feat: sync main branch (#31938)

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.codex/skills/component-refactoring

@@ -0,0 +1 @@
+../../.agents/skills/component-refactoring

.codex/skills/frontend-code-review

@@ -0,0 +1 @@
+../../.agents/skills/frontend-code-review

.codex/skills/frontend-testing

@@ -0,0 +1 @@
+../../.agents/skills/frontend-testing

.codex/skills/orpc-contract-first

@@ -0,0 +1 @@
+../../.agents/skills/orpc-contract-first

.github/CODEOWNERS

@@ -24,10 +24,6 @@
 /api/services/tools/mcp_tools_manage_service.py @Nov1c444
 /api/controllers/mcp/ @Nov1c444
 /api/controllers/console/app/mcp_server.py @Nov1c444
-
-# Backend - Tests
-/api/tests/ @laipz8200 @QuantumGhost
-
 /api/tests/**/*mcp* @Nov1c444
 
 # Backend - Workflow - Engine (Core graph execution engine)
@@ -238,9 +234,6 @@
 # Frontend - Base Components
 /web/app/components/base/ @iamjoel @zxhlyh
 
-# Frontend - Base Components Tests
-/web/app/components/base/**/*.spec.tsx @hyoban @CodingOnStar
-
 # Frontend - Utils and Hooks
 /web/utils/classnames.ts @iamjoel @zxhlyh
 /web/utils/time.ts @iamjoel @zxhlyh

.github/workflows/autofix.yml

@@ -79,6 +79,29 @@ jobs:
           find . -name "*.py" -type f -exec sed -i.bak -E 's/"([^"]+)" \| None/Optional["\1"]/g; s/'"'"'([^'"'"']+)'"'"' \| None/Optional['"'"'\1'"'"']/g' {} \;
           find . -name "*.py.bak" -type f -delete
 
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          package_json_file: web/package.json
+          run_install: false
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: 24
+          cache: pnpm
+          cache-dependency-path: ./web/pnpm-lock.yaml
+
+      - name: Install web dependencies
+        run: |
+          cd web
+          pnpm install --frozen-lockfile
+
+      - name: ESLint autofix
+        run: |
+          cd web
+          pnpm lint:fix || true
+
       # mdformat breaks YAML front matter in markdown files. Add --exclude for directories containing YAML front matter.
       - name: mdformat
         run: |

.github/workflows/deploy-hitl.yml

@@ -4,7 +4,8 @@ on:
   workflow_run:
     workflows: ["Build and Push API & Web"]
     branches:
-      - "build/feat/hitl"
+      - "feat/hitl-frontend"
+      - "feat/hitl-backend"
     types:
       - completed
 
@@ -13,7 +14,10 @@ jobs:
     runs-on: ubuntu-latest
     if: |
       github.event.workflow_run.conclusion == 'success' &&
-      github.event.workflow_run.head_branch == 'build/feat/hitl'
+      (
+        github.event.workflow_run.head_branch == 'feat/hitl-frontend' ||
+        github.event.workflow_run.head_branch == 'feat/hitl-backend'
+      )
     steps:
       - name: Deploy to server
         uses: appleboy/ssh-action@v1

.github/workflows/web-tests.yml

@@ -39,7 +39,7 @@ jobs:
         run: pnpm install --frozen-lockfile
 
       - name: Run tests
-        run: pnpm test:ci
+        run: pnpm test:coverage
 
       - name: Coverage Summary
         if: always()

api/.importlinter

@@ -136,6 +136,7 @@ ignore_imports =
     core.workflow.nodes.llm.llm_utils -> models.provider
     core.workflow.nodes.llm.llm_utils -> services.credit_pool_service
     core.workflow.nodes.llm.node -> core.tools.signature
+    core.workflow.nodes.template_transform.template_transform_node -> configs
     core.workflow.nodes.tool.tool_node -> core.callback_handler.workflow_tool_callback_handler
     core.workflow.nodes.tool.tool_node -> core.tools.tool_engine
     core.workflow.nodes.tool.tool_node -> core.tools.tool_manager

api/README.md

@@ -122,7 +122,7 @@ These commands assume you start from the repository root.
 
    ```bash
    cd api
-   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q api_token,dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
+   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
    ```
 
 1. Optional: start Celery Beat (scheduled tasks, in a new terminal).

api/commands.py

@@ -739,10 +739,8 @@ def upgrade_db():
 
             click.echo(click.style("Database migration successful!", fg="green"))
 
-        except Exception as e:
+        except Exception:
             logger.exception("Failed to execute database migration")
-            click.echo(click.style(f"Database migration failed: {e}", fg="red"))
-            raise SystemExit(1)
         finally:
             lock.release()
     else:

api/configs/feature/__init__.py

@@ -1155,16 +1155,6 @@ class CeleryScheduleTasksConfig(BaseSettings):
         default=0,
     )
 
-    # API token last_used_at batch update
-    ENABLE_API_TOKEN_LAST_USED_UPDATE_TASK: bool = Field(
-        description="Enable periodic batch update of API token last_used_at timestamps",
-        default=True,
-    )
-    API_TOKEN_LAST_USED_UPDATE_INTERVAL: int = Field(
-        description="Interval in minutes for batch updating API token last_used_at (default 30)",
-        default=30,
-    )
-
     # Trigger provider refresh (simple version)
     ENABLE_TRIGGER_PROVIDER_REFRESH_TASK: bool = Field(
         description="Enable trigger provider refresh poller",

api/controllers/console/apikey.py

@@ -10,7 +10,6 @@ from libs.helper import TimestampField
 from libs.login import current_account_with_tenant, login_required
 from models.dataset import Dataset
 from models.model import ApiToken, App
-from services.api_token_service import ApiTokenCache
 
 from . import console_ns
 from .wraps import account_initialization_required, edit_permission_required, setup_required
@@ -132,11 +131,6 @@ class BaseApiKeyResource(Resource):
         if key is None:
             flask_restx.abort(HTTPStatus.NOT_FOUND, message="API key not found")
 
-        # Invalidate cache before deleting from database
-        # Type assertion: key is guaranteed to be non-None here because abort() raises
-        assert key is not None  # nosec - for type checker only
-        ApiTokenCache.delete(key.token, key.type)
-
         db.session.query(ApiToken).where(ApiToken.id == api_key_id).delete()
         db.session.commit()
 

api/controllers/console/app/app.py

@@ -1,4 +1,3 @@
-import logging
 import uuid
 from datetime import datetime
 from typing import Any, Literal, TypeAlias
@@ -55,8 +54,6 @@ ALLOW_CREATE_APP_MODES = ["chat", "agent-chat", "advanced-chat", "workflow", "co
 
 register_enum_models(console_ns, IconType)
 
-_logger = logging.getLogger(__name__)
-
 
 class AppListQuery(BaseModel):
     page: int = Field(default=1, ge=1, le=99999, description="Page number (1-99999)")
@@ -502,7 +499,6 @@ class AppListApi(Resource):
                     select(Workflow).where(
                         Workflow.version == Workflow.VERSION_DRAFT,
                         Workflow.app_id.in_(workflow_capable_app_ids),
-                        Workflow.tenant_id == current_tenant_id,
                     )
                 )
                 .scalars()
@@ -514,14 +510,12 @@ class AppListApi(Resource):
                 NodeType.TRIGGER_PLUGIN,
             }
             for workflow in draft_workflows:
-                node_id = None
                 try:
-                    for node_id, node_data in workflow.walk_nodes():
+                    for _, node_data in workflow.walk_nodes():
                         if node_data.get("type") in trigger_node_types:
                             draft_trigger_app_ids.add(str(workflow.app_id))
                             break
                 except Exception:
-                    _logger.exception("error while walking nodes, workflow_id=%s, node_id=%s", workflow.id, node_id)
                     continue
 
         for app in app_pagination.items:

api/controllers/console/app/generator.py

@@ -1,8 +1,12 @@
+import logging
 from collections.abc import Sequence
 
 from flask_restx import Resource
 from pydantic import BaseModel, Field
 
+logger = logging.getLogger(__name__)
+
+
 from controllers.console import console_ns
 from controllers.console.app.error import (
     CompletionRequestError,
@@ -19,6 +23,7 @@ from core.helper.code_executor.python3.python3_code_provider import Python3CodeP
 from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.llm_generator import LLMGenerator
 from core.model_runtime.errors.invoke import InvokeError
+from core.workflow.generator import WorkflowGenerator
 from extensions.ext_database import db
 from libs.login import current_account_with_tenant, login_required
 from models import App
@@ -41,6 +46,30 @@ class InstructionTemplatePayload(BaseModel):
     type: str = Field(..., description="Instruction template type")
 
 
+class PreviousWorkflow(BaseModel):
+    """Previous workflow attempt for regeneration context."""
+
+    nodes: list[dict[str, Any]] = Field(default_factory=list, description="Previously generated nodes")
+    edges: list[dict[str, Any]] = Field(default_factory=list, description="Previously generated edges")
+    warnings: list[str] = Field(default_factory=list, description="Warnings from previous generation")
+
+
+class FlowchartGeneratePayload(BaseModel):
+    instruction: str = Field(..., description="Workflow flowchart generation instruction")
+    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
+    available_nodes: list[dict[str, Any]] = Field(default_factory=list, description="Available node types")
+    existing_nodes: list[dict[str, Any]] = Field(default_factory=list, description="Existing workflow nodes")
+    existing_edges: list[dict[str, Any]] = Field(default_factory=list, description="Existing workflow edges")
+    available_tools: list[dict[str, Any]] = Field(default_factory=list, description="Available tools")
+    selected_node_ids: list[str] = Field(default_factory=list, description="IDs of selected nodes for context")
+    previous_workflow: PreviousWorkflow | None = Field(default=None, description="Previous workflow for regeneration")
+    regenerate_mode: bool = Field(default=False, description="Whether this is a regeneration request")
+    # Language preference for generated content (node titles, descriptions)
+    language: str | None = Field(default=None, description="Preferred language for generated content")
+    # Available models that user has configured (for LLM/question-classifier nodes)
+    available_models: list[dict[str, Any]] = Field(default_factory=list, description="User's configured models")
+
+
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -50,6 +79,7 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
+reg(FlowchartGeneratePayload)
 reg(ModelConfig)
 
 
@@ -240,6 +270,52 @@ class InstructionGenerateApi(Resource):
             raise CompletionRequestError(e.description)
 
 
+@console_ns.route("/flowchart-generate")
+class FlowchartGenerateApi(Resource):
+    @console_ns.doc("generate_workflow_flowchart")
+    @console_ns.doc(description="Generate workflow flowchart using LLM with intent classification")
+    @console_ns.expect(console_ns.models[FlowchartGeneratePayload.__name__])
+    @console_ns.response(200, "Flowchart generated successfully")
+    @console_ns.response(400, "Invalid request parameters")
+    @console_ns.response(402, "Provider quota exceeded")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        args = FlowchartGeneratePayload.model_validate(console_ns.payload)
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            # Convert PreviousWorkflow to dict if present
+            previous_workflow_dict = args.previous_workflow.model_dump() if args.previous_workflow else None
+
+            result = WorkflowGenerator.generate_workflow_flowchart(
+                tenant_id=current_tenant_id,
+                instruction=args.instruction,
+                model_config=args.model_config_data,
+                available_nodes=args.available_nodes,
+                existing_nodes=args.existing_nodes,
+                existing_edges=args.existing_edges,
+                available_tools=args.available_tools,
+                selected_node_ids=args.selected_node_ids,
+                previous_workflow=previous_workflow_dict,
+                regenerate_mode=args.regenerate_mode,
+                preferred_language=args.language,
+                available_models=args.available_models,
+            )
+
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+
+        return result
+
+
 @console_ns.route("/instruction-generate/template")
 class InstructionGenerationTemplateApi(Resource):
     @console_ns.doc("get_instruction_template")

api/controllers/console/datasets/datasets.py

@@ -55,7 +55,6 @@ from libs.login import current_account_with_tenant, login_required
 from models import ApiToken, Dataset, Document, DocumentSegment, UploadFile
 from models.dataset import DatasetPermissionEnum
 from models.provider_ids import ModelProviderID
-from services.api_token_service import ApiTokenCache
 from services.dataset_service import DatasetPermissionService, DatasetService, DocumentService
 
 # Register models for flask_restx to avoid dict type issues in Swagger
@@ -821,11 +820,6 @@ class DatasetApiDeleteApi(Resource):
         if key is None:
             console_ns.abort(404, message="API key not found")
 
-        # Invalidate cache before deleting from database
-        # Type assertion: key is guaranteed to be non-None here because abort() raises
-        assert key is not None  # nosec - for type checker only
-        ApiTokenCache.delete(key.token, key.type)
-
         db.session.query(ApiToken).where(ApiToken.id == api_key_id).delete()
         db.session.commit()
 

api/controllers/console/tag/tags.py

@@ -120,7 +120,7 @@ class TagUpdateDeleteApi(Resource):
 
         TagService.delete_tag(tag_id)
 
-        return "", 204
+        return 204
 
 
 @console_ns.route("/tag-bindings/create")

api/controllers/service_api/dataset/dataset.py

@@ -396,7 +396,7 @@ class DatasetApi(DatasetApiResource):
         try:
             if DatasetService.delete_dataset(dataset_id_str, current_user):
                 DatasetPermissionService.clear_partial_member_list(dataset_id_str)
-                return "", 204
+                return 204
             else:
                 raise NotFound("Dataset not found.")
         except services.errors.dataset.DatasetInUseError:
@@ -557,7 +557,7 @@ class DatasetTagsApi(DatasetApiResource):
         payload = TagDeletePayload.model_validate(service_api_ns.payload or {})
         TagService.delete_tag(payload.tag_id)
 
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/tags/binding")
@@ -581,7 +581,7 @@ class DatasetTagBindingApi(DatasetApiResource):
         payload = TagBindingPayload.model_validate(service_api_ns.payload or {})
         TagService.save_tag_binding({"tag_ids": payload.tag_ids, "target_id": payload.target_id, "type": "knowledge"})
 
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/tags/unbinding")
@@ -605,7 +605,7 @@ class DatasetTagUnbindingApi(DatasetApiResource):
         payload = TagUnbindingPayload.model_validate(service_api_ns.payload or {})
         TagService.delete_tag_binding({"tag_id": payload.tag_id, "target_id": payload.target_id, "type": "knowledge"})
 
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/<uuid:dataset_id>/tags")

api/controllers/service_api/dataset/document.py

@@ -746,4 +746,4 @@ class DocumentApi(DatasetApiResource):
         except services.errors.document.DocumentIndexingError:
             raise DocumentIndexingError("Cannot delete document during indexing.")
 
-        return "", 204
+        return 204

api/controllers/service_api/dataset/metadata.py

@@ -128,7 +128,7 @@ class DatasetMetadataServiceApi(DatasetApiResource):
         DatasetService.check_dataset_permission(dataset, current_user)
 
         MetadataService.delete_metadata(dataset_id_str, metadata_id_str)
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/<uuid:dataset_id>/metadata/built-in")

api/controllers/service_api/dataset/segment.py

@@ -233,7 +233,7 @@ class DatasetSegmentApi(DatasetApiResource):
         if not segment:
             raise NotFound("Segment not found.")
         SegmentService.delete_segment(segment, document, dataset)
-        return "", 204
+        return 204
 
     @service_api_ns.expect(service_api_ns.models[SegmentUpdatePayload.__name__])
     @service_api_ns.doc("update_segment")
@@ -499,7 +499,7 @@ class DatasetChildChunkApi(DatasetApiResource):
         except ChildChunkDeleteIndexServiceError as e:
             raise ChildChunkDeleteIndexError(str(e))
 
-        return "", 204
+        return 204
 
     @service_api_ns.expect(service_api_ns.models[ChildChunkUpdatePayload.__name__])
     @service_api_ns.doc("update_child_chunk")

api/controllers/service_api/wraps.py

@@ -1,24 +1,27 @@
 import logging
 import time
 from collections.abc import Callable
+from datetime import timedelta
 from enum import StrEnum, auto
 from functools import wraps
-from typing import Concatenate, ParamSpec, TypeVar, cast
+from typing import Concatenate, ParamSpec, TypeVar
 
 from flask import current_app, request
 from flask_login import user_logged_in
 from flask_restx import Resource
 from pydantic import BaseModel
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, NotFound, Unauthorized
 
 from enums.cloud_plan import CloudPlan
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
+from libs.datetime_utils import naive_utc_now
 from libs.login import current_user
 from models import Account, Tenant, TenantAccountJoin, TenantStatus
 from models.dataset import Dataset, RateLimitLog
 from models.model import ApiToken, App
-from services.api_token_service import ApiTokenCache, fetch_token_with_single_flight, record_token_usage
 from services.end_user_service import EndUserService
 from services.feature_service import FeatureService
 
@@ -293,14 +296,7 @@ def validate_dataset_token(view: Callable[Concatenate[T, P], R] | None = None):
 
 def validate_and_get_api_token(scope: str | None = None):
     """
-    Validate and get API token with Redis caching.
-
-    This function uses a two-tier approach:
-    1. First checks Redis cache for the token
-    2. If not cached, queries database and caches the result
-
-    The last_used_at field is updated asynchronously via Celery task
-    to avoid blocking the request.
+    Validate and get API token.
     """
     auth_header = request.headers.get("Authorization")
     if auth_header is None or " " not in auth_header:
@@ -312,18 +308,29 @@ def validate_and_get_api_token(scope: str | None = None):
     if auth_scheme != "bearer":
         raise Unauthorized("Authorization scheme must be 'Bearer'")
 
-    # Try to get token from cache first
-    # Returns a CachedApiToken (plain Python object), not a SQLAlchemy model
-    cached_token = ApiTokenCache.get(auth_token, scope)
-    if cached_token is not None:
-        logger.debug("Token validation served from cache for scope: %s", scope)
-        # Record usage in Redis for later batch update (no Celery task per request)
-        record_token_usage(auth_token, scope)
-        return cast(ApiToken, cached_token)
+    current_time = naive_utc_now()
+    cutoff_time = current_time - timedelta(minutes=1)
+    with Session(db.engine, expire_on_commit=False) as session:
+        update_stmt = (
+            update(ApiToken)
+            .where(
+                ApiToken.token == auth_token,
+                (ApiToken.last_used_at.is_(None) | (ApiToken.last_used_at < cutoff_time)),
+                ApiToken.type == scope,
+            )
+            .values(last_used_at=current_time)
+        )
+        stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
+        result = session.execute(update_stmt)
+        api_token = session.scalar(stmt)
 
-    # Cache miss - use Redis lock for single-flight mode
-    # This ensures only one request queries DB for the same token concurrently
-    return fetch_token_with_single_flight(auth_token, scope)
+        if hasattr(result, "rowcount") and result.rowcount > 0:
+            session.commit()
+
+        if not api_token:
+            raise Unauthorized("Access token is invalid")
+
+    return api_token
 
 
 class DatasetApiResource(Resource):

api/core/app/workflow/node_factory.py

@@ -47,7 +47,6 @@ class DifyNodeFactory(NodeFactory):
         code_providers: Sequence[type[CodeNodeProvider]] | None = None,
         code_limits: CodeNodeLimits | None = None,
         template_renderer: Jinja2TemplateRenderer | None = None,
-        template_transform_max_output_length: int | None = None,
         http_request_http_client: HttpClientProtocol | None = None,
         http_request_tool_file_manager_factory: Callable[[], ToolFileManager] = ToolFileManager,
         http_request_file_manager: FileManagerProtocol | None = None,
@@ -69,9 +68,6 @@ class DifyNodeFactory(NodeFactory):
             max_object_array_length=dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH,
         )
         self._template_renderer = template_renderer or CodeExecutorJinja2TemplateRenderer()
-        self._template_transform_max_output_length = (
-            template_transform_max_output_length or dify_config.TEMPLATE_TRANSFORM_MAX_LENGTH
-        )
         self._http_request_http_client = http_request_http_client or ssrf_proxy
         self._http_request_tool_file_manager_factory = http_request_tool_file_manager_factory
         self._http_request_file_manager = http_request_file_manager or file_manager
@@ -126,7 +122,6 @@ class DifyNodeFactory(NodeFactory):
                 graph_init_params=self.graph_init_params,
                 graph_runtime_state=self.graph_runtime_state,
                 template_renderer=self._template_renderer,
-                max_output_length=self._template_transform_max_output_length,
             )
 
         if node_type == NodeType.HTTP_REQUEST:

api/core/helper/marketplace.py

@@ -6,8 +6,7 @@ from yarl import URL
 
 from configs import dify_config
 from core.helper.download import download_with_size_limit
-from core.plugin.entities.marketplace import MarketplacePluginDeclaration, MarketplacePluginSnapshot
-from extensions.ext_redis import redis_client
+from core.plugin.entities.marketplace import MarketplacePluginDeclaration
 
 marketplace_api_url = URL(str(dify_config.MARKETPLACE_API_URL))
 logger = logging.getLogger(__name__)
@@ -44,37 +43,28 @@ def batch_fetch_plugin_by_ids(plugin_ids: list[str]) -> list[dict]:
     return data.get("data", {}).get("plugins", [])
 
 
+def batch_fetch_plugin_manifests_ignore_deserialization_error(
+    plugin_ids: list[str],
+) -> Sequence[MarketplacePluginDeclaration]:
+    if len(plugin_ids) == 0:
+        return []
+
+    url = str(marketplace_api_url / "api/v1/plugins/batch")
+    response = httpx.post(url, json={"plugin_ids": plugin_ids}, headers={"X-Dify-Version": dify_config.project.version})
+    response.raise_for_status()
+    result: list[MarketplacePluginDeclaration] = []
+    for plugin in response.json()["data"]["plugins"]:
+        try:
+            result.append(MarketplacePluginDeclaration.model_validate(plugin))
+        except Exception:
+            logger.exception(
+                "Failed to deserialize marketplace plugin manifest for %s", plugin.get("plugin_id", "unknown")
+            )
+
+    return result
+
+
 def record_install_plugin_event(plugin_unique_identifier: str):
     url = str(marketplace_api_url / "api/v1/stats/plugins/install_count")
     response = httpx.post(url, json={"unique_identifier": plugin_unique_identifier})
     response.raise_for_status()
-
-
-def fetch_global_plugin_manifest(cache_key_prefix: str, cache_ttl: int) -> None:
-    """
-    Fetch all plugin manifests from marketplace and cache them in Redis.
-    This should be called once per check cycle to populate the instance-level cache.
-
-    Args:
-        cache_key_prefix: Redis key prefix for caching plugin manifests
-        cache_ttl: Cache TTL in seconds
-
-    Raises:
-        httpx.HTTPError: If the HTTP request fails
-        Exception: If any other error occurs during fetching or caching
-    """
-    url = str(marketplace_api_url / "api/v1/dist/plugins/manifest.json")
-    response = httpx.get(url, headers={"X-Dify-Version": dify_config.project.version}, timeout=30)
-    response.raise_for_status()
-
-    raw_json = response.json()
-    plugins_data = raw_json.get("plugins", [])
-
-    # Parse and cache all plugin snapshots
-    for plugin_data in plugins_data:
-        plugin_snapshot = MarketplacePluginSnapshot.model_validate(plugin_data)
-        redis_client.setex(
-            name=f"{cache_key_prefix}{plugin_snapshot.plugin_id}",
-            time=cache_ttl,
-            value=plugin_snapshot.model_dump_json(),
-        )

api/core/helper/ssrf_proxy.py

@@ -171,9 +171,10 @@ def make_request(method: str, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETR
             # httpx may override the Host header when using a proxy
             headers = {k: v for k, v in headers.items() if k.lower() != "host"}
             if user_provided_host is not None:
-                headers["host"] = user_provided_host
-            kwargs["headers"] = headers
-            response = client.request(method=method, url=url, **kwargs)
+                headers["Host"] = user_provided_host
+
+            request = client.build_request(method, url, headers=headers, **kwargs)
+            response = client.send(request, follow_redirects=follow_redirects)
 
             # Check for SSRF protection by Squid proxy
             if response.status_code in (401, 403):

api/core/llm_generator/llm_generator.py

@@ -1,6 +1,5 @@
 import json
 import logging
-import re
 from collections.abc import Sequence
 from typing import Protocol, cast
 
@@ -14,8 +13,6 @@ from core.llm_generator.prompts import (
     CONVERSATION_TITLE_PROMPT,
     GENERATOR_QA_PROMPT,
     JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE,
-    LLM_MODIFY_CODE_SYSTEM,
-    LLM_MODIFY_PROMPT_SYSTEM,
     PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE,
     SUGGESTED_QUESTIONS_MAX_TOKENS,
     SUGGESTED_QUESTIONS_TEMPERATURE,
@@ -32,6 +29,7 @@ from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
 from core.ops.utils import measure_time
 from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
+from core.workflow.generator import WorkflowGenerator
 from extensions.ext_database import db
 from extensions.ext_storage import storage
 from models import App, Message, WorkflowNodeExecutionModel
@@ -285,6 +283,35 @@ class LLMGenerator:
 
         return rule_config
 
+    @classmethod
+    def generate_workflow_flowchart(
+        cls,
+        tenant_id: str,
+        instruction: str,
+        model_config: dict,
+        available_nodes: Sequence[dict[str, object]] | None = None,
+        existing_nodes: Sequence[dict[str, object]] | None = None,
+        available_tools: Sequence[dict[str, object]] | None = None,
+        selected_node_ids: Sequence[str] | None = None,
+        previous_workflow: dict[str, object] | None = None,
+        regenerate_mode: bool = False,
+        preferred_language: str | None = None,
+        available_models: Sequence[dict[str, object]] | None = None,
+    ):
+        return WorkflowGenerator.generate_workflow_flowchart(
+            tenant_id=tenant_id,
+            instruction=instruction,
+            model_config=model_config,
+            available_nodes=available_nodes,
+            existing_nodes=existing_nodes,
+            available_tools=available_tools,
+            selected_node_ids=selected_node_ids,
+            previous_workflow=previous_workflow,
+            regenerate_mode=regenerate_mode,
+            preferred_language=preferred_language,
+            available_models=available_models,
+        )
+
     @classmethod
     def generate_code(
         cls,

api/core/llm_generator/prompts.py

@@ -143,6 +143,50 @@ Based on task description, please create a well-structured prompt template that
 Please generate the full prompt template with at least 300 words and output only the prompt template.
 """  # noqa: E501
 
+WORKFLOW_FLOWCHART_PROMPT_TEMPLATE = """
+You are an expert workflow designer. Generate a Mermaid flowchart based on the user's request.
+
+Constraints:
+- Detect the language of the user's request. Generate all node titles in the same language as the user's input.
+- If the input language cannot be determined, use {{PREFERRED_LANGUAGE}} as the fallback language.
+- Use only node types listed in <available_nodes>.
+- Use only tools listed in <available_tools>. When using a tool node, set type=tool and tool=<tool_key>.
+- Tools may include MCP providers (provider_type=mcp). Tool selection still uses tool_key.
+- Prefer reusing node titles from <existing_nodes> when possible.
+- Output must be valid Mermaid flowchart syntax, no markdown, no extra text.
+- First line must be: flowchart LR
+- Every node must be declared on its own line using:
+  <id>["type=<type>|title=<title>|tool=<tool_key>"]
+  - type is required and must match a type in <available_nodes>.
+  - title is required for non-tool nodes.
+  - tool is required only when type=tool, otherwise omit tool.
+- Declare all node lines before any edges.
+- Edges must use:
+  <id> --> <id>
+  <id> -->|true| <id>
+  <id> -->|false| <id>
+- Keep node ids unique and simple (N1, N2, ...).
+- For complex orchestration:
+  - Break the request into stages (ingest, transform, decision, action, output).
+  - Use IfElse for branching and label edges true/false only.
+  - Fan-in branches by connecting multiple nodes into a shared downstream node.
+  - Avoid cycles unless explicitly requested.
+  - Keep each branch complete with a clear downstream target.
+
+<user_request>
+{{TASK_DESCRIPTION}}
+</user_request>
+<available_nodes>
+{{AVAILABLE_NODES}}
+</available_nodes>
+<existing_nodes>
+{{EXISTING_NODES}}
+</existing_nodes>
+<available_tools>
+{{AVAILABLE_TOOLS}}
+</available_tools>
+"""
+
 RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """
 Here is a task description for which I would like you to create a high-quality prompt template for:
 <task_description>

api/core/plugin/entities/marketplace.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, computed_field, model_validator
+from pydantic import BaseModel, Field, model_validator
 
 from core.model_runtime.entities.provider_entities import ProviderEntity
 from core.plugin.entities.endpoint import EndpointProviderDeclaration
@@ -48,15 +48,3 @@ class MarketplacePluginDeclaration(BaseModel):
         if "tool" in data and not data["tool"]:
             del data["tool"]
         return data
-
-
-class MarketplacePluginSnapshot(BaseModel):
-    org: str
-    name: str
-    latest_version: str
-    latest_package_identifier: str
-    latest_package_url: str
-
-    @computed_field
-    def plugin_id(self) -> str:
-        return f"{self.org}/{self.name}"

api/core/variables/variables.py

@@ -112,7 +112,7 @@ class ArrayBooleanVariable(ArrayBooleanSegment, ArrayVariable):
 
 class RAGPipelineVariable(BaseModel):
     belong_to_node_id: str = Field(description="belong to which node id, shared means public")
-    type: str = Field(description="variable type, text-input, paragraph, select, number, file, file-list")
+    type: str = Field(description="variable type, text-input, paragraph, select, number,  file, file-list")
     label: str = Field(description="label")
     description: str | None = Field(description="description", default="")
     variable: str = Field(description="variable key", default="")

api/core/workflow/generator/__init__.py

@@ -0,0 +1 @@
+from .runner import WorkflowGenerator

api/core/workflow/generator/config/__init__.py

@@ -0,0 +1,29 @@
+"""
+Vibe Workflow Generator Configuration Module.
+
+This module centralizes configuration for the Vibe workflow generation feature,
+including node schemas, fallback rules, and response templates.
+"""
+
+from core.workflow.generator.config.node_schemas import (
+    BUILTIN_NODE_SCHEMAS,
+    FALLBACK_RULES,
+    FIELD_NAME_CORRECTIONS,
+    NODE_TYPE_ALIASES,
+    get_builtin_node_schemas,
+    get_corrected_field_name,
+    validate_node_schemas,
+)
+from core.workflow.generator.config.responses import DEFAULT_SUGGESTIONS, OFF_TOPIC_RESPONSES
+
+__all__ = [
+    "BUILTIN_NODE_SCHEMAS",
+    "DEFAULT_SUGGESTIONS",
+    "FALLBACK_RULES",
+    "FIELD_NAME_CORRECTIONS",
+    "NODE_TYPE_ALIASES",
+    "OFF_TOPIC_RESPONSES",
+    "get_builtin_node_schemas",
+    "get_corrected_field_name",
+    "validate_node_schemas",
+]

api/core/workflow/generator/config/node_schemas.py

@@ -0,0 +1,501 @@
+"""
+Unified Node Configuration for Vibe Workflow Generation.
+
+This module centralizes all node-related configuration:
+- Node schemas (parameter definitions)
+- Fallback rules (keyword-based node type inference)
+- Node type aliases (natural language to canonical type mapping)
+- Field name corrections (LLM output normalization)
+- Validation utilities
+
+Note: These definitions are the single source of truth.
+Frontend has a mirrored copy at web/app/components/workflow/hooks/use-workflow-vibe-config.ts
+"""
+
+from typing import Any
+
+# =============================================================================
+# NODE SCHEMAS
+# =============================================================================
+
+# Built-in node schemas with parameter definitions
+# These help the model understand what config each node type requires
+_HARDCODED_SCHEMAS: dict[str, dict[str, Any]] = {
+    "http-request": {
+        "description": "Send HTTP requests to external APIs or fetch web content",
+        "required": ["url", "method"],
+        "parameters": {
+            "url": {
+                "type": "string",
+                "description": "Full URL including protocol (https://...)",
+                "example": "{{#start.url#}} or https://api.example.com/data",
+            },
+            "method": {
+                "type": "enum",
+                "options": ["GET", "POST", "PUT", "DELETE", "PATCH", "HEAD"],
+                "description": "HTTP method",
+            },
+            "headers": {
+                "type": "string",
+                "description": "HTTP headers as newline-separated 'Key: Value' pairs",
+                "example": "Content-Type: application/json\nAuthorization: Bearer {{#start.api_key#}}",
+            },
+            "params": {
+                "type": "string",
+                "description": "URL query parameters as newline-separated 'key: value' pairs",
+            },
+            "body": {
+                "type": "object",
+                "description": "Request body with type field required",
+                "example": {"type": "none", "data": []},
+            },
+            "authorization": {
+                "type": "object",
+                "description": "Authorization config",
+                "example": {"type": "no-auth"},
+            },
+            "timeout": {
+                "type": "number",
+                "description": "Request timeout in seconds",
+                "default": 60,
+            },
+        },
+        "outputs": ["body (response content)", "status_code", "headers"],
+    },
+    "code": {
+        "description": "Execute Python or JavaScript code for custom logic",
+        "required": ["code", "language"],
+        "parameters": {
+            "code": {
+                "type": "string",
+                "description": "Code to execute. Must define a main() function that returns a dict.",
+            },
+            "language": {
+                "type": "enum",
+                "options": ["python3", "javascript"],
+            },
+            "variables": {
+                "type": "array",
+                "description": "Input variables passed to the code",
+                "item_schema": {"variable": "string", "value_selector": "array"},
+            },
+            "outputs": {
+                "type": "object",
+                "description": "Output variable definitions",
+            },
+        },
+        "outputs": ["Variables defined in outputs schema"],
+    },
+    "llm": {
+        "description": "Call a large language model for text generation/processing",
+        "required": ["prompt_template"],
+        "parameters": {
+            "model": {
+                "type": "object",
+                "description": "Model configuration (provider, name, mode)",
+            },
+            "prompt_template": {
+                "type": "array",
+                "description": "Messages for the LLM",
+                "item_schema": {
+                    "role": "enum: system, user, assistant",
+                    "text": "string - message content, can include {{#node_id.field#}} references",
+                },
+            },
+            "context": {
+                "type": "object",
+                "description": "Optional context settings",
+            },
+            "memory": {
+                "type": "object",
+                "description": "Optional memory/conversation settings",
+            },
+        },
+        "outputs": ["text (generated response)"],
+    },
+    "if-else": {
+        "description": "Conditional branching based on conditions",
+        "required": ["cases"],
+        "parameters": {
+            "cases": {
+                "type": "array",
+                "description": "List of condition cases. Each case defines when 'true' branch is taken.",
+                "item_schema": {
+                    "case_id": "string - unique case identifier (e.g., 'case_1')",
+                    "logical_operator": "enum: and, or - how multiple conditions combine",
+                    "conditions": {
+                        "type": "array",
+                        "item_schema": {
+                            "variable_selector": "array of strings - path to variable, e.g. ['node_id', 'field']",
+                            "comparison_operator": (
+                                "enum: =, ≠, >, <, ≥, ≤, contains, not contains, is, is not, empty, not empty"
+                            ),
+                            "value": "string or number - value to compare against",
+                        },
+                    },
+                },
+            },
+        },
+        "outputs": ["Branches: true (first case conditions met), false (else/no case matched)"],
+    },
+    "knowledge-retrieval": {
+        "description": "Query knowledge base for relevant content",
+        "required": ["query_variable_selector", "dataset_ids"],
+        "parameters": {
+            "query_variable_selector": {
+                "type": "array",
+                "description": "Path to query variable, e.g. ['start', 'query']",
+            },
+            "dataset_ids": {
+                "type": "array",
+                "description": "List of knowledge base IDs to search",
+            },
+            "retrieval_mode": {
+                "type": "enum",
+                "options": ["single", "multiple"],
+            },
+        },
+        "outputs": ["result (retrieved documents)"],
+    },
+    "template-transform": {
+        "description": "Transform data using Jinja2 templates",
+        "required": ["template", "variables"],
+        "parameters": {
+            "template": {
+                "type": "string",
+                "description": "Jinja2 template string. Use {{ variable_name }} to reference variables.",
+            },
+            "variables": {
+                "type": "array",
+                "description": "Input variables defined for the template",
+                "item_schema": {
+                    "variable": "string - variable name to use in template",
+                    "value_selector": "array - path to source value, e.g. ['start', 'user_input']",
+                },
+            },
+        },
+        "outputs": ["output (transformed string)"],
+    },
+    "variable-aggregator": {
+        "description": "Aggregate variables from multiple branches",
+        "required": ["variables"],
+        "parameters": {
+            "variables": {
+                "type": "array",
+                "description": "List of variable selectors to aggregate",
+                "item_schema": "array of strings - path to source variable, e.g. ['node_id', 'field']",
+            },
+        },
+        "outputs": ["output (aggregated value)"],
+    },
+    "iteration": {
+        "description": "Loop over array items",
+        "required": ["iterator_selector"],
+        "parameters": {
+            "iterator_selector": {
+                "type": "array",
+                "description": "Path to array variable to iterate",
+            },
+        },
+        "outputs": ["item (current iteration item)", "index (current index)"],
+    },
+    "parameter-extractor": {
+        "description": "Extract structured parameters from user input using LLM",
+        "required": ["query", "parameters"],
+        "parameters": {
+            "model": {
+                "type": "object",
+                "description": "Model configuration (provider, name, mode)",
+            },
+            "query": {
+                "type": "array",
+                "description": "Path to input text to extract parameters from, e.g. ['start', 'user_input']",
+            },
+            "parameters": {
+                "type": "array",
+                "description": "Parameters to extract from the input",
+                "item_schema": {
+                    "name": "string - parameter name (required)",
+                    "type": (
+                        "enum: string, number, boolean, array[string], array[number], array[object], array[boolean]"
+                    ),
+                    "description": "string - description of what to extract (required)",
+                    "required": "boolean - whether this parameter is required (MUST be specified)",
+                    "options": "array of strings (optional) - for enum-like selection",
+                },
+            },
+            "instruction": {
+                "type": "string",
+                "description": "Additional instructions for extraction",
+            },
+            "reasoning_mode": {
+                "type": "enum",
+                "options": ["function_call", "prompt"],
+                "description": "How to perform extraction (defaults to function_call)",
+            },
+        },
+        "outputs": ["Extracted parameters as defined in parameters array", "__is_success", "__reason"],
+    },
+    "question-classifier": {
+        "description": "Classify user input into predefined categories using LLM",
+        "required": ["query", "classes"],
+        "parameters": {
+            "model": {
+                "type": "object",
+                "description": "Model configuration (provider, name, mode)",
+            },
+            "query": {
+                "type": "array",
+                "description": "Path to input text to classify, e.g. ['start', 'user_input']",
+            },
+            "classes": {
+                "type": "array",
+                "description": "Classification categories",
+                "item_schema": {
+                    "id": "string - unique class identifier",
+                    "name": "string - class name/label",
+                },
+            },
+            "instruction": {
+                "type": "string",
+                "description": "Additional instructions for classification",
+            },
+        },
+        "outputs": ["class_name (selected class)"],
+    },
+}
+
+
+def _get_dynamic_schemas() -> dict[str, dict[str, Any]]:
+    """
+    Dynamically load schemas from node classes.
+    Uses lazy import to avoid circular dependency.
+    """
+    from core.workflow.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
+
+    schemas = {}
+    for node_type, version_map in NODE_TYPE_CLASSES_MAPPING.items():
+        # Get the latest version class
+        node_cls = version_map.get(LATEST_VERSION)
+        if not node_cls:
+            continue
+
+        # Get schema from the class
+        schema = node_cls.get_default_config_schema()
+        if schema:
+            schemas[node_type.value] = schema
+
+    return schemas
+
+
+# Cache for built-in schemas (populated on first access)
+_builtin_schemas_cache: dict[str, dict[str, Any]] | None = None
+
+
+def get_builtin_node_schemas() -> dict[str, dict[str, Any]]:
+    """
+    Get the complete set of built-in node schemas.
+    Combines hardcoded schemas with dynamically loaded ones.
+    Results are cached after first call.
+    """
+    global _builtin_schemas_cache
+    if _builtin_schemas_cache is None:
+        _builtin_schemas_cache = {**_HARDCODED_SCHEMAS, **_get_dynamic_schemas()}
+    return _builtin_schemas_cache
+
+
+# For backward compatibility - but use get_builtin_node_schemas() for lazy loading
+BUILTIN_NODE_SCHEMAS: dict[str, dict[str, Any]] = _HARDCODED_SCHEMAS.copy()
+
+
+# =============================================================================
+# FALLBACK RULES
+# =============================================================================
+
+# Keyword rules for smart fallback detection
+# Maps node type to keywords that suggest using that node type as a fallback
+FALLBACK_RULES: dict[str, list[str]] = {
+    "http-request": [
+        "http",
+        "url",
+        "web",
+        "scrape",
+        "scraper",
+        "fetch",
+        "api",
+        "request",
+        "download",
+        "upload",
+        "webhook",
+        "endpoint",
+        "rest",
+        "get",
+        "post",
+    ],
+    "code": [
+        "code",
+        "script",
+        "calculate",
+        "compute",
+        "process",
+        "transform",
+        "parse",
+        "convert",
+        "format",
+        "filter",
+        "sort",
+        "math",
+        "logic",
+    ],
+    "llm": [
+        "analyze",
+        "summarize",
+        "summary",
+        "extract",
+        "classify",
+        "translate",
+        "generate",
+        "write",
+        "rewrite",
+        "explain",
+        "answer",
+        "chat",
+    ],
+}
+
+
+# =============================================================================
+# NODE TYPE ALIASES
+# =============================================================================
+
+# Node type aliases for inference from natural language
+# Maps common terms to canonical node type names
+NODE_TYPE_ALIASES: dict[str, str] = {
+    # Start node aliases
+    "start": "start",
+    "begin": "start",
+    "input": "start",
+    # End node aliases
+    "end": "end",
+    "finish": "end",
+    "output": "end",
+    # LLM node aliases
+    "llm": "llm",
+    "ai": "llm",
+    "gpt": "llm",
+    "model": "llm",
+    "chat": "llm",
+    # Code node aliases
+    "code": "code",
+    "script": "code",
+    "python": "code",
+    "javascript": "code",
+    # HTTP request node aliases
+    "http-request": "http-request",
+    "http": "http-request",
+    "request": "http-request",
+    "api": "http-request",
+    "fetch": "http-request",
+    "webhook": "http-request",
+    # Conditional node aliases
+    "if-else": "if-else",
+    "condition": "if-else",
+    "branch": "if-else",
+    "switch": "if-else",
+    # Loop node aliases
+    "iteration": "iteration",
+    "loop": "loop",
+    "foreach": "iteration",
+    # Tool node alias
+    "tool": "tool",
+}
+
+
+# =============================================================================
+# FIELD NAME CORRECTIONS
+# =============================================================================
+
+# Field name corrections for LLM-generated node configs
+# Maps incorrect field names to correct ones for specific node types
+FIELD_NAME_CORRECTIONS: dict[str, dict[str, str]] = {
+    "http-request": {
+        "text": "body",  # LLM might use "text" instead of "body"
+        "content": "body",
+        "response": "body",
+    },
+    "code": {
+        "text": "result",  # LLM might use "text" instead of "result"
+        "output": "result",
+    },
+    "llm": {
+        "response": "text",
+        "answer": "text",
+    },
+}
+
+
+def get_corrected_field_name(node_type: str, field: str) -> str:
+    """
+    Get the corrected field name for a node type.
+
+    Args:
+        node_type: The type of the node (e.g., "http-request", "code")
+        field: The field name to correct
+
+    Returns:
+        The corrected field name, or the original if no correction needed
+    """
+    corrections = FIELD_NAME_CORRECTIONS.get(node_type, {})
+    return corrections.get(field, field)
+
+
+# =============================================================================
+# VALIDATION UTILITIES
+# =============================================================================
+
+# Node types that are internal and don't need schemas for LLM generation
+_INTERNAL_NODE_TYPES: set[str] = {
+    # Internal workflow nodes
+    "answer",  # Internal to chatflow
+    "loop",  # Uses iteration internally
+    "assigner",  # Variable assignment utility
+    "variable-assigner",  # Variable assignment utility
+    "agent",  # Agent node (complex, handled separately)
+    "document-extractor",  # Internal document processing
+    "list-operator",  # Internal list operations
+    # Iteration internal nodes
+    "iteration-start",  # Internal to iteration loop
+    "loop-start",  # Internal to loop
+    "loop-end",  # Internal to loop
+    # Trigger nodes (not user-creatable via LLM)
+    "trigger-plugin",  # Plugin trigger
+    "trigger-schedule",  # Scheduled trigger
+    "trigger-webhook",  # Webhook trigger
+    # Other internal nodes
+    "datasource",  # Data source configuration
+    "human-input",  # Human-in-the-loop node
+    "knowledge-index",  # Knowledge indexing node
+}
+
+
+def validate_node_schemas() -> list[str]:
+    """
+    Validate that all registered node types have corresponding schemas.
+
+    This function checks if BUILTIN_NODE_SCHEMAS covers all node types
+    registered in NODE_TYPE_CLASSES_MAPPING, excluding internal node types.
+
+    Returns:
+        List of warning messages for missing schemas (empty if all valid)
+    """
+    from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
+
+    schemas = get_builtin_node_schemas()
+    warnings = []
+    for node_type in NODE_TYPE_CLASSES_MAPPING:
+        type_value = node_type.value
+        if type_value in _INTERNAL_NODE_TYPES:
+            continue
+        if type_value not in schemas:
+            warnings.append(f"Missing schema for node type: {type_value}")
+    return warnings

api/core/workflow/generator/config/responses.py

@@ -0,0 +1,72 @@
+"""
+Response Templates for Vibe Workflow Generation.
+
+This module defines templates for off-topic responses and default suggestions
+to guide users back to workflow-related requests.
+"""
+
+# Off-topic response templates for different categories
+# Each category has messages in multiple languages
+OFF_TOPIC_RESPONSES: dict[str, dict[str, str]] = {
+    "weather": {
+        "en": (
+            "I'm the workflow design assistant - I can't check the weather, "
+            "but I can help you build AI workflows! For example, I could help you "
+            "create a workflow that fetches weather data from an API."
+        ),
+        "zh": "我是工作流设计助手，无法查询天气。但我可以帮你创建一个从API获取天气数据的工作流！",
+    },
+    "math": {
+        "en": (
+            "I focus on workflow design rather than calculations. However, "
+            "if you need calculations in a workflow, I can help you add a Code node "
+            "that handles math operations!"
+        ),
+        "zh": "我专注于工作流设计而非计算。但如果您需要在工作流中进行计算，我可以帮您添加一个处理数学运算的代码节点！",
+    },
+    "joke": {
+        "en": (
+            "While I'd love to share a laugh, I'm specialized in workflow design. "
+            "How about we create something fun instead - like a workflow that generates jokes using AI?"
+        ),
+        "zh": "虽然我很想讲笑话，但我专门从事工作流设计。不如我们创建一个有趣的东西——比如使用AI生成笑话的工作流？",
+    },
+    "translation": {
+        "en": (
+            "I can't translate directly, but I can help you build a translation workflow! "
+            "Would you like to create one using an LLM node?"
+        ),
+        "zh": "我不能直接翻译，但我可以帮你构建一个翻译工作流！要创建一个使用LLM节点的翻译流程吗？",
+    },
+    "general_coding": {
+        "en": (
+            "I'm specialized in Dify workflow design rather than general coding help. "
+            "But if you want to add code logic to your workflow, I can help you configure a Code node!"
+        ),
+        "zh": (
+            "我专注于Dify工作流设计，而非通用编程帮助。但如果您想在工作流中添加代码逻辑，我可以帮您配置一个代码节点！"
+        ),
+    },
+    "default": {
+        "en": (
+            "I'm the Dify workflow design assistant. I help create AI automation workflows, "
+            "but I can't help with general questions. Would you like to create a workflow instead?"
+        ),
+        "zh": "我是Dify工作流设计助手。我帮助创建AI自动化工作流，但无法回答一般性问题。您想创建一个工作流吗？",
+    },
+}
+
+# Default suggestions for off-topic requests
+# These help guide users towards valid workflow requests
+DEFAULT_SUGGESTIONS: dict[str, list[str]] = {
+    "en": [
+        "Create a chatbot workflow",
+        "Build a document summarization pipeline",
+        "Add email notification to workflow",
+    ],
+    "zh": [
+        "创建一个聊天机器人工作流",
+        "构建文档摘要处理流程",
+        "添加邮件通知到工作流",
+    ],
+}

api/core/workflow/generator/prompts/builder_prompts.py

@@ -0,0 +1,733 @@
+# =============================================================================
+# NEW FORMAT: depends_on based prompt (for use with GraphBuilder)
+# =============================================================================
+
+BUILDER_SYSTEM_PROMPT_V2 = """<role>
+You are a Workflow Configuration Engineer.
+Your goal is to generate workflow node configurations with dependency declarations.
+The graph structure (edges, start/end nodes) will be automatically built from your output.
+</role>
+
+<language_rules>
+- Detect the language of the user's request automatically (e.g., English, Chinese, Japanese, etc.).
+- Generate ALL node titles, descriptions, and user-facing text in the SAME language as the user's input.
+- If the input language is ambiguous or cannot be determined (e.g. code-only input),
+  use {preferred_language} as the target language.
+</language_rules>
+
+<inputs>
+<plan>
+{plan_context}
+</plan>
+
+<tool_schemas>
+{tool_schemas}
+</tool_schemas>
+
+<node_specs>
+{builtin_node_specs}
+</node_specs>
+
+<available_models>
+{available_models}
+</available_models>
+
+<workflow_context>
+  <existing_nodes>
+{existing_nodes_context}
+  </existing_nodes>
+  <selected_nodes>
+{selected_nodes_context}
+  </selected_nodes>
+</workflow_context>
+</inputs>
+
+<critical_rules>
+1. **DO NOT generate start or end nodes** - they are automatically added
+2. **DO NOT generate edges** - they are automatically built from depends_on
+3. **Use depends_on array** to declare which nodes must run before this one
+4. **Leave depends_on empty []** for nodes that should start immediately (connect to start)
+</critical_rules>
+
+<rules>
+1. **Configuration**:
+   - You MUST fill ALL required parameters for every node.
+   - Use `{{{{#node_id.field#}}}}` syntax to reference outputs from previous nodes in text fields.
+
+2. **Dependency Declaration**:
+   - Each node has a `depends_on` array listing node IDs that must complete before it runs
+   - Empty depends_on `[]` means the node runs immediately after start
+   - Example: `"depends_on": ["fetch_data"]` means this node waits for fetch_data to complete
+
+3. **Variable References**:
+   - For text fields (like prompts, queries): use string format `{{{{#node_id.field#}}}}`
+   - Dependencies will be auto-inferred from variable references if not explicitly declared
+
+4. **Tools**:
+   - ONLY use the tools listed in `<tool_schemas>`.
+   - If a planned tool is missing from schemas, fallback to `http-request` or `code`.
+
+5. **Model Selection** (CRITICAL):
+   - For LLM, question-classifier, and parameter-extractor nodes, you MUST include a "model" config.
+   - You MUST use ONLY models from the `<available_models>` section above.
+   - Copy the EXACT provider and name values from available_models.
+   - NEVER use openai/gpt-4o, gpt-3.5-turbo, gpt-4, or any other models unless they appear in available_models.
+   - If available_models is empty or shows "No models configured", omit the model config entirely.
+
+6. **if-else Branching**:
+   - Add `true_branch` and `false_branch` in config to specify target node IDs
+   - Example: `"config": {{"cases": [...], "true_branch": "success_node", "false_branch": "fallback_node"}}`
+
+7. **question-classifier Branching**:
+   - Add `target` field to each class in the classes array
+   - Example: `"classes": [{{"id": "tech", "name": "Tech", "target": "tech_handler"}}, ...]`
+
+8. **Node Specifics**:
+   - For `if-else` comparison_operator, use literal symbols: `≥`, `≤`, `=`, `≠` (NOT `>=` or `==`).
+</rules>
+
+<output_format>
+Return ONLY a JSON object with a `nodes` array. Each node has:
+- id: unique identifier
+- type: node type
+- title: display name
+- config: node configuration
+- depends_on: array of node IDs this depends on
+
+```json
+{{{{
+  "nodes": [
+    {{{{
+      "id": "fetch_data",
+      "type": "http-request",
+      "title": "Fetch Data",
+      "config": {{"url": "{{{{#start.url#}}}}", "method": "GET"}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "analyze",
+      "type": "llm",
+      "title": "Analyze",
+      "config": {{"prompt_template": [{{"role": "user", "text": "Analyze: {{{{#fetch_data.body#}}}}"}}]}},
+      "depends_on": ["fetch_data"]
+    }}}}
+  ]
+}}}}
+```
+</output_format>
+
+<examples>
+<example name="simple_linear">
+```json
+{{{{
+  "nodes": [
+    {{{{
+      "id": "llm",
+      "type": "llm",
+      "title": "Generate Response",
+      "config": {{{{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Answer: {{{{#start.query#}}}}"}}]
+      }}}},
+      "depends_on": []
+    }}}}
+  ]
+}}}}
+```
+</example>
+
+<example name="parallel_then_merge">
+```json
+{{{{
+  "nodes": [
+    {{{{
+      "id": "api1",
+      "type": "http-request",
+      "title": "Fetch API 1",
+      "config": {{"url": "https://api1.example.com", "method": "GET"}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "api2",
+      "type": "http-request",
+      "title": "Fetch API 2",
+      "config": {{"url": "https://api2.example.com", "method": "GET"}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "merge",
+      "type": "llm",
+      "title": "Merge Results",
+      "config": {{{{
+        "prompt_template": [{{"role": "user", "text": "Combine: {{{{#api1.body#}}}} and {{{{#api2.body#}}}}"}}]
+      }}}},
+      "depends_on": ["api1", "api2"]
+    }}}}
+  ]
+}}}}
+```
+</example>
+
+<example name="if_else_branching">
+```json
+{{{{
+  "nodes": [
+    {{{{
+      "id": "check",
+      "type": "if-else",
+      "title": "Check Condition",
+      "config": {{{{
+        "cases": [{{{{
+          "case_id": "case_1",
+          "logical_operator": "and",
+          "conditions": [{{{{
+            "variable_selector": ["start", "score"],
+            "comparison_operator": "≥",
+            "value": "60"
+          }}}}]
+        }}}}],
+        "true_branch": "pass_handler",
+        "false_branch": "fail_handler"
+      }}}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "pass_handler",
+      "type": "llm",
+      "title": "Pass Response",
+      "config": {{"prompt_template": [{{"role": "user", "text": "Congratulations!"}}]}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "fail_handler",
+      "type": "llm",
+      "title": "Fail Response",
+      "config": {{"prompt_template": [{{"role": "user", "text": "Try again."}}]}},
+      "depends_on": []
+    }}}}
+  ]
+}}}}
+```
+Note: pass_handler and fail_handler have empty depends_on because their connections come from if-else branches.
+</example>
+
+<example name="question_classifier">
+```json
+{{{{
+  "nodes": [
+    {{{{
+      "id": "classifier",
+      "type": "question-classifier",
+      "title": "Classify Intent",
+      "config": {{{{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "query_variable_selector": ["start", "user_input"],
+        "classes": [
+          {{"id": "tech", "name": "Technical", "target": "tech_handler"}},
+          {{"id": "billing", "name": "Billing", "target": "billing_handler"}},
+          {{"id": "other", "name": "Other", "target": "other_handler"}}
+        ]
+      }}}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "tech_handler",
+      "type": "llm",
+      "title": "Tech Support",
+      "config": {{"prompt_template": [{{"role": "user", "text": "Help with tech: {{{{#start.user_input#}}}}"}}]}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "billing_handler",
+      "type": "llm",
+      "title": "Billing Support",
+      "config": {{"prompt_template": [{{"role": "user", "text": "Help with billing: {{{{#start.user_input#}}}}"}}]}},
+      "depends_on": []
+    }}}},
+    {{{{
+      "id": "other_handler",
+      "type": "llm",
+      "title": "General Support",
+      "config": {{"prompt_template": [{{"role": "user", "text": "General help: {{{{#start.user_input#}}}}"}}]}},
+      "depends_on": []
+    }}}}
+  ]
+}}}}
+```
+Note: Handler nodes have empty depends_on because their connections come from classifier branches.
+</example>
+</examples>
+"""
+
+BUILDER_USER_PROMPT_V2 = """<instruction>
+{instruction}
+</instruction>
+
+Generate the workflow nodes configuration. Remember:
+1. Do NOT generate start or end nodes
+2. Do NOT generate edges - use depends_on instead
+3. For if-else: add true_branch/false_branch in config
+4. For question-classifier: add target to each class
+"""
+
+# =============================================================================
+# LEGACY FORMAT: edges-based prompt (backward compatible)
+# =============================================================================
+
+BUILDER_SYSTEM_PROMPT = """<role>
+You are a Workflow Configuration Engineer.
+Your goal is to implement the Architect's plan by generating a precise, runnable Dify Workflow JSON configuration.
+</role>
+
+<language_rules>
+- Detect the language of the user's request automatically (e.g., English, Chinese, Japanese, etc.).
+- Generate ALL node titles, descriptions, and user-facing text in the SAME language as the user's input.
+- If the input language is ambiguous or cannot be determined (e.g. code-only input),
+  use {preferred_language} as the target language.
+</language_rules>
+
+<inputs>
+<plan>
+{plan_context}
+</plan>
+
+<tool_schemas>
+{tool_schemas}
+</tool_schemas>
+
+<node_specs>
+{builtin_node_specs}
+</node_specs>
+
+<available_models>
+{available_models}
+</available_models>
+
+<workflow_context>
+  <existing_nodes>
+{existing_nodes_context}
+  </existing_nodes>
+  <existing_edges>
+{existing_edges_context}
+  </existing_edges>
+  <selected_nodes>
+{selected_nodes_context}
+  </selected_nodes>
+</workflow_context>
+</inputs>
+
+<rules>
+1. **Configuration**:
+   - You MUST fill ALL required parameters for every node.
+   - Use `{{{{#node_id.field#}}}}` syntax to reference outputs from previous nodes in text fields.
+   - For 'start' node, define all necessary user inputs.
+
+2. **Variable References**:
+   - For text fields (like prompts, queries): use string format `{{{{#node_id.field#}}}}`
+   - For 'end' node outputs: use `value_selector` array format `["node_id", "field"]`
+   - Example: to reference 'llm' node's 'text' output in end node, use `["llm", "text"]`
+
+3. **Tools**:
+   - ONLY use the tools listed in `<tool_schemas>`.
+   - If a planned tool is missing from schemas, fallback to `http-request` or `code`.
+
+4. **Model Selection** (CRITICAL):
+   - For LLM, question-classifier, and parameter-extractor nodes, you MUST include a "model" config.
+   - You MUST use ONLY models from the `<available_models>` section above.
+   - Copy the EXACT provider and name values from available_models.
+   - NEVER use openai/gpt-4o, gpt-3.5-turbo, gpt-4, or any other models unless they appear in available_models.
+   - If available_models is empty or shows "No models configured", omit the model config entirely.
+
+5. **Node Specifics**:
+   - For `if-else` comparison_operator, use literal symbols: `≥`, `≤`, `=`, `≠` (NOT `>=` or `==`).
+
+6. **Modification Mode**:
+   - If `<existing_nodes>` contains nodes, you are MODIFYING an existing workflow.
+   - Keep nodes that are NOT mentioned in the user's instruction UNCHANGED.
+   - Only modify/add/remove nodes that the user explicitly requested.
+   - Preserve node IDs for unchanged nodes to maintain connections.
+   - If user says "add X", append new nodes to existing workflow.
+   - If user says "change Y to Z", only modify that specific node.
+   - If user says "remove X", exclude that node from output.
+   
+   **Edge Modification**:
+   - Use `<existing_edges>` to understand current node connections.
+   - If user mentions "fix edge", "connect", "link", or "add connection",
+     review existing_edges and correct missing/wrong connections.
+   - For multi-branch nodes (if-else, question-classifier),
+     ensure EACH branch has proper sourceHandle (e.g., "true"/"false") and target.
+   - Common edge issues to fix:
+     * Missing edge: Two nodes should connect but don't - add the edge
+     * Wrong target: Edge points to wrong node - update the target
+     * Missing sourceHandle: if-else/classifier branches lack sourceHandle - add "true"/"false"
+     * Disconnected nodes: Node has no incoming or outgoing edges - connect it properly
+   - When modifying edges, ensure logical flow makes sense (start → middle → end).
+   - ALWAYS output complete edges array, even if only modifying one edge.
+   
+   **Validation Feedback** (Automatic Retry):
+   - If `<validation_feedback>` is present, you are RETRYING after validation errors.
+   - Focus ONLY on fixing the specific validation issues mentioned.
+   - Keep everything else from the previous attempt UNCHANGED (preserve node IDs, edges, etc).
+   - Common validation issues and fixes:
+     * "Missing required connection" → Add the missing edge
+     * "Invalid node configuration" → Fix the specific node's config section
+     * "Type mismatch in variable reference" → Correct the variable selector path
+     * "Unknown variable" → Update variable reference to existing output
+   - When fixing, make MINIMAL changes to address each specific error.
+
+7. **Output**:
+   - Return ONLY the JSON object with `nodes` and `edges`.
+   - Do NOT generate Mermaid diagrams.
+   - Do NOT generate explanations.
+</rules>
+
+<edge_rules priority="critical">
+**EDGES ARE CRITICAL** - Every node except 'end' MUST have at least one outgoing edge.
+
+1. **Linear Flow**: Simple source -> target connection
+   ```
+   {{"source": "node_a", "target": "node_b"}}
+   ```
+
+2. **question-classifier Branching**: Each class MUST have a separate edge with `sourceHandle` = class `id`
+   - If you define classes: [{{"id": "cls_refund", "name": "Refund"}}, {{"id": "cls_inquiry", "name": "Inquiry"}}]
+   - You MUST create edges:
+     - {{"source": "classifier", "sourceHandle": "cls_refund", "target": "refund_handler"}}
+     - {{"source": "classifier", "sourceHandle": "cls_inquiry", "target": "inquiry_handler"}}
+
+3. **if-else Branching**: MUST have exactly TWO edges with sourceHandle "true" and "false"
+   - {{"source": "condition", "sourceHandle": "true", "target": "true_branch"}}
+   - {{"source": "condition", "sourceHandle": "false", "target": "false_branch"}}
+
+4. **Branch Convergence**: Multiple branches can connect to same downstream node
+   - Both true_branch and false_branch can connect to the same 'end' node
+
+5. **NEVER leave orphan nodes**: Every node must be connected in the graph
+</edge_rules>
+
+<examples>
+<example name="simple_linear">
+```json
+{{
+  "nodes": [
+    {{
+      "id": "start",
+      "type": "start",
+      "title": "Start",
+      "config": {{
+        "variables": [{{"variable": "query", "label": "Query", "type": "text-input"}}]
+      }}
+    }},
+    {{
+      "id": "llm",
+      "type": "llm",
+      "title": "Generate Response",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Answer: {{{{#start.query#}}}}"}}]
+      }}
+    }},
+    {{
+      "id": "end",
+      "type": "end",
+      "title": "End",
+      "config": {{
+        "outputs": [
+          {{"variable": "result", "value_selector": ["llm", "text"]}}
+        ]
+      }}
+    }}
+  ],
+  "edges": [
+    {{"source": "start", "target": "llm"}},
+    {{"source": "llm", "target": "end"}}
+  ]
+}}
+```
+</example>
+
+<example name="question_classifier_branching" description="Customer service with intent classification">
+```json
+{{
+  "nodes": [
+    {{
+      "id": "start",
+      "type": "start",
+      "title": "Start",
+      "config": {{
+        "variables": [{{"variable": "user_input", "label": "User Message", "type": "text-input", "required": true}}]
+      }}
+    }},
+    {{
+      "id": "classifier",
+      "type": "question-classifier",
+      "title": "Classify Intent",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "query_variable_selector": ["start", "user_input"],
+        "classes": [
+          {{"id": "cls_refund", "name": "Refund Request"}},
+          {{"id": "cls_inquiry", "name": "Product Inquiry"}},
+          {{"id": "cls_complaint", "name": "Complaint"}},
+          {{"id": "cls_other", "name": "Other"}}
+        ],
+        "instruction": "Classify the user's intent"
+      }}
+    }},
+    {{
+      "id": "handle_refund",
+      "type": "llm",
+      "title": "Handle Refund",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Extract order number and respond: {{{{#start.user_input#}}}}"}}]
+      }}
+    }},
+    {{
+      "id": "handle_inquiry",
+      "type": "llm",
+      "title": "Handle Inquiry",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Answer product question: {{{{#start.user_input#}}}}"}}]
+      }}
+    }},
+    {{
+      "id": "handle_complaint",
+      "type": "llm",
+      "title": "Handle Complaint",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Respond with empathy: {{{{#start.user_input#}}}}"}}]
+      }}
+    }},
+    {{
+      "id": "handle_other",
+      "type": "llm",
+      "title": "Handle Other",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Provide general response: {{{{#start.user_input#}}}}"}}]
+      }}
+    }},
+    {{
+      "id": "end",
+      "type": "end",
+      "title": "End",
+      "config": {{
+        "outputs": [{{"variable": "response", "value_selector": ["handle_refund", "text"]}}]
+      }}
+    }}
+  ],
+  "edges": [
+    {{"source": "start", "target": "classifier"}},
+    {{"source": "classifier", "sourceHandle": "cls_refund", "target": "handle_refund"}},
+    {{"source": "classifier", "sourceHandle": "cls_inquiry", "target": "handle_inquiry"}},
+    {{"source": "classifier", "sourceHandle": "cls_complaint", "target": "handle_complaint"}},
+    {{"source": "classifier", "sourceHandle": "cls_other", "target": "handle_other"}},
+    {{"source": "handle_refund", "target": "end"}},
+    {{"source": "handle_inquiry", "target": "end"}},
+    {{"source": "handle_complaint", "target": "end"}},
+    {{"source": "handle_other", "target": "end"}}
+  ]
+}}
+```
+CRITICAL: Notice that each class id (cls_refund, cls_inquiry, etc.) becomes a sourceHandle in the edges!
+</example>
+
+<example name="if_else_branching" description="Conditional logic with if-else">
+```json
+{{
+  "nodes": [
+    {{
+      "id": "start",
+      "type": "start",
+      "title": "Start",
+      "config": {{
+        "variables": [{{"variable": "years", "label": "Years of Experience", "type": "number", "required": true}}]
+      }}
+    }},
+    {{
+      "id": "check_experience",
+      "type": "if-else",
+      "title": "Check Experience",
+      "config": {{
+        "cases": [
+          {{
+            "case_id": "case_1",
+            "logical_operator": "and",
+            "conditions": [
+              {{
+                "variable_selector": ["start", "years"],
+                "comparison_operator": "≥",
+                "value": "3"
+              }}
+            ]
+          }}
+        ]
+      }}
+    }},
+    {{
+      "id": "qualified",
+      "type": "llm",
+      "title": "Qualified Response",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Generate qualified candidate response"}}]
+      }}
+    }},
+    {{
+      "id": "not_qualified",
+      "type": "llm",
+      "title": "Not Qualified Response",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Generate rejection response"}}]
+      }}
+    }},
+    {{
+      "id": "end",
+      "type": "end",
+      "title": "End",
+      "config": {{
+        "outputs": [{{"variable": "result", "value_selector": ["qualified", "text"]}}]
+      }}
+    }}
+  ],
+  "edges": [
+    {{"source": "start", "target": "check_experience"}},
+    {{"source": "check_experience", "sourceHandle": "true", "target": "qualified"}},
+    {{"source": "check_experience", "sourceHandle": "false", "target": "not_qualified"}},
+    {{"source": "qualified", "target": "end"}},
+    {{"source": "not_qualified", "target": "end"}}
+  ]
+}}
+```
+CRITICAL: if-else MUST have exactly two edges with sourceHandle "true" and "false"!
+</example>
+
+<example name="parameter_extractor" description="Extract structured data from text">
+```json
+{{
+  "nodes": [
+    {{
+      "id": "start",
+      "type": "start",
+      "title": "Start",
+      "config": {{
+        "variables": [{{"variable": "resume", "label": "Resume Text", "type": "paragraph", "required": true}}]
+      }}
+    }},
+    {{
+      "id": "extract",
+      "type": "parameter-extractor",
+      "title": "Extract Info",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "query": ["start", "resume"],
+        "parameters": [
+          {{"name": "name", "type": "string", "description": "Candidate name", "required": true}},
+          {{"name": "years", "type": "number", "description": "Years of experience", "required": true}},
+          {{"name": "skills", "type": "array[string]", "description": "List of skills", "required": true}}
+        ],
+        "instruction": "Extract candidate information from resume"
+      }}
+    }},
+    {{
+      "id": "process",
+      "type": "llm",
+      "title": "Process Data",
+      "config": {{
+        "model": {{"provider": "openai", "name": "gpt-4o", "mode": "chat"}},
+        "prompt_template": [{{"role": "user", "text": "Name: {{{{#extract.name#}}}}, Years: {{{{#extract.years#}}}}"}}]
+      }}
+    }},
+    {{
+      "id": "end",
+      "type": "end",
+      "title": "End",
+      "config": {{
+        "outputs": [{{"variable": "result", "value_selector": ["process", "text"]}}]
+      }}
+    }}
+  ],
+  "edges": [
+    {{"source": "start", "target": "extract"}},
+    {{"source": "extract", "target": "process"}},
+    {{"source": "process", "target": "end"}}
+  ]
+}}
+```
+</example>
+</examples>
+
+<edge_checklist>
+Before finalizing, verify:
+1. [ ] Every node (except 'end') has at least one outgoing edge
+2. [ ] 'start' node has exactly one outgoing edge
+3. [ ] 'question-classifier' has one edge per class, each with sourceHandle = class id
+4. [ ] 'if-else' has exactly two edges: sourceHandle "true" and sourceHandle "false"
+5. [ ] All branches eventually connect to 'end' (directly or through other nodes)
+6. [ ] No orphan nodes exist (every node is reachable from 'start')
+</edge_checklist>
+"""
+
+BUILDER_USER_PROMPT = """<instruction>
+{instruction}
+</instruction>
+
+Generate the full workflow configuration now. Pay special attention to:
+1. Creating edges for ALL branches of question-classifier and if-else nodes
+2. Using correct sourceHandle values for branching nodes
+3. Ensuring every node is connected in the graph
+"""
+
+
+def format_existing_nodes(nodes: list[dict] | None) -> str:
+    """Format existing workflow nodes for context."""
+    if not nodes:
+        return "No existing nodes in workflow (creating from scratch)."
+
+    lines = []
+    for node in nodes:
+        node_id = node.get("id", "unknown")
+        node_type = node.get("type", "unknown")
+        title = node.get("title", "Untitled")
+        lines.append(f"- [{node_id}] {title} ({node_type})")
+    return "\n".join(lines)
+
+
+def format_selected_nodes(
+    selected_ids: list[str] | None,
+    existing_nodes: list[dict] | None,
+) -> str:
+    """Format selected nodes for modification context."""
+    if not selected_ids:
+        return "No nodes selected (generating new workflow)."
+
+    node_map = {n.get("id"): n for n in (existing_nodes or [])}
+    lines = []
+    for node_id in selected_ids:
+        if node_id in node_map:
+            node = node_map[node_id]
+            lines.append(f"- [{node_id}] {node.get('title', 'Untitled')} ({node.get('type', 'unknown')})")
+        else:
+            lines.append(f"- [{node_id}] (not found in current workflow)")
+    return "\n".join(lines)
+
+
+def format_existing_edges(edges: list[dict] | None) -> str:
+    """Format existing workflow edges to show connections."""
+    if not edges:
+        return "No existing edges (creating new workflow)."
+
+    lines = []
+    for edge in edges:
+        source = edge.get("source", "unknown")
+        target = edge.get("target", "unknown")
+        source_handle = edge.get("sourceHandle", "")
+        if source_handle:
+            lines.append(f"- {source} ({source_handle}) -> {target}")
+        else:
+            lines.append(f"- {source} -> {target}")
+    return "\n".join(lines)

api/core/workflow/generator/prompts/planner_prompts.py

@@ -0,0 +1,75 @@
+PLANNER_SYSTEM_PROMPT = """<role>
+You are an expert Workflow Architect.
+Your job is to analyze user requests and plan a high-level automation workflow.
+</role>
+
+<task>
+1. **Classify Intent**:
+   - Is the user asking to create an automation/workflow? -> Intent: "generate"
+   - Is it general chat/weather/jokes? -> Intent: "off_topic"
+
+2. **Plan Steps** (if intent is "generate"):
+   - Break down the user's goal into logical steps.
+   - For each step, identify if a specific capability/tool is needed.
+   - Select the MOST RELEVANT tools from the available_tools list.
+   - DO NOT configure parameters yet. Just identify the tool.
+
+3. **Output Format**:
+   Return a JSON object.
+</task>
+
+<available_tools>
+{tools_summary}
+</available_tools>
+
+<response_format>
+If intent is "generate":
+```json
+{{
+  "intent": "generate",
+  "plan_thought": "Brief explanation of the plan...",
+  "steps": [
+    {{ "step": 1, "description": "Fetch data from URL", "tool": "http-request" }},
+    {{ "step": 2, "description": "Summarize content", "tool": "llm" }},
+    {{ "step": 3, "description": "Search for info", "tool": "google_search" }}
+  ],
+  "required_tool_keys": ["google_search"] 
+}}
+```
+(Note: 'http-request', 'llm', 'code' are built-in, you don't need to list them in required_tool_keys,
+only external tools)
+
+If intent is "off_topic":
+```json
+{{
+  "intent": "off_topic",
+  "message": "I can only help you build workflows. Try asking me to 'Create a workflow that...'",
+  "suggestions": ["Scrape a website", "Summarize a PDF"]
+}}
+```
+</response_format>
+"""
+
+PLANNER_USER_PROMPT = """<user_request>
+{instruction}
+</user_request>
+"""
+
+
+def format_tools_for_planner(tools: list[dict]) -> str:
+    """Format tools list for planner (Lightweight: Name + Description only)."""
+    if not tools:
+        return "No external tools available."
+
+    lines = []
+    for t in tools:
+        key = t.get("tool_key") or t.get("tool_name")
+        provider = t.get("provider_id") or t.get("provider", "")
+        desc = t.get("tool_description") or t.get("description", "")
+        label = t.get("tool_label") or key
+
+        # Format: - [provider/key] Label: Description
+        full_key = f"{provider}/{key}" if provider else key
+        lines.append(f"- [{full_key}] {label}: {desc}")
+
+    return "\n".join(lines)

api/core/workflow/generator/runner.py

@@ -0,0 +1,349 @@
+import json
+import logging
+import re
+from collections.abc import Sequence
+
+import json_repair
+
+from core.model_manager import ModelManager
+from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
+from core.model_runtime.entities.model_entities import ModelType
+from core.workflow.generator.prompts.builder_prompts import (
+    BUILDER_SYSTEM_PROMPT,
+    BUILDER_SYSTEM_PROMPT_V2,
+    BUILDER_USER_PROMPT,
+    BUILDER_USER_PROMPT_V2,
+    format_existing_edges,
+    format_existing_nodes,
+    format_selected_nodes,
+)
+from core.workflow.generator.prompts.planner_prompts import (
+    PLANNER_SYSTEM_PROMPT,
+    PLANNER_USER_PROMPT,
+    format_tools_for_planner,
+)
+from core.workflow.generator.prompts.vibe_prompts import (
+    format_available_models,
+    format_available_nodes,
+    format_available_tools,
+    parse_vibe_response,
+)
+from core.workflow.generator.utils.graph_builder import CyclicDependencyError, GraphBuilder
+from core.workflow.generator.utils.mermaid_generator import generate_mermaid
+from core.workflow.generator.utils.workflow_validator import ValidationHint, WorkflowValidator
+
+logger = logging.getLogger(__name__)
+
+
+class WorkflowGenerator:
+    """
+    Refactored Vibe Workflow Generator (Planner-Builder Architecture).
+    Extracts Vibe logic from the monolithic LLMGenerator.
+    """
+
+    @classmethod
+    def generate_workflow_flowchart(
+        cls,
+        tenant_id: str,
+        instruction: str,
+        model_config: dict,
+        available_nodes: Sequence[dict[str, object]] | None = None,
+        existing_nodes: Sequence[dict[str, object]] | None = None,
+        existing_edges: Sequence[dict[str, object]] | None = None,
+        available_tools: Sequence[dict[str, object]] | None = None,
+        selected_node_ids: Sequence[str] | None = None,
+        previous_workflow: dict[str, object] | None = None,
+        regenerate_mode: bool = False,
+        preferred_language: str | None = None,
+        available_models: Sequence[dict[str, object]] | None = None,
+        use_graph_builder: bool = False,
+    ):
+        """
+        Generates a Dify Workflow Flowchart from natural language instruction.
+
+        Pipeline:
+        1. Planner: Analyze intent & select tools.
+        2. Context Filter: Filter relevant tools (reduce tokens).
+        3. Builder: Generate node configurations.
+        4. Repair: Fix common node/edge issues (NodeRepair, EdgeRepair).
+        5. Validator: Check for errors & generate friendly hints.
+        6. Renderer: Deterministic Mermaid generation.
+        """
+        model_manager = ModelManager()
+        model_instance = model_manager.get_model_instance(
+            tenant_id=tenant_id,
+            model_type=ModelType.LLM,
+            provider=model_config.get("provider", ""),
+            model=model_config.get("name", ""),
+        )
+        model_parameters = model_config.get("completion_params", {})
+        available_tools_list = list(available_tools) if available_tools else []
+
+        # Check if this is modification mode (user is refining existing workflow)
+        has_existing_nodes = existing_nodes and len(list(existing_nodes)) > 0
+
+        # --- STEP 1: PLANNER (Skip in modification mode) ---
+        if has_existing_nodes:
+            # In modification mode, skip Planner:
+            # - User intent is clear: modify the existing workflow
+            # - Tools are already in use (from existing nodes)
+            # - No need for intent classification or tool selection
+            plan_data = {"intent": "generate", "steps": [], "required_tool_keys": []}
+            filtered_tools = available_tools_list  # Use all available tools
+        else:
+            # In creation mode, run Planner to validate intent and select tools
+            planner_tools_context = format_tools_for_planner(available_tools_list)
+            planner_system = PLANNER_SYSTEM_PROMPT.format(tools_summary=planner_tools_context)
+            planner_user = PLANNER_USER_PROMPT.format(instruction=instruction)
+
+            try:
+                response = model_instance.invoke_llm(
+                    prompt_messages=[
+                        SystemPromptMessage(content=planner_system),
+                        UserPromptMessage(content=planner_user),
+                    ],
+                    model_parameters=model_parameters,
+                    stream=False,
+                )
+                plan_content = response.message.content
+                # Reuse parse_vibe_response logic or simple load
+                plan_data = parse_vibe_response(plan_content)
+            except Exception as e:
+                logger.exception("Planner failed")
+                return {"intent": "error", "error": f"Planning failed: {str(e)}"}
+
+            if plan_data.get("intent") == "off_topic":
+                return {
+                    "intent": "off_topic",
+                    "message": plan_data.get("message", "I can only help with workflow creation."),
+                    "suggestions": plan_data.get("suggestions", []),
+                }
+
+            # --- STEP 2: CONTEXT FILTERING ---
+            required_tools = plan_data.get("required_tool_keys", [])
+
+            filtered_tools = []
+            if required_tools:
+                # Simple linear search (optimized version would use a map)
+                for tool in available_tools_list:
+                    t_key = tool.get("tool_key") or tool.get("tool_name")
+                    provider = tool.get("provider_id") or tool.get("provider")
+                    full_key = f"{provider}/{t_key}" if provider else t_key
+
+                    # Check if this tool is in required list (match either full key or short name)
+                    if t_key in required_tools or full_key in required_tools:
+                        filtered_tools.append(tool)
+            else:
+                # If logic only, no tools needed
+                filtered_tools = []
+
+        # --- STEP 3: BUILDER (with retry loop) ---
+        MAX_GLOBAL_RETRIES = 2  # Total attempts: 1 initial + 1 retry
+
+        workflow_data = None
+        mermaid_code = None
+        all_warnings = []
+        all_fixes = []
+        retry_count = 0
+        validation_hints = []
+
+        for attempt in range(MAX_GLOBAL_RETRIES):
+            retry_count = attempt
+            logger.info("Generation attempt %s/%s", attempt + 1, MAX_GLOBAL_RETRIES)
+
+            # Prepare context
+            tool_schemas = format_available_tools(filtered_tools)
+            node_specs = format_available_nodes(list(available_nodes) if available_nodes else [])
+            existing_nodes_context = format_existing_nodes(list(existing_nodes) if existing_nodes else None)
+            existing_edges_context = format_existing_edges(list(existing_edges) if existing_edges else None)
+            selected_nodes_context = format_selected_nodes(
+                list(selected_node_ids) if selected_node_ids else None, list(existing_nodes) if existing_nodes else None
+            )
+
+            # Build retry context
+            retry_context = ""
+
+            # NOTE: Manual regeneration/refinement mode removed
+            # Only handle automatic retry (validation errors)
+
+            # For automatic retry (validation errors)
+            if attempt > 0 and validation_hints:
+                severe_issues = [h for h in validation_hints if h.severity == "error"]
+                if severe_issues:
+                    retry_context = "\n<validation_feedback>\n"
+                    retry_context += "The previous generation had validation errors:\n"
+                    for idx, hint in enumerate(severe_issues[:5], 1):
+                        retry_context += f"{idx}. {hint.message}\n"
+                    retry_context += "\nPlease fix these specific issues while keeping everything else UNCHANGED.\n"
+                    retry_context += "</validation_feedback>\n"
+
+            # Select prompt version based on use_graph_builder flag
+            if use_graph_builder:
+                builder_system = BUILDER_SYSTEM_PROMPT_V2.format(
+                    plan_context=json.dumps(plan_data.get("steps", []), indent=2),
+                    tool_schemas=tool_schemas,
+                    builtin_node_specs=node_specs,
+                    available_models=format_available_models(list(available_models or [])),
+                    preferred_language=preferred_language or "English",
+                    existing_nodes_context=existing_nodes_context,
+                    selected_nodes_context=selected_nodes_context,
+                )
+                builder_user = BUILDER_USER_PROMPT_V2.format(instruction=instruction) + retry_context
+            else:
+                builder_system = BUILDER_SYSTEM_PROMPT.format(
+                    plan_context=json.dumps(plan_data.get("steps", []), indent=2),
+                    tool_schemas=tool_schemas,
+                    builtin_node_specs=node_specs,
+                    available_models=format_available_models(list(available_models or [])),
+                    preferred_language=preferred_language or "English",
+                    existing_nodes_context=existing_nodes_context,
+                    existing_edges_context=existing_edges_context,
+                    selected_nodes_context=selected_nodes_context,
+                )
+                builder_user = BUILDER_USER_PROMPT.format(instruction=instruction) + retry_context
+
+            try:
+                build_res = model_instance.invoke_llm(
+                    prompt_messages=[
+                        SystemPromptMessage(content=builder_system),
+                        UserPromptMessage(content=builder_user),
+                    ],
+                    model_parameters=model_parameters,
+                    stream=False,
+                )
+                # Builder output is raw JSON nodes/edges
+                build_content = build_res.message.content
+                match = re.search(r"```(?:json)?\s*([\s\S]+?)```", build_content)
+                if match:
+                    build_content = match.group(1)
+
+                workflow_data = json_repair.loads(build_content)
+
+                if "nodes" not in workflow_data:
+                    workflow_data["nodes"] = []
+
+                # --- GraphBuilder Mode: Build graph from depends_on ---
+                if use_graph_builder:
+                    try:
+                        # Extract nodes from LLM output (without start/end)
+                        llm_nodes = workflow_data.get("nodes", [])
+
+                        # Build complete graph with start/end and edges
+                        complete_nodes, edges = GraphBuilder.build_graph(llm_nodes)
+
+                        workflow_data["nodes"] = complete_nodes
+                        workflow_data["edges"] = edges
+
+                        logger.info(
+                            "GraphBuilder: built %d nodes, %d edges from %d LLM nodes",
+                            len(complete_nodes),
+                            len(edges),
+                            len(llm_nodes),
+                        )
+
+                    except CyclicDependencyError as e:
+                        logger.warning("GraphBuilder: cyclic dependency detected: %s", e)
+                        # Add to validation hints for retry
+                        validation_hints.append(
+                            ValidationHint(
+                                node_id="",
+                                field="depends_on",
+                                message=f"Cyclic dependency detected: {e}. Please fix the dependency chain.",
+                                severity="error",
+                            )
+                        )
+                        if attempt == MAX_GLOBAL_RETRIES - 1:
+                            return {
+                                "intent": "error",
+                                "error": "Failed to build workflow: cyclic dependency detected.",
+                            }
+                        continue  # Retry with error feedback
+
+                    except Exception as e:
+                        logger.exception("GraphBuilder failed on attempt %d", attempt + 1)
+                        if attempt == MAX_GLOBAL_RETRIES - 1:
+                            return {"intent": "error", "error": f"Graph building failed: {str(e)}"}
+                        continue
+                else:
+                    # Legacy mode: edges from LLM output
+                    if "edges" not in workflow_data:
+                        workflow_data["edges"] = []
+
+            except Exception as e:
+                logger.exception("Builder failed on attempt %d", attempt + 1)
+                if attempt == MAX_GLOBAL_RETRIES - 1:
+                    return {"intent": "error", "error": f"Building failed: {str(e)}"}
+                continue  # Try again
+
+            # NOTE: NodeRepair and EdgeRepair have been removed.
+            # Validation will detect structural issues, and LLM will fix them on retry.
+            # This is more accurate because LLM understands the workflow context.
+
+            # --- STEP 4: RENDERER (Generate Mermaid early for validation) ---
+            mermaid_code = generate_mermaid(workflow_data)
+
+            # --- STEP 5: VALIDATOR ---
+            is_valid, validation_hints = WorkflowValidator.validate(workflow_data, available_tools_list)
+
+            # --- STEP 6: GRAPH VALIDATION (structural checks using graph algorithms) ---
+            if attempt < MAX_GLOBAL_RETRIES - 1:
+                try:
+                    from core.workflow.generator.utils.graph_validator import GraphValidator
+
+                    graph_result = GraphValidator.validate(workflow_data)
+
+                    if not graph_result.success:
+                        # Convert graph errors to validation hints
+                        for graph_error in graph_result.errors:
+                            validation_hints.append(
+                                ValidationHint(
+                                    node_id=graph_error.node_id,
+                                    field="edges",
+                                    message=f"[Graph] {graph_error.message}",
+                                    severity="error",
+                                )
+                            )
+                        # Also add warnings (dead ends) as hints
+                        for graph_warning in graph_result.warnings:
+                            validation_hints.append(
+                                ValidationHint(
+                                    node_id=graph_warning.node_id,
+                                    field="edges",
+                                    message=f"[Graph] {graph_warning.message}",
+                                    severity="warning",
+                                )
+                            )
+                except Exception as e:
+                    logger.warning("Graph validation error: %s", e)
+            # Collect all validation warnings
+            all_warnings = [h.message for h in validation_hints]
+
+            # Check if we should retry
+            severe_issues = [h for h in validation_hints if h.severity == "error"]
+
+            if not severe_issues or attempt == MAX_GLOBAL_RETRIES - 1:
+                break
+
+            # Has severe errors and retries remaining - continue to next attempt
+
+        # Collect all validation warnings
+        all_warnings = [h.message for h in validation_hints]
+
+        # Add stability warning (as requested by user)
+        stability_warning = "The generated workflow may require debugging."
+        if preferred_language and preferred_language.startswith("zh"):
+            stability_warning = "生成的 Workflow 可能需要调试。"
+        all_warnings.append(stability_warning)
+
+        return {
+            "intent": "generate",
+            "flowchart": mermaid_code,
+            "nodes": workflow_data["nodes"],
+            "edges": workflow_data["edges"],
+            "message": plan_data.get("plan_thought", "Generated workflow based on your request."),
+            "warnings": all_warnings,
+            "tool_recommendations": [],  # Legacy field
+            "error": "",
+            "fixed_issues": all_fixes,  # Track what was auto-fixed
+            "retry_count": retry_count,  # Track how many retries were needed
+        }

api/core/workflow/generator/types.py

@@ -0,0 +1,217 @@
+"""
+Type definitions for Vibe Workflow Generator.
+
+This module provides:
+- TypedDict classes for lightweight type hints (no runtime overhead)
+- Pydantic models for runtime validation where needed
+
+Usage:
+    # For type hints only (no runtime validation):
+    from core.workflow.generator.types import WorkflowNodeDict, WorkflowEdgeDict
+
+    # For runtime validation:
+    from core.workflow.generator.types import WorkflowNode, WorkflowEdge
+"""
+
+from typing import Any, TypedDict
+
+from pydantic import BaseModel, Field
+
+# ============================================================
+# TypedDict definitions (lightweight, for type hints only)
+# ============================================================
+
+
+class WorkflowNodeDict(TypedDict, total=False):
+    """
+    Workflow node structure (TypedDict for hints).
+
+    Attributes:
+        id: Unique node identifier
+        type: Node type (e.g., "start", "end", "llm", "if-else", "http-request")
+        title: Human-readable node title
+        config: Node-specific configuration
+        data: Additional node data
+    """
+
+    id: str
+    type: str
+    title: str
+    config: dict[str, Any]
+    data: dict[str, Any]
+
+
+class WorkflowEdgeDict(TypedDict, total=False):
+    """
+    Workflow edge structure (TypedDict for hints).
+
+    Attributes:
+        source: Source node ID
+        target: Target node ID
+        sourceHandle: Branch handle for if-else/question-classifier nodes
+    """
+
+    source: str
+    target: str
+    sourceHandle: str
+
+
+class AvailableModelDict(TypedDict):
+    """
+    Available model structure.
+
+    Attributes:
+        provider: Model provider (e.g., "openai", "anthropic")
+        model: Model name (e.g., "gpt-4", "claude-3")
+    """
+
+    provider: str
+    model: str
+
+
+class ToolParameterDict(TypedDict, total=False):
+    """
+    Tool parameter structure.
+
+    Attributes:
+        name: Parameter name
+        type: Parameter type (e.g., "string", "number", "boolean")
+        required: Whether parameter is required
+        human_description: Human-readable description
+        llm_description: LLM-oriented description
+        options: Available options for enum-type parameters
+    """
+
+    name: str
+    type: str
+    required: bool
+    human_description: str | dict[str, str]
+    llm_description: str
+    options: list[Any]
+
+
+class AvailableToolDict(TypedDict, total=False):
+    """
+    Available tool structure.
+
+    Attributes:
+        provider_id: Tool provider ID
+        provider: Tool provider name (alternative to provider_id)
+        tool_key: Unique tool key
+        tool_name: Tool name (alternative to tool_key)
+        tool_description: Tool description
+        description: Alternative description field
+        is_team_authorization: Whether tool is configured/authorized
+        parameters: List of tool parameters
+    """
+
+    provider_id: str
+    provider: str
+    tool_key: str
+    tool_name: str
+    tool_description: str
+    description: str
+    is_team_authorization: bool
+    parameters: list[ToolParameterDict]
+
+
+class WorkflowDataDict(TypedDict, total=False):
+    """
+    Complete workflow data structure.
+
+    Attributes:
+        nodes: List of workflow nodes
+        edges: List of workflow edges
+        warnings: List of warning messages
+    """
+
+    nodes: list[WorkflowNodeDict]
+    edges: list[WorkflowEdgeDict]
+    warnings: list[str]
+
+
+# ============================================================
+# Pydantic models (for runtime validation)
+# ============================================================
+
+
+class WorkflowNode(BaseModel):
+    """
+    Workflow node with runtime validation.
+
+    Use this model when you need to validate node data at runtime.
+    For lightweight type hints without validation, use WorkflowNodeDict.
+    """
+
+    id: str
+    type: str
+    title: str = ""
+    config: dict[str, Any] = Field(default_factory=dict)
+    data: dict[str, Any] = Field(default_factory=dict)
+
+
+class WorkflowEdge(BaseModel):
+    """
+    Workflow edge with runtime validation.
+
+    Use this model when you need to validate edge data at runtime.
+    For lightweight type hints without validation, use WorkflowEdgeDict.
+    """
+
+    source: str
+    target: str
+    sourceHandle: str | None = None
+
+
+class AvailableModel(BaseModel):
+    """
+    Available model with runtime validation.
+
+    Use this model when you need to validate model data at runtime.
+    For lightweight type hints without validation, use AvailableModelDict.
+    """
+
+    provider: str
+    model: str
+
+
+class ToolParameter(BaseModel):
+    """Tool parameter with runtime validation."""
+
+    name: str = ""
+    type: str = "string"
+    required: bool = False
+    human_description: str | dict[str, str] = ""
+    llm_description: str = ""
+    options: list[Any] = Field(default_factory=list)
+
+
+class AvailableTool(BaseModel):
+    """
+    Available tool with runtime validation.
+
+    Use this model when you need to validate tool data at runtime.
+    For lightweight type hints without validation, use AvailableToolDict.
+    """
+
+    provider_id: str = ""
+    provider: str = ""
+    tool_key: str = ""
+    tool_name: str = ""
+    tool_description: str = ""
+    description: str = ""
+    is_team_authorization: bool = False
+    parameters: list[ToolParameter] = Field(default_factory=list)
+
+
+class WorkflowData(BaseModel):
+    """
+    Complete workflow data with runtime validation.
+
+    Use this model when you need to validate workflow data at runtime.
+    For lightweight type hints without validation, use WorkflowDataDict.
+    """
+
+    nodes: list[WorkflowNode] = Field(default_factory=list)
+    edges: list[WorkflowEdge] = Field(default_factory=list)
+    warnings: list[str] = Field(default_factory=list)

api/core/workflow/generator/utils/edge_repair.py

@@ -0,0 +1,384 @@
+"""
+Edge Repair Utility for Vibe Workflow Generation.
+
+This module provides intelligent edge repair capabilities for generated workflows.
+It can detect and fix common edge issues:
+- Missing edges between sequential nodes
+- Incomplete branches for question-classifier and if-else nodes
+- Orphaned nodes without connections
+
+The repair logic is deterministic and doesn't require LLM calls.
+"""
+
+import logging
+from dataclasses import dataclass, field
+
+from core.workflow.generator.types import WorkflowDataDict, WorkflowEdgeDict, WorkflowNodeDict
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RepairResult:
+    """Result of edge repair operation."""
+
+    nodes: list[WorkflowNodeDict]
+    edges: list[WorkflowEdgeDict]
+    repairs_made: list[str] = field(default_factory=list)
+    warnings: list[str] = field(default_factory=list)
+
+    @property
+    def was_repaired(self) -> bool:
+        """Check if any repairs were made."""
+        return len(self.repairs_made) > 0
+
+
+class EdgeRepair:
+    """
+    Intelligent edge repair for workflow graphs.
+
+    Repairs are applied in order:
+    1. Infer linear connections from node order (if no edges exist)
+    2. Add missing branch edges for question-classifier
+    3. Add missing branch edges for if-else
+    4. Connect orphaned nodes
+    """
+
+    @classmethod
+    def repair(cls, workflow_data: WorkflowDataDict) -> RepairResult:
+        """
+        Repair edges in the workflow data.
+
+        Args:
+            workflow_data: Dict containing 'nodes' and 'edges'
+
+        Returns:
+            RepairResult with repaired nodes, edges, and repair logs
+        """
+        nodes = list(workflow_data.get("nodes", []))
+        edges = list(workflow_data.get("edges", []))
+        repairs: list[str] = []
+        warnings: list[str] = []
+
+        logger.info("[EDGE REPAIR] Starting repair process for %s nodes, %s edges", len(nodes), len(edges))
+
+        # Build node lookup
+
+        # Build node lookup
+        node_map = {n.get("id"): n for n in nodes if n.get("id")}
+        node_ids = set(node_map.keys())
+
+        # 1. If no edges at all, infer linear chain
+        if not edges and len(nodes) > 1:
+            edges, inferred_repairs = cls._infer_linear_chain(nodes)
+            repairs.extend(inferred_repairs)
+
+        # 2. Build edge index for analysis
+        outgoing_edges: dict[str, list[WorkflowEdgeDict]] = {}
+        incoming_edges: dict[str, list[WorkflowEdgeDict]] = {}
+        for edge in edges:
+            src = edge.get("source")
+            tgt = edge.get("target")
+            if src:
+                outgoing_edges.setdefault(src, []).append(edge)
+            if tgt:
+                incoming_edges.setdefault(tgt, []).append(edge)
+
+        # 3. Repair question-classifier branches
+        for node in nodes:
+            if node.get("type") == "question-classifier":
+                new_edges, branch_repairs, branch_warnings = cls._repair_classifier_branches(
+                    node, edges, outgoing_edges, node_ids
+                )
+                edges.extend(new_edges)
+                repairs.extend(branch_repairs)
+                warnings.extend(branch_warnings)
+                # Update outgoing index
+                for edge in new_edges:
+                    outgoing_edges.setdefault(edge.get("source"), []).append(edge)
+
+        # 4. Repair if-else branches
+        for node in nodes:
+            if node.get("type") == "if-else":
+                new_edges, branch_repairs, branch_warnings = cls._repair_if_else_branches(
+                    node, edges, outgoing_edges, node_ids
+                )
+                edges.extend(new_edges)
+                repairs.extend(branch_repairs)
+                warnings.extend(branch_warnings)
+                # Update outgoing index
+                for edge in new_edges:
+                    outgoing_edges.setdefault(edge.get("source"), []).append(edge)
+
+        # 5. Connect orphaned nodes (nodes with no incoming edge, except start)
+        new_edges, orphan_repairs = cls._connect_orphaned_nodes(nodes, edges, outgoing_edges, incoming_edges)
+        edges.extend(new_edges)
+        repairs.extend(orphan_repairs)
+
+        # 6. Connect nodes with no outgoing edge to 'end' (except end nodes)
+        new_edges, terminal_repairs = cls._connect_terminal_nodes(nodes, edges, outgoing_edges)
+        edges.extend(new_edges)
+        repairs.extend(terminal_repairs)
+
+        if repairs:
+            logger.info("[EDGE REPAIR] Completed with %s repairs:", len(repairs))
+            for i, repair in enumerate(repairs, 1):
+                logger.info("[EDGE REPAIR]   %s. %s", i, repair)
+        else:
+            logger.info("[EDGE REPAIR] Completed - no repairs needed")
+
+        return RepairResult(
+            nodes=nodes,
+            edges=edges,
+            repairs_made=repairs,
+            warnings=warnings,
+        )
+
+    @classmethod
+    def _infer_linear_chain(cls, nodes: list[WorkflowNodeDict]) -> tuple[list[WorkflowEdgeDict], list[str]]:
+        """
+        Infer a linear chain of edges from node order.
+
+        This is used when no edges are provided at all.
+        """
+        edges: list[WorkflowEdgeDict] = []
+        repairs: list[str] = []
+
+        # Filter to get ordered node IDs
+        node_ids = [n.get("id") for n in nodes if n.get("id")]
+
+        if len(node_ids) < 2:
+            return edges, repairs
+
+        # Create edges between consecutive nodes
+        for i in range(len(node_ids) - 1):
+            src = node_ids[i]
+            tgt = node_ids[i + 1]
+            edges.append({"source": src, "target": tgt})
+            repairs.append(f"Inferred edge: {src} -> {tgt}")
+
+        return edges, repairs
+
+    @classmethod
+    def _repair_classifier_branches(
+        cls,
+        node: WorkflowNodeDict,
+        edges: list[WorkflowEdgeDict],
+        outgoing_edges: dict[str, list[WorkflowEdgeDict]],
+        valid_node_ids: set[str],
+    ) -> tuple[list[WorkflowEdgeDict], list[str], list[str]]:
+        """
+        Repair missing branches for question-classifier nodes.
+
+        For each class that doesn't have an edge, create one pointing to 'end'.
+        """
+        new_edges: list[WorkflowEdgeDict] = []
+        repairs: list[str] = []
+        warnings: list[str] = []
+
+        node_id = node.get("id")
+        if not node_id:
+            return new_edges, repairs, warnings
+
+        config = node.get("config", {})
+        classes = config.get("classes", [])
+
+        if not classes:
+            return new_edges, repairs, warnings
+
+        # Get existing sourceHandles for this node
+        existing_handles = set()
+        for edge in outgoing_edges.get(node_id, []):
+            handle = edge.get("sourceHandle")
+            if handle:
+                existing_handles.add(handle)
+
+        # Find 'end' node as default target
+        end_node_id = "end"
+        if "end" not in valid_node_ids:
+            # Try to find an end node
+            for nid in valid_node_ids:
+                if "end" in nid.lower():
+                    end_node_id = nid
+                    break
+
+        # Add missing branches
+        for cls_def in classes:
+            if not isinstance(cls_def, dict):
+                continue
+            cls_id = cls_def.get("id")
+            cls_name = cls_def.get("name", cls_id)
+
+            if cls_id and cls_id not in existing_handles:
+                new_edge = {
+                    "source": node_id,
+                    "sourceHandle": cls_id,
+                    "target": end_node_id,
+                }
+                new_edges.append(new_edge)
+                repairs.append(f"Added missing branch edge for class '{cls_name}' -> {end_node_id}")
+                warnings.append(
+                    f"Auto-connected question-classifier branch '{cls_name}' to '{end_node_id}'. "
+                    "You may want to redirect this to a specific handler node."
+                )
+
+        return new_edges, repairs, warnings
+
+    @classmethod
+    def _repair_if_else_branches(
+        cls,
+        node: WorkflowNodeDict,
+        edges: list[WorkflowEdgeDict],
+        outgoing_edges: dict[str, list[WorkflowEdgeDict]],
+        valid_node_ids: set[str],
+    ) -> tuple[list[WorkflowEdgeDict], list[str], list[str]]:
+        """
+        Repair missing branches for if-else nodes.
+
+        If-else in Dify uses case_id as sourceHandle for each condition,
+        plus 'false' for the else branch.
+        """
+        new_edges: list[WorkflowEdgeDict] = []
+        repairs: list[str] = []
+        warnings: list[str] = []
+
+        node_id = node.get("id")
+        if not node_id:
+            return new_edges, repairs, warnings
+
+        # Get existing sourceHandles
+        existing_handles = set()
+        for edge in outgoing_edges.get(node_id, []):
+            handle = edge.get("sourceHandle")
+            if handle:
+                existing_handles.add(handle)
+
+        # Find 'end' node as default target
+        end_node_id = "end"
+        if "end" not in valid_node_ids:
+            for nid in valid_node_ids:
+                if "end" in nid.lower():
+                    end_node_id = nid
+                    break
+
+        # Get required branches from config
+        config = node.get("config", {})
+        cases = config.get("cases", [])
+
+        # Build required handles: each case_id + 'false' for else
+        required_branches = set()
+        for case in cases:
+            case_id = case.get("case_id")
+            if case_id:
+                required_branches.add(case_id)
+        required_branches.add("false")  # else branch
+
+        # Add missing branches
+        for branch in required_branches:
+            if branch not in existing_handles:
+                new_edge = {
+                    "source": node_id,
+                    "sourceHandle": branch,
+                    "target": end_node_id,
+                }
+                new_edges.append(new_edge)
+                repairs.append(f"Added missing if-else branch '{branch}' -> {end_node_id}")
+                warnings.append(
+                    f"Auto-connected if-else branch '{branch}' to '{end_node_id}'. "
+                    "You may want to redirect this to a specific handler node."
+                )
+
+        return new_edges, repairs, warnings
+
+    @classmethod
+    def _connect_orphaned_nodes(
+        cls,
+        nodes: list[WorkflowNodeDict],
+        edges: list[WorkflowEdgeDict],
+        outgoing_edges: dict[str, list[WorkflowEdgeDict]],
+        incoming_edges: dict[str, list[WorkflowEdgeDict]],
+    ) -> tuple[list[WorkflowEdgeDict], list[str]]:
+        """
+        Connect orphaned nodes to the previous node in sequence.
+
+        An orphaned node has no incoming edges and is not a 'start' node.
+        """
+        new_edges: list[WorkflowEdgeDict] = []
+        repairs: list[str] = []
+
+        node_ids = [n.get("id") for n in nodes if n.get("id")]
+        node_types = {n.get("id"): n.get("type") for n in nodes}
+
+        for i, node_id in enumerate(node_ids):
+            node_type = node_types.get(node_id)
+
+            # Skip start nodes - they don't need incoming edges
+            if node_type == "start":
+                continue
+
+            # Check if node has incoming edges
+            if node_id not in incoming_edges or not incoming_edges[node_id]:
+                # Find previous node to connect from
+                if i > 0:
+                    prev_node_id = node_ids[i - 1]
+                    new_edge = {"source": prev_node_id, "target": node_id}
+                    new_edges.append(new_edge)
+                    repairs.append(f"Connected orphaned node: {prev_node_id} -> {node_id}")
+
+                    # Update incoming_edges for subsequent checks
+                    incoming_edges.setdefault(node_id, []).append(new_edge)
+
+        return new_edges, repairs
+
+    @classmethod
+    def _connect_terminal_nodes(
+        cls,
+        nodes: list[WorkflowNodeDict],
+        edges: list[WorkflowEdgeDict],
+        outgoing_edges: dict[str, list[WorkflowEdgeDict]],
+    ) -> tuple[list[WorkflowEdgeDict], list[str]]:
+        """
+        Connect terminal nodes (no outgoing edges) to 'end'.
+
+        A terminal node has no outgoing edges and is not an 'end' node.
+        This ensures all branches eventually reach 'end'.
+        """
+        new_edges: list[WorkflowEdgeDict] = []
+        repairs: list[str] = []
+
+        # Find end node
+        end_node_id = None
+        node_ids = set()
+        for n in nodes:
+            nid = n.get("id")
+            ntype = n.get("type")
+            if nid:
+                node_ids.add(nid)
+            if ntype == "end":
+                end_node_id = nid
+
+        if not end_node_id:
+            # No end node found, can't connect
+            return new_edges, repairs
+
+        for node in nodes:
+            node_id = node.get("id")
+            node_type = node.get("type")
+
+            # Skip end nodes
+            if node_type == "end":
+                continue
+
+            # Skip nodes that already have outgoing edges
+            if outgoing_edges.get(node_id):
+                continue
+
+            # Connect to end
+            new_edge = {"source": node_id, "target": end_node_id}
+            new_edges.append(new_edge)
+            repairs.append(f"Connected terminal node to end: {node_id} -> {end_node_id}")
+
+            # Update for subsequent checks
+            outgoing_edges.setdefault(node_id, []).append(new_edge)
+
+        return new_edges, repairs

api/core/workflow/generator/utils/graph_builder.py

@@ -0,0 +1,615 @@
+"""
+GraphBuilder: Automatic workflow graph construction from node list.
+
+This module implements the core logic for building complete workflow graphs
+from LLM-generated node lists with dependency declarations.
+
+Key features:
+- Automatic start/end node generation
+- Dependency inference from variable references
+- Topological sorting with cycle detection
+- Special handling for branching nodes (if-else, question-classifier)
+- Silent error recovery where possible
+"""
+
+import json
+import logging
+import re
+import uuid
+from collections import defaultdict
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# Pattern to match variable references like {{#node_id.field#}}
+VAR_PATTERN = re.compile(r"\{\{#([^.#]+)\.[^#]+#\}\}")
+
+# System variable prefixes to exclude from dependency inference
+SYSTEM_VAR_PREFIXES = {"sys", "start", "env"}
+
+# Node types that have special branching behavior
+BRANCHING_NODE_TYPES = {"if-else", "question-classifier"}
+
+# Container node types (iteration, loop) - these have internal subgraphs
+# but behave as single-input-single-output nodes in the external graph
+CONTAINER_NODE_TYPES = {"iteration", "loop"}
+
+
+class GraphBuildError(Exception):
+    """Raised when graph cannot be built due to unrecoverable errors."""
+
+    pass
+
+
+class CyclicDependencyError(GraphBuildError):
+    """Raised when cyclic dependencies are detected."""
+
+    pass
+
+
+class GraphBuilder:
+    """
+    Builds complete workflow graphs from LLM-generated node lists.
+
+    This class handles the conversion from a simplified node list format
+    (with depends_on declarations) to a full workflow graph with nodes and edges.
+
+    The LLM only needs to generate:
+    - Node configurations with depends_on arrays
+    - Branch targets in config for branching nodes
+
+    The GraphBuilder automatically:
+    - Adds start and end nodes
+    - Generates all edges from dependencies
+    - Infers implicit dependencies from variable references
+    - Handles branching nodes (if-else, question-classifier)
+    - Validates graph structure (no cycles, proper connectivity)
+    """
+
+    @classmethod
+    def build_graph(
+        cls,
+        nodes: list[dict[str, Any]],
+        start_config: dict[str, Any] | None = None,
+        end_config: dict[str, Any] | None = None,
+    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
+        """
+        Build a complete workflow graph from a node list.
+
+        Args:
+            nodes: LLM-generated nodes (without start/end)
+            start_config: Optional configuration for start node
+            end_config: Optional configuration for end node
+
+        Returns:
+            Tuple of (complete_nodes, edges) where:
+            - complete_nodes includes start, user nodes, and end
+            - edges contains all connections
+
+        Raises:
+            CyclicDependencyError: If cyclic dependencies are detected
+            GraphBuildError: If graph cannot be built
+        """
+        if not nodes:
+            # Empty node list - create minimal workflow
+            start_node = cls._create_start_node([], start_config)
+            end_node = cls._create_end_node([], end_config)
+            edge = cls._create_edge("start", "end")
+            return [start_node, end_node], [edge]
+
+        # Build node index for quick lookup
+        node_map = {node["id"]: node for node in nodes}
+
+        # Step 1: Extract explicit dependencies from depends_on
+        dependencies = cls._extract_explicit_dependencies(nodes)
+
+        # Step 2: Infer implicit dependencies from variable references
+        dependencies = cls._infer_dependencies_from_variables(nodes, dependencies, node_map)
+
+        # Step 3: Validate and fix dependencies (remove invalid references)
+        dependencies = cls._validate_dependencies(dependencies, node_map)
+
+        # Step 4: Topological sort (detects cycles)
+        sorted_node_ids = cls._topological_sort(nodes, dependencies)
+
+        # Step 5: Generate start node
+        start_node = cls._create_start_node(nodes, start_config)
+
+        # Step 6: Generate edges
+        edges = cls._generate_edges(nodes, sorted_node_ids, dependencies, node_map)
+
+        # Step 7: Find terminal nodes and generate end node
+        terminal_nodes = cls._find_terminal_nodes(nodes, dependencies, node_map)
+        end_node = cls._create_end_node(terminal_nodes, end_config)
+
+        # Step 8: Add edges from terminal nodes to end
+        for terminal_id in terminal_nodes:
+            edges.append(cls._create_edge(terminal_id, "end"))
+
+        # Step 9: Assemble complete node list
+        all_nodes = [start_node, *nodes, end_node]
+
+        return all_nodes, edges
+
+    @classmethod
+    def _extract_explicit_dependencies(
+        cls,
+        nodes: list[dict[str, Any]],
+    ) -> dict[str, list[str]]:
+        """
+        Extract explicit dependencies from depends_on field.
+
+        Args:
+            nodes: List of nodes with optional depends_on field
+
+        Returns:
+            Dictionary mapping node_id -> list of dependency node_ids
+        """
+        dependencies: dict[str, list[str]] = {}
+
+        for node in nodes:
+            node_id = node.get("id", "")
+            depends_on = node.get("depends_on", [])
+
+            # Ensure depends_on is a list
+            if isinstance(depends_on, str):
+                depends_on = [depends_on] if depends_on else []
+            elif not isinstance(depends_on, list):
+                depends_on = []
+
+            dependencies[node_id] = list(depends_on)
+
+        return dependencies
+
+    @classmethod
+    def _infer_dependencies_from_variables(
+        cls,
+        nodes: list[dict[str, Any]],
+        explicit_deps: dict[str, list[str]],
+        node_map: dict[str, dict[str, Any]],
+    ) -> dict[str, list[str]]:
+        """
+        Infer implicit dependencies from variable references in config.
+
+        Scans node configurations for patterns like {{#node_id.field#}}
+        and adds those as dependencies if not already declared.
+
+        Args:
+            nodes: List of nodes
+            explicit_deps: Already extracted explicit dependencies
+            node_map: Map of node_id -> node for validation
+
+        Returns:
+            Updated dependencies dictionary
+        """
+        for node in nodes:
+            node_id = node.get("id", "")
+            config = node.get("config", {})
+
+            # Serialize config to search for variable references
+            try:
+                config_str = json.dumps(config, ensure_ascii=False)
+            except (TypeError, ValueError):
+                continue
+
+            # Find all variable references
+            referenced_nodes = set(VAR_PATTERN.findall(config_str))
+
+            # Filter out system variables
+            referenced_nodes -= SYSTEM_VAR_PREFIXES
+
+            # Ensure node_id exists in dependencies
+            if node_id not in explicit_deps:
+                explicit_deps[node_id] = []
+
+            # Add inferred dependencies
+            for ref in referenced_nodes:
+                # Skip self-references (e.g., loop nodes referencing their own outputs)
+                if ref == node_id:
+                    logger.debug(
+                        "Skipping self-reference: %s -> %s",
+                        node_id,
+                        ref,
+                    )
+                    continue
+
+                if ref in node_map and ref not in explicit_deps[node_id]:
+                    explicit_deps[node_id].append(ref)
+                    logger.debug(
+                        "Inferred dependency: %s -> %s (from variable reference)",
+                        node_id,
+                        ref,
+                    )
+
+        return explicit_deps
+
+    @classmethod
+    def _validate_dependencies(
+        cls,
+        dependencies: dict[str, list[str]],
+        node_map: dict[str, dict[str, Any]],
+    ) -> dict[str, list[str]]:
+        """
+        Validate dependencies and remove invalid references.
+
+        Silent fix: References to non-existent nodes are removed.
+
+        Args:
+            dependencies: Dependencies to validate
+            node_map: Map of valid node IDs
+
+        Returns:
+            Validated dependencies
+        """
+        valid_deps: dict[str, list[str]] = {}
+
+        for node_id, deps in dependencies.items():
+            valid_deps[node_id] = []
+            for dep in deps:
+                if dep in node_map:
+                    valid_deps[node_id].append(dep)
+                else:
+                    logger.warning(
+                        "Removed invalid dependency: %s -> %s (node does not exist)",
+                        node_id,
+                        dep,
+                    )
+
+        return valid_deps
+
+    @classmethod
+    def _topological_sort(
+        cls,
+        nodes: list[dict[str, Any]],
+        dependencies: dict[str, list[str]],
+    ) -> list[str]:
+        """
+        Perform topological sort on nodes based on dependencies.
+
+        Uses Kahn's algorithm for cycle detection.
+
+        Args:
+            nodes: List of nodes
+            dependencies: Dependency graph
+
+        Returns:
+            List of node IDs in topological order
+
+        Raises:
+            CyclicDependencyError: If cyclic dependencies are detected
+        """
+        # Build in-degree map
+        in_degree: dict[str, int] = defaultdict(int)
+        reverse_deps: dict[str, list[str]] = defaultdict(list)
+
+        node_ids = {node["id"] for node in nodes}
+
+        for node_id in node_ids:
+            in_degree[node_id] = 0
+
+        for node_id, deps in dependencies.items():
+            for dep in deps:
+                if dep in node_ids:
+                    in_degree[node_id] += 1
+                    reverse_deps[dep].append(node_id)
+
+        # Start with nodes that have no dependencies
+        queue = [nid for nid in node_ids if in_degree[nid] == 0]
+        sorted_ids: list[str] = []
+
+        while queue:
+            current = queue.pop(0)
+            sorted_ids.append(current)
+
+            for dependent in reverse_deps[current]:
+                in_degree[dependent] -= 1
+                if in_degree[dependent] == 0:
+                    queue.append(dependent)
+
+        # Check for cycles
+        if len(sorted_ids) != len(node_ids):
+            remaining = node_ids - set(sorted_ids)
+            raise CyclicDependencyError(f"Cyclic dependency detected involving nodes: {remaining}")
+
+        return sorted_ids
+
+    @classmethod
+    def _generate_edges(
+        cls,
+        nodes: list[dict[str, Any]],
+        sorted_node_ids: list[str],
+        dependencies: dict[str, list[str]],
+        node_map: dict[str, dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        """
+        Generate all edges based on dependencies and special node handling.
+
+        Args:
+            nodes: List of nodes
+            sorted_node_ids: Topologically sorted node IDs
+            dependencies: Dependency graph
+            node_map: Map of node_id -> node
+
+        Returns:
+            List of edge dictionaries
+        """
+        edges: list[dict[str, Any]] = []
+        nodes_with_incoming: set[str] = set()
+
+        # Track which nodes have outgoing edges from branching
+        branching_sources: set[str] = set()
+
+        # First pass: Handle branching nodes
+        for node in nodes:
+            node_id = node.get("id", "")
+            node_type = node.get("type", "")
+
+            if node_type == "if-else":
+                branch_edges = cls._handle_if_else_node(node)
+                edges.extend(branch_edges)
+                branching_sources.add(node_id)
+                nodes_with_incoming.update(edge["target"] for edge in branch_edges)
+
+            elif node_type == "question-classifier":
+                branch_edges = cls._handle_question_classifier_node(node)
+                edges.extend(branch_edges)
+                branching_sources.add(node_id)
+                nodes_with_incoming.update(edge["target"] for edge in branch_edges)
+
+        # Second pass: Generate edges from dependencies
+        for node_id in sorted_node_ids:
+            deps = dependencies.get(node_id, [])
+
+            if deps:
+                # Connect from each dependency
+                for dep_id in deps:
+                    dep_node = node_map.get(dep_id, {})
+                    dep_type = dep_node.get("type", "")
+
+                    # Skip if dependency is a branching node (edges handled above)
+                    if dep_type in BRANCHING_NODE_TYPES:
+                        continue
+
+                    edges.append(cls._create_edge(dep_id, node_id))
+                    nodes_with_incoming.add(node_id)
+            else:
+                # No dependencies - connect from start
+                # But skip if this node receives edges from branching nodes
+                if node_id not in nodes_with_incoming:
+                    edges.append(cls._create_edge("start", node_id))
+                    nodes_with_incoming.add(node_id)
+
+        return edges
+
+    @classmethod
+    def _handle_if_else_node(
+        cls,
+        node: dict[str, Any],
+    ) -> list[dict[str, Any]]:
+        """
+        Handle if-else node branching.
+
+        Expects config to contain true_branch and/or false_branch.
+
+        Args:
+            node: If-else node
+
+        Returns:
+            List of branch edges
+        """
+        edges: list[dict[str, Any]] = []
+        node_id = node.get("id", "")
+        config = node.get("config", {})
+
+        true_branch = config.get("true_branch")
+        false_branch = config.get("false_branch")
+
+        if true_branch:
+            edges.append(cls._create_edge(node_id, true_branch, source_handle="true"))
+
+        if false_branch:
+            edges.append(cls._create_edge(node_id, false_branch, source_handle="false"))
+
+        # If no branches specified, log warning
+        if not true_branch and not false_branch:
+            logger.warning(
+                "if-else node %s has no branch targets specified",
+                node_id,
+            )
+
+        return edges
+
+    @classmethod
+    def _handle_question_classifier_node(
+        cls,
+        node: dict[str, Any],
+    ) -> list[dict[str, Any]]:
+        """
+        Handle question-classifier node branching.
+
+        Expects config.classes to contain class definitions with target fields.
+
+        Args:
+            node: Question-classifier node
+
+        Returns:
+            List of branch edges
+        """
+        edges: list[dict[str, Any]] = []
+        node_id = node.get("id", "")
+        config = node.get("config", {})
+        classes = config.get("classes", [])
+
+        if not classes:
+            logger.warning(
+                "question-classifier node %s has no classes defined",
+                node_id,
+            )
+            return edges
+
+        for cls_def in classes:
+            class_id = cls_def.get("id", "")
+            target = cls_def.get("target")
+
+            if target:
+                edges.append(cls._create_edge(node_id, target, source_handle=class_id))
+            else:
+                # Silent fix: Connect to end if no target specified
+                edges.append(cls._create_edge(node_id, "end", source_handle=class_id))
+                logger.debug(
+                    "question-classifier class %s has no target, connecting to end",
+                    class_id,
+                )
+
+        return edges
+
+    @classmethod
+    def _find_terminal_nodes(
+        cls,
+        nodes: list[dict[str, Any]],
+        dependencies: dict[str, list[str]],
+        node_map: dict[str, dict[str, Any]],
+    ) -> list[str]:
+        """
+        Find nodes that should connect to the end node.
+
+        Terminal nodes are those that:
+        - Are not dependencies of any other node
+        - Are not branching nodes (those connect to their branches)
+
+        Args:
+            nodes: List of nodes
+            dependencies: Dependency graph
+            node_map: Map of node_id -> node
+
+        Returns:
+            List of terminal node IDs
+        """
+        # Build set of all nodes that are depended upon
+        depended_upon: set[str] = set()
+        for deps in dependencies.values():
+            depended_upon.update(deps)
+
+        # Also track nodes that are branch targets
+        branch_targets: set[str] = set()
+        branching_nodes: set[str] = set()
+
+        for node in nodes:
+            node_id = node.get("id", "")
+            node_type = node.get("type", "")
+            config = node.get("config", {})
+
+            if node_type == "if-else":
+                branching_nodes.add(node_id)
+                if config.get("true_branch"):
+                    branch_targets.add(config["true_branch"])
+                if config.get("false_branch"):
+                    branch_targets.add(config["false_branch"])
+
+            elif node_type == "question-classifier":
+                branching_nodes.add(node_id)
+                for cls_def in config.get("classes", []):
+                    if cls_def.get("target"):
+                        branch_targets.add(cls_def["target"])
+
+        # Find terminal nodes
+        terminal_nodes: list[str] = []
+        for node in nodes:
+            node_id = node.get("id", "")
+            node_type = node.get("type", "")
+
+            # Skip branching nodes - they don't connect to end directly
+            if node_type in BRANCHING_NODE_TYPES:
+                continue
+
+            # Terminal if not depended upon and not a branch target that leads elsewhere
+            if node_id not in depended_upon:
+                terminal_nodes.append(node_id)
+
+        # If no terminal nodes found (shouldn't happen), use all non-branching nodes
+        if not terminal_nodes:
+            terminal_nodes = [node["id"] for node in nodes if node.get("type") not in BRANCHING_NODE_TYPES]
+            logger.warning("No terminal nodes found, using all non-branching nodes")
+
+        return terminal_nodes
+
+    @classmethod
+    def _create_start_node(
+        cls,
+        nodes: list[dict[str, Any]],
+        config: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        """
+        Create a start node.
+
+        Args:
+            nodes: User nodes (for potential config inference)
+            config: Optional start node configuration
+
+        Returns:
+            Start node dictionary
+        """
+        return {
+            "id": "start",
+            "type": "start",
+            "title": "Start",
+            "config": config or {},
+            "data": {},
+        }
+
+    @classmethod
+    def _create_end_node(
+        cls,
+        terminal_nodes: list[str],
+        config: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        """
+        Create an end node.
+
+        Args:
+            terminal_nodes: Nodes that will connect to end
+            config: Optional end node configuration
+
+        Returns:
+            End node dictionary
+        """
+        return {
+            "id": "end",
+            "type": "end",
+            "title": "End",
+            "config": config or {},
+            "data": {},
+        }
+
+    @classmethod
+    def _create_edge(
+        cls,
+        source: str,
+        target: str,
+        source_handle: str | None = None,
+    ) -> dict[str, Any]:
+        """
+        Create an edge dictionary.
+
+        Args:
+            source: Source node ID
+            target: Target node ID
+            source_handle: Optional handle for branching (e.g., "true", "false", class_id)
+
+        Returns:
+            Edge dictionary
+        """
+        edge: dict[str, Any] = {
+            "id": f"{source}-{target}-{uuid.uuid4().hex[:8]}",
+            "source": source,
+            "target": target,
+        }
+
+        if source_handle:
+            edge["sourceHandle"] = source_handle
+        else:
+            edge["sourceHandle"] = "source"
+
+        edge["targetHandle"] = "target"
+
+        return edge

api/core/workflow/generator/utils/graph_validator.py

@@ -0,0 +1,280 @@
+"""
+Graph Validator for Workflow Generation
+
+Validates workflow graph structure using graph algorithms:
+- Reachability from start node (BFS)
+- Reachability to end node (reverse BFS)
+- Branch edge validation for if-else and classifier nodes
+"""
+
+import time
+from collections import deque
+from dataclasses import dataclass, field
+
+
+@dataclass
+class GraphError:
+    """Represents a structural error in the workflow graph."""
+
+    node_id: str
+    node_type: str
+    error_type: str  # "unreachable", "dead_end", "cycle", "missing_start", "missing_end"
+    message: str
+
+
+@dataclass
+class GraphValidationResult:
+    """Result of graph validation."""
+
+    success: bool
+    errors: list[GraphError] = field(default_factory=list)
+    warnings: list[GraphError] = field(default_factory=list)
+    execution_time: float = 0.0
+    stats: dict = field(default_factory=dict)
+
+
+class GraphValidator:
+    """
+    Validates workflow graph structure using proper graph algorithms.
+
+    Performs:
+    1. Forward reachability analysis (BFS from start)
+    2. Backward reachability analysis (reverse BFS from end)
+    3. Branch edge validation for if-else and classifier nodes
+    """
+
+    @staticmethod
+    def _build_adjacency(
+        nodes: dict[str, dict], edges: list[dict]
+    ) -> tuple[dict[str, list[str]], dict[str, list[str]]]:
+        """Build forward and reverse adjacency lists from edges."""
+        outgoing: dict[str, list[str]] = {node_id: [] for node_id in nodes}
+        incoming: dict[str, list[str]] = {node_id: [] for node_id in nodes}
+
+        for edge in edges:
+            source = edge.get("source")
+            target = edge.get("target")
+            if source in outgoing and target in incoming:
+                outgoing[source].append(target)
+                incoming[target].append(source)
+
+        return outgoing, incoming
+
+    @staticmethod
+    def _bfs_reachable(start: str, adjacency: dict[str, list[str]]) -> set[str]:
+        """BFS to find all nodes reachable from start node."""
+        if start not in adjacency:
+            return set()
+
+        visited = set()
+        queue = deque([start])
+        visited.add(start)
+
+        while queue:
+            current = queue.popleft()
+            for neighbor in adjacency.get(current, []):
+                if neighbor not in visited:
+                    visited.add(neighbor)
+                    queue.append(neighbor)
+
+        return visited
+
+    @staticmethod
+    def validate(workflow_data: dict) -> GraphValidationResult:
+        """Validate workflow graph structure."""
+        start_time = time.time()
+        errors: list[GraphError] = []
+        warnings: list[GraphError] = []
+
+        nodes_list = workflow_data.get("nodes", [])
+        edges_list = workflow_data.get("edges", [])
+        nodes = {n["id"]: n for n in nodes_list if n.get("id")}
+
+        # Find start and end nodes
+        start_node_id = None
+        end_node_ids = []
+
+        for node_id, node in nodes.items():
+            node_type = node.get("type")
+            if node_type == "start":
+                start_node_id = node_id
+            elif node_type == "end":
+                end_node_ids.append(node_id)
+
+        # Check start node exists
+        if not start_node_id:
+            errors.append(
+                GraphError(
+                    node_id="workflow",
+                    node_type="workflow",
+                    error_type="missing_start",
+                    message="Workflow has no start node",
+                )
+            )
+
+        # Check end node exists
+        if not end_node_ids:
+            errors.append(
+                GraphError(
+                    node_id="workflow",
+                    node_type="workflow",
+                    error_type="missing_end",
+                    message="Workflow has no end node",
+                )
+            )
+
+        # If missing start or end, can't do reachability analysis
+        if not start_node_id or not end_node_ids:
+            execution_time = time.time() - start_time
+            return GraphValidationResult(
+                success=False,
+                errors=errors,
+                warnings=warnings,
+                execution_time=execution_time,
+                stats={"nodes": len(nodes), "edges": len(edges_list)},
+            )
+
+        # Build adjacency lists
+        outgoing, incoming = GraphValidator._build_adjacency(nodes, edges_list)
+
+        # --- FORWARD REACHABILITY: BFS from start ---
+        reachable_from_start = GraphValidator._bfs_reachable(start_node_id, outgoing)
+
+        # Find unreachable nodes
+        unreachable_nodes = set(nodes.keys()) - reachable_from_start
+        for node_id in unreachable_nodes:
+            node = nodes[node_id]
+            errors.append(
+                GraphError(
+                    node_id=node_id,
+                    node_type=node.get("type", "unknown"),
+                    error_type="unreachable",
+                    message=f"Node '{node_id}' is not reachable from start node",
+                )
+            )
+
+        # --- BACKWARD REACHABILITY: Reverse BFS from end nodes ---
+        can_reach_end: set[str] = set()
+        for end_id in end_node_ids:
+            can_reach_end.update(GraphValidator._bfs_reachable(end_id, incoming))
+
+        # Find dead-end nodes (can't reach any end node)
+        dead_end_nodes = set(nodes.keys()) - can_reach_end
+        for node_id in dead_end_nodes:
+            if node_id in unreachable_nodes:
+                continue
+            node = nodes[node_id]
+            warnings.append(
+                GraphError(
+                    node_id=node_id,
+                    node_type=node.get("type", "unknown"),
+                    error_type="dead_end",
+                    message=f"Node '{node_id}' cannot reach any end node (dead end)",
+                )
+            )
+
+        # --- Start node has outgoing edges? ---
+        if not outgoing.get(start_node_id):
+            errors.append(
+                GraphError(
+                    node_id=start_node_id,
+                    node_type="start",
+                    error_type="disconnected",
+                    message="Start node has no outgoing connections",
+                )
+            )
+
+        # --- End nodes have incoming edges? ---
+        for end_id in end_node_ids:
+            if not incoming.get(end_id):
+                errors.append(
+                    GraphError(
+                        node_id=end_id,
+                        node_type="end",
+                        error_type="disconnected",
+                        message="End node has no incoming connections",
+                    )
+                )
+
+        # --- BRANCH EDGE VALIDATION ---
+        edge_handles: dict[str, set[str]] = {}
+        for edge in edges_list:
+            source = edge.get("source")
+            handle = edge.get("sourceHandle", "")
+            if source:
+                if source not in edge_handles:
+                    edge_handles[source] = set()
+                edge_handles[source].add(handle)
+
+        # Check if-else and question-classifier nodes
+        for node_id, node in nodes.items():
+            node_type = node.get("type")
+
+            if node_type == "if-else":
+                handles = edge_handles.get(node_id, set())
+                config = node.get("config", {})
+                cases = config.get("cases", [])
+
+                required_handles = set()
+                for case in cases:
+                    case_id = case.get("case_id")
+                    if case_id:
+                        required_handles.add(case_id)
+                required_handles.add("false")
+
+                missing = required_handles - handles
+                for handle in missing:
+                    errors.append(
+                        GraphError(
+                            node_id=node_id,
+                            node_type=node_type,
+                            error_type="missing_branch",
+                            message=f"If-else node '{node_id}' missing edge for branch '{handle}'",
+                        )
+                    )
+
+            elif node_type == "question-classifier":
+                handles = edge_handles.get(node_id, set())
+                config = node.get("config", {})
+                classes = config.get("classes", [])
+
+                required_handles = set()
+                for cls in classes:
+                    if isinstance(cls, dict):
+                        cls_id = cls.get("id")
+                        if cls_id:
+                            required_handles.add(cls_id)
+
+                missing = required_handles - handles
+                for handle in missing:
+                    cls_name = handle
+                    for cls in classes:
+                        if isinstance(cls, dict) and cls.get("id") == handle:
+                            cls_name = cls.get("name", handle)
+                            break
+                    errors.append(
+                        GraphError(
+                            node_id=node_id,
+                            node_type=node_type,
+                            error_type="missing_branch",
+                            message=f"Classifier '{node_id}' missing edge for class '{cls_name}'",
+                        )
+                    )
+
+        execution_time = time.time() - start_time
+        success = len(errors) == 0
+
+        return GraphValidationResult(
+            success=success,
+            errors=errors,
+            warnings=warnings,
+            execution_time=execution_time,
+            stats={
+                "nodes": len(nodes),
+                "edges": len(edges_list),
+                "reachable_from_start": len(reachable_from_start),
+                "can_reach_end": len(can_reach_end),
+                "unreachable": len(unreachable_nodes),
+                "dead_ends": len(dead_end_nodes - unreachable_nodes),
+            },
+        )

api/core/workflow/generator/utils/mermaid_generator.py

@@ -0,0 +1,113 @@
+import logging
+
+from core.workflow.generator.types import WorkflowDataDict
+
+logger = logging.getLogger(__name__)
+
+
+def generate_mermaid(workflow_data: WorkflowDataDict) -> str:
+    """
+    Generate a Mermaid flowchart from workflow data consisting of nodes and edges.
+
+    Args:
+        workflow_data: Dict containing 'nodes' (list) and 'edges' (list)
+
+    Returns:
+        String containing the Mermaid flowchart syntax
+    """
+    nodes = workflow_data.get("nodes", [])
+    edges = workflow_data.get("edges", [])
+
+    lines = ["flowchart TD"]
+
+    # 1. Define Nodes
+    # Format: node_id["title<br/>type"] or similar
+    # We will use the Vibe Workflow standard format: id["type=TYPE|title=TITLE"]
+    # Or specifically for tool nodes: id["type=tool|title=TITLE|tool=TOOL_KEY"]
+
+    # Map of original IDs to safe Mermaid IDs
+    id_map = {}
+
+    def get_safe_id(original_id: str) -> str:
+        if original_id == "end":
+            return "end_node"
+        if original_id == "subgraph":
+            return "subgraph_node"
+        # Mermaid IDs should be alphanumeric.
+        # If the ID has special chars, we might need to escape or hash, but Vibe usually generates simple IDs.
+        # We'll trust standard IDs but handle the reserved keyword 'end'.
+        return original_id
+
+    for node in nodes:
+        node_id = node.get("id")
+        if not node_id:
+            continue
+
+        safe_id = get_safe_id(node_id)
+        id_map[node_id] = safe_id
+
+        node_type = node.get("type", "unknown")
+        title = node.get("title", "Untitled")
+
+        # Escape quotes in title
+        safe_title = title.replace('"', "'")
+
+        if node_type == "tool":
+            config = node.get("config", {})
+            # Try multiple fields for tool reference
+            tool_ref = (
+                config.get("tool_key")
+                or config.get("tool")
+                or config.get("tool_name")
+                or node.get("tool_name")
+                or "unknown"
+            )
+            node_def = f'{safe_id}["type={node_type}|title={safe_title}|tool={tool_ref}"]'
+        else:
+            node_def = f'{safe_id}["type={node_type}|title={safe_title}"]'
+
+        lines.append(f"  {node_def}")
+
+    # 2. Define Edges
+    # Format: source --> target
+
+    # Track defined nodes to avoid edge errors
+    defined_node_ids = {n.get("id") for n in nodes if n.get("id")}
+
+    for edge in edges:
+        source = edge.get("source")
+        target = edge.get("target")
+
+        # Skip invalid edges
+        if not source or not target:
+            continue
+
+        if source not in defined_node_ids or target not in defined_node_ids:
+            continue
+
+        safe_source = id_map.get(source, source)
+        safe_target = id_map.get(target, target)
+
+        # Handle conditional branches (true/false) if present
+        # In Dify workflow, sourceHandle is often used for this
+        source_handle = edge.get("sourceHandle")
+        label = ""
+
+        if source_handle == "true":
+            label = "|true|"
+        elif source_handle == "false":
+            label = "|false|"
+        elif source_handle and source_handle != "source":
+            # For question-classifier or other multi-path nodes
+            # Clean up handle for display if needed
+            safe_handle = str(source_handle).replace('"', "'")
+            label = f"|{safe_handle}|"
+
+        edge_line = f"  {safe_source} -->{label} {safe_target}"
+        lines.append(edge_line)
+
+    # Start/End nodes are implicitly handled if they are in the 'nodes' list
+    # If not, we might need to add them, but usually the Builder should produce them.
+
+    result = "\n".join(lines)
+    return result

api/core/workflow/generator/utils/node_repair.py

@@ -0,0 +1,304 @@
+"""
+Node Repair Utility for Vibe Workflow Generation.
+
+This module provides intelligent node configuration repair capabilities.
+It can detect and fix common node configuration issues:
+- Invalid comparison operators in if-else nodes (e.g. '>=' -> '≥')
+"""
+
+import copy
+import logging
+import uuid
+from dataclasses import dataclass, field
+
+from core.workflow.generator.types import WorkflowNodeDict
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class NodeRepairResult:
+    """Result of node repair operation."""
+
+    nodes: list[WorkflowNodeDict]
+    repairs_made: list[str] = field(default_factory=list)
+    warnings: list[str] = field(default_factory=list)
+
+    @property
+    def was_repaired(self) -> bool:
+        """Check if any repairs were made."""
+        return len(self.repairs_made) > 0
+
+
+class NodeRepair:
+    """
+    Intelligent node configuration repair.
+    """
+
+    OPERATOR_MAP = {
+        ">=": "≥",
+        "<=": "≤",
+        "!=": "≠",
+        "==": "=",
+    }
+
+    TYPE_MAPPING = {
+        "json": "object",
+        "dict": "object",
+        "dictionary": "object",
+        "float": "number",
+        "int": "number",
+        "integer": "number",
+        "double": "number",
+        "str": "string",
+        "text": "string",
+        "bool": "boolean",
+        "list": "array[object]",
+        "array": "array[object]",
+    }
+
+    _REPAIR_HANDLERS = {
+        "if-else": "_repair_if_else_operators",
+        "variable-aggregator": "_repair_variable_aggregator_variables",
+        "code": "_repair_code_node_config",
+    }
+
+    @classmethod
+    def repair(
+        cls,
+        nodes: list[WorkflowNodeDict],
+        llm_callback=None,
+    ) -> NodeRepairResult:
+        """
+        Repair node configurations.
+
+        Args:
+            nodes: List of node dictionaries
+            llm_callback: Optional callback(node, issue_desc) -> fixed_config_part
+
+        Returns:
+            NodeRepairResult with repaired nodes and logs
+        """
+        # Deep copy to avoid mutating original
+        nodes = copy.deepcopy(nodes)
+        repairs: list[str] = []
+        warnings: list[str] = []
+
+        logger.info("[NODE REPAIR] Starting repair process for %s nodes", len(nodes))
+
+        for node in nodes:
+            node_type = node.get("type")
+
+            # 1. Rule-based repairs
+            handler_name = cls._REPAIR_HANDLERS.get(node_type)
+            if handler_name:
+                handler = getattr(cls, handler_name)
+                # Check if handler accepts llm_callback (inspect signature or just pass generic kwargs?)
+                # Simplest for now: handlers signature: (node, repairs, llm_callback=None)
+                try:
+                    handler(node, repairs, llm_callback=llm_callback)
+                except TypeError:
+                    # Fallback for handlers that don't accept llm_callback yet
+                    handler(node, repairs)
+
+            # Add other node type repairs here as needed
+
+        if repairs:
+            logger.info("[NODE REPAIR] Completed with %s repairs:", len(repairs))
+            for i, repair in enumerate(repairs, 1):
+                logger.info("[NODE REPAIR]   %s. %s", i, repair)
+        else:
+            logger.info("[NODE REPAIR] Completed - no repairs needed")
+
+        return NodeRepairResult(
+            nodes=nodes,
+            repairs_made=repairs,
+            warnings=warnings,
+        )
+
+    @classmethod
+    def _repair_if_else_operators(cls, node: WorkflowNodeDict, repairs: list[str], **kwargs):
+        """
+        Normalize comparison operators in if-else nodes.
+        And ensure 'id' field exists for cases and conditions (frontend requirement).
+        """
+        node_id = node.get("id", "unknown")
+        config = node.get("config", {})
+        cases = config.get("cases", [])
+
+        for case in cases:
+            # Ensure case_id
+            if "case_id" not in case:
+                case["case_id"] = str(uuid.uuid4())
+                repairs.append(f"Generated missing case_id for case in node '{node_id}'")
+
+            conditions = case.get("conditions", [])
+            for condition in conditions:
+                # Ensure condition id
+                if "id" not in condition:
+                    condition["id"] = str(uuid.uuid4())
+                    # Not logging this repair to avoid clutter, as it's a structural fix
+
+                # Ensure value type (LLM might return int/float, but we need str/bool/list)
+                val = condition.get("value")
+                if isinstance(val, (int, float)) and not isinstance(val, bool):
+                    condition["value"] = str(val)
+                    repairs.append(f"Coerced numeric value to string in node '{node_id}'")
+
+                op = condition.get("comparison_operator")
+                if op in cls.OPERATOR_MAP:
+                    new_op = cls.OPERATOR_MAP[op]
+                    condition["comparison_operator"] = new_op
+                    repairs.append(f"Normalized operator '{op}' to '{new_op}' in node '{node_id}'")
+
+    @classmethod
+    def _repair_variable_aggregator_variables(cls, node: WorkflowNodeDict, repairs: list[str]):
+        """
+        Repair variable-aggregator variables format.
+        Converts dict format to list[list[str]] format.
+        Expected: [["node_id", "field"], ["node_id2", "field2"]]
+        May receive: [{"name": "...", "value_selector": ["node_id", "field"]}, ...]
+        """
+        node_id = node.get("id", "unknown")
+        config = node.get("config", {})
+        variables = config.get("variables", [])
+
+        if not variables:
+            return
+
+        repaired = False
+        repaired_variables = []
+
+        for var in variables:
+            if isinstance(var, dict):
+                # Convert dict format to array format
+                value_selector = var.get("value_selector") or var.get("selector") or var.get("path")
+                if isinstance(value_selector, list) and len(value_selector) > 0:
+                    repaired_variables.append(value_selector)
+                    repaired = True
+                else:
+                    # Try to extract from name field - LLM may generate {"name": "node_id.field"}
+                    name = var.get("name")
+                    if isinstance(name, str) and "." in name:
+                        # Try to parse "node_id.field" format
+                        parts = name.split(".", 1)
+                        if len(parts) == 2:
+                            repaired_variables.append([parts[0], parts[1]])
+                            repaired = True
+                        else:
+                            logger.warning(
+                                "Variable aggregator node '%s' has invalid variable format: %s",
+                                node_id,
+                                var,
+                            )
+                            repaired_variables.append([])  # Empty array as fallback
+                    else:
+                        # If no valid selector or name, skip this variable
+                        logger.warning(
+                            "Variable aggregator node '%s' has invalid variable format: %s",
+                            node_id,
+                            var,
+                        )
+                        # Don't add empty array - skip invalid variables
+            elif isinstance(var, list):
+                # Already in correct format
+                repaired_variables.append(var)
+            else:
+                # Unknown format, skip
+                logger.warning("Variable aggregator node '%s' has unknown variable format: %s", node_id, var)
+                # Don't add empty array - skip invalid variables
+
+        if repaired:
+            config["variables"] = repaired_variables
+            repairs.append(f"Repaired variable-aggregator variables format in node '{node_id}'")
+
+    @classmethod
+    def _repair_code_node_config(cls, node: WorkflowNodeDict, repairs: list[str], llm_callback=None):
+        """
+        Repair code node configuration (outputs and variables).
+        1. Outputs: Converts list format to dict format AND normalizes types.
+        2. Variables: Ensures value_selector exists.
+        """
+        node_id = node.get("id", "unknown")
+        config = node.get("config", {})
+
+        if "variables" not in config:
+            config["variables"] = []
+
+        # --- Repair Variables ---
+        variables = config.get("variables")
+        if isinstance(variables, list):
+            for var in variables:
+                if isinstance(var, dict):
+                    # Ensure value_selector exists (frontend crashes if missing)
+                    if "value_selector" not in var:
+                        var["value_selector"] = []
+                        # Not logging trivial repairs
+
+        # --- Repair Outputs ---
+        outputs = config.get("outputs")
+
+        if not outputs:
+            return
+
+        # Helper to normalize type
+        def normalize_type(t: str) -> str:
+            t_lower = str(t).lower()
+            return cls.TYPE_MAPPING.get(t_lower, t)
+
+        # 1. Handle Dict format (Standard) - Check for invalid types
+        if isinstance(outputs, dict):
+            changed = False
+            for var_name, var_config in outputs.items():
+                if isinstance(var_config, dict):
+                    original_type = var_config.get("type")
+                    if original_type:
+                        new_type = normalize_type(original_type)
+                        if new_type != original_type:
+                            var_config["type"] = new_type
+                            changed = True
+                            repairs.append(
+                                f"Normalized type '{original_type}' to '{new_type}' "
+                                f"for var '{var_name}' in node '{node_id}'"
+                            )
+            return
+
+        # 2. Handle List format (Repair needed)
+        if isinstance(outputs, list):
+            new_outputs = {}
+            for item in outputs:
+                if isinstance(item, dict):
+                    var_name = item.get("variable") or item.get("name")
+                    var_type = item.get("type")
+                    if var_name and var_type:
+                        norm_type = normalize_type(var_type)
+                        new_outputs[var_name] = {"type": norm_type}
+                        if norm_type != var_type:
+                            repairs.append(
+                                f"Normalized type '{var_type}' to '{norm_type}' "
+                                f"during list conversion in node '{node_id}'"
+                            )
+
+            if new_outputs:
+                config["outputs"] = new_outputs
+                repairs.append(f"Repaired code node outputs format in node '{node_id}'")
+            else:
+                # Fallback: Try LLM if available
+                if llm_callback:
+                    try:
+                        # Attempt to fix using LLM
+                        fixed_outputs = llm_callback(
+                            node,
+                            "outputs must be a dictionary like {'var_name': {'type': 'string'}}, "
+                            "but got a list or valid conversion failed.",
+                        )
+                        if isinstance(fixed_outputs, dict) and fixed_outputs:
+                            config["outputs"] = fixed_outputs
+                            repairs.append(f"Repaired code node outputs format using LLM in node '{node_id}'")
+                            return
+                    except Exception as e:
+                        logger.warning("LLM fallback repair failed for node '%s': %s", node_id, e)
+
+                # If conversion/LLM failed, set to empty dict
+                config["outputs"] = {}
+                repairs.append(f"Reset invalid code node outputs to empty dict in node '{node_id}'")

api/core/workflow/generator/utils/workflow_validator.py

@@ -0,0 +1,101 @@
+from dataclasses import dataclass
+
+from core.workflow.generator.types import AvailableModelDict, AvailableToolDict, WorkflowDataDict
+from core.workflow.generator.validation.context import ValidationContext
+from core.workflow.generator.validation.engine import ValidationEngine
+from core.workflow.generator.validation.rules import Severity
+
+
+@dataclass
+class ValidationHint:
+    """Legacy compatibility class for validation hints."""
+
+    node_id: str
+    field: str
+    message: str
+    severity: str  # 'error', 'warning'
+    suggestion: str = None
+    node_type: str = None  # Added for test compatibility
+
+    # Alias for potential old code using 'type' instead of 'severity'
+    @property
+    def type(self) -> str:
+        return self.severity
+
+    @property
+    def element_id(self) -> str:
+        return self.node_id
+
+
+FriendlyHint = ValidationHint  # Alias for backward compatibility
+
+
+class WorkflowValidator:
+    """
+    Validates the generated workflow configuration (nodes and edges).
+    Wraps the new ValidationEngine for backward compatibility.
+    """
+
+    @classmethod
+    def validate(
+        cls,
+        workflow_data: WorkflowDataDict,
+        available_tools: list[AvailableToolDict],
+        available_models: list[AvailableModelDict] | None = None,
+    ) -> tuple[bool, list[ValidationHint]]:
+        """
+        Validate workflow data and return validity status and hints.
+
+        Args:
+            workflow_data: Dict containing 'nodes' and 'edges'
+            available_tools: List of available tool configurations
+            available_models: List of available models (added for Vibe compat)
+
+        Returns:
+            Tuple(max_severity_is_not_error, list_of_hints)
+        """
+        nodes = workflow_data.get("nodes", [])
+        edges = workflow_data.get("edges", [])
+
+        # Create context
+        context = ValidationContext(
+            nodes=nodes,
+            edges=edges,
+            available_models=available_models or [],
+            available_tools=available_tools or [],
+        )
+
+        # Run validation engine
+        engine = ValidationEngine()
+        result = engine.validate(context)
+
+        # Convert engine errors to legacy hints
+        hints: list[ValidationHint] = []
+
+        error_count = 0
+        warning_count = 0
+
+        for error in result.all_errors:
+            # Map severity
+            severity = "error" if error.severity == Severity.ERROR else "warning"
+
+            if severity == "error":
+                error_count += 1
+            else:
+                warning_count += 1
+
+            # Map field from message or details if possible (heuristic)
+            field_name = error.details.get("field", "unknown")
+
+            hints.append(
+                ValidationHint(
+                    node_id=error.node_id,
+                    field=field_name,
+                    message=error.message,
+                    severity=severity,
+                    suggestion=error.fix_hint,
+                    node_type=error.node_type,
+                )
+            )
+
+        return result.is_valid, hints

api/core/workflow/generator/validation/__init__.py

@@ -0,0 +1,42 @@
+"""
+Validation Rule Engine for Vibe Workflow Generation.
+
+This module provides a declarative, schema-based validation system for
+generated workflow nodes. It classifies errors into fixable (LLM can auto-fix)
+and user-required (needs manual intervention) categories.
+
+Usage:
+    from core.workflow.generator.validation import ValidationEngine, ValidationContext
+
+    context = ValidationContext(
+        available_models=[...],
+        available_tools=[...],
+        nodes=[...],
+        edges=[...],
+    )
+    engine = ValidationEngine()
+    result = engine.validate(context)
+
+    # Access classified errors
+    fixable_errors = result.fixable_errors
+    user_required_errors = result.user_required_errors
+"""
+
+from core.workflow.generator.validation.context import ValidationContext
+from core.workflow.generator.validation.engine import ValidationEngine, ValidationResult
+from core.workflow.generator.validation.rules import (
+    RuleCategory,
+    Severity,
+    ValidationError,
+    ValidationRule,
+)
+
+__all__ = [
+    "RuleCategory",
+    "Severity",
+    "ValidationContext",
+    "ValidationEngine",
+    "ValidationError",
+    "ValidationResult",
+    "ValidationRule",
+]

api/core/workflow/generator/validation/context.py

@@ -0,0 +1,115 @@
+"""
+Validation Context for the Rule Engine.
+
+The ValidationContext holds all the data needed for validation:
+- Generated nodes and edges
+- Available models, tools, and datasets
+- Node output schemas for variable reference validation
+"""
+
+from dataclasses import dataclass, field
+
+from core.workflow.generator.types import (
+    AvailableModelDict,
+    AvailableToolDict,
+    WorkflowEdgeDict,
+    WorkflowNodeDict,
+)
+
+
+@dataclass
+class ValidationContext:
+    """
+    Context object containing all data needed for validation.
+
+    This is passed to each validation rule, providing access to:
+    - The nodes being validated
+    - Edge connections between nodes
+    - Available external resources (models, tools)
+    """
+
+    # Generated workflow data
+    nodes: list[WorkflowNodeDict] = field(default_factory=list)
+    edges: list[WorkflowEdgeDict] = field(default_factory=list)
+
+    # Available external resources
+    available_models: list[AvailableModelDict] = field(default_factory=list)
+    available_tools: list[AvailableToolDict] = field(default_factory=list)
+
+    # Cached lookups (populated lazily)
+    _node_map: dict[str, WorkflowNodeDict] | None = field(default=None, repr=False)
+    _model_set: set[tuple[str, str]] | None = field(default=None, repr=False)
+    _tool_set: set[str] | None = field(default=None, repr=False)
+    _configured_tool_set: set[str] | None = field(default=None, repr=False)
+
+    @property
+    def node_map(self) -> dict[str, WorkflowNodeDict]:
+        """Get a map of node_id -> node for quick lookup."""
+        if self._node_map is None:
+            self._node_map = {node.get("id", ""): node for node in self.nodes}
+        return self._node_map
+
+    @property
+    def model_set(self) -> set[tuple[str, str]]:
+        """Get a set of (provider, model_name) tuples for quick lookup."""
+        if self._model_set is None:
+            self._model_set = {(m.get("provider", ""), m.get("model", "")) for m in self.available_models}
+        return self._model_set
+
+    @property
+    def tool_set(self) -> set[str]:
+        """Get a set of all tool keys (both configured and unconfigured)."""
+        if self._tool_set is None:
+            self._tool_set = set()
+            for tool in self.available_tools:
+                provider = tool.get("provider_id") or tool.get("provider", "")
+                tool_key = tool.get("tool_key") or tool.get("tool_name", "")
+                if provider and tool_key:
+                    self._tool_set.add(f"{provider}/{tool_key}")
+                if tool_key:
+                    self._tool_set.add(tool_key)
+        return self._tool_set
+
+    @property
+    def configured_tool_set(self) -> set[str]:
+        """Get a set of configured (authorized) tool keys."""
+        if self._configured_tool_set is None:
+            self._configured_tool_set = set()
+            for tool in self.available_tools:
+                if not tool.get("is_team_authorization", False):
+                    continue
+                provider = tool.get("provider_id") or tool.get("provider", "")
+                tool_key = tool.get("tool_key") or tool.get("tool_name", "")
+                if provider and tool_key:
+                    self._configured_tool_set.add(f"{provider}/{tool_key}")
+                if tool_key:
+                    self._configured_tool_set.add(tool_key)
+        return self._configured_tool_set
+
+    def has_model(self, provider: str, model_name: str) -> bool:
+        """Check if a model is available."""
+        return (provider, model_name) in self.model_set
+
+    def has_tool(self, tool_key: str) -> bool:
+        """Check if a tool exists (configured or not)."""
+        return tool_key in self.tool_set
+
+    def is_tool_configured(self, tool_key: str) -> bool:
+        """Check if a tool is configured and ready to use."""
+        return tool_key in self.configured_tool_set
+
+    def get_node(self, node_id: str) -> WorkflowNodeDict | None:
+        """Get a node by its ID."""
+        return self.node_map.get(node_id)
+
+    def get_node_ids(self) -> set[str]:
+        """Get all node IDs in the workflow."""
+        return set(self.node_map.keys())
+
+    def get_upstream_nodes(self, node_id: str) -> list[str]:
+        """Get IDs of nodes that connect to this node (upstream)."""
+        return [edge.get("source", "") for edge in self.edges if edge.get("target") == node_id]
+
+    def get_downstream_nodes(self, node_id: str) -> list[str]:
+        """Get IDs of nodes that this node connects to (downstream)."""
+        return [edge.get("target", "") for edge in self.edges if edge.get("source") == node_id]

api/core/workflow/generator/validation/engine.py

@@ -0,0 +1,260 @@
+"""
+Validation Engine - Core validation logic.
+
+The ValidationEngine orchestrates rule execution and aggregates results.
+It provides a clean interface for validating workflow nodes.
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from core.workflow.generator.types import (
+    AvailableModelDict,
+    AvailableToolDict,
+    WorkflowEdgeDict,
+    WorkflowNodeDict,
+)
+from core.workflow.generator.validation.context import ValidationContext
+from core.workflow.generator.validation.rules import (
+    RuleCategory,
+    Severity,
+    ValidationError,
+    get_registry,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ValidationResult:
+    """
+    Result of validation containing all errors classified by fixability.
+
+    Attributes:
+        all_errors: All validation errors found
+        fixable_errors: Errors that LLM can automatically fix
+        user_required_errors: Errors that require user intervention
+        warnings: Non-blocking warnings
+        stats: Validation statistics
+    """
+
+    all_errors: list[ValidationError] = field(default_factory=list)
+    fixable_errors: list[ValidationError] = field(default_factory=list)
+    user_required_errors: list[ValidationError] = field(default_factory=list)
+    warnings: list[ValidationError] = field(default_factory=list)
+    stats: dict[str, int] = field(default_factory=dict)
+
+    @property
+    def has_errors(self) -> bool:
+        """Check if there are any errors (excluding warnings)."""
+        return len(self.fixable_errors) > 0 or len(self.user_required_errors) > 0
+
+    @property
+    def has_fixable_errors(self) -> bool:
+        """Check if there are fixable errors."""
+        return len(self.fixable_errors) > 0
+
+    @property
+    def is_valid(self) -> bool:
+        """Check if validation passed (no errors, warnings are OK)."""
+        return not self.has_errors
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for API response."""
+        return {
+            "fixable": [e.to_dict() for e in self.fixable_errors],
+            "user_required": [e.to_dict() for e in self.user_required_errors],
+            "warnings": [e.to_dict() for e in self.warnings],
+            "all_warnings": [e.message for e in self.all_errors],
+            "stats": self.stats,
+        }
+
+    def get_error_messages(self) -> list[str]:
+        """Get all error messages as strings."""
+        return [e.message for e in self.all_errors]
+
+    def get_fixable_by_node(self) -> dict[str, list[ValidationError]]:
+        """Group fixable errors by node ID."""
+        result: dict[str, list[ValidationError]] = {}
+        for error in self.fixable_errors:
+            if error.node_id not in result:
+                result[error.node_id] = []
+            result[error.node_id].append(error)
+        return result
+
+
+class ValidationEngine:
+    """
+    The main validation engine.
+
+    Usage:
+        engine = ValidationEngine()
+        context = ValidationContext(nodes=[...], available_models=[...])
+        result = engine.validate(context)
+    """
+
+    def __init__(self):
+        self._registry = get_registry()
+
+    def validate(self, context: ValidationContext) -> ValidationResult:
+        """
+        Validate all nodes in the context.
+
+        Args:
+            context: ValidationContext with nodes, edges, and available resources
+
+        Returns:
+            ValidationResult with classified errors
+        """
+        result = ValidationResult()
+        stats = {
+            "total_nodes": len(context.nodes),
+            "total_rules_checked": 0,
+            "total_errors": 0,
+            "fixable_count": 0,
+            "user_required_count": 0,
+            "warning_count": 0,
+        }
+
+        # Validate each node
+        for node in context.nodes:
+            node_type = node.get("type", "unknown")
+            node_id = node.get("id", "unknown")
+
+            # Get applicable rules for this node type
+            rules = self._registry.get_rules_for_node(node_type)
+
+            for rule in rules:
+                stats["total_rules_checked"] += 1
+
+                try:
+                    errors = rule.check(node, context)
+                    for error in errors:
+                        result.all_errors.append(error)
+                        stats["total_errors"] += 1
+
+                        # Classify by severity and fixability
+                        if error.severity == Severity.WARNING:
+                            result.warnings.append(error)
+                            stats["warning_count"] += 1
+                        elif error.is_fixable:
+                            result.fixable_errors.append(error)
+                            stats["fixable_count"] += 1
+                        else:
+                            result.user_required_errors.append(error)
+                            stats["user_required_count"] += 1
+
+                except Exception:
+                    logger.exception(
+                        "Rule '%s' failed for node '%s'",
+                        rule.id,
+                        node_id,
+                    )
+                    # Don't let a rule failure break the entire validation
+                    continue
+
+        # Validate edges separately
+        edge_errors = self._validate_edges(context)
+        for error in edge_errors:
+            result.all_errors.append(error)
+            stats["total_errors"] += 1
+            if error.is_fixable:
+                result.fixable_errors.append(error)
+                stats["fixable_count"] += 1
+            else:
+                result.user_required_errors.append(error)
+                stats["user_required_count"] += 1
+
+        result.stats = stats
+
+        return result
+
+    def _validate_edges(self, context: ValidationContext) -> list[ValidationError]:
+        """Validate edge connections."""
+        errors: list[ValidationError] = []
+        valid_node_ids = context.get_node_ids()
+
+        for edge in context.edges:
+            source = edge.get("source", "")
+            target = edge.get("target", "")
+
+            if source and source not in valid_node_ids:
+                errors.append(
+                    ValidationError(
+                        rule_id="edge.source.invalid",
+                        node_id=source,
+                        node_type="edge",
+                        category=RuleCategory.SEMANTIC,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Edge source '{source}' does not exist",
+                        fix_hint="Update edge to reference existing node",
+                    )
+                )
+
+            if target and target not in valid_node_ids:
+                errors.append(
+                    ValidationError(
+                        rule_id="edge.target.invalid",
+                        node_id=target,
+                        node_type="edge",
+                        category=RuleCategory.SEMANTIC,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Edge target '{target}' does not exist",
+                        fix_hint="Update edge to reference existing node",
+                    )
+                )
+
+        return errors
+
+    def validate_single_node(
+        self,
+        node: WorkflowNodeDict,
+        context: ValidationContext,
+    ) -> list[ValidationError]:
+        """
+        Validate a single node.
+
+        Useful for incremental validation when a node is added/modified.
+        """
+        node_type = node.get("type", "unknown")
+        rules = self._registry.get_rules_for_node(node_type)
+
+        errors: list[ValidationError] = []
+        for rule in rules:
+            try:
+                errors.extend(rule.check(node, context))
+            except Exception:
+                logger.exception("Rule '%s' failed", rule.id)
+
+        return errors
+
+
+def validate_nodes(
+    nodes: list[WorkflowNodeDict],
+    edges: list[WorkflowEdgeDict] | None = None,
+    available_models: list[AvailableModelDict] | None = None,
+    available_tools: list[AvailableToolDict] | None = None,
+) -> ValidationResult:
+    """
+    Convenience function to validate nodes without creating engine/context manually.
+
+    Args:
+        nodes: List of workflow nodes to validate
+        edges: Optional list of edges
+        available_models: Optional list of available models
+        available_tools: Optional list of available tools
+
+    Returns:
+        ValidationResult with classified errors
+    """
+    context = ValidationContext(
+        nodes=nodes,
+        edges=edges or [],
+        available_models=available_models or [],
+        available_tools=available_tools or [],
+    )
+    engine = ValidationEngine()
+    return engine.validate(context)

api/core/workflow/generator/validation/rules.py

@@ -0,0 +1,947 @@
+"""
+Validation Rules Definition and Registry.
+
+This module defines:
+- ValidationRule: The rule structure
+- RuleCategory: Categories of validation rules
+- Severity: Error severity levels
+- ValidationError: Error output structure
+- All built-in validation rules
+"""
+
+import re
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import TYPE_CHECKING, Any
+
+from core.workflow.generator.types import WorkflowNodeDict
+
+if TYPE_CHECKING:
+    from core.workflow.generator.validation.context import ValidationContext
+
+
+class RuleCategory(Enum):
+    """Categories of validation rules."""
+
+    STRUCTURE = "structure"  # Field existence, types, formats
+    SEMANTIC = "semantic"  # Variable references, edge connections
+    REFERENCE = "reference"  # External resources (models, tools, datasets)
+
+
+class Severity(Enum):
+    """Severity levels for validation errors."""
+
+    ERROR = "error"  # Must be fixed
+    WARNING = "warning"  # Should be fixed but not blocking
+
+
+@dataclass
+class ValidationError:
+    """
+    Represents a validation error found during rule execution.
+
+    Attributes:
+        rule_id: The ID of the rule that generated this error
+        node_id: The ID of the node with the error
+        node_type: The type of the node
+        category: The rule category
+        severity: Error severity
+        is_fixable: Whether LLM can auto-fix this error
+        message: Human-readable error message
+        fix_hint: Hint for LLM to fix the error
+        details: Additional error details
+    """
+
+    rule_id: str
+    node_id: str
+    node_type: str
+    category: RuleCategory
+    severity: Severity
+    is_fixable: bool
+    message: str
+    fix_hint: str = ""
+    details: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for API response."""
+        return {
+            "rule_id": self.rule_id,
+            "node_id": self.node_id,
+            "node_type": self.node_type,
+            "category": self.category.value,
+            "severity": self.severity.value,
+            "is_fixable": self.is_fixable,
+            "message": self.message,
+            "fix_hint": self.fix_hint,
+            "details": self.details,
+        }
+
+
+# Type alias for rule check functions
+RuleCheckFn = Callable[
+    [WorkflowNodeDict, "ValidationContext"],
+    list[ValidationError],
+]
+
+
+@dataclass
+class ValidationRule:
+    """
+    A validation rule definition.
+
+    Attributes:
+        id: Unique rule identifier (e.g., "llm.model.required")
+        node_types: List of node types this rule applies to, or ["*"] for all
+        category: The rule category
+        severity: Default severity for errors from this rule
+        is_fixable: Whether errors from this rule can be auto-fixed by LLM
+        check: The validation function
+        description: Human-readable description of what this rule checks
+        fix_hint: Default hint for fixing errors from this rule
+    """
+
+    id: str
+    node_types: list[str]
+    category: RuleCategory
+    severity: Severity
+    is_fixable: bool
+    check: RuleCheckFn
+    description: str = ""
+    fix_hint: str = ""
+
+    def applies_to(self, node_type: str) -> bool:
+        """Check if this rule applies to a given node type."""
+        return "*" in self.node_types or node_type in self.node_types
+
+
+# =============================================================================
+# Rule Registry
+# =============================================================================
+
+
+class RuleRegistry:
+    """
+    Registry for validation rules.
+
+    Rules are registered here and can be retrieved by category or node type.
+    """
+
+    def __init__(self):
+        self._rules: list[ValidationRule] = []
+
+    def register(self, rule: ValidationRule) -> None:
+        """Register a validation rule."""
+        self._rules.append(rule)
+
+    def get_rules_for_node(self, node_type: str) -> list[ValidationRule]:
+        """Get all rules that apply to a given node type."""
+        return [r for r in self._rules if r.applies_to(node_type)]
+
+    def get_rules_by_category(self, category: RuleCategory) -> list[ValidationRule]:
+        """Get all rules in a given category."""
+        return [r for r in self._rules if r.category == category]
+
+    def get_all_rules(self) -> list[ValidationRule]:
+        """Get all registered rules."""
+        return list(self._rules)
+
+
+# Global rule registry instance
+_registry = RuleRegistry()
+
+
+def register_rule(rule: ValidationRule) -> ValidationRule:
+    """Decorator/function to register a rule with the global registry."""
+    _registry.register(rule)
+    return rule
+
+
+def get_registry() -> RuleRegistry:
+    """Get the global rule registry."""
+    return _registry
+
+
+# =============================================================================
+# Helper Functions for Rule Implementations
+# =============================================================================
+
+# Explicit placeholder value defined in prompt contract
+# See: api/core/workflow/generator/prompts/vibe_prompts.py
+PLACEHOLDER_VALUE = "__PLACEHOLDER__"
+
+# Variable reference pattern: {{#node_id.field#}}
+VARIABLE_REF_PATTERN = re.compile(r"\{\{#([^.#]+)\.([^#]+)#\}\}")
+
+
+def is_placeholder(value: Any) -> bool:
+    """Check if a value appears to be a placeholder."""
+    if not isinstance(value, str):
+        return False
+    return value == PLACEHOLDER_VALUE or PLACEHOLDER_VALUE in value
+
+
+def extract_variable_refs(text: str) -> list[tuple[str, str]]:
+    """
+    Extract variable references from text.
+
+    Returns list of (node_id, field_name) tuples.
+    """
+    return VARIABLE_REF_PATTERN.findall(text)
+
+
+def check_required_field(
+    config: dict[str, Any],
+    field_name: str,
+    node_id: str,
+    node_type: str,
+    rule_id: str,
+    fix_hint: str = "",
+) -> ValidationError | None:
+    """Helper to check if a required field exists and is non-empty."""
+    value = config.get(field_name)
+    if value is None or value == "" or (isinstance(value, list) and len(value) == 0):
+        return ValidationError(
+            rule_id=rule_id,
+            node_id=node_id,
+            node_type=node_type,
+            category=RuleCategory.STRUCTURE,
+            severity=Severity.ERROR,
+            is_fixable=True,
+            message=f"Node '{node_id}': missing required field '{field_name}'",
+            fix_hint=fix_hint or f"Add '{field_name}' to the node config",
+        )
+    return None
+
+
+# =============================================================================
+# Structure Rules - Field existence, types, formats
+# =============================================================================
+
+
+def _check_llm_prompt_template(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that LLM node has prompt_template."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    err = check_required_field(
+        config,
+        "prompt_template",
+        node_id,
+        "llm",
+        "llm.prompt_template.required",
+        "Add prompt_template with system and user messages",
+    )
+    if err:
+        errors.append(err)
+
+    return errors
+
+
+def _check_http_request_url(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that http-request node has url and method."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    # Check url
+    url = config.get("url", "")
+    if not url:
+        errors.append(
+            ValidationError(
+                rule_id="http.url.required",
+                node_id=node_id,
+                node_type="http-request",
+                category=RuleCategory.STRUCTURE,
+                severity=Severity.ERROR,
+                is_fixable=True,
+                message=f"Node '{node_id}': http-request missing required 'url'",
+                fix_hint="Add url - use {{#start.url#}} or a concrete URL",
+            )
+        )
+    elif is_placeholder(url):
+        errors.append(
+            ValidationError(
+                rule_id="http.url.placeholder",
+                node_id=node_id,
+                node_type="http-request",
+                category=RuleCategory.STRUCTURE,
+                severity=Severity.ERROR,
+                is_fixable=True,
+                message=f"Node '{node_id}': url contains placeholder value",
+                fix_hint="Replace placeholder with actual URL or variable reference",
+            )
+        )
+
+    # Check method
+    method = config.get("method", "")
+    if not method:
+        errors.append(
+            ValidationError(
+                rule_id="http.method.required",
+                node_id=node_id,
+                node_type="http-request",
+                category=RuleCategory.STRUCTURE,
+                severity=Severity.ERROR,
+                is_fixable=True,
+                message=f"Node '{node_id}': http-request missing 'method'",
+                fix_hint="Add method: GET, POST, PUT, DELETE, or PATCH",
+            )
+        )
+
+    return errors
+
+
+def _check_code_node(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that code node has code and language."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    err = check_required_field(
+        config,
+        "code",
+        node_id,
+        "code",
+        "code.code.required",
+        "Add code with a main() function that returns a dict",
+    )
+    if err:
+        errors.append(err)
+
+    err = check_required_field(
+        config,
+        "language",
+        node_id,
+        "code",
+        "code.language.required",
+        "Add language: python3 or javascript",
+    )
+    if err:
+        errors.append(err)
+
+    return errors
+
+
+def _check_question_classifier(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that question-classifier has classes."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    err = check_required_field(
+        config,
+        "classes",
+        node_id,
+        "question-classifier",
+        "classifier.classes.required",
+        "Add classes array with id and name for each classification",
+    )
+    if err:
+        errors.append(err)
+
+    return errors
+
+
+def _check_parameter_extractor(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that parameter-extractor has parameters and instruction."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    err = check_required_field(
+        config,
+        "parameters",
+        node_id,
+        "parameter-extractor",
+        "extractor.parameters.required",
+        "Add parameters array with name, type, description fields",
+    )
+    if err:
+        errors.append(err)
+    else:
+        # Check individual parameters for required fields
+        parameters = config.get("parameters", [])
+        if isinstance(parameters, list):
+            for i, param in enumerate(parameters):
+                if isinstance(param, dict):
+                    # Check for 'required' field (boolean)
+                    if "required" not in param:
+                        errors.append(
+                            ValidationError(
+                                rule_id="extractor.param.required_field.missing",
+                                node_id=node_id,
+                                node_type="parameter-extractor",
+                                category=RuleCategory.STRUCTURE,
+                                severity=Severity.ERROR,
+                                is_fixable=True,
+                                message=f"Node '{node_id}': parameter[{i}] missing 'required' field",
+                                fix_hint=f"Add 'required': True to parameter '{param.get('name', 'unknown')}'",
+                                details={"param_index": i, "param_name": param.get("name")},
+                            )
+                        )
+
+    # instruction is recommended but not strictly required
+    if not config.get("instruction"):
+        errors.append(
+            ValidationError(
+                rule_id="extractor.instruction.recommended",
+                node_id=node_id,
+                node_type="parameter-extractor",
+                category=RuleCategory.STRUCTURE,
+                severity=Severity.WARNING,
+                is_fixable=True,
+                message=f"Node '{node_id}': parameter-extractor should have 'instruction'",
+                fix_hint="Add instruction describing what to extract",
+            )
+        )
+
+    return errors
+
+
+def _check_knowledge_retrieval(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that knowledge-retrieval has dataset_ids."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    dataset_ids = config.get("dataset_ids", [])
+    if not dataset_ids:
+        errors.append(
+            ValidationError(
+                rule_id="knowledge.dataset.required",
+                node_id=node_id,
+                node_type="knowledge-retrieval",
+                category=RuleCategory.STRUCTURE,
+                severity=Severity.ERROR,
+                is_fixable=False,  # User must select knowledge base
+                message=f"Node '{node_id}': knowledge-retrieval missing 'dataset_ids'",
+                fix_hint="User must select knowledge bases in the UI",
+            )
+        )
+    else:
+        # Check for placeholder values
+        for ds_id in dataset_ids:
+            if is_placeholder(ds_id):
+                errors.append(
+                    ValidationError(
+                        rule_id="knowledge.dataset.placeholder",
+                        node_id=node_id,
+                        node_type="knowledge-retrieval",
+                        category=RuleCategory.STRUCTURE,
+                        severity=Severity.ERROR,
+                        is_fixable=False,
+                        message=f"Node '{node_id}': dataset_ids contains placeholder",
+                        fix_hint="User must replace placeholder with actual knowledge base ID",
+                        details={"placeholder_value": ds_id},
+                    )
+                )
+                break
+
+    return errors
+
+
+def _check_end_node(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that end node has outputs defined."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    config = node.get("config", {})
+
+    outputs = config.get("outputs", [])
+    if not outputs:
+        errors.append(
+            ValidationError(
+                rule_id="end.outputs.recommended",
+                node_id=node_id,
+                node_type="end",
+                category=RuleCategory.STRUCTURE,
+                severity=Severity.WARNING,
+                is_fixable=True,
+                message="End node should define output variables",
+                fix_hint="Add outputs array with variable and value_selector",
+            )
+        )
+
+    return errors
+
+
+# =============================================================================
+# Semantic Rules - Variable references, edge connections
+# =============================================================================
+
+
+def _check_variable_references(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that variable references point to valid nodes."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    node_type = node.get("type", "unknown")
+    config = node.get("config", {})
+
+    # Get all valid node IDs (including 'start' which is always valid)
+    valid_node_ids = ctx.get_node_ids()
+    valid_node_ids.add("start")
+    valid_node_ids.add("sys")  # System variables
+
+    def check_text_for_refs(text: str, field_path: str) -> None:
+        if not isinstance(text, str):
+            return
+        refs = extract_variable_refs(text)
+        for ref_node_id, ref_field in refs:
+            if ref_node_id not in valid_node_ids:
+                errors.append(
+                    ValidationError(
+                        rule_id="variable.ref.invalid_node",
+                        node_id=node_id,
+                        node_type=node_type,
+                        category=RuleCategory.SEMANTIC,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Node '{node_id}': references non-existent node '{ref_node_id}'",
+                        fix_hint=f"Change {{{{#{ref_node_id}.{ref_field}#}}}} to reference a valid node",
+                        details={"field_path": field_path, "invalid_ref": ref_node_id},
+                    )
+                )
+
+    # Check prompt_template for LLM nodes
+    prompt_template = config.get("prompt_template", [])
+    if isinstance(prompt_template, list):
+        for i, msg in enumerate(prompt_template):
+            if isinstance(msg, dict):
+                text = msg.get("text", "")
+                check_text_for_refs(text, f"prompt_template[{i}].text")
+
+    # Check instruction field
+    instruction = config.get("instruction", "")
+    check_text_for_refs(instruction, "instruction")
+
+    # Check url for http-request
+    url = config.get("url", "")
+    check_text_for_refs(url, "url")
+
+    return errors
+
+
+# NOTE: _check_node_has_outgoing_edge removed - handled by GraphValidator
+
+
+# NOTE: _check_node_has_incoming_edge removed - handled by GraphValidator
+
+
+# NOTE: _check_question_classifier_branches removed - handled by EdgeRepair
+
+
+# NOTE: _check_if_else_branches removed - handled by EdgeRepair
+
+
+def _check_if_else_operators(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that if-else comparison operators are valid."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    node_type = node.get("type", "unknown")
+
+    if node_type != "if-else":
+        return errors
+
+    valid_operators = {
+        "contains",
+        "not contains",
+        "start with",
+        "end with",
+        "is",
+        "is not",
+        "empty",
+        "not empty",
+        "in",
+        "not in",
+        "all of",
+        "=",
+        "≠",
+        ">",
+        "<",
+        "≥",
+        "≤",
+        "null",
+        "not null",
+        "exists",
+        "not exists",
+    }
+
+    config = node.get("config", {})
+    cases = config.get("cases", [])
+
+    for case in cases:
+        conditions = case.get("conditions", [])
+        for condition in conditions:
+            op = condition.get("comparison_operator")
+            if op and op not in valid_operators:
+                errors.append(
+                    ValidationError(
+                        rule_id="ifelse.operator.invalid",
+                        node_id=node_id,
+                        node_type=node_type,
+                        category=RuleCategory.SEMANTIC,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Invalid operator '{op}' in if-else node",
+                        fix_hint=f"Use one of: {', '.join(sorted(valid_operators))}",
+                        details={"invalid_operator": op, "field": "config.cases.conditions.comparison_operator"},
+                    )
+                )
+
+    return errors
+
+
+def _check_edge_targets_exist(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that edge targets reference existing nodes."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    node_type = node.get("type", "unknown")
+
+    valid_node_ids = ctx.get_node_ids()
+
+    # Check all outgoing edges from this node
+    for edge in ctx.edges:
+        if edge.get("source") == node_id:
+            target = edge.get("target")
+            if target and target not in valid_node_ids:
+                errors.append(
+                    ValidationError(
+                        rule_id="edge.target.invalid",
+                        node_id=node_id,
+                        node_type=node_type,
+                        category=RuleCategory.SEMANTIC,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Edge from '{node_id}' targets non-existent node '{target}'",
+                        fix_hint=f"Change edge target from '{target}' to an existing node",
+                        details={"invalid_target": target, "field": "edges"},
+                    )
+                )
+
+    return errors
+
+
+# =============================================================================
+# Reference Rules - External resources (models, tools, datasets)
+# =============================================================================
+
+# Node types that require model configuration
+MODEL_REQUIRED_NODE_TYPES = {"llm", "question-classifier", "parameter-extractor"}
+
+
+def _check_model_config(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that model configuration is valid."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    node_type = node.get("type", "unknown")
+    config = node.get("config", {})
+
+    if node_type not in MODEL_REQUIRED_NODE_TYPES:
+        return errors
+
+    model = config.get("model")
+
+    # Check if model config exists
+    if not model:
+        if ctx.available_models:
+            errors.append(
+                ValidationError(
+                    rule_id="model.required",
+                    node_id=node_id,
+                    node_type=node_type,
+                    category=RuleCategory.REFERENCE,
+                    severity=Severity.ERROR,
+                    is_fixable=True,
+                    message=f"Node '{node_id}' ({node_type}): missing required 'model' configuration",
+                    fix_hint="Add model config using one of the available models",
+                )
+            )
+        else:
+            errors.append(
+                ValidationError(
+                    rule_id="model.no_available",
+                    node_id=node_id,
+                    node_type=node_type,
+                    category=RuleCategory.REFERENCE,
+                    severity=Severity.ERROR,
+                    is_fixable=False,
+                    message=f"Node '{node_id}' ({node_type}): needs model but no models available",
+                    fix_hint="User must configure a model provider first",
+                )
+            )
+        return errors
+
+    # Check if model config is valid
+    if isinstance(model, dict):
+        provider = model.get("provider", "")
+        name = model.get("name", "")
+
+        # Check for placeholder values
+        if is_placeholder(provider) or is_placeholder(name):
+            if ctx.available_models:
+                errors.append(
+                    ValidationError(
+                        rule_id="model.placeholder",
+                        node_id=node_id,
+                        node_type=node_type,
+                        category=RuleCategory.REFERENCE,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Node '{node_id}': model config contains placeholder",
+                        fix_hint="Replace placeholder with actual model from available_models",
+                    )
+                )
+            return errors
+
+        # Check if model exists in available_models
+        if ctx.available_models and provider and name:
+            if not ctx.has_model(provider, name):
+                errors.append(
+                    ValidationError(
+                        rule_id="model.not_found",
+                        node_id=node_id,
+                        node_type=node_type,
+                        category=RuleCategory.REFERENCE,
+                        severity=Severity.ERROR,
+                        is_fixable=True,
+                        message=f"Node '{node_id}': model '{provider}/{name}' not in available models",
+                        fix_hint="Replace with a model from available_models",
+                        details={"provider": provider, "model": name},
+                    )
+                )
+
+    return errors
+
+
+def _check_tool_reference(node: WorkflowNodeDict, ctx: "ValidationContext") -> list[ValidationError]:
+    """Check that tool references are valid and configured."""
+    errors: list[ValidationError] = []
+    node_id = node.get("id", "unknown")
+    node_type = node.get("type", "unknown")
+
+    if node_type != "tool":
+        return errors
+
+    config = node.get("config", {})
+    tool_ref = (
+        config.get("tool_key")
+        or config.get("tool_name")
+        or config.get("provider_id", "") + "/" + config.get("tool_name", "")
+    )
+
+    if not tool_ref:
+        errors.append(
+            ValidationError(
+                rule_id="tool.key.required",
+                node_id=node_id,
+                node_type=node_type,
+                category=RuleCategory.REFERENCE,
+                severity=Severity.ERROR,
+                is_fixable=True,
+                message=f"Node '{node_id}': tool node missing tool_key",
+                fix_hint="Add tool_key from available_tools",
+            )
+        )
+        return errors
+
+    # Check if tool exists
+    if not ctx.has_tool(tool_ref):
+        errors.append(
+            ValidationError(
+                rule_id="tool.not_found",
+                node_id=node_id,
+                node_type=node_type,
+                category=RuleCategory.REFERENCE,
+                severity=Severity.ERROR,
+                is_fixable=True,  # Can be replaced with http-request fallback
+                message=f"Node '{node_id}': tool '{tool_ref}' not found",
+                fix_hint="Use http-request or code node as fallback",
+                details={"tool_ref": tool_ref},
+            )
+        )
+    elif not ctx.is_tool_configured(tool_ref):
+        errors.append(
+            ValidationError(
+                rule_id="tool.not_configured",
+                node_id=node_id,
+                node_type=node_type,
+                category=RuleCategory.REFERENCE,
+                severity=Severity.WARNING,
+                is_fixable=False,  # User needs to configure
+                message=f"Node '{node_id}': tool '{tool_ref}' requires configuration",
+                fix_hint="Configure the tool in Tools settings",
+                details={"tool_ref": tool_ref},
+            )
+        )
+
+    return errors
+
+
+# =============================================================================
+# Register All Rules
+# =============================================================================
+
+# Structure Rules
+register_rule(
+    ValidationRule(
+        id="llm.prompt_template.required",
+        node_types=["llm"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_llm_prompt_template,
+        description="LLM node must have prompt_template",
+        fix_hint="Add prompt_template with system and user messages",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="http.config.required",
+        node_types=["http-request"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_http_request_url,
+        description="HTTP request node must have url and method",
+        fix_hint="Add url and method to config",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="code.config.required",
+        node_types=["code"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_code_node,
+        description="Code node must have code and language",
+        fix_hint="Add code with main() function and language",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="classifier.classes.required",
+        node_types=["question-classifier"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_question_classifier,
+        description="Question classifier must have classes",
+        fix_hint="Add classes array with classification options",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="extractor.config.required",
+        node_types=["parameter-extractor"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_parameter_extractor,
+        description="Parameter extractor must have parameters",
+        fix_hint="Add parameters array",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="knowledge.config.required",
+        node_types=["knowledge-retrieval"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.ERROR,
+        is_fixable=False,
+        check=_check_knowledge_retrieval,
+        description="Knowledge retrieval must have dataset_ids",
+        fix_hint="User must select knowledge base",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="end.outputs.check",
+        node_types=["end"],
+        category=RuleCategory.STRUCTURE,
+        severity=Severity.WARNING,
+        is_fixable=True,
+        check=_check_end_node,
+        description="End node should have outputs",
+        fix_hint="Add outputs array",
+    )
+)
+
+# Semantic Rules
+register_rule(
+    ValidationRule(
+        id="variable.references.valid",
+        node_types=["*"],
+        category=RuleCategory.SEMANTIC,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_variable_references,
+        description="Variable references must point to valid nodes",
+        fix_hint="Fix variable reference to use valid node ID",
+    )
+)
+
+# Edge Validation Rules
+# NOTE: Edge connectivity and branch completeness are now handled by:
+# - GraphValidator (BFS-based reachability analysis)
+# - EdgeRepair (automatic branch edge repair)
+
+register_rule(
+    ValidationRule(
+        id="edge.targets.valid",
+        node_types=["*"],
+        category=RuleCategory.SEMANTIC,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_edge_targets_exist,
+        description="Edge targets must reference existing nodes",
+        fix_hint="Change edge target to an existing node ID",
+    )
+)
+
+# Reference Rules
+register_rule(
+    ValidationRule(
+        id="model.config.valid",
+        node_types=["llm", "question-classifier", "parameter-extractor"],
+        category=RuleCategory.REFERENCE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_model_config,
+        description="Model configuration must be valid",
+        fix_hint="Add valid model from available_models",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="tool.reference.valid",
+        node_types=["tool"],
+        category=RuleCategory.REFERENCE,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_tool_reference,
+        description="Tool reference must be valid and configured",
+        fix_hint="Use valid tool or fallback node",
+    )
+)
+
+register_rule(
+    ValidationRule(
+        id="ifelse.operator.valid",
+        node_types=["if-else"],
+        category=RuleCategory.SEMANTIC,
+        severity=Severity.ERROR,
+        is_fixable=True,
+        check=_check_if_else_operators,
+        description="If-else operators must be valid",
+        fix_hint="Use standard operators like ≥, ≤, =, ≠",
+    )
+)

api/core/workflow/nodes/base/node.py

@@ -199,6 +199,14 @@ class Node(Generic[NodeDataT]):
 
         return None
 
+    @classmethod
+    def get_default_config_schema(cls) -> dict[str, Any] | None:
+        """
+        Get the default configuration schema for the node.
+        Used for LLM generation.
+        """
+        return None
+
     # Global registry populated via __init_subclass__
     _registry: ClassVar[dict[NodeType, dict[str, type[Node]]]] = {}
 

api/core/workflow/nodes/end/end_node.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 from core.workflow.enums import NodeExecutionType, NodeType, WorkflowNodeExecutionStatus
 from core.workflow.node_events import NodeRunResult
 from core.workflow.nodes.base.node import Node
@@ -9,6 +11,24 @@ class EndNode(Node[EndNodeData]):
     node_type = NodeType.END
     execution_type = NodeExecutionType.RESPONSE
 
+    @classmethod
+    def get_default_config_schema(cls) -> dict[str, Any] | None:
+        return {
+            "description": "Workflow exit point - defines output variables",
+            "required": ["outputs"],
+            "parameters": {
+                "outputs": {
+                    "type": "array",
+                    "description": "Output variables to return",
+                    "item_schema": {
+                        "variable": "string - output variable name",
+                        "type": "enum: string, number, object, array",
+                        "value_selector": "array - path to source value, e.g. ['node_id', 'field']",
+                    },
+                },
+            },
+        }
+
     @classmethod
     def version(cls) -> str:
         return "1"

api/core/workflow/nodes/start/start_node.py

@@ -14,6 +14,27 @@ class StartNode(Node[StartNodeData]):
     node_type = NodeType.START
     execution_type = NodeExecutionType.ROOT
 
+    @classmethod
+    def get_default_config_schema(cls) -> dict[str, Any] | None:
+        return {
+            "description": "Workflow entry point - defines input variables",
+            "required": [],
+            "parameters": {
+                "variables": {
+                    "type": "array",
+                    "description": "Input variables for the workflow",
+                    "item_schema": {
+                        "variable": "string - variable name",
+                        "label": "string - display label",
+                        "type": "enum: text-input, paragraph, number, select, file, file-list",
+                        "required": "boolean",
+                        "max_length": "number (optional)",
+                    },
+                },
+            },
+            "outputs": ["All defined variables are available as {{#start.variable_name#}}"],
+        }
+
     @classmethod
     def version(cls) -> str:
         return "1"

api/core/workflow/nodes/template_transform/template_transform_node.py

@@ -1,6 +1,7 @@
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
+from configs import dify_config
 from core.workflow.enums import NodeType, WorkflowNodeExecutionStatus
 from core.workflow.node_events import NodeRunResult
 from core.workflow.nodes.base.node import Node
@@ -15,13 +16,12 @@ if TYPE_CHECKING:
     from core.workflow.entities import GraphInitParams
     from core.workflow.runtime import GraphRuntimeState
 
-DEFAULT_TEMPLATE_TRANSFORM_MAX_OUTPUT_LENGTH = 400_000
+MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH = dify_config.TEMPLATE_TRANSFORM_MAX_LENGTH
 
 
 class TemplateTransformNode(Node[TemplateTransformNodeData]):
     node_type = NodeType.TEMPLATE_TRANSFORM
     _template_renderer: Jinja2TemplateRenderer
-    _max_output_length: int
 
     def __init__(
         self,
@@ -31,7 +31,6 @@ class TemplateTransformNode(Node[TemplateTransformNodeData]):
         graph_runtime_state: "GraphRuntimeState",
         *,
         template_renderer: Jinja2TemplateRenderer | None = None,
-        max_output_length: int | None = None,
     ) -> None:
         super().__init__(
             id=id,
@@ -41,10 +40,6 @@ class TemplateTransformNode(Node[TemplateTransformNodeData]):
         )
         self._template_renderer = template_renderer or CodeExecutorJinja2TemplateRenderer()
 
-        if max_output_length is not None and max_output_length <= 0:
-            raise ValueError("max_output_length must be a positive integer")
-        self._max_output_length = max_output_length or DEFAULT_TEMPLATE_TRANSFORM_MAX_OUTPUT_LENGTH
-
     @classmethod
     def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:
         """
@@ -74,11 +69,11 @@ class TemplateTransformNode(Node[TemplateTransformNodeData]):
         except TemplateRenderError as e:
             return NodeRunResult(inputs=variables, status=WorkflowNodeExecutionStatus.FAILED, error=str(e))
 
-        if len(rendered) > self._max_output_length:
+        if len(rendered) > MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH:
             return NodeRunResult(
                 inputs=variables,
                 status=WorkflowNodeExecutionStatus.FAILED,
-                error=f"Output length exceeds {self._max_output_length} characters",
+                error=f"Output length exceeds {MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH} characters",
             )
 
         return NodeRunResult(

api/core/workflow/nodes/tool/tool_node.py

@@ -50,6 +50,19 @@ class ToolNode(Node[ToolNodeData]):
     def version(cls) -> str:
         return "1"
 
+    @classmethod
+    def get_default_config_schema(cls) -> dict[str, Any] | None:
+        return {
+            "description": "Execute an external tool",
+            "required": ["provider_id", "tool_id", "tool_parameters"],
+            "parameters": {
+                "provider_id": {"type": "string"},
+                "provider_type": {"type": "string"},
+                "tool_id": {"type": "string"},
+                "tool_parameters": {"type": "object"},
+            },
+        }
+
     def _run(self) -> Generator[NodeEventBase, None, None]:
         """
         Run the tool node

api/docker/entrypoint.sh

@@ -35,10 +35,10 @@ if [[ "${MODE}" == "worker" ]]; then
   if [[ -z "${CELERY_QUEUES}" ]]; then
     if [[ "${EDITION}" == "CLOUD" ]]; then
       # Cloud edition: separate queues for dataset and trigger tasks
-      DEFAULT_QUEUES="api_token,dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow_professional,workflow_team,workflow_sandbox,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention"
+      DEFAULT_QUEUES="dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow_professional,workflow_team,workflow_sandbox,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention"
     else
       # Community edition (SELF_HOSTED): dataset, pipeline and workflow have separate queues
-      DEFAULT_QUEUES="api_token,dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention"
+      DEFAULT_QUEUES="dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention"
     fi
   else
     DEFAULT_QUEUES="${CELERY_QUEUES}"

api/extensions/ext_celery.py

@@ -184,14 +184,6 @@ def init_app(app: DifyApp) -> Celery:
             "task": "schedule.trigger_provider_refresh_task.trigger_provider_refresh",
             "schedule": timedelta(minutes=dify_config.TRIGGER_PROVIDER_REFRESH_INTERVAL),
         }
-
-    if dify_config.ENABLE_API_TOKEN_LAST_USED_UPDATE_TASK:
-        imports.append("schedule.update_api_token_last_used_task")
-        beat_schedule["batch_update_api_token_last_used"] = {
-            "task": "schedule.update_api_token_last_used_task.batch_update_api_token_last_used",
-            "schedule": timedelta(minutes=dify_config.API_TOKEN_LAST_USED_UPDATE_INTERVAL),
-        }
-
     celery_app.conf.update(beat_schedule=beat_schedule, imports=imports)
 
     return celery_app

api/migrations/versions/2025_12_25_1039-7df29de0f6be_add_credit_pool.py

@@ -10,10 +10,6 @@ import models as models
 import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
 
-
-def _is_pg(conn):
-    return conn.dialect.name == "postgresql"
-
 # revision identifiers, used by Alembic.
 revision = '7df29de0f6be'
 down_revision = '03ea244985ce'
@@ -23,31 +19,16 @@ depends_on = None
 
 def upgrade():
     # ### commands auto generated by Alembic - please adjust! ###
-    conn = op.get_bind()
-
-    if _is_pg(conn):
-        op.create_table('tenant_credit_pools',
-        sa.Column('id', models.types.StringUUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
-        sa.Column('tenant_id', models.types.StringUUID(), nullable=False),
-        sa.Column('pool_type', sa.String(length=40), server_default='trial', nullable=False),
-        sa.Column('quota_limit', sa.BigInteger(), nullable=False),
-        sa.Column('quota_used', sa.BigInteger(), nullable=False),
-        sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
-        sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
-        sa.PrimaryKeyConstraint('id', name='tenant_credit_pool_pkey')
-        )
-    else:
-        # For MySQL and other databases, UUID should be generated at application level
-        op.create_table('tenant_credit_pools',
-        sa.Column('id', models.types.StringUUID(), nullable=False),
-        sa.Column('tenant_id', models.types.StringUUID(), nullable=False),
-        sa.Column('pool_type', sa.String(length=40), server_default='trial', nullable=False),
-        sa.Column('quota_limit', sa.BigInteger(), nullable=False),
-        sa.Column('quota_used', sa.BigInteger(), nullable=False),
-        sa.Column('created_at', sa.DateTime(), server_default=sa.func.current_timestamp(), nullable=False),
-        sa.Column('updated_at', sa.DateTime(), server_default=sa.func.current_timestamp(), nullable=False),
-        sa.PrimaryKeyConstraint('id', name='tenant_credit_pool_pkey')
-        )
+    op.create_table('tenant_credit_pools',
+    sa.Column('id', models.types.StringUUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
+    sa.Column('tenant_id', models.types.StringUUID(), nullable=False),
+    sa.Column('pool_type', sa.String(length=40), server_default='trial', nullable=False),
+    sa.Column('quota_limit', sa.BigInteger(), nullable=False),
+    sa.Column('quota_used', sa.BigInteger(), nullable=False),
+    sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.PrimaryKeyConstraint('id', name='tenant_credit_pool_pkey')
+    )
     with op.batch_alter_table('tenant_credit_pools', schema=None) as batch_op:
         batch_op.create_index('tenant_credit_pool_pool_type_idx', ['pool_type'], unique=False)
         batch_op.create_index('tenant_credit_pool_tenant_id_idx', ['tenant_id'], unique=False)

api/models/model.py

@@ -2166,9 +2166,7 @@ class TenantCreditPool(TypeBase):
         sa.Index("tenant_credit_pool_pool_type_idx", "pool_type"),
     )
 
-    id: Mapped[str] = mapped_column(
-        StringUUID, insert_default=lambda: str(uuid4()), default_factory=lambda: str(uuid4()), init=False
-    )
+    id: Mapped[str] = mapped_column(StringUUID, primary_key=True, server_default=text("uuid_generate_v4()"), init=False)
     tenant_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
     pool_type: Mapped[str] = mapped_column(String(40), nullable=False, default="trial", server_default="trial")
     quota_limit: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)

api/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dify-api"
-version = "1.12.1"
+version = "1.12.0"
 requires-python = ">=3.11,<3.13"
 
 dependencies = [

api/schedule/check_upgradable_plugin_task.py

@@ -1,24 +1,16 @@
-import logging
 import math
 import time
 
 import click
 
 import app
-from core.helper.marketplace import fetch_global_plugin_manifest
 from extensions.ext_database import db
 from models.account import TenantPluginAutoUpgradeStrategy
 from tasks import process_tenant_plugin_autoupgrade_check_task as check_task
 
-logger = logging.getLogger(__name__)
-
 AUTO_UPGRADE_MINIMAL_CHECKING_INTERVAL = 15 * 60  # 15 minutes
 MAX_CONCURRENT_CHECK_TASKS = 20
 
-# Import cache constants from the task module
-CACHE_REDIS_KEY_PREFIX = check_task.CACHE_REDIS_KEY_PREFIX
-CACHE_REDIS_TTL = check_task.CACHE_REDIS_TTL
-
 
 @app.celery.task(queue="plugin")
 def check_upgradable_plugin_task():
@@ -48,22 +40,6 @@ def check_upgradable_plugin_task():
     )  # make sure all strategies are checked in this interval
     batch_interval_time = (AUTO_UPGRADE_MINIMAL_CHECKING_INTERVAL / batch_chunk_count) if batch_chunk_count > 0 else 0
 
-    if total_strategies == 0:
-        click.echo(click.style("no strategies to process, skipping plugin manifest fetch.", fg="green"))
-        return
-
-    # Fetch and cache all plugin manifests before processing tenants
-    # This reduces load on marketplace from 300k requests to 1 request per check cycle
-    logger.info("fetching global plugin manifest from marketplace")
-    try:
-        fetch_global_plugin_manifest(CACHE_REDIS_KEY_PREFIX, CACHE_REDIS_TTL)
-        logger.info("successfully fetched and cached global plugin manifest")
-    except Exception as e:
-        logger.exception("failed to fetch global plugin manifest")
-        click.echo(click.style(f"failed to fetch global plugin manifest: {e}", fg="red"))
-        click.echo(click.style("skipping plugin upgrade check for this cycle", fg="yellow"))
-        return
-
     for i in range(0, total_strategies, MAX_CONCURRENT_CHECK_TASKS):
         batch_strategies = strategies[i : i + MAX_CONCURRENT_CHECK_TASKS]
         for strategy in batch_strategies:

api/schedule/update_api_token_last_used_task.py

@@ -1,114 +0,0 @@
-"""
-Scheduled task to batch-update API token last_used_at timestamps.
-
-Instead of updating the database on every request, token usage is recorded
-in Redis as lightweight SET keys (api_token_active:{scope}:{token}).
-This task runs periodically (default every 30 minutes) to flush those
-records into the database in a single batch operation.
-"""
-
-import logging
-import time
-from datetime import datetime
-
-import click
-from sqlalchemy import update
-from sqlalchemy.orm import Session
-
-import app
-from extensions.ext_database import db
-from extensions.ext_redis import redis_client
-from models.model import ApiToken
-from services.api_token_service import ACTIVE_TOKEN_KEY_PREFIX
-
-logger = logging.getLogger(__name__)
-
-
-@app.celery.task(queue="api_token")
-def batch_update_api_token_last_used():
-    """
-    Batch update last_used_at for all recently active API tokens.
-
-    Scans Redis for api_token_active:* keys, parses the token and scope
-    from each key, and performs a batch database update.
-    """
-    click.echo(click.style("batch_update_api_token_last_used: start.", fg="green"))
-    start_at = time.perf_counter()
-
-    updated_count = 0
-    scanned_count = 0
-
-    try:
-        # Collect all active token keys and their values (the actual usage timestamps)
-        token_entries: list[tuple[str, str | None, datetime]] = []  # (token, scope, usage_time)
-        keys_to_delete: list[str | bytes] = []
-
-        for key in redis_client.scan_iter(match=f"{ACTIVE_TOKEN_KEY_PREFIX}*", count=200):
-            if isinstance(key, bytes):
-                key = key.decode("utf-8")
-            scanned_count += 1
-
-            # Read the value (ISO timestamp recorded at actual request time)
-            value = redis_client.get(key)
-            if not value:
-                keys_to_delete.append(key)
-                continue
-
-            if isinstance(value, bytes):
-                value = value.decode("utf-8")
-
-            try:
-                usage_time = datetime.fromisoformat(value)
-            except (ValueError, TypeError):
-                logger.warning("Invalid timestamp in key %s: %s", key, value)
-                keys_to_delete.append(key)
-                continue
-
-            # Parse token info from key: api_token_active:{scope}:{token}
-            suffix = key[len(ACTIVE_TOKEN_KEY_PREFIX) :]
-            parts = suffix.split(":", 1)
-            if len(parts) == 2:
-                scope_str, token = parts
-                scope = None if scope_str == "None" else scope_str
-                token_entries.append((token, scope, usage_time))
-            keys_to_delete.append(key)
-
-        if not token_entries:
-            click.echo(click.style("batch_update_api_token_last_used: no active tokens found.", fg="yellow"))
-            # Still clean up any invalid keys
-            if keys_to_delete:
-                redis_client.delete(*keys_to_delete)
-            return
-
-        # Update each token in its own short transaction to avoid long transactions
-        for token, scope, usage_time in token_entries:
-            with Session(db.engine, expire_on_commit=False) as session, session.begin():
-                stmt = (
-                    update(ApiToken)
-                    .where(
-                        ApiToken.token == token,
-                        ApiToken.type == scope,
-                        (ApiToken.last_used_at.is_(None) | (ApiToken.last_used_at < usage_time)),
-                    )
-                    .values(last_used_at=usage_time)
-                )
-                result = session.execute(stmt)
-                rowcount = getattr(result, "rowcount", 0)
-                if rowcount > 0:
-                    updated_count += 1
-
-        # Delete processed keys from Redis
-        if keys_to_delete:
-            redis_client.delete(*keys_to_delete)
-
-    except Exception:
-        logger.exception("batch_update_api_token_last_used failed")
-
-    elapsed = time.perf_counter() - start_at
-    click.echo(
-        click.style(
-            f"batch_update_api_token_last_used: done. "
-            f"scanned={scanned_count}, updated={updated_count}, elapsed={elapsed:.2f}s",
-            fg="green",
-        )
-    )

api/services/account_service.py

@@ -327,17 +327,6 @@ class AccountService:
     @staticmethod
     def delete_account(account: Account):
         """Delete account. This method only adds a task to the queue for deletion."""
-        # Queue account deletion sync tasks for all workspaces BEFORE account deletion (enterprise only)
-        from services.enterprise.account_deletion_sync import sync_account_deletion
-
-        sync_success = sync_account_deletion(account_id=account.id, source="account_deleted")
-        if not sync_success:
-            logger.warning(
-                "Enterprise account deletion sync failed for account %s; proceeding with local deletion.",
-                account.id,
-            )
-
-        # Now proceed with async account deletion
         delete_account_task.delay(account.id)
 
     @staticmethod
@@ -1241,19 +1230,6 @@ class TenantService:
         if dify_config.BILLING_ENABLED:
             BillingService.clean_billing_info_cache(tenant.id)
 
-        # Queue account deletion sync task for enterprise backend to reassign resources (enterprise only)
-        from services.enterprise.account_deletion_sync import sync_workspace_member_removal
-
-        sync_success = sync_workspace_member_removal(
-            workspace_id=tenant.id, member_id=account.id, source="workspace_member_removed"
-        )
-        if not sync_success:
-            logger.warning(
-                "Enterprise workspace member removal sync failed: workspace_id=%s, member_id=%s",
-                tenant.id,
-                account.id,
-            )
-
     @staticmethod
     def update_member_role(tenant: Tenant, member: Account, new_role: str, operator: Account):
         """Update member role"""

api/services/api_token_service.py

@@ -1,330 +0,0 @@
-"""
-API Token Service
-
-Handles all API token caching, validation, and usage recording.
-Includes Redis cache operations, database queries, and single-flight concurrency control.
-"""
-
-import logging
-from datetime import datetime
-from typing import Any
-
-from pydantic import BaseModel
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-from werkzeug.exceptions import Unauthorized
-
-from extensions.ext_database import db
-from extensions.ext_redis import redis_client, redis_fallback
-from libs.datetime_utils import naive_utc_now
-from models.model import ApiToken
-
-logger = logging.getLogger(__name__)
-
-
-# ---------------------------------------------------------------------
-# Pydantic DTO
-# ---------------------------------------------------------------------
-
-
-class CachedApiToken(BaseModel):
-    """
-    Pydantic model for cached API token data.
-
-    This is NOT a SQLAlchemy model instance, but a plain Pydantic model
-    that mimics the ApiToken model interface for read-only access.
-    """
-
-    id: str
-    app_id: str | None
-    tenant_id: str | None
-    type: str
-    token: str
-    last_used_at: datetime | None
-    created_at: datetime | None
-
-    def __repr__(self) -> str:
-        return f"<CachedApiToken id={self.id} type={self.type}>"
-
-
-# ---------------------------------------------------------------------
-# Cache configuration
-# ---------------------------------------------------------------------
-
-CACHE_KEY_PREFIX = "api_token"
-CACHE_TTL_SECONDS = 600  # 10 minutes
-CACHE_NULL_TTL_SECONDS = 60  # 1 minute for non-existent tokens
-ACTIVE_TOKEN_KEY_PREFIX = "api_token_active:"
-
-
-# ---------------------------------------------------------------------
-# Cache class
-# ---------------------------------------------------------------------
-
-
-class ApiTokenCache:
-    """
-    Redis cache wrapper for API tokens.
-    Handles serialization, deserialization, and cache invalidation.
-    """
-
-    @staticmethod
-    def make_active_key(token: str, scope: str | None = None) -> str:
-        """Generate Redis key for recording token usage."""
-        return f"{ACTIVE_TOKEN_KEY_PREFIX}{scope}:{token}"
-
-    @staticmethod
-    def _make_tenant_index_key(tenant_id: str) -> str:
-        """Generate Redis key for tenant token index."""
-        return f"tenant_tokens:{tenant_id}"
-
-    @staticmethod
-    def _make_cache_key(token: str, scope: str | None = None) -> str:
-        """Generate cache key for the given token and scope."""
-        scope_str = scope or "any"
-        return f"{CACHE_KEY_PREFIX}:{scope_str}:{token}"
-
-    @staticmethod
-    def _serialize_token(api_token: Any) -> bytes:
-        """Serialize ApiToken object to JSON bytes."""
-        if isinstance(api_token, CachedApiToken):
-            return api_token.model_dump_json().encode("utf-8")
-
-        cached = CachedApiToken(
-            id=str(api_token.id),
-            app_id=str(api_token.app_id) if api_token.app_id else None,
-            tenant_id=str(api_token.tenant_id) if api_token.tenant_id else None,
-            type=api_token.type,
-            token=api_token.token,
-            last_used_at=api_token.last_used_at,
-            created_at=api_token.created_at,
-        )
-        return cached.model_dump_json().encode("utf-8")
-
-    @staticmethod
-    def _deserialize_token(cached_data: bytes | str) -> Any:
-        """Deserialize JSON bytes/string back to a CachedApiToken Pydantic model."""
-        if cached_data in {b"null", "null"}:
-            return None
-
-        try:
-            if isinstance(cached_data, bytes):
-                cached_data = cached_data.decode("utf-8")
-            return CachedApiToken.model_validate_json(cached_data)
-        except (ValueError, Exception) as e:
-            logger.warning("Failed to deserialize token from cache: %s", e)
-            return None
-
-    @staticmethod
-    @redis_fallback(default_return=None)
-    def get(token: str, scope: str | None) -> Any | None:
-        """Get API token from cache."""
-        cache_key = ApiTokenCache._make_cache_key(token, scope)
-        cached_data = redis_client.get(cache_key)
-
-        if cached_data is None:
-            logger.debug("Cache miss for token key: %s", cache_key)
-            return None
-
-        logger.debug("Cache hit for token key: %s", cache_key)
-        return ApiTokenCache._deserialize_token(cached_data)
-
-    @staticmethod
-    def _add_to_tenant_index(tenant_id: str | None, cache_key: str) -> None:
-        """Add cache key to tenant index for efficient invalidation."""
-        if not tenant_id:
-            return
-
-        try:
-            index_key = ApiTokenCache._make_tenant_index_key(tenant_id)
-            redis_client.sadd(index_key, cache_key)
-            redis_client.expire(index_key, CACHE_TTL_SECONDS + 60)
-        except Exception as e:
-            logger.warning("Failed to update tenant index: %s", e)
-
-    @staticmethod
-    def _remove_from_tenant_index(tenant_id: str | None, cache_key: str) -> None:
-        """Remove cache key from tenant index."""
-        if not tenant_id:
-            return
-
-        try:
-            index_key = ApiTokenCache._make_tenant_index_key(tenant_id)
-            redis_client.srem(index_key, cache_key)
-        except Exception as e:
-            logger.warning("Failed to remove from tenant index: %s", e)
-
-    @staticmethod
-    @redis_fallback(default_return=False)
-    def set(token: str, scope: str | None, api_token: Any | None, ttl: int = CACHE_TTL_SECONDS) -> bool:
-        """Set API token in cache."""
-        cache_key = ApiTokenCache._make_cache_key(token, scope)
-
-        if api_token is None:
-            cached_value = b"null"
-            ttl = CACHE_NULL_TTL_SECONDS
-        else:
-            cached_value = ApiTokenCache._serialize_token(api_token)
-
-        try:
-            redis_client.setex(cache_key, ttl, cached_value)
-
-            if api_token is not None and hasattr(api_token, "tenant_id"):
-                ApiTokenCache._add_to_tenant_index(api_token.tenant_id, cache_key)
-
-            logger.debug("Cached token with key: %s, ttl: %ss", cache_key, ttl)
-            return True
-        except Exception as e:
-            logger.warning("Failed to cache token: %s", e)
-            return False
-
-    @staticmethod
-    @redis_fallback(default_return=False)
-    def delete(token: str, scope: str | None = None) -> bool:
-        """Delete API token from cache."""
-        if scope is None:
-            pattern = f"{CACHE_KEY_PREFIX}:*:{token}"
-            try:
-                keys_to_delete = list(redis_client.scan_iter(match=pattern))
-                if keys_to_delete:
-                    redis_client.delete(*keys_to_delete)
-                    logger.info("Deleted %d cache entries for token", len(keys_to_delete))
-                return True
-            except Exception as e:
-                logger.warning("Failed to delete token cache with pattern: %s", e)
-                return False
-        else:
-            cache_key = ApiTokenCache._make_cache_key(token, scope)
-            try:
-                tenant_id = None
-                try:
-                    cached_data = redis_client.get(cache_key)
-                    if cached_data and cached_data != b"null":
-                        cached_token = ApiTokenCache._deserialize_token(cached_data)
-                        if cached_token:
-                            tenant_id = cached_token.tenant_id
-                except Exception as e:
-                    logger.debug("Failed to get tenant_id for cache cleanup: %s", e)
-
-                redis_client.delete(cache_key)
-
-                if tenant_id:
-                    ApiTokenCache._remove_from_tenant_index(tenant_id, cache_key)
-
-                logger.info("Deleted cache for key: %s", cache_key)
-                return True
-            except Exception as e:
-                logger.warning("Failed to delete token cache: %s", e)
-                return False
-
-    @staticmethod
-    @redis_fallback(default_return=False)
-    def invalidate_by_tenant(tenant_id: str) -> bool:
-        """Invalidate all API token caches for a specific tenant via tenant index."""
-        try:
-            index_key = ApiTokenCache._make_tenant_index_key(tenant_id)
-            cache_keys = redis_client.smembers(index_key)
-
-            if cache_keys:
-                deleted_count = 0
-                for cache_key in cache_keys:
-                    if isinstance(cache_key, bytes):
-                        cache_key = cache_key.decode("utf-8")
-                    redis_client.delete(cache_key)
-                    deleted_count += 1
-
-                redis_client.delete(index_key)
-
-                logger.info(
-                    "Invalidated %d token cache entries for tenant: %s",
-                    deleted_count,
-                    tenant_id,
-                )
-            else:
-                logger.info(
-                    "No tenant index found for %s, relying on TTL expiration",
-                    tenant_id,
-                )
-
-            return True
-
-        except Exception as e:
-            logger.warning("Failed to invalidate tenant token cache: %s", e)
-            return False
-
-
-# ---------------------------------------------------------------------
-# Token usage recording (for batch update)
-# ---------------------------------------------------------------------
-
-
-def record_token_usage(auth_token: str, scope: str | None) -> None:
-    """
-    Record token usage in Redis for later batch update by a scheduled job.
-
-    Instead of dispatching a Celery task per request, we simply SET a key in Redis.
-    A Celery Beat scheduled task will periodically scan these keys and batch-update
-    last_used_at in the database.
-    """
-    try:
-        key = ApiTokenCache.make_active_key(auth_token, scope)
-        redis_client.set(key, naive_utc_now().isoformat(), ex=3600)
-    except Exception as e:
-        logger.warning("Failed to record token usage: %s", e)
-
-
-# ---------------------------------------------------------------------
-# Database query + single-flight
-# ---------------------------------------------------------------------
-
-
-def query_token_from_db(auth_token: str, scope: str | None) -> ApiToken:
-    """
-    Query API token from database and cache the result.
-
-    Raises Unauthorized if token is invalid.
-    """
-    with Session(db.engine, expire_on_commit=False) as session:
-        stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
-        api_token = session.scalar(stmt)
-
-        if not api_token:
-            ApiTokenCache.set(auth_token, scope, None)
-            raise Unauthorized("Access token is invalid")
-
-        ApiTokenCache.set(auth_token, scope, api_token)
-        record_token_usage(auth_token, scope)
-        return api_token
-
-
-def fetch_token_with_single_flight(auth_token: str, scope: str | None) -> ApiToken | Any:
-    """
-    Fetch token from DB with single-flight pattern using Redis lock.
-
-    Ensures only one concurrent request queries the database for the same token.
-    Falls back to direct query if lock acquisition fails.
-    """
-    logger.debug("Token cache miss, attempting to acquire query lock for scope: %s", scope)
-
-    lock_key = f"api_token_query_lock:{scope}:{auth_token}"
-    lock = redis_client.lock(lock_key, timeout=10, blocking_timeout=5)
-
-    try:
-        if lock.acquire(blocking=True):
-            try:
-                cached_token = ApiTokenCache.get(auth_token, scope)
-                if cached_token is not None:
-                    logger.debug("Token cached by concurrent request, using cached version")
-                    return cached_token
-
-                return query_token_from_db(auth_token, scope)
-            finally:
-                lock.release()
-        else:
-            logger.warning("Lock timeout for token: %s, proceeding with direct query", auth_token[:10])
-            return query_token_from_db(auth_token, scope)
-    except Unauthorized:
-        raise
-    except Exception as e:
-        logger.warning("Redis lock failed for token query: %s, proceeding anyway", e)
-        return query_token_from_db(auth_token, scope)

api/services/enterprise/account_deletion_sync.py

@@ -1,115 +0,0 @@
-import json
-import logging
-import uuid
-from datetime import UTC, datetime
-
-from redis import RedisError
-
-from configs import dify_config
-from extensions.ext_database import db
-from extensions.ext_redis import redis_client
-from models.account import TenantAccountJoin
-
-logger = logging.getLogger(__name__)
-
-ACCOUNT_DELETION_SYNC_QUEUE = "enterprise:member:sync:queue"
-ACCOUNT_DELETION_SYNC_TASK_TYPE = "sync_member_deletion_from_workspace"
-
-
-def _queue_task(workspace_id: str, member_id: str, *, source: str) -> bool:
-    """
-    Queue an account deletion sync task to Redis.
-
-    Internal helper function. Do not call directly - use the public functions instead.
-
-    Args:
-        workspace_id: The workspace/tenant ID to sync
-        member_id: The member/account ID that was removed
-        source: Source of the sync request (for debugging/tracking)
-
-    Returns:
-        bool: True if task was queued successfully, False otherwise
-    """
-    try:
-        task = {
-            "task_id": str(uuid.uuid4()),
-            "workspace_id": workspace_id,
-            "member_id": member_id,
-            "retry_count": 0,
-            "created_at": datetime.now(UTC).isoformat(),
-            "source": source,
-            "type": ACCOUNT_DELETION_SYNC_TASK_TYPE,
-        }
-
-        # Push to Redis list (queue) - LPUSH adds to the head, worker consumes from tail with RPOP
-        redis_client.lpush(ACCOUNT_DELETION_SYNC_QUEUE, json.dumps(task))
-
-        logger.info(
-            "Queued account deletion sync task for workspace %s, member %s, task_id: %s, source: %s",
-            workspace_id,
-            member_id,
-            task["task_id"],
-            source,
-        )
-        return True
-
-    except (RedisError, TypeError) as e:
-        logger.error(
-            "Failed to queue account deletion sync for workspace %s, member %s: %s",
-            workspace_id,
-            member_id,
-            str(e),
-            exc_info=True,
-        )
-        # Don't raise - we don't want to fail member deletion if queueing fails
-        return False
-
-
-def sync_workspace_member_removal(workspace_id: str, member_id: str, *, source: str) -> bool:
-    """
-    Sync a single workspace member removal (enterprise only).
-
-    Queues a task for the enterprise backend to reassign resources from the removed member.
-    Handles enterprise edition check internally. Safe to call in community edition (no-op).
-
-    Args:
-        workspace_id: The workspace/tenant ID
-        member_id: The member/account ID that was removed
-        source: Source of the sync request (e.g., "workspace_member_removed")
-
-    Returns:
-        bool: True if task was queued (or skipped in community), False if queueing failed
-    """
-    if not dify_config.ENTERPRISE_ENABLED:
-        return True
-
-    return _queue_task(workspace_id=workspace_id, member_id=member_id, source=source)
-
-
-def sync_account_deletion(account_id: str, *, source: str) -> bool:
-    """
-    Sync full account deletion across all workspaces (enterprise only).
-
-    Fetches all workspace memberships for the account and queues a sync task for each.
-    Handles enterprise edition check internally. Safe to call in community edition (no-op).
-
-    Args:
-        account_id: The account ID being deleted
-        source: Source of the sync request (e.g., "account_deleted")
-
-    Returns:
-        bool: True if all tasks were queued (or skipped in community), False if any queueing failed
-    """
-    if not dify_config.ENTERPRISE_ENABLED:
-        return True
-
-    # Fetch all workspaces the account belongs to
-    workspace_joins = db.session.query(TenantAccountJoin).filter_by(account_id=account_id).all()
-
-    # Queue sync task for each workspace
-    success = True
-    for join in workspace_joins:
-        if not _queue_task(workspace_id=join.tenant_id, member_id=account_id, source=source):
-            success = False
-
-    return success

api/tasks/batch_clean_document_task.py

@@ -14,9 +14,6 @@ from models.model import UploadFile
 
 logger = logging.getLogger(__name__)
 
-# Batch size for database operations to keep transactions short
-BATCH_SIZE = 1000
-
 
 @shared_task(queue="dataset")
 def batch_clean_document_task(document_ids: list[str], dataset_id: str, doc_form: str | None, file_ids: list[str]):
@@ -34,179 +31,63 @@ def batch_clean_document_task(document_ids: list[str], dataset_id: str, doc_form
     if not doc_form:
         raise ValueError("doc_form is required")
 
-    storage_keys_to_delete: list[str] = []
-    index_node_ids: list[str] = []
-    segment_ids: list[str] = []
-    total_image_upload_file_ids: list[str] = []
+    with session_factory.create_session() as session:
+        try:
+            dataset = session.query(Dataset).where(Dataset.id == dataset_id).first()
+
+            if not dataset:
+                raise Exception("Document has no dataset")
+
+            session.query(DatasetMetadataBinding).where(
+                DatasetMetadataBinding.dataset_id == dataset_id,
+                DatasetMetadataBinding.document_id.in_(document_ids),
+            ).delete(synchronize_session=False)
 
-    try:
-        # ============ Step 1: Query segment and file data (short read-only transaction) ============
-        with session_factory.create_session() as session:
-            # Get segments info
             segments = session.scalars(
                 select(DocumentSegment).where(DocumentSegment.document_id.in_(document_ids))
             ).all()
-
+            # check segment is exist
             if segments:
                 index_node_ids = [segment.index_node_id for segment in segments]
-                segment_ids = [segment.id for segment in segments]
+                index_processor = IndexProcessorFactory(doc_form).init_index_processor()
+                index_processor.clean(
+                    dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
+                )
 
-                # Collect image file IDs from segment content
                 for segment in segments:
                     image_upload_file_ids = get_image_upload_file_ids(segment.content)
-                    total_image_upload_file_ids.extend(image_upload_file_ids)
-
-            # Query storage keys for image files
-            if total_image_upload_file_ids:
-                image_files = session.scalars(
-                    select(UploadFile).where(UploadFile.id.in_(total_image_upload_file_ids))
-                ).all()
-                storage_keys_to_delete.extend([f.key for f in image_files if f and f.key])
-
-            # Query storage keys for document files
+                    image_files = session.query(UploadFile).where(UploadFile.id.in_(image_upload_file_ids)).all()
+                    for image_file in image_files:
+                        try:
+                            if image_file and image_file.key:
+                                storage.delete(image_file.key)
+                        except Exception:
+                            logger.exception(
+                                "Delete image_files failed when storage deleted, \
+                                              image_upload_file_is: %s",
+                                image_file.id,
+                            )
+                    stmt = delete(UploadFile).where(UploadFile.id.in_(image_upload_file_ids))
+                    session.execute(stmt)
+                    session.delete(segment)
             if file_ids:
                 files = session.scalars(select(UploadFile).where(UploadFile.id.in_(file_ids))).all()
-                storage_keys_to_delete.extend([f.key for f in files if f and f.key])
+                for file in files:
+                    try:
+                        storage.delete(file.key)
+                    except Exception:
+                        logger.exception("Delete file failed when document deleted, file_id: %s", file.id)
+                stmt = delete(UploadFile).where(UploadFile.id.in_(file_ids))
+                session.execute(stmt)
 
-        # ============ Step 2: Clean vector index (external service, fresh session for dataset) ============
-        if index_node_ids:
-            try:
-                # Fetch dataset in a fresh session to avoid DetachedInstanceError
-                with session_factory.create_session() as session:
-                    dataset = session.query(Dataset).where(Dataset.id == dataset_id).first()
-                    if not dataset:
-                        logger.warning("Dataset not found for vector index cleanup, dataset_id: %s", dataset_id)
-                    else:
-                        index_processor = IndexProcessorFactory(doc_form).init_index_processor()
-                        index_processor.clean(
-                            dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
-                        )
-            except Exception:
-                logger.exception(
-                    "Failed to clean vector index for dataset_id: %s, document_ids: %s, index_node_ids count: %d",
-                    dataset_id,
-                    document_ids,
-                    len(index_node_ids),
-                )
+            session.commit()
 
-        # ============ Step 3: Delete metadata binding (separate short transaction) ============
-        try:
-            with session_factory.create_session() as session:
-                deleted_count = (
-                    session.query(DatasetMetadataBinding)
-                    .where(
-                        DatasetMetadataBinding.dataset_id == dataset_id,
-                        DatasetMetadataBinding.document_id.in_(document_ids),
-                    )
-                    .delete(synchronize_session=False)
+            end_at = time.perf_counter()
+            logger.info(
+                click.style(
+                    f"Cleaned documents when documents deleted latency: {end_at - start_at}",
+                    fg="green",
                 )
-                session.commit()
-                logger.debug("Deleted %d metadata bindings for dataset_id: %s", deleted_count, dataset_id)
+            )
         except Exception:
-            logger.exception(
-                "Failed to delete metadata bindings for dataset_id: %s, document_ids: %s",
-                dataset_id,
-                document_ids,
-            )
-
-        # ============ Step 4: Batch delete UploadFile records (multiple short transactions) ============
-        if total_image_upload_file_ids:
-            failed_batches = 0
-            total_batches = (len(total_image_upload_file_ids) + BATCH_SIZE - 1) // BATCH_SIZE
-            for i in range(0, len(total_image_upload_file_ids), BATCH_SIZE):
-                batch = total_image_upload_file_ids[i : i + BATCH_SIZE]
-                try:
-                    with session_factory.create_session() as session:
-                        stmt = delete(UploadFile).where(UploadFile.id.in_(batch))
-                        session.execute(stmt)
-                        session.commit()
-                except Exception:
-                    failed_batches += 1
-                    logger.exception(
-                        "Failed to delete image UploadFile batch %d-%d for dataset_id: %s",
-                        i,
-                        i + len(batch),
-                        dataset_id,
-                    )
-            if failed_batches > 0:
-                logger.warning(
-                    "Image UploadFile deletion: %d/%d batches failed for dataset_id: %s",
-                    failed_batches,
-                    total_batches,
-                    dataset_id,
-                )
-
-        # ============ Step 5: Batch delete DocumentSegment records (multiple short transactions) ============
-        if segment_ids:
-            failed_batches = 0
-            total_batches = (len(segment_ids) + BATCH_SIZE - 1) // BATCH_SIZE
-            for i in range(0, len(segment_ids), BATCH_SIZE):
-                batch = segment_ids[i : i + BATCH_SIZE]
-                try:
-                    with session_factory.create_session() as session:
-                        segment_delete_stmt = delete(DocumentSegment).where(DocumentSegment.id.in_(batch))
-                        session.execute(segment_delete_stmt)
-                        session.commit()
-                except Exception:
-                    failed_batches += 1
-                    logger.exception(
-                        "Failed to delete DocumentSegment batch %d-%d for dataset_id: %s, document_ids: %s",
-                        i,
-                        i + len(batch),
-                        dataset_id,
-                        document_ids,
-                    )
-            if failed_batches > 0:
-                logger.warning(
-                    "DocumentSegment deletion: %d/%d batches failed, document_ids: %s",
-                    failed_batches,
-                    total_batches,
-                    document_ids,
-                )
-
-        # ============ Step 6: Delete document-associated files (separate short transaction) ============
-        if file_ids:
-            try:
-                with session_factory.create_session() as session:
-                    stmt = delete(UploadFile).where(UploadFile.id.in_(file_ids))
-                    session.execute(stmt)
-                    session.commit()
-            except Exception:
-                logger.exception(
-                    "Failed to delete document UploadFile records for dataset_id: %s, file_ids: %s",
-                    dataset_id,
-                    file_ids,
-                )
-
-        # ============ Step 7: Delete storage files (I/O operations, no DB transaction) ============
-        storage_delete_failures = 0
-        for storage_key in storage_keys_to_delete:
-            try:
-                storage.delete(storage_key)
-            except Exception:
-                storage_delete_failures += 1
-                logger.exception("Failed to delete file from storage, key: %s", storage_key)
-        if storage_delete_failures > 0:
-            logger.warning(
-                "Storage file deletion completed with %d failures out of %d total files for dataset_id: %s",
-                storage_delete_failures,
-                len(storage_keys_to_delete),
-                dataset_id,
-            )
-
-        end_at = time.perf_counter()
-        logger.info(
-            click.style(
-                f"Cleaned documents when documents deleted latency: {end_at - start_at:.2f}s, "
-                f"dataset_id: {dataset_id}, document_ids: {document_ids}, "
-                f"segments: {len(segment_ids)}, image_files: {len(total_image_upload_file_ids)}, "
-                f"storage_files: {len(storage_keys_to_delete)}",
-                fg="green",
-            )
-        )
-    except Exception:
-        logger.exception(
-            "Batch clean documents failed for dataset_id: %s, document_ids: %s",
-            dataset_id,
-            document_ids,
-        )
+            logger.exception("Cleaned documents when documents deleted failed")

api/tasks/delete_segment_from_index_task.py

@@ -3,7 +3,6 @@ import time
 
 import click
 from celery import shared_task
-from sqlalchemy import delete
 
 from core.db.session_factory import session_factory
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
@@ -68,14 +67,8 @@ def delete_segment_from_index_task(
                 if segment_attachment_bindings:
                     attachment_ids = [binding.attachment_id for binding in segment_attachment_bindings]
                     index_processor.clean(dataset=dataset, node_ids=attachment_ids, with_keywords=False)
-                    segment_attachment_bind_ids = [i.id for i in segment_attachment_bindings]
-
-                    for i in range(0, len(segment_attachment_bind_ids), 1000):
-                        segment_attachment_bind_delete_stmt = delete(SegmentAttachmentBinding).where(
-                            SegmentAttachmentBinding.id.in_(segment_attachment_bind_ids[i : i + 1000])
-                        )
-                        session.execute(segment_attachment_bind_delete_stmt)
-
+                    for binding in segment_attachment_bindings:
+                        session.delete(binding)
                     # delete upload file
                     session.query(UploadFile).where(UploadFile.id.in_(attachment_ids)).delete(synchronize_session=False)
                     session.commit()

api/tasks/document_indexing_sync_task.py

@@ -28,7 +28,7 @@ def document_indexing_sync_task(dataset_id: str, document_id: str):
     logger.info(click.style(f"Start sync document: {document_id}", fg="green"))
     start_at = time.perf_counter()
 
-    with session_factory.create_session() as session, session.begin():
+    with session_factory.create_session() as session:
         document = session.query(Document).where(Document.id == document_id, Document.dataset_id == dataset_id).first()
 
         if not document:
@@ -68,6 +68,7 @@ def document_indexing_sync_task(dataset_id: str, document_id: str):
                 document.indexing_status = "error"
                 document.error = "Datasource credential not found. Please reconnect your Notion workspace."
                 document.stopped_at = naive_utc_now()
+                session.commit()
                 return
 
             loader = NotionExtractor(
@@ -84,6 +85,7 @@ def document_indexing_sync_task(dataset_id: str, document_id: str):
             if last_edited_time != page_edited_time:
                 document.indexing_status = "parsing"
                 document.processing_started_at = naive_utc_now()
+                session.commit()
 
                 # delete all document segment and index
                 try:

api/tasks/document_indexing_update_task.py

@@ -8,6 +8,7 @@ from sqlalchemy import delete, select
 from core.db.session_factory import session_factory
 from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
+from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
 from models.dataset import Dataset, Document, DocumentSegment
 
@@ -26,7 +27,7 @@ def document_indexing_update_task(dataset_id: str, document_id: str):
     logger.info(click.style(f"Start update document: {document_id}", fg="green"))
     start_at = time.perf_counter()
 
-    with session_factory.create_session() as session, session.begin():
+    with session_factory.create_session() as session:
         document = session.query(Document).where(Document.id == document_id, Document.dataset_id == dataset_id).first()
 
         if not document:
@@ -35,6 +36,7 @@ def document_indexing_update_task(dataset_id: str, document_id: str):
 
         document.indexing_status = "parsing"
         document.processing_started_at = naive_utc_now()
+        session.commit()
 
         # delete all document segment and index
         try:
@@ -54,7 +56,7 @@ def document_indexing_update_task(dataset_id: str, document_id: str):
                 segment_ids = [segment.id for segment in segments]
                 segment_delete_stmt = delete(DocumentSegment).where(DocumentSegment.id.in_(segment_ids))
                 session.execute(segment_delete_stmt)
-
+                db.session.commit()
             end_at = time.perf_counter()
             logger.info(
                 click.style(

api/tasks/process_tenant_plugin_autoupgrade_check_task.py

@@ -6,8 +6,8 @@ import typing
 import click
 from celery import shared_task
 
-from core.helper.marketplace import record_install_plugin_event
-from core.plugin.entities.marketplace import MarketplacePluginSnapshot
+from core.helper import marketplace
+from core.helper.marketplace import MarketplacePluginDeclaration
 from core.plugin.entities.plugin import PluginInstallationSource
 from core.plugin.impl.plugin import PluginInstaller
 from extensions.ext_redis import redis_client
@@ -16,7 +16,7 @@ from models.account import TenantPluginAutoUpgradeStrategy
 logger = logging.getLogger(__name__)
 
 RETRY_TIMES_OF_ONE_PLUGIN_IN_ONE_TENANT = 3
-CACHE_REDIS_KEY_PREFIX = "plugin_autoupgrade_check_task:cached_plugin_snapshot:"
+CACHE_REDIS_KEY_PREFIX = "plugin_autoupgrade_check_task:cached_plugin_manifests:"
 CACHE_REDIS_TTL = 60 * 60  # 1 hour
 
 
@@ -25,11 +25,11 @@ def _get_redis_cache_key(plugin_id: str) -> str:
     return f"{CACHE_REDIS_KEY_PREFIX}{plugin_id}"
 
 
-def _get_cached_manifest(plugin_id: str) -> typing.Union[MarketplacePluginSnapshot, None, bool]:
+def _get_cached_manifest(plugin_id: str) -> typing.Union[MarketplacePluginDeclaration, None, bool]:
     """
     Get cached plugin manifest from Redis.
     Returns:
-        - MarketplacePluginSnapshot: if found in cache
+        - MarketplacePluginDeclaration: if found in cache
         - None: if cached as not found (marketplace returned no result)
         - False: if not in cache at all
     """
@@ -43,31 +43,76 @@ def _get_cached_manifest(plugin_id: str) -> typing.Union[MarketplacePluginSnapsh
         if cached_json is None:
             return None
 
-        return MarketplacePluginSnapshot.model_validate(cached_json)
+        return MarketplacePluginDeclaration.model_validate(cached_json)
     except Exception:
         logger.exception("Failed to get cached manifest for plugin %s", plugin_id)
         return False
 
 
+def _set_cached_manifest(plugin_id: str, manifest: typing.Union[MarketplacePluginDeclaration, None]) -> None:
+    """
+    Cache plugin manifest in Redis.
+    Args:
+        plugin_id: The plugin ID
+        manifest: The manifest to cache, or None if not found in marketplace
+    """
+    try:
+        key = _get_redis_cache_key(plugin_id)
+        if manifest is None:
+            # Cache the fact that this plugin was not found
+            redis_client.setex(key, CACHE_REDIS_TTL, json.dumps(None))
+        else:
+            # Cache the manifest data
+            redis_client.setex(key, CACHE_REDIS_TTL, manifest.model_dump_json())
+    except Exception:
+        # If Redis fails, continue without caching
+        # traceback.print_exc()
+        logger.exception("Failed to set cached manifest for plugin %s", plugin_id)
+
+
 def marketplace_batch_fetch_plugin_manifests(
     plugin_ids_plain_list: list[str],
-) -> list[MarketplacePluginSnapshot]:
-    """
-    Fetch plugin manifests from Redis cache only.
-    This function assumes fetch_global_plugin_manifest() has been called
-    to pre-populate the cache with all marketplace plugins.
-    """
-    result: list[MarketplacePluginSnapshot] = []
+) -> list[MarketplacePluginDeclaration]:
+    """Fetch plugin manifests with Redis caching support."""
+    cached_manifests: dict[str, typing.Union[MarketplacePluginDeclaration, None]] = {}
+    not_cached_plugin_ids: list[str] = []
 
     # Check Redis cache for each plugin
     for plugin_id in plugin_ids_plain_list:
         cached_result = _get_cached_manifest(plugin_id)
-        if not isinstance(cached_result, MarketplacePluginSnapshot):
-            # cached_result is False (not in cache) or None (cached as not found)
-            logger.warning("plugin %s not found in cache, skipping", plugin_id)
-            continue
+        if cached_result is False:
+            # Not in cache, need to fetch
+            not_cached_plugin_ids.append(plugin_id)
+        else:
+            # Either found manifest or cached as None (not found in marketplace)
+            # At this point, cached_result is either MarketplacePluginDeclaration or None
+            if isinstance(cached_result, bool):
+                # This should never happen due to the if condition above, but for type safety
+                continue
+            cached_manifests[plugin_id] = cached_result
 
-        result.append(cached_result)
+    # Fetch uncached plugins from marketplace
+    if not_cached_plugin_ids:
+        manifests = marketplace.batch_fetch_plugin_manifests_ignore_deserialization_error(not_cached_plugin_ids)
+
+        # Cache the fetched manifests
+        for manifest in manifests:
+            cached_manifests[manifest.plugin_id] = manifest
+            _set_cached_manifest(manifest.plugin_id, manifest)
+
+        # Cache plugins that were not found in marketplace
+        fetched_plugin_ids = {manifest.plugin_id for manifest in manifests}
+        for plugin_id in not_cached_plugin_ids:
+            if plugin_id not in fetched_plugin_ids:
+                cached_manifests[plugin_id] = None
+                _set_cached_manifest(plugin_id, None)
+
+    # Build result list from cached manifests
+    result: list[MarketplacePluginDeclaration] = []
+    for plugin_id in plugin_ids_plain_list:
+        cached_manifest: typing.Union[MarketplacePluginDeclaration, None] = cached_manifests.get(plugin_id)
+        if cached_manifest is not None:
+            result.append(cached_manifest)
 
     return result
 
@@ -166,7 +211,7 @@ def process_tenant_plugin_autoupgrade_check_task(
                         # execute upgrade
                         new_unique_identifier = manifest.latest_package_identifier
 
-                        record_install_plugin_event(new_unique_identifier)
+                        marketplace.record_install_plugin_event(new_unique_identifier)
                         click.echo(
                             click.style(
                                 f"Upgrade plugin: {original_unique_identifier} -> {new_unique_identifier}",

api/tasks/remove_app_and_related_data_task.py

@@ -48,7 +48,6 @@ from models.workflow import (
     WorkflowArchiveLog,
 )
 from repositories.factory import DifyAPIRepositoryFactory
-from services.api_token_service import ApiTokenCache
 
 logger = logging.getLogger(__name__)
 
@@ -135,12 +134,6 @@ def _delete_app_mcp_servers(tenant_id: str, app_id: str):
 
 def _delete_app_api_tokens(tenant_id: str, app_id: str):
     def del_api_token(session, api_token_id: str):
-        # Fetch token details for cache invalidation
-        token_obj = session.query(ApiToken).where(ApiToken.id == api_token_id).first()
-        if token_obj:
-            # Invalidate cache before deletion
-            ApiTokenCache.delete(token_obj.token, token_obj.type)
-
         session.query(ApiToken).where(ApiToken.id == api_token_id).delete(synchronize_session=False)
 
     _delete_records(
@@ -266,8 +259,8 @@ def _delete_app_workflow_app_logs(tenant_id: str, app_id: str):
 
 
 def _delete_app_workflow_archive_logs(tenant_id: str, app_id: str):
-    def del_workflow_archive_log(session, workflow_archive_log_id: str):
-        session.query(WorkflowArchiveLog).where(WorkflowArchiveLog.id == workflow_archive_log_id).delete(
+    def del_workflow_archive_log(workflow_archive_log_id: str):
+        db.session.query(WorkflowArchiveLog).where(WorkflowArchiveLog.id == workflow_archive_log_id).delete(
             synchronize_session=False
         )
 
@@ -427,7 +420,7 @@ def delete_draft_variables_batch(app_id: str, batch_size: int = 1000) -> int:
     total_files_deleted = 0
 
     while True:
-        with session_factory.create_session() as session, session.begin():
+        with session_factory.create_session() as session:
             # Get a batch of draft variable IDs along with their file_ids
             query_sql = """
                 SELECT id, file_id FROM workflow_draft_variables

api/tests/integration_tests/libs/test_api_token_cache_integration.py

@@ -1,375 +0,0 @@
-"""
-Integration tests for API Token Cache with Redis.
-
-These tests require:
-- Redis server running
-- Test database configured
-"""
-
-import time
-from datetime import datetime, timedelta
-from unittest.mock import patch
-
-import pytest
-
-from extensions.ext_redis import redis_client
-from models.model import ApiToken
-from services.api_token_service import ApiTokenCache, CachedApiToken
-
-
-class TestApiTokenCacheRedisIntegration:
-    """Integration tests with real Redis."""
-
-    def setup_method(self):
-        """Setup test fixtures and clean Redis."""
-        self.test_token = "test-integration-token-123"
-        self.test_scope = "app"
-        self.cache_key = f"api_token:{self.test_scope}:{self.test_token}"
-
-        # Clean up any existing test data
-        self._cleanup()
-
-    def teardown_method(self):
-        """Cleanup test data from Redis."""
-        self._cleanup()
-
-    def _cleanup(self):
-        """Remove test data from Redis."""
-        try:
-            redis_client.delete(self.cache_key)
-            redis_client.delete(ApiTokenCache._make_tenant_index_key("test-tenant-id"))
-            redis_client.delete(ApiTokenCache.make_active_key(self.test_token, self.test_scope))
-        except Exception:
-            pass  # Ignore cleanup errors
-
-    def test_cache_set_and_get_with_real_redis(self):
-        """Test cache set and get operations with real Redis."""
-        from unittest.mock import MagicMock
-
-        mock_token = MagicMock()
-        mock_token.id = "test-id-123"
-        mock_token.app_id = "test-app-456"
-        mock_token.tenant_id = "test-tenant-789"
-        mock_token.type = "app"
-        mock_token.token = self.test_token
-        mock_token.last_used_at = datetime.now()
-        mock_token.created_at = datetime.now() - timedelta(days=30)
-
-        # Set in cache
-        result = ApiTokenCache.set(self.test_token, self.test_scope, mock_token)
-        assert result is True
-
-        # Verify in Redis
-        cached_data = redis_client.get(self.cache_key)
-        assert cached_data is not None
-
-        # Get from cache
-        cached_token = ApiTokenCache.get(self.test_token, self.test_scope)
-        assert cached_token is not None
-        assert isinstance(cached_token, CachedApiToken)
-        assert cached_token.id == "test-id-123"
-        assert cached_token.app_id == "test-app-456"
-        assert cached_token.tenant_id == "test-tenant-789"
-        assert cached_token.type == "app"
-        assert cached_token.token == self.test_token
-
-    def test_cache_ttl_with_real_redis(self):
-        """Test cache TTL is set correctly."""
-        from unittest.mock import MagicMock
-
-        mock_token = MagicMock()
-        mock_token.id = "test-id"
-        mock_token.app_id = "test-app"
-        mock_token.tenant_id = "test-tenant"
-        mock_token.type = "app"
-        mock_token.token = self.test_token
-        mock_token.last_used_at = None
-        mock_token.created_at = datetime.now()
-
-        ApiTokenCache.set(self.test_token, self.test_scope, mock_token)
-
-        ttl = redis_client.ttl(self.cache_key)
-        assert 595 <= ttl <= 600  # Should be around 600 seconds (10 minutes)
-
-    def test_cache_null_value_for_invalid_token(self):
-        """Test caching null value for invalid tokens."""
-        result = ApiTokenCache.set(self.test_token, self.test_scope, None)
-        assert result is True
-
-        cached_data = redis_client.get(self.cache_key)
-        assert cached_data == b"null"
-
-        cached_token = ApiTokenCache.get(self.test_token, self.test_scope)
-        assert cached_token is None
-
-        ttl = redis_client.ttl(self.cache_key)
-        assert 55 <= ttl <= 60
-
-    def test_cache_delete_with_real_redis(self):
-        """Test cache deletion with real Redis."""
-        from unittest.mock import MagicMock
-
-        mock_token = MagicMock()
-        mock_token.id = "test-id"
-        mock_token.app_id = "test-app"
-        mock_token.tenant_id = "test-tenant"
-        mock_token.type = "app"
-        mock_token.token = self.test_token
-        mock_token.last_used_at = None
-        mock_token.created_at = datetime.now()
-
-        ApiTokenCache.set(self.test_token, self.test_scope, mock_token)
-        assert redis_client.exists(self.cache_key) == 1
-
-        result = ApiTokenCache.delete(self.test_token, self.test_scope)
-        assert result is True
-        assert redis_client.exists(self.cache_key) == 0
-
-    def test_tenant_index_creation(self):
-        """Test tenant index is created when caching token."""
-        from unittest.mock import MagicMock
-
-        tenant_id = "test-tenant-id"
-        mock_token = MagicMock()
-        mock_token.id = "test-id"
-        mock_token.app_id = "test-app"
-        mock_token.tenant_id = tenant_id
-        mock_token.type = "app"
-        mock_token.token = self.test_token
-        mock_token.last_used_at = None
-        mock_token.created_at = datetime.now()
-
-        ApiTokenCache.set(self.test_token, self.test_scope, mock_token)
-
-        index_key = ApiTokenCache._make_tenant_index_key(tenant_id)
-        assert redis_client.exists(index_key) == 1
-
-        members = redis_client.smembers(index_key)
-        cache_keys = [m.decode("utf-8") if isinstance(m, bytes) else m for m in members]
-        assert self.cache_key in cache_keys
-
-    def test_invalidate_by_tenant_via_index(self):
-        """Test tenant-wide cache invalidation using index (fast path)."""
-        from unittest.mock import MagicMock
-
-        tenant_id = "test-tenant-id"
-
-        for i in range(3):
-            token_value = f"test-token-{i}"
-            mock_token = MagicMock()
-            mock_token.id = f"test-id-{i}"
-            mock_token.app_id = "test-app"
-            mock_token.tenant_id = tenant_id
-            mock_token.type = "app"
-            mock_token.token = token_value
-            mock_token.last_used_at = None
-            mock_token.created_at = datetime.now()
-
-            ApiTokenCache.set(token_value, "app", mock_token)
-
-        for i in range(3):
-            key = f"api_token:app:test-token-{i}"
-            assert redis_client.exists(key) == 1
-
-        result = ApiTokenCache.invalidate_by_tenant(tenant_id)
-        assert result is True
-
-        for i in range(3):
-            key = f"api_token:app:test-token-{i}"
-            assert redis_client.exists(key) == 0
-
-        assert redis_client.exists(ApiTokenCache._make_tenant_index_key(tenant_id)) == 0
-
-    def test_concurrent_cache_access(self):
-        """Test concurrent cache access doesn't cause issues."""
-        import concurrent.futures
-        from unittest.mock import MagicMock
-
-        mock_token = MagicMock()
-        mock_token.id = "test-id"
-        mock_token.app_id = "test-app"
-        mock_token.tenant_id = "test-tenant"
-        mock_token.type = "app"
-        mock_token.token = self.test_token
-        mock_token.last_used_at = None
-        mock_token.created_at = datetime.now()
-
-        ApiTokenCache.set(self.test_token, self.test_scope, mock_token)
-
-        def get_from_cache():
-            return ApiTokenCache.get(self.test_token, self.test_scope)
-
-        with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
-            futures = [executor.submit(get_from_cache) for _ in range(50)]
-            results = [f.result() for f in concurrent.futures.as_completed(futures)]
-
-        assert len(results) == 50
-        assert all(r is not None for r in results)
-        assert all(isinstance(r, CachedApiToken) for r in results)
-
-
-class TestTokenUsageRecording:
-    """Tests for recording token usage in Redis (batch update approach)."""
-
-    def setup_method(self):
-        self.test_token = "test-usage-token"
-        self.test_scope = "app"
-        self.active_key = ApiTokenCache.make_active_key(self.test_token, self.test_scope)
-
-    def teardown_method(self):
-        try:
-            redis_client.delete(self.active_key)
-        except Exception:
-            pass
-
-    def test_record_token_usage_sets_redis_key(self):
-        """Test that record_token_usage writes an active key to Redis."""
-        from services.api_token_service import record_token_usage
-
-        record_token_usage(self.test_token, self.test_scope)
-
-        # Key should exist
-        assert redis_client.exists(self.active_key) == 1
-
-        # Value should be an ISO timestamp
-        value = redis_client.get(self.active_key)
-        if isinstance(value, bytes):
-            value = value.decode("utf-8")
-        datetime.fromisoformat(value)  # Should not raise
-
-    def test_record_token_usage_has_ttl(self):
-        """Test that active keys have a TTL as safety net."""
-        from services.api_token_service import record_token_usage
-
-        record_token_usage(self.test_token, self.test_scope)
-
-        ttl = redis_client.ttl(self.active_key)
-        assert 3595 <= ttl <= 3600  # ~1 hour
-
-    def test_record_token_usage_overwrites(self):
-        """Test that repeated calls overwrite the same key (no accumulation)."""
-        from services.api_token_service import record_token_usage
-
-        record_token_usage(self.test_token, self.test_scope)
-        first_value = redis_client.get(self.active_key)
-
-        time.sleep(0.01)  # Tiny delay so timestamp differs
-
-        record_token_usage(self.test_token, self.test_scope)
-        second_value = redis_client.get(self.active_key)
-
-        # Key count should still be 1 (overwritten, not accumulated)
-        assert redis_client.exists(self.active_key) == 1
-
-
-class TestEndToEndCacheFlow:
-    """End-to-end integration test for complete cache flow."""
-
-    @pytest.mark.usefixtures("db_session")
-    def test_complete_flow_cache_miss_then_hit(self, db_session):
-        """
-        Test complete flow:
-        1. First request (cache miss) -> query DB -> cache result
-        2. Second request (cache hit) -> return from cache
-        3. Verify Redis state
-        """
-        test_token_value = "test-e2e-token"
-        test_scope = "app"
-
-        test_token = ApiToken()
-        test_token.id = "test-e2e-id"
-        test_token.token = test_token_value
-        test_token.type = test_scope
-        test_token.app_id = "test-app"
-        test_token.tenant_id = "test-tenant"
-        test_token.last_used_at = None
-        test_token.created_at = datetime.now()
-
-        db_session.add(test_token)
-        db_session.commit()
-
-        try:
-            # Step 1: Cache miss - set token in cache
-            ApiTokenCache.set(test_token_value, test_scope, test_token)
-
-            cache_key = f"api_token:{test_scope}:{test_token_value}"
-            assert redis_client.exists(cache_key) == 1
-
-            # Step 2: Cache hit - get from cache
-            cached_token = ApiTokenCache.get(test_token_value, test_scope)
-            assert cached_token is not None
-            assert cached_token.id == test_token.id
-            assert cached_token.token == test_token_value
-
-            # Step 3: Verify tenant index
-            index_key = ApiTokenCache._make_tenant_index_key(test_token.tenant_id)
-            assert redis_client.exists(index_key) == 1
-            assert cache_key.encode() in redis_client.smembers(index_key)
-
-            # Step 4: Delete and verify cleanup
-            ApiTokenCache.delete(test_token_value, test_scope)
-            assert redis_client.exists(cache_key) == 0
-            assert cache_key.encode() not in redis_client.smembers(index_key)
-
-        finally:
-            db_session.delete(test_token)
-            db_session.commit()
-            redis_client.delete(f"api_token:{test_scope}:{test_token_value}")
-            redis_client.delete(ApiTokenCache._make_tenant_index_key(test_token.tenant_id))
-
-    def test_high_concurrency_simulation(self):
-        """Simulate high concurrency access to cache."""
-        import concurrent.futures
-        from unittest.mock import MagicMock
-
-        test_token_value = "test-concurrent-token"
-        test_scope = "app"
-
-        mock_token = MagicMock()
-        mock_token.id = "concurrent-id"
-        mock_token.app_id = "test-app"
-        mock_token.tenant_id = "test-tenant"
-        mock_token.type = test_scope
-        mock_token.token = test_token_value
-        mock_token.last_used_at = datetime.now()
-        mock_token.created_at = datetime.now()
-
-        ApiTokenCache.set(test_token_value, test_scope, mock_token)
-
-        try:
-
-            def read_cache():
-                return ApiTokenCache.get(test_token_value, test_scope)
-
-            start_time = time.time()
-            with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
-                futures = [executor.submit(read_cache) for _ in range(100)]
-                results = [f.result() for f in concurrent.futures.as_completed(futures)]
-            elapsed = time.time() - start_time
-
-            assert len(results) == 100
-            assert all(r is not None for r in results)
-
-            assert elapsed < 1.0, f"Too slow: {elapsed}s for 100 cache reads"
-
-        finally:
-            ApiTokenCache.delete(test_token_value, test_scope)
-            redis_client.delete(ApiTokenCache._make_tenant_index_key(mock_token.tenant_id))
-
-
-class TestRedisFailover:
-    """Test behavior when Redis is unavailable."""
-
-    @patch("services.api_token_service.redis_client")
-    def test_graceful_degradation_when_redis_fails(self, mock_redis):
-        """Test system degrades gracefully when Redis is unavailable."""
-        from redis import RedisError
-
-        mock_redis.get.side_effect = RedisError("Connection failed")
-        mock_redis.setex.side_effect = RedisError("Connection failed")
-
-        result_get = ApiTokenCache.get("test-token", "app")
-        assert result_get is None
-
-        result_set = ApiTokenCache.set("test-token", "app", None)
-        assert result_set is False

api/tests/integration_tests/tasks/test_remove_app_and_related_data_task.py

@@ -10,10 +10,7 @@ from models import Tenant
 from models.enums import CreatorUserRole
 from models.model import App, UploadFile
 from models.workflow import WorkflowDraftVariable, WorkflowDraftVariableFile
-from tasks.remove_app_and_related_data_task import (
-    _delete_draft_variables,
-    delete_draft_variables_batch,
-)
+from tasks.remove_app_and_related_data_task import _delete_draft_variables, delete_draft_variables_batch
 
 
 @pytest.fixture
@@ -300,18 +297,12 @@ class TestDeleteDraftVariablesWithOffloadIntegration:
     def test_delete_draft_variables_with_offload_data(self, mock_storage, setup_offload_test_data):
         data = setup_offload_test_data
         app_id = data["app"].id
-        upload_file_ids = [uf.id for uf in data["upload_files"]]
-        variable_file_ids = [vf.id for vf in data["variable_files"]]
         mock_storage.delete.return_value = None
 
         with session_factory.create_session() as session:
             draft_vars_before = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-            var_files_before = (
-                session.query(WorkflowDraftVariableFile)
-                .where(WorkflowDraftVariableFile.id.in_(variable_file_ids))
-                .count()
-            )
-            upload_files_before = session.query(UploadFile).where(UploadFile.id.in_(upload_file_ids)).count()
+            var_files_before = session.query(WorkflowDraftVariableFile).count()
+            upload_files_before = session.query(UploadFile).count()
         assert draft_vars_before == 3
         assert var_files_before == 2
         assert upload_files_before == 2
@@ -324,12 +315,8 @@ class TestDeleteDraftVariablesWithOffloadIntegration:
         assert draft_vars_after == 0
 
         with session_factory.create_session() as session:
-            var_files_after = (
-                session.query(WorkflowDraftVariableFile)
-                .where(WorkflowDraftVariableFile.id.in_(variable_file_ids))
-                .count()
-            )
-            upload_files_after = session.query(UploadFile).where(UploadFile.id.in_(upload_file_ids)).count()
+            var_files_after = session.query(WorkflowDraftVariableFile).count()
+            upload_files_after = session.query(UploadFile).count()
         assert var_files_after == 0
         assert upload_files_after == 0
 
@@ -342,8 +329,6 @@ class TestDeleteDraftVariablesWithOffloadIntegration:
     def test_delete_draft_variables_storage_failure_continues_cleanup(self, mock_storage, setup_offload_test_data):
         data = setup_offload_test_data
         app_id = data["app"].id
-        upload_file_ids = [uf.id for uf in data["upload_files"]]
-        variable_file_ids = [vf.id for vf in data["variable_files"]]
         mock_storage.delete.side_effect = [Exception("Storage error"), None]
 
         deleted_count = delete_draft_variables_batch(app_id, batch_size=10)
@@ -354,12 +339,8 @@ class TestDeleteDraftVariablesWithOffloadIntegration:
         assert draft_vars_after == 0
 
         with session_factory.create_session() as session:
-            var_files_after = (
-                session.query(WorkflowDraftVariableFile)
-                .where(WorkflowDraftVariableFile.id.in_(variable_file_ids))
-                .count()
-            )
-            upload_files_after = session.query(UploadFile).where(UploadFile.id.in_(upload_file_ids)).count()
+            var_files_after = session.query(WorkflowDraftVariableFile).count()
+            upload_files_after = session.query(UploadFile).count()
         assert var_files_after == 0
         assert upload_files_after == 0
 
@@ -414,275 +395,3 @@ class TestDeleteDraftVariablesWithOffloadIntegration:
                 if app2_obj:
                     session.delete(app2_obj)
                 session.commit()
-
-
-class TestDeleteDraftVariablesSessionCommit:
-    """Test suite to verify session commit behavior in delete_draft_variables_batch."""
-
-    @pytest.fixture
-    def setup_offload_test_data(self, app_and_tenant):
-        """Create test data with offload files for session commit tests."""
-        from core.variables.types import SegmentType
-        from libs.datetime_utils import naive_utc_now
-
-        tenant, app = app_and_tenant
-
-        with session_factory.create_session() as session:
-            upload_file1 = UploadFile(
-                tenant_id=tenant.id,
-                storage_type="local",
-                key="test/file1.json",
-                name="file1.json",
-                size=1024,
-                extension="json",
-                mime_type="application/json",
-                created_by_role=CreatorUserRole.ACCOUNT,
-                created_by=str(uuid.uuid4()),
-                created_at=naive_utc_now(),
-                used=False,
-            )
-            upload_file2 = UploadFile(
-                tenant_id=tenant.id,
-                storage_type="local",
-                key="test/file2.json",
-                name="file2.json",
-                size=2048,
-                extension="json",
-                mime_type="application/json",
-                created_by_role=CreatorUserRole.ACCOUNT,
-                created_by=str(uuid.uuid4()),
-                created_at=naive_utc_now(),
-                used=False,
-            )
-            session.add(upload_file1)
-            session.add(upload_file2)
-            session.flush()
-
-            var_file1 = WorkflowDraftVariableFile(
-                tenant_id=tenant.id,
-                app_id=app.id,
-                user_id=str(uuid.uuid4()),
-                upload_file_id=upload_file1.id,
-                size=1024,
-                length=10,
-                value_type=SegmentType.STRING,
-            )
-            var_file2 = WorkflowDraftVariableFile(
-                tenant_id=tenant.id,
-                app_id=app.id,
-                user_id=str(uuid.uuid4()),
-                upload_file_id=upload_file2.id,
-                size=2048,
-                length=20,
-                value_type=SegmentType.OBJECT,
-            )
-            session.add(var_file1)
-            session.add(var_file2)
-            session.flush()
-
-            draft_var1 = WorkflowDraftVariable.new_node_variable(
-                app_id=app.id,
-                node_id="node_1",
-                name="large_var_1",
-                value=StringSegment(value="truncated..."),
-                node_execution_id=str(uuid.uuid4()),
-                file_id=var_file1.id,
-            )
-            draft_var2 = WorkflowDraftVariable.new_node_variable(
-                app_id=app.id,
-                node_id="node_2",
-                name="large_var_2",
-                value=StringSegment(value="truncated..."),
-                node_execution_id=str(uuid.uuid4()),
-                file_id=var_file2.id,
-            )
-            draft_var3 = WorkflowDraftVariable.new_node_variable(
-                app_id=app.id,
-                node_id="node_3",
-                name="regular_var",
-                value=StringSegment(value="regular_value"),
-                node_execution_id=str(uuid.uuid4()),
-            )
-            session.add(draft_var1)
-            session.add(draft_var2)
-            session.add(draft_var3)
-            session.commit()
-
-            data = {
-                "app": app,
-                "tenant": tenant,
-                "upload_files": [upload_file1, upload_file2],
-                "variable_files": [var_file1, var_file2],
-                "draft_variables": [draft_var1, draft_var2, draft_var3],
-            }
-
-        yield data
-
-        with session_factory.create_session() as session:
-            for table, ids in [
-                (WorkflowDraftVariable, [v.id for v in data["draft_variables"]]),
-                (WorkflowDraftVariableFile, [vf.id for vf in data["variable_files"]]),
-                (UploadFile, [uf.id for uf in data["upload_files"]]),
-            ]:
-                cleanup_query = delete(table).where(table.id.in_(ids)).execution_options(synchronize_session=False)
-                session.execute(cleanup_query)
-            session.commit()
-
-    @pytest.fixture
-    def setup_commit_test_data(self, app_and_tenant):
-        """Create test data for session commit tests."""
-        tenant, app = app_and_tenant
-        variable_ids: list[str] = []
-
-        with session_factory.create_session() as session:
-            variables = []
-            for i in range(10):
-                var = WorkflowDraftVariable.new_node_variable(
-                    app_id=app.id,
-                    node_id=f"node_{i}",
-                    name=f"var_{i}",
-                    value=StringSegment(value="test_value"),
-                    node_execution_id=str(uuid.uuid4()),
-                )
-                session.add(var)
-                variables.append(var)
-            session.commit()
-            variable_ids = [v.id for v in variables]
-
-        yield {
-            "app": app,
-            "tenant": tenant,
-            "variable_ids": variable_ids,
-        }
-
-        with session_factory.create_session() as session:
-            cleanup_query = (
-                delete(WorkflowDraftVariable)
-                .where(WorkflowDraftVariable.id.in_(variable_ids))
-                .execution_options(synchronize_session=False)
-            )
-            session.execute(cleanup_query)
-            session.commit()
-
-    def test_session_commit_is_called_after_each_batch(self, setup_commit_test_data):
-        """Test that session.begin() is used for automatic transaction management."""
-        data = setup_commit_test_data
-        app_id = data["app"].id
-
-        # Since session.begin() is used, the transaction is automatically committed
-        # when the with block exits successfully. We verify this by checking that
-        # data is actually persisted.
-        deleted_count = delete_draft_variables_batch(app_id, batch_size=3)
-
-        # Verify all data was deleted (proves transaction was committed)
-        with session_factory.create_session() as session:
-            remaining_count = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-
-        assert deleted_count == 10
-        assert remaining_count == 0
-
-    def test_data_persisted_after_batch_deletion(self, setup_commit_test_data):
-        """Test that data is actually persisted to database after batch deletion with commits."""
-        data = setup_commit_test_data
-        app_id = data["app"].id
-        variable_ids = data["variable_ids"]
-
-        # Verify initial state
-        with session_factory.create_session() as session:
-            initial_count = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-        assert initial_count == 10
-
-        # Perform deletion with small batch size to force multiple commits
-        deleted_count = delete_draft_variables_batch(app_id, batch_size=3)
-
-        assert deleted_count == 10
-
-        # Verify all data is deleted in a new session (proves commits worked)
-        with session_factory.create_session() as session:
-            final_count = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-        assert final_count == 0
-
-        # Verify specific IDs are deleted
-        with session_factory.create_session() as session:
-            remaining_vars = (
-                session.query(WorkflowDraftVariable).where(WorkflowDraftVariable.id.in_(variable_ids)).count()
-            )
-        assert remaining_vars == 0
-
-    def test_session_commit_with_empty_dataset(self, setup_commit_test_data):
-        """Test session behavior when deleting from an empty dataset."""
-        nonexistent_app_id = str(uuid.uuid4())
-
-        # Should not raise any errors and should return 0
-        deleted_count = delete_draft_variables_batch(nonexistent_app_id, batch_size=10)
-        assert deleted_count == 0
-
-    def test_session_commit_with_single_batch(self, setup_commit_test_data):
-        """Test that commit happens correctly when all data fits in a single batch."""
-        data = setup_commit_test_data
-        app_id = data["app"].id
-
-        with session_factory.create_session() as session:
-            initial_count = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-        assert initial_count == 10
-
-        # Delete all in a single batch
-        deleted_count = delete_draft_variables_batch(app_id, batch_size=100)
-        assert deleted_count == 10
-
-        # Verify data is persisted
-        with session_factory.create_session() as session:
-            final_count = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-        assert final_count == 0
-
-    def test_invalid_batch_size_raises_error(self, setup_commit_test_data):
-        """Test that invalid batch size raises ValueError."""
-        data = setup_commit_test_data
-        app_id = data["app"].id
-
-        with pytest.raises(ValueError, match="batch_size must be positive"):
-            delete_draft_variables_batch(app_id, batch_size=0)
-
-        with pytest.raises(ValueError, match="batch_size must be positive"):
-            delete_draft_variables_batch(app_id, batch_size=-1)
-
-    @patch("extensions.ext_storage.storage")
-    def test_session_commit_with_offload_data_cleanup(self, mock_storage, setup_offload_test_data):
-        """Test that session commits correctly when cleaning up offload data."""
-        data = setup_offload_test_data
-        app_id = data["app"].id
-        upload_file_ids = [uf.id for uf in data["upload_files"]]
-        mock_storage.delete.return_value = None
-
-        # Verify initial state
-        with session_factory.create_session() as session:
-            draft_vars_before = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-            var_files_before = (
-                session.query(WorkflowDraftVariableFile)
-                .where(WorkflowDraftVariableFile.id.in_([vf.id for vf in data["variable_files"]]))
-                .count()
-            )
-            upload_files_before = session.query(UploadFile).where(UploadFile.id.in_(upload_file_ids)).count()
-        assert draft_vars_before == 3
-        assert var_files_before == 2
-        assert upload_files_before == 2
-
-        # Delete variables with offload data
-        deleted_count = delete_draft_variables_batch(app_id, batch_size=10)
-        assert deleted_count == 3
-
-        # Verify all data is persisted (deleted) in new session
-        with session_factory.create_session() as session:
-            draft_vars_after = session.query(WorkflowDraftVariable).filter_by(app_id=app_id).count()
-            var_files_after = (
-                session.query(WorkflowDraftVariableFile)
-                .where(WorkflowDraftVariableFile.id.in_([vf.id for vf in data["variable_files"]]))
-                .count()
-            )
-            upload_files_after = session.query(UploadFile).where(UploadFile.id.in_(upload_file_ids)).count()
-        assert draft_vars_after == 0
-        assert var_files_after == 0
-        assert upload_files_after == 0
-
-        # Verify storage cleanup was called
-        assert mock_storage.delete.call_count == 2

api/tests/test_containers_integration_tests/services/test_account_service.py

@@ -1016,7 +1016,7 @@ class TestAccountService:
 
     def test_delete_account(self, db_session_with_containers, mock_external_service_dependencies):
         """
-        Test account deletion (should add task to queue and sync to enterprise).
+        Test account deletion (should add task to queue).
         """
         fake = Faker()
         email = fake.email()
@@ -1034,18 +1034,10 @@ class TestAccountService:
             password=password,
         )
 
-        with (
-            patch("services.account_service.delete_account_task") as mock_delete_task,
-            patch("services.enterprise.account_deletion_sync.sync_account_deletion") as mock_sync,
-        ):
-            mock_sync.return_value = True
-
+        with patch("services.account_service.delete_account_task") as mock_delete_task:
             # Delete account
             AccountService.delete_account(account)
 
-            # Verify sync was called
-            mock_sync.assert_called_once_with(account_id=account.id, source="account_deleted")
-
             # Verify task was added to queue
             mock_delete_task.delay.assert_called_once_with(account.id)
 
@@ -1724,7 +1716,7 @@ class TestTenantService:
 
     def test_remove_member_from_tenant_success(self, db_session_with_containers, mock_external_service_dependencies):
         """
-        Test successful member removal from tenant (should sync to enterprise).
+        Test successful member removal from tenant.
         """
         fake = Faker()
         tenant_name = fake.company()
@@ -1759,15 +1751,7 @@ class TestTenantService:
         TenantService.create_tenant_member(tenant, member_account, role="normal")
 
         # Remove member
-        with patch("services.enterprise.account_deletion_sync.sync_workspace_member_removal") as mock_sync:
-            mock_sync.return_value = True
-
-            TenantService.remove_member_from_tenant(tenant, member_account, owner_account)
-
-            # Verify sync was called
-            mock_sync.assert_called_once_with(
-                workspace_id=tenant.id, member_id=member_account.id, source="workspace_member_removed"
-            )
+        TenantService.remove_member_from_tenant(tenant, member_account, owner_account)
 
         # Verify member was removed
         from extensions.ext_database import db

api/tests/test_containers_integration_tests/tasks/test_document_indexing_update_task.py

@@ -1,182 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import pytest
-from faker import Faker
-
-from models import Account, Tenant, TenantAccountJoin, TenantAccountRole
-from models.dataset import Dataset, Document, DocumentSegment
-from tasks.document_indexing_update_task import document_indexing_update_task
-
-
-class TestDocumentIndexingUpdateTask:
-    @pytest.fixture
-    def mock_external_dependencies(self):
-        """Patch external collaborators used by the update task.
-        - IndexProcessorFactory.init_index_processor().clean(...)
-        - IndexingRunner.run([...])
-        """
-        with (
-            patch("tasks.document_indexing_update_task.IndexProcessorFactory") as mock_factory,
-            patch("tasks.document_indexing_update_task.IndexingRunner") as mock_runner,
-        ):
-            processor_instance = MagicMock()
-            mock_factory.return_value.init_index_processor.return_value = processor_instance
-
-            runner_instance = MagicMock()
-            mock_runner.return_value = runner_instance
-
-            yield {
-                "factory": mock_factory,
-                "processor": processor_instance,
-                "runner": mock_runner,
-                "runner_instance": runner_instance,
-            }
-
-    def _create_dataset_document_with_segments(self, db_session_with_containers, *, segment_count: int = 2):
-        fake = Faker()
-
-        # Account and tenant
-        account = Account(
-            email=fake.email(),
-            name=fake.name(),
-            interface_language="en-US",
-            status="active",
-        )
-        db_session_with_containers.add(account)
-        db_session_with_containers.commit()
-
-        tenant = Tenant(name=fake.company(), status="normal")
-        db_session_with_containers.add(tenant)
-        db_session_with_containers.commit()
-
-        join = TenantAccountJoin(
-            tenant_id=tenant.id,
-            account_id=account.id,
-            role=TenantAccountRole.OWNER,
-            current=True,
-        )
-        db_session_with_containers.add(join)
-        db_session_with_containers.commit()
-
-        # Dataset and document
-        dataset = Dataset(
-            tenant_id=tenant.id,
-            name=fake.company(),
-            description=fake.text(max_nb_chars=64),
-            data_source_type="upload_file",
-            indexing_technique="high_quality",
-            created_by=account.id,
-        )
-        db_session_with_containers.add(dataset)
-        db_session_with_containers.commit()
-
-        document = Document(
-            tenant_id=tenant.id,
-            dataset_id=dataset.id,
-            position=0,
-            data_source_type="upload_file",
-            batch="test_batch",
-            name=fake.file_name(),
-            created_from="upload_file",
-            created_by=account.id,
-            indexing_status="waiting",
-            enabled=True,
-            doc_form="text_model",
-        )
-        db_session_with_containers.add(document)
-        db_session_with_containers.commit()
-
-        # Segments
-        node_ids = []
-        for i in range(segment_count):
-            node_id = f"node-{i + 1}"
-            seg = DocumentSegment(
-                tenant_id=tenant.id,
-                dataset_id=dataset.id,
-                document_id=document.id,
-                position=i,
-                content=fake.text(max_nb_chars=32),
-                answer=None,
-                word_count=10,
-                tokens=5,
-                index_node_id=node_id,
-                status="completed",
-                created_by=account.id,
-            )
-            db_session_with_containers.add(seg)
-            node_ids.append(node_id)
-        db_session_with_containers.commit()
-
-        # Refresh to ensure ORM state
-        db_session_with_containers.refresh(dataset)
-        db_session_with_containers.refresh(document)
-
-        return dataset, document, node_ids
-
-    def test_cleans_segments_and_reindexes(self, db_session_with_containers, mock_external_dependencies):
-        dataset, document, node_ids = self._create_dataset_document_with_segments(db_session_with_containers)
-
-        # Act
-        document_indexing_update_task(dataset.id, document.id)
-
-        # Ensure we see committed changes from another session
-        db_session_with_containers.expire_all()
-
-        # Assert document status updated before reindex
-        updated = db_session_with_containers.query(Document).where(Document.id == document.id).first()
-        assert updated.indexing_status == "parsing"
-        assert updated.processing_started_at is not None
-
-        # Segments should be deleted
-        remaining = (
-            db_session_with_containers.query(DocumentSegment).where(DocumentSegment.document_id == document.id).count()
-        )
-        assert remaining == 0
-
-        # Assert index processor clean was called with expected args
-        clean_call = mock_external_dependencies["processor"].clean.call_args
-        assert clean_call is not None
-        args, kwargs = clean_call
-        # args[0] is a Dataset instance (from another session) — validate by id
-        assert getattr(args[0], "id", None) == dataset.id
-        # args[1] should contain our node_ids
-        assert set(args[1]) == set(node_ids)
-        assert kwargs.get("with_keywords") is True
-        assert kwargs.get("delete_child_chunks") is True
-
-        # Assert indexing runner invoked with the updated document
-        run_call = mock_external_dependencies["runner_instance"].run.call_args
-        assert run_call is not None
-        run_docs = run_call[0][0]
-        assert len(run_docs) == 1
-        first = run_docs[0]
-        assert getattr(first, "id", None) == document.id
-
-    def test_clean_error_is_logged_and_indexing_continues(self, db_session_with_containers, mock_external_dependencies):
-        dataset, document, node_ids = self._create_dataset_document_with_segments(db_session_with_containers)
-
-        # Force clean to raise; task should continue to indexing
-        mock_external_dependencies["processor"].clean.side_effect = Exception("boom")
-
-        document_indexing_update_task(dataset.id, document.id)
-
-        # Ensure we see committed changes from another session
-        db_session_with_containers.expire_all()
-
-        # Indexing should still be triggered
-        mock_external_dependencies["runner_instance"].run.assert_called_once()
-
-        # Segments should remain (since clean failed before DB delete)
-        remaining = (
-            db_session_with_containers.query(DocumentSegment).where(DocumentSegment.document_id == document.id).count()
-        )
-        assert remaining > 0
-
-    def test_document_not_found_noop(self, db_session_with_containers, mock_external_dependencies):
-        fake = Faker()
-        # Act with non-existent document id
-        document_indexing_update_task(dataset_id=fake.uuid4(), document_id=fake.uuid4())
-
-        # Neither processor nor runner should be called
-        mock_external_dependencies["processor"].clean.assert_not_called()
-        mock_external_dependencies["runner_instance"].run.assert_not_called()

api/tests/unit_tests/core/llm_generator/test_graph_builder.py

@@ -0,0 +1,400 @@
+"""
+Unit tests for GraphBuilder.
+
+Tests the automatic graph construction from node lists with dependency declarations.
+"""
+
+import pytest
+
+from core.workflow.generator.utils.graph_builder import (
+    CyclicDependencyError,
+    GraphBuilder,
+)
+
+
+class TestGraphBuilderBasic:
+    """Basic functionality tests."""
+
+    def test_empty_nodes_creates_minimal_workflow(self):
+        """Empty node list creates start -> end workflow."""
+        result_nodes, result_edges = GraphBuilder.build_graph([])
+
+        assert len(result_nodes) == 2
+        assert result_nodes[0]["type"] == "start"
+        assert result_nodes[1]["type"] == "end"
+        assert len(result_edges) == 1
+        assert result_edges[0]["source"] == "start"
+        assert result_edges[0]["target"] == "end"
+
+    def test_simple_linear_workflow(self):
+        """Simple linear workflow: start -> fetch -> process -> end."""
+        nodes = [
+            {"id": "fetch", "type": "http-request", "depends_on": []},
+            {"id": "process", "type": "llm", "depends_on": ["fetch"]},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should have: start + 2 user nodes + end = 4
+        assert len(result_nodes) == 4
+        assert result_nodes[0]["type"] == "start"
+        assert result_nodes[-1]["type"] == "end"
+
+        # Should have: start->fetch, fetch->process, process->end = 3
+        assert len(result_edges) == 3
+
+        # Verify edge connections
+        edge_pairs = [(e["source"], e["target"]) for e in result_edges]
+        assert ("start", "fetch") in edge_pairs
+        assert ("fetch", "process") in edge_pairs
+        assert ("process", "end") in edge_pairs
+
+
+class TestParallelWorkflow:
+    """Tests for parallel node handling."""
+
+    def test_parallel_workflow(self):
+        """Parallel workflow: multiple nodes from start, merging to one."""
+        nodes = [
+            {"id": "api1", "type": "http-request", "depends_on": []},
+            {"id": "api2", "type": "http-request", "depends_on": []},
+            {"id": "merge", "type": "llm", "depends_on": ["api1", "api2"]},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # start should connect to both api1 and api2
+        start_edges = [e for e in result_edges if e["source"] == "start"]
+        assert len(start_edges) == 2
+
+        start_targets = {e["target"] for e in start_edges}
+        assert start_targets == {"api1", "api2"}
+
+        # Both api1 and api2 should connect to merge
+        merge_incoming = [e for e in result_edges if e["target"] == "merge"]
+        assert len(merge_incoming) == 2
+
+    def test_multiple_terminal_nodes(self):
+        """Multiple terminal nodes all connect to end."""
+        nodes = [
+            {"id": "branch1", "type": "llm", "depends_on": []},
+            {"id": "branch2", "type": "llm", "depends_on": []},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Both branches should connect to end
+        end_incoming = [e for e in result_edges if e["target"] == "end"]
+        assert len(end_incoming) == 2
+
+
+class TestIfElseWorkflow:
+    """Tests for if-else branching."""
+
+    def test_if_else_workflow(self):
+        """Conditional branching workflow."""
+        nodes = [
+            {
+                "id": "check",
+                "type": "if-else",
+                "config": {"true_branch": "success", "false_branch": "fallback"},
+                "depends_on": [],
+            },
+            {"id": "success", "type": "llm", "depends_on": []},
+            {"id": "fallback", "type": "code", "depends_on": []},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should have true and false branch edges
+        branch_edges = [e for e in result_edges if e["source"] == "check"]
+        assert len(branch_edges) == 2
+        assert any(e.get("sourceHandle") == "true" for e in branch_edges)
+        assert any(e.get("sourceHandle") == "false" for e in branch_edges)
+
+        # Verify targets
+        true_edge = next(e for e in branch_edges if e.get("sourceHandle") == "true")
+        false_edge = next(e for e in branch_edges if e.get("sourceHandle") == "false")
+        assert true_edge["target"] == "success"
+        assert false_edge["target"] == "fallback"
+
+    def test_if_else_missing_branch_no_error(self):
+        """if-else with only true branch doesn't error (warning only)."""
+        nodes = [
+            {
+                "id": "check",
+                "type": "if-else",
+                "config": {"true_branch": "success"},
+                "depends_on": [],
+            },
+            {"id": "success", "type": "llm", "depends_on": []},
+        ]
+        # Should not raise
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should have one branch edge
+        branch_edges = [e for e in result_edges if e["source"] == "check"]
+        assert len(branch_edges) == 1
+        assert branch_edges[0].get("sourceHandle") == "true"
+
+
+class TestQuestionClassifierWorkflow:
+    """Tests for question-classifier branching."""
+
+    def test_question_classifier_workflow(self):
+        """Question classifier with multiple classes."""
+        nodes = [
+            {
+                "id": "classifier",
+                "type": "question-classifier",
+                "config": {
+                    "query": ["start", "user_input"],
+                    "classes": [
+                        {"id": "tech", "name": "技术问题", "target": "tech_handler"},
+                        {"id": "sales", "name": "销售咨询", "target": "sales_handler"},
+                        {"id": "other", "name": "其他问题", "target": "other_handler"},
+                    ],
+                },
+                "depends_on": [],
+            },
+            {"id": "tech_handler", "type": "llm", "depends_on": []},
+            {"id": "sales_handler", "type": "llm", "depends_on": []},
+            {"id": "other_handler", "type": "llm", "depends_on": []},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should have 3 branch edges from classifier
+        classifier_edges = [e for e in result_edges if e["source"] == "classifier"]
+        assert len(classifier_edges) == 3
+
+        # Each should use class id as sourceHandle
+        assert any(e.get("sourceHandle") == "tech" and e["target"] == "tech_handler" for e in classifier_edges)
+        assert any(e.get("sourceHandle") == "sales" and e["target"] == "sales_handler" for e in classifier_edges)
+        assert any(e.get("sourceHandle") == "other" and e["target"] == "other_handler" for e in classifier_edges)
+
+    def test_question_classifier_missing_target(self):
+        """Classes without target connect to end."""
+        nodes = [
+            {
+                "id": "classifier",
+                "type": "question-classifier",
+                "config": {
+                    "classes": [
+                        {"id": "known", "name": "已知问题", "target": "handler"},
+                        {"id": "unknown", "name": "未知问题"},  # Missing target
+                    ],
+                },
+                "depends_on": [],
+            },
+            {"id": "handler", "type": "llm", "depends_on": []},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Missing target should connect to end
+        classifier_edges = [e for e in result_edges if e["source"] == "classifier"]
+        assert any(e.get("sourceHandle") == "unknown" and e["target"] == "end" for e in classifier_edges)
+
+
+class TestVariableDependencyInference:
+    """Tests for automatic dependency inference from variables."""
+
+    def test_variable_dependency_inference(self):
+        """Dependencies inferred from variable references."""
+        nodes = [
+            {"id": "fetch", "type": "http-request", "depends_on": []},
+            {
+                "id": "process",
+                "type": "llm",
+                "config": {"prompt_template": [{"text": "{{#fetch.body#}}"}]},
+                # No explicit depends_on, but references fetch
+            },
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should automatically infer process depends on fetch
+        assert any(e["source"] == "fetch" and e["target"] == "process" for e in result_edges)
+
+    def test_system_variable_not_inferred(self):
+        """System variables (sys, start) not inferred as dependencies."""
+        nodes = [
+            {
+                "id": "process",
+                "type": "llm",
+                "config": {"prompt_template": [{"text": "{{#sys.query#}} {{#start.input#}}"}]},
+                "depends_on": [],
+            },
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should connect to start, not create dependency on sys or start
+        edge_sources = {e["source"] for e in result_edges}
+        assert "sys" not in edge_sources
+        assert "start" in edge_sources
+
+
+class TestCycleDetection:
+    """Tests for cyclic dependency detection."""
+
+    def test_cyclic_dependency_detected(self):
+        """Cyclic dependencies raise error."""
+        nodes = [
+            {"id": "a", "type": "llm", "depends_on": ["c"]},
+            {"id": "b", "type": "llm", "depends_on": ["a"]},
+            {"id": "c", "type": "llm", "depends_on": ["b"]},
+        ]
+
+        with pytest.raises(CyclicDependencyError):
+            GraphBuilder.build_graph(nodes)
+
+    def test_self_dependency_detected(self):
+        """Self-dependency raises error."""
+        nodes = [
+            {"id": "a", "type": "llm", "depends_on": ["a"]},
+        ]
+
+        with pytest.raises(CyclicDependencyError):
+            GraphBuilder.build_graph(nodes)
+
+
+class TestErrorRecovery:
+    """Tests for silent error recovery."""
+
+    def test_invalid_dependency_removed(self):
+        """Invalid dependencies (non-existent nodes) are silently removed."""
+        nodes = [
+            {"id": "process", "type": "llm", "depends_on": ["nonexistent"]},
+        ]
+        # Should not raise, invalid dependency silently removed
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Process should connect from start (since invalid dep was removed)
+        assert any(e["source"] == "start" and e["target"] == "process" for e in result_edges)
+
+    def test_depends_on_as_string(self):
+        """depends_on as string is converted to list."""
+        nodes = [
+            {"id": "fetch", "type": "http-request", "depends_on": []},
+            {"id": "process", "type": "llm", "depends_on": "fetch"},  # String instead of list
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should work correctly
+        assert any(e["source"] == "fetch" and e["target"] == "process" for e in result_edges)
+
+
+class TestContainerNodes:
+    """Tests for container nodes (iteration, loop)."""
+
+    def test_iteration_node_as_regular_node(self):
+        """Iteration nodes behave as regular single-in-single-out nodes."""
+        nodes = [
+            {"id": "prepare", "type": "code", "depends_on": []},
+            {
+                "id": "loop",
+                "type": "iteration",
+                "config": {"iterator_selector": ["prepare", "items"]},
+                "depends_on": ["prepare"],
+            },
+            {"id": "process_result", "type": "llm", "depends_on": ["loop"]},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should have standard edges: start->prepare, prepare->loop, loop->process_result, process_result->end
+        edge_pairs = [(e["source"], e["target"]) for e in result_edges]
+        assert ("start", "prepare") in edge_pairs
+        assert ("prepare", "loop") in edge_pairs
+        assert ("loop", "process_result") in edge_pairs
+        assert ("process_result", "end") in edge_pairs
+
+    def test_loop_node_as_regular_node(self):
+        """Loop nodes behave as regular single-in-single-out nodes."""
+        nodes = [
+            {"id": "init", "type": "code", "depends_on": []},
+            {
+                "id": "repeat",
+                "type": "loop",
+                "config": {"loop_count": 5},
+                "depends_on": ["init"],
+            },
+            {"id": "finish", "type": "llm", "depends_on": ["repeat"]},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Standard edge flow
+        edge_pairs = [(e["source"], e["target"]) for e in result_edges]
+        assert ("init", "repeat") in edge_pairs
+        assert ("repeat", "finish") in edge_pairs
+
+    def test_iteration_with_variable_inference(self):
+        """Iteration node dependencies can be inferred from iterator_selector."""
+        nodes = [
+            {"id": "data_source", "type": "http-request", "depends_on": []},
+            {
+                "id": "process_each",
+                "type": "iteration",
+                "config": {
+                    "iterator_selector": ["data_source", "items"],
+                },
+                # No explicit depends_on, but references data_source
+            },
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Should infer dependency from iterator_selector reference
+        # Note: iterator_selector format is different from {{#...#}}, so this tests
+        # that explicit depends_on is properly handled when not provided
+        # In this case, process_each has no depends_on, so it connects to start
+        edge_pairs = [(e["source"], e["target"]) for e in result_edges]
+        # Without explicit depends_on, connects to start
+        assert ("start", "process_each") in edge_pairs or ("data_source", "process_each") in edge_pairs
+
+    def test_loop_node_self_reference_not_cycle(self):
+        """Loop nodes referencing their own outputs should not create cycle."""
+        nodes = [
+            {"id": "init", "type": "code", "depends_on": []},
+            {
+                "id": "my_loop",
+                "type": "loop",
+                "config": {
+                    "loop_count": 5,
+                    # Loop node referencing its own output (common pattern)
+                    "prompt": "Previous: {{#my_loop.output#}}, continue...",
+                },
+                "depends_on": ["init"],
+            },
+            {"id": "finish", "type": "llm", "depends_on": ["my_loop"]},
+        ]
+        # Should NOT raise CyclicDependencyError
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        # Verify the graph is built correctly
+        assert len(result_nodes) == 5  # start + 3 + end
+        edge_pairs = [(e["source"], e["target"]) for e in result_edges]
+        assert ("init", "my_loop") in edge_pairs
+        assert ("my_loop", "finish") in edge_pairs
+
+
+class TestEdgeStructure:
+    """Tests for edge structure correctness."""
+
+    def test_edge_has_required_fields(self):
+        """Edges have all required fields."""
+        nodes = [
+            {"id": "node1", "type": "llm", "depends_on": []},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        for edge in result_edges:
+            assert "id" in edge
+            assert "source" in edge
+            assert "target" in edge
+            assert "sourceHandle" in edge
+            assert "targetHandle" in edge
+
+    def test_edge_id_unique(self):
+        """Each edge has a unique ID."""
+        nodes = [
+            {"id": "a", "type": "llm", "depends_on": []},
+            {"id": "b", "type": "llm", "depends_on": []},
+            {"id": "c", "type": "llm", "depends_on": ["a", "b"]},
+        ]
+        result_nodes, result_edges = GraphBuilder.build_graph(nodes)
+
+        edge_ids = [e["id"] for e in result_edges]
+        assert len(edge_ids) == len(set(edge_ids))  # All unique

api/tests/unit_tests/core/llm_generator/test_mermaid_generator.py

@@ -0,0 +1,287 @@
+"""
+Unit tests for the Mermaid Generator.
+
+Tests cover:
+- Basic workflow rendering
+- Reserved word handling ('end' → 'end_node')
+- Question classifier multi-branch edges
+- If-else branch labels
+- Edge validation and skipping
+- Tool node formatting
+"""
+
+from core.workflow.generator.utils.mermaid_generator import generate_mermaid
+
+
+class TestBasicWorkflow:
+    """Tests for basic workflow Mermaid generation."""
+
+    def test_simple_start_end_workflow(self):
+        """Test simple Start → End workflow."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "title": "Start"},
+                {"id": "end", "type": "end", "title": "End"},
+            ],
+            "edges": [{"source": "start", "target": "end"}],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "flowchart TD" in result
+        assert 'start["type=start|title=Start"]' in result
+        assert 'end_node["type=end|title=End"]' in result
+        assert "start --> end_node" in result
+
+    def test_start_llm_end_workflow(self):
+        """Test Start → LLM → End workflow."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "title": "Start"},
+                {"id": "llm", "type": "llm", "title": "Generate"},
+                {"id": "end", "type": "end", "title": "End"},
+            ],
+            "edges": [
+                {"source": "start", "target": "llm"},
+                {"source": "llm", "target": "end"},
+            ],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert 'llm["type=llm|title=Generate"]' in result
+        assert "start --> llm" in result
+        assert "llm --> end_node" in result
+
+    def test_empty_workflow(self):
+        """Test empty workflow returns minimal output."""
+        workflow_data = {"nodes": [], "edges": []}
+        result = generate_mermaid(workflow_data)
+
+        assert result == "flowchart TD"
+
+    def test_missing_keys_handled(self):
+        """Test workflow with missing keys doesn't crash."""
+        workflow_data = {}
+        result = generate_mermaid(workflow_data)
+
+        assert "flowchart TD" in result
+
+
+class TestReservedWords:
+    """Tests for reserved word handling in node IDs."""
+
+    def test_end_node_id_is_replaced(self):
+        """Test 'end' node ID is replaced with 'end_node'."""
+        workflow_data = {
+            "nodes": [{"id": "end", "type": "end", "title": "End"}],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Should use end_node instead of end
+        assert "end_node[" in result
+        assert '"type=end|title=End"' in result
+
+    def test_subgraph_node_id_is_replaced(self):
+        """Test 'subgraph' node ID is replaced with 'subgraph_node'."""
+        workflow_data = {
+            "nodes": [{"id": "subgraph", "type": "code", "title": "Process"}],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "subgraph_node[" in result
+
+    def test_edge_uses_safe_ids(self):
+        """Test edges correctly reference safe IDs after replacement."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "title": "Start"},
+                {"id": "end", "type": "end", "title": "End"},
+            ],
+            "edges": [{"source": "start", "target": "end"}],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Edge should use end_node, not end
+        assert "start --> end_node" in result
+        assert "start --> end\n" not in result
+
+
+class TestBranchEdges:
+    """Tests for branching node edge labels."""
+
+    def test_question_classifier_source_handles(self):
+        """Test question-classifier edges with sourceHandle labels."""
+        workflow_data = {
+            "nodes": [
+                {"id": "classifier", "type": "question-classifier", "title": "Classify"},
+                {"id": "refund", "type": "llm", "title": "Handle Refund"},
+                {"id": "inquiry", "type": "llm", "title": "Handle Inquiry"},
+            ],
+            "edges": [
+                {"source": "classifier", "target": "refund", "sourceHandle": "refund"},
+                {"source": "classifier", "target": "inquiry", "sourceHandle": "inquiry"},
+            ],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "classifier -->|refund| refund" in result
+        assert "classifier -->|inquiry| inquiry" in result
+
+    def test_if_else_true_false_handles(self):
+        """Test if-else edges with true/false labels."""
+        workflow_data = {
+            "nodes": [
+                {"id": "ifelse", "type": "if-else", "title": "Check"},
+                {"id": "yes_branch", "type": "llm", "title": "Yes"},
+                {"id": "no_branch", "type": "llm", "title": "No"},
+            ],
+            "edges": [
+                {"source": "ifelse", "target": "yes_branch", "sourceHandle": "true"},
+                {"source": "ifelse", "target": "no_branch", "sourceHandle": "false"},
+            ],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "ifelse -->|true| yes_branch" in result
+        assert "ifelse -->|false| no_branch" in result
+
+    def test_source_handle_source_is_ignored(self):
+        """Test sourceHandle='source' doesn't add label."""
+        workflow_data = {
+            "nodes": [
+                {"id": "llm1", "type": "llm", "title": "LLM 1"},
+                {"id": "llm2", "type": "llm", "title": "LLM 2"},
+            ],
+            "edges": [{"source": "llm1", "target": "llm2", "sourceHandle": "source"}],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Should be plain arrow without label
+        assert "llm1 --> llm2" in result
+        assert "llm1 -->|source|" not in result
+
+
+class TestEdgeValidation:
+    """Tests for edge validation and error handling."""
+
+    def test_edge_with_missing_source_is_skipped(self):
+        """Test edge with non-existent source node is skipped."""
+        workflow_data = {
+            "nodes": [{"id": "end", "type": "end", "title": "End"}],
+            "edges": [{"source": "nonexistent", "target": "end"}],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Should not contain the invalid edge
+        assert "nonexistent" not in result
+        assert "-->" not in result or "nonexistent" not in result
+
+    def test_edge_with_missing_target_is_skipped(self):
+        """Test edge with non-existent target node is skipped."""
+        workflow_data = {
+            "nodes": [{"id": "start", "type": "start", "title": "Start"}],
+            "edges": [{"source": "start", "target": "nonexistent"}],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Edge should be skipped
+        assert "start --> nonexistent" not in result
+
+    def test_edge_without_source_or_target_is_skipped(self):
+        """Test edge missing source or target is skipped."""
+        workflow_data = {
+            "nodes": [{"id": "start", "type": "start", "title": "Start"}],
+            "edges": [{"source": "start"}, {"target": "start"}, {}],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # No edges should be rendered
+        assert result.count("-->") == 0
+
+
+class TestToolNodes:
+    """Tests for tool node formatting."""
+
+    def test_tool_node_includes_tool_key(self):
+        """Test tool node includes tool_key in label."""
+        workflow_data = {
+            "nodes": [
+                {
+                    "id": "search",
+                    "type": "tool",
+                    "title": "Search",
+                    "config": {"tool_key": "google/search"},
+                }
+            ],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert 'search["type=tool|title=Search|tool=google/search"]' in result
+
+    def test_tool_node_with_tool_name_fallback(self):
+        """Test tool node uses tool_name as fallback."""
+        workflow_data = {
+            "nodes": [
+                {
+                    "id": "tool1",
+                    "type": "tool",
+                    "title": "My Tool",
+                    "config": {"tool_name": "my_tool"},
+                }
+            ],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "tool=my_tool" in result
+
+    def test_tool_node_missing_tool_key_shows_unknown(self):
+        """Test tool node without tool_key shows 'unknown'."""
+        workflow_data = {
+            "nodes": [{"id": "tool1", "type": "tool", "title": "Tool", "config": {}}],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "tool=unknown" in result
+
+
+class TestNodeFormatting:
+    """Tests for node label formatting."""
+
+    def test_quotes_in_title_are_escaped(self):
+        """Test double quotes in title are replaced with single quotes."""
+        workflow_data = {
+            "nodes": [{"id": "llm", "type": "llm", "title": 'Say "Hello"'}],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Double quotes should be replaced
+        assert "Say 'Hello'" in result
+        assert 'Say "Hello"' not in result
+
+    def test_node_without_id_is_skipped(self):
+        """Test node without id is skipped."""
+        workflow_data = {
+            "nodes": [{"type": "llm", "title": "No ID"}],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        # Should only have flowchart header
+        lines = [line for line in result.split("\n") if line.strip()]
+        assert len(lines) == 1
+
+    def test_node_default_values(self):
+        """Test node with missing type/title uses defaults."""
+        workflow_data = {
+            "nodes": [{"id": "node1"}],
+            "edges": [],
+        }
+        result = generate_mermaid(workflow_data)
+
+        assert "type=unknown" in result
+        assert "title=Untitled" in result

api/tests/unit_tests/core/llm_generator/test_node_repair.py

@@ -0,0 +1,81 @@
+from core.workflow.generator.utils.node_repair import NodeRepair
+
+
+class TestNodeRepair:
+    """Tests for NodeRepair utility."""
+
+    def test_repair_if_else_valid_operators(self):
+        """Test that valid operators remain unchanged."""
+        nodes = [
+            {
+                "id": "node1",
+                "type": "if-else",
+                "config": {
+                    "cases": [
+                        {
+                            "conditions": [
+                                {"comparison_operator": "≥", "value": "1"},
+                                {"comparison_operator": "=", "value": "2"},
+                            ]
+                        }
+                    ]
+                },
+            }
+        ]
+        result = NodeRepair.repair(nodes)
+        assert result.was_repaired is False
+        assert result.nodes == nodes
+
+    def test_repair_if_else_invalid_operators(self):
+        """Test that invalid operators are normalized."""
+        nodes = [
+            {
+                "id": "node1",
+                "type": "if-else",
+                "config": {
+                    "cases": [
+                        {
+                            "conditions": [
+                                {"comparison_operator": ">=", "value": "1"},
+                                {"comparison_operator": "<=", "value": "2"},
+                                {"comparison_operator": "!=", "value": "3"},
+                                {"comparison_operator": "==", "value": "4"},
+                            ]
+                        }
+                    ]
+                },
+            }
+        ]
+        result = NodeRepair.repair(nodes)
+        assert result.was_repaired is True
+        assert len(result.repairs_made) == 4
+
+        conditions = result.nodes[0]["config"]["cases"][0]["conditions"]
+        assert conditions[0]["comparison_operator"] == "≥"
+        assert conditions[1]["comparison_operator"] == "≤"
+        assert conditions[2]["comparison_operator"] == "≠"
+        assert conditions[3]["comparison_operator"] == "="
+
+    def test_repair_ignores_other_nodes(self):
+        """Test that other node types are ignored."""
+        nodes = [{"id": "node1", "type": "llm", "config": {"some_field": ">="}}]
+        result = NodeRepair.repair(nodes)
+        assert result.was_repaired is False
+        assert result.nodes[0]["config"]["some_field"] == ">="
+
+    def test_repair_handles_missing_config(self):
+        """Test robustness against missing fields."""
+        nodes = [
+            {
+                "id": "node1",
+                "type": "if-else",
+                # Missing config
+            },
+            {
+                "id": "node2",
+                "type": "if-else",
+                "config": {},  # Missing cases
+            },
+        ]
+        result = NodeRepair.repair(nodes)
+        assert result.was_repaired is False

api/tests/unit_tests/core/llm_generator/test_node_schemas_validation.py

@@ -0,0 +1,99 @@
+"""
+Tests for node schemas validation.
+
+Ensures that the node configuration stays in sync with registered node types.
+"""
+
+from core.workflow.generator.config.node_schemas import (
+    get_builtin_node_schemas,
+    validate_node_schemas,
+)
+
+
+class TestNodeSchemasValidation:
+    """Tests for node schema validation utilities."""
+
+    def test_validate_node_schemas_returns_no_warnings(self):
+        """Ensure all registered node types have corresponding schemas."""
+        warnings = validate_node_schemas()
+        # If this test fails, it means a new node type was added but
+        # no schema was defined for it in node_schemas.py
+        assert len(warnings) == 0, (
+            f"Missing schemas for node types: {warnings}. "
+            "Please add schemas for these node types in node_schemas.py "
+            "or add them to _INTERNAL_NODE_TYPES if they don't need schemas."
+        )
+
+    def test_builtin_node_schemas_not_empty(self):
+        """Ensure BUILTIN_NODE_SCHEMAS contains expected node types."""
+        # get_builtin_node_schemas() includes dynamic schemas
+        all_schemas = get_builtin_node_schemas()
+        assert len(all_schemas) > 0
+        # Core node types should always be present
+        expected_types = ["llm", "code", "http-request", "if-else"]
+        for node_type in expected_types:
+            assert node_type in all_schemas, f"Missing schema for core node type: {node_type}"
+
+    def test_schema_structure(self):
+        """Ensure each schema has required fields."""
+        all_schemas = get_builtin_node_schemas()
+        for node_type, schema in all_schemas.items():
+            assert "description" in schema, f"Missing 'description' in schema for {node_type}"
+            # 'parameters' is optional but if present should be a dict
+            if "parameters" in schema:
+                assert isinstance(schema["parameters"], dict), (
+                    f"'parameters' in schema for {node_type} should be a dict"
+                )
+
+
+class TestNodeSchemasMerged:
+    """Tests to verify the merged configuration works correctly."""
+
+    def test_fallback_rules_available(self):
+        """Ensure FALLBACK_RULES is available from node_schemas."""
+        from core.workflow.generator.config.node_schemas import FALLBACK_RULES
+
+        assert len(FALLBACK_RULES) > 0
+        assert "http-request" in FALLBACK_RULES
+        assert "code" in FALLBACK_RULES
+        assert "llm" in FALLBACK_RULES
+
+    def test_node_type_aliases_available(self):
+        """Ensure NODE_TYPE_ALIASES is available from node_schemas."""
+        from core.workflow.generator.config.node_schemas import NODE_TYPE_ALIASES
+
+        assert len(NODE_TYPE_ALIASES) > 0
+        assert NODE_TYPE_ALIASES.get("gpt") == "llm"
+        assert NODE_TYPE_ALIASES.get("api") == "http-request"
+
+    def test_field_name_corrections_available(self):
+        """Ensure FIELD_NAME_CORRECTIONS is available from node_schemas."""
+        from core.workflow.generator.config.node_schemas import (
+            FIELD_NAME_CORRECTIONS,
+            get_corrected_field_name,
+        )
+
+        assert len(FIELD_NAME_CORRECTIONS) > 0
+        # Test the helper function
+        assert get_corrected_field_name("http-request", "text") == "body"
+        assert get_corrected_field_name("llm", "response") == "text"
+        assert get_corrected_field_name("code", "unknown") == "unknown"
+
+    def test_config_init_exports(self):
+        """Ensure config __init__.py exports all needed symbols."""
+        from core.workflow.generator.config import (
+            BUILTIN_NODE_SCHEMAS,
+            FALLBACK_RULES,
+            FIELD_NAME_CORRECTIONS,
+            NODE_TYPE_ALIASES,
+            get_corrected_field_name,
+            validate_node_schemas,
+        )
+
+        # Just verify imports work
+        assert BUILTIN_NODE_SCHEMAS is not None
+        assert FALLBACK_RULES is not None
+        assert FIELD_NAME_CORRECTIONS is not None
+        assert NODE_TYPE_ALIASES is not None
+        assert callable(get_corrected_field_name)
+        assert callable(validate_node_schemas)

api/tests/unit_tests/core/llm_generator/test_planner_prompts.py

@@ -0,0 +1,172 @@
+"""
+Unit tests for the Planner Prompts.
+
+Tests cover:
+- Tool formatting for planner context
+- Edge cases with missing fields
+- Empty tool lists
+"""
+
+from core.workflow.generator.prompts.planner_prompts import format_tools_for_planner
+
+
+class TestFormatToolsForPlanner:
+    """Tests for format_tools_for_planner function."""
+
+    def test_empty_tools_returns_default_message(self):
+        """Test empty tools list returns default message."""
+        result = format_tools_for_planner([])
+
+        assert result == "No external tools available."
+
+    def test_none_tools_returns_default_message(self):
+        """Test None tools list returns default message."""
+        result = format_tools_for_planner(None)
+
+        assert result == "No external tools available."
+
+    def test_single_tool_formatting(self):
+        """Test single tool is formatted correctly."""
+        tools = [
+            {
+                "provider_id": "google",
+                "tool_key": "search",
+                "tool_label": "Google Search",
+                "tool_description": "Search the web using Google",
+            }
+        ]
+        result = format_tools_for_planner(tools)
+
+        assert "[google/search]" in result
+        assert "Google Search" in result
+        assert "Search the web using Google" in result
+
+    def test_multiple_tools_formatting(self):
+        """Test multiple tools are formatted correctly."""
+        tools = [
+            {
+                "provider_id": "google",
+                "tool_key": "search",
+                "tool_label": "Search",
+                "tool_description": "Web search",
+            },
+            {
+                "provider_id": "slack",
+                "tool_key": "send_message",
+                "tool_label": "Send Message",
+                "tool_description": "Send a Slack message",
+            },
+        ]
+        result = format_tools_for_planner(tools)
+
+        lines = result.strip().split("\n")
+        assert len(lines) == 2
+        assert "[google/search]" in result
+        assert "[slack/send_message]" in result
+
+    def test_tool_without_provider_uses_key_only(self):
+        """Test tool without provider_id uses tool_key only."""
+        tools = [
+            {
+                "tool_key": "my_tool",
+                "tool_label": "My Tool",
+                "tool_description": "A custom tool",
+            }
+        ]
+        result = format_tools_for_planner(tools)
+
+        # Should format as [my_tool] without provider prefix
+        assert "[my_tool]" in result
+        assert "My Tool" in result
+
+    def test_tool_with_tool_name_fallback(self):
+        """Test tool uses tool_name when tool_key is missing."""
+        tools = [
+            {
+                "tool_name": "fallback_tool",
+                "description": "Fallback description",
+            }
+        ]
+        result = format_tools_for_planner(tools)
+
+        assert "fallback_tool" in result
+        assert "Fallback description" in result
+
+    def test_tool_with_missing_description(self):
+        """Test tool with missing description doesn't crash."""
+        tools = [
+            {
+                "provider_id": "test",
+                "tool_key": "tool1",
+                "tool_label": "Tool 1",
+            }
+        ]
+        result = format_tools_for_planner(tools)
+
+        assert "[test/tool1]" in result
+        assert "Tool 1" in result
+
+    def test_tool_with_all_missing_fields(self):
+        """Test tool with all fields missing uses defaults."""
+        tools = [{}]
+        result = format_tools_for_planner(tools)
+
+        # Should not crash, may produce minimal output
+        assert isinstance(result, str)
+
+    def test_tool_uses_provider_fallback(self):
+        """Test tool uses 'provider' when 'provider_id' is missing."""
+        tools = [
+            {
+                "provider": "openai",
+                "tool_key": "dalle",
+                "tool_label": "DALL-E",
+                "tool_description": "Generate images",
+            }
+        ]
+        result = format_tools_for_planner(tools)
+
+        assert "[openai/dalle]" in result
+
+    def test_tool_label_fallback_to_key(self):
+        """Test tool_label falls back to tool_key when missing."""
+        tools = [
+            {
+                "provider_id": "test",
+                "tool_key": "my_key",
+                "tool_description": "Description here",
+            }
+        ]
+        result = format_tools_for_planner(tools)
+
+        # Label should fallback to key
+        assert "my_key" in result
+        assert "Description here" in result
+
+
+class TestPlannerPromptConstants:
+    """Tests for planner prompt constant availability."""
+
+    def test_planner_system_prompt_exists(self):
+        """Test PLANNER_SYSTEM_PROMPT is defined."""
+        from core.workflow.generator.prompts.planner_prompts import PLANNER_SYSTEM_PROMPT
+
+        assert PLANNER_SYSTEM_PROMPT is not None
+        assert len(PLANNER_SYSTEM_PROMPT) > 0
+        assert "{tools_summary}" in PLANNER_SYSTEM_PROMPT
+
+    def test_planner_user_prompt_exists(self):
+        """Test PLANNER_USER_PROMPT is defined."""
+        from core.workflow.generator.prompts.planner_prompts import PLANNER_USER_PROMPT
+
+        assert PLANNER_USER_PROMPT is not None
+        assert "{instruction}" in PLANNER_USER_PROMPT
+
+    def test_planner_system_prompt_has_required_sections(self):
+        """Test PLANNER_SYSTEM_PROMPT has required XML sections."""
+        from core.workflow.generator.prompts.planner_prompts import PLANNER_SYSTEM_PROMPT
+
+        assert "<role>" in PLANNER_SYSTEM_PROMPT
+        assert "<task>" in PLANNER_SYSTEM_PROMPT
+        assert "<available_tools>" in PLANNER_SYSTEM_PROMPT
+        assert "<response_format>" in PLANNER_SYSTEM_PROMPT

api/tests/unit_tests/core/llm_generator/test_validation_engine.py

@@ -0,0 +1,510 @@
+"""
+Unit tests for the Validation Rule Engine.
+
+Tests cover:
+- Structure rules (required fields, types, formats)
+- Semantic rules (variable references, edge connections)
+- Reference rules (model exists, tool configured, dataset valid)
+- ValidationEngine integration
+"""
+
+from core.workflow.generator.validation import (
+    ValidationContext,
+    ValidationEngine,
+)
+from core.workflow.generator.validation.rules import (
+    extract_variable_refs,
+    is_placeholder,
+)
+
+
+class TestPlaceholderDetection:
+    """Tests for placeholder detection utility."""
+
+    def test_detects_please_select(self):
+        assert is_placeholder("PLEASE_SELECT_YOUR_MODEL") is True
+
+    def test_detects_your_prefix(self):
+        assert is_placeholder("YOUR_API_KEY") is True
+
+    def test_detects_todo(self):
+        assert is_placeholder("TODO: fill this in") is True
+
+    def test_detects_placeholder(self):
+        assert is_placeholder("PLACEHOLDER_VALUE") is True
+
+    def test_detects_example_prefix(self):
+        assert is_placeholder("EXAMPLE_URL") is True
+
+    def test_detects_replace_prefix(self):
+        assert is_placeholder("REPLACE_WITH_ACTUAL") is True
+
+    def test_case_insensitive(self):
+        assert is_placeholder("please_select") is True
+        assert is_placeholder("Please_Select") is True
+
+    def test_valid_values_not_detected(self):
+        assert is_placeholder("https://api.example.com") is False
+        assert is_placeholder("gpt-4") is False
+        assert is_placeholder("my_variable") is False
+
+    def test_non_string_returns_false(self):
+        assert is_placeholder(123) is False
+        assert is_placeholder(None) is False
+        assert is_placeholder(["list"]) is False
+
+
+class TestVariableRefExtraction:
+    """Tests for variable reference extraction."""
+
+    def test_extracts_simple_ref(self):
+        refs = extract_variable_refs("Hello {{#start.query#}}")
+        assert refs == [("start", "query")]
+
+    def test_extracts_multiple_refs(self):
+        refs = extract_variable_refs("{{#node1.output#}} and {{#node2.text#}}")
+        assert refs == [("node1", "output"), ("node2", "text")]
+
+    def test_extracts_nested_field(self):
+        refs = extract_variable_refs("{{#http_request.body#}}")
+        assert refs == [("http_request", "body")]
+
+    def test_no_refs_returns_empty(self):
+        refs = extract_variable_refs("No references here")
+        assert refs == []
+
+    def test_handles_malformed_refs(self):
+        refs = extract_variable_refs("{{#invalid}} and {{incomplete#}}")
+        assert refs == []
+
+
+class TestValidationContext:
+    """Tests for ValidationContext."""
+
+    def test_node_map_lookup(self):
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "start", "type": "start"},
+                {"id": "llm_1", "type": "llm"},
+            ]
+        )
+        assert ctx.get_node("start") == {"id": "start", "type": "start"}
+        assert ctx.get_node("nonexistent") is None
+
+    def test_model_set(self):
+        ctx = ValidationContext(
+            available_models=[
+                {"provider": "openai", "model": "gpt-4"},
+                {"provider": "anthropic", "model": "claude-3"},
+            ]
+        )
+        assert ctx.has_model("openai", "gpt-4") is True
+        assert ctx.has_model("anthropic", "claude-3") is True
+        assert ctx.has_model("openai", "gpt-3.5") is False
+
+    def test_tool_set(self):
+        ctx = ValidationContext(
+            available_tools=[
+                {"provider_id": "google", "tool_key": "search", "is_team_authorization": True},
+                {"provider_id": "slack", "tool_key": "send_message", "is_team_authorization": False},
+            ]
+        )
+        assert ctx.has_tool("google/search") is True
+        assert ctx.has_tool("search") is True
+        assert ctx.is_tool_configured("google/search") is True
+        assert ctx.is_tool_configured("slack/send_message") is False
+
+    def test_upstream_downstream_nodes(self):
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "start", "type": "start"},
+                {"id": "llm", "type": "llm"},
+                {"id": "end", "type": "end"},
+            ],
+            edges=[
+                {"source": "start", "target": "llm"},
+                {"source": "llm", "target": "end"},
+            ],
+        )
+        assert ctx.get_upstream_nodes("llm") == ["start"]
+        assert ctx.get_downstream_nodes("llm") == ["end"]
+
+
+class TestStructureRules:
+    """Tests for structure validation rules."""
+
+    def test_llm_missing_prompt_template(self):
+        ctx = ValidationContext(nodes=[{"id": "llm_1", "type": "llm", "config": {}}])
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        assert result.has_errors
+        errors = [e for e in result.all_errors if e.rule_id == "llm.prompt_template.required"]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is True
+
+    def test_llm_with_prompt_template_passes(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {
+                        "prompt_template": [
+                            {"role": "system", "text": "You are helpful"},
+                            {"role": "user", "text": "Hello"},
+                        ]
+                    },
+                }
+            ]
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        # No prompt_template errors
+        errors = [e for e in result.all_errors if "prompt_template" in e.rule_id]
+        assert len(errors) == 0
+
+    def test_http_request_missing_url(self):
+        ctx = ValidationContext(nodes=[{"id": "http_1", "type": "http-request", "config": {}}])
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if "http.url" in e.rule_id]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is True
+
+    def test_http_request_placeholder_url(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "http_1",
+                    "type": "http-request",
+                    "config": {"url": "PLEASE_SELECT_YOUR_URL", "method": "GET"},
+                }
+            ]
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if "placeholder" in e.rule_id]
+        assert len(errors) == 1
+
+    def test_code_node_missing_fields(self):
+        ctx = ValidationContext(nodes=[{"id": "code_1", "type": "code", "config": {}}])
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        error_rules = {e.rule_id for e in result.all_errors}
+        assert "code.code.required" in error_rules
+        assert "code.language.required" in error_rules
+
+    def test_knowledge_retrieval_missing_dataset(self):
+        ctx = ValidationContext(nodes=[{"id": "kb_1", "type": "knowledge-retrieval", "config": {}}])
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if "knowledge.dataset" in e.rule_id]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is False  # User must configure
+
+
+class TestSemanticRules:
+    """Tests for semantic validation rules."""
+
+    def test_valid_variable_reference(self):
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "Process: {{#start.query#}}"}]},
+                },
+            ]
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        # No variable reference errors
+        errors = [e for e in result.all_errors if "variable.ref" in e.rule_id]
+        assert len(errors) == 0
+
+    def test_invalid_variable_reference(self):
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "Process: {{#nonexistent.field#}}"}]},
+                },
+            ]
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if "variable.ref" in e.rule_id]
+        assert len(errors) == 1
+        assert "nonexistent" in errors[0].message
+
+    def test_edge_validation(self):
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "start", "type": "start", "config": {}},
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            edges=[
+                {"source": "start", "target": "end"},
+                {"source": "nonexistent", "target": "end"},
+            ],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if "edge" in e.rule_id]
+        assert len(errors) == 1
+        assert "nonexistent" in errors[0].message
+
+
+class TestReferenceRules:
+    """Tests for reference validation rules (models, tools)."""
+
+    def test_llm_missing_model_with_available(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "Hi"}]},
+                }
+            ],
+            available_models=[{"provider": "openai", "model": "gpt-4"}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if e.rule_id == "model.required"]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is True
+
+    def test_llm_missing_model_no_available(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "Hi"}]},
+                }
+            ],
+            available_models=[],  # No models available
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if e.rule_id == "model.no_available"]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is False
+
+    def test_llm_with_valid_model(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {
+                        "prompt_template": [{"role": "user", "text": "Hi"}],
+                        "model": {"provider": "openai", "name": "gpt-4"},
+                    },
+                }
+            ],
+            available_models=[{"provider": "openai", "model": "gpt-4"}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if "model" in e.rule_id]
+        assert len(errors) == 0
+
+    def test_llm_with_invalid_model(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {
+                        "prompt_template": [{"role": "user", "text": "Hi"}],
+                        "model": {"provider": "openai", "name": "gpt-99"},
+                    },
+                }
+            ],
+            available_models=[{"provider": "openai", "model": "gpt-4"}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if e.rule_id == "model.not_found"]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is True
+
+    def test_tool_node_not_found(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "tool_1",
+                    "type": "tool",
+                    "config": {"tool_key": "nonexistent/tool"},
+                }
+            ],
+            available_tools=[],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if e.rule_id == "tool.not_found"]
+        assert len(errors) == 1
+
+    def test_tool_node_not_configured(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "tool_1",
+                    "type": "tool",
+                    "config": {"tool_key": "google/search"},
+                }
+            ],
+            available_tools=[{"provider_id": "google", "tool_key": "search", "is_team_authorization": False}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        errors = [e for e in result.all_errors if e.rule_id == "tool.not_configured"]
+        assert len(errors) == 1
+        assert errors[0].is_fixable is False
+
+
+class TestValidationResult:
+    """Tests for ValidationResult classification."""
+
+    def test_has_errors(self):
+        ctx = ValidationContext(nodes=[{"id": "llm_1", "type": "llm", "config": {}}])
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        assert result.has_errors is True
+        assert result.is_valid is False
+
+    def test_has_fixable_errors(self):
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "Hi"}]},
+                }
+            ],
+            available_models=[{"provider": "openai", "model": "gpt-4"}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        assert result.has_fixable_errors is True
+        assert len(result.fixable_errors) > 0
+
+    def test_get_fixable_by_node(self):
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "llm_1", "type": "llm", "config": {}},
+                {"id": "http_1", "type": "http-request", "config": {}},
+            ]
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        by_node = result.get_fixable_by_node()
+        assert "llm_1" in by_node
+        assert "http_1" in by_node
+
+    def test_to_dict(self):
+        ctx = ValidationContext(nodes=[{"id": "llm_1", "type": "llm", "config": {}}])
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        d = result.to_dict()
+        assert "fixable" in d
+        assert "user_required" in d
+        assert "warnings" in d
+        assert "all_warnings" in d
+        assert "stats" in d
+
+
+class TestIntegration:
+    """Integration tests for the full validation pipeline."""
+
+    def test_complete_workflow_validation(self):
+        """Test validation of a complete workflow."""
+        ctx = ValidationContext(
+            nodes=[
+                {
+                    "id": "start",
+                    "type": "start",
+                    "config": {"variables": [{"variable": "query", "type": "text-input"}]},
+                },
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {
+                        "model": {"provider": "openai", "name": "gpt-4"},
+                        "prompt_template": [{"role": "user", "text": "{{#start.query#}}"}],
+                    },
+                },
+                {
+                    "id": "end",
+                    "type": "end",
+                    "config": {"outputs": [{"variable": "result", "value_selector": ["llm_1", "text"]}]},
+                },
+            ],
+            edges=[
+                {"source": "start", "target": "llm_1"},
+                {"source": "llm_1", "target": "end"},
+            ],
+            available_models=[{"provider": "openai", "model": "gpt-4"}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        # Should have no errors
+        assert result.is_valid is True
+        assert len(result.fixable_errors) == 0
+        assert len(result.user_required_errors) == 0
+
+    def test_workflow_with_multiple_errors(self):
+        """Test workflow with multiple types of errors."""
+        ctx = ValidationContext(
+            nodes=[
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "llm_1",
+                    "type": "llm",
+                    "config": {},  # Missing prompt_template and model
+                },
+                {
+                    "id": "kb_1",
+                    "type": "knowledge-retrieval",
+                    "config": {"dataset_ids": ["PLEASE_SELECT_YOUR_DATASET"]},
+                },
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            available_models=[{"provider": "openai", "model": "gpt-4"}],
+        )
+        engine = ValidationEngine()
+        result = engine.validate(ctx)
+
+        # Should have multiple errors
+        assert result.has_errors is True
+        assert len(result.fixable_errors) >= 2  # model, prompt_template
+        assert len(result.user_required_errors) >= 1  # dataset placeholder
+
+        # Check stats
+        assert result.stats["total_nodes"] == 4
+        assert result.stats["total_errors"] >= 3

api/tests/unit_tests/core/llm_generator/test_workflow_validator_vibe.py

@@ -0,0 +1,434 @@
+"""
+Unit tests for the Vibe Workflow Validator.
+
+Tests cover:
+- Basic validation function
+- User-friendly validation hints
+- Edge cases and error handling
+"""
+
+from core.workflow.generator.utils.workflow_validator import ValidationHint, WorkflowValidator
+
+
+class TestValidationHint:
+    """Tests for ValidationHint dataclass."""
+
+    def test_hint_creation(self):
+        """Test creating a validation hint."""
+        hint = ValidationHint(
+            node_id="llm_1",
+            field="model",
+            message="Model is not configured",
+            severity="error",
+        )
+        assert hint.node_id == "llm_1"
+        assert hint.field == "model"
+        assert hint.message == "Model is not configured"
+        assert hint.severity == "error"
+
+    def test_hint_with_suggestion(self):
+        """Test hint with suggestion."""
+        hint = ValidationHint(
+            node_id="http_1",
+            field="url",
+            message="URL is required",
+            severity="error",
+            suggestion="Add a valid URL like https://api.example.com",
+        )
+        assert hint.suggestion is not None
+
+
+class TestWorkflowValidatorBasic:
+    """Tests for basic validation scenarios."""
+
+    def test_empty_workflow_is_valid(self):
+        """Test empty workflow passes validation."""
+        workflow_data = {"nodes": [], "edges": []}
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        # Empty but valid structure
+        assert is_valid is True
+        assert len(hints) == 0
+
+    def test_minimal_valid_workflow(self):
+        """Test minimal Start → End workflow."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [{"source": "start", "target": "end"}],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        assert is_valid is True
+
+    def test_complete_workflow_with_llm(self):
+        """Test complete workflow with LLM node."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {"variables": []}},
+                {
+                    "id": "llm",
+                    "type": "llm",
+                    "config": {
+                        "model": {"provider": "openai", "name": "gpt-4"},
+                        "prompt_template": [{"role": "user", "text": "Hello"}],
+                    },
+                },
+                {"id": "end", "type": "end", "config": {"outputs": []}},
+            ],
+            "edges": [
+                {"source": "start", "target": "llm"},
+                {"source": "llm", "target": "end"},
+            ],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        # Should pass with no critical errors
+        errors = [h for h in hints if h.severity == "error"]
+        assert len(errors) == 0
+
+
+class TestVariableReferenceValidation:
+    """Tests for variable reference validation."""
+
+    def test_valid_variable_reference(self):
+        """Test valid variable reference passes."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "llm",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "Query: {{#start.query#}}"}]},
+                },
+            ],
+            "edges": [{"source": "start", "target": "llm"}],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        ref_errors = [h for h in hints if "reference" in h.message.lower()]
+        assert len(ref_errors) == 0
+
+    def test_invalid_variable_reference(self):
+        """Test invalid variable reference generates hint."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "llm",
+                    "type": "llm",
+                    "config": {"prompt_template": [{"role": "user", "text": "{{#nonexistent.field#}}"}]},
+                },
+            ],
+            "edges": [{"source": "start", "target": "llm"}],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        # Should have a hint about invalid reference
+        ref_hints = [h for h in hints if "nonexistent" in h.message or "reference" in h.message.lower()]
+        assert len(ref_hints) >= 1
+
+
+class TestEdgeValidation:
+    """Tests for edge validation."""
+
+    def test_edge_with_invalid_source(self):
+        """Test edge with non-existent source generates hint."""
+        workflow_data = {
+            "nodes": [{"id": "end", "type": "end", "config": {}}],
+            "edges": [{"source": "nonexistent", "target": "end"}],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        # Should have hint about invalid edge
+        edge_hints = [h for h in hints if "edge" in h.message.lower() or "source" in h.message.lower()]
+        assert len(edge_hints) >= 1
+
+    def test_edge_with_invalid_target(self):
+        """Test edge with non-existent target generates hint."""
+        workflow_data = {
+            "nodes": [{"id": "start", "type": "start", "config": {}}],
+            "edges": [{"source": "start", "target": "nonexistent"}],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        edge_hints = [h for h in hints if "edge" in h.message.lower() or "target" in h.message.lower()]
+        assert len(edge_hints) >= 1
+
+
+class TestToolValidation:
+    """Tests for tool node validation."""
+
+    def test_tool_node_found_in_available(self):
+        """Test tool node that exists in available tools."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "tool1",
+                    "type": "tool",
+                    "config": {"tool_key": "google/search"},
+                },
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [{"source": "start", "target": "tool1"}, {"source": "tool1", "target": "end"}],
+        }
+        available_tools = [{"provider_id": "google", "tool_key": "search", "is_team_authorization": True}]
+        is_valid, hints = WorkflowValidator.validate(workflow_data, available_tools)
+
+        tool_errors = [h for h in hints if h.severity == "error" and "tool" in h.message.lower()]
+        assert len(tool_errors) == 0
+
+    def test_tool_node_not_found(self):
+        """Test tool node not in available tools generates hint."""
+        workflow_data = {
+            "nodes": [
+                {
+                    "id": "tool1",
+                    "type": "tool",
+                    "config": {"tool_key": "unknown/tool"},
+                }
+            ],
+            "edges": [],
+        }
+        available_tools = []
+        is_valid, hints = WorkflowValidator.validate(workflow_data, available_tools)
+
+        tool_hints = [h for h in hints if "tool" in h.message.lower()]
+        assert len(tool_hints) >= 1
+
+
+class TestQuestionClassifierValidation:
+    """Tests for question-classifier node validation."""
+
+    def test_question_classifier_with_classes(self):
+        """Test question-classifier with valid classes."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "classifier",
+                    "type": "question-classifier",
+                    "config": {
+                        "classes": [
+                            {"id": "class1", "name": "Class 1"},
+                            {"id": "class2", "name": "Class 2"},
+                        ],
+                        "model": {"provider": "openai", "name": "gpt-4", "mode": "chat"},
+                    },
+                },
+                {"id": "h1", "type": "llm", "config": {}},
+                {"id": "h2", "type": "llm", "config": {}},
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [
+                {"source": "start", "target": "classifier"},
+                {"source": "classifier", "sourceHandle": "class1", "target": "h1"},
+                {"source": "classifier", "sourceHandle": "class2", "target": "h2"},
+                {"source": "h1", "target": "end"},
+                {"source": "h2", "target": "end"},
+            ],
+        }
+        available_models = [{"provider": "openai", "model": "gpt-4", "mode": "chat"}]
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [], available_models=available_models)
+
+        class_errors = [h for h in hints if "class" in h.message.lower() and h.severity == "error"]
+        assert len(class_errors) == 0
+
+    def test_question_classifier_missing_classes(self):
+        """Test question-classifier without classes generates hint."""
+        workflow_data = {
+            "nodes": [
+                {
+                    "id": "classifier",
+                    "type": "question-classifier",
+                    "config": {"model": {"provider": "openai", "name": "gpt-4", "mode": "chat"}},
+                }
+            ],
+            "edges": [],
+        }
+        available_models = [{"provider": "openai", "model": "gpt-4", "mode": "chat"}]
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [], available_models=available_models)
+
+        # Should have hint about missing classes
+        class_hints = [h for h in hints if "class" in h.message.lower()]
+        assert len(class_hints) >= 1
+
+
+class TestHttpRequestValidation:
+    """Tests for HTTP request node validation."""
+
+    def test_http_request_with_url(self):
+        """Test HTTP request with valid URL."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "http",
+                    "type": "http-request",
+                    "config": {"url": "https://api.example.com", "method": "GET"},
+                },
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [{"source": "start", "target": "http"}, {"source": "http", "target": "end"}],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        url_errors = [h for h in hints if "url" in h.message.lower() and h.severity == "error"]
+        assert len(url_errors) == 0
+
+    def test_http_request_missing_url(self):
+        """Test HTTP request without URL generates hint."""
+        workflow_data = {
+            "nodes": [
+                {
+                    "id": "http",
+                    "type": "http-request",
+                    "config": {"method": "GET"},
+                }
+            ],
+            "edges": [],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+
+        url_hints = [h for h in hints if "url" in h.message.lower()]
+        assert len(url_hints) >= 1
+
+
+class TestParameterExtractorValidation:
+    """Tests for parameter-extractor node validation."""
+
+    def test_parameter_extractor_valid_params(self):
+        """Test parameter-extractor with valid parameters."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "extractor",
+                    "type": "parameter-extractor",
+                    "config": {
+                        "instruction": "Extract info",
+                        "parameters": [
+                            {
+                                "name": "name",
+                                "type": "string",
+                                "description": "Name",
+                                "required": True,
+                            }
+                        ],
+                        "model": {"provider": "openai", "name": "gpt-4", "mode": "chat"},
+                    },
+                },
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [{"source": "start", "target": "extractor"}, {"source": "extractor", "target": "end"}],
+        }
+        available_models = [{"provider": "openai", "model": "gpt-4", "mode": "chat"}]
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [], available_models=available_models)
+
+        errors = [h for h in hints if h.severity == "error"]
+        assert len(errors) == 0
+
+    def test_parameter_extractor_missing_required_field(self):
+        """Test parameter-extractor missing 'required' field in parameter item."""
+        workflow_data = {
+            "nodes": [
+                {
+                    "id": "extractor",
+                    "type": "parameter-extractor",
+                    "config": {
+                        "instruction": "Extract info",
+                        "parameters": [
+                            {
+                                "name": "name",
+                                "type": "string",
+                                "description": "Name",
+                                # Missing 'required'
+                            }
+                        ],
+                        "model": {"provider": "openai", "name": "gpt-4", "mode": "chat"},
+                    },
+                }
+            ],
+            "edges": [],
+        }
+        available_models = [{"provider": "openai", "model": "gpt-4", "mode": "chat"}]
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [], available_models=available_models)
+
+        errors = [h for h in hints if "required" in h.message and h.severity == "error"]
+        assert len(errors) >= 1
+        assert "parameter-extractor" in errors[0].node_type
+
+
+class TestIfElseValidation:
+    """Tests for if-else node validation."""
+
+    def test_if_else_valid_operators(self):
+        """Test if-else with valid operators."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "ifelse",
+                    "type": "if-else",
+                    "config": {
+                        "cases": [{"case_id": "c1", "conditions": [{"comparison_operator": "≥", "value": "1"}]}]
+                    },
+                },
+                {"id": "t", "type": "llm", "config": {}},
+                {"id": "f", "type": "llm", "config": {}},
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [
+                {"source": "start", "target": "ifelse"},
+                {"source": "ifelse", "sourceHandle": "true", "target": "t"},
+                {"source": "ifelse", "sourceHandle": "false", "target": "f"},
+                {"source": "t", "target": "end"},
+                {"source": "f", "target": "end"},
+            ],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+        errors = [h for h in hints if h.severity == "error"]
+        # Filter out LLM model errors if any (available tools/models check might trigger)
+        # (actually available_models empty list might trigger model error?
+        # No, model config validation skips if model field not present? No, LLM has model config.
+        # But logic skips check if key missing? Let's check logic.
+        # _check_model_config checks if provider/name match available. If available is empty, it fails.
+        # But wait, validate default available_models is None?
+        # I should provide mock available_models or ignore model errors.
+
+        # Actually LLM node "config": {} implies missing model config. Rules check if config structure is valid?
+        # Let's filter specifically for operator errors.
+        operator_errors = [h for h in errors if "operator" in h.message]
+        assert len(operator_errors) == 0
+
+    def test_if_else_invalid_operators(self):
+        """Test if-else with invalid operators."""
+        workflow_data = {
+            "nodes": [
+                {"id": "start", "type": "start", "config": {}},
+                {
+                    "id": "ifelse",
+                    "type": "if-else",
+                    "config": {
+                        "cases": [{"case_id": "c1", "conditions": [{"comparison_operator": ">=", "value": "1"}]}]
+                    },
+                },
+                {"id": "t", "type": "llm", "config": {}},
+                {"id": "f", "type": "llm", "config": {}},
+                {"id": "end", "type": "end", "config": {}},
+            ],
+            "edges": [
+                {"source": "start", "target": "ifelse"},
+                {"source": "ifelse", "sourceHandle": "true", "target": "t"},
+                {"source": "ifelse", "sourceHandle": "false", "target": "f"},
+                {"source": "t", "target": "end"},
+                {"source": "f", "target": "end"},
+            ],
+        }
+        is_valid, hints = WorkflowValidator.validate(workflow_data, [])
+        operator_errors = [h for h in hints if "operator" in h.message and h.severity == "error"]
+        assert len(operator_errors) > 0
+        assert "≥" in operator_errors[0].suggestion

api/tests/unit_tests/core/workflow/nodes/template_transform/template_transform_node_spec.py

@@ -217,6 +217,7 @@ class TestTemplateTransformNode:
     @patch(
         "core.workflow.nodes.template_transform.template_transform_node.CodeExecutorJinja2TemplateRenderer.render_template"
     )
+    @patch("core.workflow.nodes.template_transform.template_transform_node.MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH", 10)
     def test_run_output_length_exceeds_limit(
         self, mock_execute, basic_node_data, mock_graph, mock_graph_runtime_state, graph_init_params
     ):
@@ -230,7 +231,6 @@ class TestTemplateTransformNode:
             graph_init_params=graph_init_params,
             graph=mock_graph,
             graph_runtime_state=mock_graph_runtime_state,
-            max_output_length=10,
         )
 
         result = node._run()

api/tests/unit_tests/extensions/test_celery_ssl.py

@@ -132,8 +132,6 @@ class TestCelerySSLConfiguration:
         mock_config.WORKFLOW_SCHEDULE_MAX_DISPATCH_PER_TICK = 0
         mock_config.ENABLE_TRIGGER_PROVIDER_REFRESH_TASK = False
         mock_config.TRIGGER_PROVIDER_REFRESH_INTERVAL = 15
-        mock_config.ENABLE_API_TOKEN_LAST_USED_UPDATE_TASK = False
-        mock_config.API_TOKEN_LAST_USED_UPDATE_INTERVAL = 30
 
         with patch("extensions.ext_celery.dify_config", mock_config):
             from dify_app import DifyApp

api/tests/unit_tests/libs/test_api_token_cache.py

@@ -1,250 +0,0 @@
-"""
-Unit tests for API Token Cache module.
-"""
-
-import json
-from datetime import datetime
-from unittest.mock import MagicMock, patch
-
-from services.api_token_service import (
-    CACHE_KEY_PREFIX,
-    CACHE_NULL_TTL_SECONDS,
-    CACHE_TTL_SECONDS,
-    ApiTokenCache,
-    CachedApiToken,
-)
-
-
-class TestApiTokenCache:
-    """Test cases for ApiTokenCache class."""
-
-    def setup_method(self):
-        """Setup test fixtures."""
-        self.mock_token = MagicMock()
-        self.mock_token.id = "test-token-id-123"
-        self.mock_token.app_id = "test-app-id-456"
-        self.mock_token.tenant_id = "test-tenant-id-789"
-        self.mock_token.type = "app"
-        self.mock_token.token = "test-token-value-abc"
-        self.mock_token.last_used_at = datetime(2026, 2, 3, 10, 0, 0)
-        self.mock_token.created_at = datetime(2026, 1, 1, 0, 0, 0)
-
-    def test_make_cache_key(self):
-        """Test cache key generation."""
-        # Test with scope
-        key = ApiTokenCache._make_cache_key("my-token", "app")
-        assert key == f"{CACHE_KEY_PREFIX}:app:my-token"
-
-        # Test without scope
-        key = ApiTokenCache._make_cache_key("my-token", None)
-        assert key == f"{CACHE_KEY_PREFIX}:any:my-token"
-
-    def test_serialize_token(self):
-        """Test token serialization."""
-        serialized = ApiTokenCache._serialize_token(self.mock_token)
-        data = json.loads(serialized)
-
-        assert data["id"] == "test-token-id-123"
-        assert data["app_id"] == "test-app-id-456"
-        assert data["tenant_id"] == "test-tenant-id-789"
-        assert data["type"] == "app"
-        assert data["token"] == "test-token-value-abc"
-        assert data["last_used_at"] == "2026-02-03T10:00:00"
-        assert data["created_at"] == "2026-01-01T00:00:00"
-
-    def test_serialize_token_with_nulls(self):
-        """Test token serialization with None values."""
-        mock_token = MagicMock()
-        mock_token.id = "test-id"
-        mock_token.app_id = None
-        mock_token.tenant_id = None
-        mock_token.type = "dataset"
-        mock_token.token = "test-token"
-        mock_token.last_used_at = None
-        mock_token.created_at = datetime(2026, 1, 1, 0, 0, 0)
-
-        serialized = ApiTokenCache._serialize_token(mock_token)
-        data = json.loads(serialized)
-
-        assert data["app_id"] is None
-        assert data["tenant_id"] is None
-        assert data["last_used_at"] is None
-
-    def test_deserialize_token(self):
-        """Test token deserialization."""
-        cached_data = json.dumps(
-            {
-                "id": "test-id",
-                "app_id": "test-app",
-                "tenant_id": "test-tenant",
-                "type": "app",
-                "token": "test-token",
-                "last_used_at": "2026-02-03T10:00:00",
-                "created_at": "2026-01-01T00:00:00",
-            }
-        )
-
-        result = ApiTokenCache._deserialize_token(cached_data)
-
-        assert isinstance(result, CachedApiToken)
-        assert result.id == "test-id"
-        assert result.app_id == "test-app"
-        assert result.tenant_id == "test-tenant"
-        assert result.type == "app"
-        assert result.token == "test-token"
-        assert result.last_used_at == datetime(2026, 2, 3, 10, 0, 0)
-        assert result.created_at == datetime(2026, 1, 1, 0, 0, 0)
-
-    def test_deserialize_null_token(self):
-        """Test deserialization of null token (cached miss)."""
-        result = ApiTokenCache._deserialize_token("null")
-        assert result is None
-
-    def test_deserialize_invalid_json(self):
-        """Test deserialization with invalid JSON."""
-        result = ApiTokenCache._deserialize_token("invalid-json{")
-        assert result is None
-
-    @patch("services.api_token_service.redis_client")
-    def test_get_cache_hit(self, mock_redis):
-        """Test cache hit scenario."""
-        cached_data = json.dumps(
-            {
-                "id": "test-id",
-                "app_id": "test-app",
-                "tenant_id": "test-tenant",
-                "type": "app",
-                "token": "test-token",
-                "last_used_at": "2026-02-03T10:00:00",
-                "created_at": "2026-01-01T00:00:00",
-            }
-        ).encode("utf-8")
-        mock_redis.get.return_value = cached_data
-
-        result = ApiTokenCache.get("test-token", "app")
-
-        assert result is not None
-        assert isinstance(result, CachedApiToken)
-        assert result.app_id == "test-app"
-        mock_redis.get.assert_called_once_with(f"{CACHE_KEY_PREFIX}:app:test-token")
-
-    @patch("services.api_token_service.redis_client")
-    def test_get_cache_miss(self, mock_redis):
-        """Test cache miss scenario."""
-        mock_redis.get.return_value = None
-
-        result = ApiTokenCache.get("test-token", "app")
-
-        assert result is None
-        mock_redis.get.assert_called_once()
-
-    @patch("services.api_token_service.redis_client")
-    def test_set_valid_token(self, mock_redis):
-        """Test setting a valid token in cache."""
-        result = ApiTokenCache.set("test-token", "app", self.mock_token)
-
-        assert result is True
-        mock_redis.setex.assert_called_once()
-        args = mock_redis.setex.call_args[0]
-        assert args[0] == f"{CACHE_KEY_PREFIX}:app:test-token"
-        assert args[1] == CACHE_TTL_SECONDS
-
-    @patch("services.api_token_service.redis_client")
-    def test_set_null_token(self, mock_redis):
-        """Test setting a null token (cache penetration prevention)."""
-        result = ApiTokenCache.set("invalid-token", "app", None)
-
-        assert result is True
-        mock_redis.setex.assert_called_once()
-        args = mock_redis.setex.call_args[0]
-        assert args[0] == f"{CACHE_KEY_PREFIX}:app:invalid-token"
-        assert args[1] == CACHE_NULL_TTL_SECONDS
-        assert args[2] == b"null"
-
-    @patch("services.api_token_service.redis_client")
-    def test_delete_with_scope(self, mock_redis):
-        """Test deleting token cache with specific scope."""
-        result = ApiTokenCache.delete("test-token", "app")
-
-        assert result is True
-        mock_redis.delete.assert_called_once_with(f"{CACHE_KEY_PREFIX}:app:test-token")
-
-    @patch("services.api_token_service.redis_client")
-    def test_delete_without_scope(self, mock_redis):
-        """Test deleting token cache without scope (delete all)."""
-        # Mock scan_iter to return an iterator of keys
-        mock_redis.scan_iter.return_value = iter(
-            [
-                b"api_token:app:test-token",
-                b"api_token:dataset:test-token",
-            ]
-        )
-
-        result = ApiTokenCache.delete("test-token", None)
-
-        assert result is True
-        # Verify scan_iter was called with the correct pattern
-        mock_redis.scan_iter.assert_called_once()
-        call_args = mock_redis.scan_iter.call_args
-        assert call_args[1]["match"] == f"{CACHE_KEY_PREFIX}:*:test-token"
-
-        # Verify delete was called with all matched keys
-        mock_redis.delete.assert_called_once_with(
-            b"api_token:app:test-token",
-            b"api_token:dataset:test-token",
-        )
-
-    @patch("services.api_token_service.redis_client")
-    def test_redis_fallback_on_exception(self, mock_redis):
-        """Test Redis fallback when Redis is unavailable."""
-        from redis import RedisError
-
-        mock_redis.get.side_effect = RedisError("Connection failed")
-
-        result = ApiTokenCache.get("test-token", "app")
-
-        # Should return None (fallback) instead of raising exception
-        assert result is None
-
-
-class TestApiTokenCacheIntegration:
-    """Integration test scenarios."""
-
-    @patch("services.api_token_service.redis_client")
-    def test_full_cache_lifecycle(self, mock_redis):
-        """Test complete cache lifecycle: set -> get -> delete."""
-        # Setup mock token
-        mock_token = MagicMock()
-        mock_token.id = "id-123"
-        mock_token.app_id = "app-456"
-        mock_token.tenant_id = "tenant-789"
-        mock_token.type = "app"
-        mock_token.token = "token-abc"
-        mock_token.last_used_at = datetime(2026, 2, 3, 10, 0, 0)
-        mock_token.created_at = datetime(2026, 1, 1, 0, 0, 0)
-
-        # 1. Set token in cache
-        ApiTokenCache.set("token-abc", "app", mock_token)
-        assert mock_redis.setex.called
-
-        # 2. Simulate cache hit
-        cached_data = ApiTokenCache._serialize_token(mock_token)
-        mock_redis.get.return_value = cached_data  # bytes from model_dump_json().encode()
-
-        retrieved = ApiTokenCache.get("token-abc", "app")
-        assert retrieved is not None
-        assert isinstance(retrieved, CachedApiToken)
-
-        # 3. Delete from cache
-        ApiTokenCache.delete("token-abc", "app")
-        assert mock_redis.delete.called
-
-    @patch("services.api_token_service.redis_client")
-    def test_cache_penetration_prevention(self, mock_redis):
-        """Test that non-existent tokens are cached as null."""
-        # Set null token (cache miss)
-        ApiTokenCache.set("non-existent-token", "app", None)
-
-        args = mock_redis.setex.call_args[0]
-        assert args[2] == b"null"
-        assert args[1] == CACHE_NULL_TTL_SECONDS  # Shorter TTL for null values

api/tests/unit_tests/services/enterprise/test_account_deletion_sync.py

@@ -1,276 +0,0 @@
-"""Unit tests for account deletion synchronization.
-
-This test module verifies the enterprise account deletion sync functionality,
-including Redis queuing, error handling, and community vs enterprise behavior.
-"""
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-from redis import RedisError
-
-from services.enterprise.account_deletion_sync import (
-    _queue_task,
-    sync_account_deletion,
-    sync_workspace_member_removal,
-)
-
-
-class TestQueueTask:
-    """Unit tests for the _queue_task helper function."""
-
-    @pytest.fixture
-    def mock_redis_client(self):
-        """Mock redis_client for testing."""
-        with patch("services.enterprise.account_deletion_sync.redis_client") as mock_redis:
-            yield mock_redis
-
-    @pytest.fixture
-    def mock_uuid(self):
-        """Mock UUID generation for predictable task IDs."""
-        with patch("services.enterprise.account_deletion_sync.uuid.uuid4") as mock_uuid_gen:
-            mock_uuid_gen.return_value = MagicMock(hex="test-task-id-1234")
-            yield mock_uuid_gen
-
-    def test_queue_task_success(self, mock_redis_client, mock_uuid):
-        """Test successful task queueing to Redis."""
-        # Arrange
-        workspace_id = "ws-123"
-        member_id = "member-456"
-        source = "test_source"
-
-        # Act
-        result = _queue_task(workspace_id=workspace_id, member_id=member_id, source=source)
-
-        # Assert
-        assert result is True
-        mock_redis_client.lpush.assert_called_once()
-
-        # Verify the task payload structure
-        call_args = mock_redis_client.lpush.call_args[0]
-        assert call_args[0] == "enterprise:member:sync:queue"
-
-        import json
-
-        task_data = json.loads(call_args[1])
-        assert task_data["workspace_id"] == workspace_id
-        assert task_data["member_id"] == member_id
-        assert task_data["source"] == source
-        assert task_data["type"] == "sync_member_deletion_from_workspace"
-        assert task_data["retry_count"] == 0
-        assert "task_id" in task_data
-        assert "created_at" in task_data
-
-    def test_queue_task_redis_error(self, mock_redis_client, caplog):
-        """Test handling of Redis connection errors."""
-        # Arrange
-        mock_redis_client.lpush.side_effect = RedisError("Connection failed")
-
-        # Act
-        result = _queue_task(workspace_id="ws-123", member_id="member-456", source="test_source")
-
-        # Assert
-        assert result is False
-        assert "Failed to queue account deletion sync" in caplog.text
-
-    def test_queue_task_type_error(self, mock_redis_client, caplog):
-        """Test handling of JSON serialization errors."""
-        # Arrange
-        mock_redis_client.lpush.side_effect = TypeError("Cannot serialize")
-
-        # Act
-        result = _queue_task(workspace_id="ws-123", member_id="member-456", source="test_source")
-
-        # Assert
-        assert result is False
-        assert "Failed to queue account deletion sync" in caplog.text
-
-
-class TestSyncWorkspaceMemberRemoval:
-    """Unit tests for sync_workspace_member_removal function."""
-
-    @pytest.fixture
-    def mock_queue_task(self):
-        """Mock _queue_task for testing."""
-        with patch("services.enterprise.account_deletion_sync._queue_task") as mock_queue:
-            mock_queue.return_value = True
-            yield mock_queue
-
-    def test_sync_workspace_member_removal_enterprise_enabled(self, mock_queue_task):
-        """Test sync when ENTERPRISE_ENABLED is True."""
-        # Arrange
-        workspace_id = "ws-123"
-        member_id = "member-456"
-        source = "workspace_member_removed"
-
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = True
-
-            # Act
-            result = sync_workspace_member_removal(workspace_id=workspace_id, member_id=member_id, source=source)
-
-            # Assert
-            assert result is True
-            mock_queue_task.assert_called_once_with(workspace_id=workspace_id, member_id=member_id, source=source)
-
-    def test_sync_workspace_member_removal_enterprise_disabled(self, mock_queue_task):
-        """Test sync when ENTERPRISE_ENABLED is False (community edition)."""
-        # Arrange
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = False
-
-            # Act
-            result = sync_workspace_member_removal(workspace_id="ws-123", member_id="member-456", source="test_source")
-
-            # Assert
-            assert result is True
-            mock_queue_task.assert_not_called()
-
-    def test_sync_workspace_member_removal_queue_failure(self, mock_queue_task):
-        """Test handling of queue task failures."""
-        # Arrange
-        mock_queue_task.return_value = False
-
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = True
-
-            # Act
-            result = sync_workspace_member_removal(workspace_id="ws-123", member_id="member-456", source="test_source")
-
-            # Assert
-            assert result is False
-
-
-class TestSyncAccountDeletion:
-    """Unit tests for sync_account_deletion function."""
-
-    @pytest.fixture
-    def mock_db_session(self):
-        """Mock database session for testing."""
-        with patch("services.enterprise.account_deletion_sync.db.session") as mock_session:
-            yield mock_session
-
-    @pytest.fixture
-    def mock_queue_task(self):
-        """Mock _queue_task for testing."""
-        with patch("services.enterprise.account_deletion_sync._queue_task") as mock_queue:
-            mock_queue.return_value = True
-            yield mock_queue
-
-    def test_sync_account_deletion_enterprise_disabled(self, mock_db_session, mock_queue_task):
-        """Test sync when ENTERPRISE_ENABLED is False (community edition)."""
-        # Arrange
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = False
-
-            # Act
-            result = sync_account_deletion(account_id="acc-123", source="account_deleted")
-
-            # Assert
-            assert result is True
-            mock_db_session.query.assert_not_called()
-            mock_queue_task.assert_not_called()
-
-    def test_sync_account_deletion_multiple_workspaces(self, mock_db_session, mock_queue_task):
-        """Test sync for account with multiple workspace memberships."""
-        # Arrange
-        account_id = "acc-123"
-
-        # Mock workspace joins
-        mock_join1 = MagicMock()
-        mock_join1.tenant_id = "tenant-1"
-        mock_join2 = MagicMock()
-        mock_join2.tenant_id = "tenant-2"
-        mock_join3 = MagicMock()
-        mock_join3.tenant_id = "tenant-3"
-
-        mock_query = MagicMock()
-        mock_query.filter_by.return_value.all.return_value = [mock_join1, mock_join2, mock_join3]
-        mock_db_session.query.return_value = mock_query
-
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = True
-
-            # Act
-            result = sync_account_deletion(account_id=account_id, source="account_deleted")
-
-            # Assert
-            assert result is True
-            assert mock_queue_task.call_count == 3
-
-            # Verify each workspace was queued
-            mock_queue_task.assert_any_call(workspace_id="tenant-1", member_id=account_id, source="account_deleted")
-            mock_queue_task.assert_any_call(workspace_id="tenant-2", member_id=account_id, source="account_deleted")
-            mock_queue_task.assert_any_call(workspace_id="tenant-3", member_id=account_id, source="account_deleted")
-
-    def test_sync_account_deletion_no_workspaces(self, mock_db_session, mock_queue_task):
-        """Test sync for account with no workspace memberships."""
-        # Arrange
-        mock_query = MagicMock()
-        mock_query.filter_by.return_value.all.return_value = []
-        mock_db_session.query.return_value = mock_query
-
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = True
-
-            # Act
-            result = sync_account_deletion(account_id="acc-123", source="account_deleted")
-
-            # Assert
-            assert result is True
-            mock_queue_task.assert_not_called()
-
-    def test_sync_account_deletion_partial_failure(self, mock_db_session, mock_queue_task):
-        """Test sync when some tasks fail to queue."""
-        # Arrange
-        account_id = "acc-123"
-
-        # Mock workspace joins
-        mock_join1 = MagicMock()
-        mock_join1.tenant_id = "tenant-1"
-        mock_join2 = MagicMock()
-        mock_join2.tenant_id = "tenant-2"
-        mock_join3 = MagicMock()
-        mock_join3.tenant_id = "tenant-3"
-
-        mock_query = MagicMock()
-        mock_query.filter_by.return_value.all.return_value = [mock_join1, mock_join2, mock_join3]
-        mock_db_session.query.return_value = mock_query
-
-        # Mock queue_task to fail for second workspace
-        def queue_side_effect(workspace_id, member_id, source):
-            return workspace_id != "tenant-2"
-
-        mock_queue_task.side_effect = queue_side_effect
-
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = True
-
-            # Act
-            result = sync_account_deletion(account_id=account_id, source="account_deleted")
-
-            # Assert
-            assert result is False  # Should return False if any task fails
-            assert mock_queue_task.call_count == 3
-
-    def test_sync_account_deletion_all_failures(self, mock_db_session, mock_queue_task):
-        """Test sync when all tasks fail to queue."""
-        # Arrange
-        mock_join = MagicMock()
-        mock_join.tenant_id = "tenant-1"
-
-        mock_query = MagicMock()
-        mock_query.filter_by.return_value.all.return_value = [mock_join]
-        mock_db_session.query.return_value = mock_query
-
-        mock_queue_task.return_value = False
-
-        with patch("services.enterprise.account_deletion_sync.dify_config") as mock_config:
-            mock_config.ENTERPRISE_ENABLED = True
-
-            # Act
-            result = sync_account_deletion(account_id="acc-123", source="account_deleted")
-
-            # Assert
-            assert result is False
-            mock_queue_task.assert_called_once()

api/tests/unit_tests/tasks/test_document_indexing_sync_task.py

@@ -114,21 +114,6 @@ def mock_db_session():
         session = MagicMock()
         # Ensure tests can observe session.close() via context manager teardown
         session.close = MagicMock()
-        session.commit = MagicMock()
-
-        # Mock session.begin() context manager to auto-commit on exit
-        begin_cm = MagicMock()
-        begin_cm.__enter__.return_value = session
-
-        def _begin_exit_side_effect(*args, **kwargs):
-            # session.begin().__exit__() should commit if no exception
-            if args[0] is None:  # No exception
-                session.commit()
-
-        begin_cm.__exit__.side_effect = _begin_exit_side_effect
-        session.begin.return_value = begin_cm
-
-        # Mock create_session() context manager
         cm = MagicMock()
         cm.__enter__.return_value = session
 

api/tests/unit_tests/tasks/test_remove_app_and_related_data_task.py

@@ -350,7 +350,7 @@ class TestDeleteWorkflowArchiveLogs:
         mock_query.where.return_value = mock_delete_query
         mock_db.session.query.return_value = mock_query
 
-        delete_func(mock_db.session, "log-1")
+        delete_func("log-1")
 
         mock_db.session.query.assert_called_once_with(WorkflowArchiveLog)
         mock_query.where.assert_called_once()

api/uv.lock

@@ -1368,7 +1368,7 @@ wheels = [
 
 [[package]]
 name = "dify-api"
-version = "1.12.1"
+version = "1.12.0"
 source = { virtual = "." }
 dependencies = [
     { name = "aliyun-log-python-sdk" },

docker/docker-compose-template.yaml

@@ -21,7 +21,7 @@ services:
 
   # API service
   api:
-    image: langgenius/dify-api:1.12.1
+    image: langgenius/dify-api:1.12.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -63,7 +63,7 @@ services:
   # worker service
   # The Celery worker for processing all queues (dataset, workflow, mail, etc.)
   worker:
-    image: langgenius/dify-api:1.12.1
+    image: langgenius/dify-api:1.12.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -102,7 +102,7 @@ services:
   # worker_beat service
   # Celery beat for scheduling periodic tasks.
   worker_beat:
-    image: langgenius/dify-api:1.12.1
+    image: langgenius/dify-api:1.12.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -132,7 +132,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:1.12.1
+    image: langgenius/dify-web:1.12.0
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}

docker/docker-compose.yaml

@@ -707,7 +707,7 @@ services:
 
   # API service
   api:
-    image: langgenius/dify-api:1.12.1
+    image: langgenius/dify-api:1.12.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -749,7 +749,7 @@ services:
   # worker service
   # The Celery worker for processing all queues (dataset, workflow, mail, etc.)
   worker:
-    image: langgenius/dify-api:1.12.1
+    image: langgenius/dify-api:1.12.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -788,7 +788,7 @@ services:
   # worker_beat service
   # Celery beat for scheduling periodic tasks.
   worker_beat:
-    image: langgenius/dify-api:1.12.1
+    image: langgenius/dify-api:1.12.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -818,7 +818,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:1.12.1
+    image: langgenius/dify-web:1.12.0
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}

web/__tests__/goto-anything/command-selector.test.tsx

@@ -1,326 +0,0 @@
-import type { ActionItem } from '../../app/components/goto-anything/actions/types'
-import { fireEvent, render, screen } from '@testing-library/react'
-import * as React from 'react'
-import CommandSelector from '../../app/components/goto-anything/command-selector'
-
-vi.mock('cmdk', () => ({
-  Command: {
-    Group: ({ children, className }: any) => <div className={className}>{children}</div>,
-    Item: ({ children, onSelect, value, className }: any) => (
-      <div
-        className={className}
-        onClick={() => onSelect?.()}
-        data-value={value}
-        data-testid={`command-item-${value}`}
-      >
-        {children}
-      </div>
-    ),
-  },
-}))
-
-describe('CommandSelector', () => {
-  const mockActions: Record<string, ActionItem> = {
-    app: {
-      key: '@app',
-      shortcut: '@app',
-      title: 'Search Applications',
-      description: 'Search apps',
-      search: vi.fn(),
-    },
-    knowledge: {
-      key: '@knowledge',
-      shortcut: '@kb',
-      title: 'Search Knowledge',
-      description: 'Search knowledge bases',
-      search: vi.fn(),
-    },
-    plugin: {
-      key: '@plugin',
-      shortcut: '@plugin',
-      title: 'Search Plugins',
-      description: 'Search plugins',
-      search: vi.fn(),
-    },
-    node: {
-      key: '@node',
-      shortcut: '@node',
-      title: 'Search Nodes',
-      description: 'Search workflow nodes',
-      search: vi.fn(),
-    },
-  }
-
-  const mockOnCommandSelect = vi.fn()
-  const mockOnCommandValueChange = vi.fn()
-
-  beforeEach(() => {
-    vi.clearAllMocks()
-  })
-
-  describe('Basic Rendering', () => {
-    it('should render all actions when no filter is provided', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@app')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@kb')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@plugin')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@node')).toBeInTheDocument()
-    })
-
-    it('should render empty filter as showing all actions', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter=""
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@app')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@kb')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@plugin')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@node')).toBeInTheDocument()
-    })
-  })
-
-  describe('Filtering Functionality', () => {
-    it('should filter actions based on searchFilter - single match', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="k"
-        />,
-      )
-
-      expect(screen.queryByTestId('command-item-@app')).not.toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@kb')).toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@plugin')).not.toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@node')).not.toBeInTheDocument()
-    })
-
-    it('should filter actions with multiple matches', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="p"
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@app')).toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@kb')).not.toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@plugin')).toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@node')).not.toBeInTheDocument()
-    })
-
-    it('should be case-insensitive when filtering', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="APP"
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@app')).toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@kb')).not.toBeInTheDocument()
-    })
-
-    it('should match partial strings', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="od"
-        />,
-      )
-
-      expect(screen.queryByTestId('command-item-@app')).not.toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@kb')).not.toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@plugin')).not.toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@node')).toBeInTheDocument()
-    })
-  })
-
-  describe('Empty State', () => {
-    it('should show empty state when no matches found', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="xyz"
-        />,
-      )
-
-      expect(screen.queryByTestId('command-item-@app')).not.toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@kb')).not.toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@plugin')).not.toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@node')).not.toBeInTheDocument()
-
-      expect(screen.getByText('app.gotoAnything.noMatchingCommands')).toBeInTheDocument()
-      expect(screen.getByText('app.gotoAnything.tryDifferentSearch')).toBeInTheDocument()
-    })
-
-    it('should not show empty state when filter is empty', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter=""
-        />,
-      )
-
-      expect(screen.queryByText('app.gotoAnything.noMatchingCommands')).not.toBeInTheDocument()
-    })
-  })
-
-  describe('Selection and Highlight Management', () => {
-    it('should call onCommandValueChange when filter changes and first item differs', () => {
-      const { rerender } = render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter=""
-          commandValue="@app"
-          onCommandValueChange={mockOnCommandValueChange}
-        />,
-      )
-
-      rerender(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="k"
-          commandValue="@app"
-          onCommandValueChange={mockOnCommandValueChange}
-        />,
-      )
-
-      expect(mockOnCommandValueChange).toHaveBeenCalledWith('@kb')
-    })
-
-    it('should not call onCommandValueChange if current value still exists', () => {
-      const { rerender } = render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter=""
-          commandValue="@app"
-          onCommandValueChange={mockOnCommandValueChange}
-        />,
-      )
-
-      rerender(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="a"
-          commandValue="@app"
-          onCommandValueChange={mockOnCommandValueChange}
-        />,
-      )
-
-      expect(mockOnCommandValueChange).not.toHaveBeenCalled()
-    })
-
-    it('should handle onCommandSelect callback correctly', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="k"
-        />,
-      )
-
-      const knowledgeItem = screen.getByTestId('command-item-@kb')
-      fireEvent.click(knowledgeItem)
-
-      expect(mockOnCommandSelect).toHaveBeenCalledWith('@kb')
-    })
-  })
-
-  describe('Edge Cases', () => {
-    it('should handle empty actions object', () => {
-      render(
-        <CommandSelector
-          actions={{}}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter=""
-        />,
-      )
-
-      expect(screen.getByText('app.gotoAnything.noMatchingCommands')).toBeInTheDocument()
-    })
-
-    it('should handle special characters in filter', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="@"
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@app')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@kb')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@plugin')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@node')).toBeInTheDocument()
-    })
-
-    it('should handle undefined onCommandValueChange gracefully', () => {
-      const { rerender } = render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter=""
-        />,
-      )
-
-      expect(() => {
-        rerender(
-          <CommandSelector
-            actions={mockActions}
-            onCommandSelect={mockOnCommandSelect}
-            searchFilter="k"
-          />,
-        )
-      }).not.toThrow()
-    })
-  })
-
-  describe('Backward Compatibility', () => {
-    it('should work without searchFilter prop (backward compatible)', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@app')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@kb')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@plugin')).toBeInTheDocument()
-      expect(screen.getByTestId('command-item-@node')).toBeInTheDocument()
-    })
-
-    it('should work without commandValue and onCommandValueChange props', () => {
-      render(
-        <CommandSelector
-          actions={mockActions}
-          onCommandSelect={mockOnCommandSelect}
-          searchFilter="k"
-        />,
-      )
-
-      expect(screen.getByTestId('command-item-@kb')).toBeInTheDocument()
-      expect(screen.queryByTestId('command-item-@app')).not.toBeInTheDocument()
-    })
-  })
-})

web/__tests__/goto-anything/match-action.test.ts

@@ -1,236 +0,0 @@
-import type { Mock } from 'vitest'
-import type { ActionItem } from '../../app/components/goto-anything/actions/types'
-
-// Import after mocking to get mocked version
-import { matchAction } from '../../app/components/goto-anything/actions'
-import { slashCommandRegistry } from '../../app/components/goto-anything/actions/commands/registry'
-
-// Mock the entire actions module to avoid import issues
-vi.mock('../../app/components/goto-anything/actions', () => ({
-  matchAction: vi.fn(),
-}))
-
-vi.mock('../../app/components/goto-anything/actions/commands/registry')
-
-// Implement the actual matchAction logic for testing
-const actualMatchAction = (query: string, actions: Record<string, ActionItem>) => {
-  const result = Object.values(actions).find((action) => {
-    // Special handling for slash commands
-    if (action.key === '/') {
-      // Get all registered commands from the registry
-      const allCommands = slashCommandRegistry.getAllCommands()
-
-      // Check if query matches any registered command
-      return allCommands.some((cmd) => {
-        const cmdPattern = `/${cmd.name}`
-
-        // For direct mode commands, don't match (keep in command selector)
-        if (cmd.mode === 'direct')
-          return false
-
-        // For submenu mode commands, match when complete command is entered
-        return query === cmdPattern || query.startsWith(`${cmdPattern} `)
-      })
-    }
-
-    const reg = new RegExp(`^(${action.key}|${action.shortcut})(?:\\s|$)`)
-    return reg.test(query)
-  })
-  return result
-}
-
-// Replace mock with actual implementation
-;(matchAction as Mock).mockImplementation(actualMatchAction)
-
-describe('matchAction Logic', () => {
-  const mockActions: Record<string, ActionItem> = {
-    app: {
-      key: '@app',
-      shortcut: '@a',
-      title: 'Search Applications',
-      description: 'Search apps',
-      search: vi.fn(),
-    },
-    knowledge: {
-      key: '@knowledge',
-      shortcut: '@kb',
-      title: 'Search Knowledge',
-      description: 'Search knowledge bases',
-      search: vi.fn(),
-    },
-    slash: {
-      key: '/',
-      shortcut: '/',
-      title: 'Commands',
-      description: 'Execute commands',
-      search: vi.fn(),
-    },
-  }
-
-  beforeEach(() => {
-    vi.clearAllMocks()
-    ;(slashCommandRegistry.getAllCommands as Mock).mockReturnValue([
-      { name: 'docs', mode: 'direct' },
-      { name: 'community', mode: 'direct' },
-      { name: 'feedback', mode: 'direct' },
-      { name: 'account', mode: 'direct' },
-      { name: 'theme', mode: 'submenu' },
-      { name: 'language', mode: 'submenu' },
-    ])
-  })
-
-  describe('@ Actions Matching', () => {
-    it('should match @app with key', () => {
-      const result = matchAction('@app', mockActions)
-      expect(result).toBe(mockActions.app)
-    })
-
-    it('should match @app with shortcut', () => {
-      const result = matchAction('@a', mockActions)
-      expect(result).toBe(mockActions.app)
-    })
-
-    it('should match @knowledge with key', () => {
-      const result = matchAction('@knowledge', mockActions)
-      expect(result).toBe(mockActions.knowledge)
-    })
-
-    it('should match @knowledge with shortcut @kb', () => {
-      const result = matchAction('@kb', mockActions)
-      expect(result).toBe(mockActions.knowledge)
-    })
-
-    it('should match with text after action', () => {
-      const result = matchAction('@app search term', mockActions)
-      expect(result).toBe(mockActions.app)
-    })
-
-    it('should not match partial @ actions', () => {
-      const result = matchAction('@ap', mockActions)
-      expect(result).toBeUndefined()
-    })
-  })
-
-  describe('Slash Commands Matching', () => {
-    describe('Direct Mode Commands', () => {
-      it('should not match direct mode commands', () => {
-        const result = matchAction('/docs', mockActions)
-        expect(result).toBeUndefined()
-      })
-
-      it('should not match direct mode with arguments', () => {
-        const result = matchAction('/docs something', mockActions)
-        expect(result).toBeUndefined()
-      })
-
-      it('should not match any direct mode command', () => {
-        expect(matchAction('/community', mockActions)).toBeUndefined()
-        expect(matchAction('/feedback', mockActions)).toBeUndefined()
-        expect(matchAction('/account', mockActions)).toBeUndefined()
-      })
-    })
-
-    describe('Submenu Mode Commands', () => {
-      it('should match submenu mode commands exactly', () => {
-        const result = matchAction('/theme', mockActions)
-        expect(result).toBe(mockActions.slash)
-      })
-
-      it('should match submenu mode with arguments', () => {
-        const result = matchAction('/theme dark', mockActions)
-        expect(result).toBe(mockActions.slash)
-      })
-
-      it('should match all submenu commands', () => {
-        expect(matchAction('/language', mockActions)).toBe(mockActions.slash)
-        expect(matchAction('/language en', mockActions)).toBe(mockActions.slash)
-      })
-    })
-
-    describe('Slash Without Command', () => {
-      it('should not match single slash', () => {
-        const result = matchAction('/', mockActions)
-        expect(result).toBeUndefined()
-      })
-
-      it('should not match unregistered commands', () => {
-        const result = matchAction('/unknown', mockActions)
-        expect(result).toBeUndefined()
-      })
-    })
-  })
-
-  describe('Edge Cases', () => {
-    it('should handle empty query', () => {
-      const result = matchAction('', mockActions)
-      expect(result).toBeUndefined()
-    })
-
-    it('should handle whitespace only', () => {
-      const result = matchAction('  ', mockActions)
-      expect(result).toBeUndefined()
-    })
-
-    it('should handle regular text without actions', () => {
-      const result = matchAction('search something', mockActions)
-      expect(result).toBeUndefined()
-    })
-
-    it('should handle special characters', () => {
-      const result = matchAction('#tag', mockActions)
-      expect(result).toBeUndefined()
-    })
-
-    it('should handle multiple @ or /', () => {
-      expect(matchAction('@@app', mockActions)).toBeUndefined()
-      expect(matchAction('//theme', mockActions)).toBeUndefined()
-    })
-  })
-
-  describe('Mode-based Filtering', () => {
-    it('should filter direct mode commands from matching', () => {
-      ;(slashCommandRegistry.getAllCommands as Mock).mockReturnValue([
-        { name: 'test', mode: 'direct' },
-      ])
-
-      const result = matchAction('/test', mockActions)
-      expect(result).toBeUndefined()
-    })
-
-    it('should allow submenu mode commands to match', () => {
-      ;(slashCommandRegistry.getAllCommands as Mock).mockReturnValue([
-        { name: 'test', mode: 'submenu' },
-      ])
-
-      const result = matchAction('/test', mockActions)
-      expect(result).toBe(mockActions.slash)
-    })
-
-    it('should treat undefined mode as submenu', () => {
-      ;(slashCommandRegistry.getAllCommands as Mock).mockReturnValue([
-        { name: 'test' }, // No mode specified
-      ])
-
-      const result = matchAction('/test', mockActions)
-      expect(result).toBe(mockActions.slash)
-    })
-  })
-
-  describe('Registry Integration', () => {
-    it('should call getAllCommands when matching slash', () => {
-      matchAction('/theme', mockActions)
-      expect(slashCommandRegistry.getAllCommands).toHaveBeenCalled()
-    })
-
-    it('should not call getAllCommands for @ actions', () => {
-      matchAction('@app', mockActions)
-      expect(slashCommandRegistry.getAllCommands).not.toHaveBeenCalled()
-    })
-
-    it('should handle empty command list', () => {
-      ;(slashCommandRegistry.getAllCommands as Mock).mockReturnValue([])
-      const result = matchAction('/anything', mockActions)
-      expect(result).toBeUndefined()
-    })
-  })
-})

web/__tests__/goto-anything/search-error-handling.test.ts

@@ -9,10 +9,8 @@ import type { MockedFunction } from 'vitest'
  * 4. Ensure errors don't propagate to UI layer causing "search failed"
  */
 
-import { Actions, searchAnything } from '@/app/components/goto-anything/actions'
-import { fetchAppList } from '@/service/apps'
-import { postMarketplace } from '@/service/base'
-import { fetchDatasets } from '@/service/datasets'
+import { appScope, knowledgeScope, pluginScope, searchAnything } from '@/app/components/goto-anything/actions'
+import { searchApps, searchDatasets, searchPlugins } from '@/service/use-goto-anything'
 
 // Mock react-i18next before importing modules that use it
 vi.mock('react-i18next', () => ({
@@ -22,22 +20,17 @@ vi.mock('react-i18next', () => ({
   }),
 }))
 
-// Mock API functions
-vi.mock('@/service/base', () => ({
-  postMarketplace: vi.fn(),
+// Mock the new oRPC-based service functions
+vi.mock('@/service/use-goto-anything', () => ({
+  searchApps: vi.fn(),
+  searchDatasets: vi.fn(),
+  searchPlugins: vi.fn(),
 }))
 
-vi.mock('@/service/apps', () => ({
-  fetchAppList: vi.fn(),
-}))
-
-vi.mock('@/service/datasets', () => ({
-  fetchDatasets: vi.fn(),
-}))
-
-const mockPostMarketplace = postMarketplace as MockedFunction<typeof postMarketplace>
-const mockFetchAppList = fetchAppList as MockedFunction<typeof fetchAppList>
-const mockFetchDatasets = fetchDatasets as MockedFunction<typeof fetchDatasets>
+const mockSearchApps = searchApps as MockedFunction<typeof searchApps>
+const mockSearchDatasets = searchDatasets as MockedFunction<typeof searchDatasets>
+const mockSearchPlugins = searchPlugins as MockedFunction<typeof searchPlugins>
+const searchScopes = [appScope, knowledgeScope, pluginScope]
 
 describe('GotoAnything Search Error Handling', () => {
   beforeEach(() => {
@@ -55,45 +48,33 @@ describe('GotoAnything Search Error Handling', () => {
   describe('@plugin search error handling', () => {
     it('should return empty array when API fails instead of throwing error', async () => {
       // Mock marketplace API failure (403 permission denied)
-      mockPostMarketplace.mockRejectedValue(new Error('HTTP 403: Forbidden'))
+      mockSearchPlugins.mockRejectedValue(new Error('HTTP 403: Forbidden'))
 
-      const pluginAction = Actions.plugin
-
-      // Directly call plugin action's search method
-      const result = await pluginAction.search('@plugin', 'test', 'en')
+      const result = await pluginScope.search('@plugin', 'test', 'en')
 
       // Should return empty array instead of throwing error
       expect(result).toEqual([])
-      expect(mockPostMarketplace).toHaveBeenCalledWith('/plugins/search/advanced', {
-        body: {
-          page: 1,
-          page_size: 10,
-          query: 'test',
-          type: 'plugin',
-        },
-      })
+      expect(mockSearchPlugins).toHaveBeenCalledWith('test')
     })
 
     it('should return empty array when user has no plugin data', async () => {
       // Mock marketplace returning empty data
-      mockPostMarketplace.mockResolvedValue({
-        data: { plugins: [] },
+      mockSearchPlugins.mockResolvedValue({
+        data: { plugins: [], total: 0 },
       })
 
-      const pluginAction = Actions.plugin
-      const result = await pluginAction.search('@plugin', '', 'en')
+      const result = await pluginScope.search('@plugin', '', 'en')
 
       expect(result).toEqual([])
     })
 
     it('should return empty array when API returns unexpected data structure', async () => {
       // Mock API returning unexpected data structure
-      mockPostMarketplace.mockResolvedValue({
+      mockSearchPlugins.mockResolvedValue({
         data: null,
-      })
+      } as any)
 
-      const pluginAction = Actions.plugin
-      const result = await pluginAction.search('@plugin', 'test', 'en')
+      const result = await pluginScope.search('@plugin', 'test', 'en')
 
       expect(result).toEqual([])
     })
@@ -102,20 +83,18 @@ describe('GotoAnything Search Error Handling', () => {
   describe('Other search types error handling', () => {
     it('@app search should return empty array when API fails', async () => {
       // Mock app API failure
-      mockFetchAppList.mockRejectedValue(new Error('API Error'))
+      mockSearchApps.mockRejectedValue(new Error('API Error'))
 
-      const appAction = Actions.app
-      const result = await appAction.search('@app', 'test', 'en')
+      const result = await appScope.search('@app', 'test', 'en')
 
       expect(result).toEqual([])
     })
 
     it('@knowledge search should return empty array when API fails', async () => {
       // Mock knowledge API failure
-      mockFetchDatasets.mockRejectedValue(new Error('API Error'))
+      mockSearchDatasets.mockRejectedValue(new Error('API Error'))
 
-      const knowledgeAction = Actions.knowledge
-      const result = await knowledgeAction.search('@knowledge', 'test', 'en')
+      const result = await knowledgeScope.search('@knowledge', 'test', 'en')
 
       expect(result).toEqual([])
     })
@@ -124,11 +103,11 @@ describe('GotoAnything Search Error Handling', () => {
   describe('Unified search entry error handling', () => {
     it('regular search (without @prefix) should return successful results even when partial APIs fail', async () => {
       // Set app and knowledge success, plugin failure
-      mockFetchAppList.mockResolvedValue({ data: [], has_more: false, limit: 10, page: 1, total: 0 })
-      mockFetchDatasets.mockResolvedValue({ data: [], has_more: false, limit: 10, page: 1, total: 0 })
-      mockPostMarketplace.mockRejectedValue(new Error('Plugin API failed'))
+      mockSearchApps.mockResolvedValue({ data: [], has_more: false, limit: 10, page: 1, total: 0 })
+      mockSearchDatasets.mockResolvedValue({ data: [], has_more: false, limit: 10, page: 1, total: 0 })
+      mockSearchPlugins.mockRejectedValue(new Error('Plugin API failed'))
 
-      const result = await searchAnything('en', 'test')
+      const result = await searchAnything('en', 'test', undefined, searchScopes)
 
       // Should return successful results even if plugin search fails
       expect(result).toEqual([])
@@ -137,10 +116,9 @@ describe('GotoAnything Search Error Handling', () => {
 
     it('@plugin dedicated search should return empty array when API fails', async () => {
       // Mock plugin API failure
-      mockPostMarketplace.mockRejectedValue(new Error('Plugin service unavailable'))
+      mockSearchPlugins.mockRejectedValue(new Error('Plugin service unavailable'))
 
-      const pluginAction = Actions.plugin
-      const result = await searchAnything('en', '@plugin test', pluginAction)
+      const result = await searchAnything('en', '@plugin test', pluginScope, searchScopes)
 
       // Should return empty array instead of throwing error
       expect(result).toEqual([])
@@ -148,10 +126,9 @@ describe('GotoAnything Search Error Handling', () => {
 
     it('@app dedicated search should return empty array when API fails', async () => {
       // Mock app API failure
-      mockFetchAppList.mockRejectedValue(new Error('App service unavailable'))
+      mockSearchApps.mockRejectedValue(new Error('App service unavailable'))
 
-      const appAction = Actions.app
-      const result = await searchAnything('en', '@app test', appAction)
+      const result = await searchAnything('en', '@app test', appScope, searchScopes)
 
       expect(result).toEqual([])
     })
@@ -160,14 +137,14 @@ describe('GotoAnything Search Error Handling', () => {
   describe('Error handling consistency validation', () => {
     it('all search types should return empty array when encountering errors', async () => {
       // Mock all APIs to fail
-      mockPostMarketplace.mockRejectedValue(new Error('Plugin API failed'))
-      mockFetchAppList.mockRejectedValue(new Error('App API failed'))
-      mockFetchDatasets.mockRejectedValue(new Error('Dataset API failed'))
+      mockSearchPlugins.mockRejectedValue(new Error('Plugin API failed'))
+      mockSearchApps.mockRejectedValue(new Error('App API failed'))
+      mockSearchDatasets.mockRejectedValue(new Error('Dataset API failed'))
 
       const actions = [
-        { name: '@plugin', action: Actions.plugin },
-        { name: '@app', action: Actions.app },
-        { name: '@knowledge', action: Actions.knowledge },
+        { name: '@plugin', action: pluginScope },
+        { name: '@app', action: appScope },
+        { name: '@knowledge', action: knowledgeScope },
       ]
 
       for (const { name, action } of actions) {
@@ -179,9 +156,9 @@ describe('GotoAnything Search Error Handling', () => {
 
   describe('Edge case testing', () => {
     it('empty search term should be handled properly', async () => {
-      mockPostMarketplace.mockResolvedValue({ data: { plugins: [] } })
+      mockSearchPlugins.mockResolvedValue({ data: { plugins: [], total: 0 } })
 
-      const result = await searchAnything('en', '@plugin ', Actions.plugin)
+      const result = await searchAnything('en', '@plugin ', pluginScope, searchScopes)
       expect(result).toEqual([])
     })
 
@@ -189,17 +166,17 @@ describe('GotoAnything Search Error Handling', () => {
       const timeoutError = new Error('Network timeout')
       timeoutError.name = 'TimeoutError'
 
-      mockPostMarketplace.mockRejectedValue(timeoutError)
+      mockSearchPlugins.mockRejectedValue(timeoutError)
 
-      const result = await searchAnything('en', '@plugin test', Actions.plugin)
+      const result = await searchAnything('en', '@plugin test', pluginScope, searchScopes)
       expect(result).toEqual([])
     })
 
     it('JSON parsing errors should be handled correctly', async () => {
       const parseError = new SyntaxError('Unexpected token in JSON')
-      mockPostMarketplace.mockRejectedValue(parseError)
+      mockSearchPlugins.mockRejectedValue(parseError)
 
-      const result = await searchAnything('en', '@plugin test', Actions.plugin)
+      const result = await searchAnything('en', '@plugin test', pluginScope, searchScopes)
       expect(result).toEqual([])
     })
   })

web/app/components/app/configuration/config/agent/agent-tools/index.tsx

@@ -109,7 +109,6 @@ const AgentTools: FC = () => {
       tool_parameters: paramsWithDefaultValue,
       notAuthor: !tool.is_team_authorization,
       enabled: true,
-      type: tool.provider_type as CollectionType,
     }
   }
   const handleSelectTool = (tool: ToolDefaultValue) => {

web/app/components/app/configuration/config/automatic/version-selector.tsx

@@ -10,9 +10,15 @@ type VersionSelectorProps = {
   versionLen: number
   value: number
   onChange: (index: number) => void
+  contentClassName?: string
 }
 
-const VersionSelector: React.FC<VersionSelectorProps> = ({ versionLen, value, onChange }) => {
+const VersionSelector: React.FC<VersionSelectorProps> = ({
+  versionLen,
+  value,
+  onChange,
+  contentClassName,
+}) => {
   const { t } = useTranslation()
   const [isOpen, {
     setFalse: handleOpenFalse,
@@ -64,6 +70,7 @@ const VersionSelector: React.FC<VersionSelectorProps> = ({ versionLen, value, on
       </PortalToFollowElemTrigger>
       <PortalToFollowElemContent className={cn(
         'z-[99]',
+        contentClassName,
       )}
       >
         <div

web/app/components/app/create-app-modal/index.spec.tsx

@@ -1,4 +1,3 @@
-import type { App } from '@/types/app'
 import { fireEvent, render, screen, waitFor } from '@testing-library/react'
 import { useRouter } from 'next/navigation'
 import { afterAll, beforeEach, describe, expect, it, vi } from 'vitest'
@@ -14,8 +13,8 @@ import { getRedirection } from '@/utils/app-redirection'
 import CreateAppModal from './index'
 
 vi.mock('ahooks', () => ({
-  useDebounceFn: <T extends (...args: unknown[]) => unknown>(fn: T) => {
-    const run = (...args: Parameters<T>) => fn(...args)
+  useDebounceFn: (fn: (...args: any[]) => any) => {
+    const run = (...args: any[]) => fn(...args)
     const cancel = vi.fn()
     const flush = vi.fn()
     return { run, cancel, flush }
@@ -84,7 +83,7 @@ describe('CreateAppModal', () => {
 
   beforeEach(() => {
     vi.clearAllMocks()
-    mockUseRouter.mockReturnValue({ push: mockPush } as unknown as ReturnType<typeof useRouter>)
+    mockUseRouter.mockReturnValue({ push: mockPush } as any)
     mockUseProviderContext.mockReturnValue({
       plan: {
         type: AppModeEnum.ADVANCED_CHAT,
@@ -93,10 +92,10 @@ describe('CreateAppModal', () => {
         reset: {},
       },
       enableBilling: true,
-    } as unknown as ReturnType<typeof useProviderContext>)
+    } as any)
     mockUseAppContext.mockReturnValue({
       isCurrentWorkspaceEditor: true,
-    } as unknown as ReturnType<typeof useAppContext>)
+    } as any)
     mockSetItem.mockClear()
     Object.defineProperty(window, 'localStorage', {
       value: {
@@ -119,8 +118,8 @@ describe('CreateAppModal', () => {
   })
 
   it('creates an app, notifies success, and fires callbacks', async () => {
-    const mockApp: Partial<App> = { id: 'app-1', mode: AppModeEnum.ADVANCED_CHAT }
-    mockCreateApp.mockResolvedValue(mockApp as App)
+    const mockApp = { id: 'app-1', mode: AppModeEnum.ADVANCED_CHAT }
+    mockCreateApp.mockResolvedValue(mockApp as any)
     const { onClose, onSuccess } = renderModal()
 
     const nameInput = screen.getByPlaceholderText('app.newApp.appNamePlaceholder')

web/app/components/base/effect/index.stories.tsx

@@ -1,3 +1,4 @@
+/* eslint-disable tailwindcss/classnames-order */
 import type { Meta, StoryObj } from '@storybook/nextjs-vite'
 import Effect from '.'
 
@@ -28,8 +29,8 @@ type Story = StoryObj<typeof meta>
 export const Playground: Story = {
   render: () => (
     <div className="relative h-40 w-72 overflow-hidden rounded-2xl border border-divider-subtle bg-background-default-subtle">
-      <Effect className="left-8 top-6" />
-      <Effect className="bg-util-colors-purple-brand-purple-brand-500 right-10 top-14" />
+      <Effect className="top-6 left-8" />
+      <Effect className="top-14 right-10 bg-util-colors-purple-brand-purple-brand-500" />
       <div className="absolute inset-x-0 bottom-4 flex justify-center text-xs text-text-secondary">
         Accent glow
       </div>

web/app/components/base/tooltip/index.tsx

@@ -4,7 +4,7 @@ import type { FC } from 'react'
 import { RiQuestionLine } from '@remixicon/react'
 import { useBoolean } from 'ahooks'
 import * as React from 'react'
-import { useCallback, useEffect, useRef, useState } from 'react'
+import { useEffect, useRef, useState } from 'react'
 import { PortalToFollowElem, PortalToFollowElemContent, PortalToFollowElemTrigger } from '@/app/components/base/portal-to-follow-elem'
 import { cn } from '@/utils/classnames'
 import { tooltipManager } from './TooltipManager'
@@ -61,20 +61,6 @@ const Tooltip: FC<TooltipProps> = ({
     isHoverTriggerRef.current = isHoverTrigger
   }, [isHoverTrigger])
 
-  const closeTimeoutRef = useRef<ReturnType<typeof setTimeout> | null>(null)
-  const clearCloseTimeout = useCallback(() => {
-    if (closeTimeoutRef.current) {
-      clearTimeout(closeTimeoutRef.current)
-      closeTimeoutRef.current = null
-    }
-  }, [])
-
-  useEffect(() => {
-    return () => {
-      clearCloseTimeout()
-    }
-  }, [clearCloseTimeout])
-
   const close = () => setOpen(false)
 
   const handleLeave = (isTrigger: boolean) => {
@@ -85,9 +71,7 @@ const Tooltip: FC<TooltipProps> = ({
 
     // give time to move to the popup
     if (needsDelay) {
-      clearCloseTimeout()
-      closeTimeoutRef.current = setTimeout(() => {
-        closeTimeoutRef.current = null
+      setTimeout(() => {
         if (!isHoverPopupRef.current && !isHoverTriggerRef.current) {
           setOpen(false)
           tooltipManager.clear(close)
@@ -95,7 +79,6 @@ const Tooltip: FC<TooltipProps> = ({
       }, 300)
     }
     else {
-      clearCloseTimeout()
       setOpen(false)
       tooltipManager.clear(close)
     }
@@ -112,7 +95,6 @@ const Tooltip: FC<TooltipProps> = ({
         onClick={() => triggerMethod === 'click' && setOpen(v => !v)}
         onMouseEnter={() => {
           if (triggerMethod === 'hover') {
-            clearCloseTimeout()
             setHoverTrigger()
             tooltipManager.register(close)
             setOpen(true)
@@ -133,12 +115,7 @@ const Tooltip: FC<TooltipProps> = ({
               !noDecoration && 'system-xs-regular relative max-w-[300px] break-words rounded-md bg-components-panel-bg px-3 py-2 text-left text-text-tertiary shadow-lg',
               popupClassName,
             )}
-            onMouseEnter={() => {
-              if (triggerMethod === 'hover') {
-                clearCloseTimeout()
-                setHoverPopup()
-              }
-            }}
+            onMouseEnter={() => triggerMethod === 'hover' && setHoverPopup()}
             onMouseLeave={() => triggerMethod === 'hover' && handleLeave(false)}
           >
             {popupContent}

web/app/components/datasets/common/image-uploader/utils.spec.ts

@@ -216,22 +216,13 @@ describe('image-uploader utils', () => {
     type FileCallback = (file: MockFile) => void
     type EntriesCallback = (entries: FileSystemEntry[]) => void
 
-    // Helper to create mock FileSystemEntry with required properties
-    const createMockEntry = (props: {
-      isFile: boolean
-      isDirectory: boolean
-      name?: string
-      file?: (callback: FileCallback) => void
-      createReader?: () => { readEntries: (callback: EntriesCallback) => void }
-    }): FileSystemEntry => props as unknown as FileSystemEntry
-
     it('should resolve with file array for file entry', async () => {
       const mockFile: MockFile = { name: 'test.png' }
-      const mockEntry = createMockEntry({
+      const mockEntry = {
         isFile: true,
         isDirectory: false,
         file: (callback: FileCallback) => callback(mockFile),
-      })
+      }
 
       const result = await traverseFileEntry(mockEntry)
       expect(result).toHaveLength(1)
@@ -241,11 +232,11 @@ describe('image-uploader utils', () => {
 
     it('should resolve with file array with prefix for nested file', async () => {
       const mockFile: MockFile = { name: 'test.png' }
-      const mockEntry = createMockEntry({
+      const mockEntry = {
         isFile: true,
         isDirectory: false,
         file: (callback: FileCallback) => callback(mockFile),
-      })
+      }
 
       const result = await traverseFileEntry(mockEntry, 'folder/')
       expect(result).toHaveLength(1)
@@ -253,24 +244,24 @@ describe('image-uploader utils', () => {
     })
 
     it('should resolve empty array for unknown entry type', async () => {
-      const mockEntry = createMockEntry({
+      const mockEntry = {
         isFile: false,
         isDirectory: false,
-      })
+      }
 
       const result = await traverseFileEntry(mockEntry)
       expect(result).toEqual([])
     })
 
     it('should handle directory with no files', async () => {
-      const mockEntry = createMockEntry({
+      const mockEntry = {
         isFile: false,
         isDirectory: true,
         name: 'empty-folder',
         createReader: () => ({
           readEntries: (callback: EntriesCallback) => callback([]),
         }),
-      })
+      }
 
       const result = await traverseFileEntry(mockEntry)
       expect(result).toEqual([])
@@ -280,20 +271,20 @@ describe('image-uploader utils', () => {
       const mockFile1: MockFile = { name: 'file1.png' }
       const mockFile2: MockFile = { name: 'file2.png' }
 
-      const mockFileEntry1 = createMockEntry({
+      const mockFileEntry1 = {
         isFile: true,
         isDirectory: false,
         file: (callback: FileCallback) => callback(mockFile1),
-      })
+      }
 
-      const mockFileEntry2 = createMockEntry({
+      const mockFileEntry2 = {
         isFile: true,
         isDirectory: false,
         file: (callback: FileCallback) => callback(mockFile2),
-      })
+      }
 
       let readCount = 0
-      const mockEntry = createMockEntry({
+      const mockEntry = {
         isFile: false,
         isDirectory: true,
         name: 'folder',
@@ -301,14 +292,14 @@ describe('image-uploader utils', () => {
           readEntries: (callback: EntriesCallback) => {
             if (readCount === 0) {
               readCount++
-              callback([mockFileEntry1, mockFileEntry2])
+              callback([mockFileEntry1, mockFileEntry2] as unknown as FileSystemEntry[])
             }
             else {
               callback([])
             }
           },
         }),
-      })
+      }
 
       const result = await traverseFileEntry(mockEntry)
       expect(result).toHaveLength(2)

web/app/components/datasets/common/image-uploader/utils.ts

@@ -18,17 +18,17 @@ type FileWithPath = {
   relativePath?: string
 } & File
 
-export const traverseFileEntry = (entry: FileSystemEntry, prefix = ''): Promise<FileWithPath[]> => {
+export const traverseFileEntry = (entry: any, prefix = ''): Promise<FileWithPath[]> => {
   return new Promise((resolve) => {
     if (entry.isFile) {
-      (entry as FileSystemFileEntry).file((file: FileWithPath) => {
+      entry.file((file: FileWithPath) => {
         file.relativePath = `${prefix}${file.name}`
         resolve([file])
       })
     }
     else if (entry.isDirectory) {
-      const reader = (entry as FileSystemDirectoryEntry).createReader()
-      const entries: FileSystemEntry[] = []
+      const reader = entry.createReader()
+      const entries: any[] = []
       const read = () => {
         reader.readEntries(async (results: FileSystemEntry[]) => {
           if (!results.length) {