update

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Crazywoola <100913391+crazywoola@users.noreply.github.com>

.agents/skills/component-refactoring/SKILL.md

@@ -187,53 +187,13 @@ const Template = useMemo(() => {
 
 **When**: Component directly handles API calls, data transformation, or complex async operations.
 
-**Dify Convention**: Use `@tanstack/react-query` hooks from `web/service/use-*.ts` or create custom data hooks.
-
-```typescript
-// ❌ Before: API logic in component
-const MCPServiceCard = () => {
-  const [basicAppConfig, setBasicAppConfig] = useState({})
-  
-  useEffect(() => {
-    if (isBasicApp && appId) {
-      (async () => {
-        const res = await fetchAppDetail({ url: '/apps', id: appId })
-        setBasicAppConfig(res?.model_config || {})
-      })()
-    }
-  }, [appId, isBasicApp])
-  
-  // More API-related logic...
-}
-
-// ✅ After: Extract to data hook using React Query
-// use-app-config.ts
-import { useQuery } from '@tanstack/react-query'
-import { get } from '@/service/base'
-
-const NAME_SPACE = 'appConfig'
-
-export const useAppConfig = (appId: string, isBasicApp: boolean) => {
-  return useQuery({
-    enabled: isBasicApp && !!appId,
-    queryKey: [NAME_SPACE, 'detail', appId],
-    queryFn: () => get<AppDetailResponse>(`/apps/${appId}`),
-    select: data => data?.model_config || {},
-  })
-}
-
-// Component becomes cleaner
-const MCPServiceCard = () => {
-  const { data: config, isLoading } = useAppConfig(appId, isBasicApp)
-  // UI only
-}
-```
-
-**React Query Best Practices in Dify**:
-- Define `NAME_SPACE` for query key organization
-- Use `enabled` option for conditional fetching
-- Use `select` for data transformation
-- Export invalidation hooks: `useInvalidXxx`
+**Dify Convention**:
+- This skill is for component decomposition, not query/mutation design.
+- When refactoring data fetching, follow `web/AGENTS.md`.
+- Use `orpc-contract-first` for contracts, query shape, data-fetching wrappers, and query/mutation call-site patterns.
+- Use `web/docs/query-mutation.md` for Dify-specific conditional query, invalidation, and mutation error-handling rules.
+- Do not introduce deprecated `useInvalid` / `useReset`.
+- Do not add thin passthrough `useQuery` wrappers during refactoring; only extract a custom hook when it truly orchestrates multiple queries/mutations or shared derived state.
 
 **Dify Examples**:
 - `web/service/use-workflow.ts`

.agents/skills/component-refactoring/references/hook-extraction.md

@@ -155,48 +155,15 @@ const Configuration: FC = () => {
 
 ## Common Hook Patterns in Dify
 
-### 1. Data Fetching Hook (React Query)
+### 1. Data Fetching / Mutation Hooks
 
-```typescript
-// Pattern: Use @tanstack/react-query for data fetching
-import { useQuery, useQueryClient } from '@tanstack/react-query'
-import { get } from '@/service/base'
-import { useInvalid } from '@/service/use-base'
+When hook extraction touches query or mutation code, do not use this reference as the source of truth for data-layer patterns.
 
-const NAME_SPACE = 'appConfig'
-
-// Query keys for cache management
-export const appConfigQueryKeys = {
-  detail: (appId: string) => [NAME_SPACE, 'detail', appId] as const,
-}
-
-// Main data hook
-export const useAppConfig = (appId: string) => {
-  return useQuery({
-    enabled: !!appId,
-    queryKey: appConfigQueryKeys.detail(appId),
-    queryFn: () => get<AppDetailResponse>(`/apps/${appId}`),
-    select: data => data?.model_config || null,
-  })
-}
-
-// Invalidation hook for refreshing data
-export const useInvalidAppConfig = () => {
-  return useInvalid([NAME_SPACE])
-}
-
-// Usage in component
-const Component = () => {
-  const { data: config, isLoading, error, refetch } = useAppConfig(appId)
-  const invalidAppConfig = useInvalidAppConfig()
-  
-  const handleRefresh = () => {
-    invalidAppConfig() // Invalidates cache and triggers refetch
-  }
-  
-  return <div>...</div>
-}
-```
+- Follow `web/AGENTS.md` first.
+- Use `orpc-contract-first` for contracts, query shape, data-fetching wrappers, and query/mutation call-site patterns.
+- Use `web/docs/query-mutation.md` for conditional query, invalidation, and mutation error-handling rules.
+- Do not introduce deprecated `useInvalid` / `useReset`.
+- Do not extract thin passthrough `useQuery` hooks; only extract orchestration hooks.
 
 ### 2. Form State Hook
 

.github/actions/setup-web/action.yml

@@ -1,33 +1,13 @@
 name: Setup Web Environment
-description: Setup pnpm, Node.js, and install web dependencies.
-
-inputs:
-  node-version:
-    description: Node.js version to use
-    required: false
-    default: "22"
-  install-dependencies:
-    description: Whether to install web dependencies after setting up Node.js
-    required: false
-    default: "true"
 
 runs:
   using: composite
   steps:
-    - name: Install pnpm
-      uses: pnpm/action-setup@41ff72655975bd51cab0327fa583b6e92b6d3061 # v4.2.0
+    - name: Setup Vite+
+      uses: voidzero-dev/setup-vp@b5d848f5a62488f3d3d920f8aa6ac318a60c5f07 # v1
       with:
-        package_json_file: web/package.json
-        run_install: false
-
-    - name: Setup Node.js
-      uses: actions/setup-node@53b83947a5a98c8d113130e565377fae1a50d02f # v6.3.0
-      with:
-        node-version: ${{ inputs.node-version }}
-        cache: pnpm
-        cache-dependency-path: ./web/pnpm-lock.yaml
-
-    - name: Install dependencies
-      if: ${{ inputs.install-dependencies == 'true' }}
-      shell: bash
-      run: pnpm --dir web install --frozen-lockfile
+        node-version-file: "./web/.nvmrc"
+        cache: true
+        run-install: |
+          - cwd: ./web
+            args: ['--frozen-lockfile']

.github/workflows/autofix.yml

@@ -102,13 +102,11 @@ jobs:
       - name: Setup web environment
         if: steps.web-changes.outputs.any_changed == 'true'
         uses: ./.github/actions/setup-web
-        with:
-          node-version: "24"
 
       - name: ESLint autofix
         if: steps.web-changes.outputs.any_changed == 'true'
         run: |
           cd web
-          pnpm eslint --concurrency=2 --prune-suppressions --quiet || true
+          vp exec eslint --concurrency=2 --prune-suppressions --quiet || true
 
       - uses: autofix-ci/action@7a166d7532b277f34e16238930461bf77f9d7ed8 # v1.3.3

.github/workflows/style.yml

@@ -88,7 +88,7 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         working-directory: ./web
         run: |
-          pnpm run lint:ci
+          vp run lint:ci
         # pnpm run lint:report
         # continue-on-error: true
 
@@ -102,17 +102,17 @@ jobs:
       - name: Web tsslint
         if: steps.changed-files.outputs.any_changed == 'true'
         working-directory: ./web
-        run: pnpm run lint:tss
+        run: vp run lint:tss
 
       - name: Web type check
         if: steps.changed-files.outputs.any_changed == 'true'
         working-directory: ./web
-        run: pnpm run type-check
+        run: vp run type-check
 
       - name: Web dead code check
         if: steps.changed-files.outputs.any_changed == 'true'
         working-directory: ./web
-        run: pnpm run knip
+        run: vp run knip
 
   superlinter:
     name: SuperLinter

.github/workflows/translate-i18n-claude.yml

@@ -50,8 +50,6 @@ jobs:
 
       - name: Setup web environment
         uses: ./.github/actions/setup-web
-        with:
-          install-dependencies: "false"
 
       - name: Detect changed files and generate diff
         id: detect_changes

.github/workflows/web-tests.yml

@@ -43,7 +43,7 @@ jobs:
         uses: ./.github/actions/setup-web
 
       - name: Run tests
-        run: pnpm vitest run --reporter=blob --shard=${{ matrix.shardIndex }}/${{ matrix.shardTotal }} --coverage
+        run: vp test run --reporter=blob --shard=${{ matrix.shardIndex }}/${{ matrix.shardTotal }} --coverage
 
       - name: Upload blob report
         if: ${{ !cancelled() }}
@@ -84,7 +84,7 @@ jobs:
           merge-multiple: true
 
       - name: Merge reports
-        run: pnpm vitest --merge-reports --reporter=json --reporter=agent --coverage
+        run: vp test --merge-reports --reporter=json --reporter=agent --coverage
 
       - name: Check app/components diff coverage
         env:
@@ -447,4 +447,4 @@ jobs:
       - name: Web build check
         if: steps.changed-files.outputs.any_changed == 'true'
         working-directory: ./web
-        run: pnpm run build
+        run: vp run build

.gitignore

@@ -237,3 +237,6 @@ scripts/stress-test/reports/
 # settings
 *.local.json
 *.local.md
+
+# Code Agent Folder
+.qoder/*

api/.env.example

@@ -22,10 +22,10 @@ APP_WEB_URL=http://localhost:3000
 # Files URL
 FILES_URL=http://localhost:5001
 
-# INTERNAL_FILES_URL is used for plugin daemon communication within Docker network.
-# Set this to the internal Docker service URL for proper plugin file access.
-# Example: INTERNAL_FILES_URL=http://api:5001
-INTERNAL_FILES_URL=http://127.0.0.1:5001
+# INTERNAL_FILES_URL is used by services running in Docker to reach the API file endpoints.
+# For Docker Desktop (Mac/Windows), use http://host.docker.internal:5001 when the API runs on the host.
+# For Docker Compose on Linux, use http://api:5001 when the API runs inside the Docker network.
+INTERNAL_FILES_URL=http://host.docker.internal:5001
 
 # TRIGGER URL
 TRIGGER_URL=http://localhost:5001
@@ -180,7 +180,7 @@ CONSOLE_CORS_ALLOW_ORIGINS=http://localhost:3000,*
 COOKIE_DOMAIN=
 
 # Vector database configuration
-# Supported values are `weaviate`, `oceanbase`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`,  `opengauss`, `tablestore`,`vastbase`,`tidb`,`tidb_on_qdrant`,`baidu`,`lindorm`,`huawei_cloud`,`upstash`, `matrixone`.
+# Supported values are `weaviate`, `oceanbase`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`,  `opengauss`, `tablestore`,`vastbase`,`tidb`,`tidb_on_qdrant`,`baidu`,`lindorm`,`huawei_cloud`,`upstash`, `matrixone`, `hologres`.
 VECTOR_STORE=weaviate
 # Prefix used to create collection name in vector database
 VECTOR_INDEX_NAME_PREFIX=Vector_index
@@ -217,6 +217,20 @@ COUCHBASE_PASSWORD=password
 COUCHBASE_BUCKET_NAME=Embeddings
 COUCHBASE_SCOPE_NAME=_default
 
+# Hologres configuration
+# access_key_id is used as the PG username, access_key_secret is used as the PG password
+HOLOGRES_HOST=
+HOLOGRES_PORT=80
+HOLOGRES_DATABASE=
+HOLOGRES_ACCESS_KEY_ID=
+HOLOGRES_ACCESS_KEY_SECRET=
+HOLOGRES_SCHEMA=public
+HOLOGRES_TOKENIZER=jieba
+HOLOGRES_DISTANCE_METHOD=Cosine
+HOLOGRES_BASE_QUANTIZATION_TYPE=rabitq
+HOLOGRES_MAX_DEGREE=64
+HOLOGRES_EF_CONSTRUCTION=400
+
 # Milvus configuration
 MILVUS_URI=http://127.0.0.1:19530
 MILVUS_TOKEN=

api/.importlinter

@@ -43,7 +43,6 @@ forbidden_modules =
     extensions.ext_redis
 allow_indirect_imports = True
 ignore_imports =
-    dify_graph.nodes.agent.agent_node -> extensions.ext_database
     dify_graph.nodes.llm.node -> extensions.ext_database
     dify_graph.model_runtime.model_providers.__base.ai_model -> extensions.ext_redis
     dify_graph.model_runtime.model_providers.model_provider_factory -> extensions.ext_redis
@@ -90,9 +89,6 @@ forbidden_modules =
     core.trigger
     core.variables
 ignore_imports =
-    dify_graph.nodes.agent.agent_node -> core.model_manager
-    dify_graph.nodes.agent.agent_node -> core.provider_manager
-    dify_graph.nodes.agent.agent_node -> core.tools.tool_manager
     dify_graph.nodes.llm.llm_utils -> core.model_manager
     dify_graph.nodes.llm.protocols -> core.model_manager
     dify_graph.nodes.llm.llm_utils -> dify_graph.model_runtime.model_providers.__base.large_language_model
@@ -100,9 +96,6 @@ ignore_imports =
     dify_graph.nodes.tool.tool_node -> core.callback_handler.workflow_tool_callback_handler
     dify_graph.nodes.tool.tool_node -> core.tools.tool_engine
     dify_graph.nodes.tool.tool_node -> core.tools.tool_manager
-    dify_graph.nodes.agent.agent_node -> core.agent.entities
-    dify_graph.nodes.agent.agent_node -> core.agent.plugin_entities
-    dify_graph.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.app.app_config.entities
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.advanced_prompt_transform
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.simple_prompt_transform
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> dify_graph.model_runtime.model_providers.__base.large_language_model
@@ -110,12 +103,10 @@ ignore_imports =
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.model_manager
     dify_graph.nodes.question_classifier.question_classifier_node -> core.model_manager
     dify_graph.nodes.tool.tool_node -> core.tools.utils.message_transformer
-    dify_graph.nodes.agent.agent_node -> models.model
     dify_graph.nodes.llm.node -> core.helper.code_executor
     dify_graph.nodes.llm.node -> core.llm_generator.output_parser.errors
     dify_graph.nodes.llm.node -> core.llm_generator.output_parser.structured_output
     dify_graph.nodes.llm.node -> core.model_manager
-    dify_graph.nodes.agent.entities -> core.prompt.entities.advanced_prompt_entities
     dify_graph.nodes.llm.entities -> core.prompt.entities.advanced_prompt_entities
     dify_graph.nodes.llm.node -> core.prompt.entities.advanced_prompt_entities
     dify_graph.nodes.llm.node -> core.prompt.utils.prompt_message_util
@@ -124,17 +115,12 @@ ignore_imports =
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.utils.prompt_message_util
     dify_graph.nodes.question_classifier.entities -> core.prompt.entities.advanced_prompt_entities
     dify_graph.nodes.question_classifier.question_classifier_node -> core.prompt.utils.prompt_message_util
-    dify_graph.nodes.knowledge_index.entities -> core.rag.retrieval.retrieval_methods
     dify_graph.nodes.llm.node -> models.dataset
-    dify_graph.nodes.agent.agent_node -> core.tools.utils.message_transformer
     dify_graph.nodes.llm.file_saver -> core.tools.signature
     dify_graph.nodes.llm.file_saver -> core.tools.tool_file_manager
     dify_graph.nodes.tool.tool_node -> core.tools.errors
-    dify_graph.nodes.agent.agent_node -> extensions.ext_database
     dify_graph.nodes.llm.node -> extensions.ext_database
-    dify_graph.nodes.agent.agent_node -> models
     dify_graph.nodes.llm.node -> models.model
-    dify_graph.nodes.agent.agent_node -> services
     dify_graph.nodes.tool.tool_node -> services
     dify_graph.model_runtime.model_providers.__base.ai_model -> configs
     dify_graph.model_runtime.model_providers.__base.ai_model -> extensions.ext_redis

api/commands/vector.py

@@ -160,6 +160,7 @@ def migrate_knowledge_vector_database():
     }
     lower_collection_vector_types = {
         VectorType.ANALYTICDB,
+        VectorType.HOLOGRES,
         VectorType.CHROMA,
         VectorType.MYSCALE,
         VectorType.PGVECTO_RS,

api/configs/middleware/__init__.py

@@ -26,6 +26,7 @@ from .vdb.chroma_config import ChromaConfig
 from .vdb.clickzetta_config import ClickzettaConfig
 from .vdb.couchbase_config import CouchbaseConfig
 from .vdb.elasticsearch_config import ElasticsearchConfig
+from .vdb.hologres_config import HologresConfig
 from .vdb.huawei_cloud_config import HuaweiCloudConfig
 from .vdb.iris_config import IrisVectorConfig
 from .vdb.lindorm_config import LindormConfig
@@ -347,6 +348,7 @@ class MiddlewareConfig(
     AnalyticdbConfig,
     ChromaConfig,
     ClickzettaConfig,
+    HologresConfig,
     HuaweiCloudConfig,
     IrisVectorConfig,
     MilvusConfig,

api/configs/middleware/vdb/hologres_config.py

@@ -0,0 +1,68 @@
+from holo_search_sdk.types import BaseQuantizationType, DistanceType, TokenizerType
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class HologresConfig(BaseSettings):
+    """
+    Configuration settings for Hologres vector database.
+
+    Hologres is compatible with PostgreSQL protocol.
+    access_key_id is used as the PostgreSQL username,
+    and access_key_secret is used as the PostgreSQL password.
+    """
+
+    HOLOGRES_HOST: str | None = Field(
+        description="Hostname or IP address of the Hologres instance.",
+        default=None,
+    )
+
+    HOLOGRES_PORT: int = Field(
+        description="Port number for connecting to the Hologres instance.",
+        default=80,
+    )
+
+    HOLOGRES_DATABASE: str | None = Field(
+        description="Name of the Hologres database to connect to.",
+        default=None,
+    )
+
+    HOLOGRES_ACCESS_KEY_ID: str | None = Field(
+        description="Alibaba Cloud AccessKey ID, also used as the PostgreSQL username.",
+        default=None,
+    )
+
+    HOLOGRES_ACCESS_KEY_SECRET: str | None = Field(
+        description="Alibaba Cloud AccessKey Secret, also used as the PostgreSQL password.",
+        default=None,
+    )
+
+    HOLOGRES_SCHEMA: str = Field(
+        description="Schema name in the Hologres database.",
+        default="public",
+    )
+
+    HOLOGRES_TOKENIZER: TokenizerType = Field(
+        description="Tokenizer for full-text search index (e.g., 'jieba', 'ik', 'standard', 'simple').",
+        default="jieba",
+    )
+
+    HOLOGRES_DISTANCE_METHOD: DistanceType = Field(
+        description="Distance method for vector index (e.g., 'Cosine', 'Euclidean', 'InnerProduct').",
+        default="Cosine",
+    )
+
+    HOLOGRES_BASE_QUANTIZATION_TYPE: BaseQuantizationType = Field(
+        description="Base quantization type for vector index (e.g., 'rabitq', 'sq8', 'fp16', 'fp32').",
+        default="rabitq",
+    )
+
+    HOLOGRES_MAX_DEGREE: int = Field(
+        description="Max degree (M) parameter for HNSW vector index.",
+        default=64,
+    )
+
+    HOLOGRES_EF_CONSTRUCTION: int = Field(
+        description="ef_construction parameter for HNSW vector index.",
+        default=400,
+    )

api/controllers/console/app/app.py

@@ -25,7 +25,8 @@ from controllers.console.wraps import (
 )
 from core.ops.ops_trace_manager import OpsTraceManager
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
-from dify_graph.enums import NodeType, WorkflowExecutionStatus
+from core.trigger.constants import TRIGGER_NODE_TYPES
+from dify_graph.enums import WorkflowExecutionStatus
 from dify_graph.file import helpers as file_helpers
 from extensions.ext_database import db
 from libs.login import current_account_with_tenant, login_required
@@ -508,11 +509,7 @@ class AppListApi(Resource):
                 .scalars()
                 .all()
             )
-            trigger_node_types = {
-                NodeType.TRIGGER_WEBHOOK,
-                NodeType.TRIGGER_SCHEDULE,
-                NodeType.TRIGGER_PLUGIN,
-            }
+            trigger_node_types = TRIGGER_NODE_TYPES
             for workflow in draft_workflows:
                 node_id = None
                 try:

api/controllers/console/app/mcp_server.py

@@ -1,5 +1,4 @@
 import json
-from enum import StrEnum
 
 from flask_restx import Resource, marshal_with
 from pydantic import BaseModel, Field
@@ -11,6 +10,7 @@ from controllers.console.wraps import account_initialization_required, edit_perm
 from extensions.ext_database import db
 from fields.app_fields import app_server_fields
 from libs.login import current_account_with_tenant, login_required
+from models.enums import AppMCPServerStatus
 from models.model import AppMCPServer
 
 DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
@@ -19,11 +19,6 @@ DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 app_server_model = console_ns.model("AppServer", app_server_fields)
 
 
-class AppMCPServerStatus(StrEnum):
-    ACTIVE = "active"
-    INACTIVE = "inactive"
-
-
 class MCPServerCreatePayload(BaseModel):
     description: str | None = Field(default=None, description="Server description")
     parameters: dict = Field(..., description="Server parameters configuration")
@@ -117,9 +112,10 @@ class AppMCPServerController(Resource):
 
         server.parameters = json.dumps(payload.parameters, ensure_ascii=False)
         if payload.status:
-            if payload.status not in [status.value for status in AppMCPServerStatus]:
+            try:
+                server.status = AppMCPServerStatus(payload.status)
+            except ValueError:
                 raise ValueError("Invalid status")
-            server.status = payload.status
         db.session.commit()
         return server
 

api/controllers/console/app/workflow.py

@@ -22,6 +22,7 @@ from core.app.apps.workflow.app_generator import SKIP_PREPARE_USER_INPUTS_KEY
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.helper.trace_id_helper import get_external_trace_id
 from core.plugin.impl.exc import PluginInvokeError
+from core.trigger.constants import TRIGGER_SCHEDULE_NODE_TYPE
 from core.trigger.debug.event_selectors import (
     TriggerDebugEvent,
     TriggerDebugEventPoller,
@@ -1209,7 +1210,7 @@ class DraftWorkflowTriggerNodeApi(Resource):
         node_type: NodeType = draft_workflow.get_node_type_from_node_config(node_config)
         event: TriggerDebugEvent | None = None
         # for schedule trigger, when run single node, just execute directly
-        if node_type == NodeType.TRIGGER_SCHEDULE:
+        if node_type == TRIGGER_SCHEDULE_NODE_TYPE:
             event = TriggerDebugEvent(
                 workflow_args={},
                 node_id=node_id,

api/controllers/console/datasets/datasets.py

@@ -263,6 +263,7 @@ def _get_retrieval_methods_by_vector_type(vector_type: str | None, is_mock: bool
         VectorType.BAIDU,
         VectorType.ALIBABACLOUD_MYSQL,
         VectorType.IRIS,
+        VectorType.HOLOGRES,
     }
 
     semantic_methods = {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}

api/controllers/console/workspace/account.py

@@ -43,6 +43,7 @@ from libs.datetime_utils import naive_utc_now
 from libs.helper import EmailStr, TimestampField, extract_remote_ip, timezone
 from libs.login import current_account_with_tenant, login_required
 from models import AccountIntegrate, InvitationCode
+from models.account import AccountStatus, InvitationCodeStatus
 from services.account_service import AccountService
 from services.billing_service import BillingService
 from services.errors.account import CurrentPasswordIncorrectError as ServiceCurrentPasswordIncorrectError
@@ -215,7 +216,7 @@ class AccountInitApi(Resource):
                 db.session.query(InvitationCode)
                 .where(
                     InvitationCode.code == args.invitation_code,
-                    InvitationCode.status == "unused",
+                    InvitationCode.status == InvitationCodeStatus.UNUSED,
                 )
                 .first()
             )
@@ -223,7 +224,7 @@ class AccountInitApi(Resource):
             if not invitation_code:
                 raise InvalidInvitationCodeError()
 
-            invitation_code.status = "used"
+            invitation_code.status = InvitationCodeStatus.USED
             invitation_code.used_at = naive_utc_now()
             invitation_code.used_by_tenant_id = account.current_tenant_id
             invitation_code.used_by_account_id = account.id
@@ -231,7 +232,7 @@ class AccountInitApi(Resource):
         account.interface_language = args.interface_language
         account.timezone = args.timezone
         account.interface_theme = "light"
-        account.status = "active"
+        account.status = AccountStatus.ACTIVE
         account.initialized_at = naive_utc_now()
         db.session.commit()
 

api/controllers/console/workspace/plugin.py

@@ -5,6 +5,7 @@ from typing import Any, Literal
 from flask import request, send_file
 from flask_restx import Resource
 from pydantic import BaseModel, Field
+from werkzeug.datastructures import FileStorage
 from werkzeug.exceptions import Forbidden
 
 from configs import dify_config
@@ -169,6 +170,20 @@ register_enum_models(
 )
 
 
+def _read_upload_content(file: FileStorage, max_size: int) -> bytes:
+    """
+    Read the uploaded file and validate its actual size before delegating to the plugin service.
+
+    FileStorage.content_length is not reliable for multipart test uploads and may be zero even when
+    content exists, so the controllers validate against the loaded bytes instead.
+    """
+    content = file.read()
+    if len(content) > max_size:
+        raise ValueError("File size exceeds the maximum allowed size")
+
+    return content
+
+
 @console_ns.route("/workspaces/current/plugin/debugging-key")
 class PluginDebuggingKeyApi(Resource):
     @setup_required
@@ -284,12 +299,7 @@ class PluginUploadFromPkgApi(Resource):
         _, tenant_id = current_account_with_tenant()
 
         file = request.files["pkg"]
-
-        # check file size
-        if file.content_length > dify_config.PLUGIN_MAX_PACKAGE_SIZE:
-            raise ValueError("File size exceeds the maximum allowed size")
-
-        content = file.read()
+        content = _read_upload_content(file, dify_config.PLUGIN_MAX_PACKAGE_SIZE)
         try:
             response = PluginService.upload_pkg(tenant_id, content)
         except PluginDaemonClientSideError as e:
@@ -328,12 +338,7 @@ class PluginUploadFromBundleApi(Resource):
         _, tenant_id = current_account_with_tenant()
 
         file = request.files["bundle"]
-
-        # check file size
-        if file.content_length > dify_config.PLUGIN_MAX_BUNDLE_SIZE:
-            raise ValueError("File size exceeds the maximum allowed size")
-
-        content = file.read()
+        content = _read_upload_content(file, dify_config.PLUGIN_MAX_BUNDLE_SIZE)
         try:
             response = PluginService.upload_bundle(tenant_id, content)
         except PluginDaemonClientSideError as e:

api/controllers/mcp/mcp.py

@@ -6,13 +6,13 @@ from pydantic import BaseModel, Field, ValidationError
 from sqlalchemy.orm import Session
 
 from controllers.common.schema import register_schema_model
-from controllers.console.app.mcp_server import AppMCPServerStatus
 from controllers.mcp import mcp_ns
 from core.mcp import types as mcp_types
 from core.mcp.server.streamable_http import handle_mcp_request
 from dify_graph.variables.input_entities import VariableEntity
 from extensions.ext_database import db
 from libs import helper
+from models.enums import AppMCPServerStatus
 from models.model import App, AppMCPServer, AppMode, EndUser
 
 

api/core/agent/cot_agent_runner.py

@@ -6,6 +6,7 @@ from typing import Any
 
 from core.agent.base_agent_runner import BaseAgentRunner
 from core.agent.entities import AgentScratchpadUnit
+from core.agent.errors import AgentMaxIterationError
 from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
 from core.app.apps.base_app_queue_manager import PublishFrom
 from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
@@ -22,7 +23,6 @@ from dify_graph.model_runtime.entities.message_entities import (
     ToolPromptMessage,
     UserPromptMessage,
 )
-from dify_graph.nodes.agent.exc import AgentMaxIterationError
 from models.model import Message
 
 logger = logging.getLogger(__name__)

api/core/agent/errors.py

@@ -0,0 +1,9 @@
+class AgentMaxIterationError(Exception):
+    """Raised when an agent runner exceeds the configured max iteration count."""
+
+    def __init__(self, max_iteration: int):
+        self.max_iteration = max_iteration
+        super().__init__(
+            f"Agent exceeded the maximum iteration limit of {max_iteration}. "
+            f"The agent was unable to complete the task within the allowed number of iterations."
+        )

api/core/agent/fc_agent_runner.py

@@ -5,6 +5,7 @@ from copy import deepcopy
 from typing import Any, Union
 
 from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.errors import AgentMaxIterationError
 from core.app.apps.base_app_queue_manager import PublishFrom
 from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
 from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
@@ -25,7 +26,6 @@ from dify_graph.model_runtime.entities import (
     UserPromptMessage,
 )
 from dify_graph.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from dify_graph.nodes.agent.exc import AgentMaxIterationError
 from models.model import Message
 
 logger = logging.getLogger(__name__)

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -69,7 +69,7 @@ from dify_graph.entities.pause_reason import HumanInputRequired
 from dify_graph.enums import WorkflowExecutionStatus
 from dify_graph.model_runtime.entities.llm_entities import LLMUsage
 from dify_graph.model_runtime.utils.encoders import jsonable_encoder
-from dify_graph.nodes import NodeType
+from dify_graph.nodes import BuiltinNodeTypes
 from dify_graph.repositories.draft_variable_repository import DraftVariableSaverFactory
 from dify_graph.runtime import GraphRuntimeState
 from dify_graph.system_variable import SystemVariable
@@ -357,7 +357,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     ) -> Generator[StreamResponse, None, None]:
         """Handle node succeeded events."""
         # Record files if it's an answer node or end node
-        if event.node_type in [NodeType.ANSWER, NodeType.END, NodeType.LLM]:
+        if event.node_type in [BuiltinNodeTypes.ANSWER, BuiltinNodeTypes.END, BuiltinNodeTypes.LLM]:
             self._recorded_files.extend(
                 self._workflow_response_converter.fetch_files_from_node_outputs(event.outputs or {})
             )

api/core/app/apps/common/workflow_response_converter.py

@@ -48,12 +48,13 @@ from core.app.entities.task_entities import (
 from core.plugin.impl.datasource import PluginDatasourceManager
 from core.tools.entities.tool_entities import ToolProviderType
 from core.tools.tool_manager import ToolManager
+from core.trigger.constants import TRIGGER_PLUGIN_NODE_TYPE
 from core.trigger.trigger_manager import TriggerManager
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities.pause_reason import HumanInputRequired
 from dify_graph.entities.workflow_start_reason import WorkflowStartReason
 from dify_graph.enums import (
-    NodeType,
+    BuiltinNodeTypes,
     SystemVariableKey,
     WorkflowExecutionStatus,
     WorkflowNodeExecutionMetadataKey,
@@ -442,7 +443,7 @@ class WorkflowResponseConverter:
         event: QueueNodeStartedEvent,
         task_id: str,
     ) -> NodeStartStreamResponse | None:
-        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+        if event.node_type in {BuiltinNodeTypes.ITERATION, BuiltinNodeTypes.LOOP}:
             return None
         run_id = self._ensure_workflow_run_id()
         snapshot = self._store_snapshot(event)
@@ -464,13 +465,13 @@ class WorkflowResponseConverter:
         )
 
         try:
-            if event.node_type == NodeType.TOOL:
+            if event.node_type == BuiltinNodeTypes.TOOL:
                 response.data.extras["icon"] = ToolManager.get_tool_icon(
                     tenant_id=self._application_generate_entity.app_config.tenant_id,
                     provider_type=ToolProviderType(event.provider_type),
                     provider_id=event.provider_id,
                 )
-            elif event.node_type == NodeType.DATASOURCE:
+            elif event.node_type == BuiltinNodeTypes.DATASOURCE:
                 manager = PluginDatasourceManager()
                 provider_entity = manager.fetch_datasource_provider(
                     self._application_generate_entity.app_config.tenant_id,
@@ -479,7 +480,7 @@ class WorkflowResponseConverter:
                 response.data.extras["icon"] = provider_entity.declaration.identity.generate_datasource_icon_url(
                     self._application_generate_entity.app_config.tenant_id
                 )
-            elif event.node_type == NodeType.TRIGGER_PLUGIN:
+            elif event.node_type == TRIGGER_PLUGIN_NODE_TYPE:
                 response.data.extras["icon"] = TriggerManager.get_trigger_plugin_icon(
                     self._application_generate_entity.app_config.tenant_id,
                     event.provider_id,
@@ -496,7 +497,7 @@ class WorkflowResponseConverter:
         event: QueueNodeSucceededEvent | QueueNodeFailedEvent | QueueNodeExceptionEvent,
         task_id: str,
     ) -> NodeFinishStreamResponse | None:
-        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+        if event.node_type in {BuiltinNodeTypes.ITERATION, BuiltinNodeTypes.LOOP}:
             return None
         run_id = self._ensure_workflow_run_id()
         snapshot = self._pop_snapshot(event.node_execution_id)
@@ -554,7 +555,7 @@ class WorkflowResponseConverter:
         event: QueueNodeRetryEvent,
         task_id: str,
     ) -> NodeRetryStreamResponse | None:
-        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+        if event.node_type in {BuiltinNodeTypes.ITERATION, BuiltinNodeTypes.LOOP}:
             return None
         run_id = self._ensure_workflow_run_id()
 
@@ -612,7 +613,7 @@ class WorkflowResponseConverter:
             data=IterationNodeStartStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 created_at=int(time.time()),
                 extras={},
@@ -635,7 +636,7 @@ class WorkflowResponseConverter:
             data=IterationNodeNextStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 index=event.index,
                 created_at=int(time.time()),
@@ -662,7 +663,7 @@ class WorkflowResponseConverter:
             data=IterationNodeCompletedStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 outputs=new_outputs,
                 outputs_truncated=outputs_truncated,
@@ -692,7 +693,7 @@ class WorkflowResponseConverter:
             data=LoopNodeStartStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 created_at=int(time.time()),
                 extras={},
@@ -715,7 +716,7 @@ class WorkflowResponseConverter:
             data=LoopNodeNextStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 index=event.index,
                 # The `pre_loop_output` field is not utilized by the frontend.
@@ -744,7 +745,7 @@ class WorkflowResponseConverter:
             data=LoopNodeCompletedStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 outputs=new_outputs,
                 outputs_truncated=outputs_truncated,

api/core/app/apps/pipeline/pipeline_runner.py

@@ -12,7 +12,7 @@ from core.app.entities.app_invoke_entities import (
     build_dify_run_context,
 )
 from core.app.workflow.layers.persistence import PersistenceWorkflowInfo, WorkflowPersistenceLayer
-from core.workflow.node_factory import DifyNodeFactory
+from core.workflow.node_factory import DifyNodeFactory, get_default_root_node_id
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities.graph_init_params import GraphInitParams
 from dify_graph.enums import WorkflowType
@@ -274,6 +274,8 @@ class PipelineRunner(WorkflowBasedAppRunner):
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
         )
+        if start_node_id is None:
+            start_node_id = get_default_root_node_id(graph_config)
         graph = Graph.init(graph_config=graph_config, node_factory=node_factory, root_node_id=start_node_id)
 
         if not graph:

api/core/app/apps/workflow_app_runner.py

@@ -3,7 +3,10 @@ import time
 from collections.abc import Mapping, Sequence
 from typing import Any, cast
 
+from pydantic import ValidationError
+
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
+from core.app.entities.agent_strategy import AgentStrategyInfo
 from core.app.entities.app_invoke_entities import InvokeFrom, UserFrom, build_dify_run_context
 from core.app.entities.queue_entities import (
     AppQueueEvent,
@@ -29,7 +32,8 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
-from core.workflow.node_factory import DifyNodeFactory
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
+from core.workflow.node_factory import DifyNodeFactory, get_default_root_node_id, resolve_workflow_node_class
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities import GraphInitParams
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
@@ -63,7 +67,6 @@ from dify_graph.graph_events import (
     NodeRunSucceededEvent,
 )
 from dify_graph.graph_events.graph import GraphRunAbortedEvent
-from dify_graph.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
 from dify_graph.runtime import GraphRuntimeState, VariablePool
 from dify_graph.system_variable import SystemVariable
 from dify_graph.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader, load_into_variable_pool
@@ -137,6 +140,9 @@ class WorkflowBasedAppRunner:
             graph_runtime_state=graph_runtime_state,
         )
 
+        if root_node_id is None:
+            root_node_id = get_default_root_node_id(graph_config)
+
         # init graph
         graph = Graph.init(graph_config=graph_config, node_factory=node_factory, root_node_id=root_node_id)
 
@@ -308,7 +314,7 @@ class WorkflowBasedAppRunner:
         # Get node class
         node_type = target_node_config["data"].type
         node_version = str(target_node_config["data"].version)
-        node_cls = NODE_TYPE_CLASSES_MAPPING[node_type][node_version]
+        node_cls = resolve_workflow_node_class(node_type=node_type, node_version=node_version)
 
         # Use the variable pool from graph_runtime_state instead of creating a new one
         variable_pool = graph_runtime_state.variable_pool
@@ -336,6 +342,18 @@ class WorkflowBasedAppRunner:
 
         return graph, variable_pool
 
+    @staticmethod
+    def _build_agent_strategy_info(event: NodeRunStartedEvent) -> AgentStrategyInfo | None:
+        raw_agent_strategy = event.extras.get("agent_strategy")
+        if raw_agent_strategy is None:
+            return None
+
+        try:
+            return AgentStrategyInfo.model_validate(raw_agent_strategy)
+        except ValidationError:
+            logger.warning("Invalid agent strategy payload for node %s", event.node_id, exc_info=True)
+            return None
+
     def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent):
         """
         Handle event
@@ -421,7 +439,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    agent_strategy=event.agent_strategy,
+                    agent_strategy=self._build_agent_strategy_info(event),
                     provider_type=event.provider_type,
                     provider_id=event.provider_id,
                 )
@@ -490,7 +508,9 @@ class WorkflowBasedAppRunner:
         elif isinstance(event, NodeRunRetrieverResourceEvent):
             self._publish_event(
                 QueueRetrieverResourcesEvent(
-                    retriever_resources=event.retriever_resources,
+                    retriever_resources=[
+                        RetrievalSourceMetadata.model_validate(resource) for resource in event.retriever_resources
+                    ],
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
                 )

api/core/app/entities/__init__.py

@@ -0,0 +1,3 @@
+from .agent_strategy import AgentStrategyInfo
+
+__all__ = ["AgentStrategyInfo"]

api/core/app/entities/agent_strategy.py

@@ -0,0 +1,8 @@
+from pydantic import BaseModel, ConfigDict
+
+
+class AgentStrategyInfo(BaseModel):
+    name: str
+    icon: str | None = None
+
+    model_config = ConfigDict(extra="forbid")

api/core/app/entities/queue_entities.py

@@ -5,13 +5,12 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
+from core.app.entities.agent_strategy import AgentStrategyInfo
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from dify_graph.entities import AgentNodeStrategyInit
 from dify_graph.entities.pause_reason import PauseReason
 from dify_graph.entities.workflow_start_reason import WorkflowStartReason
-from dify_graph.enums import WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from dify_graph.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
-from dify_graph.nodes import NodeType
 
 
 class QueueEvent(StrEnum):
@@ -314,7 +313,7 @@ class QueueNodeStartedEvent(AppQueueEvent):
     in_iteration_id: str | None = None
     in_loop_id: str | None = None
     start_at: datetime
-    agent_strategy: AgentNodeStrategyInit | None = None
+    agent_strategy: AgentStrategyInfo | None = None
 
     # FIXME(-LAN-): only for ToolNode, need to refactor
     provider_type: str  # should be a core.tools.entities.tool_entities.ToolProviderType

api/core/app/entities/task_entities.py

@@ -4,8 +4,8 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
+from core.app.entities.agent_strategy import AgentStrategyInfo
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from dify_graph.entities import AgentNodeStrategyInit
 from dify_graph.entities.workflow_start_reason import WorkflowStartReason
 from dify_graph.enums import WorkflowExecutionStatus, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from dify_graph.model_runtime.entities.llm_entities import LLMResult, LLMUsage
@@ -349,7 +349,7 @@ class NodeStartStreamResponse(StreamResponse):
         extras: dict[str, object] = Field(default_factory=dict)
         iteration_id: str | None = None
         loop_id: str | None = None
-        agent_strategy: AgentNodeStrategyInit | None = None
+        agent_strategy: AgentStrategyInfo | None = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
     workflow_run_id: str

api/core/app/layers/conversation_variable_persist_layer.py

@@ -2,7 +2,7 @@ import logging
 
 from dify_graph.constants import CONVERSATION_VARIABLE_NODE_ID
 from dify_graph.conversation_variable_updater import ConversationVariableUpdater
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_events import GraphEngineEvent, NodeRunSucceededEvent
 from dify_graph.nodes.variable_assigner.common import helpers as common_helpers
@@ -22,7 +22,7 @@ class ConversationVariablePersistenceLayer(GraphEngineLayer):
     def on_event(self, event: GraphEngineEvent) -> None:
         if not isinstance(event, NodeRunSucceededEvent):
             return
-        if event.node_type != NodeType.VARIABLE_ASSIGNER:
+        if event.node_type != BuiltinNodeTypes.VARIABLE_ASSIGNER:
             return
         if self.graph_runtime_state is None:
             return

api/core/app/workflow/layers/llm_quota.py

@@ -12,7 +12,7 @@ from typing_extensions import override
 from core.app.llm import deduct_llm_quota, ensure_llm_quota_available
 from core.errors.error import QuotaExceededError
 from core.model_manager import ModelInstance
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.graph_engine.entities.commands import AbortCommand, CommandType
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_events import GraphEngineEvent, GraphNodeEventBase
@@ -113,11 +113,11 @@ class LLMQuotaLayer(GraphEngineLayer):
     def _extract_model_instance(node: Node) -> ModelInstance | None:
         try:
             match node.node_type:
-                case NodeType.LLM:
+                case BuiltinNodeTypes.LLM:
                     return cast("LLMNode", node).model_instance
-                case NodeType.PARAMETER_EXTRACTOR:
+                case BuiltinNodeTypes.PARAMETER_EXTRACTOR:
                     return cast("ParameterExtractorNode", node).model_instance
-                case NodeType.QUESTION_CLASSIFIER:
+                case BuiltinNodeTypes.QUESTION_CLASSIFIER:
                     return cast("QuestionClassifierNode", node).model_instance
                 case _:
                     return None

api/core/app/workflow/layers/observability.py

@@ -16,7 +16,7 @@ from opentelemetry.trace import Span, SpanKind, Tracer, get_tracer, set_span_in_
 from typing_extensions import override
 
 from configs import dify_config
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_events import GraphNodeEventBase
 from dify_graph.nodes.base.node import Node
@@ -74,16 +74,13 @@ class ObservabilityLayer(GraphEngineLayer):
     def _build_parser_registry(self) -> None:
         """Initialize parser registry for node types."""
         self._parsers = {
-            NodeType.TOOL: ToolNodeOTelParser(),
-            NodeType.LLM: LLMNodeOTelParser(),
-            NodeType.KNOWLEDGE_RETRIEVAL: RetrievalNodeOTelParser(),
+            BuiltinNodeTypes.TOOL: ToolNodeOTelParser(),
+            BuiltinNodeTypes.LLM: LLMNodeOTelParser(),
+            BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL: RetrievalNodeOTelParser(),
         }
 
     def _get_parser(self, node: Node) -> NodeOTelParser:
-        node_type = getattr(node, "node_type", None)
-        if isinstance(node_type, NodeType):
-            return self._parsers.get(node_type, self._default_parser)
-        return self._default_parser
+        return self._parsers.get(node.node_type, self._default_parser)
 
     @override
     def on_graph_start(self) -> None:

api/core/callback_handler/index_tool_callback_handler.py

@@ -12,6 +12,7 @@ from core.rag.models.document import Document
 from extensions.ext_database import db
 from models.dataset import ChildChunk, DatasetQuery, DocumentSegment
 from models.dataset import Document as DatasetDocument
+from models.enums import CreatorUserRole
 
 _logger = logging.getLogger(__name__)
 
@@ -38,7 +39,9 @@ class DatasetIndexToolCallbackHandler:
             source="app",
             source_app_id=self._app_id,
             created_by_role=(
-                "account" if self._invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end_user"
+                CreatorUserRole.ACCOUNT
+                if self._invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER}
+                else CreatorUserRole.END_USER
             ),
             created_by=self._user_id,
         )

api/core/datasource/datasource_manager.py

@@ -24,12 +24,12 @@ from core.datasource.utils.message_transformer import DatasourceFileMessageTrans
 from core.datasource.website_crawl.website_crawl_provider import WebsiteCrawlDatasourcePluginProviderController
 from core.db.session_factory import session_factory
 from core.plugin.impl.datasource import PluginDatasourceManager
+from core.workflow.nodes.datasource.entities import DatasourceParameter, OnlineDriveDownloadFileParam
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from dify_graph.enums import WorkflowNodeExecutionMetadataKey
 from dify_graph.file import File
 from dify_graph.file.enums import FileTransferMethod, FileType
 from dify_graph.node_events import NodeRunResult, StreamChunkEvent, StreamCompletedEvent
-from dify_graph.repositories.datasource_manager_protocol import DatasourceParameter, OnlineDriveDownloadFileParam
 from factories import file_factory
 from models.model import UploadFile
 from models.tools import ToolFile

api/core/ops/aliyun_trace/aliyun_trace.py

@@ -58,7 +58,7 @@ from core.ops.entities.trace_entity import (
 )
 from core.repositories import DifyCoreRepositoryFactory
 from dify_graph.entities import WorkflowNodeExecution
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import WorkflowNodeExecutionTriggeredFrom
 
@@ -302,11 +302,11 @@ class AliyunDataTrace(BaseTraceInstance):
         self, node_execution: WorkflowNodeExecution, trace_info: WorkflowTraceInfo, trace_metadata: TraceMetadata
     ):
         try:
-            if node_execution.node_type == NodeType.LLM:
+            if node_execution.node_type == BuiltinNodeTypes.LLM:
                 node_span = self.build_workflow_llm_span(trace_info, node_execution, trace_metadata)
-            elif node_execution.node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+            elif node_execution.node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                 node_span = self.build_workflow_retrieval_span(trace_info, node_execution, trace_metadata)
-            elif node_execution.node_type == NodeType.TOOL:
+            elif node_execution.node_type == BuiltinNodeTypes.TOOL:
                 node_span = self.build_workflow_tool_span(trace_info, node_execution, trace_metadata)
             else:
                 node_span = self.build_workflow_task_span(trace_info, node_execution, trace_metadata)

api/core/ops/arize_phoenix_trace/arize_phoenix_trace.py

@@ -155,8 +155,8 @@ def wrap_span_metadata(metadata, **kwargs):
     return metadata
 
 
-# Mapping from NodeType string values to OpenInference span kinds.
-# NodeType values not listed here default to CHAIN.
+# Mapping from built-in node type strings to OpenInference span kinds.
+# Node types not listed here default to CHAIN.
 _NODE_TYPE_TO_SPAN_KIND: dict[str, OpenInferenceSpanKindValues] = {
     "llm": OpenInferenceSpanKindValues.LLM,
     "knowledge-retrieval": OpenInferenceSpanKindValues.RETRIEVER,
@@ -168,7 +168,7 @@ _NODE_TYPE_TO_SPAN_KIND: dict[str, OpenInferenceSpanKindValues] = {
 def _get_node_span_kind(node_type: str) -> OpenInferenceSpanKindValues:
     """Return the OpenInference span kind for a given workflow node type.
 
-    Covers every ``NodeType`` enum value.  Nodes that do not have a
+    Covers every built-in node type string. Nodes that do not have a
     specialised span kind (e.g. ``start``, ``end``, ``if-else``,
     ``code``, ``loop``, ``iteration``, etc.) are mapped to ``CHAIN``.
     """

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -28,7 +28,7 @@ from core.ops.langfuse_trace.entities.langfuse_trace_entity import (
 )
 from core.ops.utils import filter_none_values
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from extensions.ext_database import db
 from models import EndUser, WorkflowNodeExecutionTriggeredFrom
 from models.enums import MessageStatus
@@ -141,7 +141,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -28,7 +28,7 @@ from core.ops.langsmith_trace.entities.langsmith_trace_entity import (
 )
 from core.ops.utils import filter_none_values, generate_dotted_order
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
@@ -163,7 +163,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}
@@ -197,7 +197,7 @@ class LangSmithDataTrace(BaseTraceInstance):
                         "ls_model_name": process_data.get("model_name", ""),
                     }
                 )
-            elif node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+            elif node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                 run_type = LangSmithRunType.retriever
             else:
                 run_type = LangSmithRunType.tool

api/core/ops/mlflow_trace/mlflow_trace.py

@@ -23,7 +23,7 @@ from core.ops.entities.trace_entity import (
     TraceTaskName,
     WorkflowTraceInfo,
 )
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from extensions.ext_database import db
 from models import EndUser
 from models.workflow import WorkflowNodeExecutionModel
@@ -145,10 +145,10 @@ class MLflowDataTrace(BaseTraceInstance):
                     "app_name": node.title,
                 }
 
-                if node.node_type in (NodeType.LLM, NodeType.QUESTION_CLASSIFIER):
+                if node.node_type in (BuiltinNodeTypes.LLM, BuiltinNodeTypes.QUESTION_CLASSIFIER):
                     inputs, llm_attributes = self._parse_llm_inputs_and_attributes(node)
                     attributes.update(llm_attributes)
-                elif node.node_type == NodeType.HTTP_REQUEST:
+                elif node.node_type == BuiltinNodeTypes.HTTP_REQUEST:
                     inputs = node.process_data  # contains request URL
 
                 if not inputs:
@@ -180,9 +180,9 @@ class MLflowDataTrace(BaseTraceInstance):
                 # End node span
                 finished_at = node.created_at + timedelta(seconds=node.elapsed_time)
                 outputs = json.loads(node.outputs) if node.outputs else {}
-                if node.node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+                if node.node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                     outputs = self._parse_knowledge_retrieval_outputs(outputs)
-                elif node.node_type == NodeType.LLM:
+                elif node.node_type == BuiltinNodeTypes.LLM:
                     outputs = outputs.get("text", outputs)
                 node_span.end(
                     outputs=outputs,
@@ -471,13 +471,13 @@ class MLflowDataTrace(BaseTraceInstance):
     def _get_node_span_type(self, node_type: str) -> str:
         """Map Dify node types to MLflow span types"""
         node_type_mapping = {
-            NodeType.LLM: SpanType.LLM,
-            NodeType.QUESTION_CLASSIFIER: SpanType.LLM,
-            NodeType.KNOWLEDGE_RETRIEVAL: SpanType.RETRIEVER,
-            NodeType.TOOL: SpanType.TOOL,
-            NodeType.CODE: SpanType.TOOL,
-            NodeType.HTTP_REQUEST: SpanType.TOOL,
-            NodeType.AGENT: SpanType.AGENT,
+            BuiltinNodeTypes.LLM: SpanType.LLM,
+            BuiltinNodeTypes.QUESTION_CLASSIFIER: SpanType.LLM,
+            BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL: SpanType.RETRIEVER,
+            BuiltinNodeTypes.TOOL: SpanType.TOOL,
+            BuiltinNodeTypes.CODE: SpanType.TOOL,
+            BuiltinNodeTypes.HTTP_REQUEST: SpanType.TOOL,
+            BuiltinNodeTypes.AGENT: SpanType.AGENT,
         }
         return node_type_mapping.get(node_type, "CHAIN")  # type: ignore[arg-type,call-overload]
 

api/core/ops/opik_trace/opik_trace.py

@@ -23,7 +23,7 @@ from core.ops.entities.trace_entity import (
     WorkflowTraceInfo,
 )
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
@@ -187,7 +187,7 @@ class OpikDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}

api/core/ops/ops_trace_manager.py

@@ -628,10 +628,10 @@ class TraceTask:
         if not message_data:
             return {}
         conversation_mode_stmt = select(Conversation.mode).where(Conversation.id == message_data.conversation_id)
-        conversation_mode = db.session.scalars(conversation_mode_stmt).all()
-        if not conversation_mode or len(conversation_mode) == 0:
+        conversation_modes = db.session.scalars(conversation_mode_stmt).all()
+        if not conversation_modes or len(conversation_modes) == 0:
             return {}
-        conversation_mode = conversation_mode[0]
+        conversation_mode = conversation_modes[0]
         created_at = message_data.created_at
         inputs = message_data.message
 

api/core/ops/tencent_trace/tencent_trace.py

@@ -27,7 +27,7 @@ from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
 from dify_graph.entities.workflow_node_execution import (
     WorkflowNodeExecution,
 )
-from dify_graph.nodes import NodeType
+from dify_graph.nodes import BuiltinNodeTypes
 from extensions.ext_database import db
 from models import Account, App, TenantAccountJoin, WorkflowNodeExecutionTriggeredFrom
 
@@ -179,7 +179,7 @@ class TencentDataTrace(BaseTraceInstance):
                     if node_span:
                         self.trace_client.add_span(node_span)
 
-                        if node_execution.node_type == NodeType.LLM:
+                        if node_execution.node_type == BuiltinNodeTypes.LLM:
                             self._record_llm_metrics(node_execution)
                 except Exception:
                     logger.exception("[Tencent APM] Failed to process node execution: %s", node_execution.id)
@@ -192,15 +192,15 @@ class TencentDataTrace(BaseTraceInstance):
     ) -> SpanData | None:
         """Build span for different node types"""
         try:
-            if node_execution.node_type == NodeType.LLM:
+            if node_execution.node_type == BuiltinNodeTypes.LLM:
                 return TencentSpanBuilder.build_workflow_llm_span(
                     trace_id, workflow_span_id, trace_info, node_execution
                 )
-            elif node_execution.node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+            elif node_execution.node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                 return TencentSpanBuilder.build_workflow_retrieval_span(
                     trace_id, workflow_span_id, trace_info, node_execution
                 )
-            elif node_execution.node_type == NodeType.TOOL:
+            elif node_execution.node_type == BuiltinNodeTypes.TOOL:
                 return TencentSpanBuilder.build_workflow_tool_span(
                     trace_id, workflow_span_id, trace_info, node_execution
                 )

api/core/ops/weave_trace/weave_trace.py

@@ -31,7 +31,7 @@ from core.ops.entities.trace_entity import (
 )
 from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
@@ -175,7 +175,7 @@ class WeaveDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}

api/core/plugin/backwards_invocation/node.py

@@ -1,5 +1,5 @@
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.nodes.parameter_extractor.entities import (
     ModelConfig as ParameterExtractorModelConfig,
 )
@@ -52,7 +52,7 @@ class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
             instruction=instruction,  # instruct with variables are not supported
         )
         node_data_dict = node_data.model_dump()
-        node_data_dict["type"] = NodeType.PARAMETER_EXTRACTOR
+        node_data_dict["type"] = BuiltinNodeTypes.PARAMETER_EXTRACTOR
         execution = workflow_service.run_free_workflow_node(
             node_data_dict,
             tenant_id=tenant_id,

api/core/provider_manager.py

@@ -627,7 +627,7 @@ class ProviderManager:
                                 tenant_id=tenant_id,
                                 # TODO: Use provider name with prefix after the data migration.
                                 provider_name=ModelProviderID(provider_name).provider_name,
-                                provider_type=ProviderType.SYSTEM.value,
+                                provider_type=ProviderType.SYSTEM,
                                 quota_type=quota.quota_type,
                                 quota_limit=0,  # type: ignore
                                 quota_used=0,

api/core/rag/datasource/vdb/hologres/hologres_vector.py

@@ -0,0 +1,361 @@
+import json
+import logging
+import time
+from typing import Any
+
+import holo_search_sdk as holo  # type: ignore
+from holo_search_sdk.types import BaseQuantizationType, DistanceType, TokenizerType
+from psycopg import sql as psql
+from pydantic import BaseModel, model_validator
+
+from configs import dify_config
+from core.rag.datasource.vdb.vector_base import BaseVector
+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+
+class HologresVectorConfig(BaseModel):
+    """
+    Configuration for Hologres vector database connection.
+
+    In Hologres, access_key_id is used as the PostgreSQL username,
+    and access_key_secret is used as the PostgreSQL password.
+    """
+
+    host: str
+    port: int = 80
+    database: str
+    access_key_id: str
+    access_key_secret: str
+    schema_name: str = "public"
+    tokenizer: TokenizerType = "jieba"
+    distance_method: DistanceType = "Cosine"
+    base_quantization_type: BaseQuantizationType = "rabitq"
+    max_degree: int = 64
+    ef_construction: int = 400
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict):
+        if not values.get("host"):
+            raise ValueError("config HOLOGRES_HOST is required")
+        if not values.get("database"):
+            raise ValueError("config HOLOGRES_DATABASE is required")
+        if not values.get("access_key_id"):
+            raise ValueError("config HOLOGRES_ACCESS_KEY_ID is required")
+        if not values.get("access_key_secret"):
+            raise ValueError("config HOLOGRES_ACCESS_KEY_SECRET is required")
+        return values
+
+
+class HologresVector(BaseVector):
+    """
+    Hologres vector storage implementation using holo-search-sdk.
+
+    Supports semantic search (vector), full-text search, and hybrid search.
+    """
+
+    def __init__(self, collection_name: str, config: HologresVectorConfig):
+        super().__init__(collection_name)
+        self._config = config
+        self._client = self._init_client(config)
+        self.table_name = f"embedding_{collection_name}".lower()
+
+    def _init_client(self, config: HologresVectorConfig):
+        """Initialize and return a holo-search-sdk client."""
+        client = holo.connect(
+            host=config.host,
+            port=config.port,
+            database=config.database,
+            access_key_id=config.access_key_id,
+            access_key_secret=config.access_key_secret,
+            schema=config.schema_name,
+        )
+        client.connect()
+        return client
+
+    def get_type(self) -> str:
+        return VectorType.HOLOGRES
+
+    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        """Create collection table with vector and full-text indexes, then add texts."""
+        dimension = len(embeddings[0])
+        self._create_collection(dimension)
+        self.add_texts(texts, embeddings)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        """Add texts with embeddings to the collection using batch upsert."""
+        if not documents:
+            return []
+
+        pks: list[str] = []
+        batch_size = 100
+        for i in range(0, len(documents), batch_size):
+            batch_docs = documents[i : i + batch_size]
+            batch_embeddings = embeddings[i : i + batch_size]
+
+            values = []
+            column_names = ["id", "text", "meta", "embedding"]
+
+            for j, doc in enumerate(batch_docs):
+                doc_id = doc.metadata.get("doc_id", "") if doc.metadata else ""
+                pks.append(doc_id)
+                values.append(
+                    [
+                        doc_id,
+                        doc.page_content,
+                        json.dumps(doc.metadata or {}),
+                        batch_embeddings[j],
+                    ]
+                )
+
+            table = self._client.open_table(self.table_name)
+            table.upsert_multi(
+                index_column="id",
+                values=values,
+                column_names=column_names,
+                update=True,
+                update_columns=["text", "meta", "embedding"],
+            )
+
+        return pks
+
+    def text_exists(self, id: str) -> bool:
+        """Check if a text with the given doc_id exists in the collection."""
+        if not self._client.check_table_exist(self.table_name):
+            return False
+
+        result = self._client.execute(
+            psql.SQL("SELECT 1 FROM {} WHERE id = {} LIMIT 1").format(
+                psql.Identifier(self.table_name), psql.Literal(id)
+            ),
+            fetch_result=True,
+        )
+        return bool(result)
+
+    def get_ids_by_metadata_field(self, key: str, value: str) -> list[str] | None:
+        """Get document IDs by metadata field key and value."""
+        result = self._client.execute(
+            psql.SQL("SELECT id FROM {} WHERE meta->>{} = {}").format(
+                psql.Identifier(self.table_name), psql.Literal(key), psql.Literal(value)
+            ),
+            fetch_result=True,
+        )
+        if result:
+            return [row[0] for row in result]
+        return None
+
+    def delete_by_ids(self, ids: list[str]):
+        """Delete documents by their doc_id list."""
+        if not ids:
+            return
+        if not self._client.check_table_exist(self.table_name):
+            return
+
+        self._client.execute(
+            psql.SQL("DELETE FROM {} WHERE id IN ({})").format(
+                psql.Identifier(self.table_name),
+                psql.SQL(", ").join(psql.Literal(id) for id in ids),
+            )
+        )
+
+    def delete_by_metadata_field(self, key: str, value: str):
+        """Delete documents by metadata field key and value."""
+        if not self._client.check_table_exist(self.table_name):
+            return
+
+        self._client.execute(
+            psql.SQL("DELETE FROM {} WHERE meta->>{} = {}").format(
+                psql.Identifier(self.table_name), psql.Literal(key), psql.Literal(value)
+            )
+        )
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        """Search for documents by vector similarity."""
+        if not self._client.check_table_exist(self.table_name):
+            return []
+
+        top_k = kwargs.get("top_k", 4)
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+
+        table = self._client.open_table(self.table_name)
+        query = (
+            table.search_vector(
+                vector=query_vector,
+                column="embedding",
+                distance_method=self._config.distance_method,
+                output_name="distance",
+            )
+            .select(["id", "text", "meta"])
+            .limit(top_k)
+        )
+
+        # Apply document_ids_filter if provided
+        document_ids_filter = kwargs.get("document_ids_filter")
+        if document_ids_filter:
+            filter_sql = psql.SQL("meta->>'document_id' IN ({})").format(
+                psql.SQL(", ").join(psql.Literal(id) for id in document_ids_filter)
+            )
+            query = query.where(filter_sql)
+
+        results = query.fetchall()
+        return self._process_vector_results(results, score_threshold)
+
+    def _process_vector_results(self, results: list, score_threshold: float) -> list[Document]:
+        """Process vector search results into Document objects."""
+        docs = []
+        for row in results:
+            # row format: (distance, id, text, meta)
+            # distance is first because search_vector() adds the computed column before selected columns
+            distance = row[0]
+            text = row[2]
+            meta = row[3]
+
+            if isinstance(meta, str):
+                meta = json.loads(meta)
+
+            # Convert distance to similarity score (consistent with pgvector)
+            score = 1 - distance
+            meta["score"] = score
+
+            if score >= score_threshold:
+                docs.append(Document(page_content=text, metadata=meta))
+
+        return docs
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        """Search for documents by full-text search."""
+        if not self._client.check_table_exist(self.table_name):
+            return []
+
+        top_k = kwargs.get("top_k", 4)
+
+        table = self._client.open_table(self.table_name)
+        search_query = table.search_text(
+            column="text",
+            expression=query,
+            return_score=True,
+            return_score_name="score",
+            return_all_columns=True,
+        ).limit(top_k)
+
+        # Apply document_ids_filter if provided
+        document_ids_filter = kwargs.get("document_ids_filter")
+        if document_ids_filter:
+            filter_sql = psql.SQL("meta->>'document_id' IN ({})").format(
+                psql.SQL(", ").join(psql.Literal(id) for id in document_ids_filter)
+            )
+            search_query = search_query.where(filter_sql)
+
+        results = search_query.fetchall()
+        return self._process_full_text_results(results)
+
+    def _process_full_text_results(self, results: list) -> list[Document]:
+        """Process full-text search results into Document objects."""
+        docs = []
+        for row in results:
+            # row format: (id, text, meta, embedding, score)
+            text = row[1]
+            meta = row[2]
+            score = row[-1]  # score is the last column from return_score
+
+            if isinstance(meta, str):
+                meta = json.loads(meta)
+
+            meta["score"] = score
+            docs.append(Document(page_content=text, metadata=meta))
+
+        return docs
+
+    def delete(self):
+        """Delete the entire collection table."""
+        if self._client.check_table_exist(self.table_name):
+            self._client.drop_table(self.table_name)
+
+    def _create_collection(self, dimension: int):
+        """Create the collection table with vector and full-text indexes."""
+        lock_name = f"vector_indexing_lock_{self._collection_name}"
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
+            if redis_client.get(collection_exist_cache_key):
+                return
+
+            if not self._client.check_table_exist(self.table_name):
+                # Create table via SQL with CHECK constraint for vector dimension
+                create_table_sql = psql.SQL("""
+                    CREATE TABLE IF NOT EXISTS {} (
+                        id TEXT PRIMARY KEY,
+                        text TEXT NOT NULL,
+                        meta JSONB NOT NULL,
+                        embedding float4[] NOT NULL
+                            CHECK (array_ndims(embedding) = 1
+                                   AND array_length(embedding, 1) = {})
+                    );
+                """).format(psql.Identifier(self.table_name), psql.Literal(dimension))
+                self._client.execute(create_table_sql)
+
+                # Wait for table to be fully ready before creating indexes
+                max_wait_seconds = 30
+                poll_interval = 2
+                for _ in range(max_wait_seconds // poll_interval):
+                    if self._client.check_table_exist(self.table_name):
+                        break
+                    time.sleep(poll_interval)
+                else:
+                    raise RuntimeError(f"Table {self.table_name} was not ready after {max_wait_seconds}s")
+
+                # Open table and set vector index
+                table = self._client.open_table(self.table_name)
+                table.set_vector_index(
+                    column="embedding",
+                    distance_method=self._config.distance_method,
+                    base_quantization_type=self._config.base_quantization_type,
+                    max_degree=self._config.max_degree,
+                    ef_construction=self._config.ef_construction,
+                    use_reorder=self._config.base_quantization_type == "rabitq",
+                )
+
+                # Create full-text search index
+                table.create_text_index(
+                    index_name=f"ft_idx_{self._collection_name}",
+                    column="text",
+                    tokenizer=self._config.tokenizer,
+                )
+
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+
+class HologresVectorFactory(AbstractVectorFactory):
+    """Factory class for creating HologresVector instances."""
+
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> HologresVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            collection_name = class_prefix
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id)
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.HOLOGRES, collection_name))
+
+        return HologresVector(
+            collection_name=collection_name,
+            config=HologresVectorConfig(
+                host=dify_config.HOLOGRES_HOST or "",
+                port=dify_config.HOLOGRES_PORT,
+                database=dify_config.HOLOGRES_DATABASE or "",
+                access_key_id=dify_config.HOLOGRES_ACCESS_KEY_ID or "",
+                access_key_secret=dify_config.HOLOGRES_ACCESS_KEY_SECRET or "",
+                schema_name=dify_config.HOLOGRES_SCHEMA,
+                tokenizer=dify_config.HOLOGRES_TOKENIZER,
+                distance_method=dify_config.HOLOGRES_DISTANCE_METHOD,
+                base_quantization_type=dify_config.HOLOGRES_BASE_QUANTIZATION_TYPE,
+                max_degree=dify_config.HOLOGRES_MAX_DEGREE,
+                ef_construction=dify_config.HOLOGRES_EF_CONSTRUCTION,
+            ),
+        )

api/core/rag/datasource/vdb/vector_factory.py

@@ -38,7 +38,7 @@ class AbstractVectorFactory(ABC):
 class Vector:
     def __init__(self, dataset: Dataset, attributes: list | None = None):
         if attributes is None:
-            attributes = ["doc_id", "dataset_id", "document_id", "doc_hash"]
+            attributes = ["doc_id", "dataset_id", "document_id", "doc_hash", "doc_type"]
         self._dataset = dataset
         self._embeddings = self._get_embeddings()
         self._attributes = attributes
@@ -191,6 +191,10 @@ class Vector:
                 from core.rag.datasource.vdb.iris.iris_vector import IrisVectorFactory
 
                 return IrisVectorFactory
+            case VectorType.HOLOGRES:
+                from core.rag.datasource.vdb.hologres.hologres_vector import HologresVectorFactory
+
+                return HologresVectorFactory
             case _:
                 raise ValueError(f"Vector store {vector_type} is not supported.")
 

api/core/rag/datasource/vdb/vector_type.py

@@ -34,3 +34,4 @@ class VectorType(StrEnum):
     MATRIXONE = "matrixone"
     CLICKZETTA = "clickzetta"
     IRIS = "iris"
+    HOLOGRES = "hologres"

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -196,6 +196,7 @@ class WeaviateVector(BaseVector):
                             ),
                             wc.Property(name="document_id", data_type=wc.DataType.TEXT),
                             wc.Property(name="doc_id", data_type=wc.DataType.TEXT),
+                            wc.Property(name="doc_type", data_type=wc.DataType.TEXT),
                             wc.Property(name="chunk_index", data_type=wc.DataType.INT),
                         ],
                         vector_config=wc.Configure.Vectors.self_provided(),
@@ -225,6 +226,8 @@ class WeaviateVector(BaseVector):
             to_add.append(wc.Property(name="document_id", data_type=wc.DataType.TEXT))
         if "doc_id" not in existing:
             to_add.append(wc.Property(name="doc_id", data_type=wc.DataType.TEXT))
+        if "doc_type" not in existing:
+            to_add.append(wc.Property(name="doc_type", data_type=wc.DataType.TEXT))
         if "chunk_index" not in existing:
             to_add.append(wc.Property(name="chunk_index", data_type=wc.DataType.INT))
 

api/core/rag/index_processor/index_processor.py

@@ -9,8 +9,8 @@ from flask import current_app
 from sqlalchemy import delete, func, select
 
 from core.db.session_factory import session_factory
-from dify_graph.nodes.knowledge_index.exc import KnowledgeIndexNodeError
-from dify_graph.repositories.index_processor_protocol import Preview, PreviewItem, QaPreview
+from core.workflow.nodes.knowledge_index.exc import KnowledgeIndexNodeError
+from core.workflow.nodes.knowledge_index.protocols import Preview, PreviewItem, QaPreview
 from models.dataset import Dataset, Document, DocumentSegment
 
 from .index_processor_factory import IndexProcessorFactory

api/core/rag/retrieval/dataset_retrieval.py

@@ -56,18 +56,18 @@ from core.rag.retrieval.template_prompts import (
 )
 from core.tools.signature import sign_upload_file
 from core.tools.utils.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
-from dify_graph.file import File, FileTransferMethod, FileType
-from dify_graph.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMUsage
-from dify_graph.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
-from dify_graph.model_runtime.entities.model_entities import ModelFeature, ModelType
-from dify_graph.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from dify_graph.nodes.knowledge_retrieval import exc
-from dify_graph.repositories.rag_retrieval_protocol import (
+from core.workflow.nodes.knowledge_retrieval import exc
+from core.workflow.nodes.knowledge_retrieval.retrieval import (
     KnowledgeRetrievalRequest,
     Source,
     SourceChildChunk,
     SourceMetadata,
 )
+from dify_graph.file import File, FileTransferMethod, FileType
+from dify_graph.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMUsage
+from dify_graph.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
+from dify_graph.model_runtime.entities.model_entities import ModelFeature, ModelType
+from dify_graph.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from libs.json_in_md_parser import parse_and_check_json_markdown
@@ -83,6 +83,7 @@ from models.dataset import (
 )
 from models.dataset import Document as DatasetDocument
 from models.dataset import Document as DocumentModel
+from models.enums import CreatorUserRole
 from services.external_knowledge_service import ExternalDatasetService
 from services.feature_service import FeatureService
 
@@ -1009,7 +1010,7 @@ class DatasetRetrieval:
                     content=json.dumps(contents),
                     source="app",
                     source_app_id=app_id,
-                    created_by_role=user_from,
+                    created_by_role=CreatorUserRole(user_from),
                     created_by=user_id,
                 )
                 dataset_queries.append(dataset_query)

api/core/repositories/sqlalchemy_workflow_execution_repository.py

@@ -146,7 +146,9 @@ class SQLAlchemyWorkflowExecutionRepository(WorkflowExecutionRepository):
 
         # No sequence number generation needed anymore
 
-        db_model.type = domain_model.workflow_type
+        from models.workflow import WorkflowType as ModelWorkflowType
+
+        db_model.type = ModelWorkflowType(domain_model.workflow_type.value)
         db_model.version = domain_model.workflow_version
         db_model.graph = json.dumps(domain_model.graph) if domain_model.graph else None
         db_model.inputs = json.dumps(domain_model.inputs) if domain_model.inputs else None

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -18,7 +18,7 @@ from tenacity import before_sleep_log, retry, retry_if_exception, stop_after_att
 
 from configs import dify_config
 from dify_graph.entities import WorkflowNodeExecution
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from dify_graph.enums import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from dify_graph.model_runtime.utils.encoders import jsonable_encoder
 from dify_graph.repositories.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
 from dify_graph.workflow_type_encoder import WorkflowRuntimeTypeConverter
@@ -146,7 +146,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             index=db_model.index,
             predecessor_node_id=db_model.predecessor_node_id,
             node_id=db_model.node_id,
-            node_type=NodeType(db_model.node_type),
+            node_type=db_model.node_type,
             title=db_model.title,
             inputs=inputs,
             process_data=process_data,

api/core/tools/utils/configuration.py

@@ -116,6 +116,7 @@ class ToolParameterConfigurationManager:
 
         return a deep copy of parameters with decrypted values
         """
+        parameters = self._deep_copy(parameters)
 
         cache = ToolParameterCache(
             tenant_id=self.tenant_id,

api/core/tools/utils/workflow_configuration_sync.py

@@ -3,7 +3,7 @@ from typing import Any
 
 from core.tools.entities.tool_entities import WorkflowToolParameterConfiguration
 from core.tools.errors import WorkflowToolHumanInputNotSupportedError
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.nodes.base.entities import OutputVariableEntity
 from dify_graph.variables.input_entities import VariableEntity
 
@@ -51,7 +51,7 @@ class WorkflowToolConfigurationUtils:
     def ensure_no_human_input_nodes(cls, graph: Mapping[str, Any]) -> None:
         nodes = graph.get("nodes", [])
         for node in nodes:
-            if node.get("data", {}).get("type") == NodeType.HUMAN_INPUT:
+            if node.get("data", {}).get("type") == BuiltinNodeTypes.HUMAN_INPUT:
                 raise WorkflowToolHumanInputNotSupportedError()
 
     @classmethod

api/core/trigger/constants.py

@@ -0,0 +1,18 @@
+from typing import Final
+
+TRIGGER_WEBHOOK_NODE_TYPE: Final[str] = "trigger-webhook"
+TRIGGER_SCHEDULE_NODE_TYPE: Final[str] = "trigger-schedule"
+TRIGGER_PLUGIN_NODE_TYPE: Final[str] = "trigger-plugin"
+TRIGGER_INFO_METADATA_KEY: Final[str] = "trigger_info"
+
+TRIGGER_NODE_TYPES: Final[frozenset[str]] = frozenset(
+    {
+        TRIGGER_WEBHOOK_NODE_TYPE,
+        TRIGGER_SCHEDULE_NODE_TYPE,
+        TRIGGER_PLUGIN_NODE_TYPE,
+    }
+)
+
+
+def is_trigger_node_type(node_type: str) -> bool:
+    return node_type in TRIGGER_NODE_TYPES

api/core/trigger/debug/event_selectors.py

@@ -11,6 +11,11 @@ from typing import Any
 from pydantic import BaseModel
 
 from core.plugin.entities.request import TriggerInvokeEventResponse
+from core.trigger.constants import (
+    TRIGGER_PLUGIN_NODE_TYPE,
+    TRIGGER_SCHEDULE_NODE_TYPE,
+    TRIGGER_WEBHOOK_NODE_TYPE,
+)
 from core.trigger.debug.event_bus import TriggerDebugEventBus
 from core.trigger.debug.events import (
     PluginTriggerDebugEvent,
@@ -19,10 +24,9 @@ from core.trigger.debug.events import (
     build_plugin_pool_key,
     build_webhook_pool_key,
 )
+from core.workflow.nodes.trigger_plugin.entities import TriggerEventNodeData
+from core.workflow.nodes.trigger_schedule.entities import ScheduleConfig
 from dify_graph.entities.graph_config import NodeConfigDict
-from dify_graph.enums import NodeType
-from dify_graph.nodes.trigger_plugin.entities import TriggerEventNodeData
-from dify_graph.nodes.trigger_schedule.entities import ScheduleConfig
 from extensions.ext_redis import redis_client
 from libs.datetime_utils import ensure_naive_utc, naive_utc_now
 from libs.schedule_utils import calculate_next_run_at
@@ -206,21 +210,19 @@ def create_event_poller(
     if not node_config:
         raise ValueError("Node data not found for node %s", node_id)
     node_type = draft_workflow.get_node_type_from_node_config(node_config)
-    match node_type:
-        case NodeType.TRIGGER_PLUGIN:
-            return PluginTriggerDebugEventPoller(
-                tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
-            )
-        case NodeType.TRIGGER_WEBHOOK:
-            return WebhookTriggerDebugEventPoller(
-                tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
-            )
-        case NodeType.TRIGGER_SCHEDULE:
-            return ScheduleTriggerDebugEventPoller(
-                tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
-            )
-        case _:
-            raise ValueError("unable to create event poller for node type %s", node_type)
+    if node_type == TRIGGER_PLUGIN_NODE_TYPE:
+        return PluginTriggerDebugEventPoller(
+            tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
+        )
+    if node_type == TRIGGER_WEBHOOK_NODE_TYPE:
+        return WebhookTriggerDebugEventPoller(
+            tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
+        )
+    if node_type == TRIGGER_SCHEDULE_NODE_TYPE:
+        return ScheduleTriggerDebugEventPoller(
+            tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
+        )
+    raise ValueError("unable to create event poller for node type %s", node_type)
 
 
 def select_trigger_debug_events(

api/core/workflow/__init__.py

@@ -1,4 +1 @@
-from .node_factory import DifyNodeFactory
-from .workflow_entry import WorkflowEntry
-
-__all__ = ["DifyNodeFactory", "WorkflowEntry"]
+"""Core workflow package."""

api/core/workflow/node_factory.py

@@ -1,4 +1,7 @@
-from collections.abc import Callable, Mapping
+import importlib
+import pkgutil
+from collections.abc import Callable, Iterator, Mapping, MutableMapping
+from functools import lru_cache
 from typing import TYPE_CHECKING, Any, TypeAlias, cast, final
 
 from sqlalchemy import select
@@ -8,7 +11,6 @@ from typing_extensions import override
 from configs import dify_config
 from core.app.entities.app_invoke_entities import DifyRunContext
 from core.app.llm.model_access import build_dify_model_access
-from core.datasource.datasource_manager import DatasourceManager
 from core.helper.code_executor.code_executor import (
     CodeExecutionError,
     CodeExecutor,
@@ -17,15 +19,19 @@ from core.helper.ssrf_proxy import ssrf_proxy
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
-from core.rag.index_processor.index_processor import IndexProcessor
-from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
-from core.rag.summary_index.summary_index import SummaryIndex
 from core.repositories.human_input_repository import HumanInputFormRepositoryImpl
 from core.tools.tool_file_manager import ToolFileManager
+from core.trigger.constants import TRIGGER_NODE_TYPES
+from core.workflow.nodes.agent.message_transformer import AgentMessageTransformer
+from core.workflow.nodes.agent.plugin_strategy_adapter import (
+    PluginAgentStrategyPresentationProvider,
+    PluginAgentStrategyResolver,
+)
+from core.workflow.nodes.agent.runtime_support import AgentRuntimeSupport
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.entities.graph_config import NodeConfigDict, NodeConfigDictAdapter
 from dify_graph.entities.graph_init_params import DIFY_RUN_CONTEXT_KEY
-from dify_graph.enums import NodeType, SystemVariableKey
+from dify_graph.enums import BuiltinNodeTypes, NodeType, SystemVariableKey
 from dify_graph.file.file_manager import file_manager
 from dify_graph.graph.graph import NodeFactory
 from dify_graph.model_runtime.entities.model_entities import ModelType
@@ -39,7 +45,6 @@ from dify_graph.nodes.document_extractor import UnstructuredApiConfig
 from dify_graph.nodes.http_request import build_http_request_config
 from dify_graph.nodes.llm.entities import LLMNodeData
 from dify_graph.nodes.llm.exc import LLMModeRequiredError, ModelNotExistError
-from dify_graph.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
 from dify_graph.nodes.parameter_extractor.entities import ParameterExtractorNodeData
 from dify_graph.nodes.question_classifier.entities import QuestionClassifierNodeData
 from dify_graph.nodes.template_transform.template_renderer import (
@@ -53,6 +58,135 @@ if TYPE_CHECKING:
     from dify_graph.entities import GraphInitParams
     from dify_graph.runtime import GraphRuntimeState
 
+LATEST_VERSION = "latest"
+_START_NODE_TYPES: frozenset[NodeType] = frozenset(
+    (BuiltinNodeTypes.START, BuiltinNodeTypes.DATASOURCE, *TRIGGER_NODE_TYPES)
+)
+
+
+def _import_node_package(package_name: str, *, excluded_modules: frozenset[str] = frozenset()) -> None:
+    package = importlib.import_module(package_name)
+    for _, module_name, _ in pkgutil.walk_packages(package.__path__, package.__name__ + "."):
+        if module_name in excluded_modules:
+            continue
+        importlib.import_module(module_name)
+
+
+@lru_cache(maxsize=1)
+def register_nodes() -> None:
+    """Import production node modules so they self-register with ``Node``."""
+    _import_node_package("dify_graph.nodes")
+    _import_node_package("core.workflow.nodes")
+
+
+def get_node_type_classes_mapping() -> Mapping[NodeType, Mapping[str, type[Node]]]:
+    """Return a read-only snapshot of the current production node registry.
+
+    The workflow layer owns node bootstrap because it must compose built-in
+    `dify_graph.nodes.*` implementations with workflow-local nodes under
+    `core.workflow.nodes.*`. Keeping this import side effect here avoids
+    reintroducing registry bootstrapping into lower-level graph primitives.
+    """
+    register_nodes()
+    return Node.get_node_type_classes_mapping()
+
+
+def resolve_workflow_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:
+    node_mapping = get_node_type_classes_mapping().get(node_type)
+    if not node_mapping:
+        raise ValueError(f"No class mapping found for node type: {node_type}")
+
+    latest_node_class = node_mapping.get(LATEST_VERSION)
+    matched_node_class = node_mapping.get(node_version)
+    node_class = matched_node_class or latest_node_class
+    if not node_class:
+        raise ValueError(f"No latest version class found for node type: {node_type}")
+    return node_class
+
+
+def is_start_node_type(node_type: NodeType) -> bool:
+    """Return True when the node type can serve as a workflow entry point."""
+    return node_type in _START_NODE_TYPES
+
+
+def get_default_root_node_id(graph_config: Mapping[str, Any]) -> str:
+    """Resolve the default entry node for a persisted top-level workflow graph.
+
+    This workflow-layer helper depends on start-node semantics defined by
+    `is_start_node_type`, so it intentionally lives next to the node registry
+    instead of in the raw `dify_graph.entities.graph_config` schema module.
+    """
+    nodes = graph_config.get("nodes")
+    if not isinstance(nodes, list):
+        raise ValueError("nodes in workflow graph must be a list")
+
+    for node in nodes:
+        if not isinstance(node, Mapping):
+            continue
+
+        if node.get("type") == "custom-note":
+            continue
+
+        node_id = node.get("id")
+        data = node.get("data")
+        if not isinstance(node_id, str) or not isinstance(data, Mapping):
+            continue
+
+        node_type = data.get("type")
+        if isinstance(node_type, str) and is_start_node_type(node_type):
+            return node_id
+
+    raise ValueError("Unable to determine default root node ID from workflow graph")
+
+
+class _LazyNodeTypeClassesMapping(MutableMapping[NodeType, Mapping[str, type[Node]]]):
+    """Mutable dict-like view over the current node registry."""
+
+    def __init__(self) -> None:
+        self._cached_snapshot: dict[NodeType, Mapping[str, type[Node]]] = {}
+        self._cached_version = -1
+        self._deleted: set[NodeType] = set()
+        self._overrides: dict[NodeType, Mapping[str, type[Node]]] = {}
+
+    def _snapshot(self) -> dict[NodeType, Mapping[str, type[Node]]]:
+        current_version = Node.get_registry_version()
+        if self._cached_version != current_version:
+            self._cached_snapshot = dict(get_node_type_classes_mapping())
+            self._cached_version = current_version
+        if not self._deleted and not self._overrides:
+            return self._cached_snapshot
+
+        snapshot = {key: value for key, value in self._cached_snapshot.items() if key not in self._deleted}
+        snapshot.update(self._overrides)
+        return snapshot
+
+    def __getitem__(self, key: NodeType) -> Mapping[str, type[Node]]:
+        return self._snapshot()[key]
+
+    def __setitem__(self, key: NodeType, value: Mapping[str, type[Node]]) -> None:
+        self._deleted.discard(key)
+        self._overrides[key] = value
+
+    def __delitem__(self, key: NodeType) -> None:
+        if key in self._overrides:
+            del self._overrides[key]
+            return
+        if key in self._cached_snapshot:
+            self._deleted.add(key)
+            return
+        raise KeyError(key)
+
+    def __iter__(self) -> Iterator[NodeType]:
+        return iter(self._snapshot())
+
+    def __len__(self) -> int:
+        return len(self._snapshot())
+
+
+# Keep the canonical node-class mapping in the workflow layer that also bootstraps
+# legacy `core.workflow.nodes.*` registrations.
+NODE_TYPE_CLASSES_MAPPING: MutableMapping[NodeType, Mapping[str, type[Node]]] = _LazyNodeTypeClassesMapping()
+
 
 LLMCompatibleNodeData: TypeAlias = LLMNodeData | QuestionClassifierNodeData | ParameterExtractorNodeData
 
@@ -97,10 +231,7 @@ class DefaultWorkflowCodeExecutor:
 @final
 class DifyNodeFactory(NodeFactory):
     """
-    Default implementation of NodeFactory that uses the traditional node mapping.
-
-    This factory creates nodes by looking up their types in NODE_TYPE_CLASSES_MAPPING
-    and instantiating the appropriate node class.
+    Default implementation of NodeFactory that resolves node classes from the live registry.
     """
 
     def __init__(
@@ -127,7 +258,6 @@ class DifyNodeFactory(NodeFactory):
         self._http_request_http_client = ssrf_proxy
         self._http_request_tool_file_manager_factory = ToolFileManager
         self._http_request_file_manager = file_manager
-        self._rag_retrieval = DatasetRetrieval()
         self._document_extractor_unstructured_api_config = UnstructuredApiConfig(
             api_url=dify_config.UNSTRUCTURED_API_URL,
             api_key=dify_config.UNSTRUCTURED_API_KEY or "",
@@ -143,6 +273,10 @@ class DifyNodeFactory(NodeFactory):
         )
 
         self._llm_credentials_provider, self._llm_model_factory = build_dify_model_access(self._dify_context.tenant_id)
+        self._agent_strategy_resolver = PluginAgentStrategyResolver()
+        self._agent_strategy_presentation_provider = PluginAgentStrategyPresentationProvider()
+        self._agent_runtime_support = AgentRuntimeSupport()
+        self._agent_message_transformer = AgentMessageTransformer()
 
     @staticmethod
     def _resolve_dify_context(run_context: Mapping[str, Any]) -> DifyRunContext:
@@ -170,55 +304,51 @@ class DifyNodeFactory(NodeFactory):
         node_class = self._resolve_node_class(node_type=node_data.type, node_version=str(node_data.version))
         node_type = node_data.type
         node_init_kwargs_factories: Mapping[NodeType, Callable[[], dict[str, object]]] = {
-            NodeType.CODE: lambda: {
+            BuiltinNodeTypes.CODE: lambda: {
                 "code_executor": self._code_executor,
                 "code_limits": self._code_limits,
             },
-            NodeType.TEMPLATE_TRANSFORM: lambda: {
+            BuiltinNodeTypes.TEMPLATE_TRANSFORM: lambda: {
                 "template_renderer": self._template_renderer,
                 "max_output_length": self._template_transform_max_output_length,
             },
-            NodeType.HTTP_REQUEST: lambda: {
+            BuiltinNodeTypes.HTTP_REQUEST: lambda: {
                 "http_request_config": self._http_request_config,
                 "http_client": self._http_request_http_client,
                 "tool_file_manager_factory": self._http_request_tool_file_manager_factory,
                 "file_manager": self._http_request_file_manager,
             },
-            NodeType.HUMAN_INPUT: lambda: {
+            BuiltinNodeTypes.HUMAN_INPUT: lambda: {
                 "form_repository": HumanInputFormRepositoryImpl(tenant_id=self._dify_context.tenant_id),
             },
-            NodeType.KNOWLEDGE_INDEX: lambda: {
-                "index_processor": IndexProcessor(),
-                "summary_index_service": SummaryIndex(),
-            },
-            NodeType.LLM: lambda: self._build_llm_compatible_node_init_kwargs(
+            BuiltinNodeTypes.LLM: lambda: self._build_llm_compatible_node_init_kwargs(
                 node_class=node_class,
                 node_data=node_data,
                 include_http_client=True,
             ),
-            NodeType.DATASOURCE: lambda: {
-                "datasource_manager": DatasourceManager,
-            },
-            NodeType.KNOWLEDGE_RETRIEVAL: lambda: {
-                "rag_retrieval": self._rag_retrieval,
-            },
-            NodeType.DOCUMENT_EXTRACTOR: lambda: {
+            BuiltinNodeTypes.DOCUMENT_EXTRACTOR: lambda: {
                 "unstructured_api_config": self._document_extractor_unstructured_api_config,
                 "http_client": self._http_request_http_client,
             },
-            NodeType.QUESTION_CLASSIFIER: lambda: self._build_llm_compatible_node_init_kwargs(
+            BuiltinNodeTypes.QUESTION_CLASSIFIER: lambda: self._build_llm_compatible_node_init_kwargs(
                 node_class=node_class,
                 node_data=node_data,
                 include_http_client=True,
             ),
-            NodeType.PARAMETER_EXTRACTOR: lambda: self._build_llm_compatible_node_init_kwargs(
+            BuiltinNodeTypes.PARAMETER_EXTRACTOR: lambda: self._build_llm_compatible_node_init_kwargs(
                 node_class=node_class,
                 node_data=node_data,
                 include_http_client=False,
             ),
-            NodeType.TOOL: lambda: {
+            BuiltinNodeTypes.TOOL: lambda: {
                 "tool_file_manager_factory": self._http_request_tool_file_manager_factory(),
             },
+            BuiltinNodeTypes.AGENT: lambda: {
+                "strategy_resolver": self._agent_strategy_resolver,
+                "presentation_provider": self._agent_strategy_presentation_provider,
+                "runtime_support": self._agent_runtime_support,
+                "message_transformer": self._agent_message_transformer,
+            },
         }
         node_init_kwargs = node_init_kwargs_factories.get(node_type, lambda: {})()
         return node_class(
@@ -238,16 +368,7 @@ class DifyNodeFactory(NodeFactory):
 
     @staticmethod
     def _resolve_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:
-        node_mapping = NODE_TYPE_CLASSES_MAPPING.get(node_type)
-        if not node_mapping:
-            raise ValueError(f"No class mapping found for node type: {node_type}")
-
-        latest_node_class = node_mapping.get(LATEST_VERSION)
-        matched_node_class = node_mapping.get(node_version)
-        node_class = matched_node_class or latest_node_class
-        if not node_class:
-            raise ValueError(f"No latest version class found for node type: {node_type}")
-        return node_class
+        return resolve_workflow_node_class(node_type=node_type, node_version=node_version)
 
     def _build_llm_compatible_node_init_kwargs(
         self,

api/core/workflow/nodes/__init__.py

@@ -0,0 +1 @@
+"""Workflow node implementations that remain under the legacy core.workflow namespace."""

api/core/workflow/nodes/agent/__init__.py

@@ -0,0 +1,4 @@
+from .agent_node import AgentNode
+from .entities import AgentNodeData
+
+__all__ = ["AgentNode", "AgentNodeData"]

api/core/workflow/nodes/agent/agent_node.py

@@ -0,0 +1,188 @@
+from __future__ import annotations
+
+from collections.abc import Generator, Mapping, Sequence
+from typing import TYPE_CHECKING, Any
+
+from dify_graph.entities.graph_config import NodeConfigDict
+from dify_graph.enums import BuiltinNodeTypes, SystemVariableKey, WorkflowNodeExecutionStatus
+from dify_graph.node_events import NodeEventBase, NodeRunResult, StreamCompletedEvent
+from dify_graph.nodes.base.node import Node
+from dify_graph.nodes.base.variable_template_parser import VariableTemplateParser
+
+from .entities import AgentNodeData
+from .exceptions import (
+    AgentInvocationError,
+    AgentMessageTransformError,
+)
+from .message_transformer import AgentMessageTransformer
+from .runtime_support import AgentRuntimeSupport
+from .strategy_protocols import AgentStrategyPresentationProvider, AgentStrategyResolver
+
+if TYPE_CHECKING:
+    from dify_graph.entities import GraphInitParams
+    from dify_graph.runtime import GraphRuntimeState
+
+
+class AgentNode(Node[AgentNodeData]):
+    node_type = BuiltinNodeTypes.AGENT
+
+    _strategy_resolver: AgentStrategyResolver
+    _presentation_provider: AgentStrategyPresentationProvider
+    _runtime_support: AgentRuntimeSupport
+    _message_transformer: AgentMessageTransformer
+
+    def __init__(
+        self,
+        id: str,
+        config: NodeConfigDict,
+        graph_init_params: GraphInitParams,
+        graph_runtime_state: GraphRuntimeState,
+        *,
+        strategy_resolver: AgentStrategyResolver,
+        presentation_provider: AgentStrategyPresentationProvider,
+        runtime_support: AgentRuntimeSupport,
+        message_transformer: AgentMessageTransformer,
+    ) -> None:
+        super().__init__(
+            id=id,
+            config=config,
+            graph_init_params=graph_init_params,
+            graph_runtime_state=graph_runtime_state,
+        )
+        self._strategy_resolver = strategy_resolver
+        self._presentation_provider = presentation_provider
+        self._runtime_support = runtime_support
+        self._message_transformer = message_transformer
+
+    @classmethod
+    def version(cls) -> str:
+        return "1"
+
+    def populate_start_event(self, event) -> None:
+        dify_ctx = self.require_dify_context()
+        event.extras["agent_strategy"] = {
+            "name": self.node_data.agent_strategy_name,
+            "icon": self._presentation_provider.get_icon(
+                tenant_id=dify_ctx.tenant_id,
+                agent_strategy_provider_name=self.node_data.agent_strategy_provider_name,
+            ),
+        }
+
+    def _run(self) -> Generator[NodeEventBase, None, None]:
+        from core.plugin.impl.exc import PluginDaemonClientSideError
+
+        dify_ctx = self.require_dify_context()
+
+        try:
+            strategy = self._strategy_resolver.resolve(
+                tenant_id=dify_ctx.tenant_id,
+                agent_strategy_provider_name=self.node_data.agent_strategy_provider_name,
+                agent_strategy_name=self.node_data.agent_strategy_name,
+            )
+        except Exception as e:
+            yield StreamCompletedEvent(
+                node_run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    inputs={},
+                    error=f"Failed to get agent strategy: {str(e)}",
+                ),
+            )
+            return
+
+        agent_parameters = strategy.get_parameters()
+
+        parameters = self._runtime_support.build_parameters(
+            agent_parameters=agent_parameters,
+            variable_pool=self.graph_runtime_state.variable_pool,
+            node_data=self.node_data,
+            strategy=strategy,
+            tenant_id=dify_ctx.tenant_id,
+            app_id=dify_ctx.app_id,
+            invoke_from=dify_ctx.invoke_from,
+        )
+        parameters_for_log = self._runtime_support.build_parameters(
+            agent_parameters=agent_parameters,
+            variable_pool=self.graph_runtime_state.variable_pool,
+            node_data=self.node_data,
+            strategy=strategy,
+            tenant_id=dify_ctx.tenant_id,
+            app_id=dify_ctx.app_id,
+            invoke_from=dify_ctx.invoke_from,
+            for_log=True,
+        )
+        credentials = self._runtime_support.build_credentials(parameters=parameters)
+
+        conversation_id = self.graph_runtime_state.variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
+
+        try:
+            message_stream = strategy.invoke(
+                params=parameters,
+                user_id=dify_ctx.user_id,
+                app_id=dify_ctx.app_id,
+                conversation_id=conversation_id.text if conversation_id else None,
+                credentials=credentials,
+            )
+        except Exception as e:
+            error = AgentInvocationError(f"Failed to invoke agent: {str(e)}", original_error=e)
+            yield StreamCompletedEvent(
+                node_run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    inputs=parameters_for_log,
+                    error=str(error),
+                )
+            )
+            return
+
+        try:
+            yield from self._message_transformer.transform(
+                messages=message_stream,
+                tool_info={
+                    "icon": self._presentation_provider.get_icon(
+                        tenant_id=dify_ctx.tenant_id,
+                        agent_strategy_provider_name=self.node_data.agent_strategy_provider_name,
+                    ),
+                    "agent_strategy": self.node_data.agent_strategy_name,
+                },
+                parameters_for_log=parameters_for_log,
+                user_id=dify_ctx.user_id,
+                tenant_id=dify_ctx.tenant_id,
+                node_type=self.node_type,
+                node_id=self._node_id,
+                node_execution_id=self.id,
+            )
+        except PluginDaemonClientSideError as e:
+            transform_error = AgentMessageTransformError(
+                f"Failed to transform agent message: {str(e)}", original_error=e
+            )
+            yield StreamCompletedEvent(
+                node_run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    inputs=parameters_for_log,
+                    error=str(transform_error),
+                )
+            )
+
+    @classmethod
+    def _extract_variable_selector_to_variable_mapping(
+        cls,
+        *,
+        graph_config: Mapping[str, Any],
+        node_id: str,
+        node_data: AgentNodeData,
+    ) -> Mapping[str, Sequence[str]]:
+        _ = graph_config  # Explicitly mark as unused
+        result: dict[str, Any] = {}
+        typed_node_data = node_data
+        for parameter_name in typed_node_data.agent_parameters:
+            input = typed_node_data.agent_parameters[parameter_name]
+            match input.type:
+                case "mixed" | "constant":
+                    selectors = VariableTemplateParser(str(input.value)).extract_variable_selectors()
+                    for selector in selectors:
+                        result[selector.variable] = selector.value_selector
+                case "variable":
+                    result[parameter_name] = input.value
+
+        result = {node_id + "." + key: value for key, value in result.items()}
+
+        return result

api/core/workflow/nodes/agent/entities.py

@@ -6,14 +6,14 @@ from pydantic import BaseModel
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.tools.entities.tool_entities import ToolSelector
 from dify_graph.entities.base_node_data import BaseNodeData
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 
 
 class AgentNodeData(BaseNodeData):
-    type: NodeType = NodeType.AGENT
-    agent_strategy_provider_name: str  # redundancy
+    type: NodeType = BuiltinNodeTypes.AGENT
+    agent_strategy_provider_name: str
     agent_strategy_name: str
-    agent_strategy_label: str  # redundancy
+    agent_strategy_label: str
     memory: MemoryConfig | None = None
     # The version of the tool parameter.
     # If this value is None, it indicates this is a previous version

api/core/workflow/nodes/agent/exceptions.py

@@ -119,14 +119,3 @@ class AgentVariableTypeError(AgentNodeError):
         self.expected_type = expected_type
         self.actual_type = actual_type
         super().__init__(message)
-
-
-class AgentMaxIterationError(AgentNodeError):
-    """Exception raised when the agent exceeds the maximum iteration limit."""
-
-    def __init__(self, max_iteration: int):
-        self.max_iteration = max_iteration
-        super().__init__(
-            f"Agent exceeded the maximum iteration limit of {max_iteration}. "
-            f"The agent was unable to complete the task within the allowed number of iterations."
-        )

api/core/workflow/nodes/agent/message_transformer.py

@@ -0,0 +1,292 @@
+from __future__ import annotations
+
+from collections.abc import Generator, Mapping
+from typing import Any, cast
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.utils.message_transformer import ToolFileMessageTransformer
+from dify_graph.enums import BuiltinNodeTypes, NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from dify_graph.file import File, FileTransferMethod
+from dify_graph.model_runtime.entities.llm_entities import LLMUsage, LLMUsageMetadata
+from dify_graph.model_runtime.utils.encoders import jsonable_encoder
+from dify_graph.node_events import (
+    AgentLogEvent,
+    NodeEventBase,
+    NodeRunResult,
+    StreamChunkEvent,
+    StreamCompletedEvent,
+)
+from dify_graph.variables.segments import ArrayFileSegment
+from extensions.ext_database import db
+from factories import file_factory
+from models import ToolFile
+from services.tools.builtin_tools_manage_service import BuiltinToolManageService
+
+from .exceptions import AgentNodeError, AgentVariableTypeError, ToolFileNotFoundError
+
+
+class AgentMessageTransformer:
+    def transform(
+        self,
+        *,
+        messages: Generator[ToolInvokeMessage, None, None],
+        tool_info: Mapping[str, Any],
+        parameters_for_log: dict[str, Any],
+        user_id: str,
+        tenant_id: str,
+        node_type: NodeType,
+        node_id: str,
+        node_execution_id: str,
+    ) -> Generator[NodeEventBase, None, None]:
+        from core.plugin.impl.plugin import PluginInstaller
+
+        message_stream = ToolFileMessageTransformer.transform_tool_invoke_messages(
+            messages=messages,
+            user_id=user_id,
+            tenant_id=tenant_id,
+            conversation_id=None,
+        )
+
+        text = ""
+        files: list[File] = []
+        json_list: list[dict | list] = []
+
+        agent_logs: list[AgentLogEvent] = []
+        agent_execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] = {}
+        llm_usage = LLMUsage.empty_usage()
+        variables: dict[str, Any] = {}
+
+        for message in message_stream:
+            if message.type in {
+                ToolInvokeMessage.MessageType.IMAGE_LINK,
+                ToolInvokeMessage.MessageType.BINARY_LINK,
+                ToolInvokeMessage.MessageType.IMAGE,
+            }:
+                assert isinstance(message.message, ToolInvokeMessage.TextMessage)
+
+                url = message.message.text
+                if message.meta:
+                    transfer_method = message.meta.get("transfer_method", FileTransferMethod.TOOL_FILE)
+                else:
+                    transfer_method = FileTransferMethod.TOOL_FILE
+
+                tool_file_id = str(url).split("/")[-1].split(".")[0]
+
+                with Session(db.engine) as session:
+                    stmt = select(ToolFile).where(ToolFile.id == tool_file_id)
+                    tool_file = session.scalar(stmt)
+                    if tool_file is None:
+                        raise ToolFileNotFoundError(tool_file_id)
+
+                mapping = {
+                    "tool_file_id": tool_file_id,
+                    "type": file_factory.get_file_type_by_mime_type(tool_file.mimetype),
+                    "transfer_method": transfer_method,
+                    "url": url,
+                }
+                file = file_factory.build_from_mapping(
+                    mapping=mapping,
+                    tenant_id=tenant_id,
+                )
+                files.append(file)
+            elif message.type == ToolInvokeMessage.MessageType.BLOB:
+                assert isinstance(message.message, ToolInvokeMessage.TextMessage)
+                assert message.meta
+
+                tool_file_id = message.message.text.split("/")[-1].split(".")[0]
+                with Session(db.engine) as session:
+                    stmt = select(ToolFile).where(ToolFile.id == tool_file_id)
+                    tool_file = session.scalar(stmt)
+                    if tool_file is None:
+                        raise ToolFileNotFoundError(tool_file_id)
+
+                mapping = {
+                    "tool_file_id": tool_file_id,
+                    "transfer_method": FileTransferMethod.TOOL_FILE,
+                }
+                files.append(
+                    file_factory.build_from_mapping(
+                        mapping=mapping,
+                        tenant_id=tenant_id,
+                    )
+                )
+            elif message.type == ToolInvokeMessage.MessageType.TEXT:
+                assert isinstance(message.message, ToolInvokeMessage.TextMessage)
+                text += message.message.text
+                yield StreamChunkEvent(
+                    selector=[node_id, "text"],
+                    chunk=message.message.text,
+                    is_final=False,
+                )
+            elif message.type == ToolInvokeMessage.MessageType.JSON:
+                assert isinstance(message.message, ToolInvokeMessage.JsonMessage)
+                if node_type == BuiltinNodeTypes.AGENT:
+                    if isinstance(message.message.json_object, dict):
+                        msg_metadata: dict[str, Any] = message.message.json_object.pop("execution_metadata", {})
+                        llm_usage = LLMUsage.from_metadata(cast(LLMUsageMetadata, msg_metadata))
+                        agent_execution_metadata = {
+                            WorkflowNodeExecutionMetadataKey(key): value
+                            for key, value in msg_metadata.items()
+                            if key in WorkflowNodeExecutionMetadataKey.__members__.values()
+                        }
+                    else:
+                        llm_usage = LLMUsage.empty_usage()
+                        agent_execution_metadata = {}
+                if message.message.json_object:
+                    json_list.append(message.message.json_object)
+            elif message.type == ToolInvokeMessage.MessageType.LINK:
+                assert isinstance(message.message, ToolInvokeMessage.TextMessage)
+                stream_text = f"Link: {message.message.text}\n"
+                text += stream_text
+                yield StreamChunkEvent(
+                    selector=[node_id, "text"],
+                    chunk=stream_text,
+                    is_final=False,
+                )
+            elif message.type == ToolInvokeMessage.MessageType.VARIABLE:
+                assert isinstance(message.message, ToolInvokeMessage.VariableMessage)
+                variable_name = message.message.variable_name
+                variable_value = message.message.variable_value
+                if message.message.stream:
+                    if not isinstance(variable_value, str):
+                        raise AgentVariableTypeError(
+                            "When 'stream' is True, 'variable_value' must be a string.",
+                            variable_name=variable_name,
+                            expected_type="str",
+                            actual_type=type(variable_value).__name__,
+                        )
+                    if variable_name not in variables:
+                        variables[variable_name] = ""
+                    variables[variable_name] += variable_value
+
+                    yield StreamChunkEvent(
+                        selector=[node_id, variable_name],
+                        chunk=variable_value,
+                        is_final=False,
+                    )
+                else:
+                    variables[variable_name] = variable_value
+            elif message.type == ToolInvokeMessage.MessageType.FILE:
+                assert message.meta is not None
+                assert isinstance(message.meta, dict)
+                if "file" not in message.meta:
+                    raise AgentNodeError("File message is missing 'file' key in meta")
+
+                if not isinstance(message.meta["file"], File):
+                    raise AgentNodeError(f"Expected File object but got {type(message.meta['file']).__name__}")
+                files.append(message.meta["file"])
+            elif message.type == ToolInvokeMessage.MessageType.LOG:
+                assert isinstance(message.message, ToolInvokeMessage.LogMessage)
+                if message.message.metadata:
+                    icon = tool_info.get("icon", "")
+                    dict_metadata = dict(message.message.metadata)
+                    if dict_metadata.get("provider"):
+                        manager = PluginInstaller()
+                        plugins = manager.list_plugins(tenant_id)
+                        try:
+                            current_plugin = next(
+                                plugin
+                                for plugin in plugins
+                                if f"{plugin.plugin_id}/{plugin.name}" == dict_metadata["provider"]
+                            )
+                            icon = current_plugin.declaration.icon
+                        except StopIteration:
+                            pass
+                        icon_dark = None
+                        try:
+                            builtin_tool = next(
+                                provider
+                                for provider in BuiltinToolManageService.list_builtin_tools(
+                                    user_id,
+                                    tenant_id,
+                                )
+                                if provider.name == dict_metadata["provider"]
+                            )
+                            icon = builtin_tool.icon
+                            icon_dark = builtin_tool.icon_dark
+                        except StopIteration:
+                            pass
+
+                        dict_metadata["icon"] = icon
+                        dict_metadata["icon_dark"] = icon_dark
+                        message.message.metadata = dict_metadata
+                agent_log = AgentLogEvent(
+                    message_id=message.message.id,
+                    node_execution_id=node_execution_id,
+                    parent_id=message.message.parent_id,
+                    error=message.message.error,
+                    status=message.message.status.value,
+                    data=message.message.data,
+                    label=message.message.label,
+                    metadata=message.message.metadata,
+                    node_id=node_id,
+                )
+
+                for log in agent_logs:
+                    if log.message_id == agent_log.message_id:
+                        log.data = agent_log.data
+                        log.status = agent_log.status
+                        log.error = agent_log.error
+                        log.label = agent_log.label
+                        log.metadata = agent_log.metadata
+                        break
+                else:
+                    agent_logs.append(agent_log)
+
+                yield agent_log
+
+        json_output: list[dict[str, Any] | list[Any]] = []
+        if agent_logs:
+            for log in agent_logs:
+                json_output.append(
+                    {
+                        "id": log.message_id,
+                        "parent_id": log.parent_id,
+                        "error": log.error,
+                        "status": log.status,
+                        "data": log.data,
+                        "label": log.label,
+                        "metadata": log.metadata,
+                        "node_id": log.node_id,
+                    }
+                )
+        if json_list:
+            json_output.extend(json_list)
+        else:
+            json_output.append({"data": []})
+
+        yield StreamChunkEvent(
+            selector=[node_id, "text"],
+            chunk="",
+            is_final=True,
+        )
+
+        for var_name in variables:
+            yield StreamChunkEvent(
+                selector=[node_id, var_name],
+                chunk="",
+                is_final=True,
+            )
+
+        yield StreamCompletedEvent(
+            node_run_result=NodeRunResult(
+                status=WorkflowNodeExecutionStatus.SUCCEEDED,
+                outputs={
+                    "text": text,
+                    "usage": jsonable_encoder(llm_usage),
+                    "files": ArrayFileSegment(value=files),
+                    "json": json_output,
+                    **variables,
+                },
+                metadata={
+                    **agent_execution_metadata,
+                    WorkflowNodeExecutionMetadataKey.TOOL_INFO: tool_info,
+                    WorkflowNodeExecutionMetadataKey.AGENT_LOG: agent_logs,
+                },
+                inputs=parameters_for_log,
+                llm_usage=llm_usage,
+            )
+        )

api/core/workflow/nodes/agent/plugin_strategy_adapter.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from factories.agent_factory import get_plugin_agent_strategy
+
+from .strategy_protocols import AgentStrategyPresentationProvider, AgentStrategyResolver, ResolvedAgentStrategy
+
+
+class PluginAgentStrategyResolver(AgentStrategyResolver):
+    def resolve(
+        self,
+        *,
+        tenant_id: str,
+        agent_strategy_provider_name: str,
+        agent_strategy_name: str,
+    ) -> ResolvedAgentStrategy:
+        return get_plugin_agent_strategy(
+            tenant_id=tenant_id,
+            agent_strategy_provider_name=agent_strategy_provider_name,
+            agent_strategy_name=agent_strategy_name,
+        )
+
+
+class PluginAgentStrategyPresentationProvider(AgentStrategyPresentationProvider):
+    def get_icon(self, *, tenant_id: str, agent_strategy_provider_name: str) -> str | None:
+        from core.plugin.impl.plugin import PluginInstaller
+
+        manager = PluginInstaller()
+        try:
+            plugins = manager.list_plugins(tenant_id)
+        except Exception:
+            return None
+
+        try:
+            current_plugin = next(
+                plugin for plugin in plugins if f"{plugin.plugin_id}/{plugin.name}" == agent_strategy_provider_name
+            )
+        except StopIteration:
+            return None
+
+        return current_plugin.declaration.icon

api/core/workflow/nodes/agent/runtime_support.py

@@ -0,0 +1,276 @@
+from __future__ import annotations
+
+import json
+from collections.abc import Sequence
+from typing import Any, cast
+
+from packaging.version import Version
+from pydantic import ValidationError
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from core.agent.entities import AgentToolEntity
+from core.agent.plugin_entities import AgentStrategyParameter
+from core.memory.token_buffer_memory import TokenBufferMemory
+from core.model_manager import ModelInstance, ModelManager
+from core.plugin.entities.request import InvokeCredentials
+from core.provider_manager import ProviderManager
+from core.tools.entities.tool_entities import ToolIdentity, ToolParameter, ToolProviderType
+from core.tools.tool_manager import ToolManager
+from dify_graph.enums import SystemVariableKey
+from dify_graph.model_runtime.entities.model_entities import AIModelEntity, ModelType
+from dify_graph.runtime import VariablePool
+from dify_graph.variables.segments import StringSegment
+from extensions.ext_database import db
+from models.model import Conversation
+
+from .entities import AgentNodeData, AgentOldVersionModelFeatures, ParamsAutoGenerated
+from .exceptions import AgentInputTypeError, AgentVariableNotFoundError
+from .strategy_protocols import ResolvedAgentStrategy
+
+
+class AgentRuntimeSupport:
+    def build_parameters(
+        self,
+        *,
+        agent_parameters: Sequence[AgentStrategyParameter],
+        variable_pool: VariablePool,
+        node_data: AgentNodeData,
+        strategy: ResolvedAgentStrategy,
+        tenant_id: str,
+        app_id: str,
+        invoke_from: Any,
+        for_log: bool = False,
+    ) -> dict[str, Any]:
+        agent_parameters_dictionary = {parameter.name: parameter for parameter in agent_parameters}
+
+        result: dict[str, Any] = {}
+        for parameter_name in node_data.agent_parameters:
+            parameter = agent_parameters_dictionary.get(parameter_name)
+            if not parameter:
+                result[parameter_name] = None
+                continue
+
+            agent_input = node_data.agent_parameters[parameter_name]
+            match agent_input.type:
+                case "variable":
+                    variable = variable_pool.get(agent_input.value)  # type: ignore[arg-type]
+                    if variable is None:
+                        raise AgentVariableNotFoundError(str(agent_input.value))
+                    parameter_value = variable.value
+                case "mixed" | "constant":
+                    try:
+                        if not isinstance(agent_input.value, str):
+                            parameter_value = json.dumps(agent_input.value, ensure_ascii=False)
+                        else:
+                            parameter_value = str(agent_input.value)
+                    except TypeError:
+                        parameter_value = str(agent_input.value)
+
+                    segment_group = variable_pool.convert_template(parameter_value)
+                    parameter_value = segment_group.log if for_log else segment_group.text
+                    try:
+                        if not isinstance(agent_input.value, str):
+                            parameter_value = json.loads(parameter_value)
+                    except json.JSONDecodeError:
+                        parameter_value = parameter_value
+                case _:
+                    raise AgentInputTypeError(agent_input.type)
+
+            value = parameter_value
+            if parameter.type == "array[tools]":
+                value = cast(list[dict[str, Any]], value)
+                value = [tool for tool in value if tool.get("enabled", False)]
+                value = self._filter_mcp_type_tool(strategy, value)
+                for tool in value:
+                    if "schemas" in tool:
+                        tool.pop("schemas")
+                    parameters = tool.get("parameters", {})
+                    if all(isinstance(v, dict) for _, v in parameters.items()):
+                        params = {}
+                        for key, param in parameters.items():
+                            if param.get("auto", ParamsAutoGenerated.OPEN) in (
+                                ParamsAutoGenerated.CLOSE,
+                                0,
+                            ):
+                                value_param = param.get("value", {})
+                                if value_param and value_param.get("type", "") == "variable":
+                                    variable_selector = value_param.get("value")
+                                    if not variable_selector:
+                                        raise ValueError("Variable selector is missing for a variable-type parameter.")
+
+                                    variable = variable_pool.get(variable_selector)
+                                    if variable is None:
+                                        raise AgentVariableNotFoundError(str(variable_selector))
+
+                                    params[key] = variable.value
+                                else:
+                                    params[key] = value_param.get("value", "") if value_param is not None else None
+                            else:
+                                params[key] = None
+                        parameters = params
+                    tool["settings"] = {k: v.get("value", None) for k, v in tool.get("settings", {}).items()}
+                    tool["parameters"] = parameters
+
+            if not for_log:
+                if parameter.type == "array[tools]":
+                    value = cast(list[dict[str, Any]], value)
+                    tool_value = []
+                    for tool in value:
+                        provider_type = ToolProviderType(tool.get("type", ToolProviderType.BUILT_IN))
+                        setting_params = tool.get("settings", {})
+                        parameters = tool.get("parameters", {})
+                        manual_input_params = [key for key, value in parameters.items() if value is not None]
+
+                        parameters = {**parameters, **setting_params}
+                        entity = AgentToolEntity(
+                            provider_id=tool.get("provider_name", ""),
+                            provider_type=provider_type,
+                            tool_name=tool.get("tool_name", ""),
+                            tool_parameters=parameters,
+                            plugin_unique_identifier=tool.get("plugin_unique_identifier", None),
+                            credential_id=tool.get("credential_id", None),
+                        )
+
+                        extra = tool.get("extra", {})
+
+                        runtime_variable_pool: VariablePool | None = None
+                        if node_data.version != "1" or node_data.tool_node_version is not None:
+                            runtime_variable_pool = variable_pool
+                        tool_runtime = ToolManager.get_agent_tool_runtime(
+                            tenant_id,
+                            app_id,
+                            entity,
+                            invoke_from,
+                            runtime_variable_pool,
+                        )
+                        if tool_runtime.entity.description:
+                            tool_runtime.entity.description.llm = (
+                                extra.get("description", "") or tool_runtime.entity.description.llm
+                            )
+                        for tool_runtime_params in tool_runtime.entity.parameters:
+                            tool_runtime_params.form = (
+                                ToolParameter.ToolParameterForm.FORM
+                                if tool_runtime_params.name in manual_input_params
+                                else tool_runtime_params.form
+                            )
+                        manual_input_value = {}
+                        if tool_runtime.entity.parameters:
+                            manual_input_value = {
+                                key: value for key, value in parameters.items() if key in manual_input_params
+                            }
+                        runtime_parameters = {
+                            **tool_runtime.runtime.runtime_parameters,
+                            **manual_input_value,
+                        }
+                        tool_value.append(
+                            {
+                                **tool_runtime.entity.model_dump(mode="json"),
+                                "runtime_parameters": runtime_parameters,
+                                "credential_id": tool.get("credential_id", None),
+                                "provider_type": provider_type.value,
+                            }
+                        )
+                    value = tool_value
+                if parameter.type == AgentStrategyParameter.AgentStrategyParameterType.MODEL_SELECTOR:
+                    value = cast(dict[str, Any], value)
+                    model_instance, model_schema = self.fetch_model(tenant_id=tenant_id, value=value)
+                    history_prompt_messages = []
+                    if node_data.memory:
+                        memory = self.fetch_memory(
+                            variable_pool=variable_pool,
+                            app_id=app_id,
+                            model_instance=model_instance,
+                        )
+                        if memory:
+                            prompt_messages = memory.get_history_prompt_messages(
+                                message_limit=node_data.memory.window.size or None
+                            )
+                            history_prompt_messages = [
+                                prompt_message.model_dump(mode="json") for prompt_message in prompt_messages
+                            ]
+                    value["history_prompt_messages"] = history_prompt_messages
+                    if model_schema:
+                        model_schema = self._remove_unsupported_model_features_for_old_version(model_schema)
+                        value["entity"] = model_schema.model_dump(mode="json")
+                    else:
+                        value["entity"] = None
+            result[parameter_name] = value
+
+        return result
+
+    def build_credentials(self, *, parameters: dict[str, Any]) -> InvokeCredentials:
+        credentials = InvokeCredentials()
+        credentials.tool_credentials = {}
+        for tool in parameters.get("tools", []):
+            if not tool.get("credential_id"):
+                continue
+            try:
+                identity = ToolIdentity.model_validate(tool.get("identity", {}))
+            except ValidationError:
+                continue
+            credentials.tool_credentials[identity.provider] = tool.get("credential_id", None)
+        return credentials
+
+    def fetch_memory(
+        self,
+        *,
+        variable_pool: VariablePool,
+        app_id: str,
+        model_instance: ModelInstance,
+    ) -> TokenBufferMemory | None:
+        conversation_id_variable = variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
+        if not isinstance(conversation_id_variable, StringSegment):
+            return None
+        conversation_id = conversation_id_variable.value
+
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(Conversation).where(Conversation.app_id == app_id, Conversation.id == conversation_id)
+            conversation = session.scalar(stmt)
+            if not conversation:
+                return None
+
+        return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+
+    def fetch_model(self, *, tenant_id: str, value: dict[str, Any]) -> tuple[ModelInstance, AIModelEntity | None]:
+        provider_manager = ProviderManager()
+        provider_model_bundle = provider_manager.get_provider_model_bundle(
+            tenant_id=tenant_id,
+            provider=value.get("provider", ""),
+            model_type=ModelType.LLM,
+        )
+        model_name = value.get("model", "")
+        model_credentials = provider_model_bundle.configuration.get_current_credentials(
+            model_type=ModelType.LLM,
+            model=model_name,
+        )
+        provider_name = provider_model_bundle.configuration.provider.provider
+        model_type_instance = provider_model_bundle.model_type_instance
+        model_instance = ModelManager().get_model_instance(
+            tenant_id=tenant_id,
+            provider=provider_name,
+            model_type=ModelType(value.get("model_type", "")),
+            model=model_name,
+        )
+        model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
+        return model_instance, model_schema
+
+    @staticmethod
+    def _remove_unsupported_model_features_for_old_version(model_schema: AIModelEntity) -> AIModelEntity:
+        if model_schema.features:
+            for feature in model_schema.features[:]:
+                try:
+                    AgentOldVersionModelFeatures(feature.value)
+                except ValueError:
+                    model_schema.features.remove(feature)
+        return model_schema
+
+    @staticmethod
+    def _filter_mcp_type_tool(
+        strategy: ResolvedAgentStrategy,
+        tools: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        meta_version = strategy.meta_version
+        if meta_version and Version(meta_version) > Version("0.0.1"):
+            return tools
+        return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP]

api/core/workflow/nodes/agent/strategy_protocols.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from collections.abc import Generator, Sequence
+from typing import Any, Protocol
+
+from core.agent.plugin_entities import AgentStrategyParameter
+from core.plugin.entities.request import InvokeCredentials
+from core.tools.entities.tool_entities import ToolInvokeMessage
+
+
+class ResolvedAgentStrategy(Protocol):
+    meta_version: str | None
+
+    def get_parameters(self) -> Sequence[AgentStrategyParameter]: ...
+
+    def invoke(
+        self,
+        *,
+        params: dict[str, Any],
+        user_id: str,
+        conversation_id: str | None = None,
+        app_id: str | None = None,
+        message_id: str | None = None,
+        credentials: InvokeCredentials | None = None,
+    ) -> Generator[ToolInvokeMessage, None, None]: ...
+
+
+class AgentStrategyResolver(Protocol):
+    def resolve(
+        self,
+        *,
+        tenant_id: str,
+        agent_strategy_provider_name: str,
+        agent_strategy_name: str,
+    ) -> ResolvedAgentStrategy: ...
+
+
+class AgentStrategyPresentationProvider(Protocol):
+    def get_icon(self, *, tenant_id: str, agent_strategy_provider_name: str) -> str | None: ...

api/core/workflow/nodes/datasource/__init__.py

@@ -0,0 +1 @@
+"""Datasource workflow node package."""

api/core/workflow/nodes/datasource/datasource_node.py

@@ -1,22 +1,17 @@
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
+from core.datasource.datasource_manager import DatasourceManager
 from core.datasource.entities.datasource_entities import DatasourceProviderType
 from core.plugin.impl.exc import PluginDaemonClientSideError
 from dify_graph.entities.graph_config import NodeConfigDict
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType, SystemVariableKey
+from dify_graph.enums import BuiltinNodeTypes, NodeExecutionType, SystemVariableKey, WorkflowNodeExecutionMetadataKey
 from dify_graph.node_events import NodeRunResult, StreamCompletedEvent
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.base.variable_template_parser import VariableTemplateParser
-from dify_graph.repositories.datasource_manager_protocol import (
-    DatasourceManagerProtocol,
-    DatasourceParameter,
-    OnlineDriveDownloadFileParam,
-)
 
-from ...entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from .entities import DatasourceNodeData
+from .entities import DatasourceNodeData, DatasourceParameter, OnlineDriveDownloadFileParam
 from .exc import DatasourceNodeError
 
 if TYPE_CHECKING:
@@ -29,7 +24,7 @@ class DatasourceNode(Node[DatasourceNodeData]):
     Datasource Node
     """
 
-    node_type = NodeType.DATASOURCE
+    node_type = BuiltinNodeTypes.DATASOURCE
     execution_type = NodeExecutionType.ROOT
 
     def __init__(
@@ -38,7 +33,6 @@ class DatasourceNode(Node[DatasourceNodeData]):
         config: NodeConfigDict,
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
-        datasource_manager: DatasourceManagerProtocol,
     ):
         super().__init__(
             id=id,
@@ -46,7 +40,11 @@ class DatasourceNode(Node[DatasourceNodeData]):
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
         )
-        self.datasource_manager = datasource_manager
+        self.datasource_manager = DatasourceManager
+
+    def populate_start_event(self, event) -> None:
+        event.provider_id = f"{self.node_data.plugin_id}/{self.node_data.provider_name}"
+        event.provider_type = self.node_data.provider_type
 
     def _run(self) -> Generator:
         """

api/core/workflow/nodes/datasource/entities.py

@@ -4,7 +4,7 @@ from pydantic import BaseModel, field_validator
 from pydantic_core.core_schema import ValidationInfo
 
 from dify_graph.entities.base_node_data import BaseNodeData
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 
 
 class DatasourceEntity(BaseModel):
@@ -17,7 +17,7 @@ class DatasourceEntity(BaseModel):
 
 
 class DatasourceNodeData(BaseNodeData, DatasourceEntity):
-    type: NodeType = NodeType.DATASOURCE
+    type: NodeType = BuiltinNodeTypes.DATASOURCE
 
     class DatasourceInput(BaseModel):
         # TODO: check this type
@@ -42,3 +42,14 @@ class DatasourceNodeData(BaseNodeData, DatasourceEntity):
             return typ
 
     datasource_parameters: dict[str, DatasourceInput] | None = None
+
+
+class DatasourceParameter(BaseModel):
+    workspace_id: str
+    page_id: str
+    type: str
+
+
+class OnlineDriveDownloadFileParam(BaseModel):
+    id: str
+    bucket: str

api/core/workflow/nodes/datasource/protocols.py

@@ -1,25 +1,10 @@
 from collections.abc import Generator
 from typing import Any, Protocol
 
-from pydantic import BaseModel
-
 from dify_graph.file import File
 from dify_graph.node_events import StreamChunkEvent, StreamCompletedEvent
 
-
-class DatasourceParameter(BaseModel):
-    workspace_id: str
-    page_id: str
-    type: str
-
-
-class OnlineDriveDownloadFileParam(BaseModel):
-    id: str
-    bucket: str
-
-
-class DatasourceFinal(BaseModel):
-    data: dict[str, Any] | None = None
+from .entities import DatasourceParameter, OnlineDriveDownloadFileParam
 
 
 class DatasourceManagerProtocol(Protocol):

api/core/workflow/nodes/knowledge_index/__init__.py

@@ -0,0 +1,5 @@
+"""Knowledge index workflow node package."""
+
+KNOWLEDGE_INDEX_NODE_TYPE = "knowledge-index"
+
+__all__ = ["KNOWLEDGE_INDEX_NODE_TYPE"]

api/core/workflow/nodes/knowledge_index/entities.py

@@ -3,6 +3,7 @@ from typing import Literal, Union
 from pydantic import BaseModel
 
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
+from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
 
@@ -156,7 +157,7 @@ class KnowledgeIndexNodeData(BaseNodeData):
     Knowledge index Node Data.
     """
 
-    type: NodeType = NodeType.KNOWLEDGE_INDEX
+    type: NodeType = KNOWLEDGE_INDEX_NODE_TYPE
     chunk_structure: str
     index_chunk_variable_selector: list[str]
     indexing_technique: str | None = None

api/core/workflow/nodes/knowledge_index/knowledge_index_node.py

@@ -2,14 +2,15 @@ import logging
 from collections.abc import Mapping
 from typing import TYPE_CHECKING, Any
 
+from core.rag.index_processor.index_processor import IndexProcessor
+from core.rag.summary_index.summary_index import SummaryIndex
+from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from dify_graph.entities.graph_config import NodeConfigDict
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType, SystemVariableKey
+from dify_graph.enums import NodeExecutionType, SystemVariableKey
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.base.template import Template
-from dify_graph.repositories.index_processor_protocol import IndexProcessorProtocol
-from dify_graph.repositories.summary_index_service_protocol import SummaryIndexServiceProtocol
 
 from .entities import KnowledgeIndexNodeData
 from .exc import (
@@ -25,7 +26,7 @@ _INVOKE_FROM_DEBUGGER = "debugger"
 
 
 class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
-    node_type = NodeType.KNOWLEDGE_INDEX
+    node_type = KNOWLEDGE_INDEX_NODE_TYPE
     execution_type = NodeExecutionType.RESPONSE
 
     def __init__(
@@ -34,12 +35,10 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
         config: NodeConfigDict,
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
-        index_processor: IndexProcessorProtocol,
-        summary_index_service: SummaryIndexServiceProtocol,
     ) -> None:
         super().__init__(id, config, graph_init_params, graph_runtime_state)
-        self.index_processor = index_processor
-        self.summary_index_service = summary_index_service
+        self.index_processor = IndexProcessor()
+        self.summary_index_service = SummaryIndex()
 
     def _run(self) -> NodeRunResult:  # type: ignore
         node_data = self.node_data

api/core/workflow/nodes/knowledge_index/protocols.py

@@ -5,21 +5,21 @@ from pydantic import BaseModel, Field
 
 
 class PreviewItem(BaseModel):
-    content: str | None = Field(None)
-    child_chunks: list[str] | None = Field(None)
-    summary: str | None = Field(None)
+    content: str | None = Field(default=None)
+    child_chunks: list[str] | None = Field(default=None)
+    summary: str | None = Field(default=None)
 
 
 class QaPreview(BaseModel):
-    answer: str | None = Field(None)
-    question: str | None = Field(None)
+    answer: str | None = Field(default=None)
+    question: str | None = Field(default=None)
 
 
 class Preview(BaseModel):
     chunk_structure: str
-    parent_mode: str | None = Field(None)
-    preview: list[PreviewItem] = Field([])
-    qa_preview: list[QaPreview] = Field([])
+    parent_mode: str | None = Field(default=None)
+    preview: list[PreviewItem] = Field(default_factory=list)
+    qa_preview: list[QaPreview] = Field(default_factory=list)
     total_segments: int
 
 
@@ -39,3 +39,9 @@ class IndexProcessorProtocol(Protocol):
     def get_preview_output(
         self, chunks: Any, dataset_id: str, document_id: str, chunk_structure: str, summary_index_setting: dict | None
     ) -> Preview: ...
+
+
+class SummaryIndexServiceProtocol(Protocol):
+    def generate_and_vectorize_summary(
+        self, dataset_id: str, document_id: str, is_preview: bool, summary_index_setting: dict | None = None
+    ) -> None: ...

api/core/workflow/nodes/knowledge_retrieval/__init__.py

@@ -0,0 +1 @@
+"""Knowledge retrieval workflow node package."""

api/core/workflow/nodes/knowledge_retrieval/entities.py

@@ -4,7 +4,7 @@ from typing import Literal
 from pydantic import BaseModel, Field
 
 from dify_graph.entities.base_node_data import BaseNodeData
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 from dify_graph.nodes.llm.entities import ModelConfig, VisionConfig
 
 
@@ -114,7 +114,7 @@ class KnowledgeRetrievalNodeData(BaseNodeData):
     Knowledge retrieval Node Data.
     """
 
-    type: NodeType = NodeType.KNOWLEDGE_RETRIEVAL
+    type: NodeType = BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL
     query_variable_selector: list[str] | None | str = None
     query_attachment_selector: list[str] | None | str = None
     dataset_ids: list[str]

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -1,12 +1,19 @@
+"""Knowledge retrieval workflow node implementation.
+
+This node now lives under ``core.workflow.nodes`` and is discovered directly by
+the workflow node registry.
+"""
+
 import logging
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal
 
 from core.app.app_config.entities import DatasetRetrieveConfigEntity
+from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from dify_graph.entities import GraphInitParams
 from dify_graph.entities.graph_config import NodeConfigDict
 from dify_graph.enums import (
-    NodeType,
+    BuiltinNodeTypes,
     WorkflowNodeExecutionMetadataKey,
     WorkflowNodeExecutionStatus,
 )
@@ -15,7 +22,6 @@ from dify_graph.model_runtime.utils.encoders import jsonable_encoder
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base import LLMUsageTrackingMixin
 from dify_graph.nodes.base.node import Node
-from dify_graph.repositories.rag_retrieval_protocol import KnowledgeRetrievalRequest, RAGRetrievalProtocol, Source
 from dify_graph.variables import (
     ArrayFileSegment,
     FileSegment,
@@ -32,6 +38,7 @@ from .exc import (
     KnowledgeRetrievalNodeError,
     RateLimitExceededError,
 )
+from .retrieval import KnowledgeRetrievalRequest, Source
 
 if TYPE_CHECKING:
     from dify_graph.file.models import File
@@ -41,7 +48,7 @@ logger = logging.getLogger(__name__)
 
 
 class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeData]):
-    node_type = NodeType.KNOWLEDGE_RETRIEVAL
+    node_type = BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL
 
     # Instance attributes specific to LLMNode.
     # Output variable for file
@@ -53,7 +60,6 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
         config: NodeConfigDict,
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
-        rag_retrieval: RAGRetrievalProtocol,
     ):
         super().__init__(
             id=id,
@@ -63,7 +69,7 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
         )
         # LLM file outputs, used for MultiModal outputs.
         self._file_outputs = []
-        self._rag_retrieval = rag_retrieval
+        self._rag_retrieval = DatasetRetrieval()
 
     @classmethod
     def version(cls):

api/core/workflow/nodes/knowledge_retrieval/retrieval.py

@@ -3,9 +3,10 @@ from typing import Any, Literal, Protocol
 from pydantic import BaseModel, Field
 
 from dify_graph.model_runtime.entities import LLMUsage
-from dify_graph.nodes.knowledge_retrieval.entities import MetadataFilteringCondition
 from dify_graph.nodes.llm.entities import ModelConfig
 
+from .entities import MetadataFilteringCondition
+
 
 class SourceChildChunk(BaseModel):
     id: str = Field(default="", description="Child chunk ID")
@@ -28,7 +29,7 @@ class SourceMetadata(BaseModel):
     segment_id: str | None = Field(default=None, description="Segment unique identifier")
     retriever_from: str = Field(default="workflow", description="Retriever source context")
     score: float = Field(default=0.0, description="Retrieval relevance score")
-    child_chunks: list[SourceChildChunk] = Field(default=[], description="List of child chunks")
+    child_chunks: list[SourceChildChunk] = Field(default_factory=list, description="List of child chunks")
     segment_hit_count: int | None = Field(default=0, description="Number of times segment was retrieved")
     segment_word_count: int | None = Field(default=0, description="Word count of the segment")
     segment_position: int | None = Field(default=0, description="Position of segment in document")
@@ -81,28 +82,7 @@ class KnowledgeRetrievalRequest(BaseModel):
 
 
 class RAGRetrievalProtocol(Protocol):
-    """Protocol for RAG-based knowledge retrieval implementations.
-
-    Implementations of this protocol handle knowledge retrieval from datasets
-    including rate limiting, dataset filtering, and document retrieval.
-    """
-
     @property
-    def llm_usage(self) -> LLMUsage:
-        """Return accumulated LLM usage for retrieval operations."""
-        ...
+    def llm_usage(self) -> LLMUsage: ...
 
-    def knowledge_retrieval(self, request: KnowledgeRetrievalRequest) -> list[Source]:
-        """Retrieve knowledge from datasets based on the provided request.
-
-        Args:
-            request: Knowledge retrieval request with search parameters
-
-        Returns:
-            List of sources matching the search criteria
-
-        Raises:
-            RateLimitExceededError: If rate limit is exceeded
-            ModelNotExistError: If specified model doesn't exist
-        """
-        ...
+    def knowledge_retrieval(self, request: KnowledgeRetrievalRequest) -> list[Source]: ...

api/core/workflow/nodes/trigger_plugin/entities.py

@@ -3,16 +3,18 @@ from typing import Any, Literal, Union
 
 from pydantic import BaseModel, Field, ValidationInfo, field_validator
 
+from core.trigger.constants import TRIGGER_PLUGIN_NODE_TYPE
 from core.trigger.entities.entities import EventParameter
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
-from dify_graph.nodes.trigger_plugin.exc import TriggerEventParameterError
+
+from .exc import TriggerEventParameterError
 
 
 class TriggerEventNodeData(BaseNodeData):
     """Plugin trigger node data"""
 
-    type: NodeType = NodeType.TRIGGER_PLUGIN
+    type: NodeType = TRIGGER_PLUGIN_NODE_TYPE
 
     class TriggerEventInput(BaseModel):
         value: Union[Any, list[str]]

api/core/workflow/nodes/trigger_plugin/trigger_event_node.py

@@ -1,8 +1,10 @@
 from collections.abc import Mapping
+from typing import Any, cast
 
+from core.trigger.constants import TRIGGER_INFO_METADATA_KEY, TRIGGER_PLUGIN_NODE_TYPE
 from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
-from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
+from dify_graph.enums import NodeExecutionType, WorkflowNodeExecutionMetadataKey
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
 
@@ -10,7 +12,7 @@ from .entities import TriggerEventNodeData
 
 
 class TriggerEventNode(Node[TriggerEventNodeData]):
-    node_type = NodeType.TRIGGER_PLUGIN
+    node_type = TRIGGER_PLUGIN_NODE_TYPE
     execution_type = NodeExecutionType.ROOT
 
     @classmethod
@@ -32,6 +34,9 @@ class TriggerEventNode(Node[TriggerEventNodeData]):
     def version(cls) -> str:
         return "1"
 
+    def populate_start_event(self, event) -> None:
+        event.provider_id = self.node_data.provider_id
+
     def _run(self) -> NodeRunResult:
         """
         Run the plugin trigger node.
@@ -41,8 +46,8 @@ class TriggerEventNode(Node[TriggerEventNodeData]):
         """
 
         # Get trigger data passed when workflow was triggered
-        metadata = {
-            WorkflowNodeExecutionMetadataKey.TRIGGER_INFO: {
+        metadata: dict[WorkflowNodeExecutionMetadataKey, Any] = {
+            cast(WorkflowNodeExecutionMetadataKey, TRIGGER_INFO_METADATA_KEY): {
                 "provider_id": self.node_data.provider_id,
                 "event_name": self.node_data.event_name,
                 "plugin_unique_identifier": self.node_data.plugin_unique_identifier,

api/core/workflow/nodes/trigger_schedule/__init__.py

@@ -0,0 +1,3 @@
+from .trigger_schedule_node import TriggerScheduleNode
+
+__all__ = ["TriggerScheduleNode"]

api/core/workflow/nodes/trigger_schedule/entities.py

@@ -2,6 +2,7 @@ from typing import Literal, Union
 
 from pydantic import BaseModel, Field
 
+from core.trigger.constants import TRIGGER_SCHEDULE_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
 
@@ -11,7 +12,7 @@ class TriggerScheduleNodeData(BaseNodeData):
     Trigger Schedule Node Data
     """
 
-    type: NodeType = NodeType.TRIGGER_SCHEDULE
+    type: NodeType = TRIGGER_SCHEDULE_NODE_TYPE
     mode: str = Field(default="visual", description="Schedule mode: visual or cron")
     frequency: str | None = Field(default=None, description="Frequency for visual mode: hourly, daily, weekly, monthly")
     cron_expression: str | None = Field(default=None, description="Cron expression for cron mode")

api/core/workflow/nodes/trigger_schedule/trigger_schedule_node.py

@@ -1,15 +1,17 @@
 from collections.abc import Mapping
 
+from core.trigger.constants import TRIGGER_SCHEDULE_NODE_TYPE
 from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.enums import NodeExecutionType
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
-from dify_graph.nodes.trigger_schedule.entities import TriggerScheduleNodeData
+
+from .entities import TriggerScheduleNodeData
 
 
 class TriggerScheduleNode(Node[TriggerScheduleNodeData]):
-    node_type = NodeType.TRIGGER_SCHEDULE
+    node_type = TRIGGER_SCHEDULE_NODE_TYPE
     execution_type = NodeExecutionType.ROOT
 
     @classmethod
@@ -19,7 +21,7 @@ class TriggerScheduleNode(Node[TriggerScheduleNodeData]):
     @classmethod
     def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:
         return {
-            "type": "trigger-schedule",
+            "type": TRIGGER_SCHEDULE_NODE_TYPE,
             "config": {
                 "mode": "visual",
                 "frequency": "daily",

api/core/workflow/nodes/trigger_webhook/entities.py

@@ -3,6 +3,7 @@ from enum import StrEnum
 
 from pydantic import BaseModel, Field, field_validator
 
+from core.trigger.constants import TRIGGER_WEBHOOK_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
 from dify_graph.variables.types import SegmentType
@@ -93,7 +94,7 @@ class WebhookData(BaseNodeData):
     class SyncMode(StrEnum):
         SYNC = "async"  # only support
 
-    type: NodeType = NodeType.TRIGGER_WEBHOOK
+    type: NodeType = TRIGGER_WEBHOOK_NODE_TYPE
     method: Method = Method.GET
     content_type: ContentType = Field(default=ContentType.JSON)
     headers: Sequence[WebhookParameter] = Field(default_factory=list)

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

@@ -2,9 +2,10 @@ import logging
 from collections.abc import Mapping
 from typing import Any
 
+from core.trigger.constants import TRIGGER_WEBHOOK_NODE_TYPE
 from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.enums import NodeExecutionType
 from dify_graph.file import FileTransferMethod
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
@@ -19,7 +20,7 @@ logger = logging.getLogger(__name__)
 
 
 class TriggerWebhookNode(Node[WebhookData]):
-    node_type = NodeType.TRIGGER_WEBHOOK
+    node_type = TRIGGER_WEBHOOK_NODE_TYPE
     execution_type = NodeExecutionType.ROOT
 
     @classmethod

api/core/workflow/workflow_entry.py

@@ -8,7 +8,7 @@ from core.app.apps.exc import GenerateTaskStoppedError
 from core.app.entities.app_invoke_entities import InvokeFrom, UserFrom, build_dify_run_context
 from core.app.workflow.layers.llm_quota import LLMQuotaLayer
 from core.app.workflow.layers.observability import ObservabilityLayer
-from core.workflow.node_factory import DifyNodeFactory
+from core.workflow.node_factory import DifyNodeFactory, resolve_workflow_node_class
 from dify_graph.constants import ENVIRONMENT_VARIABLE_NODE_ID
 from dify_graph.entities import GraphInitParams
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
@@ -21,9 +21,8 @@ from dify_graph.graph_engine.layers import DebugLoggingLayer, ExecutionLimitsLay
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_engine.protocols.command_channel import CommandChannel
 from dify_graph.graph_events import GraphEngineEvent, GraphNodeEventBase, GraphRunFailedEvent
-from dify_graph.nodes import NodeType
+from dify_graph.nodes import BuiltinNodeTypes
 from dify_graph.nodes.base.node import Node
-from dify_graph.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
 from dify_graph.runtime import ChildGraphNotFoundError, GraphRuntimeState, VariablePool
 from dify_graph.system_variable import SystemVariable
 from dify_graph.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader, load_into_variable_pool
@@ -253,7 +252,7 @@ class WorkflowEntry:
             variable_mapping=variable_mapping,
             user_inputs=user_inputs,
         )
-        if node_type != NodeType.DATASOURCE:
+        if node_type != BuiltinNodeTypes.DATASOURCE:
             cls.mapping_user_inputs_to_variable_pool(
                 variable_mapping=variable_mapping,
                 user_inputs=user_inputs,
@@ -303,7 +302,7 @@ class WorkflowEntry:
             "height": node_height,
             "type": "custom",
             "data": {
-                "type": NodeType.START,
+                "type": BuiltinNodeTypes.START,
                 "title": "Start",
                 "desc": "Start",
             },
@@ -339,11 +338,11 @@ class WorkflowEntry:
         # Create a minimal graph for single node execution
         graph_dict = cls._create_single_node_graph(node_id, node_data)
 
-        node_type = NodeType(node_data.get("type", ""))
-        if node_type not in {NodeType.PARAMETER_EXTRACTOR, NodeType.QUESTION_CLASSIFIER}:
+        node_type = node_data.get("type", "")
+        if node_type not in {BuiltinNodeTypes.PARAMETER_EXTRACTOR, BuiltinNodeTypes.QUESTION_CLASSIFIER}:
             raise ValueError(f"Node type {node_type} not supported")
 
-        node_cls = NODE_TYPE_CLASSES_MAPPING[node_type]["1"]
+        node_cls = resolve_workflow_node_class(node_type=node_type, node_version="1")
         if not node_cls:
             raise ValueError(f"Node class not found for node type {node_type}")