feat: migrate file and live post-processing pipelines from Celery to Hatchet workflow engine (#911)

* feat: migrate file and live post-processing pipelines from Celery to Hatchet workflow engine

* fix: always force reprocessing

* fix: ci tests with live pipelines

* fix: ci tests with live pipelines

docker-compose.selfhosted.yml

@@ -51,6 +51,9 @@ services:
       HF_TOKEN: ${HF_TOKEN:-}
       # WebRTC: fixed UDP port range for ICE candidates (mapped above)
       WEBRTC_PORT_RANGE: "51000-51100"
+      # Hatchet workflow engine (always-on for processing pipelines)
+      HATCHET_CLIENT_SERVER_URL: ${HATCHET_CLIENT_SERVER_URL:-http://hatchet:8888}
+      HATCHET_CLIENT_HOST_PORT: ${HATCHET_CLIENT_HOST_PORT:-hatchet:7077}
     depends_on:
       postgres:
         condition: service_healthy
@@ -75,6 +78,9 @@ services:
       CELERY_RESULT_BACKEND: redis://redis:6379/1
       # ML backend config comes from env_file (server/.env), set per-mode by setup script
       HF_TOKEN: ${HF_TOKEN:-}
+      # Hatchet workflow engine (always-on for processing pipelines)
+      HATCHET_CLIENT_SERVER_URL: ${HATCHET_CLIENT_SERVER_URL:-http://hatchet:8888}
+      HATCHET_CLIENT_HOST_PORT: ${HATCHET_CLIENT_HOST_PORT:-hatchet:7077}
     depends_on:
       postgres:
         condition: service_healthy
@@ -126,6 +132,8 @@ services:
   redis:
     image: redis:7.2-alpine
     restart: unless-stopped
+    ports:
+      - "6379:6379"
     healthcheck:
       test: ["CMD", "redis-cli", "ping"]
       interval: 30s
@@ -301,20 +309,20 @@ services:
       - server
 
   # ===========================================================
-  # Hatchet + Daily.co workers (optional — for Daily.co multitrack processing)
+  # Hatchet workflow engine + workers
-  # Auto-enabled when DAILY_API_KEY is configured in server/r
+  # Required for all processing pipelines (file, live, Daily.co multitrack).
+  # Always-on — every selfhosted deployment needs Hatchet.
   # ===========================================================
 
   hatchet:
     image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
-    profiles: [dailyco]
     restart: on-failure
     depends_on:
       postgres:
         condition: service_healthy
     ports:
-      - "8888:8888"
+      - "127.0.0.1:8888:8888"
-      - "7078:7077"
+      - "127.0.0.1:7078:7077"
     env_file:
       - ./.env.hatchet
     environment:
@@ -363,7 +371,6 @@ services:
       context: ./server
       dockerfile: Dockerfile
     image: monadicalsas/reflector-backend:latest
-    profiles: [dailyco]
     restart: unless-stopped
     env_file:
       - ./server/.env

scripts/setup-selfhosted.sh

@@ -261,9 +261,11 @@ if [[ -z "$MODEL_MODE" ]]; then
 fi
 
 # Build profiles list — one profile per feature
-# Only --gpu needs a compose profile; --cpu and --hosted use in-process/remote backends
+# Hatchet + hatchet-worker-llm are always-on (no profile needed).
+# gpu/cpu profiles only control the ML container (transcription service).
 COMPOSE_PROFILES=()
 [[ "$MODEL_MODE" == "gpu" ]] && COMPOSE_PROFILES+=("gpu")
+[[ "$MODEL_MODE" == "cpu" ]] && COMPOSE_PROFILES+=("cpu")
 [[ -n "$OLLAMA_MODE" ]] && COMPOSE_PROFILES+=("$OLLAMA_MODE")
 [[ "$USE_GARAGE" == "true" ]] && COMPOSE_PROFILES+=("garage")
 [[ "$USE_CADDY" == "true" ]] && COMPOSE_PROFILES+=("caddy")
@@ -557,12 +559,10 @@ step_server_env() {
         ok "CPU mode — file processing timeouts set to 3600s (1 hour)"
     fi
 
-    # If Daily.co is manually configured, ensure Hatchet connectivity vars are set
+    # Hatchet is always required (file, live, and multitrack pipelines all use it)
-    if env_has_key "$SERVER_ENV" "DAILY_API_KEY" && [[ -n "$(env_get "$SERVER_ENV" "DAILY_API_KEY")" ]]; then
     env_set "$SERVER_ENV" "HATCHET_CLIENT_SERVER_URL" "http://hatchet:8888"
     env_set "$SERVER_ENV" "HATCHET_CLIENT_HOST_PORT" "hatchet:7077"
-        ok "Daily.co detected — Hatchet connectivity configured"
+    ok "Hatchet connectivity configured (workflow engine for processing pipelines)"
-    fi
 
     ok "server/.env ready"
 }
@@ -886,15 +886,22 @@ step_services() {
         compose_cmd pull server web || warn "Pull failed — using cached images"
     fi
 
-    # Build hatchet workers if Daily.co is configured (same backend image)
+    # Hatchet is always needed (all processing pipelines use it)
-    if [[ "$DAILY_DETECTED" == "true" ]] && [[ "$BUILD_IMAGES" == "true" ]]; then
+    local NEEDS_HATCHET=true
+
+    # Build hatchet workers if Hatchet is needed (same backend image)
+    if [[ "$NEEDS_HATCHET" == "true" ]] && [[ "$BUILD_IMAGES" == "true" ]]; then
         info "Building Hatchet worker images..."
+        if [[ "$DAILY_DETECTED" == "true" ]]; then
             compose_cmd build hatchet-worker-cpu hatchet-worker-llm
+        else
+            compose_cmd build hatchet-worker-llm
+        fi
         ok "Hatchet worker images built"
     fi
 
     # Ensure hatchet database exists before starting hatchet (init-hatchet-db.sql only runs on fresh postgres volumes)
-    if [[ "$DAILY_DETECTED" == "true" ]]; then
+    if [[ "$NEEDS_HATCHET" == "true" ]]; then
         info "Ensuring postgres is running for Hatchet database setup..."
         compose_cmd up -d postgres
         local pg_ready=false
@@ -1049,8 +1056,7 @@ step_health() {
         fi
     fi
 
-    # Hatchet (if Daily.co detected)
+    # Hatchet (always-on)
-    if [[ "$DAILY_DETECTED" == "true" ]]; then
     info "Waiting for Hatchet workflow engine..."
     local hatchet_ok=false
     for i in $(seq 1 60); do
@@ -1067,7 +1073,6 @@ step_health() {
     else
         warn "Hatchet not ready yet. Check: docker compose logs hatchet"
     fi
-    fi
 
     # LLM warning for non-Ollama modes
     if [[ "$USES_OLLAMA" == "false" ]]; then
@@ -1087,12 +1092,10 @@ step_health() {
 }
 
 # =========================================================
-# Step 8: Hatchet token generation (Daily.co only)
+# Step 8: Hatchet token generation (gpu/cpu/Daily.co)
 # =========================================================
 step_hatchet_token() {
-    if [[ "$DAILY_DETECTED" != "true" ]]; then
+    # Hatchet is always required — no gating needed
-        return
-    fi
 
     # Skip if token already set
     if env_has_key "$SERVER_ENV" "HATCHET_CLIENT_TOKEN" && [[ -n "$(env_get "$SERVER_ENV" "HATCHET_CLIENT_TOKEN")" ]]; then
@@ -1147,7 +1150,9 @@ step_hatchet_token() {
 
     # Restart services that need the token
     info "Restarting services with new Hatchet token..."
-    compose_cmd restart server worker hatchet-worker-cpu hatchet-worker-llm
+    local restart_services="server worker hatchet-worker-llm"
+    [[ "$DAILY_DETECTED" == "true" ]] && restart_services="$restart_services hatchet-worker-cpu"
+    compose_cmd restart $restart_services
     ok "Services restarted with Hatchet token"
 }
 
@@ -1216,8 +1221,7 @@ main() {
         ok "Daily.co detected — enabling Hatchet workflow services"
     fi
 
-    # Generate .env.hatchet for hatchet dashboard config
+    # Generate .env.hatchet for hatchet dashboard config (always needed)
-    if [[ "$DAILY_DETECTED" == "true" ]]; then
     local hatchet_server_url hatchet_cookie_domain
     if [[ -n "$CUSTOM_DOMAIN" ]]; then
         hatchet_server_url="https://${CUSTOM_DOMAIN}:8888"
@@ -1234,10 +1238,6 @@ SERVER_URL=$hatchet_server_url
 SERVER_AUTH_COOKIE_DOMAIN=$hatchet_cookie_domain
 EOF
     ok "Generated .env.hatchet (dashboard URL=$hatchet_server_url)"
-    else
-        # Create empty .env.hatchet so compose doesn't fail if dailyco profile is ever activated manually
-        touch "$ROOT_DIR/.env.hatchet"
-    fi
 
     step_www_env
     echo ""

server/pyproject.toml

@@ -116,6 +116,7 @@ source = ["reflector"]
 ENVIRONMENT = "pytest"
 DATABASE_URL = "postgresql://test_user:test_password@localhost:15432/reflector_test"
 AUTH_BACKEND = "jwt"
+HATCHET_CLIENT_TOKEN = "test-dummy-token"
 
 [tool.pytest.ini_options]
 addopts = "-ra -q --disable-pytest-warnings --cov --cov-report html -v"

server/reflector/hatchet/constants.py

@@ -26,6 +26,21 @@ class TaskName(StrEnum):
     DETECT_CHUNK_TOPIC = "detect_chunk_topic"
     GENERATE_DETAILED_SUMMARY = "generate_detailed_summary"
 
+    # File pipeline tasks
+    EXTRACT_AUDIO = "extract_audio"
+    UPLOAD_AUDIO = "upload_audio"
+    TRANSCRIBE = "transcribe"
+    DIARIZE = "diarize"
+    ASSEMBLE_TRANSCRIPT = "assemble_transcript"
+    GENERATE_SUMMARIES = "generate_summaries"
+
+    # Live post-processing pipeline tasks
+    WAVEFORM = "waveform"
+    CONVERT_MP3 = "convert_mp3"
+    UPLOAD_MP3 = "upload_mp3"
+    REMOVE_UPLOAD = "remove_upload"
+    FINAL_SUMMARIES = "final_summaries"
+
 
 # Rate limit key for LLM API calls (shared across all LLM-calling tasks)
 LLM_RATE_LIMIT_KEY = "llm"

server/reflector/hatchet/run_workers_llm.py

@@ -10,6 +10,8 @@ from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     daily_multitrack_pipeline,
 )
+from reflector.hatchet.workflows.file_pipeline import file_pipeline
+from reflector.hatchet.workflows.live_post_pipeline import live_post_pipeline
 from reflector.hatchet.workflows.subject_processing import subject_workflow
 from reflector.hatchet.workflows.topic_chunk_processing import topic_chunk_workflow
 from reflector.hatchet.workflows.track_processing import track_workflow
@@ -47,6 +49,8 @@ def main():
         },
         workflows=[
             daily_multitrack_pipeline,
+            file_pipeline,
+            live_post_pipeline,
             topic_chunk_workflow,
             subject_workflow,
             track_workflow,

server/reflector/hatchet/workflows/file_pipeline.py

@@ -0,0 +1,885 @@
+"""
+Hatchet workflow: FilePipeline
+
+Processing pipeline for file uploads and Whereby recordings.
+Orchestrates: extract audio → upload → transcribe/diarize/waveform (parallel)
+→ assemble → detect topics → title/summaries (parallel) → finalize
+→ cleanup consent → post zulip / send webhook.
+
+Note: This file uses deferred imports (inside functions/tasks) intentionally.
+Hatchet workers run in forked processes; fresh imports per task ensure DB connections
+are not shared across forks, avoiding connection pooling issues.
+"""
+
+import json
+from datetime import timedelta
+from pathlib import Path
+
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.broadcast import (
+    append_event_and_broadcast,
+    set_status_and_broadcast,
+)
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.constants import (
+    TIMEOUT_HEAVY,
+    TIMEOUT_MEDIUM,
+    TIMEOUT_SHORT,
+    TIMEOUT_TITLE,
+    TaskName,
+)
+from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+    fresh_db_connection,
+    set_workflow_error_status,
+    with_error_handling,
+)
+from reflector.hatchet.workflows.models import (
+    ConsentResult,
+    TitleResult,
+    TopicsResult,
+    WaveformResult,
+    WebhookResult,
+    ZulipResult,
+)
+from reflector.logger import logger
+from reflector.pipelines import topic_processing
+from reflector.settings import settings
+from reflector.utils.audio_constants import WAVEFORM_SEGMENTS
+from reflector.utils.audio_waveform import get_audio_waveform
+
+
+class FilePipelineInput(BaseModel):
+    transcript_id: str
+    room_id: str | None = None
+
+
+# --- Result models specific to file pipeline ---
+
+
+class ExtractAudioResult(BaseModel):
+    audio_path: str
+    duration_ms: float = 0.0
+
+
+class UploadAudioResult(BaseModel):
+    audio_url: str
+    audio_path: str
+
+
+class TranscribeResult(BaseModel):
+    words: list[dict]
+    translation: str | None = None
+
+
+class DiarizeResult(BaseModel):
+    diarization: list[dict] | None = None
+
+
+class AssembleTranscriptResult(BaseModel):
+    assembled: bool
+
+
+class SummariesResult(BaseModel):
+    generated: bool
+
+
+class FinalizeResult(BaseModel):
+    status: str
+
+
+hatchet = HatchetClientManager.get_client()
+
+file_pipeline = hatchet.workflow(name="FilePipeline", input_validator=FilePipelineInput)
+
+
+@file_pipeline.task(
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.EXTRACT_AUDIO)
+async def extract_audio(input: FilePipelineInput, ctx: Context) -> ExtractAudioResult:
+    """Extract audio from upload file, convert to MP3."""
+    ctx.log(f"extract_audio: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        await set_status_and_broadcast(input.transcript_id, "processing", logger=logger)
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            raise ValueError(f"Transcript {input.transcript_id} not found")
+
+        # Clear transcript as we're going to regenerate everything
+        await transcripts_controller.update(
+            transcript,
+            {
+                "events": [],
+                "topics": [],
+            },
+        )
+
+        # Find upload file
+        audio_file = next(transcript.data_path.glob("upload.*"), None)
+        if not audio_file:
+            audio_file = next(transcript.data_path.glob("audio.*"), None)
+        if not audio_file:
+            raise ValueError("No audio file found to process")
+
+        ctx.log(f"extract_audio: processing {audio_file}")
+
+        # Extract audio and write as MP3
+        import av  # noqa: PLC0415
+
+        from reflector.processors import AudioFileWriterProcessor  # noqa: PLC0415
+
+        duration_ms_container = [0.0]
+
+        async def capture_duration(d):
+            duration_ms_container[0] = d
+
+        mp3_writer = AudioFileWriterProcessor(
+            path=transcript.audio_mp3_filename,
+            on_duration=capture_duration,
+        )
+        input_container = av.open(str(audio_file))
+        for frame in input_container.decode(audio=0):
+            await mp3_writer.push(frame)
+        await mp3_writer.flush()
+        input_container.close()
+
+        duration_ms = duration_ms_container[0]
+        audio_path = str(transcript.audio_mp3_filename)
+
+        # Persist duration to database and broadcast to websocket clients
+        from reflector.db.transcripts import TranscriptDuration  # noqa: PLC0415
+        from reflector.db.transcripts import transcripts_controller as tc
+
+        await tc.update(transcript, {"duration": duration_ms})
+        await append_event_and_broadcast(
+            input.transcript_id,
+            transcript,
+            "DURATION",
+            TranscriptDuration(duration=duration_ms),
+            logger=logger,
+        )
+
+    ctx.log(f"extract_audio complete: {audio_path}, duration={duration_ms}ms")
+    return ExtractAudioResult(audio_path=audio_path, duration_ms=duration_ms)
+
+
+@file_pipeline.task(
+    parents=[extract_audio],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.UPLOAD_AUDIO)
+async def upload_audio(input: FilePipelineInput, ctx: Context) -> UploadAudioResult:
+    """Upload audio to S3/storage, return audio_url."""
+    ctx.log(f"upload_audio: starting for transcript_id={input.transcript_id}")
+
+    extract_result = ctx.task_output(extract_audio)
+    audio_path = extract_result.audio_path
+
+    from reflector.storage import get_transcripts_storage  # noqa: PLC0415
+
+    storage = get_transcripts_storage()
+    if not storage:
+        raise ValueError(
+            "Storage backend required for file processing. "
+            "Configure TRANSCRIPT_STORAGE_* settings."
+        )
+
+    with open(audio_path, "rb") as f:
+        audio_data = f.read()
+
+    storage_path = f"file_pipeline/{input.transcript_id}/audio.mp3"
+    await storage.put_file(storage_path, audio_data)
+    audio_url = await storage.get_file_url(storage_path)
+
+    ctx.log(f"upload_audio complete: {audio_url}")
+    return UploadAudioResult(audio_url=audio_url, audio_path=audio_path)
+
+
+@file_pipeline.task(
+    parents=[upload_audio],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
+@with_error_handling(TaskName.TRANSCRIBE)
+async def transcribe(input: FilePipelineInput, ctx: Context) -> TranscribeResult:
+    """Transcribe the audio file using the configured backend."""
+    ctx.log(f"transcribe: starting for transcript_id={input.transcript_id}")
+
+    upload_result = ctx.task_output(upload_audio)
+    audio_url = upload_result.audio_url
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            raise ValueError(f"Transcript {input.transcript_id} not found")
+        source_language = transcript.source_language
+
+    from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
+        transcribe_file_with_processor,
+    )
+
+    result = await transcribe_file_with_processor(audio_url, source_language)
+
+    ctx.log(f"transcribe complete: {len(result.words)} words")
+    return TranscribeResult(
+        words=[w.model_dump() for w in result.words],
+        translation=result.translation,
+    )
+
+
+@file_pipeline.task(
+    parents=[upload_audio],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
+@with_error_handling(TaskName.DIARIZE)
+async def diarize(input: FilePipelineInput, ctx: Context) -> DiarizeResult:
+    """Diarize the audio file (speaker identification)."""
+    ctx.log(f"diarize: starting for transcript_id={input.transcript_id}")
+
+    if not settings.DIARIZATION_BACKEND:
+        ctx.log("diarize: diarization disabled, skipping")
+        return DiarizeResult(diarization=None)
+
+    upload_result = ctx.task_output(upload_audio)
+    audio_url = upload_result.audio_url
+
+    from reflector.processors.file_diarization import (  # noqa: PLC0415
+        FileDiarizationInput,
+    )
+    from reflector.processors.file_diarization_auto import (  # noqa: PLC0415
+        FileDiarizationAutoProcessor,
+    )
+
+    processor = FileDiarizationAutoProcessor()
+    input_data = FileDiarizationInput(audio_url=audio_url)
+
+    result = None
+
+    async def capture_result(diarization_output):
+        nonlocal result
+        result = diarization_output.diarization
+
+    try:
+        processor.on(capture_result)
+        await processor.push(input_data)
+        await processor.flush()
+    except Exception as e:
+        logger.error(f"Diarization failed: {e}")
+        return DiarizeResult(diarization=None)
+
+    ctx.log(f"diarize complete: {len(result) if result else 0} segments")
+    return DiarizeResult(diarization=list(result) if result else None)
+
+
+@file_pipeline.task(
+    parents=[upload_audio],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.GENERATE_WAVEFORM)
+async def generate_waveform(input: FilePipelineInput, ctx: Context) -> WaveformResult:
+    """Generate audio waveform visualization."""
+    ctx.log(f"generate_waveform: starting for transcript_id={input.transcript_id}")
+
+    upload_result = ctx.task_output(upload_audio)
+    audio_path = upload_result.audio_path
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptWaveform,
+        transcripts_controller,
+    )
+
+    waveform = get_audio_waveform(
+        path=Path(audio_path), segments_count=WAVEFORM_SEGMENTS
+    )
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            transcript.data_path.mkdir(parents=True, exist_ok=True)
+            with open(transcript.audio_waveform_filename, "w") as f:
+                json.dump(waveform, f)
+
+            waveform_data = TranscriptWaveform(waveform=waveform)
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "WAVEFORM",
+                waveform_data,
+                logger=logger,
+            )
+
+    ctx.log("generate_waveform complete")
+    return WaveformResult(waveform_generated=True)
+
+
+@file_pipeline.task(
+    parents=[transcribe, diarize, generate_waveform],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.ASSEMBLE_TRANSCRIPT)
+async def assemble_transcript(
+    input: FilePipelineInput, ctx: Context
+) -> AssembleTranscriptResult:
+    """Merge transcription + diarization results."""
+    ctx.log(f"assemble_transcript: starting for transcript_id={input.transcript_id}")
+
+    transcribe_result = ctx.task_output(transcribe)
+    diarize_result = ctx.task_output(diarize)
+
+    from reflector.processors.transcript_diarization_assembler import (  # noqa: PLC0415
+        TranscriptDiarizationAssemblerInput,
+        TranscriptDiarizationAssemblerProcessor,
+    )
+    from reflector.processors.types import (  # noqa: PLC0415
+        DiarizationSegment,
+        Word,
+    )
+    from reflector.processors.types import (  # noqa: PLC0415
+        Transcript as TranscriptType,
+    )
+
+    words = [Word(**w) for w in transcribe_result.words]
+    transcript_data = TranscriptType(
+        words=words, translation=transcribe_result.translation
+    )
+
+    diarization = None
+    if diarize_result.diarization:
+        diarization = [DiarizationSegment(**s) for s in diarize_result.diarization]
+
+    processor = TranscriptDiarizationAssemblerProcessor()
+    assembler_input = TranscriptDiarizationAssemblerInput(
+        transcript=transcript_data, diarization=diarization or []
+    )
+
+    diarized_transcript = None
+
+    async def capture_result(transcript):
+        nonlocal diarized_transcript
+        diarized_transcript = transcript
+
+    processor.on(capture_result)
+    await processor.push(assembler_input)
+    await processor.flush()
+
+    if not diarized_transcript:
+        raise ValueError("No diarized transcript captured")
+
+    # Save the assembled transcript events to the database
+    async with fresh_db_connection():
+        from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptText,
+            transcripts_controller,
+        )
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            assembled_text = diarized_transcript.text if diarized_transcript else ""
+            assembled_translation = (
+                diarized_transcript.translation if diarized_transcript else None
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "TRANSCRIPT",
+                TranscriptText(text=assembled_text, translation=assembled_translation),
+                logger=logger,
+            )
+
+    ctx.log("assemble_transcript complete")
+    return AssembleTranscriptResult(assembled=True)
+
+
+@file_pipeline.task(
+    parents=[assemble_transcript],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
+@with_error_handling(TaskName.DETECT_TOPICS)
+async def detect_topics(input: FilePipelineInput, ctx: Context) -> TopicsResult:
+    """Detect topics from the assembled transcript."""
+    ctx.log(f"detect_topics: starting for transcript_id={input.transcript_id}")
+
+    # Re-read the transcript to get the diarized words
+    transcribe_result = ctx.task_output(transcribe)
+    diarize_result = ctx.task_output(diarize)
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptTopic,
+        transcripts_controller,
+    )
+    from reflector.processors.transcript_diarization_assembler import (  # noqa: PLC0415
+        TranscriptDiarizationAssemblerInput,
+        TranscriptDiarizationAssemblerProcessor,
+    )
+    from reflector.processors.types import (  # noqa: PLC0415
+        DiarizationSegment,
+        Word,
+    )
+    from reflector.processors.types import (  # noqa: PLC0415
+        Transcript as TranscriptType,
+    )
+
+    words = [Word(**w) for w in transcribe_result.words]
+    transcript_data = TranscriptType(
+        words=words, translation=transcribe_result.translation
+    )
+
+    diarization = None
+    if diarize_result.diarization:
+        diarization = [DiarizationSegment(**s) for s in diarize_result.diarization]
+
+    # Re-assemble to get the diarized transcript for topic detection
+    processor = TranscriptDiarizationAssemblerProcessor()
+    assembler_input = TranscriptDiarizationAssemblerInput(
+        transcript=transcript_data, diarization=diarization or []
+    )
+
+    diarized_transcript = None
+
+    async def capture_result(transcript):
+        nonlocal diarized_transcript
+        diarized_transcript = transcript
+
+    processor.on(capture_result)
+    await processor.push(assembler_input)
+    await processor.flush()
+
+    if not diarized_transcript:
+        raise ValueError("No diarized transcript for topic detection")
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            raise ValueError(f"Transcript {input.transcript_id} not found")
+        target_language = transcript.target_language
+
+        empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+
+        async def on_topic_callback(data):
+            topic = TranscriptTopic(
+                title=data.title,
+                summary=data.summary,
+                timestamp=data.timestamp,
+                transcript=data.transcript.text
+                if hasattr(data.transcript, "text")
+                else "",
+                words=data.transcript.words
+                if hasattr(data.transcript, "words")
+                else [],
+            )
+            await transcripts_controller.upsert_topic(transcript, topic)
+            await append_event_and_broadcast(
+                input.transcript_id, transcript, "TOPIC", topic, logger=logger
+            )
+
+        topics = await topic_processing.detect_topics(
+            diarized_transcript,
+            target_language,
+            on_topic_callback=on_topic_callback,
+            empty_pipeline=empty_pipeline,
+        )
+
+    ctx.log(f"detect_topics complete: {len(topics)} topics")
+    return TopicsResult(topics=topics)
+
+
+@file_pipeline.task(
+    parents=[detect_topics],
+    execution_timeout=timedelta(seconds=TIMEOUT_TITLE),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
+)
+@with_error_handling(TaskName.GENERATE_TITLE)
+async def generate_title(input: FilePipelineInput, ctx: Context) -> TitleResult:
+    """Generate meeting title using LLM."""
+    ctx.log(f"generate_title: starting for transcript_id={input.transcript_id}")
+
+    topics_result = ctx.task_output(detect_topics)
+    topics = topics_result.topics
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptFinalTitle,
+        transcripts_controller,
+    )
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+    title_result = None
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            raise ValueError(f"Transcript {input.transcript_id} not found")
+
+        async def on_title_callback(data):
+            nonlocal title_result
+            title_result = data.title
+            final_title = TranscriptFinalTitle(title=data.title)
+            if not transcript.title:
+                await transcripts_controller.update(
+                    transcript, {"title": final_title.title}
+                )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_TITLE",
+                final_title,
+                logger=logger,
+            )
+
+        await topic_processing.generate_title(
+            topics,
+            on_title_callback=on_title_callback,
+            empty_pipeline=empty_pipeline,
+            logger=logger,
+        )
+
+    ctx.log(f"generate_title complete: '{title_result}'")
+    return TitleResult(title=title_result)
+
+
+@file_pipeline.task(
+    parents=[detect_topics],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
+@with_error_handling(TaskName.GENERATE_SUMMARIES)
+async def generate_summaries(input: FilePipelineInput, ctx: Context) -> SummariesResult:
+    """Generate long/short summaries and action items."""
+    ctx.log(f"generate_summaries: starting for transcript_id={input.transcript_id}")
+
+    topics_result = ctx.task_output(detect_topics)
+    topics = topics_result.topics
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptActionItems,
+        TranscriptFinalLongSummary,
+        TranscriptFinalShortSummary,
+        transcripts_controller,
+    )
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            raise ValueError(f"Transcript {input.transcript_id} not found")
+
+        async def on_long_summary_callback(data):
+            final_long = TranscriptFinalLongSummary(long_summary=data.long_summary)
+            await transcripts_controller.update(
+                transcript, {"long_summary": final_long.long_summary}
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_LONG_SUMMARY",
+                final_long,
+                logger=logger,
+            )
+
+        async def on_short_summary_callback(data):
+            final_short = TranscriptFinalShortSummary(short_summary=data.short_summary)
+            await transcripts_controller.update(
+                transcript, {"short_summary": final_short.short_summary}
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_SHORT_SUMMARY",
+                final_short,
+                logger=logger,
+            )
+
+        async def on_action_items_callback(data):
+            action_items = TranscriptActionItems(action_items=data.action_items)
+            await transcripts_controller.update(
+                transcript, {"action_items": action_items.action_items}
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "ACTION_ITEMS",
+                action_items,
+                logger=logger,
+            )
+
+        await topic_processing.generate_summaries(
+            topics,
+            transcript,
+            on_long_summary_callback=on_long_summary_callback,
+            on_short_summary_callback=on_short_summary_callback,
+            on_action_items_callback=on_action_items_callback,
+            empty_pipeline=empty_pipeline,
+            logger=logger,
+        )
+
+    ctx.log("generate_summaries complete")
+    return SummariesResult(generated=True)
+
+
+@file_pipeline.task(
+    parents=[generate_title, generate_summaries],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=5,
+)
+@with_error_handling(TaskName.FINALIZE)
+async def finalize(input: FilePipelineInput, ctx: Context) -> FinalizeResult:
+    """Set transcript status to 'ended' and broadcast."""
+    ctx.log("finalize: setting status to 'ended'")
+
+    async with fresh_db_connection():
+        await set_status_and_broadcast(input.transcript_id, "ended", logger=logger)
+
+    ctx.log("finalize complete")
+    return FinalizeResult(status="COMPLETED")
+
+
+@file_pipeline.task(
+    parents=[finalize],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.CLEANUP_CONSENT, set_error_status=False)
+async def cleanup_consent(input: FilePipelineInput, ctx: Context) -> ConsentResult:
+    """Check consent and delete audio files if any participant denied."""
+    ctx.log(f"cleanup_consent: transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.db.meetings import (  # noqa: PLC0415
+            meeting_consent_controller,
+            meetings_controller,
+        )
+        from reflector.db.recordings import recordings_controller  # noqa: PLC0415
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+        from reflector.storage import get_transcripts_storage  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            ctx.log("cleanup_consent: transcript not found")
+            return ConsentResult()
+
+        consent_denied = False
+        recording = None
+        if transcript.recording_id:
+            recording = await recordings_controller.get_by_id(transcript.recording_id)
+            if recording and recording.meeting_id:
+                meeting = await meetings_controller.get_by_id(recording.meeting_id)
+                if meeting:
+                    consent_denied = await meeting_consent_controller.has_any_denial(
+                        meeting.id
+                    )
+
+        if not consent_denied:
+            ctx.log("cleanup_consent: consent approved, keeping all files")
+            return ConsentResult()
+
+        ctx.log("cleanup_consent: consent denied, deleting audio files")
+
+        deletion_errors = []
+        if recording and recording.bucket_name:
+            keys_to_delete = []
+            if recording.track_keys:
+                keys_to_delete = recording.track_keys
+            elif recording.object_key:
+                keys_to_delete = [recording.object_key]
+
+            master_storage = get_transcripts_storage()
+            for key in keys_to_delete:
+                try:
+                    await master_storage.delete_file(key, bucket=recording.bucket_name)
+                    ctx.log(f"Deleted recording file: {recording.bucket_name}/{key}")
+                except Exception as e:
+                    error_msg = f"Failed to delete {key}: {e}"
+                    logger.error(error_msg, exc_info=True)
+                    deletion_errors.append(error_msg)
+
+        if transcript.audio_location == "storage":
+            storage = get_transcripts_storage()
+            try:
+                await storage.delete_file(transcript.storage_audio_path)
+                ctx.log(f"Deleted processed audio: {transcript.storage_audio_path}")
+            except Exception as e:
+                error_msg = f"Failed to delete processed audio: {e}"
+                logger.error(error_msg, exc_info=True)
+                deletion_errors.append(error_msg)
+
+        try:
+            if (
+                hasattr(transcript, "audio_mp3_filename")
+                and transcript.audio_mp3_filename
+            ):
+                transcript.audio_mp3_filename.unlink(missing_ok=True)
+            if (
+                hasattr(transcript, "audio_wav_filename")
+                and transcript.audio_wav_filename
+            ):
+                transcript.audio_wav_filename.unlink(missing_ok=True)
+        except Exception as e:
+            error_msg = f"Failed to delete local audio files: {e}"
+            logger.error(error_msg, exc_info=True)
+            deletion_errors.append(error_msg)
+
+        if deletion_errors:
+            logger.warning(
+                "[Hatchet] cleanup_consent completed with errors",
+                transcript_id=input.transcript_id,
+                error_count=len(deletion_errors),
+            )
+        else:
+            await transcripts_controller.update(transcript, {"audio_deleted": True})
+            ctx.log("cleanup_consent: all audio deleted successfully")
+
+    return ConsentResult()
+
+
+@file_pipeline.task(
+    parents=[cleanup_consent],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
+)
+@with_error_handling(TaskName.POST_ZULIP, set_error_status=False)
+async def post_zulip(input: FilePipelineInput, ctx: Context) -> ZulipResult:
+    """Post notification to Zulip."""
+    ctx.log(f"post_zulip: transcript_id={input.transcript_id}")
+
+    if not settings.ZULIP_REALM:
+        ctx.log("post_zulip skipped (Zulip not configured)")
+        return ZulipResult(zulip_message_id=None, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+        from reflector.zulip import post_transcript_notification  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            message_id = await post_transcript_notification(transcript)
+            ctx.log(f"post_zulip complete: zulip_message_id={message_id}")
+        else:
+            message_id = None
+
+    return ZulipResult(zulip_message_id=message_id)
+
+
+@file_pipeline.task(
+    parents=[cleanup_consent],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
+)
+@with_error_handling(TaskName.SEND_WEBHOOK, set_error_status=False)
+async def send_webhook(input: FilePipelineInput, ctx: Context) -> WebhookResult:
+    """Send completion webhook to external service."""
+    ctx.log(f"send_webhook: transcript_id={input.transcript_id}")
+
+    if not input.room_id:
+        ctx.log("send_webhook skipped (no room_id)")
+        return WebhookResult(webhook_sent=False, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.rooms import rooms_controller  # noqa: PLC0415
+        from reflector.utils.webhook import (  # noqa: PLC0415
+            fetch_transcript_webhook_payload,
+            send_webhook_request,
+        )
+
+        room = await rooms_controller.get_by_id(input.room_id)
+        if not room or not room.webhook_url:
+            ctx.log("send_webhook skipped (no webhook_url configured)")
+            return WebhookResult(webhook_sent=False, skipped=True)
+
+        payload = await fetch_transcript_webhook_payload(
+            transcript_id=input.transcript_id,
+            room_id=input.room_id,
+        )
+
+        if isinstance(payload, str):
+            ctx.log(f"send_webhook skipped (could not build payload): {payload}")
+            return WebhookResult(webhook_sent=False, skipped=True)
+
+        import httpx  # noqa: PLC0415
+
+        try:
+            response = await send_webhook_request(
+                url=room.webhook_url,
+                payload=payload,
+                event_type="transcript.completed",
+                webhook_secret=room.webhook_secret,
+                timeout=30.0,
+            )
+            ctx.log(f"send_webhook complete: status_code={response.status_code}")
+            return WebhookResult(webhook_sent=True, response_code=response.status_code)
+        except httpx.HTTPStatusError as e:
+            ctx.log(f"send_webhook failed (HTTP {e.response.status_code}), continuing")
+            return WebhookResult(
+                webhook_sent=False, response_code=e.response.status_code
+            )
+        except (httpx.ConnectError, httpx.TimeoutException) as e:
+            ctx.log(f"send_webhook failed ({e}), continuing")
+            return WebhookResult(webhook_sent=False)
+        except Exception as e:
+            ctx.log(f"send_webhook unexpected error: {e}")
+            return WebhookResult(webhook_sent=False)
+
+
+# --- On failure handler ---
+
+
+async def on_workflow_failure(input: FilePipelineInput, ctx: Context) -> None:
+    """Set transcript status to 'error' only if not already 'ended'."""
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript and transcript.status == "ended":
+            logger.info(
+                "[Hatchet] FilePipeline on_workflow_failure: transcript already ended, skipping error status",
+                transcript_id=input.transcript_id,
+            )
+            ctx.log(
+                "on_workflow_failure: transcript already ended, skipping error status"
+            )
+            return
+    await set_workflow_error_status(input.transcript_id)
+
+
+@file_pipeline.on_failure_task()
+async def _register_on_workflow_failure(input: FilePipelineInput, ctx: Context) -> None:
+    await on_workflow_failure(input, ctx)

server/reflector/hatchet/workflows/live_post_pipeline.py

@@ -0,0 +1,389 @@
+"""
+Hatchet workflow: LivePostProcessingPipeline
+
+Post-processing pipeline for live WebRTC meetings.
+Triggered after a live meeting ends. Orchestrates:
+  Left branch:  waveform → convert_mp3 → upload_mp3 → remove_upload → diarize → cleanup_consent
+  Right branch: generate_title (parallel with left branch)
+  Fan-in:       final_summaries → post_zulip → send_webhook
+
+Note: This file uses deferred imports (inside functions/tasks) intentionally.
+Hatchet workers run in forked processes; fresh imports per task ensure DB connections
+are not shared across forks, avoiding connection pooling issues.
+"""
+
+from datetime import timedelta
+
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.constants import (
+    TIMEOUT_HEAVY,
+    TIMEOUT_MEDIUM,
+    TIMEOUT_SHORT,
+    TIMEOUT_TITLE,
+    TaskName,
+)
+from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+    fresh_db_connection,
+    set_workflow_error_status,
+    with_error_handling,
+)
+from reflector.hatchet.workflows.models import (
+    ConsentResult,
+    TitleResult,
+    WaveformResult,
+    WebhookResult,
+    ZulipResult,
+)
+from reflector.logger import logger
+from reflector.settings import settings
+
+
+class LivePostPipelineInput(BaseModel):
+    transcript_id: str
+    room_id: str | None = None
+
+
+# --- Result models specific to live post pipeline ---
+
+
+class ConvertMp3Result(BaseModel):
+    converted: bool
+
+
+class UploadMp3Result(BaseModel):
+    uploaded: bool
+
+
+class RemoveUploadResult(BaseModel):
+    removed: bool
+
+
+class DiarizeResult(BaseModel):
+    diarized: bool
+
+
+class FinalSummariesResult(BaseModel):
+    generated: bool
+
+
+hatchet = HatchetClientManager.get_client()
+
+live_post_pipeline = hatchet.workflow(
+    name="LivePostProcessingPipeline", input_validator=LivePostPipelineInput
+)
+
+
+@live_post_pipeline.task(
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.WAVEFORM)
+async def waveform(input: LivePostPipelineInput, ctx: Context) -> WaveformResult:
+    """Generate waveform visualization from recorded audio."""
+    ctx.log(f"waveform: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            PipelineMainWaveform,
+        )
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            raise ValueError(f"Transcript {input.transcript_id} not found")
+
+        runner = PipelineMainWaveform(transcript_id=transcript.id)
+        await runner.run()
+
+    ctx.log("waveform complete")
+    return WaveformResult(waveform_generated=True)
+
+
+@live_post_pipeline.task(
+    execution_timeout=timedelta(seconds=TIMEOUT_TITLE),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
+)
+@with_error_handling(TaskName.GENERATE_TITLE)
+async def generate_title(input: LivePostPipelineInput, ctx: Context) -> TitleResult:
+    """Generate meeting title from topics (runs in parallel with audio chain)."""
+    ctx.log(f"generate_title: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            PipelineMainTitle,
+        )
+
+        runner = PipelineMainTitle(transcript_id=input.transcript_id)
+        await runner.run()
+
+    ctx.log("generate_title complete")
+    return TitleResult(title=None)
+
+
+@live_post_pipeline.task(
+    parents=[waveform],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.CONVERT_MP3)
+async def convert_mp3(input: LivePostPipelineInput, ctx: Context) -> ConvertMp3Result:
+    """Convert WAV recording to MP3."""
+    ctx.log(f"convert_mp3: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            pipeline_convert_to_mp3,
+        )
+
+        await pipeline_convert_to_mp3(transcript_id=input.transcript_id)
+
+    ctx.log("convert_mp3 complete")
+    return ConvertMp3Result(converted=True)
+
+
+@live_post_pipeline.task(
+    parents=[convert_mp3],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.UPLOAD_MP3)
+async def upload_mp3(input: LivePostPipelineInput, ctx: Context) -> UploadMp3Result:
+    """Upload MP3 to external storage."""
+    ctx.log(f"upload_mp3: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            pipeline_upload_mp3,
+        )
+
+        await pipeline_upload_mp3(transcript_id=input.transcript_id)
+
+    ctx.log("upload_mp3 complete")
+    return UploadMp3Result(uploaded=True)
+
+
+@live_post_pipeline.task(
+    parents=[upload_mp3],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=5,
+)
+@with_error_handling(TaskName.REMOVE_UPLOAD)
+async def remove_upload(
+    input: LivePostPipelineInput, ctx: Context
+) -> RemoveUploadResult:
+    """Remove the original upload file."""
+    ctx.log(f"remove_upload: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            pipeline_remove_upload,
+        )
+
+        await pipeline_remove_upload(transcript_id=input.transcript_id)
+
+    ctx.log("remove_upload complete")
+    return RemoveUploadResult(removed=True)
+
+
+@live_post_pipeline.task(
+    parents=[remove_upload],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
+@with_error_handling(TaskName.DIARIZE)
+async def diarize(input: LivePostPipelineInput, ctx: Context) -> DiarizeResult:
+    """Run diarization on the recorded audio."""
+    ctx.log(f"diarize: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            pipeline_diarization,
+        )
+
+        await pipeline_diarization(transcript_id=input.transcript_id)
+
+    ctx.log("diarize complete")
+    return DiarizeResult(diarized=True)
+
+
+@live_post_pipeline.task(
+    parents=[diarize],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=10,
+)
+@with_error_handling(TaskName.CLEANUP_CONSENT, set_error_status=False)
+async def cleanup_consent(input: LivePostPipelineInput, ctx: Context) -> ConsentResult:
+    """Check consent and delete audio files if any participant denied."""
+    ctx.log(f"cleanup_consent: transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            cleanup_consent as _cleanup_consent,
+        )
+
+        await _cleanup_consent(transcript_id=input.transcript_id)
+
+    ctx.log("cleanup_consent complete")
+    return ConsentResult()
+
+
+@live_post_pipeline.task(
+    parents=[cleanup_consent, generate_title],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+    backoff_factor=2.0,
+    backoff_max_seconds=30,
+)
+@with_error_handling(TaskName.FINAL_SUMMARIES)
+async def final_summaries(
+    input: LivePostPipelineInput, ctx: Context
+) -> FinalSummariesResult:
+    """Generate final summaries (fan-in after audio chain + title)."""
+    ctx.log(f"final_summaries: starting for transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
+            pipeline_summaries,
+        )
+
+        await pipeline_summaries(transcript_id=input.transcript_id)
+
+    ctx.log("final_summaries complete")
+    return FinalSummariesResult(generated=True)
+
+
+@live_post_pipeline.task(
+    parents=[final_summaries],
+    execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
+)
+@with_error_handling(TaskName.POST_ZULIP, set_error_status=False)
+async def post_zulip(input: LivePostPipelineInput, ctx: Context) -> ZulipResult:
+    """Post notification to Zulip."""
+    ctx.log(f"post_zulip: transcript_id={input.transcript_id}")
+
+    if not settings.ZULIP_REALM:
+        ctx.log("post_zulip skipped (Zulip not configured)")
+        return ZulipResult(zulip_message_id=None, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+        from reflector.zulip import post_transcript_notification  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            message_id = await post_transcript_notification(transcript)
+            ctx.log(f"post_zulip complete: zulip_message_id={message_id}")
+        else:
+            message_id = None
+
+    return ZulipResult(zulip_message_id=message_id)
+
+
+@live_post_pipeline.task(
+    parents=[final_summaries],
+    execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
+    retries=5,
+    backoff_factor=2.0,
+    backoff_max_seconds=15,
+)
+@with_error_handling(TaskName.SEND_WEBHOOK, set_error_status=False)
+async def send_webhook(input: LivePostPipelineInput, ctx: Context) -> WebhookResult:
+    """Send completion webhook to external service."""
+    ctx.log(f"send_webhook: transcript_id={input.transcript_id}")
+
+    if not input.room_id:
+        ctx.log("send_webhook skipped (no room_id)")
+        return WebhookResult(webhook_sent=False, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.rooms import rooms_controller  # noqa: PLC0415
+        from reflector.utils.webhook import (  # noqa: PLC0415
+            fetch_transcript_webhook_payload,
+            send_webhook_request,
+        )
+
+        room = await rooms_controller.get_by_id(input.room_id)
+        if not room or not room.webhook_url:
+            ctx.log("send_webhook skipped (no webhook_url configured)")
+            return WebhookResult(webhook_sent=False, skipped=True)
+
+        payload = await fetch_transcript_webhook_payload(
+            transcript_id=input.transcript_id,
+            room_id=input.room_id,
+        )
+
+        if isinstance(payload, str):
+            ctx.log(f"send_webhook skipped (could not build payload): {payload}")
+            return WebhookResult(webhook_sent=False, skipped=True)
+
+        import httpx  # noqa: PLC0415
+
+        try:
+            response = await send_webhook_request(
+                url=room.webhook_url,
+                payload=payload,
+                event_type="transcript.completed",
+                webhook_secret=room.webhook_secret,
+                timeout=30.0,
+            )
+            ctx.log(f"send_webhook complete: status_code={response.status_code}")
+            return WebhookResult(webhook_sent=True, response_code=response.status_code)
+        except httpx.HTTPStatusError as e:
+            ctx.log(f"send_webhook failed (HTTP {e.response.status_code}), continuing")
+            return WebhookResult(
+                webhook_sent=False, response_code=e.response.status_code
+            )
+        except (httpx.ConnectError, httpx.TimeoutException) as e:
+            ctx.log(f"send_webhook failed ({e}), continuing")
+            return WebhookResult(webhook_sent=False)
+        except Exception as e:
+            ctx.log(f"send_webhook unexpected error: {e}")
+            return WebhookResult(webhook_sent=False)
+
+
+# --- On failure handler ---
+
+
+async def on_workflow_failure(input: LivePostPipelineInput, ctx: Context) -> None:
+    """Set transcript status to 'error' only if not already 'ended'."""
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript and transcript.status == "ended":
+            logger.info(
+                "[Hatchet] LivePostProcessingPipeline on_workflow_failure: transcript already ended",
+                transcript_id=input.transcript_id,
+            )
+            ctx.log(
+                "on_workflow_failure: transcript already ended, skipping error status"
+            )
+            return
+    await set_workflow_error_status(input.transcript_id)
+
+
+@live_post_pipeline.on_failure_task()
+async def _register_on_workflow_failure(
+    input: LivePostPipelineInput, ctx: Context
+) -> None:
+    await on_workflow_failure(input, ctx)

server/reflector/pipelines/main_live_pipeline.py

@@ -17,7 +17,7 @@ from contextlib import asynccontextmanager
 from typing import Generic
 
 import av
-from celery import chord, current_task, group, shared_task
+from celery import current_task, shared_task
 from pydantic import BaseModel
 from structlog import BoundLogger as Logger
 
@@ -397,7 +397,9 @@ class PipelineMainLive(PipelineMainBase):
         # when the pipeline ends, connect to the post pipeline
         logger.info("Pipeline main live ended", transcript_id=self.transcript_id)
         logger.info("Scheduling pipeline main post", transcript_id=self.transcript_id)
-        pipeline_post(transcript_id=self.transcript_id)
+        transcript = await transcripts_controller.get_by_id(self.transcript_id)
+        room_id = transcript.room_id if transcript else None
+        await pipeline_post(transcript_id=self.transcript_id, room_id=room_id)
 
 
 class PipelineMainDiarization(PipelineMainBase[AudioDiarizationInput]):
@@ -792,29 +794,20 @@ async def task_pipeline_post_to_zulip(*, transcript_id: str):
     await pipeline_post_to_zulip(transcript_id=transcript_id)
 
 
-def pipeline_post(*, transcript_id: str):
+async def pipeline_post(*, transcript_id: str, room_id: str | None = None):
     """
-    Run the post pipeline
+    Run the post pipeline via Hatchet.
     """
-    chain_mp3_and_diarize = (
+    from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
-        task_pipeline_waveform.si(transcript_id=transcript_id)
-        | task_pipeline_convert_to_mp3.si(transcript_id=transcript_id)
-        | task_pipeline_upload_mp3.si(transcript_id=transcript_id)
-        | task_pipeline_remove_upload.si(transcript_id=transcript_id)
-        | task_pipeline_diarization.si(transcript_id=transcript_id)
-        | task_cleanup_consent.si(transcript_id=transcript_id)
-    )
-    chain_title_preview = task_pipeline_title.si(transcript_id=transcript_id)
-    chain_final_summaries = task_pipeline_final_summaries.si(
-        transcript_id=transcript_id
-    )
 
-    chain = chord(
+    await HatchetClientManager.start_workflow(
-        group(chain_mp3_and_diarize, chain_title_preview),
+        "LivePostProcessingPipeline",
-        chain_final_summaries,
+        {
-    ) | task_pipeline_post_to_zulip.si(transcript_id=transcript_id)
+            "transcript_id": str(transcript_id),
-
+            "room_id": str(room_id) if room_id else None,
-    return chain.delay()
+        },
+        additional_metadata={"transcript_id": str(transcript_id)},
+    )
 
 
 @get_transcript

server/reflector/services/transcript_process.py

@@ -10,7 +10,6 @@ from dataclasses import dataclass
 from typing import Literal, Union, assert_never
 
 import celery
-from celery.result import AsyncResult
 from hatchet_sdk.clients.rest.exceptions import ApiException, NotFoundException
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
@@ -18,7 +17,6 @@ from reflector.db.recordings import recordings_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.utils.string import NonEmptyString
 
 
@@ -105,11 +103,8 @@ async def validate_transcript_for_processing(
     ):
         return ValidationNotReady(detail="Recording is not ready for processing")
 
-    # Check Celery tasks
+    # Check Celery tasks (multitrack still uses Celery for some paths)
     if task_is_scheduled_or_active(
-        "reflector.pipelines.main_file_pipeline.task_pipeline_file_process",
-        transcript_id=transcript.id,
-    ) or task_is_scheduled_or_active(
         "reflector.pipelines.main_multitrack_pipeline.task_pipeline_multitrack_process",
         transcript_id=transcript.id,
     ):
@@ -175,11 +170,8 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
 
 async def dispatch_transcript_processing(
     config: ProcessingConfig, force: bool = False
-) -> AsyncResult | None:
+) -> None:
-    """Dispatch transcript processing to appropriate backend (Hatchet or Celery).
+    """Dispatch transcript processing to Hatchet workflow engine."""
-
-    Returns AsyncResult for Celery tasks, None for Hatchet workflows.
-    """
     if isinstance(config, MultitrackProcessingConfig):
         # Multitrack processing always uses Hatchet (no Celery fallback)
         # First check if we can replay (outside transaction since it's read-only)
@@ -275,7 +267,21 @@ async def dispatch_transcript_processing(
         return None
 
     elif isinstance(config, FileProcessingConfig):
-        return task_pipeline_file_process.delay(transcript_id=config.transcript_id)
+        # File processing uses Hatchet workflow
+        workflow_id = await HatchetClientManager.start_workflow(
+            workflow_name="FilePipeline",
+            input_data={"transcript_id": config.transcript_id},
+            additional_metadata={"transcript_id": config.transcript_id},
+        )
+
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
+            )
+
+        logger.info("File pipeline dispatched via Hatchet", workflow_id=workflow_id)
+        return None
     else:
         assert_never(config)
 

server/reflector/tools/process.py

@@ -7,7 +7,6 @@ import asyncio
 import json
 import shutil
 import sys
-import time
 from pathlib import Path
 from typing import Any, Dict, List, Literal, Tuple
 from urllib.parse import unquote, urlparse
@@ -15,10 +14,8 @@ from urllib.parse import unquote, urlparse
 from botocore.exceptions import BotoCoreError, ClientError, NoCredentialsError
 
 from reflector.db.transcripts import SourceKind, TranscriptTopic, transcripts_controller
+from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
-from reflector.pipelines.main_file_pipeline import (
-    task_pipeline_file_process as task_pipeline_file_process,
-)
 from reflector.pipelines.main_live_pipeline import pipeline_post as live_pipeline_post
 from reflector.pipelines.main_live_pipeline import (
     pipeline_process as live_pipeline_process,
@@ -237,29 +234,22 @@ async def process_live_pipeline(
     # assert documented behaviour: after process, the pipeline isn't ended. this is the reason of calling pipeline_post
     assert pre_final_transcript.status != "ended"
 
-    # at this point, diarization is running but we have no access to it. run diarization in parallel - one will hopefully win after polling
+    # Trigger post-processing via Hatchet (fire-and-forget)
-    result = live_pipeline_post(transcript_id=transcript_id)
+    await live_pipeline_post(transcript_id=transcript_id)
-
+    print("Live post-processing pipeline triggered via Hatchet", file=sys.stderr)
-    # result.ready() blocks even without await; it mutates result also
-    while not result.ready():
-        print(f"Status: {result.state}")
-        time.sleep(2)
 
 
 async def process_file_pipeline(
     transcript_id: TranscriptId,
 ):
-    """Process audio/video file using the optimized file pipeline"""
+    """Process audio/video file using the optimized file pipeline via Hatchet"""
 
-    # task_pipeline_file_process is a Celery task, need to use .delay() for async execution
+    await HatchetClientManager.start_workflow(
-    result = task_pipeline_file_process.delay(transcript_id=transcript_id)
+        "FilePipeline",
-
+        {"transcript_id": str(transcript_id)},
-    # Wait for the Celery task to complete
+        additional_metadata={"transcript_id": str(transcript_id)},
-    while not result.ready():
+    )
-        print(f"File pipeline status: {result.state}", file=sys.stderr)
+    print("File pipeline triggered via Hatchet", file=sys.stderr)
-        time.sleep(2)
-
-    logger.info("File pipeline processing complete")
 
 
 async def process(
@@ -293,6 +283,15 @@ async def process(
 
         await handler(transcript_id)
 
+        if pipeline == "file":
+            # File pipeline is async via Hatchet — results not available immediately.
+            # Use reflector.tools.process_transcript with --sync for polling.
+            print(
+                f"File pipeline dispatched for transcript {transcript_id}. "
+                f"Results will be available once the Hatchet workflow completes.",
+                file=sys.stderr,
+            )
+        else:
             await extract_result_from_entry(transcript_id, output_path)
     finally:
         await database.disconnect()

server/reflector/tools/process_transcript.py

@@ -11,10 +11,8 @@ Usage:
 import argparse
 import asyncio
 import sys
-import time
 from typing import Callable
 
-from celery.result import AsyncResult
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 import reflector._warnings_filter  # noqa: F401 -- side effect: suppress pydantic validate_default warning
@@ -39,7 +37,7 @@ async def process_transcript_inner(
     on_validation: Callable[[ValidationResult], None],
     on_preprocess: Callable[[PrepareResult], None],
     force: bool = False,
-) -> AsyncResult | None:
+) -> None:
     validation = await validate_transcript_for_processing(transcript)
     on_validation(validation)
     config = await prepare_transcript_processing(validation)
@@ -87,15 +85,13 @@ async def process_transcript(
             elif isinstance(config, FileProcessingConfig):
                 print(f"Dispatching file pipeline", file=sys.stderr)
 
-        result = await process_transcript_inner(
+        await process_transcript_inner(
             transcript,
             on_validation=on_validation,
             on_preprocess=on_preprocess,
             force=force,
         )
 
-        if result is None:
-            # Hatchet workflow dispatched
         if sync:
             # Re-fetch transcript to get workflow_run_id
             transcript = await transcripts_controller.get_by_id(transcript_id)
@@ -123,21 +119,6 @@ async def process_transcript(
                 "Task dispatched (use --sync to wait for completion)",
                 file=sys.stderr,
             )
-        elif sync:
-            print("Waiting for task completion...", file=sys.stderr)
-            while not result.ready():
-                print(f"  Status: {result.state}", file=sys.stderr)
-                time.sleep(5)
-
-            if result.successful():
-                print("Task completed successfully", file=sys.stderr)
-            else:
-                print(f"Task failed: {result.result}", file=sys.stderr)
-                sys.exit(1)
-        else:
-            print(
-                "Task dispatched (use --sync to wait for completion)", file=sys.stderr
-            )
 
     finally:
         await database.disconnect()

server/reflector/views/transcripts_process.py

@@ -52,8 +52,5 @@ async def transcript_process(
     if isinstance(config, ProcessError):
         raise HTTPException(status_code=500, detail=config.detail)
     else:
-        # When transcript is in error state, force a new workflow instead of replaying
+        await dispatch_transcript_processing(config, force=True)
-        # (replay would re-run from failure point with same conditions and likely fail again)
-        force = transcript.status == "error"
-        await dispatch_transcript_processing(config, force=force)
         return ProcessStatus(status="ok")

server/reflector/views/transcripts_upload.py

@@ -6,7 +6,7 @@ from pydantic import BaseModel
 
 import reflector.auth as auth
 from reflector.db.transcripts import SourceKind, transcripts_controller
-from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
+from reflector.hatchet.client import HatchetClientManager
 
 router = APIRouter()
 
@@ -95,7 +95,14 @@ async def transcript_record_upload(
         transcript, {"status": "uploaded", "source_kind": SourceKind.FILE}
     )
 
-    # launch a background task to process the file
+    # launch Hatchet workflow to process the file
-    task_pipeline_file_process.delay(transcript_id=transcript_id)
+    workflow_id = await HatchetClientManager.start_workflow(
+        "FilePipeline",
+        {"transcript_id": str(transcript_id)},
+        additional_metadata={"transcript_id": str(transcript_id)},
+    )
+
+    # Save workflow_run_id for duplicate detection and status polling
+    await transcripts_controller.update(transcript, {"workflow_run_id": workflow_id})
 
     return UploadStatus(status="ok")

server/reflector/worker/process.py

@@ -25,7 +25,6 @@ from reflector.db.transcripts import (
     transcripts_controller,
 )
 from reflector.hatchet.client import HatchetClientManager
-from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_live_pipeline import asynctask
 from reflector.pipelines.topic_processing import EmptyPipeline
 from reflector.processors import AudioFileWriterProcessor
@@ -163,7 +162,14 @@ async def process_recording(bucket_name: str, object_key: str):
 
     await transcripts_controller.update(transcript, {"status": "uploaded"})
 
-    task_pipeline_file_process.delay(transcript_id=transcript.id)
+    await HatchetClientManager.start_workflow(
+        "FilePipeline",
+        {
+            "transcript_id": str(transcript.id),
+            "room_id": str(room.id) if room else None,
+        },
+        additional_metadata={"transcript_id": str(transcript.id)},
+    )
 
 
 @shared_task

server/tests/conftest.py

@@ -1,6 +1,6 @@
 import os
 from contextlib import asynccontextmanager
-from unittest.mock import patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
@@ -538,18 +538,59 @@ def fake_mp3_upload():
 
 
 @pytest.fixture(autouse=True)
-def reset_hatchet_client():
+def mock_hatchet_client():
-    """Reset HatchetClientManager singleton before and after each test.
+    """Mock HatchetClientManager for all tests.
 
-    This ensures test isolation - each test starts with a fresh client state.
+    Prevents tests from connecting to a real Hatchet server. The dummy token
-    The fixture is autouse=True so it applies to all tests automatically.
+    in [tool.pytest_env] prevents the import-time ValueError, but the SDK
+    would still try to connect when get_client() is called. This fixture
+    mocks get_client to return a MagicMock and start_workflow to return a
+    dummy workflow ID.
     """
     from reflector.hatchet.client import HatchetClientManager
 
-    # Reset before test
     HatchetClientManager.reset()
-    yield
+
-    # Reset after test to clean up
+    mock_client = MagicMock()
+    mock_client.workflow.return_value = MagicMock()
+
+    with (
+        patch.object(
+            HatchetClientManager,
+            "get_client",
+            return_value=mock_client,
+        ),
+        patch.object(
+            HatchetClientManager,
+            "start_workflow",
+            new_callable=AsyncMock,
+            return_value="mock-workflow-id",
+        ),
+        patch.object(
+            HatchetClientManager,
+            "get_workflow_run_status",
+            new_callable=AsyncMock,
+            return_value=None,
+        ),
+        patch.object(
+            HatchetClientManager,
+            "can_replay",
+            new_callable=AsyncMock,
+            return_value=False,
+        ),
+        patch.object(
+            HatchetClientManager,
+            "cancel_workflow",
+            new_callable=AsyncMock,
+        ),
+        patch.object(
+            HatchetClientManager,
+            "replay_workflow",
+            new_callable=AsyncMock,
+        ),
+    ):
+        yield mock_client
+
     HatchetClientManager.reset()
 
 

server/tests/test_hatchet_client.py

@@ -37,18 +37,3 @@ async def test_hatchet_client_can_replay_handles_exception():
 
             # Should return False on error (workflow might be gone)
             assert can_replay is False
-
-
-def test_hatchet_client_raises_without_token():
-    """Test that get_client raises ValueError without token.
-
-    Useful: Catches if someone removes the token validation,
-    which would cause cryptic errors later.
-    """
-    from reflector.hatchet.client import HatchetClientManager
-
-    with patch("reflector.hatchet.client.settings") as mock_settings:
-        mock_settings.HATCHET_CLIENT_TOKEN = None
-
-        with pytest.raises(ValueError, match="HATCHET_CLIENT_TOKEN must be set"):
-            HatchetClientManager.get_client()

server/tests/test_hatchet_file_pipeline.py

@@ -0,0 +1,233 @@
+"""
+Tests for the FilePipeline Hatchet workflow.
+
+Tests verify:
+1. with_error_handling behavior for file pipeline input model
+2. on_workflow_failure logic (don't overwrite 'ended' status)
+3. Input model validation
+"""
+
+from contextlib import asynccontextmanager
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+from hatchet_sdk import NonRetryableException
+
+
+@asynccontextmanager
+async def _noop_db_context():
+    """Async context manager that yields without touching the DB."""
+    yield None
+
+
+@pytest.fixture(scope="module")
+def file_pipeline_module():
+    """Import file_pipeline with Hatchet client mocked."""
+    mock_client = MagicMock()
+    mock_client.workflow.return_value = MagicMock()
+    with patch(
+        "reflector.hatchet.client.HatchetClientManager.get_client",
+        return_value=mock_client,
+    ):
+        from reflector.hatchet.workflows import file_pipeline
+
+        return file_pipeline
+
+
+@pytest.fixture
+def mock_file_input():
+    """Minimal FilePipelineInput for tests."""
+    from reflector.hatchet.workflows.file_pipeline import FilePipelineInput
+
+    return FilePipelineInput(
+        transcript_id="ts-file-123",
+        room_id="room-456",
+    )
+
+
+@pytest.fixture
+def mock_ctx():
+    """Minimal Context-like object."""
+    ctx = MagicMock()
+    ctx.log = MagicMock()
+    return ctx
+
+
+def test_file_pipeline_input_model():
+    """Test FilePipelineInput validation."""
+    from reflector.hatchet.workflows.file_pipeline import FilePipelineInput
+
+    # Valid input with room_id
+    input_with_room = FilePipelineInput(transcript_id="ts-123", room_id="room-456")
+    assert input_with_room.transcript_id == "ts-123"
+    assert input_with_room.room_id == "room-456"
+
+    # Valid input without room_id
+    input_no_room = FilePipelineInput(transcript_id="ts-123")
+    assert input_no_room.room_id is None
+
+
+@pytest.mark.asyncio
+async def test_file_pipeline_error_handling_transient(
+    file_pipeline_module, mock_file_input, mock_ctx
+):
+    """Transient exception must NOT set error status."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise httpx.TimeoutException("timed out")
+
+    wrapped = with_error_handling(TaskName.EXTRACT_AUDIO)(failing_task)
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises(httpx.TimeoutException):
+            await wrapped(mock_file_input, mock_ctx)
+
+        mock_set_error.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_file_pipeline_error_handling_hard_fail(
+    file_pipeline_module, mock_file_input, mock_ctx
+):
+    """Hard-fail (ValueError) must set error status and raise NonRetryableException."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise ValueError("No audio file found")
+
+    wrapped = with_error_handling(TaskName.EXTRACT_AUDIO)(failing_task)
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises(NonRetryableException) as exc_info:
+            await wrapped(mock_file_input, mock_ctx)
+
+        assert "No audio file found" in str(exc_info.value)
+        mock_set_error.assert_called_once_with("ts-file-123")
+
+
+def test_diarize_result_uses_plain_dicts():
+    """DiarizationSegment is a TypedDict (plain dict), not a Pydantic model.
+
+    The diarize task must serialize segments as plain dicts (not call .model_dump()),
+    and assemble_transcript must be able to reconstruct them with DiarizationSegment(**s).
+    This was a real bug: 'dict' object has no attribute 'model_dump'.
+    """
+    from reflector.hatchet.workflows.file_pipeline import DiarizeResult
+    from reflector.processors.types import DiarizationSegment
+
+    # DiarizationSegment is a TypedDict — instances are plain dicts
+    segments = [
+        DiarizationSegment(start=0.0, end=1.5, speaker=0),
+        DiarizationSegment(start=1.5, end=3.0, speaker=1),
+    ]
+    assert isinstance(segments[0], dict), "DiarizationSegment should be a plain dict"
+
+    # DiarizeResult should accept list[dict] directly (no model_dump needed)
+    result = DiarizeResult(diarization=segments)
+    assert result.diarization is not None
+    assert len(result.diarization) == 2
+
+    # Consumer (assemble_transcript) reconstructs via DiarizationSegment(**s)
+    reconstructed = [DiarizationSegment(**s) for s in result.diarization]
+    assert reconstructed[0]["start"] == 0.0
+    assert reconstructed[0]["speaker"] == 0
+    assert reconstructed[1]["end"] == 3.0
+    assert reconstructed[1]["speaker"] == 1
+
+
+def test_diarize_result_handles_none():
+    """DiarizeResult with no diarization data (diarization disabled)."""
+    from reflector.hatchet.workflows.file_pipeline import DiarizeResult
+
+    result = DiarizeResult(diarization=None)
+    assert result.diarization is None
+
+    result_default = DiarizeResult()
+    assert result_default.diarization is None
+
+
+def test_transcribe_result_words_are_pydantic():
+    """TranscribeResult words come from Pydantic Word.model_dump() — verify roundtrip."""
+    from reflector.hatchet.workflows.file_pipeline import TranscribeResult
+    from reflector.processors.types import Word
+
+    words = [
+        Word(text="hello", start=0.0, end=0.5),
+        Word(text="world", start=0.5, end=1.0),
+    ]
+    # Words are Pydantic models, so model_dump() works
+    word_dicts = [w.model_dump() for w in words]
+    result = TranscribeResult(words=word_dicts)
+
+    # Consumer reconstructs via Word(**w)
+    reconstructed = [Word(**w) for w in result.words]
+    assert reconstructed[0].text == "hello"
+    assert reconstructed[1].start == 0.5
+
+
+@pytest.mark.asyncio
+async def test_file_pipeline_on_failure_sets_error_status(
+    file_pipeline_module, mock_file_input, mock_ctx
+):
+    """on_workflow_failure sets error status when transcript is processing."""
+    from reflector.hatchet.workflows.file_pipeline import on_workflow_failure
+
+    transcript_processing = MagicMock()
+    transcript_processing.status = "processing"
+
+    with patch(
+        "reflector.hatchet.workflows.file_pipeline.fresh_db_connection",
+        _noop_db_context,
+    ):
+        with patch(
+            "reflector.db.transcripts.transcripts_controller.get_by_id",
+            new_callable=AsyncMock,
+            return_value=transcript_processing,
+        ):
+            with patch(
+                "reflector.hatchet.workflows.file_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error:
+                await on_workflow_failure(mock_file_input, mock_ctx)
+                mock_set_error.assert_called_once_with(mock_file_input.transcript_id)
+
+
+@pytest.mark.asyncio
+async def test_file_pipeline_on_failure_does_not_overwrite_ended(
+    file_pipeline_module, mock_file_input, mock_ctx
+):
+    """on_workflow_failure must NOT overwrite 'ended' status."""
+    from reflector.hatchet.workflows.file_pipeline import on_workflow_failure
+
+    transcript_ended = MagicMock()
+    transcript_ended.status = "ended"
+
+    with patch(
+        "reflector.hatchet.workflows.file_pipeline.fresh_db_connection",
+        _noop_db_context,
+    ):
+        with patch(
+            "reflector.db.transcripts.transcripts_controller.get_by_id",
+            new_callable=AsyncMock,
+            return_value=transcript_ended,
+        ):
+            with patch(
+                "reflector.hatchet.workflows.file_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error:
+                await on_workflow_failure(mock_file_input, mock_ctx)
+                mock_set_error.assert_not_called()

server/tests/test_hatchet_live_post_pipeline.py

@@ -0,0 +1,218 @@
+"""
+Tests for the LivePostProcessingPipeline Hatchet workflow.
+
+Tests verify:
+1. with_error_handling behavior for live post pipeline input model
+2. on_workflow_failure logic (don't overwrite 'ended' status)
+3. Input model validation
+4. pipeline_post() now triggers Hatchet instead of Celery chord
+"""
+
+from contextlib import asynccontextmanager
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+from hatchet_sdk import NonRetryableException
+
+
+@asynccontextmanager
+async def _noop_db_context():
+    """Async context manager that yields without touching the DB."""
+    yield None
+
+
+@pytest.fixture(scope="module")
+def live_pipeline_module():
+    """Import live_post_pipeline with Hatchet client mocked."""
+    mock_client = MagicMock()
+    mock_client.workflow.return_value = MagicMock()
+    with patch(
+        "reflector.hatchet.client.HatchetClientManager.get_client",
+        return_value=mock_client,
+    ):
+        from reflector.hatchet.workflows import live_post_pipeline
+
+        return live_post_pipeline
+
+
+@pytest.fixture
+def mock_live_input():
+    """Minimal LivePostPipelineInput for tests."""
+    from reflector.hatchet.workflows.live_post_pipeline import LivePostPipelineInput
+
+    return LivePostPipelineInput(
+        transcript_id="ts-live-789",
+        room_id="room-abc",
+    )
+
+
+@pytest.fixture
+def mock_ctx():
+    """Minimal Context-like object."""
+    ctx = MagicMock()
+    ctx.log = MagicMock()
+    return ctx
+
+
+def test_live_post_pipeline_input_model():
+    """Test LivePostPipelineInput validation."""
+    from reflector.hatchet.workflows.live_post_pipeline import LivePostPipelineInput
+
+    # Valid input with room_id
+    input_with_room = LivePostPipelineInput(transcript_id="ts-123", room_id="room-456")
+    assert input_with_room.transcript_id == "ts-123"
+    assert input_with_room.room_id == "room-456"
+
+    # Valid input without room_id
+    input_no_room = LivePostPipelineInput(transcript_id="ts-123")
+    assert input_no_room.room_id is None
+
+
+@pytest.mark.asyncio
+async def test_live_pipeline_error_handling_transient(
+    live_pipeline_module, mock_live_input, mock_ctx
+):
+    """Transient exception must NOT set error status."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise httpx.TimeoutException("timed out")
+
+    wrapped = with_error_handling(TaskName.WAVEFORM)(failing_task)
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises(httpx.TimeoutException):
+            await wrapped(mock_live_input, mock_ctx)
+
+        mock_set_error.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_live_pipeline_error_handling_hard_fail(
+    live_pipeline_module, mock_live_input, mock_ctx
+):
+    """Hard-fail must set error status and raise NonRetryableException."""
+    from reflector.hatchet.workflows.daily_multitrack_pipeline import (
+        TaskName,
+        with_error_handling,
+    )
+
+    async def failing_task(input, ctx):
+        raise ValueError("Transcript not found")
+
+    wrapped = with_error_handling(TaskName.WAVEFORM)(failing_task)
+
+    with patch(
+        "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
+        new_callable=AsyncMock,
+    ) as mock_set_error:
+        with pytest.raises(NonRetryableException) as exc_info:
+            await wrapped(mock_live_input, mock_ctx)
+
+        assert "Transcript not found" in str(exc_info.value)
+        mock_set_error.assert_called_once_with("ts-live-789")
+
+
+@pytest.mark.asyncio
+async def test_live_pipeline_on_failure_sets_error_status(
+    live_pipeline_module, mock_live_input, mock_ctx
+):
+    """on_workflow_failure sets error status when transcript is processing."""
+    from reflector.hatchet.workflows.live_post_pipeline import on_workflow_failure
+
+    transcript_processing = MagicMock()
+    transcript_processing.status = "processing"
+
+    with patch(
+        "reflector.hatchet.workflows.live_post_pipeline.fresh_db_connection",
+        _noop_db_context,
+    ):
+        with patch(
+            "reflector.db.transcripts.transcripts_controller.get_by_id",
+            new_callable=AsyncMock,
+            return_value=transcript_processing,
+        ):
+            with patch(
+                "reflector.hatchet.workflows.live_post_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error:
+                await on_workflow_failure(mock_live_input, mock_ctx)
+                mock_set_error.assert_called_once_with(mock_live_input.transcript_id)
+
+
+@pytest.mark.asyncio
+async def test_live_pipeline_on_failure_does_not_overwrite_ended(
+    live_pipeline_module, mock_live_input, mock_ctx
+):
+    """on_workflow_failure must NOT overwrite 'ended' status."""
+    from reflector.hatchet.workflows.live_post_pipeline import on_workflow_failure
+
+    transcript_ended = MagicMock()
+    transcript_ended.status = "ended"
+
+    with patch(
+        "reflector.hatchet.workflows.live_post_pipeline.fresh_db_connection",
+        _noop_db_context,
+    ):
+        with patch(
+            "reflector.db.transcripts.transcripts_controller.get_by_id",
+            new_callable=AsyncMock,
+            return_value=transcript_ended,
+        ):
+            with patch(
+                "reflector.hatchet.workflows.live_post_pipeline.set_workflow_error_status",
+                new_callable=AsyncMock,
+            ) as mock_set_error:
+                await on_workflow_failure(mock_live_input, mock_ctx)
+                mock_set_error.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_pipeline_post_triggers_hatchet():
+    """pipeline_post() should trigger Hatchet LivePostProcessingPipeline workflow."""
+    with patch(
+        "reflector.hatchet.client.HatchetClientManager.start_workflow",
+        new_callable=AsyncMock,
+        return_value="workflow-run-id",
+    ) as mock_start:
+        from reflector.pipelines.main_live_pipeline import pipeline_post
+
+        await pipeline_post(transcript_id="ts-test-123", room_id="room-test")
+
+        mock_start.assert_called_once_with(
+            "LivePostProcessingPipeline",
+            {
+                "transcript_id": "ts-test-123",
+                "room_id": "room-test",
+            },
+            additional_metadata={"transcript_id": "ts-test-123"},
+        )
+
+
+@pytest.mark.asyncio
+async def test_pipeline_post_triggers_hatchet_without_room_id():
+    """pipeline_post() should handle None room_id."""
+    with patch(
+        "reflector.hatchet.client.HatchetClientManager.start_workflow",
+        new_callable=AsyncMock,
+        return_value="workflow-run-id",
+    ) as mock_start:
+        from reflector.pipelines.main_live_pipeline import pipeline_post
+
+        await pipeline_post(transcript_id="ts-test-456")
+
+        mock_start.assert_called_once_with(
+            "LivePostProcessingPipeline",
+            {
+                "transcript_id": "ts-test-456",
+                "room_id": None,
+            },
+            additional_metadata={"transcript_id": "ts-test-456"},
+        )

server/tests/test_hatchet_trigger_migration.py

@@ -0,0 +1,90 @@
+"""
+Tests verifying Celery-to-Hatchet trigger migration.
+
+Ensures that:
+1. process_recording triggers FilePipeline via Hatchet (not Celery)
+2. transcript_record_upload triggers FilePipeline via Hatchet (not Celery)
+3. Old Celery task references are no longer in active call sites
+"""
+
+
+def test_process_recording_does_not_import_celery_file_task():
+    """Verify process.py no longer imports task_pipeline_file_process."""
+    import inspect
+
+    from reflector.worker import process
+
+    source = inspect.getsource(process)
+    # Should not contain the old Celery task import
+    assert "task_pipeline_file_process" not in source
+
+
+def test_transcripts_upload_does_not_import_celery_file_task():
+    """Verify transcripts_upload.py no longer imports task_pipeline_file_process."""
+    import inspect
+
+    from reflector.views import transcripts_upload
+
+    source = inspect.getsource(transcripts_upload)
+    # Should not contain the old Celery task import
+    assert "task_pipeline_file_process" not in source
+
+
+def test_transcripts_upload_imports_hatchet():
+    """Verify transcripts_upload.py imports HatchetClientManager."""
+    import inspect
+
+    from reflector.views import transcripts_upload
+
+    source = inspect.getsource(transcripts_upload)
+    assert "HatchetClientManager" in source
+
+
+def test_pipeline_post_is_async():
+    """Verify pipeline_post is now async (Hatchet trigger)."""
+    import asyncio
+
+    from reflector.pipelines.main_live_pipeline import pipeline_post
+
+    assert asyncio.iscoroutinefunction(pipeline_post)
+
+
+def test_transcript_process_service_does_not_import_celery_file_task():
+    """Verify transcript_process.py service no longer imports task_pipeline_file_process."""
+    import inspect
+
+    from reflector.services import transcript_process
+
+    source = inspect.getsource(transcript_process)
+    assert "task_pipeline_file_process" not in source
+
+
+def test_transcript_process_service_dispatch_uses_hatchet():
+    """Verify dispatch_transcript_processing uses HatchetClientManager for file processing."""
+    import inspect
+
+    from reflector.services import transcript_process
+
+    source = inspect.getsource(transcript_process.dispatch_transcript_processing)
+    assert "HatchetClientManager" in source
+    assert "FilePipeline" in source
+
+
+def test_new_task_names_exist():
+    """Verify new TaskName constants were added for file and live pipelines."""
+    from reflector.hatchet.constants import TaskName
+
+    # File pipeline tasks
+    assert TaskName.EXTRACT_AUDIO == "extract_audio"
+    assert TaskName.UPLOAD_AUDIO == "upload_audio"
+    assert TaskName.TRANSCRIBE == "transcribe"
+    assert TaskName.DIARIZE == "diarize"
+    assert TaskName.ASSEMBLE_TRANSCRIPT == "assemble_transcript"
+    assert TaskName.GENERATE_SUMMARIES == "generate_summaries"
+
+    # Live post-processing pipeline tasks
+    assert TaskName.WAVEFORM == "waveform"
+    assert TaskName.CONVERT_MP3 == "convert_mp3"
+    assert TaskName.UPLOAD_MP3 == "upload_mp3"
+    assert TaskName.REMOVE_UPLOAD == "remove_upload"
+    assert TaskName.FINAL_SUMMARIES == "final_summaries"

server/tests/test_transcripts_process.py

@@ -1,5 +1,3 @@
-import asyncio
-import time
 from unittest.mock import AsyncMock, patch
 
 import pytest
@@ -27,8 +25,6 @@ async def client(app_lifespan):
 
 
 @pytest.mark.usefixtures("setup_database")
-@pytest.mark.usefixtures("celery_session_app")
-@pytest.mark.usefixtures("celery_session_worker")
 @pytest.mark.asyncio
 async def test_transcript_process(
     tmpdir,
@@ -39,8 +35,13 @@ async def test_transcript_process(
     dummy_storage,
     client,
     monkeypatch,
+    mock_hatchet_client,
 ):
-    # public mode: this test uses an anonymous client; allow anonymous transcript creation
+    """Test upload + process dispatch via Hatchet.
+
+    The file pipeline is now dispatched to Hatchet (fire-and-forget),
+    so we verify the workflow was triggered rather than polling for completion.
+    """
     monkeypatch.setattr(settings, "PUBLIC_MODE", True)
 
     # create a transcript
@@ -63,51 +64,43 @@ async def test_transcript_process(
     assert response.status_code == 200
     assert response.json()["status"] == "ok"
 
-    # wait for processing to finish (max 1 minute)
+    # Verify Hatchet workflow was dispatched (from upload endpoint)
-    timeout_seconds = 60
+    from reflector.hatchet.client import HatchetClientManager
-    start_time = time.monotonic()
+
-    while (time.monotonic() - start_time) < timeout_seconds:
+    HatchetClientManager.start_workflow.assert_called_once_with(
-        # fetch the transcript and check if it is ended
+        "FilePipeline",
+        {"transcript_id": tid},
+        additional_metadata={"transcript_id": tid},
+    )
+
+    # Verify transcript status was set to "uploaded"
     resp = await client.get(f"/transcripts/{tid}")
     assert resp.status_code == 200
-        if resp.json()["status"] in ("ended", "error"):
+    assert resp.json()["status"] == "uploaded"
-            break
-        await asyncio.sleep(1)
-    else:
-        pytest.fail(f"Initial processing timed out after {timeout_seconds} seconds")
 
-    # restart the processing
+    # Reset mock for reprocess test
-    response = await client.post(
+    HatchetClientManager.start_workflow.reset_mock()
-        f"/transcripts/{tid}/process",
+
-    )
+    # Clear workflow_run_id so /process endpoint can dispatch again
+    from reflector.db.transcripts import transcripts_controller
+
+    transcript = await transcripts_controller.get_by_id(tid)
+    await transcripts_controller.update(transcript, {"workflow_run_id": None})
+
+    # Reprocess via /process endpoint
+    with patch(
+        "reflector.services.transcript_process.task_is_scheduled_or_active",
+        return_value=False,
+    ):
+        response = await client.post(f"/transcripts/{tid}/process")
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
-    await asyncio.sleep(2)
 
-    # wait for processing to finish (max 1 minute)
+    # Verify second Hatchet dispatch (from /process endpoint)
-    timeout_seconds = 60
+    HatchetClientManager.start_workflow.assert_called_once()
-    start_time = time.monotonic()
+    call_kwargs = HatchetClientManager.start_workflow.call_args.kwargs
-    while (time.monotonic() - start_time) < timeout_seconds:
+    assert call_kwargs["workflow_name"] == "FilePipeline"
-        # fetch the transcript and check if it is ended
+    assert call_kwargs["input_data"]["transcript_id"] == tid
-        resp = await client.get(f"/transcripts/{tid}")
-        assert resp.status_code == 200
-        if resp.json()["status"] in ("ended", "error"):
-            break
-        await asyncio.sleep(1)
-    else:
-        pytest.fail(f"Restart processing timed out after {timeout_seconds} seconds")
-
-    # check the transcript is ended
-    transcript = resp.json()
-    assert transcript["status"] == "ended"
-    assert transcript["short_summary"] == "LLM SHORT SUMMARY"
-    assert transcript["title"] == "Llm Title"
-
-    # check topics and transcript
-    response = await client.get(f"/transcripts/{tid}/topics")
-    assert response.status_code == 200
-    assert len(response.json()) == 1
-    assert "Hello world. How are you today?" in response.json()[0]["transcript"]
 
 
 @pytest.mark.usefixtures("setup_database")
@@ -150,20 +143,25 @@ async def test_whereby_recording_uses_file_pipeline(monkeypatch, client):
 
     with (
         patch(
-            "reflector.services.transcript_process.task_pipeline_file_process"
+            "reflector.services.transcript_process.task_is_scheduled_or_active",
-        ) as mock_file_pipeline,
+            return_value=False,
+        ),
         patch(
             "reflector.services.transcript_process.HatchetClientManager"
         ) as mock_hatchet,
     ):
+        mock_hatchet.start_workflow = AsyncMock(return_value="test-workflow-id")
+
         response = await client.post(f"/transcripts/{transcript.id}/process")
 
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
 
-        # Whereby recordings should use file pipeline, not Hatchet
+        # Whereby recordings should use Hatchet FilePipeline
-        mock_file_pipeline.delay.assert_called_once_with(transcript_id=transcript.id)
+        mock_hatchet.start_workflow.assert_called_once()
-        mock_hatchet.start_workflow.assert_not_called()
+        call_kwargs = mock_hatchet.start_workflow.call_args.kwargs
+        assert call_kwargs["workflow_name"] == "FilePipeline"
+        assert call_kwargs["input_data"]["transcript_id"] == transcript.id
 
 
 @pytest.mark.usefixtures("setup_database")
@@ -224,8 +222,9 @@ async def test_dailyco_recording_uses_multitrack_pipeline(monkeypatch, client):
 
     with (
         patch(
-            "reflector.services.transcript_process.task_pipeline_file_process"
+            "reflector.services.transcript_process.task_is_scheduled_or_active",
-        ) as mock_file_pipeline,
+            return_value=False,
+        ),
         patch(
             "reflector.services.transcript_process.HatchetClientManager"
         ) as mock_hatchet,
@@ -237,7 +236,7 @@ async def test_dailyco_recording_uses_multitrack_pipeline(monkeypatch, client):
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
 
-        # Daily.co multitrack recordings should use Hatchet workflow
+        # Daily.co multitrack recordings should use Hatchet DiarizationPipeline
         mock_hatchet.start_workflow.assert_called_once()
         call_kwargs = mock_hatchet.start_workflow.call_args.kwargs
         assert call_kwargs["workflow_name"] == "DiarizationPipeline"
@@ -246,7 +245,6 @@ async def test_dailyco_recording_uses_multitrack_pipeline(monkeypatch, client):
         assert call_kwargs["input_data"]["tracks"] == [
             {"s3_key": k} for k in track_keys
         ]
-        mock_file_pipeline.delay.assert_not_called()
 
 
 @pytest.mark.usefixtures("setup_database")

server/tests/test_transcripts_rtc_ws.py

@@ -2,6 +2,10 @@
 # FIXME test status of transcript
 # FIXME test websocket connection after RTC is finished still send the full events
 # FIXME try with locked session, RTC should not work
+# TODO: add integration tests for post-processing (LivePostPipeline) with a real
+#       Hatchet instance. These tests currently only cover the live pipeline.
+#       Post-processing events (WAVEFORM, FINAL_*, DURATION, STATUS=ended, mp3)
+#       are now dispatched via Hatchet and tested in test_hatchet_live_post_pipeline.py.
 
 import asyncio
 import json
@@ -49,7 +53,7 @@ class ThreadedUvicorn:
 
 
 @pytest.fixture
-def appserver(tmpdir, setup_database, celery_session_app, celery_session_worker):
+def appserver(tmpdir, setup_database):
     import threading
 
     from reflector.app import app
@@ -119,8 +123,6 @@ def appserver(tmpdir, setup_database, celery_session_app, celery_session_worker)
 
 
 @pytest.mark.usefixtures("setup_database")
-@pytest.mark.usefixtures("celery_session_app")
-@pytest.mark.usefixtures("celery_session_worker")
 @pytest.mark.asyncio
 async def test_transcript_rtc_and_websocket(
     tmpdir,
@@ -134,6 +136,7 @@ async def test_transcript_rtc_and_websocket(
     appserver,
     client,
     monkeypatch,
+    mock_hatchet_client,
 ):
     # goal: start the server, exchange RTC, receive websocket events
     # because of that, we need to start the server in a thread
@@ -208,35 +211,30 @@ async def test_transcript_rtc_and_websocket(
     stream_client.channel.send(json.dumps({"cmd": "STOP"}))
     await stream_client.stop()
 
-    # wait the processing to finish
+    # Wait for live pipeline to flush (it dispatches post-processing to Hatchet)
-    timeout = 120
+    timeout = 30
     while True:
-        # fetch the transcript and check if it is ended
         resp = await client.get(f"/transcripts/{tid}")
         assert resp.status_code == 200
-        if resp.json()["status"] in ("ended", "error"):
+        if resp.json()["status"] in ("processing", "ended", "error"):
             break
         await asyncio.sleep(1)
         timeout -= 1
         if timeout < 0:
-            raise TimeoutError("Timeout while waiting for transcript to be ended")
+            raise TimeoutError("Timeout waiting for live pipeline to finish")
-
-    if resp.json()["status"] != "ended":
-        raise TimeoutError("Transcript processing failed")
 
     # stop websocket task
     websocket_task.cancel()
 
-    # check events
+    # check live pipeline events
     assert len(events) > 0
     from pprint import pprint
 
     pprint(events)
 
-    # get events list
     eventnames = [e["event"] for e in events]
 
-    # check events
+    # Live pipeline produces TRANSCRIPT and TOPIC events during RTC
     assert "TRANSCRIPT" in eventnames
     ev = events[eventnames.index("TRANSCRIPT")]
     assert ev["data"]["text"].startswith("Hello world.")
@@ -249,50 +247,18 @@ async def test_transcript_rtc_and_websocket(
     assert ev["data"]["transcript"].startswith("Hello world.")
     assert ev["data"]["timestamp"] == 0.0
 
-    assert "FINAL_LONG_SUMMARY" in eventnames
+    # Live pipeline status progression
-    ev = events[eventnames.index("FINAL_LONG_SUMMARY")]
-    assert ev["data"]["long_summary"] == "LLM LONG SUMMARY"
-
-    assert "FINAL_SHORT_SUMMARY" in eventnames
-    ev = events[eventnames.index("FINAL_SHORT_SUMMARY")]
-    assert ev["data"]["short_summary"] == "LLM SHORT SUMMARY"
-
-    assert "FINAL_TITLE" in eventnames
-    ev = events[eventnames.index("FINAL_TITLE")]
-    assert ev["data"]["title"] == "Llm Title"
-
-    assert "WAVEFORM" in eventnames
-    ev = events[eventnames.index("WAVEFORM")]
-    assert isinstance(ev["data"]["waveform"], list)
-    assert len(ev["data"]["waveform"]) >= 250
-    waveform_resp = await client.get(f"/transcripts/{tid}/audio/waveform")
-    assert waveform_resp.status_code == 200
-    assert waveform_resp.headers["content-type"] == "application/json"
-    assert isinstance(waveform_resp.json()["data"], list)
-    assert len(waveform_resp.json()["data"]) >= 250
-
-    # check status order
     statuses = [e["data"]["value"] for e in events if e["event"] == "STATUS"]
+    assert "recording" in statuses
+    assert "processing" in statuses
     assert statuses.index("recording") < statuses.index("processing")
-    assert statuses.index("processing") < statuses.index("ended")
 
-    # ensure the last event received is ended
+    # Post-processing (WAVEFORM, FINAL_*, DURATION, mp3, STATUS=ended) is now
-    assert events[-1]["event"] == "STATUS"
+    # dispatched to Hatchet via LivePostPipeline — not tested here.
-    assert events[-1]["data"]["value"] == "ended"
+    # See test_hatchet_live_post_pipeline.py for post-processing tests.
-
-    # check on the latest response that the audio duration is > 0
-    assert resp.json()["duration"] > 0
-    assert "DURATION" in eventnames
-
-    # check that audio/mp3 is available
-    audio_resp = await client.get(f"/transcripts/{tid}/audio/mp3")
-    assert audio_resp.status_code == 200
-    assert audio_resp.headers["Content-Type"] == "audio/mpeg"
 
 
 @pytest.mark.usefixtures("setup_database")
-@pytest.mark.usefixtures("celery_session_app")
-@pytest.mark.usefixtures("celery_session_worker")
 @pytest.mark.asyncio
 async def test_transcript_rtc_and_websocket_and_fr(
     tmpdir,
@@ -306,6 +272,7 @@ async def test_transcript_rtc_and_websocket_and_fr(
     appserver,
     client,
     monkeypatch,
+    mock_hatchet_client,
 ):
     # goal: start the server, exchange RTC, receive websocket events
     # because of that, we need to start the server in a thread
@@ -382,42 +349,34 @@ async def test_transcript_rtc_and_websocket_and_fr(
     # instead of waiting a long time, we just send a STOP
     stream_client.channel.send(json.dumps({"cmd": "STOP"}))
 
-    # wait the processing to finish
     await asyncio.sleep(2)
 
     await stream_client.stop()
 
-    # wait the processing to finish
+    # Wait for live pipeline to flush
-    timeout = 120
+    timeout = 30
     while True:
-        # fetch the transcript and check if it is ended
         resp = await client.get(f"/transcripts/{tid}")
         assert resp.status_code == 200
-        if resp.json()["status"] == "ended":
+        if resp.json()["status"] in ("processing", "ended", "error"):
             break
         await asyncio.sleep(1)
         timeout -= 1
         if timeout < 0:
-            raise TimeoutError("Timeout while waiting for transcript to be ended")
+            raise TimeoutError("Timeout waiting for live pipeline to finish")
-
-    if resp.json()["status"] != "ended":
-        raise TimeoutError("Transcript processing failed")
-
-    await asyncio.sleep(2)
 
     # stop websocket task
     websocket_task.cancel()
 
-    # check events
+    # check live pipeline events
     assert len(events) > 0
     from pprint import pprint
 
     pprint(events)
 
-    # get events list
     eventnames = [e["event"] for e in events]
 
-    # check events
+    # Live pipeline produces TRANSCRIPT with translation
     assert "TRANSCRIPT" in eventnames
     ev = events[eventnames.index("TRANSCRIPT")]
     assert ev["data"]["text"].startswith("Hello world.")
@@ -430,23 +389,11 @@ async def test_transcript_rtc_and_websocket_and_fr(
     assert ev["data"]["transcript"].startswith("Hello world.")
     assert ev["data"]["timestamp"] == 0.0
 
-    assert "FINAL_LONG_SUMMARY" in eventnames
+    # Live pipeline status progression
-    ev = events[eventnames.index("FINAL_LONG_SUMMARY")]
-    assert ev["data"]["long_summary"] == "LLM LONG SUMMARY"
-
-    assert "FINAL_SHORT_SUMMARY" in eventnames
-    ev = events[eventnames.index("FINAL_SHORT_SUMMARY")]
-    assert ev["data"]["short_summary"] == "LLM SHORT SUMMARY"
-
-    assert "FINAL_TITLE" in eventnames
-    ev = events[eventnames.index("FINAL_TITLE")]
-    assert ev["data"]["title"] == "Llm Title"
-
-    # check status order
     statuses = [e["data"]["value"] for e in events if e["event"] == "STATUS"]
+    assert "recording" in statuses
+    assert "processing" in statuses
     assert statuses.index("recording") < statuses.index("processing")
-    assert statuses.index("processing") < statuses.index("ended")
 
-    # ensure the last event received is ended
+    # Post-processing (FINAL_*, STATUS=ended) is now dispatched to Hatchet
-    assert events[-1]["event"] == "STATUS"
+    # via LivePostPipeline — not tested here.
-    assert events[-1]["data"]["value"] == "ended"

server/tests/test_transcripts_upload.py

@@ -1,12 +1,7 @@
-import asyncio
-import time
-
 import pytest
 
 
 @pytest.mark.usefixtures("setup_database")
-@pytest.mark.usefixtures("celery_session_app")
-@pytest.mark.usefixtures("celery_session_worker")
 @pytest.mark.asyncio
 async def test_transcript_upload_file(
     tmpdir,
@@ -17,6 +12,7 @@ async def test_transcript_upload_file(
     dummy_storage,
     client,
     monkeypatch,
+    mock_hatchet_client,
 ):
     from reflector.settings import settings
 
@@ -43,27 +39,16 @@ async def test_transcript_upload_file(
     assert response.status_code == 200
     assert response.json()["status"] == "ok"
 
-    # wait the processing to finish (max 1 minute)
+    # Verify Hatchet workflow was dispatched for file processing
-    timeout_seconds = 60
+    from reflector.hatchet.client import HatchetClientManager
-    start_time = time.monotonic()
+
-    while (time.monotonic() - start_time) < timeout_seconds:
+    HatchetClientManager.start_workflow.assert_called_once_with(
-        # fetch the transcript and check if it is ended
+        "FilePipeline",
+        {"transcript_id": tid},
+        additional_metadata={"transcript_id": tid},
+    )
+
+    # Verify transcript status was updated to "uploaded"
     resp = await client.get(f"/transcripts/{tid}")
     assert resp.status_code == 200
-        if resp.json()["status"] in ("ended", "error"):
+    assert resp.json()["status"] == "uploaded"
-            break
-        await asyncio.sleep(1)
-    else:
-        return pytest.fail(f"Processing timed out after {timeout_seconds} seconds")
-
-    # check the transcript is ended
-    transcript = resp.json()
-    assert transcript["status"] == "ended"
-    assert transcript["short_summary"] == "LLM SHORT SUMMARY"
-    assert transcript["title"] == "Llm Title"
-
-    # check topics and transcript
-    response = await client.get(f"/transcripts/{tid}/topics")
-    assert response.status_code == 200
-    assert len(response.json()) == 1
-    assert "Hello world. How are you today?" in response.json()[0]["transcript"]