diff --git a/backend/agents/create_agent_info.py b/backend/agents/create_agent_info.py
index 7e3b42e28..17eb17484 100644
--- a/backend/agents/create_agent_info.py
+++ b/backend/agents/create_agent_info.py
@@ -578,6 +578,7 @@ async def create_agent_config(
     system_prompt = Template(prompt_template["system_prompt"], undefined=StrictUndefined).render(render_kwargs)
 
     model_id_to_use = override_model_id if override_model_id else agent_info.get("model_id")
+    model_info = None
     model_max_tokens = 10000
     if model_id_to_use is not None:
         model_info = get_model_by_model_id(model_id_to_use, tenant_id=tenant_id)
@@ -587,6 +588,14 @@ async def create_agent_config(
     else:
         model_name = "main_model"
 
+    logger.info(
+        "Agent main LLM: agent_id=%s, model_id=%s, display_name=%s, model_name=%s",
+        agent_id,
+        model_id_to_use,
+        model_info.get("display_name") if model_info else model_name,
+        model_info.get("model_name") if model_info else model_name,
+    )
+
     # Use agent-level setting for context management, default to False.
     # When ContextManager is disabled, do not attach context_components because
     # downstream runtime may prefer component-based prompt assembly over the
@@ -759,22 +768,25 @@ async def create_tool_config_list(
                 "rerank_model": rerank_model,
             }
         elif tool_config.class_name == "AnalyzeTextFileTool":
+            selected_model_id = param_dict.get("selected_model_id")
             tool_config.metadata = {
-                "llm_model": get_llm_model(tenant_id=tenant_id),
+                "llm_model": get_llm_model(tenant_id=tenant_id, model_id=selected_model_id),
                 "storage_client": minio_client,
                 "data_process_service_url": DATA_PROCESS_SERVICE,
                 "validate_url_access": lambda urls: validate_urls_access(urls, user_id)
             }
         elif tool_config.class_name == "AnalyzeImageTool":
+            selected_model_id = param_dict.get("selected_model_id")
             tool_config.metadata = {
                 # get_vlm_model reads the first multimodal slot, now shown as image understanding.
-                "vlm_model": get_vlm_model(tenant_id=tenant_id),
+                "vlm_model": get_vlm_model(tenant_id=tenant_id, model_id=selected_model_id),
                 "storage_client": minio_client,
                 "validate_url_access": lambda urls: validate_urls_access(urls, user_id)
             }
         elif tool_config.class_name in ["AnalyzeAudioTool", "AnalyzeVideoTool"]:
+            selected_model_id = param_dict.get("selected_model_id")
             tool_config.metadata = {
-                "vlm_model": get_video_understanding_model(tenant_id=tenant_id),
+                "vlm_model": get_video_understanding_model(tenant_id=tenant_id, model_id=selected_model_id),
                 "storage_client": minio_client,
                 "validate_url_access": lambda urls: validate_urls_access(urls, user_id)
             }
diff --git a/backend/services/file_management_service.py b/backend/services/file_management_service.py
index 585669c0c..64f7ac486 100644
--- a/backend/services/file_management_service.py
+++ b/backend/services/file_management_service.py
@@ -33,6 +33,7 @@
     list_files,
     upload_fileobj,
 )
+from database.model_management_db import get_model_by_model_id
 from services.vectordatabase_service import ElasticSearchService, get_vector_db_core
 from utils.config_utils import tenant_config_manager, get_model_name_from_config
 from utils.file_management_utils import save_upload_file
@@ -448,20 +449,39 @@ async def list_files_impl(prefix: str, limit: Optional[int] = None):
     return files
 
 
-def get_llm_model(tenant_id: str):
-    # Get the tenant config
-    main_model_config = tenant_config_manager.get_model_config(
-        key=MODEL_CONFIG_MAPPING["llm"], tenant_id=tenant_id)
+def get_llm_model(tenant_id: str, model_id: Optional[int] = None):
+    if model_id:
+        main_model_config = get_model_by_model_id(int(model_id), tenant_id)
+        if not main_model_config:
+            raise ValueError(f"Model not found: {model_id}")
+        if main_model_config.get("model_type") != "llm":
+            raise ValueError(f"Selected model {model_id} is not an LLM model")
+    else:
+        # Get the tenant config
+        main_model_config = tenant_config_manager.get_model_config(
+            key=MODEL_CONFIG_MAPPING["llm"], tenant_id=tenant_id)
     timeout_seconds = main_model_config.get(
         "timeout_seconds") if main_model_config else None
+    
+    resolved_model_name = get_model_name_from_config(main_model_config)
+
+    logger.info(
+        "Using LLM model for analyze_text_file: model_id=%s, display_name=%s, model_name=%s",
+        model_id,
+        main_model_config.get("display_name") if main_model_config else None,
+        resolved_model_name
+    )
+
     long_text_to_text_model = OpenAILongContextModel(
         observer=MessageObserver(),
-        model_id=get_model_name_from_config(main_model_config),
+        model_id=resolved_model_name,
         api_base=main_model_config.get("base_url"),
         api_key=main_model_config.get("api_key"),
         max_context_tokens=main_model_config.get("max_tokens"),
         ssl_verify=main_model_config.get("ssl_verify", True),
         timeout_seconds=timeout_seconds,
+        model_factory=main_model_config.get("model_factory"),
+        display_name=main_model_config.get("display_name"),
     )
     return long_text_to_text_model
 
diff --git a/backend/services/image_service.py b/backend/services/image_service.py
index fdef3b081..76790dc23 100644
--- a/backend/services/image_service.py
+++ b/backend/services/image_service.py
@@ -3,12 +3,14 @@
 import logging
 import socket
 from http import HTTPStatus
+from typing import Optional
 from urllib.parse import urlparse, urlunparse
 
 import aiohttp
 
 from consts.const import DATA_PROCESS_SERVICE
 from consts.const import MODEL_CONFIG_MAPPING
+from database.model_management_db import get_model_by_model_id
 from utils.config_utils import tenant_config_manager, get_model_name_from_config
 
 from nexent import MessageObserver
@@ -146,14 +148,19 @@ async def proxy_image_impl(decoded_url: str):
             return result
 
 
-def get_vlm_model(tenant_id: str):
-    """Return the configured image understanding model for AnalyzeImageTool.
+def _get_model_config_by_id(tenant_id, model_id, expected_model_type):
+    if not model_id:
+        return None
 
-    The first multimodal model slot is still stored under MODEL_CONFIG_MAPPING["vlm"]
-    for compatibility, but it is the user-facing image understanding configuration.
-    """
-    vlm_model_config = tenant_config_manager.get_model_config(
-        key=MODEL_CONFIG_MAPPING["vlm"], tenant_id=tenant_id)
+    model_config = get_model_by_model_id(int(model_id), tenant_id)
+    if not model_config:
+        raise ValueError(f"Model not found: {model_id}")
+    if model_config.get("model_type") != expected_model_type:
+        raise ValueError(f"Selected model {model_id} is not a {expected_model_type} model")
+    return model_config
+
+
+def _build_vlm_model(vlm_model_config):
     if not vlm_model_config:
         return None
     return OpenAIVLModel(
@@ -167,28 +174,34 @@ def get_vlm_model(tenant_id: str):
         frequency_penalty=0.5,
         max_tokens=512,
         ssl_verify=vlm_model_config.get("ssl_verify", True),
+        model_factory=vlm_model_config.get("model_factory"),
+        display_name=vlm_model_config.get("display_name"),
     )
 
 
+def get_vlm_model(tenant_id: str, model_id: Optional[int] = None):
+    """Return the configured image understanding model for AnalyzeImageTool.
+
+    The first multimodal model slot is still stored under MODEL_CONFIG_MAPPING["vlm"]
+    for compatibility, but it is the user-facing image understanding configuration.
+    """
+    if model_id:
+        vlm_model_config = _get_model_config_by_id(tenant_id, model_id, "vlm")
+    else:
+        vlm_model_config = tenant_config_manager.get_model_config(
+            key=MODEL_CONFIG_MAPPING["vlm"], tenant_id=tenant_id)
+    return _build_vlm_model(vlm_model_config)
+
+
 def get_image_understanding_model(tenant_id: str):
     return get_vlm_model(tenant_id=tenant_id)
 
 
-def get_video_understanding_model(tenant_id: str):
+def get_video_understanding_model(tenant_id: str, model_id: Optional[int] = None):
     """Return the configured video understanding model for multimodal tools."""
-    vlm_model_config = tenant_config_manager.get_model_config(
-        key=MODEL_CONFIG_MAPPING["vlm3"], tenant_id=tenant_id)
-    if not vlm_model_config:
-        return None
-    return OpenAIVLModel(
-        observer=MessageObserver(),
-        model_id=get_model_name_from_config(
-            vlm_model_config) if vlm_model_config else "",
-        api_base=vlm_model_config.get("base_url", ""),
-        api_key=vlm_model_config.get("api_key", ""),
-        temperature=0.7,
-        top_p=0.7,
-        frequency_penalty=0.5,
-        max_tokens=512,
-        ssl_verify=vlm_model_config.get("ssl_verify", True),
-    )
+    if model_id:
+        vlm_model_config = _get_model_config_by_id(tenant_id, model_id, "vlm3")
+    else:
+        vlm_model_config = tenant_config_manager.get_model_config(
+            key=MODEL_CONFIG_MAPPING["vlm3"], tenant_id=tenant_id)
+    return _build_vlm_model(vlm_model_config)
diff --git a/backend/services/northbound_service.py b/backend/services/northbound_service.py
index c5493a551..a75b92ce0 100644
--- a/backend/services/northbound_service.py
+++ b/backend/services/northbound_service.py
@@ -133,7 +133,7 @@ def _normalize_northbound_attachments(
     tenant_id: str,
 ) -> Optional[List[Dict[str, Any]]]:
     """Convert northbound attachment references into internal minio_files objects.
-    
+
     Supports two formats:
     1. List of S3 URL strings (backward compatible): ["s3://nexent/...", "/nexent/...", "attachments/..."]
     2. List of attachment objects (full metadata): [{"object_name": "...", "name": "...", ...}]
diff --git a/backend/services/tool_configuration_service.py b/backend/services/tool_configuration_service.py
index 6e6260544..0f5de35c3 100644
--- a/backend/services/tool_configuration_service.py
+++ b/backend/services/tool_configuration_service.py
@@ -815,7 +815,8 @@ def _validate_local_tool(
                 raise ToolExecutionException(
                     f"Tenant ID and User ID are required for {tool_name} validation")
             # get_vlm_model reads the first multimodal slot, now shown as image understanding.
-            image_to_text_model = get_vlm_model(tenant_id=tenant_id)
+            selected_model_id = instantiation_params.get("selected_model_id")
+            image_to_text_model = get_vlm_model(tenant_id=tenant_id, model_id=selected_model_id)
             vlm_display_name = getattr(
                 image_to_text_model, 'display_name', None)
             set_monitoring_context(tenant_id=tenant_id)
@@ -832,7 +833,8 @@ def _validate_local_tool(
             if not tenant_id or not user_id:
                 raise ToolExecutionException(
                     f"Tenant ID and User ID are required for {tool_name} validation")
-            video_understanding_model = get_video_understanding_model(tenant_id=tenant_id)
+            selected_model_id = instantiation_params.get("selected_model_id")
+            video_understanding_model = get_video_understanding_model(tenant_id=tenant_id, model_id=selected_model_id)
             model_display_name = getattr(
                 video_understanding_model, 'display_name', None)
             set_monitoring_context(tenant_id=tenant_id)
@@ -849,7 +851,8 @@ def _validate_local_tool(
             if not tenant_id or not user_id:
                 raise ToolExecutionException(
                     f"Tenant ID and User ID are required for {tool_name} validation")
-            long_text_to_text_model = get_llm_model(tenant_id=tenant_id)
+            selected_model_id = instantiation_params.get("selected_model_id")
+            long_text_to_text_model = get_llm_model(tenant_id=tenant_id, model_id=selected_model_id)
             llm_display_name = getattr(
                 long_text_to_text_model, 'display_name', None)
             set_monitoring_context(tenant_id=tenant_id)
diff --git a/frontend/app/[locale]/agents/components/agentConfig/tool/ToolConfigModal.tsx b/frontend/app/[locale]/agents/components/agentConfig/tool/ToolConfigModal.tsx
index fbbf6db78..f249f49aa 100644
--- a/frontend/app/[locale]/agents/components/agentConfig/tool/ToolConfigModal.tsx
+++ b/frontend/app/[locale]/agents/components/agentConfig/tool/ToolConfigModal.tsx
@@ -35,12 +35,15 @@ import {
 } from "@/hooks/useKnowledgeBaseConfigChangeHandler";
 import { API_ENDPOINTS } from "@/services/api";
 import knowledgeBaseService from "@/services/knowledgeBaseService";
+import { modelService } from "@/services/modelService";
 import log from "@/lib/logger";
+import { MODEL_TYPES } from "@/const/modelConfig";
 import {
   isEmbeddingModelCompatible as isEmbeddingModelCompatibleBase,
   isMultimodalConstraintMismatch as isMultimodalConstraintMismatchBase,
 } from "@/lib/knowledgeBaseCompatibility";
 import { isZhLocale, getLocalizedDescription, getKbDisplayName, mapKbIdsToDisplayNames, parseKbIds } from "@/lib/utils";
+import { ModelOption, ModelType } from "@/types/modelConfig";
 
 export interface ToolConfigModalProps {
   isOpen: boolean;
@@ -69,6 +72,24 @@ const TOOLS_SUPPORTING_RERANK = [
   "datamate_search",
 ];
 
+const ANALYZE_TOOL_MODEL_TYPES: Record<string, ModelType> = {
+  analyze_text_file: MODEL_TYPES.LLM,
+  analyze_image: MODEL_TYPES.VLM,
+  analyze_audio: MODEL_TYPES.VLM3,
+  analyze_video: MODEL_TYPES.VLM3,
+};
+
+const ANALYZE_TOOL_MODEL_DESCRIPTIONS: Record<string, string> = {
+  analyze_text_file:
+    "Optional Nexent LLM model ID to use for text file analysis. If omitted, the default LLM model is used.",
+  analyze_image:
+    "Optional Nexent image understanding model ID to use for image analysis. If omitted, the default image understanding model is used.",
+  analyze_audio:
+    "Optional Nexent video understanding model ID to use for audio analysis. If omitted, the default video understanding model is used.",
+  analyze_video:
+    "Optional Nexent video understanding model ID to use for video analysis. If omitted, the default video understanding model is used.",
+};
+
 function withRerankParams(params: ToolParam[], toolName?: string): ToolParam[] {
   if (!toolName || !TOOLS_SUPPORTING_RERANK.includes(toolName)) return params;
 
@@ -101,6 +122,38 @@ function withRerankParams(params: ToolParam[], toolName?: string): ToolParam[] {
   return next;
 }
 
+function withAnalyzeToolModelParam(params: ToolParam[], toolName?: string): ToolParam[] {
+  if (!toolName || !ANALYZE_TOOL_MODEL_TYPES[toolName]) return params;
+
+  const normalizedParams = params.map((param) => {
+    if (param.name !== "selected_model_id") return param;
+    const value =
+      param.value === "" || param.value === undefined || param.value === null
+        ? undefined
+        : Number(param.value);
+    return { ...param, value };
+  });
+
+  if (normalizedParams.some((param) => param.name === "selected_model_id")) {
+    return normalizedParams;
+  }
+
+  return [
+    ...normalizedParams,
+    {
+      name: "selected_model_id",
+      type: "number",
+      required: false,
+      value: undefined,
+      description: ANALYZE_TOOL_MODEL_DESCRIPTIONS[toolName],
+    },
+  ];
+}
+
+function withExtraToolParams(params: ToolParam[], toolName?: string): ToolParam[] {
+  return withAnalyzeToolModelParam(withRerankParams(params, toolName), toolName);
+}
+
 export default function ToolConfigModal({
   isOpen,
   onCancel,
@@ -131,6 +184,29 @@ export default function ToolConfigModal({
 
   // Use React Query for config data
   const { data: configData } = useConfig();
+  const analyzeToolModelType = tool?.name
+    ? ANALYZE_TOOL_MODEL_TYPES[tool.name]
+    : undefined;
+  const isAnalyzeToolWithModelSelection = Boolean(analyzeToolModelType);
+  const {
+    data: registeredModels = [],
+    isFetching: registeredModelsLoading,
+  } = useQuery<ModelOption[]>({
+    queryKey: ["models", "registered", "toolConfig", analyzeToolModelType],
+    queryFn: () => modelService.getAllModels(),
+    enabled: isOpen && isAnalyzeToolWithModelSelection,
+    staleTime: 60_000,
+    gcTime: 5 * 60_000,
+  });
+  const analyzeToolModelOptions = useMemo(() => {
+    if (!analyzeToolModelType) return [];
+    return registeredModels
+      .filter((model) => model.type === analyzeToolModelType)
+      .map((model) => ({
+        value: model.id,
+        label: model.displayName || model.name,
+      }));
+  }, [registeredModels, analyzeToolModelType]);
   const [selectedKbDisplayNames, setSelectedKbDisplayNames] = useState<
     string[]
   >([]);
@@ -720,7 +796,7 @@ export default function ToolConfigModal({
     // If server_url already has a saved value, use it
     if (serverUrlParam?.value) {
       // Initialize form with saved values (including server_url)
-      const paramsWithRerank = withRerankParams(initialParams, tool.name);
+      const paramsWithRerank = withExtraToolParams(initialParams, tool.name);
       setCurrentParams(paramsWithRerank);
       const formValues: Record<string, any> = {};
       paramsWithRerank.forEach((param, index) => {
@@ -767,7 +843,7 @@ export default function ToolConfigModal({
         return param;
       });
 
-      const paramsWithRerank = withRerankParams(updatedParams, tool.name);
+      const paramsWithRerank = withExtraToolParams(updatedParams, tool.name);
       setCurrentParams(paramsWithRerank);
 
       const formValues: Record<string, any> = {};
@@ -777,7 +853,7 @@ export default function ToolConfigModal({
       form.setFieldsValue(formValues);
     } else {
       // Either no default available OR user has modified the URL, initialize with initialParams
-      const paramsWithRerank = withRerankParams(initialParams, tool.name);
+      const paramsWithRerank = withExtraToolParams(initialParams, tool.name);
       setCurrentParams(paramsWithRerank);
       const formValues: Record<string, any> = {};
       paramsWithRerank.forEach((param, index) => {
@@ -858,7 +934,7 @@ export default function ToolConfigModal({
       return param;
     });
 
-    const paramsWithRerank = withRerankParams(updatedParams, tool.name);
+    const paramsWithRerank = withExtraToolParams(updatedParams, tool.name);
     setCurrentParams(paramsWithRerank);
 
     const formValues: Record<string, any> = {};
@@ -910,7 +986,7 @@ export default function ToolConfigModal({
     // Initialize form values
     const paramsWithDefaults = applyInitParamDefaults(initialParams);
     const paramsMigrated = migrateAidpParamNames(paramsWithDefaults);
-    const paramsWithRerank = withRerankParams(paramsMigrated, tool?.name);
+    const paramsWithRerank = withExtraToolParams(paramsMigrated, tool?.name);
     setCurrentParams(paramsWithRerank);
     const formValues: Record<string, any> = {};
     paramsWithRerank.forEach((param, index) => {
@@ -1540,6 +1616,22 @@ export default function ToolConfigModal({
     // Determine if this parameter should be rendered as a select dropdown
     const isSelectType = options && options.length > 0;
 
+    if (param.name === "selected_model_id" && isAnalyzeToolWithModelSelection) {
+      return (
+        <Select
+          placeholder="未选择时使用默认模型"
+          options={analyzeToolModelOptions}
+          loading={registeredModelsLoading}
+          allowClear
+          showSearch
+          optionFilterProp="label"
+          notFoundContent={
+            registeredModelsLoading ? undefined : "暂无可选模型"
+          }
+        />
+      );
+    }
+
     // Special handling for rerank_model_name parameter - show model selector
     if (param.name === "rerank_model_name") {
       // First try to get the list of available rerank models from config
diff --git a/sdk/nexent/core/tools/analyze_audio_tool.py b/sdk/nexent/core/tools/analyze_audio_tool.py
index 1e5439443..282a0b080 100644
--- a/sdk/nexent/core/tools/analyze_audio_tool.py
+++ b/sdk/nexent/core/tools/analyze_audio_tool.py
@@ -56,6 +56,9 @@ class AnalyzeAudioTool(Tool):
     init_param_descriptions = {
         "observer": {"description": "Message observer"},
         "vlm_model": {"description": "The video understanding model to use"},
+        "selected_model_id": {
+            "description": "Optional Nexent video understanding model ID to use for audio analysis. If omitted, the default video understanding model is used."
+        },
         "storage_client": {"description": "Storage client for downloading files"},
         "validate_url_access": {
             "description": "Callback function to validate URL access permissions (passed to LoadSaveObjectManager)"
@@ -75,6 +78,9 @@ def __init__(
                 description="The video understanding model to use",
                 default=None,
                 exclude=True),
+            selected_model_id: int = Field(
+                description="Optional Nexent video understanding model ID to use for audio analysis. If omitted, the default video understanding model is used.",
+                default=None),
             storage_client: MinIOStorageClient = Field(
                 description="Storage client for downloading files from S3 URLs, HTTP URLs, and HTTPS URLs.",
                 default=None,
@@ -87,6 +93,7 @@ def __init__(
         super().__init__()
         self.observer = observer
         self.vlm_model = vlm_model
+        self.selected_model_id = selected_model_id
         self.storage_client = storage_client
         self._is_chinese = bool(observer and observer.lang == "zh")
 
diff --git a/sdk/nexent/core/tools/analyze_image_tool.py b/sdk/nexent/core/tools/analyze_image_tool.py
index f7640a9dc..9368f23fd 100644
--- a/sdk/nexent/core/tools/analyze_image_tool.py
+++ b/sdk/nexent/core/tools/analyze_image_tool.py
@@ -56,6 +56,9 @@ class AnalyzeImageTool(Tool):
         "vlm_model": {
             "description": "The image understanding model to use"
         },
+        "selected_model_id": {
+            "description": "Optional Nexent image understanding model ID to use for image analysis. If omitted, the default image understanding model is used."
+        },
         "storage_client": {
             "description": "Storage client for downloading files"
         },
@@ -77,6 +80,9 @@ def __init__(
                 description="The image understanding model to use",
                 default=None,
                 exclude=True),
+            selected_model_id: int = Field(
+                description="Optional Nexent image understanding model ID to use for image analysis. If omitted, the default image understanding model is used.",
+                default=None),
             storage_client: MinIOStorageClient = Field(
                 description="Storage client for downloading files from S3 URLs、HTTP URLs、HTTPS URLs.",
                 default=None,
@@ -89,6 +95,7 @@ def __init__(
         super().__init__()
         self.observer = observer
         self.vlm_model = vlm_model
+        self.selected_model_id = selected_model_id
         self.storage_client = storage_client
 
         # Determine if the language is Chinese for internationalization
diff --git a/sdk/nexent/core/tools/analyze_text_file_tool.py b/sdk/nexent/core/tools/analyze_text_file_tool.py
index 49b9a10ca..89c285af4 100644
--- a/sdk/nexent/core/tools/analyze_text_file_tool.py
+++ b/sdk/nexent/core/tools/analyze_text_file_tool.py
@@ -57,6 +57,9 @@ class AnalyzeTextFileTool(Tool):
         "llm_model": {
             "description": "The LLM model to use"
         },
+        "selected_model_id": {
+            "description": "Optional Nexent LLM model ID to use for text file analysis. If omitted, the default LLM model is used."
+        },
         "validate_url_access": {
             "description": "Callback function to validate URL access permissions (passed to LoadSaveObjectManager)"
         }
@@ -85,6 +88,9 @@ def __init__(
             description="The LLM model to use",
             default=None,
             exclude=True),
+        selected_model_id: int = Field(
+            description="Optional Nexent LLM model ID to use for text file analysis. If omitted, the default LLM model is used.",
+            default=None),
         validate_url_access: callable = Field(
             description="Callback function to validate URL access permissions",
             default=None,
@@ -94,6 +100,7 @@ def __init__(
         self.storage_client = storage_client
         self.observer = observer
         self.llm_model = llm_model
+        self.selected_model_id = selected_model_id
         self.data_process_service_url = data_process_service_url
 
         # Create LoadSaveObjectManager with the storage client and validation callback
diff --git a/sdk/nexent/core/tools/analyze_video_tool.py b/sdk/nexent/core/tools/analyze_video_tool.py
index e7bf84549..cb4c3929b 100644
--- a/sdk/nexent/core/tools/analyze_video_tool.py
+++ b/sdk/nexent/core/tools/analyze_video_tool.py
@@ -56,6 +56,9 @@ class AnalyzeVideoTool(Tool):
     init_param_descriptions = {
         "observer": {"description": "Message observer"},
         "vlm_model": {"description": "The video understanding model to use"},
+        "selected_model_id": {
+            "description": "Optional Nexent video understanding model ID to use for video analysis. If omitted, the default video understanding model is used."
+        },
         "storage_client": {"description": "Storage client for downloading files"},
         "validate_url_access": {
             "description": "Callback function to validate URL access permissions (passed to LoadSaveObjectManager)"
@@ -75,6 +78,9 @@ def __init__(
                 description="The video understanding model to use",
                 default=None,
                 exclude=True),
+            selected_model_id: int = Field(
+                description="Optional Nexent video understanding model ID to use for video analysis. If omitted, the default video understanding model is used.",
+                default=None),
             storage_client: MinIOStorageClient = Field(
                 description="Storage client for downloading files from S3 URLs, HTTP URLs, and HTTPS URLs.",
                 default=None,
@@ -87,6 +93,7 @@ def __init__(
         super().__init__()
         self.observer = observer
         self.vlm_model = vlm_model
+        self.selected_model_id = selected_model_id
         self.storage_client = storage_client
         self._is_chinese = bool(observer and observer.lang == "zh")
 
diff --git a/test/backend/agents/test_create_agent_info.py b/test/backend/agents/test_create_agent_info.py
index 083886c1a..6d7fef775 100644
--- a/test/backend/agents/test_create_agent_info.py
+++ b/test/backend/agents/test_create_agent_info.py
@@ -837,7 +837,7 @@ async def test_create_tool_config_list_with_analyze_image_tool(self):
 
             assert len(result) == 1
             assert result[0] is mock_tool_instance
-            mock_get_vlm_model.assert_called_once_with(tenant_id="tenant_1")
+            mock_get_vlm_model.assert_called_once_with(tenant_id="tenant_1", model_id=None)
             # Verify metadata includes validate_url_access lambda
             assert "vlm_model" in mock_tool_instance.metadata
             assert "storage_client" in mock_tool_instance.metadata
@@ -881,7 +881,7 @@ async def test_create_tool_config_list_with_audio_video_tools(self, class_name,
 
             assert len(result) == 1
             assert result[0] is mock_tool_instance
-            mock_get_video_model.assert_called_once_with(tenant_id="tenant_1")
+            mock_get_video_model.assert_called_once_with(tenant_id="tenant_1", model_id=None)
             assert mock_tool_instance.metadata["vlm_model"] == "mock_video_model"
             assert "storage_client" in mock_tool_instance.metadata
             assert callable(mock_tool_instance.metadata["validate_url_access"])
@@ -916,7 +916,7 @@ async def test_create_tool_config_list_with_analyze_text_file_tool(self):
 
             assert len(result) == 1
             assert result[0] is mock_tool_instance
-            mock_get_llm_model.assert_called_once_with(tenant_id="tenant_1")
+            mock_get_llm_model.assert_called_once_with(tenant_id="tenant_1", model_id=None)
             # Verify metadata includes validate_url_access lambda
             assert "llm_model" in mock_tool_instance.metadata
             assert "storage_client" in mock_tool_instance.metadata
diff --git a/test/backend/services/test_file_management_service.py b/test/backend/services/test_file_management_service.py
index fe0b5bc69..5a2f9fb92 100644
--- a/test/backend/services/test_file_management_service.py
+++ b/test/backend/services/test_file_management_service.py
@@ -1412,7 +1412,9 @@ def test_get_llm_model_success(self, mock_tenant_config, mock_get_model_name, mo
             api_key="test_api_key",
             max_context_tokens=4096,
             ssl_verify=True,
-            timeout_seconds=None
+            timeout_seconds=None,
+            model_factory=None,
+            display_name=None,
         )
 
     @patch('backend.services.file_management_service.MODEL_CONFIG_MAPPING', {"llm": "llm_config_key"})
diff --git a/test/backend/services/test_image_service.py b/test/backend/services/test_image_service.py
index 34cbc4420..5ba3bf7bf 100644
--- a/test/backend/services/test_image_service.py
+++ b/test/backend/services/test_image_service.py
@@ -347,7 +347,9 @@ def test_get_vlm_model_success(mock_tenant_config_manager, mock_get_model_name,
         top_p=0.7,
         frequency_penalty=0.5,
         max_tokens=512,
-        ssl_verify=True
+        ssl_verify=True,
+        model_factory=None,
+        display_name=None
     )
     assert result == mock_model_instance
 
diff --git a/test/backend/services/test_northbound_service.py b/test/backend/services/test_northbound_service.py
index e98fc4ca1..8a7726b26 100644
--- a/test/backend/services/test_northbound_service.py
+++ b/test/backend/services/test_northbound_service.py
@@ -343,10 +343,10 @@ async def test_rate_limit_cleanup_old_buckets(self):
         # First, add a request to create an old bucket
         old_bucket = str(int(ns._now_seconds() // 60) - 1)
         ns._RATE_STATE["tenant-cleanup"] = {old_bucket: 50}
-        
+
         # Make a new request - should trigger cleanup of old bucket
         await ns.check_and_consume_rate_limit("tenant-cleanup")
-        
+
         # Old bucket should be cleaned up, new bucket should have 1 request
         current_bucket = ns._minute_bucket()
         assert old_bucket not in ns._RATE_STATE["tenant-cleanup"]
@@ -946,7 +946,7 @@ class TestGetAgentInfoListErrorHandling:
     async def test_get_agent_info_by_name_success(self):
         """Test successful agent ID retrieval."""
         agent_service_mod.get_agent_id_by_name.return_value = 42
-        
+
         result = await ns.get_agent_info_by_name("test_agent", "tenant-1")
         assert result == 42
 
@@ -954,7 +954,7 @@ async def test_get_agent_info_by_name_success(self):
     async def test_get_agent_info_by_name_error(self):
         """Test that errors are wrapped properly."""
         agent_service_mod.get_agent_id_by_name.side_effect = Exception("Agent not found")
-        
+
         with pytest.raises(Exception) as exc_info:
             await ns.get_agent_info_by_name("nonexistent", "tenant-1")
         assert "Failed to get agent id" in str(exc_info.value)
diff --git a/test/backend/services/test_tool_configuration_service.py b/test/backend/services/test_tool_configuration_service.py
index 994bba212..945eeba74 100644
--- a/test/backend/services/test_tool_configuration_service.py
+++ b/test/backend/services/test_tool_configuration_service.py
@@ -3205,7 +3205,7 @@ def test_validate_local_tool_analyze_image_success(self, mock_signature, mock_ge
         )
 
         assert result == "analyze image result"
-        mock_get_vlm_model.assert_called_once_with(tenant_id="tenant1")
+        mock_get_vlm_model.assert_called_once_with(tenant_id="tenant1", model_id=None)
         mock_tool_class.assert_called_once()
         call_kwargs = mock_tool_class.call_args.kwargs
         assert 'vlm_model' in call_kwargs
@@ -3279,7 +3279,7 @@ def test_validate_local_tool_analyze_audio_video_success(
         )
 
         assert result == f"{tool_name} result"
-        mock_get_video_model.assert_called_once_with(tenant_id="tenant1")
+        mock_get_video_model.assert_called_once_with(tenant_id="tenant1", model_id=None)
         call_kwargs = mock_tool_class.call_args.kwargs
         assert call_kwargs["vlm_model"] == "mock_video_model"
         assert "storage_client" in call_kwargs
@@ -3590,7 +3590,7 @@ def test_validate_local_tool_analyze_text_file_success(self, mock_minio_client,
         mock_tool_instance.forward.assert_called_once_with(input="test input")
 
         # Verify service calls
-        mock_get_llm_model.assert_called_once_with(tenant_id="tenant1")
+        mock_get_llm_model.assert_called_once_with(tenant_id="tenant1", model_id=None)
 
     @patch('backend.services.tool_configuration_service._get_tool_class_by_name')
     def test_validate_local_tool_analyze_text_file_missing_tenant_id(self, mock_get_class):
diff --git a/test/common/test_mocks.py b/test/common/test_mocks.py
index fb7e5d8b5..95b436ea7 100644
--- a/test/common/test_mocks.py
+++ b/test/common/test_mocks.py
@@ -74,6 +74,10 @@ def bootstrap_test_env() -> Dict[str, Any]:
     nexent_module = _create_module("nexent", MessageObserver=MagicMock())
     _create_module("nexent.core")
     _create_module("nexent.core.models", OpenAIVLModel=MagicMock())
+    
+    mock_model_management_db = types.ModuleType("database.model_management_db")
+    mock_model_management_db.get_model_by_model_id = MagicMock(return_value=None)
+    sys.modules["database.model_management_db"] = mock_model_management_db
 
     return {
         "mock_const": mock_const,
diff --git a/test/sdk/core/tools/test_knowledge_base_search_tool.py b/test/sdk/core/tools/test_knowledge_base_search_tool.py
index 7a4b23ebe..8dd3d9978 100644
--- a/test/sdk/core/tools/test_knowledge_base_search_tool.py
+++ b/test/sdk/core/tools/test_knowledge_base_search_tool.py
@@ -1808,7 +1808,6 @@ def test_filter_by_document_paths_unwraps_fieldinfo_default(self, mock_vdb_core,
 
         assert len(filtered) == 1
         assert filtered[0]["path_or_url"] == "s3://bucket/doc1.txt"
-
     def test_filter_by_document_paths_unwraps_fieldinfo_default_factory(self, mock_vdb_core, mock_embedding_model):
         """Filter should tolerate a FieldInfo with default_factory."""
         try:
@@ -1862,5 +1861,3 @@ def test_set_document_paths_unwraps_fieldinfo(self, mock_vdb_core, mock_embeddin
 
         assert len(filtered) == 1
         assert filtered[0]["path_or_url"] == "s3://bucket/doc1.txt"
-
-