diff --git a/.circleci/config.yml b/.circleci/config.yml
index e7566bd764b..40076c3c7f6 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -532,7 +532,7 @@ jobs:
           command: |
             pwd
             ls
-            python -m pytest -vv tests/router_unit_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5
+            python -m pytest -vv tests/router_unit_tests --cov=litellm --cov-report=xml -x -s --junitxml=test-results/junit.xml --durations=5
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
@@ -1164,7 +1164,7 @@ jobs:
           command: |
             pwd
             ls
-            python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 8
+            python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 8
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
@@ -1396,7 +1396,7 @@ jobs:
           command: |
             pwd
             ls
-            python -m pytest -vv tests/image_gen_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5
+            python -m pytest -vv tests/image_gen_tests --cov=litellm --cov-report=xml -x -v --junitxml=test-results/junit.xml --durations=5
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt
index 8e0f1dfe7e9..2294c84813c 100644
--- a/.circleci/requirements.txt
+++ b/.circleci/requirements.txt
@@ -15,4 +15,5 @@ fastapi-sso==0.16.0
 uvloop==0.21.0
 mcp==1.10.1    # for MCP server  
 semantic_router==0.1.10 # for auto-routing with litellm
-fastuuid==0.12.0
\ No newline at end of file
+fastuuid==0.12.0
+responses==0.25.7  # for proxy client tests
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
index 6ab78d85e33..d9ea0d9a471 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -65,6 +65,10 @@ COPY --from=builder /wheels/ /wheels/
 # Install the built wheel using pip; again using a wildcard if it's the only file
 RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels
 
+# Remove test files and keys from dependencies
+RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \
+    find /usr/lib -type d -path "*/tornado/test" -delete
+
 # Install semantic_router and aurelio-sdk using script
 RUN chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh
 
diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev
index 2e886915203..f95f540a7a5 100644
--- a/docker/Dockerfile.dev
+++ b/docker/Dockerfile.dev
@@ -57,6 +57,9 @@ USER root
 # Install only runtime dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
     libssl3 \
+    libatomic1 \
+    nodejs \
+    npm \
     && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root
index 6be115ba71b..0cbdf761fe8 100644
--- a/docker/Dockerfile.non_root
+++ b/docker/Dockerfile.non_root
@@ -69,6 +69,10 @@ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ \
   && rm -f *.whl \
   && rm -rf /wheels
 
+# Remove test files and keys from dependencies
+RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \
+    find /usr/lib -type d -path "*/tornado/test" -delete
+
 # Install semantic_router and aurelio-sdk using script
 RUN chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh
 
diff --git a/docs/my-website/docs/benchmarks.md b/docs/my-website/docs/benchmarks.md
index 40285b63d71..f00732450d1 100644
--- a/docs/my-website/docs/benchmarks.md
+++ b/docs/my-website/docs/benchmarks.md
@@ -48,47 +48,6 @@ In these tests the baseline latency characteristics are measured against a fake-
 - High-percentile latencies drop significantly: P95 630 ms → 150 ms, P99 1,200 ms → 240 ms.
 - Setting workers equal to CPU count gives optimal performance.
 
-## LiteLLM vs Portkey Performance Comparison
-
-**Test Configuration**: 4 CPUs, 8 GB RAM per instance | Load: 1k concurrent users, 500 ramp-up
-
-### Multi-Instance (4×) Performance
-
-| Metric              | Portkey (no DB) | LiteLLM (with DB) |
-| ------------------- | --------------- | ----------------- |
-| **Total Requests**  | 293,796         | 312,405           |
-| **Failed Requests** | 0               | 0                 |
-| **Median Latency**  | 100 ms          | 100 ms            |
-| **p95 Latency**     | 230 ms          | 150 ms            |
-| **p99 Latency**     | 500 ms          | 240 ms            |
-| **Average Latency** | 123 ms          | 111 ms            |
-| **Current RPS**     | 1,170.9         | 1,170             |
-
-### Technical Insights
-
-**Portkey**
-
-**Pros**
-
-* Low memory footprint
-* Stable latency with minimal spikes
-
-**Cons**
-
-* CPU utilization capped around ~40%, indicating underutilization of available compute resources
-* Experienced three I/O timeout outages
-
-**LiteLLM**
-
-**Pros**
-
-* Fully utilizes available CPU capacity
-* Strong connection handling and low latency after initial warm-up spikes
-
-**Cons**
-
-* High memory usage during initialization and per request
-
 ## Machine Spec used for testing
 
 Each machine deploying LiteLLM had the following specs:
@@ -163,6 +122,48 @@ class MyUser(HttpUser):
 ```
 
 
+## LiteLLM vs Portkey Performance Comparison
+
+**Test Configuration**: 4 CPUs, 8 GB RAM per instance | Load: 1k concurrent users, 500 ramp-up
+
+### Multi-Instance (4×) Performance
+
+| Metric              | Portkey (no DB) | LiteLLM (with DB) |
+| ------------------- | --------------- | ----------------- |
+| **Total Requests**  | 293,796         | 312,405           |
+| **Failed Requests** | 0               | 0                 |
+| **Median Latency**  | 100 ms          | 100 ms            |
+| **p95 Latency**     | 230 ms          | 150 ms            |
+| **p99 Latency**     | 500 ms          | 240 ms            |
+| **Average Latency** | 123 ms          | 111 ms            |
+| **Current RPS**     | 1,170.9         | 1,170             |
+
+### Technical Insights
+
+**Portkey**
+
+**Pros**
+
+* Low memory footprint
+* Stable latency with minimal spikes
+
+**Cons**
+
+* CPU utilization capped around ~40%, indicating underutilization of available compute resources
+* Experienced three I/O timeout outages
+
+**LiteLLM**
+
+**Pros**
+
+* Fully utilizes available CPU capacity
+* Strong connection handling and low latency after initial warm-up spikes
+
+**Cons**
+
+* High memory usage during initialization and per request
+
+
 
 ## Logging Callbacks
 
diff --git a/docs/my-website/docs/completion/image_generation_chat.md b/docs/my-website/docs/completion/image_generation_chat.md
index 58ae70e2fff..98b718ef4ce 100644
--- a/docs/my-website/docs/completion/image_generation_chat.md
+++ b/docs/my-website/docs/completion/image_generation_chat.md
@@ -15,16 +15,22 @@ Supported Providers:
 - Google AI Studio (`gemini`)
 - Vertex AI (`vertex_ai/`)
 
-LiteLLM will standardize the `image` response in the assistant message for models that support image generation during chat completions.
+LiteLLM will standardize the `images` response in the assistant message for models that support image generation during chat completions.
 
 ```python title="Example response from litellm"
 "message": {
     ...
     "content": "Here's the image you requested:",
-    "image": {
-        "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...",
-        "detail": "auto"
-    }
+    "images": [
+        {
+            "image_url": {
+                "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...",
+                "detail": "auto"
+            },
+            "index": 0,
+            "type": "image_url"
+        }
+    ]
 }
 ```
 
@@ -47,7 +53,7 @@ response = completion(
 )
 
 print(response.choices[0].message.content)  # Text response
-print(response.choices[0].message.image)    # Image data
+print(response.choices[0].message.images)   # List of image objects
 ```
 
 </TabItem>
@@ -103,10 +109,16 @@ curl http://0.0.0.0:4000/v1/chat/completions \
             "message": {
                 "content": "Here's the image you requested:",
                 "role": "assistant",
-                "image": {
-                    "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...",
-                    "detail": "auto"
-                }
+                "images": [
+                    {
+                        "image_url": {
+                            "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...",
+                            "detail": "auto"
+                        },
+                        "index": 0,
+                        "type": "image_url"
+                    }
+                ]
             }
         }
     ],
@@ -141,8 +153,8 @@ response = completion(
 )
 
 for chunk in response:
-    if hasattr(chunk.choices[0].delta, "image") and chunk.choices[0].delta.image is not None:
-        print("Generated image:", chunk.choices[0].delta.image["url"])
+    if hasattr(chunk.choices[0].delta, "images") and chunk.choices[0].delta.images is not None:
+        print("Generated image:", chunk.choices[0].delta.images[0]["image_url"]["url"])
         break
 ```
 
@@ -175,7 +187,7 @@ data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084
 
 data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{"content":"Here's the image you requested:"},"finish_reason":null}]}
 
-data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{"image":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...","detail":"auto"}},"finish_reason":null}]}
+data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{"images":[{"image_url":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...","detail":"auto"},"index":0,"type":"image_url"}]},"finish_reason":null}]}
 
 data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1723323084,"model":"gemini/gemini-2.5-flash-image-preview","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
 
@@ -200,8 +212,8 @@ async def generate_image():
     )
     
     print(response.choices[0].message.content)  # Text response
-    print(response.choices[0].message.image)    # Image data
-    
+    print(response.choices[0].message.images)   # List of image objects
+
     return response
 
 # Run the async function
@@ -215,18 +227,28 @@ asyncio.run(generate_image())
 | Google AI Studio | `gemini/gemini-2.5-flash-image-preview` |
 | Vertex AI | `vertex_ai/gemini-2.5-flash-image-preview` |
 
-## Spec 
+## Spec
 
-The `image` field in the response follows this structure:
+The `images` field in the response follows this structure:
 
 ```python
-"image": {
-    "url": "data:image/png;base64,<base64_encoded_image>",
-    "detail": "auto"
-}
+"images": [
+    {
+        "image_url": {
+            "url": "data:image/png;base64,<base64_encoded_image>",
+            "detail": "auto"
+        },
+        "index": 0,
+        "type": "image_url"
+    }
+]
 ```
 
-- `url` - str: Base64 encoded image data in data URI format
-- `detail` - str: Image detail level (always "auto" for generated images)
+- `images` - List[ImageURLListItem]: Array of generated images
+  - `image_url` - ImageURLObject: Container for image data
+    - `url` - str: Base64 encoded image data in data URI format
+    - `detail` - str: Image detail level (always "auto" for generated images)
+  - `index` - int: Index of the image in the response
+  - `type` - str: Type identifier (always "image_url")
 
-The image is returned as a base64-encoded data URI that can be directly used in HTML `<img>` tags or saved to a file.
+The images are returned as base64-encoded data URIs that can be directly used in HTML `<img>` tags or saved to files.
diff --git a/docs/my-website/docs/exception_mapping.md b/docs/my-website/docs/exception_mapping.md
index 2342f444e17..efdada2a1eb 100644
--- a/docs/my-website/docs/exception_mapping.md
+++ b/docs/my-website/docs/exception_mapping.md
@@ -112,6 +112,85 @@ except openai.APITimeoutError as e:
     print(f"should_retry: {should_retry}")
 ```
 
+## Advanced
+
+### Accessing Provider-Specific Error Details
+
+LiteLLM exceptions include a `provider_specific_fields` attribute that contains additional error information specific to each provider. This is particularly useful for Azure OpenAI, which provides detailed content filtering information.
+
+#### Azure OpenAI - Content Policy Violation Inner Error Access
+
+When Azure OpenAI returns content policy violations, you can access the detailed content filtering results through the `innererror` field:
+
+```python
+import litellm
+from litellm.exceptions import ContentPolicyViolationError
+
+try:
+    response = litellm.completion(
+        model="azure/gpt-4",
+        messages=[
+            {
+                "role": "user", 
+                "content": "Some content that might violate policies"
+            }
+        ]
+    )
+except ContentPolicyViolationError as e:
+    # Access Azure-specific error details
+    if e.provider_specific_fields and "innererror" in e.provider_specific_fields:
+        innererror = e.provider_specific_fields["innererror"]
+        
+        # Access content filter results
+        content_filter_result = innererror.get("content_filter_result", {})
+        
+        print(f"Content filter code: {innererror.get('code')}")
+        print(f"Hate filtered: {content_filter_result.get('hate', {}).get('filtered')}")
+        print(f"Violence severity: {content_filter_result.get('violence', {}).get('severity')}")
+        print(f"Sexual content filtered: {content_filter_result.get('sexual', {}).get('filtered')}")
+```
+
+**Example Response Structure:**
+
+When calling the LiteLLM proxy, content policy violations will return detailed filtering information:
+
+```json
+{
+  "error": {
+    "message": "litellm.ContentPolicyViolationError: AzureException - The response was filtered due to the prompt triggering Azure OpenAI's content management policy...",
+    "type": null,
+    "param": null,
+    "code": "400",
+    "provider_specific_fields": {
+      "innererror": {
+        "code": "ResponsibleAIPolicyViolation",
+        "content_filter_result": {
+          "hate": {
+            "filtered": true,
+            "severity": "high"
+          },
+          "jailbreak": {
+            "filtered": false,
+            "detected": false
+          },
+          "self_harm": {
+            "filtered": false,
+            "severity": "safe"
+          },
+          "sexual": {
+            "filtered": false,
+            "severity": "safe"
+          },
+          "violence": {
+            "filtered": true,
+            "severity": "medium"
+          }
+        }
+      }
+    }
+  }
+}
+
 ## Details 
 
 To see how it's implemented - [check out the code](https://github.com/BerriAI/litellm/blob/a42c197e5a6de56ea576c73715e6c7c6b19fa249/litellm/utils.py#L1217)
diff --git a/docs/my-website/docs/moderation.md b/docs/my-website/docs/moderation.md
index f9c2810bc8a..1f67b0a7543 100644
--- a/docs/my-website/docs/moderation.md
+++ b/docs/my-website/docs/moderation.md
@@ -22,10 +22,19 @@ response = moderation(
 
 For `/moderations` endpoint, there is **no need to specify `model` in the request or on the litellm config.yaml**
 
-Start litellm proxy server 
+
+1. Setup config.yaml
+```yaml
+model_list:
+  - model_name: text-moderation-stable
+    litellm_params:
+      model: openai/omni-moderation-latest
+```
+
+2. Start litellm proxy server 
 
 ```
-litellm
+litellm --config /path/to/config.yaml
 ```
 
 
@@ -41,7 +50,7 @@ client = OpenAI(api_key="<proxy-api-key>", base_url="http://0.0.0.0:4000")
 
 response = client.moderations.create(
     input="hello from litellm",
-    model="text-moderation-stable" # optional, defaults to `omni-moderation-latest`
+    model="text-moderation-stable"
 )
 
 print(response)
diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md
index 08ebf8b28ce..5cb5ab3af2d 100644
--- a/docs/my-website/docs/observability/datadog.md
+++ b/docs/my-website/docs/observability/datadog.md
@@ -56,12 +56,32 @@ litellm_settings:
 
 **Step 2**: Set Required env variables for datadog
 
+#### Direct API
+
+Send logs directly to Datadog API:
+
 ```shell
 DD_API_KEY="5f2d0f310***********" # your datadog API Key
 DD_SITE="us5.datadoghq.com"       # your datadog base url
 DD_SOURCE="litellm_dev"       # [OPTIONAL] your datadog source. use to differentiate dev vs. prod deployments
 ```
 
+#### Via DataDog Agent
+
+Send logs through a local DataDog agent (useful for containerized environments):
+
+```shell
+DD_AGENT_HOST="localhost"         # hostname or IP of DataDog agent
+DD_AGENT_PORT="10518"             # [OPTIONAL] port of DataDog agent (default: 10518)
+DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth)
+DD_SOURCE="litellm_dev"           # [OPTIONAL] your datadog source
+```
+
+When `DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for:
+- Centralized log shipping in containerized environments
+- Reducing direct API calls from multiple services
+- Leveraging agent-side processing and filtering
+
 **Step 3**: Start the proxy, make a test request
 
 Start proxy
@@ -169,8 +189,10 @@ LiteLLM supports customizing the following Datadog environment variables
 
 | Environment Variable | Description | Default Value | Required |
 |---------------------|-------------|---------------|----------|
-| `DD_API_KEY` | Your Datadog API key for authentication | None | ✅ Yes |
-| `DD_SITE` | Your Datadog site (e.g., "us5.datadoghq.com") | None | ✅ Yes |
+| `DD_API_KEY` | Your Datadog API key for authentication (required for direct API, optional for agent) | None | Conditional* |
+| `DD_SITE` | Your Datadog site (e.g., "us5.datadoghq.com") (required for direct API) | None | Conditional* |
+| `DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No |
+| `DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No |
 | `DD_ENV` | Environment tag for your logs (e.g., "production", "staging") | "unknown" | ❌ No |
 | `DD_SERVICE` | Service name for your logs | "litellm-server" | ❌ No |
 | `DD_SOURCE` | Source name for your logs | "litellm" | ❌ No |
@@ -178,3 +200,6 @@ LiteLLM supports customizing the following Datadog environment variables
 | `HOSTNAME` | Hostname tag for your logs | "" | ❌ No |
 | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No |
 
+\* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required  
+\* **Optional when using DataDog Agent**: Set `DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required
+
diff --git a/docs/my-website/docs/projects/Softgen b/docs/my-website/docs/projects/Softgen
new file mode 100644
index 00000000000..2e5024a0770
--- /dev/null
+++ b/docs/my-website/docs/projects/Softgen
@@ -0,0 +1,7 @@
+# Softgen
+
+`Softgen` is an AI-powered platform that builds full-stack web apps from your plain instructions.
+LiteLLM helps `Softgen` users to choose and use different LLMs.
+
+- [Softgen](https://softgen.ai)
+- [Academy](hhttps://academy.softgen.ai)
diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md
index 1663d32ddfc..0ea042e5d98 100644
--- a/docs/my-website/docs/providers/anthropic.md
+++ b/docs/my-website/docs/providers/anthropic.md
@@ -953,7 +953,7 @@ except Exception as e:
 
 s/o @[Shekhar Patnaik](https://www.linkedin.com/in/patnaikshekhar) for requesting this!
 
-### Anthropic Hosted Tools (Computer, Text Editor, Web Search)
+### Anthropic Hosted Tools (Computer, Text Editor, Web Search, Memory)
 
 
 <Tabs>
@@ -1183,6 +1183,72 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 </Tabs>
 
 </TabItem>
+
+<TabItem value="memory" label="Memory">
+
+:::info
+The Anthropic Memory tool is currently in beta.
+:::
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import completion
+
+tools = [{
+    "type": "memory_20250818",
+    "name": "memory"
+}]
+
+model = "claude-sonnet-4-5-20250929" 
+messages = [{"role": "user", "content": "Please remember that my favorite color is blue."}]
+
+response = completion(
+    model=model,
+    messages=messages,
+    tools=tools,
+)
+
+print(response)
+```
+
+</TabItem>
+<TabItem value="proxy" label="Proxy">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+    - model_name: claude-memory-model
+    litellm_params:
+        model: anthropic/claude-sonnet-4-5-20250929
+        api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it! 
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer $LITELLM_KEY" \
+    -d '{
+    "model": "claude-memory-model",
+    "messages": [{"role": "user", "content": "Please remember that my favorite color is blue."}],
+    "tools": [{"type": "memory_20250818", "name": "memory"}]
+    }'
+```
+</TabItem>
+</Tabs>
+
+</TabItem>
+
 </Tabs>
 
 
diff --git a/docs/my-website/docs/providers/azure/videos.md b/docs/my-website/docs/providers/azure/videos.md
index 188713d6335..62f8d0df182 100644
--- a/docs/my-website/docs/providers/azure/videos.md
+++ b/docs/my-website/docs/providers/azure/videos.md
@@ -25,7 +25,6 @@ LiteLLM supports Azure OpenAI's video generation models including Sora with full
 import os 
 os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
 os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"
-os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-15-preview"
 ```
 
 ### Basic Usage
@@ -37,7 +36,6 @@ import time
 
 os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
 os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"
-os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-15-preview"
 
 # Generate video
 response = video_generation(
@@ -53,8 +51,7 @@ print(f"Initial Status: {response.status}")
 # Check status until video is ready
 while True:
     status_response = video_status(
-        video_id=response.id,
-        custom_llm_provider="azure"
+        video_id=response.id
     )
     
     print(f"Current Status: {status_response.status}")
@@ -69,8 +66,7 @@ while True:
 
 # Download video content when ready
 video_bytes = video_content(
-    video_id=response.id,
-    custom_llm_provider="azure"
+    video_id=response.id
 )
 
 # Save to file
@@ -87,7 +83,6 @@ Here's how to call Azure video generation models with the LiteLLM Proxy Server
 ```bash
 export AZURE_OPENAI_API_KEY="your-azure-api-key"
 export AZURE_OPENAI_API_BASE="https://your-resource.openai.azure.com/"
-export AZURE_OPENAI_API_VERSION="2024-02-15-preview"
 ```
 
 ### 2. Start the proxy 
@@ -102,7 +97,6 @@ model_list:
       model: azure/sora-2
       api_key: os.environ/AZURE_OPENAI_API_KEY
       api_base: os.environ/AZURE_OPENAI_API_BASE
-      api_version: "2024-02-15-preview"
 ```
 
 </TabItem>
@@ -211,8 +205,7 @@ general_settings:
 ```python
 # Download video content
 video_bytes = video_content(
-    video_id="video_1234567890",
-    model="azure/sora-2"
+    video_id="video_1234567890"
 )
 
 # Save to file
@@ -243,8 +236,7 @@ def generate_and_download_video(prompt):
     
     # Step 3: Download video
     video_bytes = litellm.video_content(
-        video_id=video_id,
-        custom_llm_provider="azure"
+        video_id=video_id
     )
     
     # Step 4: Save to file
@@ -264,9 +256,9 @@ video_file = generate_and_download_video(
 ```python
 # Video editing with reference image
 response = litellm.video_remix(
+    video_id="video_456",
     prompt="Make the cat jump higher",
     input_reference=open("path/to/image.jpg", "rb"),  # Reference image as file object
-    custom_llm_provider="azure"
     seconds="8"
 )
 
diff --git a/docs/my-website/docs/providers/fireworks_ai.md b/docs/my-website/docs/providers/fireworks_ai.md
index 98d7c33ce7e..b1b10cd71b5 100644
--- a/docs/my-website/docs/providers/fireworks_ai.md
+++ b/docs/my-website/docs/providers/fireworks_ai.md
@@ -204,7 +204,7 @@ from litellm import completion
 import os
 
 os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"
-os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.us-virginia-1.direct.fireworks.ai/v1"
+os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.api.fireworks.ai/v1"
 
 completion = litellm.completion(
     model="fireworks_ai/accounts/fireworks/models/llama-v3p3-70b-instruct",
@@ -343,7 +343,7 @@ from litellm import transcription
 import os
 
 os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"
-os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.us-virginia-1.direct.fireworks.ai/v1"
+os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.api.fireworks.ai/v1"
 
 response = transcription(
     model="fireworks_ai/whisper-v3",
@@ -363,7 +363,7 @@ model_list:
   - model_name: whisper-v3
     litellm_params:
       model: fireworks_ai/whisper-v3
-      api_base: https://audio-prod.us-virginia-1.direct.fireworks.ai/v1
+      api_base: https://audio-prod.api.fireworks.ai/v1
       api_key: os.environ/FIREWORKS_API_KEY
     model_info:
       mode: audio_transcription
diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md
index 40d64656528..31d3a491f40 100644
--- a/docs/my-website/docs/providers/gemini.md
+++ b/docs/my-website/docs/providers/gemini.md
@@ -10,7 +10,7 @@ import TabItem from '@theme/TabItem';
 | Provider Route on LiteLLM | `gemini/` |
 | Provider Doc | [Google AI Studio ↗](https://aistudio.google.com/) |
 | API Endpoint for Provider | https://generativelanguage.googleapis.com |
-| Supported OpenAI Endpoints | `/chat/completions`, [`/embeddings`](../embedding/supported_embedding#gemini-ai-embedding-models), `/completions` |
+| Supported OpenAI Endpoints | `/chat/completions`, [`/embeddings`](../embedding/supported_embedding#gemini-ai-embedding-models), `/completions`, [`/videos`](./gemini/videos.md) |
 | Pass-through Endpoint | [Supported](../pass_through/google_ai_studio.md) |
 
 <br />
diff --git a/docs/my-website/docs/providers/gemini/videos.md b/docs/my-website/docs/providers/gemini/videos.md
new file mode 100644
index 00000000000..5b5d5a8a636
--- /dev/null
+++ b/docs/my-website/docs/providers/gemini/videos.md
@@ -0,0 +1,409 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Gemini Video Generation (Veo)
+
+LiteLLM supports Google's Veo video generation models through a unified API interface.
+
+| Property | Details |
+|-------|-------|
+| Description | Google's Veo AI video generation models |
+| Provider Route on LiteLLM | `gemini/` |
+| Supported Models | `veo-3.0-generate-preview`, `veo-3.1-generate-preview` |
+| Cost Tracking | ✅ Duration-based pricing |
+| Logging Support | ✅ Full request/response logging |
+| Proxy Server Support | ✅ Full proxy integration with virtual keys |
+| Spend Management | ✅ Budget tracking and rate limiting |
+| Link to Provider Doc | [Google Veo Documentation ↗](https://ai.google.dev/gemini-api/docs/video) |
+
+## Quick Start
+
+### Required API Keys
+
+```python
+import os 
+os.environ["GEMINI_API_KEY"] = "your-google-api-key"
+# OR
+os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+```
+
+### Basic Usage
+
+```python
+from litellm import video_generation, video_status, video_content
+import os
+import time
+
+os.environ["GEMINI_API_KEY"] = "your-google-api-key"
+
+# Step 1: Generate video
+response = video_generation(
+    model="gemini/veo-3.0-generate-preview",
+    prompt="A cat playing with a ball of yarn in a sunny garden"
+)
+
+print(f"Video ID: {response.id}")
+print(f"Initial Status: {response.status}")  # "processing"
+
+# Step 2: Poll for completion
+while True:
+    status_response = video_status(
+        video_id=response.id
+    )
+    
+    print(f"Current Status: {status_response.status}")
+    
+    if status_response.status == "completed":
+        break
+    elif status_response.status == "failed":
+        print("Video generation failed")
+        break
+    
+    time.sleep(10)  # Wait 10 seconds before checking again
+
+# Step 3: Download video content
+video_bytes = video_content(
+    video_id=response.id
+)
+
+# Save to file
+with open("generated_video.mp4", "wb") as f:
+    f.write(video_bytes)
+
+print("Video downloaded successfully!")
+```
+
+## Supported Models
+
+| Model Name | Description | Max Duration | Status |
+|------------|-------------|--------------|--------|
+| veo-3.0-generate-preview | Veo 3.0 video generation | 8 seconds | Preview |
+| veo-3.1-generate-preview | Veo 3.1 video generation | 8 seconds | Preview |
+
+## Video Generation Parameters
+
+LiteLLM automatically maps OpenAI-style parameters to Veo's format:
+
+| OpenAI Parameter | Veo Parameter | Description | Example |
+|------------------|---------------|-------------|---------|
+| `prompt` | `prompt` | Text description of the video | "A cat playing" |
+| `size` | `aspectRatio` | Video dimensions → aspect ratio | "1280x720" → "16:9" |
+| `seconds` | `durationSeconds` | Duration in seconds | "8" → 8 |
+| `input_reference` | `image` | Reference image to animate | File object or path |
+| `model` | `model` | Model to use | "gemini/veo-3.0-generate-preview" |
+
+### Size to Aspect Ratio Mapping
+
+LiteLLM automatically converts size dimensions to Veo's aspect ratio format:
+- `"1280x720"`, `"1920x1080"` → `"16:9"` (landscape)
+- `"720x1280"`, `"1080x1920"` → `"9:16"` (portrait)
+
+### Supported Veo Parameters
+
+Based on Veo's API:
+- **prompt** (required): Text description with optional audio cues
+- **aspectRatio**: `"16:9"` (default) or `"9:16"`
+- **resolution**: `"720p"` (default) or `"1080p"` (Veo 3.1 only, 16:9 aspect ratio only)
+- **durationSeconds**: Video length (max 8 seconds for most models)
+- **image**: Reference image for animation
+- **negativePrompt**: What to exclude from the video (Veo 3.1)
+- **referenceImages**: Style and content references (Veo 3.1 only)
+
+## Complete Workflow Example
+
+```python
+import litellm
+import time
+
+def generate_and_download_veo_video(
+    prompt: str, 
+    output_file: str = "video.mp4",
+    size: str = "1280x720",
+    seconds: str = "8"
+):
+    """
+    Complete workflow for Veo video generation.
+    
+    Args:
+        prompt: Text description of the video
+        output_file: Where to save the video
+        size: Video dimensions (e.g., "1280x720" for 16:9)
+        seconds: Duration in seconds
+        
+    Returns:
+        bool: True if successful
+    """
+    print(f"🎬 Generating video: {prompt}")
+    
+    # Step 1: Initiate generation
+    response = litellm.video_generation(
+        model="gemini/veo-3.0-generate-preview",
+        prompt=prompt,
+        size=size,      # Maps to aspectRatio
+        seconds=seconds  # Maps to durationSeconds
+    )
+    
+    video_id = response.id
+    print(f"✓ Video generation started (ID: {video_id})")
+    
+    # Step 2: Wait for completion
+    max_wait_time = 600  # 10 minutes
+    start_time = time.time()
+    
+    while time.time() - start_time < max_wait_time:
+        status_response = litellm.video_status(video_id=video_id)
+        
+        if status_response.status == "completed":
+            print("✓ Video generation completed!")
+            break
+        elif status_response.status == "failed":
+            print("✗ Video generation failed")
+            return False
+        
+        print(f"⏳ Status: {status_response.status}")
+        time.sleep(10)
+    else:
+        print("✗ Timeout waiting for video generation")
+        return False
+    
+    # Step 3: Download video
+    print("⬇️  Downloading video...")
+    video_bytes = litellm.video_content(video_id=video_id)
+    
+    with open(output_file, "wb") as f:
+        f.write(video_bytes)
+    
+    print(f"✓ Video saved to {output_file}")
+    return True
+
+# Use it
+generate_and_download_veo_video(
+    prompt="A serene lake at sunset with mountains in the background",
+    output_file="sunset_lake.mp4"
+)
+```
+
+## Async Usage
+
+```python
+from litellm import avideo_generation, avideo_status, avideo_content
+import asyncio
+
+async def async_video_workflow():
+    # Generate video
+    response = await avideo_generation(
+        model="gemini/veo-3.0-generate-preview",
+        prompt="A cat playing with a ball of yarn"
+    )
+    
+    # Poll for completion
+    while True:
+        status = await avideo_status(video_id=response.id)
+        if status.status == "completed":
+            break
+        await asyncio.sleep(10)
+    
+    # Download content
+    video_bytes = await avideo_content(video_id=response.id)
+    
+    with open("video.mp4", "wb") as f:
+        f.write(video_bytes)
+
+# Run it
+asyncio.run(async_video_workflow())
+```
+
+## LiteLLM Proxy Usage
+
+### Configuration
+
+Add Veo models to your `config.yaml`:
+
+```yaml
+model_list:
+  - model_name: veo-3
+    litellm_params:
+      model: gemini/veo-3.0-generate-preview
+      api_key: os.environ/GEMINI_API_KEY
+```
+
+Start the proxy:
+
+```bash
+litellm --config config.yaml
+# Server running on http://0.0.0.0:4000
+```
+
+### Making Requests
+
+<Tabs>
+<TabItem value="curl" label="Curl">
+
+```bash
+# Step 1: Generate video
+curl --location 'http://0.0.0.0:4000/v1/videos' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer sk-1234' \
+--data '{
+    "model": "veo-3",
+    "prompt": "A cat playing with a ball of yarn in a sunny garden"
+}'
+
+# Response: {"id": "gemini::operations/generate_12345::...", "status": "processing", ...}
+
+# Step 2: Check status
+curl --location 'http://localhost:4000/v1/videos/{video_id}' \
+--header 'x-litellm-api-key: sk-1234'
+
+# Step 3: Download video (when status is "completed")
+curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \
+--header 'x-litellm-api-key: sk-1234' \
+--output video.mp4
+```
+
+</TabItem>
+<TabItem value="python" label="Python SDK">
+
+```python
+import litellm
+
+litellm.api_base = "http://0.0.0.0:4000"
+litellm.api_key = "sk-1234"
+
+# Generate video
+response = litellm.video_generation(
+    model="veo-3",
+    prompt="A cat playing with a ball of yarn in a sunny garden"
+)
+
+# Check status
+import time
+while True:
+    status = litellm.video_status(video_id=response.id)
+    if status.status == "completed":
+        break
+    time.sleep(10)
+
+# Download video
+video_bytes = litellm.video_content(video_id=response.id)
+with open("video.mp4", "wb") as f:
+    f.write(video_bytes)
+```
+
+</TabItem>
+</Tabs>
+
+## Cost Tracking
+
+LiteLLM automatically tracks costs for Veo video generation:
+
+```python
+response = litellm.video_generation(
+    model="gemini/veo-3.0-generate-preview",
+    prompt="A beautiful sunset"
+)
+
+# Cost is calculated based on video duration
+# Veo pricing: ~$0.10 per second (estimated)
+# Default video duration: ~5 seconds
+# Estimated cost: ~$0.50
+```
+
+## Differences from OpenAI Video API
+
+| Feature | OpenAI (Sora) | Gemini (Veo) |
+|---------|---------------|--------------|
+| Reference Images | ✅ Supported | ❌ Not supported |
+| Size Control | ✅ Supported | ❌ Not supported |
+| Duration Control | ✅ Supported | ❌ Not supported |
+| Video Remix/Edit | ✅ Supported | ❌ Not supported |
+| Video List | ✅ Supported | ❌ Not supported |
+| Prompt-based Generation | ✅ Supported | ✅ Supported |
+| Async Operations | ✅ Supported | ✅ Supported |
+
+## Error Handling
+
+```python
+from litellm import video_generation, video_status, video_content
+from litellm.exceptions import APIError, Timeout
+
+try:
+    response = video_generation(
+        model="gemini/veo-3.0-generate-preview",
+        prompt="A beautiful landscape"
+    )
+    
+    # Poll with timeout
+    max_attempts = 60  # 10 minutes (60 * 10s)
+    for attempt in range(max_attempts):
+        status = video_status(video_id=response.id)
+        
+        if status.status == "completed":
+            video_bytes = video_content(video_id=response.id)
+            with open("video.mp4", "wb") as f:
+                f.write(video_bytes)
+            break
+        elif status.status == "failed":
+            raise APIError("Video generation failed")
+        
+        time.sleep(10)
+    else:
+        raise Timeout("Video generation timed out")
+        
+except APIError as e:
+    print(f"API Error: {e}")
+except Timeout as e:
+    print(f"Timeout: {e}")
+except Exception as e:
+    print(f"Unexpected error: {e}")
+```
+
+## Best Practices
+
+1. **Always poll for completion**: Veo video generation is asynchronous and can take several minutes
+2. **Set reasonable timeouts**: Allow at least 5-10 minutes for video generation
+3. **Handle failures gracefully**: Check for `failed` status and implement retry logic
+4. **Use descriptive prompts**: More detailed prompts generally produce better results
+5. **Store video IDs**: Save the operation ID/video ID to resume polling if your application restarts
+
+## Troubleshooting
+
+### Video generation times out
+
+```python
+# Increase polling timeout
+max_wait_time = 900  # 15 minutes instead of 10
+```
+
+### Video not found when downloading
+
+```python
+# Make sure video is completed before downloading
+status = video_status(video_id=video_id)
+if status.status != "completed":
+    print("Video not ready yet!")
+```
+
+### API key errors
+
+```python
+# Verify your API key is set
+import os
+print(os.environ.get("GEMINI_API_KEY"))
+
+# Or pass it explicitly
+response = video_generation(
+    model="gemini/veo-3.0-generate-preview",
+    prompt="...",
+    api_key="your-api-key-here"
+)
+```
+
+## See Also
+
+- [OpenAI Video Generation](../openai/videos.md)
+- [Azure Video Generation](../azure/videos.md)
+- [Vertex AI Video Generation](../vertex_ai/videos.md)
+- [Video Generation API Reference](/docs/videos)
+- [Veo Pass-through Endpoints](/docs/pass_through/google_ai_studio#example-4-video-generation-with-veo)
+
diff --git a/docs/my-website/docs/providers/openai/videos.md b/docs/my-website/docs/providers/openai/videos.md
index 06d0934b180..202c79c2446 100644
--- a/docs/my-website/docs/providers/openai/videos.md
+++ b/docs/my-website/docs/providers/openai/videos.md
@@ -36,7 +36,6 @@ print(f"Status: {response.status}")
 # Download video content when ready
 video_bytes = video_content(
     video_id=response.id,
-    model="sora-2"
 )
 
 # Save to file
@@ -44,6 +43,113 @@ with open("generated_video.mp4", "wb") as f:
     f.write(video_bytes)
 ```
 
+## **LiteLLM Proxy Usage**
+
+LiteLLM provides OpenAI API compatible video endpoints for complete video generation workflow:
+
+- `/videos/generations` - Generate new videos
+- `/videos/remix` - Edit existing videos with reference images  
+- `/videos/status` - Check video generation status
+- `/videos/retrieval` - Download completed videos
+
+**Setup**
+
+Add this to your litellm proxy config.yaml
+
+```yaml
+model_list:
+  - model_name: sora-2
+    litellm_params:
+      model: openai/sora-2
+      api_key: os.environ/OPENAI_API_KEY
+```
+
+Start litellm
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+Test video generation request
+
+```bash
+curl --location 'http://localhost:4000/v1/videos' \
+--header 'Content-Type: application/json' \
+--header 'x-litellm-api-key: sk-1234' \
+--data '{
+    "model": "sora-2",
+    "prompt": "A beautiful sunset over the ocean"
+}'
+```
+
+Test video status request
+
+```bash
+# Using custom-llm-provider header
+curl --location 'http://localhost:4000/v1/videos/video_id' \
+--header 'Accept: application/json' \
+--header 'x-litellm-api-key: sk-1234' \
+--header 'custom-llm-provider: openai'
+```
+
+Test video retrieval request
+
+```bash
+# Using custom-llm-provider header
+curl --location 'http://localhost:4000/v1/videos/video_id/content' \
+--header 'Accept: application/json' \
+--header 'x-litellm-api-key: sk-1234' \
+--header 'custom-llm-provider: openai' \
+--output video.mp4
+
+# Or using query parameter
+curl --location 'http://localhost:4000/v1/videos/video_id/content?custom_llm_provider=openai' \
+--header 'Accept: application/json' \
+--header 'x-litellm-api-key: sk-1234' \
+--output video.mp4
+```
+
+Test video remix request
+
+```bash
+# Using custom_llm_provider in request body
+curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix' \
+--header 'Accept: application/json' \
+--header 'Content-Type: application/json' \
+--header 'x-litellm-api-key: sk-1234' \
+--data '{
+    "prompt": "New remix instructions",
+    "custom_llm_provider": "openai"
+}'
+
+# Or using custom-llm-provider header
+curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix' \
+--header 'Accept: application/json' \
+--header 'Content-Type: application/json' \
+--header 'x-litellm-api-key: sk-1234' \
+--header 'custom-llm-provider: openai' \
+--data '{
+    "prompt": "New remix instructions"
+}'
+```
+
+Test OpenAI video generation request
+
+```bash
+curl http://localhost:4000/v1/videos \
+  -H "Authorization: Bearer sk-1234" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "sora-2",
+    "prompt": "A cat playing with a ball of yarn in a sunny garden",
+    "seconds": "8",
+    "size": "720x1280"
+  }'
+```
+
+
 ## Supported Models
 
 | Model Name | Description | Max Duration | Supported Sizes |
@@ -64,8 +170,7 @@ with open("generated_video.mp4", "wb") as f:
 ```python
 # Download video content
 video_bytes = video_content(
-    video_id="video_1234567890",
-    custom_llm_provider="openai"  # Or use model="sora-2"
+    video_id="video_1234567890"
 )
 
 # Save to file
@@ -96,8 +201,7 @@ def generate_and_download_video(prompt):
     
     # Step 3: Download video
     video_bytes = litellm.video_content(
-        video_id=video_id,
-        custom_llm_provider="openai"  
+        video_id=video_id
     )
     
     # Step 4: Save to file
@@ -112,6 +216,7 @@ video_file = generate_and_download_video(
 )
 ```
 
+
 ## Video Editing with Reference Images
 
 ```python
@@ -133,8 +238,7 @@ from litellm.exceptions import BadRequestError, AuthenticationError
 
 try:
     response = video_generation(
-        prompt="A cat playing with a ball of yarn",
-        model="sora-2"
+        prompt="A cat playing with a ball of yarn"
     )
 except AuthenticationError as e:
     print(f"Authentication failed: {e}")
diff --git a/docs/my-website/docs/providers/vertex_ai/videos.md b/docs/my-website/docs/providers/vertex_ai/videos.md
new file mode 100644
index 00000000000..4aaf74354b1
--- /dev/null
+++ b/docs/my-website/docs/providers/vertex_ai/videos.md
@@ -0,0 +1,268 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Vertex AI Video Generation (Veo)
+
+LiteLLM supports Vertex AI's Veo video generation models using the unified OpenAI video API surface.
+
+| Property | Details |
+|-------|-------|
+| Description | Google Cloud Vertex AI Veo video generation models |
+| Provider Route on LiteLLM | `vertex_ai/` |
+| Supported Models | `veo-2.0-generate-001`, `veo-3.0-generate-preview`, `veo-3.0-fast-generate-preview`, `veo-3.1-generate-preview`, `veo-3.1-fast-generate-preview` |
+| Cost Tracking | ✅ Duration-based pricing |
+| Logging Support | ✅ Full request/response logging |
+| Proxy Server Support | ✅ Full proxy integration with virtual keys |
+| Spend Management | ✅ Budget tracking and rate limiting |
+| Link to Provider Doc | [Vertex AI Veo Documentation ↗](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo-video-generation) |
+
+## Quick Start
+
+### Required Environment Setup
+
+```python
+import json
+import os
+
+os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
+os.environ["VERTEXAI_LOCATION"] = "us-central1"
+
+# Option 1: Point to a service account file
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service_account.json"
+
+# Option 2: Store the service account JSON directly
+with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
+    os.environ["VERTEXAI_CREDENTIALS"] = f.read()
+```
+
+### Basic Usage
+
+```python
+from litellm import video_generation, video_status, video_content
+import json
+import os
+import time
+
+with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
+    vertex_credentials = f.read()
+
+response = video_generation(
+    model="vertex_ai/veo-3.0-generate-preview",
+    prompt="A cat playing with a ball of yarn in a sunny garden",
+    vertex_project="your-gcp-project-id",
+    vertex_location="us-central1",
+    vertex_credentials=vertex_credentials,
+    seconds="8",
+    size="1280x720",
+)
+
+print(f"Video ID: {response.id}")
+print(f"Initial Status: {response.status}")
+
+# Poll for completion
+while True:
+    status = video_status(
+        video_id=response.id,
+        vertex_project="your-gcp-project-id",
+        vertex_location="us-central1",
+        vertex_credentials=vertex_credentials,
+    )
+
+    print(f"Current Status: {status.status}")
+
+    if status.status == "completed":
+        break
+    if status.status == "failed":
+        raise RuntimeError("Video generation failed")
+
+    time.sleep(10)
+
+# Download the rendered video
+video_bytes = video_content(
+    video_id=response.id,
+    vertex_project="your-gcp-project-id",
+    vertex_location="us-central1",
+    vertex_credentials=vertex_credentials,
+)
+
+with open("generated_video.mp4", "wb") as f:
+    f.write(video_bytes)
+```
+
+## Supported Models
+
+| Model Name | Description | Max Duration | Status |
+|------------|-------------|--------------|--------|
+| veo-2.0-generate-001 | Veo 2.0 video generation | 5 seconds | GA |
+| veo-3.0-generate-preview | Veo 3.0 high quality | 8 seconds | Preview |
+| veo-3.0-fast-generate-preview | Veo 3.0 fast generation | 8 seconds | Preview |
+| veo-3.1-generate-preview | Veo 3.1 high quality | 10 seconds | Preview |
+| veo-3.1-fast-generate-preview | Veo 3.1 fast | 10 seconds | Preview |
+
+## Video Generation Parameters
+
+LiteLLM converts OpenAI-style parameters to Veo's API shape automatically:
+
+| OpenAI Parameter | Vertex AI Parameter | Description | Example |
+|------------------|---------------------|-------------|---------|
+| `prompt` | `instances[].prompt` | Text description of the video | "A cat playing" |
+| `size` | `parameters.aspectRatio` | Converted to `16:9` or `9:16` | "1280x720" → `16:9` |
+| `seconds` | `parameters.durationSeconds` | Clip length in seconds | "8" → `8` |
+| `input_reference` | `instances[].image` | Reference image for animation | `open("image.jpg", "rb")` |
+| Provider-specific params | `extra_body` | Forwarded to Vertex API | `{"negativePrompt": "blurry"}` |
+
+### Size to Aspect Ratio Mapping
+
+- `1280x720`, `1920x1080` → `16:9`
+- `720x1280`, `1080x1920` → `9:16`
+- Unknown sizes default to `16:9`
+
+## Async Usage
+
+```python
+from litellm import avideo_generation, avideo_status, avideo_content
+import asyncio
+import json
+
+with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
+    vertex_credentials = f.read()
+
+
+async def workflow():
+    response = await avideo_generation(
+        model="vertex_ai/veo-3.1-generate-preview",
+        prompt="Slow motion water droplets splashing into a pool",
+        seconds="10",
+        vertex_project="your-gcp-project-id",
+        vertex_location="us-central1",
+        vertex_credentials=vertex_credentials,
+    )
+
+    while True:
+        status = await avideo_status(
+            video_id=response.id,
+            vertex_project="your-gcp-project-id",
+            vertex_location="us-central1",
+            vertex_credentials=vertex_credentials,
+        )
+
+        if status.status == "completed":
+            break
+        if status.status == "failed":
+            raise RuntimeError("Video generation failed")
+
+        await asyncio.sleep(10)
+
+    video_bytes = await avideo_content(
+        video_id=response.id,
+        vertex_project="your-gcp-project-id",
+        vertex_location="us-central1",
+        vertex_credentials=vertex_credentials,
+    )
+
+    with open("veo_water.mp4", "wb") as f:
+        f.write(video_bytes)
+
+asyncio.run(workflow())
+```
+
+## LiteLLM Proxy Usage
+
+Add Veo models to your `config.yaml`:
+
+```yaml
+model_list:
+  - model_name: veo-3
+    litellm_params:
+      model: vertex_ai/veo-3.0-generate-preview
+      vertex_project: os.environ/VERTEXAI_PROJECT
+      vertex_location: os.environ/VERTEXAI_LOCATION
+      vertex_credentials: os.environ/VERTEXAI_CREDENTIALS
+```
+
+Start the proxy and make requests:
+
+<Tabs>
+<TabItem value="curl" label="Curl">
+
+```bash
+# Step 1: Generate video
+curl --location 'http://0.0.0.0:4000/videos' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer sk-1234' \
+--data '{
+  "model": "veo-3",
+  "prompt": "Aerial shot over a futuristic city at sunrise",
+  "seconds": "8"
+}'
+
+# Step 2: Poll status
+curl --location 'http://localhost:4000/v1/videos/{video_id}' \
+--header 'x-litellm-api-key: sk-1234'
+
+# Step 3: Download video
+curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \
+--header 'x-litellm-api-key: sk-1234' \
+--output video.mp4
+```
+
+</TabItem>
+<TabItem value="python" label="Python SDK">
+
+```python
+import litellm
+
+litellm.api_base = "http://0.0.0.0:4000"
+litellm.api_key = "sk-1234"
+
+response = litellm.video_generation(
+    model="veo-3",
+    prompt="Aerial shot over a futuristic city at sunrise",
+)
+
+status = litellm.video_status(video_id=response.id)
+while status.status not in ["completed", "failed"]:
+    status = litellm.video_status(video_id=response.id)
+
+if status.status == "completed":
+    content = litellm.video_content(video_id=response.id)
+    with open("veo_city.mp4", "wb") as f:
+        f.write(content)
+```
+
+</TabItem>
+</Tabs>
+
+## Cost Tracking
+
+LiteLLM records the duration returned by Veo so you can apply duration-based pricing.
+
+```python
+with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
+    vertex_credentials = f.read()
+
+response = video_generation(
+    model="vertex_ai/veo-2.0-generate-001",
+    prompt="Flowers blooming in fast forward",
+    seconds="5",
+    vertex_project="your-gcp-project-id",
+    vertex_location="us-central1",
+    vertex_credentials=vertex_credentials,
+)
+
+print(response.usage)  # {"duration_seconds": 5.0}
+```
+
+## Troubleshooting
+
+- **`vertex_project is required`**: set `VERTEXAI_PROJECT` env var or pass `vertex_project` in the request.
+- **`Permission denied`**: ensure the service account has the `Vertex AI User` role and the correct region enabled.
+- **Video stuck in `processing`**: Veo operations are long-running. Continue polling every 10–15 seconds up to ~10 minutes.
+
+## See Also
+
+- [OpenAI Video Generation](../openai/videos.md)
+- [Azure Video Generation](../azure/videos.md)
+- [Gemini Video Generation](../gemini/videos.md)
+- [Video Generation API Reference](/docs/videos)
+
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index b5a27770ed6..fbdbe6ea7f3 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -399,6 +399,8 @@ router_settings:
 | AZURE_COMPUTER_USE_INPUT_COST_PER_1K_TOKENS | Input cost per 1K tokens for Azure Computer Use service
 | AZURE_COMPUTER_USE_OUTPUT_COST_PER_1K_TOKENS | Output cost per 1K tokens for Azure Computer Use service
 | AZURE_DEFAULT_RESPONSES_API_VERSION | Version of the Azure Default Responses API being used. Default is "preview"
+| AZURE_DOCUMENT_INTELLIGENCE_API_VERSION | API version for Azure Document Intelligence service
+| AZURE_DOCUMENT_INTELLIGENCE_DEFAULT_DPI | Default DPI (dots per inch) setting for Azure Document Intelligence service
 | AZURE_TENANT_ID | Tenant ID for Azure Active Directory
 | AZURE_USERNAME | Username for Azure services, use in conjunction with AZURE_PASSWORD for azure ad token with basic username/password workflow
 | AZURE_PASSWORD | Password for Azure services, use in conjunction with AZURE_USERNAME for azure ad token with basic username/password workflow
@@ -429,6 +431,12 @@ router_settings:
 | CLOUDZERO_MAX_FETCHED_DATA_RECORDS | Maximum number of data records to fetch from CloudZero
 | CLOUDZERO_TIMEZONE | Timezone for date handling (default: UTC)
 | CONFIG_FILE_PATH | File path for configuration file
+| CYBERARK_ACCOUNT | CyberArk account name for secret management
+| CYBERARK_API_BASE | Base URL for CyberArk API
+| CYBERARK_API_KEY | API key for CyberArk secret management service
+| CYBERARK_CLIENT_CERT | Path to client certificate for CyberArk authentication
+| CYBERARK_CLIENT_KEY | Path to client key for CyberArk authentication
+| CYBERARK_USERNAME | Username for CyberArk authentication
 | CONFIDENT_API_KEY | API key for DeepEval integration
 | CUSTOM_TIKTOKEN_CACHE_DIR | Custom directory for Tiktoken cache
 | CONFIDENT_API_KEY | API key for Confident AI (Deepeval) Logging service
@@ -452,6 +460,8 @@ router_settings:
 | DD_BASE_URL | Base URL for Datadog integration
 | DATADOG_BASE_URL | (Alternative to DD_BASE_URL) Base URL for Datadog integration
 | _DATADOG_BASE_URL | (Alternative to DD_BASE_URL) Base URL for Datadog integration
+| DD_AGENT_HOST | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API
+| DD_AGENT_PORT | Port of DataDog agent for log intake. Default is 10518
 | DD_API_KEY | API key for Datadog integration
 | DD_SITE | Site URL for Datadog (e.g., datadoghq.com)
 | DD_SOURCE | Source identifier for Datadog logs
@@ -470,6 +480,7 @@ router_settings:
 | DEFAULT_FAILURE_THRESHOLD_PERCENT | Threshold percentage of failures to cool down a deployment. Default is 0.5 (50%)
 | DEFAULT_FLUSH_INTERVAL_SECONDS | Default interval in seconds for flushing operations. Default is 5
 | DEFAULT_HEALTH_CHECK_INTERVAL | Default interval in seconds for health checks. Default is 300 (5 minutes)
+| DEFAULT_HEALTH_CHECK_PROMPT | Default prompt used during health checks for non-image models. Default is "test from litellm"
 | DEFAULT_IMAGE_HEIGHT | Default height for images. Default is 300
 | DEFAULT_IMAGE_TOKEN_COUNT | Default token count for images. Default is 250
 | DEFAULT_IMAGE_WIDTH | Default width for images. Default is 300
@@ -496,6 +507,7 @@ router_settings:
 | DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH | Default minimal reasoning effort thinking budget for Gemini 2.5 Flash. Default is 512
 | DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE | Default minimal reasoning effort thinking budget for Gemini 2.5 Flash Lite. Default is 512
 | DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO | Default minimal reasoning effort thinking budget for Gemini 2.5 Pro. Default is 512
+| DEFAULT_REDIS_MAJOR_VERSION | Default Redis major version to assume when version cannot be determined. Default is 7
 | DEFAULT_REDIS_SYNC_INTERVAL | Default Redis synchronization interval in seconds. Default is 1
 | DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND | Default price per second for Replicate GPU. Default is 0.001400
 | DEFAULT_REPLICATE_POLLING_DELAY_SECONDS | Default delay in seconds for Replicate polling. Default is 1
@@ -507,6 +519,7 @@ router_settings:
 | DEFAULT_SLACK_ALERTING_THRESHOLD | Default threshold for Slack alerting. Default is 300
 | DEFAULT_SOFT_BUDGET | Default soft budget for LiteLLM proxy keys. Default is 50.0
 | DEFAULT_TRIM_RATIO | Default ratio of tokens to trim from prompt end. Default is 0.75
+| DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS | Default duration for video generation in seconds in google. Default is 8
 | DIRECT_URL | Direct URL for service endpoint
 | DISABLE_ADMIN_UI | Toggle to disable the admin UI
 | DISABLE_AIOHTTP_TRANSPORT | Flag to disable aiohttp transport. When this is set to True, litellm will use httpx instead of aiohttp. **Default is False**
@@ -581,9 +594,14 @@ router_settings:
 | HEROKU_API_KEY | API key for Heroku services
 | HF_API_BASE | Base URL for Hugging Face API
 | HCP_VAULT_ADDR | Address for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
+| HCP_VAULT_APPROLE_MOUNT_PATH | Mount path for AppRole authentication in [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault). Default is "approle"
+| HCP_VAULT_APPROLE_ROLE_ID | Role ID for AppRole authentication in [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
+| HCP_VAULT_APPROLE_SECRET_ID | Secret ID for AppRole authentication in [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
 | HCP_VAULT_CLIENT_CERT | Path to client certificate for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
 | HCP_VAULT_CLIENT_KEY | Path to client key for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
+| HCP_VAULT_MOUNT_NAME | Mount name for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
 | HCP_VAULT_NAMESPACE | Namespace for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
+| HCP_VAULT_PATH_PREFIX | Path prefix for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
 | HCP_VAULT_TOKEN | Token for [Hashicorp Vault Secret Manager](../secret.md#hashicorp-vault)
 | HCP_VAULT_CERT_ROLE | Role for [Hashicorp Vault Secret Manager Auth](../secret.md#hashicorp-vault)
 | HELICONE_API_KEY | API key for Helicone service
@@ -650,6 +668,7 @@ router_settings:
 | LITELLM_OTEL_INTEGRATION_ENABLE_METRICS | Optionally enable emantic metrics for OTEL
 | LITELLM_MASTER_KEY | Master key for proxy authentication
 | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development)
+| LITELLM_NON_ROOT | Flag to run LiteLLM in non-root mode for enhanced security in Docker containers
 | LITELLM_RATE_LIMIT_WINDOW_SIZE | Rate limit window size for LiteLLM. Default is 60
 | LITELLM_SALT_KEY | Salt key for encryption in LiteLLM
 | LITELLM_SSL_CIPHERS | SSL/TLS cipher configuration for faster handshakes. Controls cipher suite preferences for OpenSSL connections.
diff --git a/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md b/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md
new file mode 100644
index 00000000000..29183c693a4
--- /dev/null
+++ b/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md
@@ -0,0 +1,455 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Image from '@theme/IdealImage';
+
+
+# LiteLLM Content Filter
+
+**Built-in guardrail** for detecting and filtering sensitive information using regex patterns and keyword matching. No external dependencies required.
+
+## Overview
+
+| Property | Details |
+|----------|---------|
+| Description | On-device guardrail for detecting and filtering sensitive information using regex patterns and keyword matching. Built into LiteLLM with no external dependencies. |
+| Guardrail Name | `litellm_content_filter` |
+| Detection Methods | Prebuilt regex patterns, custom regex, keyword matching |
+| Actions | `BLOCK` (reject request), `MASK` (redact content) |
+| Supported Modes | `pre_call`, `post_call`, `during_call` (streaming) |
+| Performance | Fast - runs locally, no external API calls |
+
+## Quick Start
+
+## LiteLLM UI
+
+### Step 1: Select LiteLLM Content Filter
+
+Click "Add New Guardrail" and select "LiteLLM Content Filter" as your guardrail provider.
+
+<Image img={require('../../../img/create_guard.gif')} alt="Select LiteLLM Content Filter" />
+
+### Step 2: Configure Pattern Detection
+
+Select the prebuilt entities you want to block or mask. In this example, we select "Email" to detect and block email addresses.
+
+If you need to block a custom entity, you can add a custom regex pattern by clicking "Add custom regex".
+
+<Image img={require('../../../img/add_Guard2.gif')} alt="Select prebuilt entities or add custom regex" />
+
+### Step 3: Add Blocked Keywords
+
+Enter specific keywords you want to block. This is useful if you have policies to block certain words or phrases.
+
+<Image img={require('../../../img/create_guard3.gif')} alt="Add blocked keywords" />
+
+### Step 4: Test Your Guardrail
+
+After creating the guardrail, navigate to "Test Playground" to test it. Select the guardrail you just created.
+
+Test examples:
+- **Blocked keyword test**: Entering "hi blue" will trigger the block since we set "blue" as a blocked keyword
+- **Pattern detection test**: Entering "Hi ishaan@berri.ai" will trigger the email pattern detector
+
+<Image img={require('../../../img/add_guard5.gif')} alt="Test guardrail in playground" />
+
+## LiteLLM Config.yaml Setup
+
+### Step 1: Define Guardrails in config.yaml
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: openai/gpt-3.5-turbo
+      api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+  - guardrail_name: "content-filter-pre"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      
+      # Prebuilt patterns for common PII
+      patterns:
+        - pattern_type: "prebuilt"
+          pattern_name: "us_ssn"
+          action: "BLOCK"
+        
+        - pattern_type: "prebuilt"
+          pattern_name: "email"
+          action: "MASK"
+      
+      # Custom blocked keywords
+      blocked_words:
+        - keyword: "confidential"
+          action: "BLOCK"
+          description: "Sensitive internal information"
+```
+
+### Step 2: Start LiteLLM Gateway
+
+```shell
+litellm --config config.yaml
+```
+
+### Step 3: Test Request
+
+<Tabs>
+<TabItem label="SSN Blocked" value="ssn-blocked">
+
+```shell
+curl -i http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+      {"role": "user", "content": "My SSN is 123-45-6789"}
+    ],
+    "guardrails": ["content-filter-pre"]
+  }'
+```
+
+**Response: HTTP 400 Error**
+```json
+{
+  "error": {
+    "message": {
+      "error": "Content blocked: us_ssn pattern detected",
+      "pattern": "us_ssn"
+    },
+    "code": "400"
+  }
+}
+```
+
+</TabItem>
+
+<TabItem label="Email Masked" value="email-masked">
+
+```shell
+curl -i http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+      {"role": "user", "content": "Contact me at john@example.com"}
+    ],
+    "guardrails": ["content-filter-pre"]
+  }'
+```
+
+The request is sent to the LLM with the email masked:
+```
+Contact me at [EMAIL_REDACTED]
+```
+
+</TabItem>
+</Tabs>
+
+## Configuration
+
+### Supported Modes
+
+- **`pre_call`** - Run before LLM call, filters input messages
+- **`post_call`** - Run after LLM call, filters output responses
+- **`during_call`** - Run during streaming, filters each chunk in real-time
+
+### Actions
+
+- **`BLOCK`** - Reject the request with HTTP 400 error
+- **`MASK`** - Replace sensitive content with redaction tags (e.g., `[EMAIL_REDACTED]`)
+
+## Prebuilt Patterns
+
+### Available Patterns
+
+| Pattern Name | Description | Example |
+|-------------|-------------|---------|
+| `us_ssn` | US Social Security Numbers | `123-45-6789` |
+| `email` | Email addresses | `user@example.com` |
+| `phone` | Phone numbers | `+1-555-123-4567` |
+| `visa` | Visa credit cards | `4532-1234-5678-9010` |
+| `mastercard` | Mastercard credit cards | `5425-2334-3010-9903` |
+| `amex` | American Express cards | `3782-822463-10005` |
+| `aws_access_key` | AWS access keys | `AKIAIOSFODNN7EXAMPLE` |
+| `aws_secret_key` | AWS secret keys | `wJalrXUtnFEMI/K7MDENG/bPxRfi...` |
+| `github_token` | GitHub tokens | `ghp_16C7e42F292c6912E7710c838347Ae178B4a` |
+
+### Using Prebuilt Patterns
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+  - guardrail_name: "pii-filter"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      patterns:
+        - pattern_type: "prebuilt"
+          pattern_name: "us_ssn"
+          action: "BLOCK"
+        
+        - pattern_type: "prebuilt"
+          pattern_name: "email"
+          action: "MASK"
+        
+        - pattern_type: "prebuilt"
+          pattern_name: "aws_access_key"
+          action: "BLOCK"
+```
+
+## Custom Regex Patterns
+
+Define your own regex patterns for domain-specific sensitive data:
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+  - guardrail_name: "custom-patterns"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      patterns:
+        # Custom employee ID format
+        - pattern_type: "regex"
+          pattern: '\b[A-Z]{3}-\d{4}\b'
+          name: "employee_id"
+          action: "MASK"
+        
+        # Custom project code format
+        - pattern_type: "regex"
+          pattern: 'PROJECT-\d{6}'
+          name: "project_code"
+          action: "BLOCK"
+```
+
+## Keyword Filtering
+
+Block or mask specific keywords:
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+  - guardrail_name: "keyword-filter"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      blocked_words:
+        - keyword: "confidential"
+          action: "BLOCK"
+          description: "Internal confidential information"
+        
+        - keyword: "proprietary"
+          action: "MASK"
+          description: "Proprietary company data"
+        
+        - keyword: "secret_project"
+          action: "BLOCK"
+```
+
+### Loading Keywords from File
+
+For large keyword lists, use a YAML file:
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+  - guardrail_name: "keyword-file-filter"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      blocked_words_file: "/path/to/sensitive_keywords.yaml"
+```
+
+```yaml showLineNumbers title="sensitive_keywords.yaml"
+blocked_words:
+  - keyword: "project_apollo"
+    action: "BLOCK"
+    description: "Confidential project codename"
+  
+  - keyword: "internal_api"
+    action: "MASK"
+    description: "Internal API references"
+  
+  - keyword: "customer_database"
+    action: "BLOCK"
+    description: "Protected database name"
+```
+
+## Streaming Support
+
+Content filter works with streaming responses by checking each chunk:
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+  - guardrail_name: "streaming-filter"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "during_call"  # Check each streaming chunk
+      patterns:
+        - pattern_type: "prebuilt"
+          pattern_name: "email"
+          action: "MASK"
+```
+
+```python
+import openai
+
+client = openai.OpenAI(
+    api_key="sk-1234",
+    base_url="http://localhost:4000"
+)
+
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    messages=[{"role": "user", "content": "Tell me about yourself"}],
+    stream=True,
+    extra_body={"guardrails": ["streaming-filter"]}
+)
+
+for chunk in response:
+    print(chunk.choices[0].delta.content)
+    # Emails automatically masked in real-time
+```
+
+## Customizing Redaction Tags
+
+When using the `MASK` action, sensitive content is replaced with redaction tags. You can customize how these tags appear.
+
+### Default Behavior
+
+**Patterns:** Each pattern type gets its own tag based on the pattern name
+```
+Input:  "My email is john@example.com and SSN is 123-45-6789"
+Output: "My email is [EMAIL_REDACTED] and SSN is [US_SSN_REDACTED]"
+```
+
+**Keywords:** All keywords use the same generic tag
+```
+Input:  "This is confidential and proprietary information"
+Output: "This is [KEYWORD_REDACTED] and [KEYWORD_REDACTED] information"
+```
+
+### Customizing Tags
+
+Use `pattern_redaction_format` and `keyword_redaction_tag` to change the redaction format:
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+  - guardrail_name: "custom-redaction"
+    litellm_params:
+      guardrail: litellm_content_filter
+      mode: "pre_call"
+      pattern_redaction_format: "***{pattern_name}***"  # Use {pattern_name} placeholder
+      keyword_redaction_tag: "***REDACTED***"
+      patterns:
+        - pattern_type: "prebuilt"
+          pattern_name: "email"
+          action: "MASK"
+        - pattern_type: "prebuilt"
+          pattern_name: "us_ssn"
+          action: "MASK"
+      blocked_words:
+        - keyword: "confidential"
+          action: "MASK"
+```
+
+**Output:**
+```
+Input:  "Email john@example.com, SSN 123-45-6789, confidential data"
+Output: "Email ***EMAIL***, SSN ***US_SSN***, ***REDACTED*** data"
+```
+
+**Key Points:**
+- `pattern_redaction_format` must include `{pattern_name}` placeholder
+- Pattern names are automatically uppercased (e.g., `email` → `EMAIL`)
+- `keyword_redaction_tag` is a fixed string (no placeholders)
+
+## Use Cases
+
+### 1. PII Protection
+Block or mask personally identifiable information before sending to LLMs:
+
+```yaml
+patterns:
+  - pattern_type: "prebuilt"
+    pattern_name: "us_ssn"
+    action: "BLOCK"
+  - pattern_type: "prebuilt"
+    pattern_name: "email"
+    action: "MASK"
+```
+
+### 2. Credential Detection
+Prevent API keys and secrets from being exposed:
+
+```yaml
+patterns:
+  - pattern_type: "prebuilt"
+    pattern_name: "aws_access_key"
+    action: "BLOCK"
+  - pattern_type: "prebuilt"
+    pattern_name: "github_token"
+    action: "BLOCK"
+```
+
+### 3. Sensitive Internal Data Protection
+Block or mask references to confidential internal projects, codenames, or proprietary information:
+
+```yaml
+blocked_words:
+  - keyword: "project_titan"
+    action: "BLOCK"
+    description: "Confidential project codename"
+  - keyword: "internal_api"
+    action: "MASK"
+    description: "Internal system references"
+```
+
+For large lists of sensitive terms, use a file:
+```yaml
+blocked_words_file: "/path/to/sensitive_terms.yaml"
+```
+
+### 4. Compliance
+Ensure regulatory compliance by filtering sensitive data types:
+
+```yaml
+patterns:
+  - pattern_type: "prebuilt"
+    pattern_name: "visa"
+    action: "BLOCK"
+  - pattern_type: "prebuilt"
+    pattern_name: "us_ssn"
+    action: "BLOCK"
+```
+
+## Troubleshooting
+
+### Pattern Not Matching
+
+**Issue:** Regex pattern isn't detecting expected content
+
+**Solution:** Test your regex pattern:
+```python
+import re
+pattern = r'\b[A-Z]{3}-\d{4}\b'
+test_text = "Employee ID: ABC-1234"
+print(re.search(pattern, test_text))  # Should match
+```
+
+### Multiple Pattern Matches
+
+**Issue:** Text contains multiple sensitive patterns
+
+**Solution:** First matching pattern/keyword is processed. Order patterns by priority:
+```yaml
+patterns:
+  # Most critical first
+  - pattern_type: "prebuilt"
+    pattern_name: "us_ssn"
+    action: "BLOCK"
+  # Less critical
+  - pattern_type: "prebuilt"
+    pattern_name: "email"
+    action: "MASK"
+```
+
diff --git a/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md b/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md
index e1d6ddf5928..edf2a05d24c 100644
--- a/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md
+++ b/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md
@@ -4,12 +4,12 @@ import TabItem from '@theme/TabItem';
 
 # PANW Prisma AIRS
 
-LiteLLM supports PANW Prisma AIRS (AI Runtime Security) guardrails via the [Prisma AIRS Scan API](https://pan.dev/prisma-airs/api/airuntimesecurity/scan-sync-request/). This integration provides **Security-as-Code** for AI applications using Palo Alto Networks' AI security platform.
+LiteLLM supports PANW Prisma AIRS (AI Runtime Security) guardrails via the [Prisma AIRS Scan API](https://pan.dev/prisma-airs/api/airuntimesecurity/airuntimesecurityapi//). This integration provides **Security-as-Code** for AI applications using Palo Alto Networks' AI security platform.
 
 ## Features
 
 - ✅ **Real-time prompt injection detection**
-- ✅ **Malicious content filtering** 
+- ✅ **Malicious URL detection** 
 - ✅ **Data loss prevention (DLP)**
 - ✅ **Sensitive content masking** - Automatically mask PII, credit cards, SSNs instead of blocking
 - ✅ **Comprehensive threat detection** for AI models and datasets
@@ -17,6 +17,7 @@ LiteLLM supports PANW Prisma AIRS (AI Runtime Security) guardrails via the [Pris
 - ✅ **Synchronous scanning** with immediate response
 - ✅ **Configurable security profiles**
 - ✅ **Streaming support** - Real-time masking for streaming responses
+- ✅ **Multi-turn conversation tracking** - Automatic session grouping in Prisma AIRS SCM logs
 - ✅ **Fail-closed security** - Blocks requests if PANW API is unavailable (maximum security)
 
 ## Quick Start
@@ -237,6 +238,74 @@ You can override guardrail settings on a per-request basis using the `metadata`
 - **Note:** If your API key is not linked to a profile, you must provide `profile_name` or `profile_id`
 :::
 
+## Multi-Turn Conversation Tracking
+
+PANW Prisma AIRS automatically tracks multi-turn conversations using LiteLLM's `litellm_trace_id`. This enables you to:
+
+- **Group related requests** - All requests in a conversation share the same AI Session ID in Prisma AIRS SCM logs
+- **Track conversation context** - See the full history of prompts and responses for a user session
+- **Analyze attack patterns** - Identify sophisticated multi-turn attacks across conversation history
+
+### How It Works
+
+LiteLLM automatically generates a unique `litellm_trace_id` for each conversation session. The PANW guardrail uses this as the PANW transaction ID (which maps to "AI Session ID" in Strata Cloud Manager):
+
+```
+Conversation Session: litellm_trace_id = "abc-123-def-456"
+
+Turn 1 (User):    "What's the capital of France?"
+  → Scan ID: scan_001 | Prisma AIRS AI Session ID: abc-123-def-456
+
+Turn 2 (Assistant): "Paris is the capital of France."
+  → Scan ID: scan_002 | Prisma AIRS AI Session ID: abc-123-def-456
+
+Turn 3 (User):    "What's the population?"
+  → Scan ID: scan_003 | Prisma AIRS AI Session ID: abc-123-def-456
+
+Turn 4 (Assistant): "Paris has approximately 2.1 million residents."
+  → Scan ID: scan_004 | Prisma AIRS AI Session ID: abc-123-def-456
+```
+
+All scans appear under the same AI Session ID in Prisma AIRS logs, making it easy to:
+- Review complete conversation history (all 4 turns grouped together)
+- Identify patterns across multiple turns
+- Correlate security events within a session
+- Track the flow of user prompts and AI responses
+
+### Session Tracking
+
+LiteLLM automatically generates a unique `litellm_trace_id` for each request, which the PANW guardrail uses as the AI Session ID in Strata Cloud Manager. All prompt and response scans for a request are automatically grouped under the same session.
+
+#### Custom Session IDs (Per-App Tracking)
+
+You can provide your own `litellm_trace_id` to track sessions on a per-app or per-conversation basis:
+
+```bash
+curl -X POST http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [{"role": "user", "content": "capital of France"}],
+    "litellm_trace_id": "my-app-session-123",             # Custom AI Session ID
+    "metadata": {
+      "profile_name": "dev-allow-all-profile",            # Override security profile
+      "user_ip": "192.168.1.1",                           # Track user IP
+      "app_name": "eng"                                   # Custom app identifier
+    },
+    "guardrails": ["panw-prisma-airs-pre-guard", "panw-prisma-airs-post-guard"]
+  }'
+```
+
+**Result in PANW SCM:**
+- AI Session ID: `my-app-session-123`
+- All prompt and response scans will be grouped under this custom session ID
+- Perfect for tracking multi-turn conversations or per-application sessions
+
+:::tip Viewing Sessions in Prisma AIRS SCM Logs
+In Strata Cloud Manager, navigate to **AI Runtime > Sessions** to view all AI Session IDs and their associated scans. Click on a session to see the complete conversation history with security analysis.
+:::
+
 ## Environment Variables
 
 ```bash
diff --git a/docs/my-website/docs/proxy/guardrails/test_playground.md b/docs/my-website/docs/proxy/guardrails/test_playground.md
new file mode 100644
index 00000000000..832a912e114
--- /dev/null
+++ b/docs/my-website/docs/proxy/guardrails/test_playground.md
@@ -0,0 +1,46 @@
+import Image from '@theme/IdealImage';
+
+# Guardrail Testing Playground
+
+Test and compare multiple guardrails in real-time with an interactive playground interface.
+
+<Image img={require('../../../img/guardrail_playground.png')} alt="Guardrail Test Playground" />
+
+## How to Use the Guardrail Testing Playground
+
+The Guardrail Testing Playground allows you to quickly test and compare the behavior of different guardrails with sample inputs.
+
+### Steps to Test Guardrails
+
+1. **Navigate to the Guardrails Section**
+   - Open the LiteLLM Admin UI
+   - Go to the **Guardrails** section
+
+2. **Open Test Playground**
+   - Click on the **Test Playground** tab at the top of the page
+
+3. **Select Guardrails to Test**
+   - Check the guardrails you want to compare
+   - You can select multiple guardrails to see how they each respond to the same input
+
+4. **Enter Your Input**
+   - Type or paste your test input in the text area
+   - This could be a prompt, message, or any text you want to validate against the guardrails
+
+5. **Run the Test**
+   - Click the **Test guardrails** button (or press Enter)
+
+6. **View Results**
+   - See the output from each selected guardrail
+   - Compare how different guardrails handle the same input
+   - Results will show whether the input passed or was blocked by each guardrail
+
+## Use Cases
+
+This is ideal for **Security Teams** & **LiteLLM Admins** evaluating guardrail solutions.
+
+This brings the following benefits for LiteLLM users:
+
+- **Compare guardrail responses**: test the same prompt across multiple providers (Lakera, Noma AI, Bedrock Guardrails, etc.) simultaneously.
+
+- **Validate configurations**: verify your guardrails catch the threats you care about before production deployment.
diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md
index 7df7685f335..6f98265e40a 100644
--- a/docs/my-website/docs/proxy/health.md
+++ b/docs/my-website/docs/proxy/health.md
@@ -106,6 +106,13 @@ model_list:
       mode: image_generation # 👈 ADD THIS
 ```
 
+#### Custom Health Check Prompt
+
+By default, health checks use the prompt `"test from litellm"`. You can customize this prompt globally by setting an environment variable, or per-model via config:
+
+```bash
+DEFAULT_HEALTH_CHECK_PROMPT="this is a test prompt"
+```
 
 ### Text Completion Models 
 
diff --git a/docs/my-website/docs/secret_managers/hashicorp_vault.md b/docs/my-website/docs/secret_managers/hashicorp_vault.md
index 4d0ef05a326..9e536270988 100644
--- a/docs/my-website/docs/secret_managers/hashicorp_vault.md
+++ b/docs/my-website/docs/secret_managers/hashicorp_vault.md
@@ -16,21 +16,28 @@ import Image from '@theme/IdealImage';
 |---------|----------|-------------|
 | Reading Secrets | ✅ | Read secrets e.g `OPENAI_API_KEY` |
 | Writing Secrets | ✅ | Store secrets e.g `Virtual Keys` |
+| Authentication Methods to Hashicorp Vault | ✅ | AppRole, TLS Certificate, Token |
 
 Read secrets from [Hashicorp Vault](https://developer.hashicorp.com/vault/docs/secrets/kv/kv-v2)
 
 **Step 1.** Add Hashicorp Vault details in your environment
 
-LiteLLM supports two methods of authentication:
+LiteLLM supports three methods of authentication:
 
-1. TLS cert authentication - `HCP_VAULT_CLIENT_CERT` and `HCP_VAULT_CLIENT_KEY`
-2. Token authentication - `HCP_VAULT_TOKEN`
+1. AppRole authentication (recommended) - `HCP_VAULT_APPROLE_ROLE_ID` and `HCP_VAULT_APPROLE_SECRET_ID`
+2. TLS cert authentication - `HCP_VAULT_CLIENT_CERT` and `HCP_VAULT_CLIENT_KEY`
+3. Token authentication - `HCP_VAULT_TOKEN`
 
 ```bash
 HCP_VAULT_ADDR="https://test-cluster-public-vault-0f98180c.e98296b2.z1.hashicorp.cloud:8200"
 HCP_VAULT_NAMESPACE="admin"
 
-# Authentication via TLS cert
+# Authentication via AppRole (recommended)
+HCP_VAULT_APPROLE_ROLE_ID="your-role-id"
+HCP_VAULT_APPROLE_SECRET_ID="your-secret-id"
+HCP_VAULT_APPROLE_MOUNT_PATH="approle" # OPTIONAL. defaults to "approle"
+
+# OR - Authentication via TLS cert
 HCP_VAULT_CLIENT_CERT="path/to/client.pem"
 HCP_VAULT_CLIENT_KEY="path/to/client.key"
 
@@ -64,6 +71,80 @@ $ litellm --config /path/to/config.yaml
 [Quick Test Proxy](../proxy/user_keys)
 
 
+## Authentication Methods
+
+LiteLLM supports three authentication methods for Hashicorp Vault, with the following priority:
+
+1. **AppRole** - Recommended for production applications
+2. **TLS Certificate** - For certificate-based authentication
+3. **Token** - Direct token authentication
+
+### 1. AppRole Authentication
+
+To set up AppRole authentication:
+
+1. Enable AppRole auth in Vault:
+```bash
+vault auth enable approle
+```
+
+2. Create a policy and role for LiteLLM:
+```bash
+# Create a policy file (litellm-policy.hcl)
+path "secret/data/*" {
+  capabilities = ["create", "read", "update", "delete", "list"]
+}
+
+# Apply the policy
+vault policy write litellm-policy litellm-policy.hcl
+
+# Create an AppRole
+vault write auth/approle/role/litellm \
+    token_policies="litellm-policy" \
+    token_ttl=32d \
+    token_max_ttl=32d
+```
+
+3. Get your Role ID and Secret ID:
+```bash
+# Get Role ID
+vault read auth/approle/role/litellm/role-id
+
+# Generate Secret ID
+vault write -f auth/approle/role/litellm/secret-id
+```
+
+4. Set the environment variables:
+```bash
+export HCP_VAULT_APPROLE_ROLE_ID="your-role-id"
+export HCP_VAULT_APPROLE_SECRET_ID="your-secret-id"
+```
+
+### 2. TLS Certificate Authentication
+
+TLS Certificate authentication uses client certificates for mutual TLS authentication with Vault.
+
+**Environment Variables:**
+```bash
+export HCP_VAULT_CLIENT_CERT="path/to/client.pem"
+export HCP_VAULT_CLIENT_KEY="path/to/client.key"
+export HCP_VAULT_CERT_ROLE="your-cert-role"  # Optional
+```
+
+**How it works:**
+- LiteLLM uses the client certificate and key for mutual TLS authentication
+- Vault validates the certificate and issues a temporary token
+- The token is cached for the duration of its lease
+
+### 3. Token Authentication
+
+Direct token authentication uses a static Vault token.
+
+**Environment Variables:**
+```bash
+export HCP_VAULT_TOKEN="hvs.CAESIG52gL6ljBSdmq*****"
+```
+
 ## How it works
 
 **Reading Secrets**
diff --git a/docs/my-website/docs/videos.md b/docs/my-website/docs/videos.md
index 96ff4c8190a..cc9f1bc9cea 100644
--- a/docs/my-website/docs/videos.md
+++ b/docs/my-website/docs/videos.md
@@ -9,7 +9,7 @@ Fallbacks | ✅ (Between supported models) |
 | Guardrails Support | ✅ Content moderation and safety checks |
 | Proxy Server Support | ✅ Full proxy integration with virtual keys |
 | Spend Management | ✅ Budget tracking and rate limiting |
-| Supported Providers | `openai`, `azure` |
+| Supported Providers | `openai`, `azure`, `gemini`, `vertex_ai` |
 
 :::tip
 
@@ -41,8 +41,7 @@ print(f"Initial Status: {response.status}")
 # Check status until video is ready
 while True:
     status_response = video_status(
-        video_id=response.id,
-        custom_llm_provider="openai"
+        video_id=response.id
     )
     
     print(f"Current Status: {status_response.status}")
@@ -57,8 +56,7 @@ while True:
 
 # Download video content when ready
 video_bytes = video_content(
-    video_id=response.id,
-    custom_llm_provider="openai"
+    video_id=response.id
 )
 
 # Save to file
@@ -88,8 +86,7 @@ async def test_async_video():
     # Check status until video is ready
     while True:
         status_response = await avideo_status(
-            video_id=response.id,
-            custom_llm_provider="openai"
+            video_id=response.id
         )
         
         print(f"Current Status: {status_response.status}")
@@ -104,8 +101,7 @@ async def test_async_video():
     
     # Download video content when ready
     video_bytes = await avideo_content(
-        video_id=response.id,
-        custom_llm_provider="openai"
+        video_id=response.id
     )
     
     # Save to file
@@ -120,21 +116,27 @@ asyncio.run(test_async_video())
 ```python
 from litellm import video_status
 
-# Check the status of a video generation
 status_response = video_status(
-    video_id="video_1234567890",
-    custom_llm_provider="openai"
+    video_id="video_1234567890"
 )
 
 print(f"Video Status: {status_response.status}")
 print(f"Created At: {status_response.created_at}")
 print(f"Model: {status_response.model}")
+```
 
-# Possible status values:
-# - "queued": Video is in the queue
-# - "processing": Video is being generated
-# - "completed": Video is ready for download
-# - "failed": Video generation failed
+### List Videos
+
+For listing videos, you need to specify the provider since there's no video_id to decode from:
+
+```python
+from litellm import video_list
+
+# List videos from OpenAI
+videos = video_list(custom_llm_provider="openai")
+
+for video in videos:
+    print(f"Video ID: {video['id']}")
 ```
 
 ### Video Generation with Reference Image
@@ -207,7 +209,7 @@ print(f"Video ID: {response.id}")
 
 LiteLLM provides OpenAI API compatible video endpoints for complete video generation workflow:
 
-- `/videos/generations` - Generate new videos
+- `/videos` - Generate new videos
 - `/videos/remix` - Edit existing videos with reference images  
 - `/videos/status` - Check video generation status
 - `/videos/retrieval` - Download completed videos
@@ -227,7 +229,6 @@ model_list:
       model: azure/sora-2
       api_key: os.environ/AZURE_OPENAI_API_KEY
       api_base: os.environ/AZURE_OPENAI_API_BASE
-      api_version: "2024-02-15-preview"
 ```
 
 Start litellm
@@ -253,31 +254,14 @@ curl --location 'http://localhost:4000/v1/videos' \
 Test video status request
 
 ```bash
-# Using custom-llm-provider header
-curl --location 'http://localhost:4000/v1/videos/video_id' \
---header 'Accept: application/json' \
---header 'x-litellm-api-key: sk-1234' \
---header 'custom-llm-provider: azure'
-
-# Or using query parameter
-curl --location 'http://localhost:4000/v1/videos/video_id?custom_llm_provider=azure' \
---header 'Accept: application/json' \
+curl --location 'http://localhost:4000/v1/videos/{video_id}' \
 --header 'x-litellm-api-key: sk-1234'
 ```
 
 Test video retrieval request
 
 ```bash
-# Using custom-llm-provider header
-curl --location 'http://localhost:4000/v1/videos/video_id/content' \
---header 'Accept: application/json' \
---header 'x-litellm-api-key: sk-1234' \
---header 'custom-llm-provider: openai' \
---output video.mp4
-
-# Or using query parameter
-curl --location 'http://localhost:4000/v1/videos/video_id/content?custom_llm_provider=openai' \
---header 'Accept: application/json' \
+curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \
 --header 'x-litellm-api-key: sk-1234' \
 --output video.mp4
 ```
@@ -285,27 +269,27 @@ curl --location 'http://localhost:4000/v1/videos/video_id/content?custom_llm_pro
 Test video remix request
 
 ```bash
-# Using custom_llm_provider in request body
-curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix' \
---header 'Accept: application/json' \
+curl --location --request POST 'http://localhost:4000/v1/videos/{video_id}/remix' \
 --header 'Content-Type: application/json' \
 --header 'x-litellm-api-key: sk-1234' \
---data '{
-    "prompt": "New remix instructions",
-    "custom_llm_provider": "azure"
-}'
-
-# Or using custom-llm-provider header
-curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix' \
---header 'Accept: application/json' \
---header 'Content-Type: application/json' \
---header 'x-litellm-api-key: sk-1234' \
---header 'custom-llm-provider: azure' \
 --data '{
     "prompt": "New remix instructions"
 }'
 ```
 
+Test video list request (requires custom_llm_provider)
+
+```bash
+# Note: video_list requires custom_llm_provider since there's no video_id to decode from
+curl --location 'http://localhost:4000/v1/videos?custom_llm_provider=openai' \
+--header 'x-litellm-api-key: sk-1234'
+
+# Or using header
+curl --location 'http://localhost:4000/v1/videos' \
+--header 'x-litellm-api-key: sk-1234' \
+--header 'custom-llm-provider: azure'
+```
+
 Test Azure video generation request
 
 ```bash
@@ -618,4 +602,6 @@ The response follows OpenAI's video generation format with the following structu
 | Provider    | Link to Usage      |
 |-------------|--------------------|
 | OpenAI      |   [Usage](providers/openai/videos)  |
-| Azure       |   [Usage](providers/azure/videos)   |
\ No newline at end of file
+| Azure       |   [Usage](providers/azure/videos)   |
+| Gemini       |   [Usage](providers/gemini/videos)   |
+| Vertex AI   |   [Usage](providers/vertex_ai/videos) |
diff --git a/docs/my-website/img/add_Guard2.gif b/docs/my-website/img/add_Guard2.gif
new file mode 100644
index 00000000000..9df8f771815
Binary files /dev/null and b/docs/my-website/img/add_Guard2.gif differ
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diff --git a/docs/my-website/img/release_notes/built_in_guard.png b/docs/my-website/img/release_notes/built_in_guard.png
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diff --git a/docs/my-website/release_notes/v1.79.1-stable/index.md b/docs/my-website/release_notes/v1.79.1-stable/index.md
index 986ea42c680..ea8cfeae740 100644
--- a/docs/my-website/release_notes/v1.79.1-stable/index.md
+++ b/docs/my-website/release_notes/v1.79.1-stable/index.md
@@ -1,5 +1,5 @@
 ---
-title: "[Preview] v1.79.1-stable - FAL AI Support"
+title: "v1.79.1-stable - Guardrail Playground"
 slug: "v1-79-1"
 date: 2025-11-01T10:00:00
 authors:
@@ -27,7 +27,7 @@ import TabItem from '@theme/TabItem';
 docker run \
 -e STORE_MODEL_IN_DB=True \
 -p 4000:4000 \
-ghcr.io/berriai/litellm:v1.80.0-stable
+ghcr.io/berriai/litellm:v1.79.1-stable
 ```
 
 </TabItem>
@@ -151,7 +151,7 @@ pip install litellm==1.80.0
 
 #### Features
 
-- **[Container API](../../docs/container_api)**
+- **[Container API](../../docs/containers)**
     - Add end-to-end OpenAI Container API support to LiteLLM SDK - [PR #16136](https://github.com/BerriAI/litellm/pull/16136)
     - Add proxy support for container APIs - [PR #16049](https://github.com/BerriAI/litellm/pull/16049)
     - Add logging support for Container API - [PR #16049](https://github.com/BerriAI/litellm/pull/16049)
diff --git a/docs/my-website/release_notes/v1.79.3-stable/index.md b/docs/my-website/release_notes/v1.79.3-stable/index.md
new file mode 100644
index 00000000000..f081fa614eb
--- /dev/null
+++ b/docs/my-website/release_notes/v1.79.3-stable/index.md
@@ -0,0 +1,444 @@
+---
+title: "[Preview] v1.79.3-stable - Built-in Guardrails on AI Gateway"
+slug: "v1-79-3"
+date: 2025-11-08T10:00:00
+authors:
+  - name: Krrish Dholakia
+    title: CEO, LiteLLM
+    url: https://www.linkedin.com/in/krish-d/
+    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+  - name: Ishaan Jaff
+    title: CTO, LiteLLM
+    url: https://www.linkedin.com/in/reffajnaahsi/
+    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+<Tabs>
+<TabItem value="docker" label="Docker">
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+ghcr.io/berriai/litellm:v1.79.3.rc.1
+```
+
+</TabItem>
+
+<TabItem value="pip" label="Pip">
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.79.3.rc.1
+```
+
+</TabItem>
+</Tabs>
+
+---
+
+## Key Highlights
+
+- **LiteLLM Custom Guardrail** - Built-in guardrail with UI configuration support
+- **Performance Improvements** - `/responses` API 19× Lower Median Latency
+- **Veo3 Video Generation (Vertex AI + Google AI Studio)** - Use OpenAI Video API to generate videos with Vertex AI and Google AI Studio Veo3 models
+
+---
+
+### Built-in Guardrails on AI Gateway
+
+<Image 
+  img={require('../../img/release_notes/built_in_guard.png')}
+  style={{width: '100%', display: 'block', margin: '2rem auto'}}
+/>
+
+<br/>
+
+This release introduces built-in guardrails for LiteLLM AI Gateway, allowing you to enforce protections without depending on an external guardrail API.
+
+- **Blocking Keywords** - Block known sensitive keywords like "litellm", "python", etc.
+- **Pattern Detection** - Block known sensitive patterns like emails, Social Security Numbers, API keys, etc.
+- **Custom Regex Patterns** - Define custom regex patterns for your specific use case.
+
+
+Get started with the built-in guardrails on AI Gateway [here](https://docs.litellm.ai/docs/proxy/guardrails/litellm_content_filter).
+
+---
+
+### Performance – `/responses` 19× Lower Median Latency
+
+This update significantly improves `/responses` latency by integrating our internal network management for connection handling, eliminating per-request setup overhead.
+
+#### Results
+
+| Metric | Before | After | Improvement |
+|--------|--------|-------|-------------|
+| Median latency | 3,600 ms | **190 ms** | **−95% (~19× faster)** |
+| p95 latency | 4,300 ms | **280 ms** | −93% |
+| p99 latency | 4,600 ms | **590 ms** | −87% |
+| Average latency | 3,571 ms | **208 ms** | −94% |
+| RPS | 231 | **1,059** | +358% |
+
+#### Test Setup
+
+| Category | Specification |
+|----------|---------------|
+| **Load Testing** | Locust: 1,000 concurrent users, 500 ramp-up |
+| **System** | 4 vCPUs, 8 GB RAM, 4 workers, 4 instances |
+| **Database** | PostgreSQL (Redis unused) |
+| **Configuration** | [config.yaml](https://gist.github.com/AlexsanderHamir/550791675fd752befcac6a9e44024652) |
+| **Load Script** | [no_cache_hits.py](https://gist.github.com/AlexsanderHamir/99d673bf74cdd81fd39f59fa9048f2e8) |
+
+---
+
+## New Models / Updated Models
+
+#### New Model Support
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| Azure | `azure/gpt-5-pro` | 272K | $15.00 | $120.00 | Responses API, reasoning, vision, PDF input |
+| Azure | `azure/gpt-image-1-mini` | - | - | - | Image generation - per pixel pricing |
+| Azure | `azure/container` | - | - | - | Container API - $0.03/session |
+| OpenAI | `openai/container` | - | - | - | Container API - $0.03/session |
+| Cohere | `cohere/embed-v4.0` | 128K | $0.12 | - | Embeddings with image input support |
+| Gemini | `gemini/gemini-live-2.5-flash-preview-native-audio-09-2025` | 1M | $0.30 | $2.00 | Native audio, vision, web search |
+| Vertex AI | `vertex_ai/minimaxai/minimax-m2-maas` | 196K | $0.30 | $1.20 | Function calling, tool choice |
+| NVIDIA | `nvidia/nemotron-nano-9b-v2` | - | - | - | Chat completions |
+
+#### OCR Models
+
+| Provider | Model | Cost Per Page | Features |
+| -------- | ----- | ------------- | -------- |
+| Azure AI | `azure_ai/doc-intelligence/prebuilt-read` | $0.0015 | Document reading |
+| Azure AI | `azure_ai/doc-intelligence/prebuilt-layout` | $0.01 | Layout analysis |
+| Azure AI | `azure_ai/doc-intelligence/prebuilt-document` | $0.01 | Document processing |
+| Vertex AI | `vertex_ai/mistral-ocr-2505` | $0.0005 | OCR processing |
+
+#### Search Models
+
+| Provider | Model | Pricing | Features |
+| -------- | ----- | ------- | -------- |
+| Firecrawl | `firecrawl/search` | Tiered: $0.00166-$0.0166/query | 10-100 results per query |
+| SearXNG | `searxng/search` | Free | Open-source metasearch |
+
+#### Features
+
+- **[Azure](../../docs/providers/azure)**
+    - Add Azure GPT-5-Pro Responses API support with reasoning capabilities - [PR #16235](https://github.com/BerriAI/litellm/pull/16235)
+    - Add gpt-image-1-mini pricing for Azure with quality tiers (low/medium/high) - [PR #16182](https://github.com/BerriAI/litellm/pull/16182)
+    - Add support for returning Azure Content Policy error information when exceptions from Azure OpenAI occur - [PR #16231](https://github.com/BerriAI/litellm/pull/16231)
+    - Fix Azure GPT-5 incorrectly routed to O-series config (temperature parameter unsupported) - [PR #16246](https://github.com/BerriAI/litellm/pull/16246)
+    - Fix Azure doesn't accept extra body param - [PR #16116](https://github.com/BerriAI/litellm/pull/16116)
+    - Fix Azure DALL-E-3 health check content policy violation by using safe default prompt - [PR #16329](https://github.com/BerriAI/litellm/pull/16329)
+
+- **[Bedrock](../../docs/providers/bedrock)**
+    - Fix empty assistant message handling in AWS Bedrock Converse API to prevent 400 Bad Request errors - [PR #15850](https://github.com/BerriAI/litellm/pull/15850)
+    - Fix: Filter AWS authentication params from Bedrock InvokeModel request body - [PR #16315](https://github.com/BerriAI/litellm/pull/16315)
+    - Fix Bedrock proxy adding name to file content, breaks when cache_control in use - [PR #16275](https://github.com/BerriAI/litellm/pull/16275)
+    - Fix global.anthropic.claude-haiku-4-5-20251001-v1:0 supports_reasoning flag and update pricing - [PR #16263](https://github.com/BerriAI/litellm/pull/16263)
+
+- **[Gemini (Google AI Studio + Vertex AI)](../../docs/providers/gemini)**
+    - Add gemini live audio model cost in model map - [PR #16183](https://github.com/BerriAI/litellm/pull/16183)
+    - Fix translation problem with Gemini parallel tool calls - [PR #16194](https://github.com/BerriAI/litellm/pull/16194)
+    - Fix: Send Gemini API key via x-goog-api-key header with custom api_base - [PR #16085](https://github.com/BerriAI/litellm/pull/16085)
+    - Fix image_config.aspect_ratio not working for gemini-2.5-flash-image - [PR #15999](https://github.com/BerriAI/litellm/pull/15999)
+    - Fix Gemini minimal reasoning env overrides disabling thoughts - [PR #16347](https://github.com/BerriAI/litellm/pull/16347)
+    - Fix cache_read_input_token_cost for gemini-2.5-flash - [PR #16354](https://github.com/BerriAI/litellm/pull/16354)
+
+- **[Anthropic](../../docs/providers/anthropic)**
+    - Fix Anthropic token counting for VertexAI - [PR #16171](https://github.com/BerriAI/litellm/pull/16171)
+    - Fix anthropic-adapter: properly translate Anthropic image format to OpenAI - [PR #16202](https://github.com/BerriAI/litellm/pull/16202)
+    - Enable automated prompt caching message format for Claude on Databricks - [PR #16200](https://github.com/BerriAI/litellm/pull/16200)
+    - Add support for Anthropic Memory Tool - [PR #16115](https://github.com/BerriAI/litellm/pull/16115)
+    - Propagate cache creation/read token costs for model info to fix Anthropic long context cost calculations - [PR #16376](https://github.com/BerriAI/litellm/pull/16376)
+
+- **[Vertex AI](../../docs/providers/vertex_ai)**
+    - Add Vertex MiniMAX m2 model support - [PR #16373](https://github.com/BerriAI/litellm/pull/16373)
+    - Correctly map 429 Resource Exhausted to RateLimitError - [PR #16363](https://github.com/BerriAI/litellm/pull/16363)
+    - Add `vertex_credentials` support to `litellm.rerank()` for Vertex AI - [PR #16266](https://github.com/BerriAI/litellm/pull/16266)
+
+- **[Databricks](../../docs/providers/databricks)**
+    - Fix databricks streaming - [PR #16368](https://github.com/BerriAI/litellm/pull/16368)
+
+- **[Deepgram](../../docs/providers/deepgram)**
+    - Return the diarized transcript when it's required in the request - [PR #16133](https://github.com/BerriAI/litellm/pull/16133)
+
+- **[Fireworks](../../docs/providers/fireworks_ai)**
+    - Update Fireworks audio endpoints to new `api.fireworks.ai` domains - [PR #16346](https://github.com/BerriAI/litellm/pull/16346)
+
+- **[Cohere](../../docs/providers/cohere)**
+    - Add cohere embed-v4.0 model support - [PR #16358](https://github.com/BerriAI/litellm/pull/16358)
+
+- **[Watsonx](../../docs/providers/watsonx)**
+    - Support `reasoning_effort` for watsonx chat models - [PR #16261](https://github.com/BerriAI/litellm/pull/16261)
+
+- **[OpenAI](../../docs/providers/openai)**
+    - Remove automatic summary from reasoning_effort transformation - [PR #16210](https://github.com/BerriAI/litellm/pull/16210)
+
+- **[XAI](../../docs/providers/xai)**
+    - Remove Grok 4 Models Reasoning Effort Parameter - [PR #16265](https://github.com/BerriAI/litellm/pull/16265)
+
+- **[Hosted VLLM](../../docs/providers/vllm)**
+    - Fix HostedVLLMRerankConfig will not be used - [PR #16352](https://github.com/BerriAI/litellm/pull/16352)
+
+#### New Provider Support
+
+- **[Bedrock Agentcore](../../docs/providers/bedrock)**
+    - Add Bedrock Agentcore as a provider on LiteLLM Python SDK and LiteLLM AI Gateway - [PR #16252](https://github.com/BerriAI/litellm/pull/16252)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **[OCR API](../../docs/ocr)**
+    - Add VertexAI OCR provider support + cost tracking - [PR #16216](https://github.com/BerriAI/litellm/pull/16216)
+    - Add Azure AI Doc Intelligence OCR support - [PR #16219](https://github.com/BerriAI/litellm/pull/16219)
+
+- **[Search API](../../docs/search)**
+    - Add firecrawl search API support with tiered pricing - [PR #16257](https://github.com/BerriAI/litellm/pull/16257)
+    - Add searxng search API provider - [PR #16259](https://github.com/BerriAI/litellm/pull/16259)
+
+- **[Responses API](../../docs/response_api)**
+    - Support responses API streaming in langfuse otel - [PR #16153](https://github.com/BerriAI/litellm/pull/16153)
+    - Pass extra_body parameters to provider in Responses API requests - [PR #16320](https://github.com/BerriAI/litellm/pull/16320)
+
+- **[Container API](../../docs/container_api)**
+    - Add E2E Container API Support - [PR #16136](https://github.com/BerriAI/litellm/pull/16136)
+    - Update container documentation to be similar to others - [PR #16327](https://github.com/BerriAI/litellm/pull/16327)
+
+- **[Video Generation API](../../docs/video_generation)**
+    - Add Vertex and Gemini Videos API with Cost Tracking + UI support - [PR #16323](https://github.com/BerriAI/litellm/pull/16323)
+    - Add `custom_llm_provider` support for video endpoints (non-generation) - [PR #16121](https://github.com/BerriAI/litellm/pull/16121)
+
+- **[Audio API](../../docs/audio)**
+    - Add gpt-4o-transcribe cost tracking - [PR #16412](https://github.com/BerriAI/litellm/pull/16412)
+
+- **[Vector Stores](../../docs/vector_stores)**
+    - Milvus - search vector store support + support multi-part form data on passthrough - [PR #16035](https://github.com/BerriAI/litellm/pull/16035)
+    - Azure AI Vector Stores - support "virtual" indexes + create vector store on passthrough API - [PR #16160](https://github.com/BerriAI/litellm/pull/16160)
+    - Milvus - Passthrough API support - adds create + read vector store support via passthrough API's - [PR #16170](https://github.com/BerriAI/litellm/pull/16170)
+
+- **[Embeddings API](../../docs/embedding/supported_embedding)**
+    - Use valid CallTypes enum value in embeddings endpoint - [PR #16328](https://github.com/BerriAI/litellm/pull/16328)
+
+- **[Rerank API](../../docs/rerank)**
+    - Generalize tiered pricing in generic cost calculator - [PR #16150](https://github.com/BerriAI/litellm/pull/16150)
+
+#### Bugs
+
+- **General**
+    - Fix index field not populated in streaming mode with n>1 and tool calls - [PR #15962](https://github.com/BerriAI/litellm/pull/15962)
+    - Pass aws_region_name in litellm_params - [PR #16321](https://github.com/BerriAI/litellm/pull/16321)
+    - Add `retry-after` header support for errors `502`, `503`, `504` - [PR #16288](https://github.com/BerriAI/litellm/pull/16288)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Virtual Keys**
+    - UI - Delete Team Member with friction - [PR #16167](https://github.com/BerriAI/litellm/pull/16167)
+    - UI - Litellm test key audio support - [PR #16251](https://github.com/BerriAI/litellm/pull/16251)
+    - UI - Test Key Page Revert Model To Single Select - [PR #16390](https://github.com/BerriAI/litellm/pull/16390)
+
+- **Models + Endpoints**
+    - UI - Add Model Existing Credentials Improvement - [PR #16166](https://github.com/BerriAI/litellm/pull/16166)
+    - UI - Add Azure AD Token field and Azure API Key optional - [PR #16331](https://github.com/BerriAI/litellm/pull/16331)
+    - UI - Fixed Label for vLLM in Model Create Flow - [PR #16285](https://github.com/BerriAI/litellm/pull/16285)
+    - UI - Include Model Access Group Models on Team Models Table - [PR #16298](https://github.com/BerriAI/litellm/pull/16298)
+    - Fix /model_group/info Returning Entire Model List for SSO Users - [PR #16296](https://github.com/BerriAI/litellm/pull/16296)
+    - Litellm non root docker Model Hub Table fix - [PR #16282](https://github.com/BerriAI/litellm/pull/16282)
+
+- **Guardrails**
+    - UI - Fix regression where Guardrail Entity Could not be selected and entity was not displayed - [PR #16165](https://github.com/BerriAI/litellm/pull/16165)
+    - UI - Guardrail Info Page Show PII Config - [PR #16164](https://github.com/BerriAI/litellm/pull/16164)
+    - Change guardrail_information to list type - [PR #16127](https://github.com/BerriAI/litellm/pull/16127)
+    - UI - LiteLLM Guardrail - ensure you can see UI Friendly name for PII Patterns - [PR #16382](https://github.com/BerriAI/litellm/pull/16382)
+    - UI - Guardrails - LiteLLM Content Filter, Allow Viewing/Editing Content Filter Settings - [PR #16383](https://github.com/BerriAI/litellm/pull/16383)
+    - UI - Guardrails - allow updating guardrails through UI. Ensure litellm_params actually get updated in memory - [PR #16384](https://github.com/BerriAI/litellm/pull/16384)
+
+- **SSO Settings**
+    - Support dot notation on ui sso - [PR #16135](https://github.com/BerriAI/litellm/pull/16135)
+    - UI - Prevent trailing slash in sso proxy base url input - [PR #16244](https://github.com/BerriAI/litellm/pull/16244)
+    - UI - SSO Proxy Base URL input validation and remove normalizing / - [PR #16332](https://github.com/BerriAI/litellm/pull/16332)
+    - UI - Surface SSO Create errors on create flow - [PR #16369](https://github.com/BerriAI/litellm/pull/16369)
+
+- **Usage & Analytics**
+    - UI - Tag Usage Top Model Table View and Label Fix - [PR #16249](https://github.com/BerriAI/litellm/pull/16249)
+    - UI - Litellm usage date picker - [PR #16264](https://github.com/BerriAI/litellm/pull/16264)
+
+- **Cache Settings**
+    - UI - Cache Settings Redis Add Semantic Cache Settings - [PR #16398](https://github.com/BerriAI/litellm/pull/16398)
+
+#### Bugs
+
+- **General**
+    - UI - Remove encoding_format in request for embedding models - [PR #16367](https://github.com/BerriAI/litellm/pull/16367)
+    - UI - Revert Changes for Test Key Multiple Model Select - [PR #16372](https://github.com/BerriAI/litellm/pull/16372)
+    - UI - Various Small Issues - [PR #16406](https://github.com/BerriAI/litellm/pull/16406)
+
+---
+
+## AI Integrations
+
+### Logging
+
+- **[Langfuse](../../docs/proxy/logging#langfuse)**
+    - Fix langfuse input tokens logic for cached tokens - [PR #16203](https://github.com/BerriAI/litellm/pull/16203)
+
+- **[Opik](../../docs/proxy/logging#opik)**
+    - Fix the bug with not incorrect attachment to existing trace & refactor - [PR #15529](https://github.com/BerriAI/litellm/pull/15529)
+
+- **[S3](../../docs/proxy/logging#s3)**
+    - S3 logger, add support for ssl_verify when using minio logger - [PR #16211](https://github.com/BerriAI/litellm/pull/16211)
+    - Strip base64 in s3 - [PR #16157](https://github.com/BerriAI/litellm/pull/16157)
+    - Add allowing Key based prefix to s3 path - [PR #16237](https://github.com/BerriAI/litellm/pull/16237)
+    - Add Prometheus metric to track callback logging failures in S3 - [PR #16209](https://github.com/BerriAI/litellm/pull/16209)
+
+- **[OpenTelemetry](../../docs/proxy/logging#opentelemetry)**
+    - OTEL - Log Cost Breakdown on OTEL Logger - [PR #16334](https://github.com/BerriAI/litellm/pull/16334)
+
+- **[DataDog](../../docs/proxy/logging#datadog)**
+    - Add DD Agent Host support for `datadog` callback - [PR #16379](https://github.com/BerriAI/litellm/pull/16379)
+
+### Guardrails
+
+- **[Noma](../../docs/proxy/guardrails)**
+    - Revert Noma Apply Guardrail implementation - [PR #16214](https://github.com/BerriAI/litellm/pull/16214)
+    - Litellm noma guardrail support images - [PR #16199](https://github.com/BerriAI/litellm/pull/16199)
+
+- **[PANW Prisma AIRS](../../docs/proxy/guardrails)**
+    - PANW prisma airs guardrail deduplication and enhanced session tracking - [PR #16273](https://github.com/BerriAI/litellm/pull/16273)
+
+- **[LiteLLM Custom Guardrail](../../docs/proxy/guardrails)**
+    - Add LiteLLM Gateway built in guardrail - [PR #16338](https://github.com/BerriAI/litellm/pull/16338)
+    - UI - Allow configuring LiteLLM Custom Guardrail - [PR #16339](https://github.com/BerriAI/litellm/pull/16339)
+    - Bug Fix: Content Filter Guard - [PR #16414](https://github.com/BerriAI/litellm/pull/16414)
+
+### Secret Managers
+
+- **[CyberArk](../../docs/secret_managers)**
+    - Add CyberArk Secrets Manager Integration - [PR #16278](https://github.com/BerriAI/litellm/pull/16278)
+    - Cyber Ark - Add Key Rotations support - [PR #16289](https://github.com/BerriAI/litellm/pull/16289)
+
+- **[HashiCorp Vault](../../docs/secret_managers)**
+    - Add configurable mount name and path prefix for HashiCorp Vault - [PR #16253](https://github.com/BerriAI/litellm/pull/16253)
+    - Secret Manager - Hashicorp, add auth via approle - [PR #16374](https://github.com/BerriAI/litellm/pull/16374)
+
+- **[AWS Secrets Manager](../../docs/secret_managers)**
+    - Add tags and descriptions support to aws secrets manager - [PR #16224](https://github.com/BerriAI/litellm/pull/16224)
+
+- **[Custom Secret Manager](../../docs/secret_managers)**
+    - Add Custom Secret Manager - Allow users to define and write a custom secret manager - [PR #16297](https://github.com/BerriAI/litellm/pull/16297)
+
+- **General**
+    - Email Notifications - Ensure Users get Key Rotated Email - [PR #16292](https://github.com/BerriAI/litellm/pull/16292)
+    - Fix verify ssl on sts boto3 - [PR #16313](https://github.com/BerriAI/litellm/pull/16313)
+
+---
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- **Cost Tracking**
+    - Fix OpenAI Responses API streaming tests usage field names and cost calculation - [PR #16236](https://github.com/BerriAI/litellm/pull/16236)
+
+---
+
+## MCP Gateway
+
+- **Configuration**
+    - Configure static mcp header - [PR #16179](https://github.com/BerriAI/litellm/pull/16179)
+    - Persist mcp credentials in db - [PR #16308](https://github.com/BerriAI/litellm/pull/16308)
+
+
+## Performance / Loadbalancing / Reliability improvements
+
+- **Memory Leak Fixes**
+    - Resolve memory accumulation caused by Pydantic 2.11+ deprecation warnings - [PR #16110](https://github.com/BerriAI/litellm/pull/16110)
+
+- **Session Management**
+    - Add shared_session support to responses API - [PR #16260](https://github.com/BerriAI/litellm/pull/16260)
+
+- **Error Handling**
+    - Gracefully handle connection closed errors during streaming - [PR #16294](https://github.com/BerriAI/litellm/pull/16294)
+    - Handle None values in daily spend sort key - [PR #16245](https://github.com/BerriAI/litellm/pull/16245)
+
+- **Configuration**
+    - Remove minimum validation for cache control injection index - [PR #16149](https://github.com/BerriAI/litellm/pull/16149)
+    - Improve clearing logic - only remove unvisited endpoints - [PR #16400](https://github.com/BerriAI/litellm/pull/16400)
+
+- **Redis**
+    - Handle float redis_version from AWS ElastiCache Valkey - [PR #16207](https://github.com/BerriAI/litellm/pull/16207)
+
+- **Hooks**
+    - Add parallel execution handling in during_call_hook - [PR #16279](https://github.com/BerriAI/litellm/pull/16279)
+
+- **Infrastructure**
+    - Install runtime node for prisma - [PR #16410](https://github.com/BerriAI/litellm/pull/16410)
+
+
+
+---
+
+## Documentation Updates
+
+- **Provider Documentation**
+    - Docs - v1.79.1 - [PR #16163](https://github.com/BerriAI/litellm/pull/16163)
+    - Fix broken link on model_management.md - [PR #16217](https://github.com/BerriAI/litellm/pull/16217)
+    - Fix image generation response format - use 'images' array instead of 'image' object - [PR #16378](https://github.com/BerriAI/litellm/pull/16378)
+
+- **General Documentation**
+    - Add minimum resource requirement for production - [PR #16146](https://github.com/BerriAI/litellm/pull/16146)
+    - Add benchmark comparison with other AI gateways - [PR #16248](https://github.com/BerriAI/litellm/pull/16248)
+    - LiteLLM content filter guard documentation - [PR #16413](https://github.com/BerriAI/litellm/pull/16413)
+    - Fix typo of the word orginal - [PR #16255](https://github.com/BerriAI/litellm/pull/16255)
+
+- **Security**
+    - Remove tornado test files (including test.key), fixes Python 3.13 security issues - [PR #16342](https://github.com/BerriAI/litellm/pull/16342)
+
+---
+
+## New Contributors
+
+* @steve-gore-snapdocs made their first contribution in [PR #16149](https://github.com/BerriAI/litellm/pull/16149)
+* @timbmg made their first contribution in [PR #16120](https://github.com/BerriAI/litellm/pull/16120)
+* @Nivg made their first contribution in [PR #16202](https://github.com/BerriAI/litellm/pull/16202)
+* @pablobgar made their first contribution in [PR #16194](https://github.com/BerriAI/litellm/pull/16194)
+* @AlanPonnachan made their first contribution in [PR #16150](https://github.com/BerriAI/litellm/pull/16150)
+* @Chesars made their first contribution in [PR #16236](https://github.com/BerriAI/litellm/pull/16236)
+* @bowenliang123 made their first contribution in [PR #16255](https://github.com/BerriAI/litellm/pull/16255)
+* @dean-zavad made their first contribution in [PR #16199](https://github.com/BerriAI/litellm/pull/16199)
+* @alexkuzmik made their first contribution in [PR #15529](https://github.com/BerriAI/litellm/pull/15529)
+* @Granine made their first contribution in [PR #16281](https://github.com/BerriAI/litellm/pull/16281)
+* @Oodapow made their first contribution in [PR #16279](https://github.com/BerriAI/litellm/pull/16279)
+* @jgoodyear made their first contribution in [PR #16275](https://github.com/BerriAI/litellm/pull/16275)
+* @Qanpi made their first contribution in [PR #16321](https://github.com/BerriAI/litellm/pull/16321)
+* @ShimonMimoun made their first contribution in [PR #16313](https://github.com/BerriAI/litellm/pull/16313)
+* @andriykislitsyn made their first contribution in [PR #16288](https://github.com/BerriAI/litellm/pull/16288)
+* @reckless-huang made their first contribution in [PR #16263](https://github.com/BerriAI/litellm/pull/16263)
+* @chenmoneygithub made their first contribution in [PR #16368](https://github.com/BerriAI/litellm/pull/16368)
+* @stembe-digitalex made their first contribution in [PR #16354](https://github.com/BerriAI/litellm/pull/16354)
+* @jfcherng made their first contribution in [PR #16352](https://github.com/BerriAI/litellm/pull/16352)
+* @xingyaoww made their first contribution in [PR #16246](https://github.com/BerriAI/litellm/pull/16246)
+* @emerzon made their first contribution in [PR #16373](https://github.com/BerriAI/litellm/pull/16373)
+* @wwwillchen made their first contribution in [PR #16376](https://github.com/BerriAI/litellm/pull/16376)
+* @fabriciojoc made their first contribution in [PR #16203](https://github.com/BerriAI/litellm/pull/16203)
+* @jroberts2600 made their first contribution in [PR #16273](https://github.com/BerriAI/litellm/pull/16273)
+
+---
+
+## Full Changelog
+
+**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/compare/v1.79.1-nightly...v1.79.2.rc.1)**
+
+
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 0b303b8c6af..f009abd766a 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -31,6 +31,7 @@ const sidebars = {
       label: "Guardrails",
       items: [
         "proxy/guardrails/quick_start",
+        "proxy/guardrails/test_playground",
         ...[
           "adding_provider/adding_guardrail_support",
           "proxy/guardrails/aim_security",
@@ -41,6 +42,7 @@ const sidebars = {
           "proxy/guardrails/ibm_guardrails",
           "proxy/guardrails/grayswan",
           "proxy/guardrails/lasso_security",
+          "proxy/guardrails/litellm_content_filter",
           "proxy/guardrails/guardrails_ai",
           "proxy/guardrails/lakera_ai",
           "proxy/guardrails/model_armor",
@@ -477,6 +479,7 @@ const sidebars = {
           label: "Vertex AI",
           items: [
             "providers/vertex",
+            "providers/vertex_ai/videos",
             "providers/vertex_partner",
             "providers/vertex_self_deployed",
             "providers/vertex_image",
@@ -489,6 +492,7 @@ const sidebars = {
           label: "Google AI Studio",
           items: [
             "providers/gemini",
+            "providers/gemini/videos",
             "providers/google_ai_studio/files",
             "providers/google_ai_studio/image_gen",
             "providers/google_ai_studio/realtime",
@@ -780,6 +784,7 @@ const sidebars = {
             "projects/GPTLocalhost",
             "projects/HolmesGPT",
             "projects/Railtracks",
+            "projects/Softgen",
           ],
         },
         "extras/code_quality",
diff --git a/enterprise/enterprise_hooks/aporia_ai.py b/enterprise/enterprise_hooks/aporia_ai.py
index 55ba6071820..28b49bfce21 100644
--- a/enterprise/enterprise_hooks/aporia_ai.py
+++ b/enterprise/enterprise_hooks/aporia_ai.py
@@ -8,6 +8,8 @@
 import os
 import sys
 
+from litellm.types.utils import CallTypesLiteral
+
 sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
@@ -166,16 +168,7 @@ class AporiaGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,
diff --git a/enterprise/enterprise_hooks/google_text_moderation.py b/enterprise/enterprise_hooks/google_text_moderation.py
index c1c932dcb04..1f26d52adf8 100644
--- a/enterprise/enterprise_hooks/google_text_moderation.py
+++ b/enterprise/enterprise_hooks/google_text_moderation.py
@@ -6,14 +6,13 @@
 # +-----------------------------------------------+
 #  Thank you users! We ❤️ you! - Krrish & Ishaan
 
-from typing import Literal
-
 from fastapi import HTTPException
 
 import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy._types import UserAPIKeyAuth
+from litellm.types.utils import CallTypesLiteral
 
 
 class _ENTERPRISE_GoogleTextModeration(CustomLogger):
@@ -89,16 +88,7 @@ class _ENTERPRISE_GoogleTextModeration(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         - Calls Google's Text Moderation API
diff --git a/enterprise/enterprise_hooks/openai_moderation.py b/enterprise/enterprise_hooks/openai_moderation.py
index 4464fff25c9..a1db9818e5e 100644
--- a/enterprise/enterprise_hooks/openai_moderation.py
+++ b/enterprise/enterprise_hooks/openai_moderation.py
@@ -12,7 +12,6 @@ sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 import sys
-from typing import Literal
 
 from fastapi import HTTPException
 
@@ -20,6 +19,7 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy._types import UserAPIKeyAuth
+from litellm.types.utils import CallTypesLiteral
 
 
 class _ENTERPRISE_OpenAI_Moderation(CustomLogger):
@@ -35,16 +35,7 @@ class _ENTERPRISE_OpenAI_Moderation(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         text = ""
         if "messages" in data and isinstance(data["messages"], list):
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/llama_guard.py b/enterprise/litellm_enterprise/enterprise_callbacks/llama_guard.py
index 80de7a396a4..5e1aebdbdfb 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/llama_guard.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/llama_guard.py
@@ -23,7 +23,7 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy._types import UserAPIKeyAuth
-from litellm.types.utils import Choices, ModelResponse
+from litellm.types.utils import CallTypesLiteral, Choices, ModelResponse
 
 
 class _ENTERPRISE_LlamaGuard(CustomLogger):
@@ -98,16 +98,7 @@ class _ENTERPRISE_LlamaGuard(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         - Calls the Llama Guard Endpoint
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py b/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py
index 6f07250a61a..ad8aabf77b6 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py
@@ -17,6 +17,7 @@ from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.utils import CallTypesLiteral
 from litellm.utils import get_formatted_prompt
 
 
@@ -120,16 +121,7 @@ class _ENTERPRISE_LLMGuard(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         - Calls the LLM Guard Endpoint
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
index 3162c2f12f8..e481cdc995c 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
@@ -31,6 +31,7 @@ from litellm.types.integrations.pagerduty import (
     PagerDutyRequestBody,
 )
 from litellm.types.utils import (
+    CallTypesLiteral,
     StandardLoggingPayload,
     StandardLoggingPayloadErrorInformation,
 )
@@ -142,18 +143,7 @@ class PagerDutyAlerting(SlackAlerting):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, dict]]:
         """
         Example of detecting hanging requests by waiting a given threshold.
diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
index c55a4f03898..80cc77883fe 100644
--- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
+++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
@@ -36,6 +36,7 @@ from litellm.types.llms.openai import (
     OpenAIFilesPurpose,
 )
 from litellm.types.utils import (
+    CallTypesLiteral,
     LiteLLMBatch,
     LiteLLMFineTuningJob,
     LLMResponseTypes,
@@ -272,28 +273,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: Dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "acreate_batch",
-            "aretrieve_batch",
-            "acreate_file",
-            "afile_list",
-            "afile_delete",
-            "afile_content",
-            "acreate_fine_tuning_job",
-            "aretrieve_fine_tuning_job",
-            "alist_fine_tuning_jobs",
-            "acancel_fine_tuning_job",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, Dict, None]:
         """
         - Detect litellm_proxy/ file_id
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.2-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.2-py3-none-any.whl
new file mode 100644
index 00000000000..f65cee904be
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.2-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.2.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.2.tar.gz
new file mode 100644
index 00000000000..389a114c7da
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.2.tar.gz differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.3-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.3-py3-none-any.whl
new file mode 100644
index 00000000000..aaca139a071
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.3-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.3.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.3.tar.gz
new file mode 100644
index 00000000000..9b41e61f6dc
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.3.tar.gz differ
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251104220043_add_credentials_to_mcp_servers/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251104220043_add_credentials_to_mcp_servers/migration.sql
new file mode 100644
index 00000000000..800c96f18b7
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251104220043_add_credentials_to_mcp_servers/migration.sql
@@ -0,0 +1,2 @@
+-- AlterTable
+ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN     "credentials" JSONB DEFAULT '{}';
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index 9702c390288..1ab193bba7c 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -174,6 +174,7 @@ model LiteLLM_MCPServerTable {
   url          String?
   transport    String         @default("sse")
   auth_type    String?        
+  credentials  Json?          @default("{}")
   created_at   DateTime?      @default(now()) @map("created_at")
   created_by   String?
   updated_at   DateTime?      @default(now()) @updatedAt @map("updated_at")
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 07077759397..6c782eace2f 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm-proxy-extras"
-version = "0.4.1"
+version = "0.4.3"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 authors = ["BerriAI"]
 readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "0.4.1"
+version = "0.4.3"
 version_files = [
     "pyproject.toml:version",
     "../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 20fe4a2aeac..5f3b1156c92 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -4,7 +4,9 @@ import warnings
 warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*")
 # Suppress Pydantic 2.11+ deprecation warning about accessing model_fields on instances
 # This warning can accumulate during streaming and cause memory leaks
-warnings.filterwarnings("ignore", message=".*Accessing the.*attribute on the instance is deprecated.*")
+warnings.filterwarnings(
+    "ignore", message=".*Accessing the.*attribute on the instance is deprecated.*"
+)
 ### INIT VARIABLES #######################
 import threading
 import os
@@ -32,7 +34,7 @@ from litellm.types.utils import (
     all_litellm_params as _litellm_completion_params,
     CredentialItem,
     PriorityReservationDict,
-)  # maintain backwards compatibility for root param
+)  # maintain backwards compatibility for root param.
 from litellm._logging import (
     set_verbose,
     _turn_on_debug,
@@ -179,22 +181,22 @@ prometheus_initialize_budget_metrics: Optional[bool] = False
 require_auth_for_metrics_endpoint: Optional[bool] = False
 argilla_batch_size: Optional[int] = None
 datadog_use_v1: Optional[bool] = False  # if you want to use v1 datadog logged payload.
-gcs_pub_sub_use_v1: Optional[bool] = (
-    False  # if you want to use v1 gcs pubsub logged payload
-)
-generic_api_use_v1: Optional[bool] = (
-    False  # if you want to use v1 generic api logged payload
-)
+gcs_pub_sub_use_v1: Optional[
+    bool
+] = False  # if you want to use v1 gcs pubsub logged payload
+generic_api_use_v1: Optional[
+    bool
+] = False  # if you want to use v1 generic api logged payload
 argilla_transformation_object: Optional[Dict[str, Any]] = None
-_async_input_callback: List[Union[str, Callable, CustomLogger]] = (
-    []
-)  # internal variable - async custom callbacks are routed here.
-_async_success_callback: List[Union[str, Callable, CustomLogger]] = (
-    []
-)  # internal variable - async custom callbacks are routed here.
-_async_failure_callback: List[Union[str, Callable, CustomLogger]] = (
-    []
-)  # internal variable - async custom callbacks are routed here.
+_async_input_callback: List[
+    Union[str, Callable, CustomLogger]
+] = []  # internal variable - async custom callbacks are routed here.
+_async_success_callback: List[
+    Union[str, Callable, CustomLogger]
+] = []  # internal variable - async custom callbacks are routed here.
+_async_failure_callback: List[
+    Union[str, Callable, CustomLogger]
+] = []  # internal variable - async custom callbacks are routed here.
 pre_call_rules: List[Callable] = []
 post_call_rules: List[Callable] = []
 turn_off_message_logging: Optional[bool] = False
@@ -202,18 +204,18 @@ log_raw_request_response: bool = False
 redact_messages_in_exceptions: Optional[bool] = False
 redact_user_api_key_info: Optional[bool] = False
 filter_invalid_headers: Optional[bool] = False
-add_user_information_to_llm_headers: Optional[bool] = (
-    None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
-)
+add_user_information_to_llm_headers: Optional[
+    bool
+] = None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
 store_audit_logs = False  # Enterprise feature, allow users to see audit logs
 ### end of callbacks #############
 
-email: Optional[str] = (
-    None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
-token: Optional[str] = (
-    None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
+email: Optional[
+    str
+] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+token: Optional[
+    str
+] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
 telemetry = True
 max_tokens: int = DEFAULT_MAX_TOKENS  # OpenAI Defaults
 drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
@@ -269,9 +271,9 @@ use_client: bool = False
 ssl_verify: Union[str, bool] = True
 ssl_security_level: Optional[str] = None
 ssl_certificate: Optional[str] = None
-ssl_ecdh_curve: Optional[str] = (
-    None  # Set to 'X25519' to disable PQC and improve performance
-)
+ssl_ecdh_curve: Optional[
+    str
+] = None  # Set to 'X25519' to disable PQC and improve performance
 disable_streaming_logging: bool = False
 disable_token_counter: bool = False
 disable_add_transform_inline_image_block: bool = False
@@ -317,24 +319,20 @@ enable_loadbalancing_on_batch_endpoints: Optional[bool] = None
 enable_caching_on_provider_specific_optional_params: bool = (
     False  # feature-flag for caching on optional params - e.g. 'top_k'
 )
-caching: bool = (
-    False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
-caching_with_models: bool = (
-    False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
-cache: Optional[Cache] = (
-    None  # cache object <- use this - https://docs.litellm.ai/docs/caching
-)
+caching: bool = False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+caching_with_models: bool = False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+cache: Optional[
+    Cache
+] = None  # cache object <- use this - https://docs.litellm.ai/docs/caching
 default_in_memory_ttl: Optional[float] = None
 default_redis_ttl: Optional[float] = None
 default_redis_batch_cache_expiry: Optional[float] = None
 model_alias_map: Dict[str, str] = {}
 model_group_settings: Optional["ModelGroupSettings"] = None
 max_budget: float = 0.0  # set the max budget across all providers
-budget_duration: Optional[str] = (
-    None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
-)
+budget_duration: Optional[
+    str
+] = None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
 default_soft_budget: float = (
     DEFAULT_SOFT_BUDGET  # by default all litellm proxy keys have a soft budget of 50.0
 )
@@ -343,15 +341,11 @@ forward_traceparent_to_llm_provider: bool = False
 
 _current_cost = 0.0  # private variable, used if max budget is set
 error_logs: Dict = {}
-add_function_to_prompt: bool = (
-    False  # if function calling not supported by api, append function call details to system prompt
-)
+add_function_to_prompt: bool = False  # if function calling not supported by api, append function call details to system prompt
 client_session: Optional[httpx.Client] = None
 aclient_session: Optional[httpx.AsyncClient] = None
 model_fallbacks: Optional[List] = None  # Deprecated for 'litellm.fallbacks'
-model_cost_map_url: str = (
-    "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
-)
+model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 suppress_debug_info = False
 dynamodb_table_name: Optional[str] = None
 s3_callback_params: Optional[Dict] = None
@@ -381,9 +375,7 @@ prometheus_metrics_config: Optional[List] = None
 disable_add_prefix_to_prompt: bool = (
     False  # used by anthropic, to disable adding prefix to prompt
 )
-disable_copilot_system_to_assistant: bool = (
-    False  # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
-)
+disable_copilot_system_to_assistant: bool = False  # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
 public_model_groups: Optional[List[str]] = None
 public_model_groups_links: Dict[str, str] = {}
 #### REQUEST PRIORITIZATION #######
@@ -394,17 +386,13 @@ priority_reservation_settings: "PriorityReservationSettings" = (
 
 
 ######## Networking Settings ########
-use_aiohttp_transport: bool = (
-    True  # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
-)
+use_aiohttp_transport: bool = True  # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
 aiohttp_trust_env: bool = False  # set to true to use HTTP_ Proxy settings
 disable_aiohttp_transport: bool = False  # Set this to true to use httpx instead
 disable_aiohttp_trust_env: bool = (
     False  # When False, aiohttp will respect HTTP(S)_PROXY env vars
 )
-force_ipv4: bool = (
-    False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
-)
+force_ipv4: bool = False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
 module_level_aclient = AsyncHTTPHandler(
     timeout=request_timeout, client_alias="module level aclient"
 )
@@ -418,13 +406,13 @@ fallbacks: Optional[List] = None
 context_window_fallbacks: Optional[List] = None
 content_policy_fallbacks: Optional[List] = None
 allowed_fails: int = 3
-num_retries_per_request: Optional[int] = (
-    None  # for the request overall (incl. fallbacks + model retries)
-)
+num_retries_per_request: Optional[
+    int
+] = None  # for the request overall (incl. fallbacks + model retries)
 ####### SECRET MANAGERS #####################
-secret_manager_client: Optional[Any] = (
-    None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
-)
+secret_manager_client: Optional[
+    Any
+] = None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
 _google_kms_resource_name: Optional[str] = None
 _key_management_system: Optional[KeyManagementSystem] = None
 _key_management_settings: KeyManagementSettings = KeyManagementSettings()
@@ -434,9 +422,9 @@ output_parse_pii: bool = False
 from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
 
 model_cost = get_model_cost_map(url=model_cost_map_url)
-cost_discount_config: Dict[str, float] = (
-    {}
-)  # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
+cost_discount_config: Dict[
+    str, float
+] = {}  # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
 custom_prompt_dict: Dict[str, dict] = {}
 check_provider_endpoint = False
 
@@ -492,6 +480,7 @@ vertex_deepseek_models: Set = set()
 vertex_ai_ai21_models: Set = set()
 vertex_mistral_models: Set = set()
 vertex_openai_models: Set = set()
+vertex_minimax_models: Set = set()
 ai21_models: Set = set()
 ai21_chat_models: Set = set()
 nlp_cloud_models: Set = set()
@@ -652,6 +641,9 @@ def add_known_models():
         elif value.get("litellm_provider") == "vertex_ai-openai_models":
             key = key.replace("vertex_ai/", "")
             vertex_openai_models.add(key)
+        elif value.get("litellm_provider") == "vertex_ai-minimax_models":
+            key = key.replace("vertex_ai/", "")
+            vertex_minimax_models.add(key)
         elif value.get("litellm_provider") == "ai21":
             if value.get("mode") == "chat":
                 ai21_chat_models.add(key)
@@ -907,7 +899,8 @@ models_by_provider: dict = {
     | vertex_anthropic_models
     | vertex_vision_models
     | vertex_language_models
-    | vertex_deepseek_models,
+    | vertex_deepseek_models
+    | vertex_minimax_models,
     "ai21": ai21_models,
     "bedrock": bedrock_models | bedrock_converse_models,
     "petals": petals_models,
@@ -1105,6 +1098,7 @@ from .llms.azure_ai.rerank.transformation import AzureAIRerankConfig
 from .llms.infinity.rerank.transformation import InfinityRerankConfig
 from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig
 from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig
+from .llms.hosted_vllm.rerank.transformation import HostedVLLMRerankConfig
 from .llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig
 from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
 from .llms.clarifai.chat.transformation import ClarifaiConfig
@@ -1345,6 +1339,7 @@ from .exceptions import (
     NotFoundError,
     RateLimitError,
     ServiceUnavailableError,
+    BadGatewayError,
     OpenAIError,
     ContextWindowExceededError,
     ContentPolicyViolationError,
@@ -1400,12 +1395,12 @@ from .types.llms.custom_llm import CustomLLMItem
 from .types.utils import GenericStreamingChunk
 
 custom_provider_map: List[CustomLLMItem] = []
-_custom_providers: List[str] = (
-    []
-)  # internal helper util, used to track names of custom providers
-disable_hf_tokenizer_download: Optional[bool] = (
-    None  # disable huggingface tokenizer download. Defaults to openai clk100
-)
+_custom_providers: List[
+    str
+] = []  # internal helper util, used to track names of custom providers
+disable_hf_tokenizer_download: Optional[
+    bool
+] = None  # disable huggingface tokenizer download. Defaults to openai clk100
 global_disable_no_log_param: bool = False
 
 ### CLI UTILITIES ###
diff --git a/litellm/constants.py b/litellm/constants.py
index 7565aa7d3ae..43fc37ad1c7 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -1,6 +1,7 @@
 import os
 from typing import List, Literal
 
+DEFAULT_HEALTH_CHECK_PROMPT = str(os.getenv("DEFAULT_HEALTH_CHECK_PROMPT", "test from litellm"))
 AZURE_DEFAULT_RESPONSES_API_VERSION = str(
     os.getenv("AZURE_DEFAULT_RESPONSES_API_VERSION", "preview")
 )
@@ -219,7 +220,7 @@ REDIS_SOCKET_TIMEOUT = float(os.getenv("REDIS_SOCKET_TIMEOUT", 0.1))
 REDIS_CONNECTION_POOL_TIMEOUT = int(os.getenv("REDIS_CONNECTION_POOL_TIMEOUT", 5))
 # Default Redis major version to assume when version cannot be determined
 # Using 7 as it's the modern version that supports LPOP with count parameter
-DEFAULT_REDIS_MAJOR_VERSION = 7
+DEFAULT_REDIS_MAJOR_VERSION = int(os.getenv("DEFAULT_REDIS_MAJOR_VERSION", 7))
 NON_LLM_CONNECTION_TIMEOUT = int(
     os.getenv("NON_LLM_CONNECTION_TIMEOUT", 15)
 )  # timeout for adjacent services (e.g. jwt auth)
@@ -279,6 +280,8 @@ ANTHROPIC_WEB_SEARCH_TOOL_MAX_USES = {
 DEFAULT_IMAGE_ENDPOINT_MODEL = "dall-e-2"
 DEFAULT_VIDEO_ENDPOINT_MODEL = "sora-2"
 
+DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS = int(os.getenv("DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS", 8))
+
 ### DATAFORSEO CONSTANTS ###
 DEFAULT_DATAFORSEO_LOCATION_CODE = int(
     os.getenv("DEFAULT_DATAFORSEO_LOCATION_CODE", 2250)
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 42fcabcf680..d4a4c441eb7 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -17,6 +17,9 @@ from litellm.constants import (
 from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import (
     StandardBuiltInToolCostTracking,
 )
+from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import (
+    TranscriptionUsageObjectTransformation,
+)
 from litellm.litellm_core_utils.llm_cost_calc.utils import (
     CostCalculatorUtils,
     _generic_cost_per_character,
@@ -81,6 +84,8 @@ from litellm.types.utils import (
     LlmProvidersSet,
     ModelInfo,
     StandardBuiltInToolsParams,
+    TranscriptionUsageDurationObject,
+    TranscriptionUsageTokensObject,
     Usage,
     VectorStoreSearchResponse,
 )
@@ -319,20 +324,32 @@ def cost_per_token(  # noqa: PLR0915
             usage=usage_block, model=model, custom_llm_provider=custom_llm_provider
         )
     elif call_type == "atranscription" or call_type == "transcription":
-        return openai_cost_per_second(
-            model=model,
-            custom_llm_provider=custom_llm_provider,
-            duration=audio_transcription_file_duration,
-        )
+
+        if model == "gpt-4o-mini-transcribe":
+            return openai_cost_per_token(
+                model=model,
+                usage=usage_block,
+                service_tier=service_tier,
+            )
+        else:
+            return openai_cost_per_second(
+                model=model,
+                custom_llm_provider=custom_llm_provider,
+                duration=audio_transcription_file_duration,
+            )
     elif call_type == "search" or call_type == "asearch":
         # Search providers use per-query pricing
         from litellm.search import search_provider_cost_per_query
-        
+
         return search_provider_cost_per_query(
             model=model,
             custom_llm_provider=custom_llm_provider,
             number_of_queries=number_of_queries or 1,
-            optional_params=response._hidden_params if response and hasattr(response, "_hidden_params") else None
+            optional_params=(
+                response._hidden_params
+                if response and hasattr(response, "_hidden_params")
+                else None
+            ),
         )
     elif custom_llm_provider == "vertex_ai":
         cost_router = google_cost_router(
@@ -509,16 +526,18 @@ def _select_model_name_for_cost_calc(
         else:
             return_model = model
 
-    if base_model is not None:
+    elif base_model is not None:
         return_model = base_model
 
-    if completion_response_model is None and hidden_params is not None:
+    elif completion_response_model is None and hidden_params is not None:
         if (
             hidden_params.get("model", None) is not None
             and len(hidden_params["model"]) > 0
         ):
             return_model = hidden_params.get("model", model)
-    if hidden_params is not None and hidden_params.get("region_name", None) is not None:
+    elif (
+        hidden_params is not None and hidden_params.get("region_name", None) is not None
+    ):
         region_name = hidden_params.get("region_name", None)
 
     if return_model is None and completion_response_model is not None:
@@ -573,6 +592,19 @@ def _get_usage_object(
         return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
             usage_obj
         )
+    elif TranscriptionUsageObjectTransformation.is_transcription_usage_object(
+        usage_obj
+    ):
+        return (
+            TranscriptionUsageObjectTransformation.transform_transcription_usage_object(
+                cast(
+                    Union[
+                        TranscriptionUsageDurationObject, TranscriptionUsageTokensObject
+                    ],
+                    usage_obj,
+                )
+            )
+        )
     elif isinstance(usage_obj, dict):
         return Usage(**usage_obj)
     elif isinstance(usage_obj, BaseModel):
@@ -586,8 +618,12 @@ def _get_usage_object(
 
 def _is_known_usage_objects(usage_obj):
     """Returns True if the usage obj is a known Usage type"""
-    return isinstance(usage_obj, litellm.Usage) or isinstance(
-        usage_obj, ResponseAPIUsage
+    return (
+        isinstance(usage_obj, litellm.Usage)
+        or isinstance(usage_obj, ResponseAPIUsage)
+        or TranscriptionUsageObjectTransformation.is_transcription_usage_object(
+            usage_obj
+        )
     )
 
 
@@ -827,6 +863,22 @@ def completion_cost(  # noqa: PLR0915
                         _usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
                             _usage
                         ).model_dump()
+                    elif TranscriptionUsageObjectTransformation.is_transcription_usage_object(
+                        _usage
+                    ):
+                        tr_usage = TranscriptionUsageObjectTransformation.transform_transcription_usage_object(
+                            cast(
+                                Union[
+                                    TranscriptionUsageDurationObject,
+                                    TranscriptionUsageTokensObject,
+                                ],
+                                _usage,
+                            )
+                        )
+                        if tr_usage is not None:
+                            _usage = tr_usage.model_dump()
+                    else:
+                        _usage = _usage
 
                     # get input/output tokens from completion_response
                     prompt_tokens = _usage.get("prompt_tokens", 0)
@@ -853,15 +905,6 @@ def completion_cost(  # noqa: PLR0915
                             "custom_llm_provider", custom_llm_provider or None
                         )
                         region_name = hidden_params.get("region_name", region_name)
-                        size = hidden_params.get("optional_params", {}).get(
-                            "size", "1024-x-1024"
-                        )  # openai default
-                        quality = hidden_params.get("optional_params", {}).get(
-                            "quality", "standard"
-                        )  # openai default
-                        n = hidden_params.get("optional_params", {}).get(
-                            "n", 1
-                        )  # openai default
                 else:
                     if model is None:
                         raise ValueError(
@@ -888,7 +931,9 @@ def completion_cost(  # noqa: PLR0915
                                 str(e)
                             )
                         )
-                if CostCalculatorUtils._call_type_has_image_response(call_type):
+                if CostCalculatorUtils._call_type_has_image_response(
+                    call_type
+                ) and isinstance(completion_response, ImageResponse):
                     ### IMAGE GENERATION COST CALCULATION ###
                     return CostCalculatorUtils.route_image_generation_cost_calculator(
                         model=model,
@@ -906,27 +951,32 @@ def completion_cost(  # noqa: PLR0915
                     or call_type == CallTypes.avideo_remix.value
                 ):
                     ### VIDEO GENERATION COST CALCULATION ###
-                    if completion_response is not None and hasattr(completion_response, 'usage'):
-                        usage_obj = completion_response.usage
+                    usage_obj = getattr(completion_response, "usage", None)
+                    if completion_response is not None and usage_obj:
                         # Handle both dict and Pydantic Usage object
                         if isinstance(usage_obj, dict):
-                            duration_seconds = usage_obj.get('duration_seconds', None)
+                            duration_seconds = usage_obj.get("duration_seconds", None)
                         else:
-                            duration_seconds = getattr(usage_obj, 'duration_seconds', None)
+                            duration_seconds = getattr(
+                                usage_obj, "duration_seconds", None
+                            )
 
                         if duration_seconds is not None:
                             # Calculate cost based on video duration using video-specific cost calculation
-                            from litellm.llms.openai.cost_calculation import video_generation_cost
+                            from litellm.llms.openai.cost_calculation import (
+                                video_generation_cost,
+                            )
+
                             return video_generation_cost(
                                 model=model,
                                 duration_seconds=duration_seconds,
-                                custom_llm_provider=custom_llm_provider
+                                custom_llm_provider=custom_llm_provider,
                             )
                     # Fallback to default video cost calculation if no duration available
                     return default_video_cost_calculator(
                         model=model,
                         duration_seconds=0.0,  # Default to 0 if no duration available
-                        custom_llm_provider=custom_llm_provider
+                        custom_llm_provider=custom_llm_provider,
                     )
                 elif (
                     call_type == CallTypes.speech.value
@@ -1460,13 +1510,13 @@ def default_video_cost_calculator(
         model_name_without_custom_llm_provider = model.replace(
             f"{custom_llm_provider}/", ""
         )
-        base_model_name = f"{custom_llm_provider}/{model_name_without_custom_llm_provider}"
+        base_model_name = (
+            f"{custom_llm_provider}/{model_name_without_custom_llm_provider}"
+        )
 
-    verbose_logger.debug(
-        f"Looking up cost for video model: {base_model_name}"
-    )
+    verbose_logger.debug(f"Looking up cost for video model: {base_model_name}")
 
-    model_without_provider = model.split('/')[-1]
+    model_without_provider = model.split("/")[-1]
 
     # Try model with provider first, fall back to base model name
     cost_info: Optional[dict] = None
@@ -1480,7 +1530,7 @@ def default_video_cost_calculator(
         if _model is not None and _model in litellm.model_cost:
             cost_info = litellm.model_cost[_model]
             break
-    
+
     # If still not found, try with custom_llm_provider prefix
     if cost_info is None and custom_llm_provider:
         prefixed_model = f"{custom_llm_provider}/{model}"
@@ -1495,12 +1545,12 @@ def default_video_cost_calculator(
     video_cost_per_second = cost_info.get("output_cost_per_video_per_second")
     if video_cost_per_second is not None:
         return video_cost_per_second * duration_seconds
-    
+
     # Fallback to general output cost per second
     output_cost_per_second = cost_info.get("output_cost_per_second")
     if output_cost_per_second is not None:
         return output_cost_per_second * duration_seconds
-    
+
     # If no cost information found, return 0
     verbose_logger.info(
         f"No cost information found for video model {model}. Please add pricing to model_prices_and_context_window.json"
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index ccb3ce90e9c..d963cac754c 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -450,6 +450,7 @@ class ContentPolicyViolationError(BadRequestError):  # type: ignore
         llm_provider,
         response: Optional[httpx.Response] = None,
         litellm_debug_info: Optional[str] = None,
+        provider_specific_fields: Optional[dict] = None,
     ):
         self.status_code = 400
         self.message = "litellm.ContentPolicyViolationError: {}".format(message)
@@ -458,6 +459,8 @@ class ContentPolicyViolationError(BadRequestError):  # type: ignore
         self.litellm_debug_info = litellm_debug_info
         request = httpx.Request(method="POST", url="https://api.openai.com/v1")
         self.response = httpx.Response(status_code=400, request=request)
+        self.provider_specific_fields = provider_specific_fields
+        
         super().__init__(
             message=self.message,
             model=self.model,  # type: ignore
@@ -465,16 +468,18 @@ class ContentPolicyViolationError(BadRequestError):  # type: ignore
             response=self.response,
             litellm_debug_info=self.litellm_debug_info,
         )  # Call the base class constructor with the parameters it needs
+    
 
     def __str__(self):
-        _message = self.message
-        if self.num_retries:
-            _message += f" LiteLLM Retried: {self.num_retries} times"
-        if self.max_retries:
-            _message += f", LiteLLM Max Retries: {self.max_retries}"
-        return _message
+        return self._transform_error_to_string()
 
     def __repr__(self):
+        return self._transform_error_to_string()
+
+    def _transform_error_to_string(self) -> str:
+        """
+        Transform the error to a string
+        """
         _message = self.message
         if self.num_retries:
             _message += f" LiteLLM Retried: {self.num_retries} times"
@@ -501,8 +506,62 @@ class ServiceUnavailableError(openai.APIStatusError):  # type: ignore
         self.litellm_debug_info = litellm_debug_info
         self.max_retries = max_retries
         self.num_retries = num_retries
+        _response_headers = (
+            getattr(response, "headers", None) if response is not None else None
+        )
         self.response = httpx.Response(
             status_code=self.status_code,
+            headers=_response_headers,
+            request=httpx.Request(
+                method="POST",
+                url=" https://cloud.google.com/vertex-ai/",
+            ),
+        )
+        super().__init__(
+            self.message, response=self.response, body=None
+        )  # Call the base class constructor with the parameters it needs
+
+    def __str__(self):
+        _message = self.message
+        if self.num_retries:
+            _message += f" LiteLLM Retried: {self.num_retries} times"
+        if self.max_retries:
+            _message += f", LiteLLM Max Retries: {self.max_retries}"
+        return _message
+
+    def __repr__(self):
+        _message = self.message
+        if self.num_retries:
+            _message += f" LiteLLM Retried: {self.num_retries} times"
+        if self.max_retries:
+            _message += f", LiteLLM Max Retries: {self.max_retries}"
+        return _message
+
+
+class BadGatewayError(openai.APIStatusError):  # type: ignore
+    def __init__(
+        self,
+        message,
+        llm_provider,
+        model,
+        response: Optional[httpx.Response] = None,
+        litellm_debug_info: Optional[str] = None,
+        max_retries: Optional[int] = None,
+        num_retries: Optional[int] = None,
+    ):
+        self.status_code = 502
+        self.message = "litellm.BadGatewayError: {}".format(message)
+        self.llm_provider = llm_provider
+        self.model = model
+        self.litellm_debug_info = litellm_debug_info
+        self.max_retries = max_retries
+        self.num_retries = num_retries
+        _response_headers = (
+            getattr(response, "headers", None) if response is not None else None
+        )
+        self.response = httpx.Response(
+            status_code=self.status_code,
+            headers=_response_headers,
             request=httpx.Request(
                 method="POST",
                 url=" https://cloud.google.com/vertex-ai/",
@@ -547,8 +606,12 @@ class InternalServerError(openai.InternalServerError):  # type: ignore
         self.litellm_debug_info = litellm_debug_info
         self.max_retries = max_retries
         self.num_retries = num_retries
+        _response_headers = (
+            getattr(response, "headers", None) if response is not None else None
+        )
         self.response = httpx.Response(
             status_code=self.status_code,
+            headers=_response_headers,
             request=httpx.Request(
                 method="POST",
                 url=" https://cloud.google.com/vertex-ai/",
@@ -754,6 +817,7 @@ LITELLM_EXCEPTION_TYPES = [
     ContentPolicyViolationError,
     InternalServerError,
     ServiceUnavailableError,
+    BadGatewayError,
     APIError,
     APIConnectionError,
     APIResponseValidationError,
diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py
index 2a08408f7de..a3e67d8a73f 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -8,7 +8,6 @@ from typing import (
     AsyncGenerator,
     Dict,
     List,
-    Literal,
     Optional,
     Tuple,
     Union,
@@ -24,6 +23,7 @@ from litellm.types.llms.openai import AllMessageValues, ChatCompletionRequest
 from litellm.types.utils import (
     AdapterCompletionStreamWrapper,
     CallTypes,
+    CallTypesLiteral,
     LLMResponseTypes,
     ModelResponse,
     ModelResponseStream,
@@ -65,12 +65,11 @@ _BASE64_INLINE_PATTERN = re.compile(
 class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
     # Class variables or attributes
     def __init__(
-        self, 
+        self,
         turn_off_message_logging: bool = False,
-
         # deprecated param, use `turn_off_message_logging` instead
         message_logging: bool = True,
-        **kwargs
+        **kwargs,
     ) -> None:
         """
         Args:
@@ -259,7 +258,7 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
     ) -> Optional[Any]:
         """
         Allow modifying streaming chunks just before they're returned to the user.
-        
+
         This is called for each streaming chunk in the response.
         """
         pass
@@ -330,18 +329,7 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[
         Union[Exception, str, dict]
     ]:  # raise exception if invalid, return a str for the user to receive - if rejected, or return a modified dictionary for passing into litellm
@@ -380,16 +368,7 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Any:
         pass
 
@@ -473,7 +452,6 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
     # MCP TOOL CALL HOOKS
     #########################################################
 
-
     async def async_post_mcp_tool_call_hook(
         self, kwargs, response_obj: MCPPostCallResponseObject, start_time, end_time
     ) -> Optional[MCPPostCallResponseObject]:
@@ -557,33 +535,32 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         if LITELLM_METADATA_FIELD in request_kwargs:
             return LITELLM_METADATA_FIELD
         return OLD_LITELLM_METADATA_FIELD
-    
+
     def redact_standard_logging_payload_from_model_call_details(
         self, model_call_details: Dict
     ) -> Dict:
         """
         Only redacts messages and responses when self.turn_off_message_logging is True
-        
+
 
         By default, self.turn_off_message_logging is False and this does nothing.
-        
+
         Return a redacted deepcopy of the provided logging payload.
-        
+
         This is useful for logging payloads that contain sensitive information.
         """
         from copy import copy
 
         from litellm import Choices, Message, ModelResponse
-        from litellm.types.utils import LiteLLMCommonStrings
         turn_off_message_logging: bool = getattr(self, "turn_off_message_logging", False)
         
         if turn_off_message_logging is False:
             return model_call_details
-        
+
         # Only make a shallow copy of the top-level dict to avoid deepcopy issues
         # with complex objects like AuthenticationError that may be present
         model_call_details_copy = copy(model_call_details)
-        redacted_str = LiteLLMCommonStrings.redacted_by_litellm.value
+        redacted_str = "redacted-by-litellm"
         standard_logging_object = model_call_details.get("standard_logging_object")
         if standard_logging_object is None:
             return model_call_details_copy
@@ -592,20 +569,40 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         standard_logging_object_copy = copy(standard_logging_object)
 
         if standard_logging_object_copy.get("messages") is not None:
-            standard_logging_object_copy["messages"] = [Message(content=redacted_str).model_dump()]
+            standard_logging_object_copy["messages"] = [
+                Message(content=redacted_str).model_dump()
+            ]
 
         if standard_logging_object_copy.get("response") is not None:
-            model_response = ModelResponse(
-                choices=[Choices(message=Message(content=redacted_str))]
-            )
-            model_response_dict = model_response.model_dump()
-            standard_logging_object_copy["response"] = model_response_dict
+            response = standard_logging_object_copy["response"]
+            # Check if this is a ResponsesAPIResponse (has "output" field)
+            if isinstance(response, dict) and "output" in response:
+                # Make a copy to avoid modifying the original
+                from copy import deepcopy
+                response_copy = deepcopy(response)
+                # Redact content in output array
+                if isinstance(response_copy.get("output"), list):
+                    for output_item in response_copy["output"]:
+                        if isinstance(output_item, dict) and "content" in output_item:
+                            if isinstance(output_item["content"], list):
+                                # Redact text in content items
+                                for content_item in output_item["content"]:
+                                    if isinstance(content_item, dict) and "text" in content_item:
+                                        content_item["text"] = redacted_str
+                standard_logging_object_copy["response"] = response_copy
+            else:
+                # Standard ModelResponse format
+                model_response = ModelResponse(
+                    choices=[Choices(message=Message(content=redacted_str))]
+                )
+                model_response_dict = model_response.model_dump()
+                standard_logging_object_copy["response"] = model_response_dict
 
-        model_call_details_copy["standard_logging_object"] = standard_logging_object_copy
+        model_call_details_copy["standard_logging_object"] = (
+            standard_logging_object_copy
+        )
         return model_call_details_copy
-    
 
-    
     async def get_proxy_server_request_from_cold_storage_with_object_key(
         self,
         object_key: str,
@@ -643,7 +640,9 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
             verbose_logger.debug(f"Error in handle_callback_failure for {callback_name}: {str(e)}")
 
     async def _strip_base64_from_messages(
-        self, payload: "StandardLoggingPayload", max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER
+        self,
+        payload: "StandardLoggingPayload",
+        max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER,
     ) -> "StandardLoggingPayload":
         """
         Removes or redacts base64-encoded file data (e.g., PDFs, images, audio)
@@ -692,7 +691,9 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         verbose_logger.debug(f"[CustomLogger] Stripping base64 from {len(messages)} messages")
 
         if messages:
-            payload["messages"] = self._process_messages(messages=messages, max_depth=max_depth)
+            payload["messages"] = self._process_messages(
+                messages=messages, max_depth=max_depth
+            )
 
         total_items = 0
         for m in payload.get("messages", []) or []:
@@ -705,9 +706,13 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
             f"[CustomLogger] Completed base64 strip; retained {total_items} content items"
         )
         return payload
-    
 
-    def _redact_base64(self, value: Any, depth: int = 0, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER) -> Any:
+    def _redact_base64(
+        self,
+        value: Any,
+        depth: int = 0,
+        max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER,
+    ) -> Any:
         """Recursively redact inline base64 from any nested structure with a max recursion depth limit."""
         if depth > max_depth:
             verbose_logger.warning(
@@ -724,10 +729,16 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
             return value
 
         if isinstance(value, list):
-            return [self._redact_base64(value=v, depth=depth + 1, max_depth=max_depth) for v in value]
+            return [
+                self._redact_base64(value=v, depth=depth + 1, max_depth=max_depth)
+                for v in value
+            ]
 
         if isinstance(value, dict):
-            return {k: self._redact_base64(value=v, depth=depth + 1, max_depth=max_depth) for k, v in value.items()}
+            return {
+                k: self._redact_base64(value=v, depth=depth + 1, max_depth=max_depth)
+                for k, v in value.items()
+            }
 
         return value
 
@@ -750,10 +761,14 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
                 cleaned: List[Any] = []
                 for c in contents:
                     if self._should_keep_content(content=c):
-                        cleaned.append(self._redact_base64(value=c, max_depth=max_depth))
+                        cleaned.append(
+                            self._redact_base64(value=c, max_depth=max_depth)
+                        )
                 msg["content"] = cleaned
             else:
-                msg["content"] = self._redact_base64(value=contents, max_depth=max_depth)
+                msg["content"] = self._redact_base64(
+                    value=contents, max_depth=max_depth
+                )
 
             for key, val in list(msg.items()):
                 if key != "content":
diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py
index 0c62667f749..46e1a2c201f 100644
--- a/litellm/integrations/datadog/datadog.py
+++ b/litellm/integrations/datadog/datadog.py
@@ -17,7 +17,6 @@ import asyncio
 import datetime
 import os
 import traceback
-from litellm._uuid import uuid
 from datetime import datetime as datetimeObj
 from typing import Any, Dict, List, Optional, Union
 
@@ -26,6 +25,7 @@ from httpx import Response
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm._uuid import uuid
 from litellm.integrations.custom_batch_logger import CustomBatchLogger
 from litellm.llms.custom_httpx.http_handler import (
     _get_httpx_client,
@@ -60,17 +60,19 @@ class DataDogLogger(
         """
         Initializes the datadog logger, checks if the correct env variables are set
 
-        Required environment variables:
+        Required environment variables (Direct API):
         `DD_API_KEY` - your datadog api key
         `DD_SITE` - your datadog site, example = `"us5.datadoghq.com"`
+
+        Optional environment variables (DataDog Agent):
+        `DD_AGENT_HOST` - hostname or IP of DataDog agent, example = `"localhost"`
+        `DD_AGENT_PORT` - port of DataDog agent (default: 10518 for logs)
+        
+        Note: If DD_AGENT_HOST is set, logs will be sent to the agent instead of directly to DataDog API.
+        In this case, DD_API_KEY and DD_SITE are not required (agent handles authentication).
         """
         try:
             verbose_logger.debug("Datadog: in init datadog logger")
-            # check if the correct env variables are set
-            if os.getenv("DD_API_KEY", None) is None:
-                raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>")
-            if os.getenv("DD_SITE", None) is None:
-                raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>")
             
             #########################################################
             # Handle datadog_params set as litellm.datadog_params
@@ -81,21 +83,16 @@ class DataDogLogger(
             self.async_client = get_async_httpx_client(
                 llm_provider=httpxSpecialProvider.LoggingCallback
             )
-            self.DD_API_KEY = os.getenv("DD_API_KEY")
-            self.intake_url = (
-                f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
-            )
-
-            ###################################
-            # OPTIONAL -only used for testing
-            dd_base_url: Optional[str] = (
-                os.getenv("_DATADOG_BASE_URL")
-                or os.getenv("DATADOG_BASE_URL")
-                or os.getenv("DD_BASE_URL")
-            )
-            if dd_base_url is not None:
-                self.intake_url = f"{dd_base_url}/api/v2/logs"
-            ###################################
+            
+            # Configure DataDog endpoint (Agent or Direct API)
+            dd_agent_host = os.getenv("DD_AGENT_HOST")
+            if dd_agent_host:
+                self._configure_dd_agent(dd_agent_host=dd_agent_host)
+            else:
+                self._configure_dd_direct_api()
+            
+            # Optional override for testing
+            self._apply_dd_base_url_override()
             self.sync_client = _get_httpx_client()
             asyncio.create_task(self.periodic_flush())
             self.flush_lock = asyncio.Lock()
@@ -123,6 +120,47 @@ class DataDogLogger(
                 dict_datadog_params = DatadogInitParams(**litellm.datadog_params).model_dump()
         return dict_datadog_params
 
+    def _configure_dd_agent(self, dd_agent_host: str) -> None:
+        """
+        Configure DataDog Agent for log forwarding
+        
+        Args:
+            dd_agent_host: Hostname or IP of DataDog agent
+        """
+        dd_agent_port = os.getenv("DD_AGENT_PORT", "10518")  # default port for logs
+        self.intake_url = f"http://{dd_agent_host}:{dd_agent_port}/api/v2/logs"
+        self.DD_API_KEY = os.getenv("DD_API_KEY")  # Optional when using agent
+        verbose_logger.debug(f"Datadog: Using DD Agent at {self.intake_url}")
+
+    def _configure_dd_direct_api(self) -> None:
+        """
+        Configure direct DataDog API connection
+        
+        Raises:
+            Exception: If required environment variables are not set
+        """
+        if os.getenv("DD_API_KEY", None) is None:
+            raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>")
+        if os.getenv("DD_SITE", None) is None:
+            raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>")
+        
+        self.DD_API_KEY = os.getenv("DD_API_KEY")
+        self.intake_url = (
+            f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
+        )
+
+    def _apply_dd_base_url_override(self) -> None:
+        """
+        Apply base URL override for testing purposes
+        """
+        dd_base_url: Optional[str] = (
+            os.getenv("_DATADOG_BASE_URL")
+            or os.getenv("DATADOG_BASE_URL")
+            or os.getenv("DD_BASE_URL")
+        )
+        if dd_base_url is not None:
+            self.intake_url = f"{dd_base_url}/api/v2/logs"
+
     async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
         """
         Async Log success events to Datadog
@@ -226,12 +264,16 @@ class DataDogLogger(
                     end_time=end_time,
                 )
 
+            # Build headers
+            headers = {}
+            # Add API key if available (required for direct API, optional for agent)
+            if self.DD_API_KEY:
+                headers["DD-API-KEY"] = self.DD_API_KEY
+            
             response = self.sync_client.post(
                 url=self.intake_url,
                 json=dd_payload,  # type: ignore
-                headers={
-                    "DD-API-KEY": self.DD_API_KEY,
-                },
+                headers=headers,
             )
 
             response.raise_for_status()
@@ -342,14 +384,21 @@ class DataDogLogger(
 
         from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
         compressed_data = gzip.compress(safe_dumps(data).encode("utf-8"))
+        
+        # Build headers
+        headers = {
+            "Content-Encoding": "gzip",
+            "Content-Type": "application/json",
+        }
+        
+        # Add API key if available (required for direct API, optional for agent)
+        if self.DD_API_KEY:
+            headers["DD-API-KEY"] = self.DD_API_KEY
+        
         response = await self.async_client.post(
             url=self.intake_url,
             data=compressed_data,  # type: ignore
-            headers={
-                "DD-API-KEY": self.DD_API_KEY,
-                "Content-Encoding": "gzip",
-                "Content-Type": "application/json",
-            },
+            headers=headers,
         )
         return response
 
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
index 9a17244a06d..53b7825b3d3 100644
--- a/litellm/integrations/opentelemetry.py
+++ b/litellm/integrations/opentelemetry.py
@@ -10,6 +10,7 @@ from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.types.services import ServiceLoggerPayload
 from litellm.types.utils import (
     ChatCompletionMessageToolCall,
+    CostBreakdown,
     Function,
     StandardCallbackDynamicParams,
     StandardLoggingPayload,
@@ -1076,6 +1077,16 @@ class OpenTelemetry(CustomLogger):
                 self.safe_set_attribute(
                     span=span, key="hidden_params", value=safe_dumps(hidden_params)
                 )
+            # Cost breakdown tracking
+            cost_breakdown: Optional[CostBreakdown] = standard_logging_payload.get("cost_breakdown")
+            if cost_breakdown:
+                for key, value in cost_breakdown.items():
+                    if value is not None:
+                        self.safe_set_attribute(
+                            span=span,
+                            key=f"gen_ai.cost.{key}",
+                            value=value,
+                        )
             #############################################
             ########## LLM Request Attributes ###########
             #############################################
diff --git a/litellm/integrations/opik/utils.py b/litellm/integrations/opik/utils.py
index 8989a861cdc..b0ab5991c91 100644
--- a/litellm/integrations/opik/utils.py
+++ b/litellm/integrations/opik/utils.py
@@ -1,7 +1,7 @@
 import configparser
 import os
 import time
-from typing import Any, Dict, Final, List, Optional
+from typing import Any, Dict, Final, List, Optional, Tuple
 
 CONFIG_FILE_PATH_DEFAULT: Final[str] = "~/.opik.config"
 
@@ -106,7 +106,7 @@ def _remove_nulls(x: Dict[str, Any]) -> Dict[str, Any]:
 
 def get_traces_and_spans_from_payload(
     payload: List[Dict[str, Any]]
-) -> tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
     """
     Separate traces and spans from payload.
 
diff --git a/litellm/litellm_core_utils/audio_utils/utils.py b/litellm/litellm_core_utils/audio_utils/utils.py
index fc0c8aca842..2f0db4978ff 100644
--- a/litellm/litellm_core_utils/audio_utils/utils.py
+++ b/litellm/litellm_core_utils/audio_utils/utils.py
@@ -4,6 +4,7 @@ Utils used for litellm.transcription() and litellm.atranscription()
 
 import os
 from dataclasses import dataclass
+from typing import Optional
 
 from litellm.types.files import get_file_mime_type_from_extension
 from litellm.types.utils import FileTypes
@@ -13,12 +14,13 @@ from litellm.types.utils import FileTypes
 class ProcessedAudioFile:
     """
     Processed audio file data.
-    
+
     Attributes:
         file_content: The binary content of the audio file
         filename: The filename (extracted or generated)
         content_type: The MIME type of the audio file
     """
+
     file_content: bytes
     filename: str
     content_type: str
@@ -27,61 +29,63 @@ class ProcessedAudioFile:
 def process_audio_file(audio_file: FileTypes) -> ProcessedAudioFile:
     """
     Common utility function to process audio files for audio transcription APIs.
-    
+
     Handles various input types:
     - File paths (str, os.PathLike)
     - Raw bytes/bytearray
     - Tuples (filename, content, optional content_type)
     - File-like objects with read() method
-    
+
     Args:
         audio_file: The audio file input in various formats
-        
+
     Returns:
         ProcessedAudioFile: Structured data with file content, filename, and content type
-        
+
     Raises:
         ValueError: If audio_file type is unsupported or content cannot be extracted
     """
     file_content = None
     filename = None
-    
+
     if isinstance(audio_file, (bytes, bytearray)):
         # Raw bytes
-        filename = 'audio.wav'
+        filename = "audio.wav"
         file_content = bytes(audio_file)
     elif isinstance(audio_file, (str, os.PathLike)):
         # File path or PathLike
         file_path = str(audio_file)
-        with open(file_path, 'rb') as f:
+        with open(file_path, "rb") as f:
             file_content = f.read()
-        filename = file_path.split('/')[-1]
+        filename = file_path.split("/")[-1]
     elif isinstance(audio_file, tuple):
         # Tuple format: (filename, content, content_type) or (filename, content)
         if len(audio_file) >= 2:
-            filename = audio_file[0] or 'audio.wav'
+            filename = audio_file[0] or "audio.wav"
             content = audio_file[1]
             if isinstance(content, (bytes, bytearray)):
                 file_content = bytes(content)
             elif isinstance(content, (str, os.PathLike)):
                 # File path or PathLike
-                with open(str(content), 'rb') as f:
+                with open(str(content), "rb") as f:
                     file_content = f.read()
-            elif hasattr(content, 'read'):
+            elif hasattr(content, "read"):
                 # File-like object
                 file_content = content.read()
-                if hasattr(content, 'seek'):
+                if hasattr(content, "seek"):
                     content.seek(0)
             else:
                 raise ValueError(f"Unsupported content type in tuple: {type(content)}")
         else:
             raise ValueError("Tuple must have at least 2 elements: (filename, content)")
-    elif hasattr(audio_file, 'read') and not isinstance(audio_file, (str, bytes, bytearray, tuple, os.PathLike)):
+    elif hasattr(audio_file, "read") and not isinstance(
+        audio_file, (str, bytes, bytearray, tuple, os.PathLike)
+    ):
         # File-like object (IO) - check this after all other types
-        filename = getattr(audio_file, 'name', 'audio.wav')
+        filename = getattr(audio_file, "name", "audio.wav")
         file_content = audio_file.read()  # type: ignore
         # Reset file pointer if possible
-        if hasattr(audio_file, 'seek'):
+        if hasattr(audio_file, "seek"):
             audio_file.seek(0)  # type: ignore
     else:
         raise ValueError(f"Unsupported audio_file type: {type(audio_file)}")
@@ -90,20 +94,18 @@ def process_audio_file(audio_file: FileTypes) -> ProcessedAudioFile:
         raise ValueError("Could not extract file content from audio_file")
 
     # Determine content type using LiteLLM's file type utilities
-    content_type = 'audio/wav'  # Default fallback
+    content_type = "audio/wav"  # Default fallback
     if filename:
         try:
             # Extract extension from filename
-            extension = filename.split('.')[-1].lower() if '.' in filename else 'wav'
+            extension = filename.split(".")[-1].lower() if "." in filename else "wav"
             content_type = get_file_mime_type_from_extension(extension)
         except ValueError:
             # If extension is not recognized, fallback to audio/wav
-            content_type = 'audio/wav'
-    
+            content_type = "audio/wav"
+
     return ProcessedAudioFile(
-        file_content=file_content,
-        filename=filename,
-        content_type=content_type
+        file_content=file_content, filename=filename, content_type=content_type
     )
 
 
@@ -134,3 +136,74 @@ def get_audio_file_for_health_check() -> FileTypes:
     pwd = os.path.dirname(os.path.realpath(__file__))
     file_path = os.path.join(pwd, "audio_health_check.wav")
     return open(file_path, "rb")
+
+
+def calculate_request_duration(file: FileTypes) -> Optional[float]:
+    """
+    Calculate audio duration from file content.
+
+    Args:
+        file: The audio file (can be file path, bytes, or file-like object)
+
+    Returns:
+        Duration in seconds, or None if extraction fails or soundfile is not available
+    """
+    try:
+        import soundfile as sf
+    except ImportError:
+        # soundfile not available, cannot extract duration
+        return None
+
+    try:
+        import io
+
+        # Handle different file input types
+        file_content: Optional[bytes] = None
+
+        if isinstance(file, (bytes, bytearray)):
+            # Raw bytes
+            file_content = bytes(file)
+        elif isinstance(file, (str, os.PathLike)):
+            # File path
+            with open(str(file), "rb") as f:
+                file_content = f.read()
+        elif isinstance(file, tuple):
+            # Tuple format: (filename, content, optional content_type)
+            if len(file) >= 2:
+                content = file[1]
+                if isinstance(content, bytes):
+                    file_content = content
+                elif hasattr(content, "read") and not isinstance(
+                    content, (str, os.PathLike)
+                ):
+                    # File-like object in tuple
+                    current_pos = getattr(content, "tell", lambda: None)()
+                    # Seek to start to ensure we read the entire content
+                    if hasattr(content, "seek"):
+                        content.seek(0)
+                    file_content = content.read()
+                    if current_pos is not None and hasattr(content, "seek"):
+                        content.seek(current_pos)
+        elif hasattr(file, "read") and not isinstance(file, tuple):
+            # File-like object (including BytesIO)
+            current_position = file.tell() if hasattr(file, "tell") else None
+            # Seek to start to ensure we read the entire content
+            if hasattr(file, "seek"):
+                file.seek(0)
+            file_content = file.read()
+            # Reset file position if possible
+            if current_position is not None and hasattr(file, "seek"):
+                file.seek(current_position)
+
+        if file_content is None or not isinstance(file_content, bytes):
+            return None
+
+        # Extract duration using soundfile
+        file_object = io.BytesIO(file_content)
+        with sf.SoundFile(file_object) as audio:
+            duration = len(audio) / audio.samplerate
+            return duration
+
+    except Exception:
+        # Silently fail if duration extraction fails
+        return None
diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py
index 61551b04236..1a43ff2e176 100644
--- a/litellm/litellm_core_utils/exception_mapping_utils.py
+++ b/litellm/litellm_core_utils/exception_mapping_utils.py
@@ -12,6 +12,7 @@ from ..exceptions import (
     APIConnectionError,
     APIError,
     AuthenticationError,
+    BadGatewayError,
     BadRequestError,
     ContentPolicyViolationError,
     ContextWindowExceededError,
@@ -43,16 +44,16 @@ class ExceptionCheckers:
         """
         if not isinstance(error_str, str):
             return False
-        
+
         if "429" in error_str or "rate limit" in error_str.lower():
             return True
-        
+
         #######################################
         # Mistral API returns this error string
         #########################################
         if "service tier capacity exceeded" in error_str.lower():
             return True
-        
+
         return False
 
     @staticmethod
@@ -73,6 +74,24 @@ class ExceptionCheckers:
             if substring in _error_str_lowercase:
                 return True
         return False
+    
+    @staticmethod
+    def is_azure_content_policy_violation_error(error_str: str) -> bool:
+        """
+        Check if an error string indicates a content policy violation error.
+        """
+        known_exception_substrings = [
+            "invalid_request_error",
+            "content_policy_violation",
+            "the response was filtered due to the prompt triggering azure openai's content management",
+            "your task failed as a result of our safety system",
+            "the model produced invalid content",
+            "content_filter_policy",
+        ]
+        for substring in known_exception_substrings:
+            if substring in error_str.lower():
+                return True
+        return False
 
 
 def get_error_message(error_obj) -> Optional[str]:
@@ -507,6 +526,15 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             response=getattr(original_exception, "response", None),
                             litellm_debug_info=extra_information,
                         )
+                    elif original_exception.status_code == 502:
+                        exception_mapping_worked = True
+                        raise BadGatewayError(
+                            message=f"BadGatewayError: {exception_provider} - {message}",
+                            model=model,
+                            llm_provider=custom_llm_provider,
+                            response=getattr(original_exception, "response", None),
+                            litellm_debug_info=extra_information,
+                        )
                     elif original_exception.status_code == 503:
                         exception_mapping_worked = True
                         raise ServiceUnavailableError(
@@ -637,6 +665,15 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.",
                             llm_provider="anthropic",
                             model=model,
+                            response=getattr(original_exception, "response", None),
+                        )
+                    elif original_exception.status_code == 502:
+                        exception_mapping_worked = True
+                        raise BadGatewayError(
+                            message=f"AnthropicException BadGatewayError - {error_str}",
+                            llm_provider="anthropic",
+                            model=model,
+                            response=getattr(original_exception, "response", None),
                         )
                     elif original_exception.status_code == 503:
                         exception_mapping_worked = True
@@ -644,6 +681,15 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.",
                             llm_provider="anthropic",
                             model=model,
+                            response=getattr(original_exception, "response", None),
+                        )
+                    elif original_exception.status_code == 504:  # gateway timeout error
+                        exception_mapping_worked = True
+                        raise Timeout(
+                            message=f"AnthropicException Timeout - {error_str}",
+                            model=model,
+                            llm_provider="anthropic",
+                            exception_status_code=original_exception.status_code,
                         )
             elif custom_llm_provider == "replicate":
                 if "Incorrect authentication token" in error_str:
@@ -1260,6 +1306,7 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                 elif (
                     "429 Quota exceeded" in error_str
                     or "Quota exceeded for" in error_str
+                    or "Resource exhausted" in error_str
                     or "IndexError: list index out of range" in error_str
                     or "429 Unable to submit request because the service is temporarily out of capacity."
                     in error_str
@@ -1992,26 +2039,19 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         response=getattr(original_exception, "response", None),
                     )
                 elif (
-                    (
-                        "invalid_request_error" in error_str
-                        and "content_policy_violation" in error_str
-                    )
-                    or (
-                        "The response was filtered due to the prompt triggering Azure OpenAI's content management"
-                        in error_str
-                    )
-                    or "Your task failed as a result of our safety system" in error_str
-                    or "The model produced invalid content" in error_str
-                    or "content_filter_policy" in error_str
+                    ExceptionCheckers.is_azure_content_policy_violation_error(error_str)
                 ):
                     exception_mapping_worked = True
-                    raise ContentPolicyViolationError(
-                        message=f"litellm.ContentPolicyViolationError: AzureException - {message}",
-                        llm_provider="azure",
-                        model=model,
-                        litellm_debug_info=extra_information,
-                        response=getattr(original_exception, "response", None),
+                    from litellm.llms.azure.exception_mapping import (
+                        AzureOpenAIExceptionMapping,
                     )
+                    raise AzureOpenAIExceptionMapping.create_content_policy_violation_error(
+                        message=message,
+                        model=model,
+                        extra_information=extra_information,
+                        original_exception=original_exception,
+                    )
+                    
                 elif "invalid_request_error" in error_str:
                     exception_mapping_worked = True
                     raise BadRequestError(
@@ -2089,6 +2129,15 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                             litellm_debug_info=extra_information,
                             response=getattr(original_exception, "response", None),
                         )
+                    elif original_exception.status_code == 502:
+                        exception_mapping_worked = True
+                        raise BadGatewayError(
+                            message=f"AzureException BadGatewayError - {message}",
+                            model=model,
+                            llm_provider="azure",
+                            litellm_debug_info=extra_information,
+                            response=getattr(original_exception, "response", None),
+                        )
                     elif original_exception.status_code == 503:
                         exception_mapping_worked = True
                         raise ServiceUnavailableError(
diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py
index c167c202e5d..d5675a2ac51 100644
--- a/litellm/litellm_core_utils/get_litellm_params.py
+++ b/litellm/litellm_core_utils/get_litellm_params.py
@@ -120,5 +120,6 @@ def get_litellm_params(
         "vertex_project": kwargs.get("vertex_project"),
         "use_litellm_proxy": use_litellm_proxy,
         "litellm_request_debug": litellm_request_debug,
+        "aws_region_name": kwargs.get("aws_region_name"),
     }
     return litellm_params
diff --git a/litellm/litellm_core_utils/health_check_helpers.py b/litellm/litellm_core_utils/health_check_helpers.py
index 9cbee7fc70d..cc3916af069 100644
--- a/litellm/litellm_core_utils/health_check_helpers.py
+++ b/litellm/litellm_core_utils/health_check_helpers.py
@@ -97,6 +97,7 @@ class HealthCheckHelpers:
             "audio_speech",
             "audio_transcription",
             "image_generation",
+            "video_generation",
             "rerank",
             "realtime",
             "batch",
@@ -159,6 +160,10 @@ class HealthCheckHelpers:
                 **_filter_model_params(model_params=model_params),
                 prompt=prompt,
             ),
+            "video_generation": lambda: litellm.avideo_generation(
+                **_filter_model_params(model_params=model_params),
+                prompt=prompt or "test video generation",
+            ),
             "rerank": lambda: litellm.arerank(
                 **_filter_model_params(model_params=model_params),
                 query=prompt or "",
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index d7be4d296b3..41a5eed55d8 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -308,9 +308,9 @@ class Logging(LiteLLMLoggingBaseClass):
         self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4())
         self.function_id = function_id
         self.streaming_chunks: List[Any] = []  # for generating complete stream response
-        self.sync_streaming_chunks: List[
-            Any
-        ] = []  # for generating complete stream response
+        self.sync_streaming_chunks: List[Any] = (
+            []
+        )  # for generating complete stream response
         self.log_raw_request_response = log_raw_request_response
 
         # Initialize dynamic callbacks
@@ -686,9 +686,9 @@ class Logging(LiteLLMLoggingBaseClass):
         if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook(
             non_default_params
         ):
-            self.model_call_details[
-                "prompt_integration"
-            ] = anthropic_cache_control_logger.__class__.__name__
+            self.model_call_details["prompt_integration"] = (
+                anthropic_cache_control_logger.__class__.__name__
+            )
             return anthropic_cache_control_logger
 
         #########################################################
@@ -700,9 +700,9 @@ class Logging(LiteLLMLoggingBaseClass):
                 internal_usage_cache=None,
                 llm_router=None,
             )
-            self.model_call_details[
-                "prompt_integration"
-            ] = vector_store_custom_logger.__class__.__name__
+            self.model_call_details["prompt_integration"] = (
+                vector_store_custom_logger.__class__.__name__
+            )
             # Add to global callbacks so post-call hooks are invoked
             if (
                 vector_store_custom_logger
@@ -762,9 +762,9 @@ class Logging(LiteLLMLoggingBaseClass):
             model
         ):  # if model name was changes pre-call, overwrite the initial model call name with the new one
             self.model_call_details["model"] = model
-        self.model_call_details["litellm_params"][
-            "api_base"
-        ] = self._get_masked_api_base(additional_args.get("api_base", ""))
+        self.model_call_details["litellm_params"]["api_base"] = (
+            self._get_masked_api_base(additional_args.get("api_base", ""))
+        )
 
     def pre_call(self, input, api_key, model=None, additional_args={}):  # noqa: PLR0915
         # Log the exact input to the LLM API
@@ -793,10 +793,10 @@ class Logging(LiteLLMLoggingBaseClass):
                 try:
                     # [Non-blocking Extra Debug Information in metadata]
                     if turn_off_message_logging is True:
-                        _metadata[
-                            "raw_request"
-                        ] = "redacted by litellm. \
+                        _metadata["raw_request"] = (
+                            "redacted by litellm. \
                             'litellm.turn_off_message_logging=True'"
+                        )
                     else:
                         curl_command = self._get_request_curl_command(
                             api_base=additional_args.get("api_base", ""),
@@ -807,32 +807,32 @@ class Logging(LiteLLMLoggingBaseClass):
 
                         _metadata["raw_request"] = str(curl_command)
                         # split up, so it's easier to parse in the UI
-                        self.model_call_details[
-                            "raw_request_typed_dict"
-                        ] = RawRequestTypedDict(
-                            raw_request_api_base=str(
-                                additional_args.get("api_base") or ""
-                            ),
-                            raw_request_body=self._get_raw_request_body(
-                                additional_args.get("complete_input_dict", {})
-                            ),
-                            raw_request_headers=self._get_masked_headers(
-                                additional_args.get("headers", {}) or {},
-                                ignore_sensitive_headers=True,
-                            ),
-                            error=None,
+                        self.model_call_details["raw_request_typed_dict"] = (
+                            RawRequestTypedDict(
+                                raw_request_api_base=str(
+                                    additional_args.get("api_base") or ""
+                                ),
+                                raw_request_body=self._get_raw_request_body(
+                                    additional_args.get("complete_input_dict", {})
+                                ),
+                                raw_request_headers=self._get_masked_headers(
+                                    additional_args.get("headers", {}) or {},
+                                    ignore_sensitive_headers=True,
+                                ),
+                                error=None,
+                            )
                         )
                 except Exception as e:
-                    self.model_call_details[
-                        "raw_request_typed_dict"
-                    ] = RawRequestTypedDict(
-                        error=str(e),
+                    self.model_call_details["raw_request_typed_dict"] = (
+                        RawRequestTypedDict(
+                            error=str(e),
+                        )
                     )
-                    _metadata[
-                        "raw_request"
-                    ] = "Unable to Log \
+                    _metadata["raw_request"] = (
+                        "Unable to Log \
                         raw request: {}".format(
-                        str(e)
+                            str(e)
+                        )
                     )
             if getattr(self, "logger_fn", None) and callable(self.logger_fn):
                 try:
@@ -1133,13 +1133,13 @@ class Logging(LiteLLMLoggingBaseClass):
         for callback in callbacks:
             try:
                 if isinstance(callback, CustomLogger):
-                    response: Optional[
-                        MCPPostCallResponseObject
-                    ] = await callback.async_post_mcp_tool_call_hook(
-                        kwargs=kwargs,
-                        response_obj=post_mcp_tool_call_response_obj,
-                        start_time=start_time,
-                        end_time=end_time,
+                    response: Optional[MCPPostCallResponseObject] = (
+                        await callback.async_post_mcp_tool_call_hook(
+                            kwargs=kwargs,
+                            response_obj=post_mcp_tool_call_response_obj,
+                            start_time=start_time,
+                            end_time=end_time,
+                        )
                     )
                     ######################################################################
                     # if any of the callbacks modify the response, use the modified response
@@ -1243,6 +1243,7 @@ class Logging(LiteLLMLoggingBaseClass):
 
         used for consistent cost calculation across response headers + logging integrations.
         """
+
         if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"):
             hidden_params = getattr(result, "_hidden_params", {})
             if (
@@ -1302,9 +1303,9 @@ class Logging(LiteLLMLoggingBaseClass):
             verbose_logger.debug(
                 f"response_cost_failure_debug_information: {debug_info}"
             )
-            self.model_call_details[
-                "response_cost_failure_debug_information"
-            ] = debug_info
+            self.model_call_details["response_cost_failure_debug_information"] = (
+                debug_info
+            )
             return None
 
         try:
@@ -1330,9 +1331,9 @@ class Logging(LiteLLMLoggingBaseClass):
             verbose_logger.debug(
                 f"response_cost_failure_debug_information: {debug_info}"
             )
-            self.model_call_details[
-                "response_cost_failure_debug_information"
-            ] = debug_info
+            self.model_call_details["response_cost_failure_debug_information"] = (
+                debug_info
+            )
 
         return None
 
@@ -1461,6 +1462,51 @@ class Logging(LiteLLMLoggingBaseClass):
                 )
         return logging_result
 
+    def _process_hidden_params_and_response_cost(
+        self,
+        logging_result,
+        start_time,
+        end_time,
+    ):
+        hidden_params = getattr(logging_result, "_hidden_params", {})
+        if hidden_params:
+            if self.model_call_details.get("litellm_params") is not None:
+                self.model_call_details["litellm_params"].setdefault("metadata", {})
+                if self.model_call_details["litellm_params"]["metadata"] is None:
+                    self.model_call_details["litellm_params"]["metadata"] = {}
+                self.model_call_details["litellm_params"]["metadata"]["hidden_params"] = getattr(logging_result, "_hidden_params", {})  # type: ignore
+        
+        if "response_cost" in hidden_params:
+            self.model_call_details["response_cost"] = hidden_params["response_cost"]
+        else:
+            self.model_call_details["response_cost"] = self._response_cost_calculator(result=logging_result)
+        
+        self.model_call_details["standard_logging_object"] = get_standard_logging_object_payload(
+            kwargs=self.model_call_details,
+            init_response_obj=logging_result,
+            start_time=start_time,
+            end_time=end_time,
+            logging_obj=self,
+            status="success",
+            standard_built_in_tools_params=self.standard_built_in_tools_params,
+        )
+
+    def _transform_usage_objects(self, result):
+        if isinstance(result, ResponsesAPIResponse):
+            result = result.model_copy()
+            transformed_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(result.usage)
+            setattr(result, "usage", transformed_usage.model_dump() if hasattr(transformed_usage, "model_dump") else dict(transformed_usage))
+            if (standard_logging_payload := self.model_call_details.get("standard_logging_object")) is not None:
+                standard_logging_payload["response"] = result.model_dump() if hasattr(result, "model_dump") else dict(result)
+        elif isinstance(result, TranscriptionResponse):
+            from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import (
+                TranscriptionUsageObjectTransformation,
+            )
+            result = result.model_copy()
+            transformed_usage = TranscriptionUsageObjectTransformation.transform_transcription_usage_object(result.usage)  # type: ignore
+            setattr(result, "usage", transformed_usage)
+        return result
+
     def _success_handler_helper_fn(
         self,
         result=None,
@@ -1476,82 +1522,24 @@ class Logging(LiteLLMLoggingBaseClass):
                 end_time = datetime.datetime.now()
             if self.completion_start_time is None:
                 self.completion_start_time = end_time
-                self.model_call_details[
-                    "completion_start_time"
-                ] = self.completion_start_time
+                self.model_call_details["completion_start_time"] = self.completion_start_time
+            
             self.model_call_details["log_event_type"] = "successful_api_call"
             self.model_call_details["end_time"] = end_time
             self.model_call_details["cache_hit"] = cache_hit
+            
             if self.call_type == CallTypes.anthropic_messages.value:
                 result = self._handle_anthropic_messages_response_logging(result=result)
-            elif (
-                self.call_type == CallTypes.generate_content.value
-                or self.call_type == CallTypes.agenerate_content.value
-            ):
-                result = self._handle_non_streaming_google_genai_generate_content_response_logging(
-                    result=result
-                )
-            ## if model in model cost map - log the response cost
-            ## else set cost to None
-
+            elif self.call_type == CallTypes.generate_content.value or self.call_type == CallTypes.agenerate_content.value:
+                result = self._handle_non_streaming_google_genai_generate_content_response_logging(result=result)
+            
             logging_result = self.normalize_logging_result(result=result)
 
-            if (
-                standard_logging_object is None
-                and result is not None
-                and self.stream is not True
-            ):
-                if self._is_recognized_call_type_for_logging(
-                    logging_result=logging_result
-                ):
-                    ## HIDDEN PARAMS ##
-                    hidden_params = getattr(logging_result, "_hidden_params", {})
-                    if hidden_params:
-                        # add to metadata for logging
-                        if self.model_call_details.get("litellm_params") is not None:
-                            self.model_call_details["litellm_params"].setdefault(
-                                "metadata", {}
-                            )
-                            if (
-                                self.model_call_details["litellm_params"]["metadata"]
-                                is None
-                            ):
-                                self.model_call_details["litellm_params"][
-                                    "metadata"
-                                ] = {}
-
-                            self.model_call_details["litellm_params"]["metadata"][  # type: ignore
-                                "hidden_params"
-                            ] = getattr(
-                                logging_result, "_hidden_params", {}
-                            )
-                    ## RESPONSE COST - Only calculate if not in hidden_params ##
-                    if "response_cost" in hidden_params:
-                        self.model_call_details["response_cost"] = hidden_params[
-                            "response_cost"
-                        ]
-                    else:
-                        self.model_call_details[
-                            "response_cost"
-                        ] = self._response_cost_calculator(result=logging_result)
-                    ## STANDARDIZED LOGGING PAYLOAD
-
-                    self.model_call_details[
-                        "standard_logging_object"
-                    ] = get_standard_logging_object_payload(
-                        kwargs=self.model_call_details,
-                        init_response_obj=logging_result,
-                        start_time=start_time,
-                        end_time=end_time,
-                        logging_obj=self,
-                        status="success",
-                        standard_built_in_tools_params=self.standard_built_in_tools_params,
-                    )
+            if standard_logging_object is None and result is not None and self.stream is not True:
+                if self._is_recognized_call_type_for_logging(logging_result=logging_result):
+                    self._process_hidden_params_and_response_cost(logging_result=logging_result, start_time=start_time, end_time=end_time)
                 elif isinstance(result, dict) or isinstance(result, list):
-                    ## STANDARDIZED LOGGING PAYLOAD
-                    self.model_call_details[
-                        "standard_logging_object"
-                    ] = get_standard_logging_object_payload(
+                    self.model_call_details["standard_logging_object"] = get_standard_logging_object_payload(
                         kwargs=self.model_call_details,
                         init_response_obj=result,
                         start_time=start_time,
@@ -1561,31 +1549,13 @@ class Logging(LiteLLMLoggingBaseClass):
                         standard_built_in_tools_params=self.standard_built_in_tools_params,
                     )
             elif standard_logging_object is not None:
-                self.model_call_details[
-                    "standard_logging_object"
-                ] = standard_logging_object
-            else:  # streaming chunks + image gen.
+                self.model_call_details["standard_logging_object"] = standard_logging_object
+            else:
                 self.model_call_details["response_cost"] = None
 
-            ## RESPONSES API USAGE OBJECT TRANSFORMATION ##
-            # MAP RESPONSES API USAGE OBJECT TO LITELLM USAGE OBJECT
-            if isinstance(result, ResponsesAPIResponse):
-                result = result.model_copy()
-                setattr(
-                    result,
-                    "usage",
-                    ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
-                        result.usage
-                    ),
-                )
-
-            if (
-                litellm.max_budget
-                and self.stream is False
-                and result is not None
-                and isinstance(result, dict)
-                and "content" in result
-            ):
+            result = self._transform_usage_objects(result=result)
+            
+            if litellm.max_budget and self.stream is False and result is not None and isinstance(result, dict) and "content" in result:
                 time_diff = (end_time - start_time).total_seconds()
                 float_diff = float(time_diff)
                 litellm._current_cost += litellm.completion_cost(
@@ -1625,7 +1595,7 @@ class Logging(LiteLLMLoggingBaseClass):
             or isinstance(logging_result, OCRResponse)  # OCR
             or isinstance(logging_result, dict)
             and logging_result.get("object") == "vector_store.search_results.page"
-            or isinstance(logging_result, VideoObject) 
+            or isinstance(logging_result, VideoObject)
             or isinstance(logging_result, ContainerObject)
             or (self.call_type == CallTypes.call_mcp_tool.value)
         ):
@@ -1719,23 +1689,23 @@ class Logging(LiteLLMLoggingBaseClass):
                 verbose_logger.debug(
                     "Logging Details LiteLLM-Success Call streaming complete"
                 )
-                self.model_call_details[
-                    "complete_streaming_response"
-                ] = complete_streaming_response
-                self.model_call_details[
-                    "response_cost"
-                ] = self._response_cost_calculator(result=complete_streaming_response)
+                self.model_call_details["complete_streaming_response"] = (
+                    complete_streaming_response
+                )
+                self.model_call_details["response_cost"] = (
+                    self._response_cost_calculator(result=complete_streaming_response)
+                )
                 ## STANDARDIZED LOGGING PAYLOAD
-                self.model_call_details[
-                    "standard_logging_object"
-                ] = get_standard_logging_object_payload(
-                    kwargs=self.model_call_details,
-                    init_response_obj=complete_streaming_response,
-                    start_time=start_time,
-                    end_time=end_time,
-                    logging_obj=self,
-                    status="success",
-                    standard_built_in_tools_params=self.standard_built_in_tools_params,
+                self.model_call_details["standard_logging_object"] = (
+                    get_standard_logging_object_payload(
+                        kwargs=self.model_call_details,
+                        init_response_obj=complete_streaming_response,
+                        start_time=start_time,
+                        end_time=end_time,
+                        logging_obj=self,
+                        status="success",
+                        standard_built_in_tools_params=self.standard_built_in_tools_params,
+                    )
                 )
             callbacks = self.get_combined_callback_list(
                 dynamic_success_callbacks=self.dynamic_success_callbacks,
@@ -2063,10 +2033,10 @@ class Logging(LiteLLMLoggingBaseClass):
                             )
                         else:
                             if self.stream and complete_streaming_response:
-                                self.model_call_details[
-                                    "complete_response"
-                                ] = self.model_call_details.get(
-                                    "complete_streaming_response", {}
+                                self.model_call_details["complete_response"] = (
+                                    self.model_call_details.get(
+                                        "complete_streaming_response", {}
+                                    )
                                 )
                                 result = self.model_call_details["complete_response"]
                             openMeterLogger.log_success_event(
@@ -2105,10 +2075,10 @@ class Logging(LiteLLMLoggingBaseClass):
                             )
                         else:
                             if self.stream and complete_streaming_response:
-                                self.model_call_details[
-                                    "complete_response"
-                                ] = self.model_call_details.get(
-                                    "complete_streaming_response", {}
+                                self.model_call_details["complete_response"] = (
+                                    self.model_call_details.get(
+                                        "complete_streaming_response", {}
+                                    )
                                 )
                                 result = self.model_call_details["complete_response"]
 
@@ -2251,9 +2221,9 @@ class Logging(LiteLLMLoggingBaseClass):
         if complete_streaming_response is not None:
             print_verbose("Async success callbacks: Got a complete streaming response")
 
-            self.model_call_details[
-                "async_complete_streaming_response"
-            ] = complete_streaming_response
+            self.model_call_details["async_complete_streaming_response"] = (
+                complete_streaming_response
+            )
 
             try:
                 if self.model_call_details.get("cache_hit", False) is True:
@@ -2264,10 +2234,10 @@ class Logging(LiteLLMLoggingBaseClass):
                         model_call_details=self.model_call_details
                     )
                     # base_model defaults to None if not set on model_info
-                    self.model_call_details[
-                        "response_cost"
-                    ] = self._response_cost_calculator(
-                        result=complete_streaming_response
+                    self.model_call_details["response_cost"] = (
+                        self._response_cost_calculator(
+                            result=complete_streaming_response
+                        )
                     )
 
                 verbose_logger.debug(
@@ -2280,16 +2250,16 @@ class Logging(LiteLLMLoggingBaseClass):
                 self.model_call_details["response_cost"] = None
 
             ## STANDARDIZED LOGGING PAYLOAD
-            self.model_call_details[
-                "standard_logging_object"
-            ] = get_standard_logging_object_payload(
-                kwargs=self.model_call_details,
-                init_response_obj=complete_streaming_response,
-                start_time=start_time,
-                end_time=end_time,
-                logging_obj=self,
-                status="success",
-                standard_built_in_tools_params=self.standard_built_in_tools_params,
+            self.model_call_details["standard_logging_object"] = (
+                get_standard_logging_object_payload(
+                    kwargs=self.model_call_details,
+                    init_response_obj=complete_streaming_response,
+                    start_time=start_time,
+                    end_time=end_time,
+                    logging_obj=self,
+                    status="success",
+                    standard_built_in_tools_params=self.standard_built_in_tools_params,
+                )
             )
         callbacks = self.get_combined_callback_list(
             dynamic_success_callbacks=self.dynamic_async_success_callbacks,
@@ -2478,26 +2448,24 @@ class Logging(LiteLLMLoggingBaseClass):
     def _handle_callback_failure(self, callback: Any):
         """
         Handle callback logging failures by incrementing Prometheus metrics.
-        
+
         Works for both sync and async contexts since Prometheus counter increment is synchronous.
-        
+
         Args:
             callback: The callback that failed
         """
         try:
             callback_name = self._get_callback_name(callback)
-            
+
             all_callbacks = litellm.logging_callback_manager._get_all_callbacks()
-                        
+
             for callback_obj in all_callbacks:
-                if hasattr(callback_obj, 'increment_callback_logging_failure'):
+                if hasattr(callback_obj, "increment_callback_logging_failure"):
                     callback_obj.increment_callback_logging_failure(callback_name=callback_name)  # type: ignore
                     break  # Only increment once
-                    
+
         except Exception as e:
-            verbose_logger.debug(
-                f"Error in _handle_callback_failure: {str(e)}"
-            )
+            verbose_logger.debug(f"Error in _handle_callback_failure: {str(e)}")
 
     def _failure_handler_helper_fn(
         self, exception, traceback_exception, start_time=None, end_time=None
@@ -2527,18 +2495,18 @@ class Logging(LiteLLMLoggingBaseClass):
 
         ## STANDARDIZED LOGGING PAYLOAD
 
-        self.model_call_details[
-            "standard_logging_object"
-        ] = get_standard_logging_object_payload(
-            kwargs=self.model_call_details,
-            init_response_obj={},
-            start_time=start_time,
-            end_time=end_time,
-            logging_obj=self,
-            status="failure",
-            error_str=str(exception),
-            original_exception=exception,
-            standard_built_in_tools_params=self.standard_built_in_tools_params,
+        self.model_call_details["standard_logging_object"] = (
+            get_standard_logging_object_payload(
+                kwargs=self.model_call_details,
+                init_response_obj={},
+                start_time=start_time,
+                end_time=end_time,
+                logging_obj=self,
+                status="failure",
+                error_str=str(exception),
+                original_exception=exception,
+                standard_built_in_tools_params=self.standard_built_in_tools_params,
+            )
         )
         return start_time, end_time
 
@@ -2975,12 +2943,12 @@ class Logging(LiteLLMLoggingBaseClass):
         """
         if isinstance(cb, str):
             return cb
-        if hasattr(cb, "__class__"):
-            return cb.__class__.__name__
         if hasattr(cb, "__name__"):
             return cb.__name__
         if hasattr(cb, "__func__"):
             return cb.__func__.__name__
+        if hasattr(cb, "__class__"):
+            return cb.__class__.__name__
         return str(cb)
 
     def _is_internal_litellm_proxy_callback(self, cb) -> bool:
@@ -3036,13 +3004,19 @@ class Logging(LiteLLMLoggingBaseClass):
         elif isinstance(result, ResponseCompletedEvent):
             ## return unified Usage object
             if isinstance(result.response.usage, ResponseAPIUsage):
+                transformed_usage = (
+                    ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
+                        result.response.usage
+                    )
+                )
+                # Set as dict instead of Usage object so model_dump() serializes it correctly
                 setattr(
                     result.response,
                     "usage",
                     (
-                        ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
-                            result.response.usage
-                        )
+                        transformed_usage.model_dump()
+                        if hasattr(transformed_usage, "model_dump")
+                        else dict(transformed_usage)
                     ),
                 )
             return result.response
@@ -3443,9 +3417,9 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
                 endpoint=arize_config.endpoint,
             )
 
-            os.environ[
-                "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
-            ] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
+            os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+                f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
+            )
             for callback in _in_memory_loggers:
                 if (
                     isinstance(callback, ArizeLogger)
@@ -3469,9 +3443,9 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
 
             # auth can be disabled on local deployments of arize phoenix
             if arize_phoenix_config.otlp_auth_headers is not None:
-                os.environ[
-                    "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
-                ] = arize_phoenix_config.otlp_auth_headers
+                os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+                    arize_phoenix_config.otlp_auth_headers
+                )
 
             for callback in _in_memory_loggers:
                 if (
@@ -3603,9 +3577,9 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
                 exporter="otlp_http",
                 endpoint="https://langtrace.ai/api/trace",
             )
-            os.environ[
-                "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
-            ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}"
+            os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+                f"api_key={os.getenv('LANGTRACE_API_KEY')}"
+            )
             for callback in _in_memory_loggers:
                 if (
                     isinstance(callback, OpenTelemetry)
@@ -4305,10 +4279,10 @@ class StandardLoggingPayloadSetup:
             for key in StandardLoggingHiddenParams.__annotations__.keys():
                 if key in hidden_params:
                     if key == "additional_headers":
-                        clean_hidden_params[
-                            "additional_headers"
-                        ] = StandardLoggingPayloadSetup.get_additional_headers(
-                            hidden_params[key]
+                        clean_hidden_params["additional_headers"] = (
+                            StandardLoggingPayloadSetup.get_additional_headers(
+                                hidden_params[key]
+                            )
                         )
                     else:
                         clean_hidden_params[key] = hidden_params[key]  # type: ignore
@@ -4369,7 +4343,7 @@ class StandardLoggingPayloadSetup:
 
             s3_object_key = get_s3_object_key(
                 s3_path=s3_path,  # Use actual s3_path from logger configuration
-                team_alias_prefix="",  # Don't split by team alias for cold storage
+                prefix="",  # Don't split by team alias for cold storage
                 start_time=start_time,
                 s3_file_name=s3_file_name,
             )
@@ -4871,9 +4845,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]):
     ):
         for k, v in metadata["user_api_key_metadata"].items():
             if k == "logging":  # prevent logging user logging keys
-                cleaned_user_api_key_metadata[
-                    k
-                ] = "scrubbed_by_litellm_for_sensitive_keys"
+                cleaned_user_api_key_metadata[k] = (
+                    "scrubbed_by_litellm_for_sensitive_keys"
+                )
             else:
                 cleaned_user_api_key_metadata[k] = v
 
diff --git a/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py b/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py
new file mode 100644
index 00000000000..1432e912fd8
--- /dev/null
+++ b/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py
@@ -0,0 +1,38 @@
+from typing import Any, Optional, Union
+
+from litellm.types.utils import (
+    PromptTokensDetailsWrapper,
+    TranscriptionUsageDurationObject,
+    TranscriptionUsageTokensObject,
+    Usage,
+)
+
+
+class TranscriptionUsageObjectTransformation:
+    @staticmethod
+    def is_transcription_usage_object(
+        usage_object: Any,
+    ) -> bool:
+        return isinstance(usage_object, TranscriptionUsageDurationObject) or isinstance(
+            usage_object, TranscriptionUsageTokensObject
+        )
+
+    @staticmethod
+    def transform_transcription_usage_object(
+        usage_object: Union[
+            TranscriptionUsageDurationObject, TranscriptionUsageTokensObject
+        ],
+    ) -> Optional[Usage]:
+        if isinstance(usage_object, TranscriptionUsageDurationObject):
+            return None
+        elif isinstance(usage_object, TranscriptionUsageTokensObject):
+            return Usage(
+                prompt_tokens=usage_object.input_tokens,
+                completion_tokens=usage_object.output_tokens,
+                total_tokens=usage_object.total_tokens,
+                prompt_tokens_details=PromptTokensDetailsWrapper(
+                    text_tokens=usage_object.input_token_details.text_tokens,
+                    audio_tokens=usage_object.input_token_details.audio_tokens,
+                ),
+            )
+        return None
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index d9dbc703d3a..99f3853d21a 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -1,7 +1,7 @@
 # What is this?
 ## Helper utilities for cost_per_token()
 
-from typing import Any, Literal, Optional, Tuple, TypedDict, cast
+from typing import Literal, Optional, Tuple, TypedDict, cast
 
 import litellm
 from litellm._logging import verbose_logger
@@ -118,21 +118,21 @@ def _generic_cost_per_character(
 def _get_service_tier_cost_key(base_key: str, service_tier: Optional[str]) -> str:
     """
     Get the appropriate cost key based on service tier.
-    
+
     Args:
         base_key: The base cost key (e.g., "input_cost_per_token")
         service_tier: The service tier ("flex", "priority", or None for standard)
-        
+
     Returns:
         str: The cost key to use (e.g., "input_cost_per_token_flex" or "input_cost_per_token")
     """
     if service_tier is None:
         return base_key
-    
+
     # Only use service tier specific keys for "flex" and "priority"
     if service_tier.lower() in [ServiceTier.FLEX.value, ServiceTier.PRIORITY.value]:
         return f"{base_key}_{service_tier.lower()}"
-    
+
     # For any other service tier, use standard pricing
     return base_key
 
@@ -152,15 +152,15 @@ def _get_token_base_cost(
     # Get service tier aware cost keys
     input_cost_key = _get_service_tier_cost_key("input_cost_per_token", service_tier)
     output_cost_key = _get_service_tier_cost_key("output_cost_per_token", service_tier)
-    cache_creation_cost_key = _get_service_tier_cost_key("cache_creation_input_token_cost", service_tier)
-    cache_read_cost_key = _get_service_tier_cost_key("cache_read_input_token_cost", service_tier)
-    
-    prompt_base_cost = cast(
-        float, _get_cost_per_unit(model_info, input_cost_key)
+    cache_creation_cost_key = _get_service_tier_cost_key(
+        "cache_creation_input_token_cost", service_tier
     )
-    completion_base_cost = cast(
-        float, _get_cost_per_unit(model_info, output_cost_key)
+    cache_read_cost_key = _get_service_tier_cost_key(
+        "cache_read_input_token_cost", service_tier
     )
+
+    prompt_base_cost = cast(float, _get_cost_per_unit(model_info, input_cost_key))
+    completion_base_cost = cast(float, _get_cost_per_unit(model_info, output_cost_key))
     cache_creation_cost = cast(
         float, _get_cost_per_unit(model_info, cache_creation_cost_key)
     )
@@ -168,9 +168,7 @@ def _get_token_base_cost(
         float,
         _get_cost_per_unit(model_info, "cache_creation_input_token_cost_above_1hr"),
     )
-    cache_read_cost = cast(
-        float, _get_cost_per_unit(model_info, cache_read_cost_key)
-    )
+    cache_read_cost = cast(float, _get_cost_per_unit(model_info, cache_read_cost_key))
 
     ## CHECK IF ABOVE THRESHOLD
     threshold: Optional[float] = None
@@ -278,7 +276,7 @@ def _get_cost_per_unit(
             verbose_logger.exception(
                 f"litellm.litellm_core_utils.llm_cost_calc.utils.py::calculate_cost_per_component(): Exception occured - {cost_per_unit}\nDefaulting to 0.0"
             )
-    
+
     # If the service tier key doesn't exist or is None, try to fall back to the standard key
     if cost_per_unit is None:
         # Check if any service tier suffix exists in the cost key using ServiceTier enum
@@ -286,7 +284,7 @@ def _get_cost_per_unit(
             suffix = f"_{service_tier.value}"
             if suffix in cost_key:
                 # Extract the base key by removing the matched suffix
-                base_key = cost_key.replace(suffix, '')
+                base_key = cost_key.replace(suffix, "")
                 fallback_cost = model_info.get(base_key)
                 if isinstance(fallback_cost, float):
                     return fallback_cost
@@ -300,7 +298,7 @@ def _get_cost_per_unit(
                             f"litellm.litellm_core_utils.llm_cost_calc.utils.py::_get_cost_per_unit(): Exception occured - {fallback_cost}\nDefaulting to 0.0"
                         )
                 break  # Only try the first matching suffix
-    
+
     return default_value
 
 
@@ -495,7 +493,10 @@ def _calculate_input_cost(
 
 
 def generic_cost_per_token(
-    model: str, usage: Usage, custom_llm_provider: str, service_tier: Optional[str] = None
+    model: str,
+    usage: Usage,
+    custom_llm_provider: str,
+    service_tier: Optional[str] = None,
 ) -> Tuple[float, float]:
     """
     Calculates the cost per token for a given model, prompt tokens, and completion tokens.
@@ -547,7 +548,9 @@ def generic_cost_per_token(
         cache_creation_cost,
         cache_creation_cost_above_1hr,
         cache_read_cost,
-    ) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier)
+    ) = _get_token_base_cost(
+        model_info=model_info, usage=usage, service_tier=service_tier
+    )
 
     prompt_cost = _calculate_input_cost(
         prompt_tokens_details=prompt_tokens_details,
@@ -631,7 +634,7 @@ class CostCalculatorUtils:
     @staticmethod
     def route_image_generation_cost_calculator(
         model: str,
-        completion_response: Any,
+        completion_response: ImageResponse,
         custom_llm_provider: Optional[str] = None,
         quality: Optional[str] = None,
         n: Optional[int] = None,
@@ -658,6 +661,13 @@ class CostCalculatorUtils:
             cost_calculator as vertex_ai_image_cost_calculator,
         )
 
+        if size is None:
+            size = completion_response.size or "1024-x-1024"
+        if quality is None:
+            quality = completion_response.quality or "standard"
+        if n is None:
+            n = len(completion_response.data) if completion_response.data else 0
+
         if custom_llm_provider == litellm.LlmProviders.VERTEX_AI.value:
             if isinstance(completion_response, ImageResponse):
                 return vertex_ai_image_cost_calculator(
diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
index 6ed9d5725e9..5a50806218f 100644
--- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
+++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
@@ -37,6 +37,8 @@ from litellm.types.utils import (
     TextChoices,
     TextCompletionResponse,
     TranscriptionResponse,
+    TranscriptionUsageDurationObject,
+    TranscriptionUsageTokensObject,
     Usage,
 )
 
@@ -684,6 +686,24 @@ def convert_to_model_response_object(  # noqa: PLR0915
                 if key in response_object:
                     setattr(model_response_object, key, response_object[key])
 
+            if "usage" in response_object and response_object["usage"] is not None:
+                tr_usage_object: Optional[
+                    Union[
+                        TranscriptionUsageDurationObject, TranscriptionUsageTokensObject
+                    ]
+                ] = None
+
+                if response_object["usage"].get("type", None) == "duration":
+                    tr_usage_object = TranscriptionUsageDurationObject(
+                        **response_object["usage"]
+                    )
+                elif response_object["usage"].get("type", None) == "tokens":
+                    tr_usage_object = TranscriptionUsageTokensObject(
+                        **response_object["usage"]
+                    )
+                if tr_usage_object is not None:
+                    setattr(model_response_object, "usage", tr_usage_object)
+
             if hidden_params is not None:
                 model_response_object._hidden_params = hidden_params
 
diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py
index 849cb20cdc5..0effed3db70 100644
--- a/litellm/litellm_core_utils/redact_messages.py
+++ b/litellm/litellm_core_utils/redact_messages.py
@@ -7,17 +7,17 @@
 #
 #  Thank you users! We ❤️ you! - Krrish & Ishaan
 
+import asyncio
 import copy
 from typing import TYPE_CHECKING, Any, Optional
 
 import litellm
 from litellm.integrations.custom_logger import CustomLogger
-from litellm.secret_managers.main import str_to_bool
-from litellm.types.utils import StandardCallbackDynamicParams
 from litellm.litellm_core_utils.core_helpers import (
     get_metadata_variable_name_from_kwargs,
 )
-import asyncio
+from litellm.secret_managers.main import str_to_bool
+from litellm.types.utils import StandardCallbackDynamicParams
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import (
diff --git a/litellm/litellm_core_utils/safe_json_dumps.py b/litellm/litellm_core_utils/safe_json_dumps.py
index c714e36b5f9..8b50e41a795 100644
--- a/litellm/litellm_core_utils/safe_json_dumps.py
+++ b/litellm/litellm_core_utils/safe_json_dumps.py
@@ -49,4 +49,4 @@ def safe_dumps(data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH) -> str:
                 return "Unserializable Object"
 
     safe_data = _serialize(data, set(), 0)
-    return json.dumps(safe_data, default=str)
+    return json.dumps(safe_data, default=str)
\ No newline at end of file
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 691b46af8da..ced5a089fcc 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -82,9 +82,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
     to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
     """
 
-    max_tokens: Optional[int] = (
-        DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS  # anthropic requires a default value (Opus, Sonnet, and Haiku have the same default)
-    )
+    max_tokens: Optional[
+        int
+    ] = DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS  # anthropic requires a default value (Opus, Sonnet, and Haiku have the same default)
     stop_sequences: Optional[list] = None
     temperature: Optional[int] = None
     top_p: Optional[int] = None
@@ -118,7 +118,6 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         return super().get_config()
 
     def get_supported_openai_params(self, model: str):
-
         params = [
             "stream",
             "stop",
@@ -465,11 +464,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 if mcp_servers:
                     optional_params["mcp_servers"] = mcp_servers
             if param == "tool_choice" or param == "parallel_tool_calls":
-                _tool_choice: Optional[AnthropicMessagesToolChoice] = (
-                    self._map_tool_choice(
-                        tool_choice=non_default_params.get("tool_choice"),
-                        parallel_tool_use=non_default_params.get("parallel_tool_calls"),
-                    )
+                _tool_choice: Optional[
+                    AnthropicMessagesToolChoice
+                ] = self._map_tool_choice(
+                    tool_choice=non_default_params.get("tool_choice"),
+                    parallel_tool_use=non_default_params.get("parallel_tool_calls"),
                 )
 
                 if _tool_choice is not None:
@@ -517,6 +516,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 self._add_tools_to_optional_params(
                     optional_params=optional_params, tools=[hosted_web_search_tool]
                 )
+            elif param == "extra_headers":
+                optional_params["extra_headers"] = value
 
         ## handle thinking tokens
         self.update_optional_params_with_thinking_tokens(
@@ -575,9 +576,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                         text=system_message_block["content"],
                     )
                     if "cache_control" in system_message_block:
-                        anthropic_system_message_content["cache_control"] = (
-                            system_message_block["cache_control"]
-                        )
+                        anthropic_system_message_content[
+                            "cache_control"
+                        ] = system_message_block["cache_control"]
                     anthropic_system_message_list.append(
                         anthropic_system_message_content
                     )
@@ -591,9 +592,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                             )
                         )
                         if "cache_control" in _content:
-                            anthropic_system_message_content["cache_control"] = (
-                                _content["cache_control"]
-                            )
+                            anthropic_system_message_content[
+                                "cache_control"
+                            ] = _content["cache_control"]
 
                         anthropic_system_message_list.append(
                             anthropic_system_message_content
@@ -641,13 +642,25 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 )
             )
         return tools
-    
-    def update_headers_with_optional_anthropic_beta(self, headers: dict, optional_params: dict) -> dict:
+
+    def update_headers_with_optional_anthropic_beta(
+        self, headers: dict, optional_params: dict
+    ) -> dict:
         """Update headers with optional anthropic beta."""
         _tools = optional_params.get("tools", [])
         for tool in _tools:
-            if tool.get("type", None) and tool.get("type").startswith(ANTHROPIC_HOSTED_TOOLS.WEB_FETCH.value):
-                headers["anthropic-beta"] = ANTHROPIC_BETA_HEADER_VALUES.WEB_FETCH_2025_09_10.value
+            if tool.get("type", None) and tool.get("type").startswith(
+                ANTHROPIC_HOSTED_TOOLS.WEB_FETCH.value
+            ):
+                headers[
+                    "anthropic-beta"
+                ] = ANTHROPIC_BETA_HEADER_VALUES.WEB_FETCH_2025_09_10.value
+            elif tool.get("type", None) and tool.get("type").startswith(
+                ANTHROPIC_HOSTED_TOOLS.MEMORY.value
+            ):
+                headers[
+                    "anthropic-beta"
+                ] = ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
         return headers
 
     def transform_request(
@@ -685,7 +698,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                     llm_provider="anthropic",
                 )
 
-        headers = self.update_headers_with_optional_anthropic_beta(headers=headers, optional_params=optional_params)
+        headers = self.update_headers_with_optional_anthropic_beta(
+            headers=headers, optional_params=optional_params
+        )
 
         # Separate system prompt from rest of message
         anthropic_system_message_list = self.translate_system_message(messages=messages)
@@ -764,7 +779,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 )
         return _message
 
-    def extract_response_content(self, completion_response: dict) -> Tuple[
+    def extract_response_content(
+        self, completion_response: dict
+    ) -> Tuple[
         str,
         Optional[List[Any]],
         Optional[
diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py
index 488aed4e031..e7aa93ac882 100644
--- a/litellm/llms/azure/azure.py
+++ b/litellm/llms/azure/azure.py
@@ -36,6 +36,7 @@ from .common_utils import (
     process_azure_headers,
     select_azure_base_url_or_endpoint,
 )
+from .image_generation import get_azure_image_generation_config
 
 
 class AzureOpenAIAssistantsAPIConfig:
@@ -1011,7 +1012,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
     async def aimage_generation(
         self,
         data: dict,
-        model_response: ModelResponse,
+        model_response: Optional[ImageResponse],
         azure_client_params: dict,
         api_key: str,
         input: list,
@@ -1020,6 +1021,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
         client=None,
         timeout=None,
     ) -> litellm.ImageResponse:
+
         response: Optional[dict] = None
         try:
             # response = await azure_client.images.generate(**data, timeout=timeout)
@@ -1052,21 +1054,38 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
                 data=data,
                 headers=headers,
             )
-            response = httpx_response.json()
 
-            stringified_response = response
-            ## LOGGING
-            logging_obj.post_call(
-                input=input,
-                api_key=api_key,
-                additional_args={"complete_input_dict": data},
-                original_response=stringified_response,
-            )
-            return convert_to_model_response_object(  # type: ignore
-                response_object=stringified_response,
-                model_response_object=model_response,
-                response_type="image_generation",
+            provider_config = get_azure_image_generation_config(
+                data.get("model", "dall-e-2")
             )
+            if provider_config is not None:
+                return provider_config.transform_image_generation_response(
+                    model=data.get("model", "dall-e-2"),
+                    raw_response=httpx_response,
+                    model_response=model_response or ImageResponse(),
+                    logging_obj=logging_obj,
+                    request_data=data,
+                    optional_params=data,
+                    litellm_params=data,
+                    encoding=litellm.encoding,
+                )
+
+            else:
+                response = httpx_response.json()
+
+                stringified_response = response
+                ## LOGGING
+                logging_obj.post_call(
+                    input=input,
+                    api_key=api_key,
+                    additional_args={"complete_input_dict": data},
+                    original_response=stringified_response,
+                )
+                return convert_to_model_response_object(  # type: ignore
+                    response_object=stringified_response,
+                    model_response_object=model_response,
+                    response_type="image_generation",
+                )
         except Exception as e:
             ## LOGGING
             logging_obj.post_call(
@@ -1124,9 +1143,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
             if api_key is None and azure_ad_token_provider is not None:
                 azure_ad_token = azure_ad_token_provider()
                 if azure_ad_token:
-                    headers.pop(
-                        "api-key", None
-                    )
+                    headers.pop("api-key", None)
                     headers["Authorization"] = f"Bearer {azure_ad_token}"
 
             # init AzureOpenAI Client
diff --git a/litellm/llms/azure/exception_mapping.py b/litellm/llms/azure/exception_mapping.py
new file mode 100644
index 00000000000..70c2609c6b4
--- /dev/null
+++ b/litellm/llms/azure/exception_mapping.py
@@ -0,0 +1,42 @@
+from typing import Optional
+
+from litellm.exceptions import ContentPolicyViolationError
+
+
+class AzureOpenAIExceptionMapping:
+    """
+    Class for creating Azure OpenAI specific exceptions
+    """
+    @staticmethod
+    def create_content_policy_violation_error(
+        message: str,
+        model: str,
+        extra_information: str,
+        original_exception: Exception,
+    ) -> ContentPolicyViolationError:
+        """
+        Create a content policy violation error
+        """    
+        raise ContentPolicyViolationError(
+            message=f"litellm.ContentPolicyViolationError: AzureException - {message}",
+            llm_provider="azure",
+            model=model,
+            litellm_debug_info=extra_information,
+            response=getattr(original_exception, "response", None),
+            provider_specific_fields={
+                "innererror": AzureOpenAIExceptionMapping._get_innererror_from_exception(original_exception)
+            },
+        )
+    
+    @staticmethod
+    def _get_innererror_from_exception(original_exception: Exception) -> Optional[dict]:
+        """
+        Azure OpenAI returns the innererror in the body of the exception
+        This method extracts the innererror from the exception
+        """
+        innererror = None
+        body_dict = getattr(original_exception, "body", None) or {}
+        if isinstance(body_dict, dict):
+            innererror = body_dict.get("innererror")
+        return innererror
+            
\ No newline at end of file
diff --git a/litellm/llms/azure_ai/rerank/transformation.py b/litellm/llms/azure_ai/rerank/transformation.py
index 4465e0d70a2..a47b6082c37 100644
--- a/litellm/llms/azure_ai/rerank/transformation.py
+++ b/litellm/llms/azure_ai/rerank/transformation.py
@@ -18,7 +18,12 @@ class AzureAIRerankConfig(CohereRerankConfig):
     Azure AI Rerank - Follows the same Spec as Cohere Rerank
     """
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         if api_base is None:
             raise ValueError(
                 "Azure AI API Base is required. api_base=None. Set in call or via `AZURE_AI_API_BASE` env var."
@@ -32,6 +37,7 @@ class AzureAIRerankConfig(CohereRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         if api_key is None:
             api_key = get_secret_str("AZURE_AI_API_KEY") or litellm.azure_key
diff --git a/litellm/llms/base_llm/rerank/transformation.py b/litellm/llms/base_llm/rerank/transformation.py
index 6e9c03dee89..b22d85e82be 100644
--- a/litellm/llms/base_llm/rerank/transformation.py
+++ b/litellm/llms/base_llm/rerank/transformation.py
@@ -23,6 +23,7 @@ class BaseRerankConfig(ABC):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         pass
 
@@ -50,7 +51,12 @@ class BaseRerankConfig(ABC):
         return model_response
 
     @abstractmethod
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         """
         OPTIONAL
 
diff --git a/litellm/llms/base_llm/videos/transformation.py b/litellm/llms/base_llm/videos/transformation.py
index 7234093778c..16341932fe8 100644
--- a/litellm/llms/base_llm/videos/transformation.py
+++ b/litellm/llms/base_llm/videos/transformation.py
@@ -92,10 +92,11 @@ class BaseVideoConfig(ABC):
         self,
         model: str,
         prompt: str,
+        api_base: str,
         video_create_optional_request_params: Dict,
         litellm_params: GenericLiteLLMParams,
         headers: dict,
-    ) -> Tuple[Dict, RequestFiles]:
+    ) -> Tuple[Dict, RequestFiles, str]:
         pass
 
     @abstractmethod
@@ -104,6 +105,8 @@ class BaseVideoConfig(ABC):
         model: str,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+        request_data: Optional[Dict] = None,
     ) -> VideoObject:
         pass
 
@@ -154,6 +157,7 @@ class BaseVideoConfig(ABC):
         self,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
     ) -> VideoObject:
         pass
 
@@ -181,6 +185,7 @@ class BaseVideoConfig(ABC):
         self,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
     ) -> Dict[str,str]:
         pass
 
@@ -229,6 +234,7 @@ class BaseVideoConfig(ABC):
         self,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
     ) -> VideoObject:
         pass
 
diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py
index d73860e1bc9..baaec996535 100644
--- a/litellm/llms/bedrock/common_utils.py
+++ b/litellm/llms/bedrock/common_utils.py
@@ -237,6 +237,7 @@ def init_bedrock_client(
             "sts",
             aws_access_key_id=aws_access_key_id,
             aws_secret_access_key=aws_secret_access_key,
+            verify=ssl_verify
         )
 
         sts_response = sts_client.assume_role(
diff --git a/litellm/llms/cohere/rerank/transformation.py b/litellm/llms/cohere/rerank/transformation.py
index f9c979712da..d085cb13c44 100644
--- a/litellm/llms/cohere/rerank/transformation.py
+++ b/litellm/llms/cohere/rerank/transformation.py
@@ -20,7 +20,12 @@ class CohereRerankConfig(BaseRerankConfig):
     def __init__(self) -> None:
         pass
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         if api_base:
             # Remove trailing slashes and ensure clean base URL
             api_base = api_base.rstrip("/")
@@ -72,6 +77,7 @@ class CohereRerankConfig(BaseRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         if api_key is None:
             api_key = (
diff --git a/litellm/llms/cohere/rerank_v2/transformation.py b/litellm/llms/cohere/rerank_v2/transformation.py
index eb551a8a949..01309d937f9 100644
--- a/litellm/llms/cohere/rerank_v2/transformation.py
+++ b/litellm/llms/cohere/rerank_v2/transformation.py
@@ -12,7 +12,12 @@ class CohereRerankV2Config(CohereRerankConfig):
     def __init__(self) -> None:
         pass
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         if api_base:
             # Remove trailing slashes and ensure clean base URL
             api_base = api_base.rstrip("/")
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index dd39c29203a..883f2de44df 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -921,11 +921,13 @@ class BaseLLMHTTPHandler:
             api_key=api_key,
             headers=headers or {},
             model=model,
+            optional_params=optional_rerank_params,
         )
 
         api_base = provider_config.get_complete_url(
             api_base=api_base,
             model=model,
+            optional_params=optional_rerank_params,
         )
 
         data = provider_config.transform_rerank_request(
@@ -2009,6 +2011,9 @@ class BaseLLMHTTPHandler:
             headers=headers,
         )
 
+        if extra_body:
+            data.update(extra_body)
+
         ## LOGGING
         logging_obj.pre_call(
             input=input,
@@ -2135,6 +2140,9 @@ class BaseLLMHTTPHandler:
             headers=headers,
         )
 
+        if extra_body:
+            data.update(extra_body)
+
         ## LOGGING
         logging_obj.pre_call(
             input=input,
@@ -4091,7 +4099,7 @@ class BaseLLMHTTPHandler:
             or {},
             model=model,
         )
-
+        
         if extra_headers:
             headers.update(extra_headers)
 
@@ -4101,12 +4109,13 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        data, files = video_generation_provider_config.transform_video_create_request(
+        data, files, api_base = video_generation_provider_config.transform_video_create_request(
             model=model,
             prompt=prompt,
             video_create_optional_request_params=video_generation_optional_request_params,
             litellm_params=litellm_params,
             headers=headers,
+            api_base=api_base,
         )
 
         ## LOGGING
@@ -4132,8 +4141,8 @@ class BaseLLMHTTPHandler:
                     timeout=timeout,
                 )
 
-            # --- END MOCK VIDEO RESPONSE ---
             else:
+                # Use JSON content type for POST requests without files
                 response = sync_httpx_client.post(
                     url=api_base,
                     headers=headers,
@@ -4151,6 +4160,8 @@ class BaseLLMHTTPHandler:
             model=model,
             raw_response=response,
             logging_obj=logging_obj,
+            custom_llm_provider=custom_llm_provider,
+            request_data=data,
         )
 
     async def async_video_generation_handler(
@@ -4198,9 +4209,10 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        data, files = video_generation_provider_config.transform_video_create_request(
+        data, files, api_base = video_generation_provider_config.transform_video_create_request(
             model=model,
             prompt=prompt,
+            api_base=api_base,
             video_create_optional_request_params=video_generation_optional_request_params,
             litellm_params=litellm_params,
             headers=headers,
@@ -4218,7 +4230,7 @@ class BaseLLMHTTPHandler:
         )
 
         try:
-            # Use JSON when no files, otherwise use form data with files
+            #Use JSON when no files, otherwise use form data with files
             if files is None or len(files) == 0:
                 response = await async_httpx_client.post(
                     url=api_base,
@@ -4245,6 +4257,8 @@ class BaseLLMHTTPHandler:
             model=model,
             raw_response=response,
             logging_obj=logging_obj,
+            custom_llm_provider=custom_llm_provider,
+            request_data=data,
         )
 
     ###### VIDEO CONTENT HANDLER ######
@@ -4300,7 +4314,7 @@ class BaseLLMHTTPHandler:
         )
 
         # Transform the request using the provider config
-        url, params = video_content_provider_config.transform_video_content_request(
+        url, data = video_content_provider_config.transform_video_content_request(
             video_id=video_id,
             api_base=api_base,
             litellm_params=litellm_params,
@@ -4308,12 +4322,21 @@ class BaseLLMHTTPHandler:
         )
 
         try:
-            # Make the GET request to download content
-            response = sync_httpx_client.get(
-                url=url,
-                headers=headers,
-                params=params,
-            )
+            # Use POST if params contains data (e.g., Vertex AI fetchPredictOperation)
+            # Otherwise use GET (e.g., OpenAI video content download)
+            if data:
+                response = sync_httpx_client.post(
+                    url=url,
+                    headers=headers,
+                    json=data,
+                )
+            else:
+                # Otherwise it's a GET request with query params
+                response = sync_httpx_client.get(
+                    url=url,
+                    headers=headers,
+                    params=data,
+                )
 
             # Transform the response using the provider config
             return video_content_provider_config.transform_video_content_response(
@@ -4366,7 +4389,7 @@ class BaseLLMHTTPHandler:
         )
 
         # Transform the request using the provider config
-        url, params = video_content_provider_config.transform_video_content_request(
+        url, data = video_content_provider_config.transform_video_content_request(
             video_id=video_id,
             api_base=api_base,
             litellm_params=litellm_params,
@@ -4374,12 +4397,21 @@ class BaseLLMHTTPHandler:
         )
 
         try:
-            # Make the GET request to download content
-            response = await async_httpx_client.get(
-                url=url,
-                headers=headers,
-                params=params,
-            )
+            # Use POST if params contains data (e.g., Vertex AI fetchPredictOperation)
+            # Otherwise use GET (e.g., OpenAI video content download)
+            if data:
+                response = await async_httpx_client.post(
+                    url=url,
+                    headers=headers,
+                    json=data,
+                )
+            else:
+                # Otherwise it's a GET request with query params
+                response = await async_httpx_client.get(
+                    url=url,
+                    headers=headers,
+                    params=data,
+                )
 
             # Transform the response using the provider config
             return video_content_provider_config.transform_video_content_response(
@@ -4484,6 +4516,7 @@ class BaseLLMHTTPHandler:
             return video_remix_provider_config.transform_video_remix_response(
                 raw_response=response,
                 logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
             )
 
         except Exception as e:
@@ -4565,6 +4598,7 @@ class BaseLLMHTTPHandler:
             return video_remix_provider_config.transform_video_remix_response(
                 raw_response=response,
                 logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
             )
 
         except Exception as e:
@@ -4700,6 +4734,7 @@ class BaseLLMHTTPHandler:
             return video_list_provider_config.transform_video_list_response(
                 raw_response=response,
                 logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
             )
 
         except Exception as e:
@@ -4855,17 +4890,29 @@ class BaseLLMHTTPHandler:
                 "api_base": url,
                 "headers": headers,
                 "video_id": video_id,
+                "data": data,
             },
         )
 
         try:
-            response = sync_httpx_client.get(
-                url=url,
-                headers=headers,
-            )
+            # Use POST if data is provided (e.g., Vertex AI fetchPredictOperation)
+            # Otherwise use GET (e.g., OpenAI video status)
+            if data:
+                response = sync_httpx_client.post(
+                    url=url,
+                    headers=headers,
+                    json=data,
+                )
+            else:
+                response = sync_httpx_client.get(
+                    url=url,
+                    headers=headers,
+                )
+
             return video_status_provider_config.transform_video_status_retrieve_response(
                 raw_response=response,
                 logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
             )
 
         except Exception as e:
@@ -4929,17 +4976,28 @@ class BaseLLMHTTPHandler:
                 "api_base": url,
                 "headers": headers,
                 "video_id": video_id,
+                "data": data,
             },
         )
 
         try:
-            response = await async_httpx_client.get(
-                url=url,
-                headers=headers,
-            )
+            # Use POST if data is provided (e.g., Vertex AI fetchPredictOperation)
+            # Otherwise use GET (e.g., OpenAI video status)
+            if data:
+                response = await async_httpx_client.post(
+                    url=url,
+                    headers=headers,
+                    json=data,
+                )
+            else:
+                response = await async_httpx_client.get(
+                    url=url,
+                    headers=headers,
+                )
             return video_status_provider_config.transform_video_status_retrieve_response(
                 raw_response=response,
                 logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
             )
 
         except Exception as e:
diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py
index 9ee6cd25d20..ac3be0c3518 100644
--- a/litellm/llms/databricks/chat/transformation.py
+++ b/litellm/llms/databricks/chat/transformation.py
@@ -640,7 +640,7 @@ class DatabricksChatResponseIterator(BaseModelResponseIterator):
                     for _tc in tool_calls:
                         if _tc.get("function", {}).get("arguments") == "{}":
                             _tc["function"]["arguments"] = ""  # avoid invalid json
-                if isinstance(choice["delta"]["content"], list) and (
+                if isinstance(choice["delta"].get("content"), list) and (
                     content := choice["delta"]["content"]
                 ):
                     if citations := content[0].get("citations"):
diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py
index 69c7dabebd8..47f47418cb2 100644
--- a/litellm/llms/deepinfra/rerank/transformation.py
+++ b/litellm/llms/deepinfra/rerank/transformation.py
@@ -28,7 +28,12 @@ class DeepinfraRerankConfig(BaseRerankConfig):
     Deepinfra Rerank - Follows the same Spec as Cohere Rerank
     """
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         """
         Constructs the complete DeepInfra inference endpoint URL for rerank.
 
@@ -63,6 +68,7 @@ class DeepinfraRerankConfig(BaseRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         if api_key is None:
             api_key = get_secret_str("DEEPINFRA_API_KEY")
diff --git a/litellm/llms/gemini/videos/__init__.py b/litellm/llms/gemini/videos/__init__.py
new file mode 100644
index 00000000000..c5aed2db2d0
--- /dev/null
+++ b/litellm/llms/gemini/videos/__init__.py
@@ -0,0 +1,5 @@
+# Gemini Video Generation Support
+from .transformation import GeminiVideoConfig
+
+__all__ = ["GeminiVideoConfig"]
+
diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py
new file mode 100644
index 00000000000..d1ae47af269
--- /dev/null
+++ b/litellm/llms/gemini/videos/transformation.py
@@ -0,0 +1,523 @@
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+import base64
+
+import httpx
+from httpx._types import RequestFiles
+
+from litellm.types.videos.main import VideoCreateOptionalRequestParams, VideoObject
+from litellm.types.router import GenericLiteLLMParams
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.videos.utils import (
+    encode_video_id_with_provider,
+    extract_original_video_id,
+)
+from litellm.images.utils import ImageEditRequestUtils
+import litellm
+from litellm.types.llms.gemini import GeminiLongRunningOperationResponse, GeminiVideoGenerationInstance, GeminiVideoGenerationParameters, GeminiVideoGenerationRequest
+from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+    from ...base_llm.videos.transformation import BaseVideoConfig as _BaseVideoConfig
+    from ...base_llm.chat.transformation import BaseLLMException as _BaseLLMException
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+    BaseVideoConfig = _BaseVideoConfig
+    BaseLLMException = _BaseLLMException
+else:
+    LiteLLMLoggingObj = Any
+    BaseVideoConfig = Any
+    BaseLLMException = Any
+
+
+def _convert_image_to_gemini_format(image_file) -> Dict[str, str]:
+    """
+    Convert image file to Gemini format with base64 encoding and MIME type.
+    
+    Args:
+        image_file: File-like object opened in binary mode (e.g., open("path", "rb"))
+    
+    Returns:
+        Dict with bytesBase64Encoded and mimeType
+    """
+    mime_type = ImageEditRequestUtils.get_image_content_type(image_file)
+    
+    if hasattr(image_file, 'seek'):
+        image_file.seek(0)
+    image_bytes = image_file.read()
+    base64_encoded = base64.b64encode(image_bytes).decode("utf-8")
+    
+    return {
+        "bytesBase64Encoded": base64_encoded,
+        "mimeType": mime_type
+    }
+
+
+class GeminiVideoConfig(BaseVideoConfig):
+    """
+    Configuration class for Gemini (Veo) video generation.
+    
+    Veo uses a long-running operation model:
+    1. POST to :predictLongRunning returns operation name
+    2. Poll operation until done=true
+    3. Extract video URI from response
+    4. Download video using file API
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get the list of supported OpenAI parameters for Veo video generation.
+        Veo supports minimal parameters compared to OpenAI.
+        """
+        return [
+            "model",
+            "prompt",
+            "input_reference",
+            "seconds",
+            "size"
+        ]
+
+    def map_openai_params(
+        self,
+        video_create_optional_params: VideoCreateOptionalRequestParams,
+        model: str,
+        drop_params: bool,
+    ) -> Dict[str, Any]:
+        """
+        Map OpenAI-style parameters to Veo format.
+        
+        Mappings:
+        - prompt → prompt
+        - input_reference → image
+        - size → aspectRatio (e.g., "1280x720" → "16:9")
+        - seconds → durationSeconds (defaults to 4 seconds if not provided)
+        
+        All other params are passed through as-is to support Gemini-specific parameters.
+        """
+        mapped_params: Dict[str, Any] = {}
+        
+        # Get supported OpenAI params (exclude "model" and "prompt" which are handled separately)
+        supported_openai_params = self.get_supported_openai_params(model)
+        openai_params_to_map = {
+            param for param in supported_openai_params 
+            if param not in {"model", "prompt"}
+        }
+        
+        # Map input_reference to image
+        if "input_reference" in video_create_optional_params:
+            mapped_params["image"] = video_create_optional_params["input_reference"]
+        
+        # Map size to aspectRatio
+        if "size" in video_create_optional_params:
+            size = video_create_optional_params["size"]
+            if size is not None:
+                aspect_ratio = self._convert_size_to_aspect_ratio(size)
+                if aspect_ratio:
+                    mapped_params["aspectRatio"] = aspect_ratio
+        
+        # Map seconds to durationSeconds, default to 4 seconds (matching OpenAI)
+        if "seconds" in video_create_optional_params:
+            seconds = video_create_optional_params["seconds"]
+            try:
+                duration = int(seconds) if isinstance(seconds, str) else seconds
+                if duration is not None:
+                    mapped_params["durationSeconds"] = duration
+            except (ValueError, TypeError):
+                # If conversion fails, use default
+                pass
+        
+        # Pass through any other params that weren't mapped (Gemini-specific params)
+        for key, value in video_create_optional_params.items():
+            if key not in openai_params_to_map and key not in mapped_params:
+                mapped_params[key] = value
+        
+        return mapped_params
+    
+    def _convert_size_to_aspect_ratio(self, size: str) -> Optional[str]:
+        """
+        Convert OpenAI size format to Veo aspectRatio format.
+        
+        https://cloud.google.com/vertex-ai/generative-ai/docs/image/generate-videos
+        
+        Supported aspect ratios: 9:16 (portrait), 16:9 (landscape)
+        """
+        if not size:
+            return None
+        
+        aspect_ratio_map = {
+            "1280x720": "16:9",
+            "1920x1080": "16:9",
+            "720x1280": "9:16",
+            "1080x1920": "9:16",
+        }
+        
+        return aspect_ratio_map.get(size, "16:9")
+
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        api_key: Optional[str] = None,
+    ) -> dict:
+        """
+        Validate environment and add Gemini API key to headers.
+        Gemini uses x-goog-api-key header for authentication.
+        """
+        api_key = (
+            api_key
+            or litellm.api_key
+            or get_secret_str("GOOGLE_API_KEY")
+            or get_secret_str("GEMINI_API_KEY")
+        )
+        
+        if not api_key:
+            raise ValueError(
+                "GEMINI_API_KEY or GOOGLE_API_KEY is required for Veo video generation. "
+                "Set it via environment variable or pass it as api_key parameter."
+            )
+        
+        headers.update({
+            "x-goog-api-key": api_key,
+            "Content-Type": "application/json",
+        })
+        return headers
+
+    def get_complete_url(
+        self,
+        model: str,
+        api_base: Optional[str],
+        litellm_params: dict,
+    ) -> str:
+        """
+        Get the complete URL for Veo video generation.
+        For video creation: returns full URL with :predictLongRunning
+        For status/delete: returns base URL only
+        """
+        if api_base is None:
+            api_base = get_secret_str("GEMINI_API_BASE") or "https://generativelanguage.googleapis.com"
+        
+        if not model or model == "":
+            return api_base.rstrip('/')
+        
+        model_name = model.replace("gemini/", "")
+        url = f"{api_base.rstrip('/')}/v1beta/models/{model_name}:predictLongRunning"
+        
+        return url
+
+    def transform_video_create_request(
+        self,
+        model: str,
+        prompt: str,
+        api_base: str,
+        video_create_optional_request_params: Dict,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[Dict, RequestFiles, str]:
+        """
+        Transform the video creation request for Veo API.
+        
+        Veo expects:
+        {
+            "instances": [
+                {
+                    "prompt": "A cat playing with a ball of yarn"
+                }
+            ],
+            "parameters": {
+                "aspectRatio": "16:9",
+                "durationSeconds": 8,
+                "resolution": "720p"
+            }
+        }
+        """
+        instance = GeminiVideoGenerationInstance(prompt=prompt)
+        
+        params_copy = video_create_optional_request_params.copy()
+        
+        if "image" in params_copy and params_copy["image"] is not None:
+            image_data = _convert_image_to_gemini_format(params_copy["image"])
+            params_copy["image"] = image_data
+        
+        parameters = GeminiVideoGenerationParameters(**params_copy)
+        
+        request_body_obj = GeminiVideoGenerationRequest(
+            instances=[instance],
+            parameters=parameters
+        )
+        
+        request_data = request_body_obj.model_dump(exclude_none=True)
+        
+        return request_data, [], api_base
+
+    def transform_video_create_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+        request_data: Optional[Dict] = None,
+    ) -> VideoObject:
+        """
+        Transform the Veo video creation response.
+        
+        Veo returns:
+        {
+            "name": "operations/generate_1234567890",
+            "metadata": {...},
+            "done": false,
+            "error": {...}
+        }
+        
+        We return this as a VideoObject with:
+        - id: operation name (used for polling)
+        - status: "processing"
+        - usage: includes duration_seconds for cost calculation
+        """        
+        response_data = raw_response.json()
+        
+        # Parse response using Pydantic model for type safety
+        try:
+            operation_response = GeminiLongRunningOperationResponse(**response_data)
+        except Exception as e:
+            raise ValueError(f"Failed to parse operation response: {e}")
+        
+        operation_name = operation_response.name
+        if not operation_name:
+            raise ValueError(f"No operation name in Veo response: {response_data}")
+        
+        if custom_llm_provider:
+            video_id = encode_video_id_with_provider(operation_name, custom_llm_provider, model)
+        else:
+            video_id = operation_name
+        
+        video_obj = VideoObject(
+            id=video_id,
+            object="video",
+            status="processing",
+            model=model,
+        )
+        
+        usage_data = {}
+        if request_data:
+            parameters = request_data.get("parameters", {})
+            duration = parameters.get("durationSeconds") or DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS
+            if duration is not None:
+                try:
+                    usage_data["duration_seconds"] = float(duration)
+                except (ValueError, TypeError):
+                    pass
+        
+        video_obj.usage = usage_data
+        return video_obj
+
+    def transform_video_status_retrieve_request(
+        self,
+        video_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the video status retrieve request for Veo API.
+        
+        Veo polls operations at:
+        GET https://generativelanguage.googleapis.com/v1beta/{operation_name}
+        """
+        operation_name = extract_original_video_id(video_id)
+        url = f"{api_base.rstrip('/')}/v1beta/{operation_name}"
+        params: Dict[str, Any] = {}
+        
+        return url, params
+
+    def transform_video_status_retrieve_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+    ) -> VideoObject:
+        """
+        Transform the Veo operation status response.
+        
+        Veo returns:
+        {
+            "name": "operations/generate_1234567890",
+            "done": false  # or true when complete
+        }
+        
+        When done=true:
+        {
+            "name": "operations/generate_1234567890",
+            "done": true,
+            "response": {
+                "generateVideoResponse": {
+                    "generatedSamples": [
+                        {
+                            "video": {
+                                "uri": "files/abc123..."
+                            }
+                        }
+                    ]
+                }
+            }
+        }
+        """        
+        response_data = raw_response.json()
+        # Parse response using Pydantic model for type safety
+        operation_response = GeminiLongRunningOperationResponse(**response_data)
+        
+        operation_name = operation_response.name
+        is_done = operation_response.done
+        
+        if custom_llm_provider:
+            video_id = encode_video_id_with_provider(operation_name, custom_llm_provider, None)
+        else:
+            video_id = operation_name
+                   
+        video_obj = VideoObject(
+            id=video_id,
+            object="video",
+            status="processing" if not is_done else "completed"
+        )
+        return video_obj
+
+    def transform_video_content_request(
+        self,
+        video_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the video content request for Veo API.
+        
+        For Veo, we need to:
+        1. Get operation status to extract video URI
+        2. Return download URL for the video
+        """        
+        operation_name = extract_original_video_id(video_id)
+        
+        status_url = f"{api_base.rstrip('/')}/v1beta/{operation_name}"
+        client = litellm.module_level_client
+        status_response = client.get(url=status_url, headers=headers)
+        status_response.raise_for_status()
+        response_data = status_response.json()
+        
+        operation_response = GeminiLongRunningOperationResponse(**response_data)
+
+        if not operation_response.done:
+            raise ValueError(
+                "Video generation is not complete yet. "
+                "Please check status with video_status() before downloading."
+            )
+        
+        if not operation_response.response:
+            raise ValueError("No response data in completed operation")
+        
+        generated_samples = operation_response.response.generateVideoResponse.generatedSamples
+        download_url = generated_samples[0].video.uri
+        
+        params: Dict[str, Any] = {}
+        
+        return download_url, params
+
+    def transform_video_content_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> bytes:
+        """
+        Transform the Veo video content download response.
+        Returns the video bytes directly.
+        """
+        return raw_response.content
+
+    def transform_video_remix_request(
+        self,
+        video_id: str,
+        prompt: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+        extra_body: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[str, Dict]:
+        """
+        Video remix is not supported by Veo API.
+        """
+        raise NotImplementedError(
+            "Video remix is not supported by Google Veo. "
+            "Please use video_generation() to create new videos."
+        )
+
+    def transform_video_remix_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+    ) -> VideoObject:
+        """Video remix is not supported."""
+        raise NotImplementedError("Video remix is not supported by Google Veo.")
+
+    def transform_video_list_request(
+        self,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+        order: Optional[str] = None,
+        extra_query: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[str, Dict]:
+        """
+        Video list is not supported by Veo API.
+        """
+        raise NotImplementedError(
+            "Video list is not supported by Google Veo. "
+            "Use the operations endpoint directly if you need to list operations."
+        )
+
+    def transform_video_list_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+    ) -> Dict[str, str]:
+        """Video list is not supported."""
+        raise NotImplementedError("Video list is not supported by Google Veo.")
+
+    def transform_video_delete_request(
+        self,
+        video_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Video delete is not supported by Veo API.
+        """
+        raise NotImplementedError(
+            "Video delete is not supported by Google Veo. "
+            "Videos are automatically cleaned up by Google."
+        )
+
+    def transform_video_delete_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> VideoObject:
+        """Video delete is not supported."""
+        raise NotImplementedError("Video delete is not supported by Google Veo.")
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        from ..common_utils import GeminiError
+
+        return GeminiError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
+
diff --git a/litellm/llms/hosted_vllm/rerank/transformation.py b/litellm/llms/hosted_vllm/rerank/transformation.py
index 2faef2c4c73..8316e923df3 100644
--- a/litellm/llms/hosted_vllm/rerank/transformation.py
+++ b/litellm/llms/hosted_vllm/rerank/transformation.py
@@ -37,7 +37,12 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
     def __init__(self) -> None:
         pass
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         if api_base:
             # Remove trailing slashes and ensure clean base URL
             api_base = api_base.rstrip("/")
@@ -91,6 +96,7 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         if api_key is None:
             api_key = get_secret_str("HOSTED_VLLM_API_KEY") or "fake-api-key"
@@ -150,7 +156,7 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
                 f"Error parsing response: {raw_response.text}, status_code={raw_response.status_code}"
             )
 
-        return RerankResponse(**raw_response_json)
+        return self._transform_response(raw_response_json)
 
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
diff --git a/litellm/llms/huggingface/rerank/transformation.py b/litellm/llms/huggingface/rerank/transformation.py
index 1454328cc13..b386daf1c83 100644
--- a/litellm/llms/huggingface/rerank/transformation.py
+++ b/litellm/llms/huggingface/rerank/transformation.py
@@ -60,7 +60,12 @@ class HuggingFaceRerankConfig(BaseRerankConfig):
         else:
             return "https://api-inference.huggingface.co"
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         """
         Get the complete URL for the API call, including the /rerank suffix if necessary.
         """
@@ -117,6 +122,7 @@ class HuggingFaceRerankConfig(BaseRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
         api_base: Optional[str] = None,
     ) -> dict:
         # Get API credentials
diff --git a/litellm/llms/infinity/rerank/transformation.py b/litellm/llms/infinity/rerank/transformation.py
index 55aac6033d5..1c15de714b6 100644
--- a/litellm/llms/infinity/rerank/transformation.py
+++ b/litellm/llms/infinity/rerank/transformation.py
@@ -26,7 +26,12 @@ from ..common_utils import InfinityError
 
 
 class InfinityRerankConfig(CohereRerankConfig):
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         if api_base is None:
             raise ValueError("api_base is required for Infinity rerank")
         # Remove trailing slashes and ensure clean base URL
@@ -40,6 +45,7 @@ class InfinityRerankConfig(CohereRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         if api_key is None:
             api_key = (
diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py
index 3ba24680fd4..0fddd754a9c 100644
--- a/litellm/llms/jina_ai/rerank/transformation.py
+++ b/litellm/llms/jina_ai/rerank/transformation.py
@@ -55,7 +55,12 @@ class JinaAIRerankConfig(BaseRerankConfig):
             **optional_params,
         ))
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         base_path = "/v1/rerank"
 
         if api_base is None:
@@ -122,7 +127,11 @@ class JinaAIRerankConfig(BaseRerankConfig):
         )  # Return response
 
     def validate_environment(
-        self, headers: Dict, model: str, api_key: Optional[str] = None
+        self, 
+        headers: Dict, 
+        model: str, 
+        api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> Dict:
         if api_key is None:
             raise ValueError(
diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py
index cb9fd4bebaa..5bbe16e5381 100644
--- a/litellm/llms/nvidia_nim/rerank/transformation.py
+++ b/litellm/llms/nvidia_nim/rerank/transformation.py
@@ -55,7 +55,12 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
     def __init__(self) -> None:
         pass
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
         """
         Construct the Nvidia NIM rerank URL.
         
@@ -131,6 +136,7 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
     ) -> dict:
         """
         Validate that the Nvidia NIM API key is present.
diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py
index 65a50224bb8..e5349db3af7 100644
--- a/litellm/llms/openai/cost_calculation.py
+++ b/litellm/llms/openai/cost_calculation.py
@@ -18,7 +18,9 @@ def cost_router(call_type: CallTypes) -> Literal["cost_per_token", "cost_per_sec
         return "cost_per_token"
 
 
-def cost_per_token(model: str, usage: Usage, service_tier: Optional[str] = None) -> Tuple[float, float]:
+def cost_per_token(
+    model: str, usage: Usage, service_tier: Optional[str] = None
+) -> Tuple[float, float]:
     """
     Calculates the cost per token for a given model, prompt tokens, and completion tokens.
 
@@ -31,7 +33,10 @@ def cost_per_token(model: str, usage: Usage, service_tier: Optional[str] = None)
     """
     ## CALCULATE INPUT COST
     return generic_cost_per_token(
-        model=model, usage=usage, custom_llm_provider="openai", service_tier=service_tier
+        model=model,
+        usage=usage,
+        custom_llm_provider="openai",
+        service_tier=service_tier,
     )
     # ### Non-cached text tokens
     # non_cached_text_tokens = usage.prompt_tokens
@@ -92,6 +97,7 @@ def cost_per_second(
     Returns:
         Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
     """
+
     ## GET MODEL INFO
     model_info = get_model_info(
         model=model, custom_llm_provider=custom_llm_provider or "openai"
@@ -123,18 +129,16 @@ def cost_per_second(
 
 
 def video_generation_cost(
-    model: str, 
-    duration_seconds: float, 
-    custom_llm_provider: Optional[str] = None
+    model: str, duration_seconds: float, custom_llm_provider: Optional[str] = None
 ) -> float:
     """
     Calculates the cost for video generation based on duration in seconds.
-    
+
     Input:
         - model: str, the model name without provider prefix
         - duration_seconds: float, the duration of the generated video in seconds
         - custom_llm_provider: str, the custom llm provider
-        
+
     Returns:
         float - total_cost_in_usd
     """
@@ -142,7 +146,7 @@ def video_generation_cost(
     model_info = get_model_info(
         model=model, custom_llm_provider=custom_llm_provider or "openai"
     )
-    
+
     # Check for video-specific cost per second
     video_cost_per_second = model_info.get("output_cost_per_video_per_second")
     if video_cost_per_second is not None:
@@ -150,7 +154,7 @@ def video_generation_cost(
             f"For model={model} - output_cost_per_video_per_second: {video_cost_per_second}; duration: {duration_seconds}"
         )
         return video_cost_per_second * duration_seconds
-    
+
     # Fallback to general output cost per second
     output_cost_per_second = model_info.get("output_cost_per_second")
     if output_cost_per_second is not None:
@@ -158,7 +162,7 @@ def video_generation_cost(
             f"For model={model} - output_cost_per_second: {output_cost_per_second}; duration: {duration_seconds}"
         )
         return output_cost_per_second * duration_seconds
-    
+
     # If no cost information found, return 0
     verbose_logger.warning(
         f"No cost information found for video model {model}. Please add pricing to model_prices_and_context_window.json"
diff --git a/litellm/llms/openai/image_generation/dall_e_2_transformation.py b/litellm/llms/openai/image_generation/dall_e_2_transformation.py
index 8e306a83375..22c2349a837 100644
--- a/litellm/llms/openai/image_generation/dall_e_2_transformation.py
+++ b/litellm/llms/openai/image_generation/dall_e_2_transformation.py
@@ -1,9 +1,16 @@
-from typing import List
+from typing import TYPE_CHECKING, Any, List, Optional
+
+import httpx
 
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
 from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams
+from litellm.types.utils import ImageResponse
+from litellm.utils import convert_to_model_response_object
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj
 
 
 class DallE2ImageGenerationConfig(BaseImageGenerationConfig):
@@ -36,3 +43,45 @@ class DallE2ImageGenerationConfig(BaseImageGenerationConfig):
                     )
 
         return optional_params
+
+    def transform_image_generation_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ImageResponse,
+        logging_obj: "LiteLLMLoggingObj",
+        request_data: dict,
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ImageResponse:
+        response = raw_response.json()
+
+        stringified_response = response
+        ## LOGGING
+        logging_obj.post_call(
+            input=request_data.get("prompt", ""),
+            api_key=api_key,
+            additional_args={"complete_input_dict": request_data},
+            original_response=stringified_response,
+        )
+        image_response: ImageResponse = convert_to_model_response_object(  # type: ignore
+            response_object=stringified_response,
+            model_response_object=model_response,
+            response_type="image_generation",
+        )
+
+        # set optional params
+        image_response.size = optional_params.get(
+            "size", "1024x1024"
+        )  # default is always 1024x1024
+        image_response.quality = optional_params.get(
+            "quality", "standard"
+        )  # always standard for dall-e-2
+        image_response.output_format = optional_params.get(
+            "output_format", "png"
+        )  # always png for dall-e-2
+
+        return image_response
diff --git a/litellm/llms/openai/image_generation/dall_e_3_transformation.py b/litellm/llms/openai/image_generation/dall_e_3_transformation.py
index c4b0b66e112..9e2bdabc3a1 100644
--- a/litellm/llms/openai/image_generation/dall_e_3_transformation.py
+++ b/litellm/llms/openai/image_generation/dall_e_3_transformation.py
@@ -1,9 +1,16 @@
-from typing import List
+from typing import TYPE_CHECKING, Any, List, Optional
+
+import httpx
 
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
 from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams
+from litellm.types.utils import ImageResponse
+from litellm.utils import convert_to_model_response_object
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj
 
 
 class DallE3ImageGenerationConfig(BaseImageGenerationConfig):
@@ -36,3 +43,45 @@ class DallE3ImageGenerationConfig(BaseImageGenerationConfig):
                     )
 
         return optional_params
+
+    def transform_image_generation_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ImageResponse,
+        logging_obj: "LiteLLMLoggingObj",
+        request_data: dict,
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ImageResponse:
+        response = raw_response.json()
+
+        stringified_response = response
+        ## LOGGING
+        logging_obj.post_call(
+            input=request_data.get("prompt", ""),
+            api_key=api_key,
+            additional_args={"complete_input_dict": request_data},
+            original_response=stringified_response,
+        )
+        image_response: ImageResponse = convert_to_model_response_object(  # type: ignore
+            response_object=stringified_response,
+            model_response_object=model_response,
+            response_type="image_generation",
+        )
+
+        # set optional params
+        image_response.size = optional_params.get(
+            "size", "1024x1024"
+        )  # default is always 1024x1024
+        image_response.quality = optional_params.get(
+            "quality", "hd"
+        )  # always hd for dall-e-3
+        image_response.output_format = optional_params.get(
+            "output_format", "png"
+        )  # always png for dall-e-3
+
+        return image_response
diff --git a/litellm/llms/openai/image_generation/gpt_transformation.py b/litellm/llms/openai/image_generation/gpt_transformation.py
index 1cee13784e7..c106d7f17b6 100644
--- a/litellm/llms/openai/image_generation/gpt_transformation.py
+++ b/litellm/llms/openai/image_generation/gpt_transformation.py
@@ -1,9 +1,16 @@
-from typing import List
+from typing import TYPE_CHECKING, Any, List, Optional
+
+import httpx
 
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
 from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams
+from litellm.types.utils import ImageResponse
+from litellm.utils import convert_to_model_response_object
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj
 
 
 class GPTImageGenerationConfig(BaseImageGenerationConfig):
@@ -45,3 +52,45 @@ class GPTImageGenerationConfig(BaseImageGenerationConfig):
                     )
 
         return optional_params
+
+    def transform_image_generation_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ImageResponse,
+        logging_obj: "LiteLLMLoggingObj",
+        request_data: dict,
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ImageResponse:
+        response = raw_response.json()
+
+        stringified_response = response
+        ## LOGGING
+        logging_obj.post_call(
+            input=request_data.get("prompt", ""),
+            api_key=api_key,
+            additional_args={"complete_input_dict": request_data},
+            original_response=stringified_response,
+        )
+        image_response: ImageResponse = convert_to_model_response_object(  # type: ignore
+            response_object=stringified_response,
+            model_response_object=model_response,
+            response_type="image_generation",
+        )
+
+        # set optional params
+        image_response.size = optional_params.get(
+            "size", "1024x1024"
+        )  # default is always 1024x1024
+        image_response.quality = optional_params.get(
+            "quality", "high"
+        )  # always hd for dall-e-3
+        image_response.output_format = optional_params.get(
+            "response_format", "png"
+        )  # always png for dall-e-3
+
+        return image_response
diff --git a/litellm/llms/openai/transcriptions/handler.py b/litellm/llms/openai/transcriptions/handler.py
index 19b303bb968..4d60b8a8310 100644
--- a/litellm/llms/openai/transcriptions/handler.py
+++ b/litellm/llms/openai/transcriptions/handler.py
@@ -213,6 +213,7 @@ class OpenAIAudioTranscription(OpenAIChatCompletion):
             # Extract the actual model from data instead of hardcoding "whisper-1"
             actual_model = data.get("model", "whisper-1")
             hidden_params = {"model": actual_model, "custom_llm_provider": "openai"}
+
             return convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, hidden_params=hidden_params, response_type="audio_transcription")  # type: ignore
         except Exception as e:
             ## LOGGING
diff --git a/litellm/llms/openai/videos/transformation.py b/litellm/llms/openai/videos/transformation.py
index c573f3b59b0..9848477f32d 100644
--- a/litellm/llms/openai/videos/transformation.py
+++ b/litellm/llms/openai/videos/transformation.py
@@ -9,6 +9,7 @@ from litellm.types.llms.openai import CreateVideoRequest
 from litellm.types.router import GenericLiteLLMParams
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.videos.main import VideoObject
+from litellm.types.videos.utils import encode_video_id_with_provider, extract_original_video_id
 import litellm
 from litellm.llms.openai.image_edit.transformation import ImageEditRequestUtils
 if TYPE_CHECKING:
@@ -94,17 +95,18 @@ class OpenAIVideoConfig(BaseVideoConfig):
         self,
         model: str,
         prompt: str,
+        api_base: str,
         video_create_optional_request_params: Dict,
         litellm_params: GenericLiteLLMParams,
         headers: dict,
-    ) -> Tuple[Dict, RequestFiles]:
+    ) -> Tuple[Dict, RequestFiles, str]:
         """
         Transform the video creation request for OpenAI API.
         """
         # Remove model and extra_headers from optional params as they're handled separately
         video_create_optional_request_params = {
             k: v for k, v in video_create_optional_request_params.items()
-            if k not in ["model", "extra_headers"]
+            if k not in ["model", "extra_headers", "prompt"]
         }
         
         # Create the request data
@@ -129,26 +131,24 @@ class OpenAIVideoConfig(BaseVideoConfig):
                 image=_input_reference,
                 field_name="input_reference",
             )
-        # Convert to dict for JSON serialization
-        return data_without_files, files_list
+        return data_without_files, files_list, api_base
 
     def transform_video_create_response(
         self,
         model: str,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+        request_data: Optional[Dict] = None,
     ) -> VideoObject:
-        """
-        Transform the OpenAI video creation response.
-        """
+        """Transform the OpenAI video creation response."""
         response_data = raw_response.json()
-        
-        # Transform the response data
     
         video_obj = VideoObject(**response_data)  # type: ignore[arg-type]
         
-        # Create usage object with duration information for cost calculation
-        # Video generation API doesn't provide usage, so we create one with duration
+        if custom_llm_provider and video_obj.id:
+            video_obj.id = encode_video_id_with_provider(video_obj.id, custom_llm_provider, model)
+        
         usage_data = {}
         if video_obj:
             if hasattr(video_obj, 'seconds') and video_obj.seconds:
@@ -156,9 +156,7 @@ class OpenAIVideoConfig(BaseVideoConfig):
                     usage_data["duration_seconds"] = float(video_obj.seconds)
                 except (ValueError, TypeError):
                     pass
-        # Create the response
         video_obj.usage = usage_data
-
         
         return video_obj
 
@@ -175,11 +173,13 @@ class OpenAIVideoConfig(BaseVideoConfig):
         OpenAI API expects the following request:
         - GET /v1/videos/{video_id}/content
         """
+        original_video_id = extract_original_video_id(video_id)
+        
         # Construct the URL for video content download
-        url = f"{api_base.rstrip('/')}/{video_id}/content"
+        url = f"{api_base.rstrip('/')}/{original_video_id}/content"
         
         # Add video_id as query parameter
-        params = {"video_id": video_id}
+        params = {"video_id": original_video_id}
         
         return url, params
 
@@ -198,8 +198,10 @@ class OpenAIVideoConfig(BaseVideoConfig):
         OpenAI API expects the following request:
         - POST /v1/videos/{video_id}/remix
         """
+        original_video_id = extract_original_video_id(video_id)
+        
         # Construct the URL for video remix
-        url = f"{api_base.rstrip('/')}/{video_id}/remix"
+        url = f"{api_base.rstrip('/')}/{original_video_id}/remix"
         
         # Prepare the request data
         data = {"prompt": prompt}
@@ -215,17 +217,14 @@ class OpenAIVideoConfig(BaseVideoConfig):
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
     ) -> bytes:
-        """
-        Transform the OpenAI video content download response.
-        Returns raw video content as bytes.
-        """
-        # For video content download, return the raw content as bytes
+        """Transform the OpenAI video content download response."""
         return raw_response.content
 
     def transform_video_remix_response(
         self,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
     ) -> VideoObject:
         """
         Transform the OpenAI video remix response.
@@ -235,6 +234,9 @@ class OpenAIVideoConfig(BaseVideoConfig):
         # Transform the response data
         video_obj = VideoObject(**response_data)  # type: ignore[arg-type]
         
+        if custom_llm_provider and video_obj.id:
+            video_obj.id = encode_video_id_with_provider(video_obj.id, custom_llm_provider, None)
+        
         # Create usage object with duration information for cost calculation
         # Video remix API doesn't provide usage, so we create one with duration
         usage_data = {}
@@ -287,8 +289,20 @@ class OpenAIVideoConfig(BaseVideoConfig):
         self,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
     ) -> Dict[str,str]:
-        return raw_response.json()
+        response_data = raw_response.json()
+        
+        if custom_llm_provider and "data" in response_data:
+            for video_obj in response_data.get("data", []):
+                if isinstance(video_obj, dict) and "id" in video_obj:
+                    video_obj["id"] = encode_video_id_with_provider(
+                        video_obj["id"], 
+                        custom_llm_provider, 
+                        video_obj.get("model")
+                    )
+        
+        return response_data
 
     def transform_video_delete_request(
         self,
@@ -303,8 +317,10 @@ class OpenAIVideoConfig(BaseVideoConfig):
         OpenAI API expects the following request:
         - DELETE /v1/videos/{video_id}
         """
+        original_video_id = extract_original_video_id(video_id)
+        
         # Construct the URL for video delete
-        url = f"{api_base.rstrip('/')}/{video_id}"
+        url = f"{api_base.rstrip('/')}/{original_video_id}"
         
         # No data needed for DELETE request
         data: Dict[str, Any] = {}
@@ -336,8 +352,11 @@ class OpenAIVideoConfig(BaseVideoConfig):
         """
         Transform the OpenAI video retrieve request.
         """
+        # Extract the original video_id (remove provider encoding if present)
+        original_video_id = extract_original_video_id(video_id)
+        
         # For video retrieve, we just need to construct the URL
-        url = f"{api_base.rstrip('/')}/{video_id}"
+        url = f"{api_base.rstrip('/')}/{original_video_id}"
         
         # No additional data needed for GET request
         data: Dict[str, Any] = {}
@@ -348,6 +367,7 @@ class OpenAIVideoConfig(BaseVideoConfig):
         self,
         raw_response: httpx.Response,
         logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
     ) -> VideoObject:
         """
         Transform the OpenAI video retrieve response.
@@ -355,6 +375,9 @@ class OpenAIVideoConfig(BaseVideoConfig):
         response_data = raw_response.json()
         # Transform the response data
         video_obj = VideoObject(**response_data)  # type: ignore[arg-type]
+        
+        if custom_llm_provider and video_obj.id:
+            video_obj.id = encode_video_id_with_provider(video_obj.id, custom_llm_provider, None)
 
         return video_obj
 
diff --git a/litellm/llms/perplexity/chat/__init__.py b/litellm/llms/perplexity/chat/__init__.py
deleted file mode 100644
index f4f9edf38e5..00000000000
--- a/litellm/llms/perplexity/chat/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Perplexity chat completion transformations."""
diff --git a/litellm/llms/perplexity/chat/transformation.py b/litellm/llms/perplexity/chat/transformation.py
index 831b009de7a..27e6415ff8b 100644
--- a/litellm/llms/perplexity/chat/transformation.py
+++ b/litellm/llms/perplexity/chat/transformation.py
@@ -1,32 +1,25 @@
-"""Translate from OpenAI's `/v1/chat/completions` to Perplexity's `/v1/chat/completions`."""
+"""
+Translate from OpenAI's `/v1/chat/completions` to Perplexity's `/v1/chat/completions`
+"""
 
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
+import httpx
 import litellm
 from litellm._logging import verbose_logger
-from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
 from litellm.secret_managers.main import get_secret_str
-from litellm.types.utils import ModelResponse, PromptTokensDetailsWrapper, Usage
-
-if TYPE_CHECKING:
-    import httpx
-
-    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
-    from litellm.types.llms.openai import (
-        AllMessageValues,
-        ChatCompletionAnnotation,
-        ChatCompletionAnnotationURLCitation,
-    )
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import Usage, PromptTokensDetailsWrapper
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
+from litellm.types.utils import ModelResponse
+from litellm.types.llms.openai import ChatCompletionAnnotation
+from litellm.types.llms.openai import ChatCompletionAnnotationURLCitation
 
 
 class PerplexityChatConfig(OpenAIGPTConfig):
-    """Configuration for Perplexity chat completions."""
-
     @property
-    def custom_llm_provider(self) -> str | None:
-        """Return the custom LLM provider name."""
+    def custom_llm_provider(self) -> Optional[str]:
         return "perplexity"
 
     def _get_openai_compatible_provider_info(
@@ -40,38 +33,6 @@ class PerplexityChatConfig(OpenAIGPTConfig):
         )
         return api_base, dynamic_api_key
 
-    def validate_environment(
-        self,
-        headers: dict,
-        model: str,
-        messages: list,
-        optional_params: dict,
-        litellm_params: dict,
-        api_key: Optional[str] = None,
-        api_base: Optional[str] = None,
-    ) -> dict:
-        """Validate Perplexity environment and set headers."""
-        # Get API key from environment if not provided
-        if api_key is None:
-            _, api_key = self._get_openai_compatible_provider_info(
-                api_base=api_base, api_key=api_key
-            )
-        
-        # Validate API key is present
-        if api_key is None:
-            raise ValueError(
-                "The api_key client option must be set either by passing api_key to the client or by setting the PERPLEXITY_API_KEY environment variable"
-            )
-        
-        # Set authorization header
-        headers["Authorization"] = f"Bearer {api_key}"
-        
-        # Ensure Content-Type is set to application/json
-        if "content-type" not in headers and "Content-Type" not in headers:
-            headers["Content-Type"] = "application/json"
-        
-        return headers
-
     def get_supported_openai_params(self, model: str) -> list:
         """
         Perplexity supports a subset of OpenAI params
@@ -111,8 +72,7 @@ class PerplexityChatConfig(OpenAIGPTConfig):
         
         return base_openai_params
 
-
-    def transform_response(  # noqa: PLR0913
+    def transform_response(
         self,
         model: str,
         raw_response: httpx.Response,
@@ -122,11 +82,10 @@ class PerplexityChatConfig(OpenAIGPTConfig):
         messages: List[AllMessageValues],
         optional_params: dict,
         litellm_params: dict,
-        encoding: Any,  
+        encoding: Any,
         api_key: Optional[str] = None,
-        json_mode: Optional[bool] = None,  
+        json_mode: Optional[bool] = None,
     ) -> ModelResponse:
-        """Transform Perplexity response to standard format."""
         # Call the parent transform_response first to handle the standard transformation
         model_response = super().transform_response(
             model=model,
@@ -145,29 +104,28 @@ class PerplexityChatConfig(OpenAIGPTConfig):
         # Extract and enhance usage with Perplexity-specific fields
         try:
             raw_response_json = raw_response.json()
-            self.add_cost_to_usage(model_response, raw_response_json)
             self._enhance_usage_with_perplexity_fields(
-                model_response, raw_response_json,
+                model_response, raw_response_json
             )
             self._add_citations_as_annotations(model_response, raw_response_json)
-        except (ValueError, TypeError, KeyError) as e:
+        except Exception as e:
             verbose_logger.debug(f"Error extracting Perplexity-specific usage fields: {e}")
 
         return model_response
 
-    def _enhance_usage_with_perplexity_fields(  
-        self, model_response: ModelResponse, raw_response_json: dict,
+    def _enhance_usage_with_perplexity_fields(
+        self, model_response: ModelResponse, raw_response_json: dict
     ) -> None:
-        """Extract citation tokens and search queries from Perplexity API response.
-
-        Add them to the usage object using standard LiteLLM fields.
+        """
+        Extract citation tokens and search queries from Perplexity API response
+        and add them to the usage object using standard LiteLLM fields.
         """
         if not hasattr(model_response, "usage") or model_response.usage is None:
             # Create a usage object if it doesn't exist (when usage was None)
             model_response.usage = Usage(  # type: ignore[attr-defined]
                 prompt_tokens=0,
                 completion_tokens=0,
-                total_tokens=0,
+                total_tokens=0
             )
 
         usage = model_response.usage  # type: ignore[attr-defined]
@@ -188,7 +146,7 @@ class PerplexityChatConfig(OpenAIGPTConfig):
         # Extract search queries count from usage or response metadata
         # Perplexity might include this in the usage object or as separate metadata
         perplexity_usage = raw_response_json.get("usage", {})
-
+        
         # Try to extract search queries from usage field first, then root level
         num_search_queries = perplexity_usage.get("num_search_queries")
         if num_search_queries is None:
@@ -197,18 +155,18 @@ class PerplexityChatConfig(OpenAIGPTConfig):
             num_search_queries = perplexity_usage.get("search_queries")
         if num_search_queries is None:
             num_search_queries = raw_response_json.get("search_queries")
-
+        
         # Create or update prompt_tokens_details to include web search requests and citation tokens
         if citation_tokens > 0 or (
             num_search_queries is not None and num_search_queries > 0
         ):
             if usage.prompt_tokens_details is None:
                 usage.prompt_tokens_details = PromptTokensDetailsWrapper()
-
+            
             # Store citation tokens count for cost calculation
             if citation_tokens > 0:
-                usage.citation_tokens = citation_tokens
-
+                setattr(usage, "citation_tokens", citation_tokens)
+            
             # Store search queries count in the standard web_search_requests field
             if num_search_queries is not None and num_search_queries > 0:
                 usage.prompt_tokens_details.web_search_requests = num_search_queries
@@ -290,35 +248,4 @@ class PerplexityChatConfig(OpenAIGPTConfig):
         if citations:
             setattr(model_response, "citations", citations)
         if search_results:
-            setattr(model_response, "search_results", search_results)
-
-    def add_cost_to_usage(self, model_response: ModelResponse, raw_response_json: dict) -> None:
-        """Add the cost to the usage object."""
-        try:
-            usage_data = raw_response_json.get("usage")
-            if usage_data:
-                # Try different possible cost field locations
-                response_cost = None
-
-                # Check if cost is directly in usage (flat structure)
-                if "total_cost" in usage_data:
-                    response_cost = usage_data["total_cost"]
-                # Check if cost is nested (cost.total_cost structure)
-                elif "cost" in usage_data and isinstance(usage_data["cost"], dict):
-                    response_cost = usage_data["cost"].get("total_cost")
-                # Check if cost is a simple value
-                elif "cost" in usage_data:
-                    response_cost = usage_data["cost"]
-
-                if response_cost is not None:
-                    # Store cost in hidden params for the cost calculator to use
-                    if not hasattr(model_response, "_hidden_params"):
-                        model_response._hidden_params = {}  
-                    if "additional_headers" not in model_response._hidden_params:  
-                        model_response._hidden_params["additional_headers"] = {}  
-                    model_response._hidden_params["additional_headers"][  
-                        "llm_provider-x-litellm-response-cost"
-                    ] = float(response_cost)
-        except (ValueError, TypeError, KeyError) as e:
-            verbose_logger.debug(f"Error adding cost to usage: {e}")
-            # If we can't extract cost, continue without it - don't fail the response
+            setattr(model_response, "search_results", search_results)
\ No newline at end of file
diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py
index 966368bc3cc..c3cdd2b0fb6 100644
--- a/litellm/llms/vertex_ai/rerank/transformation.py
+++ b/litellm/llms/vertex_ai/rerank/transformation.py
@@ -27,19 +27,40 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
     def __init__(self) -> None:
         super().__init__()
 
-    def get_complete_url(self, api_base: Optional[str], model: str) -> str:
+    def get_complete_url(
+        self, 
+        api_base: Optional[str], 
+        model: str,
+        optional_params: Optional[Dict] = None,
+    ) -> str:
         """
         Get the complete URL for the Vertex AI Discovery Engine ranking API
         """
-        # Get project ID from environment or litellm config
+        # Try to get project ID from optional_params first (e.g., vertex_project parameter)
+        params = optional_params or {}
+        
+        # Get credentials to extract project ID if needed
+        vertex_credentials = self.get_vertex_ai_credentials(params.copy())
+        vertex_project = self.get_vertex_ai_project(params.copy())
+        
+        # Use _ensure_access_token to extract project_id from credentials
+        # This is the same method used in vertex embeddings
+        _, vertex_project = self._ensure_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider="vertex_ai",
+        )
+        
+        # Fallback to environment or litellm config
         project_id = (
-            get_secret_str("VERTEXAI_PROJECT") 
+            vertex_project
+            or get_secret_str("VERTEXAI_PROJECT") 
             or litellm.vertex_project
         )
         
         if not project_id:
             raise ValueError(
-                "Vertex AI project ID is required. Please set 'VERTEXAI_PROJECT' or 'litellm.vertex_project'"
+                "Vertex AI project ID is required. Please set 'VERTEXAI_PROJECT', 'litellm.vertex_project', or pass 'vertex_project' parameter"
             )
         
         return f"https://discoveryengine.googleapis.com/v1/projects/{project_id}/locations/global/rankingConfigs/default_ranking_config:rank"
@@ -49,13 +70,15 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
         headers: dict,
         model: str,
         api_key: Optional[str] = None,
+        optional_params: Optional[Dict] = None,
     ) -> dict:
         """
         Validate and set up authentication for Vertex AI Discovery Engine API
         """
-        # Get credentials and project info
-        vertex_credentials = self.get_vertex_ai_credentials({})
-        vertex_project = self.get_vertex_ai_project({})
+        # Get credentials and project info from optional_params (which contains vertex_credentials, etc.)
+        litellm_params = optional_params or {}
+        vertex_credentials = self.get_vertex_ai_credentials(litellm_params)
+        vertex_project = self.get_vertex_ai_project(litellm_params)
         
         # Get access token using the base class method
         access_token, project_id = self._ensure_access_token(
@@ -218,10 +241,12 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
         """
         Map Cohere rerank params to Vertex AI format
         """
-        return {
+        result = {
             "query": query,
             "documents": documents,
             "top_n": top_n,
             "return_documents": return_documents,
         }
+        result.update(non_default_params)
+        return result
 
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
index 85b1a6bc0db..624e682ec59 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
@@ -38,6 +38,7 @@ class PartnerModelPrefixes(str, Enum):
     CLAUDE_PREFIX = "claude"
     QWEN_PREFIX = "qwen"
     GPT_OSS_PREFIX = "openai/gpt-oss-"
+    MINIMAX_PREFIX = "minimaxai/"
 
 
 class VertexAIPartnerModels(VertexBase):
@@ -62,6 +63,7 @@ class VertexAIPartnerModels(VertexBase):
             or model.startswith(PartnerModelPrefixes.CLAUDE_PREFIX)
             or model.startswith(PartnerModelPrefixes.QWEN_PREFIX)
             or model.startswith(PartnerModelPrefixes.GPT_OSS_PREFIX)
+            or model.startswith(PartnerModelPrefixes.MINIMAX_PREFIX)
         ):
             return True
         return False
@@ -73,6 +75,7 @@ class VertexAIPartnerModels(VertexBase):
             PartnerModelPrefixes.DEEPSEEK_PREFIX,
             PartnerModelPrefixes.QWEN_PREFIX,
             PartnerModelPrefixes.GPT_OSS_PREFIX,
+            PartnerModelPrefixes.MINIMAX_PREFIX,
         ]
         if any(provider in model for provider in OPENAI_LIKE_VERTEX_PROVIDERS):
             return True
diff --git a/litellm/llms/vertex_ai/videos/__init__.py b/litellm/llms/vertex_ai/videos/__init__.py
new file mode 100644
index 00000000000..1dcdbdf4ded
--- /dev/null
+++ b/litellm/llms/vertex_ai/videos/__init__.py
@@ -0,0 +1,10 @@
+"""
+Vertex AI Video Generation Module
+
+This module provides support for Vertex AI's Veo video generation API.
+"""
+
+from .transformation import VertexAIVideoConfig
+
+__all__ = ["VertexAIVideoConfig"]
+
diff --git a/litellm/llms/vertex_ai/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py
new file mode 100644
index 00000000000..2b6d43dd708
--- /dev/null
+++ b/litellm/llms/vertex_ai/videos/transformation.py
@@ -0,0 +1,597 @@
+"""
+Vertex AI Video Generation Transformation
+
+Handles transformation of requests/responses for Vertex AI's Veo video generation API.
+Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo-video-generation
+"""
+
+import base64
+import time
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+
+import httpx
+from httpx._types import RequestFiles
+
+from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
+from litellm.llms.vertex_ai.common_utils import (
+    _convert_vertex_datetime_to_openai_datetime,
+)
+from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.videos.main import VideoCreateOptionalRequestParams, VideoObject
+from litellm.types.videos.utils import (
+    encode_video_id_with_provider,
+    extract_original_video_id,
+)
+from litellm.images.utils import ImageEditRequestUtils
+from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+    from litellm.llms.base_llm.chat.transformation import (
+        BaseLLMException as _BaseLLMException,
+    )
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+    BaseLLMException = _BaseLLMException
+else:
+    LiteLLMLoggingObj = Any
+    BaseLLMException = Any
+
+
+def _convert_image_to_vertex_format(image_file) -> Dict[str, str]:
+    """
+    Convert image file to Vertex AI format with base64 encoding and MIME type.
+
+    Args:
+        image_file: File-like object opened in binary mode (e.g., open("path", "rb"))
+
+    Returns:
+        Dict with bytesBase64Encoded and mimeType
+    """
+    mime_type = ImageEditRequestUtils.get_image_content_type(image_file)
+
+    if hasattr(image_file, "seek"):
+        image_file.seek(0)
+    image_bytes = image_file.read()
+    base64_encoded = base64.b64encode(image_bytes).decode("utf-8")
+
+    return {"bytesBase64Encoded": base64_encoded, "mimeType": mime_type}
+
+
+class VertexAIVideoConfig(BaseVideoConfig, VertexBase):
+    """
+    Configuration class for Vertex AI (Veo) video generation.
+
+    Veo uses a long-running operation model:
+    1. POST to :predictLongRunning returns operation name
+    2. Poll operation using :fetchPredictOperation until done=true
+    3. Extract video data (base64) from response
+    """
+
+    def __init__(self):
+        BaseVideoConfig.__init__(self)
+        VertexBase.__init__(self)
+
+    @staticmethod
+    def extract_model_from_operation_name(operation_name: str) -> Optional[str]:
+        """
+        Extract the model name from a Vertex AI operation name.
+        
+        Args:
+            operation_name: Operation name in format:
+                projects/PROJECT/locations/LOCATION/publishers/google/models/MODEL/operations/OPERATION_ID
+        
+        Returns:
+            Model name (e.g., "veo-2.0-generate-001") or None if extraction fails
+        """
+        parts = operation_name.split("/")
+        # Model is at index 7 in the operation name format
+        if len(parts) >= 8:
+            return parts[7]
+        return None
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get the list of supported OpenAI parameters for Veo video generation.
+        Veo supports minimal parameters compared to OpenAI.
+        """
+        return ["model", "prompt", "input_reference", "seconds", "size"]
+
+    def map_openai_params(
+        self,
+        video_create_optional_params: VideoCreateOptionalRequestParams,
+        model: str,
+        drop_params: bool,
+    ) -> Dict[str, Any]:
+        """
+        Map OpenAI-style parameters to Veo format.
+
+        Mappings:
+        - prompt → prompt (in instances)
+        - input_reference → image (in instances)
+        - size → aspectRatio (e.g., "1280x720" → "16:9")
+        - seconds → durationSeconds (defaults to 4 seconds if not provided)
+        """
+        mapped_params: Dict[str, Any] = {}
+
+        # Map input_reference to image (will be processed in transform_video_create_request)
+        if "input_reference" in video_create_optional_params:
+            mapped_params["image"] = video_create_optional_params["input_reference"]
+
+        # Map size to aspectRatio
+        if "size" in video_create_optional_params:
+            size = video_create_optional_params["size"]
+            if size is not None:
+                aspect_ratio = self._convert_size_to_aspect_ratio(size)
+                if aspect_ratio:
+                    mapped_params["aspectRatio"] = aspect_ratio
+
+        # Map seconds to durationSeconds, default to 4 seconds (matching OpenAI)
+        if "seconds" in video_create_optional_params:
+            seconds = video_create_optional_params["seconds"]
+            try:
+                duration = int(seconds) if isinstance(seconds, str) else seconds
+                if duration is not None:
+                    mapped_params["durationSeconds"] = duration
+            except (ValueError, TypeError):
+                # If conversion fails, use default
+                pass
+
+        return mapped_params
+
+    def _convert_size_to_aspect_ratio(self, size: str) -> Optional[str]:
+        """
+        Convert OpenAI size format to Veo aspectRatio format.
+
+        Supported aspect ratios: 9:16 (portrait), 16:9 (landscape)
+        """
+        if not size:
+            return None
+
+        aspect_ratio_map = {
+            "1280x720": "16:9",
+            "1920x1080": "16:9",
+            "720x1280": "9:16",
+            "1080x1920": "9:16",
+        }
+
+        return aspect_ratio_map.get(size, "16:9")
+
+    def validate_environment(
+        self,
+        headers: Dict,
+        model: str,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        litellm_params: Optional[dict] = None,
+        **kwargs,
+    ) -> Dict:
+        """
+        Validate environment and return headers for Vertex AI OCR.
+        
+        Vertex AI uses Bearer token authentication with access token from credentials.
+        """
+        # Extract Vertex AI parameters using safe helpers from VertexBase
+        # Use safe_get_* methods that don't mutate litellm_params dict
+        litellm_params = litellm_params or {}
+        
+        vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=litellm_params)
+        vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params=litellm_params)
+        
+        # Get access token from Vertex credentials
+        access_token, project_id = self.get_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+        )
+
+        headers = {
+            "Authorization": f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            **headers,
+        }
+
+        return headers
+
+    def get_complete_url(
+        self,
+        model: str,
+        api_base: Optional[str],
+        litellm_params: dict,
+    ) -> str:
+        """
+        Get the complete URL for Veo video generation.
+
+        Returns URL for :predictLongRunning endpoint:
+        https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/publishers/google/models/MODEL:predictLongRunning
+        """
+        vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params)
+        vertex_location = VertexBase.safe_get_vertex_ai_location(litellm_params)
+
+        if not vertex_project:
+            raise ValueError(
+                "vertex_project is required for Vertex AI video generation. "
+                "Set it via environment variable VERTEXAI_PROJECT or pass as parameter."
+            )
+
+        # Default to us-central1 if no location specified
+        vertex_location = vertex_location or "us-central1"
+
+        # Extract model name (remove vertex_ai/ prefix if present)
+        model_name = model.replace("vertex_ai/", "")
+
+        # Construct the URL
+        if api_base:
+            base_url = api_base.rstrip("/")
+        else:
+            base_url = f"https://{vertex_location}-aiplatform.googleapis.com"
+
+        url = f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}"
+
+        return url
+
+    def transform_video_create_request(
+        self,
+        model: str,
+        prompt: str,
+        api_base: str,
+        video_create_optional_request_params: Dict,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[Dict, RequestFiles, str]:
+        """
+        Transform the video creation request for Veo API.
+
+        Veo expects:
+        {
+            "instances": [
+                {
+                    "prompt": "A cat playing with a ball of yarn",
+                    "image": {
+                        "bytesBase64Encoded": "...",
+                        "mimeType": "image/jpeg"
+                    }
+                }
+            ],
+            "parameters": {
+                "aspectRatio": "16:9",
+                "durationSeconds": 8
+            }
+        }
+        """
+        # Build instance with prompt
+        instance_dict: Dict[str, Any] = {"prompt": prompt}
+        params_copy = video_create_optional_request_params.copy()
+
+
+        # Check if user wants to provide full instance dict
+        if "instances" in params_copy and isinstance(params_copy["instances"], dict):
+            # Replace/merge with user-provided instance
+            instance_dict.update(params_copy["instances"])
+            params_copy.pop("instances")
+        elif "image" in params_copy and params_copy["image"] is not None:
+            image_data = _convert_image_to_vertex_format(params_copy["image"])
+            instance_dict["image"] = image_data
+            params_copy.pop("image")
+
+        # Build request data directly (TypedDict doesn't have model_dump)
+        request_data: Dict[str, Any] = {"instances": [instance_dict]}
+
+        # Only add parameters if there are any
+        if params_copy:
+            request_data["parameters"] = params_copy
+
+        # Append :predictLongRunning endpoint to api_base
+        url = f"{api_base}:predictLongRunning"
+
+        # No files needed - everything is in JSON
+        return request_data, [], url
+
+    def transform_video_create_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+        request_data: Optional[Dict] = None,
+    ) -> VideoObject:
+        """
+        Transform the Veo video creation response.
+
+        Veo returns:
+        {
+            "name": "projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL/operations/OPERATION_ID"
+        }
+
+        We return this as a VideoObject with:
+        - id: operation name (used for polling)
+        - status: "processing"
+        - usage: includes duration_seconds for cost calculation
+        """
+        response_data = raw_response.json()
+
+        operation_name = response_data.get("name")
+        if not operation_name:
+            raise ValueError(f"No operation name in Veo response: {response_data}")
+
+        if custom_llm_provider:
+            video_id = encode_video_id_with_provider(
+                operation_name, custom_llm_provider, model
+            )
+        else:
+            video_id = operation_name
+
+
+        video_obj = VideoObject(
+            id=video_id,
+            object="video",
+            status="processing",
+            model=model
+        )
+
+        usage_data = {}
+        if request_data:
+            parameters = request_data.get("parameters", {})
+            duration = parameters.get("durationSeconds") or DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS
+            if duration is not None:
+                try:
+                    usage_data["duration_seconds"] = float(duration)
+                except (ValueError, TypeError):
+                    pass
+        
+        video_obj.usage = usage_data
+        return video_obj
+
+    def transform_video_status_retrieve_request(
+        self,
+        video_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the video status retrieve request for Veo API.
+
+        Veo polls operations using :fetchPredictOperation endpoint with POST request.
+        """
+        operation_name = extract_original_video_id(video_id)
+        model = self.extract_model_from_operation_name(operation_name)
+        
+        if not model:
+            raise ValueError(
+                f"Invalid operation name format: {operation_name}. "
+                "Expected format: projects/PROJECT/locations/LOCATION/publishers/google/models/MODEL/operations/OPERATION_ID"
+            )
+
+        # Construct the full URL including model ID
+        # URL format: https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/publishers/google/models/MODEL:fetchPredictOperation
+        # Strip trailing slashes from api_base and append model
+        url = f"{api_base.rstrip('/')}/{model}:fetchPredictOperation"
+
+        # Request body contains the operation name
+        params = {"operationName": operation_name}
+
+        return url, params
+
+    def transform_video_status_retrieve_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+    ) -> VideoObject:
+        """
+        Transform the Veo operation status response.
+
+        Veo returns:
+        {
+            "name": "projects/.../operations/OPERATION_ID",
+            "done": false  # or true when complete
+        }
+
+        When done=true:
+        {
+            "name": "projects/.../operations/OPERATION_ID",
+            "done": true,
+            "response": {
+                "@type": "type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse",
+                "raiMediaFilteredCount": 0,
+                "videos": [
+                    {
+                        "bytesBase64Encoded": "...",
+                        "mimeType": "video/mp4"
+                    }
+                ]
+            }
+        }
+        """
+        response_data = raw_response.json()
+
+        operation_name = response_data.get("name", "")
+        is_done = response_data.get("done", False)
+        error_data = response_data.get("error")
+
+        # Extract model from operation name
+        model = self.extract_model_from_operation_name(operation_name)
+
+        if custom_llm_provider:
+            video_id = encode_video_id_with_provider(
+                operation_name, custom_llm_provider, model
+            )
+        else:
+            video_id = operation_name
+
+        # Convert createTime to Unix timestamp
+        create_time_str = response_data.get("metadata", {}).get("createTime")
+        if create_time_str:
+            try:
+                created_at = _convert_vertex_datetime_to_openai_datetime(
+                    create_time_str
+                )
+            except Exception:
+                created_at = int(time.time())
+        else:
+            created_at = int(time.time())
+
+        if error_data:
+            status = "failed"
+        elif is_done:
+            status = "completed"
+        else:
+            status = "processing"
+
+        video_obj = VideoObject(
+            id=video_id,
+            object="video",
+            status=status,
+            model=model,
+            created_at=created_at,
+            error=error_data,
+        )
+        return video_obj
+
+    def transform_video_content_request(
+        self,
+        video_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the video content request for Veo API.
+
+        For Veo, we need to:
+        1. Poll the operation status to ensure it's complete
+        2. Extract the base64 video data from the response
+        3. Return it for decoding
+
+        Since we need to make an HTTP call here, we'll use the same fetchPredictOperation
+        approach as status retrieval.
+        """
+        return self.transform_video_status_retrieve_request(video_id, api_base, litellm_params, headers)
+
+    def transform_video_content_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> bytes:
+        """
+        Transform the Veo video content download response.
+
+        Extracts the base64 encoded video from the response and decodes it to bytes.
+        """
+        response_data = raw_response.json()
+
+        if not response_data.get("done", False):
+            raise ValueError(
+                "Video generation is not complete yet. "
+                "Please check status with video_status() before downloading."
+            )
+
+        try:
+            video_response = response_data.get("response", {})
+            videos = video_response.get("videos", [])
+
+            if not videos or len(videos) == 0:
+                raise ValueError("No video data found in completed operation")
+
+            # Get the first video
+            video_data = videos[0]
+            base64_encoded = video_data.get("bytesBase64Encoded")
+
+            if not base64_encoded:
+                raise ValueError("No base64 encoded video data found")
+
+            # Decode base64 to bytes
+            video_bytes = base64.b64decode(base64_encoded)
+            return video_bytes
+
+        except (KeyError, IndexError) as e:
+            raise ValueError(f"Failed to extract video data: {e}")
+
+    def transform_video_remix_request(
+        self,
+        video_id: str,
+        prompt: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+        extra_body: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[str, Dict]:
+        """
+        Video remix is not supported by Veo API.
+        """
+        raise NotImplementedError(
+            "Video remix is not supported by Vertex AI Veo. "
+            "Please use video_generation() to create new videos."
+        )
+
+    def transform_video_remix_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+    ) -> VideoObject:
+        """Video remix is not supported."""
+        raise NotImplementedError("Video remix is not supported by Vertex AI Veo.")
+
+    def transform_video_list_request(
+        self,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+        order: Optional[str] = None,
+        extra_query: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[str, Dict]:
+        """
+        Video list is not supported by Veo API.
+        """
+        raise NotImplementedError(
+            "Video list is not supported by Vertex AI Veo. "
+            "Use the operations endpoint directly if you need to list operations."
+        )
+
+    def transform_video_list_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+    ) -> Dict[str, str]:
+        """Video list is not supported."""
+        raise NotImplementedError("Video list is not supported by Vertex AI Veo.")
+
+    def transform_video_delete_request(
+        self,
+        video_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Video delete is not supported by Veo API.
+        """
+        raise NotImplementedError(
+            "Video delete is not supported by Vertex AI Veo. "
+            "Videos are automatically cleaned up by Google."
+        )
+
+    def transform_video_delete_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> VideoObject:
+        """Video delete is not supported."""
+        raise NotImplementedError("Video delete is not supported by Vertex AI Veo.")
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        from litellm.llms.vertex_ai.common_utils import VertexAIError
+
+        return VertexAIError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
+
diff --git a/litellm/llms/xai/responses/__init__.py b/litellm/llms/xai/responses/__init__.py
deleted file mode 100644
index 9610a119787..00000000000
--- a/litellm/llms/xai/responses/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# XAI Responses API
-from .transformation import XAIResponsesAPIConfig
-
-__all__ = ["XAIResponsesAPIConfig"]
-
diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py
index 5767177b7a6..bd422c8d81e 100644
--- a/litellm/llms/xai/responses/transformation.py
+++ b/litellm/llms/xai/responses/transformation.py
@@ -85,7 +85,7 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
                     # XAI supports code_interpreter but doesn't use the container field
                     # Keep only the type field
                     verbose_logger.debug(
-                        f"XAI: Transforming code_interpreter tool, removing container field"
+                        "XAI: Transforming code_interpreter tool, removing container field"
                     )
                     transformed_tools.append({"type": "code_interpreter"})
                 else:
diff --git a/litellm/main.py b/litellm/main.py
index 9cae34d1678..2ad444a9a20 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -65,7 +65,10 @@ from litellm.constants import (
 )
 from litellm.exceptions import LiteLLMUnknownProvider
 from litellm.integrations.custom_logger import CustomLogger
-from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_for_health_check
+from litellm.litellm_core_utils.audio_utils.utils import (
+    calculate_request_duration,
+    get_audio_file_for_health_check,
+)
 from litellm.litellm_core_utils.dd_tracing import tracer
 from litellm.litellm_core_utils.get_provider_specific_headers import (
     ProviderSpecificHeaderUtils,
@@ -2033,36 +2036,11 @@ def completion(  # type: ignore # noqa: PLR0915
             logging.post_call(
                 input=messages, api_key=api_key, original_response=response
             )
-        elif custom_llm_provider == "perplexity":
-            response = base_llm_http_handler.completion(
-                model=model,
-                messages=messages,
-                headers=headers,
-                model_response=model_response,
-                api_key=api_key,
-                api_base=api_base,
-                acompletion=acompletion,
-                logging_obj=logging,
-                optional_params=optional_params,
-                litellm_params=litellm_params,
-                shared_session=shared_session,
-                timeout=timeout,
-                client=client,
-                custom_llm_provider=custom_llm_provider,
-                encoding=encoding,
-                stream=stream,
-                provider_config=provider_config,
-            )
-
-            ## LOGGING - Call after response has been processed by transform_response
-            logging.post_call(
-                input=messages, api_key=api_key, original_response=response
-            )
-            
         elif (
             model in litellm.open_ai_chat_completion_models
             or custom_llm_provider == "custom_openai"
             or custom_llm_provider == "deepinfra"
+            or custom_llm_provider == "perplexity"
             or custom_llm_provider == "nvidia_nim"
             or custom_llm_provider == "cerebras"
             or custom_llm_provider == "baseten"
@@ -5431,6 +5409,7 @@ async def atranscription(*args, **kwargs) -> TranscriptionResponse:
     model = args[0] if len(args) > 0 else kwargs["model"]
     ### PASS ARGS TO Image Generation ###
     kwargs["atranscription"] = True
+    file = kwargs.get("file", None)
     custom_llm_provider = None
     try:
         # Use a partial function to pass your keyword arguments
@@ -5459,6 +5438,20 @@ async def atranscription(*args, **kwargs) -> TranscriptionResponse:
             raise ValueError(
                 f"Invalid response from transcription provider, expected TranscriptionResponse, but got {type(response)}"
             )
+
+        # Calculate and add duration if response is missing it
+        if (
+            response is not None
+            and not isinstance(response, Coroutine)
+            and file is not None
+        ):
+            # Check if response is missing duration
+            existing_duration = getattr(response, "duration", None)
+            if existing_duration is None:
+                calculated_duration = calculate_request_duration(file)
+                if calculated_duration is not None:
+                    setattr(response, "duration", calculated_duration)
+
         return response
     except Exception as e:
         custom_llm_provider = custom_llm_provider or "openai"
@@ -5669,6 +5662,16 @@ def transcription(
             headers={},
             provider_config=provider_config,
         )
+
+    # Calculate and add duration if response is missing it
+    if response is not None and not isinstance(response, Coroutine):
+        # Check if response is missing duration
+        existing_duration = getattr(response, "duration", None)
+        if existing_duration is None:
+            calculated_duration = calculate_request_duration(file)
+            if calculated_duration is not None:
+                setattr(response, "duration", calculated_duration)
+
     if response is None:
         raise ValueError("Unmapped provider passed in. Unable to get the response.")
     return response
@@ -6026,6 +6029,7 @@ async def ahealth_check(
             "audio_speech",
             "audio_transcription",
             "image_generation",
+            "video_generation",
             "batch",
             "rerank",
             "realtime",
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 49236741fff..f571dfb5243 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -3704,7 +3704,6 @@
         "output_cost_per_token": 2.75e-05,
         "source": "https://azure.microsoft.com/en-us/blog/grok-4-is-now-available-in-azure-ai-foundry-unlock-frontier-intelligence-and-business-ready-capabilities/",
         "supports_function_calling": true,
-        "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_web_search": true
@@ -3732,7 +3731,6 @@
         "mode": "chat",
         "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-the-grok-4-fast-models-from-xai-now-available-in-azure-ai-foundry/4456701",
         "supports_function_calling": true,
-        "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_web_search": true
@@ -5755,6 +5753,16 @@
         "output_vector_size": 1536,
         "supports_embedding_image_input": true
     },
+    "cohere/embed-v4.0": {
+        "input_cost_per_token": 1.2e-07,
+        "litellm_provider": "cohere",
+        "max_input_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "embedding",
+        "output_cost_per_token": 0.0,
+        "output_vector_size": 1536,
+        "supports_embedding_image_input": true
+    },
     "cohere.rerank-v3-5:0": {
         "input_cost_per_query": 0.002,
         "input_cost_per_token": 0.0,
@@ -10342,6 +10350,7 @@
         "mode": "chat",
         "output_cost_per_audio_token": 1.2e-05,
         "output_cost_per_token": 2e-06,
+        "rpm": 100000,
         "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
@@ -10368,7 +10377,8 @@
         "supports_tool_choice": true,
         "supports_url_context": true,
         "supports_vision": true,
-        "supports_web_search": true
+        "supports_web_search": true,
+        "tpm": 8000000
     },
     "gemini-2.5-flash-lite-preview-06-17": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -11607,7 +11617,7 @@
         "tpm": 1000000
     },
     "gemini/gemini-2.5-flash": {
-        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost": 3e-08,
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "gemini",
@@ -12658,6 +12668,34 @@
             "video"
         ]
     },
+    "gemini/veo-3.1-fast-generate-preview": {
+        "litellm_provider": "gemini",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.15,
+        "source": "https://ai.google.dev/gemini-api/docs/video",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
+    "gemini/veo-3.1-generate-preview": {
+        "litellm_provider": "gemini",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.40,
+        "source": "https://ai.google.dev/gemini-api/docs/video",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
     "google_pse/search": {
         "input_cost_per_query": 0.005,
         "litellm_provider": "google_pse",
@@ -12724,19 +12762,21 @@
         "tool_use_system_prompt_tokens": 159
     },
     "global.anthropic.claude-haiku-4-5-20251001-v1:0": {
-        "cache_creation_input_token_cost": 1.25e-06,
-        "cache_read_input_token_cost": 1e-07,
-        "input_cost_per_token": 1e-06,
+        "cache_creation_input_token_cost": 1.375e-06,
+        "cache_read_input_token_cost": 1.1e-07,
+        "input_cost_per_token": 1.1e-06,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 200000,
         "max_output_tokens": 8192,
         "max_tokens": 8192,
         "mode": "chat",
-        "output_cost_per_token": 5e-06,
+        "output_cost_per_token": 5.5e-06,
+        "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_pdf_input": true,
         "supports_prompt_caching": true,
+        "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
@@ -23096,6 +23136,18 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supports_tool_choice": true
     },
+    "vertex_ai/minimaxai/minimax-m2-maas": {
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "vertex_ai-minimax_models",
+        "max_input_tokens": 196608,
+        "max_output_tokens": 196608,
+        "max_tokens": 196608,
+        "mode": "chat",
+        "output_cost_per_token": 1.2e-06,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
     "vertex_ai/mistral-medium-3": {
         "input_cost_per_token": 4e-07,
         "litellm_provider": "vertex_ai-mistral_models",
@@ -23350,6 +23402,34 @@
             "video"
         ]
     },
+    "vertex_ai/veo-3.1-generate-preview": {
+        "litellm_provider": "vertex_ai-video-models",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.4,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
+    "vertex_ai/veo-3.1-fast-generate-preview": {
+        "litellm_provider": "vertex_ai-video-models",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.15,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
     "voyage/rerank-2": {
         "input_cost_per_query": 5e-08,
         "input_cost_per_token": 5e-08,
diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py
index dca9af62c72..a9734233a61 100644
--- a/litellm/proxy/_experimental/mcp_server/db.py
+++ b/litellm/proxy/_experimental/mcp_server/db.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Set, Union
+from typing import Any, Dict, Iterable, List, Optional, Set, Union, cast
 
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid
@@ -11,7 +11,12 @@ from litellm.proxy._types import (
     UpdateMCPServerRequest,
     UserAPIKeyAuth,
 )
+from litellm.proxy.common_utils.encrypt_decrypt_utils import (
+    _get_salt_key,
+    encrypt_value_helper,
+)
 from litellm.proxy.utils import PrismaClient
+from litellm.types.mcp import MCPCredentials
 
 
 def _prepare_mcp_server_data(
@@ -35,6 +40,14 @@ def _prepare_mcp_server_data(
     if "alias" not in data_dict:
         data_dict["alias"] = getattr(data, "alias", None)
 
+    # Handle credentials serialization
+    credentials = data_dict.get("credentials")
+    if credentials is not None:
+        data_dict["credentials"] = encrypt_credentials(
+            credentials=credentials, encryption_key=_get_salt_key()
+        )
+        data_dict["credentials"] = safe_dumps(data_dict["credentials"])
+
     # Handle static_headers serialization
     if data.static_headers is not None:
         data_dict["static_headers"] = safe_dumps(data.static_headers)
@@ -52,6 +65,30 @@ def _prepare_mcp_server_data(
     return data_dict
 
 
+def encrypt_credentials(
+    credentials: MCPCredentials, encryption_key: Optional[str]
+) -> MCPCredentials:
+    auth_value = credentials.get("auth_value")
+    if auth_value is not None:
+        credentials["auth_value"] = encrypt_value_helper(
+            value=auth_value,
+            new_encryption_key=encryption_key,
+        )
+    client_id = credentials.get("client_id")
+    if client_id is not None:
+        credentials["client_id"] = encrypt_value_helper(
+            value=client_id,
+            new_encryption_key=encryption_key,
+        )
+    client_secret = credentials.get("client_secret")
+    if client_secret is not None:
+        credentials["client_secret"] = encrypt_value_helper(
+            value=client_secret,
+            new_encryption_key=encryption_key,
+        )
+    return credentials
+
+
 async def get_all_mcp_servers(
     prisma_client: PrismaClient,
 ) -> List[LiteLLM_MCPServerTable]:
@@ -303,3 +340,32 @@ async def update_mcp_server(
     )
 
     return updated_mcp_server
+
+
+async def rotate_mcp_server_credentials_master_key(
+    prisma_client: PrismaClient, touched_by: str, new_master_key: str
+):
+    mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many()
+
+    for mcp_server in mcp_servers:
+        credentials = mcp_server.credentials
+        if not credentials:
+            continue
+
+        credentials_copy = dict(credentials)
+        encrypted_credentials = encrypt_credentials(
+            credentials=cast(MCPCredentials, credentials_copy),
+            encryption_key=new_master_key,
+        )
+
+        from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
+        serialized_credentials = safe_dumps(encrypted_credentials)
+
+        await prisma_client.db.litellm_mcpservertable.update(
+            where={"server_id": mcp_server.server_id},
+            data={
+                "credentials": serialized_credentials,
+                "updated_by": touched_by,
+            },
+        )
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index 49ff1effd19..5b1dc5933c3 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -38,6 +38,9 @@ from litellm.proxy._types import (
     MCPTransportType,
     UserAPIKeyAuth,
 )
+from litellm.proxy.common_utils.encrypt_decrypt_utils import (
+    decrypt_value_helper,
+)
 from litellm.proxy.utils import ProxyLogging
 from litellm.types.mcp import MCPAuth, MCPStdioConfig
 from litellm.types.mcp_server.mcp_server_manager import MCPInfo, MCPServer
@@ -400,6 +403,20 @@ class MCPServerManager:
                 static_headers_dict = _deserialize_json_dict(
                     getattr(mcp_server, "static_headers", None)
                 )
+                credentials_dict = _deserialize_json_dict(
+                    getattr(mcp_server, "credentials", None)
+                )
+
+                encrypted_auth_value: Optional[str] = None
+                if credentials_dict:
+                    encrypted_auth_value = credentials_dict.get("auth_value")
+
+                auth_value: Optional[str] = None
+                if encrypted_auth_value:
+                    auth_value = decrypt_value_helper(
+                        value=encrypted_auth_value,
+                        key="auth_value",
+                    )
                 # Use alias for name if present, else server_name
                 name_for_prefix = (
                     mcp_server.alias or mcp_server.server_name or mcp_server.server_id
@@ -422,6 +439,7 @@ class MCPServerManager:
                     url=mcp_server.url,
                     transport=cast(MCPTransportType, mcp_server.transport),
                     auth_type=cast(MCPAuthType, mcp_server.auth_type),
+                    authentication_token=auth_value,
                     mcp_info=mcp_info,
                     extra_headers=getattr(mcp_server, "extra_headers", None),
                     static_headers=static_headers_dict,
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1162-278deed893787c5d.js b/litellm/proxy/_experimental/out/_next/static/chunks/1162-278deed893787c5d.js
deleted file mode 100644
index 42e084fc3b7..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1162-278deed893787c5d.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/131-66e1fb73fd8f2361.js b/litellm/proxy/_experimental/out/_next/static/chunks/131-2eebef89ebe87d26.js
similarity index 100%
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-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[2162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return r.Z},xv:function(){return s.Z},zx:function(){return a.Z}});var a=n(20831),r=n(12514),s=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return r.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return s.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),r=n(97214),s=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return r}});var a=n(49817);let r=e=>{let t;let{apiKeySource:n,accessToken:r,apiKey:s,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?r:s,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(h,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(j,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(j,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(_,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(j,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(j,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(_,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(j,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:t='\nresponse = client.embeddings.create(\n	input="'.concat(i||"Your string here",'",\n	model="').concat(j,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(y,"\n").concat(t)}},49817:function(e,t,n){var a,r,s,i;n.d(t,{KP:function(){return r},vf:function(){return l}}),(s=a||(a={})).IMAGE_GENERATION="image_generation",s.CHAT="chat",s.RESPONSES="responses",s.IMAGE_EDITS="image_edits",s.ANTHROPIC_MESSAGES="anthropic_messages",(i=r||(r={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings";let 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2344-169e12738d6439ab.js b/litellm/proxy/_experimental/out/_next/static/chunks/2344-cbe5939116545b80.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/2344-169e12738d6439ab.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/2344-cbe5939116545b80.js
index 89ac20e063c..3d10b6e7929 100644
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0!==t.activeTooltipIndex){var u=t.chartX,l=t.chartY,s=t.activeTooltipIndex,f=c.state,p=f.offset,h=f.tooltipTicks;if(!p)return;if("function"==typeof r)s=r(h,t);else if("value"===r){s=-1;for(var d=0;d<h.length;d++)if(h[d].value===t.activeLabel){s=d;break}}var y=ee(ee({},p),{},{x:p.left,y:p.top}),v=Math.min(u,y.x+y.width),m=Math.min(l,y.y+y.height),g=h[s]&&h[s].value,b=ef(c.state,c.props.data,s),x=h[s]?{x:"horizontal"===n?h[s].coordinate:v,y:"horizontal"===n?m:h[s].coordinate}:ea;c.setState(ee(ee({},t),{},{activeLabel:g,activeCoordinate:x,activePayload:b,activeTooltipIndex:s}))}else c.setState(t)}),en(t3(c),"renderCursor",function(t){var e,o=c.state,i=o.isTooltipActive,a=o.activeCoordinate,u=o.activePayload,l=o.offset,s=o.activeTooltipIndex,f=o.tooltipAxisBandSize,p=c.getTooltipEventType(),h=null!==(e=t.props.active)&&void 0!==e?e:i,d=c.props.layout,y=t.key||"_recharts-cursor";return r.createElement(tK,{key:y,activeCoordinate:a,activePayload:u,activeTooltipIndex:s,chartName:n,element:t,isActive:h,layout:d,offset:l,tooltipAxisBandSize:f,tooltipEventType:p})}),en(t3(c),"renderPolarAxis",function(t,e,n){var o=f()(t,"type.axisType"),i=f()(c.state,"".concat(o,"Map")),a=i&&i[t.props["".concat(o,"Id")]];return(0,r.cloneElement)(t,ee(ee({},a),{},{className:o,key:t.key||"".concat(e,"-").concat(n),ticks:(0,T.uY)(a,!0)}))}),en(t3(c),"renderGrid",function(t){var e=c.state,n=e.xAxisMap,o=e.yAxisMap,i=e.offset,a=(0,C.Kt)(n),u=m()(o,function(t){return b()(t.domain,Number.isFinite)})||(0,C.Kt)(o),l=t.props||{};return(0,r.cloneElement)(t,{key:t.key||"grid",x:(0,C.hj)(l.x)?l.x:i.left,y:(0,C.hj)(l.y)?l.y:i.top,width:(0,C.hj)(l.width)?l.width:i.width,height:(0,C.hj)(l.height)?l.height:i.height,xAxis:a,yAxis:u,verticalCoordinatesGenerator:l.verticalCoordinatesGenerator,horizontalCoordinatesGenerator:l.horizontalCoordinatesGenerator})}),en(t3(c),"renderPolarGrid",function(t){var e=t.props,n=e.radialLines,o=e.polarAngles,i=e.polarRadius,a=c.state,u=a.radiusAxisMap,l=a.angleAxisMap,s=(0,C.Kt)(u),f=(0,C.Kt)(l),p=f.cx,h=f.cy,d=f.innerRadius,y=f.outerRadius;return(0,r.cloneElement)(t,{polarAngles:Array.isArray(o)?o:(0,T.uY)(f,!0).map(function(t){return t.coordinate}),polarRadius:Array.isArray(i)?i:(0,T.uY)(s,!0).map(function(t){return t.coordinate}),cx:p,cy:h,innerRadius:d,outerRadius:y,key:t.key||"polar-grid",radialLines:n})}),en(t3(c),"renderLegend",function(){var t=c.state.formattedGraphicalItems,e=c.props,n=e.children,o=e.width,i=e.height,a=c.props.margin||{},u=o-(a.left||0)-(a.right||0),l=(0,Q.z)({children:n,formattedGraphicalItems:t,legendWidth:u,legendContent:h});if(!l)return null;var s=l.item,f=t5(l,tJ);return(0,r.cloneElement)(s,ee(ee({},f),{},{chartWidth:o,chartHeight:i,margin:a,onBBoxUpdate:c.handleLegendBBoxUpdate}))}),en(t3(c),"renderTooltip",function(){var t,e=c.props.children,n=(0,A.sP)(e,S.u);if(!n)return null;var o=c.state,i=o.isTooltipActive,a=o.activeCoordinate,u=o.activePayload,l=o.activeLabel,s=o.offset,f=null!==(t=n.props.active)&&void 0!==t?t:i;return(0,r.cloneElement)(n,{viewBox:ee(ee({},s),{},{x:s.left,y:s.top}),active:f,label:l,payload:f?u:[],coordinate:a})}),en(t3(c),"renderBrush",function(t){var e=c.props,n=e.margin,o=e.data,i=c.state,a=i.offset,u=i.dataStartIndex,l=i.dataEndIndex,s=i.updateId;return(0,r.cloneElement)(t,{key:t.key||"_recharts-brush",onChange:(0,T.DO)(c.handleBrushChange,t.props.onChange),data:o,x:(0,C.hj)(t.props.x)?t.props.x:a.left,y:(0,C.hj)(t.props.y)?t.props.y:a.top+a.height+a.brushBottom-(n.bottom||0),width:(0,C.hj)(t.props.width)?t.props.width:a.width,startIndex:u,endIndex:l,updateId:"brush-".concat(s)})}),en(t3(c),"renderReferenceElement",function(t,e,n){if(!t)return null;var o=t3(c).clipPathId,i=c.state,a=i.xAxisMap,u=i.yAxisMap,l=i.offset,s=t.props,f=s.xAxisId,p=s.yAxisId;return(0,r.cloneElement)(t,{key:t.key||"".concat(e,"-").concat(n),xAxis:a[f],yAxis:u[p],viewBox:{x:l.left,y:l.top,width:l.width,height:l.height},clipPathId:o})}),en(t3(c),"renderActivePoints",function(t){var e=t.item,n=t.activePoint,r=t.basePoint,o=t.childIndex,i=t.isRange,a=[],u=e.props.key,c=e.item.props,s=c.activeDot,f=ee(ee({index:o,dataKey:c.dataKey,cx:n.x,cy:n.y,r:4,fill:(0,T.fk)(e.item),strokeWidth:2,stroke:"#fff",payload:n.payload,value:n.value,key:"".concat(u,"-activePoint-").concat(o)},(0,A.L6)(s,!1)),(0,tD.Ym)(s));return a.push(l.renderActiveDot(s,f)),r?a.push(l.renderActiveDot(s,ee(ee({},f),{},{cx:r.x,cy:r.y,key:"".concat(u,"-basePoint-").concat(o)}))):i&&a.push(null),a}),en(t3(c),"renderGraphicChild",function(t,e,n){var o=c.filterFormatItem(t,e,n);if(!o)return null;var a=c.getTooltipEventType(),u=c.state,l=u.isTooltipActive,s=u.tooltipAxis,f=u.activeTooltipIndex,p=u.activeLabel,h=c.props.children,d=(0,A.sP)(h,S.u),y=o.props,v=y.points,m=y.isRange,g=y.baseLine,b=o.item.props,x=b.activeDot,O=b.hide,w=b.activeBar,j=b.activeShape,E={};"axis"!==a&&d&&"click"===d.props.trigger?E={onClick:(0,T.DO)(c.handleItemMouseEnter,t.props.onClick)}:"axis"!==a&&(E={onMouseLeave:(0,T.DO)(c.handleItemMouseLeave,t.props.onMouseLeave),onMouseEnter:(0,T.DO)(c.handleItemMouseEnter,t.props.onMouseEnter)});var k=(0,r.cloneElement)(t,ee(ee({},o.props),E));if(!O&&l&&d&&(x||w||j)){if(f>=0){if(s.dataKey&&!s.allowDuplicatedCategory){var P="function"==typeof s.dataKey?function(t){return"function"==typeof s.dataKey?s.dataKey(t.payload):null}:"payload.".concat(s.dataKey.toString());_=(0,C.Ap)(v,P,p),N=m&&g&&(0,C.Ap)(g,P,p)}else _=null==v?void 0:v[f],N=m&&g&&g[f];if(j||w){var M=void 0!==t.props.activeIndex?t.props.activeIndex:f;return[(0,r.cloneElement)(t,ee(ee(ee({},o.props),E),{},{activeIndex:M})),null,null]}if(!i()(_))return[k].concat(t8(c.renderActivePoints({item:o,activePoint:_,basePoint:N,childIndex:f,isRange:m})))}else{var _,N,D,I=(null!==(D=c.getItemByXY(c.state.activeCoordinate))&&void 0!==D?D:{graphicalItem:k}).graphicalItem,L=I.item,B=void 0===L?t:L,R=I.childIndex,z=ee(ee(ee({},o.props),E),{},{activeIndex:R});return[(0,r.cloneElement)(B,z),null,null]}}return m?[k,null,null]:[k,null]}),en(t3(c),"renderCustomized",function(t,e,n){return(0,r.cloneElement)(t,ee(ee({key:"recharts-customized-".concat(n)},c.props),c.state))}),en(t3(c),"renderMap",{CartesianGrid:{handler:c.renderGrid,once:!0},ReferenceArea:{handler:c.renderReferenceElement},ReferenceLine:{handler:eu},ReferenceDot:{handler:c.renderReferenceElement},XAxis:{handler:eu},YAxis:{handler:eu},Brush:{handler:c.renderBrush,once:!0},Bar:{handler:c.renderGraphicChild},Line:{handler:c.renderGraphicChild},Area:{handler:c.renderGraphicChild},Radar:{handler:c.renderGraphicChild},RadialBar:{handler:c.renderGraphicChild},Scatter:{handler:c.renderGraphicChild},Pie:{handler:c.renderGraphicChild},Funnel:{handler:c.renderGraphicChild},Tooltip:{handler:c.renderCursor,once:!0},PolarGrid:{handler:c.renderPolarGrid,once:!0},PolarAngleAxis:{handler:c.renderPolarAxis},PolarRadiusAxis:{handler:c.renderPolarAxis},Customized:{handler:c.renderCustomized}}),c.clipPathId="".concat(null!==(e=t.id)&&void 0!==e?e:(0,C.EL)("recharts"),"-clip"),c.throttleTriggeredAfterMouseMove=y()(c.triggeredAfterMouseMove,null!==(o=t.throttleDelay)&&void 0!==o?o:1e3/60),c.state={},c}return o=[{key:"componentDidMount",value:function(){var t,e;this.addListener(),this.accessibilityManager.setDetails({container:this.container,offset:{left:null!==(t=this.props.margin.left)&&void 0!==t?t:0,top:null!==(e=this.props.margin.top)&&void 0!==e?e:0},coordinateList:this.state.tooltipTicks,mouseHandlerCallback:this.triggeredAfterMouseMove,layout:this.props.layout}),this.displayDefaultTooltip()}},{key:"displayDefaultTooltip",value:function(){var t=this.props,e=t.children,n=t.data,r=t.height,o=t.layout,i=(0,A.sP)(e,S.u);if(i){var a=i.props.defaultIndex;if("number"==typeof a&&!(a<0)&&!(a>this.state.tooltipTicks.length)){var u=this.state.tooltipTicks[a]&&this.state.tooltipTicks[a].value,c=ef(this.state,n,a,u),l=this.state.tooltipTicks[a].coordinate,s=(this.state.offset.top+r)/2,f="horizontal"===o?{x:l,y:s}:{y:l,x:s},p=this.state.formattedGraphicalItems.find(function(t){return"Scatter"===t.item.type.name});p&&(f=ee(ee({},f),p.props.points[a].tooltipPosition),c=p.props.points[a].tooltipPayload);var h={activeTooltipIndex:a,isTooltipActive:!0,activeLabel:u,activePayload:c,activeCoordinate:f};this.setState(h),this.renderCursor(i),this.accessibilityManager.setIndex(a)}}}},{key:"getSnapshotBeforeUpdate",value:function(t,e){if(!this.props.accessibilityLayer)return null;if(this.state.tooltipTicks!==e.tooltipTicks&&this.accessibilityManager.setDetails({coordinateList:this.state.tooltipTicks}),this.props.layout!==t.layout&&this.accessibilityManager.setDetails({layout:this.props.layout}),this.props.margin!==t.margin){var n,r;this.accessibilityManager.setDetails({offset:{left:null!==(n=this.props.margin.left)&&void 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s=(0,C.Kt)(c).scale,f=(0,C.Kt)(l).scale,p=s&&s.invert?s.invert(o.chartX):null,h=f&&f.invert?f.invert(o.chartY):null;return ee(ee({},o),{},{xValue:p,yValue:h})}var d=ep(this.state,this.props.data,this.props.layout,a);return d?ee(ee({},o),d):null}},{key:"inRange",value:function(t,e){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,r=this.props.layout,o=t/n,i=e/n;if("horizontal"===r||"vertical"===r){var a=this.state.offset;return o>=a.left&&o<=a.left+a.width&&i>=a.top&&i<=a.top+a.height?{x:o,y:i}:null}var u=this.state,c=u.angleAxisMap,l=u.radiusAxisMap;if(c&&l){var s=(0,C.Kt)(c);return(0,tM.z3)({x:o,y:i},s)}return null}},{key:"parseEventsOfWrapper",value:function(){var t=this.props.children,e=this.getTooltipEventType(),n=(0,A.sP)(t,S.u),r={};return n&&"axis"===e&&(r="click"===n.props.trigger?{onClick:this.handleClick}:{onMouseEnter:this.handleMouseEnter,onMouseMove:this.handleMouseMove,onMouseLeave:this.handleMouseLeave,onTouchMove:this.handleTouchMove,onTouchStart:this.handleTouchStart,onTouchEnd:this.handleTouchEnd}),ee(ee({},(0,tD.Ym)(this.props,this.handleOuterEvent)),r)}},{key:"addListener",value:function(){tC.on(tN,this.handleReceiveSyncEvent)}},{key:"removeListener",value:function(){tC.removeListener(tN,this.handleReceiveSyncEvent)}},{key:"filterFormatItem",value:function(t,e,n){for(var r=this.state.formattedGraphicalItems,o=0,i=r.length;o<i;o++){var a=r[o];if(a.item===t||a.props.key===t.key||e===(0,A.Gf)(a.item.type)&&n===a.childIndex)return a}return null}},{key:"renderClipPath",value:function(){var t=this.clipPathId,e=this.state.offset,n=e.left,o=e.top,i=e.height,a=e.width;return r.createElement("defs",null,r.createElement("clipPath",{id:t},r.createElement("rect",{x:n,y:o,height:i,width:a})))}},{key:"getXScales",value:function(){var t=this.state.xAxisMap;return t?Object.entries(t).reduce(function(t,e){var n=t2(e,2),r=n[0],o=n[1];return ee(ee({},t),{},en({},r,o.scale))},{}):null}},{key:"getYScales",value:function(){var t=this.state.yAxisMap;return t?Object.entries(t).reduce(function(t,e){var n=t2(e,2),r=n[0],o=n[1];return ee(ee({},t),{},en({},r,o.scale))},{}):null}},{key:"getXScaleByAxisId",value:function(t){var e;return null===(e=this.state.xAxisMap)||void 0===e||null===(e=e[t])||void 0===e?void 0:e.scale}},{key:"getYScaleByAxisId",value:function(t){var e;return null===(e=this.state.yAxisMap)||void 0===e||null===(e=e[t])||void 0===e?void 0:e.scale}},{key:"getItemByXY",value:function(t){var e=this.state,n=e.formattedGraphicalItems,r=e.activeItem;if(n&&n.length)for(var o=0,i=n.length;o<i;o++){var a=n[o],u=a.props,c=a.item,l=(0,A.Gf)(c.type);if("Bar"===l){var 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0!==t.activeTooltipIndex){var u=t.chartX,l=t.chartY,s=t.activeTooltipIndex,f=c.state,p=f.offset,h=f.tooltipTicks;if(!p)return;if("function"==typeof r)s=r(h,t);else if("value"===r){s=-1;for(var d=0;d<h.length;d++)if(h[d].value===t.activeLabel){s=d;break}}var y=ee(ee({},p),{},{x:p.left,y:p.top}),v=Math.min(u,y.x+y.width),m=Math.min(l,y.y+y.height),g=h[s]&&h[s].value,b=ef(c.state,c.props.data,s),x=h[s]?{x:"horizontal"===n?h[s].coordinate:v,y:"horizontal"===n?m:h[s].coordinate}:ea;c.setState(ee(ee({},t),{},{activeLabel:g,activeCoordinate:x,activePayload:b,activeTooltipIndex:s}))}else c.setState(t)}),en(t3(c),"renderCursor",function(t){var e,o=c.state,i=o.isTooltipActive,a=o.activeCoordinate,u=o.activePayload,l=o.offset,s=o.activeTooltipIndex,f=o.tooltipAxisBandSize,p=c.getTooltipEventType(),h=null!==(e=t.props.active)&&void 0!==e?e:i,d=c.props.layout,y=t.key||"_recharts-cursor";return r.createElement(tK,{key:y,activeCoordinate:a,activePayload:u,activeTooltipIndex:s,chartName:n,element:t,isActive:h,layout:d,offset:l,tooltipAxisBandSize:f,tooltipEventType:p})}),en(t3(c),"renderPolarAxis",function(t,e,n){var o=f()(t,"type.axisType"),i=f()(c.state,"".concat(o,"Map")),a=i&&i[t.props["".concat(o,"Id")]];return(0,r.cloneElement)(t,ee(ee({},a),{},{className:o,key:t.key||"".concat(e,"-").concat(n),ticks:(0,T.uY)(a,!0)}))}),en(t3(c),"renderGrid",function(t){var e=c.state,n=e.xAxisMap,o=e.yAxisMap,i=e.offset,a=(0,C.Kt)(n),u=m()(o,function(t){return b()(t.domain,Number.isFinite)})||(0,C.Kt)(o),l=t.props||{};return(0,r.cloneElement)(t,{key:t.key||"grid",x:(0,C.hj)(l.x)?l.x:i.left,y:(0,C.hj)(l.y)?l.y:i.top,width:(0,C.hj)(l.width)?l.width:i.width,height:(0,C.hj)(l.height)?l.height:i.height,xAxis:a,yAxis:u,verticalCoordinatesGenerator:l.verticalCoordinatesGenerator,horizontalCoordinatesGenerator:l.horizontalCoordinatesGenerator})}),en(t3(c),"renderPolarGrid",function(t){var e=t.props,n=e.radialLines,o=e.polarAngles,i=e.polarRadius,a=c.state,u=a.radiusAxisMap,l=a.angleAxisMap,s=(0,C.Kt)(u),f=(0,C.Kt)(l),p=f.cx,h=f.cy,d=f.innerRadius,y=f.outerRadius;return(0,r.cloneElement)(t,{polarAngles:Array.isArray(o)?o:(0,T.uY)(f,!0).map(function(t){return t.coordinate}),polarRadius:Array.isArray(i)?i:(0,T.uY)(s,!0).map(function(t){return t.coordinate}),cx:p,cy:h,innerRadius:d,outerRadius:y,key:t.key||"polar-grid",radialLines:n})}),en(t3(c),"renderLegend",function(){var t=c.state.formattedGraphicalItems,e=c.props,n=e.children,o=e.width,i=e.height,a=c.props.margin||{},u=o-(a.left||0)-(a.right||0),l=(0,Q.z)({children:n,formattedGraphicalItems:t,legendWidth:u,legendContent:h});if(!l)return null;var s=l.item,f=t5(l,tJ);return(0,r.cloneElement)(s,ee(ee({},f),{},{chartWidth:o,chartHeight:i,margin:a,onBBoxUpdate:c.handleLegendBBoxUpdate}))}),en(t3(c),"renderTooltip",function(){var t,e=c.props.children,n=(0,A.sP)(e,S.u);if(!n)return null;var o=c.state,i=o.isTooltipActive,a=o.activeCoordinate,u=o.activePayload,l=o.activeLabel,s=o.offset,f=null!==(t=n.props.active)&&void 0!==t?t:i;return(0,r.cloneElement)(n,{viewBox:ee(ee({},s),{},{x:s.left,y:s.top}),active:f,label:l,payload:f?u:[],coordinate:a})}),en(t3(c),"renderBrush",function(t){var e=c.props,n=e.margin,o=e.data,i=c.state,a=i.offset,u=i.dataStartIndex,l=i.dataEndIndex,s=i.updateId;return(0,r.cloneElement)(t,{key:t.key||"_recharts-brush",onChange:(0,T.DO)(c.handleBrushChange,t.props.onChange),data:o,x:(0,C.hj)(t.props.x)?t.props.x:a.left,y:(0,C.hj)(t.props.y)?t.props.y:a.top+a.height+a.brushBottom-(n.bottom||0),width:(0,C.hj)(t.props.width)?t.props.width:a.width,startIndex:u,endIndex:l,updateId:"brush-".concat(s)})}),en(t3(c),"renderReferenceElement",function(t,e,n){if(!t)return null;var o=t3(c).clipPathId,i=c.state,a=i.xAxisMap,u=i.yAxisMap,l=i.offset,s=t.props,f=s.xAxisId,p=s.yAxisId;return(0,r.cloneElement)(t,{key:t.key||"".concat(e,"-").concat(n),xAxis:a[f],yAxis:u[p],viewBox:{x:l.left,y:l.top,width:l.width,height:l.height},clipPathId:o})}),en(t3(c),"renderActivePoints",function(t){var e=t.item,n=t.activePoint,r=t.basePoint,o=t.childIndex,i=t.isRange,a=[],u=e.props.key,c=e.item.props,s=c.activeDot,f=ee(ee({index:o,dataKey:c.dataKey,cx:n.x,cy:n.y,r:4,fill:(0,T.fk)(e.item),strokeWidth:2,stroke:"#fff",payload:n.payload,value:n.value,key:"".concat(u,"-activePoint-").concat(o)},(0,A.L6)(s,!1)),(0,tD.Ym)(s));return a.push(l.renderActiveDot(s,f)),r?a.push(l.renderActiveDot(s,ee(ee({},f),{},{cx:r.x,cy:r.y,key:"".concat(u,"-basePoint-").concat(o)}))):i&&a.push(null),a}),en(t3(c),"renderGraphicChild",function(t,e,n){var o=c.filterFormatItem(t,e,n);if(!o)return null;var a=c.getTooltipEventType(),u=c.state,l=u.isTooltipActive,s=u.tooltipAxis,f=u.activeTooltipIndex,p=u.activeLabel,h=c.props.children,d=(0,A.sP)(h,S.u),y=o.props,v=y.points,m=y.isRange,g=y.baseLine,b=o.item.props,x=b.activeDot,O=b.hide,w=b.activeBar,j=b.activeShape,E={};"axis"!==a&&d&&"click"===d.props.trigger?E={onClick:(0,T.DO)(c.handleItemMouseEnter,t.props.onClick)}:"axis"!==a&&(E={onMouseLeave:(0,T.DO)(c.handleItemMouseLeave,t.props.onMouseLeave),onMouseEnter:(0,T.DO)(c.handleItemMouseEnter,t.props.onMouseEnter)});var k=(0,r.cloneElement)(t,ee(ee({},o.props),E));if(!O&&l&&d&&(x||w||j)){if(f>=0){if(s.dataKey&&!s.allowDuplicatedCategory){var P="function"==typeof s.dataKey?function(t){return"function"==typeof s.dataKey?s.dataKey(t.payload):null}:"payload.".concat(s.dataKey.toString());_=(0,C.Ap)(v,P,p),N=m&&g&&(0,C.Ap)(g,P,p)}else _=null==v?void 0:v[f],N=m&&g&&g[f];if(j||w){var M=void 0!==t.props.activeIndex?t.props.activeIndex:f;return[(0,r.cloneElement)(t,ee(ee(ee({},o.props),E),{},{activeIndex:M})),null,null]}if(!i()(_))return[k].concat(t8(c.renderActivePoints({item:o,activePoint:_,basePoint:N,childIndex:f,isRange:m})))}else{var _,N,D,I=(null!==(D=c.getItemByXY(c.state.activeCoordinate))&&void 0!==D?D:{graphicalItem:k}).graphicalItem,L=I.item,B=void 0===L?t:L,R=I.childIndex,z=ee(ee(ee({},o.props),E),{},{activeIndex:R});return[(0,r.cloneElement)(B,z),null,null]}}return m?[k,null,null]:[k,null]}),en(t3(c),"renderCustomized",function(t,e,n){return(0,r.cloneElement)(t,ee(ee({key:"recharts-customized-".concat(n)},c.props),c.state))}),en(t3(c),"renderMap",{CartesianGrid:{handler:c.renderGrid,once:!0},ReferenceArea:{handler:c.renderReferenceElement},ReferenceLine:{handler:eu},ReferenceDot:{handler:c.renderReferenceElement},XAxis:{handler:eu},YAxis:{handler:eu},Brush:{handler:c.renderBrush,once:!0},Bar:{handler:c.renderGraphicChild},Line:{handler:c.renderGraphicChild},Area:{handler:c.renderGraphicChild},Radar:{handler:c.renderGraphicChild},RadialBar:{handler:c.renderGraphicChild},Scatter:{handler:c.renderGraphicChild},Pie:{handler:c.renderGraphicChild},Funnel:{handler:c.renderGraphicChild},Tooltip:{handler:c.renderCursor,once:!0},PolarGrid:{handler:c.renderPolarGrid,once:!0},PolarAngleAxis:{handler:c.renderPolarAxis},PolarRadiusAxis:{handler:c.renderPolarAxis},Customized:{handler:c.renderCustomized}}),c.clipPathId="".concat(null!==(e=t.id)&&void 0!==e?e:(0,C.EL)("recharts"),"-clip"),c.throttleTriggeredAfterMouseMove=y()(c.triggeredAfterMouseMove,null!==(o=t.throttleDelay)&&void 0!==o?o:1e3/60),c.state={},c}return o=[{key:"componentDidMount",value:function(){var t,e;this.addListener(),this.accessibilityManager.setDetails({container:this.container,offset:{left:null!==(t=this.props.margin.left)&&void 0!==t?t:0,top:null!==(e=this.props.margin.top)&&void 0!==e?e:0},coordinateList:this.state.tooltipTicks,mouseHandlerCallback:this.triggeredAfterMouseMove,layout:this.props.layout}),this.displayDefaultTooltip()}},{key:"displayDefaultTooltip",value:function(){var t=this.props,e=t.children,n=t.data,r=t.height,o=t.layout,i=(0,A.sP)(e,S.u);if(i){var a=i.props.defaultIndex;if("number"==typeof a&&!(a<0)&&!(a>this.state.tooltipTicks.length)){var u=this.state.tooltipTicks[a]&&this.state.tooltipTicks[a].value,c=ef(this.state,n,a,u),l=this.state.tooltipTicks[a].coordinate,s=(this.state.offset.top+r)/2,f="horizontal"===o?{x:l,y:s}:{y:l,x:s},p=this.state.formattedGraphicalItems.find(function(t){return"Scatter"===t.item.type.name});p&&(f=ee(ee({},f),p.props.points[a].tooltipPosition),c=p.props.points[a].tooltipPayload);var h={activeTooltipIndex:a,isTooltipActive:!0,activeLabel:u,activePayload:c,activeCoordinate:f};this.setState(h),this.renderCursor(i),this.accessibilityManager.setIndex(a)}}}},{key:"getSnapshotBeforeUpdate",value:function(t,e){if(!this.props.accessibilityLayer)return null;if(this.state.tooltipTicks!==e.tooltipTicks&&this.accessibilityManager.setDetails({coordinateList:this.state.tooltipTicks}),this.props.layout!==t.layout&&this.accessibilityManager.setDetails({layout:this.props.layout}),this.props.margin!==t.margin){var n,r;this.accessibilityManager.setDetails({offset:{left:null!==(n=this.props.margin.left)&&void 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s=(0,C.Kt)(c).scale,f=(0,C.Kt)(l).scale,p=s&&s.invert?s.invert(o.chartX):null,h=f&&f.invert?f.invert(o.chartY):null;return ee(ee({},o),{},{xValue:p,yValue:h})}var d=ep(this.state,this.props.data,this.props.layout,a);return d?ee(ee({},o),d):null}},{key:"inRange",value:function(t,e){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,r=this.props.layout,o=t/n,i=e/n;if("horizontal"===r||"vertical"===r){var a=this.state.offset;return o>=a.left&&o<=a.left+a.width&&i>=a.top&&i<=a.top+a.height?{x:o,y:i}:null}var u=this.state,c=u.angleAxisMap,l=u.radiusAxisMap;if(c&&l){var s=(0,C.Kt)(c);return(0,tM.z3)({x:o,y:i},s)}return null}},{key:"parseEventsOfWrapper",value:function(){var t=this.props.children,e=this.getTooltipEventType(),n=(0,A.sP)(t,S.u),r={};return n&&"axis"===e&&(r="click"===n.props.trigger?{onClick:this.handleClick}:{onMouseEnter:this.handleMouseEnter,onMouseMove:this.handleMouseMove,onMouseLeave:this.handleMouseLeave,onTouchMove:this.handleTouchMove,onTouchStart:this.handleTouchStart,onTouchEnd:this.handleTouchEnd}),ee(ee({},(0,tD.Ym)(this.props,this.handleOuterEvent)),r)}},{key:"addListener",value:function(){tC.on(tN,this.handleReceiveSyncEvent)}},{key:"removeListener",value:function(){tC.removeListener(tN,this.handleReceiveSyncEvent)}},{key:"filterFormatItem",value:function(t,e,n){for(var r=this.state.formattedGraphicalItems,o=0,i=r.length;o<i;o++){var a=r[o];if(a.item===t||a.props.key===t.key||e===(0,A.Gf)(a.item.type)&&n===a.childIndex)return a}return null}},{key:"renderClipPath",value:function(){var t=this.clipPathId,e=this.state.offset,n=e.left,o=e.top,i=e.height,a=e.width;return r.createElement("defs",null,r.createElement("clipPath",{id:t},r.createElement("rect",{x:n,y:o,height:i,width:a})))}},{key:"getXScales",value:function(){var t=this.state.xAxisMap;return t?Object.entries(t).reduce(function(t,e){var n=t2(e,2),r=n[0],o=n[1];return ee(ee({},t),{},en({},r,o.scale))},{}):null}},{key:"getYScales",value:function(){var t=this.state.yAxisMap;return t?Object.entries(t).reduce(function(t,e){var n=t2(e,2),r=n[0],o=n[1];return ee(ee({},t),{},en({},r,o.scale))},{}):null}},{key:"getXScaleByAxisId",value:function(t){var e;return null===(e=this.state.xAxisMap)||void 0===e||null===(e=e[t])||void 0===e?void 0:e.scale}},{key:"getYScaleByAxisId",value:function(t){var e;return null===(e=this.state.yAxisMap)||void 0===e||null===(e=e[t])||void 0===e?void 0:e.scale}},{key:"getItemByXY",value:function(t){var e=this.state,n=e.formattedGraphicalItems,r=e.activeItem;if(n&&n.length)for(var o=0,i=n.length;o<i;o++){var a=n[o],u=a.props,c=a.item,l=(0,A.Gf)(c.type);if("Bar"===l){var 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similarity index 51%
rename from litellm/proxy/_experimental/out/_next/static/chunks/3801-1fb81a288323ae68.js
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index b2e4d2be99e..114ab6fc5ae 100644
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/4289-85d72de0c6c33d90.js b/litellm/proxy/_experimental/out/_next/static/chunks/4289-1afc296c9a6702e2.js
similarity index 72%
rename from litellm/proxy/_experimental/out/_next/static/chunks/4289-85d72de0c6c33d90.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/4289-1afc296c9a6702e2.js
index 820ded9ffc3..1dc7a7acf1a 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/4289-85d72de0c6c33d90.js
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o=e.state,r=o.dragChildrenKeys,a=o.flattenNodes,l=o.keyEntities,c=o.expandedKeys,i=o.indent,d=e.props,s=d.onDragOver,u=d.allowDrop,f=d.direction,p=(0,eX.Z)(e).dragNode;if(p){var m=ta(t,p,n,i,e.dragStartMousePosition,u,a,l,c,f),g=m.dropPosition,h=m.dropLevelOffset,v=m.dropTargetKey,b=m.dropContainerKey,y=m.dropAllowed,x=m.dropTargetPos,k=m.dragOverNodeKey;-1===r.indexOf(v)&&y&&(p.props.eventKey===v&&0===h?null===e.state.dropPosition&&null===e.state.dropLevelOffset&&null===e.state.dropTargetKey&&null===e.state.dropContainerKey&&null===e.state.dropTargetPos&&!1===e.state.dropAllowed&&null===e.state.dragOverNodeKey||e.resetDragState():g===e.state.dropPosition&&h===e.state.dropLevelOffset&&v===e.state.dropTargetKey&&b===e.state.dropContainerKey&&x===e.state.dropTargetPos&&y===e.state.dropAllowed&&k===e.state.dragOverNodeKey||e.setState({dropPosition:g,dropLevelOffset:h,dropTargetKey:v,dropContainerKey:b,dropTargetPos:x,dropAllowed:y,dragOverNodeKey:k}),null==s||s({event:t,node:e8(n.props)}))}},e.onNodeDragLeave=function(t,n){e.currentMouseOverDroppableNodeKey!==n.props.eventKey||t.currentTarget.contains(t.relatedTarget)||(e.resetDragState(),e.currentMouseOverDroppableNodeKey=null);var o=e.props.onDragLeave;null==o||o({event:t,node:e8(n.props)})},e.onWindowDragEnd=function(t){e.onNodeDragEnd(t,null,!0),window.removeEventListener("dragend",e.onWindowDragEnd)},e.onNodeDragEnd=function(t,n){var o=e.props.onDragEnd;e.setState({dragOverNodeKey:null}),e.cleanDragState(),null==o||o({event:t,node:e8(n.props)}),e.dragNode=null,window.removeEventListener("dragend",e.onWindowDragEnd)},e.onNodeDrop=function(t,n){var o,r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],a=e.state,l=a.dragChildrenKeys,c=a.dropPosition,i=a.dropTargetKey,d=a.dropTargetPos;if(a.dropAllowed){var s=e.props.onDrop;if(e.setState({dragOverNodeKey:null}),e.cleanDragState(),null!==i){var u=(0,E.Z)((0,E.Z)({},e6(i,e.getTreeNodeRequiredProps())),{},{active:(null===(o=e.getActiveItem())||void 0===o?void 0:o.key)===i,data:e.state.keyEntities[i].node}),f=-1!==l.indexOf(i);(0,R.ZP)(!f,"Can not drop to dragNode's children node. 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t=e.state;return{expandedKeys:t.expandedKeys||[],selectedKeys:t.selectedKeys||[],loadedKeys:t.loadedKeys||[],loadingKeys:t.loadingKeys||[],checkedKeys:t.checkedKeys||[],halfCheckedKeys:t.halfCheckedKeys||[],dragOverNodeKey:t.dragOverNodeKey,dropPosition:t.dropPosition,keyEntities:t.keyEntities}},e.setExpandedKeys=function(t){var n=e.state,o=e3(n.treeData,t,n.fieldNames);e.setUncontrolledState({expandedKeys:t,flattenNodes:o},!0)},e.onNodeExpand=function(t,n){var o=e.state.expandedKeys,r=e.state,a=r.listChanging,l=r.fieldNames,c=e.props,i=c.onExpand,d=c.loadData,s=n.expanded,u=n[l.key];if(!a){var f=o.indexOf(u),p=!s;if((0,R.ZP)(s&&-1!==f||!s&&-1===f,"Expand state not sync with index check"),o=p?to(o,u):tn(o,u),e.setExpandedKeys(o),null==i||i(o,{node:n,expanded:p,nativeEvent:t.nativeEvent}),p&&d){var m=e.onNodeLoad(n);m&&m.then(function(){var t=e3(e.state.treeData,o,l);e.setUncontrolledState({flattenNodes:t})}).catch(function(){var 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e,t,n=this.state,o=n.focused,r=n.flattenNodes,l=n.keyEntities,c=n.draggingNodeKey,i=n.activeKey,d=n.dropLevelOffset,s=n.dropContainerKey,u=n.dropTargetKey,f=n.dropPosition,p=n.dragOverNodeKey,m=n.indent,h=this.props,v=h.prefixCls,b=h.className,y=h.style,x=h.showLine,k=h.focusable,C=h.tabIndex,w=h.selectable,E=h.showIcon,Z=h.icon,I=h.switcherIcon,K=h.draggable,R=h.checkable,P=h.checkStrictly,M=h.disabled,D=h.motion,T=h.loadData,j=h.filterTreeNode,z=h.height,B=h.itemHeight,H=h.virtual,L=h.titleRender,A=h.dropIndicatorRender,_=h.onContextMenu,W=h.onScroll,q=h.direction,F=h.rootClassName,X=h.rootStyle,U=(0,V.Z)(this.props,{aria:!0,data:!0});return K&&(t="object"===(0,S.Z)(K)?K:"function"==typeof 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not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tR;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tR;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,_,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,a,"m",E).call(this);let{response:i,data:o}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(i),o))en(this,a,"m",R).call(this,e);if(o.controller.signal?.aborted)throw new es;en(this,a,"m",T).call(this)}_connected(e){this.ended||(et(this,k,e,"f"),et(this,w,e?.headers.get("request-id"),"f"),en(this,c,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,g,"f")}get errored(){return en(this,y,"f")}get aborted(){return en(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,m,"f")[e]||(en(this,m,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,m,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,m,"f")[e]||(en(this,m,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,b,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,b,!0,"f"),await en(this,f,"f")}get currentMessage(){return en(this,s,"f")}async finalMessage(){return await this.done(),en(this,a,"m",x).call(this)}async finalText(){return await this.done(),en(this,a,"m",S).call(this)}_emit(e,...t){if(en(this,g,"f"))return;"end"===e&&(et(this,g,!0,"f"),en(this,d,"f").call(this));let n=en(this,m,"f")[e];if(n&&(en(this,m,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,b,"f")||n?.length||Promise.reject(e),en(this,h,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,b,"f")||n?.length||Promise.reject(e),en(this,h,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,a,"m",x).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,a,"m",E).call(this),this._connected(null);let r=eZ.fromReadableStream(e,this.controller);for await(let e of r)en(this,a,"m",R).call(this,e);if(r.controller.signal?.aborted)throw new es;en(this,a,"m",T).call(this)}[(s=new WeakMap,u=new WeakMap,c=new WeakMap,h=new WeakMap,f=new WeakMap,d=new WeakMap,p=new WeakMap,m=new WeakMap,g=new WeakMap,y=new WeakMap,v=new WeakMap,b=new WeakMap,k=new WeakMap,w=new WeakMap,_=new WeakMap,a=new WeakSet,x=function(){if(0===this.receivedMessages.length)throw new el("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},S=function(){if(0===this.receivedMessages.length)throw new el("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new el("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||et(this,s,void 0,"f")},R=function(e){if(this.ended)return;let t=en(this,a,"m",P).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tE(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,s,t,"f")}},T=function(){if(this.ended)throw new el("stream has ended, this shouldn't happen");let e=en(this,s,"f");if(!e)throw new el("request ended without sending any chunks");return et(this,s,void 0,"f"),e},P=function(e){let t=en(this,s,"f");if("message_start"===e.type){if(t)throw new el(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new el(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tE(n)){let t=n[t_]||"";if(Object.defineProperty(n,t_,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tS(t)}catch(n){let e=new el(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${n}. JSON: ${t}`);en(this,_,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eZ(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tT={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tP={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tC extends tu{constructor(){super(...arguments),this.batches=new tv(this._client)}create(e,t){let{betas:n,...r}=e;r.model in tP&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tP[r.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let i=this._client._options.timeout;if(!r.stream&&null==i){let e=tT[r.model]??void 0;i=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:i??6e5,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tR.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tf([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tC.Batches=tv;class tM extends tu{constructor(){super(...arguments),this.models=new tg(this._client),this.messages=new tC(this._client),this.files=new tm(this._client)}}tM.Models=tg,tM.Messages=tC,tM.Files=tm;class tA extends tu{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tI="__json_buf";function tO(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tz{constructor(){C.add(this),this.messages=[],this.receivedMessages=[],M.set(this,void 0),this.controller=new AbortController,A.set(this,void 0),I.set(this,()=>{}),O.set(this,()=>{}),z.set(this,void 0),L.set(this,()=>{}),j.set(this,()=>{}),D.set(this,{}),F.set(this,!1),N.set(this,!1),H.set(this,!1),U.set(this,!1),B.set(this,void 0),q.set(this,void 0),W.set(this,e=>{if(et(this,N,!0,"f"),ei(e)&&(e=new es),e instanceof es)return et(this,H,!0,"f"),this._emit("abort",e);if(e instanceof el)return this._emit("error",e);if(e instanceof Error){let t=new el(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new el(String(e)))}),et(this,A,new Promise((e,t)=>{et(this,I,e,"f"),et(this,O,t,"f")}),"f"),et(this,z,new Promise((e,t)=>{et(this,L,e,"f"),et(this,j,t,"f")}),"f"),en(this,A,"f").catch(()=>{}),en(this,z,"f").catch(()=>{})}get response(){return en(this,B,"f")}get request_id(){return en(this,q,"f")}async withResponse(){let e=await en(this,A,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tz;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tz;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,W,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,C,"m",Z).call(this);let{response:i,data:o}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(i),o))en(this,C,"m",X).call(this,e);if(o.controller.signal?.aborted)throw new es;en(this,C,"m",J).call(this)}_connected(e){this.ended||(et(this,B,e,"f"),et(this,q,e?.headers.get("request-id"),"f"),en(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,F,"f")}get errored(){return en(this,N,"f")}get aborted(){return en(this,H,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,D,"f")[e]||(en(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,D,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,D,"f")[e]||(en(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,U,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,U,!0,"f"),await en(this,z,"f")}get currentMessage(){return en(this,M,"f")}async finalMessage(){return await this.done(),en(this,C,"m",$).call(this)}async finalText(){return await this.done(),en(this,C,"m",V).call(this)}_emit(e,...t){if(en(this,F,"f"))return;"end"===e&&(et(this,F,!0,"f"),en(this,L,"f").call(this));let n=en(this,D,"f")[e];if(n&&(en(this,D,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,U,"f")||n?.length||Promise.reject(e),en(this,O,"f").call(this,e),en(this,j,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,U,"f")||n?.length||Promise.reject(e),en(this,O,"f").call(this,e),en(this,j,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,C,"m",$).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,C,"m",Z).call(this),this._connected(null);let r=eZ.fromReadableStream(e,this.controller);for await(let e of r)en(this,C,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new es;en(this,C,"m",J).call(this)}[(M=new WeakMap,A=new WeakMap,I=new WeakMap,O=new WeakMap,z=new WeakMap,L=new WeakMap,j=new WeakMap,D=new WeakMap,F=new WeakMap,N=new WeakMap,H=new WeakMap,U=new WeakMap,B=new WeakMap,q=new WeakMap,W=new WeakMap,C=new WeakSet,$=function(){if(0===this.receivedMessages.length)throw new el("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},V=function(){if(0===this.receivedMessages.length)throw new el("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new el("stream ended without producing a content block with type=text");return e.join(" ")},Z=function(){this.ended||et(this,M,void 0,"f")},X=function(e){if(this.ended)return;let t=en(this,C,"m",K).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tO(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,M,t,"f")}},J=function(){if(this.ended)throw new el("stream has ended, this shouldn't happen");let e=en(this,M,"f");if(!e)throw new el("request ended without sending any chunks");return et(this,M,void 0,"f"),e},K=function(e){let t=en(this,M,"f");if("message_start"===e.type){if(t)throw new el(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new el(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tO(n)){let t=n[tI]||"";Object.defineProperty(n,tI,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tS(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eZ(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tL extends tu{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tp`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(tp`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tp`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new el(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:tf([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>ty.fromResponse(t.response,t.controller))}}class tj extends tu{constructor(){super(...arguments),this.batches=new tL(this._client)}create(e,t){e.model in tD&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tD[e.model]}
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n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tE(n)){let t=n[t_]||"";if(Object.defineProperty(n,t_,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tS(t)}catch(n){let e=new el(`Unable to parse tool parameter JSON from model. 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JSON: ${t}`);en(this,_,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eZ(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tC={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tR={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tM extends tu{constructor(){super(...arguments),this.batches=new tv(this._client)}create(e,t){let{betas:n,...r}=e;r.model in tR&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tR[r.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let i=this._client._options.timeout;if(!r.stream&&null==i){let e=tC[r.model]??void 0;i=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:i??6e5,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tP.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tf([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tM.Batches=tv;class tT extends tu{constructor(){super(...arguments),this.models=new tg(this._client),this.messages=new tM(this._client),this.files=new tm(this._client)}}tT.Models=tg,tT.Messages=tM,tT.Files=tm;class tA extends tu{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tO="__json_buf";function tI(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tz{constructor(){M.add(this),this.messages=[],this.receivedMessages=[],T.set(this,void 0),this.controller=new AbortController,A.set(this,void 0),O.set(this,()=>{}),I.set(this,()=>{}),z.set(this,void 0),L.set(this,()=>{}),j.set(this,()=>{}),D.set(this,{}),F.set(this,!1),N.set(this,!1),H.set(this,!1),U.set(this,!1),B.set(this,void 0),q.set(this,void 0),W.set(this,e=>{if(et(this,N,!0,"f"),ei(e)&&(e=new es),e instanceof es)return et(this,H,!0,"f"),this._emit("abort",e);if(e instanceof el)return this._emit("error",e);if(e instanceof Error){let t=new el(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new el(String(e)))}),et(this,A,new Promise((e,t)=>{et(this,O,e,"f"),et(this,I,t,"f")}),"f"),et(this,z,new Promise((e,t)=>{et(this,L,e,"f"),et(this,j,t,"f")}),"f"),en(this,A,"f").catch(()=>{}),en(this,z,"f").catch(()=>{})}get response(){return en(this,B,"f")}get request_id(){return en(this,q,"f")}async withResponse(){let e=await en(this,A,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tz;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tz;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,W,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,M,"m",Z).call(this);let{response:i,data:o}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(i),o))en(this,M,"m",X).call(this,e);if(o.controller.signal?.aborted)throw new es;en(this,M,"m",J).call(this)}_connected(e){this.ended||(et(this,B,e,"f"),et(this,q,e?.headers.get("request-id"),"f"),en(this,O,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,F,"f")}get errored(){return en(this,N,"f")}get aborted(){return en(this,H,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,D,"f")[e]||(en(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,D,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,D,"f")[e]||(en(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,U,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,U,!0,"f"),await en(this,z,"f")}get currentMessage(){return en(this,T,"f")}async finalMessage(){return await this.done(),en(this,M,"m",V).call(this)}async finalText(){return await this.done(),en(this,M,"m",$).call(this)}_emit(e,...t){if(en(this,F,"f"))return;"end"===e&&(et(this,F,!0,"f"),en(this,L,"f").call(this));let n=en(this,D,"f")[e];if(n&&(en(this,D,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,U,"f")||n?.length||Promise.reject(e),en(this,I,"f").call(this,e),en(this,j,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,U,"f")||n?.length||Promise.reject(e),en(this,I,"f").call(this,e),en(this,j,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,M,"m",V).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,M,"m",Z).call(this),this._connected(null);let r=eZ.fromReadableStream(e,this.controller);for await(let e of r)en(this,M,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new es;en(this,M,"m",J).call(this)}[(T=new WeakMap,A=new WeakMap,O=new WeakMap,I=new WeakMap,z=new WeakMap,L=new WeakMap,j=new WeakMap,D=new WeakMap,F=new WeakMap,N=new WeakMap,H=new WeakMap,U=new WeakMap,B=new WeakMap,q=new WeakMap,W=new WeakMap,M=new WeakSet,V=function(){if(0===this.receivedMessages.length)throw new el("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},$=function(){if(0===this.receivedMessages.length)throw new el("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new el("stream ended without producing a content block with type=text");return e.join(" ")},Z=function(){this.ended||et(this,T,void 0,"f")},X=function(e){if(this.ended)return;let t=en(this,M,"m",K).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tI(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,T,t,"f")}},J=function(){if(this.ended)throw new el("stream has ended, this shouldn't happen");let e=en(this,T,"f");if(!e)throw new el("request ended without sending any chunks");return et(this,T,void 0,"f"),e},K=function(e){let t=en(this,T,"f");if("message_start"===e.type){if(t)throw new el(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new el(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tI(n)){let t=n[tO]||"";Object.defineProperty(n,tO,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tS(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eZ(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tL extends tu{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tp`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(tp`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tp`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new el(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:tf([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>ty.fromResponse(t.response,t.controller))}}class tj extends tu{constructor(){super(...arguments),this.batches=new tL(this._client)}create(e,t){e.model in tD&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tD[e.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let n=this._client._options.timeout;if(!e.stream&&null==n){let t=tC[e.model]??void 0;n=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:n??6e5,...t,stream:e.stream??!1})}stream(e,t){return tz.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tD={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tj.Batches=tL;class tF extends tu{retrieve(e,t={},n){let{betas:r}=t??{};return this._client.get(tp`/v1/models/${e}`,{...n,headers:tf([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},n?.headers])})}list(e={},t){let{betas:n,...r}=e??{};return this._client.getAPIList("/v1/models",e2,{query:r,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers])})}}let tN=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tH{constructor({baseURL:e=tN("ANTHROPIC_BASE_URL"),apiKey:t=tN("ANTHROPIC_API_KEY")??null,authToken:n=tN("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Q.set(this,void 0);let i={apiKey:t,authToken:n,...r,baseURL:e||"https://api.anthropic.com"};if(!i.dangerouslyAllowBrowser&&ez())throw new el("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=i.baseURL,this.timeout=i.timeout??tU.DEFAULT_TIMEOUT,this.logger=i.logger??console;let o="warn";this.logLevel=o,this.logLevel=eP(i.logLevel,"ClientOptions.logLevel",this)??eP(tN("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??o,this.fetchOptions=i.fetchOptions,this.maxRetries=i.maxRetries??2,this.fetch=i.fetch??function(){if("undefined"!=typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),et(this,Q,eq,"f"),this._options=i,this.apiKey=t,this.authToken=n}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization")||t.has("authorization")))throw Error('Could not resolve authentication method. 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deleted file mode 100644
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index 0841fece93b..00000000000
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similarity index 100%
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/9358-83d8d82499a7d0b5.js b/litellm/proxy/_experimental/out/_next/static/chunks/6190-838cf0edd54d6216.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/9358-83d8d82499a7d0b5.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6190-838cf0edd54d6216.js
index c07493a3f19..9546c2de099 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6925-c4d6db49a055b630.js b/litellm/proxy/_experimental/out/_next/static/chunks/6925-058fe84b7bd44718.js
similarity index 84%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6925-c4d6db49a055b630.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6925-058fe84b7bd44718.js
index 9006f54922e..488dab126ec 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/6925-c4d6db49a055b630.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/6925-058fe84b7bd44718.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7526-64e8890047228caf.js b/litellm/proxy/_experimental/out/_next/static/chunks/7526-64e8890047228caf.js
new file mode 100644
index 00000000000..8e1809ac4f9
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/7526-64e8890047228caf.js
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+"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[7526],{19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,selectedVoice:p,endpointType:u,selectedModel:g,selectedSdk:h}=e,x="session"===n?s:r,f=window.location.origin,_=i||"Your prompt here",b=_.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),j=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let y=g||"your-model-name",N="azure"===h?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(f,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(f,'"\n)');switch(u){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=j.length>0?j:[{role:"user",content:_}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(y,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(y,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(b,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=j.length>0?j:[{role:"user",content:_}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(y,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(y,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(b,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===h?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(y,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(y,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===h?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(y,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(y,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:t='\nresponse = client.embeddings.create(\n	input="'.concat(i||"Your string here",'",\n	model="').concat(y,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:t='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(y,'",\n	file=audio_file').concat(i?',\n	prompt="'.concat(i.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:t='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(y,'",\n	input="').concat(i||"Your text to convert to speech here",'",\n	voice="').concat(p,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(y,'",\n#     input="').concat(i||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(N,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return 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t(e){e.languages.sqf=e.languages.extend("clike",{string:{pattern:/"(?:(?:"")?[^"])*"(?!")|'(?:[^'])*'/,greedy:!0},keyword:/\b(?:breakOut|breakTo|call|case|catch|default|do|echo|else|execFSM|execVM|exitWith|for|forEach|forEachMember|forEachMemberAgent|forEachMemberTeam|from|goto|if|nil|preprocessFile|preprocessFileLineNumbers|private|scopeName|spawn|step|switch|then|throw|to|try|while|with)\b/i,boolean:/\b(?:false|true)\b/i,function:/\b(?:abs|accTime|acos|action|actionIDs|actionKeys|actionKeysImages|actionKeysNames|actionKeysNamesArray|actionName|actionParams|activateAddons|activatedAddons|activateKey|add3DENConnection|add3DENEventHandler|add3DENLayer|addAction|addBackpack|addBackpackCargo|addBackpackCargoGlobal|addBackpackGlobal|addCamShake|addCuratorAddons|addCuratorCameraArea|addCuratorEditableObjects|addCuratorEditingArea|addCuratorPoints|addEditorObject|addEventHandler|addForce|addForceGeneratorRTD|addGoggles|addGroupIcon|addHandgunItem|addHeadgear|addItem|addItemCargo|addItemCargoGlobal|addItemPool|addItemToBackpack|addItemToUniform|addItemToVest|addLiveStats|addMagazine|addMagazineAmmoCargo|addMagazineCargo|addMagazineCargoGlobal|addMagazineGlobal|addMagazinePool|addMagazines|addMagazineTurret|addMenu|addMenuItem|addMissionEventHandler|addMPEventHandler|addMusicEventHandler|addOwnedMine|addPlayerScores|addPrimaryWeaponItem|addPublicVariableEventHandler|addRating|addResources|addScore|addScoreSide|addSecondaryWeaponItem|addSwitchableUnit|addTeamMember|addToRemainsCollector|addTorque|addUniform|addVehicle|addVest|addWaypoint|addWeapon|addWeaponCargo|addWeaponCargoGlobal|addWeaponGlobal|addWeaponItem|addWeaponPool|addWeaponTurret|admin|agent|agents|AGLToASL|aimedAtTarget|aimPos|airDensityCurveRTD|airDensityRTD|airplaneThrottle|airportSide|AISFinishHeal|alive|all3DENEntities|allAirports|allControls|allCurators|allCutLayers|allDead|allDeadMen|allDisplays|allGroups|allMapMarkers|allMines|allMissionObjects|allow3DMode|allowCrewInImmobile|allowCuratorLogicIgnoreAreas|allowDamage|allowDammage|allowFileOperations|allowFleeing|allowGetIn|allowSprint|allPlayers|allSimpleObjects|allSites|allTurrets|allUnits|allUnitsUAV|allVariables|ammo|ammoOnPylon|animate|animateBay|animateDoor|animatePylon|animateSource|animationNames|animationPhase|animationSourcePhase|animationState|append|apply|armoryPoints|arrayIntersect|asin|ASLToAGL|ASLToATL|assert|assignAsCargo|assignAsCargoIndex|assignAsCommander|assignAsDriver|assignAsGunner|assignAsTurret|assignCurator|assignedCargo|assignedCommander|assignedDriver|assignedGunner|assignedItems|assignedTarget|assignedTeam|assignedVehicle|assignedVehicleRole|assignItem|assignTeam|assignToAirport|atan|atan2|atg|ATLToASL|attachedObject|attachedObjects|attachedTo|attachObject|attachTo|attackEnabled|backpack|backpackCargo|backpackContainer|backpackItems|backpackMagazines|backpackSpaceFor|behaviour|benchmark|binocular|blufor|boundingBox|boundingBoxReal|boundingCenter|briefingName|buildingExit|buildingPos|buldozer_EnableRoadDiag|buldozer_IsEnabledRoadDiag|buldozer_LoadNewRoads|buldozer_reloadOperMap|buttonAction|buttonSetAction|cadetMode|callExtension|camCommand|camCommit|camCommitPrepared|camCommitted|camConstuctionSetParams|camCreate|camDestroy|cameraEffect|cameraEffectEnableHUD|cameraInterest|cameraOn|cameraView|campaignConfigFile|camPreload|camPreloaded|camPrepareBank|camPrepareDir|camPrepareDive|camPrepareFocus|camPrepareFov|camPrepareFovRange|camPreparePos|camPrepareRelPos|camPrepareTarget|camSetBank|camSetDir|camSetDive|camSetFocus|camSetFov|camSetFovRange|camSetPos|camSetRelPos|camSetTarget|camTarget|camUseNVG|canAdd|canAddItemToBackpack|canAddItemToUniform|canAddItemToVest|cancelSimpleTaskDestination|canFire|canMove|canSlingLoad|canStand|canSuspend|canTriggerDynamicSimulation|canUnloadInCombat|canVehicleCargo|captive|captiveNum|cbChecked|cbSetChecked|ceil|channelEnabled|cheatsEnabled|checkAIFeature|checkVisibility|civilian|className|clear3DENAttribute|clear3DENInventory|clearAllItemsFromBackpack|clearBackpackCargo|clearBackpackCargoGlobal|clearForcesRTD|clearGroupIcons|clearItemCargo|clearItemCargoGlobal|clearItemPool|clearMagazineCargo|clearMagazineCargoGlobal|clearMagazinePool|clearOverlay|clearRadio|clearVehicleInit|clearWeaponCargo|clearWeaponCargoGlobal|clearWeaponPool|clientOwner|closeDialog|closeDisplay|closeOverlay|collapseObjectTree|collect3DENHistory|collectiveRTD|combatMode|commandArtilleryFire|commandChat|commander|commandFire|commandFollow|commandFSM|commandGetOut|commandingMenu|commandMove|commandRadio|commandStop|commandSuppressiveFire|commandTarget|commandWatch|comment|commitOverlay|compile|compileFinal|completedFSM|composeText|configClasses|configFile|configHierarchy|configName|configNull|configProperties|configSourceAddonList|configSourceMod|configSourceModList|confirmSensorTarget|connectTerminalToUAV|controlNull|controlsGroupCtrl|copyFromClipboard|copyToClipboard|copyWaypoints|cos|count|countEnemy|countFriendly|countSide|countType|countUnknown|create3DENComposition|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t(e){e.languages.sqf=e.languages.extend("clike",{string:{pattern:/"(?:(?:"")?[^"])*"(?!")|'(?:[^'])*'/,greedy:!0},keyword:/\b(?:breakOut|breakTo|call|case|catch|default|do|echo|else|execFSM|execVM|exitWith|for|forEach|forEachMember|forEachMemberAgent|forEachMemberTeam|from|goto|if|nil|preprocessFile|preprocessFileLineNumbers|private|scopeName|spawn|step|switch|then|throw|to|try|while|with)\b/i,boolean:/\b(?:false|true)\b/i,function:/\b(?:abs|accTime|acos|action|actionIDs|actionKeys|actionKeysImages|actionKeysNames|actionKeysNamesArray|actionName|actionParams|activateAddons|activatedAddons|activateKey|add3DENConnection|add3DENEventHandler|add3DENLayer|addAction|addBackpack|addBackpackCargo|addBackpackCargoGlobal|addBackpackGlobal|addCamShake|addCuratorAddons|addCuratorCameraArea|addCuratorEditableObjects|addCuratorEditingArea|addCuratorPoints|addEditorObject|addEventHandler|addForce|addForceGeneratorRTD|addGoggles|addGroupIcon|addHandgunItem|addHeadgear|addItem|addItemCargo|addItemCargoGlobal|addItemPool|addItemToBackpack|addItemToUniform|addItemToVest|addLiveStats|addMagazine|addMagazineAmmoCargo|addMagazineCargo|addMagazineCargoGlobal|addMagazineGlobal|addMagazinePool|addMagazines|addMagazineTurret|addMenu|addMenuItem|addMissionEventHandler|addMPEventHandler|addMusicEventHandler|addOwnedMine|addPlayerScores|addPrimaryWeaponItem|addPublicVariableEventHandler|addRating|addResources|addScore|addScoreSide|addSecondaryWeaponItem|addSwitchableUnit|addTeamMember|addToRemainsCollector|addTorque|addUniform|addVehicle|addVest|addWaypoint|addWeapon|addWeaponCargo|addWeaponCargoGlobal|addWeaponGlobal|addWeaponItem|addWeaponPool|addWeaponTurret|admin|agent|agents|AGLToASL|aimedAtTarget|aimPos|airDensityCurveRTD|airDensityRTD|airplaneThrottle|airportSide|AISFinishHeal|alive|all3DENEntities|allAirports|allControls|allCurators|allCutLayers|allDead|allDeadMen|allDisplays|allGroups|allMapMarkers|allMines|allMissionObjects|allow3DMode|allowCrewInImmobile|allowCuratorLogicIgnoreAreas|allowDamage|allowDammage|allowFileOperations|allowFleeing|allowGetIn|allowSprint|allPlayers|allSimpleObjects|allSites|allTurrets|allUnits|allUnitsUAV|allVariables|ammo|ammoOnPylon|animate|animateBay|animateDoor|animatePylon|animateSource|animationNames|animationPhase|animationSourcePhase|animationState|append|apply|armoryPoints|arrayIntersect|asin|ASLToAGL|ASLToATL|assert|assignAsCargo|assignAsCargoIndex|assignAsCommander|assignAsDriver|assignAsGunner|assignAsTurret|assignCurator|assignedCargo|assignedCommander|assignedDriver|assignedGunner|assignedItems|assignedTarget|assignedTeam|assignedVehicle|assignedVehicleRole|assignItem|assignTeam|assignToAirport|atan|atan2|atg|ATLToASL|attachedObject|attachedObjects|attachedTo|attachObject|attachTo|attackEnabled|backpack|backpackCargo|backpackContainer|backpackItems|backpackMagazines|backpackSpaceFor|behaviour|benchmark|binocular|blufor|boundingBox|boundingBoxReal|boundingCenter|briefingName|buildingExit|buildingPos|buldozer_EnableRoadDiag|buldozer_IsEnabledRoadDiag|buldozer_LoadNewRoads|buldozer_reloadOperMap|buttonAction|buttonSetAction|cadetMode|callExtension|camCommand|camCommit|camCommitPrepared|camCommitted|camConstuctionSetParams|camCreate|camDestroy|cameraEffect|cameraEffectEnableHUD|cameraInterest|cameraOn|cameraView|campaignConfigFile|camPreload|camPreloaded|camPrepareBank|camPrepareDir|camPrepareDive|camPrepareFocus|camPrepareFov|camPrepareFovRange|camPreparePos|camPrepareRelPos|camPrepareTarget|camSetBank|camSetDir|camSetDive|camSetFocus|camSetFov|camSetFovRange|camSetPos|camSetRelPos|camSetTarget|camTarget|camUseNVG|canAdd|canAddItemToBackpack|canAddItemToUniform|canAddItemToVest|cancelSimpleTaskDestination|canFire|canMove|canSlingLoad|canStand|canSuspend|canTriggerDynamicSimulation|canUnloadInCombat|canVehicleCargo|captive|captiveNum|cbChecked|cbSetChecked|ceil|channelEnabled|cheatsEnabled|checkAIFeature|checkVisibility|civilian|className|clear3DENAttribute|clear3DENInventory|clearAllItemsFromBackpack|clearBackpackCargo|clearBackpackCargoGlobal|clearForcesRTD|clearGroupIcons|clearItemCargo|clearItemCargoGlobal|clearItemPool|clearMagazineCargo|clearMagazineCargoGlobal|clearMagazinePool|clearOverlay|clearRadio|clearVehicleInit|clearWeaponCargo|clearWeaponCargoGlobal|clearWeaponPool|clientOwner|closeDialog|closeDisplay|closeOverlay|collapseObjectTree|collect3DENHistory|collectiveRTD|combatMode|commandArtilleryFire|commandChat|commander|commandFire|commandFollow|commandFSM|commandGetOut|commandingMenu|commandMove|commandRadio|commandStop|commandSuppressiveFire|commandTarget|commandWatch|comment|commitOverlay|compile|compileFinal|completedFSM|composeText|configClasses|configFile|configHierarchy|configName|configNull|configProperties|configSourceAddonList|configSourceMod|configSourceModList|confirmSensorTarget|connectTerminalToUAV|controlNull|controlsGroupCtrl|copyFromClipboard|copyToClipboard|copyWaypoints|cos|count|countEnemy|countFriendly|countSide|countType|countUnknown|create3DENComposition|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deleted file mode 100644
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--- a/litellm/proxy/_experimental/out/_next/static/chunks/8049-b89d8be2044ba775.js
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console.log(c),c}catch(e){throw console.error("Failed to create key:",e),e}},eK=async e=>{try{let t=d?"".concat(d,"/user/get_requests"):"/user/get_requests";console.log("in userGetRequesedtModelsCall:",t);let o=await fetch(t,{method:"GET",headers:{[y]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok){let e=await o.json(),t=oR(e);throw w(t),Error(t)}let a=await o.json();return console.log(a),a}catch(e){throw console.error("Failed to get requested models:",e),e}},eQ=async(e,t,o)=>{try{let a=d?"".concat(d,"/user/daily/activity/aggregated"):"/user/daily/activity/aggregated",r=new URLSearchParams,n=e=>{let t=e.getFullYear(),o=String(e.getMonth()+1).padStart(2,"0"),a=String(e.getDate()).padStart(2,"0");return"".concat(t,"-").concat(o,"-").concat(a)};r.append("start_date",n(t)),r.append("end_date",n(o));let c=r.toString();c&&(a+="?".concat(c));let l=await fetch(a,{method:"GET",headers:{[y]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!l.ok){let e=await 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8160-08526425824fd908.js b/litellm/proxy/_experimental/out/_next/static/chunks/8160-12bc82b6a0051fb3.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/8160-08526425824fd908.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/8160-12bc82b6a0051fb3.js
index 9e102b0a4a9..068c10286e1 100644
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/874-e11bf6df41972648.js b/litellm/proxy/_experimental/out/_next/static/chunks/874-d2dc6ab8e9242cf1.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/874-e11bf6df41972648.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/874-d2dc6ab8e9242cf1.js
index c44fce85f11..6a3c54dbed9 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/874-e11bf6df41972648.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/874-d2dc6ab8e9242cf1.js
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similarity index 98%
rename from litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/budgets/page-3c8898eb84bdb422.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/budgets/page-c8d0b319f577e903.js
index d0b5208b495..31a1c1ca08c 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/caching/page-f8f9909f807222c8.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/caching/page-f8f9909f807222c8.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/old-usage/page-010fa8dfb79d1c9f.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/old-usage/page-010fa8dfb79d1c9f.js
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similarity index 94%
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/ui-theme/page-e8ad96f1f538ee8c.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/ui-theme/page-0a2f67a8126ad082.js
similarity index 98%
rename from litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/ui-theme/page-e8ad96f1f538ee8c.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/ui-theme/page-0a2f67a8126ad082.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/teams/page-5c65127c78cc8c3e.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/teams/page-61d65b25f3e3c300.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/teams/page-5c65127c78cc8c3e.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/teams/page-61d65b25f3e3c300.js
index e424e525957..680a46293ee 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-02e9423ce539cb48.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-02e9423ce539cb48.js
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encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(w,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(w,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(b,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(w,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(w,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(x,"\n").concat(n)}},51601:function(e,n,t){"use strict";t.d(n,{p:function(){return i}});var a=t(19250);let i=async e=>{try{let n=await (0,a.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},49817:function(e,n,t){"use strict";var a,i,o,r;t.d(n,{KP:function(){return i},vf:function(){return l}}),(o=a||(a={})).IMAGE_GENERATION="image_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",(r=i||(i={})).IMAGE="image",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings";let s={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},67479:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(52787),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,disabled:p}=e,[c,m]=(0,i.useState)([]),[u,d]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){d(!0);try{let e=await (0,r.getGuardrailsList)(l);console.log("Guardrails response:",e),e.guardrails&&(console.log("Guardrails data:",e.guardrails),m(e.guardrails))}catch(e){console.error("Error fetching guardrails:",e)}finally{d(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",disabled:p,placeholder:p?"Setting guardrails is a premium feature.":"Select guardrails",onChange:e=>{console.log("Selected guardrails:",e),n(e)},value:t,loading:u,className:s,options:c.map(e=>(console.log("Mapping guardrail:",e),{label:"".concat(e.guardrail_name),value:e.guardrail_name})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"}})})}},97415:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(52787),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:c=!1}=e,[m,u]=(0,i.useState)([]),[d,g]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&u(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,options:m.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:c})})}}},function(e){e.O(0,[1114,1491,4556,2417,3709,9775,7908,9011,5319,7906,4851,6433,9888,8049,3240,2971,2117,1744],function(){return e(e.s=22859)}),_N_E=e.O()}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-f536da4064bd1547.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-f536da4064bd1547.js
new file mode 100644
index 00000000000..3271ac15586
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openai.AzureOpenAI(\n	api_key="'.concat(f||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(h,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(f||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(h,'"\n)');switch(d){case a.KP.CHAT:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:b}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(x,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(x,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(v,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:b}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(x,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(x,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(v,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===_?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(x,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(v,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(x,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===_?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(v,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(x,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(v,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(x,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(x,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(x,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(x,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(m,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(x,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(E,"\n").concat(n)}},51601:function(e,n,t){"use strict";t.d(n,{p:function(){return i}});var a=t(19250);let i=async e=>{try{let n=await (0,a.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},49817:function(e,n,t){"use strict";var a,i,o,r;t.d(n,{KP:function(){return i},vf:function(){return l}}),(o=a||(a={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",(r=i||(i={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},67479:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(52787),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,disabled:p}=e,[c,u]=(0,i.useState)([]),[m,d]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){d(!0);try{let e=await (0,r.getGuardrailsList)(l);console.log("Guardrails response:",e),e.guardrails&&(console.log("Guardrails data:",e.guardrails),u(e.guardrails))}catch(e){console.error("Error fetching guardrails:",e)}finally{d(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",disabled:p,placeholder:p?"Setting guardrails is a premium feature.":"Select guardrails",onChange:e=>{console.log("Selected guardrails:",e),n(e)},value:t,loading:m,className:s,options:c.map(e=>(console.log("Mapping guardrail:",e),{label:"".concat(e.guardrail_name),value:e.guardrail_name})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"}})})}},97415:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(52787),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:c=!1}=e,[u,m]=(0,i.useState)([]),[d,g]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&m(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,options:u.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:c})})}}},function(e){e.O(0,[1114,1491,4556,2417,3709,9775,7908,9011,5319,7906,4851,6433,4388,4688,7029,2971,2117,1744],function(){return e(e.s=35831)}),_N_E=e.O()}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/tools/mcp-servers/page-3851c0d7162337c4.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/tools/mcp-servers/page-3851c0d7162337c4.js
new file mode 100644
index 00000000000..97b571bcf52
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/tools/mcp-servers/page-3851c0d7162337c4.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/page-3ac57035eb8ce002.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/page-3ac57035eb8ce002.js
new file mode 100644
index 00000000000..e29d90ed589
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/main-app-c6945ec5b2d5e671.js b/litellm/proxy/_experimental/out/_next/static/chunks/main-app-77a6ca3c04ee9adf.js
similarity index 81%
rename from litellm/proxy/_experimental/out/_next/static/chunks/main-app-c6945ec5b2d5e671.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/main-app-77a6ca3c04ee9adf.js
index e0441585089..3be8daf3eb0 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/main-app-c6945ec5b2d5e671.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/css/9bba2469df3eb88d.css b/litellm/proxy/_experimental/out/_next/static/css/9bba2469df3eb88d.css
new file mode 100644
index 00000000000..1684927aff1
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/css/9bba2469df3eb88d.css
@@ -0,0 +1,3 @@
+/*
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diff --git a/litellm/proxy/_experimental/out/_next/static/D4fwNrBygvLONmgebv5_0/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/nynjENYG9eyWwqRWz7fGE/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/D4fwNrBygvLONmgebv5_0/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/nynjENYG9eyWwqRWz7fGE/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/D4fwNrBygvLONmgebv5_0/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/nynjENYG9eyWwqRWz7fGE/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/D4fwNrBygvLONmgebv5_0/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/nynjENYG9eyWwqRWz7fGE/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/api-reference/index.html b/litellm/proxy/_experimental/out/api-reference.html
similarity index 90%
rename from litellm/proxy/_experimental/out/api-reference/index.html
rename to litellm/proxy/_experimental/out/api-reference.html
index 8ea2b156019..3bd2ffe544b 100644
--- a/litellm/proxy/_experimental/out/api-reference/index.html
+++ b/litellm/proxy/_experimental/out/api-reference.html
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/experimental/old-usage.txt b/litellm/proxy/_experimental/out/experimental/old-usage.txt
index 7d999f074e8..de000a78188 100644
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diff --git a/litellm/proxy/_experimental/out/models-and-endpoints.txt b/litellm/proxy/_experimental/out/models-and-endpoints.txt
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diff --git a/litellm/proxy/_experimental/out/teams.txt b/litellm/proxy/_experimental/out/teams.txt
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diff --git a/litellm/proxy/_experimental/out/virtual-keys/index.html b/litellm/proxy/_experimental/out/virtual-keys/index.html
deleted file mode 100644
index 4f80b0d29f1..00000000000
--- a/litellm/proxy/_experimental/out/virtual-keys/index.html
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml
index 9ef9812dd49..3a4cf84609f 100644
--- a/litellm/proxy/_new_secret_config.yaml
+++ b/litellm/proxy/_new_secret_config.yaml
@@ -6,16 +6,8 @@ model_list:
     litellm_params:
       model: openai/text-embedding-3-large
 
-vector_store_registry:
-  - vector_store_name: "vertex-ai-litellm-website-knowledgebase"
+  - model_name: gpt-4o-mini-transcribe
     litellm_params:
-      vector_store_id: "litellm-docs_1761094140318"
-      custom_llm_provider: "vertex_ai/search_api"
-      vertex_project: "test-vector-store-db"
-      vertex_location: "global"
-  - vector_store_name: "milvus-litellm-website-knowledgebase"
-    litellm_params:
-      vector_store_id: "can-be-anything"
-      custom_llm_provider: "milvus"
-      api_base: os.environ/MILVUS_API_BASE
-      api_key: os.environ/MILVUS_API_KEY
\ No newline at end of file
+      model: openai/gpt-4o-mini-transcribe
+      api_key: os.environ/OPENAI_API_KEY
+
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 17be7c18aab..612a2c914f4 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -17,7 +17,13 @@ from typing_extensions import Required, TypedDict
 from litellm._uuid import uuid
 from litellm.types.integrations.slack_alerting import AlertType
 from litellm.types.llms.openai import AllMessageValues, OpenAIFileObject
-from litellm.types.mcp import MCPAuthType, MCPTransport, MCPTransportType
+from litellm.types.mcp import (
+    MCPAuth,
+    MCPAuthType,
+    MCPCredentials,
+    MCPTransport,
+    MCPTransportType,
+)
 from litellm.types.mcp_server.mcp_server_manager import MCPInfo
 from litellm.types.router import RouterErrors, UpdateRouterConfig
 from litellm.types.secret_managers.main import KeyManagementSystem
@@ -229,6 +235,7 @@ class LiteLLMRoutes(enum.Enum):
         "completion",
         "embeddings",
         "image_generation",
+        "video_generation",
         "audio_transcriptions",
         "moderations",
         "model_list",  # OpenAI /v1/models route
@@ -959,6 +966,7 @@ class NewMCPServerRequest(LiteLLMPydanticObjectBase):
     description: Optional[str] = None
     transport: MCPTransportType = MCPTransport.sse
     auth_type: Optional[MCPAuthType] = None
+    credentials: Optional[MCPCredentials] = None
     url: Optional[str] = None
     mcp_info: Optional[MCPInfo] = None
     mcp_access_groups: List[str] = Field(default_factory=list)
@@ -985,6 +993,28 @@ class NewMCPServerRequest(LiteLLMPydanticObjectBase):
                     raise ValueError("url is required for HTTP/SSE transport")
         return values
 
+    @model_validator(mode="before")
+    @classmethod
+    def validate_credentials_requirements(cls, values):
+        if not isinstance(values, dict):
+            return values
+
+        auth_type = values.get("auth_type")
+        if auth_type in {MCPAuth.api_key, MCPAuth.bearer_token, MCPAuth.basic}:
+            credentials = values.get("credentials")
+            auth_value = None
+            if isinstance(credentials, dict):
+                auth_value = credentials.get("auth_value")
+            elif hasattr(credentials, "get"):
+                auth_value = credentials.get("auth_value")  # type: ignore[attr-defined]
+
+            if not auth_value:
+                raise ValueError(
+                    "auth_value is required when auth_type is api_key, bearer_token, or basic"
+                )
+
+        return values
+
 
 class UpdateMCPServerRequest(LiteLLMPydanticObjectBase):
     server_id: str
@@ -993,6 +1023,7 @@ class UpdateMCPServerRequest(LiteLLMPydanticObjectBase):
     description: Optional[str] = None
     transport: MCPTransportType = MCPTransport.sse
     auth_type: Optional[MCPAuthType] = None
+    credentials: Optional[MCPCredentials] = None
     url: Optional[str] = None
     mcp_info: Optional[MCPInfo] = None
     mcp_access_groups: List[str] = Field(default_factory=list)
@@ -1028,6 +1059,7 @@ class LiteLLM_MCPServerTable(LiteLLMPydanticObjectBase):
     url: Optional[str] = None
     transport: MCPTransportType
     auth_type: Optional[MCPAuthType] = None
+    credentials: Optional[MCPCredentials] = None
     created_at: Optional[datetime] = None
     created_by: Optional[str] = None
     updated_at: Optional[datetime] = None
@@ -2645,6 +2677,7 @@ class ProxyException(Exception):
         code: Optional[Union[int, str]] = None,  # maps to status code
         headers: Optional[Dict[str, str]] = None,
         openai_code: Optional[str] = None,  # maps to 'code'  in openai
+        provider_specific_fields: Optional[dict] = None,
     ):
         self.message = str(message)
         self.type = type
@@ -2659,7 +2692,7 @@ class ProxyException(Exception):
                 if not isinstance(v, str):
                     headers[k] = str(v)
         self.headers = headers or {}
-
+        self.provider_specific_fields = provider_specific_fields
         # rules for proxyExceptions
         # Litellm router.py returns "No healthy deployment available" when there are no deployments available
         # Should map to 429 errors https://github.com/BerriAI/litellm/issues/2487
@@ -2673,12 +2706,15 @@ class ProxyException(Exception):
 
     def to_dict(self) -> dict:
         """Converts the ProxyException instance to a dictionary."""
-        return {
+        error_dict: Dict[str, Optional[Union[str, Dict]]] = {
             "message": self.message,
             "type": self.type,
             "param": self.param,
             "code": self.code,
         }
+        if self.provider_specific_fields:
+            error_dict["provider_specific_fields"] = self.provider_specific_fields
+        return error_dict
 
 
 class CommonProxyErrors(str, enum.Enum):
diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py
index 82e54c7ee9e..448528bd684 100644
--- a/litellm/proxy/common_request_processing.py
+++ b/litellm/proxy/common_request_processing.py
@@ -746,6 +746,7 @@ class ProxyBaseLLMRequestProcessing:
                 type=getattr(e, "type", "None"),
                 param=getattr(e, "param", "None"),
                 code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+                provider_specific_fields=getattr(e, "provider_specific_fields", None),
                 headers=headers,
             )
         elif isinstance(e, httpx.HTTPStatusError):
@@ -765,6 +766,7 @@ class ProxyBaseLLMRequestProcessing:
             param=getattr(e, "param", "None"),
             openai_code=getattr(e, "code", None),
             code=getattr(e, "status_code", 500),
+            provider_specific_fields=getattr(e, "provider_specific_fields", None),
             headers=headers,
         )
 
diff --git a/litellm/proxy/example_config_yaml/custom_callbacks1.py b/litellm/proxy/example_config_yaml/custom_callbacks1.py
index 7e7f8133f4d..b261f5a83af 100644
--- a/litellm/proxy/example_config_yaml/custom_callbacks1.py
+++ b/litellm/proxy/example_config_yaml/custom_callbacks1.py
@@ -3,6 +3,7 @@ from typing import Literal, Optional
 import litellm
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy.proxy_server import DualCache, UserAPIKeyAuth
+from litellm.types.utils import CallTypesLiteral
 
 
 # This file includes the custom callbacks for LiteLLM Proxy
@@ -21,18 +22,7 @@ class MyCustomHandler(
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         return data
 
@@ -58,16 +48,7 @@ class MyCustomHandler(
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         pass
 
diff --git a/litellm/proxy/example_config_yaml/custom_guardrail.py b/litellm/proxy/example_config_yaml/custom_guardrail.py
index 95324583158..48eedcde5c0 100644
--- a/litellm/proxy/example_config_yaml/custom_guardrail.py
+++ b/litellm/proxy/example_config_yaml/custom_guardrail.py
@@ -6,6 +6,7 @@ from litellm.caching.caching import DualCache
 from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata
+from litellm.types.utils import CallTypesLiteral
 
 
 class myCustomGuardrail(CustomGuardrail):
@@ -23,18 +24,7 @@ class myCustomGuardrail(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, dict]]:
         """
         Runs before the LLM API call
@@ -62,16 +52,7 @@ class myCustomGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         Runs in parallel to LLM API call
diff --git a/litellm/proxy/guardrails/guardrail_endpoints.py b/litellm/proxy/guardrails/guardrail_endpoints.py
index a09185c6e3e..e64fbe9084e 100644
--- a/litellm/proxy/guardrails/guardrail_endpoints.py
+++ b/litellm/proxy/guardrails/guardrail_endpoints.py
@@ -564,8 +564,7 @@ async def patch_guardrail(guardrail_id: str, request: PatchGuardrailRequest):
         guardrail_name = result.get("guardrail_name", "Unknown")
 
         try:
-            IN_MEMORY_GUARDRAIL_HANDLER.update_in_memory_guardrail(
-                guardrail_id=guardrail_id,
+            IN_MEMORY_GUARDRAIL_HANDLER.sync_guardrail_from_db(
                 guardrail=guardrail,
             )
             verbose_proxy_logger.info(
@@ -693,7 +692,13 @@ async def get_guardrail_ui_settings():
     - Supported entities for guardrails
     - Supported modes for guardrails
     - PII entity categories for UI organization
+    - Content filter settings (patterns and categories)
     """
+    from litellm.proxy.guardrails.guardrail_hooks.litellm_content_filter.patterns import (
+        PATTERN_CATEGORIES,
+        get_pattern_metadata,
+    )
+
     # Convert the PII_ENTITY_CATEGORIES_MAP to the format expected by the UI
     category_maps = []
     for category, entities in PII_ENTITY_CATEGORIES_MAP.items():
@@ -707,9 +712,111 @@ async def get_guardrail_ui_settings():
         supported_actions=[action.value for action in PiiAction],
         supported_modes=[mode.value for mode in GuardrailEventHooks],
         pii_entity_categories=category_maps,
+        content_filter_settings={
+            "prebuilt_patterns": get_pattern_metadata(),
+            "pattern_categories": list(PATTERN_CATEGORIES.keys()),
+            "supported_actions": ["BLOCK", "MASK"],
+        },
     )
 
 
+@router.post(
+    "/guardrails/validate_blocked_words_file",
+    tags=["Guardrails"],
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def validate_blocked_words_file(request: Dict[str, str]):
+    """
+    Validate a blocked_words YAML file content.
+    
+    Args:
+        request: Dictionary with 'file_content' key containing the YAML string
+    
+    Returns:
+        Dictionary with 'valid' boolean and either 'message'/'errors' depending on result
+    
+    Example Request:
+    ```json
+    {
+        "file_content": "blocked_words:\\n  - keyword: \\"test\\"\\n    action: \\"BLOCK\\""
+    }
+    ```
+    
+    Example Success Response:
+    ```json
+    {
+        "valid": true,
+        "message": "Valid YAML file with 2 blocked words"
+    }
+    ```
+    
+    Example Error Response:
+    ```json
+    {
+        "valid": false,
+        "errors": ["Entry 0: missing 'action' field"]
+    }
+    ```
+    """
+    import yaml
+    
+    try:
+        file_content = request.get("file_content", "")
+        if not file_content:
+            return {
+                "valid": False,
+                "error": "No file content provided"
+            }
+        
+        data = yaml.safe_load(file_content)
+        
+        if not isinstance(data, dict) or "blocked_words" not in data:
+            return {
+                "valid": False,
+                "error": "Invalid format: file must contain 'blocked_words' key with a list"
+            }
+        
+        blocked_words_list = data["blocked_words"]
+        if not isinstance(blocked_words_list, list):
+            return {
+                "valid": False,
+                "error": "'blocked_words' must be a list"
+            }
+        
+        # Validate each entry
+        errors = []
+        for idx, word_data in enumerate(blocked_words_list):
+            if not isinstance(word_data, dict):
+                errors.append(f"Entry {idx}: must be an object")
+                continue
+            
+            if "keyword" not in word_data:
+                errors.append(f"Entry {idx}: missing 'keyword' field")
+            elif not isinstance(word_data["keyword"], str):
+                errors.append(f"Entry {idx}: 'keyword' must be a string")
+                
+            if "action" not in word_data:
+                errors.append(f"Entry {idx}: missing 'action' field")
+            elif word_data["action"] not in ["BLOCK", "MASK"]:
+                errors.append(f"Entry {idx}: action must be 'BLOCK' or 'MASK', got '{word_data['action']}'")
+            
+            if "description" in word_data and not isinstance(word_data["description"], str):
+                errors.append(f"Entry {idx}: 'description' must be a string")
+        
+        if errors:
+            return {"valid": False, "errors": errors}
+        
+        return {
+            "valid": True,
+            "message": f"Valid YAML file with {len(blocked_words_list)} blocked word(s)"
+        }
+    except yaml.YAMLError as e:
+        return {"valid": False, "error": f"Invalid YAML syntax: {str(e)}"}
+    except Exception as e:
+        verbose_proxy_logger.exception("Error validating blocked words file")
+        return {"valid": False, "error": f"Validation error: {str(e)}"}
+
+
 def _get_field_type_from_annotation(field_annotation: Any) -> str:
     """
     Convert a Python type annotation to a UI-friendly type string
diff --git a/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py b/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py
index fd9b89d0834..3a0b0c3202b 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py
@@ -7,7 +7,7 @@
 import asyncio
 import json
 import os
-from typing import TYPE_CHECKING, Any, AsyncGenerator, Literal, Optional, Type, Union
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional, Type, Union
 
 from fastapi import HTTPException
 from pydantic import BaseModel
@@ -23,6 +23,7 @@ from litellm.llms.custom_httpx.http_handler import (
 )
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.types.utils import (
+    CallTypesLiteral,
     Choices,
     EmbeddingResponse,
     ImageResponse,
@@ -67,18 +68,7 @@ class AimGuardrail(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         verbose_proxy_logger.debug("Inside AIM Pre-Call Hook")
         return await self.call_aim_guardrail(
@@ -89,16 +79,7 @@ class AimGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         verbose_proxy_logger.debug("Inside AIM Moderation Hook")
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
index 9b71c49f2a9..e8460528de5 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
@@ -42,6 +42,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import (
 )
 from litellm.types.utils import (
     CallTypes,
+    CallTypesLiteral,
     Choices,
     GuardrailStatus,
     ModelResponse,
@@ -608,18 +609,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         verbose_proxy_logger.debug(
             "Inside Bedrock Pre-Call Hook for call_type: %s", call_type
@@ -685,16 +675,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,
@@ -1163,7 +1144,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
 
         This method allows users to test Bedrock guardrails without making actual LLM calls.
         It creates a mock request and response to test the guardrail functionality.
-        
+
         Args:
             text: The text to analyze
             language: Optional language parameter (not used by Bedrock)
@@ -1175,11 +1156,11 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
             mock_messages: List[AllMessageValues] = [
                 ChatCompletionUserMessage(role="user", content=text)
             ]
-            
+
             # Use provided request_data or create a mock one for testing
             if request_data is None:
                 request_data = {"messages": mock_messages}
-            
+
             bedrock_response = await self.make_bedrock_api_request(
                 source="INPUT",
                 messages=mock_messages,
diff --git a/litellm/proxy/guardrails/guardrail_hooks/dynamoai/dynamoai.py b/litellm/proxy/guardrails/guardrail_hooks/dynamoai/dynamoai.py
index 9ea5f4cc747..6915286a2d7 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/dynamoai/dynamoai.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/dynamoai/dynamoai.py
@@ -7,16 +7,7 @@
 
 import os
 from datetime import datetime
-from typing import (
-    Any,
-    AsyncGenerator,
-    Dict,
-    List,
-    Literal,
-    Optional,
-    Type,
-    Union,
-)
+from typing import Any, AsyncGenerator, Dict, List, Optional, Type, Union
 
 import httpx
 
@@ -36,7 +27,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.dynamoai import (
     DynamoAIRequest,
     DynamoAIResponse,
 )
-from litellm.types.utils import GuardrailStatus, ModelResponseStream
+from litellm.types.utils import CallTypesLiteral, GuardrailStatus, ModelResponseStream
 
 GUARDRAIL_NAME = "dynamoai"
 
@@ -44,9 +35,10 @@ GUARDRAIL_NAME = "dynamoai"
 class DynamoAIGuardrails(CustomGuardrail):
     """
     DynamoAI Guardrails integration for LiteLLM.
-    
+
     Provides content moderation and policy enforcement using DynamoAI's guardrail API.
     """
+
     def __init__(
         self,
         guardrail_name: str = "litellm_test",
@@ -59,28 +51,30 @@ class DynamoAIGuardrails(CustomGuardrail):
         self.async_handler = get_async_httpx_client(
             llm_provider=httpxSpecialProvider.GuardrailCallback
         )
-        
+
         # Set API configuration
         self.api_key = api_key or os.getenv("DYNAMOAI_API_KEY")
         if not self.api_key:
             raise ValueError(
                 "DynamoAI API key is required. Set DYNAMOAI_API_KEY environment variable or pass api_key parameter."
             )
-        
+
         self.api_base = api_base or os.getenv(
             "DYNAMOAI_API_BASE", "https://api.dynamo.ai"
         )
         self.api_url = f"{self.api_base}/v1/moderation/analyze/"
-        
+
         # Model ID for tracking/logging purposes
         self.model_id = model_id or os.getenv("DYNAMOAI_MODEL_ID", "")
-        
+
         # Policy IDs - get from parameter, env var, or use empty list
         env_policy_ids = os.getenv("DYNAMOAI_POLICY_IDS", "")
-        self.policy_ids = policy_ids or (env_policy_ids.split(",") if env_policy_ids else [])
+        self.policy_ids = policy_ids or (
+            env_policy_ids.split(",") if env_policy_ids else []
+        )
         self.guardrail_name = guardrail_name
         self.guardrail_provider = "dynamoai"
-        
+
         # store kwargs as optional_params
         self.optional_params = kwargs
 
@@ -108,38 +102,38 @@ class DynamoAIGuardrails(CustomGuardrail):
     ) -> DynamoAIResponse:
         """
         Call DynamoAI Guardrails API to analyze messages for policy violations.
-        
+
         Args:
             messages: List of messages to analyze
             text_type: Type of text being analyzed ("input" or "output")
             request_data: Optional request data for logging purposes
-            
+
         Returns:
             DynamoAIResponse: Response from the DynamoAI Guardrails API
         """
         start_time = datetime.now()
-        
+
         payload: DynamoAIRequest = {
             "messages": messages,
         }
-        
+
         # Add optional fields if provided
         if self.policy_ids:
             payload["policyIds"] = self.policy_ids
         if self.model_id:
             payload["modelId"] = self.model_id
-        
+
         headers = {
             "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}"
+            "Authorization": f"Bearer {self.api_key}",
         }
-        
+
         verbose_proxy_logger.debug(
             "DynamoAI request to %s with payload=%s",
             self.api_url,
             payload,
         )
-        
+
         try:
             response = await self.async_handler.post(
                 url=self.api_url,
@@ -148,10 +142,10 @@ class DynamoAIGuardrails(CustomGuardrail):
             )
             response.raise_for_status()
             response_json = response.json()
-            
+
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
-            
+
             # Add guardrail information to request trace
             if request_data:
                 guardrail_status = self._determine_guardrail_status(response_json)
@@ -164,17 +158,15 @@ class DynamoAIGuardrails(CustomGuardrail):
                     end_time=end_time.timestamp(),
                     duration=duration,
                 )
-            
+
             return response_json
-            
+
         except httpx.HTTPError as e:
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
-            
-            verbose_proxy_logger.error(
-                "DynamoAI API request failed: %s", str(e)
-            )
-            
+
+            verbose_proxy_logger.error("DynamoAI API request failed: %s", str(e))
+
             # Add guardrail information with failure status
             if request_data:
                 self.add_standard_logging_guardrail_information_to_request_data(
@@ -186,7 +178,7 @@ class DynamoAIGuardrails(CustomGuardrail):
                     end_time=end_time.timestamp(),
                     duration=duration,
                 )
-            
+
             raise
 
     def _process_dynamoai_guardrails_response(
@@ -194,35 +186,35 @@ class DynamoAIGuardrails(CustomGuardrail):
     ) -> DynamoAIProcessedResult:
         """
         Process the response from the DynamoAI Guardrails API
-        
+
         Args:
             response: The response from the API with 'finalAction' and 'appliedPolicies' keys
-            
+
         Returns:
             DynamoAIProcessedResult: Processed response with detected violations
         """
         final_action = response.get("finalAction", "NONE")
         applied_policies = response.get("appliedPolicies", [])
-        
+
         violations_detected: List[str] = []
         violation_details: Dict[str, Any] = {}
-        
+
         # For now, only handle BLOCK action
         if final_action == "BLOCK":
             for applied_policy in applied_policies:
                 policy_info = applied_policy.get("policy", {})
                 policy_outputs = applied_policy.get("outputs", {})
-                
+
                 # Get policy name and action
                 policy_name = policy_info.get("name", "unknown")
-                
+
                 # Check for action in multiple places
                 policy_action = (
-                    applied_policy.get("action") or 
-                    (policy_outputs.get("action") if policy_outputs else None) or
-                    "NONE"
+                    applied_policy.get("action")
+                    or (policy_outputs.get("action") if policy_outputs else None)
+                    or "NONE"
                 )
-                
+
                 # Only include policies with BLOCK action
                 if policy_action == "BLOCK":
                     violations_detected.append(policy_name)
@@ -231,12 +223,14 @@ class DynamoAIGuardrails(CustomGuardrail):
                         "action": policy_action,
                         "method": policy_info.get("method"),
                         "description": policy_info.get("description"),
-                        "message": policy_outputs.get("message") if policy_outputs else None,
+                        "message": (
+                            policy_outputs.get("message") if policy_outputs else None
+                        ),
                     }
-        
+
         return {
             "violations_detected": violations_detected,
-            "violation_details": violation_details
+            "violation_details": violation_details,
         }
 
     def _determine_guardrail_status(
@@ -244,7 +238,7 @@ class DynamoAIGuardrails(CustomGuardrail):
     ) -> GuardrailStatus:
         """
         Determine the guardrail status based on DynamoAI API response.
-        
+
         Returns:
             "success": Content allowed through with no violations (finalAction is NONE)
             "guardrail_intervened": Content blocked (finalAction is BLOCK)
@@ -253,21 +247,21 @@ class DynamoAIGuardrails(CustomGuardrail):
         try:
             if not isinstance(response_json, dict):
                 return "guardrail_failed_to_respond"
-            
+
             # Check for error in response
             if response_json.get("error"):
                 return "guardrail_failed_to_respond"
-            
+
             final_action = response_json.get("finalAction", "NONE")
-            
+
             if final_action == "NONE":
                 return "success"
             elif final_action == "BLOCK":
                 return "guardrail_intervened"
-            
+
             # For now, treat other actions as success (WARN, REDACT, SANITIZE not implemented yet)
             return "success"
-            
+
         except Exception as e:
             verbose_proxy_logger.error(
                 "Error determining DynamoAI guardrail status: %s", str(e)
@@ -277,22 +271,24 @@ class DynamoAIGuardrails(CustomGuardrail):
     def _create_error_message(self, processed_result: DynamoAIProcessedResult) -> str:
         """
         Create a detailed error message from processed guardrail results.
-        
+
         Args:
             processed_result: Processed response with detected violations
-            
+
         Returns:
             Formatted error message string
         """
         violations_detected = processed_result["violations_detected"]
         violation_details = processed_result["violation_details"]
-        
-        error_message = f"Guardrail failed: {len(violations_detected)} violation(s) detected\n\n"
-        
+
+        error_message = (
+            f"Guardrail failed: {len(violations_detected)} violation(s) detected\n\n"
+        )
+
         for policy_name in violations_detected:
             error_message += f"- {policy_name.upper()}:\n"
             details = violation_details.get(policy_name, {})
-            
+
             # Format violation details
             if details.get("action"):
                 error_message += f"  Action: {details['action']}\n"
@@ -305,7 +301,7 @@ class DynamoAIGuardrails(CustomGuardrail):
             if details.get("policyId"):
                 error_message += f"  Policy ID: {details['policyId']}\n"
             error_message += "\n"
-        
+
         return error_message.strip()
 
     async def async_pre_call_hook(
@@ -313,18 +309,7 @@ class DynamoAIGuardrails(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         """
         Runs before the LLM API call
@@ -349,7 +334,9 @@ class DynamoAIGuardrails(CustomGuardrail):
                 request_data=data,
             )
 
-            verbose_proxy_logger.debug("Guardrails async_pre_call_hook result=%s", result)
+            verbose_proxy_logger.debug(
+                "Guardrails async_pre_call_hook result=%s", result
+            )
 
             # Process the guardrails response
             processed_result = self._process_dynamoai_guardrails_response(result)
@@ -364,23 +351,14 @@ class DynamoAIGuardrails(CustomGuardrail):
         add_guardrail_to_applied_guardrails_header(
             request_data=data, guardrail_name=self.guardrail_name
         )
-        
+
         return data
 
     async def async_moderation_hook(
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         Runs in parallel to LLM API call
@@ -404,7 +382,9 @@ class DynamoAIGuardrails(CustomGuardrail):
                 request_data=data,
             )
 
-            verbose_proxy_logger.debug("Guardrails async_moderation_hook result=%s", result)
+            verbose_proxy_logger.debug(
+                "Guardrails async_moderation_hook result=%s", result
+            )
 
             # Process the guardrails response
             processed_result = self._process_dynamoai_guardrails_response(result)
@@ -449,22 +429,26 @@ class DynamoAIGuardrails(CustomGuardrail):
             return
 
         verbose_proxy_logger.debug("async_post_call_success_hook response=%s", response)
-        
+
         # Check if the ModelResponse has text content in its choices
         # to avoid sending empty content to DynamoAI (e.g., during tool calls)
         if isinstance(response, litellm.ModelResponse):
             has_text_content = False
             dynamoai_messages: List[Dict[str, Any]] = []
-            
+
             for choice in response.choices:
                 if isinstance(choice, litellm.Choices):
-                    if choice.message.content and isinstance(choice.message.content, str):
+                    if choice.message.content and isinstance(
+                        choice.message.content, str
+                    ):
                         has_text_content = True
-                        dynamoai_messages.append({
-                            "role": choice.message.role or "assistant",
-                            "content": choice.message.content
-                        })
-            
+                        dynamoai_messages.append(
+                            {
+                                "role": choice.message.role or "assistant",
+                                "content": choice.message.content,
+                            }
+                        )
+
             if not has_text_content:
                 verbose_proxy_logger.warning(
                     "DynamoAI: not running guardrail. No output text in response"
@@ -478,7 +462,9 @@ class DynamoAIGuardrails(CustomGuardrail):
                     request_data=data,
                 )
 
-                verbose_proxy_logger.debug("Guardrails async_post_call_success_hook result=%s", result)
+                verbose_proxy_logger.debug(
+                    "Guardrails async_post_call_success_hook result=%s", result
+                )
 
                 # Process the guardrails response
                 processed_result = self._process_dynamoai_guardrails_response(result)
@@ -517,4 +503,3 @@ class DynamoAIGuardrails(CustomGuardrail):
         )
 
         return DynamoAIGuardrailConfigModel
-
diff --git a/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py b/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py
index d41599c1e19..952daaafb2b 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py
@@ -7,7 +7,7 @@
 
 import os
 from datetime import datetime
-from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Optional, Union
 
 import httpx
 
@@ -25,7 +25,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.enkryptai import (
     EnkryptAIProcessedResult,
     EnkryptAIResponse,
 )
-from litellm.types.utils import GuardrailStatus, ModelResponseStream
+from litellm.types.utils import CallTypesLiteral, GuardrailStatus, ModelResponseStream
 
 GUARDRAIL_NAME = "enkryptai"
 
@@ -284,18 +284,7 @@ class EnkryptAIGuardrails(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         """
         Runs before the LLM API call
@@ -348,16 +337,7 @@ class EnkryptAIGuardrails(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         Runs in parallel to LLM API call
diff --git a/litellm/proxy/guardrails/guardrail_hooks/ibm_guardrails/ibm_detector.py b/litellm/proxy/guardrails/guardrail_hooks/ibm_guardrails/ibm_detector.py
index e7e5beb0dba..72e0b3f013c 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/ibm_guardrails/ibm_detector.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/ibm_guardrails/ibm_detector.py
@@ -7,7 +7,7 @@
 
 import os
 from datetime import datetime
-from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Optional, Union
 from urllib.parse import urlencode
 
 import httpx
@@ -26,7 +26,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.ibm import (
     IBMDetectorDetection,
     IBMDetectorResponseOrchestrator,
 )
-from litellm.types.utils import GuardrailStatus, ModelResponseStream
+from litellm.types.utils import CallTypesLiteral, GuardrailStatus, ModelResponseStream
 
 GUARDRAIL_NAME = "ibm_guardrails"
 
@@ -47,7 +47,7 @@ class IBMGuardrailDetector(CustomGuardrail):
     ):
         self.async_handler = get_async_httpx_client(
             llm_provider=httpxSpecialProvider.GuardrailCallback,
-            params={"ssl_verify": verify_ssl}
+            params={"ssl_verify": verify_ssl},
         )
 
         # Set API configuration
@@ -436,18 +436,7 @@ class IBMGuardrailDetector(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         """
         Runs before the LLM API call
@@ -533,16 +522,7 @@ class IBMGuardrailDetector(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         Runs in parallel to LLM API call
diff --git a/litellm/proxy/guardrails/guardrail_hooks/javelin/javelin.py b/litellm/proxy/guardrails/guardrail_hooks/javelin/javelin.py
index 9c82b511f56..6d4ed089818 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/javelin/javelin.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/javelin/javelin.py
@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Type, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Type, Union
 
 from fastapi import HTTPException
 
@@ -18,7 +18,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.javelin import (
     JavelinGuardRequest,
     JavelinGuardResponse,
 )
-from litellm.types.utils import GuardrailStatus
+from litellm.types.utils import CallTypesLiteral, GuardrailStatus
 
 if TYPE_CHECKING:
     from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
@@ -165,18 +165,7 @@ class JavelinGuardrail(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: litellm.DualCache,
         data: Dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, Dict]]:
         """
         Pre-call hook for the Javelin guardrail.
diff --git a/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py b/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py
index 9a0b21e6859..c9d88badde8 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py
@@ -1,7 +1,7 @@
 import copy
 import os
 from datetime import datetime
-from typing import Dict, List, Literal, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 from fastapi import HTTPException
 
@@ -20,7 +20,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.lakera_ai_v2 import (
     LakeraAIRequest,
     LakeraAIResponse,
 )
-from litellm.types.utils import GuardrailStatus
+from litellm.types.utils import CallTypesLiteral, GuardrailStatus
 
 
 class LakeraAIGuardrail(CustomGuardrail):
@@ -183,18 +183,7 @@ class LakeraAIGuardrail(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: litellm.DualCache,
         data: Dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, Dict]]:
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,
@@ -257,16 +246,7 @@ class LakeraAIGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ):
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,
@@ -333,7 +313,7 @@ class LakeraAIGuardrail(CustomGuardrail):
         breakdown = lakera_response.get("breakdown", []) or []
         if not breakdown:
             return False
-        
+
         has_violations = False
         for item in breakdown:
             if item.get("detected", False):
@@ -341,7 +321,7 @@ class LakeraAIGuardrail(CustomGuardrail):
                 detector_type = item.get("detector_type", "") or ""
                 if not detector_type.startswith("pii/"):
                     return False
-        
+
         # Return True only if there are violations and they are all PII
         return has_violations
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/__init__.py
new file mode 100644
index 00000000000..89bb53ef72b
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/__init__.py
@@ -0,0 +1,52 @@
+from typing import TYPE_CHECKING
+
+import litellm
+from litellm.proxy.guardrails.guardrail_hooks.litellm_content_filter.content_filter import (
+    ContentFilterGuardrail,
+)
+from litellm.types.guardrails import SupportedGuardrailIntegrations
+
+if TYPE_CHECKING:
+    from litellm.types.guardrails import Guardrail, LitellmParams
+
+
+def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"):
+    """
+    Initialize the Content Filter Guardrail.
+    
+    Args:
+        litellm_params: Guardrail configuration parameters
+        guardrail: Guardrail metadata
+        
+    Returns:
+        Initialized ContentFilterGuardrail instance
+    """
+    guardrail_name = guardrail.get("guardrail_name")
+    if not guardrail_name:
+        raise ValueError("Content Filter: guardrail_name is required")
+    
+    content_filter_guardrail = ContentFilterGuardrail(
+        guardrail_name=guardrail_name,
+        patterns=litellm_params.patterns,
+        blocked_words=litellm_params.blocked_words,
+        blocked_words_file=litellm_params.blocked_words_file,
+        event_hook=litellm_params.mode,  # type: ignore
+        default_on=litellm_params.default_on or False,
+    )
+    
+    litellm.logging_callback_manager.add_litellm_callback(
+        content_filter_guardrail
+    )
+    
+    return content_filter_guardrail
+
+
+guardrail_initializer_registry = {
+    SupportedGuardrailIntegrations.LITELLM_CONTENT_FILTER.value: initialize_guardrail,
+}
+
+
+guardrail_class_registry = {
+    SupportedGuardrailIntegrations.LITELLM_CONTENT_FILTER.value: ContentFilterGuardrail,
+}
+
diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py
new file mode 100644
index 00000000000..644ecaf72ee
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py
@@ -0,0 +1,414 @@
+"""
+Content Filter Guardrail for LiteLLM.
+
+This guardrail provides regex pattern matching and keyword filtering
+to detect and block/mask sensitive content.
+"""
+
+import re
+from typing import Any, AsyncGenerator, Dict, List, Optional, Pattern, Tuple, Union
+
+import yaml
+from fastapi import HTTPException
+
+from litellm._logging import verbose_proxy_logger
+from litellm.integrations.custom_guardrail import CustomGuardrail
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.types.guardrails import (
+    BlockedWord,
+    ContentFilterAction,
+    ContentFilterPattern,
+    GuardrailEventHooks,
+    Mode,
+    PiiEntityType,
+)
+from litellm.types.utils import ModelResponseStream
+
+from .patterns import get_compiled_pattern
+
+
+class ContentFilterGuardrail(CustomGuardrail):
+    """
+    Content filter guardrail that detects sensitive information using:
+    - Prebuilt regex patterns (SSN, credit cards, API keys, etc.)
+    - Custom user-defined regex patterns
+    - Dictionary-based keyword matching
+
+    Actions:
+    - BLOCK: Reject the request with an error
+    - MASK: Replace the sensitive content with a redacted placeholder
+    """
+
+    # Redaction format constants
+    PATTERN_REDACTION_FORMAT = "[{pattern_name}_REDACTED]"
+    KEYWORD_REDACTION_STR = "[KEYWORD_REDACTED]"
+
+    def __init__(
+        self,
+        guardrail_name: Optional[str] = None,
+        patterns: Optional[List[ContentFilterPattern]] = None,
+        blocked_words: Optional[List[BlockedWord]] = None,
+        blocked_words_file: Optional[str] = None,
+        event_hook: Optional[Union[GuardrailEventHooks, List[GuardrailEventHooks], Mode]] = None,
+        default_on: bool = False,
+        pattern_redaction_format: Optional[str] = None,
+        keyword_redaction_tag: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Initialize the Content Filter Guardrail.
+        
+        Args:
+            guardrail_name: Name of this guardrail instance
+            patterns: List of ContentFilterPattern objects to detect
+            blocked_words: List of BlockedWord objects with keywords and actions
+            blocked_words_file: Path to YAML file containing blocked_words list
+            event_hook: When to run this guardrail (pre_call, post_call, etc.)
+            default_on: If True, runs on all requests by default
+            pattern_redaction_format: Format string for pattern redaction (use {pattern_name} placeholder)
+            keyword_redaction_tag: Tag to use for keyword redaction
+        """
+        super().__init__(
+            guardrail_name=guardrail_name,
+            supported_event_hooks=[
+                GuardrailEventHooks.pre_call,
+                GuardrailEventHooks.post_call,
+                GuardrailEventHooks.during_call,
+            ],
+            event_hook=event_hook or GuardrailEventHooks.pre_call,
+            default_on=default_on,
+            **kwargs,
+        )
+        
+        self.guardrail_provider = "litellm_content_filter"
+        self.pattern_redaction_format = pattern_redaction_format or self.PATTERN_REDACTION_FORMAT
+        self.keyword_redaction_tag = keyword_redaction_tag or self.KEYWORD_REDACTION_STR
+        
+        # Normalize inputs: convert dicts to Pydantic models for consistent handling
+        normalized_patterns: List[ContentFilterPattern] = []
+        if patterns:
+            for pattern_config in patterns:
+                if isinstance(pattern_config, dict):
+                    normalized_patterns.append(ContentFilterPattern(**pattern_config))
+                else:
+                    normalized_patterns.append(pattern_config)
+        
+        normalized_blocked_words: List[BlockedWord] = []
+        if blocked_words:
+            for word in blocked_words:
+                if isinstance(word, dict):
+                    normalized_blocked_words.append(BlockedWord(**word))
+                else:
+                    normalized_blocked_words.append(word)
+        
+        # Compile regex patterns
+        self.compiled_patterns: List[Tuple[Pattern, str, ContentFilterAction]] = []
+        for pattern_config in normalized_patterns:
+            self._add_pattern(pattern_config)
+        
+        # Load blocked words - always initialize as dict
+        self.blocked_words: Dict[str, Tuple[ContentFilterAction, Optional[str]]] = {}
+        for word in normalized_blocked_words:
+            self.blocked_words[word.keyword.lower()] = (word.action, word.description)
+        
+        # Defensive check: ensure blocked_words is a dict (not a list)
+        if not isinstance(self.blocked_words, dict):
+            verbose_proxy_logger.error(
+                f"blocked_words is not a dict, got {type(self.blocked_words)}. Resetting to empty dict."
+            )
+            self.blocked_words = {}
+        
+        # Load blocked words from file if provided
+        if blocked_words_file:
+            self._load_blocked_words_file(blocked_words_file)
+        
+        verbose_proxy_logger.debug(
+            f"ContentFilterGuardrail initialized with {len(self.compiled_patterns)} patterns "
+            f"and {len(self.blocked_words)} blocked words"
+        )
+
+    def _add_pattern(self, pattern_config: ContentFilterPattern) -> None:
+        """
+        Add a pattern to the compiled patterns list.
+        
+        Args:
+            pattern_config: ContentFilterPattern configuration
+        """
+        try:
+            if pattern_config.pattern_type == "prebuilt":
+                if not pattern_config.pattern_name:
+                    raise ValueError("pattern_name is required for prebuilt patterns")
+                compiled = get_compiled_pattern(pattern_config.pattern_name)
+                pattern_name = pattern_config.pattern_name
+            elif pattern_config.pattern_type == "regex":
+                if not pattern_config.pattern:
+                    raise ValueError("pattern is required for regex patterns")
+                compiled = re.compile(pattern_config.pattern, re.IGNORECASE)
+                pattern_name = pattern_config.name or "custom_regex"
+            else:
+                raise ValueError(f"Unknown pattern_type: {pattern_config.pattern_type}")
+            
+            self.compiled_patterns.append((compiled, pattern_name, pattern_config.action))
+            verbose_proxy_logger.debug(f"Added pattern: {pattern_name} with action {pattern_config.action}")
+        except Exception as e:
+            verbose_proxy_logger.error(f"Error adding pattern {pattern_config}: {e}")
+            raise
+
+    def _load_blocked_words_file(self, file_path: str) -> None:
+        """
+        Load blocked words from a YAML file.
+        
+        Args:
+            file_path: Path to YAML file containing blocked_words list
+            
+        Expected format:
+        ```yaml
+        blocked_words:
+          - keyword: "sensitive_term"
+            action: "BLOCK"
+            description: "Optional description"
+        ```
+        """
+        try:
+            with open(file_path, "r") as f:
+                data = yaml.safe_load(f)
+            
+            if not isinstance(data, dict) or "blocked_words" not in data:
+                raise ValueError(
+                    "Invalid format: file must contain 'blocked_words' key with list of words"
+                )
+            
+            for word_data in data["blocked_words"]:
+                if not isinstance(word_data, dict) or "keyword" not in word_data or "action" not in word_data:
+                    verbose_proxy_logger.warning(f"Skipping invalid word entry: {word_data}")
+                    continue
+                
+                keyword = word_data["keyword"].lower()
+                action = ContentFilterAction(word_data["action"])
+                description = word_data.get("description")
+                
+                self.blocked_words[keyword] = (action, description)
+            
+            verbose_proxy_logger.info(
+                f"Loaded {len(data['blocked_words'])} blocked words from {file_path}"
+            )
+        except FileNotFoundError:
+            raise FileNotFoundError(f"Blocked words file not found: {file_path}")
+        except Exception as e:
+            raise Exception(f"Error loading blocked words file {file_path}: {str(e)}")
+
+    def _check_patterns(self, text: str) -> Optional[Tuple[str, str, ContentFilterAction]]:
+        """
+        Check text against all compiled regex patterns.
+        
+        Args:
+            text: Text to check
+            
+        Returns:
+            Tuple of (matched_text, pattern_name, action) if match found, None otherwise
+        """
+        for compiled_pattern, pattern_name, action in self.compiled_patterns:
+            match = compiled_pattern.search(text)
+            if match:
+                matched_text = match.group(0)
+                verbose_proxy_logger.debug(
+                    f"Pattern '{pattern_name}' matched: {matched_text[:20]}..."
+                )
+                return (matched_text, pattern_name, action)
+        return None
+
+    def _check_blocked_words(self, text: str) -> Optional[Tuple[str, ContentFilterAction, Optional[str]]]:
+        """
+        Check text for blocked keywords.
+        
+        Args:
+            text: Text to check
+            
+        Returns:
+            Tuple of (keyword, action, description) if match found, None otherwise
+        """
+        # Handle case where blocked_words might still be a list (old instances)
+        if isinstance(self.blocked_words, list):
+            verbose_proxy_logger.warning(
+                "blocked_words is a list instead of dict. Re-initializing. "
+                "This suggests an old guardrail instance is still in use. Please restart the server."
+            )
+            # Convert list to dict on-the-fly
+            temp_dict: Dict[str, Tuple[ContentFilterAction, Optional[str]]] = {}
+            for word in self.blocked_words:
+                if isinstance(word, dict):
+                    temp_dict[word.get("keyword", "").lower()] = (
+                        word.get("action", ContentFilterAction.BLOCK),
+                        word.get("description")
+                    )
+            self.blocked_words = temp_dict
+        
+        if not self.blocked_words:
+            return None
+            
+        text_lower = text.lower()
+        for keyword, (action, description) in self.blocked_words.items():
+            if keyword in text_lower:
+                verbose_proxy_logger.debug(
+                    f"Blocked word '{keyword}' found with action {action}"
+                )
+                return (keyword, action, description)
+        return None
+
+    def _mask_content(self, text: str, pattern_name: str) -> str:
+        """
+        Mask sensitive content in text.
+        
+        Args:
+            text: Text containing sensitive content
+            pattern_name: Name of the pattern that matched
+            
+        Returns:
+            Text with sensitive content masked
+        """
+        redaction_tag = self.pattern_redaction_format.format(
+            pattern_name=pattern_name.upper()
+        )
+        return redaction_tag
+
+    async def apply_guardrail(
+        self,
+        text: str,
+        language: Optional[str] = None,
+        entities: Optional[List[PiiEntityType]] = None,
+        request_data: Optional[dict] = None,
+    ) -> str:
+        """
+        Apply content filtering guardrail to the given text.
+        
+        This method checks for sensitive patterns and blocked keywords,
+        either blocking the request or masking the sensitive content.
+        
+        Args:
+            text: The text to apply the guardrail to
+            language: Optional language parameter (not used)
+            entities: Optional entities parameter (not used)
+            request_data: Optional request data dictionary for logging metadata
+            
+        Returns:
+            Text with sensitive content masked (if action is MASK)
+            
+        Raises:
+            HTTPException: If sensitive content is detected and action is BLOCK
+        """
+        verbose_proxy_logger.debug("ContentFilterGuardrail: Applying guardrail to text")
+        
+        # Check regex patterns
+        pattern_match = self._check_patterns(text)
+        if pattern_match:
+            matched_text, pattern_name, action = pattern_match
+            
+            if action == ContentFilterAction.BLOCK:
+                error_msg = f"Content blocked: {pattern_name} pattern detected"
+                verbose_proxy_logger.warning(error_msg)
+                raise HTTPException(
+                    status_code=400,
+                    detail={"error": error_msg, "pattern": pattern_name},
+                )
+            elif action == ContentFilterAction.MASK:
+                # Replace the matched text with redaction tag
+                redaction_tag = self._mask_content(matched_text, pattern_name)
+                text = text.replace(matched_text, redaction_tag)
+                verbose_proxy_logger.info(f"Masked {pattern_name} in content")
+        
+        # Check blocked words
+        word_match = self._check_blocked_words(text)
+        if word_match:
+            keyword, action, description = word_match
+            
+            if action == ContentFilterAction.BLOCK:
+                error_msg = f"Content blocked: keyword '{keyword}' detected"
+                if description:
+                    error_msg += f" ({description})"
+                verbose_proxy_logger.warning(error_msg)
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": error_msg,
+                        "keyword": keyword,
+                        "description": description,
+                    },
+                )
+            elif action == ContentFilterAction.MASK:
+                # Replace keyword with redaction tag (case-insensitive)
+                text = re.sub(
+                    re.escape(keyword),
+                    self.keyword_redaction_tag,
+                    text,
+                    flags=re.IGNORECASE,
+                )
+                verbose_proxy_logger.info(f"Masked keyword '{keyword}' in content")
+        
+        verbose_proxy_logger.debug("ContentFilterGuardrail: Guardrail applied successfully")
+        return text
+
+    async def async_post_call_streaming_iterator_hook(
+        self,
+        user_api_key_dict: UserAPIKeyAuth,
+        response: Any,
+        request_data: dict,
+    ) -> AsyncGenerator[ModelResponseStream, None]:
+        """
+        Streaming hook to check each chunk as it's yielded.
+        
+        This implementation checks each chunk individually and yields it immediately,
+        allowing for low-latency streaming with content filtering.
+        
+        Args:
+            user_api_key_dict: User API key authentication
+            response: Async generator of response chunks
+            request_data: Original request data
+            
+        Yields:
+            Checked and potentially masked chunks
+            
+        Raises:
+            HTTPException: If chunk content should be blocked
+        """
+        verbose_proxy_logger.debug(
+            "ContentFilterGuardrail: Running streaming check (per-chunk mode)"
+        )
+                
+        # Process each chunk individually
+        async for chunk in response:
+            if isinstance(chunk, ModelResponseStream):
+                for choice in chunk.choices:
+                    if hasattr(choice, "delta") and choice.delta.content:
+                        if isinstance(choice.delta.content, str):
+                            # Check the chunk content using apply_guardrail
+                            try:
+                                processed_content = await self.apply_guardrail(
+                                    text=choice.delta.content,
+                                    request_data=request_data,
+                                )
+                                if processed_content != choice.delta.content:
+                                    choice.delta.content = processed_content
+                                    verbose_proxy_logger.debug(
+                                        "ContentFilterGuardrail: Modified streaming chunk"
+                                    )
+                            except HTTPException as e:
+                                # If content should be blocked, raise immediately
+                                verbose_proxy_logger.warning(
+                                    f"ContentFilterGuardrail: Blocked streaming chunk: {e.detail}"
+                                )
+                                raise
+            
+            yield chunk
+        
+        verbose_proxy_logger.debug(
+            "ContentFilterGuardrail: Streaming check completed"
+        )
+    
+    @staticmethod
+    def get_config_model():
+        from litellm.types.proxy.guardrails.guardrail_hooks.litellm_content_filter import (
+            LitellmContentFilterGuardrailConfigModel,
+        )
+
+        return LitellmContentFilterGuardrailConfigModel
+
diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json
new file mode 100644
index 00000000000..06dabced5af
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json
@@ -0,0 +1,124 @@
+{
+  "patterns": [
+    {
+      "name": "us_ssn",
+      "display_name": "SSN (Social Security Number)",
+      "pattern": "\\b\\d{3}-\\d{2}-\\d{4}\\b",
+      "category": "PII Patterns",
+      "description": "Detects US Social Security Numbers (XXX-XX-XXXX format)"
+    },
+    {
+      "name": "us_ssn_no_dash",
+      "display_name": "SSN (No Dashes)",
+      "pattern": "\\b(?!000|666|9\\d{2})\\d{3}(?!00)\\d{2}(?!0000)\\d{4}\\b",
+      "category": "PII Patterns",
+      "description": "Detects US SSN without dashes (XXXXXXXXX format)"
+    },
+    {
+      "name": "email",
+      "display_name": "Email Address",
+      "pattern": "\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b",
+      "category": "PII Patterns",
+      "description": "Detects email addresses"
+    },
+    {
+      "name": "us_phone",
+      "display_name": "Phone Number (US)",
+      "pattern": "\\b(?:\\+?1[\\s.-]?)?\\(?([0-9]{3})\\)?[\\s.-]?([0-9]{3})[\\s.-]?([0-9]{4})\\b",
+      "category": "PII Patterns",
+      "description": "Detects US phone numbers in various formats"
+    },
+    {
+      "name": "visa",
+      "display_name": "Credit Card (Visa)",
+      "pattern": "\\b4\\d{3}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}\\b",
+      "category": "Payment Card Patterns",
+      "description": "Detects Visa credit card numbers"
+    },
+    {
+      "name": "mastercard",
+      "display_name": "Credit Card (Mastercard)",
+      "pattern": "\\b5[1-5]\\d{2}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}\\b",
+      "category": "Payment Card Patterns",
+      "description": "Detects Mastercard credit card numbers"
+    },
+    {
+      "name": "amex",
+      "display_name": "Credit Card (American Express)",
+      "pattern": "\\b3[47]\\d{2}[\\s\\-]?\\d{6}[\\s\\-]?\\d{5}\\b",
+      "category": "Payment Card Patterns",
+      "description": "Detects American Express credit card numbers"
+    },
+    {
+      "name": "discover",
+      "display_name": "Credit Card (Discover)",
+      "pattern": "\\b6(?:011|5\\d{2})[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}\\b",
+      "category": "Payment Card Patterns",
+      "description": "Detects Discover credit card numbers"
+    },
+    {
+      "name": "credit_card",
+      "display_name": "Credit Card (Any)",
+      "pattern": "(?:\\b4\\d{3}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}\\b|\\b5[1-5]\\d{2}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}\\b|\\b3[47]\\d{2}[\\s\\-]?\\d{6}[\\s\\-]?\\d{5}\\b|\\b6(?:011|5\\d{2})[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}[\\s\\-]?\\d{4}\\b)",
+      "category": "Payment Card Patterns",
+      "description": "Detects any major credit card number"
+    },
+    {
+      "name": "aws_access_key",
+      "display_name": "AWS Access Key",
+      "pattern": "\\b(AKIA[0-9A-Z]{16})\\b",
+      "category": "Credential Patterns",
+      "description": "Detects AWS access keys (AKIA...)"
+    },
+    {
+      "name": "aws_secret_key",
+      "display_name": "AWS Secret Key",
+      "pattern": "\\b([A-Za-z0-9/+=]{40})\\b",
+      "category": "Credential Patterns",
+      "description": "Detects AWS secret keys (40 characters)"
+    },
+    {
+      "name": "github_token",
+      "display_name": "GitHub Token",
+      "pattern": "\\b(gh[ps]_[a-zA-Z0-9]{36})\\b",
+      "category": "Credential Patterns",
+      "description": "Detects GitHub personal access tokens"
+    },
+    {
+      "name": "slack_token",
+      "display_name": "Slack Token",
+      "pattern": "\\b(xox[pboa]-[0-9]{10,13}-[0-9]{10,13}-[a-zA-Z0-9]{24,32})\\b",
+      "category": "Credential Patterns",
+      "description": "Detects Slack API tokens"
+    },
+    {
+      "name": "generic_api_key",
+      "display_name": "API Key (Generic)",
+      "pattern": "\\b([Aa][Pp][Ii][-_]?[Kk][Ee][Yy][\\s:=]+['\"]?[A-Za-z0-9_\\-]{20,}['\"]?)\\b",
+      "category": "Credential Patterns",
+      "description": "Detects generic API key patterns"
+    },
+    {
+      "name": "ipv4",
+      "display_name": "IP Address (IPv4)",
+      "pattern": "\\b(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\b",
+      "category": "Network Patterns",
+      "description": "Detects IPv4 addresses"
+    },
+    {
+      "name": "ipv6",
+      "display_name": "IP Address (IPv6)",
+      "pattern": "\\b(?:[0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}\\b",
+      "category": "Network Patterns",
+      "description": "Detects IPv6 addresses"
+    },
+    {
+      "name": "url",
+      "display_name": "URL",
+      "pattern": "\\b(?:https?://|www\\.)[^\\s/$.?#].[^\\s]*\\b",
+      "category": "Network Patterns",
+      "description": "Detects URLs (http/https)"
+    }
+  ]
+}
+
diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.py b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.py
new file mode 100644
index 00000000000..b4649d73e34
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.py
@@ -0,0 +1,115 @@
+"""
+Prebuilt regex patterns for content filtering.
+
+This module loads predefined regex patterns from patterns.json for detecting 
+sensitive information like SSNs, credit cards, API keys, etc.
+"""
+
+import json
+import os
+import re
+from enum import Enum
+from typing import Dict, List, Pattern
+
+
+def _load_patterns_from_json() -> Dict:
+    """Load pattern definitions from patterns.json file"""
+    json_path = os.path.join(os.path.dirname(__file__), "patterns.json")
+    with open(json_path, "r") as f:
+        return json.load(f)
+
+
+# Load patterns from JSON
+_PATTERNS_DATA = _load_patterns_from_json()
+
+
+class PrebuiltPatternName(str, Enum):
+    """Enum for prebuilt pattern names - dynamically generated from JSON"""
+    pass
+
+
+# Dynamically create enum values from JSON
+for pattern_data in _PATTERNS_DATA["patterns"]:
+    setattr(PrebuiltPatternName, pattern_data["name"].upper(), pattern_data["name"])
+
+
+# Build lookup dictionaries from JSON
+PREBUILT_PATTERNS: Dict[str, str] = {
+    pattern_data["name"]: pattern_data["pattern"]
+    for pattern_data in _PATTERNS_DATA["patterns"]
+}
+
+
+def get_compiled_pattern(pattern_name: str) -> Pattern:
+    """
+    Get a compiled regex pattern by name.
+    
+    Args:
+        pattern_name: Name of the prebuilt pattern
+        
+    Returns:
+        Compiled regex pattern
+        
+    Raises:
+        ValueError: If pattern_name is not found in PREBUILT_PATTERNS
+    """
+    if pattern_name not in PREBUILT_PATTERNS:
+        available_patterns = ", ".join(PREBUILT_PATTERNS.keys())
+        raise ValueError(
+            f"Unknown pattern name: '{pattern_name}'. "
+            f"Available patterns: {available_patterns}"
+        )
+    
+    return re.compile(PREBUILT_PATTERNS[pattern_name], re.IGNORECASE)
+
+
+def get_all_pattern_names() -> List[str]:
+    """
+    Get a list of all available prebuilt pattern names.
+    
+    Returns:
+        List of pattern names
+    """
+    return list(PREBUILT_PATTERNS.keys())
+
+
+# Build category mapping from JSON
+PATTERN_CATEGORIES: Dict[str, List[str]] = {}
+for pattern_data in _PATTERNS_DATA["patterns"]:
+    category = pattern_data["category"]
+    if category not in PATTERN_CATEGORIES:
+        PATTERN_CATEGORIES[category] = []
+    PATTERN_CATEGORIES[category].append(pattern_data["name"])
+
+
+# Build display names mapping from JSON
+PATTERN_DISPLAY_NAMES: Dict[str, str] = {
+    pattern_data["name"]: pattern_data["display_name"]
+    for pattern_data in _PATTERNS_DATA["patterns"]
+}
+
+
+# Build descriptions mapping from JSON
+PATTERN_DESCRIPTIONS: Dict[str, str] = {
+    pattern_data["name"]: pattern_data["description"]
+    for pattern_data in _PATTERNS_DATA["patterns"]
+}
+
+
+def get_pattern_metadata() -> List[Dict[str, str]]:
+    """
+    Return pattern metadata for UI display.
+    
+    Returns:
+        List of dictionaries containing pattern name, display_name, category, and description
+    """
+    return [
+        {
+            "name": pattern_data["name"],
+            "display_name": pattern_data["display_name"],
+            "category": pattern_data["category"],
+            "description": pattern_data["description"],
+        }
+        for pattern_data in _PATTERNS_DATA["patterns"]
+    ]
+
diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py
index 9848fe59479..c2e00b0c7c5 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py
@@ -6,11 +6,22 @@
 # +-------------------------------------------------------------+
 
 import asyncio
+import json
 import os
 from datetime import datetime
-from typing import TYPE_CHECKING, Any, Dict, Final, Literal, Optional, Type, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncGenerator,
+    Dict,
+    Final,
+    List,
+    Literal,
+    Optional,
+    Type,
+    Union,
+)
 from urllib.parse import urljoin
-import json
 
 from fastapi import HTTPException
 
@@ -18,25 +29,22 @@ import litellm
 from litellm import DualCache, ModelResponse
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_guardrail import CustomGuardrail
+from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
 from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
     httpxSpecialProvider,
 )
+from litellm.main import stream_chunk_builder
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.types.guardrails import GuardrailEventHooks
-from litellm.types.utils import EmbeddingResponse, GuardrailStatus, ImageResponse
-
 from litellm.types.utils import (
+    CallTypesLiteral,
+    EmbeddingResponse,
+    GuardrailStatus,
+    ImageResponse,
     ModelResponseStream,
+    TextCompletionResponse,
 )
-from typing import (
-    List,
-    AsyncGenerator
-)
-
-from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
-from litellm.main import stream_chunk_builder
-from litellm.types.utils import TextCompletionResponse
 
 # Constants
 USER_ROLE: Final[Literal["user"]] = "user"
@@ -48,9 +56,9 @@ MessageRole = Literal["user", "assistant"]
 LLMResponse = Union[Any, ModelResponse, EmbeddingResponse, ImageResponse]
 
 if TYPE_CHECKING:
-  from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
+    from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
+
 
-  
 class NomaBlockedMessage(HTTPException):
     """Exception raised when Noma guardrail blocks a message"""
 
@@ -160,13 +168,7 @@ class NomaGuardrail(CustomGuardrail):
             return None
 
         payload = {
-            "input": [
-                {
-                    "type": "message",
-                    "role": "user",
-                    "content": user_message
-                }
-            ]
+            "input": [{"type": "message", "role": "user", "content": user_message}]
         }
         response_json = await self._call_noma_api(
             payload=payload,
@@ -175,13 +177,13 @@ class NomaGuardrail(CustomGuardrail):
             user_auth=user_auth,
             extra_data=extra_data,
         )
-        
+
         end_time = datetime.now()
         duration = (end_time - start_time).total_seconds()
 
         # Determine guardrail status based on response
         guardrail_status = self._determine_guardrail_status(response_json)
-        
+
         # Always log guardrail information for consistency
         self.add_standard_logging_guardrail_information_to_request_data(
             guardrail_provider="noma",
@@ -222,7 +224,7 @@ class NomaGuardrail(CustomGuardrail):
         user_auth: UserAPIKeyAuth,
     ) -> Optional[str]:
         """Shared logic for processing LLM response checks"""
-        
+
         start_time = datetime.now()
         extra_data = self.get_guardrail_dynamic_request_body_params(request_data)
 
@@ -243,12 +245,7 @@ class NomaGuardrail(CustomGuardrail):
                 {
                     "type": "message",
                     "role": "assistant",
-                    "content": [
-                        {
-                            "type": "input_text",
-                            "text": content
-                        }
-                    ]
+                    "content": [{"type": "input_text", "text": content}],
                 }
             ]
         }
@@ -260,13 +257,13 @@ class NomaGuardrail(CustomGuardrail):
             user_auth=user_auth,
             extra_data=extra_data,
         )
-        
+
         end_time = datetime.now()
         duration = (end_time - start_time).total_seconds()
 
         # Determine guardrail status based on response
         guardrail_status = self._determine_guardrail_status(response_json)
-        
+
         # Always log guardrail information for consistency
         self.add_standard_logging_guardrail_information_to_request_data(
             guardrail_provider="noma",
@@ -303,10 +300,10 @@ class NomaGuardrail(CustomGuardrail):
     def _determine_guardrail_status(self, response_json: dict) -> GuardrailStatus:
         """
         Determine the guardrail status based on NOMA API response.
-        
+
         Args:
             response_json: Response from NOMA API
-            
+
         Returns:
             "success": Content allowed through with no violations
             "guardrail_intervened": Content blocked due to policy violations
@@ -316,24 +313,26 @@ class NomaGuardrail(CustomGuardrail):
             # Check if we got a valid response structure
             if not isinstance(response_json, dict):
                 return "guardrail_failed_to_respond"
-            
+
             # Get the aggregatedScanResult from the response
             # aggregatedScanResult=True means unsafe (block), False means safe (allow)
             aggregated_scan_result = response_json.get("aggregatedScanResult", False)
-            
+
             # If aggregatedScanResult is False, content is safe/allowed
             if aggregated_scan_result is False:
                 return "success"
-            
+
             # If aggregatedScanResult is True, content is blocked/flagged
             if aggregated_scan_result is True:
                 return "guardrail_intervened"
-                
+
             # If aggregatedScanResult is missing or invalid, treat as failure
             return "guardrail_failed_to_respond"
-            
+
         except Exception as e:
-            verbose_proxy_logger.error(f"Error determining NOMA guardrail status: {str(e)}")
+            verbose_proxy_logger.error(
+                f"Error determining NOMA guardrail status: {str(e)}"
+            )
             return "guardrail_failed_to_respond"
 
     def _should_only_sensitive_data_failed(self, classification_obj: dict) -> bool:
@@ -392,12 +391,16 @@ class NomaGuardrail(CustomGuardrail):
         scan_result = response_json.get("scanResult", [])
         if not scan_result:
             return None
-        
+
         # Find the scan result matching the message type (role)
         for result_item in scan_result:
             if result_item.get("role") == message_type:
-                return result_item.get("results", {}).get("anonymizedContent", {}).get("anonymized", "")
-        
+                return (
+                    result_item.get("results", {})
+                    .get("anonymizedContent", {})
+                    .get("anonymized", "")
+                )
+
         return None
 
     def _should_anonymize(self, response_json: dict, message_type: MessageRole) -> bool:
@@ -423,7 +426,7 @@ class NomaGuardrail(CustomGuardrail):
 
         # aggregatedScanResult=False means safe, True means unsafe
         aggregated_scan_result = response_json.get("aggregatedScanResult", False)
-        
+
         # If aggregatedScanResult is False, content is safe - anonymize if available
         if not aggregated_scan_result:
             return True
@@ -432,13 +435,15 @@ class NomaGuardrail(CustomGuardrail):
         scan_result = response_json.get("scanResult", [])
         if not scan_result:
             return False
-        
+
         if not isinstance(scan_result, list) or len(scan_result) == 0:
             return False
-        
+
         for result_item in scan_result:
             if result_item.get("role") == message_type:
-                return self._should_only_sensitive_data_failed(result_item.get("results", {}))
+                return self._should_only_sensitive_data_failed(
+                    result_item.get("results", {})
+                )
 
         return False
 
@@ -534,7 +539,7 @@ class NomaGuardrail(CustomGuardrail):
         try:
             # aggregatedScanResult=True means blocked, False means allowed
             aggregated_scan_result = response_json.get("aggregatedScanResult", False)
-            
+
             if aggregated_scan_result:  # True = unsafe
                 msg = f"Noma guardrail blocked {type} message: {message}"
                 verbose_proxy_logger.warning(msg)
@@ -551,20 +556,9 @@ class NomaGuardrail(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, dict]]:
-    
+
         verbose_proxy_logger.debug("Running Noma pre-call hook")
 
         if (
@@ -595,6 +589,7 @@ class NomaGuardrail(CustomGuardrail):
         except Exception as e:
             # Log technical failures
             from datetime import datetime
+
             start_time = datetime.now()
             self.add_standard_logging_guardrail_information_to_request_data(
                 guardrail_provider="noma",
@@ -605,7 +600,7 @@ class NomaGuardrail(CustomGuardrail):
                 end_time=start_time.timestamp(),
                 duration=0.0,
             )
-            
+
             verbose_proxy_logger.error(f"Noma pre-call hook failed: {str(e)}")
 
             if self.block_failures:
@@ -616,16 +611,7 @@ class NomaGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal[
-            "completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "responses",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         event_type: GuardrailEventHooks = GuardrailEventHooks.during_call
         if self.should_run_guardrail(data=data, event_type=event_type) is not True:
@@ -651,6 +637,7 @@ class NomaGuardrail(CustomGuardrail):
         except Exception as e:
             # Log technical failures
             from datetime import datetime
+
             start_time = datetime.now()
             self.add_standard_logging_guardrail_information_to_request_data(
                 guardrail_provider="noma",
@@ -661,7 +648,7 @@ class NomaGuardrail(CustomGuardrail):
                 end_time=start_time.timestamp(),
                 duration=0.0,
             )
-            
+
             verbose_proxy_logger.error(f"Noma moderation hook failed: {str(e)}")
 
             if self.block_failures:
@@ -700,6 +687,7 @@ class NomaGuardrail(CustomGuardrail):
         except Exception as e:
             # Log technical failures
             from datetime import datetime
+
             start_time = datetime.now()
             self.add_standard_logging_guardrail_information_to_request_data(
                 guardrail_provider="noma",
@@ -710,7 +698,7 @@ class NomaGuardrail(CustomGuardrail):
                 end_time=start_time.timestamp(),
                 duration=0.0,
             )
-            
+
             verbose_proxy_logger.error(f"Noma post-call hook failed: {str(e)}")
             if self.block_failures:
                 raise
@@ -756,37 +744,31 @@ class NomaGuardrail(CustomGuardrail):
 
         last_user_message = user_messages[-1].get("content", "")
         if isinstance(last_user_message, str):
-            return [{
-                "type": "input_text",
-                "text": last_user_message
-            }]
+            return [{"type": "input_text", "text": last_user_message}]
         elif isinstance(last_user_message, list):
             converted_messages = []
             for message in last_user_message:
-                converted_message = self._convert_single_user_message_to_payload(message)
+                converted_message = self._convert_single_user_message_to_payload(
+                    message
+                )
                 if converted_message is not None:
                     converted_messages.append(converted_message)
             return converted_messages
         else:
             return None
 
-    
-    def _convert_single_user_message_to_payload(self, user_message: Any) -> Optional[dict]:
+    def _convert_single_user_message_to_payload(
+        self, user_message: Any
+    ) -> Optional[dict]:
         if isinstance(user_message, str):
-            return {
-                "type": "input_text",
-                "text": user_message
-            }
+            return {"type": "input_text", "text": user_message}
         elif user_message.get("type", "") == "image_url":
             return {
                 "type": "input_image",
-                "image_url": user_message.get("image_url", {}).get("url", "")
+                "image_url": user_message.get("image_url", {}).get("url", ""),
             }
         elif user_message.get("type", "") == "text":
-            return {
-                "type": "input_text",
-                "text": user_message.get("text", "")
-            }
+            return {"type": "input_text", "text": user_message.get("text", "")}
         else:
             return None
 
@@ -816,13 +798,16 @@ class NomaGuardrail(CustomGuardrail):
                     "applicationId": extra_data.get("application_id")
                     or request_data.get("metadata", {})
                     .get("headers", {})
-                    .get("x-noma-application-id") or self.application_id,
+                    .get("x-noma-application-id")
+                    or self.application_id,
                     "ipAddress": request_data.get("metadata", {}).get(
                         "requester_ip_address", None
                     ),
-                    "userId": user_auth.user_email
-                    if user_auth.user_email
-                    else user_auth.user_id,
+                    "userId": (
+                        user_auth.user_email
+                        if user_auth.user_email
+                        else user_auth.user_id
+                    ),
                     "sessionId": call_id,
                     "requestId": llm_request_id,
                 },
@@ -844,7 +829,7 @@ class NomaGuardrail(CustomGuardrail):
         """
         # aggregatedScanResult=True means blocked, False means allowed
         aggregated_scan_result = response_json.get("aggregatedScanResult", False)
-        
+
         if aggregated_scan_result:  # True = unsafe, block it
             msg = f"Noma guardrail blocked {type} message: {message}"
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/panw_prisma_airs/panw_prisma_airs.py b/litellm/proxy/guardrails/guardrail_hooks/panw_prisma_airs/panw_prisma_airs.py
index 814fb6deb2c..36fdfecaab8 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/panw_prisma_airs/panw_prisma_airs.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/panw_prisma_airs/panw_prisma_airs.py
@@ -205,11 +205,10 @@ class PanwPrismaAirsHandler(CustomGuardrail):
         if not content.strip():
             return {"action": "allow", "category": "empty"}
 
-        # Use litellm_call_id if provided, fallback to generating UUID
-        litellm_call_id = call_id or str(uuid.uuid4())
-
-        # Build transaction ID with prompt/response prefix + full call_id for correlation
-        transaction_id = f"litellm-{'resp' if is_response else 'req'}-{litellm_call_id}"
+        # Use litellm_trace_id as Prisma AIRS AI Session ID for session grouping
+        transaction_id = metadata.get("litellm_trace_id") if metadata else None
+        if not transaction_id:
+            transaction_id = call_id or str(uuid.uuid4())
 
         # Build Prisma AIRS API metadata
         # Handle app_name: LiteLLM by default, or LiteLLM-{user_app_name} if user provides one
@@ -264,7 +263,7 @@ class PanwPrismaAirsHandler(CustomGuardrail):
             payload["ai_profile"] = ai_profile
 
         if is_response:
-            payload["metadata"]["is_response"] = True  # type: ignore[index]
+            payload["metadata"]["is_response"] = True  # type: ignore[call-overload, index]
 
         headers = {
             "Content-Type": "application/json",
@@ -275,7 +274,7 @@ class PanwPrismaAirsHandler(CustomGuardrail):
         try:
             # Use LiteLLM's async HTTP client
             async_client = get_async_httpx_client(
-                llm_provider=httpxSpecialProvider.LoggingCallback
+                llm_provider=httpxSpecialProvider.GuardrailCallback
             )
 
             response = await async_client.post(
@@ -496,8 +495,69 @@ class PanwPrismaAirsHandler(CustomGuardrail):
         if "app_name" in user_metadata:
             metadata["app_name"] = user_metadata["app_name"]
 
+        # Include litellm_trace_id for session tracking
+        if data.get("litellm_trace_id"):
+            metadata["litellm_trace_id"] = data["litellm_trace_id"]
+
         return metadata
 
+    def _check_and_mark_scanned(self, data: dict, scan_type: str) -> bool:
+        """
+        Check if request has already been scanned and mark it as scanned.
+
+        Args:
+            data: Request data dictionary
+            scan_type: Type of scan ('pre', 'post', 'streaming')
+
+        Returns:
+            True if already scanned (should skip), False if needs scanning
+        """
+        call_id = data.get("litellm_call_id")
+        if not call_id:
+            call_id = str(uuid.uuid4())
+            data["litellm_call_id"] = call_id
+
+        scan_key = f"_panw_{scan_type}_scanned_{call_id}"
+        litellm_metadata = data.setdefault("litellm_metadata", {})
+
+        if litellm_metadata.get(scan_key):
+            verbose_proxy_logger.debug(
+                f"PANW Prisma AIRS: Skipping duplicate {scan_type}-call scan"
+            )
+            return True  # Already scanned
+
+        litellm_metadata[scan_key] = True
+        return False  # Needs scanning
+
+    def _extract_prompt_from_request(self, data: dict) -> str:
+        """
+        Extract prompt text from request data.
+
+        Handles both chat completion (messages) and text completion (prompt) formats.
+
+        Args:
+            data: Request data dictionary
+
+        Returns:
+            Extracted prompt text, or empty string if not found
+        """
+        # Extract from messages (chat completion)
+        messages = data.get("messages", [])
+        prompt_text = self._extract_text_from_messages(messages)
+
+        # Fallback to prompt field for text completion requests
+        if not prompt_text:
+            prompt_value = data.get("prompt")
+            if isinstance(prompt_value, str):
+                prompt_text = prompt_value
+            elif isinstance(prompt_value, list):
+                # Handle list of prompts (batch text completion)
+                prompt_text = " ".join(str(p) for p in prompt_value if p)
+            else:
+                prompt_text = ""
+
+        return prompt_text
+
     @log_guardrail_information
     async def async_pre_call_hook(
         self,
@@ -534,21 +594,14 @@ class PanwPrismaAirsHandler(CustomGuardrail):
         if self.should_run_guardrail(data=data, event_type=event_type) is not True:
             return data
 
-        try:
-            # Extract prompt text from messages (chat completion) or prompt (text completion)
-            messages = data.get("messages", [])
-            prompt_text = self._extract_text_from_messages(messages)
+        # Prevent duplicate scans by checking if already processed
+        if self._check_and_mark_scanned(data, "pre"):
+            return data
 
-            # Fallback to prompt field for text completion requests
-            if not prompt_text:
-                prompt_value = data.get("prompt")
-                if isinstance(prompt_value, str):
-                    prompt_text = prompt_value
-                elif isinstance(prompt_value, list):
-                    # Handle list of prompts (batch text completion)
-                    prompt_text = " ".join(str(p) for p in prompt_value if p)
-                else:
-                    prompt_text = ""
+        try:
+            # Extract prompt text from request
+            prompt_text = self._extract_prompt_from_request(data)
+            messages = data.get("messages", [])  # Keep for masking operations
 
             if not prompt_text:
                 verbose_proxy_logger.warning(
@@ -659,6 +712,10 @@ class PanwPrismaAirsHandler(CustomGuardrail):
         if self.should_run_guardrail(data=data, event_type=event_type) is not True:
             return response
 
+        # Prevent duplicate scans by checking if already processed
+        if self._check_and_mark_scanned(data, "post"):
+            return response
+
         try:
             # Extract response text
             response_text = self._extract_response_text(response)
@@ -819,6 +876,12 @@ class PanwPrismaAirsHandler(CustomGuardrail):
                 yield chunk
             return
 
+        # Prevent duplicate scans by checking if already processed
+        if self._check_and_mark_scanned(request_data, "streaming"):
+            async for chunk in response:
+                yield chunk
+            return
+
         verbose_proxy_logger.info("PANW Prisma AIRS: Running post-call streaming scan")
 
         all_chunks = []
diff --git a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py
index 5355f308fd0..824ed4e0b0c 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py
@@ -6,7 +6,7 @@ Unified Guardrail, leveraging LiteLLM's /applyGuardrail endpoint
 3. Implements a way to call /applyGuardrail endpoint for `/chat/completions` + `/v1/messages` requests on async_post_call_streaming_iterator_hook
 """
 
-from typing import Any, AsyncGenerator, Literal, Union
+from typing import Any, AsyncGenerator, Union
 
 from litellm._logging import verbose_proxy_logger
 from litellm.caching.caching import DualCache
@@ -16,7 +16,7 @@ from litellm.integrations.custom_logger import CustomLogger
 from litellm.llms import load_guardrail_translation_mappings
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.types.guardrails import GuardrailEventHooks
-from litellm.types.utils import CallTypes, ModelResponseStream
+from litellm.types.utils import CallTypes, CallTypesLiteral, ModelResponseStream
 
 GUARDRAIL_NAME = "unified_llm_guardrails"
 endpoint_guardrail_translation_mappings = None
@@ -43,18 +43,7 @@ class UnifiedLLMGuardrails(CustomLogger):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Union[Exception, str, dict, None]:
         """
         Runs before the LLM API call
diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py
index b5ae8437d86..c175cd54a50 100644
--- a/litellm/proxy/guardrails/guardrail_registry.py
+++ b/litellm/proxy/guardrails/guardrail_registry.py
@@ -2,12 +2,12 @@
 
 import importlib
 import os
-from litellm._uuid import uuid
 from datetime import datetime, timezone
-from typing import Dict, List, Optional, Type, cast
+from typing import Any, Dict, List, Optional, Type, cast
 
 import litellm
 from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
 from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.proxy.utils import PrismaClient
@@ -232,7 +232,13 @@ class GuardrailRegistry:
         """
         try:
             guardrail_name = guardrail.get("guardrail_name")
-            litellm_params: str = safe_dumps(dict(guardrail.get("litellm_params", {})))
+            # Properly serialize LitellmParams Pydantic model to dict
+            litellm_params_obj: Any = guardrail.get("litellm_params", {})
+            if hasattr(litellm_params_obj, 'model_dump'):
+                litellm_params_dict = litellm_params_obj.model_dump()
+            else:
+                litellm_params_dict = dict(litellm_params_obj) if litellm_params_obj else {}
+            litellm_params: str = safe_dumps(litellm_params_dict)
             guardrail_info: str = safe_dumps(guardrail.get("guardrail_info", {}))
 
             # Create guardrail in DB
@@ -278,7 +284,13 @@ class GuardrailRegistry:
         """
         try:
             guardrail_name = guardrail.get("guardrail_name")
-            litellm_params: str = safe_dumps(dict(guardrail.get("litellm_params", {})))
+            # Properly serialize LitellmParams Pydantic model to dict
+            litellm_params_obj: Any = guardrail.get("litellm_params", {})
+            if hasattr(litellm_params_obj, 'model_dump'):
+                litellm_params_dict = litellm_params_obj.model_dump()
+            else:
+                litellm_params_dict = dict(litellm_params_obj) if litellm_params_obj else {}
+            litellm_params: str = safe_dumps(litellm_params_dict)
             guardrail_info: str = safe_dumps(guardrail.get("guardrail_info", {}))
 
             # Update in DB
@@ -525,9 +537,19 @@ class InMemoryGuardrailHandler:
 
     def delete_in_memory_guardrail(self, guardrail_id: str) -> None:
         """
-        Delete a guardrail in memory
+        Delete a guardrail in memory and remove from litellm callbacks.
         """
+        # Remove from in-memory storage
         self.IN_MEMORY_GUARDRAILS.pop(guardrail_id, None)
+        
+        # Remove the callback from litellm.callbacks
+        custom_guardrail_callback = self.guardrail_id_to_custom_guardrail.pop(guardrail_id, None)
+        if custom_guardrail_callback:
+            litellm.logging_callback_manager.remove_callback_from_list_by_object(
+                callback_list=litellm.callbacks,
+                obj=custom_guardrail_callback,
+                require_self=False
+            )
 
     def list_in_memory_guardrails(self) -> List[Guardrail]:
         """
@@ -541,6 +563,102 @@ class InMemoryGuardrailHandler:
         """
         return self.IN_MEMORY_GUARDRAILS.get(guardrail_id)
 
+    def _has_guardrail_params_changed(
+        self, guardrail_id: str, new_guardrail: Guardrail
+    ) -> bool:
+        """
+        Check if guardrail params or name have changed compared to in-memory version.
+        Returns True if params/name changed or guardrail doesn't exist in memory.
+        """
+        existing = self.IN_MEMORY_GUARDRAILS.get(guardrail_id)
+        if existing is None:
+            return True
+        
+        # Compare guardrail_name
+        if existing.get("guardrail_name") != new_guardrail.get("guardrail_name"):
+            return True
+        
+        # Compare litellm_params
+        existing_params = existing.get("litellm_params")
+        new_params = new_guardrail.get("litellm_params")
+        
+        # Convert to dicts for comparison
+        existing_dict = (
+            existing_params.model_dump() 
+            if isinstance(existing_params, LitellmParams) 
+            else existing_params
+        )
+        new_dict = (
+            new_params.model_dump() 
+            if isinstance(new_params, LitellmParams) 
+            else new_params
+        )
+        
+        # Compare and identify specific differences
+        changed_fields = {}
+        if existing_dict is not None and new_dict is not None:
+            all_keys = set(existing_dict.keys()) | set(new_dict.keys())
+            for key in all_keys:
+                old_val = existing_dict.get(key)
+                new_val = new_dict.get(key)
+                if old_val != new_val:
+                    changed_fields[key] = {"old": old_val, "new": new_val}
+        elif existing_dict != new_dict:
+            changed_fields = {"litellm_params": {"old": existing_dict, "new": new_dict}}
+        
+        # Log differences if any found
+        if changed_fields:
+            verbose_proxy_logger.debug(
+                f"Guardrail params changed. Differences: {changed_fields}"
+            )
+        
+        # Return True if any fields changed
+        return len(changed_fields) > 0
+
+    def reinitialize_guardrail(
+        self, guardrail: Guardrail, config_file_path: Optional[str] = None
+    ) -> Optional[Guardrail]:
+        """
+        Force re-initialization of a guardrail even if it exists in memory.
+        Removes old callback from litellm.callbacks and creates fresh instance.
+        """
+        guardrail_id = guardrail.get("guardrail_id")
+        if not guardrail_id:
+            verbose_proxy_logger.error("Cannot reinitialize guardrail without guardrail_id")
+            return None
+        
+        # Remove from memory if exists (also removes from callbacks)
+        if guardrail_id in self.IN_MEMORY_GUARDRAILS:
+            self.delete_in_memory_guardrail(guardrail_id)
+        
+        # Initialize fresh (will add new callback to litellm.callbacks)
+        return self.initialize_guardrail(
+            guardrail=guardrail, config_file_path=config_file_path
+        )
+
+    def sync_guardrail_from_db(
+        self, guardrail: Guardrail, config_file_path: Optional[str] = None
+    ) -> Optional[Guardrail]:
+        """
+        Sync a guardrail from DB - initializes if new, re-initializes if changed.
+        This is the method to call during DB polling.
+        """
+        guardrail_id = guardrail.get("guardrail_id")
+        if not guardrail_id:
+            verbose_proxy_logger.error("Cannot sync guardrail without guardrail_id")
+            return None
+        
+        if self._has_guardrail_params_changed(guardrail_id, guardrail):
+            guardrail_name = guardrail.get("guardrail_name", "Unknown")
+            verbose_proxy_logger.info(
+                f"Guardrail '{guardrail_name}' (ID: {guardrail_id}) params changed, re-initializing..."
+            )
+            return self.reinitialize_guardrail(
+                guardrail=guardrail, config_file_path=config_file_path
+            )
+        
+        return self.IN_MEMORY_GUARDRAILS.get(guardrail_id)
+
 
 ########################################################
 # In Memory Guardrail Handler for LiteLLM Proxy
diff --git a/litellm/proxy/health_check.py b/litellm/proxy/health_check.py
index 2f4bb081819..427a16a9801 100644
--- a/litellm/proxy/health_check.py
+++ b/litellm/proxy/health_check.py
@@ -8,7 +8,7 @@ from typing import List, Optional
 import litellm
 
 logger = logging.getLogger(__name__)
-from litellm.constants import HEALTH_CHECK_TIMEOUT_SECONDS
+from litellm.constants import HEALTH_CHECK_TIMEOUT_SECONDS, DEFAULT_HEALTH_CHECK_PROMPT
 
 ILLEGAL_DISPLAY_PARAMS = [
     "messages",
@@ -99,7 +99,7 @@ async def _perform_health_check(model_list: list, details: Optional[bool] = True
             litellm.ahealth_check(
                 model["litellm_params"],
                 mode=mode,
-                prompt="test from litellm",
+                prompt=DEFAULT_HEALTH_CHECK_PROMPT,
                 input=["test from litellm"],
             ),
             timeout,
diff --git a/litellm/proxy/health_endpoints/_health_endpoints.py b/litellm/proxy/health_endpoints/_health_endpoints.py
index b42f800734a..1af6c9cf287 100644
--- a/litellm/proxy/health_endpoints/_health_endpoints.py
+++ b/litellm/proxy/health_endpoints/_health_endpoints.py
@@ -947,6 +947,7 @@ async def test_model_connection(
             "audio_speech",
             "audio_transcription",
             "image_generation",
+            "video_generation",
             "batch",
             "rerank",
             "realtime",
diff --git a/litellm/proxy/hooks/dynamic_rate_limiter.py b/litellm/proxy/hooks/dynamic_rate_limiter.py
index e7ddc9c36d8..f1c1d487cc1 100644
--- a/litellm/proxy/hooks/dynamic_rate_limiter.py
+++ b/litellm/proxy/hooks/dynamic_rate_limiter.py
@@ -4,7 +4,7 @@
 
 import asyncio
 import os
-from typing import List, Literal, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 from fastapi import HTTPException
 
@@ -15,6 +15,7 @@ from litellm.caching.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.types.router import ModelGroupInfo
+from litellm.types.utils import CallTypesLiteral
 from litellm.utils import get_utc_datetime
 
 from .rate_limiter_utils import convert_priority_to_percent
@@ -102,10 +103,10 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
         """
         try:
             # Get model info first for conversion
-            model_group_info: Optional[
-                ModelGroupInfo
-            ] = self.llm_router.get_model_group_info(model_group=model)
-            
+            model_group_info: Optional[ModelGroupInfo] = (
+                self.llm_router.get_model_group_info(model_group=model)
+            )
+
             weight: float = 1
             if (
                 litellm.priority_reservation is None
@@ -193,18 +194,7 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[
         Union[Exception, str, dict]
     ]:  # raise exception if invalid, return a str for the user to receive - if rejected, or return a modified dictionary for passing into litellm
@@ -287,16 +277,16 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
                 ) = await self.check_available_usage(
                     model=model_info["model_name"], priority=key_priority
                 )
-                response._hidden_params[
-                    "additional_headers"
-                ] = {  # Add additional response headers - easier debugging
-                    "x-litellm-model_group": model_info["model_name"],
-                    "x-ratelimit-remaining-litellm-project-tokens": available_tpm,
-                    "x-ratelimit-remaining-litellm-project-requests": available_rpm,
-                    "x-ratelimit-remaining-model-tokens": model_tpm,
-                    "x-ratelimit-remaining-model-requests": model_rpm,
-                    "x-ratelimit-current-active-projects": active_projects,
-                }
+                response._hidden_params["additional_headers"] = (
+                    {  # Add additional response headers - easier debugging
+                        "x-litellm-model_group": model_info["model_name"],
+                        "x-ratelimit-remaining-litellm-project-tokens": available_tpm,
+                        "x-ratelimit-remaining-litellm-project-requests": available_rpm,
+                        "x-ratelimit-remaining-model-tokens": model_tpm,
+                        "x-ratelimit-remaining-model-requests": model_rpm,
+                        "x-ratelimit-current-active-projects": active_projects,
+                    }
+                )
 
                 return response
             return await super().async_post_call_success_hook(
diff --git a/litellm/proxy/hooks/dynamic_rate_limiter_v3.py b/litellm/proxy/hooks/dynamic_rate_limiter_v3.py
index 5bf2c0aba61..7e6ec1dc151 100644
--- a/litellm/proxy/hooks/dynamic_rate_limiter_v3.py
+++ b/litellm/proxy/hooks/dynamic_rate_limiter_v3.py
@@ -4,7 +4,7 @@ Dynamic rate limiter v3 - Saturation-aware priority-based rate limiting
 
 import os
 from datetime import datetime
-from typing import Callable, Dict, List, Literal, Optional, Union
+from typing import Callable, Dict, List, Optional, Union
 
 from fastapi import HTTPException
 
@@ -22,19 +22,20 @@ from litellm.proxy.hooks.parallel_request_limiter_v3 import (
 from litellm.proxy.hooks.rate_limiter_utils import convert_priority_to_percent
 from litellm.proxy.utils import InternalUsageCache
 from litellm.types.router import ModelGroupInfo
+from litellm.types.utils import CallTypesLiteral
 
 
 class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     """
     Saturation-aware priority-based rate limiter using v3 infrastructure.
-    
+
     Key features:
     1. Model capacity ALWAYS enforced at 100% (prevents over-allocation)
     2. Priority usage tracked from first request (accurate accounting)
     3. Priority limits only enforced when saturated >= threshold
     4. Three-phase checking prevents partial counter increments
     5. Reuses v3 limiter's Redis-based tracking (multi-instance safe)
-    
+
     How it works:
     - Phase 1: Read-only check of ALL limits (no increments)
     - Phase 2: Decide enforcement based on saturation
@@ -43,6 +44,7 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     - When saturated: strict priority-based limits enforced (fair)
     - Uses v3 limiter's atomic Lua scripts for race-free increments
     """
+
     def __init__(
         self,
         internal_usage_cache: DualCache,
@@ -56,7 +58,9 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     def update_variables(self, llm_router: Router):
         self.llm_router = llm_router
 
-    def _get_priority_weight(self, priority: Optional[str], model_info: Optional[ModelGroupInfo] = None) -> float:
+    def _get_priority_weight(
+        self, priority: Optional[str], model_info: Optional[ModelGroupInfo] = None
+    ) -> float:
         """Get the weight for a given priority from litellm.priority_reservation"""
         weight: float = litellm.priority_reservation_settings.default_priority
         if (
@@ -76,30 +80,32 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                 weight = convert_priority_to_percent(value, model_info)
         return weight
 
-    def _normalize_priority_weights(self, model_info: ModelGroupInfo) -> Dict[str, float]:
+    def _normalize_priority_weights(
+        self, model_info: ModelGroupInfo
+    ) -> Dict[str, float]:
         """
         Normalize priority weights if they sum to > 1.0
-        
+
         Handles over-allocation: {key_a: 0.60, key_b: 0.80} -> {key_a: 0.43, key_b: 0.57}
         Converts absolute rpm/tpm values to percentages based on model capacity.
         """
         if litellm.priority_reservation is None:
             return {}
-        
+
         # Convert all values to percentages first
         weights: Dict[str, float] = {}
         for k, v in litellm.priority_reservation.items():
             weights[k] = convert_priority_to_percent(v, model_info)
-        
+
         total_weight = sum(weights.values())
-        
+
         if total_weight > 1.0:
             normalized = {k: v / total_weight for k, v in weights.items()}
             verbose_proxy_logger.debug(
                 f"Normalized over-allocated priorities: {weights} -> {normalized}"
             )
             return normalized
-        
+
         return weights
 
     def _get_priority_allocation(
@@ -111,29 +117,31 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     ) -> tuple[float, str]:
         """
         Get priority weight and pool key for a given priority.
-        
+
         For explicit priorities: returns specific allocation and unique pool key
         For default priority: returns default allocation and shared pool key
-        
+
         Args:
             model: Model name
             priority: Priority level (None for default)
             normalized_weights: Pre-computed normalized weights
             model_info: Model configuration (optional, for fallback conversion)
-            
+
         Returns:
             tuple: (priority_weight, priority_key)
         """
         # Check if this key has an explicit priority in litellm.priority_reservation
         has_explicit_priority = (
-            priority is not None 
-            and litellm.priority_reservation is not None 
+            priority is not None
+            and litellm.priority_reservation is not None
             and priority in litellm.priority_reservation
         )
-        
+
         if has_explicit_priority and priority is not None:
             # Explicit priority: get its specific allocation
-            priority_weight = normalized_weights.get(priority, self._get_priority_weight(priority, model_info))
+            priority_weight = normalized_weights.get(
+                priority, self._get_priority_weight(priority, model_info)
+            )
             # Use unique key per priority level
             priority_key = f"{model}:{priority}"
         else:
@@ -141,7 +149,7 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
             priority_weight = litellm.priority_reservation_settings.default_priority
             # Use shared key for all default-priority requests
             priority_key = f"{model}:default_pool"
-        
+
         return priority_weight, priority_key
 
     async def _check_model_saturation(
@@ -151,16 +159,16 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     ) -> float:
         """
         Check current saturation by directly querying v3 limiter's cache keys.
-        
+
         Reuses v3 limiter's Redis-based tracking (works across multiple instances).
         Reads counters WITHOUT incrementing them.
-        
+
         Returns:
             float: Saturation ratio (0.0 = empty, 1.0 = at capacity, >1.0 = over)
         """
         try:
             max_saturation = 0.0
-            
+
             # Query RPM saturation
             if model_group_info.rpm is not None and model_group_info.rpm > 0:
                 # Use v3 limiter's key format: {key:value}:rate_limit_type
@@ -169,24 +177,24 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                     value=model,
                     rate_limit_type="requests",
                 )
-                
+
                 # Query cache for current counter value
                 counter_value = await self.internal_usage_cache.async_get_cache(
                     key=counter_key,
                     litellm_parent_otel_span=None,
                     local_only=False,  # Check Redis too
                 )
-                
+
                 if counter_value is not None:
                     current_requests = int(counter_value)
                     rpm_saturation = current_requests / model_group_info.rpm
                     max_saturation = max(max_saturation, rpm_saturation)
-                    
+
                     verbose_proxy_logger.debug(
                         f"Model {model} RPM: {current_requests}/{model_group_info.rpm} "
                         f"({rpm_saturation:.1%})"
                     )
-            
+
             # Query TPM saturation
             if model_group_info.tpm is not None and model_group_info.tpm > 0:
                 counter_key = self.v3_limiter.create_rate_limit_keys(
@@ -194,29 +202,29 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                     value=model,
                     rate_limit_type="tokens",
                 )
-                
+
                 counter_value = await self.internal_usage_cache.async_get_cache(
                     key=counter_key,
                     litellm_parent_otel_span=None,
                     local_only=False,
                 )
-                
+
                 if counter_value is not None:
                     current_tokens = float(counter_value)
                     tpm_saturation = current_tokens / model_group_info.tpm
                     max_saturation = max(max_saturation, tpm_saturation)
-                    
+
                     verbose_proxy_logger.debug(
                         f"Model {model} TPM: {current_tokens}/{model_group_info.tpm} "
                         f"({tpm_saturation:.1%})"
                     )
-            
+
             verbose_proxy_logger.debug(
                 f"Model {model} overall saturation: {max_saturation:.1%}"
             )
-            
+
             return max_saturation
-            
+
         except Exception as e:
             verbose_proxy_logger.error(
                 f"Error checking saturation for {model}: {str(e)}"
@@ -232,17 +240,17 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     ) -> List[RateLimitDescriptor]:
         """
         Create rate limit descriptors with normalized priority weights.
-        
+
         Uses normalized weights to handle over-allocation scenarios.
-        
+
         For explicit priorities: each priority gets its own pool (e.g., prod gets 75%)
         For default priority: ALL keys without explicit priority share ONE pool (e.g., all share 25%)
         """
         descriptors: List[RateLimitDescriptor] = []
-        
+
         # Get model group info
-        model_group_info: Optional[ModelGroupInfo] = self.llm_router.get_model_group_info(
-            model_group=model
+        model_group_info: Optional[ModelGroupInfo] = (
+            self.llm_router.get_model_group_info(model_group=model)
         )
         if model_group_info is None:
             return descriptors
@@ -255,21 +263,21 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
             normalized_weights=normalized_weights,
             model_info=model_group_info,
         )
-        
+
         rate_limit_config: RateLimitDescriptorRateLimitObject = {}
-        
+
         # Apply priority weight to model limits
         if model_group_info.tpm is not None:
             reserved_tpm = int(model_group_info.tpm * priority_weight)
             rate_limit_config["tokens_per_unit"] = reserved_tpm
-            
+
         if model_group_info.rpm is not None:
             reserved_rpm = int(model_group_info.rpm * priority_weight)
             rate_limit_config["requests_per_unit"] = reserved_rpm
 
         if rate_limit_config:
             rate_limit_config["window_size"] = self.v3_limiter.window_size
-            
+
             descriptors.append(
                 RateLimitDescriptor(
                     key="priority_model",
@@ -288,12 +296,12 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     ) -> RateLimitDescriptor:
         """
         Create a descriptor for tracking model-wide usage.
-        
+
         Args:
             model: Model name
             model_group_info: Model configuration with RPM/TPM limits
             high_limit_multiplier: Multiplier for limits (use >1 for tracking-only)
-            
+
         Returns:
             Rate limit descriptor for model-wide tracking
         """
@@ -302,18 +310,19 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
             value=model,
             rate_limit={
                 "requests_per_unit": (
-                    model_group_info.rpm * high_limit_multiplier 
-                    if model_group_info.rpm else None
+                    model_group_info.rpm * high_limit_multiplier
+                    if model_group_info.rpm
+                    else None
                 ),
                 "tokens_per_unit": (
-                    model_group_info.tpm * high_limit_multiplier 
-                    if model_group_info.tpm else None
+                    model_group_info.tpm * high_limit_multiplier
+                    if model_group_info.tpm
+                    else None
                 ),
                 "window_size": self.v3_limiter.window_size,
             },
         )
 
-
     async def _check_rate_limits(
         self,
         model: str,
@@ -325,23 +334,23 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
     ) -> None:
         """
         Check rate limits using THREE-PHASE approach to prevent partial increments.
-        
+
         Phase 1: Read-only check of ALL limits (no increments)
         Phase 2: Decide which limits to enforce based on saturation
         Phase 3: Increment ALL counters atomically (model + priority)
-        
+
         This prevents the bug where:
         - Model counter increments in stage 1
         - Priority check fails in stage 2
         - Request blocked but model counter already incremented
-        
+
         Key behaviors:
         - All checks performed first (read-only)
         - Only increment counters if request will be allowed
         - Model capacity: Always enforced at 100%
         - Priority limits: Only enforced when saturated >= threshold
         - Both counters tracked from first request (accurate accounting)
-        
+
         Args:
             model: Model name
             model_group_info: Model configuration
@@ -349,17 +358,20 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
             key_priority: User's priority level
             saturation: Current saturation level
             data: Request data dictionary
-            
+
         Raises:
             HTTPException: If any limit is exceeded
         """
         import json
-        saturation_threshold = litellm.priority_reservation_settings.saturation_threshold
+
+        saturation_threshold = (
+            litellm.priority_reservation_settings.saturation_threshold
+        )
         should_enforce_priority = saturation >= saturation_threshold
-        
+
         # Build ALL descriptors upfront
         descriptors_to_check: List[RateLimitDescriptor] = []
-        
+
         # Model-wide descriptor (always enforce)
         model_wide_descriptor = self._create_model_tracking_descriptor(
             model=model,
@@ -367,7 +379,7 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
             high_limit_multiplier=1,
         )
         descriptors_to_check.append(model_wide_descriptor)
-        
+
         # Priority descriptors (always track, conditionally enforce)
         priority_descriptors = self._create_priority_based_descriptors(
             model=model,
@@ -376,31 +388,33 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
         )
         if priority_descriptors:
             descriptors_to_check.extend(priority_descriptors)
-        
+
         # PHASE 1: Read-only check of ALL limits (no increments)
         check_response = await self.v3_limiter.should_rate_limit(
             descriptors=descriptors_to_check,
             parent_otel_span=user_api_key_dict.parent_otel_span,
             read_only=True,  # CRITICAL: Don't increment counters yet
         )
-        
-        verbose_proxy_logger.debug(f"Read-only check: {json.dumps(check_response, indent=2)}")
-        
+
+        verbose_proxy_logger.debug(
+            f"Read-only check: {json.dumps(check_response, indent=2)}"
+        )
+
         # PHASE 2: Decide which limits to enforce
         if check_response["overall_code"] == "OVER_LIMIT":
             for status in check_response["statuses"]:
                 if status["code"] == "OVER_LIMIT":
                     descriptor_key = status["descriptor_key"]
-                    
+
                     # Model-wide limit exceeded (ALWAYS enforce)
                     if descriptor_key == "model_saturation_check":
                         raise HTTPException(
                             status_code=429,
                             detail={
                                 "error": f"Model capacity reached for {model}. "
-                                        f"Priority: {key_priority}, "
-                                        f"Rate limit type: {status['rate_limit_type']}, "
-                                        f"Remaining: {status['limit_remaining']}"
+                                f"Priority: {key_priority}, "
+                                f"Rate limit type: {status['rate_limit_type']}, "
+                                f"Remaining: {status['limit_remaining']}"
                             },
                             headers={
                                 "retry-after": str(self.v3_limiter.window_size),
@@ -408,7 +422,7 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                                 "x-litellm-priority": key_priority or "default",
                             },
                         )
-                    
+
                     # Priority limit exceeded (ONLY enforce when saturated)
                     elif descriptor_key == "priority_model" and should_enforce_priority:
                         verbose_proxy_logger.debug(
@@ -419,10 +433,10 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                             status_code=429,
                             detail={
                                 "error": f"Priority-based rate limit exceeded. "
-                                        f"Priority: {key_priority}, "
-                                        f"Rate limit type: {status['rate_limit_type']}, "
-                                        f"Remaining: {status['limit_remaining']}, "
-                                        f"Model saturation: {saturation:.1%}"
+                                f"Priority: {key_priority}, "
+                                f"Rate limit type: {status['rate_limit_type']}, "
+                                f"Remaining: {status['limit_remaining']}, "
+                                f"Model saturation: {saturation:.1%}"
                             },
                             headers={
                                 "retry-after": str(self.v3_limiter.window_size),
@@ -431,12 +445,12 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                                 "x-litellm-saturation": f"{saturation:.2%}",
                             },
                         )
-        
+
         # PHASE 3: Increment counters separately to avoid early-exit issues
         # Model counter must ALWAYS increment, but priority counter might be over limit
         # If we increment them together, v3_limiter's in-memory check will exit early
         # and skip incrementing the model counter
-        
+
         # Step 3a: Increment model-wide counter (always)
         model_increment_response = await self.v3_limiter.should_rate_limit(
             descriptors=[model_wide_descriptor],
@@ -451,11 +465,12 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                 parent_otel_span=user_api_key_dict.parent_otel_span,
                 read_only=False,
             )
-            
+
             # Combine responses for post-call hook
             combined_response = {
                 "overall_code": model_increment_response["overall_code"],
-                "statuses": model_increment_response["statuses"] + priority_increment_response["statuses"]
+                "statuses": model_increment_response["statuses"]
+                + priority_increment_response["statuses"],
             }
             data["litellm_proxy_rate_limit_response"] = combined_response
         else:
@@ -466,50 +481,39 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
         user_api_key_dict: UserAPIKeyAuth,
         cache: DualCache,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion", 
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, dict]]:
         """
         Saturation-aware pre-call hook for priority-based rate limiting.
-        
+
         Flow:
         1. Check current saturation level
         2. THREE-PHASE rate limit check:
            - PHASE 1: Read-only check of ALL limits (no increments)
            - PHASE 2: Decide which limits to enforce based on saturation
            - PHASE 3: Increment ALL counters atomically if request allowed
-        
+
         This three-phase approach ensures:
         - Model capacity is NEVER exceeded (always enforced at 100%)
         - Priority usage tracked from first request (accurate metrics)
         - Counters only increment when request will be allowed (prevents phantom usage)
         - When under-saturated: priorities can borrow unused capacity (generous)
         - When saturated: fair allocation based on normalized priority weights (strict)
-        
+
         Example with 100 RPM model, 60% priority allocation, 80% threshold:
         - Saturation < 80%: Priority can use up to 100 RPM (model limit enforced only)
         - Saturation >= 80%: Priority limited to 60 RPM (both limits enforced)
-        
+
         Prevents bugs where:
         - Model counter increments but priority check fails → model over-capacity
         - Priority counter increments but not enforced → inaccurate metrics
-        
+
         Args:
             user_api_key_dict: User authentication and metadata
             cache: Dual cache instance
             data: Request data containing model name
             call_type: Type of API call being made
-            
+
         Returns:
             None if request is allowed, otherwise raises HTTPException
         """
@@ -518,26 +522,29 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
 
         model = data["model"]
         key_priority: Optional[str] = user_api_key_dict.metadata.get("priority", None)
-        
+
         # Get model configuration
-        model_group_info: Optional[ModelGroupInfo] = self.llm_router.get_model_group_info(
-            model_group=model
+        model_group_info: Optional[ModelGroupInfo] = (
+            self.llm_router.get_model_group_info(model_group=model)
         )
         if model_group_info is None:
-            verbose_proxy_logger.debug(f"No model group info for {model}, allowing request")
+            verbose_proxy_logger.debug(
+                f"No model group info for {model}, allowing request"
+            )
             return None
 
         try:
             # STEP 1: Check current saturation level
             saturation = await self._check_model_saturation(model, model_group_info)
-            
-            saturation_threshold = litellm.priority_reservation_settings.saturation_threshold
-            
+
+            saturation_threshold = (
+                litellm.priority_reservation_settings.saturation_threshold
+            )
+
             verbose_proxy_logger.debug(
                 f"[Dynamic Rate Limiter] Model={model}, Saturation={saturation:.1%}, "
                 f"Threshold={saturation_threshold:.1%}, Priority={key_priority}"
             )
-            
 
             # STEP 2: Check rate limits in THREE phases
             # Phase 1: Read-only check of ALL limits (no increments)
@@ -552,7 +559,7 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
                 saturation=saturation,
                 data=data,
             )
-                
+
         except HTTPException:
             raise
         except Exception as e:
@@ -579,15 +586,22 @@ class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
 
             # Add additional priority-specific headers
             if isinstance(response, ModelResponse):
-                key_priority: Optional[str] = user_api_key_dict.metadata.get("priority", None)
-                
+                key_priority: Optional[str] = user_api_key_dict.metadata.get(
+                    "priority", None
+                )
+
                 # Get existing additional headers
-                additional_headers = getattr(response, "_hidden_params", {}).get("additional_headers", {}) or {}
-                
+                additional_headers = (
+                    getattr(response, "_hidden_params", {}).get(
+                        "additional_headers", {}
+                    )
+                    or {}
+                )
+
                 # Add priority information
                 additional_headers["x-litellm-priority"] = key_priority or "default"
                 additional_headers["x-litellm-rate-limiter-version"] = "v3"
-                
+
                 # Update response
                 if not hasattr(response, "_hidden_params"):
                     response._hidden_params = {}
diff --git a/litellm/proxy/hooks/responses_id_security.py b/litellm/proxy/hooks/responses_id_security.py
index 15b2c1f4f2e..2718fcf4da9 100644
--- a/litellm/proxy/hooks/responses_id_security.py
+++ b/litellm/proxy/hooks/responses_id_security.py
@@ -5,16 +5,7 @@ This hook uses the DBSpendUpdateWriter to batch-write response IDs to the databa
 instead of writing immediately on each request.
 """
 
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    AsyncGenerator,
-    Literal,
-    Optional,
-    Tuple,
-    Union,
-    cast,
-)
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional, Tuple, Union, cast
 
 from fastapi import HTTPException
 
@@ -29,7 +20,7 @@ from litellm.types.llms.openai import (
     BaseLiteLLMOpenAIResponseObject,
     ResponsesAPIResponse,
 )
-from litellm.types.utils import LLMResponseTypes, SpecialEnums
+from litellm.types.utils import CallTypesLiteral, LLMResponseTypes, SpecialEnums
 
 if TYPE_CHECKING:
     from litellm.caching.caching import DualCache
@@ -45,18 +36,7 @@ class ResponsesIDSecurity(CustomLogger):
         user_api_key_dict: "UserAPIKeyAuth",
         cache: "DualCache",
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[Union[Exception, str, dict]]:
         # MAP all the responses api response ids to the encrypted response ids
         responses_api_call_types = {
@@ -136,7 +116,7 @@ class ResponsesIDSecurity(CustomLogger):
         split_result = response_id.split("resp_")
         if len(split_result) < 2:
             return False
-        
+
         remaining_string = split_result[1]
         decrypted_value = decrypt_value_helper(
             value=remaining_string, key="response_id", return_original_value=True
@@ -161,7 +141,7 @@ class ResponsesIDSecurity(CustomLogger):
         split_result = response_id.split("resp_")
         if len(split_result) < 2:
             return response_id, None, None
-        
+
         remaining_string = split_result[1]
         decrypted_value = decrypt_value_helper(
             value=remaining_string, key="response_id", return_original_value=True
diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py
index 472cb81f93d..a8def13869f 100644
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@@ -24,8 +24,15 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid
 from litellm.caching import DualCache
-from litellm.constants import LENGTH_OF_LITELLM_GENERATED_KEY, UI_SESSION_TOKEN_TEAM_ID
+from litellm.constants import (
+    LENGTH_OF_LITELLM_GENERATED_KEY,
+    LITELLM_PROXY_ADMIN_NAME,
+    UI_SESSION_TOKEN_TEAM_ID,
+)
 from litellm.litellm_core_utils.duration_parser import duration_in_seconds
+from litellm.proxy._experimental.mcp_server.db import (
+    rotate_mcp_server_credentials_master_key,
+)
 from litellm.proxy._types import *
 from litellm.proxy._types import LiteLLM_VerificationToken
 from litellm.proxy.auth.auth_checks import (
@@ -640,9 +647,9 @@ async def _common_key_generation_helper(  # noqa: PLR0915
         request_type="key", **data_json, table_name="key"
     )
 
-    response["soft_budget"] = (
-        data.soft_budget
-    )  # include the user-input soft budget in the response
+    response[
+        "soft_budget"
+    ] = data.soft_budget  # include the user-input soft budget in the response
 
     response = GenerateKeyResponse(**response)
 
@@ -1299,7 +1306,6 @@ async def prepare_key_update_data(
     data: Union[UpdateKeyRequest, RegenerateKeyRequest],
     existing_key_row: LiteLLM_VerificationToken,
 ):
-
     data_json: dict = data.model_dump(exclude_unset=True)
     data_json.pop("key", None)
     data_json.pop("new_key", None)
@@ -2357,10 +2363,10 @@ async def delete_verification_tokens(
     try:
         if prisma_client:
             tokens = [_hash_token_if_needed(token=key) for key in tokens]
-            _keys_being_deleted: List[LiteLLM_VerificationToken] = (
-                await prisma_client.db.litellm_verificationtoken.find_many(
-                    where={"token": {"in": tokens}}
-                )
+            _keys_being_deleted: List[
+                LiteLLM_VerificationToken
+            ] = await prisma_client.db.litellm_verificationtoken.find_many(
+                where={"token": {"in": tokens}}
             )
 
             if len(_keys_being_deleted) == 0:
@@ -2468,9 +2474,9 @@ async def _rotate_master_key(
     from litellm.proxy.proxy_server import proxy_config
 
     try:
-        models: Optional[List] = (
-            await prisma_client.db.litellm_proxymodeltable.find_many()
-        )
+        models: Optional[
+            List
+        ] = await prisma_client.db.litellm_proxymodeltable.find_many()
     except Exception:
         models = None
     # 2. process model table
@@ -2524,6 +2530,13 @@ async def _rotate_master_key(
                     data={"param_value": jsonify_object(encrypted_env_vars)},
                 )
 
+    # 4. process MCP server table
+    await rotate_mcp_server_credentials_master_key(
+        prisma_client=prisma_client,
+        touched_by=user_api_key_dict.user_id or LITELLM_PROXY_ADMIN_NAME,
+        new_master_key=new_master_key,
+    )
+
 
 def get_new_token(data: Optional[RegenerateKeyRequest]) -> str:
     if data and data.new_key is not None:
@@ -2781,11 +2794,11 @@ async def validate_key_list_check(
             param="user_id",
             code=status.HTTP_403_FORBIDDEN,
         )
-    complete_user_info_db_obj: Optional[BaseModel] = (
-        await prisma_client.db.litellm_usertable.find_unique(
-            where={"user_id": user_api_key_dict.user_id},
-            include={"organization_memberships": True},
-        )
+    complete_user_info_db_obj: Optional[
+        BaseModel
+    ] = await prisma_client.db.litellm_usertable.find_unique(
+        where={"user_id": user_api_key_dict.user_id},
+        include={"organization_memberships": True},
     )
 
     if complete_user_info_db_obj is None:
@@ -2871,10 +2884,10 @@ async def get_admin_team_ids(
     if complete_user_info is None:
         return []
     # Get all teams that user is an admin of
-    teams: Optional[List[BaseModel]] = (
-        await prisma_client.db.litellm_teamtable.find_many(
-            where={"team_id": {"in": complete_user_info.teams}}
-        )
+    teams: Optional[
+        List[BaseModel]
+    ] = await prisma_client.db.litellm_teamtable.find_many(
+        where={"team_id": {"in": complete_user_info.teams}}
     )
     if teams is None:
         return []
diff --git a/litellm/proxy/management_endpoints/mcp_management_endpoints.py b/litellm/proxy/management_endpoints/mcp_management_endpoints.py
index b0c09b1e2e8..22874ca8f13 100644
--- a/litellm/proxy/management_endpoints/mcp_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/mcp_management_endpoints.py
@@ -58,6 +58,24 @@ if MCP_AVAILABLE:
     from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view
     from litellm.proxy.management_helpers.utils import management_endpoint_wrapper
 
+    def _redact_mcp_credentials(
+        mcp_server: LiteLLM_MCPServerTable,
+    ) -> LiteLLM_MCPServerTable:
+        """Return a copy of the MCP server object with credentials removed."""
+
+        try:
+            redacted_server = mcp_server.model_copy(deep=True)
+        except AttributeError:
+            redacted_server = mcp_server.copy(deep=True)  # type: ignore[attr-defined]
+
+        redacted_server.credentials = None
+        return redacted_server
+
+    def _redact_mcp_credentials_list(
+        mcp_servers: Iterable[LiteLLM_MCPServerTable],
+    ) -> List[LiteLLM_MCPServerTable]:
+        return [_redact_mcp_credentials(server) for server in mcp_servers]
+
     def get_prisma_client_or_throw(message: str):
         from litellm.proxy.proxy_server import prisma_client
 
@@ -273,11 +291,12 @@ if MCP_AVAILABLE:
         ```
         """
         # Use server manager to get all servers with health and team data
-        return (
+        mcp_servers = (
             await global_mcp_server_manager.get_all_mcp_servers_with_health_and_teams(
                 user_api_key_auth=user_api_key_dict
             )
         )
+        return _redact_mcp_credentials_list(mcp_servers)
 
     @router.get(
         "/server/{server_id}",
@@ -335,7 +354,7 @@ if MCP_AVAILABLE:
 
         # Implement authz restriction from requested user
         if _user_has_admin_view(user_api_key_dict):
-            return mcp_server
+            return _redact_mcp_credentials(mcp_server)
 
         # Perform authz check to filter the mcp servers user has access to
         mcp_server_records = await get_all_mcp_servers_for_user(
@@ -345,7 +364,7 @@ if MCP_AVAILABLE:
 
         if exists:
             global_mcp_server_manager.add_update_server(mcp_server)
-            return mcp_server
+            return _redact_mcp_credentials(mcp_server)
         else:
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,
@@ -428,7 +447,7 @@ if MCP_AVAILABLE:
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
                 detail={"error": f"Error creating mcp server: {str(e)}"},
             )
-        return new_mcp_server
+        return _redact_mcp_credentials(new_mcp_server)
 
     @router.delete(
         "/server/{server_id}",
@@ -563,4 +582,4 @@ if MCP_AVAILABLE:
         if litellm.store_audit_logs:
             pass
 
-        return mcp_server_record_updated
+        return _redact_mcp_credentials(mcp_server_record_updated)
diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
index 89a93247ea8..2f101b13d83 100644
--- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
@@ -942,10 +942,14 @@ async def _parse_request_data_by_content_type(
 
     if "application/json" in content_type:
         # ✅ Handle JSON
-        body = await request.json()
-        query_params_data = body.get("query_params")
-        custom_body_data = body.get("custom_body")
-        stream = body.get("stream")
+        try:
+            body = await request.json()
+            query_params_data = body.get("query_params")
+            custom_body_data = body.get("custom_body")
+            stream = body.get("stream")
+        except json.JSONDecodeError:
+            # Handle requests with no body (e.g., DELETE requests)
+            pass
     elif "multipart/form-data" in content_type:
         # ✅ Handle multipart form-data
         form = await request.form()
@@ -1889,6 +1893,11 @@ class InitPassThroughEndpointHelpers:
         """Clear all pass-through routes from the registry"""
         _registered_pass_through_routes.clear()
 
+    @staticmethod
+    def get_registered_pass_through_endpoints_keys() -> List[str]:
+        """Get all registered pass-through endpoints from the registry"""
+        return list(_registered_pass_through_routes.keys())
+
     @staticmethod
     def is_registered_pass_through_route(route: str) -> bool:
         """
@@ -1991,7 +2000,16 @@ async def initialize_pass_through_endpoints(
         combined_pass_through_endpoints = pass_through_endpoints  # type: ignore
 
     ## clear all existing pass-through endpoints from the FastAPI app routes
-    InitPassThroughEndpointHelpers.clear_all_pass_through_routes()
+    # InitPassThroughEndpointHelpers.clear_all_pass_through_routes()
+
+    # get a list of all registered pass-through endpoints
+    # mark the ones that are visited in the list
+    # remove the ones that are not visited from the list
+    registered_pass_through_endpoints = (
+        InitPassThroughEndpointHelpers.get_registered_pass_through_endpoints_keys()
+    )
+
+    visited_endpoints = set()
 
     for endpoint in combined_pass_through_endpoints:
         if isinstance(endpoint, PassThroughGenericEndpoint):
@@ -2045,6 +2063,8 @@ async def initialize_pass_through_endpoints(
             endpoint_id=endpoint_id,
         )
 
+        visited_endpoints.add(f"{endpoint_id}:exact:{_path}")
+
         # Add wildcard route for sub-paths
         if endpoint.get("include_subpath", False) is True:
             InitPassThroughEndpointHelpers.add_subpath_route(
@@ -2059,10 +2079,17 @@ async def initialize_pass_through_endpoints(
                 endpoint_id=endpoint_id,
             )
 
+            visited_endpoints.add(f"{endpoint_id}:subpath:{_path}")
+
         verbose_proxy_logger.debug(
             "Added new pass through endpoint: %s (ID: %s)", _path, endpoint_id
         )
 
+    # remove the ones that are not visited from the list
+    for endpoint_key in registered_pass_through_endpoints:
+        if endpoint_key not in visited_endpoints:
+            InitPassThroughEndpointHelpers.remove_endpoint_routes(endpoint_key)
+
 
 async def _get_pass_through_endpoints_from_db(
     endpoint_id: Optional[str] = None,
diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml
index 089de860e6d..040db4aa426 100644
--- a/litellm/proxy/proxy_config.yaml
+++ b/litellm/proxy/proxy_config.yaml
@@ -1,10 +1,3 @@
-general_settings:
-  key_management_system: "cyberark"
-  key_management_settings: 
-    store_virtual_keys: true
-    prefix_for_stored_virtual_keys: "litellm/"
-    access_mode: "read_and_write"
-
 model_list:
   - model_name: aws/anthropic/bedrock-claude-3-5-sonnet-v1
     litellm_params:
@@ -52,4 +45,8 @@ litellm_settings:
   cache: True
   cache_params:
     type: local
-  drop_params: True
\ No newline at end of file
+  drop_params: True
+
+
+general_settings:
+  store_prompts_in_spend_logs: True
\ No newline at end of file
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 4acd2639d8f..090757da4ad 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -271,9 +271,7 @@ from litellm.proxy.management_endpoints.customer_endpoints import (
 from litellm.proxy.management_endpoints.internal_user_endpoints import (
     router as internal_user_router,
 )
-from litellm.proxy.management_endpoints.internal_user_endpoints import (
-    user_update,
-)
+from litellm.proxy.management_endpoints.internal_user_endpoints import user_update
 from litellm.proxy.management_endpoints.key_management_endpoints import (
     delete_verification_tokens,
     duration_in_seconds,
@@ -324,9 +322,7 @@ from litellm.proxy.ocr_endpoints.endpoints import router as ocr_router
 from litellm.proxy.openai_files_endpoints.files_endpoints import (
     router as openai_files_router,
 )
-from litellm.proxy.openai_files_endpoints.files_endpoints import (
-    set_files_config,
-)
+from litellm.proxy.openai_files_endpoints.files_endpoints import set_files_config
 from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
     passthrough_endpoint_router,
 )
@@ -413,9 +409,7 @@ from litellm.types.proxy.management_endpoints.ui_sso import (
     LiteLLM_UpperboundKeyGenerateParams,
 )
 from litellm.types.realtime import RealtimeQueryParams
-from litellm.types.router import (
-    DeploymentTypedDict,
-)
+from litellm.types.router import DeploymentTypedDict
 from litellm.types.router import ModelInfo as RouterModelInfo
 from litellm.types.router import (
     RouterGeneralSettings,
@@ -878,18 +872,12 @@ class UserAPIKeyCacheTTLEnum(enum.Enum):
 async def openai_exception_handler(request: Request, exc: ProxyException):
     # NOTE: DO NOT MODIFY THIS, its crucial to map to Openai exceptions
     headers = exc.headers
+    error_dict = exc.to_dict()
     return JSONResponse(
         status_code=(
             int(exc.code) if exc.code else status.HTTP_500_INTERNAL_SERVER_ERROR
         ),
-        content={
-            "error": {
-                "message": exc.message,
-                "type": exc.type,
-                "param": exc.param,
-                "code": exc.code,
-            }
-        },
+        content={"error": error_dict},
         headers=headers,
     )
 
@@ -908,15 +896,23 @@ try:
     # Support both "true" and "True" for case-insensitive comparison
     if os.getenv("LITELLM_NON_ROOT", "").lower() == "true":
         non_root_ui_path = "/tmp/litellm_ui"
-        
+
         # Check if the UI was built and exists at the expected location
         if os.path.exists(non_root_ui_path) and os.listdir(non_root_ui_path):
-            verbose_proxy_logger.info(f"Using pre-built UI for non-root Docker: {non_root_ui_path}")
-            verbose_proxy_logger.info(f"UI files found: {len(os.listdir(non_root_ui_path))} items")
+            verbose_proxy_logger.info(
+                f"Using pre-built UI for non-root Docker: {non_root_ui_path}"
+            )
+            verbose_proxy_logger.info(
+                f"UI files found: {len(os.listdir(non_root_ui_path))} items"
+            )
             ui_path = non_root_ui_path
         else:
-            verbose_proxy_logger.error(f"UI not found at {non_root_ui_path}. UI will not be available.")
-            verbose_proxy_logger.error(f"Path exists: {os.path.exists(non_root_ui_path)}, Has content: {os.path.exists(non_root_ui_path) and bool(os.listdir(non_root_ui_path))}")
+            verbose_proxy_logger.error(
+                f"UI not found at {non_root_ui_path}. UI will not be available."
+            )
+            verbose_proxy_logger.error(
+                f"Path exists: {os.path.exists(non_root_ui_path)}, Has content: {os.path.exists(non_root_ui_path) and bool(os.listdir(non_root_ui_path))}"
+            )
 
     # Only modify files if a custom server root path is set
     if server_root_path and server_root_path != "/":
@@ -992,7 +988,9 @@ try:
                 dst = os.path.join(folder_path, "index.html")
                 os.rename(src, dst)
     else:
-        verbose_proxy_logger.info("Skipping runtime HTML restructuring for non-root Docker (already done at build time)")
+        verbose_proxy_logger.info(
+            "Skipping runtime HTML restructuring for non-root Docker (already done at build time)"
+        )
 
 except Exception:
     pass
@@ -1853,8 +1851,12 @@ class ProxyConfig:
         """
         global llm_router
         import litellm
-        
-        if llm_router is not None and litellm.cache is not None and llm_router.cache_responses is not True:
+
+        if (
+            llm_router is not None
+            and litellm.cache is not None
+            and llm_router.cache_responses is not True
+        ):
             llm_router.cache_responses = True
             verbose_proxy_logger.debug(
                 "Set router.cache_responses=True after initializing cache"
@@ -2309,12 +2311,12 @@ class ProxyConfig:
                 litellm._key_management_settings = KeyManagementSettings(
                     **key_management_settings
                 )
-            
+
             ### LOAD SECRET MANAGER ###
             key_management_system = general_settings.get("key_management_system", None)
             self.initialize_secret_manager(
                 key_management_system=key_management_system,
-                config_file_path=config_file_path
+                config_file_path=config_file_path,
             )
             ### [DEPRECATED] LOAD FROM GOOGLE KMS ### old way of loading from google kms
             use_google_kms = general_settings.get("use_google_kms", False)
@@ -2648,7 +2650,9 @@ class ProxyConfig:
         pass
 
     def initialize_secret_manager(
-        self, key_management_system: Optional[str], config_file_path: Optional[str] = None
+        self,
+        key_management_system: Optional[str],
+        config_file_path: Optional[str] = None,
     ):
         """
         Initialize the relevant secret manager if `key_management_system` is provided
@@ -3030,14 +3034,16 @@ class ProxyConfig:
                     "Error setting env variable: %s - %s", k, str(e)
                 )
         return decrypted_env_vars
-    
+
     def _decrypt_db_variables(self, variables_dict: dict) -> dict:
         """
         Decrypts a dictionary of variables and returns them.
         """
         decrypted_variables = {}
         for k, v in variables_dict.items():
-            decrypted_value = decrypt_value_helper(value=v, key=k, return_original_value=True)
+            decrypted_value = decrypt_value_helper(
+                value=v, key=k, return_original_value=True
+            )
             decrypted_variables[k] = decrypted_value
         return decrypted_variables
 
@@ -3435,6 +3441,7 @@ class ProxyConfig:
             from litellm.proxy.management_endpoints.cache_settings_endpoints import (
                 CacheSettingsManager,
             )
+
             await CacheSettingsManager.init_cache_settings_in_db(
                 prisma_client=prisma_client, proxy_config=self
             )
@@ -3596,7 +3603,7 @@ class ProxyConfig:
                 "guardrails from the DB %s", str(guardrails_in_db)
             )
             for guardrail in guardrails_in_db:
-                IN_MEMORY_GUARDRAIL_HANDLER.initialize_guardrail(
+                IN_MEMORY_GUARDRAIL_HANDLER.sync_guardrail_from_db(
                     guardrail=cast(Guardrail, guardrail),
                 )
         except Exception as e:
@@ -3690,7 +3697,7 @@ class ProxyConfig:
         Initialize search tools from database into the router on startup.
         """
         global llm_router
-        
+
         from litellm.proxy.search_endpoints.search_tool_registry import (
             SearchToolRegistry,
         )
@@ -4210,7 +4217,7 @@ class ProxyStartupEvent:
         # Key fixes:
         # 1. Remove/minimize jitter to avoid normalize() memory explosion
         # 2. Use larger misfire_grace_time to prevent backlog calculations
-        # 3. Set replace_existing=True to avoid duplicate jobs (must be passed per-job, not as default)
+        # 3. Set replace_existing=True to avoid duplicate jobs
         from apscheduler.executors.asyncio import AsyncIOExecutor
         from apscheduler.jobstores.memory import MemoryJobStore
 
@@ -5082,7 +5089,9 @@ async def embeddings(  # noqa: PLR0915
 
         ### CALL HOOKS ### - modify incoming data / reject request before calling the model
         data = await proxy_logging_obj.pre_call_hook(
-            user_api_key_dict=user_api_key_dict, data=data, call_type="embeddings"
+            user_api_key_dict=user_api_key_dict,
+            data=data,
+            call_type=CallTypes.aembedding.value,
         )
 
         tasks = []
@@ -5090,7 +5099,7 @@ async def embeddings(  # noqa: PLR0915
             proxy_logging_obj.during_call_hook(
                 data=data,
                 user_api_key_dict=user_api_key_dict,
-                call_type="embeddings",
+                call_type="aembedding",
             )
         )
 
@@ -5495,7 +5504,7 @@ async def audio_transcriptions(
             data = await proxy_logging_obj.pre_call_hook(
                 user_api_key_dict=user_api_key_dict,
                 data=data,
-                call_type="audio_transcription",
+                call_type="transcription",
             )
 
             ## ROUTE TO CORRECT ENDPOINT ##
diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma
index 760954a1d60..025a1a0e3ce 100644
--- a/litellm/proxy/schema.prisma
+++ b/litellm/proxy/schema.prisma
@@ -174,6 +174,7 @@ model LiteLLM_MCPServerTable {
   url          String?
   transport    String         @default("sse")
   auth_type    String?        
+  credentials  Json?          @default("{}")
   created_at   DateTime?      @default(now()) @map("created_at")
   created_by   String?
   updated_at   DateTime?      @default(now()) @updatedAt @map("updated_at")
diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py
index 02adcac0eca..303f0016503 100644
--- a/litellm/proxy/spend_tracking/spend_tracking_utils.py
+++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py
@@ -51,7 +51,7 @@ def _get_spend_logs_metadata(
     vector_store_request_metadata: Optional[
         List[StandardLoggingVectorStoreRequest]
     ] = None,
-    guardrail_information: Optional[list[StandardLoggingGuardrailInformation]] = None,
+    guardrail_information: Optional[List[StandardLoggingGuardrailInformation]] = None,
     usage_object: Optional[dict] = None,
     model_map_information: Optional[StandardLoggingModelInformation] = None,
     cold_storage_object_key: Optional[str] = None,
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index 62ab69c32ea..015540caafd 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -33,7 +33,7 @@ from litellm.proxy._types import (
     SpendLogsPayload,
 )
 from litellm.types.guardrails import GuardrailEventHooks
-from litellm.types.utils import CallTypes
+from litellm.types.utils import CallTypes, CallTypesLiteral
 
 try:
     import backoff
@@ -796,18 +796,7 @@ class ProxyLogging:
         callback: CustomGuardrail,
         data: dict,
         user_api_key_dict: Optional[UserAPIKeyAuth],
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[dict]:
         """
         Process a guardrail callback during pre-call hook.
@@ -865,18 +854,7 @@ class ProxyLogging:
         self,
         user_api_key_dict: UserAPIKeyAuth,
         data: None,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> None:
         pass
 
@@ -885,18 +863,7 @@ class ProxyLogging:
         self,
         user_api_key_dict: UserAPIKeyAuth,
         data: dict,
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> dict:
         pass
 
@@ -904,18 +871,7 @@ class ProxyLogging:
         self,
         user_api_key_dict: UserAPIKeyAuth,
         data: Optional[dict],
-        call_type: Literal[
-            "completion",
-            "text_completion",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "pass_through_endpoint",
-            "rerank",
-            "mcp_call",
-            "anthropic_messages",
-        ],
+        call_type: CallTypesLiteral,
     ) -> Optional[dict]:
         """
         Allows users to modify/reject the incoming request to the proxy, without having to deal with parsing Request body.
@@ -1014,7 +970,7 @@ class ProxyLogging:
                         user_api_key_dict=user_api_key_dict,
                         cache=self.call_details["user_api_key_cache"],
                         data=data,  # type: ignore
-                        call_type=call_type,
+                        call_type=call_type,  # type: ignore
                     )
                     if response is not None:
                         data = await self.process_pre_call_hook_response(
@@ -1042,15 +998,7 @@ class ProxyLogging:
         self,
         data: dict,
         user_api_key_dict: Optional[UserAPIKeyAuth],
-        call_type: Literal[
-            "completion",
-            "responses",
-            "embeddings",
-            "image_generation",
-            "moderation",
-            "audio_transcription",
-            "mcp_call",
-        ],
+        call_type: CallTypesLiteral,
     ):
         """
         Runs the CustomGuardrail's async_moderation_hook() in parallel
@@ -1098,7 +1046,7 @@ class ProxyLogging:
                     callback.async_moderation_hook(
                         data=data,
                         user_api_key_dict=user_api_key_auth_dict,  # type: ignore
-                        call_type=call_type,
+                        call_type=call_type,  # type: ignore
                     )
                 )
 
diff --git a/litellm/proxy/video_endpoints/endpoints.py b/litellm/proxy/video_endpoints/endpoints.py
index 9cd4a698035..25114921dbc 100644
--- a/litellm/proxy/video_endpoints/endpoints.py
+++ b/litellm/proxy/video_endpoints/endpoints.py
@@ -15,6 +15,7 @@ from litellm.proxy.common_utils.openai_endpoint_utils import (
     get_custom_llm_provider_from_request_headers,
     get_custom_llm_provider_from_request_query,
 )
+from litellm.types.videos.utils import decode_video_id_with_provider
 
 router = APIRouter()
 
@@ -237,13 +238,15 @@ async def video_status(
     # Create data with video_id
     data: Dict[str, Any] = {"video_id": video_id}
 
-    # Extract custom_llm_provider from headers, query params, or body
+    decoded = decode_video_id_with_provider(video_id)
+    provider_from_id = decoded.get("custom_llm_provider")
+
     custom_llm_provider = (
         get_custom_llm_provider_from_request_headers(request=request)
         or get_custom_llm_provider_from_request_query(request=request)
         or await get_custom_llm_provider_from_request_body(request=request)
+        or provider_from_id
         or "openai"
-
     )
     if custom_llm_provider:
         data["custom_llm_provider"] = custom_llm_provider
@@ -304,7 +307,7 @@ async def video_content(
     
     Example:
     ```bash
-    curl -X GET "http://localhost:4000/v1/videos/video_123/content" \
+    curl -X GET "http://localhost:4000/v1/videos/{video_id}/content" \
         -H "Authorization: Bearer sk-1234" \
         --output video.mp4
     ```
@@ -326,11 +329,14 @@ async def video_content(
     # Create data with video_id
     data: Dict[str, Any] = {"video_id": video_id}
 
-    # Extract custom_llm_provider from headers, query params, or body
+    decoded = decode_video_id_with_provider(video_id)
+    provider_from_id = decoded.get("custom_llm_provider")
+    
     custom_llm_provider = (
         get_custom_llm_provider_from_request_headers(request=request)
         or get_custom_llm_provider_from_request_query(request=request)
         or await get_custom_llm_provider_from_request_body(request=request)
+        or provider_from_id
     )
     if custom_llm_provider:
         data["custom_llm_provider"] = custom_llm_provider
@@ -428,11 +434,14 @@ async def video_remix(
     data = orjson.loads(body)
     data["video_id"] = video_id
 
-    # Extract custom_llm_provider from headers, query params, or body
+    decoded = decode_video_id_with_provider(video_id)
+    provider_from_id = decoded.get("custom_llm_provider")
+
     custom_llm_provider = (
         get_custom_llm_provider_from_request_headers(request=request)
         or get_custom_llm_provider_from_request_query(request=request)
         or data.get("custom_llm_provider")
+        or provider_from_id
     )
     if custom_llm_provider:
         data["custom_llm_provider"] = custom_llm_provider
diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py
index 3754959f6ab..8eecc3e8211 100644
--- a/litellm/responses/streaming_iterator.py
+++ b/litellm/responses/streaming_iterator.py
@@ -188,9 +188,15 @@ class ResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
 
     def _handle_logging_completed_response(self):
         """Handle logging for completed responses in async context"""
+        # Create a deep copy for logging to avoid modifying the response object that will be returned to the user
+        # The logging handlers may transform usage from Responses API format (input_tokens/output_tokens) 
+        # to chat completion format (prompt_tokens/completion_tokens) for internal logging
+        import copy
+        logging_response = copy.deepcopy(self.completed_response)
+        
         asyncio.create_task(
             self.logging_obj.async_success_handler(
-                result=self.completed_response,
+                result=logging_response,
                 start_time=self.start_time,
                 end_time=datetime.now(),
                 cache_hit=None,
@@ -199,7 +205,7 @@ class ResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
 
         executor.submit(
             self.logging_obj.success_handler,
-            result=self.completed_response,
+            result=logging_response,
             cache_hit=None,
             start_time=self.start_time,
             end_time=datetime.now(),
@@ -258,9 +264,15 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
 
     def _handle_logging_completed_response(self):
         """Handle logging for completed responses in sync context"""
+        # Create a deep copy for logging to avoid modifying the response object that will be returned to the user
+        # The logging handlers may transform usage from Responses API format (input_tokens/output_tokens) 
+        # to chat completion format (prompt_tokens/completion_tokens) for internal logging
+        import copy
+        logging_response = copy.deepcopy(self.completed_response)
+        
         run_async_function(
             async_function=self.logging_obj.async_success_handler,
-            result=self.completed_response,
+            result=logging_response,
             start_time=self.start_time,
             end_time=datetime.now(),
             cache_hit=None,
@@ -268,7 +280,7 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
 
         executor.submit(
             self.logging_obj.success_handler,
-            result=self.completed_response,
+            result=logging_response,
             cache_hit=None,
             start_time=self.start_time,
             end_time=datetime.now(),
diff --git a/litellm/router_utils/batch_utils.py b/litellm/router_utils/batch_utils.py
index 0baad322fa9..aef8f5cc012 100644
--- a/litellm/router_utils/batch_utils.py
+++ b/litellm/router_utils/batch_utils.py
@@ -1,7 +1,7 @@
 import io
 import json
 from os import PathLike
-from typing import Optional
+from typing import List, Optional
 
 from litellm._logging import verbose_logger
 from litellm.types.llms.openai import FileTypes, OpenAIFilesPurpose
@@ -16,7 +16,7 @@ class InMemoryFile(io.BytesIO):
         self.content_type = content_type
 
 
-def parse_jsonl_with_embedded_newlines(content: str) -> list[dict]:
+def parse_jsonl_with_embedded_newlines(content: str) -> List[dict]:
     """
     Parse JSONL content that may contain JSON objects with embedded newlines in string values.
 
diff --git a/litellm/secret_managers/hashicorp_secret_manager.py b/litellm/secret_managers/hashicorp_secret_manager.py
index 90705de02cb..fe26f5c332c 100644
--- a/litellm/secret_managers/hashicorp_secret_manager.py
+++ b/litellm/secret_managers/hashicorp_secret_manager.py
@@ -37,11 +37,12 @@ class HashicorpSecretManager(BaseSecretManager):
         self.tls_key_path = os.getenv("HCP_VAULT_CLIENT_KEY", "")
         self.vault_cert_role = os.getenv("HCP_VAULT_CERT_ROLE", None)
 
-        # Validate environment
-        if not self.vault_token:
-            raise ValueError(
-                "Missing Vault token. Please set HCP_VAULT_TOKEN in your environment."
-            )
+        # Optional config for AppRole auth
+        self.approle_role_id = os.getenv("HCP_VAULT_APPROLE_ROLE_ID", "")
+        self.approle_secret_id = os.getenv("HCP_VAULT_APPROLE_SECRET_ID", "")
+        self.approle_mount_path = os.getenv("HCP_VAULT_APPROLE_MOUNT_PATH", "approle")
+
+        self._verify_required_credentials_exist()
 
         litellm.secret_manager_client = self
         litellm._key_management_system = KeyManagementSystem.HASHICORP_VAULT
@@ -62,6 +63,92 @@ class HashicorpSecretManager(BaseSecretManager):
                 f"Hashicorp secret manager is only available for premium users. {CommonProxyErrors.not_premium_user.value}"
             )
 
+    def _verify_required_credentials_exist(self) -> None:
+        """
+        Validate that at least one authentication method is configured.
+        
+        Raises:
+            ValueError: If no valid authentication credentials are provided
+        """
+        if not self.vault_token and not (self.approle_role_id and self.approle_secret_id):
+            raise ValueError(
+                "Missing Vault authentication credentials. Please set either:\n"
+                "  - HCP_VAULT_TOKEN for token-based auth, or\n"
+                "  - HCP_VAULT_APPROLE_ROLE_ID and HCP_VAULT_APPROLE_SECRET_ID for AppRole auth"
+            )
+
+    def _auth_via_approle(self) -> str:
+        """
+        Authenticate to Vault using AppRole auth method.
+        
+        Ref: https://developer.hashicorp.com/vault/api-docs/auth/approle
+
+        Request:
+        ```
+        curl \
+            --request POST \
+            --header "X-Vault-Namespace: mynamespace/" \
+            --data '{"role_id": "...", "secret_id": "..."}' \
+            http://127.0.0.1:8200/v1/auth/approle/login
+        ```
+
+        Response:
+        ```
+        {
+            "auth": {
+                "client_token": "hvs.CAESI...",
+                "accessor": "hmac-sha256...",
+                "policies": ["default", "dev-policy"],
+                "token_policies": ["default", "dev-policy"],
+                "lease_duration": 2764800,
+                "renewable": true
+            }
+        }
+        ```
+        """
+        verbose_logger.debug("Using AppRole auth for Hashicorp Vault")
+        
+        # Check cache first
+        cached_token = self.cache.get_cache(key="hcp_vault_approle_token")
+        if cached_token:
+            verbose_logger.debug("Using cached Vault token from AppRole auth")
+            return cached_token
+        
+        # Vault endpoint for AppRole login
+        login_url = f"{self.vault_addr}/v1/auth/{self.approle_mount_path}/login"
+
+        headers = {}
+        if hasattr(self, "vault_namespace") and self.vault_namespace:
+            headers["X-Vault-Namespace"] = self.vault_namespace
+        
+        try:
+            client = _get_httpx_client()
+            resp = client.post(
+                url=login_url,
+                headers=headers,
+                json={
+                    "role_id": self.approle_role_id,
+                    "secret_id": self.approle_secret_id,
+                },
+            )
+            resp.raise_for_status()
+            
+            auth_data = resp.json()["auth"]
+            token = auth_data["client_token"]
+            _lease_duration = auth_data["lease_duration"]
+            
+            verbose_logger.debug(
+                f"Successfully obtained Vault token via AppRole auth. Lease duration: {_lease_duration}s"
+            )
+            
+            # Cache the token with its lease duration
+            self.cache.set_cache(
+                key="hcp_vault_approle_token", value=token, ttl=_lease_duration
+            )
+            return token
+        except Exception as e:
+            raise RuntimeError(f"Could not authenticate to Vault via AppRole: {e}")
+
     def _auth_via_tls_cert(self) -> str:
         """
         Ref: https://developer.hashicorp.com/vault/api-docs/auth/cert
@@ -144,8 +231,23 @@ class HashicorpSecretManager(BaseSecretManager):
         return _url
 
     def _get_request_headers(self) -> dict:
+        """
+        Get the headers for Vault API requests.
+        
+        Authentication priority:
+        1. AppRole (if role_id and secret_id are configured)
+        2. TLS Certificate (if cert paths are configured)
+        3. Direct token (if HCP_VAULT_TOKEN is set)
+        """
+        # Priority 1: AppRole auth
+        if self.approle_role_id and self.approle_secret_id:
+            return {"X-Vault-Token": self._auth_via_approle()}
+        
+        # Priority 2: TLS cert auth
         if self.tls_cert_path and self.tls_key_path:
             return {"X-Vault-Token": self._auth_via_tls_cert()}
+        
+        # Priority 3: Direct token
         return {"X-Vault-Token": self.vault_token}
 
     async def async_read_secret(
diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py
index f832fdfd7df..931d9d9d149 100644
--- a/litellm/types/guardrails.py
+++ b/litellm/types/guardrails.py
@@ -52,6 +52,7 @@ class SupportedGuardrailIntegrations(Enum):
     JAVELIN = "javelin"
     ENKRYPTAI = "enkryptai"
     IBM_GUARDRAILS = "ibm_guardrails"
+    LITELLM_CONTENT_FILTER = "litellm_content_filter"
 
 
 class Role(Enum):
@@ -443,6 +444,65 @@ class JavelinGuardrailConfigModel(BaseModel):
     )
 
 
+class ContentFilterAction(str, Enum):
+    """Action to take when content filter detects a match"""
+
+    BLOCK = "BLOCK"
+    MASK = "MASK"
+
+
+class BlockedWord(BaseModel):
+    """Represents a blocked word with its action and optional description"""
+
+    keyword: str = Field(description="The keyword to block or mask")
+    action: ContentFilterAction = Field(
+        description="Action to take when keyword is detected (BLOCK or MASK)"
+    )
+    description: Optional[str] = Field(
+        default=None, description="Optional description explaining why this keyword is sensitive"
+    )
+
+
+class ContentFilterPattern(BaseModel):
+    """Represents a content filter pattern (prebuilt or custom regex)"""
+
+    pattern_type: Literal["prebuilt", "regex"] = Field(
+        description="Type of pattern: 'prebuilt' for predefined patterns or 'regex' for custom"
+    )
+    pattern_name: Optional[str] = Field(
+        default=None,
+        description="Name of prebuilt pattern (e.g., 'us_ssn', 'credit_card'). Required if pattern_type is 'prebuilt'"
+    )
+    pattern: Optional[str] = Field(
+        default=None,
+        description="Custom regex pattern. Required if pattern_type is 'regex'"
+    )
+    name: Optional[str] = Field(
+        default=None,
+        description="Name for this pattern (used in logging and error messages)"
+    )
+    action: ContentFilterAction = Field(
+        description="Action to take when pattern matches (BLOCK or MASK)"
+    )
+
+
+class ContentFilterConfigModel(BaseModel):
+    """Configuration parameters for the content filter guardrail"""
+
+    patterns: Optional[List[ContentFilterPattern]] = Field(
+        default=None,
+        description="List of patterns (prebuilt or custom regex) to detect"
+    )
+    blocked_words: Optional[List[BlockedWord]] = Field(
+        default=None,
+        description="List of blocked words with individual actions"
+    )
+    blocked_words_file: Optional[str] = Field(
+        default=None,
+        description="Path to YAML file containing blocked_words list"
+    )
+
+
 class BaseLitellmParams(BaseModel):  # works for new and patch update guardrails
     api_key: Optional[str] = Field(
         default=None, description="API key for the guardrail service"
@@ -534,6 +594,7 @@ class LitellmParams(
     NomaGuardrailConfigModel,
     ToolPermissionGuardrailConfigModel,
     JavelinGuardrailConfigModel,
+    ContentFilterConfigModel,
     BaseLitellmParams,
     EnkryptAIGuardrailConfigs,
     IBMGuardrailsBaseConfigModel,
@@ -615,6 +676,7 @@ class GuardrailUIAddGuardrailSettings(BaseModel):
     supported_actions: List[str]
     supported_modes: List[str]
     pii_entity_categories: List[PiiEntityCategoryMap]
+    content_filter_settings: Optional[Dict[str, Any]] = None
 
 
 class PresidioPerRequestConfig(BaseModel):
diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py
index 582d238e21c..1f20d4b0c77 100644
--- a/litellm/types/llms/anthropic.py
+++ b/litellm/types/llms/anthropic.py
@@ -4,7 +4,11 @@ from typing import Dict, Iterable, List, Optional, Union
 from pydantic import BaseModel
 from typing_extensions import Literal, Required, TypedDict
 
-from .openai import ChatCompletionCachedContent, ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock
+from .openai import (
+    ChatCompletionCachedContent,
+    ChatCompletionThinkingBlock,
+    ChatCompletionRedactedThinkingBlock,
+)
 
 
 class AnthropicMessagesToolChoice(TypedDict, total=False):
@@ -65,12 +69,19 @@ class AnthropicCodeExecutionTool(TypedDict, total=False):
     cache_control: Optional[Union[dict, ChatCompletionCachedContent]]
 
 
+class AnthropicMemoryTool(TypedDict, total=False):
+    type: Required[str]
+    name: Required[Literal["memory"]]
+    cache_control: Optional[Union[dict, ChatCompletionCachedContent]]
+
+
 AllAnthropicToolsValues = Union[
     AnthropicComputerTool,
     AnthropicHostedTools,
     AnthropicMessagesTool,
     AnthropicWebSearchTool,
     AnthropicCodeExecutionTool,
+    AnthropicMemoryTool,
 ]
 
 
@@ -162,6 +173,7 @@ class AnthropicCitationPageLocation(TypedDict, total=False):
     Anthropic citation for page-based references.
     Used when citing from documents with page numbers.
     """
+
     type: Literal["page_location"]
     cited_text: str  # The exact text being cited (not counted towards output tokens)
     document_index: int  # Index referencing the cited document
@@ -175,6 +187,7 @@ class AnthropicCitationCharLocation(TypedDict, total=False):
     Anthropic citation for character-based references.
     Used when citing from text with character positions.
     """
+
     type: Literal["char_location"]
     cited_text: str  # The exact text being cited (not counted towards output tokens)
     document_index: int  # Index referencing the cited document
@@ -317,7 +330,11 @@ class ContentBlockDelta(TypedDict):
     type: Literal["content_block_delta"]
     index: int
     delta: Union[
-        ContentTextBlockDelta, ContentJsonBlockDelta, ContentCitationsBlockDelta, ContentThinkingBlockDelta, ContentThinkingSignatureBlockDelta
+        ContentTextBlockDelta,
+        ContentJsonBlockDelta,
+        ContentCitationsBlockDelta,
+        ContentThinkingBlockDelta,
+        ContentThinkingSignatureBlockDelta,
     ]
 
 
@@ -360,7 +377,9 @@ class ContentBlockStartText(TypedDict):
     content_block: TextBlock
 
 
-ContentBlockContentBlockDict = Union[ToolUseBlock, TextBlock, ChatCompletionThinkingBlock]
+ContentBlockContentBlockDict = Union[
+    ToolUseBlock, TextBlock, ChatCompletionThinkingBlock
+]
 
 ContentBlockStart = Union[ContentBlockStartToolUse, ContentBlockStartText]
 
@@ -463,7 +482,12 @@ class AnthropicResponse(BaseModel):
     """Conversational role of the generated message. This will always be "assistant"."""
 
     content: List[
-        Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking]
+        Union[
+            AnthropicResponseContentBlockText,
+            AnthropicResponseContentBlockToolUse,
+            AnthropicResponseContentBlockThinking,
+            AnthropicResponseContentBlockRedactedThinking,
+        ]
     ]
     """Content generated by the model."""
 
@@ -502,15 +526,20 @@ class AnthropicThinkingParam(TypedDict, total=False):
     type: Literal["enabled"]
     budget_tokens: int
 
+
 class ANTHROPIC_HOSTED_TOOLS(str, Enum):
     WEB_SEARCH = "web_search"
     BASH = "bash"
     TEXT_EDITOR = "text_editor"
     CODE_EXECUTION = "code_execution"
     WEB_FETCH = "web_fetch"
+    MEMORY = "memory"
+
 
 class ANTHROPIC_BETA_HEADER_VALUES(str, Enum):
     """
     Known beta header values for Anthropic.
     """
-    WEB_FETCH_2025_09_10 = "web-fetch-2025-09-10"
\ No newline at end of file
+
+    WEB_FETCH_2025_09_10 = "web-fetch-2025-09-10"
+    CONTEXT_MANAGEMENT_2025_06_27 = "context-management-2025-06-27"
diff --git a/litellm/types/llms/gemini.py b/litellm/types/llms/gemini.py
index cfc13cc44a8..e29a2cc19a0 100644
--- a/litellm/types/llms/gemini.py
+++ b/litellm/types/llms/gemini.py
@@ -221,3 +221,125 @@ class GeminiImageGenerationPrediction(TypedDict):
 class GeminiImageGenerationResponse(TypedDict):
     """Complete response body from Gemini image generation API"""
     predictions: List[GeminiImageGenerationPrediction]
+
+# Video Generation Types
+class GeminiVideoGenerationInstance(TypedDict):
+    """Instance data for Gemini video generation request"""
+    prompt: str
+
+
+class GeminiVideoGenerationParameters(BaseModel):
+    """
+    Parameters for Gemini video generation request.
+    
+    See: Veo 3/3.1 parameter guide.
+    """
+    aspectRatio: Optional[str] = None
+    """Aspect ratio for generated video (e.g., '16:9', '9:16')."""
+
+    durationSeconds: Optional[int] = None
+    """
+    Length of the generated video in seconds (e.g., 4, 5, 6, 8).
+    Must be 8 when using extension/interpolation or referenceImages.
+    """
+
+    resolution: Optional[str] = None
+    """
+    Video resolution (e.g., '720p', '1080p').
+    '1080p' only supports 8s duration; extension only supports '720p'.
+    """
+
+    negativePrompt: Optional[str] = None
+    """Text describing what not to include in the video."""
+
+    image: Optional[Any] = None
+    """
+    An initial image to animate (Image object).
+    """
+
+    lastFrame: Optional[Any] = None
+    """
+    The final image for interpolation video to transition.
+    Should be used with the 'image' parameter.
+    """
+
+    referenceImages: Optional[list] = None
+    """
+    Up to three images to be used as style/content references.
+    Only supported in Veo 3.1 (list of VideoGenerationReferenceImage objects).
+    """
+
+    video: Optional[Any] = None
+    """
+    Video to be used for video extension (Video object).
+    Only supported in Veo 3.1 & Veo 3 Fast.
+    """
+
+    personGeneration: Optional[str] = None
+    """
+    Controls the generation of people.
+    Text-to-video & Extension: "allow_all" only
+    Image-to-video, Interpolation, & Reference images (Veo 3.x): "allow_adult" only
+    See documentation for region restrictions & more.
+    """
+
+
+class GeminiVideoGenerationRequest(BaseModel):
+    """Complete request body for Gemini video generation"""
+    instances: List[GeminiVideoGenerationInstance]
+    parameters: Optional[GeminiVideoGenerationParameters] = None
+
+
+# Video Generation Operation Response Types
+class GeminiVideoUri(BaseModel):
+    """Video URI in the generated sample"""
+    uri: str
+    """File URI of the generated video (e.g., 'files/abc123...')"""
+
+
+class GeminiGeneratedVideoSample(BaseModel):
+    """Individual generated video sample"""
+    video: GeminiVideoUri
+    """Video object containing the URI"""
+
+
+class GeminiGenerateVideoResponse(BaseModel):
+    """Generate video response containing the samples"""
+    generatedSamples: List[GeminiGeneratedVideoSample]
+    """List of generated video samples"""
+
+
+class GeminiOperationResponse(BaseModel):
+    """Response object in the operation when done"""
+    generateVideoResponse: GeminiGenerateVideoResponse
+    """Video generation response"""
+
+
+class GeminiOperationMetadata(BaseModel):
+    """Metadata for the operation"""
+    createTime: Optional[str] = None
+    """Creation timestamp"""
+    model: Optional[str] = None
+    """Model used for generation"""
+
+
+class GeminiLongRunningOperationResponse(BaseModel):
+    """
+    Complete response for a long-running operation.
+    
+    Used when polling operation status and extracting results.
+    """
+    name: str
+    """Operation name (e.g., 'operations/generate_1234567890')"""
+    
+    done: bool = False
+    """Whether the operation is complete"""
+    
+    metadata: Optional[GeminiOperationMetadata] = None
+    """Operation metadata"""
+    
+    response: Optional[GeminiOperationResponse] = None
+    """Response object when operation is complete"""
+    
+    error: Optional[Dict[str, Any]] = None
+    """Error details if operation failed"""
diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py
index cb2075981a8..768818a6107 100644
--- a/litellm/types/llms/vertex_ai.py
+++ b/litellm/types/llms/vertex_ai.py
@@ -638,6 +638,52 @@ class VertexBatchPredictionResponse(TypedDict, total=False):
     modelVersionId: str
 
 
+class VertexVideoImage(TypedDict, total=False):
+    """Image input for video generation"""
+
+    bytesBase64Encoded: str
+    mimeType: str
+
+
+class VertexVideoGenerationInstance(TypedDict, total=False):
+    """Instance object for Vertex AI video generation request"""
+
+    prompt: Required[str]
+    image: VertexVideoImage
+
+
+class VertexVideoGenerationParameters(TypedDict, total=False):
+    """Parameters for Vertex AI video generation"""
+
+    aspectRatio: Literal["9:16", "16:9"]
+    durationSeconds: int
+
+
+class VertexVideoGenerationRequest(TypedDict):
+    """Complete request body for Vertex AI video generation"""
+
+    instances: Required[List[VertexVideoGenerationInstance]]
+    parameters: VertexVideoGenerationParameters
+
+
+class VertexVideoOutput(TypedDict, total=False):
+    """Video output in response"""
+
+    bytesBase64Encoded: str
+    mimeType: str
+    gcsUri: str
+
+
+class VertexVideoGenerationResponse(TypedDict, total=False):
+    """Response body for Vertex AI video generation"""
+
+    name: str
+    done: bool
+    response: Dict[str, Any]
+    metadata: Dict[str, Any]
+    error: Dict[str, Any]
+
+
 VERTEX_CREDENTIALS_TYPES = Union[str, Dict[str, str]]
 
 
diff --git a/litellm/types/management_endpoints/cache_settings_endpoints.py b/litellm/types/management_endpoints/cache_settings_endpoints.py
index 4afef21b194..d2a8a39e4f8 100644
--- a/litellm/types/management_endpoints/cache_settings_endpoints.py
+++ b/litellm/types/management_endpoints/cache_settings_endpoints.py
@@ -150,6 +150,25 @@ CACHE_SETTINGS_FIELDS: List[CacheSettingsField] = [
         ui_field_name="Sentinel Password",
         redis_type="sentinel",
     ),
+    # Semantic-specific fields
+    CacheSettingsField(
+        field_name="similarity_threshold",
+        field_type="Float",
+        field_value=None,
+        field_description="Similarity threshold for semantic cache",
+        field_default=0.8,
+        ui_field_name="Similarity Threshold",
+        redis_type="semantic",
+    ),
+    CacheSettingsField(
+        field_name="redis_semantic_cache_embedding_model",
+        field_type="Models_Select",
+        field_value=None,
+        field_description="Embedding model for semantic cache",
+        field_default=None,
+        ui_field_name="Embedding Model",
+        redis_type="semantic",
+    ),
     # GCP IAM authentication fields
     CacheSettingsField(
         field_name="gcp_service_account",
diff --git a/litellm/types/mcp.py b/litellm/types/mcp.py
index bbb9e7d7b47..ce771c40d3d 100644
--- a/litellm/types/mcp.py
+++ b/litellm/types/mcp.py
@@ -54,6 +54,28 @@ MCPAuthType = Optional[
 ]
 
 
+class MCPCredentials(TypedDict, total=False):
+    auth_value: Optional[str]
+    """
+    Authentication value
+    """
+
+    client_id: Optional[str]
+    """
+    OAuth 2.0 client identifier used when auth_type is oauth2
+    """
+
+    client_secret: Optional[str]
+    """
+    OAuth 2.0 client secret used when auth_type is oauth2
+    """
+
+    scopes: Optional[List[str]]
+    """
+    OAuth 2.0 scopes to request when exchanging the client credentials
+    """
+
+
 class MCPServerCostInfo(TypedDict, total=False):
     default_cost_per_query: Optional[float]
     """
diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/litellm_content_filter.py b/litellm/types/proxy/guardrails/guardrail_hooks/litellm_content_filter.py
new file mode 100644
index 00000000000..4ccab3718ed
--- /dev/null
+++ b/litellm/types/proxy/guardrails/guardrail_hooks/litellm_content_filter.py
@@ -0,0 +1,7 @@
+from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
+
+
+class LitellmContentFilterGuardrailConfigModel(GuardrailConfigModel):
+    @staticmethod
+    def ui_friendly_name() -> str:
+        return "LiteLLM Content Filter"
\ No newline at end of file
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 05fc23a2c6b..fd78987b2fe 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -1,16 +1,7 @@
 import json
 import time
 from enum import Enum
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    List,
-    Literal,
-    Mapping,
-    Optional,
-    Union,
-)
+from typing import TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional, Union
 
 from aiohttp import FormData
 from openai._models import BaseModel as OpenAIObject
@@ -128,6 +119,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
         float
     ]  # OpenAI priority service tier pricing
     cache_creation_input_token_cost: Optional[float]
+    cache_creation_input_token_cost_above_200k_tokens: Optional[float]
     cache_creation_input_token_cost_above_1hr: Optional[float]
     cache_read_input_token_cost: Optional[float]
     cache_read_input_token_cost_flex: Optional[
@@ -136,6 +128,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
     cache_read_input_token_cost_priority: Optional[
         float
     ]  # OpenAI priority service tier pricing
+    cache_read_input_token_cost_above_200k_tokens: Optional[float]
     input_cost_per_character: Optional[float]  # only for vertex ai models
     input_cost_per_audio_token: Optional[float]
     input_cost_per_token_above_128k_tokens: Optional[float]  # only for vertex ai models
@@ -297,7 +290,7 @@ class CallTypes(str, Enum):
     avideo_retrieve_job = "avideo_retrieve_job"
     video_delete = "video_delete"
     avideo_delete = "avideo_delete"
-    
+
     #########################################################
     # Container Call Types
     #########################################################
@@ -309,7 +302,7 @@ class CallTypes(str, Enum):
     aretrieve_container = "aretrieve_container"
     delete_container = "delete_container"
     adelete_container = "adelete_container"
-    
+
     acancel_fine_tuning_job = "acancel_fine_tuning_job"
     cancel_fine_tuning_job = "cancel_fine_tuning_job"
     alist_fine_tuning_jobs = "alist_fine_tuning_jobs"
@@ -372,6 +365,7 @@ CallTypesLiteral = Literal[
     "aocr",
     "avector_store_search",
     "vector_store_search",
+    "call_mcp_tool",
 ]
 
 
@@ -819,6 +813,8 @@ class Delta(OpenAIObject):
                     if tool_call.get("index", None) is None:
                         tool_call["index"] = current_index
                         current_index += 1
+                    if tool_call.get("type", None) is None:
+                        tool_call["type"] = "function"
                     self.tool_calls.append(ChatCompletionDeltaToolCall(**tool_call))
                 elif isinstance(tool_call, ChatCompletionDeltaToolCall):
                     self.tool_calls.append(tool_call)
@@ -1771,6 +1767,10 @@ class ImageResponse(OpenAIImageResponse, BaseLiteLLMOpenAIResponseObject):
             total_tokens=0,
         )
         super().__init__(created=created, data=_data, usage=_usage)  # type: ignore
+
+        self.quality = kwargs.get("quality", None)
+        self.output_format = kwargs.get("output_format", None)
+        self.size = kwargs.get("size", None)
         self._hidden_params = hidden_params or {}
 
     def __contains__(self, key):
@@ -1797,8 +1797,29 @@ class ImageResponse(OpenAIImageResponse, BaseLiteLLMOpenAIResponseObject):
             return self.dict()
 
 
+class TranscriptionUsageDurationObject(BaseModel):
+    type: Literal["duration"]
+    seconds: int
+
+
+class TranscriptionUsageInputTokenDetailsObject(BaseModel):
+    audio_tokens: int
+    text_tokens: int
+
+
+class TranscriptionUsageTokensObject(BaseModel):
+    type: Literal["tokens"]
+    input_tokens: int
+    output_tokens: int
+    total_tokens: int
+    input_token_details: TranscriptionUsageInputTokenDetailsObject
+
+
 class TranscriptionResponse(OpenAIObject):
     text: Optional[str] = None
+    usage: Optional[
+        Union[TranscriptionUsageDurationObject, TranscriptionUsageTokensObject]
+    ] = None
 
     _hidden_params: dict = {}
     _response_headers: Optional[dict] = None
@@ -2784,6 +2805,10 @@ class SpecialEnums(Enum):
 
     LITELLM_MANAGED_GENERIC_RESPONSE_COMPLETE_STR = "litellm_proxy;model_id:{};generic_response_id:{}"  # generic implementation of 'managed batches' - used for finetuning and any future work.
 
+    LITELLM_MANAGED_VIDEO_COMPLETE_STR = (
+        "litellm:custom_llm_provider:{};model_id:{};video_id:{}"
+    )
+
 
 class ServiceTier(Enum):
     """Enum for service tier types used in cost calculations."""
diff --git a/litellm/types/videos/main.py b/litellm/types/videos/main.py
index f33e1d5a441..65c4cfe0e00 100644
--- a/litellm/types/videos/main.py
+++ b/litellm/types/videos/main.py
@@ -10,7 +10,7 @@ class VideoObject(BaseModel):
     id: str
     object: Literal["video"]
     status: str
-    created_at: int
+    created_at: Optional[int] = None
     completed_at: Optional[int] = None
     expires_at: Optional[int] = None
     error: Optional[Dict[str, Any]] = None
@@ -87,3 +87,10 @@ class VideoCreateRequestParams(VideoCreateOptionalRequestParams, total=False):
     Params here: https://platform.openai.com/docs/api-reference/videos/create
     """
     prompt: str
+
+class DecodedVideoId(TypedDict, total=False):
+    """Structure representing a decoded video ID"""
+
+    custom_llm_provider: Optional[str]
+    model_id: Optional[str]
+    video_id: str
\ No newline at end of file
diff --git a/litellm/types/videos/utils.py b/litellm/types/videos/utils.py
new file mode 100644
index 00000000000..329aea645c5
--- /dev/null
+++ b/litellm/types/videos/utils.py
@@ -0,0 +1,100 @@
+"""
+Utility functions for video ID encoding/decoding with provider information.
+
+Follows the pattern used in responses/utils.py for consistency.
+Format: vid_{base64_encoded_string}
+"""
+import base64
+from typing import Tuple, Optional
+from litellm.types.utils import SpecialEnums
+from litellm.types.videos.main import DecodedVideoId    
+from litellm._logging import verbose_logger
+
+
+
+VIDEO_ID_PREFIX = "video_"
+
+
+def encode_video_id_with_provider(
+    video_id: str, 
+    provider: str,
+    model_id: Optional[str] = None
+) -> str:
+    """Encode provider and model_id into video_id using base64."""
+    if not provider or not video_id:
+        return video_id
+    
+    if video_id.startswith(VIDEO_ID_PREFIX):
+        return video_id
+    
+    assembled_id = str(
+        SpecialEnums.LITELLM_MANAGED_VIDEO_COMPLETE_STR.value
+    ).format(provider, model_id or "", video_id)
+    
+    base64_encoded_id: str = base64.b64encode(assembled_id.encode("utf-8")).decode("utf-8")
+    
+    return f"{VIDEO_ID_PREFIX}{base64_encoded_id}"
+
+
+def decode_video_id_with_provider(encoded_video_id: str) -> DecodedVideoId:
+    """Decode provider and model_id from encoded video_id."""
+    if not encoded_video_id:
+        return DecodedVideoId(
+            custom_llm_provider=None,
+            model_id=None,
+            video_id=encoded_video_id,
+        )
+    
+    if not encoded_video_id.startswith(VIDEO_ID_PREFIX):
+        return DecodedVideoId(
+            custom_llm_provider=None,
+            model_id=None,
+            video_id=encoded_video_id,
+        )
+    
+    try:
+        cleaned_id = encoded_video_id.replace(VIDEO_ID_PREFIX, "")
+        decoded_id = base64.b64decode(cleaned_id.encode("utf-8")).decode("utf-8")
+
+        if ";" not in decoded_id:
+            return DecodedVideoId(
+                custom_llm_provider=None,
+                model_id=None,
+                video_id=encoded_video_id,
+            )
+
+        parts = decoded_id.split(";")
+
+        custom_llm_provider = None
+        model_id = None
+        decoded_video_id = encoded_video_id
+
+        if len(parts) >= 3:
+            custom_llm_provider_part = parts[0]
+            model_id_part = parts[1]
+            video_id_part = parts[2]
+
+            custom_llm_provider = custom_llm_provider_part.replace(
+                "litellm:custom_llm_provider:", ""
+            )
+            model_id = model_id_part.replace("model_id:", "")
+            decoded_video_id = video_id_part.replace("video_id:", "")
+
+        return DecodedVideoId(
+            custom_llm_provider=custom_llm_provider,
+            model_id=model_id,
+            video_id=decoded_video_id,
+        )
+    except Exception as e:
+        verbose_logger.debug(f"Error decoding video_id '{encoded_video_id}': {e}")
+        return DecodedVideoId(
+            custom_llm_provider=None,
+            model_id=None,
+            video_id=encoded_video_id,
+        )
+
+
+def extract_original_video_id(encoded_video_id: str) -> str:
+    """Extract original video ID without encoding."""
+    decoded = decode_video_id_with_provider(encoded_video_id)
+    return decoded.get("video_id", encoded_video_id)
diff --git a/litellm/utils.py b/litellm/utils.py
index f487adb5fcd..f32c27ebee9 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -4952,9 +4952,15 @@ def _get_model_info_helper(  # noqa: PLR0915
                 cache_creation_input_token_cost=_model_info.get(
                     "cache_creation_input_token_cost", None
                 ),
+                cache_creation_input_token_cost_above_200k_tokens=_model_info.get(
+                    "cache_creation_input_token_cost_above_200k_tokens", None
+                ),
                 cache_read_input_token_cost=_model_info.get(
                     "cache_read_input_token_cost", None
                 ),
+                cache_read_input_token_cost_above_200k_tokens=_model_info.get(
+                    "cache_read_input_token_cost_above_200k_tokens", None
+                ),
                 cache_read_input_token_cost_flex=_model_info.get(
                     "cache_read_input_token_cost_flex", None
                 ),
@@ -7288,6 +7294,8 @@ class ProviderConfigManager:
             return litellm.InfinityRerankConfig()
         elif litellm.LlmProviders.JINA_AI == provider:
             return litellm.JinaAIRerankConfig()
+        elif litellm.LlmProviders.HOSTED_VLLM == provider:
+            return litellm.HostedVLLMRerankConfig()
         elif litellm.LlmProviders.HUGGINGFACE == provider:
             return litellm.HuggingFaceRerankConfig()
         elif litellm.LlmProviders.DEEPINFRA == provider:
@@ -7357,14 +7365,19 @@ class ProviderConfigManager:
             return litellm.OpenAIResponsesAPIConfig()
         elif litellm.LlmProviders.AZURE == provider:
             # Check if it's an O-series model
-            if model and ("o_series" in model.lower() or supports_reasoning(model)):
+            # Note: GPT models (gpt-3.5, gpt-4, gpt-5, etc.) support temperature parameter
+            # O-series models (o1, o3) do not contain "gpt" and have different parameter restrictions
+            is_gpt_model = model and "gpt" in model.lower()
+            is_o_series = model and ("o_series" in model.lower() or (supports_reasoning(model) and not is_gpt_model))
+            
+            if is_o_series:
                 return litellm.AzureOpenAIOSeriesResponsesAPIConfig()
             else:
                 return litellm.AzureOpenAIResponsesAPIConfig()
-        elif litellm.LlmProviders.LITELLM_PROXY == provider:
-            return litellm.LiteLLMProxyResponsesAPIConfig()
         elif litellm.LlmProviders.XAI == provider:
             return litellm.XAIResponsesAPIConfig()
+        elif litellm.LlmProviders.LITELLM_PROXY == provider:
+            return litellm.LiteLLMProxyResponsesAPIConfig()
         return None
 
     @staticmethod
@@ -7637,6 +7650,16 @@ class ProviderConfigManager:
             from litellm.llms.azure.videos.transformation import AzureVideoConfig
 
             return AzureVideoConfig()
+        elif LlmProviders.GEMINI == provider:
+            from litellm.llms.gemini.videos.transformation import GeminiVideoConfig
+
+            return GeminiVideoConfig()
+        elif LlmProviders.VERTEX_AI == provider:
+            from litellm.llms.vertex_ai.videos.transformation import (
+                VertexAIVideoConfig,
+            )
+
+            return VertexAIVideoConfig()
         return None
 
     @staticmethod
diff --git a/litellm/videos/main.py b/litellm/videos/main.py
index cbc59169a7a..be95b4ab9b2 100644
--- a/litellm/videos/main.py
+++ b/litellm/videos/main.py
@@ -19,6 +19,7 @@ from litellm.types.router import GenericLiteLLMParams
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
+from litellm.types.videos.utils import decode_video_id_with_provider
 
 #################### Initialize provider clients ####################
 llm_http_handler: BaseLLMHTTPHandler = BaseLLMHTTPHandler()
@@ -303,13 +304,10 @@ def video_content(
         ```python
         import litellm
 
-        # Download video content
         video_bytes = litellm.video_content(
-            video_id="video_123",
-            custom_llm_provider="openai"
+            video_id="video_123"
         )
 
-        # Save to file
         with open("video.mp4", "wb") as f:
             f.write(video_bytes)
         ```
@@ -320,9 +318,10 @@ def video_content(
         litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
         _is_async = kwargs.pop("async_call", False) is True
 
-        # Ensure custom_llm_provider is not None - default to openai if not provided
+        # Try to decode provider from video_id if not explicitly provided
         if custom_llm_provider is None:
-            custom_llm_provider = "openai"
+            decoded = decode_video_id_with_provider(video_id)
+            custom_llm_provider = decoded.get("custom_llm_provider") or "openai"
 
         # get llm provider logic
         litellm_params = GenericLiteLLMParams(**kwargs)
@@ -594,9 +593,10 @@ def video_remix(  # noqa: PLR0915
             response = VideoObject(**mock_response)
             return response
 
-        # Ensure custom_llm_provider is not None - default to openai if not provided
+        # Try to decode provider from video_id if not explicitly provided
         if custom_llm_provider is None:
-            custom_llm_provider = "openai"
+            decoded = decode_video_id_with_provider(video_id)
+            custom_llm_provider = decoded.get("custom_llm_provider") or "openai"
 
         # get llm provider logic
         litellm_params = GenericLiteLLMParams(**kwargs)
@@ -907,7 +907,7 @@ async def avideo_status(
 
     Returns:
     - `response` (VideoObject): The response returned by the `video_status` function.
-"""
+    """
     local_vars = locals()
     try:
         loop = asyncio.get_event_loop()
@@ -1015,8 +1015,7 @@ def video_status(  # noqa: PLR0915
 
         # Get video status
         video_status = litellm.video_status(
-            video_id="video_123",
-            custom_llm_provider="openai"
+            video_id="video_123"
         )
 
         print(f"Video status: {video_status.status}")
@@ -1038,9 +1037,10 @@ def video_status(  # noqa: PLR0915
             response = VideoObject(**mock_response)
             return response
 
-        # Ensure custom_llm_provider is not None - default to openai if not provided
+        # Try to decode provider from video_id if not explicitly provided
         if custom_llm_provider is None:
-            custom_llm_provider = "openai"
+            decoded = decode_video_id_with_provider(video_id)
+            custom_llm_provider = decoded.get("custom_llm_provider") or "openai"
 
         # get llm provider logic
         litellm_params = GenericLiteLLMParams(**kwargs)
diff --git a/litellm/videos/utils.py b/litellm/videos/utils.py
index 7cfccab6720..e04ab9fe180 100644
--- a/litellm/videos/utils.py
+++ b/litellm/videos/utils.py
@@ -1,8 +1,9 @@
-from typing import Any, Dict, cast, get_type_hints
+from typing import Any, Dict, cast
 
 import litellm
 from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 from litellm.types.videos.main import VideoCreateOptionalRequestParams
+from litellm.utils import filter_out_litellm_params
 
 
 class VideoGenerationRequestUtils:
@@ -25,25 +26,6 @@ class VideoGenerationRequestUtils:
         Returns:
             A dictionary of supported parameters for the video generation API
         """
-        # Get supported parameters for the model
-        supported_params = video_generation_provider_config.get_supported_openai_params(model)
-
-        # Check for unsupported parameters
-        unsupported_params = [
-            param
-            for param in video_generation_optional_params
-            if param not in supported_params
-        ]
-
-        if unsupported_params:
-            raise litellm.UnsupportedParamsError(
-                model=model,
-                message=(
-                    f"The following parameters are not supported for model {model}: "
-                    f"{', '.join(unsupported_params)}"
-                ),
-            )
-
         # Map parameters to provider-specific format
         mapped_params = video_generation_provider_config.map_openai_params(
             video_create_optional_params=video_generation_optional_params,
@@ -51,6 +33,15 @@ class VideoGenerationRequestUtils:
             drop_params=litellm.drop_params,
         )
 
+        # Merge extra_body params if present (for provider-specific parameters)
+        if "extra_body" in video_generation_optional_params:
+            extra_body = video_generation_optional_params["extra_body"]
+            if extra_body and isinstance(extra_body, dict):
+                # extra_body params override mapped params
+                mapped_params.update(extra_body)
+            # Remove extra_body from mapped_params since it's not sent to the API
+            mapped_params.pop("extra_body", None)
+
         return mapped_params
 
     @staticmethod
@@ -66,9 +57,44 @@ class VideoGenerationRequestUtils:
         Returns:
             VideoCreateOptionalRequestParams instance with only the valid parameters
         """
-        valid_keys = get_type_hints(VideoCreateOptionalRequestParams).keys()
-        filtered_params = {
-            k: v for k, v in params.items() if k in valid_keys and v is not None
+        params = dict(params or {})
+
+        raw_kwargs = params.get("kwargs", {})
+        if not isinstance(raw_kwargs, dict):
+            raw_kwargs = {}
+
+        kwargs_extra_body = raw_kwargs.pop("extra_body", None)
+        top_level_extra_body = params.get("extra_body")
+
+        base_params_raw = {
+            key: value
+            for key, value in params.items()
+            if key not in {"kwargs", "extra_body", "prompt", "model"} and value is not None
+        }
+        base_params = filter_out_litellm_params(kwargs=base_params_raw)
+
+        cleaned_kwargs = filter_out_litellm_params(
+            kwargs={k: v for k, v in raw_kwargs.items() if v is not None}
+        )
+
+        optional_params: Dict[str, Any] = {
+            **base_params,
+            **cleaned_kwargs,
         }
 
-        return cast(VideoCreateOptionalRequestParams, filtered_params)
+        merged_extra_body: Dict[str, Any] = {}
+        for extra_body_candidate in (top_level_extra_body, kwargs_extra_body):
+            if isinstance(extra_body_candidate, dict):
+                for key, value in extra_body_candidate.items():
+                    if value is not None:
+                        merged_extra_body[key] = value
+
+        if merged_extra_body:
+            merged_extra_body = filter_out_litellm_params(kwargs=merged_extra_body)
+            if merged_extra_body:
+                optional_params["extra_body"] = merged_extra_body
+                optional_params.update(merged_extra_body)
+
+        optional_params.pop("timeout", None)
+
+        return cast(VideoCreateOptionalRequestParams, optional_params)
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 5b901946b39..a887579a1ed 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -5753,6 +5753,16 @@
         "output_vector_size": 1536,
         "supports_embedding_image_input": true
     },
+    "cohere/embed-v4.0": {
+        "input_cost_per_token": 1.2e-07,
+        "litellm_provider": "cohere",
+        "max_input_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "embedding",
+        "output_cost_per_token": 0.0,
+        "output_vector_size": 1536,
+        "supports_embedding_image_input": true
+    },
     "cohere.rerank-v3-5:0": {
         "input_cost_per_query": 0.002,
         "input_cost_per_token": 0.0,
@@ -10340,6 +10350,7 @@
         "mode": "chat",
         "output_cost_per_audio_token": 1.2e-05,
         "output_cost_per_token": 2e-06,
+        "rpm": 100000,
         "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
@@ -10366,7 +10377,8 @@
         "supports_tool_choice": true,
         "supports_url_context": true,
         "supports_vision": true,
-        "supports_web_search": true
+        "supports_web_search": true,
+        "tpm": 8000000
     },
     "gemini-2.5-flash-lite-preview-06-17": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -11605,7 +11617,7 @@
         "tpm": 1000000
     },
     "gemini/gemini-2.5-flash": {
-        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost": 3e-08,
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "gemini",
@@ -12656,6 +12668,34 @@
             "video"
         ]
     },
+    "gemini/veo-3.1-fast-generate-preview": {
+        "litellm_provider": "gemini",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.15,
+        "source": "https://ai.google.dev/gemini-api/docs/video",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
+    "gemini/veo-3.1-generate-preview": {
+        "litellm_provider": "gemini",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.40,
+        "source": "https://ai.google.dev/gemini-api/docs/video",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
     "google_pse/search": {
         "input_cost_per_query": 0.005,
         "litellm_provider": "google_pse",
@@ -12722,19 +12762,21 @@
         "tool_use_system_prompt_tokens": 159
     },
     "global.anthropic.claude-haiku-4-5-20251001-v1:0": {
-        "cache_creation_input_token_cost": 1.25e-06,
-        "cache_read_input_token_cost": 1e-07,
-        "input_cost_per_token": 1e-06,
+        "cache_creation_input_token_cost": 1.375e-06,
+        "cache_read_input_token_cost": 1.1e-07,
+        "input_cost_per_token": 1.1e-06,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 200000,
         "max_output_tokens": 8192,
         "max_tokens": 8192,
         "mode": "chat",
-        "output_cost_per_token": 5e-06,
+        "output_cost_per_token": 5.5e-06,
+        "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_pdf_input": true,
         "supports_prompt_caching": true,
+        "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
@@ -16582,6 +16624,20 @@
         "source": "https://platform.moonshot.ai/docs/pricing",
         "supports_vision": true
     },
+    "moonshot/kimi-k2-thinking": {
+        "cache_read_input_token_cost": 1.5e-7,
+        "input_cost_per_token": 6e-7,
+        "litellm_provider": "moonshot",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 2.5e-6,
+        "source": "https://platform.moonshot.ai/docs/pricing/chat#generation-model-kimi-k2",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_web_search": true
+    },
     "moonshot/moonshot-v1-128k": {
         "input_cost_per_token": 2e-06,
         "litellm_provider": "moonshot",
@@ -23094,6 +23150,18 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supports_tool_choice": true
     },
+    "vertex_ai/minimaxai/minimax-m2-maas": {
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "vertex_ai-minimax_models",
+        "max_input_tokens": 196608,
+        "max_output_tokens": 196608,
+        "max_tokens": 196608,
+        "mode": "chat",
+        "output_cost_per_token": 1.2e-06,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
     "vertex_ai/mistral-medium-3": {
         "input_cost_per_token": 4e-07,
         "litellm_provider": "vertex_ai-mistral_models",
@@ -23348,6 +23416,34 @@
             "video"
         ]
     },
+    "vertex_ai/veo-3.1-generate-preview": {
+        "litellm_provider": "vertex_ai-video-models",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.4,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
+    "vertex_ai/veo-3.1-fast-generate-preview": {
+        "litellm_provider": "vertex_ai-video-models",
+        "max_input_tokens": 1024,
+        "max_tokens": 1024,
+        "mode": "video_generation",
+        "output_cost_per_second": 0.15,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ]
+    },
     "voyage/rerank-2": {
         "input_cost_per_query": 5e-08,
         "input_cost_per_token": 5e-08,
diff --git a/package-lock.json b/package-lock.json
index 1b3c2a690a4..302ec856788 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -8172,4 +8172,4 @@
       }
     }
   }
-}
+}
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index ab34c863518..d71712dd9f2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -740,13 +740,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudpickle"
-version = "3.1.1"
+version = "3.1.2"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e"},
-    {file = "cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64"},
+    {file = "cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a"},
+    {file = "cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414"},
 ]
 
 [[package]]
@@ -955,18 +955,18 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "databricks-sdk"
-version = "0.71.0"
+version = "0.73.0"
 description = "Databricks SDK for Python (Beta)"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "databricks_sdk-0.71.0-py3-none-any.whl", hash = "sha256:f5b7b5d7970f6411c75c30c6ddddcc8dccf8274f13b79135b2427f24e9e77980"},
-    {file = "databricks_sdk-0.71.0.tar.gz", hash = "sha256:5ef299ab746eba9d7edcd7450a9d030a5208fe816be7f43aa23b6d5db34593be"},
+    {file = "databricks_sdk-0.73.0-py3-none-any.whl", hash = "sha256:a4d3cfd19357a2b459d2dc3101454d7f0d1b62865ce099c35d0c342b66ac64ff"},
+    {file = "databricks_sdk-0.73.0.tar.gz", hash = "sha256:db09eaaacd98e07dded78d3e7ab47d2f6c886e0380cb577977bd442bace8bd8d"},
 ]
 
 [package.dependencies]
 google-auth = ">=2.0,<3.0"
-protobuf = ">=4.21.0,<7.0"
+protobuf = ">=4.25.8,<5.26.dev0 || >5.29.0,<5.29.1 || >5.29.1,<5.29.2 || >5.29.2,<5.29.3 || >5.29.3,<5.29.4 || >5.29.4,<6.30.0 || >6.30.0,<6.30.1 || >6.30.1,<6.31.0 || >6.31.0,<7.0"
 requests = ">=2.28.1,<3"
 
 [package.extras]
@@ -1100,13 +1100,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.120.4"
+version = "0.121.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.120.4-py3-none-any.whl", hash = "sha256:9bdf192308676480d3593e10fd05094e56d6fdc7d9283db26053d8104d5f82a0"},
-    {file = "fastapi-0.120.4.tar.gz", hash = "sha256:2d856bc847893ca4d77896d4504ffdec0fb04312b705065fca9104428eca3868"},
+    {file = "fastapi-0.121.0-py3-none-any.whl", hash = "sha256:8bdf1b15a55f4e4b0d6201033da9109ea15632cb76cf156e7b8b4019f2172106"},
+    {file = "fastapi-0.121.0.tar.gz", hash = "sha256:06663356a0b1ee93e875bbf05a31fb22314f5bed455afaaad2b2dad7f26e98fa"},
 ]
 
 [package.dependencies]
@@ -1688,13 +1688,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.42.1"
+version = "2.43.0"
 description = "Google Authentication Library"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "google_auth-2.42.1-py2.py3-none-any.whl", hash = "sha256:eb73d71c91fc95dbd221a2eb87477c278a355e7367a35c0d84e6b0e5f9b4ad11"},
-    {file = "google_auth-2.42.1.tar.gz", hash = "sha256:30178b7a21aa50bffbdc1ffcb34ff770a2f65c712170ecd5446c4bef4dc2b94e"},
+    {file = "google_auth-2.43.0-py2.py3-none-any.whl", hash = "sha256:af628ba6fa493f75c7e9dbe9373d148ca9f4399b5ea29976519e0a3848eddd16"},
+    {file = "google_auth-2.43.0.tar.gz", hash = "sha256:88228eee5fc21b62a1b5fe773ca15e67778cb07dc8363adcb4a8827b52d81483"},
 ]
 
 [package.dependencies]
@@ -1772,13 +1772,13 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.71.0"
+version = "1.72.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis_common_protos-1.71.0-py3-none-any.whl", hash = "sha256:59034a1d849dc4d18971997a72ac56246570afdd17f9369a0ff68218d50ab78c"},
-    {file = "googleapis_common_protos-1.71.0.tar.gz", hash = "sha256:1aec01e574e29da63c80ba9f7bbf1ccfaacf1da877f23609fe236ca7c72a2e2e"},
+    {file = "googleapis_common_protos-1.72.0-py3-none-any.whl", hash = "sha256:4299c5a82d5ae1a9702ada957347726b167f9f8d1fc352477702a1e851ff4038"},
+    {file = "googleapis_common_protos-1.72.0.tar.gz", hash = "sha256:e55a601c1b32b52d7a3e65f43563e2aa61bcd737998ee672ac9b951cd49319f5"},
 ]
 
 [package.dependencies]
@@ -1811,13 +1811,13 @@ test = ["coveralls (>=3.3,<5)", "pytest (>=8,<9)", "pytest-asyncio (>=0.16,<2)",
 
 [[package]]
 name = "graphql-core"
-version = "3.2.6"
+version = "3.2.7"
 description = "GraphQL implementation for Python, a port of GraphQL.js, the JavaScript reference implementation for GraphQL."
 optional = true
-python-versions = "<4,>=3.6"
+python-versions = "<4,>=3.7"
 files = [
-    {file = "graphql_core-3.2.6-py3-none-any.whl", hash = "sha256:78b016718c161a6fb20a7d97bbf107f331cd1afe53e45566c59f776ed7f0b45f"},
-    {file = "graphql_core-3.2.6.tar.gz", hash = "sha256:c08eec22f9e40f0bd61d805907e3b3b1b9a320bc606e23dc145eebca07c8fbab"},
+    {file = "graphql_core-3.2.7-py3-none-any.whl", hash = "sha256:17fc8f3ca4a42913d8e24d9ac9f08deddf0a0b2483076575757f6c412ead2ec0"},
+    {file = "graphql_core-3.2.7.tar.gz", hash = "sha256:27b6904bdd3b43f2a0556dad5d579bdfdeab1f38e8e8788e555bdcb586a6f62c"},
 ]
 
 [[package]]
@@ -2148,6 +2148,21 @@ files = [
     {file = "httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d"},
 ]
 
+[[package]]
+name = "huey"
+version = "2.5.4"
+description = "huey, a little task queue"
+optional = true
+python-versions = "*"
+files = [
+    {file = "huey-2.5.4-py3-none-any.whl", hash = "sha256:0eac1fb2711f6366a1db003629354a0cea470a3db720d5bab0d140c28e993f9c"},
+    {file = "huey-2.5.4.tar.gz", hash = "sha256:4b7fb217b640fbb46efc4f4681b446b40726593522f093e8ef27c4a8fcb6cfbb"},
+]
+
+[package.extras]
+backends = ["redis (>=3.0.0)"]
+redis = ["redis (>=3.0.0)"]
+
 [[package]]
 name = "huggingface-hub"
 version = "0.36.0"
@@ -2645,13 +2660,13 @@ files = [
 
 [[package]]
 name = "litellm-proxy-extras"
-version = "0.4.1"
+version = "0.4.3"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 optional = true
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 files = [
-    {file = "litellm_proxy_extras-0.4.1-py3-none-any.whl", hash = "sha256:9f9b29c9824fe153a5cce5001211884fab858a47185bc7fcda39e5fca07c7b9b"},
-    {file = "litellm_proxy_extras-0.4.1.tar.gz", hash = "sha256:5d055352e0592d062604fdd4067294e52b0692575cef140c09b11b3cc633169a"},
+    {file = "litellm_proxy_extras-0.4.3-py3-none-any.whl", hash = "sha256:e7ab09aa78d04d02dc48975620defa36784e1a0baa6d04a078b98b5717fcae24"},
+    {file = "litellm_proxy_extras-0.4.3.tar.gz", hash = "sha256:420400d0db186319695526f6765d3d481206fe025b70bc74a1ce895a7d720bee"},
 ]
 
 [[package]]
@@ -2934,13 +2949,13 @@ dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
 
 [[package]]
 name = "mlflow"
-version = "3.5.1"
+version = "3.6.0"
 description = "MLflow is an open source platform for the complete machine learning lifecycle"
 optional = true
 python-versions = ">=3.10"
 files = [
-    {file = "mlflow-3.5.1-py3-none-any.whl", hash = "sha256:ebbf5fef59787161a15f2878f210877a62d54d943ad6cea140621687b2393f85"},
-    {file = "mlflow-3.5.1.tar.gz", hash = "sha256:32630f2aaadeb6dc6ccbde56247a1500518b38d0a7cc12f714be1703b6ee3ea1"},
+    {file = "mlflow-3.6.0-py3-none-any.whl", hash = "sha256:04d1691facd412be8e61b963fad859286cfeb2dbcafaea294e6aa0b83a15fc04"},
+    {file = "mlflow-3.6.0.tar.gz", hash = "sha256:d945d259b5c6b551a9f26846db8979fd84c78114a027b77ada3298f821a9b0e1"},
 ]
 
 [package.dependencies]
@@ -2951,12 +2966,13 @@ Flask = "<4"
 Flask-CORS = "<7"
 graphene = "<4"
 gunicorn = {version = "<24", markers = "platform_system != \"Windows\""}
+huey = ">=2.5.0,<3"
 matplotlib = "<4"
-mlflow-skinny = "3.5.1"
-mlflow-tracing = "3.5.1"
+mlflow-skinny = "3.6.0"
+mlflow-tracing = "3.6.0"
 numpy = "<3"
 pandas = "<3"
-pyarrow = ">=4.0.0,<22"
+pyarrow = ">=4.0.0,<23"
 scikit-learn = "<2"
 scipy = "<2"
 sqlalchemy = ">=1.4.0,<3"
@@ -2970,21 +2986,20 @@ extras = ["azureml-core (>=1.2.0)", "boto3", "botocore", "google-cloud-storage (
 gateway = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<2)"]
 genai = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<2)"]
 jfrog = ["mlflow-jfrog-plugin"]
-jobs = ["huey (>=2.5.0,<3)"]
-langchain = ["langchain (>=0.3.3,<=0.3.27)"]
+langchain = ["langchain (>=0.3.7,<=0.3.27)"]
 mcp = ["fastmcp (>=2.0.0,<3)"]
 mlserver = ["mlserver (>=1.2.0,!=1.3.1,<2.0.0)", "mlserver-mlflow (>=1.2.0,!=1.3.1,<2.0.0)"]
 sqlserver = ["mlflow-dbstore"]
 
 [[package]]
 name = "mlflow-skinny"
-version = "3.5.1"
+version = "3.6.0"
 description = "MLflow is an open source platform for the complete machine learning lifecycle"
 optional = true
 python-versions = ">=3.10"
 files = [
-    {file = "mlflow_skinny-3.5.1-py3-none-any.whl", hash = "sha256:e5f96977d21a093a3ffda789bee90070855dbfe1b9d0703c0c3e34d2f8d7fba8"},
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 [package.dependencies]
@@ -3000,7 +3015,7 @@ opentelemetry-proto = ">=1.9.0,<3"
 opentelemetry-sdk = ">=1.9.0,<3"
 packaging = "<26"
 protobuf = ">=3.12.0,<7"
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 requests = ">=2.17.3,<3"
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 packaging = "<26"
 protobuf = ">=3.12.0,<7"
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 [[package]]
 name = "msal"
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+    {file = "pyarrow-22.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:6dda1ddac033d27421c20d7a7943eec60be44e0db4e079f33cc5af3b8280ccde"},
+    {file = "pyarrow-22.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:84378110dd9a6c06323b41b56e129c504d157d1a983ce8f5443761eb5256bafc"},
+    {file = "pyarrow-22.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:854794239111d2b88b40b6ef92aa478024d1e5074f364033e73e21e3f76b25e0"},
+    {file = "pyarrow-22.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:b883fe6fd85adad7932b3271c38ac289c65b7337c2c132e9569f9d3940620730"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:7a820d8ae11facf32585507c11f04e3f38343c1e784c9b5a8b1da5c930547fe2"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:c6ec3675d98915bf1ec8b3c7986422682f7232ea76cad276f4c8abd5b7319b70"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:3e739edd001b04f654b166204fc7a9de896cf6007eaff33409ee9e50ceaff754"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:7388ac685cab5b279a41dfe0a6ccd99e4dbf322edfb63e02fc0443bf24134e91"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:f633074f36dbc33d5c05b5dc75371e5660f1dbf9c8b1d95669def05e5425989c"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4c19236ae2402a8663a2c8f21f1870a03cc57f0bef7e4b6eb3238cc82944de80"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:0c34fe18094686194f204a3b1787a27456897d8a2d62caf84b61e8dfbc0252ae"},
+    {file = "pyarrow-22.0.0.tar.gz", hash = "sha256:3d600dc583260d845c7d8a6db540339dd883081925da2bd1c5cb808f720b3cd9"},
 ]
 
-[package.extras]
-test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
-
 [[package]]
 name = "pyasn1"
 version = "0.6.1"
@@ -5391,6 +5409,29 @@ files = [
     {file = "snowballstemmer-3.0.1.tar.gz", hash = "sha256:6d5eeeec8e9f84d4d56b847692bacf79bc2c8e90c7f80ca4444ff8b6f2e52895"},
 ]
 
+[[package]]
+name = "soundfile"
+version = "0.12.1"
+description = "An audio library based on libsndfile, CFFI and NumPy"
+optional = true
+python-versions = "*"
+files = [
+    {file = "soundfile-0.12.1-py2.py3-none-any.whl", hash = "sha256:828a79c2e75abab5359f780c81dccd4953c45a2c4cd4f05ba3e233ddf984b882"},
+    {file = "soundfile-0.12.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:d922be1563ce17a69582a352a86f28ed8c9f6a8bc951df63476ffc310c064bfa"},
+    {file = "soundfile-0.12.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:bceaab5c4febb11ea0554566784bcf4bc2e3977b53946dda2b12804b4fe524a8"},
+    {file = "soundfile-0.12.1-py2.py3-none-manylinux_2_17_x86_64.whl", hash = "sha256:2dc3685bed7187c072a46ab4ffddd38cef7de9ae5eb05c03df2ad569cf4dacbc"},
+    {file = "soundfile-0.12.1-py2.py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:074247b771a181859d2bc1f98b5ebf6d5153d2c397b86ee9e29ba602a8dfe2a6"},
+    {file = "soundfile-0.12.1-py2.py3-none-win32.whl", hash = "sha256:59dfd88c79b48f441bbf6994142a19ab1de3b9bb7c12863402c2bc621e49091a"},
+    {file = "soundfile-0.12.1-py2.py3-none-win_amd64.whl", hash = "sha256:0d86924c00b62552b650ddd28af426e3ff2d4dc2e9047dae5b3d8452e0a49a77"},
+    {file = "soundfile-0.12.1.tar.gz", hash = "sha256:e8e1017b2cf1dda767aef19d2fd9ee5ebe07e050d430f77a0a7c66ba08b8cdae"},
+]
+
+[package.dependencies]
+cffi = ">=1.0"
+
+[package.extras]
+numpy = ["numpy"]
+
 [[package]]
 name = "sphinx"
 version = "7.1.2"
@@ -6512,11 +6553,11 @@ type = ["pytest-mypy"]
 caching = ["diskcache"]
 extra-proxy = ["azure-identity", "azure-keyvault-secrets", "google-cloud-iam", "google-cloud-kms", "prisma", "redisvl", "resend"]
 mlflow = ["mlflow"]
-proxy = ["PyJWT", "apscheduler", "azure-identity", "azure-storage-blob", "backoff", "boto3", "cryptography", "fastapi", "fastapi-sso", "gunicorn", "litellm-enterprise", "litellm-proxy-extras", "mcp", "orjson", "polars", "pynacl", "python-multipart", "pyyaml", "rich", "rq", "uvicorn", "uvloop", "websockets"]
+proxy = ["PyJWT", "apscheduler", "azure-identity", "azure-storage-blob", "backoff", "boto3", "cryptography", "fastapi", "fastapi-sso", "gunicorn", "litellm-enterprise", "litellm-proxy-extras", "mcp", "orjson", "polars", "pynacl", "python-multipart", "pyyaml", "rich", "rq", "soundfile", "uvicorn", "uvloop", "websockets"]
 semantic-router = ["semantic-router"]
 utils = ["numpydoc"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0, !=3.9.7"
-content-hash = "4aab4263275116fad0eaefa44fb10d8a9934525bf506bd35db39ca9310822691"
+content-hash = "9aa69423e29fd687063c54a6afa789fd19f3828cddcd94cbf4ee5bda17d13b32"
diff --git a/pyproject.toml b/pyproject.toml
index 6b219307ec8..ebfa3345fa7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.79.1"
+version = "1.79.3"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -59,13 +59,14 @@ websockets = {version = "^13.1.0", optional = true}
 boto3 = {version = "1.36.0", optional = true}
 redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
 mcp = {version = "^1.10.0", optional = true, python = ">=3.10"}
-litellm-proxy-extras = {version = "0.4.1", optional = true}
+litellm-proxy-extras = {version = "0.4.3", optional = true}
 rich = {version = "13.7.1", optional = true}
 litellm-enterprise = {version = "0.1.20", optional = true}
 diskcache = {version = "^5.6.1", optional = true}
 polars = {version = "^1.31.0", optional = true, python = ">=3.10"}
 semantic-router = {version = "*", optional = true, python = ">=3.9"}
 mlflow = {version = ">3.1.4", optional = true, python = ">=3.10"}
+soundfile = {version = "^0.12.1", optional = true}
 
 [tool.poetry.extras]
 proxy = [
@@ -92,6 +93,7 @@ proxy = [
     "litellm-enterprise",
     "rich",
     "polars",
+    "soundfile",
 ]
 
 extra_proxy = [
@@ -157,7 +159,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.79.1"
+version = "1.79.3"
 version_files = [
     "pyproject.toml:^version"
 ]
diff --git a/requirements.txt b/requirements.txt
index ce1c41ce268..cf2ce85e52f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -43,7 +43,7 @@ sentry_sdk==2.21.0 # for sentry error handling
 detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests
 cryptography==44.0.1
 tzdata==2025.1 # IANA time zone database
-litellm-proxy-extras==0.4.1 # for proxy extras - e.g. prisma migrations
+litellm-proxy-extras==0.4.3 # for proxy extras - e.g. prisma migrations
 ### LITELLM PACKAGE DEPENDENCIES
 python-dotenv==1.0.1 # for env
 tiktoken==0.8.0 # for calculating usage
@@ -58,6 +58,7 @@ tenacity==8.5.0  # for retrying requests, when litellm.num_retries set
 pydantic==2.10.2 # proxy + openai req.
 jsonschema==4.22.0 # validating json schema
 websockets==13.1.0 # for realtime API
+soundfile==0.12.1 # for audio file processing
 
 ########################
 # LITELLM ENTERPRISE DEPENDENCIES
diff --git a/schema.prisma b/schema.prisma
index 760954a1d60..025a1a0e3ce 100644
--- a/schema.prisma
+++ b/schema.prisma
@@ -174,6 +174,7 @@ model LiteLLM_MCPServerTable {
   url          String?
   transport    String         @default("sse")
   auth_type    String?        
+  credentials  Json?          @default("{}")
   created_at   DateTime?      @default(now()) @map("created_at")
   created_by   String?
   updated_at   DateTime?      @default(now()) @updatedAt @map("updated_at")
diff --git a/tests/code_coverage_tests/enforce_llms_folder_style.py b/tests/code_coverage_tests/enforce_llms_folder_style.py
index b8ac177ff6a..7588f7771b0 100644
--- a/tests/code_coverage_tests/enforce_llms_folder_style.py
+++ b/tests/code_coverage_tests/enforce_llms_folder_style.py
@@ -12,6 +12,8 @@ SEARCH_PROVIDERS = [
     "google_pse",
     "parallel_ai",
     "exa_ai",
+    "firecrawl",
+    "searxng",
 ]
 
 ALLOWED_FILES_IN_LLMS_FOLDER = [
diff --git a/tests/guardrails_tests/test_tracing_guardrails.py b/tests/guardrails_tests/test_tracing_guardrails.py
index d7589c53879..068ecae7bc8 100644
--- a/tests/guardrails_tests/test_tracing_guardrails.py
+++ b/tests/guardrails_tests/test_tracing_guardrails.py
@@ -34,9 +34,72 @@ async def test_standard_logging_payload_includes_guardrail_information():
     presidio_guard = _OPTIONAL_PresidioPIIMasking(
         guardrail_name="presidio_guard",
         event_hook=GuardrailEventHooks.pre_call,
-        presidio_analyzer_api_base=os.getenv("PRESIDIO_ANALYZER_API_BASE"),
-        presidio_anonymizer_api_base=os.getenv("PRESIDIO_ANONYMIZER_API_BASE"),
+        presidio_analyzer_api_base="https://mock-presidio-analyzer.com/",
+        presidio_anonymizer_api_base="https://mock-presidio-anonymizer.com/",
     )
+    
+    # Mock the Presidio API responses
+    mock_analyze_response = [
+        {
+            "analysis_explanation": {"recognizer": "PhoneRecognizer", "pattern": "phone"},
+            "start": 26,
+            "end": 40,
+            "score": 0.75,
+            "entity_type": "PHONE_NUMBER"
+        }
+    ]
+    
+    mock_anonymize_response = {
+        "text": "Hello, my phone number is <PHONE_NUMBER>",
+        "items": [
+            {
+                "start": 26,
+                "end": 40,
+                "entity_type": "PHONE_NUMBER",
+                "text": "<PHONE_NUMBER>",
+                "operator": "replace"
+            }
+        ]
+    }
+    
+    # Create mock response objects
+    mock_analyze_resp = MagicMock()
+    mock_analyze_resp.json = AsyncMock(return_value=mock_analyze_response)
+    
+    mock_anonymize_resp = MagicMock()
+    mock_anonymize_resp.json = AsyncMock(return_value=mock_anonymize_response)
+    
+    # Mock the aiohttp ClientSession with global call tracking
+    call_counter = {"count": 0}
+    
+    class MockClientSession:
+        def __init__(self):
+            pass
+            
+        async def __aenter__(self):
+            return self
+            
+        async def __aexit__(self, exc_type, exc_val, exc_tb):
+            pass
+            
+        def post(self, url, json=None):
+            class MockResponse:
+                def __init__(self, response_obj):
+                    self.response_obj = response_obj
+                    
+                async def __aenter__(self):
+                    return self.response_obj
+                    
+                async def __aexit__(self, exc_type, exc_val, exc_tb):
+                    pass
+            
+            # Return analyze response first, then anonymize response
+            call_counter["count"] += 1
+            if "analyze" in url:
+                return MockResponse(mock_analyze_resp)
+            else:
+                return MockResponse(mock_anonymize_resp)
+    
     # 1. call the pre call hook with guardrail
     request_data = {
         "model": "gpt-4o",
@@ -47,12 +110,14 @@ async def test_standard_logging_payload_includes_guardrail_information():
         "guardrails": ["presidio_guard"],
         "metadata": {},
     }
-    await presidio_guard.async_pre_call_hook(
-        user_api_key_dict={},
-        cache=None,
-        data=request_data,
-        call_type="acompletion"
-    )
+    
+    with patch("aiohttp.ClientSession", MockClientSession):
+        await presidio_guard.async_pre_call_hook(
+            user_api_key_dict={},
+            cache=None,
+            data=request_data,
+            call_type="acompletion"
+        )
 
     # 2. call litellm.acompletion
     response = await litellm.acompletion(**request_data)
@@ -62,11 +127,17 @@ async def test_standard_logging_payload_includes_guardrail_information():
     print("got standard logging payload=", json.dumps(test_custom_logger.standard_logging_payload, indent=4, default=str))
     assert test_custom_logger.standard_logging_payload is not None
     assert test_custom_logger.standard_logging_payload["guardrail_information"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_name"] == "presidio_guard"
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_mode"] == GuardrailEventHooks.pre_call
+    
+    # guardrail_information is now a list
+    assert isinstance(test_custom_logger.standard_logging_payload["guardrail_information"], list)
+    assert len(test_custom_logger.standard_logging_payload["guardrail_information"]) > 0
+    
+    guardrail_info = test_custom_logger.standard_logging_payload["guardrail_information"][0]
+    assert guardrail_info["guardrail_name"] == "presidio_guard"
+    assert guardrail_info["guardrail_mode"] == GuardrailEventHooks.pre_call
 
     # assert that the guardrail_response is a response from presidio analyze
-    presidio_response = test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_response"]
+    presidio_response = guardrail_info["guardrail_response"]
     assert isinstance(presidio_response, list)
     for response_item in presidio_response:
         assert "analysis_explanation" in response_item
@@ -76,12 +147,12 @@ async def test_standard_logging_payload_includes_guardrail_information():
         assert "entity_type" in response_item
 
     # assert that the duration is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["duration"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["duration"] > 0
+    assert guardrail_info["duration"] is not None
+    assert guardrail_info["duration"] > 0
 
     # assert that we get the count of masked entities
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["masked_entity_count"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["masked_entity_count"]["PHONE_NUMBER"] == 1
+    assert guardrail_info["masked_entity_count"] is not None
+    assert guardrail_info["masked_entity_count"]["PHONE_NUMBER"] == 1
 
 
 
@@ -255,10 +326,13 @@ async def test_bedrock_guardrail_status_blocked():
     # Verify the standard logging payload was captured
     assert test_custom_logger.standard_logging_payload is not None
     assert test_custom_logger.standard_logging_payload["guardrail_information"] is not None
+    assert isinstance(test_custom_logger.standard_logging_payload["guardrail_information"], list)
+    assert len(test_custom_logger.standard_logging_payload["guardrail_information"]) > 0
     
-    # Verify guardrail information fields
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_status"] == "guardrail_intervened"
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_provider"] == "bedrock"
+    # Verify guardrail information fields (guardrail_information is now a list)
+    guardrail_info = test_custom_logger.standard_logging_payload["guardrail_information"][0]
+    assert guardrail_info["guardrail_status"] == "guardrail_intervened"
+    assert guardrail_info["guardrail_provider"] == "bedrock"
     
     # Verify the new typed status fields
     # guardrail_status should be "guardrail_intervened" when content is blocked
@@ -334,8 +408,12 @@ async def test_bedrock_guardrail_status_success():
     # Check standard logging payload status fields
     assert test_custom_logger.standard_logging_payload is not None
     assert test_custom_logger.standard_logging_payload["guardrail_information"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_status"] == "success"
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_provider"] == "bedrock"
+    assert isinstance(test_custom_logger.standard_logging_payload["guardrail_information"], list)
+    assert len(test_custom_logger.standard_logging_payload["guardrail_information"]) > 0
+    
+    guardrail_info = test_custom_logger.standard_logging_payload["guardrail_information"][0]
+    assert guardrail_info["guardrail_status"] == "success"
+    assert guardrail_info["guardrail_provider"] == "bedrock"
     
     # Check status fields
     status_fields = test_custom_logger.standard_logging_payload.get("status_fields", {})
@@ -410,8 +488,12 @@ async def test_bedrock_guardrail_status_failure():
     # Check standard logging payload status fields
     assert test_custom_logger.standard_logging_payload is not None
     assert test_custom_logger.standard_logging_payload["guardrail_information"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_status"] == "guardrail_failed_to_respond"
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_provider"] == "bedrock"
+    assert isinstance(test_custom_logger.standard_logging_payload["guardrail_information"], list)
+    assert len(test_custom_logger.standard_logging_payload["guardrail_information"]) > 0
+    
+    guardrail_info = test_custom_logger.standard_logging_payload["guardrail_information"][0]
+    assert guardrail_info["guardrail_status"] == "guardrail_failed_to_respond"
+    assert guardrail_info["guardrail_provider"] == "bedrock"
     
     # Check status fields
     status_fields = test_custom_logger.standard_logging_payload.get("status_fields", {})
@@ -454,6 +536,7 @@ async def test_noma_guardrail_status_blocked():
     mock_response.status_code = 200
     mock_response.json.return_value = {
         "verdict": False,
+        "aggregatedScanResult": True,
         "originalResponse": {
             "prompt": {
                 "topicDetector": {"harmful": {"result": True}}
@@ -490,8 +573,12 @@ async def test_noma_guardrail_status_blocked():
     # Check standard logging payload status fields
     assert test_custom_logger.standard_logging_payload is not None
     assert test_custom_logger.standard_logging_payload["guardrail_information"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_status"] == "guardrail_intervened"
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_provider"] == "noma"
+    assert isinstance(test_custom_logger.standard_logging_payload["guardrail_information"], list)
+    assert len(test_custom_logger.standard_logging_payload["guardrail_information"]) > 0
+    
+    guardrail_info = test_custom_logger.standard_logging_payload["guardrail_information"][0]
+    assert guardrail_info["guardrail_status"] == "guardrail_intervened"
+    assert guardrail_info["guardrail_provider"] == "noma"
     
     # Check status fields
     status_fields = test_custom_logger.standard_logging_payload.get("status_fields", {})
@@ -534,6 +621,7 @@ async def test_noma_guardrail_status_success():
     mock_response.status_code = 200
     mock_response.json.return_value = {
         "verdict": True,
+        "aggregatedScanResult": False,
         "originalResponse": {"prompt": {}}
     }
     mock_response.raise_for_status = MagicMock()
@@ -562,8 +650,12 @@ async def test_noma_guardrail_status_success():
     # Check standard logging payload status fields
     assert test_custom_logger.standard_logging_payload is not None
     assert test_custom_logger.standard_logging_payload["guardrail_information"] is not None
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_status"] == "success"
-    assert test_custom_logger.standard_logging_payload["guardrail_information"]["guardrail_provider"] == "noma"
+    assert isinstance(test_custom_logger.standard_logging_payload["guardrail_information"], list)
+    assert len(test_custom_logger.standard_logging_payload["guardrail_information"]) > 0
+    
+    guardrail_info = test_custom_logger.standard_logging_payload["guardrail_information"][0]
+    assert guardrail_info["guardrail_status"] == "success"
+    assert guardrail_info["guardrail_provider"] == "noma"
     
     # Check status fields
     status_fields = test_custom_logger.standard_logging_payload.get("status_fields", {})
@@ -586,7 +678,8 @@ def test_guardrail_status_fields_computation():
     from litellm.litellm_core_utils.litellm_logging import _get_status_fields
     
     # Test guardrail_intervened status (content was blocked by guardrail)
-    intervened_info = {"guardrail_status": "guardrail_intervened"}
+    # guardrail_information is now a list
+    intervened_info = [{"guardrail_status": "guardrail_intervened"}]
     status_fields_intervened = _get_status_fields(
         status="success",
         guardrail_information=intervened_info,
@@ -596,7 +689,7 @@ def test_guardrail_status_fields_computation():
     assert status_fields_intervened["guardrail_status"] == "guardrail_intervened"
     
     # Test legacy blocked status (for backward compatibility)
-    blocked_info = {"guardrail_status": "blocked"}
+    blocked_info = [{"guardrail_status": "blocked"}]
     status_fields_blocked = _get_status_fields(
         status="success",
         guardrail_information=blocked_info,
@@ -606,7 +699,7 @@ def test_guardrail_status_fields_computation():
     assert status_fields_blocked["guardrail_status"] == "guardrail_intervened"
     
     # Test success status
-    success_info = {"guardrail_status": "success"}
+    success_info = [{"guardrail_status": "success"}]
     status_fields_success = _get_status_fields(
         status="success",
         guardrail_information=success_info,
@@ -616,7 +709,7 @@ def test_guardrail_status_fields_computation():
     assert status_fields_success["guardrail_status"] == "success"
     
     # Test guardrail_failed_to_respond status
-    failed_info = {"guardrail_status": "guardrail_failed_to_respond"}
+    failed_info = [{"guardrail_status": "guardrail_failed_to_respond"}]
     status_fields_failed = _get_status_fields(
         status="failure",
         guardrail_information=failed_info,
@@ -626,7 +719,7 @@ def test_guardrail_status_fields_computation():
     assert status_fields_failed["guardrail_status"] == "guardrail_failed_to_respond"
     
     # Test legacy failure status (for backward compatibility)
-    failure_info = {"guardrail_status": "failure"}
+    failure_info = [{"guardrail_status": "failure"}]
     status_fields_failure = _get_status_fields(
         status="failure",
         guardrail_information=failure_info,
diff --git a/tests/image_gen_tests/base_image_generation_test.py b/tests/image_gen_tests/base_image_generation_test.py
index e18094dceb0..e3c7d79b3b1 100644
--- a/tests/image_gen_tests/base_image_generation_test.py
+++ b/tests/image_gen_tests/base_image_generation_test.py
@@ -79,7 +79,6 @@ class BaseImageGenTest(ABC):
             if "usage" in response_dict:
                 response_dict["usage"] = dict(response_dict["usage"])
             print("response usage=", response_dict.get("usage"))
-            ImagesResponse.model_validate(response_dict)
 
             for d in response.data:
                 assert isinstance(d, Image)
diff --git a/tests/image_gen_tests/test_image_generation.py b/tests/image_gen_tests/test_image_generation.py
index 4fc8a18a9d3..cca021ad163 100644
--- a/tests/image_gen_tests/test_image_generation.py
+++ b/tests/image_gen_tests/test_image_generation.py
@@ -118,6 +118,7 @@ class TestVertexImageGeneration(BaseImageGenTest):
             "n": 1,
         }
 
+
 class TestBedrockNovaCanvasTextToImage(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
         litellm.in_memory_llm_clients_cache = InMemoryCache()
@@ -164,10 +165,12 @@ class TestAimlImageGeneration(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
         return {"model": "aiml/flux-pro/v1.1"}
 
+
 class TestFAL_AI_ImageGeneration(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
         return {"model": "fal_ai/fal-ai/imagen4/preview"}
 
+
 class TestGoogleImageGen(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
         return {"model": "gemini/imagen-4.0-generate-001"}
@@ -175,7 +178,6 @@ class TestGoogleImageGen(BaseImageGenTest):
 
 class TestAzureOpenAIDalle3(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
-        litellm.set_verbose = True
         return {
             "model": "azure/dall-e-3",
             "api_version": "2024-02-01",
@@ -189,7 +191,6 @@ class TestAzureOpenAIDalle3(BaseImageGenTest):
         }
 
 
-
 @pytest.mark.skip(reason="model EOL")
 @pytest.mark.asyncio
 async def test_aimage_generation_bedrock_with_optional_params():
@@ -281,13 +282,13 @@ async def test_aiml_image_generation_with_dynamic_api_key():
         assert captured_json_data["prompt"] == "A cute baby sea otter"
         assert captured_json_data["model"] == "flux-pro/v1.1"
 
+
 @pytest.mark.asyncio
 async def test_azure_image_generation_request_body():
     from litellm import aimage_generation
+
     test_dir = os.path.dirname(__file__)
-    expected_path = os.path.join(
-        test_dir, "request_payloads", "azure_gpt_image_1.json"
-    )
+    expected_path = os.path.join(test_dir, "request_payloads", "azure_gpt_image_1.json")
     with open(expected_path, "r") as f:
         expected_body = json.load(f)
 
@@ -299,12 +300,12 @@ async def test_azure_image_generation_request_body():
 
         with pytest.raises(Exception):
             await aimage_generation(
-                    model="azure/gpt-image-1",
-                    prompt="test prompt",
-                    api_base="https://example.azure.com",
-                    api_key="test-key",
-                    api_version="2025-04-01-preview",
-                )
+                model="azure/gpt-image-1",
+                prompt="test prompt",
+                api_base="https://example.azure.com",
+                api_key="test-key",
+                api_version="2025-04-01-preview",
+            )
 
         mock_post.assert_called_once()
         call_args = mock_post.call_args
diff --git a/tests/litellm_utils_tests/test_cyberark.py b/tests/litellm_utils_tests/test_cyberark.py
index 45783b99fe9..24c84687705 100644
--- a/tests/litellm_utils_tests/test_cyberark.py
+++ b/tests/litellm_utils_tests/test_cyberark.py
@@ -9,7 +9,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 sys.path.insert(0, os.path.abspath("../.."))
-from unittest.mock import patch
+from unittest.mock import AsyncMock, MagicMock, patch
 from litellm._uuid import uuid
 
 # Set up environment variables for testing
@@ -21,42 +21,87 @@ os.environ["CYBERARK_USERNAME"] = "admin"
 from litellm.secret_managers.cyberark_secret_manager import CyberArkSecretManager
 
 
+def create_mock_response(status_code: int, text: str = ""):
+    """
+    Helper function to create a mock HTTP response.
+    """
+    mock_response = MagicMock()
+    mock_response.status_code = status_code
+    mock_response.text = text
+    mock_response.raise_for_status = MagicMock()
+    
+    if status_code >= 400:
+        import httpx
+        error = httpx.HTTPStatusError(
+            message=f"HTTP {status_code}",
+            request=MagicMock(),
+            response=mock_response
+        )
+        mock_response.raise_for_status.side_effect = error
+    
+    return mock_response
+
+
 @pytest.mark.asyncio
 async def test_cyberark_write_and_read_secret():
     """
-    Integration test: Write a secret to CyberArk Conjur and read it back to validate.
+    Test writing a secret to CyberArk Conjur and reading it back using mocked HTTP requests.
     """
     with patch("litellm.proxy.proxy_server.premium_user", True):
-        # Create CyberArk secret manager instance
-        cyberark_manager = CyberArkSecretManager()
-
         # Generate unique secret name and value
         secret_name = f"test-secret-{uuid.uuid4()}"
         secret_value = f"test-value-{uuid.uuid4()}"
 
-        # Write the secret
-        write_response = await cyberark_manager.async_write_secret(
-            secret_name=secret_name,
-            secret_value=secret_value,
+        # Mock sync httpx client (for auth, ensure variable exists, sync read)
+        mock_sync_client = MagicMock()
+        # Auth response
+        mock_sync_client.post.return_value = create_mock_response(
+            status_code=200, text="mock-token"
+        )
+        # Sync read response
+        mock_sync_client.get.return_value = create_mock_response(
+            status_code=200, text=secret_value
         )
-        print("write_response=", write_response)
 
-        # Validate write was successful
-        assert write_response["status"] == "success"
+        # Mock async httpx client (for async write)
+        mock_async_client = AsyncMock()
+        mock_async_client.post.return_value = create_mock_response(
+            status_code=201, text=""
+        )
 
-        # Read the secret back
-        read_value = cyberark_manager.sync_read_secret(secret_name=secret_name)
-        print("READ VALUE=", read_value)
+        with patch(
+            "litellm.secret_managers.cyberark_secret_manager._get_httpx_client",
+            return_value=mock_sync_client,
+        ), patch(
+            "litellm.secret_managers.cyberark_secret_manager.get_async_httpx_client",
+            return_value=mock_async_client,
+        ):
+            # Create CyberArk secret manager instance
+            cyberark_manager = CyberArkSecretManager()
 
-        # Validate the secret exists and has the correct value
-        assert read_value is not None
-        assert read_value == secret_value
+            # Write the secret
+            write_response = await cyberark_manager.async_write_secret(
+                secret_name=secret_name,
+                secret_value=secret_value,
+            )
+            print("write_response=", write_response)
+
+            # Validate write was successful
+            assert write_response["status"] == "success"
+
+            # Read the secret back
+            read_value = cyberark_manager.sync_read_secret(secret_name=secret_name)
+            print("READ VALUE=", read_value)
+
+            # Validate the secret exists and has the correct value
+            assert read_value is not None
+            assert read_value == secret_value
 
 
 @pytest.mark.asyncio
 async def test_cyberark_rotate_secret():
     """
-    Integration test: Test key rotation in CyberArk Conjur.
+    Test key rotation in CyberArk Conjur using mocked HTTP requests.
     
     This test simulates what happens when a virtual key is rotated:
     1. Write initial secret with alias (like sk-1234)
@@ -64,9 +109,6 @@ async def test_cyberark_rotate_secret():
     3. Verify reading the secret returns the NEW value
     """
     with patch("litellm.proxy.proxy_server.premium_user", True):
-        # Create CyberArk secret manager instance
-        cyberark_manager = CyberArkSecretManager()
-
         # Simulate initial virtual key creation
         secret_alias = f"test-rotation-key-{uuid.uuid4()}"
         initial_key_value = f"sk-initial-{uuid.uuid4()}"
@@ -77,45 +119,92 @@ async def test_cyberark_rotate_secret():
         print(f"Initial value: {initial_key_value}")
         print(f"Rotated value: {rotated_key_value}")
 
-        # Step 1: Write initial secret (simulates key creation)
-        write_response = await cyberark_manager.async_write_secret(
-            secret_name=secret_alias,
-            secret_value=initial_key_value,
+        # Store the current value to simulate actual storage behavior
+        current_value = {"value": initial_key_value}
+
+        # Mock sync httpx client (for auth, ensure variable exists, sync reads)
+        mock_sync_client = MagicMock()
+        # Auth response
+        mock_sync_client.post.return_value = create_mock_response(
+            status_code=200, text="mock-token"
         )
-        print(f"\n1. Initial write response: {write_response}")
-        assert write_response["status"] == "success"
-
-        # Verify initial value was written
-        initial_read = cyberark_manager.sync_read_secret(secret_name=secret_alias)
-        print(f"2. Initial read value: {initial_read}")
-        assert initial_read == initial_key_value
-
-        # Step 2: Rotate the secret (simulates key rotation)
-        # In key rotation, we keep the same secret_name but update the value
-        rotation_response = await cyberark_manager.async_rotate_secret(
-            current_secret_name=secret_alias,
-            new_secret_name=secret_alias,  # Same name = update in place
-            new_secret_value=rotated_key_value,
-        )
-        print(f"3. Rotation response: {rotation_response}")
-        assert rotation_response["status"] == "success"
-
-        # Step 3: Verify the secret now returns the NEW value
-        rotated_read = cyberark_manager.sync_read_secret(secret_name=secret_alias)
-        print(f"4. After rotation, read value: {rotated_read}")
         
-        # This is the key assertion: after rotation, reading should return the NEW value
-        assert rotated_read is not None
-        assert rotated_read == rotated_key_value
-        assert rotated_read != initial_key_value
+        # Sync reads return the current value from our simulated storage
+        def get_mock_sync_read_response(*args, **kwargs):
+            return create_mock_response(status_code=200, text=current_value["value"])
+        
+        mock_sync_client.get.side_effect = get_mock_sync_read_response
 
-        print(f"\n✅ Rotation successful: {initial_key_value} → {rotated_key_value}")
+        # Mock async httpx client (for async writes and reads)
+        mock_async_client = AsyncMock()
+        
+        # Async writes update the current value
+        async def mock_async_post(*args, **kwargs):
+            content = kwargs.get("content", "")
+            if content:
+                current_value["value"] = content
+            return create_mock_response(status_code=201, text="")
+        
+        mock_async_client.post.side_effect = mock_async_post
+        
+        # Async reads also return the current value
+        async def get_mock_async_read_response(*args, **kwargs):
+            return create_mock_response(status_code=200, text=current_value["value"])
+        
+        mock_async_client.get.side_effect = get_mock_async_read_response
+
+        with patch(
+            "litellm.secret_managers.cyberark_secret_manager._get_httpx_client",
+            return_value=mock_sync_client,
+        ), patch(
+            "litellm.secret_managers.cyberark_secret_manager.get_async_httpx_client",
+            return_value=mock_async_client,
+        ):
+            # Create CyberArk secret manager instance
+            cyberark_manager = CyberArkSecretManager()
+
+            # Step 1: Write initial secret (simulates key creation)
+            write_response = await cyberark_manager.async_write_secret(
+                secret_name=secret_alias,
+                secret_value=initial_key_value,
+            )
+            print(f"\n1. Initial write response: {write_response}")
+            assert write_response["status"] == "success"
+
+            # Verify initial value was written
+            initial_read = cyberark_manager.sync_read_secret(secret_name=secret_alias)
+            print(f"2. Initial read value: {initial_read}")
+            assert initial_read == initial_key_value
+
+            # Step 2: Rotate the secret (simulates key rotation)
+            # In key rotation, we keep the same secret_name but update the value
+            rotation_response = await cyberark_manager.async_rotate_secret(
+                current_secret_name=secret_alias,
+                new_secret_name=secret_alias,
+                new_secret_value=rotated_key_value,
+            )
+            print(f"3. Rotation response: {rotation_response}")
+            assert rotation_response["status"] == "success"
+
+            # Clear cache to force a fresh read
+            cyberark_manager.cache.flush_cache()
+
+            # Step 3: Verify the secret now returns the NEW value
+            rotated_read = cyberark_manager.sync_read_secret(secret_name=secret_alias)
+            print(f"4. After rotation, read value: {rotated_read}")
+            
+            # This is the key assertion: after rotation, reading should return the NEW value
+            assert rotated_read is not None
+            assert rotated_read == rotated_key_value
+            assert rotated_read != initial_key_value
+
+            print(f"\n✅ Rotation successful: {initial_key_value} → {rotated_key_value}")
 
 
 @pytest.mark.asyncio
 async def test_cyberark_rotate_secret_with_new_alias():
     """
-    Integration test: Test key rotation with a new alias.
+    Test key rotation with a new alias using mocked HTTP requests.
     
     This simulates rotating a key and changing its alias at the same time:
     1. Write secret with alias-v1
@@ -124,9 +213,6 @@ async def test_cyberark_rotate_secret_with_new_alias():
     4. Verify alias-v1 still exists with old value (CyberArk doesn't delete)
     """
     with patch("litellm.proxy.proxy_server.premium_user", True):
-        # Create CyberArk secret manager instance
-        cyberark_manager = CyberArkSecretManager()
-
         # Simulate key rotation with alias change
         base_alias = f"test-alias-change-{uuid.uuid4()}"
         old_alias = f"{base_alias}-v1"
@@ -138,33 +224,96 @@ async def test_cyberark_rotate_secret_with_new_alias():
         print(f"Old alias: {old_alias} = {old_value}")
         print(f"New alias: {new_alias} = {new_value}")
 
-        # Step 1: Create initial secret with old alias
-        write_response = await cyberark_manager.async_write_secret(
-            secret_name=old_alias,
-            secret_value=old_value,
+        # Store secrets in a dict to simulate actual storage
+        secrets_store = {}
+
+        # Mock sync httpx client (for auth, ensure variable exists, sync reads)
+        mock_sync_client = MagicMock()
+        # Auth response
+        mock_sync_client.post.return_value = create_mock_response(
+            status_code=200, text="mock-token"
         )
-        print(f"\n1. Initial write: {write_response}")
-        assert write_response["status"] == "success"
+        
+        # Mock sync reads to return from our store
+        def get_mock_sync_read(*args, **kwargs):
+            url = args[0] if args else kwargs.get("url", "")
+            # Extract secret name from URL
+            for secret_name, secret_val in secrets_store.items():
+                if secret_name in url:
+                    return create_mock_response(status_code=200, text=secret_val)
+            return create_mock_response(status_code=404, text="Not found")
+        
+        mock_sync_client.get.side_effect = get_mock_sync_read
 
-        # Step 2: Rotate to new alias with new value
-        rotation_response = await cyberark_manager.async_rotate_secret(
-            current_secret_name=old_alias,
-            new_secret_name=new_alias,  # Different name = new secret
-            new_secret_value=new_value,
-        )
-        print(f"2. Rotation response: {rotation_response}")
-        assert rotation_response["status"] == "success"
+        # Mock async httpx client (for async writes and reads)
+        mock_async_client = AsyncMock()
+        
+        # Mock async write to update our store
+        async def mock_async_post(*args, **kwargs):
+            url = args[0] if args else kwargs.get("url", "")
+            content = kwargs.get("content", "")
+            
+            # Extract secret name from URL and store the value
+            if old_alias in url:
+                secrets_store[old_alias] = content
+            elif new_alias in url:
+                secrets_store[new_alias] = content
+            
+            return create_mock_response(status_code=201, text="")
+        
+        mock_async_client.post.side_effect = mock_async_post
+        
+        # Mock async reads to return from our store
+        async def get_mock_async_read(*args, **kwargs):
+            url = args[0] if args else kwargs.get("url", "")
+            # Extract secret name from URL
+            for secret_name, secret_val in secrets_store.items():
+                if secret_name in url:
+                    return create_mock_response(status_code=200, text=secret_val)
+            return create_mock_response(status_code=404, text="Not found")
+        
+        mock_async_client.get.side_effect = get_mock_async_read
 
-        # Step 3: Verify new alias has new value
-        new_read = cyberark_manager.sync_read_secret(secret_name=new_alias)
-        print(f"3. Read new alias: {new_read}")
-        assert new_read == new_value
+        with patch(
+            "litellm.secret_managers.cyberark_secret_manager._get_httpx_client",
+            return_value=mock_sync_client,
+        ), patch(
+            "litellm.secret_managers.cyberark_secret_manager.get_async_httpx_client",
+            return_value=mock_async_client,
+        ):
+            # Create CyberArk secret manager instance
+            cyberark_manager = CyberArkSecretManager()
 
-        # Step 4: Verify old alias still exists (CyberArk doesn't delete via API)
-        old_read = cyberark_manager.sync_read_secret(secret_name=old_alias)
-        print(f"4. Read old alias (should still exist): {old_read}")
-        assert old_read == old_value  # Old secret still exists in CyberArk
+            # Step 1: Create initial secret with old alias
+            write_response = await cyberark_manager.async_write_secret(
+                secret_name=old_alias,
+                secret_value=old_value,
+            )
+            print(f"\n1. Initial write: {write_response}")
+            assert write_response["status"] == "success"
 
-        print(f"\n✅ Alias rotation successful: {old_alias} → {new_alias}")
-        print(f"   Note: Old alias still exists in CyberArk (expected behavior)")
+            # Step 2: Rotate to new alias with new value
+            rotation_response = await cyberark_manager.async_rotate_secret(
+                current_secret_name=old_alias,
+                new_secret_name=new_alias,
+                new_secret_value=new_value,
+            )
+            print(f"2. Rotation response: {rotation_response}")
+            assert rotation_response["status"] == "success"
+
+            # Clear cache to force fresh reads
+            cyberark_manager.cache.flush_cache()
+
+            # Step 3: Verify new alias has new value
+            new_read = cyberark_manager.sync_read_secret(secret_name=new_alias)
+            print(f"3. Read new alias: {new_read}")
+            assert new_read == new_value
+
+            # Step 4: Verify old alias still exists (CyberArk doesn't delete via API)
+            old_read = cyberark_manager.sync_read_secret(secret_name=old_alias)
+            print(f"4. Read old alias (should still exist): {old_read}")
+            assert old_read == old_value
+
+            print(f"\n✅ Alias rotation successful: {old_alias} → {new_alias}")
+            print(f"   Note: Old alias still exists in CyberArk (expected behavior)")
 
diff --git a/tests/litellm_utils_tests/test_hashicorp.py b/tests/litellm_utils_tests/test_hashicorp.py
index ef8aa889ff1..4757c72262b 100644
--- a/tests/litellm_utils_tests/test_hashicorp.py
+++ b/tests/litellm_utils_tests/test_hashicorp.py
@@ -217,6 +217,49 @@ def test_hashicorp_secret_manager_tls_cert_auth(monkeypatch):
         assert test_manager.cache.get_cache("hcp_vault_token") == "test-client-token-12345"
 
 
+def test_hashicorp_secret_manager_approle_auth(monkeypatch):
+    """
+    Test AppRole authentication makes the expected POST request to the correct URL.
+    """
+    monkeypatch.setenv("HCP_VAULT_TOKEN", "test-token-12345")
+    
+    with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "auth": {
+                "client_token": "hvs.approle-token-67890",
+                "lease_duration": 2764800,
+                "renewable": True,
+            }
+        }
+        mock_response.raise_for_status.return_value = None
+        mock_post.return_value = mock_response
+
+        test_manager = HashicorpSecretManager()
+        test_manager.approle_role_id = "test-role-id-123"
+        test_manager.approle_secret_id = "test-secret-id-456"
+        test_manager.approle_mount_path = "approle"
+        
+        token = test_manager._auth_via_approle()
+
+        assert token == "hvs.approle-token-67890"
+        
+        expected_headers = {}
+        if test_manager.vault_namespace:
+            expected_headers["X-Vault-Namespace"] = test_manager.vault_namespace
+        
+        mock_post.assert_called_once_with(
+            url=f"{test_manager.vault_addr}/v1/auth/approle/login",
+            headers=expected_headers,
+            json={
+                "role_id": "test-role-id-123",
+                "secret_id": "test-secret-id-456",
+            },
+        )
+
+        assert test_manager.cache.get_cache("hcp_vault_approle_token") == "hvs.approle-token-67890"
+
+
 def test_hashicorp_custom_mount_and_prefix():
     """Test URL construction with custom mount name and path prefix using get_url method."""
     # Save original values
diff --git a/tests/litellm_utils_tests/test_health_check.py b/tests/litellm_utils_tests/test_health_check.py
index 4d55a25f334..05c4598f82a 100644
--- a/tests/litellm_utils_tests/test_health_check.py
+++ b/tests/litellm_utils_tests/test_health_check.py
@@ -89,7 +89,7 @@ async def test_azure_img_gen_health_check():
         model_params={
             "model": "azure/dall-e-3",
             "api_base": os.getenv("AZURE_API_BASE"),
-            "api_key": os.getenv("AZURE_API_KEY")
+            "api_key": os.getenv("AZURE_API_KEY"),
         },
         mode="image_generation",
         prompt="cute baby sea otter",
@@ -99,7 +99,6 @@ async def test_azure_img_gen_health_check():
     return response
 
 
-
 @pytest.mark.skip(reason="AWS Suspended Account")
 @pytest.mark.asyncio
 async def test_sagemaker_embedding_health_check():
@@ -192,6 +191,7 @@ async def test_audio_speech_health_check_with_another_voice():
 
     print(response)
 
+
 @pytest.mark.asyncio
 async def test_audio_transcription_health_check():
     litellm.set_verbose = True
@@ -203,6 +203,8 @@ async def test_audio_transcription_health_check():
         mode="audio_transcription",
     )
 
+    print(f"response: {response}")
+
     assert "error" not in response
 
     print(response)
@@ -343,9 +345,12 @@ def test_update_litellm_params_for_health_check():
             "model": f"bedrock/{prefix}anthropic.claude-3-haiku-20240307-v1:0",
             "api_key": "fake_key",
         }
-        updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-        assert updated_params["model"] == f"{prefix}anthropic.claude-3-haiku-20240307-v1:0", \
-            f"Failed to preserve CRIS prefix: {prefix}"
+        updated_params = _update_litellm_params_for_health_check(
+            model_info, litellm_params
+        )
+        assert (
+            updated_params["model"] == f"{prefix}anthropic.claude-3-haiku-20240307-v1:0"
+        ), f"Failed to preserve CRIS prefix: {prefix}"
 
     # Test regional + CRIS combination - region should be stripped, CRIS preserved
     litellm_params = {
@@ -369,14 +374,20 @@ def test_update_litellm_params_for_health_check():
         "api_key": "fake_key",
     }
     updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-    assert updated_params["model"] == "llama/arn:aws:bedrock:us-east-1:123:imported-model/abc"
+    assert (
+        updated_params["model"]
+        == "llama/arn:aws:bedrock:us-east-1:123:imported-model/abc"
+    )
 
     litellm_params = {
         "model": "bedrock/deepseek_r1/arn:aws:bedrock:us-west-2:456:imported-model/xyz",
         "api_key": "fake_key",
     }
     updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-    assert updated_params["model"] == "deepseek_r1/arn:aws:bedrock:us-west-2:456:imported-model/xyz"
+    assert (
+        updated_params["model"]
+        == "deepseek_r1/arn:aws:bedrock:us-west-2:456:imported-model/xyz"
+    )
 
     # Test route specifications - routes should be preserved
     litellm_params = {
@@ -384,7 +395,10 @@ def test_update_litellm_params_for_health_check():
         "api_key": "fake_key",
     }
     updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-    assert updated_params["model"] == "converse/us.anthropic.claude-3-5-sonnet-20240620-v1:0"
+    assert (
+        updated_params["model"]
+        == "converse/us.anthropic.claude-3-5-sonnet-20240620-v1:0"
+    )
 
     litellm_params = {
         "model": "bedrock/invoke/us-west-2/anthropic.claude-instant-v1",
@@ -399,7 +413,10 @@ def test_update_litellm_params_for_health_check():
         "api_key": "fake_key",
     }
     updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-    assert updated_params["model"] == "arn:aws:bedrock:eu-central-1:000:application-inference-profile/abc"
+    assert (
+        updated_params["model"]
+        == "arn:aws:bedrock:eu-central-1:000:application-inference-profile/abc"
+    )
 
     # Test edge case: region + handler + ARN
     litellm_params = {
@@ -407,7 +424,10 @@ def test_update_litellm_params_for_health_check():
         "api_key": "fake_key",
     }
     updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-    assert updated_params["model"] == "llama/arn:aws:bedrock:us-east-1:123:imported-model/abc"
+    assert (
+        updated_params["model"]
+        == "llama/arn:aws:bedrock:us-east-1:123:imported-model/abc"
+    )
 
     # Test edge case: route + region + CRIS
     litellm_params = {
@@ -415,7 +435,10 @@ def test_update_litellm_params_for_health_check():
         "api_key": "fake_key",
     }
     updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
-    assert updated_params["model"] == "converse/eu.anthropic.claude-3-sonnet-20240229-v1:0"
+    assert (
+        updated_params["model"] == "converse/eu.anthropic.claude-3-sonnet-20240229-v1:0"
+    )
+
 
 @pytest.mark.asyncio
 async def test_perform_health_check_with_health_check_model():
@@ -507,6 +530,7 @@ async def test_health_check_bad_model():
             end_time - start_time < 2
         ), "Health check took longer than health_check_timeout"
 
+
 @pytest.mark.asyncio
 async def test_ahealth_check_ocr():
     litellm._turn_on_debug()
@@ -518,4 +542,67 @@ async def test_ahealth_check_ocr():
         mode="ocr",
     )
     print(response)
-    return response
\ No newline at end of file
+    return response
+
+
+@pytest.mark.asyncio
+async def test_image_generation_health_check_prompt(monkeypatch):
+    """Health checks should respect default and environment-configured prompts."""
+
+    import importlib
+    import litellm.constants as litellm_constants
+    import litellm.proxy.health_check as health_check
+
+    def reload_modules():
+        reloaded_constants = importlib.reload(litellm_constants)
+        reloaded_health_check = importlib.reload(health_check)
+        return reloaded_constants, reloaded_health_check
+
+    async def run_health_check(health_check_module):
+        health_check_calls = []
+
+        async def mock_health_check(litellm_params, mode=None, prompt=None, input=None):
+            health_check_calls.append(
+                {
+                    "mode": mode,
+                    "prompt": prompt,
+                    "model": litellm_params.get("model"),
+                }
+            )
+            return {"status": "healthy"}
+
+        model_list = [
+            {
+                "litellm_params": {"model": "dall-e-3", "api_key": "fake-key"},
+                "model_info": {
+                    "mode": "image_generation",
+                },
+            }
+        ]
+
+        with patch(
+            "litellm.proxy.health_check.litellm.ahealth_check",
+            side_effect=mock_health_check,
+        ):
+            await health_check_module._perform_health_check(model_list)
+
+        return health_check_calls
+
+    # Default prompt is used when env var is unset
+    monkeypatch.delenv("DEFAULT_HEALTH_CHECK_PROMPT", raising=False)
+    litellm_constants, health_check = reload_modules()
+    health_check_calls = await run_health_check(health_check)
+
+    assert len(health_check_calls) == 1
+    assert (
+        health_check_calls[0]["prompt"] == litellm_constants.DEFAULT_HEALTH_CHECK_PROMPT
+    )
+
+    # Environment override should change the prompt without code changes
+    override_prompt = "environment override prompt"
+    monkeypatch.setenv("DEFAULT_HEALTH_CHECK_PROMPT", override_prompt)
+    litellm_constants, health_check = reload_modules()
+    health_check_calls = await run_health_check(health_check)
+
+    assert len(health_check_calls) == 1
+    assert health_check_calls[0]["prompt"] == override_prompt
diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py
index 0941e064a7f..f68b373feae 100644
--- a/tests/llm_responses_api_testing/base_responses_api.py
+++ b/tests/llm_responses_api_testing/base_responses_api.py
@@ -189,12 +189,12 @@ class BaseResponsesAPITest(ABC):
             response_completed_event.response.usage,
         )
         assert (
-            response_completed_event.response.usage.prompt_tokens > 0
-            and response_completed_event.response.usage.prompt_tokens < 100
+            response_completed_event.response.usage.input_tokens > 0
+            and response_completed_event.response.usage.input_tokens < 100
         )
         assert (
-            response_completed_event.response.usage.completion_tokens > 0
-            and response_completed_event.response.usage.completion_tokens < 2000
+            response_completed_event.response.usage.output_tokens > 0
+            and response_completed_event.response.usage.output_tokens < 2000
         )
         assert (
             response_completed_event.response.usage.total_tokens > 0
@@ -204,8 +204,8 @@ class BaseResponsesAPITest(ABC):
         # total tokens should be the sum of input and output tokens
         assert (
             response_completed_event.response.usage.total_tokens
-            == response_completed_event.response.usage.prompt_tokens
-            + response_completed_event.response.usage.completion_tokens
+            == response_completed_event.response.usage.input_tokens
+            + response_completed_event.response.usage.output_tokens
         )
 
         # assert the response completed event includes cost when include_cost_in_streaming_usage is True
diff --git a/tests/llm_responses_api_testing/test_openai_responses_api.py b/tests/llm_responses_api_testing/test_openai_responses_api.py
index f93720620b1..7553c670774 100644
--- a/tests/llm_responses_api_testing/test_openai_responses_api.py
+++ b/tests/llm_responses_api_testing/test_openai_responses_api.py
@@ -65,7 +65,10 @@ def validate_standard_logging_payload(
     assert slp is not None, "Standard logging payload should not be None"
 
     # Validate token counts
-    print("response=", json.dumps(response, indent=4, default=str))
+    print("VALIDATING STANDARD LOGGING PAYLOAD. response=", json.dumps(response, indent=4, default=str))
+    print("FIELDS IN SLP=", json.dumps(slp, indent=4, default=str))
+    print("SLP PROMPT TOKENS=", slp["prompt_tokens"])
+    print("RESPONSE PROMPT TOKENS=", response["usage"]["input_tokens"])
     assert (
         slp["prompt_tokens"] == response["usage"]["input_tokens"]
     ), "Prompt tokens mismatch"
@@ -90,25 +93,25 @@ def validate_standard_logging_payload(
 
 
 @pytest.mark.asyncio
-async def test_basic_openai_responses_api_streaming_with_logging():
+def test_basic_openai_responses_api_streaming_with_logging():
     litellm._turn_on_debug()
     litellm.set_verbose = True
     test_custom_logger = TestCustomLogger()
     litellm.callbacks = [test_custom_logger]
     request_model = "gpt-4o"
-    response = await litellm.aresponses(
+    response = litellm.responses(
         model=request_model,
         input="hi",
         stream=True,
     )
     final_response: Optional[ResponseCompletedEvent] = None
-    async for event in response:
+    for event in response:
         if event.type == "response.completed":
             final_response = event
         print("litellm response=", json.dumps(event, indent=4, default=str))
 
     print("sleeping for 2 seconds...")
-    await asyncio.sleep(2)
+    time.sleep(2)
     print(
         "standard logging payload=",
         json.dumps(test_custom_logger.standard_logging_object, indent=4, default=str),
@@ -135,11 +138,11 @@ def validate_responses_match(slp_response, litellm_response):
 
     # Validate usage
     assert (
-        slp_response["usage"]["input_tokens"]
+        slp_response["usage"]["prompt_tokens"]
         == litellm_response["usage"]["input_tokens"]
     ), "Input tokens mismatch"
     assert (
-        slp_response["usage"]["output_tokens"]
+        slp_response["usage"]["completion_tokens"]
         == litellm_response["usage"]["output_tokens"]
     ), "Output tokens mismatch"
     assert (
@@ -177,11 +180,12 @@ async def test_basic_openai_responses_api_non_streaming_with_logging():
     print("response hidden params=", response._hidden_params)
 
     print("sleeping for 2 seconds...")
-    await asyncio.sleep(2)
+    await asyncio.sleep(5)
     print(
         "standard logging payload=",
         json.dumps(test_custom_logger.standard_logging_object, indent=4, default=str),
     )
+    print("response usage=", response.usage)
 
     assert response is not None
     assert test_custom_logger.standard_logging_object is not None
@@ -1638,7 +1642,7 @@ async def test_openai_responses_api_token_limit_error():
 
 
 async def test_openai_streaming_logging():
-    """Test that hard_limit parameter is properly sent in the HTTP request for GPT-5 models."""
+    """Test that OpenAI Responses API streaming logging is working correctly."""
     litellm._turn_on_debug()
     from litellm.integrations.custom_logger import CustomLogger
     from litellm.types.utils import Usage
@@ -1654,8 +1658,12 @@ async def test_openai_streaming_logging():
         ):
             print(f"response_obj: {response_obj.usage}")
             assert isinstance(
-                response_obj.usage, Usage
-            ), f"Expected response_obj.usage to be of type Usage, but got {type(response_obj.usage)}"
+                response_obj.usage, (Usage, dict)
+            ), f"Expected response_obj.usage to be of type Usage or dict, but got {type(response_obj.usage)}"
+            # Verify it has the chat completion format fields
+            if isinstance(response_obj.usage, dict):
+                assert "prompt_tokens" in response_obj.usage, "Usage dict should have prompt_tokens"
+                assert "completion_tokens" in response_obj.usage, "Usage dict should have completion_tokens"
             print("\n\nVALIDATED USAGE\n\n")
             self.validate_usage = True
 
@@ -1676,3 +1684,133 @@ async def test_openai_streaming_logging():
 
     await asyncio.sleep(2)
     assert tcl.validate_usage, "Usage should be validated"
+
+
+# Tests for extra_body parameter passing
+class MockResponse:
+    def __init__(self, json_data, status_code):
+        self._json_data = json_data
+        self.status_code = status_code
+        self.text = str(json_data)
+        self.headers = httpx.Headers({})
+
+    def json(self):
+        return self._json_data
+
+
+@pytest.fixture
+def extra_body_mock_response_data():
+    return {
+        "id": "resp_test123",
+        "object": "response",
+        "created_at": 1234567890,
+        "status": "completed",
+        "model": "gpt-4o",
+        "output": [
+            {
+                "type": "message",
+                "id": "msg_123",
+                "status": "completed",
+                "role": "assistant",
+                "content": [
+                    {"type": "output_text", "text": "Hello!", "annotations": []}
+                ],
+            }
+        ],
+        "usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
+        "parallel_tool_calls": True,
+        "text": {"format": {"type": "text"}},
+        "error": None,
+        "metadata": {},
+        "temperature": 1.0,
+        "reasoning": {"effort": None, "summary": None},
+    }
+
+
+@pytest.mark.asyncio
+async def test_aresponses_extra_body_params_passed(extra_body_mock_response_data):
+    """Test that extra_body parameters are passed in async mode."""
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post:
+        mock_post.return_value = MockResponse(extra_body_mock_response_data, 200)
+
+        response = await litellm.aresponses(
+            model="gpt-4o",
+            input="Test input",
+            max_output_tokens=20,
+            extra_body={
+                "custom_param_1": "value1",
+                "custom_param_2": {"nested": "value2"},
+                "experimental_feature": True,
+            },
+        )
+
+        assert response is not None
+        assert response.id is not None
+
+        request_body = mock_post.call_args.kwargs["json"]
+
+        assert "custom_param_1" in request_body
+        assert request_body["custom_param_1"] == "value1"
+        assert "custom_param_2" in request_body
+        assert request_body["custom_param_2"]["nested"] == "value2"
+        assert "experimental_feature" in request_body
+        assert request_body["experimental_feature"] is True
+        assert request_body["model"] == "gpt-4o"
+        assert request_body["input"] == "Test input"
+
+
+def test_responses_extra_body_params_passed_sync(extra_body_mock_response_data):
+    """Test that extra_body parameters are passed in sync mode."""
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
+        return_value=MockResponse(extra_body_mock_response_data, 200),
+    ) as mock_post:
+        response = litellm.responses(
+            model="gpt-4o",
+            input="Sync test",
+            max_output_tokens=20,
+            extra_body={
+                "sync_custom_param": "sync_value",
+                "another_param": 42,
+            },
+        )
+
+        assert response is not None
+        assert response.id is not None
+
+        request_body = mock_post.call_args.kwargs["json"]
+
+        assert "sync_custom_param" in request_body
+        assert request_body["sync_custom_param"] == "sync_value"
+        assert "another_param" in request_body
+        assert request_body["another_param"] == 42
+        assert request_body["model"] == "gpt-4o"
+
+
+@pytest.mark.asyncio
+async def test_extra_body_merges_with_request_data(extra_body_mock_response_data):
+    """Test that extra_body is merged into the request data."""
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post:
+        mock_post.return_value = MockResponse(extra_body_mock_response_data, 200)
+
+        await litellm.aresponses(
+            model="gpt-4o",
+            input="Test",
+            temperature=0.7,
+            max_output_tokens=20,
+            extra_body={
+                "custom_field": "custom_value",
+            },
+        )
+
+        request_body = mock_post.call_args.kwargs["json"]
+
+        assert "temperature" in request_body
+        assert "custom_field" in request_body
+        assert request_body["custom_field"] == "custom_value"
diff --git a/tests/llm_translation/test_azure_openai.py b/tests/llm_translation/test_azure_openai.py
index 671fa0abcf5..216da5db8d4 100644
--- a/tests/llm_translation/test_azure_openai.py
+++ b/tests/llm_translation/test_azure_openai.py
@@ -648,4 +648,61 @@ def test_completion_azure_deployment_id():
         ],
     )
     # Add any assertions here to check the response
-    print(response)
\ No newline at end of file
+    print(response)
+def test_azure_with_content_safety_error():
+    """
+    Verify user can access innererror from the Azure OpenAI exception
+    """
+    from litellm import completion
+    from litellm.exceptions import ContentPolicyViolationError
+    from litellm.litellm_core_utils.exception_mapping_utils import exception_type
+    from unittest.mock import MagicMock
+    
+    mock_exception = Exception("The response was filtered due to the prompt triggering Azure OpenAI's content management policy")
+    mock_exception.body = {
+        "innererror": {
+            "code": "ResponsibleAIPolicyViolation",
+            "content_filter_result": {
+                "hate": {
+                    "filtered": False,
+                    "severity": "safe"
+                },
+                "jailbreak": {
+                    "filtered": False,
+                    "detected": False
+                },
+                "self_harm": {
+                    "filtered": False,
+                    "severity": "safe"
+                },
+                "sexual": {
+                    "filtered": False,
+                    "severity": "safe"
+                },
+                "violence": {
+                    "filtered": True,
+                    "severity": "high"
+                }
+            }
+        }
+    }
+    
+    mock_response = MagicMock()
+    mock_response.status_code = 400
+    mock_exception.response = mock_response
+    
+    with pytest.raises(ContentPolicyViolationError) as exc_info:
+        exception_type(
+            model="azure/gpt-4o-new-test",
+            original_exception=mock_exception,
+            custom_llm_provider="azure"
+        )
+    
+    e = exc_info.value
+    print("got exception=", e)
+    assert e.provider_specific_fields is not None
+    print("got provider_specific_fields=", e.provider_specific_fields)
+    assert e.provider_specific_fields.get("innererror") is not None
+    assert e.provider_specific_fields["innererror"]["code"] == "ResponsibleAIPolicyViolation"
+    assert e.provider_specific_fields["innererror"]["content_filter_result"]["violence"]["filtered"] is True
+    assert e.provider_specific_fields["innererror"]["content_filter_result"]["violence"]["severity"] == "high"
diff --git a/tests/llm_translation/test_bedrock_completion.py b/tests/llm_translation/test_bedrock_completion.py
index 81593fb3f41..7f20dfaa0f5 100644
--- a/tests/llm_translation/test_bedrock_completion.py
+++ b/tests/llm_translation/test_bedrock_completion.py
@@ -2341,7 +2341,8 @@ def test_bedrock_custom_continue_message():
 
 def test_bedrock_no_default_message():
     """
-    Test that empty content is handled correctly when modify_params=False
+    Test that empty content is replaced with placeholder when modify_params=False.
+    AWS Bedrock doesn't allow empty or whitespace-only text content.
     """
     messages = [
         {"role": "user", "content": "Hello!"},
@@ -2357,15 +2358,13 @@ def test_bedrock_no_default_message():
         llm_provider="bedrock",
     )
 
-    # Verify empty message is present and valid message remains
+    # Verify empty message is replaced with placeholder and valid message remains
     assistant_messages = [
         msg for msg in formatted_messages if msg["role"] == "assistant"
     ]
-    assert len(assistant_messages) == 2  # Both empty and valid messages present
-    assert assistant_messages[0]["content"][0]["text"] == ""  # First message is empty
-    assert (
-        assistant_messages[1]["content"][0]["text"] == "Valid response"
-    )  # Second message is valid
+    assert len(assistant_messages) == 2
+    assert assistant_messages[0]["content"][0]["text"] == "."
+    assert assistant_messages[1]["content"][0]["text"] == "Valid response"
 
 
 @pytest.mark.parametrize("top_k_param", ["top_k", "topK"])
diff --git a/tests/llm_translation/test_fireworks_ai_translation.py b/tests/llm_translation/test_fireworks_ai_translation.py
index 930ef4456be..903b83aea67 100644
--- a/tests/llm_translation/test_fireworks_ai_translation.py
+++ b/tests/llm_translation/test_fireworks_ai_translation.py
@@ -93,7 +93,7 @@ class TestFireworksAIAudioTranscription(BaseLLMAudioTranscriptionTest):
     def get_base_audio_transcription_call_args(self) -> dict:
         return {
             "model": "fireworks_ai/whisper-v3",
-            "api_base": "https://audio-prod.us-virginia-1.direct.fireworks.ai/v1",
+            "api_base": "https://audio-prod.api.fireworks.ai/v1",
         }
 
     def get_custom_llm_provider(self) -> litellm.LlmProviders:
diff --git a/tests/llm_translation/test_openai.py b/tests/llm_translation/test_openai.py
index 156c5352958..1b96a996210 100644
--- a/tests/llm_translation/test_openai.py
+++ b/tests/llm_translation/test_openai.py
@@ -796,19 +796,16 @@ def test_gpt_5_reasoning_streaming():
         stream=True,
     )
 
-    has_reasoning_content = False
+    has_content = False
     for chunk in response:
         print("chunk: ", chunk)
-        if (
-            hasattr(chunk.choices[0].delta, "reasoning_content")
-            and chunk.choices[0].delta.reasoning_content is not None
-        ):
-            print("reasoning_content: ", chunk.choices[0].delta.reasoning_content)
-            has_reasoning_content = True
+        if chunk.choices[0].delta.content:
+            has_content = True
+            print("content: ", chunk.choices[0].delta.content)
 
-    assert has_reasoning_content
+    assert has_content
 
-    print("✓ gpt_5_reasoning_streaming correctly handled reasoning content")
+    print("✓ gpt_5_reasoning_streaming correctly handled streaming")
 
 
 def test_gpt_5_pro_reasoning():
@@ -824,7 +821,8 @@ def test_gpt_5_pro_reasoning():
         reasoning_effort="high",
     )
     print("response: ", response)
-    assert response.choices[0].message.reasoning_content is not None
+    # reasoning_effort string param does not request summaries (opt-in since #16210)
+    assert response.choices[0].message.content is not None  # But we should get content
 
 
 def test_openai_gpt_5_codex_reasoning():
diff --git a/tests/llm_translation/test_perplexity_reasoning.py b/tests/llm_translation/test_perplexity_reasoning.py
index 7db99014e9d..70b665ea339 100644
--- a/tests/llm_translation/test_perplexity_reasoning.py
+++ b/tests/llm_translation/test_perplexity_reasoning.py
@@ -62,12 +62,13 @@ class TestPerplexityReasoning:
         """
         Test that reasoning_effort is correctly passed in actual completion call (mocked)
         """
-        import httpx
+        from openai import OpenAI
+        from openai.types.chat.chat_completion import ChatCompletion
         
         litellm.set_verbose = True
         
         # Mock successful response with reasoning content
-        response_json = {
+        response_object = {
             "id": "cmpl-test",
             "object": "chat.completion",
             "created": 1677652288,
@@ -93,37 +94,35 @@ class TestPerplexityReasoning:
             },
         }
 
-        def mock_post(*args, **kwargs):
-            # Create a mock response
-            mock_response = MagicMock(spec=httpx.Response)
-            mock_response.status_code = 200
-            mock_response.headers = {"content-type": "application/json"}
-            mock_response.json.return_value = response_json
-            mock_response.text = json.dumps(response_json)
-            
-            # Store the request data for verification
-            mock_post.last_request_data = kwargs.get("data")
-            if isinstance(mock_post.last_request_data, (str, bytes)):
-                mock_post.last_request_data = json.loads(mock_post.last_request_data)
-            
-            return mock_response
+        pydantic_obj = ChatCompletion(**response_object)
 
-        # Mock at the HTTP handler level
-        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", side_effect=mock_post) as mock_http:
+        def _return_pydantic_obj(*args, **kwargs):
+            new_response = MagicMock()
+            new_response.headers = {"content-type": "application/json"}
+            new_response.parse.return_value = pydantic_obj
+            return new_response
+
+        openai_client = OpenAI(api_key="fake-api-key")
+
+        with patch.object(
+            openai_client.chat.completions.with_raw_response, "create", side_effect=_return_pydantic_obj
+        ) as mock_client:
             
             response = completion(
                 model=model,
                 messages=[{"role": "user", "content": "Hello, please think about this carefully."}],
                 reasoning_effort="high",
-                api_key="fake-api-key",
+                client=openai_client,
             )
             
             # Verify the call was made
-            assert mock_http.called
+            assert mock_client.called
+            
+            # Get the request data from the mock call
+            call_args = mock_client.call_args
+            request_data = call_args.kwargs
             
             # Verify reasoning_effort was included in the request
-            request_data = mock_post.last_request_data
-            assert request_data is not None
             assert "reasoning_effort" in request_data
             assert request_data["reasoning_effort"] == "high"
             
diff --git a/tests/local_testing/test_completion.py b/tests/local_testing/test_completion.py
index d8ebb059b9a..a3f3ba5729a 100644
--- a/tests/local_testing/test_completion.py
+++ b/tests/local_testing/test_completion.py
@@ -1244,9 +1244,6 @@ def test_completion_fireworks_ai_dynamic_params(api_key, api_base):
 # @pytest.mark.skip(reason="this test is flaky")
 def test_completion_perplexity_api():
     try:
-        import httpx
-        import json
-        
         response_object = {
             "id": "a8f37485-026e-45da-81a9-cf0184896840",
             "model": "llama-3-sonar-small-32k-online",
@@ -1273,17 +1270,25 @@ def test_completion_perplexity_api():
             ],
         }
 
-        def mock_post(*args, **kwargs):
-            # Create a mock response
-            mock_response = MagicMock(spec=httpx.Response)
-            mock_response.status_code = 200
-            mock_response.headers = {"content-type": "application/json"}
-            mock_response.json.return_value = response_object
-            mock_response.text = json.dumps(response_object)
-            return mock_response
+        from openai import OpenAI
+        from openai.types.chat.chat_completion import ChatCompletion
 
-        # Mock at the HTTP handler level
-        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", side_effect=mock_post):
+        pydantic_obj = ChatCompletion(**response_object)
+
+        def _return_pydantic_obj(*args, **kwargs):
+            new_response = MagicMock()
+            new_response.headers = {"hello": "world"}
+
+            new_response.parse.return_value = pydantic_obj
+            return new_response
+
+        openai_client = OpenAI()
+
+        with patch.object(
+            openai_client.chat.completions.with_raw_response,
+            "create",
+            side_effect=_return_pydantic_obj,
+        ) as mock_client:
             # litellm.set_verbose= True
             messages = [
                 {"role": "system", "content": "You're a good bot"},
@@ -1297,9 +1302,10 @@ def test_completion_perplexity_api():
                 },
             ]
             response = completion(
-                model="perplexity/llama-3-sonar-small-32k-online",
+                model="mistral-7b-instruct",
                 messages=messages,
-                api_key="fake-api-key",
+                api_base="https://api.perplexity.ai",
+                client=openai_client,
             )
             print(response)
             assert hasattr(response, "citations")
diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py
index f9bf39548da..d4edcf58ea9 100644
--- a/tests/local_testing/test_completion_cost.py
+++ b/tests/local_testing/test_completion_cost.py
@@ -311,17 +311,6 @@ def test_cost_azure_embedding():
 # test_cost_azure_embedding()
 
 
-def test_cost_openai_image_gen():
-    cost = litellm.completion_cost(
-        model="dall-e-2",
-        size="1024-x-1024",
-        quality="standard",
-        n=1,
-        call_type="image_generation",
-    )
-    assert cost == 0.019922944
-
-
 def test_cost_bedrock_pricing_actual_calls():
     litellm.set_verbose = True
     model = "anthropic.claude-3-5-sonnet-20240620-v1:0"
@@ -1159,10 +1148,10 @@ def test_completion_cost_databricks_embedding(model, monkeypatch):
     api_key = "dapimykey"
     monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
     monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
-    
+
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")
-    
+
     mock_response_data = {
         "object": "list",
         "model": model.split("/")[1],
@@ -1187,18 +1176,16 @@ def test_completion_cost_databricks_embedding(model, monkeypatch):
             "prompt_tokens_details": None,
         },
     }
-    
+
     mock_response = MagicMock(spec=httpx.Response)
     mock_response.status_code = 200
     mock_response.json.return_value = mock_response_data
-    
+
     sync_handler = HTTPHandler()
-    
+
     with patch.object(HTTPHandler, "post", return_value=mock_response):
         resp = litellm.embedding(
-            model=model, 
-            input=["hey, how's it going?"],
-            client=sync_handler
+            model=model, input=["hey, how's it going?"], client=sync_handler
         )
 
         print(resp)
diff --git a/tests/logging_callback_tests/test_datadog.py b/tests/logging_callback_tests/test_datadog.py
index db6f193327c..13125aa4952 100644
--- a/tests/logging_callback_tests/test_datadog.py
+++ b/tests/logging_callback_tests/test_datadog.py
@@ -612,7 +612,7 @@ async def test_datadog_message_redaction():
         
         # Apply redaction using the inherited method
         redacted_details = dd_logger.redact_standard_logging_payload_from_model_call_details(model_call_details)
-        redacted_str = LiteLLMCommonStrings.redacted_by_litellm.value
+        redacted_str = "redacted-by-litellm"
         
         # Verify that messages are redacted
         redacted_standard_obj = redacted_details["standard_logging_object"]
@@ -628,4 +628,30 @@ async def test_datadog_message_redaction():
     finally:
         # Clean up
         litellm.datadog_params = None
-        litellm.callbacks = []
\ No newline at end of file
+        litellm.callbacks = []
+
+
+def test_datadog_agent_configuration():
+    """
+    Test that DataDog logger correctly configures agent endpoint when DD_AGENT_HOST is set
+    """
+    test_env = {
+        "DD_AGENT_HOST": "localhost",
+        "DD_AGENT_PORT": "10518",
+    }
+    
+    # Remove DD_SITE and DD_API_KEY to verify they're not required for agent mode
+    env_to_remove = ["DD_SITE", "DD_API_KEY"]
+    
+    with patch.dict(os.environ, test_env, clear=False):
+        for key in env_to_remove:
+            os.environ.pop(key, None)
+        
+        with patch("asyncio.create_task"):
+            dd_logger = DataDogLogger()
+        
+        # Verify agent endpoint is configured correctly
+        assert dd_logger.intake_url == "http://localhost:10518/api/v2/logs", f"Expected agent URL, got {dd_logger.intake_url}"
+        
+        # Verify DD_API_KEY is optional (can be None)
+        assert dd_logger.DD_API_KEY is None or isinstance(dd_logger.DD_API_KEY, str)
\ No newline at end of file
diff --git a/tests/logging_callback_tests/test_logging_redaction_e2e_test.py b/tests/logging_callback_tests/test_logging_redaction_e2e_test.py
index 07ec6126467..b261daab6a7 100644
--- a/tests/logging_callback_tests/test_logging_redaction_e2e_test.py
+++ b/tests/logging_callback_tests/test_logging_redaction_e2e_test.py
@@ -124,7 +124,7 @@ async def test_global_redaction_off_with_dynamic_params(turn_off_message_logging
 async def test_redaction_responses_api():
     """Test redaction with ResponsesAPIResponse format"""
     litellm.turn_off_message_logging = True
-    test_custom_logger = TestCustomLogger()
+    test_custom_logger = TestCustomLogger(turn_off_message_logging=True)
     litellm.callbacks = [test_custom_logger]
     
     # Mock a ResponsesAPIResponse-style response
@@ -145,8 +145,23 @@ async def test_redaction_responses_api():
     assert standard_logging_payload is not None
     
     # Verify redaction in ResponsesAPIResponse format
-    assert standard_logging_payload["response"] == {"text": "redacted-by-litellm"}
+    # The response is now the full ResponsesAPIResponse object with transformed usage
+    assert isinstance(standard_logging_payload["response"], dict)
+    assert "usage" in standard_logging_payload["response"]
+    # Check that usage has been transformed to chat completion format
+    assert "prompt_tokens" in standard_logging_payload["response"]["usage"]
+    assert "completion_tokens" in standard_logging_payload["response"]["usage"]
+    
     assert standard_logging_payload["messages"][0]["content"] == "redacted-by-litellm"
+    
+    # Verify that output content is redacted
+    assert "output" in standard_logging_payload["response"]
+    output_items = standard_logging_payload["response"]["output"]
+    for output_item in output_items:
+        if "content" in output_item and isinstance(output_item["content"], list):
+            for content_item in output_item["content"]:
+                if "text" in content_item:
+                    assert content_item["text"] == "redacted-by-litellm", f"Expected redacted text but got: {content_item['text']}"
     print(
         "logged standard logging payload for ResponsesAPIResponse",
         json.dumps(standard_logging_payload, indent=2),
@@ -157,7 +172,7 @@ async def test_redaction_responses_api():
 async def test_redaction_responses_api_stream():
     """Test redaction with ResponsesAPIResponse format"""
     litellm.turn_off_message_logging = True
-    test_custom_logger = TestCustomLogger()
+    test_custom_logger = TestCustomLogger(turn_off_message_logging=True)
     litellm.callbacks = [test_custom_logger]
     
     # Mock a ResponsesAPIResponse-style response with streaming chunks
@@ -194,8 +209,22 @@ async def test_redaction_responses_api_stream():
     assert standard_logging_payload is not None
     
     # Verify redaction in ResponsesAPIResponse format
-    assert standard_logging_payload["response"] == {"text": "redacted-by-litellm"}
+    # The streaming response is in ModelResponse format (choices), not ResponsesAPIResponse format (output)
+    assert isinstance(standard_logging_payload["response"], dict)
     assert standard_logging_payload["messages"][0]["content"] == "redacted-by-litellm"
+    
+    # Verify that response content is redacted (ModelResponse format)
+    if "choices" in standard_logging_payload["response"]:
+        # ModelResponse format
+        assert standard_logging_payload["response"]["choices"][0]["message"]["content"] == "redacted-by-litellm"
+    elif "output" in standard_logging_payload["response"]:
+        # ResponsesAPIResponse format
+        output_items = standard_logging_payload["response"]["output"]
+        for output_item in output_items:
+            if "content" in output_item and isinstance(output_item["content"], list):
+                for content_item in output_item["content"]:
+                    if "text" in content_item:
+                        assert content_item["text"] == "redacted-by-litellm", f"Expected redacted text but got: {content_item['text']}"
     print(
         "logged standard logging payload for ResponsesAPIResponse stream",
         json.dumps(standard_logging_payload, indent=2),
diff --git a/tests/logging_callback_tests/test_otel_logging.py b/tests/logging_callback_tests/test_otel_logging.py
index ecbcc8734f5..8d1da0439d3 100644
--- a/tests/logging_callback_tests/test_otel_logging.py
+++ b/tests/logging_callback_tests/test_otel_logging.py
@@ -281,11 +281,13 @@ def validate_redacted_message_span_attributes(span):
         _all_attributes
     ), f"Missing required attributes: {required_set - _all_attributes}"
 
-    # Check that any additional attributes are metadata fields (start with "metadata.")
+    # Check that any additional attributes are metadata fields (start with "metadata.") or cost fields
     non_required_attrs = _all_attributes - required_set
     for attr in non_required_attrs:
-        assert attr.startswith("metadata.") or attr.startswith(
-            "hidden_params"
+        assert (
+            attr.startswith("metadata.")
+            or attr.startswith("hidden_params")
+            or attr.startswith("gen_ai.cost.")
         ), f"Non-metadata attribute found: {attr}"
 
     pass
diff --git a/tests/mcp_tests/test_mcp_server.py b/tests/mcp_tests/test_mcp_server.py
index 6f6127ed26c..9ee5f6a9a60 100644
--- a/tests/mcp_tests/test_mcp_server.py
+++ b/tests/mcp_tests/test_mcp_server.py
@@ -1342,6 +1342,7 @@ def test_add_update_server_with_alias():
     mock_mcp_server.url = "https://test-server.com/mcp"
     mock_mcp_server.transport = MCPTransport.http
     mock_mcp_server.auth_type = None
+    mock_mcp_server.credentials = {}
     mock_mcp_server.description = "Test server description"
     mock_mcp_server.mcp_info = {}
     mock_mcp_server.static_headers = {}
@@ -1380,6 +1381,7 @@ def test_add_update_server_without_alias():
     mock_mcp_server.url = "https://test-server.com/mcp"
     mock_mcp_server.transport = MCPTransport.http
     mock_mcp_server.auth_type = None
+    mock_mcp_server.credentials = {}
     mock_mcp_server.description = "Test server description"
     mock_mcp_server.mcp_info = {}
     mock_mcp_server.static_headers = {}
@@ -1418,6 +1420,7 @@ def test_add_update_server_fallback_to_server_id():
     mock_mcp_server.url = "https://test-server.com/mcp"
     mock_mcp_server.transport = MCPTransport.http
     mock_mcp_server.auth_type = None
+    mock_mcp_server.credentials = {}
     mock_mcp_server.description = "Test server description"
     mock_mcp_server.mcp_info = {}
     mock_mcp_server.static_headers = {}
diff --git a/tests/proxy_unit_tests/test_key_generate_prisma.py b/tests/proxy_unit_tests/test_key_generate_prisma.py
index 5a8b8c7c06e..06972c32a69 100644
--- a/tests/proxy_unit_tests/test_key_generate_prisma.py
+++ b/tests/proxy_unit_tests/test_key_generate_prisma.py
@@ -24,6 +24,7 @@ import sys
 import traceback
 from litellm._uuid import uuid
 from datetime import datetime, timezone
+from unittest import mock
 
 from dotenv import load_dotenv
 from fastapi import Request
@@ -3716,7 +3717,12 @@ async def test_user_api_key_auth_db_unavailable_not_allowed():
 
 
 @pytest.mark.asyncio
-async def test_key_generate_with_secret_manager_call(prisma_client):
+@mock.patch("litellm.secret_managers.aws_secret_manager_v2.AWSSecretsManagerV2.async_write_secret")
+@mock.patch("litellm.secret_managers.aws_secret_manager_v2.AWSSecretsManagerV2.async_read_secret")
+@mock.patch("litellm.secret_managers.aws_secret_manager_v2.AWSSecretsManagerV2.async_delete_secret")
+async def test_key_generate_with_secret_manager_call(
+    mock_delete_secret, mock_read_secret, mock_write_secret, prisma_client
+):
     """
     Generate a key
     assert it exists in the secret manager
@@ -3761,6 +3767,10 @@ async def test_key_generate_with_secret_manager_call(prisma_client):
     spend = 100
     max_budget = 400
     models = ["fake-openai-endpoint"]
+    
+    # Mock write_secret to return success
+    mock_write_secret.return_value = None
+    
     new_key = await generate_key_fn(
         data=GenerateKeyRequest(
             key_alias=key_alias, spend=spend, max_budget=max_budget, models=models
@@ -3778,6 +3788,8 @@ async def test_key_generate_with_secret_manager_call(prisma_client):
     await asyncio.sleep(2)
 
     # read from the secret manager
+    # Mock read_secret to return the generated key
+    mock_read_secret.return_value = generated_key
 
     result = await aws_secret_manager_client.async_read_secret(
         secret_name=f"{litellm._key_management_settings.prefix_for_stored_virtual_keys}{key_alias}"
@@ -3788,6 +3800,9 @@ async def test_key_generate_with_secret_manager_call(prisma_client):
     print(result)
     assert result == generated_key
 
+    # Mock delete_secret to return success
+    mock_delete_secret.return_value = None
+    
     # delete the key
     await delete_key_fn(
         data=KeyRequest(keys=[generated_key]),
@@ -3799,6 +3814,8 @@ async def test_key_generate_with_secret_manager_call(prisma_client):
     await asyncio.sleep(2)
 
     # Assert the key is deleted from the secret manager
+    # Mock read_secret to return None after deletion
+    mock_read_secret.return_value = None
 
     result = await aws_secret_manager_client.async_read_secret(
         secret_name=f"{litellm._key_management_settings.prefix_for_stored_virtual_keys}{key_alias}"
diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py
index 3aeb8a840c9..71b4503ff3c 100644
--- a/tests/proxy_unit_tests/test_proxy_server.py
+++ b/tests/proxy_unit_tests/test_proxy_server.py
@@ -545,7 +545,27 @@ def test_embedding(mock_aembedding, client_no_auth):
             "input": ["good morning from litellm"],
         }
 
-        response = client_no_auth.post("/v1/embeddings", json=test_data)
+        pre_call_return_value = {
+            **test_data,
+            "metadata": {"source": "unit-test"},
+            "proxy_server_request": {"path": "/v1/embeddings"},
+            "secret_fields": [],
+        }
+
+        with patch.object(
+            litellm.proxy.proxy_server.proxy_logging_obj,
+            "pre_call_hook",
+            new=AsyncMock(return_value=pre_call_return_value),
+        ) as mock_pre_call_hook, patch.object(
+            litellm.proxy.proxy_server.proxy_logging_obj,
+            "during_call_hook",
+            new=AsyncMock(return_value=None),
+        ) as mock_during_hook, patch.object(
+            litellm.proxy.proxy_server.proxy_logging_obj,
+            "post_call_success_hook",
+            new=AsyncMock(return_value=None),
+        ):
+            response = client_no_auth.post("/v1/embeddings", json=test_data)
 
         mock_aembedding.assert_called_once_with(
             model="azure/text-embedding-ada-002",
@@ -559,6 +579,16 @@ def test_embedding(mock_aembedding, client_no_auth):
         result = response.json()
         print(len(result["data"][0]["embedding"]))
         assert len(result["data"][0]["embedding"]) > 10  # this usually has len==1536 so
+
+        pre_call_kwargs = mock_pre_call_hook.await_args_list[0].kwargs
+        assert (
+            pre_call_kwargs.get("call_type") == "aembedding"
+        ), f"expected pre_call_hook to receive call_type='aembedding', got {pre_call_kwargs.get('call_type')}"
+
+        during_call_kwargs = mock_during_hook.await_args_list[0].kwargs
+        assert (
+            during_call_kwargs.get("call_type") == "aembedding"
+        ), f"expected during_call_hook to receive call_type='aembedding', got {during_call_kwargs.get('call_type')}"
     except Exception as e:
         pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}")
 
diff --git a/tests/router_unit_tests/test_router_endpoints.py b/tests/router_unit_tests/test_router_endpoints.py
index 3a6a955f807..04c582f6013 100644
--- a/tests/router_unit_tests/test_router_endpoints.py
+++ b/tests/router_unit_tests/test_router_endpoints.py
@@ -770,3 +770,105 @@ def test_apply_default_settings():
         mock_add_checks.assert_called_once_with([])
 
 
+
+
+def test_initialize_core_endpoints():
+    """
+    Test that _initialize_core_endpoints correctly sets up all core router endpoints.
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "anthropic/test-model",
+                    "api_key": "fake-api-key",
+                },
+            }
+        ]
+    )
+
+    router._initialize_core_endpoints()
+
+    core_endpoints = [
+        "amoderation",
+        "aanthropic_messages",
+        "agenerate_content",
+        "aadapter_generate_content",
+        "aresponses",
+        "afile_delete",
+        "afile_content",
+        "responses",
+        "aget_responses",
+        "acancel_responses",
+        "adelete_responses",
+        "alist_input_items",
+        "_arealtime",
+        "acreate_fine_tuning_job",
+        "acancel_fine_tuning_job",
+        "alist_fine_tuning_jobs",
+        "aretrieve_fine_tuning_job",
+        "afile_list",
+        "aimage_edit",
+        "allm_passthrough_route",
+    ]
+
+    for endpoint in core_endpoints:
+        assert hasattr(router, endpoint)
+        assert callable(getattr(router, endpoint))
+
+
+def test_initialize_specialized_endpoints():
+    """
+    Test that _initialize_specialized_endpoints correctly sets up specialized endpoints.
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "openai/test-model",
+                    "api_key": "fake-api-key",
+                },
+            }
+        ]
+    )
+
+    router._initialize_specialized_endpoints()
+
+    specialized_endpoints = [
+        "avector_store_search",
+        "avector_store_create",
+        "vector_store_search",
+        "vector_store_create",
+        "agenerate_content",
+        "generate_content",
+        "agenerate_content_stream",
+        "generate_content_stream",
+        "aocr",
+        "ocr",
+        "asearch",
+        "search",
+        "avideo_generation",
+        "video_generation",
+        "avideo_list",
+        "video_list",
+        "avideo_status",
+        "video_status",
+        "avideo_content",
+        "video_content",
+        "avideo_remix",
+        "video_remix",
+        "acreate_container",
+        "create_container",
+        "alist_containers",
+        "list_containers",
+        "aretrieve_container",
+        "retrieve_container",
+        "adelete_container",
+        "delete_container",
+    ]
+
+    for endpoint in specialized_endpoints:
+        assert hasattr(router, endpoint)
+        assert callable(getattr(router, endpoint))
diff --git a/tests/search_tests/test_firecrawl_search.py b/tests/search_tests/test_firecrawl_search.py
index 580f8e01569..437f12a329e 100644
--- a/tests/search_tests/test_firecrawl_search.py
+++ b/tests/search_tests/test_firecrawl_search.py
@@ -1,18 +1,40 @@
-import pytest
+from unittest.mock import Mock, patch
 import litellm
-from typing import List, Union
-
-from tests.search_tests.base_search_unit_tests import BaseSearchTest
 
 
-class TestFirecrawlSearch(BaseSearchTest):
+def test_firecrawl_search_request_body():
     """
-    Tests for Firecrawl Search functionality.
+    Test that validates the Firecrawl search request body is correctly formatted.
     """
+    mock_response = Mock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = {
+        "success": True,
+        "data": {
+            "web": [
+                {
+                    "title": "Test Title",
+                    "url": "https://example.com",
+                    "markdown": "Test content"
+                }
+            ]
+        }
+    }
     
-    def get_search_provider(self) -> str:
-        """
-        Return search_provider for Firecrawl Search.
-        """
-        return "firecrawl"
+    with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", return_value=mock_response) as mock_post:
+        litellm.search(
+            query="test query",
+            search_provider="firecrawl",
+            max_results=10,
+            country="US"
+        )
+        
+        assert mock_post.called
+        call_kwargs = mock_post.call_args.kwargs
+        request_body = call_kwargs.get("json")
+        
+        assert request_body is not None
+        assert request_body["query"] == "test query"
+        assert request_body["limit"] == 10
+        assert request_body["country"] == "US"
 
diff --git a/tests/store_model_in_db_tests/test_mcp_servers.py b/tests/store_model_in_db_tests/test_mcp_servers.py
index a396c37832a..ca9b92afb9b 100644
--- a/tests/store_model_in_db_tests/test_mcp_servers.py
+++ b/tests/store_model_in_db_tests/test_mcp_servers.py
@@ -15,6 +15,7 @@ from litellm.proxy._types import (
     MCPTransportType,
     MCPTransport,
     NewMCPServerRequest,
+    UpdateMCPServerRequest,
     LiteLLM_MCPServerTable,
     LitellmUserRoles,
     UserAPIKeyAuth,
@@ -165,6 +166,7 @@ async def test_create_mcp_server_direct():
             updated_by=LITELLM_PROXY_ADMIN_NAME,
             teams=[],
         )
+        expected_response.credentials = {"auth_value": "secret"}
 
         # Mock the database calls
         mock_get_server.return_value = None  # Server doesn't exist yet
@@ -188,6 +190,7 @@ async def test_create_mcp_server_direct():
         assert result.alias == expected_alias  # Check against normalized alias
         assert result.url == mcp_server_request.url
         assert result.transport == mcp_server_request.transport
+        assert result.credentials is None
 
         # Verify mocks were called
         mock_get_server.assert_called_once_with(mock_prisma, server_id)
@@ -353,6 +356,69 @@ async def test_create_mcp_server_invalid_alias():
         )
 
 
+@pytest.mark.asyncio
+async def test_edit_mcp_server_redacts_credentials():
+    with mock.patch(
+        "litellm.proxy.management_endpoints.mcp_management_endpoints.MCP_AVAILABLE",
+        True,
+    ), mock.patch(
+        "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw"
+    ) as mock_get_prisma, mock.patch(
+        "litellm.proxy.management_endpoints.mcp_management_endpoints.update_mcp_server",
+        new_callable=mock.AsyncMock,
+    ) as mock_update, mock.patch(
+        "litellm.proxy.management_endpoints.mcp_management_endpoints.validate_and_normalize_mcp_server_payload",
+        autospec=True,
+    ) as mock_validate, mock.patch(
+        "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager"
+    ) as mock_manager:
+        from litellm.proxy.management_endpoints.mcp_management_endpoints import (
+            edit_mcp_server,
+        )
+
+        mock_prisma = mock.Mock()
+        mock_get_prisma.return_value = mock_prisma
+
+        mock_manager.add_update_server = mock.Mock()
+        mock_manager.reload_servers_from_database = mock.AsyncMock()
+
+        server_id = str(uuid.uuid4())
+        updated_server = LiteLLM_MCPServerTable(
+            server_id=server_id,
+            alias="Updated Server",
+            url="https://updated.example.com/mcp",
+            transport=MCPTransport.http,
+            created_at=datetime.now(),
+            updated_at=datetime.now(),
+            teams=[],
+        )
+        updated_server.credentials = {"auth_value": "secret"}
+
+        mock_update.return_value = updated_server
+
+        payload = UpdateMCPServerRequest(
+            server_id=server_id,
+            alias="Updated Server",
+            url="https://updated.example.com/mcp",
+            transport=MCPTransport.http,
+        )
+
+        user_auth = UserAPIKeyAuth(
+            api_key=TEST_MASTER_KEY,
+            user_id="test-user",
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+        )
+
+        result = await edit_mcp_server(payload=payload, user_api_key_dict=user_auth)
+
+        assert result.server_id == server_id
+        assert result.credentials is None
+        assert updated_server.credentials == {"auth_value": "secret"}
+
+        mock_validate.assert_called_once()
+        mock_update.assert_awaited_once()
+        mock_manager.add_update_server.assert_called_once_with(updated_server)
+        mock_manager.reload_servers_from_database.assert_awaited_once()
 def test_validate_mcp_server_name_direct():
     """
     Test the validation function directly to ensure it works.
diff --git a/tests/test_gpt5_azure_temperature_support.py b/tests/test_gpt5_azure_temperature_support.py
new file mode 100644
index 00000000000..ac32abc0a67
--- /dev/null
+++ b/tests/test_gpt5_azure_temperature_support.py
@@ -0,0 +1,97 @@
+"""
+Test that Azure GPT-5 models support temperature parameter in Responses API.
+"""
+
+import pytest
+from litellm.utils import ProviderConfigManager
+from litellm.types.utils import LlmProviders
+
+
+def test_azure_gpt5_supports_temperature():
+    """Test that Azure GPT-5 uses the correct config that supports temperature."""
+    config = ProviderConfigManager.get_provider_responses_api_config(
+        provider=LlmProviders.AZURE,
+        model="gpt-5"
+    )
+    
+    # Should use AzureOpenAIResponsesAPIConfig, not AzureOpenAIOSeriesResponsesAPIConfig
+    assert type(config).__name__ == "AzureOpenAIResponsesAPIConfig"
+    
+    # Should support temperature parameter
+    supported_params = config.get_supported_openai_params("gpt-5")
+    assert "temperature" in supported_params, "Azure GPT-5 should support temperature parameter"
+
+
+def test_azure_o_series_does_not_support_temperature():
+    """Test that Azure O-series models still use the correct O-series config."""
+    test_models = ["o1", "o1-preview", "o3"]
+    
+    for model in test_models:
+        config = ProviderConfigManager.get_provider_responses_api_config(
+            provider=LlmProviders.AZURE,
+            model=model
+        )
+        
+        # Should use AzureOpenAIOSeriesResponsesAPIConfig
+        assert type(config).__name__ == "AzureOpenAIOSeriesResponsesAPIConfig", \
+            f"Azure {model} should use O-series config"
+        
+        # Should NOT support temperature parameter
+        supported_params = config.get_supported_openai_params(model)
+        assert "temperature" not in supported_params, \
+            f"Azure {model} should NOT support temperature parameter"
+
+
+def test_openai_gpt5_supports_temperature():
+    """Test that OpenAI GPT-5 supports temperature parameter."""
+    config = ProviderConfigManager.get_provider_responses_api_config(
+        provider=LlmProviders.OPENAI,
+        model="gpt-5"
+    )
+    
+    # Should use OpenAIResponsesAPIConfig
+    assert type(config).__name__ == "OpenAIResponsesAPIConfig"
+    
+    # Should support temperature parameter
+    supported_params = config.get_supported_openai_params("gpt-5")
+    assert "temperature" in supported_params, "OpenAI GPT-5 should support temperature parameter"
+
+
+def test_azure_gpt5_variants_support_temperature():
+    """Test that various GPT-5 model name variants support temperature."""
+    gpt5_variants = ["gpt-5", "gpt-5-turbo", "GPT-5", "azure/gpt-5"]
+    
+    for model in gpt5_variants:
+        config = ProviderConfigManager.get_provider_responses_api_config(
+            provider=LlmProviders.AZURE,
+            model=model
+        )
+        
+        # All GPT-5 variants should use the base config, not O-series config
+        assert type(config).__name__ == "AzureOpenAIResponsesAPIConfig", \
+            f"Model '{model}' should not use O-series config"
+        
+        # All should support temperature
+        supported_params = config.get_supported_openai_params(model)
+        assert "temperature" in supported_params, \
+            f"Model '{model}' should support temperature parameter"
+
+
+def test_azure_gpt_models_support_temperature():
+    """Test that all GPT models (gpt-3.5, gpt-4, gpt-5, etc.) support temperature."""
+    gpt_models = ["gpt-3.5-turbo", "gpt-4", "gpt-4-turbo", "gpt-4o", "gpt-5"]
+    
+    for model in gpt_models:
+        config = ProviderConfigManager.get_provider_responses_api_config(
+            provider=LlmProviders.AZURE,
+            model=model
+        )
+        
+        # All GPT models should use the base config, not O-series config
+        assert type(config).__name__ == "AzureOpenAIResponsesAPIConfig", \
+            f"Model '{model}' should not use O-series config"
+        
+        # All should support temperature
+        supported_params = config.get_supported_openai_params(model)
+        assert "temperature" in supported_params, \
+            f"Model '{model}' should support temperature parameter"
diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py
index f6b8af503fb..464cb0026e5 100644
--- a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py
+++ b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py
@@ -3,7 +3,7 @@ import os
 import sys
 from datetime import datetime, timedelta, timezone
 from typing import Optional
-from unittest.mock import Mock, patch, MagicMock
+from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 
@@ -406,13 +406,13 @@ async def test_dd_llms_obs_redaction(mock_env_vars):
 
     assert (
         dd_llms_obs_logger.logged_standard_logging_payload["messages"][0]["content"]
-        == LiteLLMCommonStrings.redacted_by_litellm.value
+        == "redacted-by-litellm"
     )
     assert (
         dd_llms_obs_logger.logged_standard_logging_payload["response"]["choices"][0][
             "message"
         ]["content"]
-        == LiteLLMCommonStrings.redacted_by_litellm.value
+        == "redacted-by-litellm"
     )
 
     assert test_s3_logger.logged_standard_logging_payload["messages"] == [
diff --git a/tests/test_litellm/integrations/test_opentelemetry.py b/tests/test_litellm/integrations/test_opentelemetry.py
index 2774d168701..58fccf5e42d 100644
--- a/tests/test_litellm/integrations/test_opentelemetry.py
+++ b/tests/test_litellm/integrations/test_opentelemetry.py
@@ -79,6 +79,98 @@ class TestOpenTelemetryGuardrails(unittest.TestCase):
         otel.tracer.start_span.assert_not_called()
 
 
+class TestOpenTelemetryCostBreakdown(unittest.TestCase):
+    def test_cost_breakdown_emitted_to_otel_span(self):
+        """
+        Test that cost breakdown from StandardLoggingPayload is emitted to OpenTelemetry span attributes.
+        """
+        otel = OpenTelemetry()
+        mock_span = MagicMock()
+
+        cost_breakdown = {
+            "input_cost": 0.001,
+            "output_cost": 0.002,
+            "total_cost": 0.003,
+            "tool_usage_cost": 0.0001,
+            "original_cost": 0.004,
+            "discount_percent": 0.25,
+            "discount_amount": 0.001,
+        }
+
+        kwargs = {
+            "model": "gpt-4",
+            "messages": [{"role": "user", "content": "Hello"}],
+            "optional_params": {},
+            "litellm_params": {"custom_llm_provider": "openai"},
+            "standard_logging_object": {
+                "id": "test-id",
+                "call_type": "completion",
+                "metadata": {},
+                "cost_breakdown": cost_breakdown,
+            },
+        }
+
+        response_obj = {
+            "id": "test-response-id",
+            "model": "gpt-4",
+            "choices": [],
+            "usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
+        }
+
+        otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj)
+
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.input_cost", 0.001)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.output_cost", 0.002)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.total_cost", 0.003)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.tool_usage_cost", 0.0001)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.original_cost", 0.004)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.discount_percent", 0.25)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.discount_amount", 0.001)
+
+    def test_cost_breakdown_with_partial_fields(self):
+        """
+        Test that cost breakdown works correctly when only some fields are present.
+        """
+        otel = OpenTelemetry()
+        mock_span = MagicMock()
+
+        cost_breakdown = {
+            "input_cost": 0.001,
+            "output_cost": 0.002,
+            "total_cost": 0.003,
+        }
+
+        kwargs = {
+            "model": "gpt-4",
+            "messages": [{"role": "user", "content": "Hello"}],
+            "optional_params": {},
+            "litellm_params": {"custom_llm_provider": "openai"},
+            "standard_logging_object": {
+                "id": "test-id",
+                "call_type": "completion",
+                "metadata": {},
+                "cost_breakdown": cost_breakdown,
+            },
+        }
+
+        response_obj = {
+            "id": "test-response-id",
+            "model": "gpt-4",
+            "choices": [],
+            "usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
+        }
+
+        otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj)
+
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.input_cost", 0.001)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.output_cost", 0.002)
+        mock_span.set_attribute.assert_any_call("gen_ai.cost.total_cost", 0.003)
+
+        call_args_list = [call[0] for call in mock_span.set_attribute.call_args_list]
+        assert ("gen_ai.cost.tool_usage_cost", 0.0001) not in call_args_list
+        assert ("gen_ai.cost.original_cost", 0.004) not in call_args_list
+
+
 class TestOpenTelemetry(unittest.TestCase):
     POLL_INTERVAL = 0.05
     POLL_TIMEOUT = 2.0
diff --git a/tests/test_litellm/litellm_core_utils/test_audio_utils.py b/tests/test_litellm/litellm_core_utils/test_audio_utils.py
index 9efabdede0a..2fbb21e7c90 100644
--- a/tests/test_litellm/litellm_core_utils/test_audio_utils.py
+++ b/tests/test_litellm/litellm_core_utils/test_audio_utils.py
@@ -11,6 +11,7 @@ import pytest
 
 from litellm.litellm_core_utils.audio_utils.utils import (
     ProcessedAudioFile,
+    calculate_request_duration,
     get_audio_file_for_health_check,
     get_audio_file_name,
     process_audio_file,
@@ -24,7 +25,7 @@ class TestProcessAudioFile:
         """Test processing raw bytes input"""
         audio_data = b"fake audio data"
         result = process_audio_file(audio_data)
-        
+
         assert isinstance(result, ProcessedAudioFile)
         assert result.file_content == audio_data
         assert result.filename == "audio.wav"
@@ -34,7 +35,7 @@ class TestProcessAudioFile:
         """Test processing bytearray input"""
         audio_data = bytearray(b"fake audio data")
         result = process_audio_file(audio_data)
-        
+
         assert isinstance(result, ProcessedAudioFile)
         assert result.file_content == bytes(audio_data)
         assert result.filename == "audio.wav"
@@ -43,14 +44,14 @@ class TestProcessAudioFile:
     def test_process_file_path_input(self):
         """Test processing file path input"""
         test_content = b"test audio content"
-        
+
         with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
             temp_file.write(test_content)
             temp_file_path = temp_file.name
-        
+
         try:
             result = process_audio_file(temp_file_path)
-            
+
             assert isinstance(result, ProcessedAudioFile)
             assert result.file_content == test_content
             assert result.filename == os.path.basename(temp_file_path)
@@ -63,9 +64,9 @@ class TestProcessAudioFile:
         filename = "test.wav"
         audio_data = b"fake audio data"
         audio_tuple = (filename, audio_data)
-        
+
         result = process_audio_file(audio_tuple)
-        
+
         assert isinstance(result, ProcessedAudioFile)
         assert result.file_content == audio_data
         assert result.filename == filename
@@ -74,17 +75,17 @@ class TestProcessAudioFile:
     def test_process_tuple_input_with_file_path(self):
         """Test processing tuple input with file path content"""
         test_content = b"test audio content"
-        
+
         with tempfile.NamedTemporaryFile(suffix=".flac", delete=False) as temp_file:
             temp_file.write(test_content)
             temp_file_path = temp_file.name
-        
+
         try:
             filename = "custom_name.flac"
             audio_tuple = (filename, temp_file_path)
-            
+
             result = process_audio_file(audio_tuple)
-            
+
             assert isinstance(result, ProcessedAudioFile)
             assert result.file_content == test_content
             assert result.filename == filename
@@ -97,14 +98,14 @@ class TestProcessAudioFile:
         test_content = b"test audio content"
         file_obj = io.BytesIO(test_content)
         file_obj.name = "test_audio.ogg"
-        
+
         result = process_audio_file(file_obj)
-        
+
         assert isinstance(result, ProcessedAudioFile)
         assert result.file_content == test_content
         assert result.filename == "test_audio.ogg"
         assert result.content_type == "audio/ogg"
-        
+
         # Verify file pointer was reset
         assert file_obj.tell() == 0
 
@@ -112,9 +113,9 @@ class TestProcessAudioFile:
         """Test processing file-like object without name attribute"""
         test_content = b"test audio content"
         file_obj = io.BytesIO(test_content)
-        
+
         result = process_audio_file(file_obj)
-        
+
         assert isinstance(result, ProcessedAudioFile)
         assert result.file_content == test_content
         assert result.filename == "audio.wav"
@@ -124,17 +125,17 @@ class TestProcessAudioFile:
         """Test processing tuple with file-like object as content"""
         test_content = b"test audio content"
         file_obj = io.BytesIO(test_content)
-        
+
         filename = "custom.mp3"
         audio_tuple = (filename, file_obj)
-        
+
         result = process_audio_file(audio_tuple)
-        
+
         assert isinstance(result, ProcessedAudioFile)
         assert result.file_content == test_content
         assert result.filename == filename
         assert result.content_type == "audio/mpeg"
-        
+
         # Verify file pointer was reset
         assert file_obj.tell() == 0
 
@@ -148,7 +149,7 @@ class TestProcessAudioFile:
             ("test.aac", "audio/aac"),
             ("test.m4a", "audio/x-m4a"),
         ]
-        
+
         for filename, expected_mime_type in test_cases:
             audio_tuple = (filename, b"fake content")
             result = process_audio_file(audio_tuple)
@@ -158,24 +159,25 @@ class TestProcessAudioFile:
         """Test MIME type fallback for unknown file extensions"""
         audio_tuple = ("test.unknown", b"fake content")
         result = process_audio_file(audio_tuple)
-        
+
         assert result.content_type == "audio/wav"  # Should fallback to default
 
     def test_process_pathlike_object(self):
         """Test processing os.PathLike object"""
         test_content = b"test audio content"
-        
+
         with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
             temp_file.write(test_content)
             temp_file_path = temp_file.name
-        
+
         try:
             # Convert to pathlib.Path
             from pathlib import Path
+
             path_obj = Path(temp_file_path)
-            
+
             result = process_audio_file(path_obj)
-            
+
             assert isinstance(result, ProcessedAudioFile)
             assert result.file_content == test_content
             assert result.filename == os.path.basename(temp_file_path)
@@ -202,7 +204,62 @@ class TestProcessAudioFile:
         """Test tuple with None filename gets default name"""
         audio_tuple = (None, b"fake content")
         result = process_audio_file(audio_tuple)
-        
+
         assert result.filename == "audio.wav"
         assert result.content_type == "audio/wav"
 
+
+class TestCalculateRequestDuration:
+    """Test the calculate_request_duration function"""
+
+    @pytest.mark.skipif(
+        os.environ.get("SKIP_AUDIO_TESTS") == "true",
+        reason="Skipping audio tests - soundfile may not be available",
+    )
+    def test_bytesio_at_end_position(self):
+        """
+        Test that calculate_request_duration handles BytesIO with file pointer at end.
+        This reproduces and verifies the fix for the OGG file bug where BytesIO
+        position was at the end after a previous read(), causing "Format not recognised" error.
+        """
+        # Create a simple WAV file in memory (44 bytes header + some data)
+        # This is a minimal valid WAV file
+        wav_header = (
+            b"RIFF"
+            + (36 + 8).to_bytes(4, "little")  # ChunkSize
+            + b"WAVE"
+            + b"fmt "
+            + (16).to_bytes(4, "little")  # Subchunk1Size
+            + (1).to_bytes(2, "little")  # AudioFormat (PCM)
+            + (1).to_bytes(2, "little")  # NumChannels
+            + (16000).to_bytes(4, "little")  # SampleRate
+            + (32000).to_bytes(4, "little")  # ByteRate
+            + (2).to_bytes(2, "little")  # BlockAlign
+            + (16).to_bytes(2, "little")  # BitsPerSample
+            + b"data"
+            + (8).to_bytes(4, "little")  # Subchunk2Size
+            + b"\x00\x00\x00\x00\x00\x00\x00\x00"  # Sample data
+        )
+
+        # Create BytesIO object
+        file_obj = io.BytesIO(wav_header)
+        file_obj.name = "test_audio.wav"
+
+        # Simulate the bug: something reads from the file first, moving position to end
+        _ = file_obj.read()
+        assert file_obj.tell() == len(wav_header), "File position should be at end"
+
+        # Call calculate_request_duration - this would fail before the fix
+        duration = calculate_request_duration(file_obj)
+
+        # Verify it succeeded (returns a duration, not None)
+        assert (
+            duration is not None
+        ), "Duration should be calculated even when BytesIO is at end"
+        assert isinstance(duration, float), "Duration should be a float"
+        assert duration > 0, "Duration should be positive"
+
+        # Verify the file position was restored
+        assert file_obj.tell() == len(
+            wav_header
+        ), "File position should be restored to original position"
diff --git a/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py b/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py
index 638aab8fb49..2487479a8c2 100644
--- a/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py
+++ b/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py
@@ -1,32 +1,177 @@
+import os
+import sys
+
 import pytest
 
-from litellm.litellm_core_utils.exception_mapping_utils import ExceptionCheckers
+import litellm
+
+sys.path.insert(
+    0, os.path.abspath("../../..")
+)  # Adds the parent directory to the system path
+
+from litellm.litellm_core_utils.exception_mapping_utils import (
+    ExceptionCheckers,
+    exception_type,
+)
 
 # Test cases for is_error_str_context_window_exceeded
 # Tuple format: (error_message, expected_result)
 context_window_test_cases = [
     # Positive cases (should return True)
-    ("An error occurred: The input exceeds the model's maximum context limit of 8192 tokens.", True),
-    ("Some text before, this model's maximum context length is 4096 tokens. Some text after.", True),
-    ("Validation Error: string too long. expected a string with maximum length 1000.", True),
+    (
+        "An error occurred: The input exceeds the model's maximum context limit of 8192 tokens.",
+        True,
+    ),
+    (
+        "Some text before, this model's maximum context length is 4096 tokens. Some text after.",
+        True,
+    ),
+    (
+        "Validation Error: string too long. expected a string with maximum length 1000.",
+        True,
+    ),
     ("Your prompt is longer than the model's context length of 2048.", True),
     ("AWS Bedrock Error: The request payload size has exceed context limit.", True),
-    ("Input tokens exceed the configured limit of 272000 tokens. Your messages resulted in 509178 tokens. Please reduce the length of the messages.", True),
-
+    (
+        "Input tokens exceed the configured limit of 272000 tokens. Your messages resulted in 509178 tokens. Please reduce the length of the messages.",
+        True,
+    ),
     # Test case insensitivity
     ("ERROR: THIS MODEL'S MAXIMUM CONTEXT LENGTH IS 1024.", True),
-
     # Negative cases (should return False)
     ("A generic API error occurred.", False),
     ("Invalid API Key provided.", False),
     ("Rate limit reached for requests.", False),
     ("The context is large, but acceptable.", False),
-    ("", False), # Empty string
+    ("", False),  # Empty string
 ]
 
+
 @pytest.mark.parametrize("error_str, expected", context_window_test_cases)
 def test_is_error_str_context_window_exceeded(error_str, expected):
     """
     Tests the is_error_str_context_window_exceeded function with various error strings.
     """
-    assert ExceptionCheckers.is_error_str_context_window_exceeded(error_str) == expected
\ No newline at end of file
+    assert ExceptionCheckers.is_error_str_context_window_exceeded(error_str) == expected
+
+class TestExceptionCheckers:
+    """Test the ExceptionCheckers utility methods"""
+
+    def test_is_azure_content_policy_violation_error_with_policy_violation_text(self):
+        """Test detection of Azure content policy violation with explicit policy violation text"""
+        
+        error_strings = [
+            "invalid_request_error content_policy_violation occurred",
+            "The response was filtered due to the prompt triggering Azure OpenAI's content management policy",
+            "Your task failed as a result of our safety system detecting harmful content",
+            "The model produced invalid content that violates our policy",
+            "Request blocked due to content_filter_policy restrictions"
+        ]
+        
+        for error_str in error_strings:
+            result = ExceptionCheckers.is_azure_content_policy_violation_error(error_str)
+            assert result is True, f"Should detect policy violation in: {error_str}"
+
+    def test_is_azure_content_policy_violation_error_case_insensitive(self):
+        """Test that content policy violation detection is case insensitive"""
+        
+        error_strings = [
+            "INVALID_REQUEST_ERROR CONTENT_POLICY_VIOLATION",
+            "The Response Was Filtered Due To The Prompt Triggering Azure OpenAI's Content Management",
+            "YOUR TASK FAILED AS A RESULT OF OUR SAFETY SYSTEM",
+            "Content_Filter_Policy restriction detected"
+        ]
+        
+        for error_str in error_strings:
+            result = ExceptionCheckers.is_azure_content_policy_violation_error(error_str)
+            assert result is True, f"Should detect policy violation in uppercase: {error_str}"
+
+    def test_is_azure_content_policy_violation_error_with_non_policy_errors(self):
+        """Test that non-policy violation errors are not detected as policy violations"""
+        
+        error_strings = [
+            "Invalid API key provided",
+            "Rate limit exceeded for current model",
+            "Model not found: gpt-nonexistent",
+            "Request timeout occurred",
+            "Authentication failed",
+            "Insufficient quota remaining",
+            "Bad request format",
+            "Internal server error occurred"
+        ]
+        
+        for error_str in error_strings:
+            result = ExceptionCheckers.is_azure_content_policy_violation_error(error_str)
+            assert result is False, f"Should NOT detect policy violation in: {error_str}"
+
+    def test_is_azure_content_policy_violation_error_with_partial_matches(self):
+        """Test that partial keyword matches work correctly"""
+        
+        # These should match because they contain the required substrings
+        positive_cases = [
+            "Error: content_policy_violation detected in request",
+            "Safety content management, your task failed as a result of our safety system",
+            "the model produced invalid content",
+        ]
+        
+        for error_str in positive_cases:
+            print("testing positive case=", error_str)
+            result = ExceptionCheckers.is_azure_content_policy_violation_error(error_str)
+            assert result is True, f"Should detect policy violation in: {error_str}"
+        
+        # These should not match even though they contain similar words
+        negative_cases = [
+            "Invalid content format in request",  # "invalid" but not "invalid content"
+            "Policy configuration error",         # "policy" but not policy violation context
+            "Content type not supported",         # "content" but not content filter context  
+            "Management API unavailable"          # "management" but not content management context
+        ]
+        
+        for error_str in negative_cases:
+            result = ExceptionCheckers.is_azure_content_policy_violation_error(error_str)
+            assert result is False, f"Should NOT detect policy violation in: {error_str}"
+
+# Test cases for Vertex AI RateLimitError mapping
+# As per https://github.com/BerriAI/litellm/issues/16189
+vertex_rate_limit_test_cases = [
+    ("429 Quota exceeded for model", True),
+    ("Resource exhausted. Please try again later.", True),
+    (
+        "429 Unable to submit request because the service is temporarily out of capacity.",
+        True,
+    ),
+    ("A generic error occurred.", False),  # Negative case
+]
+
+
+@pytest.mark.parametrize(
+    "error_message, should_raise_rate_limit", vertex_rate_limit_test_cases
+)
+def test_vertex_ai_rate_limit_error_mapping(error_message, should_raise_rate_limit):
+    """
+    Tests that the exception_type function correctly maps Vertex AI's
+    "Resource exhausted" error to a litellm.RateLimitError.
+    """
+    model = "gemini/gemini-2.5-flash"
+    custom_llm_provider = "vertex_ai"
+
+    # Create a generic exception with the specific error message
+    original_exception = Exception(error_message)
+
+    if should_raise_rate_limit:
+        with pytest.raises(litellm.RateLimitError) as excinfo:
+            exception_type(
+                model=model,
+                original_exception=original_exception,
+                custom_llm_provider=custom_llm_provider,
+            )
+        # Check if the raised exception is indeed a RateLimitError
+        assert isinstance(excinfo.value, litellm.RateLimitError)
+    else:
+        # For the negative case, we expect it to raise a generic APIConnectionError
+        with pytest.raises(litellm.APIConnectionError):
+            exception_type(
+                model=model,
+                original_exception=original_exception,
+                custom_llm_provider=custom_llm_provider,
+            )
diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
index 5c2f84cd872..64f3a652ba6 100644
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -585,7 +585,7 @@ async def test_e2e_generate_cold_storage_object_key_successful():
         # Verify the S3 function was called with correct parameters
         mock_get_s3_key.assert_called_once_with(
             s3_path="",  # Empty path as default
-            team_alias_prefix="",  # No team alias prefix for cold storage
+            prefix="",  # No prefix for cold storage
             start_time=start_time,
             s3_file_name="time-10-30-45-123456_chatcmpl-test-12345",
         )
@@ -635,7 +635,7 @@ async def test_e2e_generate_cold_storage_object_key_with_custom_logger_s3_path()
         # Verify the S3 function was called with the custom logger's s3_path
         mock_get_s3_key.assert_called_once_with(
             s3_path="storage",  # Should use custom logger's s3_path
-            team_alias_prefix="",
+            prefix="",
             start_time=start_time,
             s3_file_name="time-10-30-45-123456_chatcmpl-test-12345",
         )
@@ -682,7 +682,7 @@ async def test_e2e_generate_cold_storage_object_key_with_logger_no_s3_path():
         # Verify the S3 function was called with empty s3_path (fallback)
         mock_get_s3_key.assert_called_once_with(
             s3_path="",  # Should fall back to empty string
-            team_alias_prefix="",
+            prefix="",
             start_time=start_time,
             s3_file_name="time-10-30-45-123456_chatcmpl-test-12345",
         )
diff --git a/tests/test_litellm/litellm_core_utils/test_retry_after_headers.py b/tests/test_litellm/litellm_core_utils/test_retry_after_headers.py
new file mode 100644
index 00000000000..5717eab5a06
--- /dev/null
+++ b/tests/test_litellm/litellm_core_utils/test_retry_after_headers.py
@@ -0,0 +1,147 @@
+"""
+Test that retry-after headers are properly preserved for status codes 502, 503, and 504.
+
+This test module verifies that:
+1. BadGatewayError exception exists and preserves retry-after headers
+2. ServiceUnavailableError preserves retry-after headers
+3. InternalServerError preserves retry-after headers
+4. All these exceptions are in LITELLM_EXCEPTION_TYPES
+"""
+
+import httpx
+
+import litellm
+
+
+def test_bad_gateway_error_preserves_retry_after_header():
+    """Test that BadGatewayError preserves retry-after headers."""
+    response = httpx.Response(
+        status_code=502,
+        headers=httpx.Headers({"retry-after": "30"}),
+        request=httpx.Request("POST", "http://test.com"),
+    )
+
+    error = litellm.BadGatewayError(
+        message="Test bad gateway error",
+        llm_provider="test",
+        model="test-model",
+        response=response,
+    )
+
+    assert error.status_code == 502
+    assert "BadGatewayError" in str(error)
+    assert error.response is not None
+    assert error.response.headers is not None
+    assert "retry-after" in error.response.headers
+    assert error.response.headers["retry-after"] == "30"
+
+
+def test_service_unavailable_error_preserves_retry_after_header():
+    """Test that ServiceUnavailableError preserves retry-after headers."""
+    response = httpx.Response(
+        status_code=503,
+        headers=httpx.Headers({"retry-after": "60"}),
+        request=httpx.Request("POST", "http://test.com"),
+    )
+
+    error = litellm.ServiceUnavailableError(
+        message="Service unavailable",
+        llm_provider="test",
+        model="test-model",
+        response=response,
+    )
+
+    assert error.status_code == 503
+    assert error.response.headers is not None
+    assert "retry-after" in error.response.headers
+    assert error.response.headers["retry-after"] == "60"
+
+
+def test_internal_server_error_preserves_retry_after_header():
+    """Test that InternalServerError preserves retry-after headers."""
+    response = httpx.Response(
+        status_code=500,
+        headers=httpx.Headers({"retry-after": "45"}),
+        request=httpx.Request("POST", "http://test.com"),
+    )
+
+    error = litellm.InternalServerError(
+        message="Internal server error",
+        llm_provider="test",
+        model="test-model",
+        response=response,
+    )
+
+    assert error.status_code == 500
+    assert error.response.headers is not None
+    assert "retry-after" in error.response.headers
+    assert error.response.headers["retry-after"] == "45"
+
+
+def test_bad_gateway_error_in_exception_types():
+    """Test that BadGatewayError is in LITELLM_EXCEPTION_TYPES."""
+    assert litellm.BadGatewayError in litellm.LITELLM_EXCEPTION_TYPES
+
+
+def test_bad_gateway_error_attributes():
+    """Test that BadGatewayError has expected attributes."""
+    response = httpx.Response(
+        status_code=502,
+        headers=httpx.Headers({"retry-after": "20"}),
+        request=httpx.Request("POST", "http://test.com"),
+    )
+
+    error = litellm.BadGatewayError(
+        message="Test error",
+        llm_provider="openai",
+        model="gpt-3.5-turbo",
+        response=response,
+        max_retries=3,
+        num_retries=1,
+    )
+
+    assert error.status_code == 502
+    assert error.llm_provider == "openai"
+    assert error.model == "gpt-3.5-turbo"
+    assert error.max_retries == 3
+    assert error.num_retries == 1
+    assert "LiteLLM Retried: 1 times" in str(error)
+    assert "LiteLLM Max Retries: 3" in str(error)
+
+
+def test_exception_without_response_headers():
+    """Test that exceptions work even without response headers."""
+    # Test with None response
+    error = litellm.BadGatewayError(
+        message="Test error",
+        llm_provider="test",
+        model="test-model",
+        response=None,
+    )
+
+    assert error.status_code == 502
+    assert error.response is not None  # Should create a default response
+    assert (
+        error.response.headers is not None
+    )  # Should have headers object (may be empty)
+
+
+def test_exception_with_empty_headers():
+    """Test that exceptions work with empty headers."""
+    response = httpx.Response(
+        status_code=502,
+        headers=httpx.Headers({}),  # Empty headers
+        request=httpx.Request("POST", "http://test.com"),
+    )
+
+    error = litellm.BadGatewayError(
+        message="Test error",
+        llm_provider="test",
+        model="test-model",
+        response=response,
+    )
+
+    assert error.status_code == 502
+    assert error.response.headers is not None
+    # Should not have retry-after since we didn't provide it
+    assert "retry-after" not in error.response.headers
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
index b556b0e5bee..f69415fb8e8 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
@@ -364,3 +364,28 @@ def test_get_supported_params_thinking():
     config = AnthropicConfig()
     params = config.get_supported_openai_params(model="claude-sonnet-4-20250514")
     assert "thinking" in params
+
+
+def test_anthropic_memory_tool_auto_adds_beta_header():
+    """
+    Tests that LiteLLM automatically adds the required 'anthropic-beta' header
+    when the memory tool is present, and the user has NOT provided a beta header.
+    """
+
+    config = AnthropicConfig()
+    memory_tool = [{"type": "memory_20250818", "name": "memory"}]
+    messages = [{"role": "user", "content": "Remember this."}]
+
+    headers = {}
+    optional_params = {"tools": memory_tool}
+
+    config.transform_request(
+        model="claude-3-5-sonnet-20240620",
+        messages=messages,
+        optional_params=optional_params,
+        litellm_params={},
+        headers=headers,
+    )
+
+    assert "anthropic-beta" in headers
+    assert headers["anthropic-beta"] == "context-management-2025-06-27"
diff --git a/tests/test_litellm/llms/azure/test_azure_exception_mapping.py b/tests/test_litellm/llms/azure/test_azure_exception_mapping.py
new file mode 100644
index 00000000000..4fe291d4964
--- /dev/null
+++ b/tests/test_litellm/llms/azure/test_azure_exception_mapping.py
@@ -0,0 +1,193 @@
+import json
+import os
+import sys
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../../../..")
+)  # Adds the parent directory to the system path
+
+import litellm
+from litellm.exceptions import ContentPolicyViolationError
+from litellm.litellm_core_utils.exception_mapping_utils import exception_type
+
+
+class TestAzureExceptionMapping:
+    """Test Azure OpenAI exception mapping with provider-specific fields"""
+
+    def test_azure_content_policy_violation_innererror_access(self):
+        """Test that Azure content policy violation exceptions provide access to innererror details"""
+        
+        # Create a mock Azure OpenAI exception with body containing innererror
+        mock_exception = Exception("The response was filtered due to the prompt triggering Azure OpenAI's content management policy")
+        mock_exception.body = {
+            "innererror": {
+                "code": "ResponsibleAIPolicyViolation",
+                "content_filter_result": {
+                    "hate": {
+                        "filtered": True,
+                        "severity": "high"
+                    },
+                    "jailbreak": {
+                        "filtered": False,
+                        "detected": False
+                    },
+                    "self_harm": {
+                        "filtered": False,
+                        "severity": "safe"
+                    },
+                    "sexual": {
+                        "filtered": False,
+                        "severity": "safe"
+                    },
+                    "violence": {
+                        "filtered": True,
+                        "severity": "medium"
+                    }
+                }
+            }
+        }
+        
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_exception.response = mock_response
+        
+        # Test the exception mapping directly
+        with pytest.raises(ContentPolicyViolationError) as exc_info:
+            exception_type(
+                model="azure/gpt-4",
+                original_exception=mock_exception,
+                custom_llm_provider="azure"
+            )
+        
+        # Access the exception and verify provider_specific_fields
+        e = exc_info.value
+        assert e.provider_specific_fields is not None
+        assert "innererror" in e.provider_specific_fields
+        
+        innererror = e.provider_specific_fields["innererror"]
+        assert innererror["code"] == "ResponsibleAIPolicyViolation"
+        assert "content_filter_result" in innererror
+        
+        content_filter_result = innererror["content_filter_result"]
+        assert content_filter_result["hate"]["filtered"] is True
+        assert content_filter_result["hate"]["severity"] == "high"
+        assert content_filter_result["violence"]["filtered"] is True
+        assert content_filter_result["violence"]["severity"] == "medium"
+        assert content_filter_result["sexual"]["filtered"] is False
+        assert content_filter_result["self_harm"]["filtered"] is False
+        assert content_filter_result["jailbreak"]["filtered"] is False
+
+    def test_azure_content_policy_violation_different_categories(self):
+        """Test Azure content policy violation with different filtering categories"""
+        
+        # Mock exception with different content filter results  
+        mock_exception = Exception("The response was filtered due to the prompt triggering Azure OpenAI's content management policy")
+        mock_exception.body = {
+            "innererror": {
+                "code": "ResponsibleAIPolicyViolation",
+                "content_filter_result": {
+                    "hate": {
+                        "filtered": False,
+                        "severity": "safe"
+                    },
+                    "jailbreak": {
+                        "filtered": True,
+                        "detected": True
+                    },
+                    "self_harm": {
+                        "filtered": True,
+                        "severity": "high"
+                    },
+                    "sexual": {
+                        "filtered": True,
+                        "severity": "medium"
+                    },
+                    "violence": {
+                        "filtered": False,
+                        "severity": "safe"
+                    }
+                }
+            }
+        }
+        
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_exception.response = mock_response
+        
+        # Test the exception mapping directly with different violation type
+        with pytest.raises(ContentPolicyViolationError) as exc_info:
+            exception_type(
+                model="azure/gpt-4",
+                original_exception=mock_exception,
+                custom_llm_provider="azure"
+            )
+        
+        # Verify provider_specific_fields contains the expected innererror structure
+        e = exc_info.value
+        assert e.provider_specific_fields is not None
+        print("got provider_specific_fields=", e.provider_specific_fields)
+        innererror = e.provider_specific_fields["innererror"]
+        content_filter_result = innererror["content_filter_result"]
+        
+        # Check different filter categories
+        assert content_filter_result["sexual"]["filtered"] is True
+        assert content_filter_result["sexual"]["severity"] == "medium"
+        assert content_filter_result["self_harm"]["filtered"] is True
+        assert content_filter_result["self_harm"]["severity"] == "high" 
+        assert content_filter_result["jailbreak"]["filtered"] is True
+        assert content_filter_result["jailbreak"]["detected"] is True
+        assert content_filter_result["hate"]["filtered"] is False
+        assert content_filter_result["violence"]["filtered"] is False
+
+    def test_azure_content_policy_violation_missing_innererror(self):
+        """Test Azure content policy violation when innererror is missing from response"""
+        
+        # Mock exception without body attribute
+        mock_exception = Exception("The response was filtered due to the prompt triggering Azure OpenAI's content management policy")
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_exception.response = mock_response
+        # Note: no mock_exception.body attribute set
+        
+        # Test the exception mapping directly
+        with pytest.raises(ContentPolicyViolationError) as exc_info:
+            exception_type(
+                model="azure/gpt-4",
+                original_exception=mock_exception,
+                custom_llm_provider="azure"
+            )
+        
+        # Verify that even without innererror, the exception is still raised properly
+        e = exc_info.value
+        print("got exception=", e)
+        # provider_specific_fields should still exist but innererror should be None
+        assert e.provider_specific_fields is not None
+        assert e.provider_specific_fields.get("innererror") is None
+
+    def test_azure_content_policy_violation_non_dict_body(self):
+        """Test Azure content policy violation when body is not a dictionary"""
+        
+        # Mock exception with non-dict body
+        mock_exception = Exception("The response was filtered due to the prompt triggering Azure OpenAI's content management policy")
+        mock_exception.body = "invalid body format"
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_exception.response = mock_response
+        
+        # Test the exception mapping directly
+        with pytest.raises(ContentPolicyViolationError) as exc_info:
+            exception_type(
+                model="azure/gpt-4",
+                original_exception=mock_exception,
+                custom_llm_provider="azure"
+            )
+        
+        # Verify that with invalid body format, innererror should be None
+        e = exc_info.value
+        print("got exception=", e)
+        print("exception fields=", vars(e))
+        assert e.provider_specific_fields is not None
+        assert e.provider_specific_fields.get("innererror") is None 
\ No newline at end of file
diff --git a/tests/test_litellm/llms/azure/videos/test_azure_video_transformation.py b/tests/test_litellm/llms/azure/videos/test_azure_video_transformation.py
index 14bf0064e61..640933179a6 100644
--- a/tests/test_litellm/llms/azure/videos/test_azure_video_transformation.py
+++ b/tests/test_litellm/llms/azure/videos/test_azure_video_transformation.py
@@ -129,10 +129,12 @@ class TestAzureVideoConfig:
         )
         
         headers = {"Authorization": f"Bearer {self.api_key}"}
+        api_base = f"{self.api_base}/openai/v1/videos"
         
-        data, files = self.config.transform_video_create_request(
+        data, files, url = self.config.transform_video_create_request(
             model=self.model,
             prompt="A cinematic shot of a city at night",
+            api_base=api_base,
             video_create_optional_request_params=video_params,
             litellm_params=litellm_params,
             headers=headers
@@ -142,6 +144,8 @@ class TestAzureVideoConfig:
         assert data["seconds"] == 8
         assert data["size"] == "720x1280"
         assert data["model"] == self.model
+        # URL should be returned as-is for Azure
+        assert url == api_base
 
     def test_transform_video_create_response(self):
         """Test video creation response transformation."""
@@ -275,13 +279,15 @@ class TestAzureVideoConfig:
         )
         
         headers = {"Authorization": f"Bearer {self.api_key}"}
+        api_base = f"{self.api_base}/openai/v1/videos"
         
         # Mock file existence
         with patch('os.path.exists', return_value=True):
             with patch('builtins.open', mock_open(read_data=b"fake image data")):
-                data, files = self.config.transform_video_create_request(
+                data, files, url = self.config.transform_video_create_request(
                     model=self.model,
                     prompt="A video with reference image",
+                    api_base=api_base,
                     video_create_optional_request_params=video_params,
                     litellm_params=litellm_params,
                     headers=headers
@@ -291,6 +297,7 @@ class TestAzureVideoConfig:
         assert data["seconds"] == 10
         assert len(files) == 1
         assert files[0][0] == "input_reference"
+        assert url == api_base
 
     def test_error_handling_in_response_transformation(self):
         """Test error handling in response transformation methods."""
diff --git a/tests/test_litellm/llms/gemini/videos/__init__.py b/tests/test_litellm/llms/gemini/videos/__init__.py
new file mode 100644
index 00000000000..7156c063be7
--- /dev/null
+++ b/tests/test_litellm/llms/gemini/videos/__init__.py
@@ -0,0 +1,2 @@
+# Gemini Video Generation Tests
+
diff --git a/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py b/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py
new file mode 100644
index 00000000000..660974181f9
--- /dev/null
+++ b/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py
@@ -0,0 +1,680 @@
+"""
+Tests for Gemini (Veo) video generation transformation.
+"""
+import json
+import os
+from unittest.mock import MagicMock, Mock, patch
+
+import httpx
+import pytest
+
+from litellm.llms.gemini.videos.transformation import GeminiVideoConfig
+from litellm.llms.openai.cost_calculation import video_generation_cost
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.videos.main import VideoObject
+
+
+class TestGeminiVideoConfig:
+    """Test GeminiVideoConfig transformation class."""
+
+    def setup_method(self):
+        """Setup test fixtures."""
+        self.config = GeminiVideoConfig()
+        self.mock_logging_obj = Mock()
+
+    def test_get_supported_openai_params(self):
+        """Test that correct params are supported."""
+        params = self.config.get_supported_openai_params("veo-3.0-generate-preview")
+        
+        assert "model" in params
+        assert "prompt" in params
+        assert "input_reference" in params
+        assert "seconds" in params
+        assert "size" in params
+
+    def test_validate_environment_with_api_key(self):
+        """Test environment validation with API key."""
+        headers = {}
+        result = self.config.validate_environment(
+            headers=headers,
+            model="veo-3.0-generate-preview",
+            api_key="test-api-key-123"
+        )
+        
+        assert "x-goog-api-key" in result
+        assert result["x-goog-api-key"] == "test-api-key-123"
+        assert "Content-Type" in result
+        assert result["Content-Type"] == "application/json"
+
+    @patch.dict('os.environ', {}, clear=True)
+    def test_validate_environment_missing_api_key(self):
+        """Test that missing API key raises error."""
+        headers = {}
+        
+        with pytest.raises(ValueError, match="GEMINI_API_KEY or GOOGLE_API_KEY is required"):
+            self.config.validate_environment(
+                headers=headers,
+                model="veo-3.0-generate-preview",
+                api_key=None
+            )
+
+    def test_get_complete_url(self):
+        """Test URL construction for video generation."""
+        url = self.config.get_complete_url(
+            model="gemini/veo-3.0-generate-preview",
+            api_base="https://generativelanguage.googleapis.com",
+            litellm_params={}
+        )
+        
+        expected = "https://generativelanguage.googleapis.com/v1beta/models/veo-3.0-generate-preview:predictLongRunning"
+        assert url == expected
+
+    def test_get_complete_url_default_api_base(self):
+        """Test URL construction with default API base."""
+        url = self.config.get_complete_url(
+            model="gemini/veo-3.0-generate-preview",
+            api_base=None,
+            litellm_params={}
+        )
+        
+        assert url.startswith("https://generativelanguage.googleapis.com")
+        assert "veo-3.0-generate-preview:predictLongRunning" in url
+
+    def test_transform_video_create_request(self):
+        """Test transformation of video creation request."""
+        prompt = "A cat playing with a ball of yarn"
+        api_base = "https://generativelanguage.googleapis.com/v1beta/models/veo-3.0-generate-preview:predictLongRunning"
+        
+        data, files, url = self.config.transform_video_create_request(
+            model="veo-3.0-generate-preview",
+            prompt=prompt,
+            api_base=api_base,
+            video_create_optional_request_params={},
+            litellm_params=GenericLiteLLMParams(),
+            headers={}
+        )
+        
+        # Check Veo format
+        assert "instances" in data
+        assert len(data["instances"]) == 1
+        assert data["instances"][0]["prompt"] == prompt
+        
+        # Check no files are uploaded
+        assert files == []
+        
+        # URL should be returned as-is for Gemini
+        assert url == api_base
+    
+    def test_transform_video_create_request_with_params(self):
+        """Test transformation with optional parameters."""
+        prompt = "A cat playing with a ball of yarn"
+        api_base = "https://generativelanguage.googleapis.com/v1beta/models/veo-3.0-generate-preview:predictLongRunning"
+        
+        data, files, url = self.config.transform_video_create_request(
+            model="veo-3.0-generate-preview",
+            prompt=prompt,
+            api_base=api_base,
+            video_create_optional_request_params={
+                "aspectRatio": "16:9",
+                "durationSeconds": 8,
+                "resolution": "1080p"
+            },
+            litellm_params=GenericLiteLLMParams(),
+            headers={}
+        )
+        
+        # Check Veo format with instances and parameters separated
+        instance = data["instances"][0]
+        assert instance["prompt"] == prompt
+        
+        # Parameters should be in a separate object
+        assert "parameters" in data
+        assert data["parameters"]["aspectRatio"] == "16:9"
+        assert data["parameters"]["durationSeconds"] == 8
+        assert data["parameters"]["resolution"] == "1080p"
+    
+    def test_map_openai_params(self):
+        """Test parameter mapping from OpenAI format to Veo format."""
+        openai_params = {
+            "size": "1280x720",
+            "seconds": "8",
+            "input_reference": "test_image.jpg"
+        }
+        
+        mapped = self.config.map_openai_params(
+            video_create_optional_params=openai_params,
+            model="veo-3.0-generate-preview",
+            drop_params=False
+        )
+        
+        # Check mappings (prompt is not mapped, it's passed separately)
+        assert mapped["aspectRatio"] == "16:9"  # 1280x720 is landscape
+        assert mapped["durationSeconds"] == 8
+        assert mapped["image"] == "test_image.jpg"
+
+    def test_map_openai_params_default_duration(self):
+        """Test that durationSeconds is omitted when not provided."""
+        openai_params = {
+            "size": "1280x720",
+        }
+        
+        mapped = self.config.map_openai_params(
+            video_create_optional_params=openai_params,
+            model="veo-3.0-generate-preview",
+            drop_params=False
+        )
+        
+        assert mapped["aspectRatio"] == "16:9"
+        assert "durationSeconds" not in mapped
+
+    def test_map_openai_params_with_gemini_specific_params(self):
+        """Test that Gemini-specific params are passed through correctly."""
+        params_with_gemini_specific = {
+            "size": "1280x720",
+            "seconds": "8",
+            "video": {"bytesBase64Encoded": "abc123", "mimeType": "video/mp4"},
+            "negativePrompt": "no people",
+            "referenceImages": [{"bytesBase64Encoded": "xyz789"}],
+            "personGeneration": "allow"
+        }
+        
+        mapped = self.config.map_openai_params(
+            video_create_optional_params=params_with_gemini_specific,
+            model="veo-3.1-generate-preview",
+            drop_params=False
+        )
+        
+        # Check OpenAI params are mapped
+        assert mapped["aspectRatio"] == "16:9"
+        assert mapped["durationSeconds"] == 8
+        
+        # Check Gemini-specific params are passed through
+        assert "video" in mapped
+        assert mapped["video"]["bytesBase64Encoded"] == "abc123"
+        assert mapped["negativePrompt"] == "no people"
+        assert mapped["referenceImages"] == [{"bytesBase64Encoded": "xyz789"}]
+        assert mapped["personGeneration"] == "allow"
+
+    def test_map_openai_params_with_extra_body(self):
+        """Test that extra_body params are merged and extra_body is removed."""
+        from litellm.videos.utils import VideoGenerationRequestUtils
+        
+        params_with_extra_body = {
+            "seconds": "4",
+            "extra_body": {
+                "negativePrompt": "no people",
+                "personGeneration": "allow",
+                "resolution": "1080p"
+            }
+        }
+        
+        mapped = VideoGenerationRequestUtils.get_optional_params_video_generation(
+            model="veo-3.0-generate-preview",
+            video_generation_provider_config=self.config,
+            video_generation_optional_params=params_with_extra_body
+        )
+        
+        # Check OpenAI params are mapped
+        assert mapped["durationSeconds"] == 4
+        
+        # Check extra_body params are merged
+        assert mapped["negativePrompt"] == "no people"
+        assert mapped["personGeneration"] == "allow"
+        assert mapped["resolution"] == "1080p"
+        
+        # Check extra_body itself is removed
+        assert "extra_body" not in mapped
+    
+    def test_convert_size_to_aspect_ratio(self):
+        """Test size to aspect ratio conversion."""
+        # Landscape
+        assert self.config._convert_size_to_aspect_ratio("1280x720") == "16:9"
+        assert self.config._convert_size_to_aspect_ratio("1920x1080") == "16:9"
+        
+        # Portrait
+        assert self.config._convert_size_to_aspect_ratio("720x1280") == "9:16"
+        assert self.config._convert_size_to_aspect_ratio("1080x1920") == "9:16"
+        
+        # Invalid (defaults to 16:9)
+        assert self.config._convert_size_to_aspect_ratio("invalid") == "16:9"
+        # Empty string returns None (no size specified)
+        assert self.config._convert_size_to_aspect_ratio("") is None
+
+    def test_transform_video_create_response(self):
+        """Test transformation of video creation response."""
+        # Mock response
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+            "metadata": {
+                "createTime": "2024-11-04T10:00:00.123456Z"
+            }
+        }
+        
+        result = self.config.transform_video_create_response(
+            model="veo-3.0-generate-preview",
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini"
+        )
+        
+        assert isinstance(result, VideoObject)
+        # ID is base64 encoded with provider info
+        assert result.id.startswith("video_")
+        assert result.status == "processing"
+        assert result.object == "video"
+
+
+    def test_transform_video_create_response_with_cost_tracking(self):
+        """Test that duration is captured for cost tracking."""
+        # Mock response
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+        }
+        
+        # Request data with durationSeconds in parameters
+        request_data = {
+            "instances": [{"prompt": "A test video"}],
+            "parameters": {
+                "durationSeconds": 5,
+                "aspectRatio": "16:9"
+            }
+        }
+        
+        result = self.config.transform_video_create_response(
+            model="gemini/veo-3.0-generate-preview",
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini",
+            request_data=request_data
+        )
+        
+        assert isinstance(result, VideoObject)
+        assert result.usage is not None, "Usage should be set"
+        assert "duration_seconds" in result.usage, "duration_seconds should be in usage"
+        assert result.usage["duration_seconds"] == 5.0, f"Expected 5.0, got {result.usage['duration_seconds']}"
+
+    def test_transform_video_create_response_cost_tracking_with_different_durations(self):
+        """Test cost tracking with different duration values."""
+        # Mock response
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+        }
+        
+        # Test with 8 seconds
+        request_data_8s = {
+            "instances": [{"prompt": "Test"}],
+            "parameters": {"durationSeconds": 8}
+        }
+        
+        result_8s = self.config.transform_video_create_response(
+            model="gemini/veo-3.1-generate-preview",
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini",
+            request_data=request_data_8s
+        )
+        
+        assert result_8s.usage["duration_seconds"] == 8.0
+        
+        # Test with 4 seconds
+        request_data_4s = {
+            "instances": [{"prompt": "Test"}],
+            "parameters": {"durationSeconds": 4}
+        }
+        
+        result_4s = self.config.transform_video_create_response(
+            model="gemini/veo-3.1-fast-generate-preview",
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini",
+            request_data=request_data_4s
+        )
+        
+        assert result_4s.usage["duration_seconds"] == 4.0
+
+    def test_transform_video_create_response_cost_tracking_no_duration(self):
+        """Test that usage defaults to 8 seconds when no duration in request."""
+        # Mock response
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+        }
+        
+        # Request data without durationSeconds (should default to 8 seconds for Google Veo)
+        request_data = {
+            "instances": [{"prompt": "A test video"}],
+            "parameters": {
+                "aspectRatio": "16:9"
+            }
+        }
+        
+        result = self.config.transform_video_create_response(
+            model="gemini/veo-3.0-generate-preview",
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini",
+            request_data=request_data
+        )
+        
+        assert isinstance(result, VideoObject)
+        # When no duration is provided, it defaults to 8 seconds (Google Veo default)
+        assert result.usage is not None
+        assert "duration_seconds" in result.usage
+        assert result.usage["duration_seconds"] == 8.0, "Should default to 8 seconds when not provided (Google Veo default)"
+
+    def test_transform_video_status_retrieve_request(self):
+        """Test transformation of status retrieve request."""
+        video_id = "gemini::operations/generate_1234567890::veo-3.0"
+        
+        url, params = self.config.transform_video_status_retrieve_request(
+            video_id=video_id,
+            api_base="https://generativelanguage.googleapis.com",
+            litellm_params=GenericLiteLLMParams(),
+            headers={}
+        )
+        
+        assert "operations/generate_1234567890" in url
+        assert "v1beta" in url
+        assert params == {}
+
+    def test_transform_video_status_retrieve_response_processing(self):
+        """Test transformation of status response when still processing."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+            "done": False,
+            "metadata": {
+                "createTime": "2024-11-04T10:00:00.123456Z"
+            }
+        }
+        
+        result = self.config.transform_video_status_retrieve_response(
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini"
+        )
+        
+        assert isinstance(result, VideoObject)
+        assert result.status == "processing"
+
+    def test_transform_video_status_retrieve_response_completed(self):
+        """Test transformation of status response when completed."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+            "done": True,
+            "metadata": {
+                "createTime": "2024-11-04T10:00:00.123456Z"
+            },
+            "response": {
+                "generateVideoResponse": {
+                    "generatedSamples": [
+                        {
+                            "video": {
+                                "uri": "files/abc123xyz"
+                            }
+                        }
+                    ]
+                }
+            }
+        }
+        
+        result = self.config.transform_video_status_retrieve_response(
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="gemini"
+        )
+        
+        assert isinstance(result, VideoObject)
+        assert result.status == "completed"
+
+    @patch('litellm.module_level_client')
+    def test_transform_video_content_request(self, mock_client):
+        """Test transformation of content download request."""
+        video_id = "gemini::operations/generate_1234567890::veo-3.0"
+        
+        # Mock the status response
+        mock_status_response = Mock(spec=httpx.Response)
+        mock_status_response.json.return_value = {
+            "name": "operations/generate_1234567890",
+            "done": True,
+            "response": {
+                "generateVideoResponse": {
+                    "generatedSamples": [
+                        {
+                            "video": {
+                                "uri": "files/abc123xyz"
+                            }
+                        }
+                    ]
+                }
+            }
+        }
+        mock_status_response.raise_for_status = Mock()
+        mock_client.get.return_value = mock_status_response
+        
+        url, params = self.config.transform_video_content_request(
+            video_id=video_id,
+            api_base="https://generativelanguage.googleapis.com",
+            litellm_params=GenericLiteLLMParams(),
+            headers={}
+        )
+        
+        # Should return download URL (may or may not include :download suffix)
+        assert "files/abc123xyz" in url
+        # Params are empty for Gemini file URIs
+        assert params == {}
+
+    def test_transform_video_content_response_bytes(self):
+        """Test transformation of content response (returns bytes directly)."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.headers = httpx.Headers({
+            "content-type": "video/mp4"
+        })
+        mock_response.content = b"fake_video_data"
+        
+        result = self.config.transform_video_content_response(
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj
+        )
+        
+        assert result == b"fake_video_data"
+
+    def test_video_remix_not_supported(self):
+        """Test that video remix raises NotImplementedError."""
+        with pytest.raises(NotImplementedError, match="Video remix is not supported"):
+            self.config.transform_video_remix_request(
+                video_id="test_id",
+                prompt="test prompt",
+                api_base="https://test.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={}
+            )
+
+    def test_video_list_not_supported(self):
+        """Test that video list raises NotImplementedError."""
+        with pytest.raises(NotImplementedError, match="Video list is not supported"):
+            self.config.transform_video_list_request(
+                api_base="https://test.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={}
+            )
+
+    def test_video_delete_not_supported(self):
+        """Test that video delete raises NotImplementedError."""
+        with pytest.raises(NotImplementedError, match="Video delete is not supported"):
+            self.config.transform_video_delete_request(
+                video_id="test_id",
+                api_base="https://test.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={}
+            )
+
+
+class TestGeminiVideoIntegration:
+    """Integration tests for Gemini video generation workflow."""
+
+    def test_full_workflow_mock(self):
+        """Test full workflow with mocked responses."""
+        config = GeminiVideoConfig()
+        mock_logging_obj = Mock()
+        
+        # Step 1: Create request with parameters
+        prompt = "A beautiful sunset over mountains"
+        api_base = "https://generativelanguage.googleapis.com/v1beta/models/veo-3.0-generate-preview:predictLongRunning"
+        data, files, url = config.transform_video_create_request(
+            model="veo-3.0-generate-preview",
+            prompt=prompt,
+            api_base=api_base,
+            video_create_optional_request_params={
+                "aspectRatio": "16:9",
+                "durationSeconds": 8
+            },
+            litellm_params=GenericLiteLLMParams(),
+            headers={}
+        )
+        
+        # Verify instances and parameters structure
+        assert data["instances"][0]["prompt"] == prompt
+        assert data["parameters"]["aspectRatio"] == "16:9"
+        assert data["parameters"]["durationSeconds"] == 8
+        
+        # Step 2: Parse create response
+        mock_create_response = Mock(spec=httpx.Response)
+        mock_create_response.json.return_value = {
+            "name": "operations/generate_abc123",
+            "metadata": {
+                "createTime": "2024-11-04T10:00:00.123456Z"
+            }
+        }
+        
+        video_obj = config.transform_video_create_response(
+            model="veo-3.0-generate-preview",
+            raw_response=mock_create_response,
+            logging_obj=mock_logging_obj,
+            custom_llm_provider="gemini"
+        )
+        
+        assert video_obj.status == "processing"
+        assert video_obj.id.startswith("video_")
+        
+        # Step 3: Check status (completed)
+        mock_status_response = Mock(spec=httpx.Response)
+        mock_status_response.json.return_value = {
+            "name": "operations/generate_abc123",
+            "done": True,
+            "metadata": {
+                "createTime": "2024-11-04T10:00:00.123456Z"
+            },
+            "response": {
+                "generateVideoResponse": {
+                    "generatedSamples": [
+                        {
+                            "video": {
+                                "uri": "files/video123"
+                            }
+                        }
+                    ]
+                }
+            }
+        }
+        
+        status_obj = config.transform_video_status_retrieve_response(
+            raw_response=mock_status_response,
+            logging_obj=mock_logging_obj,
+            custom_llm_provider="gemini"
+        )
+        
+        assert status_obj.status == "completed"
+
+
+class TestGeminiVideoCostTracking:
+    """Test cost tracking for Gemini video generation."""
+    
+    def test_cost_calculation_with_duration(self):
+        """Test that cost is calculated correctly using duration from usage."""
+        # Test VEO 2.0 ($0.35/second)
+        cost_veo2 = video_generation_cost(
+            model="gemini/veo-2.0-generate-001",
+            duration_seconds=5.0,
+            custom_llm_provider="gemini"
+        )
+        expected_veo2 = 0.35 * 5.0  # $1.75
+        assert abs(cost_veo2 - expected_veo2) < 0.001, f"Expected ${expected_veo2}, got ${cost_veo2}"
+        
+        # Test VEO 3.0 ($0.75/second)
+        cost_veo3 = video_generation_cost(
+            model="gemini/veo-3.0-generate-preview",
+            duration_seconds=8.0,
+            custom_llm_provider="gemini"
+        )
+        expected_veo3 = 0.75 * 8.0  # $6.00
+        assert abs(cost_veo3 - expected_veo3) < 0.001, f"Expected ${expected_veo3}, got ${cost_veo3}"
+        
+        # Test VEO 3.1 Standard ($0.40/second)
+        cost_veo31 = video_generation_cost(
+            model="gemini/veo-3.1-generate-preview",
+            duration_seconds=10.0,
+            custom_llm_provider="gemini"
+        )
+        expected_veo31 = 0.40 * 10.0  # $4.00
+        assert abs(cost_veo31 - expected_veo31) < 0.001, f"Expected ${expected_veo31}, got ${cost_veo31}"
+        
+        # Test VEO 3.1 Fast ($0.15/second)
+        cost_veo31_fast = video_generation_cost(
+            model="gemini/veo-3.1-fast-generate-preview",
+            duration_seconds=6.0,
+            custom_llm_provider="gemini"
+        )
+        expected_veo31_fast = 0.15 * 6.0  # $0.90
+        assert abs(cost_veo31_fast - expected_veo31_fast) < 0.001, f"Expected ${expected_veo31_fast}, got ${cost_veo31_fast}"
+    
+    def test_cost_calculation_end_to_end(self):
+        """Test complete cost tracking flow: request -> response -> cost calculation."""
+        config = GeminiVideoConfig()
+        mock_logging_obj = Mock()
+        
+        # Create request with duration
+        request_data = {
+            "instances": [{"prompt": "A beautiful sunset"}],
+            "parameters": {"durationSeconds": 5}
+        }
+        
+        # Mock response
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "operations/generate_test123",
+        }
+        
+        # Transform response
+        video_obj = config.transform_video_create_response(
+            model="gemini/veo-3.0-generate-preview",
+            raw_response=mock_response,
+            logging_obj=mock_logging_obj,
+            custom_llm_provider="gemini",
+            request_data=request_data
+        )
+        
+        # Verify usage has duration
+        assert video_obj.usage is not None
+        assert "duration_seconds" in video_obj.usage
+        duration = video_obj.usage["duration_seconds"]
+        
+        # Calculate cost using the duration from usage
+        cost = video_generation_cost(
+            model="gemini/veo-3.0-generate-preview",
+            duration_seconds=duration,
+            custom_llm_provider="gemini"
+        )
+        
+        # Verify cost calculation (VEO 3.0 is $0.75/second)
+        expected_cost = 0.75 * 5.0  # $3.75
+        assert abs(cost - expected_cost) < 0.001, f"Expected ${expected_cost}, got ${cost}"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
+
diff --git a/tests/test_litellm/llms/perplexity/chat/test_perplexity_chat_transformation.py b/tests/test_litellm/llms/perplexity/chat/test_perplexity_chat_transformation.py
index d83c1a9f659..784e6f6fe63 100644
--- a/tests/test_litellm/llms/perplexity/chat/test_perplexity_chat_transformation.py
+++ b/tests/test_litellm/llms/perplexity/chat/test_perplexity_chat_transformation.py
@@ -7,7 +7,7 @@ from Perplexity API responses.
 
 import os
 import sys
-from unittest.mock import Mock, patch
+from unittest.mock import Mock
 
 import pytest
 
@@ -707,152 +707,4 @@ class TestPerplexityChatTransformation:
         # Check that no annotations were created (message content is None)
         assert choice.message.content is None
         # No annotations should be created since content is None
-        assert not hasattr(choice.message, 'annotations') or choice.message.annotations is None
-
-    # Tests for cost extraction functionality
-    def test_add_cost_to_usage_flat_structure(self):
-        """Test cost extraction from flat usage structure."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response with flat cost structure
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "total_cost": 0.00015
-            }
-        }
-        
-        # Test cost extraction
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Check that cost was stored in hidden params
-        assert hasattr(model_response, "_hidden_params")
-        assert "additional_headers" in model_response._hidden_params
-        assert "llm_provider-x-litellm-response-cost" in model_response._hidden_params["additional_headers"]
-        
-        cost = model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"]
-        assert cost == 0.00015
-
-    def test_add_cost_to_usage_nested_structure(self):
-        """Test cost extraction from nested usage structure."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response with nested cost structure
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "cost": {
-                    "total_cost": 0.00025
-                }
-            }
-        }
-        
-        # Test cost extraction
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Check that cost was stored in hidden params
-        assert hasattr(model_response, "_hidden_params")
-        assert "additional_headers" in model_response._hidden_params
-        assert "llm_provider-x-litellm-response-cost" in model_response._hidden_params["additional_headers"]
-        
-        cost = model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"]
-        assert cost == 0.00025
-
-    def test_add_cost_to_usage_no_cost_data(self):
-        """Test handling when no cost data is present."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response without cost
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150
-            }
-        }
-        
-        # Test cost extraction - should not raise error
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Should not have cost in hidden params
-        if hasattr(model_response, "_hidden_params"):
-            assert "llm_provider-x-litellm-response-cost" not in model_response._hidden_params.get("additional_headers", {})
-
-    def test_transform_response_includes_cost_extraction(self):
-        """Test that transform_response includes cost extraction."""
-        config = PerplexityChatConfig()
-        
-        # Mock raw response
-        mock_response = Mock()
-        mock_response.json.return_value = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "total_cost": 0.00015
-            }
-        }
-        mock_response.headers = {}
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        model_response.model = "perplexity/sonar-pro"
-        
-        # Mock the parent transform_response to return our model_response
-        with patch.object(config.__class__.__bases__[0], 'transform_response', return_value=model_response):
-            result = config.transform_response(
-                model="perplexity/sonar-pro",
-                raw_response=mock_response,
-                model_response=model_response,
-                logging_obj=Mock(),
-                request_data={},
-                messages=[{"role": "user", "content": "Test"}],
-                optional_params={},
-                litellm_params={},
-                encoding=None,
-            )
-        
-        # Check that cost was extracted and stored
-        assert hasattr(result, "_hidden_params")
-        assert "additional_headers" in result._hidden_params
-        assert "llm_provider-x-litellm-response-cost" in result._hidden_params["additional_headers"]
-        
-        cost = result._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"]
-        assert cost == 0.00015
\ No newline at end of file
+        assert not hasattr(choice.message, 'annotations') or choice.message.annotations is None
\ No newline at end of file
diff --git a/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py b/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py
index 58b037269a7..f9a52100070 100644
--- a/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py
+++ b/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py
@@ -17,8 +17,7 @@ import pytest
 sys.path.insert(0, os.path.abspath("../../../.."))
 
 import litellm
-from litellm import ModelResponse
-from litellm.cost_calculator import completion_cost, cost_per_token, response_cost_calculator
+from litellm.cost_calculator import completion_cost, cost_per_token
 from litellm.llms.perplexity.cost_calculator import cost_per_token as perplexity_cost_per_token
 from litellm.types.utils import Usage, PromptTokensDetailsWrapper
 from litellm.utils import get_model_info
@@ -371,116 +370,4 @@ class TestPerplexityCostCalculator:
         
         # Ensure costs are non-negative
         assert prompt_cost >= 0
-        assert completion_cost >= 0
-
-    def test_cost_extraction_priority_over_calculation(self):
-        """Test that extracted cost from API response takes priority over calculated cost."""
-        from litellm.cost_calculator import response_cost_calculator
-        from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig
-        
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse with extracted cost
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        model_response.model = "perplexity/sonar-pro"
-        
-        # Mock raw response with cost
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "total_cost": 0.00015  # This should be used instead of calculated cost
-            }
-        }
-        
-        # Extract cost from API response
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Test response cost calculator - should use extracted cost
-        cost = response_cost_calculator(
-            response_object=model_response,
-            model="perplexity/sonar-pro",
-            custom_llm_provider="perplexity",
-            call_type="completion",
-            optional_params={}
-        )
-        
-        # Should return the extracted cost, not calculated cost
-        assert cost == 0.00015
-
-    def test_cost_extraction_from_nested_structure(self):
-        """Test cost extraction from nested usage structure."""
-        from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig
-        
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response with nested cost structure
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "cost": {
-                    "total_cost": 0.00025
-                }
-            }
-        }
-        
-        # Test cost extraction
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Check that cost was stored in hidden params
-        assert hasattr(model_response, "_hidden_params")
-        assert "additional_headers" in model_response._hidden_params
-        assert "llm_provider-x-litellm-response-cost" in model_response._hidden_params["additional_headers"]
-        
-        cost = model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"]
-        assert cost == 0.00025
-
-    def test_cost_extraction_error_handling(self):
-        """Test error handling during cost extraction."""
-        from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig
-        
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response with invalid cost data
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "cost": "invalid_cost"  # Invalid cost type
-            }
-        }
-        
-        # Test cost extraction - should not raise error
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Should not have cost in hidden params due to error
-        if hasattr(model_response, "_hidden_params"):
-            assert "llm_provider-x-litellm-response-cost" not in model_response._hidden_params.get("additional_headers", {}) 
\ No newline at end of file
+        assert completion_cost >= 0 
\ No newline at end of file
diff --git a/tests/test_litellm/llms/perplexity/test_perplexity_integration.py b/tests/test_litellm/llms/perplexity/test_perplexity_integration.py
index b38e56ce8e8..a702e9ebc4c 100644
--- a/tests/test_litellm/llms/perplexity/test_perplexity_integration.py
+++ b/tests/test_litellm/llms/perplexity/test_perplexity_integration.py
@@ -316,169 +316,4 @@ class TestPerplexityIntegration:
         expected_completion_cost = (50 * 8e-6) + (1 / 1000 * 0.005)
         
         assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6)
-        assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6)
-
-    def test_cost_extraction_from_api_response(self):
-        """Test cost extraction from Perplexity API response."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        model_response.model = "perplexity/sonar-pro"
-        
-        # Mock raw response with cost
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "total_cost": 0.00015
-            }
-        }
-        
-        # Test cost extraction
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Check that cost was stored in hidden params
-        assert hasattr(model_response, "_hidden_params")
-        assert "additional_headers" in model_response._hidden_params
-        assert "llm_provider-x-litellm-response-cost" in model_response._hidden_params["additional_headers"]
-        
-        cost = model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"]
-        assert cost == 0.00015
-
-    def test_cost_extraction_integration_with_main_calculator(self):
-        """Test that extracted cost takes priority over calculated cost."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        model_response.model = "perplexity/sonar-pro"
-        
-        # Mock raw response with cost
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "total_cost": 0.00015
-            }
-        }
-        
-        # Extract cost
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Test main cost calculator - should use extracted cost
-        from litellm.cost_calculator import response_cost_calculator
-        cost = response_cost_calculator(
-            response_object=model_response,
-            model="perplexity/sonar-pro",
-            custom_llm_provider="perplexity",
-            call_type="completion",
-            optional_params={}
-        )
-        
-        # Should return the extracted cost, not calculated cost
-        assert cost == 0.00015
-
-    def test_cost_extraction_nested_structure(self):
-        """Test cost extraction from nested usage structure."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response with nested cost structure
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "cost": {
-                    "total_cost": 0.00025
-                }
-            }
-        }
-        
-        # Test cost extraction
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Check that cost was stored in hidden params
-        assert hasattr(model_response, "_hidden_params")
-        assert "additional_headers" in model_response._hidden_params
-        assert "llm_provider-x-litellm-response-cost" in model_response._hidden_params["additional_headers"]
-        
-        cost = model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"]
-        assert cost == 0.00025
-
-    def test_cost_extraction_error_handling(self):
-        """Test error handling during cost extraction."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response with invalid cost data
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150,
-                "cost": "invalid_cost"  # Invalid cost type
-            }
-        }
-        
-        # Test cost extraction - should not raise error
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Should not have cost in hidden params due to error
-        if hasattr(model_response, "_hidden_params"):
-            assert "llm_provider-x-litellm-response-cost" not in model_response._hidden_params.get("additional_headers", {})
-
-    def test_cost_extraction_no_usage_data(self):
-        """Test handling when no usage data is present."""
-        config = PerplexityChatConfig()
-        
-        # Create a ModelResponse
-        model_response = ModelResponse()
-        model_response.usage = Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
-        
-        # Mock raw response without usage
-        raw_response_json = {
-            "choices": [{"message": {"content": "Test response"}}]
-        }
-        
-        # Test cost extraction - should not raise error
-        config.add_cost_to_usage(model_response, raw_response_json)
-        
-        # Should not have cost in hidden params
-        if hasattr(model_response, "_hidden_params"):
-            assert "llm_provider-x-litellm-response-cost" not in model_response._hidden_params.get("additional_headers", {}) 
\ No newline at end of file
+        assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6) 
\ No newline at end of file
diff --git a/tests/test_litellm/llms/vertex_ai/__init__.py b/tests/test_litellm/llms/vertex_ai/__init__.py
new file mode 100644
index 00000000000..bd2635ea1ac
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/__init__.py
@@ -0,0 +1,2 @@
+"""Vertex AI tests package."""
+
diff --git a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
index 9a1029b1726..d5e1e8b8c1c 100644
--- a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py
@@ -18,8 +18,12 @@ class TestVertexAIRerankTransform:
         self.config = VertexAIRerankConfig()
         self.model = "semantic-ranker-default@latest"
 
-    def test_get_complete_url(self):
+    @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token')
+    def test_get_complete_url(self, mock_ensure_access_token):
         """Test URL generation for Vertex AI Discovery Engine rerank API."""
+        # Mock _ensure_access_token to return (token, project_id)
+        mock_ensure_access_token.return_value = ("mock-token", None)
+        
         # Test with project ID from environment
         with patch.dict(os.environ, {"VERTEXAI_PROJECT": "test-project-123"}):
             url = self.config.get_complete_url(api_base=None, model=self.model)
@@ -29,6 +33,7 @@ class TestVertexAIRerankTransform:
         # Test with litellm.vertex_project
         with patch.dict(os.environ, {}, clear=True):
             import litellm
+
             # Set vertex_project attribute if it doesn't exist
             if not hasattr(litellm, 'vertex_project'):
                 litellm.vertex_project = None
@@ -44,6 +49,7 @@ class TestVertexAIRerankTransform:
         # Test error when no project ID is available
         with patch.dict(os.environ, {}, clear=True):
             import litellm
+
             # Set vertex_project to None to ensure no project ID is available
             if not hasattr(litellm, 'vertex_project'):
                 litellm.vertex_project = None
@@ -351,3 +357,95 @@ class TestVertexAIRerankTransform:
         # Verify 0-based indexing
         for i, record in enumerate(request_data["records"]):
             assert record["id"] == str(i)
+
+    def test_map_cohere_rerank_params_preserves_vertex_credentials(self):
+        """Test that map_cohere_rerank_params preserves vertex-specific parameters."""
+        # Test with vertex_credentials
+        non_default_params = {
+            "documents": ["doc1", "doc2"],
+            "vertex_credentials": "path/to/credentials.json",
+            "vertex_project": "my-project-id",
+            "vertex_location": "us-central1"
+        }
+        
+        params = self.config.map_cohere_rerank_params(
+            non_default_params=non_default_params,
+            model=self.model,
+            drop_params=False,
+            query="test query",
+            documents=["doc1", "doc2"],
+            top_n=2,
+            return_documents=True
+        )
+        
+        # Verify vertex-specific parameters are preserved
+        assert params["vertex_credentials"] == "path/to/credentials.json"
+        assert params["vertex_project"] == "my-project-id"
+        assert params["vertex_location"] == "us-central1"
+        
+        # Verify standard params are still present
+        assert params["query"] == "test query"
+        assert params["documents"] == ["doc1", "doc2"]
+        assert params["top_n"] == 2
+        assert params["return_documents"] == True
+
+    def test_map_cohere_rerank_params_without_vertex_credentials(self):
+        """Test that map_cohere_rerank_params works when vertex credentials are not provided."""
+        non_default_params = {
+            "documents": ["doc1", "doc2"]
+        }
+        
+        params = self.config.map_cohere_rerank_params(
+            non_default_params=non_default_params,
+            model=self.model,
+            drop_params=False,
+            query="test query",
+            documents=["doc1", "doc2"],
+            top_n=2,
+            return_documents=True
+        )
+        
+        # Verify no vertex-specific parameters are added when not provided
+        assert "vertex_credentials" not in params
+        assert "vertex_project" not in params
+        assert "vertex_location" not in params
+        
+        # Verify standard params are still present
+        assert params["query"] == "test query"
+        assert params["documents"] == ["doc1", "doc2"]
+        assert params["top_n"] == 2
+        assert params["return_documents"] == True
+
+    @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token')
+    def test_validate_environment_with_optional_params(self, mock_ensure_access_token):
+        """Test that validate_environment accepts and uses optional_params for credentials."""
+        # Mock the authentication
+        mock_ensure_access_token.return_value = ("test-access-token", "test-project-123")
+        
+        optional_params = {
+            "vertex_credentials": "path/to/credentials.json",
+            "vertex_project": "custom-project-id",
+            "query": "test query",
+            "documents": ["doc1"]
+        }
+        
+        headers = self.config.validate_environment(
+            headers={},
+            model=self.model,
+            api_key=None,
+            optional_params=optional_params
+        )
+        
+        # Verify that _ensure_access_token was called with the credentials from optional_params
+        mock_ensure_access_token.assert_called_once()
+        call_args = mock_ensure_access_token.call_args
+        # The first call argument should be credentials (which will be the value from optional_params)
+        # We can't check the exact value easily due to how get_vertex_ai_credentials pops values,
+        # but we can verify the headers were set correctly
+        
+        expected_headers = {
+            "Authorization": "Bearer test-access-token",
+            "Content-Type": "application/json",
+            "X-Goog-User-Project": "test-project-123"
+        }
+        assert headers == expected_headers
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
index db457999a03..98977e06ffe 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
@@ -965,6 +965,8 @@ async def test_vertex_ai_partner_model_detection():
 
     # Test Meta/Llama models
     assert VertexAIPartnerModels.is_vertex_partner_model("meta/llama-3.1-405b")
+    # Test Minimax models
+    assert VertexAIPartnerModels.is_vertex_partner_model("minimaxai/minimax-m2-maas")
 
     # Test Gemini models (should NOT be detected as partner model)
     assert not VertexAIPartnerModels.is_vertex_partner_model("gemini-1.5-pro")
@@ -973,4 +975,17 @@ async def test_vertex_ai_partner_model_detection():
 
     # Test other non-partner models
     assert not VertexAIPartnerModels.is_vertex_partner_model("text-bison-001")
-    assert not VertexAIPartnerModels.is_vertex_partner_model("chat-bison-001")
\ No newline at end of file
+    assert not VertexAIPartnerModels.is_vertex_partner_model("chat-bison-001")
+
+
+def test_vertex_ai_minimax_uses_openai_handler():
+    """
+    Ensure Minimax partner models re-use the OpenAI-format handler.
+    """
+    from litellm.llms.vertex_ai.vertex_ai_partner_models.main import (
+        VertexAIPartnerModels,
+    )
+
+    assert VertexAIPartnerModels.should_use_openai_handler(
+        "minimaxai/minimax-m2-maas"
+    )
diff --git a/tests/test_litellm/llms/vertex_ai/videos/__init__.py b/tests/test_litellm/llms/vertex_ai/videos/__init__.py
new file mode 100644
index 00000000000..ab1481fbd4b
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/videos/__init__.py
@@ -0,0 +1,4 @@
+"""
+Tests for Vertex AI video generation.
+"""
+
diff --git a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
new file mode 100644
index 00000000000..7ae344e4999
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
@@ -0,0 +1,550 @@
+"""
+Tests for Vertex AI (Veo) video generation transformation.
+"""
+import json
+import os
+import pytest
+from unittest.mock import Mock, MagicMock, patch
+import httpx
+import base64
+
+from litellm.llms.vertex_ai.videos.transformation import (
+    VertexAIVideoConfig,
+    _convert_image_to_vertex_format,
+)
+from litellm.types.videos.main import VideoObject
+from litellm.types.router import GenericLiteLLMParams
+
+
+class TestVertexAIVideoConfig:
+    """Test VertexAIVideoConfig transformation class."""
+
+    def setup_method(self):
+        """Setup test fixtures."""
+        self.config = VertexAIVideoConfig()
+        self.mock_logging_obj = Mock()
+
+    def test_get_supported_openai_params(self):
+        """Test that correct params are supported."""
+        params = self.config.get_supported_openai_params("veo-002")
+
+        assert "model" in params
+        assert "prompt" in params
+        assert "input_reference" in params
+        assert "seconds" in params
+        assert "size" in params
+
+    @patch.object(VertexAIVideoConfig, 'get_access_token')
+    def test_validate_environment(self, mock_get_access_token):
+        """Test environment validation for Vertex AI."""
+        # Mock the authentication
+        mock_get_access_token.return_value = ("mock-access-token", "test-project")
+        
+        headers = {}
+        litellm_params = {"vertex_project": "test-project"}
+        
+        result = self.config.validate_environment(
+            headers=headers,
+            model="veo-002",
+            api_key=None,
+            litellm_params=litellm_params
+        )
+
+        # Should add Authorization header
+        assert "Authorization" in result
+        assert result["Authorization"] == "Bearer mock-access-token"
+        assert "Content-Type" in result
+
+    def test_get_complete_url(self):
+        """Test URL construction for Vertex AI video generation."""
+        litellm_params = {
+            "vertex_project": "test-project",
+            "vertex_location": "us-central1",
+        }
+
+        url = self.config.get_complete_url(
+            model="vertex_ai/veo-002", api_base=None, litellm_params=litellm_params
+        )
+
+        expected = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
+        assert url == expected
+        # Should NOT include endpoint - that's added by transform methods
+        assert not url.endswith(":predictLongRunning")
+
+    def test_get_complete_url_with_custom_api_base(self):
+        """Test URL construction with custom API base."""
+        litellm_params = {
+            "vertex_project": "test-project",
+            "vertex_location": "us-west1",
+        }
+
+        url = self.config.get_complete_url(
+            model="veo-002",
+            api_base="https://custom-endpoint.example.com",
+            litellm_params=litellm_params,
+        )
+
+        assert url.startswith("https://custom-endpoint.example.com")
+        assert "test-project" in url
+        assert "us-west1" in url
+        assert "veo-002" in url
+        # Should NOT include endpoint
+        assert not url.endswith(":predictLongRunning")
+
+    def test_get_complete_url_missing_project(self):
+        """Test that missing vertex_project raises error."""
+        litellm_params = {}
+
+        # Note: The method might not raise if vertex_project can be fetched from env
+        # This test verifies the behavior when completely missing
+        try:
+            url = self.config.get_complete_url(
+                model="veo-002", api_base=None, litellm_params=litellm_params
+            )
+            # If no error is raised, vertex_project was obtained from environment
+            # In that case, just verify a URL was returned
+            assert url is not None
+        except ValueError as e:
+            # Expected behavior when vertex_project is truly missing
+            assert "vertex_project is required" in str(e)
+
+    def test_get_complete_url_default_location(self):
+        """Test URL construction with default location."""
+        litellm_params = {"vertex_project": "test-project"}
+
+        url = self.config.get_complete_url(
+            model="veo-002", api_base=None, litellm_params=litellm_params
+        )
+
+        # Should default to us-central1
+        assert "us-central1" in url
+        # Should NOT include endpoint
+        assert not url.endswith(":predictLongRunning")
+
+    def test_transform_video_create_request(self):
+        """Test transformation of video creation request."""
+        prompt = "A cat playing with a ball of yarn"
+        api_base = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
+
+        data, files, url = self.config.transform_video_create_request(
+            model="veo-002",
+            prompt=prompt,
+            api_base=api_base,
+            video_create_optional_request_params={},
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+        # Check Vertex AI format
+        assert "instances" in data
+        assert len(data["instances"]) == 1
+        assert data["instances"][0]["prompt"] == prompt
+
+        # Parameters should not be present when empty
+        assert "parameters" not in data or data["parameters"] == {}
+
+        # Check URL has :predictLongRunning appended
+        assert url.endswith(":predictLongRunning")
+        assert api_base in url
+
+        # Check no files are uploaded
+        assert files == []
+
+    def test_transform_video_create_request_with_parameters(self):
+        """Test video creation request with aspect ratio and duration."""
+        prompt = "A dog running in a park"
+        api_base = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
+
+        data, files, url = self.config.transform_video_create_request(
+            model="veo-002",
+            prompt=prompt,
+            api_base=api_base,
+            video_create_optional_request_params={
+                "aspectRatio": "16:9",
+                "durationSeconds": 8,
+            },
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+        assert data["instances"][0]["prompt"] == prompt
+        assert data["parameters"]["aspectRatio"] == "16:9"
+        assert data["parameters"]["durationSeconds"] == 8
+        assert url.endswith(":predictLongRunning")
+
+    def test_transform_video_create_request_with_image(self):
+        """Test video creation request with image input."""
+        prompt = "Extend this image with animation"
+        api_base = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
+
+        # Create a mock image file
+        mock_image = Mock()
+        mock_image.read.return_value = b"fake_image_data"
+        mock_image.seek = Mock()
+
+        with patch(
+            "litellm.llms.vertex_ai.videos.transformation.ImageEditRequestUtils.get_image_content_type",
+            return_value="image/jpeg",
+        ):
+            data, files, url = self.config.transform_video_create_request(
+                model="veo-002",
+                prompt=prompt,
+                api_base=api_base,
+                video_create_optional_request_params={"image": mock_image},
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+        # Check image was converted to base64
+        assert "image" in data["instances"][0]
+        assert "bytesBase64Encoded" in data["instances"][0]["image"]
+        assert "mimeType" in data["instances"][0]["image"]
+        assert data["instances"][0]["image"]["mimeType"] == "image/jpeg"
+        assert url.endswith(":predictLongRunning")
+
+    def test_map_openai_params(self):
+        """Test parameter mapping from OpenAI to Vertex AI format."""
+        openai_params = {"seconds": "8", "size": "1280x720"}
+
+        mapped = self.config.map_openai_params(
+            video_create_optional_params=openai_params,
+            model="veo-002",
+            drop_params=False,
+        )
+
+        assert mapped["durationSeconds"] == 8
+        assert mapped["aspectRatio"] == "16:9"
+
+    def test_map_openai_params_default_duration(self):
+        """Test that durationSeconds is omitted when not provided."""
+        openai_params = {"size": "1280x720"}
+
+        mapped = self.config.map_openai_params(
+            video_create_optional_params=openai_params,
+            model="veo-002",
+            drop_params=False,
+        )
+
+        assert mapped["aspectRatio"] == "16:9"
+        assert "durationSeconds" not in mapped
+
+    def test_map_openai_params_size_conversions(self):
+        """Test size to aspect ratio conversions."""
+        test_cases = [
+            ("1280x720", "16:9"),
+            ("1920x1080", "16:9"),
+            ("720x1280", "9:16"),
+            ("1080x1920", "9:16"),
+            ("unknown", "16:9"),  # Default
+        ]
+
+        for size, expected_ratio in test_cases:
+            mapped = self.config.map_openai_params(
+                video_create_optional_params={"size": size},
+                model="veo-002",
+                drop_params=False,
+            )
+            assert mapped["aspectRatio"] == expected_ratio
+
+    def test_transform_video_create_response(self):
+        """Test transformation of video creation response."""
+        # Mock response with operation name
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "metadata": {"createTime": "2024-01-15T10:30:00.000Z"},
+        }
+
+        video_obj = self.config.transform_video_create_response(
+            model="vertex_ai/veo-002",
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="vertex_ai",
+        )
+
+        assert isinstance(video_obj, VideoObject)
+        assert video_obj.status == "processing"
+        assert video_obj.object == "video"
+        # Video ID is encoded with provider info, so just check it's not empty
+        assert video_obj.id
+        assert len(video_obj.id) > 0
+
+    def test_transform_video_create_response_missing_operation_name(self):
+        """Test that missing operation name raises error."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {}
+
+        with pytest.raises(ValueError, match="No operation name in Veo response"):
+            self.config.transform_video_create_response(
+                model="veo-002",
+                raw_response=mock_response,
+                logging_obj=self.mock_logging_obj,
+            )
+
+    def test_transform_video_status_retrieve_request(self):
+        """Test transformation of video status retrieve request."""
+        operation_name = "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345"
+        
+        # Provide an api_base that would be returned from get_complete_url
+        api_base = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
+
+        url, params = self.config.transform_video_status_retrieve_request(
+            video_id=operation_name,
+            api_base=api_base,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+        # Check URL contains fetchPredictOperation endpoint
+        assert "fetchPredictOperation" in url
+        assert "test-project" in url
+        assert "us-central1" in url
+        assert "veo-002" in url
+
+        # Check params contain operation name
+        assert params["operationName"] == operation_name
+
+    def test_transform_video_status_retrieve_request_invalid_format(self):
+        """Test that invalid operation name format raises error."""
+        invalid_operation_name = "invalid/operation/name"
+
+        with pytest.raises(ValueError, match="Invalid operation name format"):
+            self.config.transform_video_status_retrieve_request(
+                video_id=invalid_operation_name,
+                api_base=None,
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+    def test_transform_video_status_retrieve_response_processing(self):
+        """Test transformation of status response while processing."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "done": False,
+            "metadata": {"createTime": "2024-01-15T10:30:00.000Z"},
+        }
+
+        video_obj = self.config.transform_video_status_retrieve_response(
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="vertex_ai",
+        )
+
+        assert isinstance(video_obj, VideoObject)
+        assert video_obj.status == "processing"
+
+    def test_transform_video_status_retrieve_response_completed(self):
+        """Test transformation of status response when completed."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "done": True,
+            "metadata": {"createTime": "2024-01-15T10:30:00.000Z"},
+            "response": {
+                "@type": "type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse",
+                "raiMediaFilteredCount": 0,
+                "videos": [
+                    {
+                        "bytesBase64Encoded": base64.b64encode(
+                            b"fake_video_data"
+                        ).decode(),
+                        "mimeType": "video/mp4",
+                    }
+                ],
+            },
+        }
+
+        video_obj = self.config.transform_video_status_retrieve_response(
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="vertex_ai",
+        )
+
+        assert isinstance(video_obj, VideoObject)
+        assert video_obj.status == "completed"
+
+    def test_transform_video_status_retrieve_response_error(self):
+        """Test transformation of status response when an error is returned."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "done": True,
+            "metadata": {"createTime": "2024-01-15T10:30:00.000Z"},
+            "error": {
+                "code": 3,
+                "message": "Unsupported output video duration 3 seconds, supported durations are [8,5,6,7] for feature text_to_video.",
+            },
+        }
+
+        video_obj = self.config.transform_video_status_retrieve_response(
+            raw_response=mock_response,
+            logging_obj=self.mock_logging_obj,
+            custom_llm_provider="vertex_ai",
+        )
+
+        assert isinstance(video_obj, VideoObject)
+        assert video_obj.status == "failed"
+        assert video_obj.error == mock_response.json.return_value["error"]
+
+    def test_transform_video_content_request(self):
+        """Test transformation of video content request."""
+        operation_name = "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345"
+        api_base = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
+
+        url, params = self.config.transform_video_content_request(
+            video_id=operation_name,
+            api_base=api_base,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+        # Should use same fetchPredictOperation endpoint
+        assert "fetchPredictOperation" in url
+        assert params["operationName"] == operation_name
+
+    def test_transform_video_content_response(self):
+        """Test transformation of video content response."""
+        fake_video_bytes = b"fake_video_data_12345"
+        encoded_video = base64.b64encode(fake_video_bytes).decode()
+
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "done": True,
+            "response": {
+                "@type": "type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse",
+                "videos": [
+                    {"bytesBase64Encoded": encoded_video, "mimeType": "video/mp4"}
+                ],
+            },
+        }
+
+        video_bytes = self.config.transform_video_content_response(
+            raw_response=mock_response, logging_obj=self.mock_logging_obj
+        )
+
+        assert isinstance(video_bytes, bytes)
+        assert video_bytes == fake_video_bytes
+
+    def test_transform_video_content_response_not_complete(self):
+        """Test that incomplete video raises error."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "done": False,
+        }
+
+        with pytest.raises(
+            ValueError, match="Video generation is not complete yet"
+        ):
+            self.config.transform_video_content_response(
+                raw_response=mock_response, logging_obj=self.mock_logging_obj
+            )
+
+    def test_transform_video_content_response_missing_video_data(self):
+        """Test that missing video data raises error."""
+        mock_response = Mock(spec=httpx.Response)
+        mock_response.json.return_value = {
+            "name": "projects/test-project/locations/us-central1/publishers/google/models/veo-002/operations/12345",
+            "done": True,
+            "response": {"videos": []},
+        }
+
+        with pytest.raises(ValueError, match="No video data found"):
+            self.config.transform_video_content_response(
+                raw_response=mock_response, logging_obj=self.mock_logging_obj
+            )
+
+    def test_transform_video_remix_request_not_supported(self):
+        """Test that video remix raises NotImplementedError."""
+        with pytest.raises(
+            NotImplementedError, match="Video remix is not supported"
+        ):
+            self.config.transform_video_remix_request(
+                video_id="test-video-id",
+                prompt="new prompt",
+                api_base="https://example.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+    def test_transform_video_list_request_not_supported(self):
+        """Test that video list raises NotImplementedError."""
+        with pytest.raises(NotImplementedError, match="Video list is not supported"):
+            self.config.transform_video_list_request(
+                api_base="https://example.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+    def test_transform_video_delete_request_not_supported(self):
+        """Test that video delete raises NotImplementedError."""
+        with pytest.raises(
+            NotImplementedError, match="Video delete is not supported"
+        ):
+            self.config.transform_video_delete_request(
+                video_id="test-video-id",
+                api_base="https://example.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+    def test_get_error_class(self):
+        """Test error class generation."""
+        error = self.config.get_error_class(
+            error_message="Test error", status_code=500, headers={}
+        )
+
+        # Should return VertexAIError
+        from litellm.llms.vertex_ai.common_utils import VertexAIError
+
+        assert isinstance(error, VertexAIError)
+        assert error.status_code == 500
+        assert "Test error" in str(error)
+
+
+class TestConvertImageToVertexFormat:
+    """Test the _convert_image_to_vertex_format helper function."""
+
+    def test_convert_image_to_vertex_format(self):
+        """Test image conversion to Vertex AI format."""
+        fake_image_data = b"fake_jpeg_image_data"
+        mock_image = Mock()
+        mock_image.read.return_value = fake_image_data
+        mock_image.seek = Mock()
+
+        with patch(
+            "litellm.llms.vertex_ai.videos.transformation.ImageEditRequestUtils.get_image_content_type",
+            return_value="image/jpeg",
+        ):
+            result = _convert_image_to_vertex_format(mock_image)
+
+        assert "bytesBase64Encoded" in result
+        assert "mimeType" in result
+        assert result["mimeType"] == "image/jpeg"
+
+        # Verify base64 encoding
+        decoded = base64.b64decode(result["bytesBase64Encoded"])
+        assert decoded == fake_image_data
+
+    def test_convert_image_to_vertex_format_with_seek(self):
+        """Test image conversion with seek support."""
+        fake_image_data = b"fake_png_image_data"
+        mock_image = Mock()
+        mock_image.read.return_value = fake_image_data
+        mock_image.seek = Mock()
+
+        with patch(
+            "litellm.llms.vertex_ai.videos.transformation.ImageEditRequestUtils.get_image_content_type",
+            return_value="image/png",
+        ):
+            result = _convert_image_to_vertex_format(mock_image)
+
+        # Verify seek was called
+        mock_image.seek.assert_called_once_with(0)
+
+        assert result["mimeType"] == "image/png"
+        decoded = base64.b64decode(result["bytesBase64Encoded"])
+        assert decoded == fake_image_data
+
diff --git a/tests/test_litellm/llms/xai/responses/__init__.py b/tests/test_litellm/llms/xai/responses/__init__.py
deleted file mode 100644
index 451d016fb21..00000000000
--- a/tests/test_litellm/llms/xai/responses/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# XAI Responses API tests
-
diff --git a/tests/test_litellm/proxy/client/test_chat.py b/tests/test_litellm/proxy/client/test_chat.py
index 868d9287692..4de58723761 100644
--- a/tests/test_litellm/proxy/client/test_chat.py
+++ b/tests/test_litellm/proxy/client/test_chat.py
@@ -3,13 +3,14 @@ import sys
 
 import pytest
 import requests
-import responses
 
 sys.path.insert(
     0, os.path.abspath("../../..")
 )  # Adds the parent directory to the system path
 
 
+import responses
+
 from litellm.proxy.client.chat import ChatClient
 from litellm.proxy.client.exceptions import UnauthorizedError
 
diff --git a/tests/test_litellm/proxy/client/test_credentials.py b/tests/test_litellm/proxy/client/test_credentials.py
index 702872a5896..580bbb39dc0 100644
--- a/tests/test_litellm/proxy/client/test_credentials.py
+++ b/tests/test_litellm/proxy/client/test_credentials.py
@@ -3,13 +3,14 @@ import sys
 
 import pytest
 import requests
-import responses
 
 sys.path.insert(
     0, os.path.abspath("../../..")
 )  # Adds the parent directory to the system path
 
 
+import responses
+
 from litellm.proxy.client.credentials import CredentialsManagementClient
 from litellm.proxy.client.exceptions import UnauthorizedError
 
diff --git a/tests/test_litellm/proxy/client/test_http_client.py b/tests/test_litellm/proxy/client/test_http_client.py
index b85724139be..3d8fe44438a 100644
--- a/tests/test_litellm/proxy/client/test_http_client.py
+++ b/tests/test_litellm/proxy/client/test_http_client.py
@@ -6,13 +6,14 @@ import sys
 
 import pytest
 import requests
-import responses
 
 sys.path.insert(
     0, os.path.abspath("../../..")
 )  # Adds the parent directory to the system path
 
 
+import responses
+
 from litellm.proxy.client.http_client import HTTPClient
 
 
diff --git a/tests/test_litellm/proxy/client/test_http_commands.py b/tests/test_litellm/proxy/client/test_http_commands.py
index d1dea3a5541..16579cfffbc 100644
--- a/tests/test_litellm/proxy/client/test_http_commands.py
+++ b/tests/test_litellm/proxy/client/test_http_commands.py
@@ -5,7 +5,6 @@ import os
 import sys
 
 import pytest
-import responses
 from click.testing import CliRunner
 
 sys.path.insert(
@@ -13,6 +12,8 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 
 
+import responses
+
 from litellm.proxy.client.cli.commands.http import http
 
 
diff --git a/tests/test_litellm/proxy/client/test_keys.py b/tests/test_litellm/proxy/client/test_keys.py
index 06cedf13104..136408e01ac 100644
--- a/tests/test_litellm/proxy/client/test_keys.py
+++ b/tests/test_litellm/proxy/client/test_keys.py
@@ -3,13 +3,14 @@ import sys
 
 import pytest
 import requests
-import responses
 
 sys.path.insert(
     0, os.path.abspath("../../..")
 )  # Adds the parent directory to the system path
 
 
+import responses
+
 from litellm.proxy.client.exceptions import UnauthorizedError
 from litellm.proxy.client.keys import KeysManagementClient
 
diff --git a/tests/test_litellm/proxy/client/test_model_groups.py b/tests/test_litellm/proxy/client/test_model_groups.py
index 3a4f1b157c3..1c87672e723 100644
--- a/tests/test_litellm/proxy/client/test_model_groups.py
+++ b/tests/test_litellm/proxy/client/test_model_groups.py
@@ -3,13 +3,14 @@ import sys
 
 import pytest
 import requests
-import responses
 
 sys.path.insert(
     0, os.path.abspath("../../..")
 )  # Adds the parent directory to the system path
 
 
+import responses
+
 from litellm.proxy.client import Client, ModelGroupsManagementClient
 from litellm.proxy.client.exceptions import UnauthorizedError
 
diff --git a/tests/test_litellm/proxy/client/test_models.py b/tests/test_litellm/proxy/client/test_models.py
index ffb6d89f577..0957c98f9e2 100644
--- a/tests/test_litellm/proxy/client/test_models.py
+++ b/tests/test_litellm/proxy/client/test_models.py
@@ -3,7 +3,6 @@ import sys
 
 import pytest
 import requests
-import responses
 
 sys.path.insert(
     0, os.path.abspath("../../..")
@@ -11,6 +10,8 @@ sys.path.insert(
 
 
 
+import responses
+
 from litellm.proxy.client import Client, ModelsManagementClient
 from litellm.proxy.client.exceptions import NotFoundError, UnauthorizedError
 
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py
new file mode 100644
index 00000000000..e756dd6bd5d
--- /dev/null
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py
@@ -0,0 +1,520 @@
+"""
+Tests for the Content Filter Guardrail
+"""
+
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../../")
+)  # Adds the parent directory to the system path
+
+from fastapi import HTTPException
+
+import litellm
+from litellm.proxy.guardrails.guardrail_hooks.litellm_content_filter.content_filter import (
+    ContentFilterGuardrail,
+)
+from litellm.types.guardrails import (
+    BlockedWord,
+    ContentFilterAction,
+    ContentFilterPattern,
+    GuardrailEventHooks,
+)
+
+
+class TestContentFilterGuardrail:
+    """Test the ContentFilterGuardrail class"""
+
+    def test_init_with_patterns(self):
+        """
+        Test initialization with prebuilt patterns
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="us_ssn",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-content-filter",
+            patterns=patterns,
+        )
+        
+        assert guardrail.guardrail_name == "test-content-filter"
+        assert len(guardrail.compiled_patterns) == 1
+
+    def test_init_with_blocked_words(self):
+        """
+        Test initialization with blocked words
+        """
+        blocked_words = [
+            BlockedWord(
+                keyword="secret_project",
+                action=ContentFilterAction.BLOCK,
+                description="Top secret project"
+            ),
+            BlockedWord(
+                keyword="internal_api",
+                action=ContentFilterAction.MASK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-content-filter",
+            blocked_words=blocked_words,
+        )
+        
+        assert len(guardrail.blocked_words) == 2
+        assert "secret_project" in guardrail.blocked_words
+        assert guardrail.blocked_words["secret_project"][0] == ContentFilterAction.BLOCK
+
+    def test_check_patterns_ssn(self):
+        """
+        Test SSN pattern detection
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="us_ssn",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-ssn",
+            patterns=patterns,
+        )
+        
+        # Test with SSN
+        result = guardrail._check_patterns("My SSN is 123-45-6789")
+        assert result is not None
+        assert result[1] == "us_ssn"
+        assert result[2] == ContentFilterAction.BLOCK
+        
+        # Test without SSN
+        result = guardrail._check_patterns("This is a normal message")
+        assert result is None
+
+    def test_check_patterns_email(self):
+        """
+        Test email pattern detection
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="email",
+                action=ContentFilterAction.MASK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-email",
+            patterns=patterns,
+        )
+        
+        result = guardrail._check_patterns("Contact me at test@example.com")
+        assert result is not None
+        assert result[1] == "email"
+        assert result[2] == ContentFilterAction.MASK
+
+    def test_check_patterns_custom_regex(self):
+        """
+        Test custom regex pattern detection
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="regex",
+                pattern=r"\b[A-Z]{3}-\d{4}\b",
+                name="custom_id",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-custom",
+            patterns=patterns,
+        )
+        
+        result = guardrail._check_patterns("My ID is ABC-1234")
+        assert result is not None
+        assert result[1] == "custom_id"
+
+    def test_check_blocked_words(self):
+        """
+        Test blocked word detection
+        """
+        blocked_words = [
+            BlockedWord(
+                keyword="confidential",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-words",
+            blocked_words=blocked_words,
+        )
+        
+        # Test with blocked word
+        result = guardrail._check_blocked_words("This is CONFIDENTIAL information")
+        assert result is not None
+        assert result[0] == "confidential"
+        assert result[1] == ContentFilterAction.BLOCK
+        
+        # Test without blocked word
+        result = guardrail._check_blocked_words("This is normal information")
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_apply_guardrail_block(self):
+        """
+        Test apply_guardrail with BLOCK action
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="us_ssn",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-block",
+            patterns=patterns,
+        )
+        
+        with pytest.raises(HTTPException) as exc_info:
+            await guardrail.apply_guardrail(text="My SSN is 123-45-6789")
+        
+        assert exc_info.value.status_code == 400
+        assert "us_ssn" in str(exc_info.value.detail)
+
+    @pytest.mark.asyncio
+    async def test_apply_guardrail_mask(self):
+        """
+        Test apply_guardrail with MASK action
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="email",
+                action=ContentFilterAction.MASK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-mask",
+            patterns=patterns,
+        )
+        
+        result = await guardrail.apply_guardrail(text="Contact me at test@example.com")
+        
+        assert result is not None
+        assert "[EMAIL_REDACTED]" in result
+        assert "test@example.com" not in result
+
+    @pytest.mark.asyncio
+    async def test_apply_guardrail_blocked_word_mask(self):
+        """
+        Test apply_guardrail with blocked word MASK action
+        """
+        blocked_words = [
+            BlockedWord(
+                keyword="proprietary",
+                action=ContentFilterAction.MASK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-word-mask",
+            blocked_words=blocked_words,
+        )
+        
+        result = await guardrail.apply_guardrail(text="This is PROPRIETARY information")
+        
+        assert result is not None
+        assert "[KEYWORD_REDACTED]" in result
+        assert "PROPRIETARY" not in result
+
+    @pytest.mark.asyncio
+    async def test_apply_guardrail_multiple_patterns(self):
+        """
+        Test apply_guardrail with multiple patterns in the same text
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="email",
+                action=ContentFilterAction.MASK,
+            ),
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="us_ssn",
+                action=ContentFilterAction.MASK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-multiple",
+            patterns=patterns,
+        )
+        
+        result = await guardrail.apply_guardrail(
+            text="Contact user@test.com or SSN: 123-45-6789"
+        )
+        
+        assert result is not None
+        # At least one pattern should be redacted (first match wins)
+        assert "[EMAIL_REDACTED]" in result or "[US_SSN_REDACTED]" in result
+
+    def test_mask_content(self):
+        """
+        Test content masking
+        """
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-mask",
+        )
+        
+        masked = guardrail._mask_content("sensitive text", "us_ssn")
+        assert masked == "[US_SSN_REDACTED]"
+
+    def test_load_blocked_words_file(self):
+        """
+        Test loading blocked words from a YAML file
+        """
+        import tempfile
+
+        # Create a temporary blocked words file
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
+            f.write("""blocked_words:
+  - keyword: "test_keyword"
+    action: "BLOCK"
+    description: "Test keyword"
+  - keyword: "another_word"
+    action: "MASK"
+""")
+            temp_file = f.name
+        
+        try:
+            guardrail = ContentFilterGuardrail(
+                guardrail_name="test-file-load",
+                blocked_words_file=temp_file,
+            )
+            
+            assert len(guardrail.blocked_words) == 2
+            assert "test_keyword" in guardrail.blocked_words
+            assert guardrail.blocked_words["test_keyword"][0] == ContentFilterAction.BLOCK
+            assert guardrail.blocked_words["test_keyword"][1] == "Test keyword"
+            assert "another_word" in guardrail.blocked_words
+            assert guardrail.blocked_words["another_word"][0] == ContentFilterAction.MASK
+        finally:
+            os.unlink(temp_file)
+
+    def test_credit_card_patterns(self):
+        """
+        Test credit card pattern detection
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="visa",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-cc",
+            patterns=patterns,
+        )
+        
+        # Test Visa card
+        result = guardrail._check_patterns("My card is 4532-1234-5678-9010")
+        assert result is not None
+        assert result[1] == "visa"
+        
+    def test_api_key_patterns(self):
+        """
+        Test API key pattern detection
+        """
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="aws_access_key",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-api-key",
+            patterns=patterns,
+        )
+        
+        # Test AWS Access Key
+        result = guardrail._check_patterns("My key is AKIAIOSFODNN7EXAMPLE")
+        assert result is not None
+        assert result[1] == "aws_access_key"
+
+    @pytest.mark.asyncio
+    async def test_streaming_hook_mask(self):
+        """
+        Test streaming hook with MASK action
+        """
+        from unittest.mock import AsyncMock
+
+        from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
+        
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="email",
+                action=ContentFilterAction.MASK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-streaming-mask",
+            patterns=patterns,
+            event_hook=GuardrailEventHooks.during_call,
+        )
+        
+        # Create mock streaming chunks
+        async def mock_stream():
+            # Chunk 1: contains email
+            chunk1 = ModelResponseStream(
+                id="chunk1",
+                choices=[StreamingChoices(delta=Delta(content="Contact me at test@example.com"), index=0)],
+                model="gpt-4",
+            )
+            yield chunk1
+            
+            # Chunk 2: normal content
+            chunk2 = ModelResponseStream(
+                id="chunk2",
+                choices=[StreamingChoices(delta=Delta(content=" for more info"), index=0)],
+                model="gpt-4",
+            )
+            yield chunk2
+        
+        user_api_key_dict = MagicMock()
+        request_data = {}
+        
+        # Process streaming response
+        result_chunks = []
+        async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+            user_api_key_dict=user_api_key_dict,
+            response=mock_stream(),
+            request_data=request_data,
+        ):
+            result_chunks.append(chunk)
+        
+        assert len(result_chunks) == 2
+        # First chunk should have email masked
+        assert "[EMAIL_REDACTED]" in result_chunks[0].choices[0].delta.content
+        assert "test@example.com" not in result_chunks[0].choices[0].delta.content
+        # Second chunk should be unchanged
+        assert result_chunks[1].choices[0].delta.content == " for more info"
+
+    @pytest.mark.asyncio
+    async def test_streaming_hook_block(self):
+        """
+        Test streaming hook with BLOCK action
+        """
+        from unittest.mock import AsyncMock
+
+        from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
+        
+        patterns = [
+            ContentFilterPattern(
+                pattern_type="prebuilt",
+                pattern_name="us_ssn",
+                action=ContentFilterAction.BLOCK,
+            ),
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-streaming-block",
+            patterns=patterns,
+            event_hook=GuardrailEventHooks.during_call,
+        )
+        
+        # Create mock streaming chunks with SSN
+        async def mock_stream():
+            chunk = ModelResponseStream(
+                id="chunk1",
+                choices=[StreamingChoices(delta=Delta(content="SSN: 123-45-6789"), index=0)],
+                model="gpt-4",
+            )
+            yield chunk
+        
+        user_api_key_dict = MagicMock()
+        request_data = {}
+        
+        # Should raise HTTPException when SSN is detected
+        with pytest.raises(HTTPException) as exc_info:
+            async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=user_api_key_dict,
+                response=mock_stream(),
+                request_data=request_data,
+            ):
+                pass
+        
+        assert exc_info.value.status_code == 400
+        assert "us_ssn" in str(exc_info.value.detail)
+
+    def test_init_with_plain_dicts(self):
+        """
+        Test initialization with plain dicts (DB format).
+        This ensures the guardrail can handle data loaded from the database
+        without requiring Pydantic model conversion.
+        """
+        patterns = [
+            {
+                "pattern_type": "prebuilt",
+                "pattern_name": "us_ssn",
+                "action": "BLOCK",
+                "name": "us_ssn",
+                "pattern": None,
+            },
+            {
+                "pattern_type": "prebuilt",
+                "pattern_name": "email",
+                "action": "MASK",
+                "name": "email",
+                "pattern": None,
+            }
+        ]
+        
+        blocked_words = [
+            {
+                "keyword": "langchain",
+                "action": "BLOCK",
+                "description": None,
+            },
+            {
+                "keyword": "openai",
+                "action": "MASK",
+                "description": "Competitor name",
+            }
+        ]
+        
+        guardrail = ContentFilterGuardrail(
+            guardrail_name="test-db-format",
+            patterns=patterns,
+            blocked_words=blocked_words,
+        )
+        
+        assert guardrail.guardrail_name == "test-db-format"
+        assert len(guardrail.compiled_patterns) == 2
+        assert len(guardrail.blocked_words) == 2
+        
+        # Verify blocked_words are stored as dict
+        assert "langchain" in guardrail.blocked_words
+        assert guardrail.blocked_words["langchain"] == ("BLOCK", None)
+        assert "openai" in guardrail.blocked_words
+        assert guardrail.blocked_words["openai"] == ("MASK", "Competitor name")
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_patterns.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_patterns.py
new file mode 100644
index 00000000000..3380cefa653
--- /dev/null
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_patterns.py
@@ -0,0 +1,157 @@
+"""
+Tests for content filter pattern loading from JSON
+"""
+import json
+import os
+import sys
+
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../../")
+)
+
+from litellm.proxy.guardrails.guardrail_hooks.litellm_content_filter.patterns import (
+    PATTERN_CATEGORIES,
+    PATTERN_DESCRIPTIONS,
+    PATTERN_DISPLAY_NAMES,
+    PREBUILT_PATTERNS,
+    get_all_pattern_names,
+    get_compiled_pattern,
+    get_pattern_metadata,
+)
+
+
+def test_patterns_loaded_from_json():
+    """
+    Test that patterns are successfully loaded from JSON file
+    """
+    assert len(PREBUILT_PATTERNS) > 0
+    assert len(PATTERN_CATEGORIES) > 0
+    assert len(PATTERN_DISPLAY_NAMES) > 0
+    assert len(PATTERN_DESCRIPTIONS) > 0
+
+
+def test_pattern_metadata_structure():
+    """
+    Test that get_pattern_metadata returns correctly structured data
+    """
+    patterns = get_pattern_metadata()
+    
+    assert len(patterns) > 0
+    
+    for pattern in patterns:
+        assert "name" in pattern
+        assert "display_name" in pattern
+        assert "category" in pattern
+        assert "description" in pattern
+        
+        assert isinstance(pattern["name"], str)
+        assert isinstance(pattern["display_name"], str)
+        assert isinstance(pattern["category"], str)
+        assert isinstance(pattern["description"], str)
+
+
+def test_display_names_user_friendly():
+    """
+    Test that display names are user-friendly and different from internal names
+    """
+    patterns = get_pattern_metadata()
+    
+    ssn_pattern = next((p for p in patterns if p["name"] == "us_ssn"), None)
+    assert ssn_pattern is not None
+    assert ssn_pattern["display_name"] == "SSN (Social Security Number)"
+    
+    email_pattern = next((p for p in patterns if p["name"] == "email"), None)
+    assert email_pattern is not None
+    assert email_pattern["display_name"] == "Email Address"
+
+
+def test_pattern_compilation():
+    """
+    Test that patterns can be compiled into regex objects
+    """
+    pattern_names = get_all_pattern_names()
+    
+    assert len(pattern_names) > 0
+    
+    for pattern_name in pattern_names:
+        compiled_pattern = get_compiled_pattern(pattern_name)
+        assert compiled_pattern is not None
+        assert hasattr(compiled_pattern, "match")
+
+
+def test_pattern_compilation_error():
+    """
+    Test that invalid pattern names raise ValueError
+    """
+    with pytest.raises(ValueError, match="Unknown pattern name"):
+        get_compiled_pattern("invalid_pattern_name")
+
+
+def test_categories_contain_patterns():
+    """
+    Test that each category contains valid pattern names
+    """
+    all_pattern_names = set(PREBUILT_PATTERNS.keys())
+    
+    for category, patterns in PATTERN_CATEGORIES.items():
+        assert len(patterns) > 0
+        for pattern_name in patterns:
+            assert pattern_name in all_pattern_names
+
+
+def test_json_file_exists():
+    """
+    Test that patterns.json file exists and is valid JSON
+    """
+    json_path = os.path.join(
+        os.path.dirname(__file__),
+        "../../../../../../litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json"
+    )
+    
+    assert os.path.exists(json_path)
+    
+    with open(json_path, "r") as f:
+        data = json.load(f)
+        assert "patterns" in data
+        assert len(data["patterns"]) > 0
+
+
+def test_json_pattern_structure():
+    """
+    Test that each pattern in JSON has required fields
+    """
+    json_path = os.path.join(
+        os.path.dirname(__file__),
+        "../../../../../../litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json"
+    )
+    
+    with open(json_path, "r") as f:
+        data = json.load(f)
+        
+        for pattern in data["patterns"]:
+            assert "name" in pattern
+            assert "display_name" in pattern
+            assert "pattern" in pattern
+            assert "category" in pattern
+            assert "description" in pattern
+            
+            assert isinstance(pattern["name"], str)
+            assert isinstance(pattern["display_name"], str)
+            assert isinstance(pattern["pattern"], str)
+            assert isinstance(pattern["category"], str)
+            assert isinstance(pattern["description"], str)
+
+
+def test_all_dictionaries_consistent():
+    """
+    Test that all pattern dictionaries have consistent keys
+    """
+    pattern_names_from_patterns = set(PREBUILT_PATTERNS.keys())
+    pattern_names_from_display = set(PATTERN_DISPLAY_NAMES.keys())
+    pattern_names_from_descriptions = set(PATTERN_DESCRIPTIONS.keys())
+    
+    assert pattern_names_from_patterns == pattern_names_from_display
+    assert pattern_names_from_patterns == pattern_names_from_descriptions
+
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py
index 7a328c03aa4..9a6ab1b8162 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py
@@ -267,9 +267,18 @@ class TestNomaGuardrailHooks:
 
             # Verify API call details
             call_args = mock_post.call_args
-            assert call_args[0][0].endswith("/ai-dr/v2/prompt/scan")
-            assert call_args[1]["headers"]["X-Noma-AIDR-Application-ID"] == "test-app"
-            assert call_args[1]["headers"]["Authorization"] == "Bearer test-api-key"
+            # Verify the URL endpoint  
+            assert call_args.args[0].endswith("/ai-dr/v2/prompt/scan")
+            # Verify headers and JSON payload
+            if "headers" in call_args.kwargs:
+                headers = call_args.kwargs["headers"]
+                assert "Authorization" in headers
+                assert headers["Authorization"] == "Bearer test-api-key"
+            # Verify application ID is in JSON payload (not headers)
+            if "json" in call_args.kwargs:
+                json_payload = call_args.kwargs["json"]
+                assert "x-noma-context" in json_payload
+                assert json_payload["x-noma-context"]["applicationId"] == "test-app"
 
     @pytest.mark.asyncio
     async def test_pre_call_hook_blocked(
@@ -1128,7 +1137,9 @@ class TestNomaImageProcessing:
         mock_response.json.return_value = noma_response
         mock_response.raise_for_status = MagicMock()
 
-        from litellm.proxy.guardrails.guardrail_hooks.noma.noma import NomaBlockedMessage
+        from litellm.proxy.guardrails.guardrail_hooks.noma.noma import (
+            NomaBlockedMessage,
+        )
 
         with patch.object(
             noma_guardrail.async_handler, "post", return_value=mock_response
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_panw_prisma_airs.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_panw_prisma_airs.py
index 101a1d8275a..77a7daf0de4 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_panw_prisma_airs.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_panw_prisma_airs.py
@@ -55,7 +55,7 @@ class TestPanwAirsInitialization:
         guardrail_config = {"guardrail_name": "test_guardrail"}
 
         with patch("litellm.logging_callback_manager.add_litellm_callback"):
-            handler = initialize_guardrail(litellm_params, guardrail_config)  
+            handler = initialize_guardrail(litellm_params, guardrail_config)
 
         assert isinstance(handler, PanwPrismaAirsHandler)
         assert handler.guardrail_name == "test_guardrail"
@@ -70,7 +70,7 @@ class TestPanwAirsInitialization:
                 profile_name="test_profile",
                 api_key=None,  # No API key provided
                 default_on=True,
-            )  
+            )
 
     def test_api_key_with_linked_profile(self):
         """Test initialization with API key that has a linked profile (no explicit profile_name needed)."""
@@ -82,7 +82,9 @@ class TestPanwAirsInitialization:
             default_on=True,
         )
         assert handler.api_key == "test_api_key_with_linked_profile"
-        assert handler.profile_name is None  # Should be None, PANW API will use linked profile   
+        assert (
+            handler.profile_name is None
+        )  # Should be None, PANW API will use linked profile
 
 
 class TestPanwAirsPromptScanning:
@@ -390,7 +392,7 @@ class TestPanwAirsConfiguration:
         guardrail_config = {"guardrail_name": "test"}
 
         with patch("litellm.logging_callback_manager.add_litellm_callback"):
-            handler = initialize_guardrail(litellm_params, guardrail_config)  
+            handler = initialize_guardrail(litellm_params, guardrail_config)
 
         assert handler.api_base == "https://service.api.aisecurity.paloaltonetworks.com"
 
@@ -410,7 +412,7 @@ class TestPanwAirsConfiguration:
         guardrail_config = {"guardrail_name": "test"}
 
         with patch("litellm.logging_callback_manager.add_litellm_callback"):
-            handler = initialize_guardrail(litellm_params, guardrail_config)  
+            handler = initialize_guardrail(litellm_params, guardrail_config)
 
         assert handler.api_base == custom_base
 
@@ -429,7 +431,7 @@ class TestPanwAirsConfiguration:
         guardrail_config = {"guardrail_name": "test_guardrail"}
 
         with patch("litellm.logging_callback_manager.add_litellm_callback"):
-            handler = initialize_guardrail(litellm_params, guardrail_config)   
+            handler = initialize_guardrail(litellm_params, guardrail_config)
 
         assert handler.guardrail_name == "test_guardrail"
 
@@ -521,13 +523,15 @@ class TestPanwAirsMaskingFunctionality:
         user_api_key_dict = UserAPIKeyAuth()
         data = {
             "model": "gpt-3.5-turbo",
-            "messages": [{
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": "My SSN is 123-45-6789"},
-                    {"type": "image", "url": "data:image/jpeg;base64,abc123"}
-                ]
-            }],
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "My SSN is 123-45-6789"},
+                        {"type": "image", "url": "data:image/jpeg;base64,abc123"},
+                    ],
+                }
+            ],
         }
 
         mock_response = {
@@ -551,7 +555,9 @@ class TestPanwAirsMaskingFunctionality:
         assert data["messages"][0]["content"][0]["text"] == "My SSN is XXXXXXXXXX"
         # Image should remain unchanged
         assert data["messages"][0]["content"][1]["type"] == "image"
-        assert data["messages"][0]["content"][1]["url"] == "data:image/jpeg;base64,abc123"
+        assert (
+            data["messages"][0]["content"][1]["url"] == "data:image/jpeg;base64,abc123"
+        )
 
     @pytest.mark.asyncio
     async def test_response_masking_on_block(self):
@@ -610,7 +616,9 @@ class TestPanwAirsMaskingFunctionality:
             "messages": [{"role": "user", "content": "Test content"}],
         }
 
-        with patch.object(handler, "_call_panw_api", side_effect=Exception("API Error")):
+        with patch.object(
+            handler, "_call_panw_api", side_effect=Exception("API Error")
+        ):
             with pytest.raises(HTTPException) as exc_info:
                 await handler.async_pre_call_hook(
                     user_api_key_dict=user_api_key_dict,
@@ -665,7 +673,7 @@ class TestPanwAirsAdvancedFeatures:
     async def test_tool_call_extraction(self):
         """Test extraction of text from responses with tool calls."""
         from litellm.types.utils import ChatCompletionMessageToolCall, Function
-        
+
         handler = PanwPrismaAirsHandler(
             guardrail_name="test_panw_airs",
             api_key="test_api_key",
@@ -690,10 +698,10 @@ class TestPanwAirsAdvancedFeatures:
                                 type="function",
                                 function=Function(
                                     name="get_weather",
-                                    arguments='{"location": "San Francisco", "ssn": "123-45-6789"}'
-                                )
+                                    arguments='{"location": "San Francisco", "ssn": "123-45-6789"}',
+                                ),
                             )
-                        ]
+                        ],
                     ),
                 ),
             ],
@@ -710,7 +718,7 @@ class TestPanwAirsAdvancedFeatures:
     async def test_tool_call_masking(self):
         """Test masking of tool call arguments when blocked."""
         from litellm.types.utils import ChatCompletionMessageToolCall, Function
-        
+
         handler = PanwPrismaAirsHandler(
             guardrail_name="test_panw_airs",
             api_key="test_api_key",
@@ -736,10 +744,10 @@ class TestPanwAirsAdvancedFeatures:
                                 type="function",
                                 function=Function(
                                     name="get_weather",
-                                    arguments='{"location": "San Francisco", "ssn": "123-45-6789"}'
-                                )
+                                    arguments='{"location": "San Francisco", "ssn": "123-45-6789"}',
+                                ),
                             )
-                        ]
+                        ],
                     ),
                 ),
             ],
@@ -757,18 +765,23 @@ class TestPanwAirsAdvancedFeatures:
             "category": "sensitive_data",
             "response_masked_data": {
                 "data": '{"location": "San Francisco", "ssn": "XXXXXXXXXX"}'
-            }
+            },
         }
 
-        with patch.object(handler, "_call_panw_api", new_callable=AsyncMock) as mock_api:
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
             mock_api.return_value = mock_scan_result
-            
+
             result = await handler.async_post_call_success_hook(
                 user_api_key_dict=user_api_key_dict, response=response, data=data
             )
 
             # Verify arguments were masked
-            assert result.choices[0].message.tool_calls[0].function.arguments == '{"location": "San Francisco", "ssn": "XXXXXXXXXX"}'
+            assert (
+                result.choices[0].message.tool_calls[0].function.arguments
+                == '{"location": "San Francisco", "ssn": "XXXXXXXXXX"}'
+            )
 
     @pytest.mark.asyncio
     async def test_multi_choice_masking(self):
@@ -794,7 +807,9 @@ class TestPanwAirsAdvancedFeatures:
                 Choices(
                     finish_reason="stop",
                     index=1,
-                    message=Message(content="Another SSN: 987-65-4321", role="assistant"),
+                    message=Message(
+                        content="Another SSN: 987-65-4321", role="assistant"
+                    ),
                 ),
             ],
             created=1234567890,
@@ -808,12 +823,14 @@ class TestPanwAirsAdvancedFeatures:
         mock_scan_result = {
             "action": "block",
             "category": "sensitive_data",
-            "response_masked_data": {"data": "SSN is XXXXXXXXXX"}
+            "response_masked_data": {"data": "SSN is XXXXXXXXXX"},
         }
 
-        with patch.object(handler, "_call_panw_api", new_callable=AsyncMock) as mock_api:
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
             mock_api.return_value = mock_scan_result
-            
+
             result = await handler.async_post_call_success_hook(
                 user_api_key_dict=user_api_key_dict, response=response, data=data
             )
@@ -836,23 +853,35 @@ class TestPanwAirsAdvancedFeatures:
         user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
         request_data = {
             "messages": [{"role": "user", "content": "test"}],
-            "model": "gpt-4"
+            "model": "gpt-4",
         }
 
         # Create mock streaming chunks
         from litellm.types.utils import StreamingChoices, Delta
-        
+
         mock_chunks = [
             ModelResponse(
                 id="test_id",
-                choices=[StreamingChoices(delta=Delta(content="Hello", role="assistant"), finish_reason=None, index=0)],
+                choices=[
+                    StreamingChoices(
+                        delta=Delta(content="Hello", role="assistant"),
+                        finish_reason=None,
+                        index=0,
+                    )
+                ],
                 created=1234567890,
                 model="gpt-4",
                 object="chat.completion.chunk",
             ),
             ModelResponse(
                 id="test_id",
-                choices=[StreamingChoices(delta=Delta(content=" world", role="assistant"), finish_reason="stop", index=0)],
+                choices=[
+                    StreamingChoices(
+                        delta=Delta(content=" world", role="assistant"),
+                        finish_reason="stop",
+                        index=0,
+                    )
+                ],
                 created=1234567890,
                 model="gpt-4",
                 object="chat.completion.chunk",
@@ -865,10 +894,14 @@ class TestPanwAirsAdvancedFeatures:
 
         mock_scan_result = {"action": "allow", "category": "safe"}
 
-        with patch.object(handler, "_call_panw_api", new_callable=AsyncMock) as mock_api:
-            with patch("litellm.proxy.common_utils.callback_utils.add_guardrail_to_applied_guardrails_header") as mock_header:
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
+            with patch(
+                "litellm.proxy.common_utils.callback_utils.add_guardrail_to_applied_guardrails_header"
+            ) as mock_header:
                 mock_api.return_value = mock_scan_result
-                
+
                 chunks_received = []
                 async for chunk in handler.async_post_call_streaming_iterator_hook(
                     user_api_key_dict=user_api_key_dict,
@@ -880,8 +913,7 @@ class TestPanwAirsAdvancedFeatures:
                 # Verify header function was called
                 assert mock_header.called
                 mock_header.assert_called_once_with(
-                    request_data=request_data, 
-                    guardrail_name="test_panw_airs"
+                    request_data=request_data, guardrail_name="test_panw_airs"
                 )
 
 
@@ -906,12 +938,14 @@ class TestTextCompletionSupport:
         data = {
             "prompt": "Complete this sentence: AI security is",
             "model": "gpt-3.5-turbo-instruct",
-            "max_tokens": 50
+            "max_tokens": 50,
         }
 
         mock_scan_result = {"action": "allow", "category": "safe"}
 
-        with patch.object(handler, "_call_panw_api", new_callable=AsyncMock) as mock_api:
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
             mock_api.return_value = mock_scan_result
 
             result = await handler.async_pre_call_hook(
@@ -924,7 +958,9 @@ class TestTextCompletionSupport:
             # Verify API was called with the prompt text
             mock_api.assert_called_once()
             call_args = mock_api.call_args
-            assert call_args.kwargs["content"] == "Complete this sentence: AI security is"
+            assert (
+                call_args.kwargs["content"] == "Complete this sentence: AI security is"
+            )
             assert call_args.kwargs["is_response"] is False
 
             # Verify request was allowed through
@@ -954,10 +990,12 @@ class TestTextCompletionSupport:
         mock_scan_result = {
             "action": "block",
             "category": "dlp",
-            "prompt_masked_data": {"data": "Send money to account XXXXXXXXXX"}
+            "prompt_masked_data": {"data": "Send money to account XXXXXXXXXX"},
         }
 
-        with patch.object(handler, "_call_panw_api", new_callable=AsyncMock) as mock_api:
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
             mock_api.return_value = mock_scan_result
 
             result = await handler.async_pre_call_hook(
@@ -993,7 +1031,9 @@ class TestTextCompletionSupport:
 
         mock_scan_result = {"action": "allow", "category": "safe"}
 
-        with patch.object(handler, "_call_panw_api", new_callable=AsyncMock) as mock_api:
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
             mock_api.return_value = mock_scan_result
 
             await handler.async_pre_call_hook(
@@ -1010,5 +1050,324 @@ class TestTextCompletionSupport:
             assert "What is AI?" in call_args.kwargs["content"]
 
 
+class TestPanwAirsDeduplication:
+    """Test deduplication of callback invocations."""
+
+    @pytest.mark.asyncio
+    async def test_duplicate_pre_call_scan_prevented(self):
+        """Test that duplicate pre-call scans are prevented."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
+        data = {
+            "model": "gpt-3.5-turbo",
+            "messages": [{"role": "user", "content": "Test"}],
+            "litellm_call_id": "test-call-123",
+        }
+
+        mock_response = {"action": "allow", "category": "benign"}
+
+        with patch.object(
+            handler, "_call_panw_api", return_value=mock_response
+        ) as mock_api:
+            # First call - should scan
+            await handler.async_pre_call_hook(
+                user_api_key_dict=user_api_key_dict,
+                cache=None,
+                data=data,
+                call_type="completion",
+            )
+            assert mock_api.call_count == 1
+
+            # Second call with same call_id - should skip
+            await handler.async_pre_call_hook(
+                user_api_key_dict=user_api_key_dict,
+                cache=None,
+                data=data,
+                call_type="completion",
+            )
+            # Still 1 - no additional scan
+            assert mock_api.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_duplicate_post_call_scan_prevented(self):
+        """Test that duplicate post-call scans are prevented."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
+        data = {
+            "model": "gpt-3.5-turbo",
+            "litellm_call_id": "test-call-456",
+        }
+        response = ModelResponse(
+            id="test_id",
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(role="assistant", content="Test response"),
+                )
+            ],
+            model="gpt-3.5-turbo",
+        )
+
+        mock_response = {"action": "allow", "category": "benign"}
+
+        with patch.object(
+            handler, "_call_panw_api", return_value=mock_response
+        ) as mock_api:
+            # First call
+            await handler.async_post_call_success_hook(
+                data=data,
+                user_api_key_dict=user_api_key_dict,
+                response=response,
+            )
+            assert mock_api.call_count == 1
+
+            # Second call - should skip
+            await handler.async_post_call_success_hook(
+                data=data,
+                user_api_key_dict=user_api_key_dict,
+                response=response,
+            )
+            assert mock_api.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_duplicate_streaming_scan_prevented(self):
+        """Test that duplicate streaming scans are prevented."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
+        request_data = {
+            "model": "gpt-3.5-turbo",
+            "litellm_call_id": "test-call-789",
+            "messages": [{"role": "user", "content": "test"}],
+        }
+
+        # Create mock streaming chunks
+        from litellm.types.utils import StreamingChoices, Delta
+
+        mock_chunks = [
+            ModelResponse(
+                id="test_id",
+                choices=[
+                    StreamingChoices(
+                        delta=Delta(content="Hello", role="assistant"),
+                        finish_reason=None,
+                        index=0,
+                    )
+                ],
+                created=1234567890,
+                model="gpt-4",
+                object="chat.completion.chunk",
+            ),
+        ]
+
+        async def mock_response_iter():
+            for chunk in mock_chunks:
+                yield chunk
+
+        mock_scan_result = {"action": "allow", "category": "safe"}
+
+        with patch.object(
+            handler, "_call_panw_api", new_callable=AsyncMock
+        ) as mock_api:
+            mock_api.return_value = mock_scan_result
+
+            # First call - should scan
+            chunks_received = []
+            async for chunk in handler.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=user_api_key_dict,
+                response=mock_response_iter(),
+                request_data=request_data,
+            ):
+                chunks_received.append(chunk)
+
+            assert mock_api.call_count == 1
+
+            # Second call with same call_id - should skip
+            async for chunk in handler.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=user_api_key_dict,
+                response=mock_response_iter(),
+                request_data=request_data,
+            ):
+                pass
+
+            # Still 1 - no additional scan
+            assert mock_api.call_count == 1
+
+
+class TestPanwAirsSessionTracking:
+    """Test session tracking with litellm_trace_id."""
+
+    @pytest.mark.asyncio
+    async def test_litellm_trace_id_used_as_transaction_id(self):
+        """Test that litellm_trace_id is used as PANW transaction ID."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        trace_id = "abc-123-def-456"
+        metadata = {
+            "user": "test_user",
+            "model": "gpt-4",
+            "litellm_trace_id": trace_id,
+        }
+
+        with patch(
+            "litellm.proxy.guardrails.guardrail_hooks.panw_prisma_airs.panw_prisma_airs.get_async_httpx_client"
+        ) as mock_client:
+            mock_async_client = AsyncMock()
+            mock_response = MagicMock()
+            mock_response.json.return_value = {"action": "allow", "category": "benign"}
+            mock_response.raise_for_status.return_value = None
+            mock_async_client.post = AsyncMock(return_value=mock_response)
+            mock_client.return_value = mock_async_client
+
+            await handler._call_panw_api(
+                content="Test content",
+                is_response=False,
+                metadata=metadata,
+            )
+
+            # Verify tr_id in API payload matches trace_id
+            call_args = mock_async_client.post.call_args
+            payload = call_args.kwargs["json"]
+            assert payload["tr_id"] == trace_id
+
+    @pytest.mark.asyncio
+    async def test_fallback_to_call_id_when_trace_id_missing(self):
+        """Test fallback to call_id when litellm_trace_id is missing."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        call_id = "fallback-call-789"
+        metadata = {
+            "user": "test_user",
+            "model": "gpt-4",
+            # No litellm_trace_id
+        }
+
+        with patch(
+            "litellm.proxy.guardrails.guardrail_hooks.panw_prisma_airs.panw_prisma_airs.get_async_httpx_client"
+        ) as mock_client:
+            mock_async_client = AsyncMock()
+            mock_response = MagicMock()
+            mock_response.json.return_value = {"action": "allow", "category": "benign"}
+            mock_response.raise_for_status.return_value = None
+            mock_async_client.post = AsyncMock(return_value=mock_response)
+            mock_client.return_value = mock_async_client
+
+            await handler._call_panw_api(
+                content="Test content",
+                is_response=False,
+                metadata=metadata,
+                call_id=call_id,
+            )
+
+            # Verify tr_id falls back to call_id
+            call_args = mock_async_client.post.call_args
+            payload = call_args.kwargs["json"]
+            assert payload["tr_id"] == call_id
+
+    @pytest.mark.asyncio
+    async def test_trace_id_extraction_from_request_data(self):
+        """Test that litellm_trace_id is extracted from request data."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        trace_id = "session-xyz-789"
+        data = {
+            "model": "gpt-3.5-turbo",
+            "messages": [{"role": "user", "content": "Test"}],
+            "litellm_trace_id": trace_id,
+        }
+
+        # Extract metadata
+        metadata = handler._prepare_metadata_from_request(data)
+
+        # Verify trace_id is included in metadata
+        assert "litellm_trace_id" in metadata
+        assert metadata["litellm_trace_id"] == trace_id
+
+    @pytest.mark.asyncio
+    async def test_same_trace_id_for_prompt_and_response(self):
+        """Test that prompt and response scans use the same trace_id."""
+        handler = PanwPrismaAirsHandler(
+            guardrail_name="test_panw_airs",
+            api_key="test_api_key",
+            profile_name="test_profile",
+            default_on=True,
+        )
+
+        trace_id = "conversation-session-123"
+
+        with patch(
+            "litellm.proxy.guardrails.guardrail_hooks.panw_prisma_airs.panw_prisma_airs.get_async_httpx_client"
+        ) as mock_client:
+            mock_async_client = AsyncMock()
+            mock_response = MagicMock()
+            mock_response.json.return_value = {"action": "allow", "category": "benign"}
+            mock_response.raise_for_status.return_value = None
+            mock_async_client.post = AsyncMock(return_value=mock_response)
+            mock_client.return_value = mock_async_client
+
+            # Prompt scan
+            await handler._call_panw_api(
+                content="User prompt",
+                is_response=False,
+                metadata={
+                    "litellm_trace_id": trace_id,
+                    "user": "test",
+                    "model": "gpt-4",
+                },
+            )
+            prompt_payload = mock_async_client.post.call_args.kwargs["json"]
+            prompt_tr_id = prompt_payload["tr_id"]
+
+            # Response scan
+            await handler._call_panw_api(
+                content="Assistant response",
+                is_response=True,
+                metadata={
+                    "litellm_trace_id": trace_id,
+                    "user": "test",
+                    "model": "gpt-4",
+                },
+            )
+            response_payload = mock_async_client.post.call_args.kwargs["json"]
+            response_tr_id = response_payload["tr_id"]
+
+            # Both should use the same trace_id
+            assert prompt_tr_id == trace_id
+            assert response_tr_id == trace_id
+            assert prompt_tr_id == response_tr_id
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py
index 234f3a951cd..9543b61ef69 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py
@@ -577,7 +577,11 @@ async def test_presidio_sets_guardrail_information_in_request_data():
     assert "metadata" in request_data
     assert "standard_logging_guardrail_information" in request_data["metadata"]
     
-    guardrail_info = request_data["metadata"]["standard_logging_guardrail_information"]
+    guardrail_info_list = request_data["metadata"]["standard_logging_guardrail_information"]
+    assert isinstance(guardrail_info_list, list)
+    assert len(guardrail_info_list) > 0
+    
+    guardrail_info = guardrail_info_list[0]
     assert "masked_entity_count" in guardrail_info
     assert guardrail_info["masked_entity_count"]["EMAIL_ADDRESS"] == 1
     assert guardrail_info["masked_entity_count"]["PERSON"] == 1
diff --git a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py
index a2d072dc7d8..2292bf32040 100644
--- a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py
+++ b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py
@@ -751,13 +751,14 @@ async def test_patch_guardrail_endpoint(
     mock_logger = None
     if scenario == "success_with_sync":
         mock_prisma_client = mocker.Mock()
+        mock_in_memory_handler.sync_guardrail_from_db = mocker.Mock()
         mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
         mocker.patch("litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY", mock_guardrail_registry)
         mocker.patch("litellm.proxy.guardrails.guardrail_registry.IN_MEMORY_GUARDRAIL_HANDLER", mock_in_memory_handler)
         
     elif scenario == "success_sync_fails":
         mock_prisma_client = mocker.Mock()
-        mock_in_memory_handler.update_in_memory_guardrail.side_effect = Exception("Sync failed")
+        mock_in_memory_handler.sync_guardrail_from_db = mocker.Mock(side_effect=Exception("Sync failed"))
         mock_logger = mocker.patch("litellm.proxy.guardrails.guardrail_endpoints.verbose_proxy_logger")
         
         mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
@@ -792,8 +793,7 @@ async def test_patch_guardrail_endpoint(
         
         mock_guardrail_registry.update_guardrail_in_db.assert_called_once()
         
-        mock_in_memory_handler.update_in_memory_guardrail.assert_called_once_with(
-            guardrail_id="test-guardrail-id",
+        mock_in_memory_handler.sync_guardrail_from_db.assert_called_once_with(
             guardrail=mocker.ANY
         )
         
diff --git a/tests/test_litellm/proxy/management_endpoints/test_delete_callbacks_endpoint.py b/tests/test_litellm/proxy/management_endpoints/test_delete_callbacks_endpoint.py
index 4d6a81b481a..e38204fc909 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_delete_callbacks_endpoint.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_delete_callbacks_endpoint.py
@@ -9,7 +9,12 @@ sys.path.insert(
     0, os.path.abspath("../../../..")
 )
 
-from litellm.proxy._types import CallbackDelete, ConfigYAML, LitellmUserRoles, UserAPIKeyAuth
+from litellm.proxy._types import (
+    CallbackDelete,
+    ConfigYAML,
+    LitellmUserRoles,
+    UserAPIKeyAuth,
+)
 from litellm.proxy.proxy_server import app
 
 client = TestClient(app)
@@ -101,76 +106,10 @@ def mock_prisma():
     return MockPrismaClient()
 
 
-def mock_encrypt_value_helper(value):
+def mock_encrypt_value_helper(value, key=None, new_encryption_key=None):
     """Mock encryption - just return the value as-is for testing"""
     return value
 
-def mock_decrypt_value_helper(value):
+def mock_decrypt_value_helper(value, key=None, return_original_value=False):
     """Mock decryption - just return the value as-is for testing"""
     return value
-
-
-@pytest.mark.asyncio
-async def test_delete_callbacks_in_db(mock_prisma, mock_auth):
-    
-    with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), \
-         patch("litellm.proxy.proxy_server.store_model_in_db", True), \
-         patch("litellm.proxy.proxy_server.encrypt_value_helper", side_effect=mock_encrypt_value_helper), \
-         patch("litellm.proxy.proxy_server.decrypt_value_helper", side_effect=mock_decrypt_value_helper):
-        
-        # Override auth dependency
-        app.dependency_overrides[
-            lambda: __import__("litellm.proxy.proxy_server", fromlist=["user_api_key_auth"]).user_api_key_auth
-        ] = lambda: mock_auth
-        
-        # Add langfuse callback to DB via /config/update
-        config_data = {
-            "litellm_settings": {"success_callback": ["langfuse"]},
-            "environment_variables": {
-                "LANGFUSE_PUBLIC_KEY": "any-public-key",
-                "LANGFUSE_SECRET_KEY": "any-secret-key",
-                "LANGFUSE_HOST": "https://exampleopenaiendpoint-production-c715.up.railway.app",
-            },
-        }
-        
-        config_response = client.post(
-            "/config/update", 
-            json=config_data,
-            headers={"Authorization": "Bearer sk-1234"}
-        )
-        assert config_response.status_code == 200
-        
-        # Delete the langfuse callback
-        delete_data = {"callback_name": "langfuse"}
-        delete_response = client.post(
-            "/config/callback/delete",
-            json=delete_data, 
-            headers={"Authorization": "Bearer sk-1234"}
-        )
-        
-        assert delete_response.status_code == 200
-        delete_result = delete_response.json()
-        
-        # Verify delete response
-        assert "message" in delete_result
-        assert "langfuse" in delete_result.get("removed_callback", "")
-        assert "langfuse" not in delete_result.get("remaining_callbacks", [])
-        
-        # Update mock to reflect deletion for get_config test
-        mock_prisma.remove_callback_from_config("langfuse")
-        
-        # Get config and verify callback is deleted
-        config_response = client.get(
-            "/get/config/callbacks",
-            headers={"Authorization": "Bearer sk-1234"}
-        )
-        
-        assert config_response.status_code == 200
-        config_data = config_response.json()
-        
-        # Verify callback is removed from the config
-        callback_names = [callback["name"] for callback in config_data.get("callbacks", [])]
-        assert "langfuse" not in callback_names
-        
-        # Clean up
-        app.dependency_overrides.clear() 
\ No newline at end of file
diff --git a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py
index b62fd6f177e..2102fe71b1c 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py
@@ -186,6 +186,9 @@ class TestListMCPServers:
             return_value=mock_servers_with_health
         )
 
+        for idx, server in enumerate(mock_servers_with_health):
+            server.credentials = {"auth_value": f"secret_{idx}"}
+
         with patch(
             "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager",
             mock_manager,
@@ -205,6 +208,7 @@ class TestListMCPServers:
 
             # Verify results
             assert len(result) == 2
+            assert all(server.credentials is None for server in result)
 
             # Check that both config servers are returned
             server_ids = [server.server_id for server in result]
@@ -315,6 +319,9 @@ class TestListMCPServers:
             return_value=mock_servers_with_health
         )
 
+        for idx, server in enumerate(mock_servers_with_health):
+            server.credentials = {"auth_value": f"secret_{idx}"}
+
         with patch(
             "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager",
             mock_manager,
@@ -334,6 +341,7 @@ class TestListMCPServers:
 
             # Verify results
             assert len(result) == 4
+            assert all(server.credentials is None for server in result)
 
             # Check that both DB and config servers are returned
             server_ids = [server.server_id for server in result]
@@ -428,6 +436,9 @@ class TestListMCPServers:
             return_value=mock_servers_with_health
         )
 
+        for idx, server in enumerate(mock_servers_with_health):
+            server.credentials = {"auth_value": f"secret_{idx}"}
+
         with patch(
             "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager",
             mock_manager,
@@ -447,6 +458,7 @@ class TestListMCPServers:
 
             # Verify results - should only return servers user has access to
             assert len(result) == 2
+            assert all(server.credentials is None for server in result)
 
             # Check that only allowed servers are returned
             server_ids = [server.server_id for server in result]
@@ -464,6 +476,51 @@ class TestListMCPServers:
                     assert server.url == "https://actions.zapier.com/mcp/sse"
 
 
+    @pytest.mark.asyncio
+    async def test_fetch_single_mcp_server_redacts_credentials(self):
+        mock_server = generate_mock_mcp_server_db_record(
+            server_id="server-1", alias="Server 1"
+        )
+        mock_server.credentials = {"auth_value": "top-secret"}
+
+        mock_prisma_client = MagicMock()
+        mock_health_result = {
+            "status": "healthy",
+            "last_health_check": datetime.now().isoformat(),
+            "error": None,
+        }
+
+        mock_user_auth = generate_mock_user_api_key_auth(
+            user_role=LitellmUserRoles.PROXY_ADMIN
+        )
+
+        with patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw",
+            return_value=mock_prisma_client,
+        ), patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints.get_mcp_server",
+            AsyncMock(return_value=mock_server),
+        ), patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.health_check_server",
+            AsyncMock(return_value=mock_health_result),
+        ), patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view",
+            return_value=True,
+        ):
+            from litellm.proxy.management_endpoints.mcp_management_endpoints import (
+                fetch_mcp_server,
+            )
+
+            result = await fetch_mcp_server(
+                server_id="server-1", user_api_key_dict=mock_user_auth
+            )
+
+            assert result.server_id == "server-1"
+            assert result.credentials is None
+            assert mock_server.credentials == {"auth_value": "top-secret"}
+            assert result.status == "healthy"
+
+
 class TestMCPHealthCheckEndpoints:
     """Test MCP health check endpoints"""
 
@@ -721,6 +778,8 @@ class TestMCPHealthCheckEndpoints:
             return_value=[mock_server]
         )
 
+        mock_server.credentials = {"auth_value": "secret"}
+
         mock_user_auth = generate_mock_user_api_key_auth(
             user_role=LitellmUserRoles.PROXY_ADMIN
         )
@@ -749,3 +808,4 @@ class TestMCPHealthCheckEndpoints:
             assert server.status == "healthy"
             assert server.last_health_check is not None
             assert server.health_check_error is None
+            assert server.credentials is None
diff --git a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
index 1a287e95746..437b71535ba 100644
--- a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
+++ b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
@@ -351,6 +351,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
 
         # Mock user API key with permissions
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = {
             "allowed_vector_store_indexes": [
                 {"index_name": "my-index", "index_permissions": ["read"]}
@@ -387,6 +388,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
 
         # Mock user API key with permissions
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = {
             "allowed_vector_store_indexes": [
                 {"index_name": "my-index", "index_permissions": ["write"]}
@@ -423,6 +425,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
 
         # Mock user API key with only read permissions
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = {
             "allowed_vector_store_indexes": [
                 {"index_name": "my-index", "index_permissions": ["read"]}
@@ -458,6 +461,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
         mock_request.url.path = "/v1/vector_stores/my-index/search"
 
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = {}
         mock_user_api_key.team_metadata = None
 
@@ -482,6 +486,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
         mock_request.url.path = "/v1/vector_stores/my-index/unknown"
 
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = {
             "allowed_vector_store_indexes": [
                 {"index_name": "my-index", "index_permissions": ["read", "write"]}
@@ -518,6 +523,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
 
         # Mock user API key with no key metadata but team metadata
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = None
         mock_user_api_key.team_metadata = {
             "allowed_vector_store_indexes": [
@@ -553,6 +559,7 @@ class TestIsAllowedToCallVectorStoreEndpoint:
         mock_request.url.path = "/v1/vector_stores/my-index/search"
 
         mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.user_role = None
         mock_user_api_key.metadata = {}
         mock_user_api_key.team_metadata = {}
 
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index 3490650ff84..f894f321102 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -461,18 +461,18 @@ def test_gemini_25_implicit_caching_cost():
         model="gemini/gemini-2.5-flash",
     )
 
-    # From the issue:
-    # input: $0.15 / 1000000 tokens
-    # output: $0.60 / 1000000 tokens
-    # With caching: 0.15*0.25*(14316/1000000)+0.15*((15033-14316)/1000000)+0.6*(17/1000000) = 0.0006546
+    # Current pricing for gemini/gemini-2.5-flash:
+    # input: $0.30 / 1M tokens (3e-07 per token)
+    # cache_read: $0.03 / 1M tokens (3e-08 per token)
+    # output: $2.50 / 1M tokens (2.5e-06 per token)
 
     # Breakdown:
-    # - Cached tokens: 14316 * 0.15/1M * 0.25 = 0.00053685
-    # - Non-cached tokens: (15033-14316) * 0.15/1M = 717 * 0.15/1M = 0.00010755
-    # - Output tokens: 17 * 0.6/1M = 0.00001020
-    # Total: 0.00053685 + 0.00010755 + 0.00001020 = 0.0006546
+    # - Cached tokens: 14316 * 3e-08 = 0.00042948
+    # - Non-cached tokens: (15033-14316) * 3e-07 = 717 * 3e-07 = 0.00021510
+    # - Output tokens: 17 * 2.5e-06 = 0.00004250
+    # Total: 0.00042948 + 0.00021510 + 0.00004250 = 0.00068708
 
-    expected_cost = 0.0013312999999999999
+    expected_cost = 0.00068708
 
     # Allow for small floating point differences
     assert (
@@ -697,7 +697,7 @@ def test_cost_discount_vertex_ai():
 
     # Save original config
     original_discount_config = litellm.cost_discount_config.copy()
-    
+
     # Create mock response
     response = ModelResponse(
         id="test-id",
@@ -705,13 +705,9 @@ def test_cost_discount_vertex_ai():
         created=1234567890,
         model="gemini-pro",
         object="chat.completion",
-        usage=Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
+        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
     )
-    
+
     # Calculate cost without discount
     litellm.cost_discount_config = {}
     cost_without_discount = completion_cost(
@@ -719,24 +715,24 @@ def test_cost_discount_vertex_ai():
         model="vertex_ai/gemini-pro",
         custom_llm_provider="vertex_ai",
     )
-    
+
     # Set 5% discount for vertex_ai
     litellm.cost_discount_config = {"vertex_ai": 0.05}
-    
+
     # Calculate cost with discount
     cost_with_discount = completion_cost(
         completion_response=response,
         model="vertex_ai/gemini-pro",
         custom_llm_provider="vertex_ai",
     )
-    
+
     # Restore original config
     litellm.cost_discount_config = original_discount_config
-    
+
     # Verify discount is applied (5% off means 95% of original cost)
     expected_cost = cost_without_discount * 0.95
     assert cost_with_discount == pytest.approx(expected_cost, rel=1e-9)
-    
+
     print(f"✓ Cost discount test passed:")
     print(f"  - Original cost: ${cost_without_discount:.6f}")
     print(f"  - Discounted cost (5% off): ${cost_with_discount:.6f}")
@@ -752,7 +748,7 @@ def test_cost_discount_not_applied_to_other_providers():
 
     # Save original config
     original_discount_config = litellm.cost_discount_config.copy()
-    
+
     # Create mock response for OpenAI
     response = ModelResponse(
         id="test-id",
@@ -760,23 +756,19 @@ def test_cost_discount_not_applied_to_other_providers():
         created=1234567890,
         model="gpt-4",
         object="chat.completion",
-        usage=Usage(
-            prompt_tokens=100,
-            completion_tokens=50,
-            total_tokens=150
-        )
+        usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150),
     )
-    
+
     # Set discount only for vertex_ai (not openai)
     litellm.cost_discount_config = {"vertex_ai": 0.05}
-    
+
     # Calculate cost for OpenAI - should NOT have discount applied
     cost_with_selective_discount = completion_cost(
         completion_response=response,
         model="gpt-4",
         custom_llm_provider="openai",
     )
-    
+
     # Clear discount config
     litellm.cost_discount_config = {}
     cost_without_discount = completion_cost(
@@ -784,13 +776,67 @@ def test_cost_discount_not_applied_to_other_providers():
         model="gpt-4",
         custom_llm_provider="openai",
     )
-    
+
     # Restore original config
     litellm.cost_discount_config = original_discount_config
-    
+
     # Costs should be the same (no discount applied to OpenAI)
     assert cost_with_selective_discount == cost_without_discount
-    
+
     print(f"✓ Selective discount test passed:")
     print(f"  - OpenAI cost (no discount configured): ${cost_without_discount:.6f}")
     print(f"  - Cost remains unchanged: ${cost_with_selective_discount:.6f}")
+
+
+def test_azure_image_generation_cost_calculator():
+    from unittest.mock import MagicMock
+
+    from litellm.types.utils import (
+        ImageObject,
+        ImageResponse,
+        ImageUsage,
+        ImageUsageInputTokensDetails,
+    )
+
+    response_cost_calculator_kwargs = {
+        "response_object": ImageResponse(
+            created=1761785270,
+            background=None,
+            data=[
+                ImageObject(
+                    b64_json=None,
+                    revised_prompt="A futuristic, techno-inspired green duck wearing cool modern sunglasses. The duck has a sleek, metallic appearance with glowing neon green accents, standing on a high-tech urban background with holographic billboards and illuminated city lights in the distance. The duck's feathers have a glossy, high-tech sheen, resembling a robotic design but still maintaining its avian features. The scene has a vibrant, cyberpunk aesthetic with a neon color palette.",
+                    url="https://dalleprodsec.blob.core.windows.net/private/images/caa17dc4-357d-4257-8938-eeea9baa8d0a/generated_00.png?se=2025-10-31T00%3A47%3A59Z&sig=KHRjLz3vMahbw94JtxL02S6t2AueeRMaiqj4z35HKDM%3D&ske=2025-11-05T00%3A26%3A20Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2025-10-29T00%3A26%3A20Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02",
+                )
+            ],
+            output_format=None,
+            quality="hd",
+            size=None,
+            usage=ImageUsage(
+                input_tokens=0,
+                input_tokens_details=ImageUsageInputTokensDetails(
+                    image_tokens=0, text_tokens=0
+                ),
+                output_tokens=0,
+                total_tokens=0,
+            ),
+        ),
+        "model": "azure/dall-e-3",
+        "cache_hit": False,
+        "custom_llm_provider": "azure",
+        "base_model": "azure/dall-e-3",
+        "call_type": "aimage_generation",
+        "optional_params": {},
+        "custom_pricing": False,
+        "prompt": "",
+        "standard_built_in_tools_params": {
+            "web_search_options": None,
+            "file_search": None,
+        },
+        "router_model_id": "6738c432ffc9b733597c6b86613ca20dc5f49bde591fd3d03e7cd6aa25bb241e",
+        "litellm_logging_obj": MagicMock(),
+        "service_tier": None,
+    }
+
+    cost = response_cost_calculator(**response_cost_calculator_kwargs)
+    assert cost > 0.079
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index dde2b45aee1..b30b0489952 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -845,6 +845,16 @@ def test_check_provider_match():
     model_info = {"litellm_provider": "bedrock"}
     assert litellm.utils._check_provider_match(model_info, "openai") is False
 
+def test_get_provider_rerank_config():
+    """
+    Test the get_provider_rerank_config function for various providers
+    """
+    from litellm import HostedVLLMRerankConfig
+    from litellm.utils import LlmProviders, ProviderConfigManager
+
+    # Test for hosted_vllm provider
+    config = ProviderConfigManager.get_provider_rerank_config("my_model", LlmProviders.HOSTED_VLLM, 'http://localhost', [])
+    assert isinstance(config, HostedVLLMRerankConfig)
 
 # Models that should be skipped during testing
 OLD_PROVIDERS = ["aleph_alpha", "palm"]
diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py
index 6007201a676..b11e38b32bb 100644
--- a/tests/test_litellm/test_video_generation.py
+++ b/tests/test_litellm/test_video_generation.py
@@ -150,9 +150,10 @@ class TestVideoGeneration:
         config = OpenAIVideoConfig()
         
         # Test request transformation
-        data, files = config.transform_video_create_request(
+        data, files, returned_api_base = config.transform_video_create_request(
             model="sora-2",
             prompt="Test video prompt",
+            api_base="https://api.openai.com/v1/videos",
             video_create_optional_request_params={
                 "seconds": "8",
                 "size": "720x1280"
@@ -166,6 +167,7 @@ class TestVideoGeneration:
         assert data["seconds"] == "8"
         assert data["size"] == "720x1280"
         assert files == []
+        assert returned_api_base == "https://api.openai.com/v1/videos"
 
     def test_video_generation_response_transformation(self):
         """Test video generation response transformation."""
@@ -228,9 +230,10 @@ class TestVideoGeneration:
         mock_file = MagicMock()
         mock_file.read.return_value = b"fake_image_data"
         
-        data, files = config.transform_video_create_request(
+        data, files, returned_api_base = config.transform_video_create_request(
             model="sora-2",
             prompt="Test video with image",
+            api_base="https://api.openai.com/v1/videos",
             video_create_optional_request_params={
                 "input_reference": mock_file,
                 "seconds": "8",
@@ -291,42 +294,29 @@ class TestVideoGeneration:
         assert mapped_params["user"] == "test-user"
 
     def test_video_generation_unsupported_parameters(self):
-        """Test video generation with unsupported parameters."""
+        """Test video generation with provider-specific parameters via extra_body."""
         from litellm.videos.utils import VideoGenerationRequestUtils
         
-        # Test unsupported parameter detection
-        with pytest.raises(litellm.UnsupportedParamsError):
-            VideoGenerationRequestUtils.get_optional_params_video_generation(
-                model="sora-2",
-                video_generation_provider_config=OpenAIVideoConfig(),
-                video_generation_optional_params={
-                    "unsupported_param": "value"
+        # Test that provider-specific parameters can be passed via extra_body
+        # This allows support for Vertex AI and Gemini specific parameters
+        result = VideoGenerationRequestUtils.get_optional_params_video_generation(
+            model="sora-2",
+            video_generation_provider_config=OpenAIVideoConfig(),
+            video_generation_optional_params={
+                "seconds": "8",
+                "extra_body": {
+                    "vertex_ai_param": "value",
+                    "gemini_param": "value2"
                 }
-            )
-
-    def test_video_generation_request_utils(self):
-        """Test video generation request utilities."""
-        from litellm.videos.utils import VideoGenerationRequestUtils
+            }
+        )
         
-        # Test parameter filtering
-        params = {
-            "prompt": "Test video",
-            "model": "sora-2",
-            "seconds": "8",
-            "size": "720x1280",
-            "user": "test-user",
-            "invalid_param": "should_be_filtered"
-        }
-        
-        filtered_params = VideoGenerationRequestUtils.get_requested_video_generation_optional_param(params)
-        
-        # Should only contain valid parameters
-        assert "prompt" not in filtered_params  # prompt is required, not optional
-        assert "seconds" in filtered_params
-        assert "size" in filtered_params
-        assert "user" in filtered_params
-        assert "invalid_param" not in filtered_params
-        # Note: model is included in the filtered params as it's part of the TypedDict
+        # extra_body params should be merged into the result
+        assert result["seconds"] == "8"
+        assert result["vertex_ai_param"] == "value"
+        assert result["gemini_param"] == "value2"
+        # extra_body itself should be removed from the result
+        assert "extra_body" not in result
 
     def test_video_generation_types(self):
         """Test video generation type definitions."""
diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json
index 7205738a21c..6e21fc2f5f7 100644
--- a/ui/litellm-dashboard/package-lock.json
+++ b/ui/litellm-dashboard/package-lock.json
@@ -23233,4 +23233,4 @@
       }
     }
   }
-}
+}
\ No newline at end of file
diff --git a/ui/litellm-dashboard/public/assets/logos/litellm_logo.jpg b/ui/litellm-dashboard/public/assets/logos/litellm_logo.jpg
new file mode 100644
index 00000000000..6fe96e2ed35
Binary files /dev/null and b/ui/litellm-dashboard/public/assets/logos/litellm_logo.jpg differ
diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx
index 0a33fdb53d3..1f61ea260ef 100644
--- a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx
+++ b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx
@@ -1,14 +1,12 @@
 import React, { useState } from "react";
 import { Button, Text } from "@tremor/react";
 import { Select } from "antd";
-import AdvancedDatePicker from "../shared/advanced_date_picker";
 import EntityUsageExportModal from "./EntityUsageExportModal";
 import type { DateRangePickerValue } from "@tremor/react";
 import type { EntitySpendData } from "./types";
 
 interface UsageExportHeaderProps {
   dateValue: DateRangePickerValue;
-  onDateChange: (value: DateRangePickerValue) => void;
   entityType: "tag" | "team";
   spendData: EntitySpendData;
   // Optional filter props
@@ -24,7 +22,6 @@ interface UsageExportHeaderProps {
 
 const UsageExportHeader: React.FC<UsageExportHeaderProps> = ({
   dateValue,
-  onDateChange,
   entityType,
   spendData,
   showFilters = false,
@@ -38,23 +35,23 @@ const UsageExportHeader: React.FC<UsageExportHeaderProps> = ({
 }) => {
   const [isExportModalOpen, setIsExportModalOpen] = useState(false);
 
+  // Determine grid layout based on what's visible
+  const getGridCols = () => {
+    const hasFilters = showFilters && filterOptions.length > 0;
+
+    if (hasFilters) return "grid-cols-[1fr_auto]";
+    return "grid-cols-[auto]";
+  };
+
   return (
     <>
       <div className="mb-4">
         {/**
-         * Use CSS grid with items-end so all cells (date picker, filter, button)
+         * Use CSS grid with items-end so all cells (filter, button)
          * align to the same baseline regardless of label heights. This removes
          * vertical drift when the right column has a label above the input.
          */}
-        <div
-          className={`grid ${
-            showFilters && filterOptions.length > 0 ? "grid-cols-[1fr_1fr_auto]" : "grid-cols-[1fr_auto]"
-          } items-end gap-4`}
-        >
-          <div>
-            <AdvancedDatePicker value={dateValue} onValueChange={onDateChange} />
-          </div>
-
+        <div className={`grid ${getGridCols()} items-end gap-4`}>
           {showFilters && filterOptions.length > 0 && (
             <div>
               {filterLabel && <Text className="mb-2">{filterLabel}</Text>}
@@ -104,4 +101,3 @@ const UsageExportHeader: React.FC<UsageExportHeaderProps> = ({
 };
 
 export default UsageExportHeader;
-
diff --git a/ui/litellm-dashboard/src/components/OldTeams.test.tsx b/ui/litellm-dashboard/src/components/OldTeams.test.tsx
index 85fa5472ad8..c055a2f85f7 100644
--- a/ui/litellm-dashboard/src/components/OldTeams.test.tsx
+++ b/ui/litellm-dashboard/src/components/OldTeams.test.tsx
@@ -1,5 +1,7 @@
-import { describe, it, expect, vi, beforeEach } from "vitest";
+import { act, fireEvent, render } from "@testing-library/react";
+import { beforeEach, describe, expect, it, vi } from "vitest";
 import { teamCreateCall } from "./networking";
+import OldTeams from "./OldTeams";
 
 vi.mock("./networking", () => ({
   teamCreateCall: vi.fn(),
@@ -126,7 +128,7 @@ describe("OldTeams - handleCreate organization handling", () => {
 
     // Verify we're not sending an empty string
     expect(formValues.organization_id).not.toBe("");
-    
+
     // Verify it's explicitly null, not undefined
     expect(formValues.organization_id).toBeNull();
   });
@@ -189,9 +191,10 @@ describe("OldTeams - handleCreate organization handling", () => {
     };
 
     // For org admins, organization_id should be required
-    const hasOrganization = formValues.organization_id !== undefined && 
-                           formValues.organization_id !== null && 
-                           formValues.organization_id !== "";
+    const hasOrganization =
+      formValues.organization_id !== undefined &&
+      formValues.organization_id !== null &&
+      formValues.organization_id !== "";
 
     if (isOrgAdmin && !hasOrganization) {
       // This should trigger validation error
@@ -222,7 +225,7 @@ describe("OldTeams - handleCreate organization handling", () => {
 
     // Type check: organization_id should never be an array
     expect(Array.isArray(invalidFormValues.organization_id)).toBe(true);
-    
+
     // Correct it to null
     if (Array.isArray(invalidFormValues.organization_id)) {
       invalidFormValues.organization_id = null;
@@ -231,6 +234,40 @@ describe("OldTeams - handleCreate organization handling", () => {
     expect(invalidFormValues.organization_id).toBeNull();
     expect(Array.isArray(invalidFormValues.organization_id)).toBe(false);
   });
+
+  it("should clear the delete modal when the cancel button is clicked", async () => {
+    const { getByRole, getByTestId } = render(
+      <OldTeams
+        teams={[
+          {
+            team_id: "1",
+            team_alias: "Test Team",
+            organization_id: "org-123",
+            models: ["gpt-4"],
+            max_budget: 100,
+            budget_duration: "1d",
+            tpm_limit: 1000,
+            rpm_limit: 1000,
+            created_at: new Date().toISOString(),
+            keys: [],
+            members_with_roles: [],
+          },
+        ]}
+        searchParams={{}}
+        accessToken="test-token"
+        setTeams={vi.fn()}
+        userID="user-123"
+        userRole="Admin"
+        organizations={[]}
+      />,
+    );
+    const deleteTeamButton = getByTestId("delete-team-button");
+    act(() => {
+      fireEvent.click(deleteTeamButton);
+    });
+    expect(getByRole("heading", { name: "Delete Team" })).toBeInTheDocument();
+    expect(getByRole("button", { name: "Cancel" })).toBeInTheDocument();
+  });
 });
 
 describe("OldTeams - helper functions", () => {
@@ -267,33 +304,25 @@ describe("OldTeams - helper functions", () => {
           organization_id: "org-1",
           organization_alias: "Org 1",
           models: [],
-          members: [
-            { user_id: "user-123", user_role: "org_admin" },
-          ],
+          members: [{ user_id: "user-123", user_role: "org_admin" }],
         },
         {
           organization_id: "org-2",
           organization_alias: "Org 2",
           models: [],
-          members: [
-            { user_id: "user-456", user_role: "org_admin" },
-          ],
+          members: [{ user_id: "user-456", user_role: "org_admin" }],
         },
         {
           organization_id: "org-3",
           organization_alias: "Org 3",
           models: [],
-          members: [
-            { user_id: "user-123", user_role: "member" },
-          ],
+          members: [{ user_id: "user-123", user_role: "member" }],
         },
       ];
 
       // Simulate getAdminOrganizations logic
       const result = organizations.filter((org) =>
-        org.members?.some(
-          (member) => member.user_id === userID && member.user_role === "org_admin"
-        )
+        org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin"),
       );
 
       expect(result.length).toBe(1);
@@ -307,17 +336,13 @@ describe("OldTeams - helper functions", () => {
           organization_id: "org-1",
           organization_alias: "Org 1",
           models: [],
-          members: [
-            { user_id: "user-123", user_role: "org_admin" },
-          ],
+          members: [{ user_id: "user-123", user_role: "org_admin" }],
         },
       ];
 
       // Simulate getAdminOrganizations logic
       const result = organizations.filter((org) =>
-        org.members?.some(
-          (member) => member.user_id === userID && member.user_role === "org_admin"
-        )
+        org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin"),
       );
 
       expect(result.length).toBe(0);
@@ -338,16 +363,12 @@ describe("OldTeams - helper functions", () => {
           organization_id: "org-1",
           organization_alias: "Org 1",
           models: [],
-          members: [
-            { user_id: "user-123", user_role: "org_admin" },
-          ],
+          members: [{ user_id: "user-123", user_role: "org_admin" }],
         },
       ];
 
       const result = organizations.some((org) =>
-        org.members?.some(
-          (member) => member.user_id === userID && member.user_role === "org_admin"
-        )
+        org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin"),
       );
 
       expect(result).toBe(true);
@@ -361,21 +382,16 @@ describe("OldTeams - helper functions", () => {
           organization_id: "org-1",
           organization_alias: "Org 1",
           models: [],
-          members: [
-            { user_id: "user-123", user_role: "member" },
-          ],
+          members: [{ user_id: "user-123", user_role: "member" }],
         },
       ];
 
       const isAdmin = userRole === "Admin";
       const isOrgAdmin = organizations.some((org) =>
-        org.members?.some(
-          (member) => member.user_id === userID && member.user_role === "org_admin"
-        )
+        org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin"),
       );
 
       expect(isAdmin || isOrgAdmin).toBe(false);
     });
   });
 });
-
diff --git a/ui/litellm-dashboard/src/components/OldTeams.tsx b/ui/litellm-dashboard/src/components/OldTeams.tsx
index 5e40f9e257d..56c73e86914 100644
--- a/ui/litellm-dashboard/src/components/OldTeams.tsx
+++ b/ui/litellm-dashboard/src/components/OldTeams.tsx
@@ -1,18 +1,8 @@
 import React, { useState, useEffect } from "react";
 import { Typography } from "antd";
-import {
-  teamDeleteCall,
-  Organization,
-  fetchMCPAccessGroups,
-} from "./networking";
+import { teamDeleteCall, Organization, fetchMCPAccessGroups } from "./networking";
 import { fetchTeams } from "./common_components/fetch_teams";
-import {
-  PencilAltIcon,
-  RefreshIcon,
-  TrashIcon,
-  ChevronDownIcon,
-  ChevronRightIcon,
-} from "@heroicons/react/outline";
+import { PencilAltIcon, RefreshIcon, TrashIcon, ChevronDownIcon, ChevronRightIcon } from "@heroicons/react/outline";
 import { Button as Button2, Modal, Form, Input, Select as Select2, Tooltip } from "antd";
 import NumericalInput from "./shared/numerical_input";
 import {
@@ -87,11 +77,7 @@ interface EditTeamModalProps {
   onSubmit: (data: FormData) => void; // Assuming FormData is the type of data to be submitted
 }
 
-import {
-  teamCreateCall,
-  Member,
-  v2TeamListCall,
-} from "./networking";
+import { teamCreateCall, Member, v2TeamListCall } from "./networking";
 import { updateExistingKeys } from "@/utils/dataUtils";
 
 interface TeamInfo {
@@ -125,7 +111,7 @@ const getOrganizationModels = (organization: Organization | null, userModels: st
 const canCreateOrManageTeams = (
   userRole: string | null,
   userID: string | null,
-  organizations: Organization[] | null
+  organizations: Organization[] | null,
 ): boolean => {
   // Admin role always has permission
   if (userRole === "Admin") {
@@ -135,7 +121,7 @@ const canCreateOrManageTeams = (
   // Check if user is an org_admin in any organization
   if (organizations && userID) {
     return organizations.some((org) =>
-      org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin")
+      org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin"),
     );
   }
 
@@ -145,7 +131,7 @@ const canCreateOrManageTeams = (
 const getAdminOrganizations = (
   userRole: string | null,
   userID: string | null,
-  organizations: Organization[] | null
+  organizations: Organization[] | null,
 ): Organization[] => {
   // Global Admin can see all organizations
   if (userRole === "Admin") {
@@ -155,7 +141,7 @@ const getAdminOrganizations = (
   // Org Admin can only see organizations they're an admin for
   if (organizations && userID) {
     return organizations.filter((org) =>
-      org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin")
+      org.members?.some((member) => member.user_id === userID && member.user_role === "org_admin"),
     );
   }
 
@@ -235,7 +221,7 @@ const Teams: React.FC<TeamProps> = ({
   useEffect(() => {
     if (isTeamModalVisible) {
       const adminOrgs = getAdminOrganizations(userRole, userID, organizations);
-      
+
       // If there's exactly one organization the user is admin for, preselect it
       if (adminOrgs.length === 1) {
         const org = adminOrgs[0];
@@ -347,7 +333,7 @@ const Teams: React.FC<TeamProps> = ({
     try {
       await teamDeleteCall(accessToken, teamToDelete);
       // Successfully completed the deletion. Update the state to trigger a rerender.
-      fetchTeams(accessToken, userID, userRole, currentOrg, setTeams);
+      await fetchTeams(accessToken, userID, userRole, currentOrg, setTeams);
     } catch (error) {
       console.error("Error deleting the team:", error);
       // Handle any error situations, such as displaying an error message to the user.
@@ -356,12 +342,14 @@ const Teams: React.FC<TeamProps> = ({
     // Close the confirmation modal and reset the teamToDelete
     setIsDeleteModalOpen(false);
     setTeamToDelete(null);
+    setDeleteConfirmInput("");
   };
 
   const cancelDelete = () => {
     // Close the confirmation modal and reset the teamToDelete
     setIsDeleteModalOpen(false);
     setTeamToDelete(null);
+    setDeleteConfirmInput("");
   };
 
   useEffect(() => {
@@ -961,6 +949,7 @@ const Teams: React.FC<TeamProps> = ({
                                               onClick={() => handleDelete(team.team_id)}
                                               icon={TrashIcon}
                                               size="sm"
+                                              data-testid="delete-team-button"
                                             />
                                           </>
                                         ) : null}
@@ -1155,15 +1144,11 @@ const Teams: React.FC<TeamProps> = ({
                             showSearch
                             allowClear={!isOrgAdmin}
                             disabled={isSingleOrg}
-                            placeholder={
-                              hasNoOrgs
-                                ? "No organizations available"
-                                : "Search or select an Organization"
-                            }
+                            placeholder={hasNoOrgs ? "No organizations available" : "Search or select an Organization"}
                             onChange={(value) => {
                               form.setFieldValue("organization_id", value);
                               setCurrentOrgForCreateTeam(
-                                adminOrgs?.find((org) => org.organization_id === value) || null
+                                adminOrgs?.find((org) => org.organization_id === value) || null,
                               );
                             }}
                             filterOption={(input, option) => {
diff --git a/ui/litellm-dashboard/src/components/SSOModals.test.tsx b/ui/litellm-dashboard/src/components/SSOModals.test.tsx
index 002672d477c..a2e979d9eb0 100644
--- a/ui/litellm-dashboard/src/components/SSOModals.test.tsx
+++ b/ui/litellm-dashboard/src/components/SSOModals.test.tsx
@@ -91,12 +91,16 @@ describe("SSOModals", () => {
     fireEvent.click(saveButton);
 
     // Check for validation error
-    await waitFor(() => {
-      expect(getByText("URL must start with http:// or https://")).toBeInTheDocument();
-    });
+    await waitFor(
+      () => {
+        expect(getByText("URL must start with http:// or https://")).toBeInTheDocument();
+      },
+      // The validation is based on a Promise, so we need to wait for it to resolve
+      { timeout: 5000 },
+    );
   });
 
-  it("should automatically remove trailing slash from the proxy base url", async () => {
+  it("should show validation error if the proxy base url ends with a trailing slash", async () => {
     const TestWrapper = () => {
       const [form] = Form.useForm();
       return (
@@ -115,18 +119,137 @@ describe("SSOModals", () => {
       );
     };
 
-    const { getByLabelText, container } = render(<TestWrapper />);
+    const { getByLabelText, getByText, container } = render(<TestWrapper />);
 
-    // Fill in the proxy base url with a trailing slash
+    // Find and interact with the SSO provider select
+    const ssoProviderSelect = container.querySelector("#sso_provider");
+    if (ssoProviderSelect) {
+      fireEvent.mouseDown(ssoProviderSelect);
+      // Wait for dropdown and select Google
+      await waitFor(() => {
+        const googleOption = getByText("Google SSO");
+        fireEvent.click(googleOption);
+      });
+    }
+
+    // Fill in the email field
+    const emailInput = getByLabelText("Proxy Admin Email");
+    fireEvent.change(emailInput, { target: { value: "test@example.com" } });
+
+    // Fill in a URL with trailing slash
     const urlInput = getByLabelText("PROXY BASE URL") as HTMLInputElement;
     fireEvent.change(urlInput, { target: { value: "https://example.com/" } });
 
-    // Trigger blur to ensure normalization is applied
-    fireEvent.blur(urlInput);
+    // Submit the form
+    const saveButton = getByText("Save");
+    fireEvent.click(saveButton);
 
-    // Check that the trailing slash was removed by the normalize function
+    // Check for validation error
     await waitFor(() => {
-      expect(urlInput.value).toBe("https://example.com");
+      expect(getByText("URL must not end with a trailing slash")).toBeInTheDocument();
     });
   });
+
+  it("should allow typing https:// without interfering with slashes", async () => {
+    const TestWrapper = () => {
+      const [form] = Form.useForm();
+      return (
+        <SSOModals
+          isAddSSOModalVisible={true}
+          isInstructionsModalVisible={false}
+          handleAddSSOOk={() => {}}
+          handleAddSSOCancel={() => {}}
+          handleShowInstructions={() => {}}
+          handleInstructionsOk={() => {}}
+          handleInstructionsCancel={() => {}}
+          form={form}
+          accessToken={null}
+          ssoConfigured={false}
+        />
+      );
+    };
+
+    const { getByLabelText } = render(<TestWrapper />);
+
+    const urlInput = getByLabelText("PROXY BASE URL") as HTMLInputElement;
+
+    // Simulate user typing "https://"
+    fireEvent.change(urlInput, { target: { value: "h" } });
+    expect(urlInput.value).toBe("h");
+
+    fireEvent.change(urlInput, { target: { value: "ht" } });
+    expect(urlInput.value).toBe("ht");
+
+    fireEvent.change(urlInput, { target: { value: "http" } });
+    expect(urlInput.value).toBe("http");
+
+    fireEvent.change(urlInput, { target: { value: "https" } });
+    expect(urlInput.value).toBe("https");
+
+    fireEvent.change(urlInput, { target: { value: "https:" } });
+    expect(urlInput.value).toBe("https:");
+
+    fireEvent.change(urlInput, { target: { value: "https:/" } });
+    expect(urlInput.value).toBe("https:/");
+
+    fireEvent.change(urlInput, { target: { value: "https://" } });
+    expect(urlInput.value).toBe("https://");
+
+    // Continue typing the domain
+    fireEvent.change(urlInput, { target: { value: "https://example.com" } });
+    expect(urlInput.value).toBe("https://example.com");
+  });
+
+  it("should only show URL format error for incomplete URLs, not trailing slash error", async () => {
+    const TestWrapper = () => {
+      const [form] = Form.useForm();
+      return (
+        <SSOModals
+          isAddSSOModalVisible={true}
+          isInstructionsModalVisible={false}
+          handleAddSSOOk={() => {}}
+          handleAddSSOCancel={() => {}}
+          handleShowInstructions={() => {}}
+          handleInstructionsOk={() => {}}
+          handleInstructionsCancel={() => {}}
+          form={form}
+          accessToken={null}
+          ssoConfigured={false}
+        />
+      );
+    };
+
+    const { getByLabelText, getByText, queryByText, container } = render(<TestWrapper />);
+
+    // Find and interact with the SSO provider select
+    const ssoProviderSelect = container.querySelector("#sso_provider");
+    if (ssoProviderSelect) {
+      fireEvent.mouseDown(ssoProviderSelect);
+      // Wait for dropdown and select Google
+      await waitFor(() => {
+        const googleOption = getByText("Google SSO");
+        fireEvent.click(googleOption);
+      });
+    }
+
+    // Fill in the email field
+    const emailInput = getByLabelText("Proxy Admin Email");
+    fireEvent.change(emailInput, { target: { value: "test@example.com" } });
+
+    // Fill in an incomplete URL like "http:"
+    const urlInput = getByLabelText("PROXY BASE URL");
+    fireEvent.change(urlInput, { target: { value: "http:" } });
+
+    // Submit the form
+    const saveButton = getByText("Save");
+    fireEvent.click(saveButton);
+
+    // Check that only the URL format error appears
+    await waitFor(() => {
+      expect(getByText("URL must start with http:// or https://")).toBeInTheDocument();
+    });
+
+    // Verify the trailing slash error does NOT appear
+    expect(queryByText("URL must not end with a trailing slash")).not.toBeInTheDocument();
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/SSOModals.tsx b/ui/litellm-dashboard/src/components/SSOModals.tsx
index 97b015f63b2..437e4b1776f 100644
--- a/ui/litellm-dashboard/src/components/SSOModals.tsx
+++ b/ui/litellm-dashboard/src/components/SSOModals.tsx
@@ -3,6 +3,7 @@ import { Modal, Form, Input, Button as Button2, Select } from "antd";
 import { Text, TextInput } from "@tremor/react";
 import { getSSOSettings, updateSSOSettings } from "./networking";
 import NotificationsManager from "./molecules/notifications_manager";
+import { parseErrorMessage } from "./shared/errorUtils";
 
 interface SSOModalsProps {
   isAddSSOModalVisible: boolean;
@@ -182,9 +183,8 @@ const SSOModals: React.FC<SSOModalsProps> = ({
 
       // Continue with the original flow (show instructions)
       handleShowInstructions(formValues);
-    } catch (error) {
-      console.error("Failed to save SSO settings:", error);
-      NotificationsManager.fromBackend("Failed to save SSO settings");
+    } catch (error: unknown) {
+      NotificationsManager.fromBackend("Failed to save SSO settings: " + parseErrorMessage(error));
     }
   };
 
@@ -309,7 +309,7 @@ const SSOModals: React.FC<SSOModalsProps> = ({
             <Form.Item
               label="PROXY BASE URL"
               name="proxy_base_url"
-              normalize={(value) => value?.trim().replace(/\/+$/, "")}
+              normalize={(value) => value?.trim()}
               rules={[
                 { required: true, message: "Please enter the proxy base url" },
                 {
@@ -317,8 +317,13 @@ const SSOModals: React.FC<SSOModalsProps> = ({
                   message: "URL must start with http:// or https://",
                 },
                 {
-                  pattern: /^https?:\/\/[^\s]+[^\/]$/,
-                  message: "URL must not end with a trailing slash",
+                  validator: (_, value) => {
+                    // Only check for trailing slash if the URL starts with http:// or https://
+                    if (value && /^https?:\/\/.+/.test(value) && value.endsWith("/")) {
+                      return Promise.reject("URL must not end with a trailing slash");
+                    }
+                    return Promise.resolve();
+                  },
                 },
               ]}
             >
diff --git a/ui/litellm-dashboard/src/components/add_model/add_model_modes.tsx b/ui/litellm-dashboard/src/components/add_model/add_model_modes.tsx
index b6c2410a027..30deb79469c 100644
--- a/ui/litellm-dashboard/src/components/add_model/add_model_modes.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/add_model_modes.tsx
@@ -6,6 +6,7 @@ export const TEST_MODES = [
   { value: "audio_speech", label: "Audio Speech - /audio/speech" },
   { value: "audio_transcription", label: "Audio Transcription - /audio/transcriptions" },
   { value: "image_generation", label: "Image Generation - /images/generations" },
+  { value: "video_generation", label: "Video Generation - /videos" },
   { value: "rerank", label: "Rerank - /rerank" },
   { value: "realtime", label: "Realtime - /realtime" },
   { value: "batch", label: "Batch - /batch" },
diff --git a/ui/litellm-dashboard/src/components/cache_settings/CacheFieldGroup.test.tsx b/ui/litellm-dashboard/src/components/cache_settings/CacheFieldGroup.test.tsx
index 9809bec743e..6eba0718948 100644
--- a/ui/litellm-dashboard/src/components/cache_settings/CacheFieldGroup.test.tsx
+++ b/ui/litellm-dashboard/src/components/cache_settings/CacheFieldGroup.test.tsx
@@ -1,8 +1,21 @@
-import { describe, it, expect, beforeEach } from "vitest";
+import { describe, it, expect, beforeEach, vi } from "vitest";
 import { render, screen, cleanup } from "@testing-library/react";
 import CacheFieldGroup from "./CacheFieldGroup";
 
 describe("CacheFieldGroup", () => {
+  vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+    default: () => ({
+      token: "mock-token",
+      accessToken: "mock-access-token",
+      userId: "mock-user-id",
+      userEmail: "test@example.com",
+      userRole: "Admin",
+      premiumUser: false,
+      disabledPersonalKeyCreation: false,
+      showSSOBanner: false,
+    }),
+  }));
+
   beforeEach(() => {
     cleanup();
   });
@@ -40,12 +53,7 @@ describe("CacheFieldGroup", () => {
 
     // Test with cluster type - should show host and redis_startup_nodes
     const { rerender } = render(
-      <CacheFieldGroup
-        title="Cluster Settings"
-        fields={fields}
-        cacheSettings={cacheSettings}
-        redisType="cluster"
-      />
+      <CacheFieldGroup title="Cluster Settings" fields={fields} cacheSettings={cacheSettings} redisType="cluster" />,
     );
 
     expect(screen.getByText("Cluster Settings")).toBeInTheDocument();
@@ -55,12 +63,7 @@ describe("CacheFieldGroup", () => {
 
     // Test with sentinel type - should show host and sentinel_nodes
     rerender(
-      <CacheFieldGroup
-        title="Sentinel Settings"
-        fields={fields}
-        cacheSettings={cacheSettings}
-        redisType="sentinel"
-      />
+      <CacheFieldGroup title="Sentinel Settings" fields={fields} cacheSettings={cacheSettings} redisType="sentinel" />,
     );
 
     expect(screen.getByText("Sentinel Settings")).toBeInTheDocument();
@@ -69,14 +72,7 @@ describe("CacheFieldGroup", () => {
     expect(screen.queryByText("Startup Nodes")).not.toBeInTheDocument();
 
     // Test with node type - should only show host
-    rerender(
-      <CacheFieldGroup
-        title="Node Settings"
-        fields={fields}
-        cacheSettings={cacheSettings}
-        redisType="node"
-      />
-    );
+    rerender(<CacheFieldGroup title="Node Settings" fields={fields} cacheSettings={cacheSettings} redisType="node" />);
 
     expect(screen.getByText("Node Settings")).toBeInTheDocument();
     expect(screen.getAllByText("Host")).toHaveLength(1);
@@ -106,7 +102,7 @@ describe("CacheFieldGroup", () => {
         fields={fields}
         cacheSettings={cacheSettings}
         redisType="node" // No fields match this type
-      />
+      />,
     );
 
     // Component should return null, so container should be empty
@@ -131,12 +127,7 @@ describe("CacheFieldGroup", () => {
     const cacheSettings = {}; // No port value set
 
     render(
-      <CacheFieldGroup
-        title="Connection Settings"
-        fields={fields}
-        cacheSettings={cacheSettings}
-        redisType="node"
-      />
+      <CacheFieldGroup title="Connection Settings" fields={fields} cacheSettings={cacheSettings} redisType="node" />,
     );
 
     const input = screen.getByRole("spinbutton", { name: "" });
@@ -145,4 +136,3 @@ describe("CacheFieldGroup", () => {
     expect(input).toHaveValue(6379);
   });
 });
-
diff --git a/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.test.tsx b/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.test.tsx
index 67b4cadfd5f..3cfacac7fda 100644
--- a/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.test.tsx
+++ b/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.test.tsx
@@ -1,7 +1,21 @@
-import { describe, it, expect } from "vitest";
+import { describe, it, expect, vi } from "vitest";
 import { render, screen } from "@testing-library/react";
 import CacheFieldRenderer from "./CacheFieldRenderer";
 
+// Mock the useAuthorized hook to avoid Next.js router dependency
+vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+  default: () => ({
+    token: "mock-token",
+    accessToken: "mock-access-token",
+    userId: "mock-user-id",
+    userEmail: "test@example.com",
+    userRole: "Admin",
+    premiumUser: false,
+    disabledPersonalKeyCreation: false,
+    showSSOBanner: false,
+  }),
+}));
+
 describe("CacheFieldRenderer", () => {
   it("should render a checkbox for Boolean field type", () => {
     /**
@@ -110,4 +124,3 @@ describe("CacheFieldRenderer", () => {
     expect(input).toHaveValue("localhost");
   });
 });
-
diff --git a/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.tsx b/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.tsx
index e711376138d..f2f316fb146 100644
--- a/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.tsx
+++ b/ui/litellm-dashboard/src/components/cache_settings/CacheFieldRenderer.tsx
@@ -1,21 +1,45 @@
-import React from "react";
-import { TextInput, NumberInput } from "@tremor/react";
+"use client";
+
+import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
+import { NumberInput, TextInput } from "@tremor/react";
+import { Select } from "antd";
+import React, { useEffect, useState } from "react";
+import { fetchAvailableModels, ModelGroup } from "../chat_ui/llm_calls/fetch_models";
+import NumericalInput from "../shared/numerical_input";
 
 interface CacheFieldRendererProps {
   field: any;
   currentValue: any;
 }
 
-const CacheFieldRenderer: React.FC<CacheFieldRendererProps> = ({
-  field,
-  currentValue,
-}) => {
+const CacheFieldRenderer: React.FC<CacheFieldRendererProps> = ({ field, currentValue }) => {
+  const [modelInfo, setModelInfo] = useState<ModelGroup[]>([]);
+  const [selectedModel, setSelectedModel] = useState<string>(currentValue || "");
+  const { accessToken } = useAuthorized();
+
+  useEffect(() => {
+    if (!accessToken) return;
+
+    const loadModels = async () => {
+      try {
+        const uniqueModels = await fetchAvailableModels(accessToken);
+        console.log("Fetched models for selector:", uniqueModels);
+
+        if (uniqueModels.length > 0) {
+          setModelInfo(uniqueModels);
+        }
+      } catch (error) {
+        console.error("Error fetching model info:", error);
+      }
+    };
+
+    loadModels();
+  }, [accessToken]);
+
   if (field.field_type === "Boolean") {
     return (
       <div className="space-y-2">
-        <label className="text-sm font-medium text-gray-700">
-          {field.ui_field_name}
-        </label>
+        <label className="text-sm font-medium text-gray-700">{field.ui_field_name}</label>
         <div className="flex items-center">
           <input
             type="checkbox"
@@ -29,12 +53,25 @@ const CacheFieldRenderer: React.FC<CacheFieldRendererProps> = ({
     );
   }
 
+  if (field.field_type === "Integer" || field.field_type === "Float") {
+    return (
+      <div className="space-y-2">
+        <label className="text-sm font-medium text-gray-700">{field.ui_field_name}</label>
+        <NumericalInput
+          name={field.field_name}
+          type="number"
+          defaultValue={currentValue}
+          placeholder={field.field_description}
+        />
+        <p className="text-xs text-gray-500">{field.field_description}</p>
+      </div>
+    );
+  }
+
   if (field.field_type === "List") {
     return (
       <div className="space-y-2">
-        <label className="text-sm font-medium text-gray-700">
-          {field.ui_field_name}
-        </label>
+        <label className="text-sm font-medium text-gray-700">{field.ui_field_name}</label>
         <textarea
           name={field.field_name}
           defaultValue={typeof currentValue === "object" ? JSON.stringify(currentValue, null, 2) : currentValue}
@@ -47,49 +84,66 @@ const CacheFieldRenderer: React.FC<CacheFieldRendererProps> = ({
     );
   }
 
+  if (field.field_type === "Models_Select") {
+    const embeddingModels = modelInfo
+      .filter((option: ModelGroup) => option.mode === "embedding")
+      .map((option: ModelGroup) => ({
+        value: option.model_group,
+        label: option.model_group,
+      }));
+
+    return (
+      <div className="space-y-2">
+        <label className="text-sm font-medium text-gray-700">{field.ui_field_name}</label>
+        <Select
+          value={selectedModel}
+          onChange={setSelectedModel}
+          showSearch={true}
+          placeholder="Search and select a model..."
+          options={embeddingModels}
+          style={{ width: "100%" }}
+          className="rounded-md"
+          filterOption={(input, option) => (option?.label ?? "").toLowerCase().includes(input.toLowerCase())}
+        />
+        {/* Hidden input to capture the value for form submission */}
+        <input type="hidden" name={field.field_name} value={selectedModel} />
+        {field.field_description && <p className="text-xs text-gray-500">{field.field_description}</p>}
+      </div>
+    );
+  }
+
   // Render number input for numeric fields
   if (field.field_type === "Integer" || field.field_type === "Float") {
     return (
       <div className="space-y-2">
-        <label className="text-sm font-medium text-gray-700">
-          {field.ui_field_name}
-        </label>
+        <label className="text-sm font-medium text-gray-700">{field.ui_field_name}</label>
         <NumberInput
           name={field.field_name}
           defaultValue={currentValue}
           placeholder={field.field_description}
           step={field.field_type === "Float" ? 0.01 : 1}
         />
-        {field.field_description && (
-          <p className="text-xs text-gray-500">{field.field_description}</p>
-        )}
+        {field.field_description && <p className="text-xs text-gray-500">{field.field_description}</p>}
       </div>
     );
   }
 
   // Determine input type for text-based fields
-  const inputType: "text" | "password" | "email" | "url" | undefined = 
-    (field.field_name === "password" || field.field_name.includes("password")) 
-      ? "password" 
-      : "text";
+  const inputType: "text" | "password" | "email" | "url" | undefined =
+    field.field_name === "password" || field.field_name.includes("password") ? "password" : "text";
 
   return (
     <div className="space-y-2">
-      <label className="text-sm font-medium text-gray-700">
-        {field.ui_field_name}
-      </label>
+      <label className="text-sm font-medium text-gray-700">{field.ui_field_name}</label>
       <TextInput
         name={field.field_name}
         type={inputType}
         defaultValue={currentValue}
         placeholder={field.field_description}
       />
-      {field.field_description && (
-        <p className="text-xs text-gray-500">{field.field_description}</p>
-      )}
+      {field.field_description && <p className="text-xs text-gray-500">{field.field_description}</p>}
     </div>
   );
 };
 
 export default CacheFieldRenderer;
-
diff --git a/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.test.tsx b/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.test.tsx
new file mode 100644
index 00000000000..9d4d5a6d425
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.test.tsx
@@ -0,0 +1,12 @@
+import { describe, expect, it } from "vitest";
+import RedisTypeSelector from "./RedisTypeSelector";
+import { render } from "@testing-library/react";
+
+describe("RedisTypeSelector", () => {
+  it("should render the component", () => {
+    const { getAllByText } = render(
+      <RedisTypeSelector redisType="redis" redisTypeDescriptions={{}} onTypeChange={() => {}} />,
+    );
+    expect(getAllByText(/Redis/i).length).toBeGreaterThan(0);
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.tsx b/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.tsx
index 08a054b7440..a94d44dd4e0 100644
--- a/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.tsx
+++ b/ui/litellm-dashboard/src/components/cache_settings/RedisTypeSelector.tsx
@@ -7,11 +7,7 @@ interface RedisTypeSelectorProps {
   onTypeChange: (type: string) => void;
 }
 
-const RedisTypeSelector: React.FC<RedisTypeSelectorProps> = ({
-  redisType,
-  redisTypeDescriptions,
-  onTypeChange,
-}) => {
+const RedisTypeSelector: React.FC<RedisTypeSelectorProps> = ({ redisType, redisTypeDescriptions, onTypeChange }) => {
   return (
     <div className="space-y-2">
       <label className="text-sm font-medium text-gray-700">Redis Type</label>
@@ -19,14 +15,13 @@ const RedisTypeSelector: React.FC<RedisTypeSelectorProps> = ({
         <SelectItem value="node">Node (Single Instance)</SelectItem>
         <SelectItem value="cluster">Cluster</SelectItem>
         <SelectItem value="sentinel">Sentinel</SelectItem>
+        <SelectItem value="semantic">Semantic</SelectItem>
       </Select>
       <p className="text-xs text-gray-500">
-        {redisTypeDescriptions[redisType] || 
-         "Select the type of Redis deployment you're using"}
+        {redisTypeDescriptions[redisType] || "Select the type of Redis deployment you're using"}
       </p>
     </div>
   );
 };
 
 export default RedisTypeSelector;
-
diff --git a/ui/litellm-dashboard/src/components/cache_settings/cacheSettingsUtils.ts b/ui/litellm-dashboard/src/components/cache_settings/cacheSettingsUtils.ts
index 748c1debb1e..b7554136ad4 100644
--- a/ui/litellm-dashboard/src/components/cache_settings/cacheSettingsUtils.ts
+++ b/ui/litellm-dashboard/src/components/cache_settings/cacheSettingsUtils.ts
@@ -7,37 +7,36 @@ export const shouldShowField = (field: any, redisType: string): boolean => {
   if (field.redis_type === null || field.redis_type === undefined) {
     return true;
   }
-  
+
   return field.redis_type === redisType;
 };
 
 export const getFieldByName = (fields: any[], fieldName: string) => {
-  return fields.find(f => f.field_name === fieldName);
+  return fields.find((f) => f.field_name === fieldName);
 };
 
 export const groupFieldsByCategory = (fields: any[], redisType: string) => {
   // Basic fields that are always shown
   const basicFieldNames = ["host", "port", "password", "username"];
-  const basicFields = basicFieldNames
-    .map(name => getFieldByName(fields, name))
-    .filter(Boolean);
+  const basicFields = basicFieldNames.map((name) => getFieldByName(fields, name)).filter(Boolean);
 
   // Advanced field groups
   const sslFields = ["ssl", "ssl_cert_reqs", "ssl_check_hostname"]
-    .map(name => getFieldByName(fields, name))
+    .map((name) => getFieldByName(fields, name))
     .filter(Boolean);
-  
+
   const cacheManagementFields = ["namespace", "ttl", "max_connections"]
-    .map(name => getFieldByName(fields, name))
+    .map((name) => getFieldByName(fields, name))
     .filter(Boolean);
-  
+
   const gcpFields = ["gcp_service_account", "gcp_ssl_ca_certs"]
-    .map(name => getFieldByName(fields, name))
+    .map((name) => getFieldByName(fields, name))
     .filter(Boolean);
 
   // Redis type-specific fields
-  const clusterFields = fields.filter(f => f.redis_type === "cluster");
-  const sentinelFields = fields.filter(f => f.redis_type === "sentinel");
+  const clusterFields = fields.filter((f) => f.redis_type === "cluster");
+  const sentinelFields = fields.filter((f) => f.redis_type === "sentinel");
+  const semanticFields = fields.filter((f) => f.redis_type === "semantic");
 
   return {
     basicFields,
@@ -46,6 +45,7 @@ export const groupFieldsByCategory = (fields: any[], redisType: string) => {
     gcpFields,
     clusterFields,
     sentinelFields,
+    semanticFields,
   };
 };
 
@@ -108,4 +108,3 @@ export const gatherFormValues = (fields: any[], redisType: string): { [key: stri
 
   return values;
 };
-
diff --git a/ui/litellm-dashboard/src/components/cache_settings/index.tsx b/ui/litellm-dashboard/src/components/cache_settings/index.tsx
index d7581041ca8..c7d8c579af3 100644
--- a/ui/litellm-dashboard/src/components/cache_settings/index.tsx
+++ b/ui/litellm-dashboard/src/components/cache_settings/index.tsx
@@ -1,10 +1,6 @@
-import React, { useState, useEffect } from "react";
+import React, { useState, useEffect, useCallback } from "react";
 import { Button, Accordion, AccordionHeader, AccordionBody } from "@tremor/react";
-import {
-  getCacheSettingsCall,
-  testCacheConnectionCall,
-  updateCacheSettingsCall,
-} from "../networking";
+import { getCacheSettingsCall, testCacheConnectionCall, updateCacheSettingsCall } from "../networking";
 import NotificationsManager from "../molecules/notifications_manager";
 import RedisTypeSelector from "./RedisTypeSelector";
 import CacheFieldRenderer from "./CacheFieldRenderer";
@@ -24,18 +20,11 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
   const [isTesting, setIsTesting] = useState<boolean>(false);
   const [isSaving, setIsSaving] = useState<boolean>(false);
 
-  useEffect(() => {
-    if (!accessToken) {
-      return;
-    }
-    loadCacheSettings();
-  }, [accessToken]);
-
-  const loadCacheSettings = async () => {
+  const loadCacheSettings = useCallback(async () => {
     try {
       const data = await getCacheSettingsCall(accessToken!);
       console.log("cache settings from API", data);
-      
+
       if (data.fields) {
         setFields(data.fields);
       }
@@ -56,7 +45,14 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
       console.error("Failed to load cache settings:", error);
       NotificationsManager.fromBackend("Failed to load cache settings");
     }
-  };
+  }, [accessToken]);
+
+  useEffect(() => {
+    if (!accessToken) {
+      return;
+    }
+    loadCacheSettings();
+  }, [accessToken, loadCacheSettings]);
 
   const handleTestConnection = async () => {
     if (!accessToken) {
@@ -67,7 +63,7 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
     try {
       const testSettings = gatherFormValues(fields, redisType);
       const result = await testCacheConnectionCall(accessToken, testSettings);
-      
+
       if (result.status === "success") {
         NotificationsManager.success("Cache connection test successful!");
       } else {
@@ -89,6 +85,9 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
     setIsSaving(true);
     try {
       const settingsToSave = gatherFormValues(fields, redisType);
+      if (redisType === "semantic") {
+        settingsToSave.type = "redis-semantic";
+      }
       await updateCacheSettingsCall(accessToken, settingsToSave);
       NotificationsManager.success("Cache settings updated successfully");
       // Reload settings to reflect saved values
@@ -105,14 +104,8 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
     return null;
   }
 
-  const {
-    basicFields,
-    sslFields,
-    cacheManagementFields,
-    gcpFields,
-    clusterFields,
-    sentinelFields,
-  } = groupFieldsByCategory(fields, redisType);
+  const { basicFields, sslFields, cacheManagementFields, gcpFields, clusterFields, sentinelFields, semanticFields } =
+    groupFieldsByCategory(fields, redisType);
 
   return (
     <div className="w-full space-y-8 py-2">
@@ -136,13 +129,7 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
             {basicFields.map((field: any) => {
               if (!field) return null;
               const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
-              return (
-                <CacheFieldRenderer
-                  key={field.field_name}
-                  field={field}
-                  currentValue={currentValue}
-                />
-              );
+              return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
             })}
           </div>
         </div>
@@ -154,13 +141,7 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
             <div className="grid grid-cols-1 gap-6">
               {clusterFields.map((field: any) => {
                 const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
-                return (
-                  <CacheFieldRenderer
-                    key={field.field_name}
-                    field={field}
-                    currentValue={currentValue}
-                  />
-                );
+                return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
               })}
             </div>
           </div>
@@ -172,13 +153,19 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
             <div className="grid grid-cols-1 gap-6 sm:grid-cols-2">
               {sentinelFields.map((field: any) => {
                 const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
-                return (
-                  <CacheFieldRenderer
-                    key={field.field_name}
-                    field={field}
-                    currentValue={currentValue}
-                  />
-                );
+                return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
+              })}
+            </div>
+          </div>
+        )}
+
+        {redisType === "semantic" && semanticFields.length > 0 && (
+          <div className="space-y-6 pt-4 border-t border-gray-200">
+            <h4 className="text-sm font-medium text-gray-900">Semantic Configuration</h4>
+            <div className="grid grid-cols-1 gap-6 sm:grid-cols-2">
+              {semanticFields.map((field: any) => {
+                const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
+                return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
               })}
             </div>
           </div>
@@ -199,13 +186,7 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
                     {sslFields.map((field: any) => {
                       if (!field) return null;
                       const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
-                      return (
-                        <CacheFieldRenderer
-                          key={field.field_name}
-                          field={field}
-                          currentValue={currentValue}
-                        />
-                      );
+                      return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
                     })}
                   </div>
                 </div>
@@ -219,13 +200,7 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
                     {cacheManagementFields.map((field: any) => {
                       if (!field) return null;
                       const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
-                      return (
-                        <CacheFieldRenderer
-                          key={field.field_name}
-                          field={field}
-                          currentValue={currentValue}
-                        />
-                      );
+                      return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
                     })}
                   </div>
                 </div>
@@ -239,13 +214,7 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
                     {gcpFields.map((field: any) => {
                       if (!field) return null;
                       const currentValue = cacheSettings[field.field_name] ?? field.field_default ?? "";
-                      return (
-                        <CacheFieldRenderer
-                          key={field.field_name}
-                          field={field}
-                          currentValue={currentValue}
-                        />
-                      );
+                      return <CacheFieldRenderer key={field.field_name} field={field} currentValue={currentValue} />;
                     })}
                   </div>
                 </div>
@@ -257,29 +226,10 @@ const CacheSettings: React.FC<CacheSettingsProps> = ({ accessToken, userRole, us
 
       {/* Actions */}
       <div className="border-t border-gray-200 pt-6 flex justify-end gap-3">
-        <Button
-          variant="secondary"
-          size="sm"
-          onClick={handleTestConnection}
-          disabled={isTesting}
-          className="text-sm"
-        >
+        <Button variant="secondary" size="sm" onClick={handleTestConnection} disabled={isTesting} className="text-sm">
           {isTesting ? "Testing..." : "Test Connection"}
         </Button>
-        <Button
-          variant="secondary"
-          size="sm"
-          onClick={() => window.location.reload()}
-          className="text-sm"
-        >
-          Reset
-        </Button>
-        <Button
-          size="sm"
-          onClick={handleSaveChanges}
-          disabled={isSaving}
-          className="text-sm font-medium"
-        >
+        <Button size="sm" onClick={handleSaveChanges} disabled={isSaving} className="text-sm font-medium">
           {isSaving ? "Saving..." : "Save Changes"}
         </Button>
       </div>
diff --git a/ui/litellm-dashboard/src/components/chat_ui/ChatImageRenderer.tsx b/ui/litellm-dashboard/src/components/chat_ui/ChatImageRenderer.tsx
index 254bb26346d..49adaaed8c1 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/ChatImageRenderer.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/ChatImageRenderer.tsx
@@ -1,4 +1,5 @@
 import React from "react";
+import Image from "next/image";
 import { MessageType } from "./types";
 import { shouldShowChatAttachedImage } from "./ChatImageUtils";
 import { FilePdfOutlined } from "@ant-design/icons";
@@ -21,11 +22,13 @@ const ChatImageRenderer: React.FC<ChatImageRendererProps> = ({ message }) => {
           <FilePdfOutlined style={{ fontSize: "48px", color: "#dc2626" }} />
         </div>
       ) : (
-        <img
-          src={message.imagePreviewUrl}
+        <Image
+          src={message.imagePreviewUrl || ""}
           alt="User uploaded image"
+          width={256}
+          height={200}
           className="max-w-64 rounded-md border border-gray-200 shadow-sm"
-          style={{ maxHeight: "200px" }}
+          style={{ maxHeight: "200px", width: "auto", height: "auto" }}
         />
       )}
     </div>
diff --git a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.test.tsx b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.test.tsx
index 3a2b4860eaa..6670395aad5 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.test.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.test.tsx
@@ -49,197 +49,7 @@ describe("ChatUI", () => {
         disabledPersonalKeyCreation={false}
       />,
     );
-
-    await waitFor(() => {
-      expect(getByText("Test Key")).toBeInTheDocument();
-    });
-  });
-
-  it("should render the chat UI with selected models", async () => {
-    const { getByText, container, getAllByText } = render(
-      <ChatUI
-        accessToken="1234567890"
-        token="1234567890"
-        userRole="user"
-        userID="1234567890"
-        disabledPersonalKeyCreation={false}
-      />,
-    );
-
-    await waitFor(() => {
-      expect(getByText("Test Key")).toBeInTheDocument();
-    });
-
-    // Wait for the first model to be auto-selected (default behavior)
-    await waitFor(() => {
-      expect(getByText("Model 1")).toBeInTheDocument();
-    });
-
-    // Now click the Select dropdown to open it and check other models
-    const selectComponent = container.querySelectorAll(".ant-select-selector")[1]; // Second select is for models
-    expect(selectComponent).toBeTruthy();
-
-    // Click to open the dropdown
-    fireEvent.mouseDown(selectComponent!);
-
-    // Wait for dropdown to open and check for all models
-    await waitFor(() => {
-      const dropdown = document.querySelector(".ant-select-dropdown");
-      expect(dropdown).toBeTruthy();
-
-      // All three models should be in the dropdown as options
-      // Use getAllByTitle since Ant Design Select renders options with title attributes
-      const model2Elements = screen.queryAllByText("Model 2");
-      const model3Elements = screen.queryAllByText("Model 3");
-
-      expect(model2Elements.length).toBeGreaterThan(0);
-      expect(model3Elements.length).toBeGreaterThan(0);
-    });
-  });
-
-  it("should allow users to pick multiple models and render badges for selected models", async () => {
-    const { getByText, container } = render(
-      <ChatUI
-        accessToken="1234567890"
-        token="1234567890"
-        userRole="user"
-        userID="1234567890"
-        disabledPersonalKeyCreation={false}
-      />,
-    );
-
-    await waitFor(() => {
-      expect(getByText("Test Key")).toBeInTheDocument();
-    });
-
-    await waitFor(() => {
-      expect(getByText("Model 1")).toBeInTheDocument();
-    });
-
-    const selectComponent = container.querySelectorAll(".ant-select-selector")[1];
-    expect(selectComponent).toBeTruthy();
-
-    fireEvent.mouseDown(selectComponent!);
-
-    let model2Option: HTMLElement | null = null;
-    await waitFor(() => {
-      const dropdown = document.querySelector(".ant-select-dropdown");
-      expect(dropdown).toBeTruthy();
-      const options = document.querySelectorAll(".ant-select-item-option-content");
-      model2Option = Array.from(options).find((opt) => opt.textContent === "Model 2") as HTMLElement;
-      expect(model2Option).toBeTruthy();
-    });
-
-    fireEvent.click(model2Option!);
-
-    await waitFor(() => {
-      const model1Badges = screen.getAllByText("Model 1");
-      const model2Badges = screen.getAllByText("Model 2");
-
-      expect(model1Badges.length).toBeGreaterThan(0);
-      expect(model2Badges.length).toBeGreaterThan(0);
-    });
-
-    fireEvent.mouseDown(selectComponent!);
-
-    let model3Option: HTMLElement | null = null;
-    await waitFor(() => {
-      const dropdown = document.querySelector(".ant-select-dropdown");
-      expect(dropdown).toBeTruthy();
-      const options = document.querySelectorAll(".ant-select-item-option-content");
-      model3Option = Array.from(options).find((opt) => opt.textContent === "Model 3") as HTMLElement;
-      expect(model3Option).toBeTruthy();
-    });
-
-    fireEvent.click(model3Option!);
-
-    // Verify all three models have badges rendered
-    await waitFor(() => {
-      const model1Badges = screen.getAllByText("Model 1");
-      const model2Badges = screen.getAllByText("Model 2");
-      const model3Badges = screen.getAllByText("Model 3");
-
-      expect(model1Badges.length).toBeGreaterThan(0);
-      expect(model2Badges.length).toBeGreaterThan(0);
-      expect(model3Badges.length).toBeGreaterThan(0);
-    });
-    const tags = container.querySelectorAll(".ant-tag");
-    expect(tags.length).toBe(3);
-  });
-
-  it("should manage selectedModels state and its side effects", async () => {
-    const { getByText, container } = render(
-      <ChatUI
-        accessToken="1234567890"
-        token="1234567890"
-        userRole="user"
-        userID="1234567890"
-        disabledPersonalKeyCreation={false}
-      />,
-    );
-
-    await waitFor(() => {
-      expect(getByText("Test Key")).toBeInTheDocument();
-    });
-
-    await waitFor(() => {
-      expect(getByText("Model 1")).toBeInTheDocument();
-    });
-
-    await waitFor(() => {
-      const storedModels = sessionStorage.getItem("selectedModels");
-      expect(storedModels).toBeTruthy();
-      const parsedModels = JSON.parse(storedModels!);
-      expect(parsedModels).toContain("Model 1");
-    });
-
-    const selectComponent = container.querySelectorAll(".ant-select-selector")[1];
-
-    fireEvent.mouseDown(selectComponent!);
-
-    let model2Option: HTMLElement | null = null;
-    await waitFor(() => {
-      const dropdown = document.querySelector(".ant-select-dropdown");
-      expect(dropdown).toBeTruthy();
-      const options = document.querySelectorAll(".ant-select-item-option-content");
-      model2Option = Array.from(options).find((opt) => opt.textContent === "Model 2") as HTMLElement;
-      expect(model2Option).toBeTruthy();
-    });
-
-    fireEvent.click(model2Option!);
-
-    await waitFor(() => {
-      const model2Badges = screen.getAllByText("Model 2");
-      expect(model2Badges.length).toBeGreaterThan(0);
-    });
-
-    await waitFor(() => {
-      const storedModels = sessionStorage.getItem("selectedModels");
-      const parsedModels = JSON.parse(storedModels!);
-      expect(parsedModels).toContain("Model 1");
-      expect(parsedModels).toContain("Model 2");
-      expect(parsedModels.length).toBe(2);
-    });
-
-    const tags = container.querySelectorAll(".ant-tag");
-    expect(tags.length).toBe(2);
-
-    const firstTagCloseButton = tags[0].querySelector(".ant-tag-close-icon");
-    expect(firstTagCloseButton).toBeTruthy();
-    fireEvent.click(firstTagCloseButton!);
-
-    await waitFor(() => {
-      const storedModels = sessionStorage.getItem("selectedModels");
-      const parsedModels = JSON.parse(storedModels!);
-      expect(parsedModels).not.toContain("Model 1");
-      expect(parsedModels).toContain("Model 2");
-      expect(parsedModels.length).toBe(1);
-    });
-
-    await waitFor(() => {
-      const remainingTags = container.querySelectorAll(".ant-tag");
-      expect(remainingTags.length).toBe(1);
-    });
+    expect(getByText("Test Key")).toBeInTheDocument();
   });
 
   it("should show the voice selector when the endpoint type is audio_speech", async () => {
@@ -259,7 +69,7 @@ describe("ChatUI", () => {
     });
 
     // Find the endpoint selector by looking for the "Endpoint Type:" text and its associated Select
-    const endpointTypeText = getByText("Endpoint Type:");
+    const endpointTypeText = getByText("Endpoint Type");
     const selectContainer = endpointTypeText.parentElement;
     const selectElement = selectContainer?.querySelector(".ant-select-selector");
 
@@ -291,4 +101,35 @@ describe("ChatUI", () => {
     const voiceSelectElement = voiceSelectContainer?.querySelector(".ant-select");
     expect(voiceSelectElement).toBeInTheDocument();
   });
+
+  it("should allow the user to select a model", async () => {
+    const { getByText, container } = render(
+      <ChatUI
+        accessToken="1234567890"
+        token="1234567890"
+        userRole="user"
+        userID="1234567890"
+        disabledPersonalKeyCreation={false}
+      />,
+    );
+
+    // Wait for the component to render
+    await waitFor(() => {
+      expect(getByText("Test Key")).toBeInTheDocument();
+    });
+
+    await waitFor(() => {
+      expect(getByText("Model 1")).toBeInTheDocument();
+    });
+
+    const selectComponent = container.querySelectorAll(".ant-select-selector")[1];
+    expect(selectComponent).toBeTruthy();
+
+    fireEvent.mouseDown(selectComponent!);
+
+    await waitFor(() => {
+      const model1Label = screen.getAllByText("Model 1");
+      expect(model1Label.length).toBeGreaterThan(0);
+    });
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx
index f0e8bfb1aff..a006d6734b2 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx
@@ -1,7 +1,3 @@
-import { Card, Text, TextInput, Title, Button as TremorButton } from "@tremor/react";
-import React, { useEffect, useRef, useState } from "react";
-import ReactMarkdown from "react-markdown";
-import { v4 as uuidv4 } from "uuid";
 import {
   ApiOutlined,
   ArrowUpOutlined,
@@ -21,9 +17,13 @@ import {
   ToolOutlined,
   UserOutlined,
 } from "@ant-design/icons";
-import { Button, Input, Modal, Select, Spin, Tag, Tooltip, Typography, Upload } from "antd";
+import { Card, Text, TextInput, Title, Button as TremorButton } from "@tremor/react";
+import { Button, Input, Modal, Select, Spin, Tooltip, Typography, Upload } from "antd";
+import React, { useEffect, useRef, useState } from "react";
+import ReactMarkdown from "react-markdown";
 import { Prism as SyntaxHighlighter } from "react-syntax-highlighter";
 import { coy } from "react-syntax-highlighter/dist/esm/styles/prism";
+import { v4 as uuidv4 } from "uuid";
 import GuardrailSelector from "../guardrails/GuardrailSelector";
 import NotificationsManager from "../molecules/notifications_manager";
 import TagSelector from "../tag_management/TagSelector";
@@ -33,7 +33,7 @@ import { OPEN_AI_VOICE_SELECT_OPTIONS, OpenAIVoice } from "./chatConstants";
 import ChatImageRenderer from "./ChatImageRenderer";
 import ChatImageUpload from "./ChatImageUpload";
 import { createChatDisplayMessage, createChatMultimodalMessage } from "./ChatImageUtils";
-import { truncateString } from "./chatUtils";
+import { truncateString } from "../../utils/textUtils";
 import { generateCodeSnippet } from "./CodeSnippets";
 import EndpointSelector from "./EndpointSelector";
 import { makeAnthropicMessagesRequest } from "./llm_calls/anthropic_messages";
@@ -106,17 +106,10 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
       return [];
     }
   });
-  const [selectedModels, setSelectedModels] = useState<string[]>(() => {
-    const saved = sessionStorage.getItem("selectedModels");
-    try {
-      return saved ? JSON.parse(saved) : [];
-    } catch (error) {
-      console.error("Error parsing selectedModels from sessionStorage", error);
-      return [];
-    }
-  });
+  const [selectedModel, setSelectedModel] = useState<string | undefined>(
+    () => sessionStorage.getItem("selectedModel") || undefined,
+  );
   const [showCustomModelInput, setShowCustomModelInput] = useState<boolean>(false);
-  const [customModelInput, setCustomModelInput] = useState<string>("");
   const [modelInfo, setModelInfo] = useState<ModelGroup[]>([]);
   const customModelTimeout = useRef<NodeJS.Timeout | null>(null);
   const [endpointType, setEndpointType] = useState<string>(
@@ -220,7 +213,7 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
         selectedGuardrails,
         selectedMCPTools,
         endpointType,
-        selectedModel: selectedModels[0], // Use first model for code snippet
+        selectedModel,
         selectedSdk,
         selectedVoice,
       });
@@ -239,7 +232,7 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
     selectedGuardrails,
     selectedMCPTools,
     endpointType,
-    selectedModels,
+    selectedModel,
   ]);
 
   useEffect(() => {
@@ -262,7 +255,11 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
     sessionStorage.setItem("selectedMCPTools", JSON.stringify(selectedMCPTools));
     sessionStorage.setItem("selectedVoice", selectedVoice);
 
-    sessionStorage.setItem("selectedModels", JSON.stringify(selectedModels));
+    if (selectedModel) {
+      sessionStorage.setItem("selectedModel", selectedModel);
+    } else {
+      sessionStorage.removeItem("selectedModel");
+    }
     if (messageTraceId) {
       sessionStorage.setItem("messageTraceId", messageTraceId);
     } else {
@@ -277,7 +274,7 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
   }, [
     apiKeySource,
     apiKey,
-    selectedModels,
+    selectedModel,
     endpointType,
     selectedTags,
     selectedVectorStores,
@@ -286,6 +283,7 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
     responsesSessionId,
     useApiSessionManagement,
     selectedMCPTools,
+    selectedVoice,
   ]);
 
   useEffect(() => {
@@ -309,11 +307,11 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
         setModelInfo(uniqueModels);
 
         // check for selection overlap or empty model list
+        const hasSelection = uniqueModels.some((m) => m.model_group === selectedModel);
         if (!uniqueModels.length) {
-          setSelectedModels([]);
-        } else if (selectedModels.length === 0) {
-          // If no models selected, auto-select the first one
-          setSelectedModels([uniqueModels[0].model_group]);
+          setSelectedModel(undefined);
+        } else if (!hasSelection) {
+          setSelectedModel(uniqueModels[0].model_group);
         }
       } catch (error) {
         console.error("Error fetching model info:", error);
@@ -726,8 +724,7 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
     setIsLoading(true);
 
     try {
-      if (selectedModels.length > 0) {
-        const selectedModel = selectedModels[0]; // For now, use first model (will be updated later for multi-model)
+      if (selectedModel) {
         if (endpointType === EndpointType.CHAT) {
           // Create chat history for API call - strip out model field and isImage field
           // For chat completions, we preserve the multimodal content structure
@@ -935,43 +932,11 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
     );
   }
 
-  const onModelsChange = (values: string[]) => {
-    console.log(`selected models:`, values);
+  const onModelChange = (value: string) => {
+    console.log(`selected ${value}`);
+    setSelectedModel(value);
 
-    // Check if "custom" was added
-    if (values.includes("custom")) {
-      setShowCustomModelInput(true);
-      // Remove "custom" from the actual selection
-      setSelectedModels(values.filter((v) => v !== "custom"));
-    } else {
-      // Limit to 3 models maximum
-      if (values.length > 3) {
-        NotificationsManager.info("Maximum 3 models can be selected");
-        return;
-      }
-      setSelectedModels(values);
-    }
-  };
-
-  const handleAddCustomModel = () => {
-    const trimmedModel = customModelInput.trim();
-    if (!trimmedModel) return;
-
-    if (selectedModels.includes(trimmedModel)) {
-      NotificationsManager.info("Model already selected");
-      return;
-    }
-    if (selectedModels.length >= 3) {
-      NotificationsManager.info("Maximum 3 models can be selected");
-      return;
-    }
-    setSelectedModels([...selectedModels, trimmedModel]);
-    setCustomModelInput("");
-    setShowCustomModelInput(false);
-  };
-
-  const handleRemoveModel = (modelToRemove: string) => {
-    setSelectedModels(selectedModels.filter((m) => m !== modelToRemove));
+    setShowCustomModelInput(value === "custom");
   };
 
   const antIcon = <LoadingOutlined style={{ fontSize: 24 }} spin />;
@@ -1015,65 +980,40 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
 
               <div>
                 <Text className="font-medium block mb-2 text-gray-700 flex items-center">
-                  <RobotOutlined className="mr-2" /> Select Models
+                  <RobotOutlined className="mr-2" /> Select Model
                 </Text>
                 <Select
-                  mode="multiple"
-                  value={selectedModels}
-                  placeholder="Select one or more models"
-                  onChange={onModelsChange}
+                  value={selectedModel}
+                  placeholder="Select a Model"
+                  onChange={onModelChange}
                   options={[
                     ...Array.from(new Set(modelInfo.map((option) => option.model_group))).map((model_group, index) => ({
                       value: model_group,
                       label: model_group,
                       key: index,
                     })),
-                    { value: "custom", label: "+ Add custom model", key: "custom" },
+                    { value: "custom", label: "Enter custom model", key: "custom" },
                   ]}
                   style={{ width: "100%" }}
                   showSearch={true}
                   className="rounded-md"
-                  maxTagCount={0}
-                  maxTagPlaceholder={`${selectedModels.length} model${selectedModels.length !== 1 ? "s" : ""} selected`}
                 />
-
                 {showCustomModelInput && (
                   <TextInput
                     className="mt-2"
-                    placeholder="Custom Model Name (Enter to add)"
-                    value={customModelInput}
-                    onValueChange={setCustomModelInput}
-                    onKeyDown={(e) => {
-                      if (e.key === "Enter") {
-                        handleAddCustomModel();
+                    placeholder="Enter custom model name"
+                    onValueChange={(value) => {
+                      // Using setTimeout to create a simple debounce effect
+                      if (customModelTimeout.current) {
+                        clearTimeout(customModelTimeout.current);
                       }
+
+                      customModelTimeout.current = setTimeout(() => {
+                        setSelectedModel(value);
+                      }, 500); // 500ms delay after typing stops
                     }}
                   />
                 )}
-
-                {/* Selected Models Tags */}
-                {selectedModels.length > 0 && (
-                  <div className="mt-2 overflow-hidden">
-                    <div className="flex flex-wrap gap-1.5">
-                      {selectedModels.map((model) => (
-                        <Tag
-                          key={model}
-                          closable
-                          onClose={(e) => {
-                            e.preventDefault();
-                            handleRemoveModel(model);
-                          }}
-                          color="blue"
-                          className="px-2 py-0.5 text-xs max-w-[200px]"
-                        >
-                          <span className="truncate inline-block max-w-[160px] align-middle" title={model}>
-                            {model}
-                          </span>
-                        </Tag>
-                      ))}
-                    </div>
-                  </div>
-                )}
               </div>
 
               <div>
@@ -1225,8 +1165,8 @@ const ChatUI: React.FC<ChatUIProps> = ({ accessToken, token, userRole, userID, d
 
           {/* Main Chat Area */}
           <div className="w-3/4 flex flex-col bg-white">
-            <div className="p-4 border-b border-gray-200 flex items-center justify-between">
-              <Title className="text-xl font-semibold">Test Key</Title>
+            <div className="p-4 border-b border-gray-200 flex justify-between items-center">
+              <Title className="text-xl font-semibold mb-0">Test Key</Title>
               <div className="flex gap-2">
                 <TremorButton
                   onClick={clearChatHistory}
diff --git a/ui/litellm-dashboard/src/components/chat_ui/EndpointSelector.tsx b/ui/litellm-dashboard/src/components/chat_ui/EndpointSelector.tsx
index 3c4708148df..fcdab900473 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/EndpointSelector.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/EndpointSelector.tsx
@@ -1,6 +1,5 @@
-import React from "react";
 import { Select } from "antd";
-import { Text } from "@tremor/react";
+import React from "react";
 import { ENDPOINT_OPTIONS } from "./chatConstants";
 
 interface EndpointSelectorProps {
@@ -12,7 +11,6 @@ interface EndpointSelectorProps {
 const EndpointSelector: React.FC<EndpointSelectorProps> = ({ endpointType, onEndpointChange, className }) => {
   return (
     <div className={className}>
-      <Text>Endpoint Type:</Text>
       <Select
         showSearch
         value={endpointType}
diff --git a/ui/litellm-dashboard/src/components/chat_ui/chatUtils.test.ts b/ui/litellm-dashboard/src/components/chat_ui/chatUtils.test.ts
deleted file mode 100644
index 547bc72e81d..00000000000
--- a/ui/litellm-dashboard/src/components/chat_ui/chatUtils.test.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-import { describe, expect, it } from "vitest";
-import { truncateString } from "./chatUtils";
-
-describe("chatUtils", () => {
-  describe("truncateString", () => {
-    it("should truncate a string", () => {
-      expect(truncateString("Hello, world!", 5)).toBe("Hello...");
-    });
-
-    it("should return the original string if it is less than the max length", () => {
-      expect(truncateString("Hello, world!", 20)).toBe("Hello, world!");
-    });
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/chat_ui/chatUtils.ts b/ui/litellm-dashboard/src/components/chat_ui/chatUtils.ts
deleted file mode 100644
index bd590a5ae15..00000000000
--- a/ui/litellm-dashboard/src/components/chat_ui/chatUtils.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-export function truncateString(str: string, maxLength: number) {
-  return str.length > maxLength ? str.substring(0, maxLength) + "..." : str;
-}
diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx
index c8d4d49cbe0..f24a50074c6 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx
@@ -1,44 +1,40 @@
 import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
 import { makeOpenAIEmbeddingsRequest } from "./embeddings_api";
-import OpenAI from "openai";
 
-vi.mock("openai");
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => "https://example.com"),
+}));
 
 describe("embeddings_api", () => {
-  const mockCreate = vi.fn();
   const mockUpdateEmbeddingsUI = vi.fn();
+  const mockFetch = vi.fn();
 
   beforeEach(() => {
-    // Mock the response structure from OpenAI embeddings API
-    mockCreate.mockResolvedValue({
-      data: [
-        {
-          embedding: [0.1, 0.2, 0.3, 0.4, 0.5],
-          index: 0,
-          object: "embedding",
-        },
-      ],
-      model: "text-embedding-3-small",
-      object: "list",
-      usage: {
-        prompt_tokens: 5,
-        total_tokens: 5,
-      },
-    });
+    mockFetch.mockResolvedValue({
+      ok: true,
+      json: async () => ({
+        data: [
+          {
+            embedding: [0.1, 0.2, 0.3, 0.4, 0.5],
+            index: 0,
+            object: "embedding",
+          },
+        ],
+        model: "text-embedding-3-small",
+        object: "list",
+      }),
+      text: async () => "",
+    } as Response);
 
-    // Mock the OpenAI constructor and its methods
-    (OpenAI as any).mockImplementation(() => ({
-      embeddings: {
-        create: mockCreate,
-      },
-    }));
+    // @ts-ignore - assigning to global for test environment
+    global.fetch = mockFetch;
   });
 
   afterEach(() => {
     vi.clearAllMocks();
   });
 
-  it("should make a request to the embeddings API", async () => {
+  it("should make a request to the embeddings endpoint", async () => {
     await makeOpenAIEmbeddingsRequest(
       "Hello, world!",
       mockUpdateEmbeddingsUI,
@@ -47,13 +43,36 @@ describe("embeddings_api", () => {
       [],
     );
 
-    expect(mockCreate).toHaveBeenCalledWith({
-      model: "text-embedding-3-small",
-      input: "Hello, world!",
+    expect(mockFetch).toHaveBeenCalledTimes(1);
+    expect(mockFetch).toHaveBeenCalledWith("https://example.com/embeddings", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: "Bearer 1234567890",
+      },
+      body: JSON.stringify({
+        model: "text-embedding-3-small",
+        input: "Hello, world!",
+      }),
     });
     expect(mockUpdateEmbeddingsUI).toHaveBeenCalledWith(
       JSON.stringify([0.1, 0.2, 0.3, 0.4, 0.5]),
       "text-embedding-3-small",
     );
   });
+
+  it("should not include encoding_format when making the request", async () => {
+    await makeOpenAIEmbeddingsRequest("Sample text", mockUpdateEmbeddingsUI, "text-embedding-3-small", "abcdef", []);
+
+    const fetchCall = mockFetch.mock.calls[0];
+    const options = fetchCall[1] as RequestInit;
+    const body = options.body as string;
+    const parsedBody = JSON.parse(body);
+
+    expect(parsedBody).not.toHaveProperty("encoding_format");
+    expect(parsedBody).toEqual({
+      model: "text-embedding-3-small",
+      input: "Sample text",
+    });
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx
index 7a5ab21d9fc..832d29bb852 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx
@@ -1,6 +1,5 @@
 import NotificationManager from "@/components/molecules/notifications_manager";
 import { getProxyBaseUrl } from "@/components/networking";
-import openai from "openai";
 
 export async function makeOpenAIEmbeddingsRequest(
   input: string,
@@ -26,20 +25,36 @@ export async function makeOpenAIEmbeddingsRequest(
     headers["x-litellm-tags"] = tags.join(",");
   }
 
-  const client = new openai.OpenAI({
-    apiKey: accessToken,
-    baseURL: proxyBaseUrl,
-    dangerouslyAllowBrowser: true,
-    defaultHeaders: headers,
-  });
-
   try {
-    const response = await client.embeddings.create({
-      model: selectedModel,
-      input: input,
+    const normalizedBaseUrl = proxyBaseUrl.endsWith("/") ? proxyBaseUrl.slice(0, -1) : proxyBaseUrl;
+    const requestUrl = `${normalizedBaseUrl}/embeddings`;
+
+    const response = await fetch(requestUrl, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: `Bearer ${accessToken}`,
+        ...headers,
+      },
+      body: JSON.stringify({
+        model: selectedModel,
+        input,
+      }),
     });
 
-    updateEmbeddingsUI(JSON.stringify(response.data[0].embedding), selectedModel);
+    if (!response.ok) {
+      const errorText = await response.text();
+      throw new Error(errorText || `Request failed with status ${response.status}`);
+    }
+
+    const responseData = await response.json();
+    const embedding = responseData?.data?.[0]?.embedding;
+
+    if (!embedding) {
+      throw new Error("No embedding returned from server");
+    }
+
+    updateEmbeddingsUI(JSON.stringify(embedding), responseData?.model ?? selectedModel);
   } catch (error: unknown) {
     NotificationManager.fromBackend(
       `Error occurred while making embeddings request. Please try again. Error: ${error}`,
diff --git a/ui/litellm-dashboard/src/components/chat_ui/mode_endpoint_mapping.tsx b/ui/litellm-dashboard/src/components/chat_ui/mode_endpoint_mapping.tsx
index 6c1fbcd504a..2aad64fb619 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/mode_endpoint_mapping.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/mode_endpoint_mapping.tsx
@@ -5,6 +5,7 @@ export enum ModelMode {
   AUDIO_SPEECH = "audio_speech",
   AUDIO_TRANSCRIPTION = "audio_transcription",
   IMAGE_GENERATION = "image_generation",
+  VIDEO_GENERATION = "video_generation",
   CHAT = "chat",
   RESPONSES = "responses",
   IMAGE_EDITS = "image_edits",
@@ -15,6 +16,7 @@ export enum ModelMode {
 // Define an enum for the endpoint types your UI calls
 export enum EndpointType {
   IMAGE = "image",
+  VIDEO = "video",
   CHAT = "chat",
   RESPONSES = "responses",
   IMAGE_EDITS = "image_edits",
@@ -28,6 +30,7 @@ export enum EndpointType {
 // Create a mapping between the model mode and the corresponding endpoint type
 export const litellmModeMapping: Record<ModelMode, EndpointType> = {
   [ModelMode.IMAGE_GENERATION]: EndpointType.IMAGE,
+  [ModelMode.VIDEO_GENERATION]: EndpointType.VIDEO,
   [ModelMode.CHAT]: EndpointType.CHAT,
   [ModelMode.RESPONSES]: EndpointType.RESPONSES,
   [ModelMode.IMAGE_EDITS]: EndpointType.IMAGE_EDITS,
diff --git a/ui/litellm-dashboard/src/components/common_components/check_openapi_schema.tsx b/ui/litellm-dashboard/src/components/common_components/check_openapi_schema.tsx
index 85eeac4644e..9697ebad788 100644
--- a/ui/litellm-dashboard/src/components/common_components/check_openapi_schema.tsx
+++ b/ui/litellm-dashboard/src/components/common_components/check_openapi_schema.tsx
@@ -4,6 +4,7 @@ import { TextInput } from "@tremor/react";
 import { InfoCircleOutlined } from "@ant-design/icons";
 import { Tooltip } from "antd";
 import { getOpenAPISchema } from "../networking";
+import { formatLabel } from "@/utils/textUtils";
 
 interface SchemaProperty {
   type?: string;
@@ -152,7 +153,7 @@ const SchemaFormFields: React.FC<SchemaFormFieldsProps> = ({
     const type = getPropertyType(property);
     const isRequired = schemaProperties?.required?.includes(key);
 
-    const label = overrideLabels[key] || property.title || key;
+    const label = overrideLabels[key] || property.title || formatLabel(key);
     const tooltip = overrideTooltips[key] || property.description;
 
     const rules = [];
diff --git a/ui/litellm-dashboard/src/components/entity_usage.test.tsx b/ui/litellm-dashboard/src/components/entity_usage.test.tsx
new file mode 100644
index 00000000000..f80b2fad5ed
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/entity_usage.test.tsx
@@ -0,0 +1,215 @@
+import { render, screen, fireEvent, waitFor } from "@testing-library/react";
+import { describe, it, expect, vi, beforeEach, beforeAll } from "vitest";
+import EntityUsage from "./entity_usage";
+import * as networking from "./networking";
+
+// Polyfill ResizeObserver for test environment
+beforeAll(() => {
+  if (typeof window !== "undefined" && !window.ResizeObserver) {
+    window.ResizeObserver = class ResizeObserver {
+      observe() {}
+      unobserve() {}
+      disconnect() {}
+    } as any;
+  }
+});
+
+// Mock the networking module
+vi.mock("./networking", () => ({
+  tagDailyActivityCall: vi.fn(),
+  teamDailyActivityCall: vi.fn(),
+}));
+
+// Mock the child components to simplify testing
+vi.mock("./activity_metrics", () => ({
+  ActivityMetrics: () => <div>Activity Metrics</div>,
+  processActivityData: () => ({ data: [], metadata: {} }),
+}));
+
+vi.mock("./top_key_view", () => ({
+  default: () => <div>Top Keys</div>,
+}));
+
+vi.mock("./top_model_view", () => ({
+  default: () => <div>Top Models</div>,
+}));
+
+vi.mock("./EntityUsageExport", () => ({
+  UsageExportHeader: () => <div>Usage Export Header</div>,
+}));
+
+describe("EntityUsage", () => {
+  const mockTagDailyActivityCall = vi.mocked(networking.tagDailyActivityCall);
+  const mockTeamDailyActivityCall = vi.mocked(networking.teamDailyActivityCall);
+
+  const mockSpendData = {
+    results: [
+      {
+        date: "2025-01-01",
+        metrics: {
+          spend: 100.5,
+          api_requests: 1000,
+          successful_requests: 950,
+          failed_requests: 50,
+          total_tokens: 50000,
+          prompt_tokens: 30000,
+          completion_tokens: 20000,
+          cache_read_input_tokens: 0,
+          cache_creation_input_tokens: 0,
+        },
+        breakdown: {
+          entities: {
+            "tag-1": {
+              metrics: {
+                spend: 60.3,
+                api_requests: 600,
+                successful_requests: 570,
+                failed_requests: 30,
+                total_tokens: 30000,
+                prompt_tokens: 18000,
+                completion_tokens: 12000,
+                cache_read_input_tokens: 0,
+                cache_creation_input_tokens: 0,
+              },
+              metadata: {
+                team_alias: "Tag 1",
+              },
+              api_key_breakdown: {},
+            },
+          },
+          models: {},
+          api_keys: {},
+          providers: {
+            openai: {
+              metrics: {
+                spend: 100.5,
+                api_requests: 1000,
+                successful_requests: 950,
+                failed_requests: 50,
+                total_tokens: 50000,
+                prompt_tokens: 30000,
+                completion_tokens: 20000,
+                cache_read_input_tokens: 0,
+                cache_creation_input_tokens: 0,
+              },
+            },
+          },
+        },
+      },
+    ],
+    metadata: {
+      total_spend: 100.5,
+      total_api_requests: 1000,
+      total_successful_requests: 950,
+      total_failed_requests: 50,
+      total_tokens: 50000,
+    },
+  };
+
+  const defaultProps = {
+    accessToken: "test-token",
+    entityType: "tag" as const,
+    entityId: "test-tag",
+    userID: "user-123",
+    userRole: "Admin",
+    entityList: [
+      { label: "Tag 1", value: "tag-1" },
+      { label: "Tag 2", value: "tag-2" },
+    ],
+    premiumUser: true,
+    dateValue: {
+      from: new Date("2025-01-01"),
+      to: new Date("2025-01-31"),
+    },
+  };
+
+  beforeEach(() => {
+    mockTagDailyActivityCall.mockClear();
+    mockTeamDailyActivityCall.mockClear();
+    mockTagDailyActivityCall.mockResolvedValue(mockSpendData);
+    mockTeamDailyActivityCall.mockResolvedValue(mockSpendData);
+  });
+
+  it("should render with tag entity type and display spend metrics", async () => {
+    render(<EntityUsage {...defaultProps} />);
+
+    await waitFor(() => {
+      expect(mockTagDailyActivityCall).toHaveBeenCalled();
+    });
+
+    // Check that spend metrics are displayed
+    expect(screen.getByText("Tag Spend Overview")).toBeInTheDocument();
+    expect(screen.getByText("Total Spend")).toBeInTheDocument();
+
+    // Use getAllByText since $100.50 appears in multiple places
+    const spendElements = screen.getAllByText("$100.50");
+    expect(spendElements.length).toBeGreaterThan(0);
+
+    expect(screen.getByText("1,000")).toBeInTheDocument(); // Total Requests
+  });
+
+  it("should render with team entity type and call team API", async () => {
+    render(<EntityUsage {...defaultProps} entityType="team" />);
+
+    await waitFor(() => {
+      expect(mockTeamDailyActivityCall).toHaveBeenCalled();
+    });
+
+    // Check that it shows team-specific label
+    expect(screen.getByText("Team Spend Overview")).toBeInTheDocument();
+
+    // Use getAllByText since $100.50 appears in multiple places
+    const spendElements = screen.getAllByText("$100.50");
+    expect(spendElements.length).toBeGreaterThan(0);
+  });
+
+  it("should switch between tabs", async () => {
+    render(<EntityUsage {...defaultProps} />);
+
+    await waitFor(() => {
+      expect(mockTagDailyActivityCall).toHaveBeenCalled();
+    });
+
+    // Check default tab (Cost) is shown
+    expect(screen.getByText("Tag Spend Overview")).toBeInTheDocument();
+
+    // Click Model Activity tab
+    const modelActivityTab = screen.getByText("Model Activity");
+    fireEvent.click(modelActivityTab);
+
+    // Should show activity metrics
+    expect(screen.getAllByText("Activity Metrics")[0]).toBeInTheDocument();
+
+    // Click Key Activity tab
+    const keyActivityTab = screen.getByText("Key Activity");
+    fireEvent.click(keyActivityTab);
+
+    // Should show activity metrics again
+    expect(screen.getAllByText("Activity Metrics")[1]).toBeInTheDocument();
+  });
+
+  it("should handle empty data gracefully", async () => {
+    const emptyData = {
+      results: [],
+      metadata: {
+        total_spend: 0,
+        total_api_requests: 0,
+        total_successful_requests: 0,
+        total_failed_requests: 0,
+        total_tokens: 0,
+      },
+    };
+
+    mockTagDailyActivityCall.mockResolvedValue(emptyData);
+
+    render(<EntityUsage {...defaultProps} />);
+
+    await waitFor(() => {
+      expect(mockTagDailyActivityCall).toHaveBeenCalled();
+    });
+
+    // Check that zero values are displayed (component formats it as $0.00)
+    expect(screen.getByText("$0.00")).toBeInTheDocument();
+    expect(screen.getByText("Total Spend")).toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/entity_usage.tsx b/ui/litellm-dashboard/src/components/entity_usage.tsx
index 2a4229d692b..fc1d03a3729 100644
--- a/ui/litellm-dashboard/src/components/entity_usage.tsx
+++ b/ui/litellm-dashboard/src/components/entity_usage.tsx
@@ -74,6 +74,7 @@ interface EntityUsageProps {
   userRole: string | null;
   entityList: EntityList[] | null;
   premiumUser: boolean;
+  dateValue: DateRangePickerValue;
 }
 
 const EntityUsage: React.FC<EntityUsageProps> = ({
@@ -84,6 +85,7 @@ const EntityUsage: React.FC<EntityUsageProps> = ({
   userRole,
   entityList,
   premiumUser,
+  dateValue,
 }) => {
   const [spendData, setSpendData] = useState<EntitySpendData>({
     results: [],
@@ -99,10 +101,6 @@ const EntityUsage: React.FC<EntityUsageProps> = ({
   const modelMetrics = processActivityData(spendData, "models");
   const keyMetrics = processActivityData(spendData, "api_keys");
   const [selectedTags, setSelectedTags] = useState<string[]>([]);
-  const [dateValue, setDateValue] = useState<DateRangePickerValue>({
-    from: new Date(Date.now() - 28 * 24 * 60 * 60 * 1000),
-    to: new Date(),
-  });
 
   const fetchSpendData = async () => {
     if (!accessToken || !dateValue.from || !dateValue.to) return;
@@ -331,7 +329,6 @@ const EntityUsage: React.FC<EntityUsageProps> = ({
     <div style={{ width: "100%" }} className="relative">
       <UsageExportHeader
         dateValue={dateValue}
-        onDateChange={setDateValue}
         entityType={entityType}
         spendData={spendData}
         showFilters={entityList !== null && entityList.length > 0}
diff --git a/ui/litellm-dashboard/src/components/guardrails/add_guardrail_form.tsx b/ui/litellm-dashboard/src/components/guardrails/add_guardrail_form.tsx
index c416cdd7f7d..55fe800a431 100644
--- a/ui/litellm-dashboard/src/components/guardrails/add_guardrail_form.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails/add_guardrail_form.tsx
@@ -4,6 +4,7 @@ import { Button, TextInput } from "@tremor/react";
 import {
   guardrail_provider_map,
   shouldRenderPIIConfigSettings,
+  shouldRenderContentFilterConfigSettings,
   guardrailLogoMap,
   populateGuardrailProviders,
   populateGuardrailProviderMap,
@@ -14,6 +15,7 @@ import PiiConfiguration from "./pii_configuration";
 import GuardrailProviderFields from "./guardrail_provider_fields";
 import GuardrailOptionalParams from "./guardrail_optional_params";
 import NotificationsManager from "../molecules/notifications_manager";
+import ContentFilterConfiguration from "./content_filter/ContentFilterConfiguration";
 
 const { Title, Text, Link } = Typography;
 const { Option } = Select;
@@ -44,6 +46,16 @@ interface GuardrailSettings {
     category: string;
     entities: string[];
   }>;
+  content_filter_settings?: {
+    prebuilt_patterns: Array<{
+      name: string;
+      display_name: string;
+      category: string;
+      description: string;
+    }>;
+    pattern_categories: string[];
+    supported_actions: string[];
+  };
 }
 
 interface LiteLLMParams {
@@ -85,6 +97,10 @@ const AddGuardrailForm: React.FC<AddGuardrailFormProps> = ({ visible, onClose, a
   const [globalSeverityThreshold, setGlobalSeverityThreshold] = useState<number>(2);
   const [categorySpecificThresholds, setCategorySpecificThresholds] = useState<{ [key: string]: number }>({});
 
+  // Content Filter state
+  const [selectedPatterns, setSelectedPatterns] = useState<any[]>([]);
+  const [blockedWords, setBlockedWords] = useState<any[]>([]);
+
   // Fetch guardrail UI settings + provider params on mount / accessToken change
   useEffect(() => {
     if (!accessToken) return;
@@ -266,6 +282,26 @@ const AddGuardrailForm: React.FC<AddGuardrailFormProps> = ({ visible, onClose, a
           guardrailData.litellm_params.presidio_anonymizer_api_base = values.presidio_anonymizer_api_base;
         }
       }
+
+      // For Content Filter, add patterns and blocked words
+      if (shouldRenderContentFilterConfigSettings(values.provider)) {
+        if (selectedPatterns.length > 0) {
+          guardrailData.litellm_params.patterns = selectedPatterns.map((p) => ({
+            pattern_type: p.type === "prebuilt" ? "prebuilt" : "regex",
+            pattern_name: p.type === "prebuilt" ? p.name : undefined,
+            pattern: p.type === "custom" ? p.pattern : undefined,
+            name: p.name,
+            action: p.action,
+          }));
+        }
+        if (blockedWords.length > 0) {
+          guardrailData.litellm_params.blocked_words = blockedWords.map((w) => ({
+            keyword: w.keyword,
+            action: w.action,
+            description: w.description,
+          }));
+        }
+      }
       // Add config values to the guardrail_info if provided
       else if (values.config) {
         try {
@@ -524,6 +560,38 @@ const AddGuardrailForm: React.FC<AddGuardrailFormProps> = ({ visible, onClose, a
     );
   };
 
+  const renderContentFilterConfiguration = (step: "patterns" | "keywords") => {
+    if (!guardrailSettings || !shouldRenderContentFilterConfigSettings(selectedProvider)) return null;
+
+    const contentFilterSettings = guardrailSettings.content_filter_settings;
+    if (!contentFilterSettings) return null;
+
+    return (
+      <ContentFilterConfiguration
+        prebuiltPatterns={contentFilterSettings.prebuilt_patterns || []}
+        categories={contentFilterSettings.pattern_categories || []}
+        selectedPatterns={selectedPatterns}
+        blockedWords={blockedWords}
+        onPatternAdd={(pattern) => setSelectedPatterns([...selectedPatterns, pattern])}
+        onPatternRemove={(id) => setSelectedPatterns(selectedPatterns.filter((p) => p.id !== id))}
+        onPatternActionChange={(id, action) => {
+          setSelectedPatterns(
+            selectedPatterns.map((p) => (p.id === id ? { ...p, action } : p))
+          );
+        }}
+        onBlockedWordAdd={(word) => setBlockedWords([...blockedWords, word])}
+        onBlockedWordRemove={(id) => setBlockedWords(blockedWords.filter((w) => w.id !== id))}
+        onBlockedWordUpdate={(id, field, value) => {
+          setBlockedWords(
+            blockedWords.map((w) => (w.id === id ? { ...w, [field]: value } : w))
+          );
+        }}
+        accessToken={accessToken}
+        showStep={step}
+      />
+    );
+  };
+
   const renderOptionalParams = () => {
     if (!selectedProvider || !providerParams) return null;
 
@@ -545,13 +613,24 @@ const AddGuardrailForm: React.FC<AddGuardrailFormProps> = ({ visible, onClose, a
         if (shouldRenderPIIConfigSettings(selectedProvider)) {
           return renderPiiConfiguration();
         }
+        if (shouldRenderContentFilterConfigSettings(selectedProvider)) {
+          return renderContentFilterConfiguration("patterns");
+        }
         return renderOptionalParams();
+      case 2:
+        if (shouldRenderContentFilterConfigSettings(selectedProvider)) {
+          return renderContentFilterConfiguration("keywords");
+        }
+        return null;
       default:
         return null;
     }
   };
 
   const renderStepButtons = () => {
+    const totalSteps = shouldRenderContentFilterConfigSettings(selectedProvider) ? 3 : 2;
+    const isLastStep = currentStep === totalSteps - 1;
+    
     return (
       <div className="flex justify-end space-x-2 mt-4">
         {currentStep > 0 && (
@@ -559,8 +638,8 @@ const AddGuardrailForm: React.FC<AddGuardrailFormProps> = ({ visible, onClose, a
             Previous
           </Button>
         )}
-        {currentStep < 2 && <Button onClick={nextStep}>Next</Button>}
-        {currentStep === 2 && (
+        {!isLastStep && <Button onClick={nextStep}>Next</Button>}
+        {isLastStep && (
           <Button onClick={handleSubmit} loading={loading}>
             Create Guardrail
           </Button>
@@ -585,8 +664,17 @@ const AddGuardrailForm: React.FC<AddGuardrailFormProps> = ({ visible, onClose, a
         <Steps current={currentStep} className="mb-6">
           <Step title="Basic Info" />
           <Step
-            title={shouldRenderPIIConfigSettings(selectedProvider) ? "PII Configuration" : "Provider Configuration"}
+            title={
+              shouldRenderPIIConfigSettings(selectedProvider)
+                ? "PII Configuration"
+                : shouldRenderContentFilterConfigSettings(selectedProvider)
+                  ? "Pattern Detection"
+                  : "Provider Configuration"
+            }
           />
+          {shouldRenderContentFilterConfigSettings(selectedProvider) && (
+            <Step title="Blocked Keywords" />
+          )}
         </Steps>
 
         {renderStepContent()}
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterConfiguration.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterConfiguration.tsx
new file mode 100644
index 00000000000..168fefdfe64
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterConfiguration.tsx
@@ -0,0 +1,300 @@
+import React, { useState } from "react";
+import { Typography, Space, Upload, Card } from "antd";
+import { PlusOutlined, UploadOutlined } from "@ant-design/icons";
+import { Button } from "@tremor/react";
+import { validateBlockedWordsFile } from "../../networking";
+import NotificationsManager from "../../molecules/notifications_manager";
+import PatternModal from "./PatternModal";
+import CustomPatternModal from "./CustomPatternModal";
+import KeywordModal from "./KeywordModal";
+import PatternTable from "./PatternTable";
+import KeywordTable from "./KeywordTable";
+
+const { Title, Text } = Typography;
+
+interface PrebuiltPattern {
+  name: string;
+  display_name: string;
+  category: string;
+  description: string;
+}
+
+interface Pattern {
+  id: string;
+  type: "prebuilt" | "custom";
+  name: string;
+  display_name?: string;
+  pattern?: string;
+  action: "BLOCK" | "MASK";
+}
+
+interface BlockedWord {
+  id: string;
+  keyword: string;
+  action: "BLOCK" | "MASK";
+  description?: string;
+}
+
+interface ContentFilterConfigurationProps {
+  prebuiltPatterns: PrebuiltPattern[];
+  categories: string[];
+  selectedPatterns: Pattern[];
+  blockedWords: BlockedWord[];
+  onPatternAdd: (pattern: Pattern) => void;
+  onPatternRemove: (id: string) => void;
+  onPatternActionChange: (id: string, action: "BLOCK" | "MASK") => void;
+  onBlockedWordAdd: (word: BlockedWord) => void;
+  onBlockedWordRemove: (id: string) => void;
+  onBlockedWordUpdate: (id: string, field: string, value: any) => void;
+  onFileUpload?: (content: string) => void;
+  accessToken: string | null;
+  showStep?: "patterns" | "keywords";
+}
+
+const ContentFilterConfiguration: React.FC<ContentFilterConfigurationProps> = ({
+  prebuiltPatterns,
+  categories,
+  selectedPatterns,
+  blockedWords,
+  onPatternAdd,
+  onPatternRemove,
+  onPatternActionChange,
+  onBlockedWordAdd,
+  onBlockedWordRemove,
+  onBlockedWordUpdate,
+  onFileUpload,
+  accessToken,
+  showStep,
+}) => {
+  const [patternModalVisible, setPatternModalVisible] = useState(false);
+  const [keywordModalVisible, setKeywordModalVisible] = useState(false);
+  const [customPatternModalVisible, setCustomPatternModalVisible] = useState(false);
+
+  const [selectedPatternName, setSelectedPatternName] = useState<string>("");
+  const [patternAction, setPatternAction] = useState<"BLOCK" | "MASK">("BLOCK");
+  const [customPatternName, setCustomPatternName] = useState<string>("");
+  const [customPatternRegex, setCustomPatternRegex] = useState<string>("");
+  const [customPatternAction, setCustomPatternAction] = useState<"BLOCK" | "MASK">("BLOCK");
+  const [newKeyword, setNewKeyword] = useState<string>("");
+  const [newKeywordAction, setNewKeywordAction] = useState<"BLOCK" | "MASK">("BLOCK");
+  const [newKeywordDescription, setNewKeywordDescription] = useState<string>("");
+  const [uploadValidating, setUploadValidating] = useState(false);
+
+  const handleAddPrebuiltPattern = () => {
+    if (!selectedPatternName) {
+      NotificationsManager.error("Please select a pattern");
+      return;
+    }
+
+    const selectedPattern = prebuiltPatterns.find((p) => p.name === selectedPatternName);
+
+    onPatternAdd({
+      id: `pattern-${Date.now()}`,
+      type: "prebuilt",
+      name: selectedPatternName,
+      display_name: selectedPattern?.display_name,
+      action: patternAction,
+    });
+
+    setPatternModalVisible(false);
+    setSelectedPatternName("");
+    setPatternAction("BLOCK");
+  };
+
+  const handleAddCustomPattern = () => {
+    if (!customPatternName || !customPatternRegex) {
+      NotificationsManager.error("Please provide pattern name and regex");
+      return;
+    }
+
+    onPatternAdd({
+      id: `custom-${Date.now()}`,
+      type: "custom",
+      name: customPatternName,
+      pattern: customPatternRegex,
+      action: customPatternAction,
+    });
+
+    setCustomPatternModalVisible(false);
+    setCustomPatternName("");
+    setCustomPatternRegex("");
+    setCustomPatternAction("BLOCK");
+  };
+
+  const handleAddKeyword = () => {
+    if (!newKeyword) {
+      NotificationsManager.error("Please enter a keyword");
+      return;
+    }
+
+    onBlockedWordAdd({
+      id: `word-${Date.now()}`,
+      keyword: newKeyword,
+      action: newKeywordAction,
+      description: newKeywordDescription || undefined,
+    });
+
+    setKeywordModalVisible(false);
+    setNewKeyword("");
+    setNewKeywordDescription("");
+    setNewKeywordAction("BLOCK");
+  };
+
+  const handleFileUpload = async (file: File) => {
+    setUploadValidating(true);
+    try {
+      const content = await file.text();
+      
+      if (accessToken) {
+        const result = await validateBlockedWordsFile(accessToken, content);
+        if (result.valid) {
+          if (onFileUpload) {
+            onFileUpload(content);
+          }
+          NotificationsManager.success(result.message || "File uploaded successfully");
+        } else {
+          const errorMessage = result.error || (result.errors && result.errors.join(", ")) || "Invalid file";
+          NotificationsManager.error(`Validation failed: ${errorMessage}`);
+        }
+      }
+    } catch (error) {
+      NotificationsManager.error(`Failed to upload file: ${error}`);
+    } finally {
+      setUploadValidating(false);
+    }
+    return false;
+  };
+
+  const showPatterns = !showStep || showStep === "patterns";
+  const showKeywords = !showStep || showStep === "keywords";
+
+  return (
+    <div className="space-y-6">
+      {!showStep && (
+        <div>
+          <Text type="secondary">
+            Configure patterns and keywords to detect and filter sensitive information in requests and responses.
+          </Text>
+        </div>
+      )}
+
+      {showPatterns && (
+        <Card
+          title={
+            <div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
+              <Title level={5} style={{ margin: 0 }}>
+                Pattern Detection
+              </Title>
+              <Text type="secondary" style={{ fontSize: 14, fontWeight: 400 }}>
+                Detect sensitive information using regex patterns (SSN, credit cards, API keys, etc.)
+              </Text>
+            </div>
+          }
+          size="small"
+        >
+          <div style={{ marginBottom: 16 }}>
+            <Space>
+              <Button type="button" onClick={() => setPatternModalVisible(true)} icon={PlusOutlined}>
+                Add prebuilt pattern
+              </Button>
+              <Button type="button" onClick={() => setCustomPatternModalVisible(true)} variant="secondary" icon={PlusOutlined}>
+                Add custom regex
+              </Button>
+            </Space>
+          </div>
+          <PatternTable
+            patterns={selectedPatterns}
+            onActionChange={onPatternActionChange}
+            onRemove={onPatternRemove}
+          />
+        </Card>
+      )}
+
+      {showKeywords && (
+        <Card
+          title={
+            <div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
+              <Title level={5} style={{ margin: 0 }}>
+                Blocked Keywords
+              </Title>
+              <Text type="secondary" style={{ fontSize: 14, fontWeight: 400 }}>
+                Block or mask specific sensitive terms and phrases
+              </Text>
+            </div>
+          }
+          size="small"
+        >
+          <div style={{ marginBottom: 16 }}>
+            <Space>
+              <Button type="button" onClick={() => setKeywordModalVisible(true)} icon={PlusOutlined}>
+                Add keyword
+              </Button>
+              <Upload beforeUpload={handleFileUpload} accept=".yaml,.yml" showUploadList={false}>
+                <Button type="button" variant="secondary" icon={UploadOutlined} loading={uploadValidating}>
+                  Upload YAML file
+                </Button>
+              </Upload>
+            </Space>
+          </div>
+          <KeywordTable
+            keywords={blockedWords}
+            onActionChange={onBlockedWordUpdate}
+            onRemove={onBlockedWordRemove}
+          />
+        </Card>
+      )}
+
+      <PatternModal
+        visible={patternModalVisible}
+        prebuiltPatterns={prebuiltPatterns}
+        categories={categories}
+        selectedPatternName={selectedPatternName}
+        patternAction={patternAction}
+        onPatternNameChange={setSelectedPatternName}
+        onActionChange={(value) => setPatternAction(value as "BLOCK" | "MASK")}
+        onAdd={handleAddPrebuiltPattern}
+        onCancel={() => {
+          setPatternModalVisible(false);
+          setSelectedPatternName("");
+          setPatternAction("BLOCK");
+        }}
+      />
+
+      <CustomPatternModal
+        visible={customPatternModalVisible}
+        patternName={customPatternName}
+        patternRegex={customPatternRegex}
+        patternAction={customPatternAction}
+        onNameChange={setCustomPatternName}
+        onRegexChange={setCustomPatternRegex}
+        onActionChange={(value) => setCustomPatternAction(value as "BLOCK" | "MASK")}
+        onAdd={handleAddCustomPattern}
+        onCancel={() => {
+          setCustomPatternModalVisible(false);
+          setCustomPatternName("");
+          setCustomPatternRegex("");
+          setCustomPatternAction("BLOCK");
+        }}
+      />
+
+      <KeywordModal
+        visible={keywordModalVisible}
+        keyword={newKeyword}
+        action={newKeywordAction}
+        description={newKeywordDescription}
+        onKeywordChange={setNewKeyword}
+        onActionChange={(value) => setNewKeywordAction(value as "BLOCK" | "MASK")}
+        onDescriptionChange={setNewKeywordDescription}
+        onAdd={handleAddKeyword}
+        onCancel={() => {
+          setKeywordModalVisible(false);
+          setNewKeyword("");
+          setNewKeywordDescription("");
+          setNewKeywordAction("BLOCK");
+        }}
+      />
+    </div>
+  );
+};
+
+export default ContentFilterConfiguration;
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterDisplay.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterDisplay.tsx
new file mode 100644
index 00000000000..0c1e12d8860
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterDisplay.tsx
@@ -0,0 +1,82 @@
+import React from "react";
+import { Card, Text, Badge } from "@tremor/react";
+import PatternTable from "./PatternTable";
+import KeywordTable from "./KeywordTable";
+
+interface Pattern {
+  id: string;
+  type: "prebuilt" | "custom";
+  name: string;
+  display_name?: string;
+  pattern?: string;
+  action: "BLOCK" | "MASK";
+}
+
+interface BlockedWord {
+  id: string;
+  keyword: string;
+  action: "BLOCK" | "MASK";
+  description?: string;
+}
+
+interface ContentFilterDisplayProps {
+  patterns: Pattern[];
+  blockedWords: BlockedWord[];
+  readOnly?: boolean;
+  onPatternActionChange?: (id: string, action: "BLOCK" | "MASK") => void;
+  onPatternRemove?: (id: string) => void;
+  onBlockedWordUpdate?: (id: string, field: string, value: any) => void;
+  onBlockedWordRemove?: (id: string) => void;
+}
+
+const ContentFilterDisplay: React.FC<ContentFilterDisplayProps> = ({
+  patterns,
+  blockedWords,
+  readOnly = true,
+  onPatternActionChange,
+  onPatternRemove,
+  onBlockedWordUpdate,
+  onBlockedWordRemove,
+}) => {
+  if (patterns.length === 0 && blockedWords.length === 0) {
+    return null;
+  }
+
+  // No-op handlers for read-only mode
+  const noOp = () => {};
+
+  return (
+    <>
+      {patterns.length > 0 && (
+        <Card className="mt-6">
+          <div className="flex justify-between items-center mb-4">
+            <Text className="text-lg font-semibold">Pattern Detection</Text>
+            <Badge color="blue">{patterns.length} patterns configured</Badge>
+          </div>
+          <PatternTable
+            patterns={patterns}
+            onActionChange={readOnly ? noOp : (onPatternActionChange || noOp)}
+            onRemove={readOnly ? noOp : (onPatternRemove || noOp)}
+          />
+        </Card>
+      )}
+
+      {blockedWords.length > 0 && (
+        <Card className="mt-6">
+          <div className="flex justify-between items-center mb-4">
+            <Text className="text-lg font-semibold">Blocked Keywords</Text>
+            <Badge color="blue">{blockedWords.length} keywords configured</Badge>
+          </div>
+          <KeywordTable
+            keywords={blockedWords}
+            onActionChange={readOnly ? noOp : (onBlockedWordUpdate || noOp)}
+            onRemove={readOnly ? noOp : (onBlockedWordRemove || noOp)}
+          />
+        </Card>
+      )}
+    </>
+  );
+};
+
+export default ContentFilterDisplay;
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterManager.test.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterManager.test.tsx
new file mode 100644
index 00000000000..6d879e1c544
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterManager.test.tsx
@@ -0,0 +1,77 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { render, screen, waitFor } from "@testing-library/react";
+import ContentFilterManager from "./ContentFilterManager";
+import React from "react";
+
+vi.mock("./ContentFilterConfiguration", () => ({
+  default: () => <div data-testid="content-filter-config">Mock Content Filter Configuration</div>
+}));
+
+vi.mock("./ContentFilterDisplay", () => ({
+  default: () => <div data-testid="content-filter-display">Mock Content Filter Display</div>
+}));
+
+vi.mock("antd", () => ({
+  Divider: ({ children }: { children: React.ReactNode }) => <div>{children}</div>
+}));
+
+describe("ContentFilterManager - Unsaved Changes Detection", () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  it("should call onUnsavedChanges with false when component initializes with matching data", async () => {
+    /**
+     * Tests that the ContentFilterManager correctly initializes the unsaved changes
+     * detection and calls onUnsavedChanges(false) when the current state matches
+     * the original loaded state (no changes yet).
+     */
+    const mockOnUnsavedChanges = vi.fn();
+    const mockOnDataChange = vi.fn();
+
+    const guardrailData = {
+      litellm_params: {
+        guardrail: "litellm_content_filter",
+        patterns: [
+          { pattern_type: "prebuilt", pattern_name: "email", action: "BLOCK" }
+        ],
+        blocked_words: [
+          { keyword: "test", action: "BLOCK", description: null }
+        ]
+      }
+    };
+
+    const guardrailSettings = {
+      content_filter_settings: {
+        prebuilt_patterns: [],
+        pattern_categories: ["PII"],
+        supported_actions: ["BLOCK", "MASK"]
+      }
+    };
+
+    render(
+      <ContentFilterManager
+        guardrailData={guardrailData}
+        guardrailSettings={guardrailSettings}
+        isEditing={true}
+        accessToken="test-token"
+        onDataChange={mockOnDataChange}
+        onUnsavedChanges={mockOnUnsavedChanges}
+      />
+    );
+
+    // Wait for component to render in edit mode
+    await waitFor(() => {
+      expect(screen.getByTestId("content-filter-config")).toBeInTheDocument();
+    });
+
+    // Verify onUnsavedChanges was called with false (no changes initially)
+    await waitFor(() => {
+      expect(mockOnUnsavedChanges).toHaveBeenCalledWith(false);
+    });
+
+    // Verify onDataChange was called with initial data
+    expect(mockOnDataChange).toHaveBeenCalled();
+  });
+});
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterManager.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterManager.tsx
new file mode 100644
index 00000000000..3fac7e11393
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/ContentFilterManager.tsx
@@ -0,0 +1,178 @@
+import React, { useState, useEffect } from "react";
+import { Divider } from "antd";
+import ContentFilterConfiguration from "./ContentFilterConfiguration";
+import ContentFilterDisplay from "./ContentFilterDisplay";
+
+interface Pattern {
+  id: string;
+  type: "prebuilt" | "custom";
+  name: string;
+  display_name?: string;
+  pattern?: string;
+  action: "BLOCK" | "MASK";
+}
+
+interface BlockedWord {
+  id: string;
+  keyword: string;
+  action: "BLOCK" | "MASK";
+  description?: string;
+}
+
+interface GuardrailSettings {
+  content_filter_settings?: {
+    prebuilt_patterns: Array<{
+      name: string;
+      display_name: string;
+      category: string;
+      description: string;
+    }>;
+    pattern_categories: string[];
+    supported_actions: string[];
+  };
+}
+
+interface ContentFilterManagerProps {
+  guardrailData: any;
+  guardrailSettings: GuardrailSettings | null;
+  isEditing: boolean;
+  accessToken: string | null;
+  onDataChange?: (patterns: Pattern[], blockedWords: BlockedWord[]) => void;
+  onUnsavedChanges?: (hasChanges: boolean) => void;
+}
+
+const ContentFilterManager: React.FC<ContentFilterManagerProps> = ({
+  guardrailData,
+  guardrailSettings,
+  isEditing,
+  accessToken,
+  onDataChange,
+  onUnsavedChanges,
+}) => {
+  const [selectedPatterns, setSelectedPatterns] = useState<Pattern[]>([]);
+  const [blockedWords, setBlockedWords] = useState<BlockedWord[]>([]);
+  const [originalPatterns, setOriginalPatterns] = useState<Pattern[]>([]);
+  const [originalBlockedWords, setOriginalBlockedWords] = useState<BlockedWord[]>([]);
+
+  // Load data from guardrail on mount or when guardrailData changes
+  useEffect(() => {
+    if (guardrailData?.litellm_params?.patterns) {
+      const patterns = guardrailData.litellm_params.patterns.map((p: any, index: number) => ({
+        id: `pattern-${index}`,
+        type: p.pattern_type === "prebuilt" ? "prebuilt" : "custom",
+        name: p.pattern_name || p.name,
+        display_name: p.display_name,
+        pattern: p.pattern,
+        action: p.action || "BLOCK",
+      }));
+      setSelectedPatterns(patterns);
+      setOriginalPatterns(patterns);
+    } else {
+      setSelectedPatterns([]);
+      setOriginalPatterns([]);
+    }
+
+    if (guardrailData?.litellm_params?.blocked_words) {
+      const words = guardrailData.litellm_params.blocked_words.map((w: any, index: number) => ({
+        id: `word-${index}`,
+        keyword: w.keyword,
+        action: w.action || "BLOCK",
+        description: w.description,
+      }));
+      setBlockedWords(words);
+      setOriginalBlockedWords(words);
+    } else {
+      setBlockedWords([]);
+      setOriginalBlockedWords([]);
+    }
+  }, [guardrailData]);
+
+  // Notify parent component when data changes
+  useEffect(() => {
+    if (onDataChange) {
+      onDataChange(selectedPatterns, blockedWords);
+    }
+  }, [selectedPatterns, blockedWords, onDataChange]);
+
+  // Detect unsaved changes
+  const hasUnsavedChanges = React.useMemo(() => {
+    const hasPatternChanges = JSON.stringify(selectedPatterns) !== JSON.stringify(originalPatterns);
+    const hasWordChanges = JSON.stringify(blockedWords) !== JSON.stringify(originalBlockedWords);
+    return hasPatternChanges || hasWordChanges;
+  }, [selectedPatterns, blockedWords, originalPatterns, originalBlockedWords]);
+
+  useEffect(() => {
+    if (isEditing && onUnsavedChanges) {
+      onUnsavedChanges(hasUnsavedChanges);
+    }
+  }, [hasUnsavedChanges, isEditing, onUnsavedChanges]);
+
+  // Check if this is a content filter guardrail
+  if (guardrailData?.litellm_params?.guardrail !== "litellm_content_filter") {
+    return null;
+  }
+
+  // Read-only display mode
+  if (!isEditing) {
+    return <ContentFilterDisplay patterns={selectedPatterns} blockedWords={blockedWords} readOnly={true} />;
+  }
+
+  // Edit mode
+  return (
+    <>
+      <Divider orientation="left">Content Filter Configuration</Divider>
+      {hasUnsavedChanges && (
+        <div className="mb-4 px-4 py-3 bg-yellow-50 border border-yellow-200 rounded-md">
+          <p className="text-sm text-yellow-800 font-medium">
+            ⚠️ You have unsaved changes to patterns or keywords. Remember to click &quot;Save Changes&quot; at the
+            bottom.
+          </p>
+        </div>
+      )}
+      <div className="mb-6">
+        {guardrailSettings && guardrailSettings.content_filter_settings && (
+          <ContentFilterConfiguration
+            prebuiltPatterns={guardrailSettings.content_filter_settings.prebuilt_patterns || []}
+            categories={guardrailSettings.content_filter_settings.pattern_categories || []}
+            selectedPatterns={selectedPatterns}
+            blockedWords={blockedWords}
+            onPatternAdd={(pattern) => setSelectedPatterns([...selectedPatterns, pattern])}
+            onPatternRemove={(id) => setSelectedPatterns(selectedPatterns.filter((p) => p.id !== id))}
+            onPatternActionChange={(id, action) =>
+              setSelectedPatterns(selectedPatterns.map((p) => (p.id === id ? { ...p, action } : p)))
+            }
+            onBlockedWordAdd={(word) => setBlockedWords([...blockedWords, word])}
+            onBlockedWordRemove={(id) => setBlockedWords(blockedWords.filter((w) => w.id !== id))}
+            onBlockedWordUpdate={(id, field, value) =>
+              setBlockedWords(blockedWords.map((w) => (w.id === id ? { ...w, [field]: value } : w)))
+            }
+            onFileUpload={(content: string) => {
+              console.log("File uploaded:", content);
+            }}
+            accessToken={accessToken}
+          />
+        )}
+      </div>
+    </>
+  );
+};
+
+export default ContentFilterManager;
+
+// Helper function to format data for API
+export const formatContentFilterDataForAPI = (patterns: Pattern[], blockedWords: BlockedWord[]) => {
+  return {
+    patterns: patterns.map((p) => ({
+      pattern_type: p.type === "prebuilt" ? "prebuilt" : "regex",
+      pattern_name: p.type === "prebuilt" ? p.name : undefined,
+      pattern: p.type === "custom" ? p.pattern : undefined,
+      name: p.name,
+      action: p.action,
+    })),
+    blocked_words: blockedWords.map((w) => ({
+      keyword: w.keyword,
+      action: w.action,
+      description: w.description,
+    })),
+  };
+};
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/CustomPatternModal.test.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/CustomPatternModal.test.tsx
new file mode 100644
index 00000000000..fa813688d79
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/CustomPatternModal.test.tsx
@@ -0,0 +1,64 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { render, screen, waitFor } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import CustomPatternModal from "./CustomPatternModal";
+
+describe("CustomPatternModal", () => {
+  const mockOnAdd = vi.fn();
+  const mockOnCancel = vi.fn();
+  const mockOnNameChange = vi.fn();
+  const mockOnRegexChange = vi.fn();
+  const mockOnActionChange = vi.fn();
+
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  it("should allow entering regex and pattern name and call onAdd when clicking add button", async () => {
+    /**
+     * Tests that user can enter a pattern name and regex, then clicking Add
+     * calls the onAdd callback. This is the core functionality of adding custom patterns.
+     */
+    const user = userEvent.setup();
+
+    render(
+      <CustomPatternModal
+        visible={true}
+        patternName=""
+        patternRegex=""
+        patternAction="BLOCK"
+        onNameChange={mockOnNameChange}
+        onRegexChange={mockOnRegexChange}
+        onActionChange={mockOnActionChange}
+        onAdd={mockOnAdd}
+        onCancel={mockOnCancel}
+      />
+    );
+
+    // Wait for modal to be visible
+    await waitFor(() => {
+      expect(screen.getByText("Add custom regex pattern")).toBeInTheDocument();
+    });
+
+    // Find and fill the pattern name input
+    const nameInput = screen.getByPlaceholderText("e.g., internal_id, employee_code");
+    await user.type(nameInput, "employee_id");
+
+    // Find and fill the regex pattern input - use paste instead of type to avoid special char issues
+    const regexInput = screen.getByPlaceholderText("e.g., ID-[0-9]{6}");
+    await user.click(regexInput);
+    await user.paste("EMP-[0-9]{5}");
+
+    // Verify the change handlers were called
+    expect(mockOnNameChange).toHaveBeenCalled();
+    expect(mockOnRegexChange).toHaveBeenCalled();
+
+    // Find and click the Add button
+    const addButton = screen.getByRole("button", { name: /add/i });
+    await user.click(addButton);
+
+    // Verify onAdd was called
+    expect(mockOnAdd).toHaveBeenCalledTimes(1);
+  });
+});
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/CustomPatternModal.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/CustomPatternModal.tsx
new file mode 100644
index 00000000000..c69a7a0e33c
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/CustomPatternModal.tsx
@@ -0,0 +1,92 @@
+import React from "react";
+import { Typography, Select, Modal, Space } from "antd";
+import { Button, TextInput } from "@tremor/react";
+
+const { Text } = Typography;
+const { Option } = Select;
+
+interface CustomPatternModalProps {
+  visible: boolean;
+  patternName: string;
+  patternRegex: string;
+  patternAction: "BLOCK" | "MASK";
+  onNameChange: (name: string) => void;
+  onRegexChange: (regex: string) => void;
+  onActionChange: (action: "BLOCK" | "MASK") => void;
+  onAdd: () => void;
+  onCancel: () => void;
+}
+
+const CustomPatternModal: React.FC<CustomPatternModalProps> = ({
+  visible,
+  patternName,
+  patternRegex,
+  patternAction,
+  onNameChange,
+  onRegexChange,
+  onActionChange,
+  onAdd,
+  onCancel,
+}) => {
+  return (
+    <Modal
+      title="Add custom regex pattern"
+      open={visible}
+      onCancel={onCancel}
+      footer={null}
+      width={800}
+    >
+      <Space direction="vertical" style={{ width: "100%" }} size="large">
+        <div>
+          <Text strong>Pattern name</Text>
+          <TextInput
+            placeholder="e.g., internal_id, employee_code"
+            value={patternName}
+            onValueChange={onNameChange}
+            style={{ marginTop: 8 }}
+          />
+        </div>
+
+        <div>
+          <Text strong>Regex pattern</Text>
+          <TextInput
+            placeholder="e.g., ID-[0-9]{6}"
+            value={patternRegex}
+            onValueChange={onRegexChange}
+            style={{ marginTop: 8 }}
+          />
+          <Text type="secondary" style={{ fontSize: 12 }}>
+            Enter a valid regular expression to match sensitive data
+          </Text>
+        </div>
+
+        <div>
+          <Text strong>Action</Text>
+          <Text type="secondary" style={{ display: "block", marginTop: 4, marginBottom: 8 }}>
+            Choose what action the guardrail should take when this pattern is detected
+          </Text>
+          <Select
+            value={patternAction}
+            onChange={onActionChange}
+            style={{ width: "100%" }}
+          >
+            <Option value="BLOCK">Block</Option>
+            <Option value="MASK">Mask</Option>
+          </Select>
+        </div>
+      </Space>
+
+      <div style={{ display: "flex", justifyContent: "flex-end", gap: "8px", marginTop: "24px" }}>
+        <Button variant="secondary" onClick={onCancel}>
+          Cancel
+        </Button>
+        <Button onClick={onAdd}>
+          Add
+        </Button>
+      </div>
+    </Modal>
+  );
+};
+
+export default CustomPatternModal;
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/KeywordModal.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/KeywordModal.tsx
new file mode 100644
index 00000000000..2a57a3d0db1
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/KeywordModal.tsx
@@ -0,0 +1,90 @@
+import React from "react";
+import { Typography, Select, Modal, Space } from "antd";
+import { Button, TextInput, Textarea } from "@tremor/react";
+
+const { Text } = Typography;
+const { Option } = Select;
+
+interface KeywordModalProps {
+  visible: boolean;
+  keyword: string;
+  action: "BLOCK" | "MASK";
+  description: string;
+  onKeywordChange: (keyword: string) => void;
+  onActionChange: (action: "BLOCK" | "MASK") => void;
+  onDescriptionChange: (description: string) => void;
+  onAdd: () => void;
+  onCancel: () => void;
+}
+
+const KeywordModal: React.FC<KeywordModalProps> = ({
+  visible,
+  keyword,
+  action,
+  description,
+  onKeywordChange,
+  onActionChange,
+  onDescriptionChange,
+  onAdd,
+  onCancel,
+}) => {
+  return (
+    <Modal
+      title="Add blocked keyword"
+      open={visible}
+      onCancel={onCancel}
+      footer={null}
+      width={800}
+    >
+      <Space direction="vertical" style={{ width: "100%" }} size="large">
+        <div>
+          <Text strong>Keyword</Text>
+          <TextInput
+            placeholder="Enter sensitive keyword or phrase"
+            value={keyword}
+            onValueChange={onKeywordChange}
+            style={{ marginTop: 8 }}
+          />
+        </div>
+
+        <div>
+          <Text strong>Action</Text>
+          <Text type="secondary" style={{ display: "block", marginTop: 4, marginBottom: 8 }}>
+            Choose what action the guardrail should take when this keyword is detected
+          </Text>
+          <Select
+            value={action}
+            onChange={onActionChange}
+            style={{ width: "100%" }}
+          >
+            <Option value="BLOCK">Block</Option>
+            <Option value="MASK">Mask</Option>
+          </Select>
+        </div>
+
+        <div>
+          <Text strong>Description (optional)</Text>
+          <Textarea
+            placeholder="Explain why this keyword is sensitive"
+            value={description}
+            onValueChange={onDescriptionChange}
+            rows={3}
+            style={{ marginTop: 8 }}
+          />
+        </div>
+      </Space>
+
+      <div style={{ display: "flex", justifyContent: "flex-end", gap: "8px", marginTop: "24px" }}>
+        <Button variant="secondary" onClick={onCancel}>
+          Cancel
+        </Button>
+        <Button onClick={onAdd}>
+          Add
+        </Button>
+      </div>
+    </Modal>
+  );
+};
+
+export default KeywordModal;
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/KeywordTable.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/KeywordTable.tsx
new file mode 100644
index 00000000000..51f8e125bf5
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/KeywordTable.tsx
@@ -0,0 +1,95 @@
+import React from "react";
+import { Typography, Select, Table } from "antd";
+import { DeleteOutlined } from "@ant-design/icons";
+import { Button } from "@tremor/react";
+
+const { Text } = Typography;
+const { Option } = Select;
+
+interface BlockedWord {
+  id: string;
+  keyword: string;
+  action: "BLOCK" | "MASK";
+  description?: string;
+}
+
+interface KeywordTableProps {
+  keywords: BlockedWord[];
+  onActionChange: (id: string, field: string, value: any) => void;
+  onRemove: (id: string) => void;
+}
+
+const KeywordTable: React.FC<KeywordTableProps> = ({
+  keywords,
+  onActionChange,
+  onRemove,
+}) => {
+  const columns = [
+    {
+      title: "Keyword",
+      dataIndex: "keyword",
+      key: "keyword",
+    },
+    {
+      title: "Action",
+      dataIndex: "action",
+      key: "action",
+      width: 150,
+      render: (action: string, record: BlockedWord) => (
+        <Select
+          value={action}
+          onChange={(value) => onActionChange(record.id, "action", value)}
+          style={{ width: 120 }}
+          size="small"
+        >
+          <Option value="BLOCK">Block</Option>
+          <Option value="MASK">Mask</Option>
+        </Select>
+      ),
+    },
+    {
+      title: "Description",
+      dataIndex: "description",
+      key: "description",
+      render: (desc: string) => desc || "-",
+    },
+    {
+      title: "",
+      key: "actions",
+      width: 100,
+      render: (_: any, record: BlockedWord) => (
+        <Button
+          type="button"
+          variant="light"
+          color="red"
+          size="xs"
+          icon={DeleteOutlined}
+          onClick={() => onRemove(record.id)}
+        >
+          Delete
+        </Button>
+      ),
+    },
+  ];
+
+  if (keywords.length === 0) {
+    return (
+      <div style={{ textAlign: "center", padding: "40px 0", color: "#999" }}>
+        No keywords added.
+      </div>
+    );
+  }
+
+  return (
+    <Table
+      dataSource={keywords}
+      columns={columns}
+      rowKey="id"
+      pagination={false}
+      size="small"
+    />
+  );
+};
+
+export default KeywordTable;
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternModal.test.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternModal.test.tsx
new file mode 100644
index 00000000000..6686c3a6d83
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternModal.test.tsx
@@ -0,0 +1,92 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { render, screen, waitFor } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import PatternModal from "./PatternModal";
+
+describe("PatternModal", () => {
+  const mockOnAdd = vi.fn();
+  const mockOnCancel = vi.fn();
+  const mockOnPatternNameChange = vi.fn();
+  const mockOnActionChange = vi.fn();
+
+  const mockPrebuiltPatterns = [
+    { name: "us_ssn", category: "PII Patterns", description: "US Social Security Number" },
+    { name: "email", category: "PII Patterns", description: "Email addresses" },
+    { name: "visa", category: "Financial Patterns", description: "Visa credit card numbers" },
+    { name: "aws_access_key", category: "Credential Patterns", description: "AWS Access Keys" },
+  ];
+
+  const mockCategories = ["PII Patterns", "Financial Patterns", "Credential Patterns"];
+
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  it("should show dropdown with prebuilt pattern options grouped by category", async () => {
+    /**
+     * Tests that the modal displays a dropdown with prebuilt patterns
+     * organized by category. This verifies the pattern selection UI is working.
+     */
+    const user = userEvent.setup();
+
+    render(
+      <PatternModal
+        visible={true}
+        prebuiltPatterns={mockPrebuiltPatterns}
+        categories={mockCategories}
+        selectedPatternName=""
+        patternAction="BLOCK"
+        onPatternNameChange={mockOnPatternNameChange}
+        onActionChange={mockOnActionChange}
+        onAdd={mockOnAdd}
+        onCancel={mockOnCancel}
+      />
+    );
+
+    // Wait for modal to be visible
+    await waitFor(() => {
+      expect(screen.getByText("Add prebuilt pattern")).toBeInTheDocument();
+    });
+
+    // Find the pattern type dropdown by looking for the first combobox input
+    const comboboxes = screen.getAllByRole("combobox");
+    const dropdown = comboboxes[0]; // First combobox is the pattern selector
+    expect(dropdown).toBeInTheDocument();
+
+    // Click to open the dropdown
+    await user.click(dropdown);
+
+    // Verify that pattern options are available in the dropdown
+    // Ant Design renders Select options in a portal, so we need to query the whole document
+    await waitFor(() => {
+      const options = document.querySelectorAll('.ant-select-item-option');
+      expect(options.length).toBeGreaterThan(0);
+    });
+
+    // Verify categories are shown as group labels
+    await waitFor(() => {
+      expect(document.body).toHaveTextContent("PII Patterns");
+      expect(document.body).toHaveTextContent("Financial Patterns");
+      expect(document.body).toHaveTextContent("Credential Patterns");
+    });
+
+    // Verify pattern options are available
+    expect(document.body).toHaveTextContent("us_ssn");
+    expect(document.body).toHaveTextContent("email");
+    expect(document.body).toHaveTextContent("visa");
+    expect(document.body).toHaveTextContent("aws_access_key");
+
+    // Select a pattern by clicking on its option element
+    const ssnOption = Array.from(document.querySelectorAll('.ant-select-item-option')).find(
+      el => el.textContent === "us_ssn"
+    ) as HTMLElement;
+    await user.click(ssnOption);
+
+    // Verify the change handler was called with the pattern name
+    // Note: Ant Design Select calls onChange with (value, option), so we check if it was called
+    expect(mockOnPatternNameChange).toHaveBeenCalled();
+    const callArgs = mockOnPatternNameChange.mock.calls[0];
+    expect(callArgs[0]).toBe("us_ssn");
+  });
+});
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternModal.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternModal.tsx
new file mode 100644
index 00000000000..4f4f2648b5a
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternModal.tsx
@@ -0,0 +1,112 @@
+import React from "react";
+import { Typography, Select, Modal, Space } from "antd";
+import { Button } from "@tremor/react";
+
+const { Text } = Typography;
+const { Option } = Select;
+
+interface PrebuiltPattern {
+  name: string;
+  display_name: string;
+  category: string;
+  description: string;
+}
+
+interface PatternModalProps {
+  visible: boolean;
+  prebuiltPatterns: PrebuiltPattern[];
+  categories: string[];
+  selectedPatternName: string;
+  patternAction: "BLOCK" | "MASK";
+  onPatternNameChange: (name: string) => void;
+  onActionChange: (action: "BLOCK" | "MASK") => void;
+  onAdd: () => void;
+  onCancel: () => void;
+}
+
+const PatternModal: React.FC<PatternModalProps> = ({
+  visible,
+  prebuiltPatterns,
+  categories,
+  selectedPatternName,
+  patternAction,
+  onPatternNameChange,
+  onActionChange,
+  onAdd,
+  onCancel,
+}) => {
+  return (
+    <Modal
+      title="Add prebuilt pattern"
+      open={visible}
+      onCancel={onCancel}
+      footer={null}
+      width={800}
+    >
+      <Space direction="vertical" style={{ width: "100%" }} size="large">
+        <div>
+          <Text strong>Pattern type</Text>
+          <Select
+            placeholder="Choose pattern type"
+            value={selectedPatternName}
+            onChange={onPatternNameChange}
+            style={{ width: "100%", marginTop: 8 }}
+            showSearch
+            filterOption={(input, option) => {
+              const pattern = prebuiltPatterns.find((p) => p.name === option?.value);
+              if (pattern) {
+                return (
+                  pattern.display_name.toLowerCase().includes(input.toLowerCase()) ||
+                  pattern.name.toLowerCase().includes(input.toLowerCase())
+                );
+              }
+              return false;
+            }}
+          >
+            {categories.map((category) => {
+              const categoryPatterns = prebuiltPatterns.filter((p) => p.category === category);
+              if (categoryPatterns.length === 0) return null;
+              
+              return (
+                <Select.OptGroup key={category} label={category}>
+                  {categoryPatterns.map((pattern) => (
+                    <Option key={pattern.name} value={pattern.name}>
+                      {pattern.display_name}
+                    </Option>
+                  ))}
+                </Select.OptGroup>
+              );
+            })}
+          </Select>
+        </div>
+
+        <div>
+          <Text strong>Action</Text>
+          <Text type="secondary" style={{ display: "block", marginTop: 4, marginBottom: 8 }}>
+            Choose what action the guardrail should take when this pattern is detected
+          </Text>
+          <Select
+            value={patternAction}
+            onChange={onActionChange}
+            style={{ width: "100%" }}
+          >
+            <Option value="BLOCK">Block</Option>
+            <Option value="MASK">Mask</Option>
+          </Select>
+        </div>
+      </Space>
+
+      <div style={{ display: "flex", justifyContent: "flex-end", gap: "8px", marginTop: "24px" }}>
+        <Button variant="secondary" onClick={onCancel}>
+          Cancel
+        </Button>
+        <Button onClick={onAdd}>
+          Add
+        </Button>
+      </div>
+    </Modal>
+  );
+};
+
+export default PatternModal;
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternTable.tsx b/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternTable.tsx
new file mode 100644
index 00000000000..423a29c343d
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/PatternTable.tsx
@@ -0,0 +1,109 @@
+import React from "react";
+import { Typography, Select, Table, Tag } from "antd";
+import { DeleteOutlined } from "@ant-design/icons";
+import { Button } from "@tremor/react";
+
+const { Text } = Typography;
+const { Option } = Select;
+
+interface Pattern {
+  id: string;
+  type: "prebuilt" | "custom";
+  name: string;
+  display_name?: string;
+  pattern?: string;
+  action: "BLOCK" | "MASK";
+}
+
+interface PatternTableProps {
+  patterns: Pattern[];
+  onActionChange: (id: string, action: "BLOCK" | "MASK") => void;
+  onRemove: (id: string) => void;
+}
+
+const PatternTable: React.FC<PatternTableProps> = ({
+  patterns,
+  onActionChange,
+  onRemove,
+}) => {
+  const columns = [
+    {
+      title: "Type",
+      dataIndex: "type",
+      key: "type",
+      width: 100,
+      render: (type: string) => (
+        <Tag color={type === "prebuilt" ? "blue" : "green"}>
+          {type === "prebuilt" ? "Prebuilt" : "Custom"}
+        </Tag>
+      ),
+    },
+    {
+      title: "Pattern name",
+      dataIndex: "name",
+      key: "name",
+      render: (_: string, record: Pattern) => record.display_name || record.name,
+    },
+    {
+      title: "Regex pattern",
+      dataIndex: "pattern",
+      key: "pattern",
+      render: (pattern: string) => (pattern ? <Text code style={{ fontSize: 12 }}>{pattern.substring(0, 40)}...</Text> : "-"),
+    },
+    {
+      title: "Action",
+      dataIndex: "action",
+      key: "action",
+      width: 150,
+      render: (action: string, record: Pattern) => (
+        <Select
+          value={action}
+          onChange={(value) => onActionChange(record.id, value as "BLOCK" | "MASK")}
+          style={{ width: 120 }}
+          size="small"
+        >
+          <Option value="BLOCK">Block</Option>
+          <Option value="MASK">Mask</Option>
+        </Select>
+      ),
+    },
+    {
+      title: "",
+      key: "actions",
+      width: 100,
+      render: (_: any, record: Pattern) => (
+        <Button
+          type="button"
+          variant="light"
+          color="red"
+          size="xs"
+          icon={DeleteOutlined}
+          onClick={() => onRemove(record.id)}
+        >
+          Delete
+        </Button>
+      ),
+    },
+  ];
+
+  if (patterns.length === 0) {
+    return (
+      <div style={{ textAlign: "center", padding: "40px 0", color: "#999" }}>
+        No patterns added.
+      </div>
+    );
+  }
+
+  return (
+    <Table
+      dataSource={patterns}
+      columns={columns}
+      rowKey="id"
+      pagination={false}
+      size="small"
+    />
+  );
+};
+
+export default PatternTable;
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/content_filter/types.ts b/ui/litellm-dashboard/src/components/guardrails/content_filter/types.ts
new file mode 100644
index 00000000000..26e4478dc1b
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/content_filter/types.ts
@@ -0,0 +1,49 @@
+/**
+ * Type definitions for Content Filter Configuration
+ */
+
+export interface PrebuiltPattern {
+  name: string;
+  display_name: string;
+  category: string;
+  description: string;
+}
+
+export interface Pattern {
+  id: string;
+  type: "prebuilt" | "custom";
+  name: string;
+  display_name?: string;
+  pattern?: string;
+  action: "BLOCK" | "MASK";
+}
+
+export interface BlockedWord {
+  id: string;
+  keyword: string;
+  action: "BLOCK" | "MASK";
+  description?: string;
+}
+
+export interface ContentFilterSettings {
+  prebuilt_patterns: PrebuiltPattern[];
+  pattern_categories: string[];
+  supported_actions: string[];
+}
+
+export interface ContentFilterConfigurationProps {
+  prebuiltPatterns: PrebuiltPattern[];
+  categories: string[];
+  selectedPatterns: Pattern[];
+  blockedWords: BlockedWord[];
+  blockedWordsFile?: string;
+  onPatternAdd: (pattern: Pattern) => void;
+  onPatternRemove: (id: string) => void;
+  onPatternActionChange: (id: string, action: "BLOCK" | "MASK") => void;
+  onBlockedWordAdd: (word: BlockedWord) => void;
+  onBlockedWordRemove: (id: string) => void;
+  onBlockedWordUpdate: (id: string, field: string, value: any) => void;
+  onFileUpload: (content: string) => void;
+  accessToken: string | null;
+}
+
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx b/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx
index d314cbc5ebe..fa34cf55636 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx
@@ -1,4 +1,4 @@
-import React, { useState, useEffect } from "react";
+import React, { useState, useEffect, useCallback } from "react";
 import {
   Card,
   Title,
@@ -25,6 +25,7 @@ import { getGuardrailLogoAndName, guardrail_provider_map } from "./guardrail_inf
 import PiiConfiguration from "./pii_configuration";
 import GuardrailProviderFields from "./guardrail_provider_fields";
 import GuardrailOptionalParams from "./guardrail_optional_params";
+import ContentFilterManager, { formatContentFilterDataForAPI } from "./content_filter/ContentFilterManager";
 import { ArrowLeftIcon } from "@heroicons/react/outline";
 import { copyToClipboard as utilCopyToClipboard } from "@/utils/dataUtils";
 import { CheckIcon, CopyIcon } from "lucide-react";
@@ -69,8 +70,31 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
       entities: string[];
     }>;
     supported_modes: string[];
+    content_filter_settings?: {
+      prebuilt_patterns: Array<{
+        name: string;
+        display_name: string;
+        category: string;
+        description: string;
+      }>;
+      pattern_categories: string[];
+      supported_actions: string[];
+    };
   } | null>(null);
   const [copiedStates, setCopiedStates] = useState<Record<string, boolean>>({});
+  const [hasUnsavedContentFilterChanges, setHasUnsavedContentFilterChanges] = useState(false);
+
+  // Content Filter data ref (managed by ContentFilterManager)
+  const contentFilterDataRef = React.useRef<{ patterns: any[]; blockedWords: any[] }>({
+    patterns: [],
+    blockedWords: [],
+  });
+
+  // Memoize onDataChange callback to prevent unnecessary re-renders
+  const handleContentFilterDataChange = useCallback((patterns: any[], blockedWords: any[]) => {
+    contentFilterDataRef.current = { patterns, blockedWords };
+  }, []);
+
   const fetchGuardrailInfo = async () => {
     try {
       setLoading(true);
@@ -212,6 +236,25 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
         updateData.litellm_params.pii_entities_config = newPiiEntitiesConfig;
       }
 
+      // Only add Content Filter patterns if there are changes
+      if (guardrailData.litellm_params?.guardrail === "litellm_content_filter") {
+        const originalPatterns = guardrailData.litellm_params?.patterns || [];
+        const originalBlockedWords = guardrailData.litellm_params?.blocked_words || [];
+
+        const formattedData = formatContentFilterDataForAPI(
+          contentFilterDataRef.current.patterns,
+          contentFilterDataRef.current.blockedWords,
+        );
+
+        if (JSON.stringify(originalPatterns) !== JSON.stringify(formattedData.patterns)) {
+          updateData.litellm_params.patterns = formattedData.patterns;
+        }
+
+        if (JSON.stringify(originalBlockedWords) !== JSON.stringify(formattedData.blocked_words)) {
+          updateData.litellm_params.blocked_words = formattedData.blocked_words;
+        }
+      }
+
       /******************************
        * Add provider-specific params (reusing logic from add_guardrail_form.tsx)
        * ----------------------------------
@@ -293,6 +336,7 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
 
       await updateGuardrailCall(accessToken, guardrailId, updateData);
       NotificationsManager.success("Guardrail updated successfully");
+      setHasUnsavedContentFilterChanges(false);
       fetchGuardrailInfo();
       setIsEditing(false);
     } catch (error) {
@@ -443,6 +487,14 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
                   </div>
                 </Card>
               )}
+
+            {/* Content Filter Configuration Display */}
+            <ContentFilterManager
+              guardrailData={guardrailData}
+              guardrailSettings={guardrailSettings}
+              isEditing={false}
+              accessToken={accessToken}
+            />
           </TabPanel>
 
           {/* Settings Panel (only for admins) */}
@@ -512,6 +564,15 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
                       </>
                     )}
 
+                    <ContentFilterManager
+                      guardrailData={guardrailData}
+                      guardrailSettings={guardrailSettings}
+                      isEditing={true}
+                      accessToken={accessToken}
+                      onDataChange={handleContentFilterDataChange}
+                      onUnsavedChanges={setHasUnsavedContentFilterChanges}
+                    />
+
                     <Divider orientation="left">Provider Settings</Divider>
 
                     {/* Provider-specific fields */}
@@ -554,7 +615,14 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
                     </Form.Item>
 
                     <div className="flex justify-end gap-2 mt-6">
-                      <Button onClick={() => setIsEditing(false)}>Cancel</Button>
+                      <Button
+                        onClick={() => {
+                          setIsEditing(false);
+                          setHasUnsavedContentFilterChanges(false);
+                        }}
+                      >
+                        Cancel
+                      </Button>
                       <TremorButton>Save Changes</TremorButton>
                     </div>
                   </Form>
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx b/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx
index 50e22d86218..1ab0849b6bd 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx
@@ -45,6 +45,7 @@ export const guardrail_provider_map: Record<string, string> = {
   PresidioPII: "presidio",
   Bedrock: "bedrock",
   Lakera: "lakera_v2",
+  LitellmContentFilter: "litellm_content_filter",
 };
 
 // Function to populate provider map from API response - updates the original map
@@ -88,6 +89,17 @@ export const shouldRenderAzureTextModerationConfigSettings = (provider: string |
   return providerEnum === "Azure Content Safety Text Moderation";
 };
 
+// Decides if we should render the Content Filter config settings for a given provider
+export const shouldRenderContentFilterConfigSettings = (provider: string | null) => {
+  if (!provider) {
+    return false;
+  }
+  // Check both dynamic and legacy providers
+  const currentProviders = getGuardrailProviders();
+  const providerEnum = currentProviders[provider as keyof typeof currentProviders];
+  return providerEnum === "LiteLLM Content Filter";
+};
+
 const asset_logos_folder = "../ui/assets/logos/";
 
 export const guardrailLogoMap: Record<string, string> = {
@@ -108,6 +120,7 @@ export const guardrailLogoMap: Record<string, string> = {
   "AIM Guardrail": `${asset_logos_folder}aim_security.jpeg`,
   "OpenAI Moderation": `${asset_logos_folder}openai_small.svg`,
   EnkryptAI: `${asset_logos_folder}enkrypt_ai.avif`,
+  "LiteLLM Content Filter": `${asset_logos_folder}litellm_logo.jpg`,
 };
 
 export const getGuardrailLogoAndName = (guardrailValue: string): { logo: string; displayName: string } => {
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx b/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx
index 2cabfff581d..e0edb506fce 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx
@@ -7,8 +7,6 @@ import NotificationsManager from "../molecules/notifications_manager";
 
 export function ToolTestPanel({
   tool,
-  needsAuth,
-  authValue,
   onSubmit,
   isLoading,
   result,
@@ -16,8 +14,6 @@ export function ToolTestPanel({
   onClose,
 }: {
   tool: MCPTool;
-  needsAuth: boolean;
-  authValue?: string | null;
   onSubmit: (args: Record<string, any>) => void;
   isLoading: boolean;
   result: any | null;
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx b/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx
index 81842b5d749..4ec60ed276a 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx
@@ -3,7 +3,7 @@ import { Modal, Tooltip, Form, Select } from "antd";
 import { InfoCircleOutlined } from "@ant-design/icons";
 import { Button, TextInput } from "@tremor/react";
 import { createMCPServer } from "../networking";
-import { MCPServer, MCPServerCostInfo } from "./types";
+import { AUTH_TYPE, MCPServer, MCPServerCostInfo } from "./types";
 import MCPServerCostConfig from "./mcp_server_cost_config";
 import MCPConnectionStatus from "./mcp_connection_status";
 import MCPToolConfiguration from "./mcp_tool_configuration";
@@ -25,6 +25,8 @@ interface CreateMCPServerProps {
   availableAccessGroups: string[];
 }
 
+const AUTH_TYPES_REQUIRING_AUTH_VALUE = [AUTH_TYPE.API_KEY, AUTH_TYPE.BEARER_TOKEN, AUTH_TYPE.BASIC];
+
 const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
   userRole,
   accessToken,
@@ -43,6 +45,8 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
   const [transportType, setTransportType] = useState<string>("");
   const [searchValue, setSearchValue] = useState<string>("");
   const [urlWarning, setUrlWarning] = useState<string>("");
+  const authType = formValues.auth_type as string | undefined;
+  const shouldShowAuthValueField = authType ? AUTH_TYPES_REQUIRING_AUTH_VALUE.includes(authType) : false;
 
   // Function to check URL format based on transport type
   const checkUrlFormat = (url: string, transport: string) => {
@@ -63,7 +67,12 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
   const handleCreate = async (values: Record<string, any>) => {
     setIsLoading(true);
     try {
-      const { static_headers: staticHeadersList, stdio_config: rawStdioConfig, ...restValues } = values;
+      const {
+        static_headers: staticHeadersList,
+        stdio_config: rawStdioConfig,
+        credentials: credentialValues,
+        ...restValues
+      } = values;
 
       // Transform access groups into objects with name property
       const accessGroups = restValues.mcp_access_groups;
@@ -77,7 +86,27 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
             acc[header] = entry?.value ?? "";
             return acc;
           }, {})
-        : {} as Record<string, string>;
+        : ({} as Record<string, string>);
+
+      const credentialsPayload =
+        credentialValues && typeof credentialValues === "object"
+          ? Object.entries(credentialValues).reduce((acc: Record<string, any>, [key, value]) => {
+              if (value === undefined || value === null || value === "") {
+                return acc;
+              }
+              if (key === "scopes") {
+                if (Array.isArray(value)) {
+                  const filteredScopes = value.filter((scope) => scope != null && scope !== "");
+                  if (filteredScopes.length > 0) {
+                    acc[key] = filteredScopes;
+                  }
+                }
+              } else {
+                acc[key] = value;
+              }
+              return acc;
+            }, {})
+          : undefined;
 
       // Process stdio configuration if present
       let stdioFields = {};
@@ -119,7 +148,7 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
       }
 
       // Prepare the payload with cost configuration and allowed tools
-      const payload = {
+      const payload: Record<string, any> = {
         ...restValues,
         ...stdioFields,
         // Remove the raw stdio_config field as we've extracted its components
@@ -135,6 +164,13 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
         static_headers: staticHeaders,
       };
 
+      payload.static_headers = staticHeaders;
+      const includeCredentials = restValues.auth_type && AUTH_TYPES_REQUIRING_AUTH_VALUE.includes(restValues.auth_type);
+
+      if (includeCredentials && credentialsPayload && Object.keys(credentialsPayload).length > 0) {
+        payload.credentials = credentialsPayload;
+      }
+
       console.log(`Payload: ${JSON.stringify(payload)}`);
 
       if (accessToken != null) {
@@ -393,6 +429,27 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
               </Form.Item>
             )}
 
+            {transportType !== "stdio" && shouldShowAuthValueField && (
+              <Form.Item
+                label={
+                  <span className="text-sm font-medium text-gray-700 flex items-center">
+                    Authentication Value
+                    <Tooltip title="Token, password, or header value to send with each request for the selected auth type.">
+                      <InfoCircleOutlined className="ml-2 text-blue-400 hover:text-blue-600 cursor-help" />
+                    </Tooltip>
+                  </span>
+                }
+                name={["credentials", "auth_value"]}
+                rules={[{ required: true, message: "Please enter the authentication value" }]}
+              >
+                <TextInput
+                  type="password"
+                  placeholder="Enter token or secret"
+                  className="rounded-lg border-gray-300 focus:border-blue-500 focus:ring-blue-500"
+                />
+              </Form.Item>
+            )}
+
             {/* Stdio Configuration - only show for stdio transport */}
             <StdioConfiguration isVisible={transportType === "stdio"} />
           </div>
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_auth_storage.ts b/ui/litellm-dashboard/src/components/mcp_tools/mcp_auth_storage.ts
deleted file mode 100644
index 9f81e58fe66..00000000000
--- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_auth_storage.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-// Utility functions for managing MCP server authentication tokens in localStorage
-
-const MCP_AUTH_STORAGE_KEY = "litellm_mcp_auth_tokens";
-
-export interface MCPAuthToken {
-  serverId: string;
-  serverAlias?: string;
-  authValue: string;
-  authType: string;
-  timestamp: number;
-}
-
-export interface MCPAuthStorage {
-  [serverId: string]: MCPAuthToken;
-}
-
-/**
- * Get all stored MCP authentication tokens
- */
-export const getMCPAuthTokens = (): MCPAuthStorage => {
-  try {
-    const stored = localStorage.getItem(MCP_AUTH_STORAGE_KEY);
-    return stored ? JSON.parse(stored) : {};
-  } catch (error) {
-    console.error("Error reading MCP auth tokens from localStorage:", error);
-    return {};
-  }
-};
-
-/**
- * Get authentication token for a specific MCP server
- */
-export const getMCPAuthToken = (serverId: string, serverAlias?: string): string | null => {
-  try {
-    const tokens = getMCPAuthTokens();
-    const token = tokens[serverId];
-
-    // If token exists, check if serverAlias matches (both can be undefined)
-    if (token && token.serverAlias === serverAlias) {
-      return token.authValue;
-    }
-
-    // If no serverAlias was provided and token exists without serverAlias, return it
-    if (token && !serverAlias && !token.serverAlias) {
-      return token.authValue;
-    }
-
-    return null;
-  } catch (error) {
-    console.error("Error getting MCP auth token:", error);
-    return null;
-  }
-};
-
-/**
- * Store authentication token for an MCP server
- */
-export const setMCPAuthToken = (serverId: string, authValue: string, authType: string, serverAlias?: string): void => {
-  try {
-    const tokens = getMCPAuthTokens();
-
-    tokens[serverId] = {
-      serverId,
-      serverAlias,
-      authValue,
-      authType,
-      timestamp: Date.now(),
-    };
-
-    localStorage.setItem(MCP_AUTH_STORAGE_KEY, JSON.stringify(tokens));
-  } catch (error) {
-    console.error("Error storing MCP auth token:", error);
-  }
-};
-
-/**
- * Remove authentication token for an MCP server
- */
-export const removeMCPAuthToken = (serverId: string): void => {
-  try {
-    const tokens = getMCPAuthTokens();
-    delete tokens[serverId];
-    localStorage.setItem(MCP_AUTH_STORAGE_KEY, JSON.stringify(tokens));
-  } catch (error) {
-    console.error("Error removing MCP auth token:", error);
-  }
-};
-
-/**
- * Clear all MCP authentication tokens (useful for logout)
- */
-export const clearMCPAuthTokens = (): void => {
-  try {
-    localStorage.removeItem(MCP_AUTH_STORAGE_KEY);
-  } catch (error) {
-    console.error("Error clearing MCP auth tokens:", error);
-  }
-};
-
-/**
- * Check if a token exists for a server
- */
-export const hasMCPAuthToken = (serverId: string, serverAlias?: string): boolean => {
-  const token = getMCPAuthToken(serverId, serverAlias);
-  return token !== null;
-};
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_edit.tsx b/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_edit.tsx
index 54e1f9f9573..109ab809225 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_edit.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_edit.tsx
@@ -1,7 +1,8 @@
 import React, { useState, useEffect } from "react";
-import { Form, Select, Button as AntdButton } from "antd";
+import { Form, Select, Button as AntdButton, Tooltip } from "antd";
+import { InfoCircleOutlined } from "@ant-design/icons";
 import { Button, TextInput, TabGroup, TabList, Tab, TabPanels, TabPanel } from "@tremor/react";
-import { MCPServer, MCPServerCostInfo } from "./types";
+import { AUTH_TYPE, MCPServer, MCPServerCostInfo } from "./types";
 import { updateMCPServer, testMCPToolsListRequest } from "../networking";
 import MCPServerCostConfig from "./mcp_server_cost_config";
 import MCPPermissionManagement from "./MCPPermissionManagement";
@@ -17,6 +18,8 @@ interface MCPServerEditProps {
   availableAccessGroups: string[];
 }
 
+const AUTH_TYPES_REQUIRING_AUTH_VALUE = [AUTH_TYPE.API_KEY, AUTH_TYPE.BEARER_TOKEN, AUTH_TYPE.BASIC];
+
 const MCPServerEdit: React.FC<MCPServerEditProps> = ({
   mcpServer,
   accessToken,
@@ -31,6 +34,8 @@ const MCPServerEdit: React.FC<MCPServerEditProps> = ({
   const [searchValue, setSearchValue] = useState<string>("");
   const [aliasManuallyEdited, setAliasManuallyEdited] = useState(false);
   const [allowedTools, setAllowedTools] = useState<string[]>([]);
+  const authType = Form.useWatch("auth_type", form) as string | undefined;
+  const shouldShowAuthValueField = authType ? AUTH_TYPES_REQUIRING_AUTH_VALUE.includes(authType) : false;
 
   const initialStaticHeaders = React.useMemo(() => {
     if (!mcpServer.static_headers) {
@@ -148,7 +153,7 @@ const MCPServerEdit: React.FC<MCPServerEditProps> = ({
     if (!accessToken) return;
     try {
       // Ensure access groups is always a string array
-      const { static_headers: staticHeadersList, ...restValues } = values;
+      const { static_headers: staticHeadersList, credentials: credentialValues, ...restValues } = values;
 
       const accessGroups = (restValues.mcp_access_groups || []).map((g: any) =>
         typeof g === "string" ? g : g.name || String(g),
@@ -163,10 +168,30 @@ const MCPServerEdit: React.FC<MCPServerEditProps> = ({
             acc[header] = entry?.value ?? "";
             return acc;
           }, {})
-        : {} as Record<string, string>;
+        : ({} as Record<string, string>);
+
+      const credentialsPayload =
+        credentialValues && typeof credentialValues === "object"
+          ? Object.entries(credentialValues).reduce((acc: Record<string, any>, [key, value]) => {
+              if (value === undefined || value === null || value === "") {
+                return acc;
+              }
+              if (key === "scopes") {
+                if (Array.isArray(value)) {
+                  const filteredScopes = value.filter((scope) => scope != null && scope !== "");
+                  if (filteredScopes.length > 0) {
+                    acc[key] = filteredScopes;
+                  }
+                }
+              } else {
+                acc[key] = value;
+              }
+              return acc;
+            }, {})
+          : undefined;
 
       // Prepare the payload with cost configuration and permission fields
-      const payload = {
+      const payload: Record<string, any> = {
         ...restValues,
         server_id: mcpServer.server_id,
         mcp_info: {
@@ -183,6 +208,12 @@ const MCPServerEdit: React.FC<MCPServerEditProps> = ({
         static_headers: staticHeaders,
       };
 
+      const includeCredentials = restValues.auth_type && AUTH_TYPES_REQUIRING_AUTH_VALUE.includes(restValues.auth_type);
+
+      if (includeCredentials && credentialsPayload && Object.keys(credentialsPayload).length > 0) {
+        payload.credentials = credentialsPayload;
+      }
+
       const updated = await updateMCPServer(accessToken, payload);
       NotificationsManager.success("MCP Server updated successfully");
       onSuccess(updated);
@@ -250,6 +281,34 @@ const MCPServerEdit: React.FC<MCPServerEditProps> = ({
               </Select>
             </Form.Item>
 
+            {shouldShowAuthValueField && (
+              <Form.Item
+                label={
+                  <span className="text-sm font-medium text-gray-700 flex items-center">
+                    Authentication Value
+                    <Tooltip title="Token, password, or header value to send with each request for the selected auth type.">
+                      <InfoCircleOutlined className="ml-2 text-blue-400 hover:text-blue-600 cursor-help" />
+                    </Tooltip>
+                  </span>
+                }
+                name={["credentials", "auth_value"]}
+                rules={[
+                  {
+                    validator: (_, value) =>
+                      value && typeof value === "string" && value.trim() === ""
+                        ? Promise.reject(new Error("Authentication value cannot be empty"))
+                        : Promise.resolve(),
+                  },
+                ]}
+              >
+                <TextInput
+                  type="password"
+                  placeholder="Enter token or secret (leave blank to keep existing)"
+                  className="rounded-lg border-gray-300 focus:border-blue-500 focus:ring-blue-500"
+                />
+              </Form.Item>
+            )}
+
             {/* Permission Management / Access Control Section */}
             <div className="mt-6">
               <MCPPermissionManagement
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx b/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx
index 366b0ce880e..7c24b1b377b 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx
@@ -1,121 +1,11 @@
-import React, { useState, useEffect } from "react";
+import React, { useState } from "react";
 import { useQuery, useMutation } from "@tanstack/react-query";
 import { ToolTestPanel } from "./ToolTestPanel";
-import { MCPTool, MCPToolsViewerProps, CallMCPToolResponse, mcpServerHasAuth } from "./types";
+import { MCPTool, MCPToolsViewerProps, CallMCPToolResponse } from "./types";
 import { listMCPTools, callMCPTool } from "../networking";
-import { getMCPAuthToken, setMCPAuthToken, removeMCPAuthToken } from "./mcp_auth_storage";
 
-import { Modal, Input, Form } from "antd";
-import { Button, Card, Title, Text } from "@tremor/react";
-import { RobotOutlined, SafetyOutlined, ToolOutlined } from "@ant-design/icons";
-
-import { AUTH_TYPE } from "./types";
-import NotificationsManager from "../molecules/notifications_manager";
-
-type AuthModalProps = {
-  visible: boolean;
-  onOk: (values: any) => void;
-  onCancel: () => void;
-  authType?: string | null;
-};
-
-export const AuthModal = ({ visible, onOk, onCancel, authType }: AuthModalProps) => {
-  const [form] = Form.useForm();
-
-  // Handler for modal OK
-  const handleOk = () => {
-    form.validateFields().then((values) => {
-      if (authType === AUTH_TYPE.BASIC) {
-        onOk(`${values.username.trim()}:${values.password.trim()}`);
-      } else {
-        onOk(values.authValue.trim());
-      }
-    });
-  };
-
-  let content;
-  if (authType === AUTH_TYPE.API_KEY || authType === AUTH_TYPE.BEARER_TOKEN) {
-    const label = authType === AUTH_TYPE.API_KEY ? "API Key" : "Bearer Token";
-    content = (
-      <Form.Item name="authValue" label={label} rules={[{ required: true, message: `Please input your ${label}` }]}>
-        <Input.Password />
-      </Form.Item>
-    );
-  } else if (authType === AUTH_TYPE.BASIC) {
-    content = (
-      <>
-        <Form.Item name="username" label="Username" rules={[{ required: true, message: "Please input your username" }]}>
-          <Input />
-        </Form.Item>
-        <Form.Item name="password" label="Password" rules={[{ required: true, message: "Please input your password" }]}>
-          <Input.Password />
-        </Form.Item>
-      </>
-    );
-  }
-
-  return (
-    <Modal open={visible} title="Authentication" onOk={handleOk} onCancel={onCancel} destroyOnClose>
-      <Form form={form} layout="vertical">
-        {content}
-      </Form>
-    </Modal>
-  );
-};
-
-const AuthSection = ({
-  authType,
-  onAuthSubmit,
-  onClearAuth,
-  hasAuth,
-}: {
-  authType: string | null | undefined;
-  onAuthSubmit: (value: string) => void;
-  onClearAuth: () => void;
-  hasAuth: boolean;
-}) => {
-  const [modalVisible, setModalVisible] = useState(false);
-
-  const handleAddAuth = () => setModalVisible(true);
-
-  const handleModalOk = (authValue: string) => {
-    onAuthSubmit(authValue);
-    setModalVisible(false);
-  };
-
-  const handleModalCancel = () => setModalVisible(false);
-
-  const handleClearAuth = () => {
-    onClearAuth();
-  };
-
-  return (
-    <div className="space-y-3">
-      <div className="flex items-center justify-between">
-        <Text className="text-sm font-medium text-gray-700">Authentication {hasAuth ? "✓" : ""}</Text>
-        <div className="flex gap-2">
-          {hasAuth && (
-            <Button
-              onClick={handleClearAuth}
-              size="sm"
-              variant="secondary"
-              className="text-xs text-red-600 hover:text-red-700"
-            >
-              Clear
-            </Button>
-          )}
-          <Button onClick={handleAddAuth} size="sm" variant="secondary" className="text-xs">
-            {hasAuth ? "Update" : "Add Auth"}
-          </Button>
-        </div>
-      </div>
-      <Text className="text-xs text-gray-500">
-        {hasAuth ? "Authentication configured and saved locally" : "Some tools may require authentication"}
-      </Text>
-      <AuthModal visible={modalVisible} onOk={handleModalOk} onCancel={handleModalCancel} authType={authType} />
-    </div>
-  );
-};
+import { Card, Title, Text } from "@tremor/react";
+import { RobotOutlined, ToolOutlined } from "@ant-design/icons";
 
 const MCPToolsViewer = ({
   serverId,
@@ -125,47 +15,20 @@ const MCPToolsViewer = ({
   userID,
   serverAlias, // Add serverAlias prop
 }: MCPToolsViewerProps) => {
-  const [mcpAuthValue, setMcpAuthValue] = useState("");
   const [selectedTool, setSelectedTool] = useState<MCPTool | null>(null);
   const [toolResult, setToolResult] = useState<CallMCPToolResponse | null>(null);
   const [toolError, setToolError] = useState<Error | null>(null);
 
-  // Load stored auth token on component mount
-  useEffect(() => {
-    if (mcpServerHasAuth(auth_type)) {
-      const storedAuthValue = getMCPAuthToken(serverId, serverAlias || undefined);
-      if (storedAuthValue) {
-        setMcpAuthValue(storedAuthValue);
-      }
-    }
-  }, [serverId, serverAlias, auth_type]);
-
-  // Function to handle auth submission with localStorage persistence
-  const handleAuthSubmit = (authValue: string) => {
-    setMcpAuthValue(authValue);
-    if (authValue && mcpServerHasAuth(auth_type)) {
-      setMCPAuthToken(serverId, authValue, auth_type || "none", serverAlias || undefined);
-      NotificationsManager.success("Authentication token saved locally");
-    }
-  };
-
-  // Function to clear auth token
-  const handleClearAuth = () => {
-    setMcpAuthValue("");
-    removeMCPAuthToken(serverId);
-    NotificationsManager.info("Authentication token cleared");
-  };
-
   // Query to fetch MCP tools
   const {
     data: mcpToolsResponse,
     isLoading: isLoadingTools,
     error: mcpToolsError,
   } = useQuery({
-    queryKey: ["mcpTools", serverId, mcpAuthValue, serverAlias],
+    queryKey: ["mcpTools", serverId],
     queryFn: () => {
       if (!accessToken) throw new Error("Access Token required");
-      return listMCPTools(accessToken, serverId, mcpAuthValue, serverAlias || undefined);
+      return listMCPTools(accessToken, serverId);
     },
     enabled: !!accessToken,
     staleTime: 30000, // Consider data fresh for 30 seconds
@@ -173,17 +36,11 @@ const MCPToolsViewer = ({
 
   // Mutation for calling a tool
   const { mutate: executeTool, isPending: isCallingTool } = useMutation({
-    mutationFn: async (args: { tool: MCPTool; arguments: Record<string, any>; authValue: string }) => {
+    mutationFn: async (args: { tool: MCPTool; arguments: Record<string, any> }) => {
       if (!accessToken) throw new Error("Access Token required");
 
       try {
-        const result = await callMCPTool(
-          accessToken,
-          args.tool.name,
-          args.arguments,
-          args.authValue,
-          serverAlias || undefined,
-        );
+        const result = await callMCPTool(accessToken, args.tool.name, args.arguments);
         return result;
       } catch (error) {
         throw error;
@@ -200,7 +57,6 @@ const MCPToolsViewer = ({
   });
 
   const toolsData = mcpToolsResponse?.tools || [];
-  const hasAuth = mcpAuthValue !== "";
 
   return (
     <div className="w-full h-screen p-4 bg-white">
@@ -317,44 +173,6 @@ const MCPToolsViewer = ({
                   </div>
                 )}
               </div>
-
-              {/* Authentication Section - Below tools list */}
-              {mcpServerHasAuth(auth_type) && (
-                <div className="pt-4 border-t border-gray-200 flex-shrink-0 mt-6">
-                  {!hasAuth ? (
-                    /* Prominent display when auth required but not provided */
-                    <div className="p-4 bg-gradient-to-r from-orange-50 to-red-50 border border-orange-200 rounded-lg">
-                      <div className="flex items-center mb-3">
-                        <SafetyOutlined className="mr-2 text-orange-600 text-lg" />
-                        <Text className="font-semibold text-orange-800">Authentication Required</Text>
-                      </div>
-                      <Text className="text-sm text-orange-700 mb-4">
-                        This MCP server requires authentication. You must add your credentials below to access the
-                        tools.
-                      </Text>
-                      <AuthSection
-                        authType={auth_type}
-                        onAuthSubmit={handleAuthSubmit}
-                        onClearAuth={handleClearAuth}
-                        hasAuth={hasAuth}
-                      />
-                    </div>
-                  ) : (
-                    /* Subtle display when already authenticated */
-                    <>
-                      <Text className="font-medium block mb-3 text-gray-700 flex items-center">
-                        <SafetyOutlined className="mr-2" /> Authentication
-                      </Text>
-                      <AuthSection
-                        authType={auth_type}
-                        onAuthSubmit={handleAuthSubmit}
-                        onClearAuth={handleClearAuth}
-                        hasAuth={hasAuth}
-                      />
-                    </>
-                  )}
-                </div>
-              )}
             </div>
           </div>
 
@@ -379,10 +197,8 @@ const MCPToolsViewer = ({
                 <div className="h-full">
                   <ToolTestPanel
                     tool={selectedTool}
-                    needsAuth={mcpServerHasAuth(auth_type)}
-                    authValue={mcpAuthValue}
                     onSubmit={(args) => {
-                      executeTool({ tool: selectedTool, arguments: args, authValue: mcpAuthValue });
+                      executeTool({ tool: selectedTool, arguments: args });
                     }}
                     result={toolResult}
                     error={toolError}
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/types.tsx b/ui/litellm-dashboard/src/components/mcp_tools/types.tsx
index 29bb19d294f..3d5360207ac 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/types.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/types.tsx
@@ -34,10 +34,6 @@ export const handleAuth = (authType?: string | null): string => {
   return authType;
 };
 
-export const mcpServerHasAuth = (authType?: string | null): boolean => {
-  return handleAuth(authType) !== AUTH_TYPE.NONE;
-};
-
 // Define the structure for tool input schema properties
 export interface InputSchemaProperty {
   type: string;
diff --git a/ui/litellm-dashboard/src/components/model_info_view.test.tsx b/ui/litellm-dashboard/src/components/model_info_view.test.tsx
index 99b6d90998b..1e2803b8121 100644
--- a/ui/litellm-dashboard/src/components/model_info_view.test.tsx
+++ b/ui/litellm-dashboard/src/components/model_info_view.test.tsx
@@ -127,23 +127,23 @@ describe("ModelInfoView", () => {
     supported_openai_params: ["temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty"],
   };
 
+  const DEFAULT_ADMIN_PROPS = {
+    modelId: "123",
+    onClose: () => {},
+    modelData: modelData,
+    accessToken: "123",
+    userID: "123",
+    userRole: "Admin",
+    editModel: false,
+    setEditModalVisible: () => {},
+    setSelectedModel: () => {},
+    onModelUpdate: () => {},
+    modelAccessGroups: [],
+  };
+
   describe("Edit Model", () => {
     it("should render the model info view", async () => {
-      const { getByText } = render(
-        <ModelInfoView
-          modelId="123"
-          onClose={() => {}}
-          modelData={modelData}
-          accessToken="123"
-          userID="123"
-          userRole="Admin"
-          editModel={false}
-          setEditModalVisible={() => {}}
-          setSelectedModel={() => {}}
-          onModelUpdate={() => {}}
-          modelAccessGroups={[]}
-        />,
-      );
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Model Settings")).toBeInTheDocument();
       });
@@ -158,86 +158,93 @@ describe("ModelInfoView", () => {
         },
       };
 
-      const { queryByText } = render(
-        <ModelInfoView
-          modelId="123"
-          onClose={() => {}}
-          modelData={nonDbModelData}
-          accessToken="123"
-          userID="123"
-          userRole="Admin"
-          editModel={false}
-          setEditModalVisible={() => {}}
-          setSelectedModel={() => {}}
-          onModelUpdate={() => {}}
-          modelAccessGroups={[]}
-        />,
-      );
+      const NON_DB_ADMIN_PROPS = {
+        ...DEFAULT_ADMIN_PROPS,
+        modelData: nonDbModelData,
+      };
+
+      const { queryByText } = render(<ModelInfoView {...NON_DB_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(queryByText("Edit Model")).not.toBeInTheDocument();
       });
     });
 
     it("should render tags in the edit model", async () => {
-      const { getByText } = render(
-        <ModelInfoView
-          modelId="123"
-          onClose={() => {}}
-          modelData={modelData}
-          accessToken="123"
-          userID="123"
-          userRole="Admin"
-          editModel={true}
-          setEditModalVisible={() => {}}
-          setSelectedModel={() => {}}
-          onModelUpdate={() => {}}
-          modelAccessGroups={[]}
-        />,
-      );
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Tags")).toBeInTheDocument();
       });
     });
   });
 
+  it("should render a test connection button", async () => {
+    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
+    await waitFor(() => {
+      expect(getByTestId("test-connection-button")).toBeInTheDocument();
+    });
+  });
+
+  it("should render a reuse credentials button", async () => {
+    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
+    await waitFor(() => {
+      expect(getByTestId("reuse-credentials-button")).toBeInTheDocument();
+    });
+  });
+
+  it("should render a delete model button", async () => {
+    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
+    await waitFor(() => {
+      expect(getByTestId("delete-model-button")).toBeInTheDocument();
+    });
+  });
+
+  it("should render a disabled delete model button if the model is not a DB model", async () => {
+    const nonDbModelData = {
+      ...modelData,
+      model_info: {
+        ...modelData.model_info,
+        db_model: false,
+      },
+    };
+    const NON_DB_ADMIN_PROPS = {
+      ...DEFAULT_ADMIN_PROPS,
+      modelData: nonDbModelData,
+    };
+    const { getByTestId } = render(<ModelInfoView {...NON_DB_ADMIN_PROPS} />);
+    await waitFor(() => {
+      expect(getByTestId("delete-model-button")).toBeDisabled();
+    });
+  });
+
+  it("should render a disabled delete model button if the user is not an admin and model is not created by the user", async () => {
+    const nonCreatedByUserModelData = {
+      ...modelData,
+      model_info: {
+        ...modelData.model_info,
+        created_by: "456",
+      },
+    };
+    const NON_CREATED_BY_USER_ADMIN_PROPS = {
+      ...DEFAULT_ADMIN_PROPS,
+      modelData: nonCreatedByUserModelData,
+      userRole: "User",
+    };
+    const { getByTestId } = render(<ModelInfoView {...NON_CREATED_BY_USER_ADMIN_PROPS} />);
+    await waitFor(() => {
+      expect(getByTestId("delete-model-button")).toBeDisabled();
+    });
+  });
+
   describe("View Model", () => {
     it("should render the model info view", async () => {
-      const { getByText } = render(
-        <ModelInfoView
-          modelId="123"
-          onClose={() => {}}
-          modelData={modelData}
-          accessToken="123"
-          userID="123"
-          userRole="Admin"
-          editModel={false}
-          setEditModalVisible={() => {}}
-          setSelectedModel={() => {}}
-          onModelUpdate={() => {}}
-          modelAccessGroups={[]}
-        />,
-      );
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Model Settings")).toBeInTheDocument();
       });
     });
 
     it("should render tags in the view model", async () => {
-      const { getByText } = render(
-        <ModelInfoView
-          modelId="123"
-          onClose={() => {}}
-          modelData={modelData}
-          accessToken="123"
-          userID="123"
-          userRole="Admin"
-          editModel={false}
-          setEditModalVisible={() => {}}
-          setSelectedModel={() => {}}
-          onModelUpdate={() => {}}
-          modelAccessGroups={[]}
-        />,
-      );
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Tags")).toBeInTheDocument();
       });
diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx
index df5292b52e0..d9212d72d03 100644
--- a/ui/litellm-dashboard/src/components/model_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/model_info_view.tsx
@@ -1,40 +1,42 @@
-import React, { useState, useEffect } from "react";
+import { InfoCircleOutlined } from "@ant-design/icons";
+import { ArrowLeftIcon, KeyIcon, RefreshIcon, TrashIcon } from "@heroicons/react/outline";
 import {
   Card,
-  Title,
-  Text,
+  Grid,
   Tab,
-  TabList,
   TabGroup,
+  TabList,
   TabPanel,
   TabPanels,
-  Grid,
-  Button as TremorButton,
+  Text,
   TextInput,
+  Title,
+  Button as TremorButton,
 } from "@tremor/react";
-import NumericalInput from "./shared/numerical_input";
-import { ArrowLeftIcon, TrashIcon, KeyIcon } from "@heroicons/react/outline";
+import { Button, Form, Input, Modal, Select, Tooltip } from "antd";
+import { CheckIcon, CopyIcon } from "lucide-react";
+import { useEffect, useState } from "react";
+import { copyToClipboard as utilCopyToClipboard } from "../utils/dataUtils";
+import { truncateString } from "../utils/textUtils";
+import CacheControlSettings from "./add_model/cache_control_settings";
+import EditAutoRouterModal from "./edit_auto_router/edit_auto_router_modal";
+import ReuseCredentialsModal from "./model_add/reuse_credentials";
+import NotificationsManager from "./molecules/notifications_manager";
 import {
-  modelDeleteCall,
   CredentialItem,
-  credentialGetCall,
   credentialCreateCall,
+  credentialGetCall,
+  getGuardrailsList,
+  modelDeleteCall,
   modelInfoV1Call,
   modelPatchUpdateCall,
-  getGuardrailsList,
   tagListCall,
+  testConnectionRequest,
 } from "./networking";
-import { Button, Form, Input, Select, Modal, Tooltip } from "antd";
-import { InfoCircleOutlined } from "@ant-design/icons";
 import { getProviderLogoAndName } from "./provider_info_helpers";
-import { getDisplayModelName } from "./view_model/model_name_display";
-import ReuseCredentialsModal from "./model_add/reuse_credentials";
-import CacheControlSettings from "./add_model/cache_control_settings";
-import { CheckIcon, CopyIcon } from "lucide-react";
-import { copyToClipboard as utilCopyToClipboard } from "../utils/dataUtils";
-import EditAutoRouterModal from "./edit_auto_router/edit_auto_router_modal";
-import NotificationsManager from "./molecules/notifications_manager";
+import NumericalInput from "./shared/numerical_input";
 import { Tag } from "./tag_management/types";
+import { getDisplayModelName } from "./view_model/model_name_display";
 
 interface ModelInfoViewProps {
   modelId: string;
@@ -262,6 +264,37 @@ export default function ModelInfoView({
     );
   }
 
+  const handleTestConnection = async () => {
+    if (!accessToken) return;
+    try {
+      NotificationsManager.info("Testing connection...");
+      const response = await testConnectionRequest(
+        accessToken,
+        {
+          custom_llm_provider: localModelData.litellm_params.custom_llm_provider,
+          litellm_credential_name: localModelData.litellm_params.litellm_credential_name,
+          model: localModelData.litellm_model_name,
+        },
+        {
+          mode: localModelData.model_info?.mode,
+        },
+        localModelData.model_info?.mode,
+      );
+
+      if (response.status === "success") {
+        NotificationsManager.success("Connection test successful!");
+      } else {
+        throw new Error(response?.result?.error || response?.message || "Unknown error");
+      }
+    } catch (error) {
+      if (error instanceof Error) {
+        NotificationsManager.error("Error testing connection: " + truncateString(error.message, 100));
+      } else {
+        NotificationsManager.error("Error testing connection: " + String(error));
+      }
+    }
+  };
+
   const handleDelete = async () => {
     try {
       if (!accessToken) return;
@@ -323,26 +356,36 @@ export default function ModelInfoView({
           </div>
         </div>
         <div className="flex gap-2">
-          {isAdmin && (
-            <TremorButton
-              icon={KeyIcon}
-              variant="secondary"
-              onClick={() => setIsCredentialModalOpen(true)}
-              className="flex items-center"
-            >
-              Re-use Credentials
-            </TremorButton>
-          )}
-          {canEditModel && (
-            <TremorButton
-              icon={TrashIcon}
-              variant="secondary"
-              onClick={() => setIsDeleteModalOpen(true)}
-              className="flex items-center"
-            >
-              Delete Model
-            </TremorButton>
-          )}
+          <TremorButton
+            variant="secondary"
+            icon={RefreshIcon}
+            onClick={handleTestConnection}
+            className="flex items-center gap-2"
+            data-testid="test-connection-button"
+          >
+            Test Connection
+          </TremorButton>
+
+          <TremorButton
+            icon={KeyIcon}
+            variant="secondary"
+            onClick={() => setIsCredentialModalOpen(true)}
+            className="flex items-center"
+            disabled={!isAdmin}
+            data-testid="reuse-credentials-button"
+          >
+            Re-use Credentials
+          </TremorButton>
+          <TremorButton
+            icon={TrashIcon}
+            variant="secondary"
+            onClick={() => setIsDeleteModalOpen(true)}
+            className="flex items-center text-red-500 border-red-500"
+            disabled={!canEditModel}
+            data-testid="delete-model-button"
+          >
+            Delete Model
+          </TremorButton>
         </div>
       </div>
 
diff --git a/ui/litellm-dashboard/src/components/navbar.tsx b/ui/litellm-dashboard/src/components/navbar.tsx
index 0316f3bf6ac..667691168ca 100644
--- a/ui/litellm-dashboard/src/components/navbar.tsx
+++ b/ui/litellm-dashboard/src/components/navbar.tsx
@@ -15,7 +15,6 @@ import {
 import { clearTokenCookies } from "@/utils/cookieUtils";
 import { fetchProxySettings } from "@/utils/proxyUtils";
 import { useTheme } from "@/contexts/ThemeContext";
-import { clearMCPAuthTokens } from "./mcp_tools/mcp_auth_storage";
 import useFeatureFlags from "@/hooks/useFeatureFlags";
 
 interface NavbarProps {
@@ -71,7 +70,6 @@ const Navbar: React.FC<NavbarProps> = ({
 
   const handleLogout = () => {
     clearTokenCookies();
-    clearMCPAuthTokens(); // Clear MCP auth tokens on logout
     window.location.href = logoutUrl;
   };
 
diff --git a/ui/litellm-dashboard/src/components/networking.test.ts b/ui/litellm-dashboard/src/components/networking.test.ts
index eae676b52fd..34f419e4900 100644
--- a/ui/litellm-dashboard/src/components/networking.test.ts
+++ b/ui/litellm-dashboard/src/components/networking.test.ts
@@ -1,5 +1,6 @@
-import { describe, it, expect, beforeEach, vi } from "vitest";
+import { describe, it, expect, beforeEach, afterEach, vi } from "vitest";
 import { clearTokenCookies } from "@/utils/cookieUtils";
+import { updateSSOSettings } from "./networking";
 
 vi.mock("@/utils/cookieUtils", () => ({
   clearTokenCookies: vi.fn(),
@@ -16,10 +17,16 @@ vi.mock("./molecules/notifications_manager", () => ({
 }));
 
 describe("networking - expired session handling", () => {
+  const originalFetch = global.fetch;
+
   beforeEach(() => {
     vi.clearAllMocks();
   });
 
+  afterEach(() => {
+    global.fetch = originalFetch;
+  });
+
   it("should call clearTokenCookies on expired session", async () => {
     const errorData = "Authentication Error - Expired Key";
     const { default: NotificationsManager } = await import("./molecules/notifications_manager");
@@ -41,4 +48,33 @@ describe("networking - expired session handling", () => {
 
     expect(clearTokenCookies).not.toHaveBeenCalled();
   });
+
+  it("should surface backend detail error when updateSSOSettings fails", async () => {
+    expect.hasAssertions();
+
+    const backendError = {
+      detail: {
+        error: "Set `'STORE_MODEL_IN_DB='True'` in your env to enable this feature.",
+      },
+    };
+
+    const mockFetch = vi.fn().mockResolvedValue({
+      ok: false,
+      json: vi.fn().mockResolvedValue(backendError),
+    } as any);
+
+    global.fetch = mockFetch as any;
+
+    try {
+      await updateSSOSettings("token", { some: "setting" });
+    } catch (error) {
+      const thrownError = error as any;
+      expect(thrownError).toBeInstanceOf(Error);
+      expect(thrownError.message).toBe(backendError.detail.error);
+      expect(thrownError.detail).toEqual(backendError.detail);
+      expect(thrownError.rawError).toEqual(backendError);
+    }
+
+    expect(mockFetch).toHaveBeenCalledOnce();
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx
index 387e1dd0cb8..a8cb05310c8 100644
--- a/ui/litellm-dashboard/src/components/networking.tsx
+++ b/ui/litellm-dashboard/src/components/networking.tsx
@@ -4444,9 +4444,7 @@ export const getGeneralSettingsCall = async (accessToken: string) => {
 
 export const getRouterSettingsCall = async (accessToken: string) => {
   try {
-    let url = proxyBaseUrl
-      ? `${proxyBaseUrl}/router/settings`
-      : `/router/settings`;
+    let url = proxyBaseUrl ? `${proxyBaseUrl}/router/settings` : `/router/settings`;
 
     const response = await fetch(url, {
       method: "GET",
@@ -4473,9 +4471,7 @@ export const getRouterSettingsCall = async (accessToken: string) => {
 
 export const getCacheSettingsCall = async (accessToken: string) => {
   try {
-    let url = proxyBaseUrl
-      ? `${proxyBaseUrl}/cache/settings`
-      : `/cache/settings`;
+    let url = proxyBaseUrl ? `${proxyBaseUrl}/cache/settings` : `/cache/settings`;
 
     const response = await fetch(url, {
       method: "GET",
@@ -4500,14 +4496,9 @@ export const getCacheSettingsCall = async (accessToken: string) => {
   }
 };
 
-export const testCacheConnectionCall = async (
-  accessToken: string,
-  cacheSettings: Record<string, any>
-) => {
+export const testCacheConnectionCall = async (accessToken: string, cacheSettings: Record<string, any>) => {
   try {
-    let url = proxyBaseUrl
-      ? `${proxyBaseUrl}/cache/settings/test`
-      : `/cache/settings/test`;
+    let url = proxyBaseUrl ? `${proxyBaseUrl}/cache/settings/test` : `/cache/settings/test`;
 
     const response = await fetch(url, {
       method: "POST",
@@ -4535,10 +4526,7 @@ export const testCacheConnectionCall = async (
   }
 };
 
-export const updateCacheSettingsCall = async (
-  accessToken: string,
-  cacheSettings: Record<string, any>
-) => {
+export const updateCacheSettingsCall = async (accessToken: string, cacheSettings: Record<string, any>) => {
   try {
     let url = proxyBaseUrl ? `${proxyBaseUrl}/cache/settings` : `/cache/settings`;
 
@@ -5701,8 +5689,8 @@ export const deleteSearchTool = async (accessToken: string, searchToolId: string
 
 export const fetchAvailableSearchProviders = async (accessToken: string) => {
   try {
-    const url = proxyBaseUrl 
-      ? `${proxyBaseUrl}/search_tools/ui/available_providers` 
+    const url = proxyBaseUrl
+      ? `${proxyBaseUrl}/search_tools/ui/available_providers`
       : `/search_tools/ui/available_providers`;
     console.log("Fetching available search providers from:", url);
 
@@ -5730,14 +5718,9 @@ export const fetchAvailableSearchProviders = async (accessToken: string) => {
   }
 };
 
-export const testSearchToolConnection = async (
-  accessToken: string,
-  litellmParams: Record<string, any>
-) => {
+export const testSearchToolConnection = async (accessToken: string, litellmParams: Record<string, any>) => {
   try {
-    const url = proxyBaseUrl
-      ? `${proxyBaseUrl}/search_tools/test_connection`
-      : `/search_tools/test_connection`;
+    const url = proxyBaseUrl ? `${proxyBaseUrl}/search_tools/test_connection` : `/search_tools/test_connection`;
     console.log("Testing search tool connection:", url);
 
     const response = await fetch(url, {
@@ -5767,7 +5750,7 @@ export const testSearchToolConnection = async (
   }
 };
 
-export const listMCPTools = async (accessToken: string, serverId: string, authValue?: string, serverAlias?: string) => {
+export const listMCPTools = async (accessToken: string, serverId: string) => {
   try {
     // Construct base URL
     let url = proxyBaseUrl
@@ -5781,14 +5764,6 @@ export const listMCPTools = async (accessToken: string, serverId: string, authVa
       "Content-Type": "application/json",
     };
 
-    // Use new server-specific auth header format if serverAlias is provided
-    if (serverAlias && authValue) {
-      headers[`x-mcp-${serverAlias}-authorization`] = authValue;
-    } else if (authValue) {
-      // Fall back to deprecated x-mcp-auth header for backward compatibility
-      headers[MCP_AUTH_HEADER] = authValue;
-    }
-
     const response = await fetch(url, {
       method: "GET",
       headers,
@@ -5822,9 +5797,7 @@ export const listMCPTools = async (accessToken: string, serverId: string, authVa
 export const callMCPTool = async (
   accessToken: string,
   toolName: string,
-  toolArguments: Record<string, any>,
-  authValue: string,
-  serverAlias?: string,
+  toolArguments: Record<string, any>
 ) => {
   try {
     // Construct base URL
@@ -5837,14 +5810,6 @@ export const callMCPTool = async (
       "Content-Type": "application/json",
     };
 
-    // Use new server-specific auth header format if serverAlias is provided
-    if (serverAlias) {
-      headers[`x-mcp-${serverAlias}-authorization`] = authValue;
-    } else {
-      // Fall back to deprecated x-mcp-auth header for backward compatibility
-      headers[MCP_AUTH_HEADER] = authValue;
-    }
-
     const response = await fetch(url, {
       method: "POST",
       headers,
@@ -6591,7 +6556,7 @@ export const applyGuardrail = async (
     if (!response.ok) {
       const errorData = await response.text();
       let errorMessage = "Failed to apply guardrail";
-      
+
       try {
         const errorJson = JSON.parse(errorData);
         if (errorJson.error?.message) {
@@ -6604,7 +6569,7 @@ export const applyGuardrail = async (
       } catch (e) {
         errorMessage = errorData || errorMessage;
       }
-      
+
       handleError(errorData);
       throw new Error(errorMessage);
     }
@@ -6618,6 +6583,36 @@ export const applyGuardrail = async (
   }
 };
 
+export const validateBlockedWordsFile = async (accessToken: string, fileContent: string) => {
+  try {
+    const url = proxyBaseUrl
+      ? `${proxyBaseUrl}/guardrails/validate_blocked_words_file`
+      : `/guardrails/validate_blocked_words_file`;
+
+    const response = await fetch(url, {
+      method: "POST",
+      headers: {
+        [globalLitellmHeaderName]: `Bearer ${accessToken}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({ file_content: fileContent }),
+    });
+
+    if (!response.ok) {
+      const errorData = await response.text();
+      handleError(errorData);
+      throw new Error("Failed to validate blocked words file");
+    }
+
+    const data = await response.json();
+    console.log("Validate blocked words file response:", data);
+    return data;
+  } catch (error) {
+    console.error("Failed to validate blocked words file:", error);
+    throw error;
+  }
+};
+
 export const getSSOSettings = async (accessToken: string) => {
   try {
     // Construct base URL
@@ -6667,9 +6662,22 @@ export const updateSSOSettings = async (accessToken: string, settings: Record<st
 
     if (!response.ok) {
       const errorData = await response.json();
-      const errorMessage = deriveErrorMessage(errorData);
+      const detailMessage =
+        typeof errorData?.detail === "object"
+          ? errorData.detail?.error || errorData.detail?.message
+          : errorData?.detail;
+      const errorMessage =
+        typeof detailMessage === "string" && detailMessage.length > 0 ? detailMessage : deriveErrorMessage(errorData);
+
       handleError(errorMessage);
-      throw new Error(errorMessage);
+
+      const enhancedError = new Error(errorMessage);
+      if (errorData?.detail !== undefined) {
+        (enhancedError as any).detail = errorData.detail;
+      }
+      (enhancedError as any).rawError = errorData;
+
+      throw enhancedError;
     }
 
     const data = await response.json();
diff --git a/ui/litellm-dashboard/src/components/new_usage.test.tsx b/ui/litellm-dashboard/src/components/new_usage.test.tsx
new file mode 100644
index 00000000000..6c426401c54
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/new_usage.test.tsx
@@ -0,0 +1,253 @@
+import { render, screen, fireEvent, waitFor } from "@testing-library/react";
+import { describe, it, expect, vi, beforeEach, beforeAll } from "vitest";
+import NewUsagePage from "./new_usage";
+import * as networking from "./networking";
+
+// Polyfill ResizeObserver for test environment
+beforeAll(() => {
+  if (typeof window !== "undefined" && !window.ResizeObserver) {
+    window.ResizeObserver = class ResizeObserver {
+      observe() {}
+      unobserve() {}
+      disconnect() {}
+    } as any;
+  }
+});
+
+// Mock the networking module
+vi.mock("./networking", () => ({
+  userDailyActivityCall: vi.fn(),
+  userDailyActivityAggregatedCall: vi.fn(),
+  tagListCall: vi.fn(),
+}));
+
+// Mock child components to simplify testing
+vi.mock("./activity_metrics", () => ({
+  ActivityMetrics: () => <div>Activity Metrics</div>,
+  processActivityData: () => ({ data: [], metadata: {} }),
+}));
+
+vi.mock("./view_user_spend", () => ({
+  default: () => <div>View User Spend</div>,
+}));
+
+vi.mock("./top_key_view", () => ({
+  default: () => <div>Top Keys</div>,
+}));
+
+vi.mock("./entity_usage", () => ({
+  default: () => <div>Entity Usage</div>,
+  EntityList: [],
+}));
+
+vi.mock("./user_agent_activity", () => ({
+  default: () => <div>User Agent Activity</div>,
+}));
+
+vi.mock("./cloudzero_export_modal", () => ({
+  default: () => <div>CloudZero Export Modal</div>,
+}));
+
+vi.mock("./EntityUsageExport", () => ({
+  default: () => <div>Entity Usage Export Modal</div>,
+}));
+
+describe("NewUsage", () => {
+  const mockUserDailyActivityAggregatedCall = vi.mocked(networking.userDailyActivityAggregatedCall);
+  const mockTagListCall = vi.mocked(networking.tagListCall);
+
+  const mockSpendData = {
+    results: [
+      {
+        date: "2025-01-01",
+        metrics: {
+          spend: 125.75,
+          api_requests: 1500,
+          successful_requests: 1450,
+          failed_requests: 50,
+          total_tokens: 75000,
+          prompt_tokens: 45000,
+          completion_tokens: 30000,
+          cache_read_input_tokens: 0,
+          cache_creation_input_tokens: 0,
+        },
+        breakdown: {
+          models: {
+            "gpt-4": {
+              metrics: {
+                spend: 75.5,
+                api_requests: 800,
+                successful_requests: 780,
+                failed_requests: 20,
+                total_tokens: 40000,
+                prompt_tokens: 24000,
+                completion_tokens: 16000,
+                cache_read_input_tokens: 0,
+                cache_creation_input_tokens: 0,
+              },
+              metadata: {},
+              api_key_breakdown: {},
+            },
+          },
+          model_groups: {
+            "gpt-4": {
+              metrics: {
+                spend: 75.5,
+                api_requests: 800,
+                successful_requests: 780,
+                failed_requests: 20,
+                total_tokens: 40000,
+                prompt_tokens: 24000,
+                completion_tokens: 16000,
+                cache_read_input_tokens: 0,
+                cache_creation_input_tokens: 0,
+              },
+              metadata: {},
+              api_key_breakdown: {},
+            },
+          },
+          api_keys: {
+            "sk-test123": {
+              metrics: {
+                spend: 125.75,
+                api_requests: 1500,
+                successful_requests: 1450,
+                failed_requests: 50,
+                total_tokens: 75000,
+                prompt_tokens: 45000,
+                completion_tokens: 30000,
+                cache_read_input_tokens: 0,
+                cache_creation_input_tokens: 0,
+              },
+              metadata: {
+                key_alias: "Test Key",
+                tags: ["production"],
+              },
+            },
+          },
+          providers: {
+            openai: {
+              metrics: {
+                spend: 125.75,
+                api_requests: 1500,
+                successful_requests: 1450,
+                failed_requests: 50,
+                total_tokens: 75000,
+                prompt_tokens: 45000,
+                completion_tokens: 30000,
+                cache_read_input_tokens: 0,
+                cache_creation_input_tokens: 0,
+              },
+            },
+          },
+          mcp_servers: {},
+        },
+      },
+    ],
+    metadata: {
+      total_spend: 125.75,
+      total_api_requests: 1500,
+      total_successful_requests: 1450,
+      total_failed_requests: 50,
+      total_tokens: 75000,
+    },
+  };
+
+  const defaultProps = {
+    accessToken: "test-token",
+    userRole: "Admin",
+    userID: "user-123",
+    teams: [
+      {
+        team_id: "team-1",
+        team_alias: "Test Team",
+        models: [],
+        max_budget: null,
+        spend: 0,
+        tpm_limit: null,
+        rpm_limit: null,
+        blocked: false,
+        metadata: {},
+        budget_duration: null,
+        organization_id: "org-123",
+        created_at: "2025-01-01T00:00:00Z",
+        keys: [],
+        members_with_roles: [],
+      },
+    ],
+    premiumUser: true,
+  };
+
+  beforeEach(() => {
+    mockUserDailyActivityAggregatedCall.mockClear();
+    mockTagListCall.mockClear();
+    mockUserDailyActivityAggregatedCall.mockResolvedValue(mockSpendData);
+    mockTagListCall.mockResolvedValue({});
+  });
+
+  it("should render and fetch usage data on mount", async () => {
+    render(<NewUsagePage {...defaultProps} />);
+
+    // Wait for data to be fetched
+    await waitFor(() => {
+      expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled();
+    });
+
+    // Check that key metrics are displayed
+    expect(screen.getByText("Total Requests")).toBeInTheDocument();
+    expect(screen.getByText("1,500")).toBeInTheDocument();
+    expect(screen.getByText("Successful Requests")).toBeInTheDocument();
+    // Use getAllByText since this value appears in multiple places (metrics card + table)
+    const successfulRequestElements = screen.getAllByText("1,450");
+    expect(successfulRequestElements.length).toBeGreaterThan(0);
+  });
+
+  it("should display usage metrics and charts", async () => {
+    render(<NewUsagePage {...defaultProps} />);
+
+    await waitFor(() => {
+      expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled();
+    });
+
+    // Check for usage metrics cards
+    expect(screen.getByText("Total Requests")).toBeInTheDocument();
+    expect(screen.getByText("Successful Requests")).toBeInTheDocument();
+    expect(screen.getByText("Failed Requests")).toBeInTheDocument();
+    expect(screen.getByText("Total Tokens")).toBeInTheDocument();
+
+    // Check for chart titles
+    expect(screen.getByText("Daily Spend")).toBeInTheDocument();
+    expect(screen.getByText("Top API Keys")).toBeInTheDocument();
+  });
+
+  it("should switch between tabs correctly", async () => {
+    render(<NewUsagePage {...defaultProps} />);
+
+    await waitFor(() => {
+      expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled();
+    });
+
+    // Default tab should show Global Usage (for admin)
+    expect(screen.getByText("Daily Spend")).toBeInTheDocument();
+
+    // Switch to Team Usage tab
+    const teamUsageTab = screen.getByText("Team Usage");
+    fireEvent.click(teamUsageTab);
+
+    // Should render EntityUsage component
+    await waitFor(() => {
+      const entityUsageElements = screen.getAllByText("Entity Usage");
+      expect(entityUsageElements.length).toBeGreaterThan(0);
+    });
+
+    // Switch to Tag Usage tab (admin only)
+    const tagUsageTab = screen.getByText("Tag Usage");
+    fireEvent.click(tagUsageTab);
+
+    // Should still render EntityUsage component for tags
+    await waitFor(() => {
+      const entityUsageElements = screen.getAllByText("Entity Usage");
+      expect(entityUsageElements.length).toBeGreaterThan(0);
+    });
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/new_usage.tsx b/ui/litellm-dashboard/src/components/new_usage.tsx
index 2e85bc2b157..95d721ce9ba 100644
--- a/ui/litellm-dashboard/src/components/new_usage.tsx
+++ b/ui/litellm-dashboard/src/components/new_usage.tsx
@@ -408,354 +408,373 @@ const NewUsagePage: React.FC<NewUsagePageProps> = ({ accessToken, userRole, user
         </div>
       )} */}
 
-      <TabGroup>
-        <TabList variant="solid" className="mt-1">
-          {all_admin_roles.includes(userRole || "") ? <Tab>Global Usage</Tab> : <Tab>Your Usage</Tab>}
-          <Tab>Team Usage</Tab>
-          {all_admin_roles.includes(userRole || "") ? <Tab>Tag Usage</Tab> : <></>}
-          {all_admin_roles.includes(userRole || "") ? <Tab>User Agent Activity</Tab> : <></>}
-        </TabList>
-        <TabPanels>
-          {/* Your Usage Panel */}
-          <TabPanel>
-            <Grid numItems={2} className="gap-10 w-full mb-4">
-              <Col>
-                <AdvancedDatePicker value={dateValue} onValueChange={handleDateChange} />
-              </Col>
-            </Grid>
-            <TabGroup>
-              <div className="flex justify-between items-center">
-                <TabList variant="solid" className="mt-1">
-                  <Tab>Cost</Tab>
-                  <Tab>Model Activity</Tab>
-                  <Tab>Key Activity</Tab>
-                  <Tab>MCP Server Activity</Tab>
-                </TabList>
-                <Button
-                  onClick={() => setIsGlobalExportModalOpen(true)}
-                  icon={() => (
-                    <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                      <path
-                        strokeLinecap="round"
-                        strokeLinejoin="round"
-                        strokeWidth={2}
-                        d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"
-                      />
-                    </svg>
-                  )}
-                >
-                  Export Data
-                </Button>
-              </div>
-              <TabPanels>
-                {/* Cost Panel */}
-                <TabPanel>
-                  <Grid numItems={2} className="gap-2 w-full">
-                    {/* Total Spend Card */}
-                    <Col numColSpan={2}>
-                      <Text className="text-tremor-default text-tremor-content dark:text-dark-tremor-content mb-2 mt-2 text-lg">
-                        Project Spend{" "}
-                        {new Date().toLocaleString("default", {
-                          month: "long",
-                        })}{" "}
-                        1 - {new Date(new Date().getFullYear(), new Date().getMonth() + 1, 0).getDate()}
-                      </Text>
-
-                      <ViewUserSpend
-                        userID={userID}
-                        userRole={userRole}
-                        accessToken={accessToken}
-                        userSpend={totalSpend}
-                        selectedTeam={null}
-                        userMaxBudget={null}
-                      />
-                    </Col>
-
-                    <Col numColSpan={2}>
-                      <Card>
-                        <Title>Usage Metrics</Title>
-                        <Grid numItems={5} className="gap-4 mt-4">
-                          <Card>
-                            <Title>Total Requests</Title>
-                            <Text className="text-2xl font-bold mt-2">
-                              {userSpendData.metadata?.total_api_requests?.toLocaleString() || 0}
-                            </Text>
-                          </Card>
-                          <Card>
-                            <Title>Successful Requests</Title>
-                            <Text className="text-2xl font-bold mt-2 text-green-600">
-                              {userSpendData.metadata?.total_successful_requests?.toLocaleString() || 0}
-                            </Text>
-                          </Card>
-                          <Card>
-                            <Title>Failed Requests</Title>
-                            <Text className="text-2xl font-bold mt-2 text-red-600">
-                              {userSpendData.metadata?.total_failed_requests?.toLocaleString() || 0}
-                            </Text>
-                          </Card>
-                          <Card>
-                            <Title>Total Tokens</Title>
-                            <Text className="text-2xl font-bold mt-2">
-                              {userSpendData.metadata?.total_tokens?.toLocaleString() || 0}
-                            </Text>
-                          </Card>
-                          <Card>
-                            <Title>Average Cost per Request</Title>
-                            <Text className="text-2xl font-bold mt-2">
-                              $
-                              {formatNumberWithCommas(
-                                (totalSpend || 0) / (userSpendData.metadata?.total_api_requests || 1),
-                                4,
-                              )}
-                            </Text>
-                          </Card>
-                        </Grid>
-                      </Card>
-                    </Col>
-
-                    {/* Daily Spend Chart */}
-                    <Col numColSpan={2}>
-                      <Card>
-                        <Title>Daily Spend</Title>
-                        {loading ? (
-                          <ChartLoader isDateChanging={isDateChanging} />
-                        ) : (
-                          <BarChart
-                            data={[...userSpendData.results].sort(
-                              (a, b) => new Date(a.date).getTime() - new Date(b.date).getTime(),
+      {/* Global Date Picker and Tabs - Single Row */}
+      <div className="flex items-end justify-between gap-6 mb-6">
+        <div className="flex-1">
+          <TabGroup>
+            <div className="flex items-end justify-start gap-6 mb-6">
+              <TabList variant="solid">
+                {all_admin_roles.includes(userRole || "") ? <Tab>Global Usage</Tab> : <Tab>Your Usage</Tab>}
+                <Tab>Team Usage</Tab>
+                {all_admin_roles.includes(userRole || "") ? <Tab>Tag Usage</Tab> : <></>}
+                {all_admin_roles.includes(userRole || "") ? <Tab>User Agent Activity</Tab> : <></>}
+              </TabList>
+              <AdvancedDatePicker value={dateValue} onValueChange={handleDateChange} />
+            </div>
+            <TabPanels>
+              {/* Your Usage Panel */}
+              <TabPanel>
+                <TabGroup>
+                  <div className="flex justify-between items-center">
+                    <TabList variant="solid" className="mt-1">
+                      <Tab>Cost</Tab>
+                      <Tab>Model Activity</Tab>
+                      <Tab>Key Activity</Tab>
+                      <Tab>MCP Server Activity</Tab>
+                    </TabList>
+                    <Button
+                      onClick={() => setIsGlobalExportModalOpen(true)}
+                      icon={() => (
+                        <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                          <path
+                            strokeLinecap="round"
+                            strokeLinejoin="round"
+                            strokeWidth={2}
+                            d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"
+                          />
+                        </svg>
+                      )}
+                    >
+                      Export Data
+                    </Button>
+                  </div>
+                  <TabPanels>
+                    {/* Cost Panel */}
+                    <TabPanel>
+                      <Grid numItems={2} className="gap-2 w-full">
+                        {/* Total Spend Card */}
+                        <Col numColSpan={2}>
+                          <Text className="text-tremor-default text-tremor-content dark:text-dark-tremor-content mb-2 mt-2 text-lg">
+                            Project Spend{" "}
+                            {dateValue.from && dateValue.to && (
+                              <>
+                                {dateValue.from.toLocaleDateString("en-US", {
+                                  month: "short",
+                                  day: "numeric",
+                                  year:
+                                    dateValue.from.getFullYear() !== dateValue.to.getFullYear() ? "numeric" : undefined,
+                                })}
+                                {" - "}
+                                {dateValue.to.toLocaleDateString("en-US", {
+                                  month: "short",
+                                  day: "numeric",
+                                  year: "numeric",
+                                })}
+                              </>
                             )}
-                            index="date"
-                            categories={["metrics.spend"]}
-                            colors={["cyan"]}
-                            valueFormatter={valueFormatterSpend}
-                            yAxisWidth={100}
-                            showLegend={false}
-                            customTooltip={({ payload, active }) => {
-                              if (!active || !payload?.[0]) return null;
-                              const data = payload[0].payload;
-                              return (
-                                <div className="bg-white p-4 shadow-lg rounded-lg border">
-                                  <p className="font-bold">{data.date}</p>
-                                  <p className="text-cyan-500">
-                                    Spend: ${formatNumberWithCommas(data.metrics.spend, 2)}
-                                  </p>
-                                  <p className="text-gray-600">Requests: {data.metrics.api_requests}</p>
-                                  <p className="text-gray-600">Successful: {data.metrics.successful_requests}</p>
-                                  <p className="text-gray-600">Failed: {data.metrics.failed_requests}</p>
-                                  <p className="text-gray-600">Tokens: {data.metrics.total_tokens}</p>
-                                </div>
-                              );
-                            }}
-                          />
-                        )}
-                      </Card>
-                    </Col>
-                    {/* Top API Keys */}
-                    <Col numColSpan={1}>
-                      <Card className="h-full">
-                        <Title>Top API Keys</Title>
-                        <TopKeyView
-                          topKeys={getTopKeys()}
-                          accessToken={accessToken}
-                          userID={userID}
-                          userRole={userRole}
-                          teams={null}
-                          premiumUser={premiumUser}
-                        />
-                      </Card>
-                    </Col>
+                          </Text>
 
-                    {/* Top Models */}
-                    <Col numColSpan={1}>
-                      <Card className="h-full">
-                        <div className="flex justify-between items-center mb-4">
-                          <Title>{modelViewType === "groups" ? "Top Public Model Names" : "Top Litellm Models"}</Title>
-                          <div className="flex bg-gray-100 rounded-lg p-1">
-                            <button
-                              className={`px-3 py-1 text-sm rounded-md transition-colors ${
-                                modelViewType === "groups"
-                                  ? "bg-white shadow-sm text-gray-900"
-                                  : "text-gray-600 hover:text-gray-900"
-                              }`}
-                              onClick={() => setModelViewType("groups")}
-                            >
-                              Public Model Name
-                            </button>
-                            <button
-                              className={`px-3 py-1 text-sm rounded-md transition-colors ${
-                                modelViewType === "individual"
-                                  ? "bg-white shadow-sm text-gray-900"
-                                  : "text-gray-600 hover:text-gray-900"
-                              }`}
-                              onClick={() => setModelViewType("individual")}
-                            >
-                              Litellm Model Name
-                            </button>
-                          </div>
-                        </div>
-                        {loading ? (
-                          <ChartLoader isDateChanging={isDateChanging} />
-                        ) : (
-                          <BarChart
-                            className="mt-4 h-40"
-                            data={modelViewType === "groups" ? getTopModelGroups() : getTopModels()}
-                            index="key"
-                            categories={["spend"]}
-                            colors={["cyan"]}
-                            valueFormatter={valueFormatterSpend}
-                            layout="vertical"
-                            yAxisWidth={200}
-                            showLegend={false}
-                            customTooltip={({ payload, active }) => {
-                              if (!active || !payload?.[0]) return null;
-                              const data = payload[0].payload;
-                              return (
-                                <div className="bg-white p-4 shadow-lg rounded-lg border">
-                                  <p className="font-bold">{data.key}</p>
-                                  <p className="text-cyan-500">Spend: ${formatNumberWithCommas(data.spend, 2)}</p>
-                                  <p className="text-gray-600">Total Requests: {data.requests.toLocaleString()}</p>
-                                  <p className="text-green-600">
-                                    Successful: {data.successful_requests.toLocaleString()}
-                                  </p>
-                                  <p className="text-red-600">Failed: {data.failed_requests.toLocaleString()}</p>
-                                  <p className="text-gray-600">Tokens: {data.tokens.toLocaleString()}</p>
-                                </div>
-                              );
-                            }}
+                          <ViewUserSpend
+                            userID={userID}
+                            userRole={userRole}
+                            accessToken={accessToken}
+                            userSpend={totalSpend}
+                            selectedTeam={null}
+                            userMaxBudget={null}
                           />
-                        )}
-                      </Card>
-                    </Col>
+                        </Col>
 
-                    {/* Spend by Provider */}
-                    <Col numColSpan={2}>
-                      <Card className="h-full">
-                        <div className="flex justify-between items-center mb-4">
-                          <Title>Spend by Provider</Title>
-                        </div>
-                        {loading ? (
-                          <ChartLoader isDateChanging={isDateChanging} />
-                        ) : (
-                          <Grid numItems={2}>
-                            <Col numColSpan={1}>
-                              <DonutChart
-                                className="mt-4 h-40"
-                                data={getProviderSpend()}
-                                index="provider"
-                                category="spend"
-                                valueFormatter={(value) => `$${formatNumberWithCommas(value, 2)}`}
+                        <Col numColSpan={2}>
+                          <Card>
+                            <Title>Usage Metrics</Title>
+                            <Grid numItems={5} className="gap-4 mt-4">
+                              <Card>
+                                <Title>Total Requests</Title>
+                                <Text className="text-2xl font-bold mt-2">
+                                  {userSpendData.metadata?.total_api_requests?.toLocaleString() || 0}
+                                </Text>
+                              </Card>
+                              <Card>
+                                <Title>Successful Requests</Title>
+                                <Text className="text-2xl font-bold mt-2 text-green-600">
+                                  {userSpendData.metadata?.total_successful_requests?.toLocaleString() || 0}
+                                </Text>
+                              </Card>
+                              <Card>
+                                <Title>Failed Requests</Title>
+                                <Text className="text-2xl font-bold mt-2 text-red-600">
+                                  {userSpendData.metadata?.total_failed_requests?.toLocaleString() || 0}
+                                </Text>
+                              </Card>
+                              <Card>
+                                <Title>Total Tokens</Title>
+                                <Text className="text-2xl font-bold mt-2">
+                                  {userSpendData.metadata?.total_tokens?.toLocaleString() || 0}
+                                </Text>
+                              </Card>
+                              <Card>
+                                <Title>Average Cost per Request</Title>
+                                <Text className="text-2xl font-bold mt-2">
+                                  $
+                                  {formatNumberWithCommas(
+                                    (totalSpend || 0) / (userSpendData.metadata?.total_api_requests || 1),
+                                    4,
+                                  )}
+                                </Text>
+                              </Card>
+                            </Grid>
+                          </Card>
+                        </Col>
+
+                        {/* Daily Spend Chart */}
+                        <Col numColSpan={2}>
+                          <Card>
+                            <Title>Daily Spend</Title>
+                            {loading ? (
+                              <ChartLoader isDateChanging={isDateChanging} />
+                            ) : (
+                              <BarChart
+                                data={[...userSpendData.results].sort(
+                                  (a, b) => new Date(a.date).getTime() - new Date(b.date).getTime(),
+                                )}
+                                index="date"
+                                categories={["metrics.spend"]}
                                 colors={["cyan"]}
+                                valueFormatter={valueFormatterSpend}
+                                yAxisWidth={100}
+                                showLegend={false}
+                                customTooltip={({ payload, active }) => {
+                                  if (!active || !payload?.[0]) return null;
+                                  const data = payload[0].payload;
+                                  return (
+                                    <div className="bg-white p-4 shadow-lg rounded-lg border">
+                                      <p className="font-bold">{data.date}</p>
+                                      <p className="text-cyan-500">
+                                        Spend: ${formatNumberWithCommas(data.metrics.spend, 2)}
+                                      </p>
+                                      <p className="text-gray-600">Requests: {data.metrics.api_requests}</p>
+                                      <p className="text-gray-600">Successful: {data.metrics.successful_requests}</p>
+                                      <p className="text-gray-600">Failed: {data.metrics.failed_requests}</p>
+                                      <p className="text-gray-600">Tokens: {data.metrics.total_tokens}</p>
+                                    </div>
+                                  );
+                                }}
                               />
-                            </Col>
-                            <Col numColSpan={1}>
-                              <Table>
-                                <TableHead>
-                                  <TableRow>
-                                    <TableHeaderCell>Provider</TableHeaderCell>
-                                    <TableHeaderCell>Spend</TableHeaderCell>
-                                    <TableHeaderCell className="text-green-600">Successful</TableHeaderCell>
-                                    <TableHeaderCell className="text-red-600">Failed</TableHeaderCell>
-                                    <TableHeaderCell>Tokens</TableHeaderCell>
-                                  </TableRow>
-                                </TableHead>
-                                <TableBody>
-                                  {getProviderSpend()
-                                    .filter((provider) => provider.spend > 0)
-                                    .map((provider) => (
-                                      <TableRow key={provider.provider}>
-                                        <TableCell>
-                                          <div className="flex items-center space-x-2">
-                                            {provider.provider && (
-                                              <img
-                                                src={getProviderLogoAndName(provider.provider).logo}
-                                                alt={`${provider.provider} logo`}
-                                                className="w-4 h-4"
-                                                onError={(e) => {
-                                                  const target = e.target as HTMLImageElement;
-                                                  const parent = target.parentElement;
-                                                  if (parent) {
-                                                    const fallbackDiv = document.createElement("div");
-                                                    fallbackDiv.className =
-                                                      "w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs";
-                                                    fallbackDiv.textContent = provider.provider?.charAt(0) || "-";
-                                                    parent.replaceChild(fallbackDiv, target);
-                                                  }
-                                                }}
-                                              />
-                                            )}
-                                            <span>{provider.provider}</span>
-                                          </div>
-                                        </TableCell>
-                                        <TableCell>${formatNumberWithCommas(provider.spend, 2)}</TableCell>
-                                        <TableCell className="text-green-600">
-                                          {provider.successful_requests.toLocaleString()}
-                                        </TableCell>
-                                        <TableCell className="text-red-600">
-                                          {provider.failed_requests.toLocaleString()}
-                                        </TableCell>
-                                        <TableCell>{provider.tokens.toLocaleString()}</TableCell>
+                            )}
+                          </Card>
+                        </Col>
+                        {/* Top API Keys */}
+                        <Col numColSpan={1}>
+                          <Card className="h-full">
+                            <Title>Top API Keys</Title>
+                            <TopKeyView
+                              topKeys={getTopKeys()}
+                              accessToken={accessToken}
+                              userID={userID}
+                              userRole={userRole}
+                              teams={null}
+                              premiumUser={premiumUser}
+                            />
+                          </Card>
+                        </Col>
+
+                        {/* Top Models */}
+                        <Col numColSpan={1}>
+                          <Card className="h-full">
+                            <div className="flex justify-between items-center mb-4">
+                              <Title>
+                                {modelViewType === "groups" ? "Top Public Model Names" : "Top Litellm Models"}
+                              </Title>
+                              <div className="flex bg-gray-100 rounded-lg p-1">
+                                <button
+                                  className={`px-3 py-1 text-sm rounded-md transition-colors ${
+                                    modelViewType === "groups"
+                                      ? "bg-white shadow-sm text-gray-900"
+                                      : "text-gray-600 hover:text-gray-900"
+                                  }`}
+                                  onClick={() => setModelViewType("groups")}
+                                >
+                                  Public Model Name
+                                </button>
+                                <button
+                                  className={`px-3 py-1 text-sm rounded-md transition-colors ${
+                                    modelViewType === "individual"
+                                      ? "bg-white shadow-sm text-gray-900"
+                                      : "text-gray-600 hover:text-gray-900"
+                                  }`}
+                                  onClick={() => setModelViewType("individual")}
+                                >
+                                  Litellm Model Name
+                                </button>
+                              </div>
+                            </div>
+                            {loading ? (
+                              <ChartLoader isDateChanging={isDateChanging} />
+                            ) : (
+                              <BarChart
+                                className="mt-4 h-40"
+                                data={modelViewType === "groups" ? getTopModelGroups() : getTopModels()}
+                                index="key"
+                                categories={["spend"]}
+                                colors={["cyan"]}
+                                valueFormatter={valueFormatterSpend}
+                                layout="vertical"
+                                yAxisWidth={200}
+                                showLegend={false}
+                                customTooltip={({ payload, active }) => {
+                                  if (!active || !payload?.[0]) return null;
+                                  const data = payload[0].payload;
+                                  return (
+                                    <div className="bg-white p-4 shadow-lg rounded-lg border">
+                                      <p className="font-bold">{data.key}</p>
+                                      <p className="text-cyan-500">Spend: ${formatNumberWithCommas(data.spend, 2)}</p>
+                                      <p className="text-gray-600">Total Requests: {data.requests.toLocaleString()}</p>
+                                      <p className="text-green-600">
+                                        Successful: {data.successful_requests.toLocaleString()}
+                                      </p>
+                                      <p className="text-red-600">Failed: {data.failed_requests.toLocaleString()}</p>
+                                      <p className="text-gray-600">Tokens: {data.tokens.toLocaleString()}</p>
+                                    </div>
+                                  );
+                                }}
+                              />
+                            )}
+                          </Card>
+                        </Col>
+
+                        {/* Spend by Provider */}
+                        <Col numColSpan={2}>
+                          <Card className="h-full">
+                            <div className="flex justify-between items-center mb-4">
+                              <Title>Spend by Provider</Title>
+                            </div>
+                            {loading ? (
+                              <ChartLoader isDateChanging={isDateChanging} />
+                            ) : (
+                              <Grid numItems={2}>
+                                <Col numColSpan={1}>
+                                  <DonutChart
+                                    className="mt-4 h-40"
+                                    data={getProviderSpend()}
+                                    index="provider"
+                                    category="spend"
+                                    valueFormatter={(value) => `$${formatNumberWithCommas(value, 2)}`}
+                                    colors={["cyan"]}
+                                  />
+                                </Col>
+                                <Col numColSpan={1}>
+                                  <Table>
+                                    <TableHead>
+                                      <TableRow>
+                                        <TableHeaderCell>Provider</TableHeaderCell>
+                                        <TableHeaderCell>Spend</TableHeaderCell>
+                                        <TableHeaderCell className="text-green-600">Successful</TableHeaderCell>
+                                        <TableHeaderCell className="text-red-600">Failed</TableHeaderCell>
+                                        <TableHeaderCell>Tokens</TableHeaderCell>
                                       </TableRow>
-                                    ))}
-                                </TableBody>
-                              </Table>
-                            </Col>
-                          </Grid>
-                        )}
-                      </Card>
-                    </Col>
+                                    </TableHead>
+                                    <TableBody>
+                                      {getProviderSpend()
+                                        .filter((provider) => provider.spend > 0)
+                                        .map((provider) => (
+                                          <TableRow key={provider.provider}>
+                                            <TableCell>
+                                              <div className="flex items-center space-x-2">
+                                                {provider.provider && (
+                                                  <img
+                                                    src={getProviderLogoAndName(provider.provider).logo}
+                                                    alt={`${provider.provider} logo`}
+                                                    className="w-4 h-4"
+                                                    onError={(e) => {
+                                                      const target = e.target as HTMLImageElement;
+                                                      const parent = target.parentElement;
+                                                      if (parent) {
+                                                        const fallbackDiv = document.createElement("div");
+                                                        fallbackDiv.className =
+                                                          "w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs";
+                                                        fallbackDiv.textContent = provider.provider?.charAt(0) || "-";
+                                                        parent.replaceChild(fallbackDiv, target);
+                                                      }
+                                                    }}
+                                                  />
+                                                )}
+                                                <span>{provider.provider}</span>
+                                              </div>
+                                            </TableCell>
+                                            <TableCell>${formatNumberWithCommas(provider.spend, 2)}</TableCell>
+                                            <TableCell className="text-green-600">
+                                              {provider.successful_requests.toLocaleString()}
+                                            </TableCell>
+                                            <TableCell className="text-red-600">
+                                              {provider.failed_requests.toLocaleString()}
+                                            </TableCell>
+                                            <TableCell>{provider.tokens.toLocaleString()}</TableCell>
+                                          </TableRow>
+                                        ))}
+                                    </TableBody>
+                                  </Table>
+                                </Col>
+                              </Grid>
+                            )}
+                          </Card>
+                        </Col>
 
-                    {/* Usage Metrics */}
-                  </Grid>
-                </TabPanel>
+                        {/* Usage Metrics */}
+                      </Grid>
+                    </TabPanel>
 
-                {/* Activity Panel */}
-                <TabPanel>
-                  <ActivityMetrics modelMetrics={modelMetrics} />
-                </TabPanel>
-                <TabPanel>
-                  <ActivityMetrics modelMetrics={keyMetrics} />
-                </TabPanel>
-                <TabPanel>
-                  <ActivityMetrics modelMetrics={mcpServerMetrics} />
-                </TabPanel>
-              </TabPanels>
-            </TabGroup>
-          </TabPanel>
+                    {/* Activity Panel */}
+                    <TabPanel>
+                      <ActivityMetrics modelMetrics={modelMetrics} />
+                    </TabPanel>
+                    <TabPanel>
+                      <ActivityMetrics modelMetrics={keyMetrics} />
+                    </TabPanel>
+                    <TabPanel>
+                      <ActivityMetrics modelMetrics={mcpServerMetrics} />
+                    </TabPanel>
+                  </TabPanels>
+                </TabGroup>
+              </TabPanel>
 
-          {/* Team Usage Panel */}
-          <TabPanel>
-            <EntityUsage
-              accessToken={accessToken}
-              entityType="team"
-              userID={userID}
-              userRole={userRole}
-              entityList={
-                teams?.map((team) => ({
-                  label: team.team_alias,
-                  value: team.team_id,
-                })) || null
-              }
-              premiumUser={premiumUser}
-            />
-          </TabPanel>
+              {/* Team Usage Panel */}
+              <TabPanel>
+                <EntityUsage
+                  accessToken={accessToken}
+                  entityType="team"
+                  userID={userID}
+                  userRole={userRole}
+                  entityList={
+                    teams?.map((team) => ({
+                      label: team.team_alias,
+                      value: team.team_id,
+                    })) || null
+                  }
+                  premiumUser={premiumUser}
+                  dateValue={dateValue}
+                />
+              </TabPanel>
 
-          {/* Tag Usage Panel */}
-          <TabPanel>
-            <EntityUsage
-              accessToken={accessToken}
-              entityType="tag"
-              userID={userID}
-              userRole={userRole}
-              entityList={allTags}
-              premiumUser={premiumUser}
-            />
-          </TabPanel>
-          {/* User Agent Activity Panel */}
-          <TabPanel>
-            <UserAgentActivity accessToken={accessToken} userRole={userRole} />
-          </TabPanel>
-        </TabPanels>
-      </TabGroup>
+              {/* Tag Usage Panel */}
+              <TabPanel>
+                <EntityUsage
+                  accessToken={accessToken}
+                  entityType="tag"
+                  userID={userID}
+                  userRole={userRole}
+                  entityList={allTags}
+                  premiumUser={premiumUser}
+                  dateValue={dateValue}
+                />
+              </TabPanel>
+              {/* User Agent Activity Panel */}
+              <TabPanel>
+                <UserAgentActivity accessToken={accessToken} userRole={userRole} dateValue={dateValue} />
+              </TabPanel>
+            </TabPanels>
+          </TabGroup>
+        </div>
+      </div>
 
       {/* CloudZero Export Modal */}
       <CloudZeroExportModal
diff --git a/ui/litellm-dashboard/src/components/router_settings/index.tsx b/ui/litellm-dashboard/src/components/router_settings/index.tsx
index 45d6ad39c83..a94860eacd1 100644
--- a/ui/litellm-dashboard/src/components/router_settings/index.tsx
+++ b/ui/litellm-dashboard/src/components/router_settings/index.tsx
@@ -1,17 +1,11 @@
-import React, { useState, useEffect } from "react";
-import {
-  Button,
-} from "@tremor/react";
-import {
-  getCallbacksCall,
-  setCallbacksCall,
-  getRouterSettingsCall,
-} from "../networking";
+import { Button } from "@tremor/react";
+import React, { useEffect, useState } from "react";
 import NotificationsManager from "../molecules/notifications_manager";
-import RoutingStrategySelector from "./RoutingStrategySelector";
-import TagFilteringToggle from "./TagFilteringToggle";
+import { getCallbacksCall, getRouterSettingsCall, setCallbacksCall } from "../networking";
 import LatencyBasedConfiguration from "./LatencyBasedConfiguration";
 import ReliabilityRetriesSection from "./ReliabilityRetriesSection";
+import RoutingStrategySelector from "./RoutingStrategySelector";
+import TagFilteringToggle from "./TagFilteringToggle";
 
 interface RouterSettingsProps {
   accessToken: string | null;
@@ -49,44 +43,40 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
         setSelectedStrategy(router_settings.routing_strategy);
       }
     });
-      getRouterSettingsCall(accessToken).then((data) => {
-        console.log("router settings from API", data);
-        if (data.fields) {
-          // Build metadata map for easy lookup
-          const fieldsMap: { [key: string]: any } = {};
-          data.fields.forEach((field: any) => {
-            fieldsMap[field.field_name] = {
-              ui_field_name: field.ui_field_name,
-              field_description: field.field_description,
-              options: field.options,
-              link: field.link,
-            };
-          });
-          setRouterFieldsMetadata(fieldsMap);
-          
-          // Extract routing strategies from the routing_strategy field's options
-          const routingStrategyField = data.fields.find(
-            (field: any) => field.field_name === "routing_strategy"
-          );
-          if (routingStrategyField?.options) {
-            setAvailableRoutingStrategies(routingStrategyField.options);
-          }
+    getRouterSettingsCall(accessToken).then((data) => {
+      console.log("router settings from API", data);
+      if (data.fields) {
+        // Build metadata map for easy lookup
+        const fieldsMap: { [key: string]: any } = {};
+        data.fields.forEach((field: any) => {
+          fieldsMap[field.field_name] = {
+            ui_field_name: field.ui_field_name,
+            field_description: field.field_description,
+            options: field.options,
+            link: field.link,
+          };
+        });
+        setRouterFieldsMetadata(fieldsMap);
 
-          // Store routing strategy descriptions
-          if (data.routing_strategy_descriptions) {
-            setRoutingStrategyDescriptions(data.routing_strategy_descriptions);
-          }
-
-          // Set enable_tag_filtering value
-          const tagFilteringField = data.fields.find(
-            (field: any) => field.field_name === "enable_tag_filtering"
-          );
-          if (tagFilteringField?.field_value !== null && tagFilteringField?.field_value !== undefined) {
-            setEnableTagFiltering(tagFilteringField.field_value);
-          }
+        // Extract routing strategies from the routing_strategy field's options
+        const routingStrategyField = data.fields.find((field: any) => field.field_name === "routing_strategy");
+        if (routingStrategyField?.options) {
+          setAvailableRoutingStrategies(routingStrategyField.options);
         }
-      });
-    }, [accessToken, userRole, userID]);
+
+        // Store routing strategy descriptions
+        if (data.routing_strategy_descriptions) {
+          setRoutingStrategyDescriptions(data.routing_strategy_descriptions);
+        }
+
+        // Set enable_tag_filtering value
+        const tagFilteringField = data.fields.find((field: any) => field.field_name === "enable_tag_filtering");
+        if (tagFilteringField?.field_value !== null && tagFilteringField?.field_value !== undefined) {
+          setEnableTagFiltering(tagFilteringField.field_value);
+        }
+      }
+    });
+  }, [accessToken, userRole, userID]);
 
   const handleSaveChanges = (router_settings: any) => {
     if (!accessToken) {
@@ -125,24 +115,24 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
       return v;
     };
 
-      // Add enable_tag_filtering to router_settings before processing
-      const settingsToUpdate = {
-        ...router_settings,
-        enable_tag_filtering: enableTagFiltering,
-      };
+    // Add enable_tag_filtering to router_settings before processing
+    const settingsToUpdate = {
+      ...router_settings,
+      enable_tag_filtering: enableTagFiltering,
+    };
 
-      const updatedVariables = Object.fromEntries(
-        Object.entries(settingsToUpdate)
-          .map(([key, value]) => {
-            if (key !== "routing_strategy_args" && key !== "routing_strategy" && key !== "enable_tag_filtering") {
-              const inputEl = document.querySelector(`input[name="${key}"]`) as HTMLInputElement | null;
-              const parsed = parseInputValue(key, inputEl?.value, value);
-              return [key, parsed];
-            } else if (key === "routing_strategy") {
-              return [key, selectedStrategy];
-            } else if (key === "enable_tag_filtering") {
-              return [key, enableTagFiltering];
-            } else if (key === "routing_strategy_args" && selectedStrategy === "latency-based-routing") {
+    const updatedVariables = Object.fromEntries(
+      Object.entries(settingsToUpdate)
+        .map(([key, value]) => {
+          if (key !== "routing_strategy_args" && key !== "routing_strategy" && key !== "enable_tag_filtering") {
+            const inputEl = document.querySelector(`input[name="${key}"]`) as HTMLInputElement | null;
+            const parsed = parseInputValue(key, inputEl?.value, value);
+            return [key, parsed];
+          } else if (key === "routing_strategy") {
+            return [key, selectedStrategy];
+          } else if (key === "enable_tag_filtering") {
+            return [key, enableTagFiltering];
+          } else if (key === "routing_strategy_args" && selectedStrategy === "latency-based-routing") {
             let setRoutingStrategyArgs: routingStrategyArgs = {};
 
             const lowestLatencyBufferElement = document.querySelector(
@@ -184,65 +174,51 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
     return null;
   }
 
-    return (
-      <div className="w-full space-y-8 py-2">
-        {/* Routing Settings Section */}
-        <div className="space-y-6">
-          <div className="max-w-3xl">
-            <h3 className="text-sm font-medium text-gray-900">Routing Settings</h3>
-            <p className="text-xs text-gray-500 mt-1">Configure how requests are routed to deployments</p>
-          </div>
-          
-          {/* Routing Strategy */}
-          {routerSettings.routing_strategy && (
-            <RoutingStrategySelector
-              selectedStrategy={selectedStrategy || routerSettings.routing_strategy}
-              availableStrategies={availableRoutingStrategies}
-              routingStrategyDescriptions={routingStrategyDescriptions}
-              routerFieldsMetadata={routerFieldsMetadata}
-              onStrategyChange={setSelectedStrategy}
-            />
-          )}
-
-          {/* Tag Filtering */}
-          <TagFilteringToggle
-            enabled={enableTagFiltering}
-            routerFieldsMetadata={routerFieldsMetadata}
-            onToggle={setEnableTagFiltering}
-          />
+  return (
+    <div className="w-full space-y-8 py-2">
+      {/* Routing Settings Section */}
+      <div className="space-y-6">
+        <div className="max-w-3xl">
+          <h3 className="text-sm font-medium text-gray-900">Routing Settings</h3>
+          <p className="text-xs text-gray-500 mt-1">Configure how requests are routed to deployments</p>
         </div>
 
-        {/* Divider */}
-        <div className="border-t border-gray-200" />
-
-        {/* Strategy-Specific Args - Show immediately after strategy if latency-based */}
-        {selectedStrategy === "latency-based-routing" && (
-          <LatencyBasedConfiguration
-            routingStrategyArgs={routerSettings["routing_strategy_args"]}
+        {/* Routing Strategy */}
+        {routerSettings.routing_strategy && (
+          <RoutingStrategySelector
+            selectedStrategy={selectedStrategy || routerSettings.routing_strategy}
+            availableStrategies={availableRoutingStrategies}
+            routingStrategyDescriptions={routingStrategyDescriptions}
+            routerFieldsMetadata={routerFieldsMetadata}
+            onStrategyChange={setSelectedStrategy}
           />
         )}
 
-        {/* Other Settings */}
-        <ReliabilityRetriesSection
-          routerSettings={routerSettings}
+        {/* Tag Filtering */}
+        <TagFilteringToggle
+          enabled={enableTagFiltering}
           routerFieldsMetadata={routerFieldsMetadata}
+          onToggle={setEnableTagFiltering}
         />
+      </div>
+
+      {/* Divider */}
+      <div className="border-t border-gray-200" />
+
+      {/* Strategy-Specific Args - Show immediately after strategy if latency-based */}
+      {selectedStrategy === "latency-based-routing" && (
+        <LatencyBasedConfiguration routingStrategyArgs={routerSettings["routing_strategy_args"]} />
+      )}
+
+      {/* Other Settings */}
+      <ReliabilityRetriesSection routerSettings={routerSettings} routerFieldsMetadata={routerFieldsMetadata} />
 
       {/* Actions - Sticky at bottom */}
       <div className="border-t border-gray-200 pt-6 flex justify-end gap-3">
-        <Button 
-          variant="secondary"
-          size="sm"
-          onClick={() => window.location.reload()}
-          className="text-sm"
-        >
+        <Button variant="secondary" size="sm" onClick={() => window.location.reload()} className="text-sm">
           Reset
         </Button>
-        <Button 
-          size="sm"
-          onClick={() => handleSaveChanges(routerSettings)}
-          className="text-sm font-medium"
-        >
+        <Button size="sm" onClick={() => handleSaveChanges(routerSettings)} className="text-sm font-medium">
           Save Changes
         </Button>
       </div>
@@ -251,4 +227,3 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
 };
 
 export default RouterSettings;
-
diff --git a/ui/litellm-dashboard/src/components/settings.test.tsx b/ui/litellm-dashboard/src/components/settings.test.tsx
index 20dadf9d2a5..20f4988c168 100644
--- a/ui/litellm-dashboard/src/components/settings.test.tsx
+++ b/ui/litellm-dashboard/src/components/settings.test.tsx
@@ -1,142 +1,97 @@
-import { render, screen, fireEvent, waitFor } from "@testing-library/react";
-import { describe, expect, it, beforeAll, beforeEach, vi } from "vitest";
+import { render, waitFor } from "@testing-library/react";
+import { beforeAll, beforeEach, describe, expect, it, vi } from "vitest";
+import { alertingSettingsCall, getCallbacksCall } from "./networking";
 import Settings from "./settings";
-import * as networking from "./networking";
+
+vi.mock("./networking", () => ({
+  getCallbacksCall: vi.fn(),
+  setCallbacksCall: vi.fn(),
+  serviceHealthCheck: vi.fn(),
+  deleteCallback: vi.fn(),
+  alertingSettingsCall: vi.fn().mockResolvedValue([]),
+}));
+
+vi.mock("./molecules/notifications_manager", () => ({
+  __esModule: true,
+  default: {
+    success: vi.fn(),
+    fromBackend: vi.fn(),
+    info: vi.fn(),
+    warning: vi.fn(),
+    clear: vi.fn(),
+  },
+}));
+
+vi.mock("./alerting/alerting_settings", () => ({
+  __esModule: true,
+  default: () => <div>Mock Alerting Settings</div>,
+}));
+
+vi.mock("./email_settings", () => ({
+  __esModule: true,
+  default: () => <div>Mock Email Settings</div>,
+}));
+
+// Polyfill ResizeObserver for components relying on it in tests
+if (typeof window !== "undefined" && !window.ResizeObserver) {
+  window.ResizeObserver = class ResizeObserver {
+    observe() {}
+    unobserve() {}
+    disconnect() {}
+  };
+}
 
 beforeAll(() => {
   Object.defineProperty(window, "matchMedia", {
     writable: true,
-    value: (query: string) => ({
+    value: vi.fn().mockImplementation((query: string) => ({
       matches: false,
       media: query,
       onchange: null,
-      addListener: () => {},
-      removeListener: () => {},
-      addEventListener: () => {},
-      removeEventListener: () => {},
-      dispatchEvent: () => true,
-    }),
+      addListener: vi.fn(),
+      removeListener: vi.fn(),
+      addEventListener: vi.fn(),
+      removeEventListener: vi.fn(),
+      dispatchEvent: vi.fn(),
+    })),
   });
 });
 
-const mockCallbacksData = {
-  callbacks: [
-    {
-      name: "langfuse",
-      type: "success",
-      variables: {
-        LANGFUSE_PUBLIC_KEY: "test_key",
-        LANGFUSE_SECRET_KEY: "test_secret",
-      },
-    },
-    {
-      name: "datadog",
-      type: "success",
-      variables: {
-        DD_API_KEY: "test_dd_key",
-      },
-    },
-  ],
-  available_callbacks: [
-    {
-      litellm_callback_name: "langfuse",
-      ui_callback_name: "Langfuse",
-      litellm_callback_params: ["LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY"],
-    },
-    {
-      litellm_callback_name: "datadog",
-      ui_callback_name: "Datadog",
-      litellm_callback_params: ["DD_API_KEY"],
-    },
-  ],
-  alerts: [],
-};
-
 describe("Settings", () => {
-  it("should render the settings page", () => {
-    vi.spyOn(networking, "alertingSettingsCall").mockResolvedValue([]);
-    vi.spyOn(networking, "getEmailEventSettings").mockResolvedValue({ settings: [] });
-    vi.spyOn(networking, "getCallbacksCall").mockResolvedValue(mockCallbacksData);
+  const defaultProps = {
+    accessToken: "token",
+    userRole: "admin",
+    userID: "user-123",
+    premiumUser: false,
+  };
+  const mockGetCallbacksCall = vi.mocked(getCallbacksCall);
+  const mockAlertingSettingsCall = vi.mocked(alertingSettingsCall);
 
-    render(<Settings accessToken="test-token" userRole="admin" userID="test-user" premiumUser={false} />);
-  });
-});
-
-describe("Logging Callbacks Section", () => {
   beforeEach(() => {
     vi.clearAllMocks();
-    vi.spyOn(networking, "alertingSettingsCall").mockResolvedValue([]);
-    vi.spyOn(networking, "getEmailEventSettings").mockResolvedValue({ settings: [] });
-  });
-
-  it("should display the list of active callbacks", async () => {
-    vi.spyOn(networking, "getCallbacksCall").mockResolvedValue(mockCallbacksData);
-
-    render(<Settings accessToken="test-token" userRole="admin" userID="test-user" premiumUser={false} />);
-
-    await waitFor(
-      () => {
-        expect(screen.getByText("langfuse")).toBeInTheDocument();
-        expect(screen.getByText("datadog")).toBeInTheDocument();
-      },
-      { timeout: 3000 },
-    );
-  });
-
-  it("should open add callback modal and display form", async () => {
-    vi.spyOn(networking, "getCallbacksCall").mockResolvedValue(mockCallbacksData);
-
-    render(<Settings accessToken="test-token" userRole="admin" userID="test-user" premiumUser={false} />);
-
-    await waitFor(() => {
-      expect(screen.getByText("langfuse")).toBeInTheDocument();
+    mockGetCallbacksCall.mockResolvedValue({
+      callbacks: [],
+      available_callbacks: [],
+      alerts: [],
     });
+    mockAlertingSettingsCall.mockResolvedValue([]);
+  });
 
-    const addButton = screen.getByText("Add Callback");
-    fireEvent.click(addButton);
+  it("should render the logging callbacks tab when access token is provided", async () => {
+    const { getByText } = render(<Settings {...defaultProps} />);
 
     await waitFor(() => {
-      expect(screen.getByText("Add Logging Callback")).toBeInTheDocument();
-      expect(screen.getByText("LiteLLM Docs: Logging")).toBeInTheDocument();
+      expect(getByText("Active Logging Callbacks")).toBeInTheDocument();
     });
   });
 
-  it("should successfully delete a callback", async () => {
-    const getCallbacksSpy = vi.spyOn(networking, "getCallbacksCall").mockResolvedValue(mockCallbacksData);
-    const deleteCallbackSpy = vi.spyOn(networking, "deleteCallback").mockResolvedValue(undefined);
-
-    const { container } = render(
-      <Settings accessToken="test-token" userRole="admin" userID="test-user" premiumUser={false} />,
-    );
+  it("should display additional settings tabs", async () => {
+    const { getByText } = render(<Settings {...defaultProps} />);
 
     await waitFor(() => {
-      expect(screen.getByText("langfuse")).toBeInTheDocument();
-    });
-
-    const trashIcons = container.querySelectorAll("svg");
-    const trashIcon = Array.from(trashIcons).find((svg) => {
-      const parentElement = svg.parentElement;
-      return parentElement?.className.includes("text-red") || parentElement?.outerHTML.includes("red");
-    });
-
-    expect(trashIcon).toBeDefined();
-    if (trashIcon && trashIcon.parentElement) {
-      fireEvent.click(trashIcon.parentElement);
-    }
-
-    await waitFor(() => {
-      const modalText = screen.getByText((content, element) => {
-        return element?.tagName.toLowerCase() === "p" && content.includes("Are you sure you want to delete");
-      });
-      expect(modalText).toBeInTheDocument();
-    });
-
-    const deleteButton = screen.getByRole("button", { name: "Delete" });
-    fireEvent.click(deleteButton);
-
-    await waitFor(() => {
-      expect(deleteCallbackSpy).toHaveBeenCalledWith("test-token", "langfuse");
-      expect(getCallbacksSpy).toHaveBeenCalledTimes(2);
+      expect(getByText("Alerting Types")).toBeInTheDocument();
+      expect(getByText("Alerting Settings")).toBeInTheDocument();
+      expect(getByText("Email Alerts")).toBeInTheDocument();
     });
   });
 });
diff --git a/ui/litellm-dashboard/src/components/settings.tsx b/ui/litellm-dashboard/src/components/settings.tsx
index e8ac54e99dd..e9014c1af19 100644
--- a/ui/litellm-dashboard/src/components/settings.tsx
+++ b/ui/litellm-dashboard/src/components/settings.tsx
@@ -646,9 +646,9 @@ const Settings: React.FC<SettingsPageProps> = ({ accessToken, userRole, userID,
               size="large"
               className="w-full"
               showSearch
-              filterOption={(input, option) =>
-                (option?.children?.toString() ?? "").toLowerCase().includes(input.toLowerCase())
-              }
+              filterOption={(input, option) => {
+                return (option?.value?.toString() ?? "").toLowerCase().includes(input.toLowerCase());
+              }}
               onChange={(value) => {
                 handleSelectedCallbackChange(value);
               }}
diff --git a/ui/litellm-dashboard/src/components/shared/advanced_date_picker.test.tsx b/ui/litellm-dashboard/src/components/shared/advanced_date_picker.test.tsx
new file mode 100644
index 00000000000..90d75df75e4
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/shared/advanced_date_picker.test.tsx
@@ -0,0 +1,183 @@
+import { render, screen, fireEvent, waitFor } from "@testing-library/react";
+import { describe, it, expect, vi, beforeEach, beforeAll } from "vitest";
+import AdvancedDatePicker from "./advanced_date_picker";
+
+// Polyfill requestIdleCallback for test environment
+beforeAll(() => {
+  if (typeof window !== "undefined" && !window.requestIdleCallback) {
+    window.requestIdleCallback = (callback: any) => {
+      const start = Date.now();
+      return setTimeout(() => {
+        callback({
+          didTimeout: false,
+          timeRemaining: () => Math.max(0, 50 - (Date.now() - start)),
+        });
+      }, 1) as any;
+    };
+  }
+});
+
+describe("AdvancedDatePicker", () => {
+  const mockOnValueChange = vi.fn();
+  const defaultValue = {
+    from: new Date("2025-01-01T12:00:00.000Z"),
+    to: new Date("2025-01-31T12:00:00.000Z"),
+  };
+
+  beforeEach(() => {
+    mockOnValueChange.mockClear();
+  });
+
+  const openDropdown = (container: HTMLElement) => {
+    // Find the clickable div that contains the clock icon
+    const trigger = container.querySelector('[role="img"][aria-label="clock-circle"]')?.closest("div.cursor-pointer");
+    if (trigger) {
+      fireEvent.click(trigger);
+    }
+  };
+
+  it("should render with default label", () => {
+    render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+    expect(screen.getByText("Select Time Range")).toBeInTheDocument();
+  });
+
+  it("should render with custom label", () => {
+    render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} label="Custom Label" />);
+    expect(screen.getByText("Custom Label")).toBeInTheDocument();
+  });
+
+  it("should display formatted date range", () => {
+    render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+    // The component displays date range in the format "D MMM, HH:mm - D MMM, HH:mm"
+    // Just check that the clock icon is present
+    expect(screen.getByLabelText("clock-circle")).toBeInTheDocument();
+  });
+
+  it("should open dropdown when clicked", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    // Check for relative time options
+    expect(screen.getByText("Today")).toBeInTheDocument();
+    expect(screen.getByText("Last 7 days")).toBeInTheDocument();
+    expect(screen.getByText("Last 30 days")).toBeInTheDocument();
+  });
+
+  it("should display relative time options", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    expect(screen.getByText("Today")).toBeInTheDocument();
+    expect(screen.getByText("Last 7 days")).toBeInTheDocument();
+    expect(screen.getByText("Last 30 days")).toBeInTheDocument();
+    expect(screen.getByText("Month to date")).toBeInTheDocument();
+    expect(screen.getByText("Year to date")).toBeInTheDocument();
+  });
+
+  it("should show date inputs in dropdown", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const startDateInput = screen.getByDisplayValue("2025-01-01");
+    const endDateInput = screen.getByDisplayValue("2025-01-31");
+
+    expect(startDateInput).toBeInTheDocument();
+    expect(endDateInput).toBeInTheDocument();
+  });
+
+  it("should update date inputs when changed", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const startDateInput = screen.getByDisplayValue("2025-01-01") as HTMLInputElement;
+    fireEvent.change(startDateInput, { target: { value: "2025-02-01" } });
+
+    expect(startDateInput.value).toBe("2025-02-01");
+  });
+
+  it("should show Apply and Cancel buttons", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    expect(screen.getByText("Apply")).toBeInTheDocument();
+    expect(screen.getByText("Cancel")).toBeInTheDocument();
+  });
+
+  it("should close dropdown when Cancel is clicked", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const cancelButton = screen.getByText("Cancel");
+    fireEvent.click(cancelButton);
+
+    // Dropdown should be closed, so relative time options shouldn't be visible
+    expect(screen.queryByText("Today")).not.toBeInTheDocument();
+  });
+
+  it("should call onValueChange when Apply is clicked", async () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const applyButton = screen.getByText("Apply");
+    fireEvent.click(applyButton);
+
+    await waitFor(() => {
+      expect(mockOnValueChange).toHaveBeenCalled();
+    });
+  });
+
+  it("should select relative time option", () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const todayOption = screen.getByText("Today");
+    fireEvent.click(todayOption);
+
+    // The option should be highlighted (bg-blue-50)
+    expect(todayOption.closest("div")).toHaveClass("bg-blue-50");
+  });
+
+  it("should show validation error for invalid date range", async () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const startDateInput = screen.getByDisplayValue("2025-01-01");
+    const endDateInput = screen.getByDisplayValue("2025-01-31");
+
+    // Set end date before start date
+    fireEvent.change(startDateInput, { target: { value: "2025-12-01" } });
+    fireEvent.change(endDateInput, { target: { value: "2025-01-01" } });
+
+    await waitFor(() => {
+      expect(screen.getByText("End date cannot be before start date")).toBeInTheDocument();
+    });
+  });
+
+  it("should disable Apply button when validation fails", async () => {
+    const { container } = render(<AdvancedDatePicker value={defaultValue} onValueChange={mockOnValueChange} />);
+
+    openDropdown(container);
+
+    const startDateInput = screen.getByDisplayValue("2025-01-01");
+    const endDateInput = screen.getByDisplayValue("2025-01-31");
+
+    // Set end date before start date
+    fireEvent.change(startDateInput, { target: { value: "2025-12-01" } });
+    fireEvent.change(endDateInput, { target: { value: "2025-01-01" } });
+
+    await waitFor(() => {
+      // Find the button element (the Apply button's actual button element)
+      const applyButton = screen.getByText("Apply").closest("button");
+      expect(applyButton).toBeDisabled();
+    });
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/shared/advanced_date_picker.tsx b/ui/litellm-dashboard/src/components/shared/advanced_date_picker.tsx
index aeb867b859c..d9e70ef3336 100644
--- a/ui/litellm-dashboard/src/components/shared/advanced_date_picker.tsx
+++ b/ui/litellm-dashboard/src/components/shared/advanced_date_picker.tsx
@@ -273,9 +273,8 @@ const AdvancedDatePicker: React.FC<AdvancedDatePickerProps> = ({
   };
 
   return (
-    <div>
-      {label && <Text className="mb-2">{label}</Text>}
-
+    <div className="flex items-center gap-3">
+      {label && <Text className="text-sm font-medium text-gray-700 whitespace-nowrap">{label}</Text>}
       <div className="relative" ref={dropdownRef}>
         {/* Main input display */}
         <div
@@ -305,7 +304,7 @@ const AdvancedDatePicker: React.FC<AdvancedDatePickerProps> = ({
               {/* Left side - Relative time options */}
               <div className="w-1/2 border-r border-gray-200">
                 <div className="p-3 border-b border-gray-200">
-                  <span className="text-sm font-semibold text-gray-900">Relative time (today, 7d, 30d, MTD, YTD)</span>
+                  <span className="text-sm font-semibold text-gray-900">Relative time</span>
                 </div>
                 <div className="h-[350px] overflow-y-auto">
                   {relativeTimeOptions.map((option) => {
@@ -322,7 +321,7 @@ const AdvancedDatePicker: React.FC<AdvancedDatePickerProps> = ({
                           {option.label}
                         </span>
                         <span
-                          className={`text-xs px-2 py-1 rounded ${
+                          className={`text-xs px-2 py-1 rounded capitalize ${
                             isSelected ? "text-blue-700 bg-blue-100" : "text-gray-500 bg-gray-100"
                           }`}
                         >
@@ -391,11 +390,17 @@ const AdvancedDatePicker: React.FC<AdvancedDatePickerProps> = ({
                     </div>
                   )}
 
-                  {/* Current selection preview */}
+                  {/* Current selection time range */}
                   {tempValue.from && tempValue.to && validation.isValid && (
-                    <div className="bg-blue-50 p-3 rounded-md">
-                      <div className="text-xs text-blue-700 font-medium">Preview:</div>
-                      <div className="text-sm text-blue-800">{formatDisplayRange(tempValue.from, tempValue.to)}</div>
+                    <div className="bg-blue-50 p-3 rounded-md space-y-1">
+                      <div className="text-xs text-blue-800">
+                        <span className="font-medium">From:</span>{" "}
+                        {moment(tempValue.from).format("MMM D, YYYY [at] HH:mm:ss")}
+                      </div>
+                      <div className="text-xs text-blue-800">
+                        <span className="font-medium">To:</span>{" "}
+                        {moment(tempValue.to).format("MMM D, YYYY [at] HH:mm:ss")}
+                      </div>
                     </div>
                   )}
                 </div>
@@ -415,14 +420,6 @@ const AdvancedDatePicker: React.FC<AdvancedDatePickerProps> = ({
           </div>
         )}
       </div>
-
-      {/* Time range display below */}
-      {showTimeRange && value.from && value.to && (
-        <Text className="mt-2 text-xs text-gray-500">
-          {moment(value.from).format("MMM D, YYYY [at] HH:mm:ss")} -{" "}
-          {moment(value.to).format("MMM D, YYYY [at] HH:mm:ss")}
-        </Text>
-      )}
     </div>
   );
 };
diff --git a/ui/litellm-dashboard/src/components/user_agent_activity.test.tsx b/ui/litellm-dashboard/src/components/user_agent_activity.test.tsx
new file mode 100644
index 00000000000..7b67720e9e5
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/user_agent_activity.test.tsx
@@ -0,0 +1,195 @@
+import { render, screen, fireEvent, waitFor } from "@testing-library/react";
+import { describe, it, expect, vi, beforeEach, beforeAll } from "vitest";
+import UserAgentActivity from "./user_agent_activity";
+import * as networking from "./networking";
+
+// Polyfill ResizeObserver for test environment
+beforeAll(() => {
+  if (typeof window !== "undefined" && !window.ResizeObserver) {
+    window.ResizeObserver = class ResizeObserver {
+      observe() {}
+      unobserve() {}
+      disconnect() {}
+    } as any;
+  }
+});
+
+// Mock the networking module
+vi.mock("./networking", () => ({
+  userAgentSummaryCall: vi.fn(),
+  tagDauCall: vi.fn(),
+  tagWauCall: vi.fn(),
+  tagMauCall: vi.fn(),
+  tagDistinctCall: vi.fn(),
+}));
+
+// Mock PerUserUsage component
+vi.mock("./per_user_usage", () => ({
+  default: () => <div>Per User Usage</div>,
+}));
+
+describe("UserAgentActivity", () => {
+  const mockUserAgentSummaryCall = vi.mocked(networking.userAgentSummaryCall);
+  const mockTagDauCall = vi.mocked(networking.tagDauCall);
+  const mockTagWauCall = vi.mocked(networking.tagWauCall);
+  const mockTagMauCall = vi.mocked(networking.tagMauCall);
+  const mockTagDistinctCall = vi.mocked(networking.tagDistinctCall);
+
+  const mockDistinctTagsData = {
+    results: [{ tag: "User-Agent: Chrome/1.0" }, { tag: "User-Agent: Firefox/2.0" }, { tag: "User-Agent: Safari/3.0" }],
+  };
+
+  const mockSummaryData = {
+    results: [
+      {
+        tag: "User-Agent: Chrome/1.0",
+        unique_users: 100,
+        total_requests: 1000,
+        successful_requests: 950,
+        failed_requests: 50,
+        total_tokens: 50000,
+        total_spend: 25.5,
+      },
+      {
+        tag: "User-Agent: Firefox/2.0",
+        unique_users: 80,
+        total_requests: 800,
+        successful_requests: 760,
+        failed_requests: 40,
+        total_tokens: 40000,
+        total_spend: 20.3,
+      },
+    ],
+  };
+
+  const mockDauData = {
+    results: [
+      {
+        tag: "User-Agent: Chrome/1.0",
+        active_users: 50,
+        date: "2025-01-01",
+      },
+      {
+        tag: "User-Agent: Firefox/2.0",
+        active_users: 30,
+        date: "2025-01-01",
+      },
+    ],
+  };
+
+  const mockWauData = {
+    results: [
+      {
+        tag: "User-Agent: Chrome/1.0",
+        active_users: 200,
+        date: "Week 1 (Jan 1)",
+      },
+    ],
+  };
+
+  const mockMauData = {
+    results: [
+      {
+        tag: "User-Agent: Chrome/1.0",
+        active_users: 500,
+        date: "Month 1 (Jan)",
+      },
+    ],
+  };
+
+  const defaultProps = {
+    accessToken: "test-token",
+    userRole: "Admin",
+    dateValue: {
+      from: new Date("2025-01-01"),
+      to: new Date("2025-01-31"),
+    },
+  };
+
+  beforeEach(() => {
+    mockUserAgentSummaryCall.mockClear();
+    mockTagDauCall.mockClear();
+    mockTagWauCall.mockClear();
+    mockTagMauCall.mockClear();
+    mockTagDistinctCall.mockClear();
+
+    mockTagDistinctCall.mockResolvedValue(mockDistinctTagsData);
+    mockUserAgentSummaryCall.mockResolvedValue(mockSummaryData);
+    mockTagDauCall.mockResolvedValue(mockDauData);
+    mockTagWauCall.mockResolvedValue(mockWauData);
+    mockTagMauCall.mockResolvedValue(mockMauData);
+  });
+
+  it("should render summary cards with user agent data", async () => {
+    render(<UserAgentActivity {...defaultProps} />);
+
+    // Wait for data to load
+    await waitFor(() => {
+      expect(mockUserAgentSummaryCall).toHaveBeenCalled();
+      expect(mockTagDistinctCall).toHaveBeenCalled();
+    });
+
+    // Check that summary section is displayed
+    expect(screen.getByText("Summary by User Agent")).toBeInTheDocument();
+    expect(screen.getByText("Performance metrics for different user agents")).toBeInTheDocument();
+
+    // Check that user agent cards are displayed
+    await waitFor(() => {
+      expect(screen.getByText("Chrome/1.0")).toBeInTheDocument();
+      expect(screen.getByText("Firefox/2.0")).toBeInTheDocument();
+    });
+
+    // Check that metrics are displayed
+    expect(screen.getAllByText("Success Requests").length).toBeGreaterThan(0);
+    expect(screen.getAllByText("Total Tokens").length).toBeGreaterThan(0);
+    expect(screen.getAllByText("Total Cost").length).toBeGreaterThan(0);
+  });
+
+  it("should switch between DAU, WAU, and MAU tabs", async () => {
+    render(<UserAgentActivity {...defaultProps} />);
+
+    // Wait for data to load
+    await waitFor(() => {
+      expect(mockTagDauCall).toHaveBeenCalled();
+      expect(mockTagWauCall).toHaveBeenCalled();
+      expect(mockTagMauCall).toHaveBeenCalled();
+    });
+
+    // Check default DAU tab content
+    expect(screen.getByText("Daily Active Users - Last 7 Days")).toBeInTheDocument();
+
+    // Find all WAU tab buttons (there might be multiple)
+    const wauTabs = screen.getAllByText("WAU");
+    fireEvent.click(wauTabs[0]);
+
+    // Check WAU tab content
+    await waitFor(() => {
+      expect(screen.getByText("Weekly Active Users - Last 7 Weeks")).toBeInTheDocument();
+    });
+
+    // Find all MAU tab buttons
+    const mauTabs = screen.getAllByText("MAU");
+    fireEvent.click(mauTabs[0]);
+
+    // Check MAU tab content
+    await waitFor(() => {
+      expect(screen.getByText("Monthly Active Users - Last 7 Months")).toBeInTheDocument();
+    });
+  });
+
+  it("should display filter dropdown and allow tag selection", async () => {
+    render(<UserAgentActivity {...defaultProps} />);
+
+    // Wait for tags to load
+    await waitFor(() => {
+      expect(mockTagDistinctCall).toHaveBeenCalled();
+    });
+
+    // Check that filter label is present
+    expect(screen.getByText("Filter by User Agents")).toBeInTheDocument();
+
+    // The Ant Design Select component should be in the document with placeholder
+    const selectElement = screen.getByText("All User Agents");
+    expect(selectElement).toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/user_agent_activity.tsx b/ui/litellm-dashboard/src/components/user_agent_activity.tsx
index 492c7fbcd86..8b5f303670d 100644
--- a/ui/litellm-dashboard/src/components/user_agent_activity.tsx
+++ b/ui/litellm-dashboard/src/components/user_agent_activity.tsx
@@ -15,7 +15,6 @@ import {
 } from "@tremor/react";
 import { Select, Tooltip } from "antd";
 import { userAgentSummaryCall, tagDauCall, tagWauCall, tagMauCall, tagDistinctCall } from "./networking";
-import AdvancedDatePicker from "./shared/advanced_date_picker";
 import PerUserUsage from "./per_user_usage";
 import { DateRangePickerValue } from "@tremor/react";
 import { ChartLoader } from "./shared/chart_loader";
@@ -58,9 +57,11 @@ interface DistinctTagsResponse {
 interface UserAgentActivityProps {
   accessToken: string | null;
   userRole: string | null;
+  dateValue: DateRangePickerValue;
+  onDateChange?: (value: DateRangePickerValue) => void; // Optional - not used anymore
 }
 
-const UserAgentActivity: React.FC<UserAgentActivityProps> = ({ accessToken, userRole }) => {
+const UserAgentActivity: React.FC<UserAgentActivityProps> = ({ accessToken, userRole, dateValue, onDateChange }) => {
   // Maximum number of categories to show in charts to prevent color palette overflow
   const MAX_CATEGORIES = 10;
 
@@ -70,11 +71,6 @@ const UserAgentActivity: React.FC<UserAgentActivityProps> = ({ accessToken, user
   const [mauData, setMauData] = useState<ActiveUsersAnalyticsResponse>({ results: [] });
   const [summaryData, setSummaryData] = useState<TagSummaryResponse>({ results: [] });
 
-  const [dateValue, setDateValue] = useState<DateRangePickerValue>({
-    from: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000),
-    to: new Date(),
-  });
-
   const [userAgentFilter, setUserAgentFilter] = useState<string>("");
 
   // Tag filtering state
@@ -88,8 +84,6 @@ const UserAgentActivity: React.FC<UserAgentActivityProps> = ({ accessToken, user
   const [mauLoading, setMauLoading] = useState(false);
   const [summaryLoading, setSummaryLoading] = useState(false);
 
-  const [isDateChanging, setIsDateChanging] = useState(false);
-
   // Use today's date as the end date for all API calls
   const today = new Date();
 
@@ -180,20 +174,9 @@ const UserAgentActivity: React.FC<UserAgentActivityProps> = ({ accessToken, user
       console.error("Failed to fetch user agent summary data:", error);
     } finally {
       setSummaryLoading(false);
-      setIsDateChanging(false);
     }
   };
 
-  // Super responsive date change handler
-  const handleDateChange = (newValue: DateRangePickerValue) => {
-    // Instant visual feedback
-    setIsDateChanging(true);
-    setSummaryLoading(true);
-
-    // Update date immediately for UI responsiveness
-    setDateValue(newValue);
-  };
-
   // Effect to fetch available tags on mount
   useEffect(() => {
     fetchAvailableTags();
@@ -425,12 +408,11 @@ const UserAgentActivity: React.FC<UserAgentActivityProps> = ({ accessToken, user
             </div>
           </div>
 
-          {/* Date Range Picker within Summary */}
-          <AdvancedDatePicker value={dateValue} onValueChange={handleDateChange} />
+          {/* Date Range Picker is controlled by parent component */}
 
           {/* Top 4 User Agents Cards */}
           {summaryLoading ? (
-            <ChartLoader isDateChanging={isDateChanging} />
+            <ChartLoader isDateChanging={false} />
           ) : (
             <Grid numItems={4} className="gap-4">
               {(summaryData.results || []).slice(0, 4).map((tag, index) => {
diff --git a/ui/litellm-dashboard/src/hooks/useTestMCPConnection.tsx b/ui/litellm-dashboard/src/hooks/useTestMCPConnection.tsx
index 4c1f88ea2b5..0cad2fe81ab 100644
--- a/ui/litellm-dashboard/src/hooks/useTestMCPConnection.tsx
+++ b/ui/litellm-dashboard/src/hooks/useTestMCPConnection.tsx
@@ -9,6 +9,12 @@ interface MCPServerConfig {
   auth_type?: string;
   mcp_info?: any;
   static_headers?: Record<string, string>;
+  credentials?: {
+    auth_value?: string;
+    client_id?: string;
+    client_secret?: string;
+    scopes?: string[];
+  };
 }
 
 interface UseTestMCPConnectionProps {
@@ -41,6 +47,7 @@ export const useTestMCPConnection = ({
   const canFetchTools = !!(formValues.url && formValues.transport && formValues.auth_type && accessToken);
 
   const staticHeadersKey = JSON.stringify(formValues.static_headers ?? {});
+  const credentialsKey = JSON.stringify(formValues.credentials ?? {});
 
   const fetchTools = async () => {
     if (!accessToken || !formValues.url) {
@@ -74,6 +81,29 @@ export const useTestMCPConnection = ({
             )
           : {} as Record<string, string>;
 
+      const credentials =
+        formValues.credentials && typeof formValues.credentials === "object"
+          ? Object.entries(formValues.credentials).reduce(
+              (acc: Record<string, any>, [key, value]) => {
+                if (value === undefined || value === null || value === "") {
+                  return acc;
+                }
+                if (key === "scopes") {
+                  if (Array.isArray(value)) {
+                    const normalizedScopes = value.filter((scope) => scope != null && scope !== "");
+                    if (normalizedScopes.length > 0) {
+                      acc[key] = normalizedScopes;
+                    }
+                  }
+                } else {
+                  acc[key] = value;
+                }
+                return acc;
+              },
+              {},
+            )
+          : undefined;
+
       const mcpServerConfig: MCPServerConfig = {
         server_id: formValues.server_id || "",
         server_name: formValues.server_name || "",
@@ -84,6 +114,10 @@ export const useTestMCPConnection = ({
         static_headers: staticHeaders,
       };
 
+      if (credentials && Object.keys(credentials).length > 0) {
+        mcpServerConfig.credentials = credentials;
+      }
+
       const toolsResponse = await testMCPToolsListRequest(accessToken, mcpServerConfig);
 
       if (toolsResponse.tools && !toolsResponse.error) {
@@ -126,7 +160,16 @@ export const useTestMCPConnection = ({
       clearTools();
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [formValues.url, formValues.transport, formValues.auth_type, accessToken, enabled, canFetchTools, staticHeadersKey]);
+  }, [
+    formValues.url,
+    formValues.transport,
+    formValues.auth_type,
+    accessToken,
+    enabled,
+    canFetchTools,
+    staticHeadersKey,
+    credentialsKey,
+  ]);
 
   return {
     tools,
diff --git a/ui/litellm-dashboard/src/utils/textUtils.test.ts b/ui/litellm-dashboard/src/utils/textUtils.test.ts
new file mode 100644
index 00000000000..c67c6bf4e89
--- /dev/null
+++ b/ui/litellm-dashboard/src/utils/textUtils.test.ts
@@ -0,0 +1,18 @@
+import { describe, it, expect } from "vitest";
+import { formatLabel, truncateString } from "./textUtils";
+
+describe("formatLabel", () => {
+  it("should format label", () => {
+    expect(formatLabel("test_label")).toBe("Test Label");
+  });
+});
+
+describe("truncateString", () => {
+  it("should truncate a string", () => {
+    expect(truncateString("Hello, world!", 5)).toBe("Hello...");
+  });
+
+  it("should return the original string if it is less than the max length", () => {
+    expect(truncateString("Hello, world!", 20)).toBe("Hello, world!");
+  });
+});
diff --git a/ui/litellm-dashboard/src/utils/textUtils.ts b/ui/litellm-dashboard/src/utils/textUtils.ts
new file mode 100644
index 00000000000..6dd94ef137e
--- /dev/null
+++ b/ui/litellm-dashboard/src/utils/textUtils.ts
@@ -0,0 +1,12 @@
+export const formatLabel = (text: string): string => {
+  if (!text) {
+    return text;
+  }
+
+  const withSpaces = text.replace(/_/g, " ");
+  return withSpaces.replace(/\b\w/g, (char) => char.toUpperCase());
+};
+
+export function truncateString(str: string, maxLength: number) {
+  return str.length > maxLength ? str.substring(0, maxLength) + "..." : str;
+}