the whisper model on first use."""
+        if self._model is not None:
+            return
+
+        import faster_whisper
+        import torch
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        compute_type = "float16" if device == "cuda" else "int8"
+        model_name = settings.WHISPER_FILE_MODEL
+
+        self.logger.info(
+            "Loading whisper model",
+            model=model_name,
+            device=device,
+            compute_type=compute_type,
+        )
+        self._model = faster_whisper.WhisperModel(
+            model_name,
+            device=device,
+            compute_type=compute_type,
+            num_workers=1,
+        )
+
+    async def _transcript(self, data: FileTranscriptInput):
+        """Download file, run VAD segmentation, transcribe each segment."""
+        tmp_path = await download_audio_to_temp(data.audio_url)
+        try:
+            loop = asyncio.get_event_loop()
+            result = await loop.run_in_executor(
+                None,
+                self._transcribe_file_blocking,
+                str(tmp_path),
+                data.language,
+            )
+            return result
+        finally:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+    def _transcribe_file_blocking(self, file_path: str, language: str) -> Transcript:
+        """Blocking transcription with VAD pipeline."""
+        self._ensure_model()
+
+        audio_array = _load_audio_via_ffmpeg(file_path, SAMPLE_RATE)
+
+        # VAD segmentation → batch merging
+        merged_batches: list[tuple[float, float]] = []
+        batch_start = None
+        batch_end = None
+        max_duration = VAD_CONFIG["batch_max_duration"]
+
+        for seg_start, seg_end in _vad_segments(audio_array):
+            if batch_start is None:
+                batch_start, batch_end = seg_start, seg_end
+                continue
+            if seg_end - batch_start <= max_duration:
+                batch_end = seg_end
+            else:
+                merged_batches.append((batch_start, batch_end))
+                batch_start, batch_end = seg_start, seg_end
+
+        if batch_start is not None and batch_end is not None:
+            merged_batches.append((batch_start, batch_end))
+
+        # If no speech detected, try transcribing the whole file
+        if not merged_batches:
+            return self._transcribe_whole_file(file_path, language)
+
+        # Transcribe each batch
+        all_words = []
+        for start_time, end_time in merged_batches:
+            s_idx = int(start_time * SAMPLE_RATE)
+            e_idx = int(end_time * SAMPLE_RATE)
+            segment = audio_array[s_idx:e_idx]
+            segment = _pad_audio(segment, SAMPLE_RATE)
+
+            with self._lock:
+                segments, _ = self._model.transcribe(
+                    segment,
+                    language=language,
+                    beam_size=5,
+                    word_timestamps=True,
+                    vad_filter=True,
+                    vad_parameters={"min_silence_duration_ms": 500},
+                )
+                segments = list(segments)
+
+            for seg in segments:
+                for w in seg.words:
+                    all_words.append(
+                        {
+                            "word": w.word,
+                            "start": round(float(w.start) + start_time, 2),
+                            "end": round(float(w.end) + start_time, 2),
+                        }
+                    )
+
+        all_words = _enforce_word_timing_constraints(all_words)
+
+        words = [
+            Word(text=w["word"], start=w["start"], end=w["end"]) for w in all_words
+        ]
+        words.sort(key=lambda w: w.start)
+        return Transcript(words=words)
+
+    def _transcribe_whole_file(self, file_path: str, language: str) -> Transcript:
+        """Fallback: transcribe entire file without VAD segmentation."""
+        with self._lock:
+            segments, _ = self._model.transcribe(
+                file_path,
+                language=language,
+                beam_size=5,
+                word_timestamps=True,
+                vad_filter=True,
+                vad_parameters={"min_silence_duration_ms": 500},
+            )
+            segments = list(segments)
+
+        words = []
+        for seg in segments:
+            for w in seg.words:
+                words.append(
+                    Word(
+                        text=w.word,
+                        start=round(float(w.start), 2),
+                        end=round(float(w.end), 2),
+                    )
+                )
+        return Transcript(words=words)
+
+
+# --- VAD helpers (ported from gpu/self_hosted/app/services/transcriber.py) ---
+# IMPORTANT: This VAD segment logic is duplicated for deployment isolation.
+# If you modify this, consider updating the GPU service copy as well:
+#   - gpu/self_hosted/app/services/transcriber.py
+#   - gpu/modal_deployments/reflector_transcriber.py
+#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+
+
+def _load_audio_via_ffmpeg(
+    input_path: str, sample_rate: int = SAMPLE_RATE
+) -> np.ndarray:
+    """Load audio file via ffmpeg, converting to mono float32 at target sample rate."""
+    ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg"
+    cmd = [
+        ffmpeg_bin,
+        "-nostdin",
+        "-threads",
+        "1",
+        "-i",
+        input_path,
+        "-f",
+        "f32le",
+        "-acodec",
+        "pcm_f32le",
+        "-ac",
+        "1",
+        "-ar",
+        str(sample_rate),
+        "pipe:1",
+    ]
+    proc = subprocess.run(
+        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
+    )
+    return np.frombuffer(proc.stdout, dtype=np.float32)
+
+
+def _vad_segments(
+    audio_array: np.ndarray,
+    sample_rate: int = SAMPLE_RATE,
+    window_size: int = VAD_CONFIG["window_size"],
+) -> Generator[tuple[float, float], None, None]:
+    """Detect speech segments using Silero VAD."""
+    vad_model = load_silero_vad(onnx=False)
+    iterator = VADIterator(vad_model, sampling_rate=sample_rate)
+    start = None
+
+    for i in range(0, len(audio_array), window_size):
+        chunk = audio_array[i : i + window_size]
+        if len(chunk) < window_size:
+            chunk = np.pad(chunk, (0, window_size - len(chunk)), mode="constant")
+        speech = iterator(chunk)
+        if not speech:
+            continue
+        if "start" in speech:
+            start = speech["start"]
+            continue
+        if "end" in speech and start is not None:
+            end = speech["end"]
+            yield (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE))
+            start = None
+
+    # Handle case where audio ends while speech is still active
+    if start is not None:
+        audio_duration = len(audio_array) / float(sample_rate)
+        yield (start / float(SAMPLE_RATE), audio_duration)
+
+    iterator.reset_states()
+
+
+def _pad_audio(audio_array: np.ndarray, sample_rate: int = SAMPLE_RATE) -> np.ndarray:
+    """Pad short audio with silence for VAD compatibility."""
+    audio_duration = len(audio_array) / sample_rate
+    if audio_duration < VAD_CONFIG["silence_padding"]:
+        silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
+        silence = np.zeros(silence_samples, dtype=np.float32)
+        return np.concatenate([audio_array, silence])
+    return audio_array
+
+
+def _enforce_word_timing_constraints(words: list[dict]) -> list[dict]:
+    """Ensure no word end time exceeds the next word's start time."""
+    if len(words) <= 1:
+        return words
+    enforced: list[dict] = []
+    for i, word in enumerate(words):
+        current = dict(word)
+        if i < len(words) - 1:
+            next_start = words[i + 1]["start"]
+            if current["end"] > next_start:
+                current["end"] = next_start
+        enforced.append(current)
+    return enforced
+
+
+FileTranscriptAutoProcessor.register("whisper", FileTranscriptWhisperProcessor)

server/reflector/processors/transcript_topic_detector.py

@@ -14,10 +14,12 @@ class TopicResponse(BaseModel):
     title: str = Field(
         description="A descriptive title for the topic being discussed",
         validation_alias=AliasChoices("title", "Title"),
+        min_length=8,
     )
     summary: str = Field(
         description="A concise 1-2 sentence summary of the discussion",
         validation_alias=AliasChoices("summary", "Summary"),
+        min_length=8,
     )
 
 

server/reflector/processors/transcript_translator_marian.py

@@ -0,0 +1,50 @@
+"""
+MarianMT transcript translator processor using HuggingFace MarianMT in-process.
+
+Translates transcript text using HuggingFace MarianMT models
+locally. No HTTP backend needed.
+"""
+
+import asyncio
+
+from reflector.processors._marian_translator_service import translator_service
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.processors.transcript_translator_auto import (
+    TranscriptTranslatorAutoProcessor,
+)
+from reflector.processors.types import TranslationLanguages
+
+
+class TranscriptTranslatorMarianProcessor(TranscriptTranslatorProcessor):
+    """Translate transcript text using MarianMT models."""
+
+    async def _translate(self, text: str) -> str | None:
+        source_language = self.get_pref("audio:source_language", "en")
+        target_language = self.get_pref("audio:target_language", "en")
+
+        languages = TranslationLanguages()
+        assert languages.is_supported(target_language)
+
+        self.logger.debug(f"MarianMT translate {text=}")
+
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(
+            None,
+            translator_service.translate,
+            text,
+            source_language,
+            target_language,
+        )
+
+        if target_language in result["text"]:
+            translation = result["text"][target_language]
+        else:
+            translation = None
+
+        self.logger.debug(f"Translation result: {text=}, {translation=}")
+        return translation
+
+
+TranscriptTranslatorAutoProcessor.register(
+    "marian", TranscriptTranslatorMarianProcessor
+)

server/reflector/services/transcript_process.py

@@ -40,6 +40,7 @@ class MultitrackProcessingConfig:
     track_keys: list[str]
     recording_id: NonEmptyString | None = None
     room_id: NonEmptyString | None = None
+    source_platform: str = "daily"
     mode: Literal["multitrack"] = "multitrack"
 
 
@@ -256,6 +257,7 @@ async def dispatch_transcript_processing(
                 "bucket_name": config.bucket_name,
                 "transcript_id": config.transcript_id,
                 "room_id": config.room_id,
+                "source_platform": config.source_platform,
             },
             additional_metadata={
                 "transcript_id": config.transcript_id,

server/reflector/settings.py

@@ -40,14 +40,24 @@ class Settings(BaseSettings):
     # backends: silero, frames
     AUDIO_CHUNKER_BACKEND: str = "frames"
 
+    # HuggingFace token for gated models (pyannote diarization in --cpu mode)
+    HF_TOKEN: str | None = None
+
     # Audio Transcription
     # backends:
     #   - whisper: in-process model loading (no HTTP, runs in same process)
     #   - modal: HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
     TRANSCRIPT_BACKEND: str = "whisper"
+
+    # Whisper model sizes for local transcription
+    # Options: "tiny", "base", "small", "medium", "large-v2"
+    WHISPER_CHUNK_MODEL: str = "tiny"
+    WHISPER_FILE_MODEL: str = "tiny"
     TRANSCRIPT_URL: str | None = None
     TRANSCRIPT_TIMEOUT: int = 90
-    TRANSCRIPT_FILE_TIMEOUT: int = 600
+    TRANSCRIPT_FILE_TIMEOUT: int = (
+        540  # Below Hatchet TIMEOUT_HEAVY (600) to avoid timeout race
+    )
 
     # Audio Transcription: modal backend
     TRANSCRIPT_MODAL_API_KEY: str | None = None
@@ -73,6 +83,9 @@ class Settings(BaseSettings):
     DAILYCO_STORAGE_AWS_BUCKET_NAME: str | None = None
     DAILYCO_STORAGE_AWS_REGION: str | None = None
     DAILYCO_STORAGE_AWS_ROLE_ARN: str | None = None
+    # Worker credentials for reading/deleting from Daily's recording bucket
+    DAILYCO_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
+    DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
 
     # Translate into the target language
     TRANSLATION_BACKEND: str = "passthrough"
@@ -97,7 +110,7 @@ class Settings(BaseSettings):
     )
 
     # Diarization
-    # backend: modal — HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
+    # backends: modal — HTTP API client, pyannote — in-process pyannote.audio
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"
     DIARIZATION_URL: str | None = None
@@ -106,7 +119,11 @@ class Settings(BaseSettings):
     # Diarization: modal backend
     DIARIZATION_MODAL_API_KEY: str | None = None
 
-    # Audio Padding (Modal.com backend)
+    # Audio Padding
+    # backends:
+    #   - pyav: in-process PyAV padding (no HTTP, runs in same process)
+    #   - modal: HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
+    PADDING_BACKEND: str = "pyav"
     PADDING_URL: str | None = None
     PADDING_MODAL_API_KEY: str | None = None
 

server/reflector/storage/__init__.py

@@ -17,6 +17,49 @@ def get_transcripts_storage() -> Storage:
     )
 
 
+def get_source_storage(platform: str) -> Storage:
+    """Get storage for reading/deleting source recording files from the platform's bucket.
+
+    Returns an AwsStorage configured with the platform's worker credentials
+    (access keys), or falls back to get_transcripts_storage() when platform-specific
+    credentials aren't configured (e.g., single-bucket setups).
+
+    Args:
+        platform: Recording platform name ("daily", "whereby", or other).
+    """
+    if platform == "daily":
+        if (
+            settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID
+            and settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY
+            and settings.DAILYCO_STORAGE_AWS_BUCKET_NAME
+        ):
+            from reflector.storage.storage_aws import AwsStorage
+
+            return AwsStorage(
+                aws_bucket_name=settings.DAILYCO_STORAGE_AWS_BUCKET_NAME,
+                aws_region=settings.DAILYCO_STORAGE_AWS_REGION or "us-east-1",
+                aws_access_key_id=settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID,
+                aws_secret_access_key=settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY,
+            )
+
+    elif platform == "whereby":
+        if (
+            settings.WHEREBY_STORAGE_AWS_ACCESS_KEY_ID
+            and settings.WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY
+            and settings.WHEREBY_STORAGE_AWS_BUCKET_NAME
+        ):
+            from reflector.storage.storage_aws import AwsStorage
+
+            return AwsStorage(
+                aws_bucket_name=settings.WHEREBY_STORAGE_AWS_BUCKET_NAME,
+                aws_region=settings.WHEREBY_STORAGE_AWS_REGION or "us-east-1",
+                aws_access_key_id=settings.WHEREBY_STORAGE_AWS_ACCESS_KEY_ID,
+                aws_secret_access_key=settings.WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY,
+            )
+
+    return get_transcripts_storage()
+
+
 def get_whereby_storage() -> Storage:
     """
     Get storage config for Whereby (for passing to Whereby API).
@@ -47,6 +90,9 @@ def get_dailyco_storage() -> Storage:
     """
     Get storage config for Daily.co (for passing to Daily API).
 
+    Uses role_arn only — access keys are excluded because they're for
+    worker reads (get_source_storage), not for the Daily API.
+
     Usage:
         daily_storage = get_dailyco_storage()
         daily_api.create_meeting(
@@ -57,13 +103,15 @@ def get_dailyco_storage() -> Storage:
 
     Do NOT use for our file operations - use get_transcripts_storage() instead.
     """
-    # Fail fast if platform-specific config missing
     if not settings.DAILYCO_STORAGE_AWS_BUCKET_NAME:
         raise ValueError(
             "DAILYCO_STORAGE_AWS_BUCKET_NAME required for Daily.co with AWS storage"
         )
 
-    return Storage.get_instance(
+    from reflector.storage.storage_aws import AwsStorage
-        name="aws",
+
-        settings_prefix="DAILYCO_STORAGE_",
+    return AwsStorage(
+        aws_bucket_name=settings.DAILYCO_STORAGE_AWS_BUCKET_NAME,
+        aws_region=settings.DAILYCO_STORAGE_AWS_REGION or "us-east-1",
+        aws_role_arn=settings.DAILYCO_STORAGE_AWS_ROLE_ARN,
     )

server/reflector/tools/process_transcript.py

@@ -17,6 +17,7 @@ from typing import Callable
 from celery.result import AsyncResult
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
+import reflector._warnings_filter  # noqa: F401 -- side effect: suppress pydantic validate_default warning
 from reflector.db import get_database
 from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.hatchet.client import HatchetClientManager

server/reflector/utils/retry.py

@@ -30,6 +30,7 @@ def retry(fn):
             "retry_httpx_status_stop",
             (
                 401,  # auth issue
+                402,  # payment required / no credits — needs human action
                 404,  # not found
                 413,  # payload too large
                 418,  # teapot
@@ -58,7 +59,8 @@ def retry(fn):
                 result = await fn(*args, **kwargs)
                 if isinstance(result, Response):
                     result.raise_for_status()
-                if result:
+                # Return any result including falsy (e.g. "" from get_response);
+                # only retry on exception, not on empty string.
                 return result
             except HTTPStatusError as e:
                 retry_logger.exception(e)

server/reflector/views/transcripts.py

@@ -1,10 +1,10 @@
 from datetime import datetime, timedelta, timezone
 from typing import Annotated, Literal, Optional, assert_never
 
+import jwt
 from fastapi import APIRouter, Depends, HTTPException, Query
 from fastapi_pagination import Page
 from fastapi_pagination.ext.databases import apaginate
-from jose import jwt
 from pydantic import (
     AwareDatetime,
     BaseModel,

server/reflector/views/transcripts_audio.py

@@ -7,13 +7,12 @@ Transcripts audio related endpoints
 from typing import Annotated, Optional
 
 import httpx
+import jwt
 from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
-from jose import jwt
 
 import reflector.auth as auth
 from reflector.db.transcripts import AudioWaveform, transcripts_controller
 from reflector.settings import settings
-from reflector.views.transcripts import ALGORITHM
 
 from ._range_requests_response import range_requests_response
 
@@ -36,16 +35,23 @@ async def transcript_get_audio_mp3(
 ):
     user_id = user["sub"] if user else None
     if not user_id and token:
-        unauthorized_exception = HTTPException(
+        try:
+            token_user = await auth.verify_raw_token(token)
+        except Exception:
+            token_user = None
+        # Fallback: try as internal HS256 token (created by _generate_local_audio_link)
+        if not token_user:
+            try:
+                payload = jwt.decode(token, settings.SECRET_KEY, algorithms=["HS256"])
+                user_id = payload.get("sub")
+            except jwt.PyJWTError:
+                raise HTTPException(
                     status_code=status.HTTP_401_UNAUTHORIZED,
                     detail="Invalid or expired token",
                     headers={"WWW-Authenticate": "Bearer"},
                 )
-        try:
+        else:
-            payload = jwt.decode(token, settings.SECRET_KEY, algorithms=[ALGORITHM])
+            user_id = token_user["sub"]
-            user_id: str = payload.get("sub")
-        except jwt.JWTError:
-            raise unauthorized_exception
 
     transcript = await transcripts_controller.get_by_id_for_http(
         transcript_id, user_id=user_id

server/reflector/views/transcripts_process.py

@@ -50,5 +50,8 @@ async def transcript_process(
     if isinstance(config, ProcessError):
         raise HTTPException(status_code=500, detail=config.detail)
     else:
-        await dispatch_transcript_processing(config)
+        # When transcript is in error state, force a new workflow instead of replaying
+        # (replay would re-run from failure point with same conditions and likely fail again)
+        force = transcript.status == "error"
+        await dispatch_transcript_processing(config, force=force)
         return ProcessStatus(status="ok")

server/reflector/worker/app.py

@@ -24,6 +24,118 @@ RECONCILIATION_INTERVAL = _override or 30.0
 ICS_SYNC_INTERVAL = _override or 60.0
 UPCOMING_MEETINGS_INTERVAL = _override or 30.0
 
+
+def build_beat_schedule(
+    *,
+    whereby_api_key=None,
+    aws_process_recording_queue_url=None,
+    daily_api_key=None,
+    public_mode=False,
+    public_data_retention_days=None,
+    healthcheck_url=None,
+):
+    """Build the Celery beat schedule based on configured services.
+
+    Only registers tasks for services that are actually configured,
+    avoiding unnecessary worker wake-ups in selfhosted deployments.
+    """
+    beat_schedule = {}
+
+    _whereby_enabled = bool(whereby_api_key) or bool(aws_process_recording_queue_url)
+    if _whereby_enabled:
+        beat_schedule["process_messages"] = {
+            "task": "reflector.worker.process.process_messages",
+            "schedule": SQS_POLL_INTERVAL,
+        }
+        beat_schedule["reprocess_failed_recordings"] = {
+            "task": "reflector.worker.process.reprocess_failed_recordings",
+            "schedule": crontab(hour=5, minute=0),  # Midnight EST
+        }
+        logger.info(
+            "Whereby beat tasks enabled",
+            tasks=["process_messages", "reprocess_failed_recordings"],
+        )
+    else:
+        logger.info("Whereby beat tasks disabled (no WHEREBY_API_KEY or SQS URL)")
+
+    _daily_enabled = bool(daily_api_key)
+    if _daily_enabled:
+        beat_schedule["poll_daily_recordings"] = {
+            "task": "reflector.worker.process.poll_daily_recordings",
+            "schedule": POLL_DAILY_RECORDINGS_INTERVAL_SEC,
+        }
+        beat_schedule["trigger_daily_reconciliation"] = {
+            "task": "reflector.worker.process.trigger_daily_reconciliation",
+            "schedule": RECONCILIATION_INTERVAL,
+        }
+        beat_schedule["reprocess_failed_daily_recordings"] = {
+            "task": "reflector.worker.process.reprocess_failed_daily_recordings",
+            "schedule": crontab(hour=5, minute=0),  # Midnight EST
+        }
+        logger.info(
+            "Daily.co beat tasks enabled",
+            tasks=[
+                "poll_daily_recordings",
+                "trigger_daily_reconciliation",
+                "reprocess_failed_daily_recordings",
+            ],
+        )
+    else:
+        logger.info("Daily.co beat tasks disabled (no DAILY_API_KEY)")
+
+    _any_platform = _whereby_enabled or _daily_enabled
+    if _any_platform:
+        beat_schedule["process_meetings"] = {
+            "task": "reflector.worker.process.process_meetings",
+            "schedule": SQS_POLL_INTERVAL,
+        }
+        beat_schedule["sync_all_ics_calendars"] = {
+            "task": "reflector.worker.ics_sync.sync_all_ics_calendars",
+            "schedule": ICS_SYNC_INTERVAL,
+        }
+        beat_schedule["create_upcoming_meetings"] = {
+            "task": "reflector.worker.ics_sync.create_upcoming_meetings",
+            "schedule": UPCOMING_MEETINGS_INTERVAL,
+        }
+        logger.info(
+            "Platform tasks enabled",
+            tasks=[
+                "process_meetings",
+                "sync_all_ics_calendars",
+                "create_upcoming_meetings",
+            ],
+        )
+    else:
+        logger.info("Platform tasks disabled (no video platform configured)")
+
+    if public_mode:
+        beat_schedule["cleanup_old_public_data"] = {
+            "task": "reflector.worker.cleanup.cleanup_old_public_data_task",
+            "schedule": crontab(hour=3, minute=0),
+        }
+        logger.info(
+            "Public mode cleanup enabled",
+            retention_days=public_data_retention_days,
+        )
+
+    if healthcheck_url:
+        beat_schedule["healthcheck_ping"] = {
+            "task": "reflector.worker.healthcheck.healthcheck_ping",
+            "schedule": 60.0 * 10,
+        }
+        logger.info("Healthcheck enabled", url=healthcheck_url)
+    else:
+        logger.warning("Healthcheck disabled, no url configured")
+
+    logger.info(
+        "Beat schedule configured",
+        total_tasks=len(beat_schedule),
+        task_names=sorted(beat_schedule.keys()),
+    )
+
+    return beat_schedule
+
+
 if celery.current_app.main != "default":
     logger.info(f"Celery already configured ({celery.current_app})")
     app = celery.current_app
@@ -42,57 +154,11 @@ else:
         ]
     )
 
-    # crontab
+    app.conf.beat_schedule = build_beat_schedule(
-    app.conf.beat_schedule = {
+        whereby_api_key=settings.WHEREBY_API_KEY,
-        "process_messages": {
+        aws_process_recording_queue_url=settings.AWS_PROCESS_RECORDING_QUEUE_URL,
-            "task": "reflector.worker.process.process_messages",
+        daily_api_key=settings.DAILY_API_KEY,
-            "schedule": SQS_POLL_INTERVAL,
+        public_mode=settings.PUBLIC_MODE,
-        },
+        public_data_retention_days=settings.PUBLIC_DATA_RETENTION_DAYS,
-        "process_meetings": {
+        healthcheck_url=settings.HEALTHCHECK_URL,
-            "task": "reflector.worker.process.process_meetings",
-            "schedule": SQS_POLL_INTERVAL,
-        },
-        "reprocess_failed_recordings": {
-            "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": POLL_DAILY_RECORDINGS_INTERVAL_SEC,
-        },
-        "trigger_daily_reconciliation": {
-            "task": "reflector.worker.process.trigger_daily_reconciliation",
-            "schedule": RECONCILIATION_INTERVAL,
-        },
-        "sync_all_ics_calendars": {
-            "task": "reflector.worker.ics_sync.sync_all_ics_calendars",
-            "schedule": ICS_SYNC_INTERVAL,
-        },
-        "create_upcoming_meetings": {
-            "task": "reflector.worker.ics_sync.create_upcoming_meetings",
-            "schedule": UPCOMING_MEETINGS_INTERVAL,
-        },
-    }
-
-    if settings.PUBLIC_MODE:
-        app.conf.beat_schedule["cleanup_old_public_data"] = {
-            "task": "reflector.worker.cleanup.cleanup_old_public_data_task",
-            "schedule": crontab(hour=3, minute=0),
-        }
-        logger.info(
-            "Public mode cleanup enabled",
-            retention_days=settings.PUBLIC_DATA_RETENTION_DAYS,
     )
-
-    if settings.HEALTHCHECK_URL:
-        app.conf.beat_schedule["healthcheck_ping"] = {
-            "task": "reflector.worker.healthcheck.healthcheck_ping",
-            "schedule": 60.0 * 10,
-        }
-        logger.info("Healthcheck enabled", url=settings.HEALTHCHECK_URL)
-    else:
-        logger.warning("Healthcheck disabled, no url configured")

server/reflector/worker/process.py

@@ -357,6 +357,7 @@ async def _process_multitrack_recording_inner(
             "bucket_name": bucket_name,
             "transcript_id": transcript.id,
             "room_id": room.id,
+            "source_platform": "daily",
         },
         additional_metadata={
             "transcript_id": transcript.id,
@@ -1068,6 +1069,7 @@ async def reprocess_failed_daily_recordings():
                     "bucket_name": bucket_name,
                     "transcript_id": transcript.id,
                     "room_id": room.id if room else None,
+                    "source_platform": "daily",
                 },
                 additional_metadata={
                     "transcript_id": transcript.id,

server/tests/test_auth_password.py

@@ -1,8 +1,8 @@
 """Tests for the password auth backend."""
 
+import jwt
 import pytest
 from httpx import AsyncClient
-from jose import jwt
 
 from reflector.auth.password_utils import hash_password
 from reflector.settings import settings

server/tests/test_beat_schedule.py

@@ -0,0 +1,247 @@
+"""Tests for conditional Celery beat schedule registration.
+
+Verifies that beat tasks are only registered when their corresponding
+services are configured (WHEREBY_API_KEY, DAILY_API_KEY, etc.).
+"""
+
+import pytest
+
+from reflector.worker.app import build_beat_schedule
+
+
+# Override autouse fixtures from conftest — these tests don't need database or websockets
+@pytest.fixture(autouse=True)
+def setup_database():
+    yield
+
+
+@pytest.fixture(autouse=True)
+def ws_manager_in_memory():
+    yield
+
+
+@pytest.fixture(autouse=True)
+def reset_hatchet_client():
+    yield
+
+
+# Task name sets for each group
+WHEREBY_TASKS = {"process_messages", "reprocess_failed_recordings"}
+DAILY_TASKS = {
+    "poll_daily_recordings",
+    "trigger_daily_reconciliation",
+    "reprocess_failed_daily_recordings",
+}
+PLATFORM_TASKS = {
+    "process_meetings",
+    "sync_all_ics_calendars",
+    "create_upcoming_meetings",
+}
+
+
+class TestNoPlatformConfigured:
+    """When no video platform is configured, no platform tasks should be registered."""
+
+    def test_no_platform_tasks(self):
+        schedule = build_beat_schedule()
+        task_names = set(schedule.keys())
+        assert not task_names & WHEREBY_TASKS
+        assert not task_names & DAILY_TASKS
+        assert not task_names & PLATFORM_TASKS
+
+    def test_only_healthcheck_disabled_warning(self):
+        """With no config at all, schedule should be empty (healthcheck needs URL)."""
+        schedule = build_beat_schedule()
+        assert len(schedule) == 0
+
+    def test_healthcheck_only(self):
+        schedule = build_beat_schedule(healthcheck_url="https://hc.example.com/ping")
+        assert set(schedule.keys()) == {"healthcheck_ping"}
+
+    def test_public_mode_only(self):
+        schedule = build_beat_schedule(public_mode=True)
+        assert set(schedule.keys()) == {"cleanup_old_public_data"}
+
+
+class TestWherebyOnly:
+    """When only Whereby is configured."""
+
+    def test_whereby_api_key(self):
+        schedule = build_beat_schedule(whereby_api_key="test-key")
+        task_names = set(schedule.keys())
+        assert WHEREBY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+        assert not task_names & DAILY_TASKS
+
+    def test_whereby_sqs_url(self):
+        schedule = build_beat_schedule(
+            aws_process_recording_queue_url="https://sqs.us-east-1.amazonaws.com/123/queue"
+        )
+        task_names = set(schedule.keys())
+        assert WHEREBY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+        assert not task_names & DAILY_TASKS
+
+    def test_whereby_task_count(self):
+        schedule = build_beat_schedule(whereby_api_key="test-key")
+        # Whereby (2) + Platform (3) = 5
+        assert len(schedule) == 5
+
+
+class TestDailyOnly:
+    """When only Daily.co is configured."""
+
+    def test_daily_api_key(self):
+        schedule = build_beat_schedule(daily_api_key="test-daily-key")
+        task_names = set(schedule.keys())
+        assert DAILY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+        assert not task_names & WHEREBY_TASKS
+
+    def test_daily_task_count(self):
+        schedule = build_beat_schedule(daily_api_key="test-daily-key")
+        # Daily (3) + Platform (3) = 6
+        assert len(schedule) == 6
+
+
+class TestBothPlatforms:
+    """When both Whereby and Daily.co are configured."""
+
+    def test_all_tasks_registered(self):
+        schedule = build_beat_schedule(
+            whereby_api_key="test-key",
+            daily_api_key="test-daily-key",
+        )
+        task_names = set(schedule.keys())
+        assert WHEREBY_TASKS <= task_names
+        assert DAILY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+
+    def test_combined_task_count(self):
+        schedule = build_beat_schedule(
+            whereby_api_key="test-key",
+            daily_api_key="test-daily-key",
+        )
+        # Whereby (2) + Daily (3) + Platform (3) = 8
+        assert len(schedule) == 8
+
+
+class TestConditionalFlags:
+    """Test PUBLIC_MODE and HEALTHCHECK_URL interact correctly with platform tasks."""
+
+    def test_all_flags_enabled(self):
+        schedule = build_beat_schedule(
+            whereby_api_key="test-key",
+            daily_api_key="test-daily-key",
+            public_mode=True,
+            healthcheck_url="https://hc.example.com/ping",
+        )
+        task_names = set(schedule.keys())
+        assert "cleanup_old_public_data" in task_names
+        assert "healthcheck_ping" in task_names
+        assert WHEREBY_TASKS <= task_names
+        assert DAILY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+        # Whereby (2) + Daily (3) + Platform (3) + cleanup (1) + healthcheck (1) = 10
+        assert len(schedule) == 10
+
+    def test_public_mode_with_whereby(self):
+        schedule = build_beat_schedule(
+            whereby_api_key="test-key",
+            public_mode=True,
+        )
+        task_names = set(schedule.keys())
+        assert "cleanup_old_public_data" in task_names
+        assert WHEREBY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+
+    def test_healthcheck_with_daily(self):
+        schedule = build_beat_schedule(
+            daily_api_key="test-daily-key",
+            healthcheck_url="https://hc.example.com/ping",
+        )
+        task_names = set(schedule.keys())
+        assert "healthcheck_ping" in task_names
+        assert DAILY_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+
+
+class TestTaskDefinitions:
+    """Verify task definitions have correct structure."""
+
+    def test_whereby_task_paths(self):
+        schedule = build_beat_schedule(whereby_api_key="test-key")
+        assert (
+            schedule["process_messages"]["task"]
+            == "reflector.worker.process.process_messages"
+        )
+        assert (
+            schedule["reprocess_failed_recordings"]["task"]
+            == "reflector.worker.process.reprocess_failed_recordings"
+        )
+
+    def test_daily_task_paths(self):
+        schedule = build_beat_schedule(daily_api_key="test-daily-key")
+        assert (
+            schedule["poll_daily_recordings"]["task"]
+            == "reflector.worker.process.poll_daily_recordings"
+        )
+        assert (
+            schedule["trigger_daily_reconciliation"]["task"]
+            == "reflector.worker.process.trigger_daily_reconciliation"
+        )
+        assert (
+            schedule["reprocess_failed_daily_recordings"]["task"]
+            == "reflector.worker.process.reprocess_failed_daily_recordings"
+        )
+
+    def test_platform_task_paths(self):
+        schedule = build_beat_schedule(daily_api_key="test-daily-key")
+        assert (
+            schedule["process_meetings"]["task"]
+            == "reflector.worker.process.process_meetings"
+        )
+        assert (
+            schedule["sync_all_ics_calendars"]["task"]
+            == "reflector.worker.ics_sync.sync_all_ics_calendars"
+        )
+        assert (
+            schedule["create_upcoming_meetings"]["task"]
+            == "reflector.worker.ics_sync.create_upcoming_meetings"
+        )
+
+    def test_all_tasks_have_schedule(self):
+        """Every registered task must have a 'schedule' key."""
+        schedule = build_beat_schedule(
+            whereby_api_key="test-key",
+            daily_api_key="test-daily-key",
+            public_mode=True,
+            healthcheck_url="https://hc.example.com/ping",
+        )
+        for name, config in schedule.items():
+            assert "schedule" in config, f"Task '{name}' missing 'schedule' key"
+            assert "task" in config, f"Task '{name}' missing 'task' key"
+
+
+class TestEmptyStringValues:
+    """Empty strings should be treated as not configured (falsy)."""
+
+    def test_empty_whereby_key(self):
+        schedule = build_beat_schedule(whereby_api_key="")
+        assert not set(schedule.keys()) & WHEREBY_TASKS
+
+    def test_empty_daily_key(self):
+        schedule = build_beat_schedule(daily_api_key="")
+        assert not set(schedule.keys()) & DAILY_TASKS
+
+    def test_empty_sqs_url(self):
+        schedule = build_beat_schedule(aws_process_recording_queue_url="")
+        assert not set(schedule.keys()) & WHEREBY_TASKS
+
+    def test_none_values(self):
+        schedule = build_beat_schedule(
+            whereby_api_key=None,
+            daily_api_key=None,
+            aws_process_recording_queue_url=None,
+        )
+        assert len(schedule) == 0

server/tests/test_hatchet_error_handling.py

@@ -0,0 +1,303 @@
+"""
+Tests for Hatchet error handling: NonRetryable classification and error status.
+
+These tests encode the desired behavior from the Hatchet Workflow Analysis doc:
+- Transient exceptions: do NOT set error status (let Hatchet retry; user stays on "processing").
+- Hard-fail exceptions: set error status and re-raise as NonRetryableException (stop retries).
+- on_failure_task: sets error status when workflow is truly dead.
+
+Run before the fix: some tests fail (reproducing the issues).
+Run after the fix: all tests pass.
+"""
+
+from contextlib import asynccontextmanager
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+from hatchet_sdk import NonRetryableException
+
+from reflector.hatchet.error_classification import is_non_retryable
+from reflector.llm import LLMParseError
+
+# --- Tests for is_non_retryable() (pass once error_classification exists) ---
+
+
+def test_is_non_retryable_returns_true_for_value_error():
+    """ValueError (e.g. missing config) should stop retries."""
+    assert is_non_retryable(ValueError("DAILY_API_KEY must be set")) is True
+
+
+def test_is_non_retryable_returns_true_for_type_error():
+    """TypeError (bad input) should stop retries."""
+    assert is_non_retryable(TypeError("expected str")) is True
+
+
+def test_is_non_retryable_returns_true_for_http_401():
+    """HTTP 401 auth error should stop retries."""
+    resp = MagicMock()
+    resp.status_code = 401
+    err = httpx.HTTPStatusError("Unauthorized", request=MagicMock(), response=resp)
+    assert is_non_retryable(err) is True
+
+
+def test_is_non_retryable_returns_true_for_http_402():
+    """HTTP 402 (no credits) should stop retries."""
+    resp = MagicMock()
+    resp.status_code = 402
+    err = httpx.HTTPStatusError("Payment Required", request=MagicMock(), response=resp)
+    assert is_non_retryable(err) is True
+
+
+def test_is_non_retryable_returns_true_for_http_404():
+    """HTTP 404 should stop retries."""
+    resp = MagicMock()
+    resp.status_code = 404
+    err = httpx.HTTPStatusError("Not Found", request=MagicMock(), response=resp)
+    assert is_non_retryable(err) is True
+
+
+def test_is_non_retryable_returns_false_for_http_503():
+    """HTTP 503 is transient; retries are useful."""
+    resp = MagicMock()
+    resp.status_code = 503
+    err = httpx.HTTPStatusError(
+        "Service Unavailable", request=MagicMock(), response=resp
+    )
+    assert is_non_retryable(err) is False
+
+
+def test_is_non_retryable_returns_false_for_timeout():
+    """Timeout is transient."""
+    assert is_non_retryable(httpx.TimeoutException("timed out")) is False
+
+
+def test_is_non_retryable_returns_true_for_llm_parse_error():
+    """LLMParseError after internal retries should stop."""
+    from pydantic import BaseModel
+
+    class _Dummy(BaseModel):
+        pass
+
+    assert is_non_retryable(LLMParseError(_Dummy, "Failed to parse", 3)) is True
+
+
+def test_is_non_retryable_returns_true_for_non_retryable_exception():
+    """Already-wrapped NonRetryableException should stay non-retryable."""
+    assert is_non_retryable(NonRetryableException("custom")) is True
+
+
+# --- Tests for with_error_handling (need pipeline module with patch) ---
+
+
+@pytest.fixture(scope="module")
+def pipeline_module():
+    """Import daily_multitrack_pipeline with Hatchet client mocked."""
+    with patch("reflector.hatchet.client.settings") as s:
+        s.HATCHET_CLIENT_TOKEN = "test-token"
+        s.HATCHET_DEBUG = False
+    mock_client = MagicMock()
+    mock_client.workflow.return_value = MagicMock()
+    with patch(
+        "reflector.hatchet.client.HatchetClientManager.get_client",
+        return_value=mock_client,
+    ):
+        from reflector.hatchet.workflows import daily_multitrack_pipeline
+
+        return daily_multitrack_pipeline
+
+
+@pytest.fixture
+def mock_input():
+    """Minimal PipelineInput for decorator tests."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import PipelineInput
+
+    return PipelineInput(
+        recording_id="rec-1",
+        tracks=[],
+        bucket_name="bucket",
+        transcript_id="ts-123",
+        room_id=None,
+    )
+
+
+@pytest.fixture
+def mock_ctx():
+    """Minimal Context-like object."""
+    ctx = MagicMock()
+    ctx.log = MagicMock()
+    return ctx
+
+
+@pytest.mark.asyncio
+async def test_with_error_handling_transient_does_not_set_error_status(
+    pipeline_module, mock_input, mock_ctx
+):
+    """Transient exception must NOT set error status (so user stays on 'processing' during retries).
+
+    Before fix: set_workflow_error_status is called on every exception → FAIL.
+    After fix: not called for transient → PASS.
+    """
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise httpx.TimeoutException("timed out")
+
+    wrapped = with_error_handling(TaskName.GET_RECORDING)(failing_task)
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises(httpx.TimeoutException):
+            await wrapped(mock_input, mock_ctx)
+
+        # Desired: do NOT set error status for transient (Hatchet will retry)
+        mock_set_error.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_with_error_handling_hard_fail_raises_non_retryable_and_sets_status(
+    pipeline_module, mock_input, mock_ctx
+):
+    """Hard-fail (e.g. ValueError) must set error status and re-raise NonRetryableException.
+
+    Before fix: raises ValueError, set_workflow_error_status called → test would need to expect ValueError.
+    After fix: raises NonRetryableException, set_workflow_error_status called → PASS.
+    """
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise ValueError("PADDING_URL must be set")
+
+    wrapped = with_error_handling(TaskName.GET_RECORDING)(failing_task)
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises(NonRetryableException) as exc_info:
+            await wrapped(mock_input, mock_ctx)
+
+        assert "PADDING_URL" in str(exc_info.value)
+        mock_set_error.assert_called_once_with("ts-123")
+
+
+@pytest.mark.asyncio
+async def test_with_error_handling_set_error_status_false_never_sets_status(
+    pipeline_module, mock_input, mock_ctx
+):
+    """When set_error_status=False, we must never set error status (e.g. cleanup_consent)."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise ValueError("something went wrong")
+
+    wrapped = with_error_handling(TaskName.CLEANUP_CONSENT, set_error_status=False)(
+        failing_task
+    )
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises((ValueError, NonRetryableException)):
+            await wrapped(mock_input, mock_ctx)
+
+        mock_set_error.assert_not_called()
+
+
+@asynccontextmanager
+async def _noop_db_context():
+    """Async context manager that yields without touching the DB (for unit tests)."""
+    yield None
+
+
+@pytest.mark.asyncio
+async def test_on_failure_task_sets_error_status(pipeline_module, mock_input, mock_ctx):
+    """When workflow fails and transcript is not yet 'ended', on_failure sets status to 'error'."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        on_workflow_failure,
+    )
+
+    transcript_processing = MagicMock()
+    transcript_processing.status = "processing"
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
+        _noop_db_context,
+    ):
+        with patch(
+            "reflector.db.transcripts.transcripts_controller.get_by_id",
+            new_callable=AsyncMock,
+            return_value=transcript_processing,
+        ):
+            with patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error:
+                await on_workflow_failure(mock_input, mock_ctx)
+                mock_set_error.assert_called_once_with(mock_input.transcript_id)
+
+
+@pytest.mark.asyncio
+async def test_on_failure_task_does_not_overwrite_ended(
+    pipeline_module, mock_input, mock_ctx
+):
+    """When workflow fails after finalize (e.g. post_zulip), do not overwrite 'ended' with 'error'.
+
+    cleanup_consent, post_zulip, send_webhook use set_error_status=False; if one fails,
+    on_workflow_failure must not set status to 'error' when transcript is already 'ended'.
+    """
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        on_workflow_failure,
+    )
+
+    transcript_ended = MagicMock()
+    transcript_ended.status = "ended"
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
+        _noop_db_context,
+    ):
+        with patch(
+            "reflector.db.transcripts.transcripts_controller.get_by_id",
+            new_callable=AsyncMock,
+            return_value=transcript_ended,
+        ):
+            with patch(
+                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error:
+                await on_workflow_failure(mock_input, mock_ctx)
+                mock_set_error.assert_not_called()
+
+
+# --- Tests for fan-out helper (_successful_run_results) ---
+
+
+def test_successful_run_results_filters_exceptions():
+    """_successful_run_results returns only non-exception items from aio_run_many(return_exceptions=True)."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        _successful_run_results,
+    )
+
+    results = [
+        {"key": "ok1"},
+        ValueError("child failed"),
+        {"key": "ok2"},
+        RuntimeError("another"),
+    ]
+    successful = _successful_run_results(results)
+    assert len(successful) == 2
+    assert successful[0] == {"key": "ok1"}
+    assert successful[1] == {"key": "ok2"}

server/tests/test_llm_retry.py

@@ -1,13 +1,11 @@
-"""Tests for LLM parse error recovery using llama-index Workflow"""
+"""Tests for LLM structured output with astructured_predict + reflection retry"""
 
-from time import monotonic
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ValidationError
-from workflows.errors import WorkflowRuntimeError, WorkflowTimeoutError
 
-from reflector.llm import LLM, LLMParseError, StructuredOutputWorkflow
+from reflector.llm import LLM, LLMParseError
 from reflector.utils.retry import RetryException
 
 
@@ -19,50 +17,42 @@ class TestResponse(BaseModel):
     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"""
+    """Test parse error recovery with astructured_predict reflection loop"""
 
     @pytest.mark.asyncio
     async def test_parse_error_recovery_with_feedback(self, test_settings):
-        """Test that parse errors trigger retry with error feedback"""
+        """Test that parse errors trigger retry with reflection prompt"""
         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):
+        async def astructured_predict_handler(output_cls, prompt_tmpl, **kwargs):
             call_count["count"] += 1
             if call_count["count"] == 1:
-                    # First JSON formatting call returns invalid JSON
+                # First call: raise ValidationError (missing fields)
-                    return make_completion_response('{"title": "Test"}')
+                raise ValidationError.from_exception_data(
-                else:
+                    title="TestResponse",
-                    # Second call should have error feedback in prompt
+                    line_errors=[
-                    assert "Your previous response could not be parsed:" in prompt
+                        {
-                    assert '{"title": "Test"}' in prompt
+                            "type": "missing",
-                    assert "Error:" in prompt
+                            "loc": ("summary",),
-                    assert "Please try again" in prompt
+                            "msg": "Field required",
-                    return make_completion_response(
+                            "input": {"title": "Test"},
-                        '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
+                        }
+                    ],
                 )
+            else:
+                # Second call: should have reflection in the prompt
+                assert "reflection" in kwargs
+                assert "could not be parsed" in kwargs["reflection"]
+                assert "Error:" in kwargs["reflection"]
+                return TestResponse(title="Test", summary="Summary", confidence=0.95)
 
-            mock_settings.llm.acomplete = AsyncMock(side_effect=acomplete_handler)
+        with patch("reflector.llm.Settings") as mock_settings:
+            mock_settings.llm.astructured_predict = AsyncMock(
+                side_effect=astructured_predict_handler
+            )
 
             result = await llm.get_structured_response(
                 prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
@@ -71,8 +61,6 @@ class TestLLMParseErrorRecovery:
             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
@@ -80,20 +68,22 @@ class TestLLMParseErrorRecovery:
         """Test that parse error retry stops after max attempts"""
         llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
 
-        with (
+        # Always raise ValidationError
-            patch("reflector.llm.TreeSummarize") as mock_summarize,
+        async def always_fail(output_cls, prompt_tmpl, **kwargs):
-            patch("reflector.llm.Settings") as mock_settings,
+            raise ValidationError.from_exception_data(
-        ):
+                title="TestResponse",
-            mock_summarizer = MagicMock()
+                line_errors=[
-            mock_summarize.return_value = mock_summarizer
+                    {
-            mock_summarizer.aget_response = AsyncMock(return_value="Some analysis")
+                        "type": "missing",
+                        "loc": ("summary",),
+                        "msg": "Field required",
+                        "input": {},
+                    }
+                ],
+            )
 
-            # Always return invalid JSON from acomplete
+        with patch("reflector.llm.Settings") as mock_settings:
-            mock_settings.llm.acomplete = AsyncMock(
+            mock_settings.llm.astructured_predict = AsyncMock(side_effect=always_fail)
-                return_value=make_completion_response(
-                    '{"invalid": "missing required fields"}'
-                )
-            )
 
             with pytest.raises(LLMParseError, match="Failed to parse"):
                 await llm.get_structured_response(
@@ -101,36 +91,39 @@ class TestLLMParseErrorRecovery:
                 )
 
             expected_attempts = test_settings.LLM_PARSE_MAX_RETRIES + 1
-            # TreeSummarize called once, acomplete called max_retries times
+            assert mock_settings.llm.astructured_predict.call_count == expected_attempts
-            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)
 
+        call_count = {"count": 0}
+
+        async def astructured_predict_handler(output_cls, prompt_tmpl, **kwargs):
+            call_count["count"] += 1
+            if call_count["count"] == 1:
+                raise ValidationError.from_exception_data(
+                    title="TestResponse",
+                    line_errors=[
+                        {
+                            "type": "missing",
+                            "loc": ("summary",),
+                            "msg": "Field required",
+                            "input": {"title": "Test"},
+                        }
+                    ],
+                )
+            return TestResponse(title="Test", summary="Summary", confidence=0.95)
+
         with (
-            patch("reflector.llm.TreeSummarize") as mock_summarize,
             patch("reflector.llm.Settings") as mock_settings,
             caplog.at_level("ERROR"),
         ):
-            mock_summarizer = MagicMock()
+            mock_settings.llm.astructured_predict = AsyncMock(
-            mock_summarize.return_value = mock_summarizer
+                side_effect=astructured_predict_handler
-            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
             )
@@ -143,35 +136,45 @@ class TestLLMParseErrorRecovery:
 
     @pytest.mark.asyncio
     async def test_multiple_validation_errors_in_feedback(self, test_settings):
-        """Test that validation errors are included in feedback"""
+        """Test that validation errors are included in reflection 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):
+        async def astructured_predict_handler(output_cls, prompt_tmpl, **kwargs):
             call_count["count"] += 1
             if call_count["count"] == 1:
                 # Missing title and summary
-                    return make_completion_response('{"confidence": 0.5}')
+                raise ValidationError.from_exception_data(
+                    title="TestResponse",
+                    line_errors=[
+                        {
+                            "type": "missing",
+                            "loc": ("title",),
+                            "msg": "Field required",
+                            "input": {},
+                        },
+                        {
+                            "type": "missing",
+                            "loc": ("summary",),
+                            "msg": "Field required",
+                            "input": {},
+                        },
+                    ],
+                )
             else:
-                    # Should have schema validation errors in prompt
+                # Should have schema validation errors in reflection
+                assert "reflection" in kwargs
                 assert (
-                        "Schema validation errors" in prompt
+                    "Schema validation errors" in kwargs["reflection"]
-                        or "error" in prompt.lower()
+                    or "error" in kwargs["reflection"].lower()
-                    )
-                    return make_completion_response(
-                        '{"title": "Test", "summary": "Summary", "confidence": 0.95}'
                 )
+                return TestResponse(title="Test", summary="Summary", confidence=0.95)
 
-            mock_settings.llm.acomplete = AsyncMock(side_effect=acomplete_handler)
+        with patch("reflector.llm.Settings") as mock_settings:
+            mock_settings.llm.astructured_predict = AsyncMock(
+                side_effect=astructured_predict_handler
+            )
 
             result = await llm.get_structured_response(
                 prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
@@ -185,17 +188,10 @@ class TestLLMParseErrorRecovery:
         """Test that no retry happens when first attempt succeeds"""
         llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
 
-        with (
+        with patch("reflector.llm.Settings") as mock_settings:
-            patch("reflector.llm.TreeSummarize") as mock_summarize,
+            mock_settings.llm.astructured_predict = AsyncMock(
-            patch("reflector.llm.Settings") as mock_settings,
+                return_value=TestResponse(
-        ):
+                    title="Test", summary="Summary", confidence=0.95
-            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}'
                 )
             )
 
@@ -206,195 +202,28 @@ class TestLLMParseErrorRecovery:
             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.astructured_predict.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"""
+    """Test that network errors are retried by the outer retry() wrapper"""
 
     @pytest.mark.asyncio
-    async def test_network_error_propagates_after_openai_retries(self, test_settings):
+    async def test_network_error_retried_by_outer_wrapper(self, test_settings):
-        """Test that network errors are retried by OpenAILike and then propagate.
+        """Test that network errors trigger the outer retry wrapper"""
-
-        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):
+        async def astructured_predict_handler(output_cls, prompt_tmpl, **kwargs):
             call_count["count"] += 1
             if call_count["count"] == 1:
-                raise WorkflowTimeoutError("Operation timed out after 120 seconds")
+                raise ConnectionError("Connection refused")
-            return {
+            return TestResponse(title="Test", summary="Summary", confidence=0.95)
-                "success": TestResponse(
-                    title="Test", summary="Summary", confidence=0.95
-                )
-            }
 
-        with (
+        with patch("reflector.llm.Settings") as mock_settings:
-            patch("reflector.llm.StructuredOutputWorkflow") as mock_workflow_class,
+            mock_settings.llm.astructured_predict = AsyncMock(
-            patch("reflector.llm.TreeSummarize") as mock_summarize,
+                side_effect=astructured_predict_handler
-            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(
@@ -402,36 +231,16 @@ class TestWorkflowTimeoutRetry:
             )
 
             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):
+    async def test_network_error_exhausts_retries(self, test_settings):
-        """Test that timeout retry stops after max attempts"""
+        """Test that persistent network errors exhaust retry attempts"""
         llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
 
-        call_count = {"count": 0}
+        with patch("reflector.llm.Settings") as mock_settings:
-
+            mock_settings.llm.astructured_predict = AsyncMock(
-        async def workflow_run_side_effect(*args, **kwargs):
+                side_effect=ConnectionError("Connection refused")
-            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"):
@@ -439,41 +248,186 @@ class TestWorkflowTimeoutRetry:
                     prompt="Test prompt", texts=["Test text"], output_cls=TestResponse
                 )
 
-            assert call_count["count"] == 3
+            # 3 retry attempts
+            assert mock_settings.llm.astructured_predict.call_count == 3
+
+
+class TestGetResponseRetries:
+    """Test that get_response() uses the same retry() wrapper for transient errors."""
 
     @pytest.mark.asyncio
-    async def test_timeout_retry_with_backoff(self, test_settings):
+    async def test_get_response_retries_on_connection_error(self, test_settings):
-        """Test that exponential backoff is applied between retries"""
+        """Test that get_response retries on ConnectionError and returns on success."""
         llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
 
-        call_times = []
+        mock_instance = MagicMock()
-
+        mock_instance.aget_response = AsyncMock(
-        async def workflow_run_side_effect(*args, **kwargs):
+            side_effect=[
-            call_times.append(monotonic())
+                ConnectionError("Connection refused"),
-            if len(call_times) < 3:
+                "  Summary text  ",
-                raise WorkflowTimeoutError("Operation timed out after 120 seconds")
+            ]
-            return {
-                "success": TestResponse(
-                    title="Test", summary="Summary", confidence=0.95
         )
+
+        with patch("reflector.llm.TreeSummarize", return_value=mock_instance):
+            result = await llm.get_response("Prompt", ["text"])
+
+        assert result == "Summary text"
+        assert mock_instance.aget_response.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_get_response_exhausts_retries(self, test_settings):
+        """Test that get_response raises RetryException after retry attempts exceeded."""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        mock_instance = MagicMock()
+        mock_instance.aget_response = AsyncMock(
+            side_effect=ConnectionError("Connection refused")
+        )
+
+        with patch("reflector.llm.TreeSummarize", return_value=mock_instance):
+            with pytest.raises(RetryException, match="Retry attempts exceeded"):
+                await llm.get_response("Prompt", ["text"])
+
+        assert mock_instance.aget_response.call_count == 3
+
+    @pytest.mark.asyncio
+    async def test_get_response_returns_empty_string_without_retry(self, test_settings):
+        """Empty or whitespace-only LLM response must return '' and not raise RetryException.
+
+        retry() must return falsy results (e.g. '' from get_response) instead of
+        treating them as 'no result' and retrying until RetryException.
+        """
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        mock_instance = MagicMock()
+        mock_instance.aget_response = AsyncMock(return_value="   \n  ")  # strip() -> ""
+
+        with patch("reflector.llm.TreeSummarize", return_value=mock_instance):
+            result = await llm.get_response("Prompt", ["text"])
+
+        assert result == ""
+        assert mock_instance.aget_response.call_count == 1
+
+
+class TestTextsInclusion:
+    """Test that texts parameter is included in the prompt sent to astructured_predict"""
+
+    @pytest.mark.asyncio
+    async def test_texts_included_in_prompt(self, test_settings):
+        """Test that texts content is appended to the prompt for astructured_predict"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        captured_prompts = []
+
+        async def capture_prompt(output_cls, prompt_tmpl, **kwargs):
+            captured_prompts.append(kwargs.get("user_prompt", ""))
+            return TestResponse(title="Test", summary="Summary", confidence=0.95)
+
+        with patch("reflector.llm.Settings") as mock_settings:
+            mock_settings.llm.astructured_predict = AsyncMock(
+                side_effect=capture_prompt
+            )
+
+            await llm.get_structured_response(
+                prompt="Identify all participants",
+                texts=["Alice: Hello everyone", "Bob: Hi Alice"],
+                output_cls=TestResponse,
+            )
+
+            assert len(captured_prompts) == 1
+            prompt_sent = captured_prompts[0]
+            assert "Identify all participants" in prompt_sent
+            assert "Alice: Hello everyone" in prompt_sent
+            assert "Bob: Hi Alice" in prompt_sent
+
+    @pytest.mark.asyncio
+    async def test_empty_texts_uses_prompt_only(self, test_settings):
+        """Test that empty texts list sends only the prompt"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        captured_prompts = []
+
+        async def capture_prompt(output_cls, prompt_tmpl, **kwargs):
+            captured_prompts.append(kwargs.get("user_prompt", ""))
+            return TestResponse(title="Test", summary="Summary", confidence=0.95)
+
+        with patch("reflector.llm.Settings") as mock_settings:
+            mock_settings.llm.astructured_predict = AsyncMock(
+                side_effect=capture_prompt
+            )
+
+            await llm.get_structured_response(
+                prompt="Identify all participants",
+                texts=[],
+                output_cls=TestResponse,
+            )
+
+            assert len(captured_prompts) == 1
+            assert captured_prompts[0] == "Identify all participants"
+
+    @pytest.mark.asyncio
+    async def test_texts_included_in_reflection_retry(self, test_settings):
+        """Test that texts are included in the prompt even during reflection retries"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        captured_prompts = []
+        call_count = {"count": 0}
+
+        async def capture_and_fail_first(output_cls, prompt_tmpl, **kwargs):
+            call_count["count"] += 1
+            captured_prompts.append(kwargs.get("user_prompt", ""))
+            if call_count["count"] == 1:
+                raise ValidationError.from_exception_data(
+                    title="TestResponse",
+                    line_errors=[
+                        {
+                            "type": "missing",
+                            "loc": ("summary",),
+                            "msg": "Field required",
+                            "input": {},
                         }
-
+                    ],
-        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}'
                 )
+            return TestResponse(title="Test", summary="Summary", confidence=0.95)
+
+        with patch("reflector.llm.Settings") as mock_settings:
+            mock_settings.llm.astructured_predict = AsyncMock(
+                side_effect=capture_and_fail_first
+            )
+
+            await llm.get_structured_response(
+                prompt="Summarize this",
+                texts=["The meeting covered project updates"],
+                output_cls=TestResponse,
+            )
+
+            # Both first attempt and reflection retry should include the texts
+            assert len(captured_prompts) == 2
+            for prompt_sent in captured_prompts:
+                assert "Summarize this" in prompt_sent
+                assert "The meeting covered project updates" in prompt_sent
+
+
+class TestReflectionRetryBackoff:
+    """Test the reflection retry timing behavior"""
+
+    @pytest.mark.asyncio
+    async def test_value_error_triggers_reflection(self, test_settings):
+        """Test that ValueError (parse failure) also triggers reflection retry"""
+        llm = LLM(settings=test_settings, temperature=0.4, max_tokens=100)
+
+        call_count = {"count": 0}
+
+        async def astructured_predict_handler(output_cls, prompt_tmpl, **kwargs):
+            call_count["count"] += 1
+            if call_count["count"] == 1:
+                raise ValueError("Could not parse output")
+            assert "reflection" in kwargs
+            return TestResponse(title="Test", summary="Summary", confidence=0.95)
+
+        with patch("reflector.llm.Settings") as mock_settings:
+            mock_settings.llm.astructured_predict = AsyncMock(
+                side_effect=astructured_predict_handler
             )
 
             result = await llm.get_structured_response(
@@ -481,8 +435,20 @@ class TestWorkflowTimeoutRetry:
             )
 
             assert result.title == "Test"
-            if len(call_times) >= 2:
+            assert call_count["count"] == 2
-                time_between_calls = call_times[1] - call_times[0]
+
-                assert (
+    @pytest.mark.asyncio
-                    time_between_calls >= 1.5
+    async def test_format_validation_error_method(self, test_settings):
-                ), f"Expected ~2s backoff, got {time_between_calls}s"
+        """Test _format_validation_error produces correct feedback"""
+        # ValidationError
+        try:
+            TestResponse(title="x", summary="y", confidence=5.0)  # confidence > 1
+        except ValidationError as e:
+            result = LLM._format_validation_error(e)
+            assert "Schema validation errors" in result
+            assert "confidence" in result
+
+        # ValueError
+        result = LLM._format_validation_error(ValueError("bad input"))
+        assert "Parse error:" in result
+        assert "bad input" in result

server/tests/test_processors_cpu.py

@@ -0,0 +1,450 @@
+"""
+Tests for in-process processor backends (--cpu mode).
+
+All ML model calls are mocked — no actual model loading needed.
+Tests verify processor registration, wiring, error handling, and data flow.
+"""
+
+import os
+import tempfile
+from pathlib import Path
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from reflector.processors.file_diarization import (
+    FileDiarizationInput,
+    FileDiarizationOutput,
+)
+from reflector.processors.types import (
+    AudioDiarizationInput,
+    TitleSummaryWithId,
+    Transcript,
+    Word,
+)
+
+# ── Registration Tests ──────────────────────────────────────────────────
+
+
+def test_audio_diarization_pyannote_registers():
+    """Verify AudioDiarizationPyannoteProcessor registers with 'pyannote' backend."""
+    # Importing the module triggers registration
+    import reflector.processors.audio_diarization_pyannote  # noqa: F401
+    from reflector.processors.audio_diarization_auto import (
+        AudioDiarizationAutoProcessor,
+    )
+
+    assert "pyannote" in AudioDiarizationAutoProcessor._registry
+
+
+def test_file_diarization_pyannote_registers():
+    """Verify FileDiarizationPyannoteProcessor registers with 'pyannote' backend."""
+    import reflector.processors.file_diarization_pyannote  # noqa: F401
+    from reflector.processors.file_diarization_auto import FileDiarizationAutoProcessor
+
+    assert "pyannote" in FileDiarizationAutoProcessor._registry
+
+
+def test_transcript_translator_marian_registers():
+    """Verify TranscriptTranslatorMarianProcessor registers with 'marian' backend."""
+    import reflector.processors.transcript_translator_marian  # noqa: F401
+    from reflector.processors.transcript_translator_auto import (
+        TranscriptTranslatorAutoProcessor,
+    )
+
+    assert "marian" in TranscriptTranslatorAutoProcessor._registry
+
+
+def test_file_transcript_whisper_registers():
+    """Verify FileTranscriptWhisperProcessor registers with 'whisper' backend."""
+    import reflector.processors.file_transcript_whisper  # noqa: F401
+    from reflector.processors.file_transcript_auto import FileTranscriptAutoProcessor
+
+    assert "whisper" in FileTranscriptAutoProcessor._registry
+
+
+# ── Audio Download Utility Tests ────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_download_audio_to_temp_success():
+    """Verify download_audio_to_temp downloads to a temp file and returns path."""
+    from reflector.processors._audio_download import download_audio_to_temp
+
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.headers = {"content-type": "audio/wav"}
+    mock_response.iter_content.return_value = [b"fake audio data"]
+    mock_response.raise_for_status = MagicMock()
+
+    with patch("reflector.processors._audio_download.requests.get") as mock_get:
+        mock_get.return_value = mock_response
+
+        result = await download_audio_to_temp("https://example.com/test.wav")
+
+        assert isinstance(result, Path)
+        assert result.exists()
+        assert result.read_bytes() == b"fake audio data"
+        assert result.suffix == ".wav"
+
+        # Cleanup
+        os.unlink(result)
+
+
+@pytest.mark.asyncio
+async def test_download_audio_to_temp_cleanup_on_error():
+    """Verify temp file is cleaned up when download fails mid-write."""
+    from reflector.processors._audio_download import download_audio_to_temp
+
+    mock_response = MagicMock()
+    mock_response.headers = {"content-type": "audio/wav"}
+    mock_response.raise_for_status = MagicMock()
+
+    def fail_iter(*args, **kwargs):
+        raise ConnectionError("Download interrupted")
+
+    mock_response.iter_content = fail_iter
+
+    with patch("reflector.processors._audio_download.requests.get") as mock_get:
+        mock_get.return_value = mock_response
+
+        with pytest.raises(ConnectionError, match="Download interrupted"):
+            await download_audio_to_temp("https://example.com/test.wav")
+
+
+def test_detect_extension_from_url():
+    """Verify extension detection from URL path."""
+    from reflector.processors._audio_download import _detect_extension
+
+    assert _detect_extension("https://example.com/test.wav", "") == ".wav"
+    assert _detect_extension("https://example.com/test.mp3?signed=1", "") == ".mp3"
+    assert _detect_extension("https://example.com/test.webm", "") == ".webm"
+
+
+def test_detect_extension_from_content_type():
+    """Verify extension detection from content-type header."""
+    from reflector.processors._audio_download import _detect_extension
+
+    assert _detect_extension("https://s3.aws/uuid", "audio/mpeg") == ".mp3"
+    assert _detect_extension("https://s3.aws/uuid", "audio/wav") == ".wav"
+    assert _detect_extension("https://s3.aws/uuid", "audio/webm") == ".webm"
+
+
+def test_detect_extension_fallback():
+    """Verify fallback extension when neither URL nor content-type is recognized."""
+    from reflector.processors._audio_download import _detect_extension
+
+    assert (
+        _detect_extension("https://s3.aws/uuid", "application/octet-stream") == ".audio"
+    )
+
+
+# ── Audio Diarization Pyannote Processor Tests ──────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_audio_diarization_pyannote_diarize():
+    """Verify pyannote audio diarization downloads, diarizes, and cleans up."""
+    from reflector.processors.audio_diarization_pyannote import (
+        AudioDiarizationPyannoteProcessor,
+    )
+
+    mock_diarization_result = {
+        "diarization": [
+            {"start": 0.0, "end": 2.5, "speaker": 0},
+            {"start": 2.5, "end": 5.0, "speaker": 1},
+        ]
+    }
+
+    # Create a temp file to simulate download
+    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    tmp.write(b"fake audio")
+    tmp.close()
+    tmp_path = Path(tmp.name)
+
+    processor = AudioDiarizationPyannoteProcessor()
+
+    with (
+        patch(
+            "reflector.processors.audio_diarization_pyannote.download_audio_to_temp",
+            new_callable=AsyncMock,
+            return_value=tmp_path,
+        ),
+        patch(
+            "reflector.processors.audio_diarization_pyannote.diarization_service"
+        ) as mock_svc,
+    ):
+        mock_svc.diarize_file.return_value = mock_diarization_result
+
+        data = AudioDiarizationInput(
+            audio_url="https://example.com/test.wav",
+            topics=[
+                TitleSummaryWithId(
+                    id="topic-1",
+                    title="Test Topic",
+                    summary="A test topic",
+                    timestamp=0.0,
+                    duration=5.0,
+                    transcript=Transcript(
+                        words=[Word(text="hello", start=0.0, end=1.0)]
+                    ),
+                )
+            ],
+        )
+        result = await processor._diarize(data)
+
+        assert result == mock_diarization_result["diarization"]
+        mock_svc.diarize_file.assert_called_once()
+
+
+# ── File Diarization Pyannote Processor Tests ───────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_file_diarization_pyannote_diarize():
+    """Verify pyannote file diarization returns FileDiarizationOutput."""
+    from reflector.processors.file_diarization_pyannote import (
+        FileDiarizationPyannoteProcessor,
+    )
+
+    mock_diarization_result = {
+        "diarization": [
+            {"start": 0.0, "end": 3.0, "speaker": 0},
+            {"start": 3.0, "end": 6.0, "speaker": 1},
+        ]
+    }
+
+    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    tmp.write(b"fake audio")
+    tmp.close()
+    tmp_path = Path(tmp.name)
+
+    processor = FileDiarizationPyannoteProcessor()
+
+    with (
+        patch(
+            "reflector.processors.file_diarization_pyannote.download_audio_to_temp",
+            new_callable=AsyncMock,
+            return_value=tmp_path,
+        ),
+        patch(
+            "reflector.processors.file_diarization_pyannote.diarization_service"
+        ) as mock_svc,
+    ):
+        mock_svc.diarize_file.return_value = mock_diarization_result
+
+        data = FileDiarizationInput(audio_url="https://example.com/test.wav")
+        result = await processor._diarize(data)
+
+        assert isinstance(result, FileDiarizationOutput)
+        assert len(result.diarization) == 2
+        assert result.diarization[0]["start"] == 0.0
+        assert result.diarization[1]["speaker"] == 1
+
+
+# ── Transcript Translator Marian Processor Tests ───────────────────────
+
+
+@pytest.mark.asyncio
+async def test_transcript_translator_marian_translate():
+    """Verify MarianMT translator calls service and extracts translation."""
+    from reflector.processors.transcript_translator_marian import (
+        TranscriptTranslatorMarianProcessor,
+    )
+
+    mock_result = {"text": {"en": "Hello world", "fr": "Bonjour le monde"}}
+
+    processor = TranscriptTranslatorMarianProcessor()
+
+    def fake_get_pref(key, default=None):
+        prefs = {"audio:source_language": "en", "audio:target_language": "fr"}
+        return prefs.get(key, default)
+
+    with (
+        patch.object(processor, "get_pref", side_effect=fake_get_pref),
+        patch(
+            "reflector.processors.transcript_translator_marian.translator_service"
+        ) as mock_svc,
+    ):
+        mock_svc.translate.return_value = mock_result
+
+        result = await processor._translate("Hello world")
+
+        assert result == "Bonjour le monde"
+        mock_svc.translate.assert_called_once_with("Hello world", "en", "fr")
+
+
+@pytest.mark.asyncio
+async def test_transcript_translator_marian_no_translation():
+    """Verify translator returns None when target language not in result."""
+    from reflector.processors.transcript_translator_marian import (
+        TranscriptTranslatorMarianProcessor,
+    )
+
+    mock_result = {"text": {"en": "Hello world"}}
+
+    processor = TranscriptTranslatorMarianProcessor()
+
+    def fake_get_pref(key, default=None):
+        prefs = {"audio:source_language": "en", "audio:target_language": "fr"}
+        return prefs.get(key, default)
+
+    with (
+        patch.object(processor, "get_pref", side_effect=fake_get_pref),
+        patch(
+            "reflector.processors.transcript_translator_marian.translator_service"
+        ) as mock_svc,
+    ):
+        mock_svc.translate.return_value = mock_result
+
+        result = await processor._translate("Hello world")
+
+        assert result is None
+
+
+# ── File Transcript Whisper Processor Tests ─────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_file_transcript_whisper_transcript():
+    """Verify whisper file processor downloads, transcribes, and returns Transcript."""
+    from reflector.processors.file_transcript import FileTranscriptInput
+    from reflector.processors.file_transcript_whisper import (
+        FileTranscriptWhisperProcessor,
+    )
+
+    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    tmp.write(b"fake audio")
+    tmp.close()
+    tmp_path = Path(tmp.name)
+
+    processor = FileTranscriptWhisperProcessor()
+
+    # Mock the blocking transcription method
+    mock_transcript = Transcript(
+        words=[
+            Word(text="Hello", start=0.0, end=0.5),
+            Word(text=" world", start=0.5, end=1.0),
+        ]
+    )
+
+    with (
+        patch(
+            "reflector.processors.file_transcript_whisper.download_audio_to_temp",
+            new_callable=AsyncMock,
+            return_value=tmp_path,
+        ),
+        patch.object(
+            processor,
+            "_transcribe_file_blocking",
+            return_value=mock_transcript,
+        ),
+    ):
+        data = FileTranscriptInput(
+            audio_url="https://example.com/test.wav", language="en"
+        )
+        result = await processor._transcript(data)
+
+        assert isinstance(result, Transcript)
+        assert len(result.words) == 2
+        assert result.words[0].text == "Hello"
+
+
+# ── VAD Helper Tests ────────────────────────────────────────────────────
+
+
+def test_enforce_word_timing_constraints():
+    """Verify word timing enforcement prevents overlapping times."""
+    from reflector.processors.file_transcript_whisper import (
+        _enforce_word_timing_constraints,
+    )
+
+    words = [
+        {"word": "hello", "start": 0.0, "end": 1.5},
+        {"word": "world", "start": 1.0, "end": 2.0},  # overlaps with previous
+        {"word": "test", "start": 2.0, "end": 3.0},
+    ]
+
+    result = _enforce_word_timing_constraints(words)
+
+    assert result[0]["end"] == 1.0  # Clamped to next word's start
+    assert result[1]["end"] == 2.0  # Clamped to next word's start
+    assert result[2]["end"] == 3.0  # Last word unchanged
+
+
+def test_enforce_word_timing_constraints_empty():
+    """Verify timing enforcement handles empty and single-word lists."""
+    from reflector.processors.file_transcript_whisper import (
+        _enforce_word_timing_constraints,
+    )
+
+    assert _enforce_word_timing_constraints([]) == []
+    assert _enforce_word_timing_constraints([{"word": "a", "start": 0, "end": 1}]) == [
+        {"word": "a", "start": 0, "end": 1}
+    ]
+
+
+def test_pad_audio_short():
+    """Verify short audio gets padded with silence."""
+    import numpy as np
+
+    from reflector.processors.file_transcript_whisper import _pad_audio
+
+    short_audio = np.zeros(100, dtype=np.float32)  # Very short
+    result = _pad_audio(short_audio, sample_rate=16000)
+
+    # Should be padded to at least silence_padding duration
+    assert len(result) > len(short_audio)
+
+
+def test_pad_audio_long():
+    """Verify long audio is not padded."""
+    import numpy as np
+
+    from reflector.processors.file_transcript_whisper import _pad_audio
+
+    long_audio = np.zeros(32000, dtype=np.float32)  # 2 seconds
+    result = _pad_audio(long_audio, sample_rate=16000)
+
+    assert len(result) == len(long_audio)
+
+
+# ── Translator Service Tests ────────────────────────────────────────────
+
+
+def test_translator_service_resolve_model():
+    """Verify model resolution for known and unknown language pairs."""
+    from reflector.processors._marian_translator_service import MarianTranslatorService
+
+    svc = MarianTranslatorService()
+
+    assert svc._resolve_model_name("en", "fr") == "Helsinki-NLP/opus-mt-en-fr"
+    assert svc._resolve_model_name("es", "en") == "Helsinki-NLP/opus-mt-es-en"
+    assert svc._resolve_model_name("en", "de") == "Helsinki-NLP/opus-mt-en-de"
+    # Unknown pair falls back to en->fr
+    assert svc._resolve_model_name("ja", "ko") == "Helsinki-NLP/opus-mt-en-fr"
+
+
+# ── Diarization Service Tests ───────────────────────────────────────────
+
+
+def test_diarization_service_singleton():
+    """Verify diarization_service is a module-level singleton."""
+    from reflector.processors._pyannote_diarization_service import (
+        PyannoteDiarizationService,
+        diarization_service,
+    )
+
+    assert isinstance(diarization_service, PyannoteDiarizationService)
+    assert diarization_service._pipeline is None  # Not loaded until first use
+
+
+def test_translator_service_singleton():
+    """Verify translator_service is a module-level singleton."""
+    from reflector.processors._marian_translator_service import (
+        MarianTranslatorService,
+        translator_service,
+    )
+
+    assert isinstance(translator_service, MarianTranslatorService)
+    assert translator_service._pipeline is None  # Not loaded until first use

server/tests/test_retry_decorator.py

@@ -49,6 +49,15 @@ async def test_retry_httpx(httpx_mock):
             )
 
 
+@pytest.mark.asyncio
+async def test_retry_402_stops_by_default(httpx_mock):
+    """402 (payment required / no credits) is in default retry_httpx_status_stop — do not retry."""
+    httpx_mock.add_response(status_code=402, json={"error": "insufficient_credits"})
+    async with httpx.AsyncClient() as client:
+        with pytest.raises(RetryHTTPException):
+            await retry(client.get)("https://test_url", retry_timeout=5)
+
+
 @pytest.mark.asyncio
 async def test_retry_normal():
     left = 3

server/tests/test_storage.py

@@ -367,3 +367,390 @@ async def test_aws_storage_none_endpoint_url():
     assert storage.base_url == "https://reflector-bucket.s3.amazonaws.com/"
     # No s3 addressing_style override — boto_config should only have retries
     assert not hasattr(storage.boto_config, "s3") or storage.boto_config.s3 is None
+
+
+# --- Tests for get_source_storage() ---
+
+
+def test_get_source_storage_daily_with_credentials():
+    """Daily platform with access keys returns AwsStorage with Daily credentials."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID = "daily-key"
+        mock_settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY = "daily-secret"
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = "us-west-2"
+
+        from reflector.storage import get_source_storage
+
+        storage = get_source_storage("daily")
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._bucket_name == "daily-bucket"
+        assert storage._region == "us-west-2"
+        assert storage._access_key_id == "daily-key"
+        assert storage._secret_access_key == "daily-secret"
+        assert storage._endpoint_url is None
+
+
+def test_get_source_storage_daily_falls_back_without_credentials():
+    """Daily platform without access keys falls back to transcript storage."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID = None
+        mock_settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY = None
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.TRANSCRIPT_STORAGE_BACKEND = "aws"
+        mock_settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME = "transcript-bucket"
+        mock_settings.TRANSCRIPT_STORAGE_AWS_REGION = "us-east-1"
+        mock_settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID = "transcript-key"
+        mock_settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY = "transcript-secret"
+        mock_settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL = None
+
+        from reflector.storage import get_source_storage
+
+        with patch("reflector.storage.get_transcripts_storage") as mock_get_transcripts:
+            fallback = AwsStorage(
+                aws_bucket_name="transcript-bucket",
+                aws_region="us-east-1",
+                aws_access_key_id="transcript-key",
+                aws_secret_access_key="transcript-secret",
+            )
+            mock_get_transcripts.return_value = fallback
+
+            storage = get_source_storage("daily")
+
+            mock_get_transcripts.assert_called_once()
+            assert storage is fallback
+
+
+def test_get_source_storage_whereby_with_credentials():
+    """Whereby platform with access keys returns AwsStorage with Whereby credentials."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.WHEREBY_STORAGE_AWS_ACCESS_KEY_ID = "whereby-key"
+        mock_settings.WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY = "whereby-secret"
+        mock_settings.WHEREBY_STORAGE_AWS_BUCKET_NAME = "whereby-bucket"
+        mock_settings.WHEREBY_STORAGE_AWS_REGION = "eu-west-1"
+
+        from reflector.storage import get_source_storage
+
+        storage = get_source_storage("whereby")
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._bucket_name == "whereby-bucket"
+        assert storage._region == "eu-west-1"
+        assert storage._access_key_id == "whereby-key"
+        assert storage._secret_access_key == "whereby-secret"
+
+
+def test_get_source_storage_unknown_platform_falls_back():
+    """Unknown platform falls back to transcript storage."""
+    with patch("reflector.storage.settings"):
+        from reflector.storage import get_source_storage
+
+        with patch("reflector.storage.get_transcripts_storage") as mock_get_transcripts:
+            fallback = MagicMock()
+            mock_get_transcripts.return_value = fallback
+
+            storage = get_source_storage("unknown-platform")
+
+            mock_get_transcripts.assert_called_once()
+            assert storage is fallback
+
+
+@pytest.mark.asyncio
+async def test_source_storage_presigns_for_correct_bucket():
+    """Source storage presigns URLs using the platform's credentials and the override bucket."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID = "daily-key"
+        mock_settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY = "daily-secret"
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = "us-west-2"
+
+        from reflector.storage import get_source_storage
+
+        storage = get_source_storage("daily")
+
+        mock_client = AsyncMock()
+        mock_client.generate_presigned_url = AsyncMock(
+            return_value="https://daily-bucket.s3.amazonaws.com/track.webm?signed"
+        )
+        mock_client.__aenter__ = AsyncMock(return_value=mock_client)
+        mock_client.__aexit__ = AsyncMock(return_value=None)
+
+        with patch.object(storage.session, "client", return_value=mock_client):
+            url = await storage.get_file_url(
+                "track.webm",
+                operation="get_object",
+                expires_in=3600,
+                bucket="override-bucket",
+            )
+
+            assert "track.webm" in url
+            mock_client.generate_presigned_url.assert_called_once()
+            call_kwargs = mock_client.generate_presigned_url.call_args
+            params = call_kwargs[1].get("Params") or call_kwargs[0][1]
+            assert params["Bucket"] == "override-bucket"
+            assert params["Key"] == "track.webm"
+
+
+def test_get_source_storage_daily_default_region():
+    """Daily platform without region falls back to us-east-1."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID = "daily-key"
+        mock_settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY = "daily-secret"
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = None
+
+        from reflector.storage import get_source_storage
+
+        storage = get_source_storage("daily")
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._region == "us-east-1"
+
+
+# --- Tests for get_dailyco_storage() ---
+
+
+def test_get_dailyco_storage_with_role_arn():
+    """get_dailyco_storage returns AwsStorage with role_arn for Daily API."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = "us-west-2"
+        mock_settings.DAILYCO_STORAGE_AWS_ROLE_ARN = "arn:aws:iam::123:role/DailyAccess"
+
+        from reflector.storage import get_dailyco_storage
+
+        storage = get_dailyco_storage()
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._bucket_name == "daily-bucket"
+        assert storage._region == "us-west-2"
+        assert storage._role_arn == "arn:aws:iam::123:role/DailyAccess"
+        assert storage._access_key_id is None
+        assert storage._secret_access_key is None
+
+
+def test_get_dailyco_storage_no_conflict_when_access_keys_also_set():
+    """get_dailyco_storage ignores access keys even when set (avoids mixed-auth error).
+
+    This is the key regression test: DAILYCO_STORAGE_AWS_ACCESS_KEY_ID and
+    SECRET_ACCESS_KEY are for get_source_storage(), not for get_dailyco_storage().
+    """
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = "us-west-2"
+        mock_settings.DAILYCO_STORAGE_AWS_ROLE_ARN = "arn:aws:iam::123:role/DailyAccess"
+        # These are set for get_source_storage but must NOT leak into get_dailyco_storage
+        mock_settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID = "AKIA-worker-key"
+        mock_settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY = "worker-secret"
+
+        from reflector.storage import get_dailyco_storage
+
+        # Must NOT raise "cannot use both aws_role_arn and access keys"
+        storage = get_dailyco_storage()
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._role_arn == "arn:aws:iam::123:role/DailyAccess"
+        assert storage._access_key_id is None
+        assert storage._secret_access_key is None
+
+
+def test_get_dailyco_storage_default_region():
+    """get_dailyco_storage falls back to us-east-1 when region is None."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = None
+        mock_settings.DAILYCO_STORAGE_AWS_ROLE_ARN = "arn:aws:iam::123:role/DailyAccess"
+
+        from reflector.storage import get_dailyco_storage
+
+        storage = get_dailyco_storage()
+
+        assert storage._region == "us-east-1"
+
+
+def test_get_dailyco_storage_raises_without_bucket():
+    """get_dailyco_storage raises ValueError when bucket is not configured."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = None
+
+        from reflector.storage import get_dailyco_storage
+
+        with pytest.raises(
+            ValueError, match="DAILYCO_STORAGE_AWS_BUCKET_NAME required"
+        ):
+            get_dailyco_storage()
+
+
+def test_get_dailyco_storage_exposes_role_credential():
+    """get_dailyco_storage().role_credential returns the role ARN."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-bucket"
+        mock_settings.DAILYCO_STORAGE_AWS_REGION = "us-east-1"
+        mock_settings.DAILYCO_STORAGE_AWS_ROLE_ARN = "arn:aws:iam::123:role/DailyAccess"
+
+        from reflector.storage import get_dailyco_storage
+
+        storage = get_dailyco_storage()
+
+        assert storage.role_credential == "arn:aws:iam::123:role/DailyAccess"
+        assert storage.bucket_name == "daily-bucket"
+        assert storage.region == "us-east-1"
+
+
+# --- Tests for get_whereby_storage() ---
+
+
+def test_get_whereby_storage_with_access_keys():
+    """get_whereby_storage returns AwsStorage with Whereby access keys."""
+    whereby_settings = [
+        ("WHEREBY_STORAGE_AWS_BUCKET_NAME", "whereby-bucket"),
+        ("WHEREBY_STORAGE_AWS_REGION", "eu-west-1"),
+        ("WHEREBY_STORAGE_AWS_ACCESS_KEY_ID", "whereby-key"),
+        ("WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY", "whereby-secret"),
+    ]
+    mock_settings = MagicMock()
+    mock_settings.WHEREBY_STORAGE_AWS_BUCKET_NAME = "whereby-bucket"
+    mock_settings.__iter__ = MagicMock(return_value=iter(whereby_settings))
+
+    # Patch both settings references: __init__.py and base.py
+    with (
+        patch("reflector.storage.settings", mock_settings),
+        patch("reflector.storage.base.settings", mock_settings),
+    ):
+        from reflector.storage import get_whereby_storage
+
+        storage = get_whereby_storage()
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._bucket_name == "whereby-bucket"
+        assert storage._region == "eu-west-1"
+        assert storage._access_key_id == "whereby-key"
+        assert storage._secret_access_key == "whereby-secret"
+
+
+def test_get_whereby_storage_raises_without_bucket():
+    """get_whereby_storage raises ValueError when bucket is not configured."""
+    with patch("reflector.storage.settings") as mock_settings:
+        mock_settings.WHEREBY_STORAGE_AWS_BUCKET_NAME = None
+
+        from reflector.storage import get_whereby_storage
+
+        with pytest.raises(
+            ValueError, match="WHEREBY_STORAGE_AWS_BUCKET_NAME required"
+        ):
+            get_whereby_storage()
+
+
+# --- Tests for get_transcripts_storage() ---
+
+
+def test_get_transcripts_storage_with_garage():
+    """get_transcripts_storage returns AwsStorage configured for Garage (custom endpoint)."""
+    garage_settings = [
+        ("TRANSCRIPT_STORAGE_AWS_BUCKET_NAME", "reflector-media"),
+        ("TRANSCRIPT_STORAGE_AWS_REGION", "garage"),
+        ("TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID", "GK-garage-key"),
+        ("TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY", "garage-secret"),
+        ("TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL", "http://garage:3900"),
+    ]
+    mock_settings = MagicMock()
+    mock_settings.TRANSCRIPT_STORAGE_BACKEND = "aws"
+    mock_settings.__iter__ = MagicMock(return_value=iter(garage_settings))
+
+    with (
+        patch("reflector.storage.settings", mock_settings),
+        patch("reflector.storage.base.settings", mock_settings),
+    ):
+        from reflector.storage import get_transcripts_storage
+
+        storage = get_transcripts_storage()
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._bucket_name == "reflector-media"
+        assert storage._endpoint_url == "http://garage:3900"
+        assert storage._access_key_id == "GK-garage-key"
+        assert storage.boto_config.s3["addressing_style"] == "path"
+
+
+def test_get_transcripts_storage_with_vanilla_aws():
+    """get_transcripts_storage returns AwsStorage configured for real AWS S3."""
+    aws_settings = [
+        ("TRANSCRIPT_STORAGE_AWS_BUCKET_NAME", "prod-transcripts"),
+        ("TRANSCRIPT_STORAGE_AWS_REGION", "us-east-1"),
+        ("TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID", "AKIA-prod-key"),
+        ("TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY", "prod-secret"),
+    ]
+    mock_settings = MagicMock()
+    mock_settings.TRANSCRIPT_STORAGE_BACKEND = "aws"
+    mock_settings.__iter__ = MagicMock(return_value=iter(aws_settings))
+
+    with (
+        patch("reflector.storage.settings", mock_settings),
+        patch("reflector.storage.base.settings", mock_settings),
+    ):
+        from reflector.storage import get_transcripts_storage
+
+        storage = get_transcripts_storage()
+
+        assert isinstance(storage, AwsStorage)
+        assert storage._bucket_name == "prod-transcripts"
+        assert storage._endpoint_url is None
+        assert storage._access_key_id == "AKIA-prod-key"
+
+
+# --- Tests for coexistence (selfhosted scenario) ---
+
+
+def test_all_factories_coexist_selfhosted_scenario():
+    """All storage factories work simultaneously with selfhosted config.
+
+    Simulates the real selfhosted setup:
+    - Transcript storage → Garage (http://garage:3900)
+    - Daily API storage → role_arn (for Daily to write recordings)
+    - Source storage → access keys (for workers to read Daily's S3 bucket)
+    """
+    transcript_settings = [
+        ("TRANSCRIPT_STORAGE_AWS_BUCKET_NAME", "reflector-media"),
+        ("TRANSCRIPT_STORAGE_AWS_REGION", "garage"),
+        ("TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID", "GK-garage-key"),
+        ("TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY", "garage-secret"),
+        ("TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL", "http://garage:3900"),
+    ]
+    mock_settings = MagicMock()
+    # Transcript storage: Garage
+    mock_settings.TRANSCRIPT_STORAGE_BACKEND = "aws"
+    mock_settings.__iter__ = MagicMock(return_value=iter(transcript_settings))
+
+    # Daily.co: both role_arn AND access keys configured
+    mock_settings.DAILYCO_STORAGE_AWS_BUCKET_NAME = "daily-recordings"
+    mock_settings.DAILYCO_STORAGE_AWS_REGION = "us-west-2"
+    mock_settings.DAILYCO_STORAGE_AWS_ROLE_ARN = "arn:aws:iam::123:role/DailyAccess"
+    mock_settings.DAILYCO_STORAGE_AWS_ACCESS_KEY_ID = "AKIA-daily-worker"
+    mock_settings.DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY = "daily-worker-secret"
+
+    with (
+        patch("reflector.storage.settings", mock_settings),
+        patch("reflector.storage.base.settings", mock_settings),
+    ):
+        from reflector.storage import (
+            get_dailyco_storage,
+            get_source_storage,
+            get_transcripts_storage,
+        )
+
+        # 1. Transcript storage → Garage
+        transcript_storage = get_transcripts_storage()
+        assert transcript_storage._endpoint_url == "http://garage:3900"
+        assert transcript_storage._access_key_id == "GK-garage-key"
+
+        # 2. Daily API storage → role_arn only (no access keys)
+        daily_api_storage = get_dailyco_storage()
+        assert daily_api_storage._role_arn == "arn:aws:iam::123:role/DailyAccess"
+        assert daily_api_storage._access_key_id is None
+
+        # 3. Source storage → access keys only (no role_arn)
+        source_storage = get_source_storage("daily")
+        assert source_storage._access_key_id == "AKIA-daily-worker"
+        assert source_storage._role_arn is None
+        assert source_storage._endpoint_url is None

server/tests/test_transcripts_audio_token_auth.py

@@ -0,0 +1,327 @@
+"""Tests for audio mp3 endpoint token query-param authentication.
+
+Covers both password (HS256) and JWT/Authentik (RS256) auth backends,
+verifying that private transcripts can be accessed via ?token= query param.
+"""
+
+import shutil
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import patch
+
+import jwt
+import pytest
+from cryptography.hazmat.primitives import serialization
+from cryptography.hazmat.primitives.asymmetric import rsa
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+OWNER_USER_ID = "test-owner-user-id"
+
+
+def _create_hs256_token(user_id: str, secret: str, expired: bool = False) -> str:
+    """Create an HS256 JWT like the password auth backend does."""
+    delta = timedelta(minutes=-5) if expired else timedelta(hours=24)
+    payload = {
+        "sub": user_id,
+        "email": "test@example.com",
+        "exp": datetime.now(timezone.utc) + delta,
+    }
+    return jwt.encode(payload, secret, algorithm="HS256")
+
+
+def _generate_rsa_keypair():
+    """Generate a fresh RSA keypair for tests."""
+    private_key = rsa.generate_private_key(public_exponent=65537, key_size=2048)
+    public_pem = private_key.public_key().public_bytes(
+        serialization.Encoding.PEM, serialization.PublicFormat.SubjectPublicKeyInfo
+    )
+    return private_key, public_pem.decode()
+
+
+def _create_rs256_token(
+    authentik_uid: str,
+    private_key,
+    audience: str,
+    expired: bool = False,
+) -> str:
+    """Create an RS256 JWT like Authentik would issue."""
+    delta = timedelta(minutes=-5) if expired else timedelta(hours=1)
+    payload = {
+        "sub": authentik_uid,
+        "email": "authentik-user@example.com",
+        "aud": audience,
+        "exp": datetime.now(timezone.utc) + delta,
+    }
+    return jwt.encode(payload, private_key, algorithm="RS256")
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+async def private_transcript(tmpdir):
+    """Create a private transcript owned by OWNER_USER_ID with an mp3 file.
+
+    Created directly via the controller (not HTTP) so no auth override
+    leaks into the test scope.
+    """
+    from reflector.db.transcripts import SourceKind, transcripts_controller
+    from reflector.settings import settings
+
+    settings.DATA_DIR = Path(tmpdir)
+
+    transcript = await transcripts_controller.add(
+        "Private audio test",
+        source_kind=SourceKind.FILE,
+        user_id=OWNER_USER_ID,
+        share_mode="private",
+    )
+    await transcripts_controller.update(transcript, {"status": "ended"})
+
+    # Copy a real mp3 to the expected location
+    audio_filename = transcript.audio_mp3_filename
+    mp3_source = Path(__file__).parent / "records" / "test_mathieu_hello.mp3"
+    audio_filename.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copy(mp3_source, audio_filename)
+
+    yield transcript
+
+
+# ---------------------------------------------------------------------------
+# Core access control tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_private_no_auth_returns_403(private_transcript, client):
+    """Without auth, accessing a private transcript's audio returns 403."""
+    response = await client.get(f"/transcripts/{private_transcript.id}/audio/mp3")
+    assert response.status_code == 403
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_with_bearer_header(private_transcript, client):
+    """Owner accessing audio via Authorization header works."""
+    from reflector.app import app
+    from reflector.auth import current_user_optional
+
+    # Temporarily override to simulate the owner being authenticated
+    app.dependency_overrides[current_user_optional] = lambda: {
+        "sub": OWNER_USER_ID,
+        "email": "test@example.com",
+    }
+    try:
+        response = await client.get(f"/transcripts/{private_transcript.id}/audio/mp3")
+    finally:
+        del app.dependency_overrides[current_user_optional]
+
+    assert response.status_code == 200
+    assert response.headers["content-type"] == "audio/mpeg"
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_public_transcript_no_auth_ok(tmpdir, client):
+    """Public transcripts are accessible without any auth."""
+    from reflector.db.transcripts import SourceKind, transcripts_controller
+    from reflector.settings import settings
+
+    settings.DATA_DIR = Path(tmpdir)
+
+    transcript = await transcripts_controller.add(
+        "Public audio test",
+        source_kind=SourceKind.FILE,
+        user_id=OWNER_USER_ID,
+        share_mode="public",
+    )
+    await transcripts_controller.update(transcript, {"status": "ended"})
+
+    audio_filename = transcript.audio_mp3_filename
+    mp3_source = Path(__file__).parent / "records" / "test_mathieu_hello.mp3"
+    audio_filename.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copy(mp3_source, audio_filename)
+
+    response = await client.get(f"/transcripts/{transcript.id}/audio/mp3")
+    assert response.status_code == 200
+    assert response.headers["content-type"] == "audio/mpeg"
+
+
+# ---------------------------------------------------------------------------
+# Password auth backend tests (?token= with HS256)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_password_token_query_param(private_transcript, client):
+    """Password backend: valid HS256 ?token= grants access to private audio."""
+    from reflector.auth.auth_password import UserInfo
+    from reflector.settings import settings
+
+    token = _create_hs256_token(OWNER_USER_ID, settings.SECRET_KEY)
+
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.return_value = UserInfo(sub=OWNER_USER_ID, email="test@example.com")
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3?token={token}"
+        )
+
+    assert response.status_code == 200
+    assert response.headers["content-type"] == "audio/mpeg"
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_password_expired_token_returns_401(private_transcript, client):
+    """Password backend: expired HS256 ?token= returns 401."""
+    from reflector.settings import settings
+
+    expired_token = _create_hs256_token(
+        OWNER_USER_ID, settings.SECRET_KEY, expired=True
+    )
+
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.side_effect = jwt.ExpiredSignatureError("token expired")
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3" f"?token={expired_token}"
+        )
+
+    assert response.status_code == 401
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_password_wrong_user_returns_403(private_transcript, client):
+    """Password backend: valid token for a different user returns 403."""
+    from reflector.auth.auth_password import UserInfo
+    from reflector.settings import settings
+
+    token = _create_hs256_token("other-user-id", settings.SECRET_KEY)
+
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.return_value = UserInfo(
+            sub="other-user-id", email="other@example.com"
+        )
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3?token={token}"
+        )
+
+    assert response.status_code == 403
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_invalid_token_returns_401(private_transcript, client):
+    """Garbage token string returns 401."""
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.return_value = None
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3" "?token=not-a-real-token"
+        )
+
+    assert response.status_code == 401
+
+
+# ---------------------------------------------------------------------------
+# JWT/Authentik auth backend tests (?token= with RS256)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_authentik_token_query_param(private_transcript, client):
+    """Authentik backend: valid RS256 ?token= grants access to private audio."""
+    from reflector.auth.auth_password import UserInfo
+
+    private_key, _ = _generate_rsa_keypair()
+    token = _create_rs256_token("authentik-abc123", private_key, "test-audience")
+
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        # Authentik flow maps authentik_uid -> internal user id
+        mock_verify.return_value = UserInfo(
+            sub=OWNER_USER_ID, email="authentik-user@example.com"
+        )
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3?token={token}"
+        )
+
+    assert response.status_code == 200
+    assert response.headers["content-type"] == "audio/mpeg"
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_authentik_expired_token_returns_401(
+    private_transcript, client
+):
+    """Authentik backend: expired RS256 ?token= returns 401."""
+    private_key, _ = _generate_rsa_keypair()
+    expired_token = _create_rs256_token(
+        "authentik-abc123", private_key, "test-audience", expired=True
+    )
+
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.side_effect = jwt.ExpiredSignatureError("token expired")
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3" f"?token={expired_token}"
+        )
+
+    assert response.status_code == 401
+
+
+@pytest.mark.asyncio
+async def test_audio_mp3_authentik_wrong_user_returns_403(private_transcript, client):
+    """Authentik backend: valid RS256 token for different user returns 403."""
+    from reflector.auth.auth_password import UserInfo
+
+    private_key, _ = _generate_rsa_keypair()
+    token = _create_rs256_token("authentik-other", private_key, "test-audience")
+
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.return_value = UserInfo(
+            sub="different-user-id", email="other@example.com"
+        )
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3?token={token}"
+        )
+
+    assert response.status_code == 403
+
+
+# ---------------------------------------------------------------------------
+# _generate_local_audio_link produces HS256 tokens — must be verifiable
+# by any auth backend
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_local_audio_link_token_works_with_authentik_backend(
+    private_transcript, client
+):
+    """_generate_local_audio_link creates an HS256 token via create_access_token.
+
+    When the Authentik (RS256) auth backend is active, verify_raw_token uses
+    JWTAuth which expects RS256 + public key. The HS256 token created by
+    _generate_local_audio_link will fail verification, returning 401.
+
+    This test documents the bug: the internal audio URL generated for the
+    diarization pipeline is unusable under the JWT auth backend.
+    """
+    from urllib.parse import parse_qs, urlparse
+
+    # Generate the internal audio link (uses create_access_token → HS256)
+    url = private_transcript._generate_local_audio_link()
+    parsed = urlparse(url)
+    token = parse_qs(parsed.query)["token"][0]
+
+    # Simulate what happens when the JWT/Authentik backend tries to verify
+    # this HS256 token: JWTAuth.verify_token expects RS256, so it raises.
+    with patch("reflector.auth.verify_raw_token") as mock_verify:
+        mock_verify.side_effect = jwt.exceptions.InvalidAlgorithmError(
+            "the specified alg value is not allowed"
+        )
+        response = await client.get(
+            f"/transcripts/{private_transcript.id}/audio/mp3?token={token}"
+        )
+
+    # BUG: this should be 200 (the token was created by our own server),
+    # but the Authentik backend rejects it because it's HS256, not RS256.
+    assert response.status_code == 200

server/tests/test_transcripts_process.py

@@ -231,3 +231,81 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
             {"s3_key": k} for k in track_keys
         ]
         mock_file_pipeline.delay.assert_not_called()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_reprocess_error_transcript_passes_force(client):
+    """When transcript status is 'error', reprocess passes force=True to start fresh workflow."""
+    from datetime import datetime, timezone
+
+    from reflector.db.recordings import Recording, recordings_controller
+    from reflector.db.rooms import rooms_controller
+    from reflector.db.transcripts import transcripts_controller
+
+    room = await rooms_controller.add(
+        name="test-room",
+        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,
+    )
+
+    transcript = await transcripts_controller.add(
+        "",
+        source_kind="room",
+        source_language="en",
+        target_language="en",
+        user_id="test-user",
+        share_mode="public",
+        room_id=room.id,
+    )
+
+    track_keys = ["recordings/test-room/track1.webm"]
+    recording = await recordings_controller.create(
+        Recording(
+            bucket_name="daily-bucket",
+            object_key="recordings/test-room",
+            meeting_id="test-meeting",
+            track_keys=track_keys,
+            recorded_at=datetime.now(timezone.utc),
+        )
+    )
+
+    await transcripts_controller.update(
+        transcript,
+        {
+            "recording_id": recording.id,
+            "status": "error",
+            "workflow_run_id": "old-failed-run",
+        },
+    )
+
+    with (
+        patch(
+            "reflector.services.transcript_process.task_is_scheduled_or_active"
+        ) as mock_celery,
+        patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet,
+        patch(
+            "reflector.views.transcripts_process.dispatch_transcript_processing",
+            new_callable=AsyncMock,
+        ) as mock_dispatch,
+    ):
+        mock_celery.return_value = False
+        from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+        mock_hatchet.get_workflow_run_status = AsyncMock(
+            return_value=V1TaskStatus.FAILED
+        )
+        response = await client.post(f"/transcripts/{transcript.id}/process")
+
+    assert response.status_code == 200
+    mock_dispatch.assert_called_once()
+    assert mock_dispatch.call_args.kwargs["force"] is True

server/tests/test_user_websocket_auth.py

@@ -67,7 +67,7 @@ def appserver_ws_user(setup_database):
 @pytest.fixture(autouse=True)
 def patch_jwt_verification(monkeypatch):
     """Patch JWT verification to accept HS256 tokens signed with SECRET_KEY for tests."""
-    from jose import jwt
+    import jwt
 
     from reflector.settings import settings
 
@@ -84,7 +84,7 @@ def _make_dummy_jwt(sub: str = "user123") -> str:
     # Create a short HS256 JWT using the app secret to pass verification in tests
     from datetime import datetime, timedelta, timezone
 
-    from jose import jwt
+    import jwt
 
     from reflector.settings import settings
 

www/.env.example

@@ -27,5 +27,5 @@ WEBSOCKET_URL=ws://127.0.0.1:1250
 AUTH_CALLBACK_URL=http://localhost:3000/auth-callback
 
 # Sentry
-# SENTRY_DSN=https://your-dsn@sentry.io/project-id
+# NEXT_PUBLIC_SENTRY_DSN=https://your-dsn@sentry.io/project-id
 # SENTRY_IGNORE_API_RESOLUTION_ERROR=1

www/.env.selfhosted.example

@@ -50,4 +50,4 @@ AUTHENTIK_REFRESH_TOKEN_URL=
 # =======================================================
 # Sentry (Optional)
 # =======================================================
-# SENTRY_DSN=
+# NEXT_PUBLIC_SENTRY_DSN=

www/.gitignore

@@ -46,3 +46,4 @@ openapi-ts-error-*.log
 
 # pnpm
 .pnpm-store
+/v10

www/app/(aboutAndPrivacy)/fullsreenModal.tsx

@@ -1,5 +1,6 @@
 import { FontAwesomeIcon } from "@fortawesome/react-fontawesome";
 import { faClose } from "@fortawesome/free-solid-svg-icons";
+import type { JSX } from "react";
 import { MouseEventHandler } from "react";
 
 type ModalProps = {

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

@@ -1,3 +1,5 @@
+"use client";
+
 import React from "react";
 import { Box, Stack, Link, Heading } from "@chakra-ui/react";
 import NextLink from "next/link";

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

@@ -1,3 +1,5 @@
+"use client";
+
 import React, { useState } from "react";
 import {
   Box,

www/app/(app)/layout.tsx

@@ -1,10 +1,8 @@
-import { Container, Flex, Link } from "@chakra-ui/react";
+import { Container, Flex } from "@chakra-ui/react";
-import { featureEnabled } from "../lib/features";
 import NextLink from "next/link";
 import Image from "next/image";
-import UserInfo from "../(auth)/userInfo";
 import AuthWrapper from "./AuthWrapper";
-import { RECORD_A_MEETING_URL } from "../api/urls";
+import MainNav from "../components/MainNav";
 
 export default async function AppLayout({
   children,
@@ -30,7 +28,7 @@ export default async function AppLayout({
         mt="1"
       >
         {/* Logo on the left */}
-        <Link as={NextLink} href="/" className="flex">
+        <NextLink href="/" className="flex">
           <Image
             src="/reach.svg"
             width={32}
@@ -46,53 +44,8 @@ export default async function AppLayout({
               Capture the signal, not the noise
             </p>
           </div>
-        </Link>
+        </NextLink>
-        <div>
+        <MainNav />
-          {/* Text link on the right */}
-          <Link
-            as={NextLink}
-            href={RECORD_A_MEETING_URL}
-            className="font-light px-2"
-          >
-            Create
-          </Link>
-          {featureEnabled("browse") ? (
-            <>
-              &nbsp;·&nbsp;
-              <Link href="/browse" as={NextLink} className="font-light px-2">
-                Browse
-              </Link>
-            </>
-          ) : (
-            <></>
-          )}
-          {featureEnabled("rooms") ? (
-            <>
-              &nbsp;·&nbsp;
-              <Link href="/rooms" as={NextLink} className="font-light px-2">
-                Rooms
-              </Link>
-            </>
-          ) : (
-            <></>
-          )}
-          {featureEnabled("requireLogin") ? (
-            <>
-              &nbsp;·&nbsp;
-              <Link
-                href="/settings/api-keys"
-                as={NextLink}
-                className="font-light px-2"
-              >
-                Settings
-              </Link>
-              &nbsp;·&nbsp;
-              <UserInfo />
-            </>
-          ) : (
-            <></>
-          )}
-        </div>
       </Flex>
 
       <AuthWrapper>{children}</AuthWrapper>

www/app/(app)/transcripts/new/page.tsx

@@ -1,13 +1,11 @@
 "use client";
 import React, { useEffect, useState } from "react";
 import useAudioDevice from "../useAudioDevice";
-import "react-select-search/style.css";
 import "../../../styles/form.scss";
 import About from "../../../(aboutAndPrivacy)/about";
 import Privacy from "../../../(aboutAndPrivacy)/privacy";
 import { useRouter } from "next/navigation";
 import useCreateTranscript from "../createTranscript";
-import SelectSearch from "react-select-search";
 import { supportedLanguages } from "../../../supportedLanguages";
 import {
   Flex,
@@ -21,6 +19,7 @@ import {
 } from "@chakra-ui/react";
 import { useAuth } from "../../../lib/AuthProvider";
 import { featureEnabled } from "../../../lib/features";
+import { SearchableLanguageSelect } from "../../../components/SearchableLanguageSelect";
 
 const TranscriptCreate = () => {
   const router = useRouter();
@@ -147,31 +146,27 @@ const TranscriptCreate = () => {
                 p={8}
                 flexDir="column"
                 my={4}
+                className="form-on-primary"
               >
                 <Heading size="xl" mb={4}>
                   Try Reflector
                 </Heading>
                 <Box mb={4}>
-                  <Text>Recording name</Text>
+                  <Text mb={1}>Recording name</Text>
-                  <div className="select-search-container">
                   <input
-                      className="select-search-input"
+                    className="form-field-input"
                     type="text"
                     onChange={nameChange}
                     placeholder="Optional"
                   />
-                  </div>
                 </Box>
                 <Box mb={4}>
-                  <Text>Do you want to enable live translation?</Text>
+                  <Text mb={1}>Do you want to enable live translation?</Text>
-                  <SelectSearch
+                  <SearchableLanguageSelect
-                    search
                     options={supportedLanguages}
                     value={targetLanguage}
                     onChange={onLanguageChange}
-                    onBlur={() => {}}
+                    placeholder="No translation"
-                    onFocus={() => {}}
-                    placeholder="Choose your language"
                   />
                 </Box>
                 {!loading ? (

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

@@ -78,10 +78,10 @@ const useMp3 = (transcriptId: string, waiting?: boolean): Mp3Response => {
 
     // Audio is not deleted, proceed to load it
     audioElement = document.createElement("audio");
-    const audioUrl = `${API_URL}/v1/transcripts/${transcriptId}/audio/mp3`;
+    const audioUrl = accessTokenInfo
-    audioElement.src = accessTokenInfo
+      ? `${API_URL}/v1/transcripts/${transcriptId}/audio/mp3?token=${encodeURIComponent(accessTokenInfo)}`
-      ? `${audioUrl}?token=${encodeURIComponent(accessTokenInfo)}`
+      : `${API_URL}/v1/transcripts/${transcriptId}/audio/mp3`;
-      : audioUrl;
+    audioElement.src = audioUrl;
     audioElement.crossOrigin = "anonymous";
     audioElement.preload = "auto";
 

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

@@ -346,7 +346,8 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
     let intentionalClose = false;
 
     const connect = () => {
-      const subprotocols = auth.accessToken
+      const subprotocols =
+        auth.status === "authenticated" && auth.accessToken
           ? ["bearer", auth.accessToken]
           : undefined;
       ws = new WebSocket(url, subprotocols);

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

@@ -28,7 +28,7 @@ function WherebyConsentDialogButton({
   meetingId: MeetingId;
   recordingType: Meeting["recording_type"];
   skipConsent: boolean;
-  wherebyRef: React.RefObject<HTMLElement>;
+  wherebyRef: React.RefObject<HTMLElement | null>;
 }) {
   const previousFocusRef = useRef<HTMLElement | null>(null);
 

www/app/[roomName]/room.tsx

@@ -49,8 +49,8 @@ export type RoomDetails = {
 
 // stages: we focus on the consent, then whereby steals focus, then we focus on the consent again, then return focus to whoever stole it initially
 const useConsentWherebyFocusManagement = (
-  acceptButtonRef: RefObject<HTMLButtonElement>,
+  acceptButtonRef: RefObject<HTMLButtonElement | null>,
-  wherebyRef: RefObject<HTMLElement>,
+  wherebyRef: RefObject<HTMLElement | null>,
 ) => {
   const currentFocusRef = useRef<HTMLElement | null>(null);
   useEffect(() => {
@@ -87,7 +87,7 @@ const useConsentWherebyFocusManagement = (
 
 const useConsentDialog = (
   meetingId: MeetingId,
-  wherebyRef: RefObject<HTMLElement> /*accessibility*/,
+  wherebyRef: RefObject<HTMLElement | null> /*accessibility*/,
 ) => {
   const { state: consentState, touch, hasAnswered } = useRecordingConsent();
   // toast would open duplicates, even with using "id=" prop
@@ -220,7 +220,7 @@ function ConsentDialogButton({
   wherebyRef,
 }: {
   meetingId: MeetingId;
-  wherebyRef: React.RefObject<HTMLElement>;
+  wherebyRef: React.RefObject<HTMLElement | null>;
 }) {
   const { showConsentModal, consentState, hasAnswered, consentLoading } =
     useConsentDialog(meetingId, wherebyRef);

www/app/api/auth/[...nextauth]/route.ts

@@ -1,6 +1,14 @@
 import NextAuth from "next-auth";
 import { authOptions } from "../../../lib/authBackend";
 
-const handler = NextAuth(authOptions());
+export const dynamic = "force-dynamic";
 
-export { handler as GET, handler as POST };
+// authOptions() is deferred to request time to avoid calling getNextEnvVar
+// during Turbopack's build-phase module evaluation (Next.js 16+)
+export function GET(req: Request, ctx: any) {
+  return NextAuth(authOptions())(req as any, ctx);
+}
+
+export function POST(req: Request, ctx: any) {
+  return NextAuth(authOptions())(req as any, ctx);
+}

www/app/api/health/route.ts

@@ -1,5 +1,7 @@
 import { NextResponse } from "next/server";
 
+export const dynamic = "force-dynamic";
+
 export async function GET() {
   const health = {
     status: "healthy",

www/app/components/MainNav.tsx

@@ -0,0 +1,46 @@
+import NextLink from "next/link";
+import { featureEnabled } from "../lib/features";
+import UserInfo from "../(auth)/userInfo";
+import { RECORD_A_MEETING_URL } from "../api/urls";
+
+function NavLink({
+  href,
+  children,
+}: {
+  href: string;
+  children: React.ReactNode;
+}) {
+  return (
+    <NextLink href={href} className="font-light px-10">
+      {children}
+    </NextLink>
+  );
+}
+
+export default function MainNav() {
+  return (
+    <nav>
+      <NavLink href={RECORD_A_MEETING_URL}>Create</NavLink>
+      {featureEnabled("browse") && (
+        <>
+          &nbsp;·&nbsp;
+          <NavLink href="/browse">Browse</NavLink>
+        </>
+      )}
+      {featureEnabled("rooms") && (
+        <>
+          &nbsp;·&nbsp;
+          <NavLink href="/rooms">Rooms</NavLink>
+        </>
+      )}
+      {featureEnabled("requireLogin") && (
+        <>
+          &nbsp;·&nbsp;
+          <NavLink href="/settings/api-keys">Settings</NavLink>
+          &nbsp;·&nbsp;
+          <UserInfo />
+        </>
+      )}
+    </nav>
+  );
+}

www/app/components/SearchableLanguageSelect.tsx

@@ -0,0 +1,98 @@
+"use client";
+
+import React, { useMemo } from "react";
+import {
+  Combobox,
+  createListCollection,
+  useComboboxContext,
+} from "@chakra-ui/react";
+
+export type LangOption = { value: string | undefined; name: string };
+
+type Item = { label: string; value: string };
+
+function FilteredComboboxItems({ items }: { items: Item[] }) {
+  const ctx = useComboboxContext();
+  const inputValue = (ctx as { inputValue?: string }).inputValue ?? "";
+  const filtered = useMemo(() => {
+    const q = inputValue.trim().toLowerCase();
+    if (!q) return items;
+    return items.filter((item) => item.label.toLowerCase().includes(q));
+  }, [items, inputValue]);
+
+  return (
+    <>
+      <Combobox.Empty>No matches</Combobox.Empty>
+      {filtered.map((item) => (
+        <Combobox.Item key={item.value} item={item}>
+          {item.label}
+        </Combobox.Item>
+      ))}
+    </>
+  );
+}
+
+type Props = {
+  options: LangOption[];
+  value: string;
+  onChange: (value: string) => void;
+  placeholder: string;
+};
+
+export function SearchableLanguageSelect({
+  options,
+  value,
+  onChange,
+  placeholder,
+}: Props) {
+  const items = useMemo(() => {
+    const result: Item[] = [];
+    let addedNone = false;
+    for (const opt of options) {
+      const val = opt.value ?? "NOTRANSLATION";
+      if (val === "NOTRANSLATION" || val === "") {
+        if (addedNone) continue;
+        addedNone = true;
+        result.push({ label: "No translation", value: "NOTRANSLATION" });
+      } else {
+        result.push({ label: opt.name, value: val });
+      }
+    }
+    return result.sort((a, b) => {
+      if (a.value === "NOTRANSLATION") return -1;
+      if (b.value === "NOTRANSLATION") return 1;
+      return a.label.localeCompare(b.label);
+    });
+  }, [options]);
+
+  const collection = useMemo(() => createListCollection({ items }), [items]);
+
+  const selectedValues = value && value !== "NOTRANSLATION" ? [value] : [];
+
+  return (
+    <Combobox.Root
+      collection={collection}
+      value={selectedValues}
+      onValueChange={(e) => onChange(e.value[0] ?? "NOTRANSLATION")}
+      openOnClick
+      closeOnSelect
+      selectionBehavior="replace"
+      placeholder={placeholder}
+      className="form-combobox"
+      size="md"
+      positioning={{ strategy: "fixed", hideWhenDetached: true }}
+    >
+      <Combobox.Control>
+        <Combobox.Input />
+        <Combobox.IndicatorGroup>
+          <Combobox.Trigger />
+        </Combobox.IndicatorGroup>
+      </Combobox.Control>
+      <Combobox.Positioner>
+        <Combobox.Content>
+          <FilteredComboboxItems items={items} />
+        </Combobox.Content>
+      </Combobox.Positioner>
+    </Combobox.Root>
+  );
+}

www/app/layout.tsx

@@ -24,10 +24,9 @@ export const viewport: Viewport = {
   maximumScale: 1,
 };
 
+export function generateMetadata(): Metadata {
   const SITE_URL = getNextEnvVar("SITE_URL");
-const env = getClientEnv();
+  return {
-
-export const metadata: Metadata = {
     metadataBase: new URL(SITE_URL),
     title: {
       template: "%s – Reflector",
@@ -38,7 +37,9 @@ export const metadata: Metadata = {
     applicationName: "Reflector",
     referrer: "origin-when-cross-origin",
     keywords: ["Reflector", "Monadical", "AI", "Meetings", "Transcription"],
-  authors: [{ name: "Monadical Team", url: "https://monadical.com/team.html" }],
+    authors: [
+      { name: "Monadical Team", url: "https://monadical.com/team.html" },
+    ],
     formatDetection: {
       email: false,
       address: false,
@@ -65,14 +66,21 @@ export const metadata: Metadata = {
       shortcut: "/r-icon.png",
       apple: "/r-icon.png",
     },
-  robots: { index: false, follow: false, noarchive: true, noimageindex: true },
+    robots: {
+      index: false,
+      follow: false,
+      noarchive: true,
+      noimageindex: true,
+    },
   };
+}
 
 export default async function RootLayout({
   children,
 }: {
   children: React.ReactNode;
 }) {
+  const env = getClientEnv();
   return (
     <html lang="en" className={poppins.className} suppressHydrationWarning>
       <body

www/app/lib/__tests__/authBackend.test.ts

@@ -0,0 +1,88 @@
+// env vars must be set before any module imports
+process.env.AUTHENTIK_REFRESH_TOKEN_URL =
+  "https://authentik.example.com/application/o/token/";
+process.env.AUTHENTIK_ISSUER =
+  "https://authentik.example.com/application/o/reflector/";
+process.env.AUTHENTIK_CLIENT_ID = "test-client-id";
+process.env.AUTHENTIK_CLIENT_SECRET = "test-client-secret";
+process.env.SERVER_API_URL = "http://localhost:1250";
+process.env.FEATURE_REQUIRE_LOGIN = "true";
+// must NOT be "credentials" so authOptions() returns the Authentik path
+delete process.env.AUTH_PROVIDER;
+
+jest.mock("../next", () => ({ isBuildPhase: false }));
+
+jest.mock("../features", () => ({
+  featureEnabled: (name: string) => name === "requireLogin",
+}));
+
+jest.mock("../redisClient", () => ({
+  tokenCacheRedis: {},
+  redlock: {
+    using: jest.fn((_keys: string[], _ttl: number, fn: () => unknown) => fn()),
+  },
+}));
+
+jest.mock("../redisTokenCache", () => ({
+  getTokenCache: jest.fn().mockResolvedValue(null),
+  setTokenCache: jest.fn().mockResolvedValue(undefined),
+  deleteTokenCache: jest.fn().mockResolvedValue(undefined),
+}));
+
+const mockFetch = jest.fn();
+global.fetch = mockFetch;
+
+import { authOptions } from "../authBackend";
+
+describe("Authentik token refresh", () => {
+  beforeEach(() => {
+    mockFetch.mockReset();
+  });
+
+  test("refresh request preserves trailing slash in token URL", async () => {
+    mockFetch.mockResolvedValue({
+      ok: true,
+      json: async () => ({
+        access_token: "new-access-token",
+        expires_in: 300,
+        refresh_token: "new-refresh-token",
+      }),
+    });
+
+    const options = authOptions();
+    const jwtCallback = options.callbacks!.jwt!;
+
+    // Simulate a returning user whose access token has expired (no account/user = not initial login)
+    const expiredToken = {
+      sub: "test-user-123",
+      accessToken: "expired-access-token",
+      accessTokenExpires: Date.now() - 60_000,
+      refreshToken: "old-refresh-token",
+    };
+
+    await jwtCallback({
+      token: expiredToken,
+      user: undefined as any,
+      account: null,
+      profile: undefined,
+      trigger: "update",
+      isNewUser: false,
+      session: undefined,
+    });
+
+    // The refresh POST must go to the exact URL from the env var (trailing slash included)
+    expect(mockFetch).toHaveBeenCalledWith(
+      "https://authentik.example.com/application/o/token/",
+      expect.objectContaining({
+        method: "POST",
+        body: expect.any(String),
+      }),
+    );
+
+    const body = new URLSearchParams(mockFetch.mock.calls[0][1].body);
+    expect(body.get("grant_type")).toBe("refresh_token");
+    expect(body.get("refresh_token")).toBe("old-refresh-token");
+    expect(body.get("client_id")).toBe("test-client-id");
+    expect(body.get("client_secret")).toBe("test-client-secret");
+  });
+});

www/app/lib/authBackend.ts

@@ -53,7 +53,7 @@ const TOKEN_CACHE_TTL = REFRESH_ACCESS_TOKEN_BEFORE;
 const getAuthentikClientId = () => getNextEnvVar("AUTHENTIK_CLIENT_ID");
 const getAuthentikClientSecret = () => getNextEnvVar("AUTHENTIK_CLIENT_SECRET");
 const getAuthentikRefreshTokenUrl = () =>
-  getNextEnvVar("AUTHENTIK_REFRESH_TOKEN_URL").replace(/\/+$/, "");
+  getNextEnvVar("AUTHENTIK_REFRESH_TOKEN_URL");
 
 const getAuthentikIssuer = () => {
   const stringUrl = getNextEnvVar("AUTHENTIK_ISSUER");
@@ -62,7 +62,7 @@ const getAuthentikIssuer = () => {
   } catch (e) {
     throw new Error("AUTHENTIK_ISSUER is not a valid URL: " + stringUrl);
   }
-  return stringUrl.replace(/\/+$/, "");
+  return stringUrl;
 };
 
 export const authOptions = (): AuthOptions => {