Merge branch 'main' into newrelic

docs/my-website/docs/providers/openai.md

@@ -632,7 +632,9 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 
 ## OpenAI Chat Completion to Responses API Bridge
 
-Call any Responses API model from OpenAI's `/chat/completions` endpoint.
+LiteLLM offers a chat completion to Responses API bridge. This lets you use the completion interface while calling the Responses API under the hood.
+
+This is useful when you want to use [Responses API](https://platform.openai.com/docs/api-reference/responses) specific features (like built-in tools, web search preview, or code interpreter).
 
 :::tip gpt-5.4 + reasoning_effort + function tools
 
@@ -649,12 +651,54 @@ response = litellm.completion(
 
 :::
 
+### When to use the `openai/responses/` prefix
+
+Each model has a `mode` property defined in [`model_prices_and_context_window.json`](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) that determines which API endpoint it uses by default:
+
+- **`mode: responses`** - Model automatically uses the Responses API
+- **`mode: chat`** - Model defaults to the Chat Completions API
+
+**Models with `mode: responses`** (automatic Responses API):
+- `o3-deep-research`, `o4-mini-deep-research`
+- `o1-pro`, `o3-pro`
+- `gpt-5.1-codex`, `gpt-5.1-codex-mini`, `gpt-5.1-codex-max`
+- `codex-mini-latest`
+
+**Models with `mode: chat`** (require `openai/responses/` prefix for built-in tools):
+- `gpt-4o`, `gpt-4o-mini`, `gpt-4.1`, `gpt-4.1-mini`
+- `gpt-5`, `gpt-5-mini`
+- `o3`, `o4-mini`
+
+To use built-in tools like `web_search_preview` with `mode: chat` models, add the `openai/responses/` prefix:
+
+```python
+# This will FAIL - gpt-4o has mode: chat, uses Chat Completions API
+response = litellm.completion(
+    model="gpt-4o",
+    messages=[{"role": "user", "content": "What is the weather in Paris today?"}],
+    tools=[{"type": "web_search_preview"}],  # Not supported in Chat Completions
+    # ... other kwargs
+)
+
+# This will WORK - prefix forces Responses API
+response = litellm.completion(
+    model="openai/responses/gpt-4o",
+    messages=[{"role": "user", "content": "What is the weather in Paris today?"}],
+    tools=[{"type": "web_search_preview"}],  # Supported in Responses API
+    # ... other kwargs
+)
+```
+
+### Examples
+
 <Tabs>
 <TabItem value="sdk" label="SDK">
 
+**Using a model with `mode: responses` (automatic):**
+
 ```python
 import litellm
-import os 
+import os
 
 os.environ["OPENAI_API_KEY"] = "sk-1234"
 
@@ -668,6 +712,26 @@ response = litellm.completion(
 )
 print(response)
 ```
+
+**Using a model with `mode: chat` (requires prefix):**
+
+```python
+import litellm
+import os
+
+os.environ["OPENAI_API_KEY"] = "sk-1234"
+
+# Use the openai/responses/ prefix to enable built-in tools
+response = litellm.completion(
+    model="openai/responses/gpt-4o",
+    messages=[{"role": "user", "content": "What is the weather in Paris today?"}],
+    tools=[
+        {"type": "web_search_preview"},
+    ],
+)
+print(response)
+```
+
 </TabItem>
 <TabItem value="proxy" label="PROXY">
 
@@ -675,10 +739,17 @@ print(response)
 
 ```yaml
 model_list:
-  - model_name: openai-model
+  # Model with mode: responses (automatic)
+  - model_name: o3-deep-research
     litellm_params:
       model: o3-deep-research-2025-06-26
       api_key: os.environ/OPENAI_API_KEY
+
+  # Model with mode: chat (use prefix for built-in tools)
+  - model_name: gpt-4o-with-tools
+    litellm_params:
+      model: openai/responses/gpt-4o
+      api_key: os.environ/OPENAI_API_KEY
 ```
 
 2. Start the proxy
@@ -693,15 +764,14 @@ litellm --config config.yaml
 curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 -H 'Content-Type: application/json' \
 -H 'Authorization: Bearer sk-1234' \
--d '{ 
-    "model": "openai-model",
+-d '{
+    "model": "gpt-4o-with-tools",
     "messages": [
-        {"role": "user", "content": "What is the capital of France?"}
+        {"role": "user", "content": "What is the weather in Paris today?"}
     ],
     "tools": [
-        {"type": "web_search_preview"},
-        {"type": "code_interpreter", "container": {"type": "auto"}},
-    ],
+        {"type": "web_search_preview"}
+    ]
 }'
 ```
 

docs/my-website/docs/tutorials/retool_assist.md

@@ -0,0 +1,143 @@
+import Image from '@theme/IdealImage';
+
+# Retool Assist
+
+This guide walks you through connecting [Retool Assist](https://docs.retool.com/apps/guides/assist/) to LiteLLM Proxy. Retool Assist uses AI to generate and edit apps from within the Retool app IDE. Using LiteLLM with Retool Assist allows you to:
+
+- Access 100+ LLMs through Retool Assist
+- Track spend and usage, set budget limits per virtual key
+- Control which models Retool Assist can access
+- Use your own LLM providers via a unified OpenAI-compatible API
+
+<div style={{ maxWidth: '100%', overflow: 'hidden', paddingBottom: '59.52%', position: 'relative', height: 0 }}>
+  <iframe 
+    style={{ position: 'absolute', top: 0, left: 0, width: '100%', height: '100%', maxWidth: '840px' }}
+    src="https://www.youtube.com/embed/aN-Iua5dHGg" 
+    frameborder="0" 
+    webkitallowfullscreen 
+    mozallowfullscreen 
+    allowfullscreen
+  ></iframe>
+</div>
+
+---
+
+:::info
+**Hosted Retool requires a public URL.** Retool Cloud runs on Retool's servers, so `localhost` will not work. You must expose your LiteLLM proxy via ngrok, Cloudflare Tunnel, or by deploying to a cloud provider.
+:::
+
+## Quick Reference
+
+| Setting | Value |
+|---------|-------|
+| Provider Schema | OpenAI |
+| Base URL | Your ngrok URL (e.g. `https://abc123.ngrok-free.app`) or deployed proxy URL |
+| API Key | Your LiteLLM Virtual Key |
+| Model | Public model name from LiteLLM (e.g. `openai/gpt-4o-mini`, `openai/gpt-5.2-2025-12-11`) |
+
+---
+
+## Prerequisites
+
+- LiteLLM Proxy running locally or deployed
+- [ngrok](https://ngrok.com/download) (or similar tunnel) for local development with hosted Retool
+- A [Retool](https://retool.com) account (Cloud or self-hosted)
+
+## 1. Start LiteLLM Proxy
+
+Set up LiteLLM Proxy following the [Getting Started Guide](https://docs.litellm.ai/docs/proxy/docker_quick_start). Ensure your proxy is running on port 4000.
+
+## 2. Expose LiteLLM with a Public URL
+
+<Image img={require('../../img/ngrok_public_url.gif')} />
+
+Retool Cloud runs on Retool's servers. You must expose your local LiteLLM proxy with a public URL.
+
+### Using ngrok
+
+- Install [ngrok](https://ngrok.com/download)
+- In a separate terminal, run:
+  
+```bash
+ngrok http 4000
+```
+- Copy the generated HTTPS URL (e.g. `https://abc123.ngrok-free.app`). This is your **Base URL** for Retool.
+
+
+### Alternative
+
+If you deploy LiteLLM to Railway, Render, Fly.io, or another cloud provider, use that public URL as your Base URL. See the [Deploy guide](https://docs.litellm.ai/docs/proxy/deploy) for details.
+
+## 3. Generate a Virtual Key
+
+<Image img={require('../../img/litellm_virtual_key.gif')} />
+
+Create a virtual key that Retool Assist will use to authenticate with LiteLLM. The key must have access to the models you want to use (e.g. `openai/*` for all OpenAI models).
+
+### Via LiteLLM UI
+
+- Navigate to [http://localhost:4000/ui](http://localhost:4000/ui)
+- Go to **Virtual Keys** → **+ Create New Key**
+- Select the models you need (or `openai/*` for all OpenAI models)
+- Copy the key
+
+## 4. Add LiteLLM as a Custom Provider in Retool
+
+Inside your Retool dashboard, configure LiteLLM as a custom AI resource:
+
+<Image img={require('../../img/retool_resource_setup.gif')} />
+
+1. Go to **Resources**
+
+2. Under the **AI** category, select **Custom Provider**
+
+3. Fill in the form:
+   - **Name:** `LiteLLM`
+   - **Description:** (optional) e.g. `LiteLLM Proxy - 100+ LLMs`
+   - **Provider Schema:** `OpenAI`
+   - **Base URL:** Your ngrok-generated URL (e.g. `https://abc123.ngrok-free.app`) or deployed proxy URL—do not add `/v1` unless Retool requires it
+   - **API Key:** Your LiteLLM virtual key from Step 3
+4. **Add model names** from your LiteLLM proxy (e.g. `openai/gpt-4o-mini`, `openai/gpt-5.2-2025-12-11`).
+5. Click **Create Resource**
+
+<Image img={require('../../img/retool_llm_setup.gif')} />
+
+## 5. Test the Connection
+
+<Image img={require('../../img/retool_litellm_connection.gif')} />
+
+- Open an app in Retool and enable **Assist** (if not already enabled in your organization)
+- Use Assist to generate or edit app elements, it will route requests through LiteLLM
+- Use the code option from the Sidebar to add a resource query, select the LiteLLM resource, and run it to test the setup.
+- Check the LiteLLM **Logs** section to verify requests and track usage
+
+<Image img={require('../../img/retool_litellm_logs.gif')} />
+
+---
+
+## Troubleshooting
+
+### 401 Unauthorized
+
+- Ensure the **API Key** in Retool matches your LiteLLM virtual key exactly
+- Verify the key is not expired or blocked in LiteLLM
+
+### 401 "key not allowed to access model"
+
+Your virtual key is restricted to specific models. Generate a new key with `openai/*` or include the model you need (e.g. `openai/gpt-5.2-2025-12-11`) in the key's allowed models list.
+
+### 500 "api_key client option must be set"
+
+LiteLLM could not use your OpenAI API key to call the provider. Ensure `OPENAI_API_KEY` is set in your LiteLLM environment (e.g. in `.env` or `docker-compose.yml`) when using `openai/*` models.
+
+### localhost does not work
+
+Retool Cloud cannot reach `localhost` it points to Retool's servers. Use ngrok or deploy LiteLLM to a public URL.
+
+---
+
+## Additional Resources
+
+- [Virtual Keys](https://docs.litellm.ai/docs/proxy/virtual_keys) – Create and manage API keys
+- [Deploy LiteLLM](https://docs.litellm.ai/docs/proxy/deploy) – Production deployment options
+- [Retool Assist Documentation](https://docs.retool.com/apps/guides/assist/) – Configure Assist and prompting guides

docs/my-website/sidebars.js

@@ -172,7 +172,8 @@ const sidebars = {
         "tutorials/litellm_gemini_cli",
         "tutorials/google_genai_sdk",
         "tutorials/litellm_qwen_code_cli",
-        "tutorials/openai_codex"
+        "tutorials/openai_codex",
+        "tutorials/retool_assist"
       ]
     },
     {

tests/mcp_tests/test_mcp_server.py

@@ -1456,6 +1456,7 @@ async def test_add_update_server_with_alias():
     mock_mcp_server.authorization_url = None
     mock_mcp_server.registration_url = None
     mock_mcp_server.token_url = None
+    mock_mcp_server.oauth2_flow = None
     # Additional fields used by build_mcp_server_from_table
     mock_mcp_server.extra_headers = None
     mock_mcp_server.allow_all_keys = False
@@ -1511,6 +1512,7 @@ async def test_add_update_server_without_alias():
     mock_mcp_server.authorization_url = None
     mock_mcp_server.registration_url = None
     mock_mcp_server.token_url = None
+    mock_mcp_server.oauth2_flow = None
     # Additional fields used by build_mcp_server_from_table
     mock_mcp_server.extra_headers = None
     mock_mcp_server.allow_all_keys = False
@@ -1566,6 +1568,7 @@ async def test_add_update_server_fallback_to_server_id():
     mock_mcp_server.authorization_url = None
     mock_mcp_server.registration_url = None
     mock_mcp_server.token_url = None
+    mock_mcp_server.oauth2_flow = None
     # Additional fields used by build_mcp_server_from_table - set explicitly
     # to avoid MagicMock objects being passed to Pydantic MCPServer constructor
     mock_mcp_server.extra_headers = None

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py

@@ -354,7 +354,7 @@ def test_generic_cost_per_token_anthropic_prompt_caching():
 
 
 def test_generic_cost_per_token_anthropic_prompt_caching_with_cache_creation():
-    model = "claude-3-5-haiku-20241022"
+    model = "claude-haiku-4-5-20251001"
     usage = Usage(
         completion_tokens=90,
         prompt_tokens=28436,
@@ -379,7 +379,7 @@ def test_generic_cost_per_token_anthropic_prompt_caching_with_cache_creation():
     )
 
     print(f"prompt_cost: {prompt_cost}")
-    assert round(prompt_cost, 3) == 0.023
+    assert round(prompt_cost, 3) == 0.029
 
 
 def test_string_cost_values():

tests/test_litellm/llms/bedrock/chat/agentcore/test_agentcore_transformation.py

@@ -219,6 +219,7 @@ class TestAgentCoreStreamingJsonFallback:
                 messages=[{"role": "user", "content": "test"}],
                 stream=True,
                 client=client,
+                api_key="test-jwt-token",
             )
 
             # Collect content across all chunks
@@ -257,6 +258,7 @@ class TestAgentCoreStreamingJsonFallback:
                 messages=[{"role": "user", "content": "test"}],
                 stream=True,
                 client=client,
+                api_key="test-jwt-token",
             )
 
             # Collect content across all chunks
@@ -289,6 +291,7 @@ class TestAgentCoreStreamingJsonFallback:
                     messages=[{"role": "user", "content": "test"}],
                     stream=True,
                     client=client,
+                    api_key="test-jwt-token",
                 )
 
     async def test_async_streaming_malformed_json_raises_error(self):
@@ -316,4 +319,5 @@ class TestAgentCoreStreamingJsonFallback:
                     messages=[{"role": "user", "content": "test"}],
                     stream=True,
                     client=client,
+                    api_key="test-jwt-token",
                 )

tests/test_litellm/test_utils.py

@@ -444,9 +444,6 @@ def test_anthropic_web_search_in_model_info():
     supported_models = [
         "anthropic/claude-4-sonnet-20250514",
         "anthropic/claude-sonnet-4-5-20250929",
-        "anthropic/claude-3-5-sonnet-20241022",
-        "anthropic/claude-3-5-haiku-20241022",
-        "anthropic/claude-3-5-haiku-latest",
     ]
     for model in supported_models:
         from litellm.utils import get_model_info

ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPServers.test.ts

@@ -74,7 +74,7 @@ describe("useMCPServers", () => {
       expect(result.current.isSuccess).toBe(true);
     });
 
-    expect(networking.fetchMCPServers).toHaveBeenCalledWith(mockAccessToken);
+    expect(networking.fetchMCPServers).toHaveBeenCalledWith(mockAccessToken, undefined);
     expect(result.current.data).toEqual(mockServers);
   });
 

ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.test.tsx

@@ -20,11 +20,15 @@ vi.mock("@/components/molecules/notifications_manager", () => ({
 
 // Mock react-query
 const mockInvalidateQueries = vi.fn();
-vi.mock("@tanstack/react-query", () => ({
-  useQueryClient: () => ({
-    invalidateQueries: mockInvalidateQueries,
-  }),
-}));
+vi.mock("@tanstack/react-query", async (importOriginal) => {
+  const actual = await importOriginal() as any;
+  return {
+    ...actual,
+    useQueryClient: () => ({
+      invalidateQueries: mockInvalidateQueries,
+    }),
+  };
+});
 
 // Mock the useModelsInfo hook
 const mockUseModelsInfo = vi.fn(() => ({
@@ -553,7 +557,7 @@ describe("AllModelsTab", () => {
 
     mockUseModelsInfo.mockReturnValue({ data: modelData, isLoading: false, error: null, refetch: vi.fn() });
 
-    render(<AllModelsTab {...defaultProps} />);
+    renderWithProviders(<AllModelsTab {...defaultProps} />);
 
     await waitFor(() => {
       expect(screen.getByText("gpt-4-delete-test")).toBeInTheDocument();
@@ -597,7 +601,7 @@ describe("AllModelsTab", () => {
 
     mockUseModelsInfo.mockReturnValue({ data: modelData, isLoading: false, error: null, refetch: vi.fn() });
 
-    render(<AllModelsTab {...defaultProps} />);
+    renderWithProviders(<AllModelsTab {...defaultProps} />);
 
     await waitFor(() => {
       expect(screen.getByText("gpt-4-clickable")).toBeInTheDocument();

ui/litellm-dashboard/src/components/mcp_server_management/MCPToolPermissions.test.tsx

@@ -72,7 +72,7 @@ describe("MCPToolPermissions", () => {
 
     // Verify API calls
     // Note: useMCPServers uses useAuthorized() internally, which returns "123" from global mock
-    expect(networking.fetchMCPServers).toHaveBeenCalledWith("123");
+    expect(networking.fetchMCPServers).toHaveBeenCalledWith("123", undefined);
     // listMCPTools uses the accessToken prop directly
     expect(networking.listMCPTools).toHaveBeenCalledWith(mockAccessToken, mockServerId);
   });

ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.test.tsx

@@ -53,8 +53,24 @@ const getServerNameInput = () => document.getElementById("server_name") as HTMLI
 /** Helper: select a dropdown option by opening a select near a label and clicking an option */
 async function selectAntOption(labelText: string, optionText: string) {
   const label = screen.getByText(labelText);
-  const formItem = label.closest(".ant-form-item")!;
-  const select = formItem.querySelector(".ant-select");
+  // First try to find a .ant-form-item ancestor (standard form fields)
+  let select: Element | null = null;
+  const formItem = label.closest(".ant-form-item");
+  if (formItem) {
+    select = formItem.querySelector(".ant-select");
+  }
+  // If not found, try .ant-collapse-content ancestor (auth type is inside a Collapse panel)
+  if (!select) {
+    const collapseContent = label.closest(".ant-collapse-item");
+    if (collapseContent) {
+      select = collapseContent.querySelector(".ant-select");
+    }
+  }
+  // Fallback: look for a sibling or nearby select
+  if (!select) {
+    const parent = label.closest("div");
+    select = parent?.querySelector(".ant-select") ?? null;
+  }
   act(() => {
     fireEvent.mouseDown(select!.querySelector(".ant-select-selector")!);
   });

ui/litellm-dashboard/src/components/mcp_tools/mcp_servers.test.tsx

@@ -126,7 +126,7 @@ describe("MCPServers", () => {
 
     // Verify the API was called
     // Note: useMCPServers uses useAuthorized() internally, which returns "123" from global mock
-    expect(networking.fetchMCPServers).toHaveBeenCalledWith("123");
+    expect(networking.fetchMCPServers).toHaveBeenCalledWith("123", undefined);
   });
 
   it("should fetch and merge health status for servers", async () => {

ui/litellm-dashboard/src/components/molecules/models/columns.test.tsx

@@ -612,18 +612,19 @@ describe("columns", () => {
 
   it("should allow Admin to delete DB models", async () => {
     const user = userEvent.setup();
-    const setSelectedModelId = vi.fn();
+    const onDeleteClick = vi.fn();
     const cols = columns(
       "Admin",
       "admin-user",
       defaultProps.premiumUser,
-      setSelectedModelId,
+      defaultProps.setSelectedModelId,
       defaultProps.setSelectedTeamId,
       defaultProps.getDisplayModelName,
       defaultProps.handleEditClick,
       defaultProps.handleRefreshClick,
       defaultProps.expandedRows,
       defaultProps.setExpandedRows,
+      onDeleteClick,
     );
 
     const model = createMockModel({
@@ -639,23 +640,24 @@ describe("columns", () => {
     expect(deleteButton).toBeInTheDocument();
 
     await user.click(deleteButton);
-    expect(setSelectedModelId).toHaveBeenCalledWith("deletable-model");
+    expect(onDeleteClick).toHaveBeenCalledWith("deletable-model");
   });
 
   it("should allow model creator to delete their own DB models", async () => {
     const user = userEvent.setup();
-    const setSelectedModelId = vi.fn();
+    const onDeleteClick = vi.fn();
     const cols = columns(
       "User",
       "model-creator",
       defaultProps.premiumUser,
-      setSelectedModelId,
+      defaultProps.setSelectedModelId,
       defaultProps.setSelectedTeamId,
       defaultProps.getDisplayModelName,
       defaultProps.handleEditClick,
       defaultProps.handleRefreshClick,
       defaultProps.expandedRows,
       defaultProps.setExpandedRows,
+      onDeleteClick,
     );
 
     const model = createMockModel({
@@ -672,7 +674,7 @@ describe("columns", () => {
     expect(deleteButton).toBeInTheDocument();
 
     await user.click(deleteButton);
-    expect(setSelectedModelId).toHaveBeenCalledWith("user-model");
+    expect(onDeleteClick).toHaveBeenCalledWith("user-model");
   });
 
 

ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx

@@ -60,7 +60,8 @@ import CodeInterpreterOutput from "./CodeInterpreterOutput";
 import CodeInterpreterTool from "./CodeInterpreterTool";
 import { generateCodeSnippet } from "./CodeSnippets";
 import EndpointSelector from "./EndpointSelector";
-import MCPEventsDisplay, { MCPEvent } from "./MCPEventsDisplay";
+import MCPEventsDisplay from "./MCPEventsDisplay";
+import type { MCPEvent } from "../../mcp_tools/types";
 import { EndpointType, getEndpointType } from "./mode_endpoint_mapping";
 import ReasoningContent from "./ReasoningContent";
 import ResponseMetrics, { TokenUsage } from "./ResponseMetrics";

ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.tsx

@@ -3,8 +3,7 @@ import { ChatCompletionMessageParam } from "openai/resources/chat/completions";
 import { TokenUsage } from "../chat_ui/ResponseMetrics";
 import { VectorStoreSearchResponse } from "../chat_ui/types";
 import { getProxyBaseUrl } from "@/components/networking";
-import { MCPServer } from "../../mcp_tools/types";
-import { MCPEvent } from "../chat_ui/MCPEventsDisplay";
+import { MCPServer, type MCPEvent } from "../../mcp_tools/types";
 
 export async function makeOpenAIChatCompletionRequest(
   chatHistory: { role: string; content: string | any[] }[],

ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx

@@ -127,15 +127,15 @@ describe("responses_api", () => {
     expect(callArgs.tools).toEqual([
       {
         type: "mcp",
-        server_label: "litellm",
-        server_url: "litellm_proxy/mcp/alpha",
+        server_label: "Alpha",
+        server_url: "https://example.com/mcp/Alpha",
         require_approval: "never",
         allowed_tools: ["toolA"],
       },
       {
         type: "mcp",
-        server_label: "litellm",
-        server_url: "litellm_proxy/mcp/Beta",
+        server_label: "Beta",
+        server_url: "https://example.com/mcp/Beta",
         require_approval: "never",
         allowed_tools: ["toolB", "toolC"],
       },

ui/litellm-dashboard/src/components/public_model_hub.tsx

@@ -136,7 +136,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
           setLoading(true);
           const _modelHubData = await modelHubPublicModelsCall();
           console.log("ModelHubData:", _modelHubData);
-          setModelHubData(_modelHubData);
+          setModelHubData(Array.isArray(_modelHubData) ? _modelHubData : []);
         } catch (error) {
           console.error("There was an error fetching the public model data", error);
           setServiceStatus("Service unavailable");
@@ -150,7 +150,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
           setAgentLoading(true);
           const _agentHubData = await agentHubPublicModelsCall();
           console.log("AgentHubData:", _agentHubData);
-          setAgentHubData(_agentHubData);
+          setAgentHubData(Array.isArray(_agentHubData) ? _agentHubData : []);
         } catch (error) {
           console.error("There was an error fetching the public agent data", error);
         } finally {
@@ -163,7 +163,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
           setMcpLoading(true);
           const _mcpHubData = await mcpHubPublicServersCall();
           console.log("MCPHubData:", _mcpHubData);
-          setMcpHubData(_mcpHubData);
+          setMcpHubData(Array.isArray(_mcpHubData) ? _mcpHubData : []);
         } catch (error) {
           console.error("There was an error fetching the public MCP server data", error);
         } finally {
@@ -199,7 +199,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
   const getUniqueProviders = (data: ModelGroupInfo[]) => {
     const providers = new Set<string>();
     data.forEach((model) => {
-      model.providers.forEach((provider) => providers.add(provider));
+      (model.providers ?? []).forEach((provider) => providers.add(provider));
     });
     return Array.from(providers);
   };
@@ -532,7 +532,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
       accessorKey: "providers",
       enableSorting: true,
       cell: ({ row }) => {
-        const providers = row.original.providers;
+        const providers = row.original.providers ?? [];
 
         return (
           <div className="flex flex-wrap gap-1">
@@ -760,7 +760,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
       accessorKey: "description",
       enableSorting: false,
       cell: ({ row }) => {
-        const description = row.original.description;
+        const description = row.original.description ?? "";
         const truncated = description.length > 80 ? description.substring(0, 80) + "..." : description;
         return (
           <Tooltip title={description}>
@@ -897,7 +897,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
       accessorKey: "mcp_info.description",
       enableSorting: false,
       cell: ({ row }) => {
-        const description = row.original.mcp_info?.description || "-";
+        const description = String(row.original.mcp_info?.description ?? "-");
         const truncated = description.length > 80 ? description.substring(0, 80) + "..." : description;
         return (
           <Tooltip title={description}>
@@ -912,7 +912,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
       accessorKey: "url",
       enableSorting: false,
       cell: ({ row }) => {
-        const url = row.original.url;
+        const url = row.original.url ?? "";
         const truncated = url.length > 40 ? url.substring(0, 40) + "..." : url;
         return (
           <Tooltip title={url}>
@@ -1336,7 +1336,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
                   <div>
                     <Text className="font-medium">Providers:</Text>
                     <div className="flex flex-wrap gap-1 mt-1">
-                      {selectedModel.providers.map((provider) => {
+                      {(selectedModel.providers ?? []).map((provider) => {
                         const { logo } = getProviderLogoAndName(provider);
                         return (
                           <Tag key={provider} color="blue">
@@ -1460,7 +1460,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
               )}
 
               {/* Supported OpenAI Parameters */}
-              {selectedModel.supported_openai_params && (
+              {selectedModel.supported_openai_params && selectedModel.supported_openai_params.length > 0 && (
                 <div>
                   <Text className="text-lg font-semibold mb-4">Supported OpenAI Parameters</Text>
                   <div className="flex flex-wrap gap-2">
@@ -1634,7 +1634,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
                   <div>
                     <Text className="font-medium">Input Modes:</Text>
                     <div className="flex flex-wrap gap-1 mt-1">
-                      {selectedAgent.defaultInputModes?.map((mode) => (
+                      {(selectedAgent.defaultInputModes ?? []).map((mode) => (
                         <Tag key={mode} color="blue">
                           {mode}
                         </Tag>
@@ -1644,7 +1644,7 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
                   <div>
                     <Text className="font-medium">Output Modes:</Text>
                     <div className="flex flex-wrap gap-1 mt-1">
-                      {selectedAgent.defaultOutputModes?.map((mode) => (
+                      {(selectedAgent.defaultOutputModes ?? []).map((mode) => (
                         <Tag key={mode} color="blue">
                           {mode}
                         </Tag>