feat: add LLM_ENABLE_THINKING env var for thinking-mode LLMs

Some LLMs (e.g. GLM-4.5-Air) default to thinking mode which returns
content in reasoning_content instead of content field, breaking
structured output parsing. This setting passes enable_thinking through
extra_body to control the behavior per deployment.

Three states: None (default, don't send), True, False.

CHANGELOG.md

@@ -1,5 +1,17 @@
 # Changelog
 
+## [0.33.0](https://github.com/Monadical-SAS/reflector/compare/v0.32.2...v0.33.0) (2026-02-05)
+
+
+### Features
+
+* Daily+hatchet default ([#846](https://github.com/Monadical-SAS/reflector/issues/846)) ([15ab2e3](https://github.com/Monadical-SAS/reflector/commit/15ab2e306eacf575494b4b5d2b2ad779d44a1c7f))
+
+
+### Bug Fixes
+
+* websocket tests ([#825](https://github.com/Monadical-SAS/reflector/issues/825)) ([1ce1c7a](https://github.com/Monadical-SAS/reflector/commit/1ce1c7a910b6c374115d2437b17f9d288ef094dc))
+
 ## [0.32.2](https://github.com/Monadical-SAS/reflector/compare/v0.32.1...v0.32.2) (2026-02-03)
 
 

server/migrations/versions/3aa20b96d963_drop_use_celery_column.py

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

server/reflector/db/rooms.py

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

server/reflector/llm.py

@@ -206,6 +206,12 @@ class LLM:
         """Configure llamaindex Settings with OpenAILike LLM"""
         session_id = llm_session_id.get() or f"fallback-{uuid4().hex}"
 
+        extra_body: dict = {"litellm_session_id": session_id}
+        # Only send enable_thinking when explicitly set (not None/unset).
+        # Models that don't support it will ignore the param.
+        if self.settings_obj.LLM_ENABLE_THINKING is not None:
+            extra_body["enable_thinking"] = self.settings_obj.LLM_ENABLE_THINKING
+
         Settings.llm = OpenAILike(
             model=self.model_name,
             api_base=self.url,
@@ -215,7 +221,7 @@ class LLM:
             is_function_calling_model=False,
             temperature=self.temperature,
             max_tokens=self.max_tokens,
-            additional_kwargs={"extra_body": {"litellm_session_id": session_id}},
+            additional_kwargs={"extra_body": extra_body},
         )
 
     async def get_response(

server/reflector/services/transcript_process.py

@@ -15,14 +15,10 @@ from hatchet_sdk.clients.rest.exceptions import ApiException, NotFoundException
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 from reflector.db.recordings import recordings_controller
-from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.utils.string import NonEmptyString
 
 
@@ -181,124 +177,98 @@ async def dispatch_transcript_processing(
     Returns AsyncResult for Celery tasks, None for Hatchet workflows.
     """
     if isinstance(config, MultitrackProcessingConfig):
-        use_celery = False
+        # Multitrack processing always uses Hatchet (no Celery fallback)
-        if config.room_id:
+        # First check if we can replay (outside transaction since it's read-only)
-            room = await rooms_controller.get_by_id(config.room_id)
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            use_celery = room.use_celery if room else False
+        if transcript and transcript.workflow_run_id and not force:
-
+            can_replay = await HatchetClientManager.can_replay(
-        use_hatchet = not use_celery
+                transcript.workflow_run_id
-
-        if use_celery:
-            logger.info(
-                "Room uses legacy Celery processing",
-                room_id=config.room_id,
-                transcript_id=config.transcript_id,
             )
-
+            if can_replay:
-        if use_hatchet:
+                await HatchetClientManager.replay_workflow(transcript.workflow_run_id)
-            # First check if we can replay (outside transaction since it's read-only)
+                logger.info(
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
+                    "Replaying Hatchet workflow",
-            if transcript and transcript.workflow_run_id and not force:
+                    workflow_id=transcript.workflow_run_id,
-                can_replay = await HatchetClientManager.can_replay(
-                    transcript.workflow_run_id
                 )
-                if can_replay:
+                return None
-                    await HatchetClientManager.replay_workflow(
+            else:
-                        transcript.workflow_run_id
+                # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
-                    )
+                # Log and proceed to start new workflow
-                    logger.info(
-                        "Replaying Hatchet workflow",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                    return None
-                else:
-                    # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
-                    # Log and proceed to start new workflow
-                    try:
-                        status = await HatchetClientManager.get_workflow_run_status(
-                            transcript.workflow_run_id
-                        )
-                        logger.info(
-                            "Old workflow not replayable, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                            old_status=status.value,
-                        )
-                    except NotFoundException:
-                        # Workflow deleted from Hatchet but ID still in DB
-                        logger.info(
-                            "Old workflow not found in Hatchet, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                        )
-
-            # Force: cancel old workflow if exists
-            if force and transcript and transcript.workflow_run_id:
-                try:
-                    await HatchetClientManager.cancel_workflow(
-                        transcript.workflow_run_id
-                    )
-                    logger.info(
-                        "Cancelled old workflow (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                except NotFoundException:
-                    logger.info(
-                        "Old workflow already deleted (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": None}
-                )
-
-            # Re-fetch and check for concurrent dispatch (optimistic approach).
-            # No database lock - worst case is duplicate dispatch, but Hatchet
-            # workflows are idempotent so this is acceptable.
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            if transcript and transcript.workflow_run_id:
-                # Another process started a workflow between validation and now
                 try:
                     status = await HatchetClientManager.get_workflow_run_status(
                         transcript.workflow_run_id
                     )
-                    if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                    logger.info(
-                        logger.info(
+                        "Old workflow not replayable, starting new",
-                            "Concurrent workflow detected, skipping dispatch",
+                        old_workflow_id=transcript.workflow_run_id,
-                            workflow_id=transcript.workflow_run_id,
+                        old_status=status.value,
-                        )
+                    )
-                        return None
+                except NotFoundException:
-                except ApiException:
+                    # Workflow deleted from Hatchet but ID still in DB
-                    # Workflow might be gone (404) or API issue - proceed with new workflow
+                    logger.info(
-                    pass
+                        "Old workflow not found in Hatchet, starting new",
+                        old_workflow_id=transcript.workflow_run_id,
+                    )
 
-            workflow_id = await HatchetClientManager.start_workflow(
+        # Force: cancel old workflow if exists
-                workflow_name="DiarizationPipeline",
+        if force and transcript and transcript.workflow_run_id:
-                input_data={
+            try:
-                    "recording_id": config.recording_id,
+                await HatchetClientManager.cancel_workflow(transcript.workflow_run_id)
-                    "tracks": [{"s3_key": k} for k in config.track_keys],
+                logger.info(
-                    "bucket_name": config.bucket_name,
+                    "Cancelled old workflow (--force)",
-                    "transcript_id": config.transcript_id,
+                    workflow_id=transcript.workflow_run_id,
-                    "room_id": config.room_id,
+                )
-                },
+            except NotFoundException:
-                additional_metadata={
+                logger.info(
-                    "transcript_id": config.transcript_id,
+                    "Old workflow already deleted (--force)",
-                    "recording_id": config.recording_id,
+                    workflow_id=transcript.workflow_run_id,
-                    "daily_recording_id": config.recording_id,
+                )
-                },
+            await transcripts_controller.update(transcript, {"workflow_run_id": None})
+
+        # Re-fetch and check for concurrent dispatch (optimistic approach).
+        # No database lock - worst case is duplicate dispatch, but Hatchet
+        # workflows are idempotent so this is acceptable.
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript and transcript.workflow_run_id:
+            # Another process started a workflow between validation and now
+            try:
+                status = await HatchetClientManager.get_workflow_run_status(
+                    transcript.workflow_run_id
+                )
+                if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                    logger.info(
+                        "Concurrent workflow detected, skipping dispatch",
+                        workflow_id=transcript.workflow_run_id,
+                    )
+                    return None
+            except ApiException:
+                # Workflow might be gone (404) or API issue - proceed with new workflow
+                pass
+
+        workflow_id = await HatchetClientManager.start_workflow(
+            workflow_name="DiarizationPipeline",
+            input_data={
+                "recording_id": config.recording_id,
+                "tracks": [{"s3_key": k} for k in config.track_keys],
+                "bucket_name": config.bucket_name,
+                "transcript_id": config.transcript_id,
+                "room_id": config.room_id,
+            },
+            additional_metadata={
+                "transcript_id": config.transcript_id,
+                "recording_id": config.recording_id,
+                "daily_recording_id": config.recording_id,
+            },
+        )
+
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
             )
 
-            if transcript:
+        logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-                await transcripts_controller.update(
+        return None
-                    transcript, {"workflow_run_id": workflow_id}
-                )
 
-            logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-            return None
-
-        # Celery pipeline (durable workflows disabled)
-        return task_pipeline_multitrack_process.delay(
-            transcript_id=config.transcript_id,
-            bucket_name=config.bucket_name,
-            track_keys=config.track_keys,
-        )
     elif isinstance(config, FileProcessingConfig):
         return task_pipeline_file_process.delay(transcript_id=config.transcript_id)
     else:

server/reflector/settings.py

@@ -1,7 +1,7 @@
 from pydantic.types import PositiveInt
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-from reflector.schemas.platform import WHEREBY_PLATFORM, Platform
+from reflector.schemas.platform import DAILY_PLATFORM, Platform
 from reflector.utils.string import NonEmptyString
 
 
@@ -75,6 +75,7 @@ class Settings(BaseSettings):
     LLM_URL: str | None = None
     LLM_API_KEY: str | None = None
     LLM_CONTEXT_WINDOW: int = 16000
+    LLM_ENABLE_THINKING: bool | None = None
 
     LLM_PARSE_MAX_RETRIES: int = (
         3  # Max retries for JSON/validation errors (total attempts = retries + 1)
@@ -155,7 +156,7 @@ class Settings(BaseSettings):
         None  # Webhook UUID for this environment. Not used by production code
     )
     # Platform Configuration
-    DEFAULT_VIDEO_PLATFORM: Platform = WHEREBY_PLATFORM
+    DEFAULT_VIDEO_PLATFORM: Platform = DAILY_PLATFORM
 
     # Zulip integration
     ZULIP_REALM: str | None = None

server/reflector/worker/process.py

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

server/tests/conftest.py

@@ -4,7 +4,7 @@ from unittest.mock import patch
 
 import pytest
 
-from reflector.schemas.platform import WHEREBY_PLATFORM
+from reflector.schemas.platform import DAILY_PLATFORM, WHEREBY_PLATFORM
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -14,6 +14,7 @@ def register_mock_platform():
     from reflector.video_platforms.registry import register_platform
 
     register_platform(WHEREBY_PLATFORM, MockPlatformClient)
+    register_platform(DAILY_PLATFORM, MockPlatformClient)
     yield
 
 

server/tests/test_llm_retry.py

@@ -8,6 +8,7 @@ from pydantic import BaseModel, Field
 from workflows.errors import WorkflowRuntimeError, WorkflowTimeoutError
 
 from reflector.llm import LLM, LLMParseError, StructuredOutputWorkflow
+from reflector.settings import Settings
 from reflector.utils.retry import RetryException
 
 
@@ -26,6 +27,57 @@ def make_completion_response(text: str):
     return response
 
 
+class TestLLMEnableThinking:
+    """Test that LLM_ENABLE_THINKING setting is passed through to OpenAILike"""
+
+    def test_enable_thinking_false_passed_in_extra_body(self):
+        """enable_thinking=False should be in extra_body when LLM_ENABLE_THINKING=False"""
+        settings = Settings(
+            LLM_ENABLE_THINKING=False,
+            LLM_URL="http://fake",
+            LLM_API_KEY="fake",
+        )
+
+        with (
+            patch("reflector.llm.OpenAILike") as mock_openai,
+            patch("reflector.llm.Settings"),
+        ):
+            LLM(settings=settings)
+            extra_body = mock_openai.call_args.kwargs["additional_kwargs"]["extra_body"]
+            assert extra_body["enable_thinking"] is False
+
+    def test_enable_thinking_true_passed_in_extra_body(self):
+        """enable_thinking=True should be in extra_body when LLM_ENABLE_THINKING=True"""
+        settings = Settings(
+            LLM_ENABLE_THINKING=True,
+            LLM_URL="http://fake",
+            LLM_API_KEY="fake",
+        )
+
+        with (
+            patch("reflector.llm.OpenAILike") as mock_openai,
+            patch("reflector.llm.Settings"),
+        ):
+            LLM(settings=settings)
+            extra_body = mock_openai.call_args.kwargs["additional_kwargs"]["extra_body"]
+            assert extra_body["enable_thinking"] is True
+
+    def test_enable_thinking_none_not_in_extra_body(self):
+        """enable_thinking should not be in extra_body when LLM_ENABLE_THINKING is None (default)"""
+        settings = Settings(
+            LLM_URL="http://fake",
+            LLM_API_KEY="fake",
+        )
+
+        with (
+            patch("reflector.llm.OpenAILike") as mock_openai,
+            patch("reflector.llm.Settings"),
+        ):
+            LLM(settings=settings)
+            extra_body = mock_openai.call_args.kwargs["additional_kwargs"]["extra_body"]
+            assert "enable_thinking" not in extra_body
+
+
 class TestLLMParseErrorRecovery:
     """Test parse error recovery with Workflow feedback loop"""
 

server/tests/test_transcripts_process.py

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