feat: add Tuner Integration to Dograh (#311)

* Add tuner integration

* bump pipecat version

* chore: update pipecat submodule to match upstream and use tuner-pipecat-sdk 0.2.0

Update pipecat submodule from 0.0.109.dev23 to 13e98d0d9 (the exact commit
upstream dograh-hq/dograh uses after v1.30.1). This installs pipecat-ai as
1.1.0.post277 via setuptools_scm, satisfying tuner-pipecat-sdk 0.2.0's
pipecat-ai>=1.0.0 requirement.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* wire tuner

* feat: refactor integrations into self contained packages

* chore: simplify ensure_public_access_token

* fix: remove NodeSpec and make DTOs the source of truth

* feat: send relevant signal to mcp using to_mcp_dict

* fix: fix tests

* cleanup: remove nango integrations

* feat: add agents.md for integrations

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Abhishek Kumar <abhishek@a6k.me>

api/tasks/campaign_tasks.py

@@ -20,7 +20,7 @@ async def sync_campaign_source(ctx: Dict, campaign_id: int) -> None:
     Phase 1: Syncs data from configured source to queued_runs table
     - Campaign state should already be 'syncing'
     - Determines source type from campaign configuration
-    - Fetches data via appropriate sync service (Google Sheets, HubSpot, etc.)
+    - Fetches data via the appropriate sync service
     - Creates queued_run entries with unique source_uuid
     - Updates campaign total_rows
     - Transitions campaign state to 'running' on success

api/tasks/run_integrations.py

@@ -1,7 +1,6 @@
 """Execute integrations (QA analysis, webhooks) after workflow run completion."""
 
 import random
-from datetime import UTC, datetime
 from typing import Any, Dict, Optional
 
 import httpx
@@ -14,6 +13,11 @@ from api.constants import BACKEND_API_ENDPOINT
 from api.db import db_client
 from api.db.models import WorkflowRunModel
 from api.enums import OrganizationConfigurationKey
+from api.services.integrations import (
+    IntegrationCompletionContext,
+    has_completion_handlers,
+    run_completion_handlers,
+)
 from api.services.pipecat.tracing_config import register_org_langfuse_credentials
 from api.services.workflow.dto import (
     QANodeData,
@@ -214,16 +218,20 @@ async def run_integrations_post_workflow_run(_ctx, workflow_run_id: int):
         nodes = workflow_definition.get("nodes", [])
         qa_nodes = [n for n in nodes if n.get("type") == "qa"]
         webhook_nodes = [n for n in nodes if n.get("type") == "webhook"]
+        has_registered_integrations = has_completion_handlers(workflow_definition)
 
-        # Step 4: Generate public access token if webhooks exist or campaign_id is set
+        # Step 4: Generate a public access token for any run that needs post-call work.
         has_campaign = workflow_run.campaign_id is not None
-        if not webhook_nodes and not qa_nodes and not has_campaign:
+        if (
+            not webhook_nodes
+            and not qa_nodes
+            and not has_registered_integrations
+            and not has_campaign
+        ):
             logger.debug("No integration nodes and no campaign, skipping")
             return
 
-        public_token = None
-        if webhook_nodes or has_campaign:
-            public_token = await db_client.ensure_public_access_token(workflow_run_id)
+        public_token = await db_client.ensure_public_access_token(workflow_run_id)
 
         # Step 5: Run QA analysis before webhooks
         if qa_nodes:
@@ -263,17 +271,37 @@ async def run_integrations_post_workflow_run(_ctx, workflow_run_id: int):
                     workflow_run_id
                 )
 
-        # Step 6: Execute webhooks
+        # Step 6: Run registered third-party integrations after uploads are complete
+        integration_results = await run_completion_handlers(
+            context=IntegrationCompletionContext(
+                workflow_run_id=workflow_run_id,
+                workflow_run=workflow_run,
+                workflow_definition=workflow_definition,
+                definition_id=definition_id,
+                organization_id=organization_id,
+                public_token=public_token,
+            )
+        )
+
+        if integration_results:
+            await db_client.update_workflow_run(
+                workflow_run_id, annotations=integration_results
+            )
+            workflow_run, _ = await db_client.get_workflow_run_with_context(
+                workflow_run_id
+            )
+
+        # Step 7: Execute webhooks
         if not webhook_nodes:
             logger.debug("No webhook nodes in workflow")
             return
 
         logger.info(f"Found {len(webhook_nodes)} webhook nodes to execute")
 
-        # Step 7: Build render context (includes annotations from QA)
+        # Step 8: Build render context (includes annotations from QA and integrations)
         render_context = _build_render_context(workflow_run, public_token)
 
-        # Step 8: Execute each webhook node
+        # Step 9: Execute each webhook node
         for node in webhook_nodes:
             node_id = node.get("id", "unknown")
             try: