finished email nodes

app.py

@@ -21,6 +21,8 @@ from datetime import datetime
 from modules.shared.configuration import APP_CONFIG
 from modules.shared.eventManagement import eventManager
 from modules.workflows.automation import subAutomationSchedule
+from modules.features.automation2.emailPoller import start as startAutomation2EmailPoller
+from modules.features.automation2.emailPoller import stop as stopAutomation2EmailPoller
 from modules.interfaces.interfaceDbApp import getRootInterface
 from modules.system.registry import loadFeatureMainModules
 
@@ -354,6 +356,7 @@ async def lifespan(app: FastAPI):
 
     # --- Init Managers ---
     subAutomationSchedule.start(eventUser)  # Automation scheduler
+    # Automation2 email poller: started on-demand when a run pauses for email.checkEmail
     eventManager.start()
     
     # Register audit log cleanup scheduler
@@ -382,6 +385,7 @@ async def lifespan(app: FastAPI):
     yield
 
     # ---  Stop Managers ---
+    stopAutomation2EmailPoller(eventUser)  # Automation2 email poller (no-op if not running)
     eventManager.stop()
     subAutomationSchedule.stop(eventUser)  # Automation scheduler
     

modules/features/automation2/emailPoller.py

@@ -0,0 +1,268 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+Background email poller for automation2.
+Checks paused runs waiting for email (email.checkEmail node) and resumes when a new matching email arrives.
+"""
+
+import asyncio
+import json
+import logging
+from datetime import datetime, timedelta, timezone
+from typing import Any, Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+# Job ID for scheduler
+POLLER_JOB_ID = "automation2_email_poller"
+POLL_INTERVAL_MINUTES = 2
+
+
+async def _pollEmailWaits(eventUser) -> None:
+    """
+    Poll for new emails for runs waiting on email.checkEmail.
+    Uses eventUser for DB access; loads owner user for each run to call readEmails.
+    Stops the poller when no runs are waiting.
+    """
+    try:
+        from modules.features.automation2.interfaceFeatureAutomation2 import getAutomation2Interface
+        from modules.features.automation2.mainAutomation2 import getAutomation2Services
+        from modules.workflows.automation2.executionEngine import executeGraph
+        from modules.workflows.processing.shared.methodDiscovery import discoverMethods
+        from modules.interfaces.interfaceDbApp import getRootInterface
+
+        root = getRootInterface()
+        if not root:
+            logger.warning("Email poller: root interface not available")
+            return
+        # Use eventUser - getRunsWaitingForEmail queries by status only
+        a2 = getAutomation2Interface(eventUser, mandateId="", featureInstanceId="")
+        runs = a2.getRunsWaitingForEmail()
+        if not runs:
+            # No workflows waiting for email - stop the poller
+            stop(eventUser)
+            return
+        logger.info("Automation2 email poller: checking %d run(s) waiting for email", len(runs))
+
+        for run in runs:
+            run_id = run.get("id")
+            workflow_id = run.get("workflowId")
+            context = run.get("context") or {}
+            wait_config = context.get("waitConfig") or {}
+            node_id = run.get("currentNodeId") or context.get("waitConfig", {}).get("_nodeId")
+            owner_id = context.get("ownerId")
+            mandate_id = context.get("mandateId")
+            instance_id = context.get("instanceId")
+            last_checked = context.get("lastCheckedAt")
+
+            if not owner_id or not mandate_id or not instance_id or not workflow_id or not node_id:
+                logger.warning("Email wait run %s missing ownerId/mandateId/instanceId/workflowId/nodeId - skipping", run_id)
+                continue
+
+            # First poll: use pausedAt (or now - 5 min) as baseline so we don't miss emails
+            # that arrived between pause and first poll
+            if last_checked is None:
+                paused_at = context.get("pausedAt")
+                if paused_at:
+                    baseline = paused_at
+                else:
+                    # Fallback: look back 5 minutes for runs created before pausedAt existed
+                    baseline = (datetime.now(timezone.utc) - timedelta(minutes=5)).strftime("%Y-%m-%dT%H:%M:%SZ")
+                last_checked = baseline
+
+            # Load owner user (root interface has broad access)
+            owner = root.getUser(owner_id) if hasattr(root, "getUser") else None
+            if not owner:
+                logger.warning("Email wait run %s: owner user %s not found", run_id, owner_id)
+                continue
+
+            # Get workflow (need scoped interface for mandate/instance)
+            a2_scoped = getAutomation2Interface(eventUser, mandateId=mandate_id, featureInstanceId=instance_id)
+            wf = a2_scoped.getWorkflow(workflow_id)
+            if not wf or not wf.get("graph"):
+                logger.warning("Email wait run %s: workflow %s not found or has no graph", run_id, workflow_id)
+                continue
+
+            # Only process runs paused at email.checkEmail – searchEmail never waits, it searches all immediately
+            nodes = (wf.get("graph") or {}).get("nodes") or []
+            paused_node = next((n for n in nodes if n.get("id") == node_id), None)
+            if paused_node and paused_node.get("type") == "email.searchEmail":
+                logger.warning("Email wait run %s: paused at email.searchEmail (should not wait) – skipping", run_id)
+                continue
+
+            services = getAutomation2Services(owner, mandateId=mandate_id, featureInstanceId=instance_id)
+            discoverMethods(services)
+
+            # Build filter with receivedDateTime – only emails received at or after baseline (new emails)
+            base_filter = wait_config.get("filter") or ""
+            dt_filter = f"receivedDateTime ge {last_checked}"
+            combined_filter = f"({base_filter}) and {dt_filter}" if base_filter else dt_filter
+            logger.debug("Email wait run %s: fetch filter (new emails only) %s", run_id, combined_filter)
+
+            from modules.workflows.processing.core.actionExecutor import ActionExecutor
+            executor = ActionExecutor(services)
+            params = {
+                "connectionReference": wait_config.get("connectionReference"),
+                "folder": wait_config.get("folder", "Inbox"),
+                "limit": min(int(wait_config.get("limit", 10)), 50),
+                "filter": combined_filter,
+            }
+
+            try:
+                result = await executor.executeAction("outlook", "readEmails", params)
+            except Exception as e:
+                logger.warning("Email wait run %s: readEmails failed: %s", run_id, e)
+                continue
+
+            # readEmails always returns 1 document (JSON wrapper); check actual email count
+            email_count = 0
+            if result and result.documents:
+                doc = result.documents[0]
+                meta = getattr(doc, "validationMetadata", None)
+                if not meta and isinstance(doc, dict):
+                    meta = doc.get("validationMetadata")
+                if meta and isinstance(meta, dict):
+                    email_count = int(meta.get("emailCount", 0))
+                else:
+                    try:
+                        data = json.loads(getattr(doc, "documentData", "") or "{}")
+                        email_count = len(data.get("emails", {}).get("emails", []))
+                    except Exception:
+                        pass
+
+            if not result or not result.success or email_count == 0:
+                # No new emails - persist lastCheckedAt so next poll uses this as baseline
+                now_iso = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+                ctx = dict(context)
+                ctx["lastCheckedAt"] = now_iso
+                a2_scoped.updateRun(run_id, context=ctx)
+                continue
+
+            # Only pass NEW emails (receivedDateTime >= last_checked) – filter server-side as safeguard
+            doc = result.documents[0]
+            raw_data = json.loads(getattr(doc, "documentData", "") or "{}")
+            emails_data = raw_data.get("emails", {})
+            all_emails = emails_data.get("emails", [])
+            new_emails = [
+                e for e in all_emails
+                if (e.get("receivedDateTime") or "") >= last_checked
+            ]
+            if not new_emails:
+                now_iso = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+                ctx = dict(context)
+                ctx["lastCheckedAt"] = now_iso
+                a2_scoped.updateRun(run_id, context=ctx)
+                continue
+
+            # Rebuild document with only new emails for downstream nodes
+            result_data = dict(raw_data)
+            result_data["emails"] = dict(emails_data)
+            result_data["emails"]["emails"] = new_emails
+            result_data["emails"]["count"] = len(new_emails)
+
+            from modules.datamodels.datamodelChat import ActionDocument
+            filtered_doc = ActionDocument(
+                documentName=getattr(doc, "documentName", "outlook_emails.json"),
+                documentData=json.dumps(result_data, indent=2),
+                mimeType=getattr(doc, "mimeType", "application/json"),
+                validationMetadata={**(getattr(doc, "validationMetadata") or {}), "emailCount": len(new_emails)},
+            )
+
+            # Build node output in same format as ActionNodeExecutor for readEmails
+            node_output = {
+                "success": result.success,
+                "error": result.error,
+                "documents": [filtered_doc.model_dump() if hasattr(filtered_doc, "model_dump") else filtered_doc],
+                "data": result.model_dump() if hasattr(result, "model_dump") else {"success": result.success, "error": result.error},
+            }
+
+            # Update lastCheckedAt before resume
+            now_iso = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+            ctx = dict(context)
+            ctx["lastCheckedAt"] = now_iso
+            a2_scoped.updateRun(run_id, status="running", context=ctx)
+
+            node_outputs = dict(run.get("nodeOutputs") or {})
+            node_outputs[node_id] = node_output
+
+            logger.info("Email wait run %s: found new email, resuming from node %s", run_id, node_id)
+
+            resume_result = await executeGraph(
+                graph=wf["graph"],
+                services=services,
+                workflowId=workflow_id,
+                instanceId=instance_id,
+                userId=owner_id,
+                mandateId=mandate_id,
+                automation2_interface=a2_scoped,
+                initialNodeOutputs=node_outputs,
+                startAfterNodeId=node_id,
+                runId=run_id,
+            )
+
+            if resume_result.get("success"):
+                logger.info("Email wait run %s: completed successfully", run_id)
+            elif resume_result.get("paused"):
+                logger.info("Email wait run %s: paused again (e.g. human task)", run_id)
+            else:
+                logger.warning("Email wait run %s: failed: %s", run_id, resume_result.get("error"))
+
+    except Exception as e:
+        logger.exception("Email poller failed: %s", e)
+
+
+def _runPollSync(ev_user):
+    """Sync job for scheduler - runs async poll. Thread-safe for both main loop and worker threads."""
+    try:
+        try:
+            loop = asyncio.get_running_loop()
+            # Already in event loop - schedule, don't block
+            loop.create_task(_pollEmailWaits(ev_user))
+        except RuntimeError:
+            # No running loop (worker thread) - run in new loop
+            asyncio.run(_pollEmailWaits(ev_user))
+    except Exception as e:
+        logger.exception("Automation2 email poller job failed: %s", e)
+
+
+def ensureRunning(eventUser) -> bool:
+    """Start the poller if not already running. Called when a run pauses for email.checkEmail."""
+    return start(eventUser)
+
+
+def start(eventUser) -> bool:
+    """Register the email poller interval job."""
+    if not eventUser:
+        logger.warning("Automation2 email poller: no eventUser, not registering")
+        return False
+    try:
+        from modules.shared.eventManagement import eventManager
+
+        # Use sync wrapper - APScheduler may run jobs in thread pool where async doesn't work
+        job_func = lambda: _runPollSync(eventUser)
+        eventManager.registerInterval(
+            POLLER_JOB_ID,
+            job_func,
+            seconds=0,
+            minutes=POLL_INTERVAL_MINUTES,
+            hours=0,
+        )
+        logger.info("Automation2 email poller started (interval=%s min)", POLL_INTERVAL_MINUTES)
+        # Run once immediately so we don't wait 2 minutes for the first check
+        _runPollSync(eventUser)
+        return True
+    except Exception as e:
+        logger.error("Failed to register automation2 email poller: %s", e)
+        return False
+
+
+def stop(eventUser) -> bool:
+    """Remove the email poller job."""
+    try:
+        from modules.shared.eventManagement import eventManager
+        eventManager.remove(POLLER_JOB_ID)
+        logger.info("Automation2 email poller removed")
+        return True
+    except Exception as e:
+        logger.warning("Error removing automation2 email poller: %s", e)
+        return True

modules/features/automation2/interfaceFeatureAutomation2.py

@@ -194,6 +194,24 @@ class Automation2Objects:
         )
         return [dict(r) for r in records] if records else []
 
+    def getRunsWaitingForEmail(self) -> List[Dict[str, Any]]:
+        """Get all paused runs waiting for a new email (for background poller)."""
+        if not self.db._ensureTableExists(Automation2WorkflowRun):
+            return []
+        records = self.db.getRecordset(
+            Automation2WorkflowRun,
+            recordFilter={"status": "paused"},
+        )
+        if not records:
+            return []
+        result = []
+        for r in records:
+            rec = dict(r)
+            ctx = rec.get("context") or {}
+            if ctx.get("waitReason") == "email":
+                result.append(rec)
+        return result
+
     # -------------------------------------------------------------------------
     # Human Tasks
     # -------------------------------------------------------------------------

modules/features/automation2/mainAutomation2.py

@@ -29,9 +29,14 @@ UI_OBJECTS = [
         "label": {"en": "Editor", "de": "Editor", "fr": "Éditeur"},
         "meta": {"area": "editor"}
     },
+    {
+        "objectKey": "ui.feature.automation2.workflows",
+        "label": {"en": "Workflows", "de": "Workflows", "fr": "Workflows"},
+        "meta": {"area": "workflows"}
+    },
     {
         "objectKey": "ui.feature.automation2.workflows-tasks",
-        "label": {"en": "Workflows & Tasks", "de": "Workflows & Tasks", "fr": "Workflows et tâches"},
+        "label": {"en": "Tasks", "de": "Tasks", "fr": "Tâches"},
         "meta": {"area": "tasks"}
     },
 ]
@@ -64,6 +69,7 @@ TEMPLATE_ROLES = [
         },
         "accessRules": [
             {"context": "UI", "item": "ui.feature.automation2.editor", "view": True},
+            {"context": "UI", "item": "ui.feature.automation2.workflows", "view": True},
             {"context": "UI", "item": "ui.feature.automation2.workflows-tasks", "view": True},
             {"context": "RESOURCE", "item": "resource.feature.automation2.dashboard", "view": True},
             {"context": "RESOURCE", "item": "resource.feature.automation2.node-types", "view": True},
@@ -94,7 +100,11 @@ def getAutomation2Services(
 
     _workflow = workflow
     if _workflow is None:
-        _workflow = type("_Placeholder", (), {"featureCode": FEATURE_CODE, "id": None, "workflowMode": None})()
+        _workflow = type(
+            "_Placeholder",
+            (),
+            {"featureCode": FEATURE_CODE, "id": None, "workflowMode": None, "messages": []},
+        )()
 
     ctx = ServiceCenterContext(
         user=user,
@@ -149,6 +159,16 @@ class _Automation2ServiceHub:
     sharepoint = None
 
 
+async def onStart(eventUser) -> None:
+    """Feature startup. Email poller is started on-demand when a run pauses for email.checkEmail."""
+
+
+async def onStop(eventUser) -> None:
+    """Feature shutdown - remove email poller if running."""
+    from modules.features.automation2.emailPoller import stop as stopEmailPoller
+    stopEmailPoller(eventUser)
+
+
 def getFeatureDefinition() -> Dict[str, Any]:
     """Return the feature definition for registration."""
     return {

modules/features/automation2/nodeDefinitions/__init__.py

@@ -5,5 +5,16 @@ from .triggers import TRIGGER_NODES
 from .flow import FLOW_NODES
 from .data import DATA_NODES
 from .input import INPUT_NODES
+from .ai import AI_NODES
+from .email import EMAIL_NODES
+from .sharepoint import SHAREPOINT_NODES
 
-STATIC_NODE_TYPES = TRIGGER_NODES + FLOW_NODES + DATA_NODES + INPUT_NODES
+STATIC_NODE_TYPES = (
+    TRIGGER_NODES
+    + FLOW_NODES
+    + DATA_NODES
+    + INPUT_NODES
+    + AI_NODES
+    + EMAIL_NODES
+    + SHAREPOINT_NODES
+)

modules/features/automation2/nodeDefinitions/ai.py

@@ -0,0 +1,113 @@
+# Copyright (c) 2025 Patrick Motsch
+# AI node definitions - map to methodAi actions.
+
+AI_NODES = [
+    {
+        "id": "ai.prompt",
+        "category": "ai",
+        "label": {"en": "Prompt", "de": "Prompt", "fr": "Invite"},
+        "description": {"en": "Enter a prompt and AI does something", "de": "Prompt eingeben und KI führt aus", "fr": "Entrer une invite et l'IA exécute"},
+        "parameters": [
+            {"name": "prompt", "type": "string", "required": True, "description": {"en": "AI prompt", "de": "KI-Prompt", "fr": "Invite IA"}},
+            {"name": "resultType", "type": "string", "required": False, "description": {"en": "Output format (txt, json, md, etc.)", "de": "Ausgabeformat", "fr": "Format de sortie"}, "default": "txt"},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-robot", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "process",
+        "_paramMap": {"prompt": "aiPrompt"},
+    },
+    {
+        "id": "ai.webResearch",
+        "category": "ai",
+        "label": {"en": "Web Research", "de": "Web-Recherche", "fr": "Recherche web"},
+        "description": {"en": "Research on the web", "de": "Recherche im Web", "fr": "Recherche sur le web"},
+        "parameters": [
+            {"name": "query", "type": "string", "required": True, "description": {"en": "Research query", "de": "Recherche-Anfrage", "fr": "Requête de recherche"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-magnify", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "webResearch",
+        "_paramMap": {"query": "prompt"},
+    },
+    {
+        "id": "ai.summarizeDocument",
+        "category": "ai",
+        "label": {"en": "Summarize Document", "de": "Dokument zusammenfassen", "fr": "Résumer document"},
+        "description": {"en": "Summarize document content", "de": "Dokumentinhalt zusammenfassen", "fr": "Résumer le contenu du document"},
+        "parameters": [
+            {"name": "summaryLength", "type": "string", "required": False, "description": {"en": "Short, medium, or long", "de": "Kurz, mittel oder lang", "fr": "Court, moyen ou long"}, "default": "medium"},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-file-document-outline", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "summarizeDocument",
+        "_paramMap": {},
+    },
+    {
+        "id": "ai.translateDocument",
+        "category": "ai",
+        "label": {"en": "Translate Document", "de": "Dokument übersetzen", "fr": "Traduire document"},
+        "description": {"en": "Translate document to target language", "de": "Dokument in Zielsprache übersetzen", "fr": "Traduire le document"},
+        "parameters": [
+            {"name": "targetLanguage", "type": "string", "required": True, "description": {"en": "Target language (e.g. en, de, fr)", "de": "Zielsprache", "fr": "Langue cible"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-translate", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "translateDocument",
+        "_paramMap": {"targetLanguage": "targetLanguage"},
+    },
+    {
+        "id": "ai.convertDocument",
+        "category": "ai",
+        "label": {"en": "Convert Document", "de": "Dokument konvertieren", "fr": "Convertir document"},
+        "description": {"en": "Convert document to another format", "de": "Dokument in anderes Format konvertieren", "fr": "Convertir le document"},
+        "parameters": [
+            {"name": "targetFormat", "type": "string", "required": True, "description": {"en": "Target format (pdf, docx, txt, etc.)", "de": "Zielformat", "fr": "Format cible"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-file-convert", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "convertDocument",
+        "_paramMap": {"targetFormat": "targetFormat"},
+    },
+    {
+        "id": "ai.generateDocument",
+        "category": "ai",
+        "label": {"en": "Generate Document", "de": "Dokument generieren", "fr": "Générer document"},
+        "description": {"en": "Generate document from prompt", "de": "Dokument aus Prompt generieren", "fr": "Générer un document"},
+        "parameters": [
+            {"name": "prompt", "type": "string", "required": True, "description": {"en": "Generation prompt", "de": "Generierungs-Prompt", "fr": "Invite de génération"}},
+            {"name": "format", "type": "string", "required": False, "description": {"en": "Output format", "de": "Ausgabeformat", "fr": "Format de sortie"}, "default": "docx"},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-file-plus", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "generateDocument",
+        "_paramMap": {"prompt": "prompt", "format": "format"},
+    },
+    {
+        "id": "ai.generateCode",
+        "category": "ai",
+        "label": {"en": "Generate Code", "de": "Code generieren", "fr": "Générer code"},
+        "description": {"en": "Generate code from description", "de": "Code aus Beschreibung generieren", "fr": "Générer du code"},
+        "parameters": [
+            {"name": "prompt", "type": "string", "required": True, "description": {"en": "Code generation prompt", "de": "Code-Generierungs-Prompt", "fr": "Invite de génération de code"}},
+            {"name": "language", "type": "string", "required": False, "description": {"en": "Programming language", "de": "Programmiersprache", "fr": "Langage de programmation"}, "default": "python"},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-code-tags", "color": "#9C27B0"},
+        "_method": "ai",
+        "_action": "generateCode",
+        "_paramMap": {"prompt": "prompt", "language": "language"},
+    },
+]

modules/features/automation2/nodeDefinitions/email.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2025 Patrick Motsch
+# Email node definitions - map to methodOutlook actions.
+# Use connectionId from user connections (like AI workspace sources).
+
+EMAIL_NODES = [
+    {
+        "id": "email.checkEmail",
+        "category": "email",
+        "label": {"en": "Check Email", "de": "E-Mail prüfen", "fr": "Vérifier email"},
+        "description": {"en": "Check for new emails (general or from specific account)", "de": "Neue E-Mails prüfen", "fr": "Vérifier les nouveaux emails"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "Email account connection", "de": "E-Mail-Konto Verbindung", "fr": "Connexion compte email"}},
+            {"name": "folder", "type": "string", "required": False, "description": {"en": "Folder (e.g. Inbox)", "de": "Ordner (z.B. Posteingang)", "fr": "Dossier (ex. Boîte de réception)"}, "default": "Inbox"},
+            {"name": "limit", "type": "number", "required": False, "description": {"en": "Max emails to fetch", "de": "Max E-Mails", "fr": "Max emails"}, "default": 100},
+            {"name": "fromAddress", "type": "string", "required": False, "description": {"en": "Only emails from this address", "de": "Nur E-Mails von dieser Adresse", "fr": "Seulement les e-mails de cette adresse"}, "default": ""},
+            {"name": "subjectContains", "type": "string", "required": False, "description": {"en": "Subject must contain this text", "de": "Betreff muss diesen Text enthalten", "fr": "Le sujet doit contenir ce texte"}, "default": ""},
+            {"name": "hasAttachment", "type": "boolean", "required": False, "description": {"en": "Only emails with attachments", "de": "Nur E-Mails mit Anhängen", "fr": "Seulement les e-mails avec pièces jointes"}, "default": False},
+            {"name": "filter", "type": "string", "required": False, "description": {"en": "Advanced: raw filter (overrides above if set)", "de": "Erweitert: Filter-Text (überschreibt obige)", "fr": "Avancé: filtre brut"}, "default": ""},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-email-check", "color": "#1976D2"},
+        "_method": "outlook",
+        "_action": "readEmails",
+        "_paramMap": {"connectionId": "connectionReference", "folder": "folder", "limit": "limit", "filter": "filter"},
+    },
+    {
+        "id": "email.searchEmail",
+        "category": "email",
+        "label": {"en": "Search Email", "de": "E-Mail suchen", "fr": "Rechercher email"},
+        "description": {"en": "Search or find emails", "de": "E-Mails suchen oder finden", "fr": "Rechercher des emails"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "Email account connection", "de": "E-Mail-Konto Verbindung", "fr": "Connexion compte email"}},
+            {"name": "query", "type": "string", "required": False, "description": {"en": "General search term (searches subject, body, from)", "de": "Suchbegriff (durchsucht Betreff, Inhalt, Absender)", "fr": "Terme de recherche (sujet, corps, expéditeur)"}, "default": ""},
+            {"name": "folder", "type": "string", "required": False, "description": {"en": "Folder to search", "de": "Ordner zum Suchen", "fr": "Dossier à rechercher"}, "default": "Inbox"},
+            {"name": "limit", "type": "number", "required": False, "description": {"en": "Max emails to return", "de": "Max E-Mails", "fr": "Max emails"}, "default": 100},
+            {"name": "fromAddress", "type": "string", "required": False, "description": {"en": "Only emails from this address", "de": "Nur E-Mails von dieser Adresse", "fr": "Seulement les e-mails de cette adresse"}, "default": ""},
+            {"name": "toAddress", "type": "string", "required": False, "description": {"en": "Only emails to this recipient", "de": "Nur E-Mails an diesen Empfänger", "fr": "Seulement les e-mails à ce destinataire"}, "default": ""},
+            {"name": "subjectContains", "type": "string", "required": False, "description": {"en": "Subject must contain this text", "de": "Betreff muss diesen Text enthalten", "fr": "Le sujet doit contenir ce texte"}, "default": ""},
+            {"name": "bodyContains", "type": "string", "required": False, "description": {"en": "Body/content must contain this text", "de": "Inhalt muss diesen Text enthalten", "fr": "Le corps doit contenir ce texte"}, "default": ""},
+            {"name": "hasAttachment", "type": "boolean", "required": False, "description": {"en": "Only emails with attachments", "de": "Nur E-Mails mit Anhängen", "fr": "Seulement les e-mails avec pièces jointes"}, "default": False},
+            {"name": "filter", "type": "string", "required": False, "description": {"en": "Advanced: raw KQL (overrides above if set)", "de": "Erweitert: KQL-Filter (überschreibt obige)", "fr": "Avancé: filtre KQL brut"}, "default": ""},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-email-search", "color": "#1976D2"},
+        "_method": "outlook",
+        "_action": "searchEmails",
+        "_paramMap": {"connectionId": "connectionReference", "query": "query", "folder": "folder", "limit": "limit", "filter": "filter"},
+    },
+    {
+        "id": "email.draftEmail",
+        "category": "email",
+        "label": {"en": "Draft Email", "de": "E-Mail entwerfen", "fr": "Brouillon email"},
+        "description": {"en": "Create a draft email", "de": "E-Mail-Entwurf erstellen", "fr": "Créer un brouillon d'email"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "Email account connection", "de": "E-Mail-Konto Verbindung", "fr": "Connexion compte email"}},
+            {"name": "subject", "type": "string", "required": True, "description": {"en": "Email subject", "de": "E-Mail-Betreff", "fr": "Sujet"}},
+            {"name": "body", "type": "string", "required": True, "description": {"en": "Email body", "de": "E-Mail-Text", "fr": "Corps de l'email"}},
+            {"name": "to", "type": "string", "required": False, "description": {"en": "Recipient(s)", "de": "Empfänger", "fr": "Destinataire(s)"}, "default": ""},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-email-edit", "color": "#1976D2"},
+        "_method": "outlook",
+        "_action": "composeAndDraftEmailWithContext",
+        "_paramMap": {"connectionId": "connectionReference", "to": "to"},
+        "_contextFrom": ["subject", "body"],
+    },
+]

modules/features/automation2/nodeDefinitions/sharepoint.py

@@ -0,0 +1,111 @@
+# Copyright (c) 2025 Patrick Motsch
+# SharePoint node definitions - map to methodSharepoint actions.
+# Use connectionId and path from connection selector (like workflow folder view).
+
+SHAREPOINT_NODES = [
+    {
+        "id": "sharepoint.findFile",
+        "category": "sharepoint",
+        "label": {"en": "Find File", "de": "Datei finden", "fr": "Trouver fichier"},
+        "description": {"en": "Find file by path or search", "de": "Datei nach Pfad oder Suche finden", "fr": "Trouver fichier par chemin ou recherche"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "SharePoint connection", "de": "SharePoint-Verbindung", "fr": "Connexion SharePoint"}},
+            {"name": "searchQuery", "type": "string", "required": True, "description": {"en": "Search query or path", "de": "Suchanfrage oder Pfad", "fr": "Requête ou chemin"}},
+            {"name": "site", "type": "string", "required": False, "description": {"en": "Optional site hint", "de": "Optionaler Site-Hinweis", "fr": "Indication de site"}, "default": ""},
+            {"name": "maxResults", "type": "number", "required": False, "description": {"en": "Max results", "de": "Max Ergebnisse", "fr": "Max résultats"}, "default": 1000},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-file-search", "color": "#0078D4"},
+        "_method": "sharepoint",
+        "_action": "findDocumentPath",
+        "_paramMap": {"connectionId": "connectionReference", "searchQuery": "searchQuery", "site": "site", "maxResults": "maxResults"},
+    },
+    {
+        "id": "sharepoint.readFile",
+        "category": "sharepoint",
+        "label": {"en": "Read File", "de": "Datei lesen", "fr": "Lire fichier"},
+        "description": {"en": "Extract content from file", "de": "Inhalt aus Datei extrahieren", "fr": "Extraire le contenu du fichier"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "SharePoint connection", "de": "SharePoint-Verbindung", "fr": "Connexion SharePoint"}},
+            {"name": "path", "type": "string", "required": True, "description": {"en": "File path or documentList from find file", "de": "Dateipfad oder documentList von Find", "fr": "Chemin ou documentList"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-file-document", "color": "#0078D4"},
+        "_method": "sharepoint",
+        "_action": "readDocuments",
+        "_paramMap": {"connectionId": "connectionReference", "path": "pathQuery"},
+    },
+    {
+        "id": "sharepoint.uploadFile",
+        "category": "sharepoint",
+        "label": {"en": "Upload File", "de": "Datei hochladen", "fr": "Téléverser fichier"},
+        "description": {"en": "Upload file to SharePoint", "de": "Datei zu SharePoint hochladen", "fr": "Téléverser fichier vers SharePoint"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "SharePoint connection", "de": "SharePoint-Verbindung", "fr": "Connexion SharePoint"}},
+            {"name": "folderPath", "type": "string", "required": True, "description": {"en": "Target folder path", "de": "Zielordner-Pfad", "fr": "Chemin du dossier cible"}},
+            {"name": "siteId", "type": "string", "required": True, "description": {"en": "SharePoint site ID", "de": "SharePoint Site-ID", "fr": "ID du site SharePoint"}},
+            {"name": "fileName", "type": "string", "required": True, "description": {"en": "File name", "de": "Dateiname", "fr": "Nom du fichier"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-upload", "color": "#0078D4"},
+        "_method": "sharepoint",
+        "_action": "uploadFile",
+        "_paramMap": {"connectionId": "connectionReference", "folderPath": "folderPath", "siteId": "siteId", "fileName": "fileName"},
+    },
+    {
+        "id": "sharepoint.listFiles",
+        "category": "sharepoint",
+        "label": {"en": "List Files", "de": "Dateien auflisten", "fr": "Lister fichiers"},
+        "description": {"en": "List files in folder or SharePoint", "de": "Dateien in Ordner oder SharePoint auflisten", "fr": "Lister les fichiers dans un dossier"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "SharePoint connection", "de": "SharePoint-Verbindung", "fr": "Connexion SharePoint"}},
+            {"name": "path", "type": "string", "required": False, "description": {"en": "Folder path (e.g. /sites/SiteName/Shared Documents)", "de": "Ordnerpfad", "fr": "Chemin du dossier"}, "default": "/"},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-folder-open", "color": "#0078D4"},
+        "_method": "sharepoint",
+        "_action": "listDocuments",
+        "_paramMap": {"connectionId": "connectionReference", "path": "pathQuery"},
+    },
+    {
+        "id": "sharepoint.downloadFile",
+        "category": "sharepoint",
+        "label": {"en": "Download File", "de": "Datei herunterladen", "fr": "Télécharger fichier"},
+        "description": {"en": "Download file from path", "de": "Datei vom Pfad herunterladen", "fr": "Télécharger le fichier"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "SharePoint connection", "de": "SharePoint-Verbindung", "fr": "Connexion SharePoint"}},
+            {"name": "siteId", "type": "string", "required": True, "description": {"en": "SharePoint site ID", "de": "SharePoint Site-ID", "fr": "ID du site"}},
+            {"name": "filePath", "type": "string", "required": True, "description": {"en": "File path", "de": "Dateipfad", "fr": "Chemin du fichier"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-download", "color": "#0078D4"},
+        "_method": "sharepoint",
+        "_action": "downloadFileByPath",
+        "_paramMap": {"connectionId": "connectionReference", "siteId": "siteId", "filePath": "filePath"},
+    },
+    {
+        "id": "sharepoint.copyFile",
+        "category": "sharepoint",
+        "label": {"en": "Copy File", "de": "Datei kopieren", "fr": "Copier fichier"},
+        "description": {"en": "Copy file to destination", "de": "Datei an Ziel kopieren", "fr": "Copier le fichier"},
+        "parameters": [
+            {"name": "connectionId", "type": "string", "required": True, "description": {"en": "SharePoint connection", "de": "SharePoint-Verbindung", "fr": "Connexion SharePoint"}},
+            {"name": "siteId", "type": "string", "required": True, "description": {"en": "SharePoint site ID", "de": "SharePoint Site-ID", "fr": "ID du site"}},
+            {"name": "sourceFolder", "type": "string", "required": True, "description": {"en": "Source folder path", "de": "Quellordner-Pfad", "fr": "Chemin dossier source"}},
+            {"name": "sourceFile", "type": "string", "required": True, "description": {"en": "Source file name", "de": "Quelldatei-Name", "fr": "Nom fichier source"}},
+            {"name": "destFolder", "type": "string", "required": True, "description": {"en": "Destination folder path", "de": "Zielordner-Pfad", "fr": "Chemin dossier cible"}},
+            {"name": "destFile", "type": "string", "required": True, "description": {"en": "Destination file name", "de": "Zieldatei-Name", "fr": "Nom fichier cible"}},
+        ],
+        "inputs": 1,
+        "outputs": 1,
+        "meta": {"icon": "mdi-content-copy", "color": "#0078D4"},
+        "_method": "sharepoint",
+        "_action": "copyFile",
+        "_paramMap": {"connectionId": "connectionReference", "siteId": "siteId", "sourceFolder": "sourceFolder", "sourceFile": "sourceFile", "destFolder": "destFolder", "destFile": "destFile"},
+    },
+]

modules/features/automation2/nodeRegistry.py

@@ -1,138 +1,37 @@
 # Copyright (c) 2025 Patrick Motsch
 # All rights reserved.
 """
-Node Type Registry for automation2 - merges static definitions with dynamic I/O nodes from methodDiscovery.
+Node Type Registry for automation2 - static node definitions (ai, email, sharepoint, trigger, flow, data, input).
+Nodes are defined first; IO/method actions are used at execution time.
 """
 
 import logging
-from typing import Dict, List, Any, Optional
+from typing import Dict, List, Any
 
 from modules.features.automation2.nodeDefinitions import STATIC_NODE_TYPES
 
 logger = logging.getLogger(__name__)
 
-# Short method names that map to I/O node category display
-METHOD_LABELS = {
-    "outlook": {"en": "Outlook", "de": "Outlook", "fr": "Outlook"},
-    "sharepoint": {"en": "SharePoint", "de": "SharePoint", "fr": "SharePoint"},
-    "context": {"en": "Context", "de": "Kontext", "fr": "Contexte"},
-    "ai": {"en": "AI", "de": "KI", "fr": "IA"},
-    "trustee": {"en": "Trustee", "de": "Trustee", "fr": "Trustee"},
-    "jira": {"en": "Jira", "de": "Jira", "fr": "Jira"},
-    "chatbot": {"en": "Chatbot", "de": "Chatbot", "fr": "Chatbot"},
-}
-
-
-def _actionNameToLabel(actionName: str) -> str:
-    """Convert camelCase actionName to readable label."""
-    import re
-    parts = re.sub(r"([A-Z])", r" \1", actionName).strip().split()
-    return " ".join(p.capitalize() for p in parts) if parts else actionName
-
-
-def _buildIoNodeFromAction(
-    shortMethod: str,
-    actionName: str,
-    actionDef: Dict[str, Any],
-    language: str = "en",
-) -> Dict[str, Any]:
-    """Build a single I/O node definition from a method action."""
-    lang = language if language in ("en", "de", "fr") else "en"
-    methodLabel = METHOD_LABELS.get(shortMethod, {}).get(lang, shortMethod)
-    actionLabel = _actionNameToLabel(actionName)
-    nodeId = f"io.{shortMethod}.{actionName}"
-    nodeLabel = {l: f"{METHOD_LABELS.get(shortMethod, {}).get(l, shortMethod)} - {_actionNameToLabel(actionName)}" for l in ("en", "de", "fr")}
-
-    parameters = []
-    paramDefs = actionDef.get("parameters", {})
-    for paramName, paramInfo in paramDefs.items():
-        if isinstance(paramInfo, dict):
-            p = {
-                "name": paramName,
-                "type": paramInfo.get("type", "str"),
-                "required": paramInfo.get("required", False),
-                "description": paramInfo.get("description", ""),
-            }
-            if paramInfo.get("default") is not None:
-                p["default"] = paramInfo["default"]
-            parameters.append(p)
-        else:
-            parameters.append({
-                "name": paramName,
-                "type": "str",
-                "required": False,
-                "description": str(paramInfo),
-            })
-
-    return {
-        "id": nodeId,
-        "category": "io",
-        "label": nodeLabel,
-        "description": actionDef.get("description") or nodeLabel,
-        "parameters": parameters,
-        "inputs": 1,
-        "outputs": 1,
-        "executor": "io",
-        "meta": {"icon": "mdi-connection", "color": "#00BCD4", "method": shortMethod, "action": actionName},
-    }
-
-
-def getIoNodesFromMethods(methods: Dict[str, Any], language: str = "en") -> List[Dict[str, Any]]:
-    """
-    Build I/O node types from methodDiscovery.methods.
-    methods: { methodName: { instance, actions: { actionName: { description, parameters, method } } } }
-    Returns list of node definitions for io.{shortMethod}.{actionName}.
-    """
-    ioNodes = []
-    processed = set()
-
-    for methodName, methodInfo in methods.items():
-        if not methodName.startswith("Method"):
-            continue
-        shortMethod = methodName.replace("Method", "").lower()
-        if shortMethod in processed:
-            continue
-        processed.add(shortMethod)
-
-        methodInstance = methodInfo.get("instance")
-        if not methodInstance:
-            continue
-
-        actions = methodInstance.actions
-        for actionName, actionDef in actions.items():
-            if not isinstance(actionDef, dict):
-                continue
-            try:
-                node = _buildIoNodeFromAction(shortMethod, actionName, actionDef, language)
-                ioNodes.append(node)
-            except Exception as e:
-                logger.warning(f"Failed to build I/O node io.{shortMethod}.{actionName}: {e}")
-                continue
-
-    return ioNodes
-
 
 def getNodeTypes(
-    services: Any,
+    services: Any = None,
     language: str = "en",
 ) -> List[Dict[str, Any]]:
     """
-    Return merged node types: static (trigger, flow, data) + dynamic I/O nodes from methodDiscovery.
-    services: Hub from getAutomation2Services (needed for discoverMethods + RBAC-filtered actions).
+    Return static node types. No dynamic I/O derivation from methodDiscovery.
+    services: Optional (kept for API compatibility, not used).
     """
-    from modules.workflows.processing.shared.methodDiscovery import discoverMethods, methods
-
-    discoverMethods(services)
-
-    static = list(STATIC_NODE_TYPES)
-    ioNodes = getIoNodesFromMethods(methods, language)
-    return static + ioNodes
+    return list(STATIC_NODE_TYPES)
 
 
 def _localizeNode(node: Dict[str, Any], language: str) -> Dict[str, Any]:
     """Apply language to label/description/parameters."""
     lang = language if language in ("en", "de", "fr") else "en"
     out = dict(node)
+    # Strip internal keys for API response
+    for key in list(out.keys()):
+        if key.startswith("_"):
+            del out[key]
     if isinstance(node.get("label"), dict):
         out["label"] = node["label"].get(lang, node["label"].get("en", str(node["label"])))
     if isinstance(node.get("description"), dict):
@@ -161,6 +60,22 @@ def getNodeTypesForApi(
         {"id": "input", "label": {"en": "Input/Human", "de": "Eingabe/Mensch", "fr": "Entrée/Humain"}},
         {"id": "flow", "label": {"en": "Flow", "de": "Ablauf", "fr": "Flux"}},
         {"id": "data", "label": {"en": "Data", "de": "Daten", "fr": "Données"}},
-        {"id": "io", "label": {"en": "I/O", "de": "E/A", "fr": "E/S"}},
+        {"id": "ai", "label": {"en": "AI", "de": "KI", "fr": "IA"}},
+        {"id": "email", "label": {"en": "Email", "de": "E-Mail", "fr": "Email"}},
+        {"id": "sharepoint", "label": {"en": "SharePoint", "de": "SharePoint", "fr": "SharePoint"}},
     ]
     return {"nodeTypes": localized, "categories": categories}
+
+
+def getNodeTypeToMethodAction() -> Dict[str, tuple]:
+    """
+    Mapping from node type id to (method, action) for execution.
+    Used by ActionNodeExecutor.
+    """
+    mapping = {}
+    for node in STATIC_NODE_TYPES:
+        method = node.get("_method")
+        action = node.get("_action")
+        if method and action:
+            mapping[node["id"]] = (method, action)
+    return mapping

modules/features/automation2/routeFeatureAutomation2.py

@@ -1,11 +1,12 @@
 # Copyright (c) 2025 Patrick Motsch
 # All rights reserved.
 """
-Automation2 routes - node-types, execute, workflows, runs, tasks.
+Automation2 routes - node-types, execute, workflows, runs, tasks, connections, browse.
 """
 
 import logging
 from fastapi import APIRouter, Depends, Path, Query, Body, Request, HTTPException
+from fastapi.responses import JSONResponse
 from modules.auth import limiter, getRequestContext, RequestContext
 
 from modules.features.automation2.mainAutomation2 import getAutomation2Services
@@ -101,6 +102,10 @@ async def post_execute(
         mandateId=mandateId,
         featureInstanceId=instanceId,
     )
+    # Ensure workflow methods (outlook, ai, sharepoint, etc.) are discovered for ActionExecutor
+    from modules.workflows.processing.shared.methodDiscovery import discoverMethods
+    discoverMethods(services)
+
     graph = body.get("graph") or body
     workflowId = body.get("workflowId")
     if workflowId:
@@ -109,6 +114,12 @@ async def post_execute(
         if wf and wf.get("graph"):
             graph = wf["graph"]
             logger.info("automation2 execute: loaded graph from workflow %s", workflowId)
+    # Use transient workflowId when none provided (e.g. execute from editor without save)
+    # Required for email.checkEmail pause/resume - run must be created
+    if not workflowId:
+        import uuid
+        workflowId = f"transient-{uuid.uuid4().hex[:12]}"
+        logger.info("automation2 execute: using transient workflowId=%s", workflowId)
     nodes_count = len(graph.get("nodes") or [])
     connections_count = len(graph.get("connections") or [])
     logger.info(
@@ -118,7 +129,7 @@ async def post_execute(
         workflowId,
         mandateId,
     )
-    a2_interface = getAutomation2Interface(context.user, mandateId, instanceId) if workflowId else None
+    a2_interface = getAutomation2Interface(context.user, mandateId, instanceId)
     result = await executeGraph(
         graph=graph,
         services=services,
@@ -139,11 +150,190 @@ async def post_execute(
     return result
 
 
+# -------------------------------------------------------------------------
+# Connections and Browse (for Email/SharePoint node config - like workspace)
+# -------------------------------------------------------------------------
+
+
+def _buildResolverDbInterface(chatService):
+    """Build a DB adapter that ConnectorResolver can use to load UserConnections."""
+    class _ResolverDbAdapter:
+        def __init__(self, appInterface):
+            self._app = appInterface
+
+        def getUserConnection(self, connectionId: str):
+            if hasattr(self._app, "getUserConnectionById"):
+                return self._app.getUserConnectionById(connectionId)
+            return None
+
+    appIf = getattr(chatService, "interfaceDbApp", None)
+    if appIf:
+        return _ResolverDbAdapter(appIf)
+    return getattr(chatService, "interfaceDbComponent", None)
+
+
+@router.get("/{instanceId}/connections")
+@limiter.limit("300/minute")
+def list_automation2_connections(
+    request: Request,
+    instanceId: str = Path(..., description="Feature instance ID"),
+    context: RequestContext = Depends(getRequestContext),
+) -> dict:
+    """Return the user's active connections (UserConnections) for Email/SharePoint node config."""
+    mandateId = _validateInstanceAccess(instanceId, context)
+    from modules.serviceCenter import getService
+    from modules.serviceCenter.context import ServiceCenterContext
+    ctx = ServiceCenterContext(
+        user=context.user,
+        mandate_id=str(context.mandateId) if context.mandateId else mandateId,
+        feature_instance_id=instanceId,
+    )
+    chatService = getService("chat", ctx)
+    connections = chatService.getUserConnections()
+    items = []
+    for c in connections or []:
+        conn = c if isinstance(c, dict) else (c.model_dump() if hasattr(c, "model_dump") else {})
+        authority = conn.get("authority")
+        if hasattr(authority, "value"):
+            authority = authority.value
+        status = conn.get("status")
+        if hasattr(status, "value"):
+            status = status.value
+        items.append({
+            "id": conn.get("id"),
+            "authority": authority,
+            "externalUsername": conn.get("externalUsername"),
+            "externalEmail": conn.get("externalEmail"),
+            "status": status,
+        })
+    return {"connections": items}
+
+
+@router.get("/{instanceId}/connections/{connectionId}/services")
+@limiter.limit("120/minute")
+async def list_connection_services(
+    request: Request,
+    instanceId: str = Path(..., description="Feature instance ID"),
+    connectionId: str = Path(..., description="Connection ID"),
+    context: RequestContext = Depends(getRequestContext),
+) -> dict:
+    """Return the available services for a specific UserConnection."""
+    mandateId = _validateInstanceAccess(instanceId, context)
+    try:
+        from modules.connectors.connectorResolver import ConnectorResolver
+        from modules.serviceCenter import getService as getSvc
+        from modules.serviceCenter.context import ServiceCenterContext
+        ctx = ServiceCenterContext(
+            user=context.user,
+            mandate_id=str(context.mandateId) if context.mandateId else mandateId,
+            feature_instance_id=instanceId,
+        )
+        chatService = getSvc("chat", ctx)
+        securityService = getSvc("security", ctx)
+        dbInterface = _buildResolverDbInterface(chatService)
+        resolver = ConnectorResolver(securityService, dbInterface)
+        provider = await resolver.resolve(connectionId)
+        services = provider.getAvailableServices()
+        _serviceLabels = {
+            "sharepoint": "SharePoint",
+            "outlook": "Outlook",
+            "teams": "Teams",
+            "onedrive": "OneDrive",
+            "drive": "Google Drive",
+            "gmail": "Gmail",
+            "files": "Files (FTP)",
+        }
+        _serviceIcons = {
+            "sharepoint": "sharepoint",
+            "outlook": "mail",
+            "teams": "chat",
+            "onedrive": "cloud",
+            "drive": "cloud",
+            "gmail": "mail",
+            "files": "folder",
+        }
+        items = [
+            {"service": s, "label": _serviceLabels.get(s, s), "icon": _serviceIcons.get(s, "folder")}
+            for s in services
+        ]
+        return {"services": items}
+    except Exception as e:
+        logger.error(f"Error listing services for connection {connectionId}: {e}")
+        return JSONResponse({"services": [], "error": str(e)}, status_code=400)
+
+
+@router.get("/{instanceId}/connections/{connectionId}/browse")
+@limiter.limit("300/minute")
+async def browse_connection_service(
+    request: Request,
+    instanceId: str = Path(..., description="Feature instance ID"),
+    connectionId: str = Path(..., description="Connection ID"),
+    service: str = Query(..., description="Service name (e.g. sharepoint, onedrive, outlook)"),
+    path: str = Query("/", description="Path within the service to browse"),
+    context: RequestContext = Depends(getRequestContext),
+) -> dict:
+    """Browse folders/items within a connection's service at a given path."""
+    mandateId = _validateInstanceAccess(instanceId, context)
+    try:
+        from modules.connectors.connectorResolver import ConnectorResolver
+        from modules.serviceCenter import getService as getSvc
+        from modules.serviceCenter.context import ServiceCenterContext
+        ctx = ServiceCenterContext(
+            user=context.user,
+            mandate_id=str(context.mandateId) if context.mandateId else mandateId,
+            feature_instance_id=instanceId,
+        )
+        chatService = getSvc("chat", ctx)
+        securityService = getSvc("security", ctx)
+        dbInterface = _buildResolverDbInterface(chatService)
+        resolver = ConnectorResolver(securityService, dbInterface)
+        adapter = await resolver.resolveService(connectionId, service)
+        entries = await adapter.browse(path, filter=None)
+        items = []
+        for entry in (entries or []):
+            items.append({
+                "name": entry.name,
+                "path": entry.path,
+                "isFolder": entry.isFolder,
+                "size": entry.size,
+                "mimeType": entry.mimeType,
+                "metadata": entry.metadata if hasattr(entry, "metadata") else {},
+            })
+        return {"items": items, "path": path, "service": service}
+    except Exception as e:
+        logger.error(f"Error browsing {service} for connection {connectionId} at '{path}': {e}")
+        return JSONResponse({"items": [], "error": str(e)}, status_code=400)
+
+
 # -------------------------------------------------------------------------
 # Workflow CRUD
 # -------------------------------------------------------------------------
 
 
+def _get_node_label_from_graph(graph: dict, nodeId: str) -> str:
+    """Extract human-readable label for a node from graph."""
+    if not graph or not nodeId:
+        return nodeId or ""
+    nodes = graph.get("nodes") or []
+    for n in nodes:
+        if n.get("id") == nodeId:
+            params = n.get("parameters") or {}
+            config = params.get("config") or {}
+            if isinstance(config, dict):
+                label = config.get("title") or config.get("label")
+            else:
+                label = None
+            return (
+                n.get("title")
+                or label
+                or params.get("title")
+                or params.get("label")
+                or n.get("type", "")
+                or nodeId
+            )
+    return nodeId or ""
+
+
 @router.get("/{instanceId}/workflows")
 @limiter.limit("60/minute")
 def get_workflows(
@@ -151,11 +341,41 @@ def get_workflows(
     instanceId: str = Path(..., description="Feature instance ID"),
     context: RequestContext = Depends(getRequestContext),
 ) -> dict:
-    """List all workflows for this feature instance."""
+    """List all workflows for this feature instance.
+    Enriches each workflow with runCount, isRunning, stuckAtNodeId, stuckAtNodeLabel,
+    createdAt, lastStartedAt.
+    """
     mandateId = _validateInstanceAccess(instanceId, context)
     a2 = getAutomation2Interface(context.user, mandateId, instanceId)
     items = a2.getWorkflows()
-    return {"workflows": items}
+    enriched = []
+    for wf in items:
+        wf_id = wf.get("id")
+        runs = a2.getRunsByWorkflow(wf_id) if wf_id else []
+        run_count = len(runs)
+        active_run = None
+        last_started_at = None
+        for r in runs:
+            ts = r.get("_createdAt")
+            if ts and (last_started_at is None or ts > last_started_at):
+                last_started_at = ts
+            if r.get("status") in ("running", "paused"):
+                active_run = r
+        stuck_at_node_id = active_run.get("currentNodeId") if active_run else None
+        stuck_at_node_label = ""
+        if stuck_at_node_id and wf.get("graph"):
+            stuck_at_node_label = _get_node_label_from_graph(wf["graph"], stuck_at_node_id)
+        enriched.append({
+            **wf,
+            "runCount": run_count,
+            "isRunning": active_run is not None,
+            "runStatus": active_run.get("status") if active_run else None,
+            "stuckAtNodeId": stuck_at_node_id,
+            "stuckAtNodeLabel": stuck_at_node_label or stuck_at_node_id or "",
+            "createdAt": wf.get("_createdAt"),
+            "lastStartedAt": last_started_at,
+        })
+    return {"workflows": enriched}
 
 
 @router.get("/{instanceId}/workflows/{workflowId}")
@@ -312,12 +532,23 @@ def get_tasks(
     status: str = Query(None, description="Filter: pending, completed, rejected"),
     context: RequestContext = Depends(getRequestContext),
 ) -> dict:
-    """Get tasks - by default those assigned to current user, or all if no assignee filter."""
+    """Get tasks - by default those assigned to current user, or all if no assignee filter.
+    Enriches each task with workflowLabel and createdAt (_createdAt).
+    """
     mandateId = _validateInstanceAccess(instanceId, context)
     a2 = getAutomation2Interface(context.user, mandateId, instanceId)
     assigneeId = str(context.user.id) if context.user else None
     items = a2.getTasks(workflowId=workflowId, status=status, assigneeId=assigneeId)
-    return {"tasks": items}
+    workflows = {w["id"]: w for w in a2.getWorkflows()}
+    enriched = []
+    for t in items:
+        wf = workflows.get(t.get("workflowId") or "")
+        enriched.append({
+            **t,
+            "workflowLabel": wf.get("label", t.get("workflowId", "")) if wf else t.get("workflowId", ""),
+            "createdAt": t.get("_createdAt"),
+        })
+    return {"tasks": enriched}
 
 
 @router.post("/{instanceId}/tasks/{taskId}/complete")

modules/shared/progressLogger.py

@@ -135,8 +135,8 @@ class ProgressLogger:
         message = f"{op['service']}"
         
         workflow = self.services.workflow
-        if not workflow:
-            logger.warning(f"Cannot log progress: no workflow available")
+        if not workflow or not getattr(workflow, "id", None):
+            # No workflow or no workflow.id (e.g. automation2 placeholder) - skip progress logging
             return None
         
         # Validate parentOperationId exists in activeOperations or finishedOperations

modules/workflows/automation2/executionEngine.py

@@ -2,6 +2,7 @@
 # Main execution engine for automation2 graphs.
 
 import logging
+from datetime import datetime, timezone
 from typing import Dict, Any, List, Set, Optional
 
 from modules.workflows.automation2.graphUtils import (
@@ -16,30 +17,19 @@ from modules.workflows.automation2.executors import (
     TriggerExecutor,
     FlowExecutor,
     DataExecutor,
-    IOExecutor,
+    ActionNodeExecutor,
     InputExecutor,
     PauseForHumanTaskError,
+    PauseForEmailWaitError,
 )
 from modules.features.automation2.nodeDefinitions import STATIC_NODE_TYPES
-from modules.workflows.processing.shared.methodDiscovery import discoverMethods, methods
 
 logger = logging.getLogger(__name__)
 
 
-def _getNodeTypeIds(services: Any) -> Set[str]:
-    """Collect all known node type IDs from static + methodDiscovery."""
-    discoverMethods(services)
-    ids = {n["id"] for n in STATIC_NODE_TYPES}
-    for methodName, methodInfo in methods.items():
-        if not methodName.startswith("Method"):
-            continue
-        shortMethod = methodName.replace("Method", "").lower()
-        instance = methodInfo.get("instance")
-        if not instance:
-            continue
-        for actionName in instance.actions:
-            ids.add(f"io.{shortMethod}.{actionName}")
-    return ids
+def _getNodeTypeIds(services: Any = None) -> Set[str]:
+    """Collect all known node type IDs from static definitions."""
+    return {n["id"] for n in STATIC_NODE_TYPES}
 
 
 def _getExecutor(
@@ -54,8 +44,8 @@ def _getExecutor(
         return FlowExecutor()
     if nodeType.startswith("data."):
         return DataExecutor()
-    if nodeType.startswith("io."):
-        return IOExecutor(services)
+    if nodeType.startswith("ai.") or nodeType.startswith("email.") or nodeType.startswith("sharepoint."):
+        return ActionNodeExecutor(services)
     if nodeType.startswith("input.") and automation2_interface:
         return InputExecutor(automation2_interface)
     return None
@@ -111,10 +101,21 @@ async def executeGraph(
     nodeOutputs: Dict[str, Any] = dict(initialNodeOutputs or {})
     is_resume = startAfterNodeId is not None
     if not runId and automation2_interface and workflowId and not is_resume:
+        run_context = {
+            "connectionMap": connectionMap,
+            "inputSources": inputSources,
+            "orderedNodeIds": ordered_ids,
+        }
+        if userId:
+            run_context["ownerId"] = userId
+        if mandateId:
+            run_context["mandateId"] = mandateId
+        if instanceId:
+            run_context["instanceId"] = instanceId
         run = automation2_interface.createRun(
             workflowId=workflowId,
             nodeOutputs=nodeOutputs,
-            context={"connectionMap": connectionMap, "inputSources": inputSources, "orderedNodeIds": ordered_ids},
+            context=run_context,
         )
         runId = run.get("id") if run else None
         logger.info("executeGraph created run %s", runId)
@@ -175,6 +176,46 @@ async def executeGraph(
                 "nodeId": e.nodeId,
                 "nodeOutputs": dict(nodeOutputs),
             }
+        except PauseForEmailWaitError as e:
+            logger.info("executeGraph paused for email wait (run %s, node %s)", e.runId, e.nodeId)
+            # Start email poller on-demand (only runs while workflows wait for email)
+            try:
+                from modules.interfaces.interfaceDbApp import getRootInterface
+                from modules.features.automation2.emailPoller import ensureRunning
+                root = getRootInterface()
+                event_user = root.getUserByUsername("event") if root else None
+                if event_user:
+                    ensureRunning(event_user)
+            except Exception as poll_err:
+                logger.warning("Could not start email poller: %s", poll_err)
+            paused_at = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+            run_ctx = {
+                "connectionMap": context.get("connectionMap"),
+                "inputSources": context.get("inputSources"),
+                "orderedNodeIds": [n.get("id") for n in context.get("_orderedNodes", []) if n.get("id")],
+                "waitReason": "email",
+                "waitConfig": e.waitConfig,
+                "pausedAt": paused_at,
+                "lastCheckedAt": None,
+                "ownerId": context.get("userId"),
+                "mandateId": context.get("mandateId"),
+                "instanceId": context.get("instanceId"),
+            }
+            automation2_interface.updateRun(
+                e.runId,
+                status="paused",
+                nodeOutputs=dict(nodeOutputs),
+                currentNodeId=e.nodeId,
+                context=run_ctx,
+            )
+            return {
+                "success": False,
+                "paused": True,
+                "waitReason": "email",
+                "runId": e.runId,
+                "nodeId": e.nodeId,
+                "nodeOutputs": dict(nodeOutputs),
+            }
         except Exception as e:
             logger.exception("executeGraph node %s (%s) FAILED: %s", nodeId, nodeType, e)
             nodeOutputs[nodeId] = {"error": str(e), "success": False}

modules/workflows/automation2/executors/__init__.py

@@ -4,14 +4,15 @@
 from .triggerExecutor import TriggerExecutor
 from .flowExecutor import FlowExecutor
 from .dataExecutor import DataExecutor
-from .ioExecutor import IOExecutor
-from .inputExecutor import InputExecutor, PauseForHumanTaskError
+from .actionNodeExecutor import ActionNodeExecutor
+from .inputExecutor import InputExecutor, PauseForHumanTaskError, PauseForEmailWaitError
 
 __all__ = [
     "TriggerExecutor",
     "FlowExecutor",
     "DataExecutor",
-    "IOExecutor",
+    "ActionNodeExecutor",
     "InputExecutor",
     "PauseForHumanTaskError",
+    "PauseForEmailWaitError",
 ]

modules/workflows/automation2/executors/actionNodeExecutor.py

@@ -0,0 +1,457 @@
+# Copyright (c) 2025 Patrick Motsch
+# Action node executor - maps ai.*, email.*, sharepoint.* to method actions via ActionExecutor.
+
+import logging
+from typing import Dict, Any, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def _getNodeDefinition(nodeType: str) -> Optional[Dict[str, Any]]:
+    """Get node definition by type id for _method, _action, _paramMap."""
+    from modules.features.automation2.nodeDefinitions import STATIC_NODE_TYPES
+    for node in STATIC_NODE_TYPES:
+        if node.get("id") == nodeType:
+            return node
+    return None
+
+
+def _resolveConnectionIdToReference(chatService, connectionId: str) -> Optional[str]:
+    """
+    Resolve connectionId (UserConnection.id) to connectionReference format.
+    connectionReference format: connection:{authority}:{externalUsername}
+    """
+    if not connectionId or not chatService:
+        return None
+    try:
+        connections = chatService.getUserConnections()
+        for c in connections or []:
+            conn = c if isinstance(c, dict) else (c.model_dump() if hasattr(c, "model_dump") else {})
+            if str(conn.get("id")) == str(connectionId):
+                authority = conn.get("authority")
+                if hasattr(authority, "value"):
+                    authority = authority.value
+                username = conn.get("externalUsername", "")
+                return f"connection:{authority}:{username}"
+        return None
+    except Exception as e:
+        logger.warning(f"Could not resolve connectionId {connectionId} to reference: {e}")
+        return None
+
+
+def _extractEmailContentFromUpstream(inp: Any) -> Optional[Dict[str, Any]]:
+    """
+    Extract {subject, body, to} from upstream node output (e.g. AI node returning JSON).
+    Expects JSON like {"subject": "...", "body": "...", "to": "..."} in documentData.
+    """
+    if not inp:
+        return None
+    import json
+    docs = inp.get("documents", inp.get("documentList", [])) if isinstance(inp, dict) else []
+    if not docs:
+        return None
+    doc = docs[0] if isinstance(docs, list) else docs
+    raw = getattr(doc, "documentData", None) if hasattr(doc, "documentData") else (doc.get("documentData") if isinstance(doc, dict) else None)
+    if not raw:
+        return None
+    try:
+        data = json.loads(raw) if isinstance(raw, str) else raw
+        if isinstance(data, dict) and data.get("subject") and data.get("body"):
+            return {
+                "subject": str(data.get("subject", "")),
+                "body": str(data.get("body", "")),
+                "to": data.get("to"),
+            }
+    except (json.JSONDecodeError, TypeError):
+        pass
+    return None
+
+
+def _getIncomingEmailFromUpstream(
+    nodeId: str,
+    inputSources: Dict[str, Dict[int, tuple]],
+    nodeOutputs: Dict[str, Any],
+    orderedNodes: List[Dict],
+) -> Optional[tuple]:
+    """
+    Walk upstream from draftEmail to find email.checkEmail/searchEmail and return (context, documentList).
+    context = formatted incoming email(s) for composeAndDraftEmail.
+    documentList = documents from the email node for attachment/context.
+    """
+    src = inputSources.get(nodeId, {}).get(0)
+    if not src:
+        return None
+    srcId, _ = src
+    srcNode = next((n for n in (orderedNodes or []) if n.get("id") == srcId), None)
+    srcType = (srcNode or {}).get("type", "")
+
+    # Direct connection to email node
+    if srcType in ("email.checkEmail", "email.searchEmail"):
+        out = nodeOutputs.get(srcId)
+        return _formatEmailOutputAsContext(out)
+
+    # Connected via AI node: walk one more step to email source
+    if srcType.startswith("ai."):
+        src2 = inputSources.get(srcId, {}).get(0)
+        if not src2:
+            return None
+        emailNodeId, _ = src2
+        emailNode = next((n for n in (orderedNodes or []) if n.get("id") == emailNodeId), None)
+        if (emailNode or {}).get("type") in ("email.checkEmail", "email.searchEmail"):
+            out = nodeOutputs.get(emailNodeId)
+            return _formatEmailOutputAsContext(out)
+    return None
+
+
+def _formatEmailOutputAsContext(out: Any) -> Optional[tuple]:
+    """Format email node output as (context, documentList, reply_to) for composeAndDraftEmail.
+    reply_to = sender address of first email (recipient for the reply).
+    """
+    if not out:
+        return None
+    docs = out.get("documents", out.get("documentList", [])) if isinstance(out, dict) else []
+    if not docs:
+        return None
+    doc = docs[0] if isinstance(docs, list) else docs
+    raw = getattr(doc, "documentData", None) if hasattr(doc, "documentData") else (doc.get("documentData") if isinstance(doc, dict) else None)
+    if not raw:
+        return None
+    import json
+    try:
+        data = json.loads(raw) if isinstance(raw, str) else raw
+    except (json.JSONDecodeError, TypeError):
+        return None
+    if not isinstance(data, dict):
+        return None
+    # readEmails: data.emails.emails | searchEmails: data.searchResults.results
+    emails_data = data.get("emails") or {}
+    emails_list = emails_data.get("emails", []) if isinstance(emails_data, dict) else []
+    if not emails_list:
+        search_results = data.get("searchResults") or {}
+        emails_list = search_results.get("results", []) if isinstance(search_results, dict) else []
+    if not emails_list:
+        return None
+    reply_to = None
+    parts = ["Reply to the following email(s):", ""]
+    for i, em in enumerate(emails_list[:5]):  # max 5
+        if not isinstance(em, dict):
+            continue
+        fr = em.get("from", em.get("sender", {}))
+        addr = fr.get("emailAddress", {}) if isinstance(fr, dict) else {}
+        from_str = addr.get("address", "") or addr.get("name", "")
+        if from_str and not reply_to:
+            reply_to = addr.get("address", "") or from_str
+        subj = em.get("subject", "")
+        body = em.get("bodyPreview", "") or (em.get("body") or {}).get("content", "") if isinstance(em.get("body"), dict) else ""
+        if body and len(str(body)) > 1500:
+            body = str(body)[:1500] + "..."
+        parts.append(f"From: {from_str}")
+        parts.append(f"Subject: {subj}")
+        parts.append(f"Content:\n{body}")
+        parts.append("")
+    if reply_to:
+        parts.insert(2, f"Recipient (reply to this address): {reply_to}")
+        parts.insert(3, "")
+    context = "\n".join(parts).strip()
+    return (context, docs, reply_to)
+
+
+def _buildSearchQuery(
+    query: str = None,
+    fromAddress: str = None,
+    toAddress: str = None,
+    subjectContains: str = None,
+    bodyContains: str = None,
+    hasAttachment: bool = None,
+    filter: str = None,
+) -> str:
+    """
+    Build Microsoft Graph $search query from discrete params.
+    Uses KQL: from:, to:, subject:, body:, hasattachments: (supported by Graph API).
+    """
+    if filter and str(filter).strip():
+        return str(filter).strip()
+    parts = []
+    if query and str(query).strip():
+        parts.append(str(query).strip())
+    if fromAddress and str(fromAddress).strip():
+        safe = str(fromAddress).strip().replace('"', '')
+        parts.append(f'from:{safe}')
+    if toAddress and str(toAddress).strip():
+        safe = str(toAddress).strip().replace('"', '')
+        parts.append(f'to:{safe}')
+    if subjectContains and str(subjectContains).strip():
+        safe = str(subjectContains).strip().replace('"', '')
+        parts.append(f'subject:{safe}')
+    if bodyContains and str(bodyContains).strip():
+        safe = str(bodyContains).strip().replace('"', '')
+        parts.append(f'body:{safe}')
+    if hasAttachment is True:
+        parts.append("hasattachments:true")
+    return " ".join(parts) if parts else "*"
+
+
+def _buildEmailFilter(fromAddress: str = None, subjectContains: str = None, hasAttachment: bool = None) -> str:
+    """
+    Build Microsoft Graph API $filter string from discrete email filter params.
+    Used for email.checkEmail (and trigger.newEmail).
+    """
+    parts = []
+    if fromAddress and str(fromAddress).strip():
+        safe = str(fromAddress).strip().replace("'", "''")
+        parts.append(f"from/emailAddress/address eq '{safe}'")
+    if subjectContains and str(subjectContains).strip():
+        safe = str(subjectContains).strip().replace("'", "''")
+        parts.append(f"contains(subject,'{safe}')")
+    if hasAttachment is True:
+        parts.append("hasAttachments eq true")
+    return " and ".join(parts) if parts else ""
+
+
+def _buildActionParams(
+    node: Dict[str, Any],
+    nodeDef: Dict[str, Any],
+    resolvedParams: Dict[str, Any],
+    chatService,
+) -> Dict[str, Any]:
+    """
+    Build params for ActionExecutor from node parameters using _paramMap.
+    Resolves connectionId -> connectionReference.
+    Handles _contextFrom for composite params (e.g. email.draftEmail subject+body -> context).
+    """
+    params = dict(resolvedParams)
+    paramMap = nodeDef.get("_paramMap") or {}
+    contextFrom = nodeDef.get("_contextFrom") or []
+
+    # email.checkEmail: build filter from discrete params (fromAddress, subjectContains, hasAttachment)
+    nodeType = node.get("type", "")
+    if nodeType == "email.checkEmail":
+        built = _buildEmailFilter(
+            fromAddress=params.get("fromAddress"),
+            subjectContains=params.get("subjectContains"),
+            hasAttachment=params.get("hasAttachment"),
+        )
+        raw_filter = (params.get("filter") or "").strip()
+        params["filter"] = built if built else (raw_filter if raw_filter else None)
+        params.pop("fromAddress", None)
+        params.pop("subjectContains", None)
+        params.pop("hasAttachment", None)
+
+    # email.searchEmail: build query from discrete params (fromAddress, toAddress, subjectContains, bodyContains, hasAttachment)
+    if nodeType == "email.searchEmail":
+        built = _buildSearchQuery(
+            query=params.get("query"),
+            fromAddress=params.get("fromAddress"),
+            toAddress=params.get("toAddress"),
+            subjectContains=params.get("subjectContains"),
+            bodyContains=params.get("bodyContains"),
+            hasAttachment=params.get("hasAttachment"),
+            filter=params.get("filter"),
+        )
+        params["query"] = built
+        params.pop("fromAddress", None)
+        params.pop("toAddress", None)
+        params.pop("subjectContains", None)
+        params.pop("bodyContains", None)
+        params.pop("hasAttachment", None)
+        params.pop("filter", None)
+
+    # Resolve connectionId to connectionReference
+    if "connectionId" in params:
+        connId = params.get("connectionId")
+        if connId and chatService:
+            ref = _resolveConnectionIdToReference(chatService, connId)
+            if ref:
+                params["connectionReference"] = ref
+            else:
+                logger.warning(f"Could not resolve connectionId {connId} to connectionReference")
+        params.pop("connectionId", None)
+
+    # Build context from multiple params (e.g. subject + body for draft email)
+    if contextFrom:
+        parts = []
+        for key in contextFrom:
+            val = params.get(key)
+            if val:
+                if key == "subject":
+                    parts.append(f"Subject: {val}")
+                elif key == "body":
+                    parts.append(f"Body:\n{val}")
+                else:
+                    parts.append(str(val))
+        if parts:
+            params["context"] = "\n\n".join(parts)
+        for k in contextFrom:
+            params.pop(k, None)
+
+    # Apply paramMap: node param name -> action param name
+    result = {}
+    mappedNodeKeys = {nodeKey for nodeKey, actionKey in paramMap.items() if actionKey and nodeKey in params}
+    for nodeKey, actionKey in paramMap.items():
+        if nodeKey in params and actionKey:
+            result[actionKey] = params[nodeKey]
+    # Pass through params not used as source for mapping
+    for k, v in params.items():
+        if k not in mappedNodeKeys and k not in result:
+            result[k] = v
+    return result
+
+
+class ActionNodeExecutor:
+    """Execute ai.*, email.*, sharepoint.* nodes by mapping to method actions."""
+
+    def __init__(self, services: Any):
+        self.services = services
+
+    async def execute(
+        self,
+        node: Dict[str, Any],
+        context: Dict[str, Any],
+    ) -> Any:
+        from modules.features.automation2.nodeRegistry import getNodeTypeToMethodAction
+        from modules.workflows.automation2.graphUtils import resolveParameterReferences
+        from modules.workflows.processing.core.actionExecutor import ActionExecutor
+
+        nodeType = node.get("type", "")
+        nodeId = node.get("id", "")
+        logger.info("ActionNodeExecutor node %s type=%s", nodeId, nodeType)
+
+        mapping = getNodeTypeToMethodAction()
+        methodAction = mapping.get(nodeType)
+        if not methodAction:
+            logger.debug("ActionNodeExecutor node %s not in mapping -> None", nodeId)
+            return None
+
+        methodName, actionName = methodAction
+        logger.info("ActionNodeExecutor node %s method=%s action=%s", nodeId, methodName, actionName)
+
+        nodeDef = _getNodeDefinition(nodeType)
+        params = dict(node.get("parameters") or {})
+        resolvedParams = resolveParameterReferences(params, context.get("nodeOutputs", {}))
+
+        # Merge input from connected nodes (documentList, etc.)
+        inputSources = context.get("inputSources", {}).get(nodeId, {})
+        if 0 in inputSources:
+            srcId, _ = inputSources[0]
+            inp = context.get("nodeOutputs", {}).get(srcId)
+            if isinstance(inp, dict):
+                resolvedParams.setdefault("documentList", inp.get("documents", inp.get("documentList", [])))
+            elif inp is not None:
+                resolvedParams.setdefault("input", inp)
+
+        # ai.prompt with email upstream: inject actual email content into prompt so AI has context
+        # (getChatDocumentsFromDocumentList fails in automation2 – workflow has no messages)
+        if nodeType.startswith("ai."):
+            orderedNodes = context.get("_orderedNodes") or []
+            if 0 in inputSources:
+                srcId, _ = inputSources[0]
+                srcNode = next((n for n in orderedNodes if n.get("id") == srcId), None)
+                srcType = (srcNode or {}).get("type", "")
+                if srcType in ("email.checkEmail", "email.searchEmail"):
+                    incoming = _getIncomingEmailFromUpstream(
+                        nodeId,
+                        context.get("inputSources", {}),
+                        context.get("nodeOutputs", {}),
+                        orderedNodes,
+                    )
+                    if incoming:
+                        ctx, _doc_list, _reply_to = incoming
+                        if ctx and ctx.strip():
+                            base_prompt = (resolvedParams.get("aiPrompt") or "").strip()
+                            resolvedParams["aiPrompt"] = (
+                                f"Eingehende E-Mail:\n{ctx}\n\nAufgabe: {base_prompt}"
+                                if base_prompt
+                                else f"Eingehende E-Mail:\n{ctx}"
+                            )
+                            logger.debug("ai.prompt: injected email context from upstream %s", srcType)
+
+        chatService = getattr(self.services, "chat", None)
+        actionParams = _buildActionParams(node, nodeDef or {}, resolvedParams, chatService)
+
+        # email.checkEmail: pause and wait for new email (background poller will resume)
+        if nodeType == "email.checkEmail":
+            runId = context.get("_runId")
+            workflowId = context.get("workflowId")
+            connRef = actionParams.get("connectionReference")
+            if runId and workflowId and connRef:
+                from modules.workflows.automation2.executors import PauseForEmailWaitError
+                waitConfig = {
+                    "connectionReference": connRef,
+                    "folder": actionParams.get("folder", "Inbox"),
+                    "limit": min(int(actionParams.get("limit") or 10), 50),
+                    "filter": actionParams.get("filter"),
+                }
+                raise PauseForEmailWaitError(runId=runId, nodeId=nodeId, waitConfig=waitConfig)
+            # Fallback: no pause (calls readEmails directly) – needs runId, workflowId, connectionReference
+            if not runId or not workflowId:
+                logger.warning(
+                    "email.checkEmail not pausing (runId=%s workflowId=%s) – run must be saved/executed as workflow",
+                    runId,
+                    workflowId,
+                )
+            elif not connRef:
+                logger.warning(
+                    "email.checkEmail not pausing – connectionReference missing (check connectionId/config)",
+                )
+
+        # email.draftEmail: use AI output as emailContent if available; else pass incoming email as context
+        if nodeType == "email.draftEmail":
+            inputSources = context.get("inputSources", {})
+            nodeOutputs = context.get("nodeOutputs", {})
+            orderedNodes = context.get("_orderedNodes") or []
+            if 0 in inputSources.get(nodeId, {}):
+                srcId, _ = inputSources[nodeId][0]
+                srcNode = next((n for n in orderedNodes if n.get("id") == srcId), None)
+                srcType = (srcNode or {}).get("type", "")
+                if srcType.startswith("ai."):
+                    inp = nodeOutputs.get(srcId)
+                    email_content = _extractEmailContentFromUpstream(inp)
+                    if email_content:
+                        actionParams["emailContent"] = email_content
+                        actionParams.setdefault("context", "(from connected AI node)")
+                    else:
+                        # AI failed or wrong format: pass incoming email from upstream as context
+                        incoming = _getIncomingEmailFromUpstream(nodeId, inputSources, nodeOutputs, orderedNodes)
+                        if incoming:
+                            ctx, doc_list, reply_to = incoming
+                            actionParams["context"] = ctx
+                            if doc_list and not actionParams.get("documentList"):
+                                actionParams["documentList"] = doc_list
+                            if reply_to and not actionParams.get("to"):
+                                actionParams["to"] = [reply_to]
+                else:
+                    # Direct connection to email.checkEmail/searchEmail: use incoming email as context
+                    if not actionParams.get("context"):
+                        incoming = _getIncomingEmailFromUpstream(nodeId, inputSources, nodeOutputs, orderedNodes)
+                        if incoming:
+                            ctx, doc_list, reply_to = incoming
+                            actionParams["context"] = ctx
+                            if doc_list and not actionParams.get("documentList"):
+                                actionParams["documentList"] = doc_list
+                            if reply_to and not actionParams.get("to"):
+                                actionParams["to"] = [reply_to]
+
+        # sharepoint.uploadFile: content from documentList (upstream) if not in params
+        if nodeType == "sharepoint.uploadFile" and "content" not in actionParams:
+            docList = actionParams.get("documentList") or resolvedParams.get("documentList")
+            if docList:
+                actionParams["content"] = docList[0] if isinstance(docList, list) and docList else docList
+
+        executor = ActionExecutor(self.services)
+        logger.info("ActionNodeExecutor node %s calling executeAction(%s, %s)", nodeId, methodName, actionName)
+        result = await executor.executeAction(methodName, actionName, actionParams)
+
+        out = {
+            "success": result.success,
+            "error": result.error,
+            "documents": [d.model_dump() if hasattr(d, "model_dump") else d for d in (result.documents or [])],
+            "data": result.model_dump() if hasattr(result, "model_dump") else {"success": result.success, "error": result.error},
+        }
+        logger.info(
+            "ActionNodeExecutor node %s result: success=%s error=%s doc_count=%d",
+            nodeId,
+            result.success,
+            result.error,
+            len(out.get("documents", [])),
+        )
+        return out

modules/workflows/automation2/executors/inputExecutor.py

@@ -17,6 +17,16 @@ class PauseForHumanTaskError(Exception):
         super().__init__(f"Pause for human task {taskId} (run {runId}, node {nodeId})")
 
 
+class PauseForEmailWaitError(Exception):
+    """Raised when execution must pause waiting for a new email. Background poller will resume."""
+
+    def __init__(self, runId: str, nodeId: str, waitConfig: Dict[str, Any]):
+        self.runId = runId
+        self.nodeId = nodeId
+        self.waitConfig = waitConfig
+        super().__init__(f"Pause for email wait (run {runId}, node {nodeId})")
+
+
 class InputExecutor:
     """
     Execute input/human nodes. Creates a HumanTask, pauses the run, and raises

modules/workflows/methods/methodOutlook/actions/composeAndDraftEmailWithContext.py

@@ -20,11 +20,44 @@ async def composeAndDraftEmailWithContext(self, parameters: Dict[str, Any]) -> A
         bcc = parameters.get("bcc") or []
         emailStyle = parameters.get("emailStyle") or "business"
         maxLength = parameters.get("maxLength") or 1000
-        
-        # Only connectionReference and context are required - to is optional for drafts
-        if not connectionReference or not context:
-            return ActionResult.isFailure(error="connectionReference and context are required")
-        
+
+        # Direct content from upstream (e.g. AI node): skip internal AI, use subject/body/to directly
+        email_content = parameters.get("emailContent")
+        if isinstance(email_content, dict):
+            direct_subject = email_content.get("subject")
+            direct_body = email_content.get("body")
+            direct_to = email_content.get("to")
+            if direct_subject and direct_body:
+                subject = str(direct_subject).strip()
+                body = str(direct_body).strip()
+                to = [direct_to] if isinstance(direct_to, str) else (direct_to or [])
+                if isinstance(to, str):
+                    to = [to]
+                ai_attachments = []
+                # Jump to create-email section (see below)
+        else:
+            direct_subject = parameters.get("subject")
+            direct_body = parameters.get("body")
+            if direct_subject and direct_body:
+                subject = str(direct_subject).strip()
+                body = str(direct_body).strip()
+                if isinstance(to, str):
+                    to = [to]
+                ai_attachments = []
+            else:
+                subject = None
+                body = None
+                ai_attachments = None
+
+        use_direct_content = bool(subject and body)
+
+        if not use_direct_content:
+            # Original path: require connectionReference and context
+            if not connectionReference or not context:
+                return ActionResult.isFailure(error="connectionReference and context are required")
+        elif not connectionReference:
+            return ActionResult.isFailure(error="connectionReference is required")
+
         # Convert single values to lists for all recipient parameters
         if isinstance(to, str):
             to = [to]
@@ -45,10 +78,10 @@ async def composeAndDraftEmailWithContext(self, parameters: Dict[str, Any]) -> A
         if not permissions_ok:
             return ActionResult.isFailure(error="Connection lacks necessary permissions for Outlook operations")
         
-        # Prepare documents for AI processing
+        # Prepare documents for AI processing (only when using AI path)
         from modules.datamodels.datamodelDocref import DocumentReferenceList
         chatDocuments = []
-        if documentList:
+        if not use_direct_content and documentList:
             # Convert to DocumentReferenceList if needed
             if isinstance(documentList, DocumentReferenceList):
                 docRefList = documentList
@@ -60,33 +93,34 @@ async def composeAndDraftEmailWithContext(self, parameters: Dict[str, Any]) -> A
                 docRefList = DocumentReferenceList(references=[])
             chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(docRefList)
         
-        # Create AI prompt for email composition
-        # Build document reference list for AI with expanded list contents when possible
-        doc_references = documentList
-        doc_list_text = ""
-        if doc_references:
-            lines = ["Available_Document_References:"]
-            for ref in doc_references:
-                # Each item is a label: resolve to its document list and render contained items
-                from modules.datamodels.datamodelDocref import DocumentReferenceList
-                list_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([ref])) or []
-                if list_docs:
-                    for d in list_docs:
-                        doc_ref_label = self.services.chat.getDocumentReferenceFromChatDocument(d)
-                        lines.append(f"- {doc_ref_label}")
-                else:
-                    lines.append("  - (no documents)")
-            doc_list_text = "\n" + "\n".join(lines)
-        else:
-            doc_list_text = "Available_Document_References: (No documents available for attachment)"
-        
-        # Escape only the user-controlled context to prevent prompt injection
-        escaped_context = context.replace('"', '\\"').replace('\n', '\\n').replace('\r', '\\r')
-        
-        # Build recipients text for prompt
-        recipients_text = f"Recipients: {to}" if to else "Recipients: (not specified - this is a draft)"
-        
-        ai_prompt = f"""Compose an email based on this context:
+        if not use_direct_content:
+            # Create AI prompt for email composition
+            # Build document reference list for AI with expanded list contents when possible
+            doc_references = documentList
+            doc_list_text = ""
+            if doc_references:
+                lines = ["Available_Document_References:"]
+                for ref in doc_references:
+                    # Each item is a label: resolve to its document list and render contained items
+                    from modules.datamodels.datamodelDocref import DocumentReferenceList
+                    list_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([ref])) or []
+                    if list_docs:
+                        for d in list_docs:
+                            doc_ref_label = self.services.chat.getDocumentReferenceFromChatDocument(d)
+                            lines.append(f"- {doc_ref_label}")
+                    else:
+                        lines.append("  - (no documents)")
+                doc_list_text = "\n" + "\n".join(lines)
+            else:
+                doc_list_text = "Available_Document_References: (No documents available for attachment)"
+
+            # Escape only the user-controlled context to prevent prompt injection
+            escaped_context = context.replace('"', '\\"').replace('\n', '\\n').replace('\r', '\\r')
+
+            # Build recipients text for prompt
+            recipients_text = f"Recipients: {to}" if to else "Recipients: (not specified - this is a draft)"
+
+            ai_prompt = f"""Compose an email based on this context:
 -------
 {escaped_context}
 -------
@@ -107,93 +141,93 @@ Return JSON:
   "attachments": ["docItem:<documentId>:<filename>"]
 }}
 """
-        
-        # Call AI service to generate email content
-        try:
-            ai_response = await self.services.ai.callAiPlanning(
-                prompt=ai_prompt,
-                placeholders=None,
-                debugType="email_composition"
-            )
-            
-            # Parse AI response
+
+            # Call AI service to generate email content
             try:
-                ai_content = ai_response
-                # Extract JSON from AI response
-                if "```json" in ai_content:
-                    json_start = ai_content.find("```json") + 7
-                    json_end = ai_content.find("```", json_start)
-                    json_content = ai_content[json_start:json_end].strip()
-                elif "{" in ai_content and "}" in ai_content:
-                    json_start = ai_content.find("{")
-                    json_end = ai_content.rfind("}") + 1
-                    json_content = ai_content[json_start:json_end]
-                else:
-                    json_content = ai_content
-                
-                email_data = json.loads(json_content)
-                subject = email_data.get("subject", "")
-                body = email_data.get("body", "")
-                ai_attachments = email_data.get("attachments", [])
-                
-                if not subject or not body:
-                    return ActionResult.isFailure(error="AI did not generate valid subject and body")
-                
-                # Use AI-selected attachments if provided, otherwise use all documents
-                normalized_ai_attachments = []
-                if documentList:
-                    try:
-                        available_refs = [documentList] if isinstance(documentList, str) else documentList
-                        from modules.datamodels.datamodelDocref import DocumentReferenceList
-                        available_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list(available_refs)) or []
-                    except Exception:
-                        available_docs = []
+                ai_response = await self.services.ai.callAiPlanning(
+                    prompt=ai_prompt,
+                    placeholders=None,
+                    debugType="email_composition"
+                )
 
-                    # Normalize AI attachments to a list of strings
-                    if isinstance(ai_attachments, str):
-                        ai_attachments = [ai_attachments]
-                    elif isinstance(ai_attachments, list):
-                        ai_attachments = [a for a in ai_attachments if isinstance(a, str)]
+                # Parse AI response
+                try:
+                    ai_content = ai_response
+                    # Extract JSON from AI response
+                    if "```json" in ai_content:
+                        json_start = ai_content.find("```json") + 7
+                        json_end = ai_content.find("```", json_start)
+                        json_content = ai_content[json_start:json_end].strip()
+                    elif "{" in ai_content and "}" in ai_content:
+                        json_start = ai_content.find("{")
+                        json_end = ai_content.rfind("}") + 1
+                        json_content = ai_content[json_start:json_end]
+                    else:
+                        json_content = ai_content
 
-                    if ai_attachments:
+                    email_data = json.loads(json_content)
+                    subject = email_data.get("subject", "")
+                    body = email_data.get("body", "")
+                    ai_attachments = email_data.get("attachments", [])
+
+                    if not subject or not body:
+                        return ActionResult.isFailure(error="AI did not generate valid subject and body")
+                
+                    # Use AI-selected attachments if provided, otherwise use all documents
+                    normalized_ai_attachments = []
+                    if documentList:
                         try:
-                            ai_refs = [ai_attachments] if isinstance(ai_attachments, str) else ai_attachments
+                            available_refs = [documentList] if isinstance(documentList, str) else documentList
                             from modules.datamodels.datamodelDocref import DocumentReferenceList
-                            ai_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list(ai_refs)) or []
+                            available_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list(available_refs)) or []
                         except Exception:
-                            ai_docs = []
+                            available_docs = []
 
-                        # Intersect by document id
-                        available_ids = {getattr(d, 'id', None) for d in available_docs}
-                        selected_docs = [d for d in ai_docs if getattr(d, 'id', None) in available_ids]
+                        # Normalize AI attachments to a list of strings
+                        if isinstance(ai_attachments, str):
+                            ai_attachments = [ai_attachments]
+                        elif isinstance(ai_attachments, list):
+                            ai_attachments = [a for a in ai_attachments if isinstance(a, str)]
 
-                        if selected_docs:
-                            # Map selected ChatDocuments back to docItem references (with full filename)
-                            documentList = [self.services.chat.getDocumentReferenceFromChatDocument(d) for d in selected_docs]
-                            # Normalize ai_attachments to full format for storage
-                            normalized_ai_attachments = documentList.copy()
-                            logger.info(f"AI selected {len(documentList)} documents for attachment (resolved via ChatDocuments)")
+                        if ai_attachments:
+                            try:
+                                ai_refs = [ai_attachments] if isinstance(ai_attachments, str) else ai_attachments
+                                from modules.datamodels.datamodelDocref import DocumentReferenceList
+                                ai_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list(ai_refs)) or []
+                            except Exception:
+                                ai_docs = []
+
+                            # Intersect by document id
+                            available_ids = {getattr(d, 'id', None) for d in available_docs}
+                            selected_docs = [d for d in ai_docs if getattr(d, 'id', None) in available_ids]
+
+                            if selected_docs:
+                                # Map selected ChatDocuments back to docItem references (with full filename)
+                                documentList = [self.services.chat.getDocumentReferenceFromChatDocument(d) for d in selected_docs]
+                                # Normalize ai_attachments to full format for storage
+                                normalized_ai_attachments = documentList.copy()
+                                logger.info(f"AI selected {len(documentList)} documents for attachment (resolved via ChatDocuments)")
+                            else:
+                                # No intersection; use all available documents
+                                documentList = [self.services.chat.getDocumentReferenceFromChatDocument(d) for d in available_docs]
+                                normalized_ai_attachments = documentList.copy()
+                                logger.warning("AI selected attachments not found in available documents, using all documents")
                         else:
-                            # No intersection; use all available documents
+                            # No AI selection; use all available documents
                             documentList = [self.services.chat.getDocumentReferenceFromChatDocument(d) for d in available_docs]
                             normalized_ai_attachments = documentList.copy()
-                            logger.warning("AI selected attachments not found in available documents, using all documents")
+                            logger.warning("AI did not specify attachments, using all available documents")
                     else:
-                        # No AI selection; use all available documents
-                        documentList = [self.services.chat.getDocumentReferenceFromChatDocument(d) for d in available_docs]
-                        normalized_ai_attachments = documentList.copy()
-                        logger.warning("AI did not specify attachments, using all available documents")
-                else:
-                    logger.info("No documents provided in documentList; skipping attachment processing")
-                    
-            except json.JSONDecodeError as e:
-                logger.error(f"Failed to parse AI response as JSON: {str(e)}")
-                logger.error(f"AI response content: {ai_response}")
-                return ActionResult.isFailure(error="AI response was not valid JSON format")
-            
-        except Exception as e:
-            logger.error(f"Error calling AI service: {str(e)}")
-            return ActionResult.isFailure(error=f"Failed to generate email content: {str(e)}")
+                        logger.info("No documents provided in documentList; skipping attachment processing")
+
+                except json.JSONDecodeError as e:
+                    logger.error(f"Failed to parse AI response as JSON: {str(e)}")
+                    logger.error(f"AI response content: {ai_response}")
+                    return ActionResult.isFailure(error="AI response was not valid JSON format")
+
+            except Exception as e:
+                logger.error(f"Error calling AI service: {str(e)}")
+                return ActionResult.isFailure(error=f"Failed to generate email content: {str(e)}")
         
         # Now create the email with AI-generated content
         try:

modules/workflows/methods/methodOutlook/actions/readEmails.py

@@ -49,9 +49,9 @@ async def readEmails(self, parameters: Dict[str, Any]) -> ActionResult:
         if filter:
             # Remove any potentially dangerous characters that could break the filter
             filter = filter.strip()
-            if len(filter) > 100:
-                logger.warning(f"Filter too long ({len(filter)} chars), truncating to 100 characters")
-                filter = filter[:100]
+            if len(filter) > 500:
+                logger.warning(f"Filter too long ({len(filter)} chars), truncating to 500 characters")
+                filter = filter[:500]
 
         
         # Get Microsoft connection

modules/workflows/methods/methodOutlook/actions/searchEmails.py

@@ -73,8 +73,12 @@ async def searchEmails(self, parameters: Dict[str, Any]) -> ActionResult:
                     logger.warning(f"Could not find folder ID for '{folder}', using folder name directly")
             
             # Build the search API request
-            api_url = f"{graph_url}/me/messages"
             params = self.emailProcessing.buildSearchParameters(query, folder_id or folder, limit)
+            # Use folder-specific URL when we have folder_id and $search - avoids InefficientFilter
+            if folder_id and params.get("$search"):
+                api_url = f"{graph_url}/me/mailFolders/{folder_id}/messages"
+            else:
+                api_url = f"{graph_url}/me/messages"
             
             # Log search parameters for debugging
             logger.debug(f"Search query: '{query}'")

modules/workflows/methods/methodOutlook/helpers/emailProcessing.py

@@ -53,7 +53,7 @@ class EmailProcessingHelper:
         # Handle common search operators
         # Recognize Graph operators including both singular and plural forms for hasAttachments
         lowered = clean_query.lower()
-        if any(op in lowered for op in ['from:', 'to:', 'subject:', 'received:', 'hasattachment:', 'hasattachments:']):
+        if any(op in lowered for op in ['from:', 'to:', 'subject:', 'body:', 'received:', 'hasattachment:', 'hasattachments:']):
             # This is an advanced search query, return as-is
             return clean_query
         
@@ -104,7 +104,7 @@ class EmailProcessingHelper:
         # Check if this is a complex search query with multiple operators
         # Recognize Graph operators including both singular and plural forms for hasAttachments
         lowered = clean_query.lower()
-        if any(op in lowered for op in ['from:', 'to:', 'subject:', 'received:', 'hasattachment:', 'hasattachments:']):
+        if any(op in lowered for op in ['from:', 'to:', 'subject:', 'body:', 'received:', 'hasattachment:', 'hasattachments:']):
             # This is an advanced search query, use $search
             # Microsoft Graph API supports complex search syntax
             params["$search"] = f'"{clean_query}"'
@@ -113,34 +113,20 @@ class EmailProcessingHelper:
             # We'll need to filter results after the API call
             # Folder filtering will be done after the API call
         else:
-            # Use $filter for basic text search, but keep it simple to avoid "InefficientFilter" error
-            # Microsoft Graph API has limitations on complex filters
+            # Use $search (KQL) instead of $filter to avoid "InefficientFilter" - Graph rejects
+            # contains(subject,x) + parentFolderId + orderby. $search handles subject:query.
             if len(clean_query) > 50:
-                # If query is too long, truncate it to avoid complex filter issues
                 clean_query = clean_query[:50]
-
-            
-            # Use only subject search to keep filter simple
-            # Handle wildcard queries specially
             if clean_query == "*" or clean_query == "":
-                # For wildcard or empty query, don't use contains filter
-                # Just use folder filter if specified
                 if folder and folder.lower() != "all":
                     params["$filter"] = f"parentFolderId eq '{folder}'"
-                else:
-                    # No filter needed for wildcard search across all folders
-                    pass
+                    params["$orderby"] = "receivedDateTime desc"
             else:
-                params["$filter"] = f"contains(subject,'{clean_query}')"
-                
-                # Add folder filter if specified
-                if folder and folder.lower() != "all":
-                    params["$filter"] = f"{params['$filter']} and parentFolderId eq '{folder}'"
-            
-            # Add orderby for basic queries
-            params["$orderby"] = "receivedDateTime desc"
+                # Use $search with subject: to avoid InefficientFilter
+                safe = clean_query.replace('"', '')
+                params["$search"] = f'"subject:{safe}"'
+                # Folder filtering done post-API in searchEmails when $search is used
 
-        
         return params
     
     def buildGraphFilter(self, filter_text: str) -> Dict[str, str]:
@@ -168,7 +154,7 @@ class EmailProcessingHelper:
         # Handle search queries (from:, to:, subject:, etc.) - check this FIRST
         # Support both singular and plural forms for hasAttachments
         lt = filter_text.lower()
-        if any(lt.startswith(prefix) for prefix in ['from:', 'to:', 'subject:', 'received:', 'hasattachment:', 'hasattachments:']):
+        if any(lt.startswith(prefix) for prefix in ['from:', 'to:', 'subject:', 'body:', 'received:', 'hasattachment:', 'hasattachments:']):
             return {"$search": f'"{filter_text}"'}
         
         # Handle email address filters (only if it's NOT a search query)

modules/workflows/methods/methodOutlook/helpers/folderManagement.py

@@ -27,10 +27,15 @@ class FolderManagementHelper:
     
     def getFolderId(self, folder_name: str, connection: Dict[str, Any]) -> Optional[str]:
         """
-        Get the folder ID for a given folder name
-        
-        This is needed for proper filtering when using advanced search queries
+        Get the folder ID for a given folder name or ID.
+        Returns the input as-is if it already looks like a Microsoft Graph folder ID.
         """
+        if not folder_name or not str(folder_name).strip():
+            return None
+        # Graph folder IDs are base64-like strings (e.g. AQMk...); return as-is
+        s = str(folder_name).strip()
+        if s.startswith("AQMk") and len(s) > 20 and " " not in s:
+            return s
         try:
             graph_url = "https://graph.microsoft.com/v1.0"
             headers = {

prompts/20260322-165228-001-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-165232-002-chapter_structure_generation_response.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Introduction",
+          "contentParts": {},
+          "generationHint": "Write an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Main Content",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email, addressing the points raised in the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Conclusion",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a polite closing statement and any necessary follow-up actions.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-165232-003-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Introduction",
+          "contentParts": {},
+          "generationHint": "Write an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Main Content",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email, addressing the points raised in the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Conclusion",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a polite closing statement and any necessary follow-up actions.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-165824-004-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-165827-005-chapter_structure_generation_response.txt

@@ -0,0 +1,39 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Email Response",
+    "filename": "email_response.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Introduction",
+        "contentParts": {},
+        "generationHint": "Generate a brief introduction for the email response.",
+        "sections": []
+      },
+      {
+        "id": "chapter_2",
+        "level": 1,
+        "title": "Main Content",
+        "contentParts": {},
+        "generationHint": "Generate the main body of the email response, addressing the points mentioned in the incoming email.",
+        "sections": []
+      },
+      {
+        "id": "chapter_3",
+        "level": 1,
+        "title": "Conclusion",
+        "contentParts": {},
+        "generationHint": "Generate a concise conclusion for the email response.",
+        "sections": []
+      }
+    ]
+  }]
+}

prompts/20260322-165827-006-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Introduction",
+          "contentParts": {},
+          "generationHint": "Generate a brief introduction for the email response.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Main Content",
+          "contentParts": {},
+          "generationHint": "Generate the main body of the email response, addressing the points mentioned in the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Conclusion",
+          "contentParts": {},
+          "generationHint": "Generate a concise conclusion for the email response.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-165827-007-email_composition_prompt.txt

@@ -0,0 +1,22 @@
+Compose an email based on this context:
+-------
+Reply to the following email(s):\n\nFrom: ida-dittrich@proton.me\nSubject: test\nContent:\nYou don't often get email from ida-dittrich@proton.me. Learn why this is important\r\n\r\n\r\ndies ist eine testmail.\r\n\r\n\r\nSent from Proton Mail for iOS.
+-------
+
+Recipients: (not specified - this is a draft)
+Style: business
+Max length: 1000 characters
+
+Available_Document_References:
+  - (no documents)
+
+Based on the context, decide which documents to attach.
+
+CRITICAL: Use EXACT document references from Available_Document_References above. For individual documents: ALWAYS use docItem:<documentId>:<filename> format (include filename)
+
+Return JSON:
+{
+  "subject": "subject line",
+  "body": "email body (HTML allowed)",
+  "attachments": ["docItem:<documentId>:<filename>"]
+}

prompts/20260322-165829-008-email_composition_response.txt

@@ -0,0 +1,7 @@
+```json
+{
+  "subject": "Re: Test",
+  "body": "Sehr geehrte Frau Dittrich,<br><br>vielen Dank für Ihre Testmail. Bitte lassen Sie mich wissen, wenn es noch etwas gibt, bei dem ich Ihnen behilflich sein kann.<br><br>Mit freundlichen Grüßen,<br>[Ihr Name]",
+  "attachments": []
+}
+```

prompts/20260322-170224-009-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-170228-010-chapter_structure_generation_response.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Generate an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email, addressing the key points from the incoming email and providing necessary information or responses.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schluss",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a closing statement, including any follow-up actions or expressions of gratitude.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-170228-011-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Generate an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email, addressing the key points from the incoming email and providing necessary information or responses.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schluss",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a closing statement, including any follow-up actions or expressions of gratitude.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171026-012-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-171030-013-chapter_structure_generation_response.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Introduction",
+          "contentParts": {},
+          "generationHint": "Generate an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Main Content",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email, addressing the points mentioned in the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Conclusion",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a polite closing statement and any necessary follow-up actions.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171030-014-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Introduction",
+          "contentParts": {},
+          "generationHint": "Generate an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Main Content",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email, addressing the points mentioned in the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Conclusion",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a polite closing statement and any necessary follow-up actions.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171117-015-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-171121-016-chapter_structure_generation_response.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Generate an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Create the main body of the email, addressing the key points from the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schluss",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a closing statement and any necessary follow-up actions.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171121-017-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Generate an introductory paragraph for the email response, setting the tone and context.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Create the main body of the email, addressing the key points from the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schluss",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a closing statement and any necessary follow-up actions.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171530-018-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-171533-019-chapter_structure_generation_response.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response Plan",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Formulate an introductory paragraph for the email response, setting the context for the reply.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email response, addressing the key points from the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schlussfolgerung",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a summary or closing remarks, ensuring a polite and professional tone.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171533-020-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response Plan",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Formulate an introductory paragraph for the email response, setting the context for the reply.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Develop the main body of the email response, addressing the key points from the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schlussfolgerung",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a summary or closing remarks, ensuring a polite and professional tone.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171534-021-email_composition_prompt.txt

@@ -0,0 +1,22 @@
+Compose an email based on this context:
+-------
+Reply to the following email(s):\n\nRecipient (reply to this address): ida-dittrich@proton.me\n\nFrom: ida-dittrich@proton.me\nSubject: test\nContent:\nYou don't often get email from ida-dittrich@proton.me. Learn why this is important\r\n\r\ndies ist eine testmail.\r\n\r\n\r\n\r\nSent from Proton Mail for iOS.\n\nFrom: p.motsch@valueon.ch\nSubject: FW: KI@SV Abgleich - Bericht Test Session\nContent:\nHallo Ida\r\n\r\nHier noch Futter für den Bericht nach der Session BEDAG von morgen.\r\nDies muss in unseren Bericht rein als Grundlage für die Entscheidungen auf Stufe Gesamtprojekt:\r\n\r\nFokus der Tests\r\n\r\n  *   Test auf «STA Sandbox» 19.03.2026, mit Cobra orgi\n\nFrom: notify@mail.notion.so\nSubject: Stephan Schellworth hat dich in Bedag Testschritt 3: Benchmark-Tests mit Cursor CLI erwähnt\nContent:\nStephan Schellworth hat dich in Bedag Testschritt 3: Benchmark-Tests mit Cursor CLI erwähnt\r\n17. März 2026, 08:15\r\n·PowerOn Startseite/05_Prozesse & Operations/Sprint Set Up/Tickets\r\nOwner\r\nI\r\nIda\r\nGehe zur Seite\r\nBenachrichtigungseinstellungen für diese \n\nFrom: notify@mail.notion.so\nSubject: Stephan Schellworth hat dich in Bedag Testschritt 4: Automatische Testfall-Ableitung erwähnt\nContent:\nStephan Schellworth hat dich in Bedag Testschritt 4: Automatische Testfall-Ableitung erwähnt\r\n17. März 2026, 08:15\r\n·PowerOn Startseite/05_Prozesse & Operations/Sprint Set Up/Tickets\r\nOwner\r\nI\r\nIda\r\nGehe zur Seite\r\nBenachrichtigungseinstellungen für diese\n\nFrom: notify@mail.notion.so\nSubject: Stephan Schellworth hat dich in Bedag Testschritt 2: Neutralisierten Code Dokumentieren erwähnt\nContent:\nStephan Schellworth hat dich in Bedag Testschritt 2: Neutralisierten Code Dokumentieren erwähnt\r\n17. März 2026, 08:15\r\n·PowerOn Startseite/05_Prozesse & Operations/Sprint Set Up/Tickets\r\nOwner\r\nI\r\nIda\r\nGehe zur Seite\r\nBenachrichtigungseinstellungen für di
+-------
+
+Recipients: ['ida-dittrich@proton.me']
+Style: business
+Max length: 1000 characters
+
+Available_Document_References:
+  - (no documents)
+
+Based on the context, decide which documents to attach.
+
+CRITICAL: Use EXACT document references from Available_Document_References above. For individual documents: ALWAYS use docItem:<documentId>:<filename> format (include filename)
+
+Return JSON:
+{
+  "subject": "subject line",
+  "body": "email body (HTML allowed)",
+  "attachments": ["docItem:<documentId>:<filename>"]
+}

prompts/20260322-171536-022-email_composition_response.txt

@@ -0,0 +1,7 @@
+```json
+{
+  "subject": "Re: Test Email",
+  "body": "Hallo Ida,<br><br>Vielen Dank für Ihre Testmail. Ich habe die Informationen aus der letzten Sitzung erhalten und werde sicherstellen, dass diese in unseren Bericht aufgenommen werden. Bitte lassen Sie mich wissen, wenn es noch weitere Details gibt, die berücksichtigt werden sollten.<br><br>Mit freundlichen Grüßen,<br><br>P. Motsch",
+  "attachments": []
+}
+```

prompts/20260322-171858-023-chapter_structure_generation_prompt.txt

@@ -0,0 +1,119 @@
+# TASK: Plan Document Structure (Documents + Chapters)
+
+This is a STRUCTURE PLANNING task. You define which documents to create and which chapters each document will have.
+Chapter CONTENT will be generated in a later step - here you only plan the STRUCTURE and assign content references.
+Return EXACTLY ONE complete JSON object. Do not generate multiple JSON objects, alternatives, or variations. Do not use separators like "---" between JSON objects.
+
+## USER REQUEST (for context)
+```
+formulier eine passende email zur eingehenden email
+```
+
+## AVAILABLE CONTENT PARTS
+
+(No content parts available)
+
+## CONTENT ASSIGNMENT RULE
+
+CRITICAL: Every chapter MUST have contentParts assigned if it relates to documents/images/data from the user request.
+If the user request mentions documents/images/data, then EVERY chapter that generates content related to those references MUST assign the relevant ContentParts explicitly.
+
+Assignment logic:
+- If chapter DISPLAYS a document/image → assign "object" format ContentPart with "caption"
+- If chapter generates text content ABOUT a document/image/data → assign ContentPart with "instruction":
+  - Prefer "extracted" format if available (contains analyzed/extracted content)
+  - If only "object" format is available, use "object" format with "instruction" (to write ABOUT the image/document)
+- If chapter's generationHint or purpose relates to a document/image/data mentioned in user request → it MUST have ContentParts assigned
+- Multiple chapters might assign the same ContentPart (e.g., one chapter displays image, another writes about it)
+- Use ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above
+- Empty contentParts are only allowed if chapter generates content WITHOUT referencing any documents/images/data from the user request
+
+CRITICAL RULE: If the user request mentions BOTH:
+  a) Documents/images/data (listed in AVAILABLE CONTENT PARTS above), AND
+  b) Generic content types (article text, main content, body text, etc.)
+Then chapters that generate those generic content types MUST assign the relevant ContentParts, because the content should relate to or be based on the provided documents/images/data.
+
+## CONTENT EFFICIENCY PRINCIPLES
+- Generate COMPACT content: Focus on essential information only
+- AVOID verbose, lengthy, or repetitive text - be concise and direct
+- Prioritize FACTS over filler text - no introductions like "In this chapter..."
+- Minimize system resources: shorter content = faster processing
+- Quality over quantity: precise, meaningful content rather than padding
+
+## CHAPTER STRUCTURE REQUIREMENTS
+- Generate chapters based on USER REQUEST - analyze what structure the user wants
+- Create ONLY the minimum chapters needed to cover the user's request - avoid over-structuring
+- HARD LIMIT: Maximum 7 chapters per document. If the topic can be covered in fewer, prefer fewer. Combine related topics into single chapters rather than creating many small ones.
+- IMPORTANT: Each chapter MUST have ALL these fields:
+  - id: Unique identifier (e.g., "chapter_1")
+  - level: Heading level (1, 2, 3, etc.)
+  - title: Chapter title
+  - contentParts: Object mapping ContentPart IDs to usage instructions (MUST assign if chapter relates to documents/data from user request)
+  - generationHint: Description of what content to generate (including formatting/styling requirements)
+  - sections: Empty array [] (REQUIRED - sections are generated in next phase)
+- contentParts: {"partId": {"instruction": "..."} or {"caption": "..."} or both} - Assign ContentParts as required by CONTENT ASSIGNMENT RULE above
+- The "instruction" field for each ContentPart MUST contain ALL relevant details from the USER REQUEST that apply to content extraction for this specific chapter. Include all formatting rules, data requirements, constraints, and specifications mentioned in the user request that are relevant for processing this ContentPart in this chapter.
+- generationHint: Keep CONCISE but include relevant details from the USER REQUEST. Focus on WHAT to generate, not HOW to phrase it verbosely.
+- The number of chapters depends on the user request - create only what is requested. Do NOT create chapters for topics without available data.
+
+CRITICAL: Only create chapters for CONTENT sections, not for formatting/styling requirements. Formatting/styling requirements to be included in each generationHint if needed.
+
+## DOCUMENT STRUCTURE
+
+For each document, determine:
+- outputFormat: From USER REQUEST (explicit mention or infer from purpose/content type). Default: "txt". Multiple documents can have different formats.
+- language: From USER REQUEST (map to ISO 639-1: de, en, fr, it...). Default: "de". Multiple documents can have different languages.
+- chapters: Structure appropriately for the format (e.g., pptx=slides, docx=sections, xlsx=worksheets). Match format capabilities and constraints.
+
+Required JSON fields:
+- metadata: {"title": "...", "language": "..."}
+- documents: Array with id, title, filename, outputFormat, language, chapters[]
+- chapters: Array with id, level, title, contentParts, generationHint, sections[]
+
+EXAMPLE STRUCTURE (for reference only - adapt to user request):
+{
+  "metadata": {
+    "title": "Document Title",
+    "language": "de"
+  },
+  "documents": [{
+    "id": "doc_1",
+    "title": "Document Title",
+    "filename": "document.txt",
+    "outputFormat": "txt",
+    "language": "de",
+    "chapters": [
+      {
+        "id": "chapter_1",
+        "level": 1,
+        "title": "Chapter Title",
+        "contentParts": {
+          "extracted_part_id": {
+            "instruction": "Use extracted content with ALL relevant details from user request"
+          }
+        },
+        "generationHint": "Detailed description including ALL relevant details from user request for this chapter",
+        "sections": []
+      }
+    ]
+  }]
+}
+
+CRITICAL INSTRUCTIONS:
+- Generate chapters based on USER REQUEST, NOT based on the example above
+- The example shows the JSON structure format, NOT the required chapters
+- Create only the chapters that match the user's request
+- Adapt chapter titles and structure to match the user's specific request
+- Determine outputFormat and language for each document by analyzing the USER REQUEST above
+- The example shows placeholders "txt" and "de" - YOU MUST REPLACE THESE with actual values determined from the USER REQUEST
+
+MANDATORY CONTENT ASSIGNMENT CHECK:
+For each chapter, verify:
+1. Does the user request mention documents/images/data? (e.g., "photo", "image", "document", "data", "based on", "about")
+2. Does this chapter's generationHint, title, or purpose relate to those documents/images/data mentioned in step 1?
+   - Examples: "article about the photo", "text describing the image", "analysis of the document", "content based on the data"
+   - Even if chapter doesn't explicitly say "about the image", if user request mentions both the image AND this chapter's content type → relate them
+3. If YES to both → chapter MUST have contentParts assigned (cannot be empty {})
+4. If ContentPart is "object" format and chapter needs to write ABOUT it → assign with "instruction" field, not just "caption"
+
+OUTPUT FORMAT: Start with { and end with }. Do NOT use markdown code fences (```json). Do NOT add explanatory text before or after the JSON. Return ONLY the JSON object itself.

prompts/20260322-171901-024-chapter_structure_generation_response.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Formulate a suitable introduction for the email response.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Develop the main content of the email response, addressing the points from the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schlussfolgerung",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a polite closing statement.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171901-025-chapter_structure_generation_final_result.txt

@@ -0,0 +1,41 @@
+{
+  "metadata": {
+    "title": "Email Response",
+    "language": "de"
+  },
+  "documents": [
+    {
+      "id": "doc_1",
+      "title": "Email Response",
+      "filename": "email_response.txt",
+      "outputFormat": "txt",
+      "language": "de",
+      "chapters": [
+        {
+          "id": "chapter_1",
+          "level": 1,
+          "title": "Einleitung",
+          "contentParts": {},
+          "generationHint": "Formulate a suitable introduction for the email response.",
+          "sections": []
+        },
+        {
+          "id": "chapter_2",
+          "level": 1,
+          "title": "Hauptteil",
+          "contentParts": {},
+          "generationHint": "Develop the main content of the email response, addressing the points from the incoming email.",
+          "sections": []
+        },
+        {
+          "id": "chapter_3",
+          "level": 1,
+          "title": "Schlussfolgerung",
+          "contentParts": {},
+          "generationHint": "Conclude the email with a polite closing statement.",
+          "sections": []
+        }
+      ]
+    }
+  ]
+}

prompts/20260322-171901-026-email_composition_prompt.txt

@@ -0,0 +1,22 @@
+Compose an email based on this context:
+-------
+Reply to the following email(s):\n\nRecipient (reply to this address): ida-dittrich@proton.me\n\nFrom: ida-dittrich@proton.me\nSubject: test\nContent:\nYou don't often get email from ida-dittrich@proton.me. Learn why this is important\r\n\r\ndies ist eine testmail.\r\n\r\n\r\n\r\nSent from Proton Mail for iOS.
+-------
+
+Recipients: ['ida-dittrich@proton.me']
+Style: business
+Max length: 1000 characters
+
+Available_Document_References:
+  - (no documents)
+
+Based on the context, decide which documents to attach.
+
+CRITICAL: Use EXACT document references from Available_Document_References above. For individual documents: ALWAYS use docItem:<documentId>:<filename> format (include filename)
+
+Return JSON:
+{
+  "subject": "subject line",
+  "body": "email body (HTML allowed)",
+  "attachments": ["docItem:<documentId>:<filename>"]
+}

prompts/20260322-171904-027-email_composition_response.txt

@@ -0,0 +1,7 @@
+```json
+{
+  "subject": "Re: test",
+  "body": "Sehr geehrte Frau Dittrich,<br><br>vielen Dank für Ihre Nachricht. Ich habe Ihre Testmail erhalten und stehe Ihnen gerne für weitere Fragen oder Anliegen zur Verfügung.<br><br>Mit freundlichen Grüßen,<br><br>[Ihr Name]",
+  "attachments": []
+}
+```