chore(main): release 0.24.0 (#793)

* Identify action items

* Add action items to mock summary

* Add action items validator

* Remove final prefix from action items

* Make on action items callback required

* Don't mutation action items response

* Assign action items to none on error

* Use timeout constant

* Exclude action items from transcript list

.github/workflows/deploy.yml

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

.github/workflows/dockerhub-backend.yml

@@ -1,35 +1,31 @@
-name: Build and Push Frontend Docker Image
+name: Build and Push Backend Docker Image (Docker Hub)
 
 on:
   push:
-    branches:
+    tags:
-      - main
+      - "v*"
-    paths:
-      - 'www/**'
-      - '.github/workflows/docker-frontend.yml'
   workflow_dispatch:
 
 env:
-  REGISTRY: ghcr.io
+  REGISTRY: docker.io
-  IMAGE_NAME: ${{ github.repository }}-frontend
+  IMAGE_NAME: monadicalsas/reflector-backend
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
     permissions:
       contents: read
-      packages: write
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
-      - name: Log in to GitHub Container Registry
+      - name: Log in to Docker Hub
         uses: docker/login-action@v3
         with:
           registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
+          username: monadicalsas
-          password: ${{ secrets.GITHUB_TOKEN }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
 
       - name: Extract metadata
         id: meta
@@ -38,7 +34,7 @@ jobs:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
           tags: |
             type=ref,event=branch
-            type=sha,prefix={{branch}}-
+            type=ref,event=tag
             type=raw,value=latest,enable={{is_default_branch}}
 
       - name: Set up Docker Buildx
@@ -47,8 +43,8 @@ jobs:
       - name: Build and push Docker image
         uses: docker/build-push-action@v5
         with:
-          context: ./www
+          context: ./server
-          file: ./www/Dockerfile
+          file: ./server/Dockerfile
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}

.github/workflows/dockerhub-frontend.yml

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

.gitignore

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

.secrets.example

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

CHANGELOG.md

@@ -1,5 +1,72 @@
 # Changelog
 
+## [0.24.0](https://github.com/Monadical-SAS/reflector/compare/v0.23.2...v0.24.0) (2025-12-18)
+
+
+### Features
+
+* identify action items ([#790](https://github.com/Monadical-SAS/reflector/issues/790)) ([964cd78](https://github.com/Monadical-SAS/reflector/commit/964cd78bb699d83d012ae4b8c96565df25b90a5d))
+
+
+### Bug Fixes
+
+* automatically reprocess daily recordings ([#797](https://github.com/Monadical-SAS/reflector/issues/797)) ([5f458aa](https://github.com/Monadical-SAS/reflector/commit/5f458aa4a7ec3d00ca5ec49d62fcc8ad232b138e))
+* daily video optimisation ([#789](https://github.com/Monadical-SAS/reflector/issues/789)) ([16284e1](https://github.com/Monadical-SAS/reflector/commit/16284e1ac3faede2b74f0d91b50c0b5612af2c35))
+* main menu login ([#800](https://github.com/Monadical-SAS/reflector/issues/800)) ([0bc971b](https://github.com/Monadical-SAS/reflector/commit/0bc971ba966a52d719c8c240b47dc7b3bdea4391))
+* retry on workflow timeout ([#798](https://github.com/Monadical-SAS/reflector/issues/798)) ([5f7dfad](https://github.com/Monadical-SAS/reflector/commit/5f7dfadabd3e8017406ad3720ba495a59963ee34))
+
+## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
+
+
+### Bug Fixes
+
+* build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
+
+## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
+
+
+### Bug Fixes
+
+* populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
+
+## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
+
+
+### Features
+
+* dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
+* llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
+
+
+### Bug Fixes
+
+* celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
+* deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
+* hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
+* return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
+
+## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
+
+
+### Bug Fixes
+
+* Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
+
+## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
+
+
+### Bug Fixes
+
+* align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
+
+## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
+
+
+### Bug Fixes
+
+* daily auto refresh fix ([#755](https://github.com/Monadical-SAS/reflector/issues/755)) ([fe47c46](https://github.com/Monadical-SAS/reflector/commit/fe47c46489c5aa0cc538109f7559cc9accb35c01))
+* Skip mixdown for multitrack ([#760](https://github.com/Monadical-SAS/reflector/issues/760)) ([b51b7aa](https://github.com/Monadical-SAS/reflector/commit/b51b7aa9176c1a53ba57ad99f5e976c804a1e80c))
+
 ## [0.22.1](https://github.com/Monadical-SAS/reflector/compare/v0.22.0...v0.22.1) (2025-11-27)
 
 

docker-compose.prod.yml

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

server/migrations/versions/05f8688d6895_add_action_items.py

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

server/pyproject.toml

@@ -126,6 +126,7 @@ markers = [
 select = [
     "I",       # isort - import sorting
     "F401",    # unused imports
+    "E402",    # module level import not at top of file
     "PLC0415", # import-outside-top-level - detect inline imports
 ]
 

server/reflector/asynctask.py

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

server/reflector/dailyco_api/__init__.py

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

server/reflector/dailyco_api/requests.py

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

server/reflector/dailyco_api/responses.py

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

server/reflector/db/recordings.py

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

server/reflector/db/transcripts.py

@@ -44,6 +44,7 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Column("title", sqlalchemy.String),
     sqlalchemy.Column("short_summary", sqlalchemy.String),
     sqlalchemy.Column("long_summary", sqlalchemy.String),
+    sqlalchemy.Column("action_items", sqlalchemy.JSON),
     sqlalchemy.Column("topics", sqlalchemy.JSON),
     sqlalchemy.Column("events", sqlalchemy.JSON),
     sqlalchemy.Column("participants", sqlalchemy.JSON),
@@ -164,6 +165,10 @@ class TranscriptFinalLongSummary(BaseModel):
     long_summary: str
 
 
+class TranscriptActionItems(BaseModel):
+    action_items: dict
+
+
 class TranscriptFinalTitle(BaseModel):
     title: str
 
@@ -204,6 +209,7 @@ class Transcript(BaseModel):
     locked: bool = False
     short_summary: str | None = None
     long_summary: str | None = None
+    action_items: dict | None = None
     topics: list[TranscriptTopic] = []
     events: list[TranscriptEvent] = []
     participants: list[TranscriptParticipant] | None = []
@@ -368,7 +374,12 @@ class TranscriptController:
         room_id: str | None = None,
         search_term: str | None = None,
         return_query: bool = False,
-        exclude_columns: list[str] = ["topics", "events", "participants"],
+        exclude_columns: list[str] = [
+            "topics",
+            "events",
+            "participants",
+            "action_items",
+        ],
     ) -> list[Transcript]:
         """
         Get all transcripts

server/reflector/db/users.py

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

server/reflector/llm.py

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

server/reflector/pipelines/main_file_pipeline.py

@@ -309,6 +309,7 @@ class PipelineMainFile(PipelineMainBase):
             transcript,
             on_long_summary_callback=self.on_long_summary,
             on_short_summary_callback=self.on_short_summary,
+            on_action_items_callback=self.on_action_items,
             empty_pipeline=self.empty_pipeline,
             logger=self.logger,
         )
@@ -340,7 +341,6 @@ async def task_send_webhook_if_needed(*, transcript_id: str):
 @asynctask
 async def task_pipeline_file_process(*, transcript_id: str):
     """Celery task for file pipeline processing"""
-
     transcript = await transcripts_controller.get_by_id(transcript_id)
     if not transcript:
         raise Exception(f"Transcript {transcript_id} not found")

server/reflector/pipelines/main_live_pipeline.py

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

server/reflector/pipelines/main_multitrack_pipeline.py

@@ -422,7 +422,15 @@ class PipelineMainMultitrack(PipelineMainBase):
             # Open all containers with cleanup guaranteed
             for i, url in enumerate(valid_track_urls):
                 try:
-                    c = av.open(url)
+                    c = av.open(
+                        url,
+                        options={
+                            # it's trying to stream from s3 by default
+                            "reconnect": "1",
+                            "reconnect_streamed": "1",
+                            "reconnect_delay_max": "5",
+                        },
+                    )
                     containers.append(c)
                 except Exception as e:
                     self.logger.warning(
@@ -451,6 +459,8 @@ class PipelineMainMultitrack(PipelineMainBase):
                         frame = next(dec)
                     except StopIteration:
                         active[i] = False
+                        # causes stream to move on / unclogs memory
+                        inputs[i].push(None)
                         continue
 
                     if frame.sample_rate != target_sample_rate:
@@ -470,8 +480,6 @@ class PipelineMainMultitrack(PipelineMainBase):
                         mixed.time_base = Fraction(1, target_sample_rate)
                         await writer.push(mixed)
 
-            for in_ctx in inputs:
-                in_ctx.push(None)
             while True:
                 try:
                     mixed = sink.pull()
@@ -764,6 +772,7 @@ class PipelineMainMultitrack(PipelineMainBase):
             transcript,
             on_long_summary_callback=self.on_long_summary,
             on_short_summary_callback=self.on_short_summary,
+            on_action_items_callback=self.on_action_items,
             empty_pipeline=self.empty_pipeline,
             logger=self.logger,
         )

server/reflector/pipelines/topic_processing.py

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

server/reflector/processors/summary/summary_builder.py

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

server/reflector/processors/transcript_final_summary.py

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

server/reflector/processors/transcript_topic_detector.py

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

server/reflector/processors/types.py

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

server/reflector/services/transcript_process.py

@@ -160,7 +160,10 @@ def dispatch_transcript_processing(config: ProcessingConfig) -> AsyncResult:
 def task_is_scheduled_or_active(task_name: str, **kwargs):
     inspect = celery.current_app.control.inspect()
 
-    for worker, tasks in (inspect.scheduled() | inspect.active()).items():
+    scheduled = inspect.scheduled() or {}
+    active = inspect.active() or {}
+    all = scheduled | active
+    for worker, tasks in all.items():
         for task in tasks:
             if task["name"] == task_name and task["kwargs"] == kwargs:
                 return True

server/reflector/settings.py

@@ -74,6 +74,13 @@ class Settings(BaseSettings):
     LLM_API_KEY: str | None = None
     LLM_CONTEXT_WINDOW: int = 16000
 
+    LLM_PARSE_MAX_RETRIES: int = (
+        3  # Max retries for JSON/validation errors (total attempts = retries + 1)
+    )
+    LLM_STRUCTURED_RESPONSE_TIMEOUT: int = (
+        300  # Timeout in seconds for structured responses (5 minutes)
+    )
+
     # Diarization
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"

server/reflector/video_platforms/daily.py

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

server/reflector/views/rooms.py

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

server/reflector/views/transcripts.py

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

server/reflector/worker/app.py

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

server/reflector/worker/process.py

@@ -12,7 +12,7 @@ from celery import shared_task
 from celery.utils.log import get_task_logger
 from pydantic import ValidationError
 
-from reflector.dailyco_api import RecordingResponse
+from reflector.dailyco_api import FinishedRecordingResponse, RecordingResponse
 from reflector.db.daily_participant_sessions import (
     DailyParticipantSession,
     daily_participant_sessions_controller,
@@ -322,16 +322,38 @@ async def poll_daily_recordings():
         )
         return
 
-    recording_ids = [rec.id for rec in api_recordings]
+    finished_recordings: List[FinishedRecordingResponse] = []
+    for rec in api_recordings:
+        finished = rec.to_finished()
+        if finished is None:
+            logger.debug(
+                "Skipping unfinished recording",
+                recording_id=rec.id,
+                room_name=rec.room_name,
+                status=rec.status,
+            )
+            continue
+        finished_recordings.append(finished)
+
+    if not finished_recordings:
+        logger.debug(
+            "No finished recordings found from Daily.co API",
+            total_api_count=len(api_recordings),
+        )
+        return
+
+    recording_ids = [rec.id for rec in finished_recordings]
     existing_recordings = await recordings_controller.get_by_ids(recording_ids)
     existing_ids = {rec.id for rec in existing_recordings}
 
-    missing_recordings = [rec for rec in api_recordings if rec.id not in existing_ids]
+    missing_recordings = [
+        rec for rec in finished_recordings if rec.id not in existing_ids
+    ]
 
     if not missing_recordings:
         logger.debug(
             "All recordings already in DB",
-            api_count=len(api_recordings),
+            api_count=len(finished_recordings),
             existing_count=len(existing_recordings),
         )
         return
@@ -339,7 +361,7 @@ async def poll_daily_recordings():
     logger.info(
         "Found recordings missing from DB",
         missing_count=len(missing_recordings),
-        total_api_count=len(api_recordings),
+        total_api_count=len(finished_recordings),
         existing_count=len(existing_recordings),
     )
 
@@ -649,7 +671,7 @@ async def reprocess_failed_recordings():
     Find recordings in Whereby S3 bucket and check if they have proper transcriptions.
     If not, requeue them for processing.
 
-    Note: Daily.co recordings are processed via webhooks, not this cron job.
+    Note: Daily.co multitrack recordings are handled by reprocess_failed_daily_recordings.
     """
     logger.info("Checking Whereby recordings that need processing or reprocessing")
 
@@ -702,6 +724,103 @@ async def reprocess_failed_recordings():
     return reprocessed_count
 
 
+@shared_task
+@asynctask
+async def reprocess_failed_daily_recordings():
+    """
+    Find Daily.co multitrack recordings in the database and check if they have proper transcriptions.
+    If not, requeue them for processing.
+    """
+    logger.info(
+        "Checking Daily.co multitrack recordings that need processing or reprocessing"
+    )
+
+    if not settings.DAILYCO_STORAGE_AWS_BUCKET_NAME:
+        logger.debug(
+            "DAILYCO_STORAGE_AWS_BUCKET_NAME not configured; skipping Daily recording reprocessing"
+        )
+        return 0
+
+    bucket_name = settings.DAILYCO_STORAGE_AWS_BUCKET_NAME
+    reprocessed_count = 0
+
+    try:
+        multitrack_recordings = (
+            await recordings_controller.get_multitrack_needing_reprocessing(bucket_name)
+        )
+
+        logger.info(
+            "Found multitrack recordings needing reprocessing",
+            count=len(multitrack_recordings),
+            bucket=bucket_name,
+        )
+
+        for recording in multitrack_recordings:
+            if not recording.meeting_id:
+                logger.debug(
+                    "Skipping recording without meeting_id",
+                    recording_id=recording.id,
+                )
+                continue
+
+            meeting = await meetings_controller.get_by_id(recording.meeting_id)
+            if not meeting:
+                logger.warning(
+                    "Meeting not found for recording",
+                    recording_id=recording.id,
+                    meeting_id=recording.meeting_id,
+                )
+                continue
+
+            transcript = None
+            try:
+                transcript = await transcripts_controller.get_by_recording_id(
+                    recording.id
+                )
+            except ValidationError:
+                await transcripts_controller.remove_by_recording_id(recording.id)
+                logger.warning(
+                    "Removed invalid transcript for recording",
+                    recording_id=recording.id,
+                )
+
+            if not recording.track_keys:
+                logger.warning(
+                    "Recording has no track_keys, cannot reprocess",
+                    recording_id=recording.id,
+                )
+                continue
+
+            logger.info(
+                "Queueing Daily recording for reprocessing",
+                recording_id=recording.id,
+                room_name=meeting.room_name,
+                track_count=len(recording.track_keys),
+                transcript_status=transcript.status if transcript else None,
+            )
+
+            process_multitrack_recording.delay(
+                bucket_name=bucket_name,
+                daily_room_name=meeting.room_name,
+                recording_id=recording.id,
+                track_keys=recording.track_keys,
+            )
+            reprocessed_count += 1
+
+    except Exception as e:
+        logger.error(
+            "Error checking Daily multitrack recordings",
+            error=str(e),
+            exc_info=True,
+        )
+
+    logger.info(
+        "Daily reprocessing complete",
+        requeued_count=reprocessed_count,
+    )
+    return reprocessed_count
+
+
 @shared_task
 @asynctask
 async def trigger_daily_reconciliation() -> None:

server/reflector/worker/webhook.py

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

server/tests/conftest.py

@@ -318,6 +318,14 @@ async def dummy_storage():
         yield
 
 
+@pytest.fixture
+def test_settings():
+    """Provide isolated settings for tests to avoid modifying global settings"""
+    from reflector.settings import Settings
+
+    return Settings()
+
+
 @pytest.fixture(scope="session")
 def celery_enable_logging():
     return True

server/tests/test_llm_retry.py

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

server/tests/test_pipeline_main_file.py

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

server/tests/test_transcripts.py

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

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

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

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

@@ -117,15 +117,6 @@ export default function TranscriptDetails(details: TranscriptDetails) {
     return <Modal title="Loading" text={"Loading transcript..."} />;
   }
 
-  if (mp3.error) {
-    return (
-      <Modal
-        title="Transcription error"
-        text={`There was an error loading the recording. Error: ${mp3.error}`}
-      />
-    );
-  }
-
   return (
     <>
       <Grid
@@ -147,7 +138,12 @@ export default function TranscriptDetails(details: TranscriptDetails) {
               />
             ) : !mp3.loading && (waveform.error || mp3.error) ? (
               <Box p={4} bg="red.100" borderRadius="md">
-                <Text>Error loading this recording</Text>
+                <Text>
+                  Error loading{" "}
+                  {[waveform.error && "waveform", mp3.error && "mp3"]
+                    .filter(Boolean)
+                    .join(" and ")}
+                </Text>
               </Box>
             ) : (
               <Skeleton h={14} />

www/app/(auth)/userInfo.tsx

@@ -13,9 +13,12 @@ export default function UserInfo() {
     <Spinner size="xs" className="mx-3" />
   ) : !isAuthenticated && !isRefreshing ? (
     <Link
-      href="/"
+      href="#"
       className="font-light px-2"
-      onClick={() => auth.signIn("authentik")}
+      onClick={(e) => {
+        e.preventDefault();
+        auth.signIn("authentik");
+      }}
     >
       Log in
     </Link>

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

@@ -11,6 +11,7 @@ import {
   recordingTypeRequiresConsent,
 } from "../../lib/consent";
 import { useRoomJoinMeeting } from "../../lib/apiHooks";
+import { assertExists } from "../../lib/utils";
 
 type Meeting = components["schemas"]["Meeting"];
 
@@ -22,16 +23,15 @@ export default function DailyRoom({ meeting }: DailyRoomProps) {
   const router = useRouter();
   const params = useParams();
   const auth = useAuth();
-  const status = auth.status;
+  const authLastUserId = auth.lastUserId;
   const containerRef = useRef<HTMLDivElement>(null);
   const joinMutation = useRoomJoinMeeting();
   const [joinedMeeting, setJoinedMeeting] = useState<Meeting | null>(null);
 
   const roomName = params?.roomName as string;
 
-  // Always call /join to get a fresh token with user_id
   useEffect(() => {
-    if (status === "loading" || !meeting?.id || !roomName) return;
+    if (authLastUserId === undefined || !meeting?.id || !roomName) return;
 
     const join = async () => {
       try {
@@ -50,18 +50,17 @@ export default function DailyRoom({ meeting }: DailyRoomProps) {
     };
 
     join();
-  }, [meeting?.id, roomName, status]);
+  }, [meeting?.id, roomName, authLastUserId]);
 
-  const roomUrl = joinedMeeting?.host_room_url || joinedMeeting?.room_url;
+  const roomUrl = joinedMeeting?.room_url;
-  const isLoading =
-    status === "loading" || joinMutation.isPending || !joinedMeeting;
 
   const handleLeave = useCallback(() => {
     router.push("/browse");
   }, [router]);
 
   useEffect(() => {
-    if (isLoading || !roomUrl || !containerRef.current) return;
+    if (authLastUserId === undefined || !roomUrl || !containerRef.current)
+      return;
 
     let frame: DailyCall | null = null;
     let destroyed = false;
@@ -92,19 +91,41 @@ export default function DailyRoom({ meeting }: DailyRoomProps) {
 
         frame.on("joined-meeting", async () => {
           try {
-            await frame.startRecording({ type: "raw-tracks" });
+            const frameInstance = assertExists(
+              frame,
+              "frame object got lost somewhere after frame.on was called",
+            );
+
+            if (meeting.recording_type === "cloud") {
+              console.log("Starting cloud recording");
+              await frameInstance.startRecording({ type: "raw-tracks" });
+            }
           } catch (error) {
             console.error("Failed to start recording:", error);
           }
         });
 
-        await frame.join({ url: roomUrl });
+        await frame.join({
+          url: roomUrl,
+          sendSettings: {
+            video: {
+              // Optimize bandwidth for camera video
+              // allowAdaptiveLayers automatically adjusts quality based on network conditions
+              allowAdaptiveLayers: true,
+              // Use bandwidth-optimized preset as fallback for browsers without adaptive support
+              maxQuality: "medium",
+            },
+            // Note: screenVideo intentionally not configured to preserve full quality for screen shares
+          },
+        });
       } catch (error) {
         console.error("Error creating Daily frame:", error);
       }
     };
 
-    createAndJoin();
+    createAndJoin().catch((error) => {
+      console.error("Failed to create and join meeting:", error);
+    });
 
     return () => {
       destroyed = true;
@@ -114,9 +135,9 @@ export default function DailyRoom({ meeting }: DailyRoomProps) {
         });
       }
     };
-  }, [roomUrl, isLoading, handleLeave]);
+  }, [roomUrl, authLastUserId, handleLeave]);
 
-  if (isLoading) {
+  if (authLastUserId === undefined) {
     return (
       <Center width="100vw" height="100vh">
         <Spinner size="xl" />

www/app/lib/AuthProvider.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { createContext, useContext } from "react";
+import { createContext, useContext, useRef } from "react";
 import { useSession as useNextAuthSession } from "next-auth/react";
 import { signOut, signIn } from "next-auth/react";
 import { configureApiAuth } from "./apiClient";
@@ -25,6 +25,9 @@ type AuthContextType = (
   update: () => Promise<Session | null>;
   signIn: typeof signIn;
   signOut: typeof signOut;
+  // TODO probably rename isLoading to isReloading and make THIS field "isLoading"
+  // undefined is "not known", null is "is certainly logged out"
+  lastUserId: CustomSession["user"]["id"] | null | undefined;
 };
 
 const AuthContext = createContext<AuthContextType | undefined>(undefined);
@@ -41,10 +44,15 @@ const noopAuthContext: AuthContextType = {
   signOut: async () => {
     throw new Error("signOut not supposed to be called");
   },
+  lastUserId: undefined,
 };
 
 export function AuthProvider({ children }: { children: React.ReactNode }) {
   const { data: session, status, update } = useNextAuthSession();
+  // referential comparison done in component, must be primitive /or cached
+  const lastUserId = useRef<CustomSession["user"]["id"] | null | undefined>(
+    null,
+  );
 
   const contextValue: AuthContextType = isAuthEnabled
     ? {
@@ -73,11 +81,16 @@ export function AuthProvider({ children }: { children: React.ReactNode }) {
             case "authenticated": {
               const customSession = assertCustomSession(session);
               if (customSession?.error === REFRESH_ACCESS_TOKEN_ERROR) {
+                // warning: call order-dependent
+                lastUserId.current = null;
                 // token had expired but next auth still returns "authenticated" so show user unauthenticated state
                 return {
                   status: "unauthenticated" as const,
                 };
               } else if (customSession?.accessToken) {
+                // updates anyways with updated properties below
+                // warning! execution order conscience, must be ran before reading lastUserId.current below
+                lastUserId.current = customSession.user.id;
                 return {
                   status,
                   accessToken: customSession.accessToken,
@@ -92,6 +105,8 @@ export function AuthProvider({ children }: { children: React.ReactNode }) {
               }
             }
             case "unauthenticated": {
+              // warning: call order-dependent
+              lastUserId.current = null;
               return { status: "unauthenticated" as const };
             }
             default: {
@@ -103,6 +118,8 @@ export function AuthProvider({ children }: { children: React.ReactNode }) {
         update,
         signIn,
         signOut,
+        // for optimistic cases when we assume "loading" doesn't immediately invalidate the user
+        lastUserId: lastUserId.current,
       }
     : noopAuthContext;
 

www/app/lib/authBackend.ts

@@ -148,7 +148,7 @@ export const authOptions = (): AuthOptions =>
           },
           async session({ session, token }) {
             const extendedToken = token as JWTWithAccessToken;
-
+            console.log("extendedToken", extendedToken);
             const userId = await getUserId(extendedToken.accessToken);
 
             return {

www/package.json

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