From 35f6fd4223065eb2c1710964ba43c60b25f9edff Mon Sep 17 00:00:00 2001
From: Ephrim Stanley <ephrim.stanley@point72.com>
Date: Sat, 28 Feb 2026 14:42:24 -0500
Subject: [PATCH 01/69] Managed batches fixes for Gemini/Vertex

---
 .../proxy/hooks/managed_files.py              |   7 ++
 litellm/batches/batch_utils.py                | 101 ++++++++----------
 litellm/files/main.py                         |   2 +-
 litellm/llms/vertex_ai/batches/handler.py     |  17 ++-
 .../llms/vertex_ai/batches/transformation.py  |   2 +-
 .../llms/vertex_ai/files/transformation.py    |  62 +++++++++--
 litellm/types/llms/vertex_ai.py               |   2 +-
 7 files changed, 121 insertions(+), 72 deletions(-)

diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
index 4fa050a84aa..37ca341fdf2 100644
--- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
+++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
@@ -589,7 +589,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             model_file_id_mapping = cast(
                 Optional[Dict[str, Dict[str, str]]], kwargs.get("model_file_id_mapping")
             )
+            # model_info may be at top-level or nested under litellm_metadata
+            # (batch/file operations use litellm_metadata)
             model_id = cast(Optional[str], kwargs.get("model_info", {}).get("id", None))
+            if model_id is None:
+                model_id = cast(
+                    Optional[str],
+                    kwargs.get("litellm_metadata", {}).get("model_info", {}).get("id", None),
+                )
             mapped_file_id: Optional[str] = None
             if input_file_id and model_file_id_mapping and model_id:
                 mapped_file_id = model_file_id_mapping.get(input_file_id, {}).get(
diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py
index 29bd99c2a60..80351664dfe 100644
--- a/litellm/batches/batch_utils.py
+++ b/litellm/batches/batch_utils.py
@@ -128,73 +128,58 @@ def calculate_vertex_ai_batch_cost_and_usage(
     model_name: Optional[str] = None,
 ) -> Tuple[float, Usage]:
     """
-    Calculate both cost and usage from Vertex AI batch responses
+    Calculate both cost and usage from Vertex AI batch responses.
+
+    Vertex AI batch output lines have format:
+      {"request": ..., "status": "", "response": {"candidates": [...], "usageMetadata": {...}}}
+
+    usageMetadata contains promptTokenCount, candidatesTokenCount, totalTokenCount.
     """
-    from litellm.litellm_core_utils.litellm_logging import Logging
-    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
-        VertexGeminiConfig,
-    )
+    from litellm.cost_calculator import batch_cost_calculator
+
     total_cost = 0.0
     total_tokens = 0
     prompt_tokens = 0
     completion_tokens = 0
-    
-    for response in vertex_ai_batch_responses:
-        if response.get("status") == "JOB_STATE_SUCCEEDED":  # Check if response was successful
-            # Transform Vertex AI response to OpenAI format if needed
+    actual_model_name = model_name or "gemini-2.0-flash-001"
 
-            # Create required arguments for the transformation method
-            model_response = ModelResponse()
-            
-            # Ensure model_name is not None
-            actual_model_name = model_name or "gemini-2.5-flash"
-            
-            # Create a real LiteLLM logging object
-            logging_obj = Logging(
+    for response in vertex_ai_batch_responses:
+        response_body = response.get("response")
+        if response_body is None:
+            continue
+
+        usage_metadata = response_body.get("usageMetadata", {})
+        _prompt = usage_metadata.get("promptTokenCount", 0) or 0
+        _completion = usage_metadata.get("candidatesTokenCount", 0) or 0
+        _total = usage_metadata.get("totalTokenCount", 0) or (_prompt + _completion)
+
+        line_usage = Usage(
+            prompt_tokens=_prompt,
+            completion_tokens=_completion,
+            total_tokens=_total,
+        )
+
+        try:
+            p_cost, c_cost = batch_cost_calculator(
+                usage=line_usage,
                 model=actual_model_name,
-                messages=[{"role": "user", "content": "batch_request"}],
-                stream=False,
-                call_type=CallTypes.aretrieve_batch,
-                start_time=time.time(),
-                litellm_call_id="batch_" + str(uuid.uuid4()),
-                function_id="batch_processing",
-                litellm_trace_id=str(uuid.uuid4()),
-                kwargs={"optional_params": {}}
-            )
-            
-            # Add the optional_params attribute that the Vertex AI transformation expects
-            logging_obj.optional_params = {}
-            raw_response = httpx.Response(200)  # Mock response object
-            
-            openai_format_response = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response(
-                completion_response=response["response"],
-                model_response=model_response,
-                model=actual_model_name,
-                logging_obj=logging_obj,
-                raw_response=raw_response,
-            )
-            
-            # Calculate cost using existing function
-            cost = litellm.completion_cost(
-                completion_response=openai_format_response,
                 custom_llm_provider="vertex_ai",
-                call_type=CallTypes.aretrieve_batch.value,
             )
-            total_cost += cost
-            
-            # Extract usage from the transformed response
-            usage_obj = getattr(openai_format_response, 'usage', None)
-            if usage_obj:
-                usage = usage_obj
-            else:
-                # Fallback: create usage from response dict
-                response_dict = openai_format_response.dict() if hasattr(openai_format_response, 'dict') else {}
-                usage = _get_batch_job_usage_from_response_body(response_dict)
-            
-            total_tokens += usage.total_tokens
-            prompt_tokens += usage.prompt_tokens
-            completion_tokens += usage.completion_tokens
-    
+            total_cost += p_cost + c_cost
+        except Exception as e:
+            verbose_logger.debug(
+                "vertex_ai batch cost calculation error for line: %s", str(e)
+            )
+
+        prompt_tokens += _prompt
+        completion_tokens += _completion
+        total_tokens += _total
+
+    verbose_logger.info(
+        "vertex_ai batch cost: cost=%s, prompt=%d, completion=%d, total=%d",
+        total_cost, prompt_tokens, completion_tokens, total_tokens,
+    )
+
     return total_cost, Usage(
         total_tokens=total_tokens,
         prompt_tokens=prompt_tokens,
diff --git a/litellm/files/main.py b/litellm/files/main.py
index 78e41bb5a68..66d3a97468d 100644
--- a/litellm/files/main.py
+++ b/litellm/files/main.py
@@ -295,7 +295,7 @@ def create_file(
 @client
 async def afile_retrieve(
     file_id: str,
-    custom_llm_provider: Literal["openai", "azure", "gemini", "hosted_vllm", "manus"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "gemini", "vertex_ai", "hosted_vllm", "manus"] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py
index 36f5e65e7a2..ba3b5fb7a2c 100644
--- a/litellm/llms/vertex_ai/batches/handler.py
+++ b/litellm/llms/vertex_ai/batches/handler.py
@@ -108,11 +108,18 @@ class VertexAIBatchPrediction(VertexLLM):
         client = get_async_httpx_client(
             llm_provider=litellm.LlmProviders.VERTEX_AI,
         )
-        response = await client.post(
-            url=api_base,
-            headers=headers,
-            data=json.dumps(vertex_batch_request),
-        )
+        try:
+            response = await client.post(
+                url=api_base,
+                headers=headers,
+                data=json.dumps(vertex_batch_request),
+            )
+        except httpx.HTTPStatusError as e:
+            error_body = e.response.text if hasattr(e, 'response') else "N/A"
+            litellm.verbose_logger.error(
+                f"Vertex AI batch create failed: status={e.response.status_code}, body={error_body[:1000]}"
+            )
+            raise
         if response.status_code != 200:
             raise Exception(f"Error: {response.status_code} {response.text}")
 
diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py
index a0adb3e55a8..7cb06fea9e2 100644
--- a/litellm/llms/vertex_ai/batches/transformation.py
+++ b/litellm/llms/vertex_ai/batches/transformation.py
@@ -29,7 +29,7 @@ class VertexAIBatchTransformation:
         if input_file_id is None:
             raise ValueError("input_file_id is required, but not provided")
         input_config: InputConfig = InputConfig(
-            gcsSource=GcsSource(uris=input_file_id), instancesFormat="jsonl"
+            gcsSource=GcsSource(uris=[input_file_id]), instancesFormat="jsonl"
         )
         model: str = cls._get_model_from_gcs_file(input_file_id)
         output_config: OutputConfig = OutputConfig(
diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py
index 2470c59bbac..f0493cd6be9 100644
--- a/litellm/llms/vertex_ai/files/transformation.py
+++ b/litellm/llms/vertex_ai/files/transformation.py
@@ -335,13 +335,37 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
             status_code=status_code, message=error_message, headers=headers
         )
 
+    def _parse_gcs_uri(self, file_id: str) -> Tuple[str, str]:
+        """
+        Parse a GCS URI (gs://bucket/path/to/object) into (bucket, url-encoded-object-path).
+        Handles both raw and URL-encoded input.
+        """
+        import urllib.parse
+
+        decoded = urllib.parse.unquote(file_id)
+        if decoded.startswith("gs://"):
+            full_path = decoded[5:]
+        else:
+            full_path = decoded
+
+        if "/" in full_path:
+            bucket_name, object_path = full_path.split("/", 1)
+        else:
+            bucket_name = full_path
+            object_path = ""
+
+        encoded_object = urllib.parse.quote(object_path, safe="")
+        return bucket_name, encoded_object
+
     def transform_retrieve_file_request(
         self,
         file_id: str,
         optional_params: dict,
         litellm_params: dict,
     ) -> tuple[str, dict]:
-        raise NotImplementedError("VertexAIFilesConfig does not support file retrieval")
+        bucket, encoded_object = self._parse_gcs_uri(file_id)
+        url = f"https://storage.googleapis.com/storage/v1/b/{bucket}/o/{encoded_object}"
+        return url, {}
 
     def transform_retrieve_file_response(
         self,
@@ -349,7 +373,21 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
         logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
     ) -> OpenAIFileObject:
-        raise NotImplementedError("VertexAIFilesConfig does not support file retrieval")
+        response_json = raw_response.json()
+        gcs_id = response_json.get("id", "")
+        gcs_id = "/".join(gcs_id.split("/")[:-1]) if gcs_id else ""
+        return OpenAIFileObject(
+            id=f"gs://{gcs_id}",
+            bytes=int(response_json.get("size", 0)),
+            created_at=_convert_vertex_datetime_to_openai_datetime(
+                vertex_datetime=response_json.get("timeCreated", "")
+            ),
+            filename=response_json.get("name", ""),
+            object="file",
+            purpose=response_json.get("metadata", {}).get("purpose", "batch"),
+            status="processed",
+            status_details=None,
+        )
 
     def transform_delete_file_request(
         self,
@@ -357,7 +395,9 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
         optional_params: dict,
         litellm_params: dict,
     ) -> tuple[str, dict]:
-        raise NotImplementedError("VertexAIFilesConfig does not support file deletion")
+        bucket, encoded_object = self._parse_gcs_uri(file_id)
+        url = f"https://storage.googleapis.com/storage/v1/b/{bucket}/o/{encoded_object}"
+        return url, {}
 
     def transform_delete_file_response(
         self,
@@ -365,7 +405,14 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
         logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
     ) -> FileDeleted:
-        raise NotImplementedError("VertexAIFilesConfig does not support file deletion")
+        file_id = "deleted"
+        if hasattr(raw_response, "request") and raw_response.request:
+            url = str(raw_response.request.url)
+            if "/o/" in url:
+                import urllib.parse
+                encoded_name = url.split("/o/")[-1].split("?")[0]
+                file_id = f"gs://{urllib.parse.unquote(encoded_name)}"
+        return FileDeleted(id=file_id, deleted=True, object="file")
 
     def transform_list_files_request(
         self,
@@ -389,7 +436,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
         optional_params: dict,
         litellm_params: dict,
     ) -> tuple[str, dict]:
-        raise NotImplementedError("VertexAIFilesConfig does not support file content retrieval")
+        file_id = file_content_request.get("file_id", "")
+        bucket, encoded_object = self._parse_gcs_uri(file_id)
+        url = f"https://storage.googleapis.com/storage/v1/b/{bucket}/o/{encoded_object}?alt=media"
+        return url, {}
 
     def transform_file_content_response(
         self,
@@ -397,7 +447,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
         logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
     ) -> HttpxBinaryResponseContent:
-        raise NotImplementedError("VertexAIFilesConfig does not support file content retrieval")
+        return HttpxBinaryResponseContent(response=raw_response)
 
 
 class VertexAIJsonlFilesTransformation(VertexGeminiConfig):
diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py
index 049a5010c79..190e680b7b9 100644
--- a/litellm/types/llms/vertex_ai.py
+++ b/litellm/types/llms/vertex_ai.py
@@ -560,7 +560,7 @@ class VertexAIBatchEmbeddingsResponseObject(TypedDict):
 
 
 class GcsSource(TypedDict):
-    uris: str
+    uris: List[str]
 
 
 class InputConfig(TypedDict):

From b16397ae1ab6d91472323673f990db6a0f3bf80f Mon Sep 17 00:00:00 2001
From: Ephrim Stanley <ephrim.stanley@point72.com>
Date: Sat, 28 Feb 2026 20:45:16 -0500
Subject: [PATCH 02/69] Managed batches fixes for Gemini/Vertex

---
 .../test_openai_batches_and_files.py          |  43 ++++
 .../proxy/hooks/test_managed_files.py         | 104 +++++++++
 .../test_vertex_ai_files_transformation.py    | 184 +++++++++++++++
 .../test_vertex_ai_batch_passthrough.py       | 219 ++++++------------
 4 files changed, 405 insertions(+), 145 deletions(-)
 create mode 100644 tests/test_litellm/llms/vertex_ai/files/test_vertex_ai_files_transformation.py

diff --git a/tests/batches_tests/test_openai_batches_and_files.py b/tests/batches_tests/test_openai_batches_and_files.py
index 055af024949..641590ad04a 100644
--- a/tests/batches_tests/test_openai_batches_and_files.py
+++ b/tests/batches_tests/test_openai_batches_and_files.py
@@ -29,6 +29,7 @@ verbose_logger.setLevel(logging.DEBUG)
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import StandardLoggingPayload
 import random
+import httpx
 from unittest.mock import patch, MagicMock
 
 
@@ -579,6 +580,48 @@ async def test_vertex_list_batches(monkeypatch):
         assert list_response["data"][1].id == "test-batch-id-789"
 
 
+@pytest.mark.asyncio
+async def test_vertex_async_create_batch_logs_error_body_on_http_error():
+    """
+    When Vertex AI returns an HTTP error (e.g. 400), _async_create_batch should
+    re-raise httpx.HTTPStatusError (not swallow it) and log the response body.
+
+    Before the fix the error body was lost because AsyncHTTPHandler.post()
+    calls raise_for_status() internally, raising before the handler's own
+    status-code check could log the body.
+    """
+    from litellm.llms.vertex_ai.batches.handler import VertexAIBatchPrediction
+
+    handler = VertexAIBatchPrediction(gcs_bucket_name="test-bucket")
+
+    error_body = '{"error": {"code": 400, "message": "Do not support publisher model gemini-2.0-flash"}}'
+
+    mock_response = MagicMock(spec=httpx.Response)
+    mock_response.status_code = 400
+    mock_response.text = error_body
+    mock_response.headers = {}
+
+    http_error = httpx.HTTPStatusError(
+        message="Bad Request",
+        request=httpx.Request("POST", "https://fake-vertex-url"),
+        response=mock_response,
+    )
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        side_effect=http_error,
+    ):
+        with pytest.raises(httpx.HTTPStatusError) as exc_info:
+            await handler._async_create_batch(
+                vertex_batch_request={},
+                api_base="https://us-central1-aiplatform.googleapis.com/v1/projects/test/locations/us-central1/batchPredictionJobs",
+                headers={"Authorization": "Bearer fake-token"},
+            )
+
+        assert exc_info.value.response.status_code == 400
+        assert "gemini-2.0-flash" in exc_info.value.response.text
+
+
 @pytest.mark.asyncio
 async def test_delete_batch_output_file():
     """
diff --git a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py
index 58efa854e7c..58fbd9e64ba 100644
--- a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py
+++ b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py
@@ -6,6 +6,7 @@ from fastapi import HTTPException
 from litellm_enterprise.proxy.hooks.managed_files import _PROXY_LiteLLMManagedFiles
 
 from litellm.caching import DualCache
+from litellm.proxy._types import CallTypes
 from litellm.proxy.openai_files_endpoints.common_utils import (
     _is_base64_encoded_unified_file_id,
 )
@@ -61,6 +62,109 @@ async def test_async_pre_call_hook_batch_retrieve():
     assert response["model"] == "my-general-azure-deployment"
 
 
+@pytest.mark.asyncio
+async def test_async_pre_call_deployment_hook_resolves_model_id_from_litellm_metadata():
+    """
+    For batch operations the router stores model_info under
+    kwargs["litellm_metadata"]["model_info"] (not top-level kwargs["model_info"]).
+    async_pre_call_deployment_hook must check both locations so the managed
+    file ID is resolved to the provider-specific file ID.
+    """
+    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
+        DualCache(), prisma_client=MagicMock()
+    )
+
+    managed_file_id = "managed-file-abc"
+    model_id = "deployment-xyz"
+    provider_file_id = "gs://bucket/path/to/file.jsonl"
+
+    # model_info is nested under litellm_metadata (batch path)
+    kwargs = {
+        "input_file_id": managed_file_id,
+        "model_file_id_mapping": {
+            managed_file_id: {model_id: provider_file_id},
+        },
+        "litellm_metadata": {
+            "model_info": {"id": model_id},
+        },
+    }
+
+    result = await proxy_managed_files.async_pre_call_deployment_hook(
+        kwargs=kwargs, call_type=CallTypes.acreate_batch
+    )
+
+    assert result["input_file_id"] == provider_file_id, (
+        f"Expected provider file ID '{provider_file_id}', got '{result['input_file_id']}'"
+    )
+
+
+@pytest.mark.asyncio
+async def test_async_pre_call_deployment_hook_prefers_top_level_model_info():
+    """
+    When model_info exists at top-level kwargs, async_pre_call_deployment_hook
+    should use it without falling back to litellm_metadata.
+    """
+    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
+        DualCache(), prisma_client=MagicMock()
+    )
+
+    managed_file_id = "managed-file-abc"
+    top_level_model_id = "deployment-top"
+    nested_model_id = "deployment-nested"
+    top_level_provider_file = "file-top-123"
+    nested_provider_file = "file-nested-456"
+
+    kwargs = {
+        "input_file_id": managed_file_id,
+        "model_file_id_mapping": {
+            managed_file_id: {
+                top_level_model_id: top_level_provider_file,
+                nested_model_id: nested_provider_file,
+            },
+        },
+        "model_info": {"id": top_level_model_id},
+        "litellm_metadata": {
+            "model_info": {"id": nested_model_id},
+        },
+    }
+
+    result = await proxy_managed_files.async_pre_call_deployment_hook(
+        kwargs=kwargs, call_type=CallTypes.acreate_batch
+    )
+
+    assert result["input_file_id"] == top_level_provider_file, (
+        "Should prefer top-level model_info over litellm_metadata"
+    )
+
+
+@pytest.mark.asyncio
+async def test_async_pre_call_deployment_hook_no_model_info_leaves_file_id_unchanged():
+    """
+    When model_info is absent from both top-level and litellm_metadata,
+    the managed file ID should remain unchanged.
+    """
+    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
+        DualCache(), prisma_client=MagicMock()
+    )
+
+    managed_file_id = "managed-file-abc"
+
+    kwargs = {
+        "input_file_id": managed_file_id,
+        "model_file_id_mapping": {
+            managed_file_id: {"some-model": "provider-file-xyz"},
+        },
+    }
+
+    result = await proxy_managed_files.async_pre_call_deployment_hook(
+        kwargs=kwargs, call_type=CallTypes.acreate_batch
+    )
+
+    assert result["input_file_id"] == managed_file_id, (
+        "File ID should remain unchanged when model_info is not available"
+    )
+
+
 # def test_list_managed_files():
 #     proxy_managed_files = _PROXY_LiteLLMManagedFiles(DualCache())
 
diff --git a/tests/test_litellm/llms/vertex_ai/files/test_vertex_ai_files_transformation.py b/tests/test_litellm/llms/vertex_ai/files/test_vertex_ai_files_transformation.py
new file mode 100644
index 00000000000..6f1d753484d
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/files/test_vertex_ai_files_transformation.py
@@ -0,0 +1,184 @@
+"""
+Tests for VertexAIFilesConfig transformation methods (Issues 5-7).
+"""
+
+import json
+import urllib.parse
+
+import httpx
+import pytest
+from unittest.mock import MagicMock
+
+from litellm.llms.vertex_ai.files.transformation import VertexAIFilesConfig
+from litellm.types.llms.openai import OpenAIFileObject, HttpxBinaryResponseContent
+from openai.types.file_deleted import FileDeleted
+
+
+@pytest.fixture
+def config():
+    return VertexAIFilesConfig()
+
+
+class TestParseGcsUri:
+    """Tests for the _parse_gcs_uri helper used by retrieve / content / delete."""
+
+    def test_should_parse_standard_gs_uri(self, config):
+        bucket, encoded = config._parse_gcs_uri(
+            "gs://my-bucket/path/to/object.jsonl"
+        )
+        assert bucket == "my-bucket"
+        assert encoded == urllib.parse.quote("path/to/object.jsonl", safe="")
+
+    def test_should_parse_uri_with_nested_publisher_path(self, config):
+        uri = "gs://litellm-local/litellm-vertex-files/publishers/google/models/gemini-2.0-flash-001/abc-123"
+        bucket, encoded = config._parse_gcs_uri(uri)
+        assert bucket == "litellm-local"
+        expected_path = "litellm-vertex-files/publishers/google/models/gemini-2.0-flash-001/abc-123"
+        assert encoded == urllib.parse.quote(expected_path, safe="")
+
+    def test_should_handle_url_encoded_input(self, config):
+        encoded_uri = urllib.parse.quote("gs://my-bucket/some/path", safe="")
+        bucket, encoded = config._parse_gcs_uri(encoded_uri)
+        assert bucket == "my-bucket"
+        assert encoded == urllib.parse.quote("some/path", safe="")
+
+    def test_should_handle_bucket_only(self, config):
+        bucket, encoded = config._parse_gcs_uri("gs://my-bucket")
+        assert bucket == "my-bucket"
+        assert encoded == ""
+
+    def test_should_handle_no_gs_prefix(self, config):
+        bucket, encoded = config._parse_gcs_uri("my-bucket/object.txt")
+        assert bucket == "my-bucket"
+        assert encoded == "object.txt"
+
+class TestTransformRetrieveFile:
+
+    def test_should_build_correct_gcs_metadata_url(self, config):
+        file_id = "gs://my-bucket/path/to/file.jsonl"
+        url, params = config.transform_retrieve_file_request(
+            file_id=file_id, optional_params={}, litellm_params={}
+        )
+        expected_encoded = urllib.parse.quote("path/to/file.jsonl", safe="")
+        assert url == f"https://storage.googleapis.com/storage/v1/b/my-bucket/o/{expected_encoded}"
+        assert params == {}
+
+    def test_should_return_openai_file_object_from_gcs_response(self, config):
+        gcs_json = {
+            "id": "my-bucket/path/to/file.jsonl/123456",
+            "name": "path/to/file.jsonl",
+            "size": "4096",
+            "timeCreated": "2025-02-15T10:00:00.000Z",
+            "metadata": {"purpose": "batch"},
+        }
+        raw_response = MagicMock(spec=httpx.Response)
+        raw_response.json.return_value = gcs_json
+
+        result = config.transform_retrieve_file_response(
+            raw_response=raw_response,
+            logging_obj=MagicMock(),
+            litellm_params={},
+        )
+
+        assert isinstance(result, OpenAIFileObject)
+        assert result.id == "gs://my-bucket/path/to/file.jsonl"
+        assert result.filename == "path/to/file.jsonl"
+        assert result.bytes == 4096
+        assert result.object == "file"
+        assert result.status == "processed"
+        assert result.purpose == "batch"
+
+    def test_should_default_purpose_to_batch_when_metadata_missing(self, config):
+        gcs_json = {
+            "id": "bucket/obj/999",
+            "name": "obj",
+            "size": "0",
+            "timeCreated": "2025-01-01T00:00:00.000Z",
+        }
+        raw_response = MagicMock(spec=httpx.Response)
+        raw_response.json.return_value = gcs_json
+
+        result = config.transform_retrieve_file_response(
+            raw_response=raw_response,
+            logging_obj=MagicMock(),
+            litellm_params={},
+        )
+        assert result.purpose == "batch"
+
+
+class TestTransformFileContent:
+
+    def test_should_build_gcs_media_download_url(self, config):
+        file_id = "gs://my-bucket/path/to/file.jsonl"
+        url, params = config.transform_file_content_request(
+            file_content_request={"file_id": file_id},
+            optional_params={},
+            litellm_params={},
+        )
+        encoded = urllib.parse.quote("path/to/file.jsonl", safe="")
+        assert url == f"https://storage.googleapis.com/storage/v1/b/my-bucket/o/{encoded}?alt=media"
+        assert params == {}
+
+    def test_should_return_binary_response_content(self, config):
+        raw_response = httpx.Response(
+            status_code=200,
+            content=b'{"line": 1}\n{"line": 2}\n',
+            headers={"content-type": "application/octet-stream"},
+            request=httpx.Request("GET", "https://example.com"),
+        )
+
+        result = config.transform_file_content_response(
+            raw_response=raw_response,
+            logging_obj=MagicMock(),
+            litellm_params={},
+        )
+
+        assert isinstance(result, HttpxBinaryResponseContent)
+        assert result.response.content == b'{"line": 1}\n{"line": 2}\n'
+
+
+class TestTransformDeleteFile:
+    def test_should_build_correct_gcs_delete_url(self, config):
+        file_id = "gs://my-bucket/path/to/file.jsonl"
+        url, params = config.transform_delete_file_request(
+            file_id=file_id, optional_params={}, litellm_params={}
+        )
+        encoded = urllib.parse.quote("path/to/file.jsonl", safe="")
+        assert url == f"https://storage.googleapis.com/storage/v1/b/my-bucket/o/{encoded}"
+        assert params == {}
+
+    def test_should_return_file_deleted_with_reconstructed_id(self, config):
+        raw_response = MagicMock(spec=httpx.Response)
+        mock_request = MagicMock()
+        encoded_name = urllib.parse.quote(
+            "litellm-vertex-files/publishers/google/models/gemini-2.0-flash-001/abc", safe=""
+        )
+        mock_request.url = (
+            f"https://storage.googleapis.com/storage/v1/b/my-bucket/o/{encoded_name}"
+        )
+        raw_response.request = mock_request
+
+        result = config.transform_delete_file_response(
+            raw_response=raw_response,
+            logging_obj=MagicMock(),
+            litellm_params={},
+        )
+
+        assert isinstance(result, FileDeleted)
+        assert result.deleted is True
+        assert result.object == "file"
+        assert "litellm-vertex-files/publishers/google/models/gemini-2.0-flash-001/abc" in result.id
+
+    def test_should_fallback_to_deleted_id_when_no_request(self, config):
+        raw_response = MagicMock(spec=httpx.Response)
+        raw_response.request = None
+
+        result = config.transform_delete_file_response(
+            raw_response=raw_response,
+            logging_obj=MagicMock(),
+            litellm_params={},
+        )
+
+        assert isinstance(result, FileDeleted)
+        assert result.id == "deleted"
+        assert result.deleted is True
diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py
index 66c063d47d8..c2f6d3fd539 100644
--- a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py
+++ b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py
@@ -227,52 +227,29 @@ class TestVertexAIBatchPassthroughHandler:
                 mock_managed_files_hook.store_unified_object_id.assert_called_once()
 
     def test_batch_cost_calculation_integration(self):
-        """Test integration with batch cost calculation"""
+        """Single Vertex AI response → non-zero cost with correct token counts."""
         from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage
-        
-        # Mock Vertex AI batch responses
+
         vertex_ai_batch_responses = [
             {
-                "status": "JOB_STATE_SUCCEEDED",
                 "response": {
-                    "candidates": [
-                        {
-                            "content": {
-                                "parts": [
-                                    {"text": "Hello, world!"}
-                                ]
-                            }
-                        }
-                    ],
                     "usageMetadata": {
                         "promptTokenCount": 10,
                         "candidatesTokenCount": 5,
-                        "totalTokenCount": 15
+                        "totalTokenCount": 15,
                     }
                 }
             }
         ]
-        
-        with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config:
-            with patch('litellm.completion_cost') as mock_completion_cost:
-                
-                # Setup mocks
-                mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = Mock(
-                    usage=Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5)
-                )
-                mock_completion_cost.return_value = 0.001
-                
-                # Test the cost calculation
-                total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
-                    vertex_ai_batch_responses, 
-                    model_name="gemini-1.5-flash"
-                )
-                
-                # Verify results
-                assert total_cost == 0.001
-                assert usage.total_tokens == 15
-                assert usage.prompt_tokens == 10
-                assert usage.completion_tokens == 5
+
+        total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
+            vertex_ai_batch_responses, model_name="gemini-1.5-flash-001"
+        )
+
+        assert usage.total_tokens == 15
+        assert usage.prompt_tokens == 10
+        assert usage.completion_tokens == 5
+        assert total_cost > 0, "batch_cost_calculator should return a non-zero cost"
 
     def test_batch_response_transformation(self):
         """Test transformation of Vertex AI batch responses to OpenAI format"""
@@ -385,155 +362,107 @@ class TestVertexAIBatchPassthroughHandler:
 
 
 class TestVertexAIBatchCostCalculation:
-    """Test cases for Vertex AI batch cost calculation functionality"""
+    """Test cases for Vertex AI batch cost calculation functionality.
 
-    def test_calculate_vertex_ai_batch_cost_and_usage_success(self):
-        """Test successful batch cost and usage calculation"""
+    The function under test (calculate_vertex_ai_batch_cost_and_usage) extracts
+    usageMetadata directly from Vertex AI response dicts and calls
+    batch_cost_calculator — no VertexGeminiConfig transformation involved.
+    """
+
+    def test_should_aggregate_cost_and_usage_across_responses(self):
+        """Two successful responses → costs and token counts are summed."""
         from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage
-        
-        # Mock successful batch responses
-        vertex_ai_batch_responses = [
+
+        responses = [
             {
-                "status": "JOB_STATE_SUCCEEDED",
                 "response": {
-                    "candidates": [
-                        {
-                            "content": {
-                                "parts": [
-                                    {"text": "Hello, world!"}
-                                ]
-                            }
-                        }
-                    ],
                     "usageMetadata": {
                         "promptTokenCount": 10,
                         "candidatesTokenCount": 5,
-                        "totalTokenCount": 15
+                        "totalTokenCount": 15,
                     }
                 }
             },
             {
-                "status": "JOB_STATE_SUCCEEDED", 
                 "response": {
-                    "candidates": [
-                        {
-                            "content": {
-                                "parts": [
-                                    {"text": "How are you?"}
-                                ]
-                            }
-                        }
-                    ],
                     "usageMetadata": {
                         "promptTokenCount": 8,
                         "candidatesTokenCount": 3,
-                        "totalTokenCount": 11
+                        "totalTokenCount": 11,
                     }
                 }
-            }
+            },
         ]
-        
-        with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config:
-            with patch('litellm.completion_cost') as mock_completion_cost:
-                
-                # Setup mocks
-                mock_model_response = Mock()
-                mock_model_response.usage = Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5)
-                mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = mock_model_response
-                mock_completion_cost.return_value = 0.001
-                
-                # Test the calculation
-                total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
-                    vertex_ai_batch_responses, 
-                    model_name="gemini-1.5-flash"
-                )
-                
-                # Verify results
-                assert total_cost == 0.002  # 2 responses * 0.001 each
-                assert usage.total_tokens == 30  # 15 + 15
-                assert usage.prompt_tokens == 20  # 10 + 10
-                assert usage.completion_tokens == 10  # 5 + 5
 
-    def test_calculate_vertex_ai_batch_cost_and_usage_with_failed_responses(self):
-        """Test batch cost calculation with some failed responses"""
+        total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
+            responses, model_name="gemini-1.5-flash-001"
+        )
+
+        assert usage.prompt_tokens == 18
+        assert usage.completion_tokens == 8
+        assert usage.total_tokens == 26
+        assert total_cost > 0, "batch_cost_calculator should return a non-zero cost"
+
+    def test_should_skip_responses_with_null_response_body(self):
+        """Failed lines (response: None) are skipped without error."""
         from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage
-        
-        # Mock batch responses with some failures
-        vertex_ai_batch_responses = [
+
+        responses = [
             {
-                "status": "JOB_STATE_SUCCEEDED",
                 "response": {
-                    "candidates": [
-                        {
-                            "content": {
-                                "parts": [
-                                    {"text": "Hello, world!"}
-                                ]
-                            }
-                        }
-                    ],
                     "usageMetadata": {
                         "promptTokenCount": 10,
                         "candidatesTokenCount": 5,
-                        "totalTokenCount": 15
+                        "totalTokenCount": 15,
                     }
                 }
             },
+            {"status": "JOB_STATE_FAILED", "response": None},
             {
-                "status": "JOB_STATE_FAILED",  # Failed response
-                "response": None
-            },
-            {
-                "status": "JOB_STATE_SUCCEEDED",
                 "response": {
-                    "candidates": [
-                        {
-                            "content": {
-                                "parts": [
-                                    {"text": "How are you?"}
-                                ]
-                            }
-                        }
-                    ],
                     "usageMetadata": {
                         "promptTokenCount": 8,
                         "candidatesTokenCount": 3,
-                        "totalTokenCount": 11
+                        "totalTokenCount": 11,
                     }
                 }
-            }
+            },
         ]
-        
-        with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config:
-            with patch('litellm.completion_cost') as mock_completion_cost:
-                
-                # Setup mocks
-                mock_model_response = Mock()
-                mock_model_response.usage = Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5)
-                mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = mock_model_response
-                mock_completion_cost.return_value = 0.001
-                
-                # Test the calculation
-                total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
-                    vertex_ai_batch_responses, 
-                    model_name="gemini-1.5-flash"
-                )
-                
-                # Verify results - should only process successful responses
-                assert total_cost == 0.002  # 2 successful responses * 0.001 each
-                assert usage.total_tokens == 30  # 15 + 15
-                assert usage.prompt_tokens == 20  # 10 + 10
-                assert usage.completion_tokens == 10  # 5 + 5
 
-    def test_calculate_vertex_ai_batch_cost_and_usage_empty_responses(self):
-        """Test batch cost calculation with empty response list"""
+        total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
+            responses, model_name="gemini-1.5-flash-001"
+        )
+
+        assert usage.prompt_tokens == 18
+        assert usage.completion_tokens == 8
+        assert usage.total_tokens == 26
+        assert total_cost > 0
+
+    def test_should_return_zeros_for_empty_response_list(self):
+        """Empty input → zero cost and zero usage."""
         from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage
-        
-        # Test with empty list
-        total_cost, usage = calculate_vertex_ai_batch_cost_and_usage([], model_name="gemini-1.5-flash")
-        
-        # Verify results
+
+        total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
+            [], model_name="gemini-1.5-flash-001"
+        )
+
         assert total_cost == 0.0
         assert usage.total_tokens == 0
         assert usage.prompt_tokens == 0
         assert usage.completion_tokens == 0
+
+    def test_should_handle_missing_usage_metadata_gracefully(self):
+        """Response without usageMetadata → 0 tokens, 0 cost for that line."""
+        from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage
+
+        responses = [
+            {"response": {"candidates": [{"content": {"parts": [{"text": "hi"}]}}]}},
+        ]
+
+        total_cost, usage = calculate_vertex_ai_batch_cost_and_usage(
+            responses, model_name="gemini-1.5-flash-001"
+        )
+
+        assert usage.prompt_tokens == 0
+        assert usage.completion_tokens == 0
+        assert usage.total_tokens == 0

From 705ef64ffcb20d9ee5cb9557e8f66e713b1b0167 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Sat, 28 Feb 2026 18:19:07 -0800
Subject: [PATCH 03/69] fix(ui): Audit logs - server-side pagination,
 filtering, and drawer view

- Replace client-side full-fetch loop with single react-query call using
  keepPreviousData; remove 5-second polling
- All filters (object ID, action, table, changed_by, team ID, key hash)
  now passed as query params to the backend
- Add object_team_id and object_key_hash params to /audit endpoint using
  Prisma JSON path filtering (PostgreSQL) to search inside before_value
  and updated_values JSON columns
- Migrate table from custom TanStack DataTable to AntD Table with
  server-side pagination
- Replace inline row expansion with a right-side AntD Drawer showing
  metadata and before/after diff
- Refactor uiAuditLogsCall to accept a structured options object

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../proxy/audit_logging_endpoints.py          |  51 +-
 .../src/components/networking.tsx             |  53 +-
 .../AuditLogDrawer/AuditLogDrawer.tsx         | 217 +++++
 .../src/components/view_logs/audit_logs.tsx   | 853 ++++++------------
 4 files changed, 571 insertions(+), 603 deletions(-)
 create mode 100644 ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx

diff --git a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py
index d1b00420d31..5ab3669b50c 100644
--- a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py
+++ b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py
@@ -1,13 +1,13 @@
 """
 AUDIT LOGGING
 
-All /audit logging endpoints. Attempting to write these as CRUD endpoints. 
+All /audit logging endpoints. Attempting to write these as CRUD endpoints.
 
 GET - /audit/{id} - Get audit log by id
 GET - /audit - Get all audit logs
 """
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 
 #### AUDIT LOGGING ####
 from fastapi import APIRouter, Depends, HTTPException, Query
@@ -22,6 +22,21 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 router = APIRouter()
 
 
+def _build_json_field_conditions(
+    field: str, json_key: str, value: str
+) -> List[Dict[str, Any]]:
+    """
+    Build OR conditions to match a value inside a JSON column at the given key.
+
+    Uses Prisma's JSON path filtering (PostgreSQL only). Returns a list of
+    two conditions — one for `before_value` and one for `updated_values` — to
+    be merged into the caller's top-level OR list.
+    """
+    return [
+        {field: {"path": [json_key], "string_contains": value}},
+    ]
+
+
 @router.get(
     "/audit",
     tags=["Audit Logging"],
@@ -49,6 +64,14 @@ async def get_audit_logs(
     ),
     start_date: Optional[str] = Query(None, description="Filter logs after this date"),
     end_date: Optional[str] = Query(None, description="Filter logs before this date"),
+    object_team_id: Optional[str] = Query(
+        None,
+        description="Filter by team_id present in before_value or updated_values JSON (PostgreSQL only)",
+    ),
+    object_key_hash: Optional[str] = Query(
+        None,
+        description="Filter by token (key hash) present in before_value or updated_values JSON (PostgreSQL only)",
+    ),
     # Sorting parameters
     sort_by: Optional[str] = Query(
         None,
@@ -60,6 +83,9 @@ async def get_audit_logs(
     Get all audit logs with filtering and pagination.
 
     Returns a paginated response of audit logs matching the specified filters.
+
+    Note: object_team_id and object_key_hash use Prisma JSON path filtering,
+    which requires PostgreSQL.
     """
     from litellm.proxy.proxy_server import prisma_client
 
@@ -82,18 +108,33 @@ async def get_audit_logs(
     if object_id:
         where_conditions["object_id"] = object_id
     if start_date or end_date:
-        date_filter = {}
+        date_filter: Dict[str, Any] = {}
         if start_date:
             date_filter["gte"] = start_date
         if end_date:
             date_filter["lte"] = end_date
         where_conditions["updated_at"] = date_filter
 
+    # JSON field filters (PostgreSQL only) — search inside before_value and
+    # updated_values for a matching key/value pair.
+    if object_team_id:
+        where_conditions["OR"] = [
+            *_build_json_field_conditions("before_value", "team_id", object_team_id),
+            *_build_json_field_conditions("updated_values", "team_id", object_team_id),
+        ]
+    if object_key_hash:
+        existing_or: List[Dict[str, Any]] = where_conditions.get("OR", [])
+        where_conditions["OR"] = [
+            *existing_or,
+            *_build_json_field_conditions("before_value", "token", object_key_hash),
+            *_build_json_field_conditions("updated_values", "token", object_key_hash),
+        ]
+
     # Build sort conditions
-    order_by = {}
+    order_by: Dict[str, Any] = {}
     if sort_by and isinstance(sort_by, str):
         order_by[sort_by] = sort_order
-    elif sort_order and isinstance(sort_order, str):
+    else:
         order_by["updated_at"] = sort_order  # Default sort by updated_at
 
     # Get paginated results
diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx
index 0df6d813d7c..919246ee851 100644
--- a/ui/litellm-dashboard/src/components/networking.tsx
+++ b/ui/litellm-dashboard/src/components/networking.tsx
@@ -8642,30 +8642,46 @@ export const updateSSOSettings = async (accessToken: string, settings: Record<st
   }
 };
 
-export const uiAuditLogsCall = async (
-  accessToken: string,
-  start_date?: string,
-  end_date?: string,
-  page?: number,
-  page_size?: number,
-) => {
+export interface UiAuditLogsParams {
+  action?: string;
+  table_name?: string;
+  object_id?: string;
+  changed_by?: string;
+  changed_by_api_key?: string;
+  object_team_id?: string;
+  object_key_hash?: string;
+  sort_by?: string;
+  sort_order?: "asc" | "desc";
+}
+
+export interface UiAuditLogsCallOptions {
+  accessToken: string;
+  page?: number;
+  page_size?: number;
+  params?: UiAuditLogsParams;
+}
+
+export const uiAuditLogsCall = async ({
+  accessToken,
+  page = 1,
+  page_size = 50,
+  params = {},
+}: UiAuditLogsCallOptions) => {
   try {
-    // Construct base URL
     let url = proxyBaseUrl ? `${proxyBaseUrl}/audit` : `/audit`;
 
-    // Add query parameters if they exist
     const queryParams = new URLSearchParams();
-    // if (start_date) queryParams.append('start_date', start_date);
-    // if (end_date) queryParams.append('end_date', end_date);
-    if (page) queryParams.append("page", page.toString());
-    if (page_size) queryParams.append("page_size", page_size.toString());
+    queryParams.append("page", page.toString());
+    queryParams.append("page_size", page_size.toString());
 
-    // Append query parameters to URL if any exist
-    const queryString = queryParams.toString();
-    if (queryString) {
-      url += `?${queryString}`;
+    for (const [key, value] of Object.entries(params)) {
+      if (value != null && value !== "") {
+        queryParams.append(key, String(value));
+      }
     }
 
+    url += `?${queryParams.toString()}`;
+
     const response = await fetch(url, {
       method: "GET",
       headers: {
@@ -8681,8 +8697,7 @@ export const uiAuditLogsCall = async (
       throw new Error(errorMessage);
     }
 
-    const data = await response.json();
-    return data;
+    return await response.json();
   } catch (error) {
     console.error("Failed to fetch audit logs:", error);
     throw error;
diff --git a/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx b/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
new file mode 100644
index 00000000000..1e8dca34f22
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
@@ -0,0 +1,217 @@
+import { Drawer, Tag, Tooltip } from "antd";
+import { CloseOutlined } from "@ant-design/icons";
+import moment from "moment";
+import { AuditLogEntry } from "../columns";
+
+interface AuditLogDrawerProps {
+  open: boolean;
+  onClose: () => void;
+  log: AuditLogEntry | null;
+}
+
+const TABLE_NAME_DISPLAY: Record<string, string> = {
+  LiteLLM_VerificationToken: "Keys",
+  LiteLLM_TeamTable: "Teams",
+  LiteLLM_UserTable: "Users",
+  LiteLLM_OrganizationTable: "Organizations",
+  LiteLLM_ProxyModelTable: "Models",
+};
+
+const ACTION_COLOR: Record<string, string> = {
+  created: "green",
+  updated: "blue",
+  deleted: "red",
+  rotated: "orange",
+};
+
+function JsonBlock({ value }: { value: Record<string, any> }) {
+  return (
+    <pre className="p-3 bg-gray-50 border rounded text-xs font-mono overflow-auto max-h-72 whitespace-pre-wrap break-all">
+      {JSON.stringify(value, null, 2)}
+    </pre>
+  );
+}
+
+function MetadataRow({ label, value }: { label: string; value: React.ReactNode }) {
+  return (
+    <div className="flex items-start gap-2 py-1.5">
+      <span className="text-xs text-gray-500 w-36 shrink-0">{label}</span>
+      <span className="text-xs text-gray-900 break-all">{value}</span>
+    </div>
+  );
+}
+
+function DiffSection({ log }: { log: AuditLogEntry }) {
+  const { action, table_name, before_value, updated_values } = log;
+  const isKeyTable = table_name === "LiteLLM_VerificationToken";
+  const isUpdateAction = action === "updated" || action === "rotated";
+
+  let displayBefore = before_value;
+  let displayAfter = updated_values;
+
+  if (isUpdateAction && before_value && updated_values) {
+    const changedBefore: Record<string, any> = {};
+    const changedAfter: Record<string, any> = {};
+    const allKeys = new Set([
+      ...Object.keys(before_value),
+      ...Object.keys(updated_values),
+    ]);
+
+    allKeys.forEach((key) => {
+      const bStr = JSON.stringify(before_value[key]);
+      const aStr = JSON.stringify(updated_values[key]);
+      if (bStr !== aStr) {
+        if (key in before_value) changedBefore[key] = before_value[key];
+        if (key in updated_values) changedAfter[key] = updated_values[key];
+      }
+    });
+
+    // Fields only in before (removed)
+    Object.keys(before_value).forEach((key) => {
+      if (!(key in updated_values) && !(key in changedBefore)) {
+        changedBefore[key] = before_value[key];
+        changedAfter[key] = undefined;
+      }
+    });
+
+    // Fields only in after (added)
+    Object.keys(updated_values).forEach((key) => {
+      if (!(key in before_value) && !(key in changedAfter)) {
+        changedAfter[key] = updated_values[key];
+        changedBefore[key] = undefined;
+      }
+    });
+
+    displayBefore =
+      Object.keys(changedBefore).length > 0
+        ? changedBefore
+        : { note: "No differing fields detected" };
+    displayAfter =
+      Object.keys(changedAfter).length > 0
+        ? changedAfter
+        : { note: "No differing fields detected" };
+  }
+
+  const renderValue = (value: Record<string, any> | null | undefined, label: string) => {
+    if (!value || Object.keys(value).length === 0) {
+      return <p className="text-xs text-gray-400 italic">N/A</p>;
+    }
+
+    // For key table updates, filter to only show meaningful fields
+    if (isKeyTable && isUpdateAction) {
+      const knownKeyFields = ["token", "spend", "max_budget"];
+      const hasOnlyKnown = Object.keys(value).every((k) => knownKeyFields.includes(k));
+      if (hasOnlyKnown && !("note" in value)) {
+        return (
+          <div className="space-y-1 text-xs">
+            {value.token !== undefined && (
+              <p><span className="text-gray-500">Token:</span> {value.token ?? "N/A"}</p>
+            )}
+            {value.spend !== undefined && (
+              <p><span className="text-gray-500">Spend:</span> ${Number(value.spend).toFixed(6)}</p>
+            )}
+            {value.max_budget !== undefined && (
+              <p><span className="text-gray-500">Max Budget:</span> ${Number(value.max_budget).toFixed(6)}</p>
+            )}
+          </div>
+        );
+      }
+    }
+
+    return <JsonBlock value={value} />;
+  };
+
+  return (
+    <div className="grid grid-cols-1 md:grid-cols-2 gap-4 mt-4">
+      <div>
+        <p className="text-xs font-semibold text-gray-600 mb-2">Before</p>
+        {renderValue(displayBefore, "before")}
+      </div>
+      <div>
+        <p className="text-xs font-semibold text-gray-600 mb-2">After</p>
+        {renderValue(displayAfter, "after")}
+      </div>
+    </div>
+  );
+}
+
+export function AuditLogDrawer({ open, onClose, log }: AuditLogDrawerProps) {
+  if (!log) return null;
+
+  const tableDisplay = TABLE_NAME_DISPLAY[log.table_name] ?? log.table_name;
+  const actionColor = ACTION_COLOR[log.action] ?? "default";
+
+  return (
+    <Drawer
+      placement="right"
+      width="60%"
+      open={open}
+      onClose={onClose}
+      closable={false}
+      mask={true}
+      maskClosable={true}
+      styles={{ body: { padding: 0 }, header: { display: "none" } }}
+    >
+      {/* Header */}
+      <div className="flex items-center justify-between px-6 py-4 border-b bg-white sticky top-0 z-10">
+        <div className="flex items-center gap-3">
+          <Tag color={actionColor} className="capitalize m-0">
+            {log.action}
+          </Tag>
+          <span className="text-sm text-gray-500">
+            {moment.utc(log.updated_at).local().format("MMM D, YYYY HH:mm:ss")}
+          </span>
+        </div>
+        <button
+          onClick={onClose}
+          className="w-8 h-8 flex items-center justify-center rounded hover:bg-gray-100 text-gray-500"
+          aria-label="Close"
+        >
+          <CloseOutlined />
+        </button>
+      </div>
+
+      {/* Body */}
+      <div className="px-6 py-5 overflow-auto h-full">
+        {/* Metadata */}
+        <div className="bg-gray-50 border rounded-lg p-4 mb-5">
+          <p className="text-xs font-semibold text-gray-700 mb-2 uppercase tracking-wide">
+            Details
+          </p>
+          <MetadataRow label="Table" value={tableDisplay} />
+          <MetadataRow
+            label="Object ID"
+            value={
+              <Tooltip title={log.object_id}>
+                <span className="font-mono">{log.object_id}</span>
+              </Tooltip>
+            }
+          />
+          <MetadataRow label="Changed By" value={log.changed_by || "—"} />
+          <MetadataRow
+            label="API Key"
+            value={
+              log.changed_by_api_key ? (
+                <Tooltip title={log.changed_by_api_key}>
+                  <span className="font-mono">
+                    {log.changed_by_api_key.slice(0, 12)}…
+                  </span>
+                </Tooltip>
+              ) : (
+                "—"
+              )
+            }
+          />
+        </div>
+
+        {/* Diff */}
+        <div>
+          <p className="text-xs font-semibold text-gray-700 mb-1 uppercase tracking-wide">
+            Changes
+          </p>
+          <DiffSection log={log} />
+        </div>
+      </div>
+    </Drawer>
+  );
+}
diff --git a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
index 918447a6149..d3372eca6ff 100644
--- a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
@@ -1,12 +1,15 @@
-import { DataTable } from "./table";
+import { useState } from "react";
+import { useQuery, keepPreviousData } from "@tanstack/react-query";
+import { Table, Tag, Input, Select, Button, Tooltip } from "antd";
+import { ReloadOutlined } from "@ant-design/icons";
+import type { ColumnsType, TablePaginationConfig } from "antd/es/table";
 import moment from "moment";
-import { useRef, useState, useEffect, useCallback, useMemo } from "react";
-import { useQuery } from "@tanstack/react-query";
-import { uiAuditLogsCall, keyListCall } from "../networking";
-import { AuditLogEntry, auditLogColumns } from "./columns";
-import { Text } from "@tremor/react";
+import { uiAuditLogsCall } from "../networking";
+import { AuditLogEntry } from "./columns";
+import { AuditLogDrawer } from "./AuditLogDrawer/AuditLogDrawer";
 import { Team } from "../key_team_helpers/key_list";
-import { formatNumberWithCommas } from "@/utils/dataUtils";
+
+const { Search } = Input;
 
 interface AuditLogsProps {
   accessToken: string | null;
@@ -21,6 +24,23 @@ interface AuditLogsProps {
 const asset_logos_folder = "../ui/assets/";
 export const auditLogsPreviewImg = `${asset_logos_folder}audit-logs-preview.png`;
 
+const TABLE_NAME_DISPLAY: Record<string, string> = {
+  LiteLLM_VerificationToken: "Keys",
+  LiteLLM_TeamTable: "Teams",
+  LiteLLM_UserTable: "Users",
+  LiteLLM_OrganizationTable: "Organizations",
+  LiteLLM_ProxyModelTable: "Models",
+};
+
+const ACTION_COLOR: Record<string, string> = {
+  created: "green",
+  updated: "blue",
+  deleted: "red",
+  rotated: "orange",
+};
+
+const PAGE_SIZE = 50;
+
 export default function AuditLogs({
   userID,
   userRole,
@@ -28,413 +48,144 @@ export default function AuditLogs({
   accessToken,
   isActive,
   premiumUser,
-  allTeams,
 }: AuditLogsProps) {
-  const [startTime, setStartTime] = useState<string>(moment().subtract(24, "hours").format("YYYY-MM-DDTHH:mm"));
+  const [page, setPage] = useState(1);
 
-  const actionFilterRef = useRef<HTMLDivElement>(null);
-  const tableFilterRef = useRef<HTMLDivElement>(null);
-  const [clientCurrentPage, setClientCurrentPage] = useState(1);
-  const [pageSize] = useState(50);
-  const [filters, setFilters] = useState<Record<string, string>>({});
-  const [selectedTeamId, setSelectedTeamId] = useState("");
-  const [selectedKeyHash, setSelectedKeyHash] = useState("");
-  const [objectIdSearch, setObjectIdSearch] = useState("");
-  const [selectedActionFilter, setSelectedActionFilter] = useState("all");
-  const [selectedTableFilter, setSelectedTableFilter] = useState("all");
-  const [actionFilterOpen, setActionFilterOpen] = useState(false);
-  const [tableFilterOpen, setTableFilterOpen] = useState(false);
+  // Filter state
+  const [objectId, setObjectId] = useState("");
+  const [changedBy, setChangedBy] = useState("");
+  const [keyHash, setKeyHash] = useState("");
+  const [teamId, setTeamId] = useState("");
+  const [action, setAction] = useState<string | undefined>(undefined);
+  const [tableName, setTableName] = useState<string | undefined>(undefined);
 
-  const allLogsQuery = useQuery<AuditLogEntry[]>({
-    queryKey: ["all_audit_logs", accessToken, token, userRole, userID, startTime],
+  // Drawer state
+  const [selectedLog, setSelectedLog] = useState<AuditLogEntry | null>(null);
+  const [drawerOpen, setDrawerOpen] = useState(false);
+
+  const query = useQuery({
+    queryKey: [
+      "audit_logs",
+      page,
+      PAGE_SIZE,
+      objectId,
+      changedBy,
+      keyHash,
+      teamId,
+      action,
+      tableName,
+    ],
     queryFn: async () => {
       if (!accessToken || !token || !userRole || !userID) {
-        return [];
+        return { audit_logs: [], total: 0, page: 1, page_size: PAGE_SIZE, total_pages: 0 };
       }
-
-      const formattedStartTimeStr = moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss");
-      const formattedEndTimeStr = moment().utc().format("YYYY-MM-DD HH:mm:ss");
-
-      let accumulatedLogs: AuditLogEntry[] = [];
-      let currentPageToFetch = 1;
-      let totalPagesFromBackend = 1;
-      const backendPageSize = 50;
-
-      do {
-        const response = await uiAuditLogsCall(
-          accessToken,
-          formattedStartTimeStr,
-          formattedEndTimeStr,
-          currentPageToFetch,
-          backendPageSize,
-        );
-        accumulatedLogs = accumulatedLogs.concat(response.audit_logs);
-        totalPagesFromBackend = response.total_pages;
-        currentPageToFetch++;
-      } while (currentPageToFetch <= totalPagesFromBackend);
-
-      return accumulatedLogs;
+      return uiAuditLogsCall({
+        accessToken,
+        page,
+        page_size: PAGE_SIZE,
+        params: {
+          object_id: objectId || undefined,
+          changed_by: changedBy || undefined,
+          object_key_hash: keyHash || undefined,
+          object_team_id: teamId || undefined,
+          action: action || undefined,
+          table_name: tableName || undefined,
+          sort_by: "updated_at",
+          sort_order: "desc",
+        },
+      });
     },
     enabled: !!accessToken && !!token && !!userRole && !!userID && isActive,
-    refetchInterval: 5000,
-    refetchIntervalInBackground: true,
+    placeholderData: keepPreviousData,
   });
 
-  const handleRefresh = () => {
-    allLogsQuery.refetch();
+  const handleFilterChange = () => {
+    // Reset to page 1 whenever a filter changes
+    setPage(1);
   };
 
-  const handleFilterChange = (newFilters: Record<string, string>) => {
-    setFilters(newFilters);
+  const handleTableChange = (pagination: TablePaginationConfig) => {
+    setPage(pagination.current ?? 1);
   };
 
-  const handleFilterReset = () => {
-    setFilters({});
-    setSelectedTeamId("");
-    setSelectedKeyHash("");
-    setObjectIdSearch("");
-    setSelectedActionFilter("all");
-    setSelectedTableFilter("all");
-    setClientCurrentPage(1);
+  const handleRowClick = (log: AuditLogEntry) => {
+    setSelectedLog(log);
+    setDrawerOpen(true);
   };
 
-  const fetchKeyHashForAlias = useCallback(
-    async (keyAlias: string) => {
-      if (!accessToken) return;
-
-      try {
-        const response = await keyListCall(accessToken, null, null, keyAlias, null, null, 1, 10);
-
-        const selectedKey = response.keys.find((key: any) => key.key_alias === keyAlias);
-
-        if (selectedKey) {
-          setSelectedKeyHash(selectedKey.token);
-        } else {
-          setSelectedKeyHash("");
-        }
-      } catch (error) {
-        console.error("Error fetching key hash for alias:", error);
-        setSelectedKeyHash("");
-      }
+  const columns: ColumnsType<AuditLogEntry> = [
+    {
+      title: "Timestamp",
+      dataIndex: "updated_at",
+      key: "updated_at",
+      width: 200,
+      render: (val: string) => (
+        <span className="font-mono text-xs whitespace-nowrap">
+          {moment.utc(val).local().format("MMM D, YYYY HH:mm:ss")}
+        </span>
+      ),
     },
-    [accessToken],
-  );
-
-  useEffect(() => {
-    if (!accessToken) return;
-
-    let teamIdChanged = false;
-    let keyHashChanged = false;
-
-    if (filters["Team ID"]) {
-      if (selectedTeamId !== filters["Team ID"]) {
-        setSelectedTeamId(filters["Team ID"]);
-        teamIdChanged = true;
-      }
-    } else {
-      if (selectedTeamId !== "") {
-        setSelectedTeamId("");
-        teamIdChanged = true;
-      }
-    }
-
-    if (filters["Key Hash"]) {
-      if (selectedKeyHash !== filters["Key Hash"]) {
-        setSelectedKeyHash(filters["Key Hash"]);
-        keyHashChanged = true;
-      }
-    } else if (filters["Key Alias"]) {
-      fetchKeyHashForAlias(filters["Key Alias"]);
-    } else {
-      if (selectedKeyHash !== "") {
-        setSelectedKeyHash("");
-        keyHashChanged = true;
-      }
-    }
-
-    if (teamIdChanged || keyHashChanged) {
-      setClientCurrentPage(1);
-    }
-  }, [filters, accessToken, fetchKeyHashForAlias, selectedTeamId, selectedKeyHash]);
-
-  useEffect(() => {
-    setClientCurrentPage(1);
-  }, [selectedTeamId, selectedKeyHash, startTime, objectIdSearch, selectedActionFilter, selectedTableFilter]);
-
-  useEffect(() => {
-    function handleClickOutside(event: MouseEvent) {
-      if (actionFilterRef.current && !actionFilterRef.current.contains(event.target as Node)) {
-        setActionFilterOpen(false);
-      }
-      if (tableFilterRef.current && !tableFilterRef.current.contains(event.target as Node)) {
-        setTableFilterOpen(false);
-      }
-    }
-
-    document.addEventListener("mousedown", handleClickOutside);
-    return () => document.removeEventListener("mousedown", handleClickOutside);
-  }, []);
-
-  const completeFilteredLogs = useMemo(() => {
-    if (!allLogsQuery.data) return [];
-    return allLogsQuery.data.filter((log) => {
-      let matchesTeam = true;
-      let matchesKey = true;
-      let matchesObjectId = true;
-      let matchesAction = true;
-      let matchesTable = true;
-
-      if (selectedTeamId) {
-        const beforeTeamId =
-          typeof log.before_value === "string" ? JSON.parse(log.before_value)?.team_id : log.before_value?.team_id;
-        const updatedTeamId =
-          typeof log.updated_values === "string"
-            ? JSON.parse(log.updated_values)?.team_id
-            : log.updated_values?.team_id;
-        matchesTeam = beforeTeamId === selectedTeamId || updatedTeamId === selectedTeamId;
-      }
-
-      if (selectedKeyHash) {
-        try {
-          const beforeBody = typeof log.before_value === "string" ? JSON.parse(log.before_value) : log.before_value;
-          const updatedBody =
-            typeof log.updated_values === "string" ? JSON.parse(log.updated_values) : log.updated_values;
-
-          const beforeKey = beforeBody?.token;
-          const updatedKey = updatedBody?.token;
-
-          matchesKey =
-            (typeof beforeKey === "string" && beforeKey.includes(selectedKeyHash)) ||
-            (typeof updatedKey === "string" && updatedKey.includes(selectedKeyHash));
-        } catch (e) {
-          matchesKey = false;
-        }
-      }
-
-      if (objectIdSearch) {
-        matchesObjectId = log.object_id?.toLowerCase().includes(objectIdSearch.toLowerCase());
-      }
-
-      if (selectedActionFilter !== "all") {
-        matchesAction = log.action?.toLowerCase() === selectedActionFilter.toLowerCase();
-      }
-
-      if (selectedTableFilter !== "all") {
-        let tableMatchName = "";
-        switch (selectedTableFilter) {
-          case "keys":
-            tableMatchName = "litellm_verificationtoken";
-            break;
-          case "teams":
-            tableMatchName = "litellm_teamtable";
-            break;
-          case "users":
-            tableMatchName = "litellm_usertable";
-            break;
-          // Add other direct table names if needed, or rely on a more generic match
-          default:
-            tableMatchName = selectedTableFilter; // Should not happen with current UI options
-        }
-        matchesTable = log.table_name?.toLowerCase() === tableMatchName;
-      }
-
-      return matchesTeam && matchesKey && matchesObjectId && matchesAction && matchesTable;
-    });
-  }, [allLogsQuery.data, selectedTeamId, selectedKeyHash, objectIdSearch, selectedActionFilter, selectedTableFilter]);
-
-  const totalFilteredItems = completeFilteredLogs.length;
-  const totalFilteredPages = Math.ceil(totalFilteredItems / pageSize) || 1;
-
-  const paginatedViewOfFilteredLogs = useMemo(() => {
-    const start = (clientCurrentPage - 1) * pageSize;
-    const end = start + pageSize;
-    return completeFilteredLogs.slice(start, end);
-  }, [completeFilteredLogs, clientCurrentPage, pageSize]);
-
-  // Check if audit logs are empty (not loading and no data)
-  const showAuditLogsInfo = !allLogsQuery.data || allLogsQuery.data.length === 0;
-
-  // Custom AuditLogsInfoMessage component
-  const AuditLogsInfoMessage = ({ show }: { show: boolean }) => {
-    if (!show) return null;
-
-    return (
-      <div className="bg-blue-50 border border-blue-200 rounded-lg p-4 flex items-start mb-6">
-        <div className="text-blue-500 mr-3 flex-shrink-0 mt-0.5">
-          <svg
-            xmlns="http://www.w3.org/2000/svg"
-            width="20"
-            height="20"
-            viewBox="0 0 24 24"
-            fill="none"
-            stroke="currentColor"
-            strokeWidth="2"
-            strokeLinecap="round"
-            strokeLinejoin="round"
-          >
-            <circle cx="12" cy="12" r="10"></circle>
-            <line x1="12" y1="16" x2="12" y2="12"></line>
-            <line x1="12" y1="8" x2="12.01" y2="8"></line>
-          </svg>
-        </div>
-        <div>
-          <h4 className="text-sm font-medium text-blue-800">Audit Logs Not Available</h4>
-          <p className="text-sm text-blue-700 mt-1">
-            To enable audit logging, add the following configuration to your LiteLLM proxy configuration file:
-          </p>
-          <pre className="mt-2 bg-white p-3 rounded border border-blue-200 text-xs font-mono overflow-auto">
-            {`litellm_settings:
-  store_audit_logs: true`}
-          </pre>
-          <p className="text-xs text-blue-700 mt-2">
-            Note: This will only affect new requests after the configuration change and proxy restart.
-          </p>
-        </div>
-      </div>
-    );
-  };
-
-  const renderSubComponent = useCallback(({ row }: { row: any }) => {
-    const AuditLogRowExpansionPanel = ({ rowData }: { rowData: AuditLogEntry }) => {
-      const { before_value, updated_values, table_name, action } = rowData;
-
-      const renderValue = (value: Record<string, any>, isKeyTable: boolean) => {
-        if (!value || Object.keys(value).length === 0) return <Text>N/A</Text>;
-
-        if (isKeyTable) {
-          const changedKeys = Object.keys(value);
-          const knownKeyFields = ["token", "spend", "max_budget"];
-
-          const onlyKnownFieldsChanged = changedKeys.every((key) => knownKeyFields.includes(key));
-
-          if (onlyKnownFieldsChanged && changedKeys.length > 0) {
-            return (
-              <div>
-                {changedKeys.includes("token") && (
-                  <p>
-                    <strong>Token:</strong> {value.token || "N/A"}
-                  </p>
-                )}
-                {changedKeys.includes("spend") && (
-                  <p>
-                    <strong>Spend:</strong>{" "}
-                    {value.spend !== undefined ? `$${formatNumberWithCommas(value.spend, 6)}` : "N/A"}
-                  </p>
-                )}
-                {changedKeys.includes("max_budget") && (
-                  <p>
-                    <strong>Max Budget:</strong>{" "}
-                    {value.max_budget !== undefined ? `$${formatNumberWithCommas(value.max_budget, 6)}` : "N/A"}
-                  </p>
-                )}
-              </div>
-            );
-          } else {
-            if (
-              value["No differing fields detected in 'before' state"] ||
-              value["No differing fields detected in 'updated' state"] ||
-              value["No fields changed"]
-            ) {
-              return <Text>{value[Object.keys(value)[0]]}</Text>; // Display the N/A message string
-            }
-            return (
-              <pre className="p-2 bg-gray-50 border rounded text-xs overflow-auto max-h-60">
-                {JSON.stringify(value, null, 2)}
-              </pre>
-            );
-          }
-        }
-
-        return (
-          <pre className="p-2 bg-gray-50 border rounded text-xs overflow-auto max-h-60">
-            {JSON.stringify(value, null, 2)}
-          </pre>
-        );
-      };
-
-      let displayBeforeValue = before_value;
-      let displayUpdatedValue = updated_values;
-
-      if ((action === "updated" || action === "rotated") && before_value && updated_values) {
-        if (
-          table_name === "LiteLLM_TeamTable" ||
-          table_name === "LiteLLM_UserTable" ||
-          table_name === "LiteLLM_VerificationToken"
-        ) {
-          const changedBefore: Record<string, any> = {};
-          const changedUpdated: Record<string, any> = {};
-          const allKeys = new Set([...Object.keys(before_value), ...Object.keys(updated_values)]);
-
-          allKeys.forEach((key) => {
-            const beforeValStr = JSON.stringify(before_value[key]);
-            const updatedValStr = JSON.stringify(updated_values[key]);
-            if (beforeValStr !== updatedValStr) {
-              if (before_value.hasOwnProperty(key)) {
-                changedBefore[key] = before_value[key];
-              }
-              if (updated_values.hasOwnProperty(key)) {
-                changedUpdated[key] = updated_values[key];
-              }
-            }
-          });
-
-          Object.keys(before_value).forEach((key) => {
-            if (!updated_values.hasOwnProperty(key) && !changedBefore.hasOwnProperty(key)) {
-              changedBefore[key] = before_value[key];
-              changedUpdated[key] = undefined;
-            }
-          });
-
-          Object.keys(updated_values).forEach((key) => {
-            if (!before_value.hasOwnProperty(key) && !changedUpdated.hasOwnProperty(key)) {
-              changedUpdated[key] = updated_values[key];
-              changedBefore[key] = undefined;
-            }
-          });
-
-          displayBeforeValue =
-            Object.keys(changedBefore).length > 0
-              ? changedBefore
-              : { "No differing fields detected in 'before' state": "N/A" };
-          displayUpdatedValue =
-            Object.keys(changedUpdated).length > 0
-              ? changedUpdated
-              : { "No differing fields detected in 'updated' state": "N/A" };
-
-          if (Object.keys(changedBefore).length === 0 && Object.keys(changedUpdated).length === 0) {
-            displayBeforeValue = { "No fields changed": "N/A" };
-            displayUpdatedValue = { "No fields changed": "N/A" };
-          }
-        }
-      }
-
-      return (
-        <div className="-mx-4 p-4 bg-slate-100 border-y border-slate-300 grid grid-cols-1 md:grid-cols-2 gap-4">
-          <div>
-            <h4 className="font-semibold mb-2 text-sm text-slate-700">Before Value:</h4>
-            {renderValue(displayBeforeValue, table_name === "LiteLLM_VerificationToken")}
-          </div>
-          <div>
-            <h4 className="font-semibold mb-2 text-sm text-slate-700">Updated Value:</h4>
-            {renderValue(displayUpdatedValue, table_name === "LiteLLM_VerificationToken")}
-          </div>
-        </div>
-      );
-    };
-
-    return <AuditLogRowExpansionPanel rowData={row.original as AuditLogEntry} />;
-  }, []);
+    {
+      title: "Action",
+      dataIndex: "action",
+      key: "action",
+      width: 100,
+      render: (val: string) => (
+        <Tag color={ACTION_COLOR[val] ?? "default"} className="capitalize">
+          {val}
+        </Tag>
+      ),
+    },
+    {
+      title: "Table",
+      dataIndex: "table_name",
+      key: "table_name",
+      width: 130,
+      render: (val: string) => TABLE_NAME_DISPLAY[val] ?? val,
+    },
+    {
+      title: "Object ID",
+      dataIndex: "object_id",
+      key: "object_id",
+      render: (val: string) => (
+        <Tooltip title={val}>
+          <span className="font-mono text-xs">{val}</span>
+        </Tooltip>
+      ),
+    },
+    {
+      title: "Changed By",
+      dataIndex: "changed_by",
+      key: "changed_by",
+      width: 200,
+      render: (val: string) => val || "—",
+    },
+    {
+      title: "API Key",
+      dataIndex: "changed_by_api_key",
+      key: "changed_by_api_key",
+      width: 140,
+      render: (val: string) =>
+        val ? (
+          <Tooltip title={val}>
+            <span className="font-mono text-xs">{val.slice(0, 12)}…</span>
+          </Tooltip>
+        ) : (
+          "—"
+        ),
+    },
+  ];
 
   if (!premiumUser) {
     return (
       <div style={{ textAlign: "center", marginTop: "20px" }}>
         <h1 style={{ display: "block", marginBottom: "10px" }}>✨ Enterprise Feature.</h1>
-        <Text style={{ display: "block", marginBottom: "10px" }}>
+        <p style={{ display: "block", marginBottom: "10px" }}>
           This is a LiteLLM Enterprise feature, and requires a valid key to use.
-        </Text>
-        <Text style={{ display: "block", marginBottom: "20px", fontStyle: "italic" }}>
+        </p>
+        <p style={{ display: "block", marginBottom: "20px", fontStyle: "italic" }}>
           Here&apos;s a preview of what Audit Logs offer:
-        </Text>
+        </p>
         <img
           src={auditLogsPreviewImg}
           alt="Audit Logs Preview"
@@ -446,7 +197,6 @@ export default function AuditLogs({
             margin: "0 auto",
           }}
           onError={(e) => {
-            console.error("Failed to load audit logs preview image");
             (e.target as HTMLImageElement).style.display = "none";
           }}
         />
@@ -454,204 +204,149 @@ export default function AuditLogs({
     );
   }
 
-  const currentDisplayItemsStart = totalFilteredItems > 0 ? (clientCurrentPage - 1) * pageSize + 1 : 0;
-  const currentDisplayItemsEnd = Math.min(clientCurrentPage * pageSize, totalFilteredItems);
+  const auditLogs: AuditLogEntry[] = query.data?.audit_logs ?? [];
+  const total: number = query.data?.total ?? 0;
 
   return (
     <>
-      <div className="flex items-center justify-between mb-4"></div>
-      {/* <FilterComponent options={auditLogFilterOptions} onApplyFilters={handleFilterChange} onResetFilters={handleFilterReset} /> */}
       <div className="bg-white rounded-lg shadow">
+        {/* Header */}
         <div className="border-b px-6 py-4">
-          <h1 className="text-xl font-semibold py-4">Audit Logs</h1>
+          <div className="flex items-center justify-between mb-4">
+            <h1 className="text-xl font-semibold">Audit Logs</h1>
+            <Button
+              icon={<ReloadOutlined spin={query.isFetching} />}
+              onClick={() => query.refetch()}
+              disabled={query.isFetching}
+            >
+              Refresh
+            </Button>
+          </div>
 
-          {/* Show Audit Logs Info Message when no data */}
-          <AuditLogsInfoMessage show={showAuditLogsInfo} />
-
-          <div className="flex flex-col md:flex-row items-start md:items-center justify-between space-y-4 md:space-y-0">
-            <div className="flex flex-wrap items-center gap-3">
-              <div className="flex items-center gap-2">
-                <div className="flex items-center">
-                  <input
-                    type="text"
-                    placeholder="Search by Object ID..."
-                    value={objectIdSearch}
-                    onChange={(e) => setObjectIdSearch(e.target.value)}
-                    className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
-                  />
-                </div>
-
-                <button
-                  onClick={handleRefresh}
-                  className="px-3 py-2 text-sm border rounded-md hover:bg-gray-50 flex items-center gap-2"
-                  title="Refresh data"
-                >
-                  <svg
-                    className={`w-4 h-4 ${allLogsQuery.isFetching ? "animate-spin" : ""}`}
-                    fill="none"
-                    stroke="currentColor"
-                    viewBox="0 0 24 24"
-                  >
-                    <path
-                      strokeLinecap="round"
-                      strokeLinejoin="round"
-                      strokeWidth={2}
-                      d="M4 4v5h.582m15.356 2A8.001 8.001 0 004.582 9m0 0H9m11 11v-5h-.581m0 0a8.003 8.003 0 01-15.357-2m15.357 2H15"
-                    />
-                  </svg>
-                  <span>Refresh</span>
-                </button>
-              </div>
-            </div>
-
-            <div className="flex items-center space-x-4">
-              {/* Custom Action Filter Dropdown */}
-              <div className="relative" ref={actionFilterRef}>
-                <label htmlFor="actionFilterDisplay" className="mr-2 text-sm font-medium text-gray-700 sr-only">
-                  Action:
-                </label>
-                <button
-                  id="actionFilterDisplay"
-                  onClick={() => setActionFilterOpen(!actionFilterOpen)}
-                  className="px-3 py-2 text-sm border rounded-md hover:bg-gray-50 flex items-center gap-2 bg-white w-40 text-left justify-between"
-                >
-                  <span>
-                    {selectedActionFilter === "all" && "All Actions"}
-                    {selectedActionFilter === "created" && "Created"}
-                    {selectedActionFilter === "updated" && "Updated"}
-                    {selectedActionFilter === "deleted" && "Deleted"}
-                    {selectedActionFilter === "rotated" && "Rotated"}
-                  </span>
-                  <svg
-                    className="w-4 h-4 text-gray-500"
-                    fill="none"
-                    stroke="currentColor"
-                    viewBox="0 0 24 24"
-                    xmlns="http://www.w3.org/2000/svg"
-                  >
-                    <path strokeLinecap="round" strokeLinejoin="round" strokeWidth="2" d="M19 9l-7 7-7-7"></path>
-                  </svg>
-                </button>
-                {actionFilterOpen && (
-                  <div className="absolute left-0 mt-2 w-40 bg-white rounded-lg shadow-lg border p-1 z-50">
-                    <div className="space-y-1">
-                      {[
-                        { label: "All Actions", value: "all" },
-                        { label: "Created", value: "created" },
-                        { label: "Updated", value: "updated" },
-                        { label: "Deleted", value: "deleted" },
-                        { label: "Rotated", value: "rotated" },
-                      ].map((option) => (
-                        <button
-                          key={option.value}
-                          className={`w-full px-3 py-2 text-left text-sm hover:bg-gray-50 rounded-md ${
-                            selectedActionFilter === option.value
-                              ? "bg-blue-50 text-blue-600 font-medium"
-                              : "font-normal"
-                          }`}
-                          onClick={() => {
-                            setSelectedActionFilter(option.value);
-                            setActionFilterOpen(false);
-                          }}
-                        >
-                          {option.label}
-                        </button>
-                      ))}
-                    </div>
-                  </div>
-                )}
-              </div>
-
-              {/* Custom Table Filter Dropdown */}
-              <div className="relative" ref={tableFilterRef}>
-                <label htmlFor="tableFilterDisplay" className="mr-2 text-sm font-medium text-gray-700 sr-only">
-                  Table:
-                </label>
-                <button
-                  id="tableFilterDisplay"
-                  onClick={() => setTableFilterOpen(!tableFilterOpen)}
-                  className="px-3 py-2 text-sm border rounded-md hover:bg-gray-50 flex items-center gap-2 bg-white w-40 text-left justify-between"
-                >
-                  <span>
-                    {selectedTableFilter === "all" && "All Tables"}
-                    {selectedTableFilter === "keys" && "Keys"}
-                    {selectedTableFilter === "teams" && "Teams"}
-                    {selectedTableFilter === "users" && "Users"}
-                  </span>
-                  <svg
-                    className="w-4 h-4 text-gray-500"
-                    fill="none"
-                    stroke="currentColor"
-                    viewBox="0 0 24 24"
-                    xmlns="http://www.w3.org/2000/svg"
-                  >
-                    <path strokeLinecap="round" strokeLinejoin="round" strokeWidth="2" d="M19 9l-7 7-7-7"></path>
-                  </svg>
-                </button>
-                {tableFilterOpen && (
-                  <div className="absolute left-0 mt-2 w-40 bg-white rounded-lg shadow-lg border p-1 z-50">
-                    <div className="space-y-1">
-                      {[
-                        { label: "All Tables", value: "all" },
-                        { label: "Keys", value: "keys" },
-                        { label: "Teams", value: "teams" },
-                        { label: "Users", value: "users" },
-                      ].map((option) => (
-                        <button
-                          key={option.value}
-                          className={`w-full px-3 py-2 text-left text-sm hover:bg-gray-50 rounded-md ${
-                            selectedTableFilter === option.value
-                              ? "bg-blue-50 text-blue-600 font-medium"
-                              : "font-normal"
-                          }`}
-                          onClick={() => {
-                            setSelectedTableFilter(option.value);
-                            setTableFilterOpen(false);
-                          }}
-                        >
-                          {option.label}
-                        </button>
-                      ))}
-                    </div>
-                  </div>
-                )}
-              </div>
-
-              <span className="text-sm text-gray-700">
-                Showing {allLogsQuery.isLoading ? "..." : currentDisplayItemsStart} -{" "}
-                {allLogsQuery.isLoading ? "..." : currentDisplayItemsEnd} of{" "}
-                {allLogsQuery.isLoading ? "..." : totalFilteredItems} results
-              </span>
-              <div className="flex items-center space-x-2">
-                <span className="text-sm text-gray-700">
-                  Page {allLogsQuery.isLoading ? "..." : clientCurrentPage} of{" "}
-                  {allLogsQuery.isLoading ? "..." : totalFilteredPages}
-                </span>
-                <button
-                  onClick={() => setClientCurrentPage((p) => Math.max(1, p - 1))}
-                  disabled={allLogsQuery.isLoading || clientCurrentPage === 1}
-                  className="px-3 py-1 text-sm border rounded-md hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed"
-                >
-                  Previous
-                </button>
-                <button
-                  onClick={() => setClientCurrentPage((p) => Math.min(totalFilteredPages, p + 1))}
-                  disabled={allLogsQuery.isLoading || clientCurrentPage === totalFilteredPages}
-                  className="px-3 py-1 text-sm border rounded-md hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed"
-                >
-                  Next
-                </button>
-              </div>
-            </div>
+          {/* Filters */}
+          <div className="flex flex-wrap gap-3">
+            <Search
+              placeholder="Object ID"
+              allowClear
+              style={{ width: 200 }}
+              onSearch={(val) => {
+                setObjectId(val);
+                handleFilterChange();
+              }}
+              onChange={(e) => {
+                if (!e.target.value) {
+                  setObjectId("");
+                  handleFilterChange();
+                }
+              }}
+            />
+            <Search
+              placeholder="Changed By"
+              allowClear
+              style={{ width: 180 }}
+              onSearch={(val) => {
+                setChangedBy(val);
+                handleFilterChange();
+              }}
+              onChange={(e) => {
+                if (!e.target.value) {
+                  setChangedBy("");
+                  handleFilterChange();
+                }
+              }}
+            />
+            <Search
+              placeholder="Team ID"
+              allowClear
+              style={{ width: 180 }}
+              onSearch={(val) => {
+                setTeamId(val);
+                handleFilterChange();
+              }}
+              onChange={(e) => {
+                if (!e.target.value) {
+                  setTeamId("");
+                  handleFilterChange();
+                }
+              }}
+            />
+            <Search
+              placeholder="Key Hash"
+              allowClear
+              style={{ width: 180 }}
+              onSearch={(val) => {
+                setKeyHash(val);
+                handleFilterChange();
+              }}
+              onChange={(e) => {
+                if (!e.target.value) {
+                  setKeyHash("");
+                  handleFilterChange();
+                }
+              }}
+            />
+            <Select
+              placeholder="All Actions"
+              allowClear
+              style={{ width: 140 }}
+              options={[
+                { label: "Created", value: "created" },
+                { label: "Updated", value: "updated" },
+                { label: "Deleted", value: "deleted" },
+                { label: "Rotated", value: "rotated" },
+              ]}
+              onChange={(val) => {
+                setAction(val);
+                handleFilterChange();
+              }}
+            />
+            <Select
+              placeholder="All Tables"
+              allowClear
+              style={{ width: 150 }}
+              options={[
+                { label: "Keys", value: "LiteLLM_VerificationToken" },
+                { label: "Teams", value: "LiteLLM_TeamTable" },
+                { label: "Users", value: "LiteLLM_UserTable" },
+                { label: "Organizations", value: "LiteLLM_OrganizationTable" },
+                { label: "Models", value: "LiteLLM_ProxyModelTable" },
+              ]}
+              onChange={(val) => {
+                setTableName(val);
+                handleFilterChange();
+              }}
+            />
           </div>
         </div>
-        <DataTable
-          columns={auditLogColumns}
-          data={paginatedViewOfFilteredLogs}
-          renderSubComponent={renderSubComponent}
-          getRowCanExpand={() => true}
+
+        {/* Table */}
+        <Table<AuditLogEntry>
+          columns={columns}
+          dataSource={auditLogs}
+          rowKey="id"
+          loading={query.isLoading}
+          size="small"
+          onRow={(record) => ({
+            onClick: () => handleRowClick(record),
+            style: { cursor: "pointer" },
+          })}
+          pagination={{
+            current: page,
+            pageSize: PAGE_SIZE,
+            total,
+            showTotal: (t) => `${t} total`,
+            showSizeChanger: false,
+            onChange: (p) => setPage(p),
+          }}
+          onChange={handleTableChange}
         />
       </div>
+
+      <AuditLogDrawer
+        open={drawerOpen}
+        onClose={() => setDrawerOpen(false)}
+        log={selectedLog}
+      />
     </>
   );
 }

From 86d5b4c632f4b3e3279e64b1cd0426d985a00dfc Mon Sep 17 00:00:00 2001
From: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Date: Mon, 2 Mar 2026 18:43:07 -0800
Subject: [PATCH 04/69] feat: add Nebius AI Studio models to
 model_prices_and_context_window.json (#22614)

Add 30 Nebius AI Studio models covering:
- Text-to-text: DeepSeek (R1, R1-0528, R1-Distill, V3, V3-0324), Meta Llama
  (3.1-8B/70B/405B, 3.3-70B), Qwen (3-235B/32B/30B/14B/4B, 2.5-72B/32B,
  2.5-Coder-7B, QwQ-32B), Mistral Nemo, NousResearch Hermes-3, NVIDIA
  Nemotron Ultra/Super, Google Gemma-3-27B, Llama-Guard-3
- Vision: Qwen2.5-VL-72B, Qwen2-VL-72B, Qwen2-VL-7B
- Embedding: BAAI/bge-en-icl, BAAI/bge-multilingual-gemma2, intfloat/e5-mistral-7b

Pricing sourced from https://nebius.com/prices-ai-studio (base flavor).
Context windows sourced from https://docs.nebius.com/studio/inference/models/

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
---
 ...odel_prices_and_context_window_backup.json | 333 +++++++++++++++++-
 model_prices_and_context_window.json          | 333 +++++++++++++++++-
 2 files changed, 662 insertions(+), 4 deletions(-)

diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 48a93830d4f..d4c5b476af6 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -23991,6 +23991,335 @@
             "/v1/images/generations"
         ]
     },
+    "nebius/deepseek-ai/DeepSeek-R1": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 8e-07,
+        "output_cost_per_token": 2.4e-06,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/deepseek-ai/DeepSeek-R1-0528": {
+        "max_tokens": 164000,
+        "max_input_tokens": 164000,
+        "max_output_tokens": 164000,
+        "input_cost_per_token": 8e-07,
+        "output_cost_per_token": 2.4e-06,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 2.5e-07,
+        "output_cost_per_token": 7.5e-07,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/deepseek-ai/DeepSeek-V3": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 5e-07,
+        "output_cost_per_token": 1.5e-06,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/deepseek-ai/DeepSeek-V3-0324": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 5e-07,
+        "output_cost_per_token": 1.5e-06,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/google/gemma-3-27b-it": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 6e-08,
+        "output_cost_per_token": 2e-07,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/meta-llama/Llama-3.3-70B-Instruct": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 1.3e-07,
+        "output_cost_per_token": 4e-07,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/meta-llama/Llama-Guard-3-8B": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
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diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index f2aa6287a56..4934f11d456 100644
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+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/Qwen/Qwen2.5-Coder-7B": {
+        "max_tokens": 32768,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 32768,
+        "input_cost_per_token": 1e-08,
+        "output_cost_per_token": 3e-08,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/Qwen/Qwen2.5-VL-72B-Instruct": {
+        "max_tokens": 131072,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "input_cost_per_token": 1.3e-07,
+        "output_cost_per_token": 4e-07,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/Qwen/Qwen2-VL-72B-Instruct": {
+        "max_tokens": 131072,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "input_cost_per_token": 1.3e-07,
+        "output_cost_per_token": 4e-07,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/Qwen/Qwen2-VL-7B-Instruct": {
+        "max_tokens": 131072,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "input_cost_per_token": 2e-08,
+        "output_cost_per_token": 6e-08,
+        "litellm_provider": "nebius",
+        "mode": "chat",
+        "supports_vision": true,
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/BAAI/bge-en-icl": {
+        "max_tokens": 32768,
+        "max_input_tokens": 32768,
+        "input_cost_per_token": 1e-08,
+        "output_cost_per_token": 0.0,
+        "litellm_provider": "nebius",
+        "mode": "embedding",
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/BAAI/bge-multilingual-gemma2": {
+        "max_tokens": 8192,
+        "max_input_tokens": 8192,
+        "input_cost_per_token": 1e-08,
+        "output_cost_per_token": 0.0,
+        "litellm_provider": "nebius",
+        "mode": "embedding",
+        "source": "https://nebius.com/prices-ai-studio"
+    },
+    "nebius/intfloat/e5-mistral-7b-instruct": {
+        "max_tokens": 32768,
+        "max_input_tokens": 32768,
+        "input_cost_per_token": 1e-08,
+        "output_cost_per_token": 0.0,
+        "litellm_provider": "nebius",
+        "mode": "embedding",
+        "source": "https://nebius.com/prices-ai-studio"
+    },
     "nvidia.nemotron-nano-12b-v2": {
         "input_cost_per_token": 2e-07,
         "litellm_provider": "bedrock_converse",
@@ -27188,7 +27517,7 @@
         "supports_function_calling": true
     },
     "perplexity/pplx-embed-v1-0.6b": {
-        "input_cost_per_token": 0.000000004,
+        "input_cost_per_token": 4e-09,
         "litellm_provider": "perplexity",
         "max_input_tokens": 32768,
         "max_tokens": 32768,
@@ -27198,7 +27527,7 @@
         "source": "https://docs.perplexity.ai/docs/embeddings/quickstart"
     },
     "perplexity/pplx-embed-v1-4b": {
-        "input_cost_per_token": 0.00000003,
+        "input_cost_per_token": 3e-08,
         "litellm_provider": "perplexity",
         "max_input_tokens": 32768,
         "max_tokens": 32768,

From 18216ac07c68dee1d9fd8d1d6f4788b62b9394ec Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 10:48:00 +0530
Subject: [PATCH 05/69] Fix: Azure ai finetuning api

---
 litellm/fine_tuning/main.py                   |  55 +++
 ...odel_prices_and_context_window_backup.json | 353 +++++++++++++++---
 litellm/types/llms/openai.py                  |  13 +-
 tests/batches_tests/test_fine_tuning_api.py   |  58 +++
 4 files changed, 423 insertions(+), 56 deletions(-)

diff --git a/litellm/fine_tuning/main.py b/litellm/fine_tuning/main.py
index f5b8b097026..db77fa32919 100644
--- a/litellm/fine_tuning/main.py
+++ b/litellm/fine_tuning/main.py
@@ -34,6 +34,44 @@ vertex_fine_tuning_apis_instance = VertexFineTuningAPI()
 #################################################
 
 
+def _prepare_azure_extra_body(
+    extra_body: Optional[Dict[str, Any]],
+    kwargs: Dict[str, Any],
+    azure_specific_hyperparams: Dict[str, Any],
+) -> Dict[str, Any]:
+    """
+    Prepare extra_body for Azure fine-tuning API by combining Azure-specific parameters.
+    
+    Azure fine-tuning API accepts additional parameters beyond the standard OpenAI spec:
+    - trainingType: Type of training (e.g., 1 for supervised fine-tuning)
+    - prompt_loss_weight: Weight for prompt loss in training
+    
+    These parameters must be passed in the extra_body field when calling the Azure OpenAI SDK.
+    
+    Args:
+        extra_body: Optional existing extra_body dict
+        kwargs: Request kwargs that may contain Azure-specific parameters
+        azure_specific_hyperparams: Dict of Azure-specific hyperparameters already extracted
+        
+    Returns:
+        Dict containing all Azure-specific parameters to be passed in extra_body
+    """
+    if extra_body is None:
+        extra_body = {}
+    
+    # Azure-specific root-level parameters
+    azure_specific_params = ["trainingType"]
+    for param in azure_specific_params:
+        if param in kwargs:
+            extra_body[param] = kwargs[param]
+    
+    # Add Azure-specific hyperparameters
+    if azure_specific_hyperparams:
+        extra_body.update(azure_specific_hyperparams)
+    
+    return extra_body
+
+
 @client
 async def acreate_fine_tuning_job(
     model: str,
@@ -114,6 +152,15 @@ def create_fine_tuning_job(
 
         # handle hyperparameters
         hyperparameters = hyperparameters or {}  # original hyperparameters
+        
+        # For Azure, extract Azure-specific hyperparameters before creating OpenAI-spec hyperparameters
+        azure_specific_hyperparams = {}
+        if custom_llm_provider == "azure":
+            azure_hyperparameter_keys = ["prompt_loss_weight"]
+            for key in azure_hyperparameter_keys:
+                if key in hyperparameters:
+                    azure_specific_hyperparams[key] = hyperparameters.pop(key)
+        
         _oai_hyperparameters: Hyperparameters = Hyperparameters(
             **hyperparameters
         )  # Typed Hyperparameters for OpenAI Spec
@@ -207,6 +254,10 @@ def create_fine_tuning_job(
                 extra_body.pop("azure_ad_token", None)
             else:
                 get_secret_str("AZURE_AD_TOKEN")  # type: ignore
+            
+            # Prepare Azure-specific parameters for extra_body
+            extra_body = _prepare_azure_extra_body(extra_body, kwargs, azure_specific_hyperparams)
+            
             create_fine_tuning_job_data = FineTuningJobCreate(
                 model=model,
                 training_file=training_file,
@@ -220,6 +271,10 @@ def create_fine_tuning_job(
             create_fine_tuning_job_data_dict = create_fine_tuning_job_data.model_dump(
                 exclude_none=True
             )
+            
+            # Add extra_body if it has Azure-specific parameters
+            if extra_body:
+                create_fine_tuning_job_data_dict["extra_body"] = extra_body
 
             response = azure_fine_tuning_apis_instance.create_fine_tuning_job(
                 api_base=api_base,
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index d4c5b476af6..4934f11d456 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -846,7 +846,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "anthropic.claude-3-opus-20240229-v1:0": {
         "input_cost_per_token": 1.5e-05,
@@ -859,7 +861,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.5e-06,
+        "cache_creation_input_token_cost": 1.875e-05
     },
     "anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -873,7 +877,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "anthropic.claude-instant-v1": {
         "input_cost_per_token": 8e-07,
@@ -1512,7 +1518,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "apac.anthropic.claude-3-5-sonnet-20241022-v2:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1545,7 +1553,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "apac.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -1581,7 +1591,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "apac.anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -6925,7 +6937,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "bedrock/sa-east-1/meta.llama3-70b-instruct-v1:0": {
         "input_cost_per_token": 4.45e-06,
@@ -7344,7 +7358,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3.6e-07,
+        "cache_creation_input_token_cost": 4.5e-06
     },
     "bedrock/us-gov-east-1/anthropic.claude-3-haiku-20240307-v1:0": {
         "input_cost_per_token": 3e-07,
@@ -7358,7 +7374,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-08,
+        "cache_creation_input_token_cost": 3.75e-07
     },
     "bedrock/us-gov-east-1/claude-sonnet-4-5-20250929-v1:0": {
         "input_cost_per_token": 3.3e-06,
@@ -7376,7 +7394,9 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3.3e-07,
+        "cache_creation_input_token_cost": 4.125e-06
     },
     "bedrock/us-gov-east-1/meta.llama3-70b-instruct-v1:0": {
         "input_cost_per_token": 2.65e-06,
@@ -7489,7 +7509,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3.6e-07,
+        "cache_creation_input_token_cost": 4.5e-06
     },
     "bedrock/us-gov-west-1/anthropic.claude-3-haiku-20240307-v1:0": {
         "input_cost_per_token": 3e-07,
@@ -7503,7 +7525,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-08,
+        "cache_creation_input_token_cost": 3.75e-07
     },
     "bedrock/us-gov-west-1/claude-sonnet-4-5-20250929-v1:0": {
         "input_cost_per_token": 3.3e-06,
@@ -7521,7 +7545,9 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3.3e-07,
+        "cache_creation_input_token_cost": 4.125e-06
     },
     "bedrock/us-gov-west-1/meta.llama3-70b-instruct-v1:0": {
         "input_cost_per_token": 2.65e-06,
@@ -9753,6 +9779,74 @@
             }
         ]
     },
+    "dashscope/qwen3-vl-plus": {
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 260096,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "mode": "chat",
+        "source": "https://www.alibabacloud.com/help/en/model-studio/models",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "tiered_pricing": [
+            {
+                "input_cost_per_token": 2e-07,
+                "output_cost_per_token": 1.6e-06,
+                "range": [
+                    0,
+                    32000.0
+                ]
+            },
+            {
+                "input_cost_per_token": 3e-07,
+                "output_cost_per_token": 2.4e-06,
+                "range": [
+                    32000.0,
+                    128000.0
+                ]
+            },
+            {
+                "input_cost_per_token": 6e-07,
+                "output_cost_per_token": 4.8e-06,
+                "range": [
+                    128000.0,
+                    256000.0
+                ]
+            }
+        ]
+    },
+    "dashscope/qwen3.5-plus": {
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 991808,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "source": "https://www.alibabacloud.com/help/en/model-studio/models",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "tiered_pricing": [
+            {
+                "input_cost_per_token": 4e-07,
+                "output_cost_per_token": 2.4e-06,
+                "range": [
+                    0,
+                    256000.0
+                ]
+            },
+            {
+                "input_cost_per_token": 5e-07,
+                "output_cost_per_token": 3e-06,
+                "range": [
+                    256000.0,
+                    1000000.0
+                ]
+            }
+        ]
+    },
     "dashscope/qwq-plus": {
         "input_cost_per_token": 8e-07,
         "litellm_provider": "dashscope",
@@ -11089,7 +11183,7 @@
         "supports_tool_choice": true
     },
     "deepinfra/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "max_tokens": 1000000,
         "max_input_tokens": 1000000,
         "max_output_tokens": 1000000,
@@ -11950,7 +12044,9 @@
         "supports_pdf_input": true,
         "supports_prompt_caching": true,
         "supports_response_schema": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "eu.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -11987,7 +12083,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "eu.anthropic.claude-3-5-sonnet-20241022-v2:0": {
         "input_cost_per_token": 3e-06,
@@ -12004,7 +12102,9 @@
         "supports_prompt_caching": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "eu.anthropic.claude-3-7-sonnet-20250219-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -12022,7 +12122,9 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "eu.anthropic.claude-3-haiku-20240307-v1:0": {
         "input_cost_per_token": 2.5e-07,
@@ -12036,7 +12138,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "eu.anthropic.claude-3-opus-20240229-v1:0": {
         "input_cost_per_token": 1.5e-05,
@@ -12049,7 +12153,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.5e-06,
+        "cache_creation_input_token_cost": 1.875e-05
     },
     "eu.anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -12063,7 +12169,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "eu.anthropic.claude-opus-4-1-20250805-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -13590,7 +13698,7 @@
     },
     "gemini-2.0-flash": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -13630,7 +13738,7 @@
     },
     "gemini-2.0-flash-001": {
         "cache_read_input_token_cost": 3.75e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -13716,7 +13824,7 @@
     },
     "gemini-2.0-flash-lite": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vertex_ai-language-models",
@@ -13752,7 +13860,7 @@
     },
     "gemini-2.0-flash-lite-001": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vertex_ai-language-models",
@@ -14669,6 +14777,7 @@
         "supports_web_search": true
     },
     "gemini-3-pro-preview": {
+        "deprecation_date": "2026-03-26",
         "cache_read_input_token_cost": 2e-07,
         "cache_read_input_token_cost_above_200k_tokens": 4e-07,
         "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07,
@@ -15805,7 +15914,7 @@
     },
     "gemini/gemini-2.0-flash": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "gemini",
@@ -15846,7 +15955,7 @@
     },
     "gemini/gemini-2.0-flash-001": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "gemini",
@@ -15934,7 +16043,7 @@
     },
     "gemini/gemini-2.0-flash-lite": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "gemini",
@@ -15970,7 +16079,7 @@
         "tpm": 4000000
     },
     "gemini/gemini-2.0-flash-lite-preview-02-05": {
-        "deprecation_date": "2025-12-02",
+        "deprecation_date": "2025-12-09",
         "cache_read_input_token_cost": 1.875e-08,
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
@@ -16925,6 +17034,7 @@
         "tpm": 800000
     },
     "gemini/gemini-3-pro-preview": {
+        "deprecation_date": "2026-03-09",
         "cache_read_input_token_cost": 2e-07,
         "cache_read_input_token_cost_above_200k_tokens": 4e-07,
         "input_cost_per_token": 2e-06,
@@ -23112,6 +23222,21 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "mistral/magistral-medium-1-2-2509": {
+        "input_cost_per_token": 2e-06,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 40000,
+        "max_output_tokens": 40000,
+        "max_tokens": 40000,
+        "mode": "chat",
+        "output_cost_per_token": 5e-06,
+        "source": "https://mistral.ai/news/magistral",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true
+    },
     "mistral/mistral-ocr-latest": {
         "litellm_provider": "mistral",
         "ocr_cost_per_page": 0.001,
@@ -23177,6 +23302,21 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "mistral/magistral-small-1-2-2509": {
+        "input_cost_per_token": 5e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 40000,
+        "max_output_tokens": 40000,
+        "max_tokens": 40000,
+        "mode": "chat",
+        "output_cost_per_token": 1.5e-06,
+        "source": "https://mistral.ai/pricing#api-pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true
+    },
     "mistral/mistral-embed": {
         "input_cost_per_token": 1e-07,
         "litellm_provider": "mistral",
@@ -23238,24 +23378,41 @@
         "supports_tool_choice": true
     },
     "mistral/mistral-large-latest": {
-        "input_cost_per_token": 2e-06,
+        "input_cost_per_token": 5e-07,
         "litellm_provider": "mistral",
-        "max_input_tokens": 128000,
-        "max_output_tokens": 128000,
-        "max_tokens": 128000,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
         "mode": "chat",
-        "output_cost_per_token": 6e-06,
+        "output_cost_per_token": 1.5e-06,
+        "source": "https://docs.mistral.ai/models/mistral-large-3-25-12",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_response_schema": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_vision": true
     },
     "mistral/mistral-large-3": {
         "input_cost_per_token": 5e-07,
         "litellm_provider": "mistral",
-        "max_input_tokens": 256000,
-        "max_output_tokens": 8191,
-        "max_tokens": 8191,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 1.5e-06,
+        "source": "https://docs.mistral.ai/models/mistral-large-3-25-12",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/mistral-large-2512": {
+        "input_cost_per_token": 5e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "source": "https://docs.mistral.ai/models/mistral-large-3-25-12",
@@ -23306,14 +23463,30 @@
         "input_cost_per_token": 4e-07,
         "litellm_provider": "mistral",
         "max_input_tokens": 131072,
-        "max_output_tokens": 8191,
-        "max_tokens": 8191,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_response_schema": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/mistral-medium-3-1-2508": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "mode": "chat",
+        "output_cost_per_token": 2e-06,
+        "source": "https://mistral.ai/news/mistral-medium-3",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
     },
     "mistral/mistral-small": {
         "input_cost_per_token": 1e-07,
@@ -23329,17 +23502,79 @@
         "supports_tool_choice": true
     },
     "mistral/mistral-small-latest": {
-        "input_cost_per_token": 1e-07,
+        "input_cost_per_token": 6e-08,
         "litellm_provider": "mistral",
-        "max_input_tokens": 32000,
-        "max_output_tokens": 8191,
-        "max_tokens": 8191,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
         "mode": "chat",
-        "output_cost_per_token": 3e-07,
+        "output_cost_per_token": 1.8e-07,
+        "source": "https://mistral.ai/pricing",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_response_schema": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/mistral-small-3-2-2506": {
+        "input_cost_per_token": 6e-08,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "mode": "chat",
+        "output_cost_per_token": 1.8e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/ministral-3-3b-2512": {
+        "input_cost_per_token": 1e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "mode": "chat",
+        "output_cost_per_token": 1e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/ministral-3-8b-2512": {
+        "input_cost_per_token": 1.5e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 1.5e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/ministral-3-14b-2512": {
+        "input_cost_per_token": 2e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 2e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
     },
     "mistral/mistral-tiny": {
         "input_cost_per_token": 2.5e-07,
@@ -25657,7 +25892,7 @@
         "supports_tool_choice": true
     },
     "openrouter/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "openrouter",
@@ -29554,7 +29789,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "us.anthropic.claude-3-5-sonnet-20241022-v2:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -29607,7 +29844,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "us.anthropic.claude-3-opus-20240229-v1:0": {
         "input_cost_per_token": 1.5e-05,
@@ -29620,7 +29859,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.5e-06,
+        "cache_creation_input_token_cost": 1.875e-05
     },
     "us.anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -29634,7 +29875,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "us.anthropic.claude-opus-4-1-20250805-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -30527,7 +30770,7 @@
         "supports_tool_choice": true
     },
     "vercel_ai_gateway/google/gemini-2.0-flash": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -30541,7 +30784,7 @@
         "supports_response_schema": true
     },
     "vercel_ai_gateway/google/gemini-2.0-flash-lite": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -37898,7 +38141,7 @@
     },
     "gemini/gemini-2.0-flash-lite-001": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "gemini",
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index f82f6a02f22..d06d879dad1 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -71,7 +71,14 @@ from openai.types.responses.response_create_params import (
     ToolParam,
 )
 from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
-from pydantic import BaseModel, ConfigDict, Discriminator, PrivateAttr, field_serializer, field_validator
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Discriminator,
+    PrivateAttr,
+    field_serializer,
+    field_validator,
+)
 from typing_extensions import Annotated, Dict, Required, TypedDict, override
 
 from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
@@ -964,6 +971,10 @@ class Hyperparameters(BaseModel):
     n_epochs: Optional[Union[str, int]] = (
         None  # "The number of epochs to train the model for"
     )
+    
+    model_config = {
+        "extra": "allow"
+    }
 
 
 class FineTuningJobCreate(BaseModel):
diff --git a/tests/batches_tests/test_fine_tuning_api.py b/tests/batches_tests/test_fine_tuning_api.py
index c6a731ea54f..7e238173480 100644
--- a/tests/batches_tests/test_fine_tuning_api.py
+++ b/tests/batches_tests/test_fine_tuning_api.py
@@ -596,3 +596,61 @@ async def test_mock_openai_retrieve_fine_tune_job():
 
         # Verify the request
         mock_retrieve.assert_called_once_with(fine_tuning_job_id="ft-123")
+
+
+@pytest.mark.asyncio
+async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
+    """Test that Azure-specific parameters are passed through extra_body"""
+    from openai import AsyncAzureOpenAI
+    from openai.types.fine_tuning.fine_tuning_job import FineTuningJob
+    from openai.types.fine_tuning.fine_tuning_job import Hyperparameters as OAIHyperparameters
+
+    mock_response = FineTuningJob(
+        id="ft-azure-123",
+        model="gpt-4.1-mini-2025-04-14",
+        created_at=1677610602,
+        status="validating_files",
+        fine_tuned_model=None,
+        object="fine_tuning.job",
+        hyperparameters=OAIHyperparameters(n_epochs=3),
+        organization_id="org-123",
+        seed=42,
+        training_file="file-123",
+        result_files=[],
+    )
+
+    with patch("litellm.llms.azure.fine_tuning.handler.AzureOpenAIFineTuningAPI.create_fine_tuning_job") as mock_create:
+        mock_create.return_value = mock_response
+
+        response = await litellm.acreate_fine_tuning_job(
+            model="gpt-4.1-mini-2025-04-14",
+            training_file="file-123",
+            custom_llm_provider="azure",
+            api_base="https://test.openai.azure.com",
+            api_key="test-key",
+            api_version="2025-04-01-preview",
+            trainingType=1,
+            hyperparameters={
+                "n_epochs": 3,
+                "prompt_loss_weight": 0.1
+            },
+        )
+
+        # Verify the request
+        mock_create.assert_called_once()
+        request_params = mock_create.call_args.kwargs
+
+        # Check that create_fine_tuning_job_data contains the correct structure
+        create_data = request_params["create_fine_tuning_job_data"]
+        assert create_data["model"] == "gpt-4.1-mini-2025-04-14"
+        assert create_data["training_file"] == "file-123"
+        assert create_data["hyperparameters"] == {"n_epochs": 3}
+        
+        # Azure-specific parameters should be in extra_body
+        assert "extra_body" in create_data
+        assert create_data["extra_body"]["trainingType"] == 1
+        assert create_data["extra_body"]["prompt_loss_weight"] == 0.1
+
+        # Verify the response
+        assert response.id == "ft-azure-123"
+        assert response.model == "gpt-4.1-mini-2025-04-14"

From 5b0238736c2b994d17dee7d96af228882f78e277 Mon Sep 17 00:00:00 2001
From: ryan-crabbe <128659760+ryan-crabbe@users.noreply.github.com>
Date: Mon, 2 Mar 2026 21:49:48 -0800
Subject: [PATCH 06/69] Add incident report: cache eviction closes in-use httpx
 clients (#22309)

---
 .../httpx_cache_eviction_incident/index.md    | 132 ++++++++++++++++++
 1 file changed, 132 insertions(+)
 create mode 100644 docs/my-website/blog/httpx_cache_eviction_incident/index.md

diff --git a/docs/my-website/blog/httpx_cache_eviction_incident/index.md b/docs/my-website/blog/httpx_cache_eviction_incident/index.md
new file mode 100644
index 00000000000..9e6152d0e63
--- /dev/null
+++ b/docs/my-website/blog/httpx_cache_eviction_incident/index.md
@@ -0,0 +1,132 @@
+---
+slug: httpx-cache-eviction-incident
+title: "Incident Report: Cache Eviction Closes In-Use httpx Clients"
+date: 2026-02-27T10:00:00
+authors:
+  - name: Ryan Crabbe
+    title: Performance Engineer, LiteLLM
+    url: https://www.linkedin.com/in/ryan-crabbe-0b9687214
+  - 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
+  - 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
+tags: [incident-report, caching, stability]
+hide_table_of_contents: false
+---
+
+**Date:** February 27, 2026
+**Duration:** ~6 days (Feb 21 merge -> Feb 27 fix)
+**Severity:** High
+**Status:** Resolved
+
+> **Note:** This fix is available starting from LiteLLM `v1.81.14.rc.2` or higher.
+
+## Summary
+
+A change to improve Redis connection pool cleanup introduced a regression that closed **httpx clients** that were still actively being used by the proxy. The `LLMClientCache` (an in-memory TTL cache) stores both Redis clients *and* httpx clients under the same eviction policy. When a cache entry expired or was evicted, the new cleanup code called `aclose()`/`close()` on the evicted value which worked correctly for Redis clients, but destroyed httpx clients that other parts of the system still held references to and were actively using for LLM API calls.
+
+**Impact:** Any proxy instance that hit the cache TTL (default 10 minutes) or capacity limit (200 entries) would have its httpx clients closed out from under it, causing requests to LLM providers to fail with connection errors.
+
+---
+
+## Background
+
+`LLMClientCache` extends `InMemoryCache` and is used to cache SDK clients (OpenAI, Anthropic, etc.) to avoid re-creating them on every request. These clients are keyed by configuration + event loop ID. The cache has:
+
+- **Max size:** 200 entries
+- **Default TTL:** 10 minutes
+
+When the cache is full or entries expire, `InMemoryCache.evict_cache()` calls `_remove_key()` to drop entries.
+
+The cached values are a mix of:
+- **Redis/async Redis clients** — owned exclusively by the cache, safe to close on eviction
+- **httpx-backed SDK clients** (OpenAI, Anthropic, etc.) — shared references, still in use by router/model instances
+
+---
+
+## Root Cause
+
+[PR #21717](https://github.com/BerriAI/litellm/pull/21717) overrode `_remove_key()` in `LLMClientCache` to close async clients on eviction:
+
+<details>
+<summary>Problematic code added in PR #21717</summary>
+
+```python
+class LLMClientCache(InMemoryCache):
+    def _remove_key(self, key: str) -> None:
+        value = self.cache_dict.get(key)
+        super()._remove_key(key)
+        if value is not None:
+            close_fn = getattr(value, "aclose", None) or getattr(value, "close", None)
+            if close_fn and asyncio.iscoroutinefunction(close_fn):
+                try:
+                    asyncio.get_running_loop().create_task(close_fn())
+                except RuntimeError:
+                    pass
+            elif close_fn and callable(close_fn):
+                try:
+                    close_fn()
+                except Exception:
+                    pass
+```
+
+</details>
+
+The intent was correct for Redis clients — prevent connection pool leaks when cached Redis clients expire. But `LLMClientCache` also stores httpx-backed SDK clients (e.g., `AsyncOpenAI`, `AsyncAnthropic`). These clients:
+
+1. Have an `aclose()` method (inherited from httpx)
+2. Are still held by references elsewhere in the codebase (router, model instances)
+3. Were being closed without any check on whether they were still in use
+
+So when the cache evicted an entry, it would call `aclose()` on an httpx client that was still being used for active LLM requests → closed transport → connection errors.
+
+---
+
+## The Fix
+
+[PR #22247](https://github.com/BerriAI/litellm/pull/22247) removed the `_remove_key` override entirely:
+
+<details>
+<summary>The fix (PR #22247)</summary>
+
+```diff
+ class LLMClientCache(InMemoryCache):
+-    def _remove_key(self, key: str) -> None:
+-        """Close async clients before evicting them to prevent connection pool leaks."""
+-        value = self.cache_dict.get(key)
+-        super()._remove_key(key)
+-        if value is not None:
+-            close_fn = getattr(value, "aclose", None) or getattr(
+-                value, "close", None
+-            )
+-            ...
+-
+     def update_cache_key_with_event_loop(self, key):
+```
+
+</details>
+
+The eviction now simply drops the reference and lets Python's GC handle cleanup, which is safe because:
+- httpx clients that are still referenced elsewhere stay alive
+- Unreferenced clients get cleaned up by GC naturally
+
+The other improvements from PR #21717 were kept:
+- **`max_connections` respected for URL-based Redis configs**, previously silently dropped
+- **`disconnect()` now closes both sync and async Redis clients**, sync client was previously leaked
+- **Connection pool passthrough**, when a pool is provided with a URL config, it's used directly instead of creating a duplicate
+
+---
+
+## Remediation
+
+| Action | Status | Code |
+|--------|--------|------|
+| Remove `_remove_key` override that closes shared clients on eviction | ✅ Done | [PR #22247](https://github.com/BerriAI/litellm/pull/22247) |
+| Add e2e test: evicted client still usable (capacity) | ✅ Done | [PR #22313](https://github.com/BerriAI/litellm/pull/22313) |
+| Add e2e test: expired client still usable (TTL) | ✅ Done | [PR #22313](https://github.com/BerriAI/litellm/pull/22313) |
+
+The e2e tests go through `get_async_httpx_client()` the same code path the proxy uses in production and assert the client is still functional after eviction. These run in CI on every PR against `main`. If anyone modifies `LLMClientCache` eviction behavior, overrides `_remove_key`, or adds any form of client cleanup on eviction, these tests will fail regardless of the implementation approach.

From 6bcba46dda1ffd8dcba33e5b19c57f1d2e66b5e7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jaeyeon=20Kim=28=EA=B9=80=EC=9E=AC=EC=97=B0=29?=
 <anencore94@gmail.com>
Date: Tue, 3 Mar 2026 06:57:54 +0100
Subject: [PATCH 07/69] fix: set mock status_code in JWT OIDC discovery tests
 (#22361)

The _resolve_jwks_url method checks response.status_code != 200, but
MagicMock returns a MagicMock object for status_code which is always
truthy (!= 200). Explicitly set mock_response.status_code = 200 so the
tests exercise the intended code path.

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
---
 tests/test_litellm/proxy/auth/test_handle_jwt.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py
index 8418dde5e9c..3c190974277 100644
--- a/tests/test_litellm/proxy/auth/test_handle_jwt.py
+++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py
@@ -1559,6 +1559,7 @@ async def test_resolve_jwks_url_caches_resolved_jwks_uri():
     jwks_url = "https://login.microsoftonline.com/tenant/discovery/keys"
 
     mock_response = MagicMock()
+    mock_response.status_code = 200
     mock_response.json.return_value = {"jwks_uri": jwks_url}
 
     with patch.object(handler.http_handler, "get", new_callable=AsyncMock, return_value=mock_response) as mock_get:
@@ -1587,6 +1588,7 @@ async def test_resolve_jwks_url_raises_if_no_jwks_uri_in_discovery_doc():
 
     discovery_url = "https://example.com/.well-known/openid-configuration"
     mock_response = MagicMock()
+    mock_response.status_code = 200
     mock_response.json.return_value = {"issuer": "https://example.com"}  # no jwks_uri
 
     with patch.object(handler.http_handler, "get", new_callable=AsyncMock, return_value=mock_response):

From 213799282b7d6c0eb76c9853f4f46d99daad7396 Mon Sep 17 00:00:00 2001
From: Shivaang <38239870+shivaaang@users.noreply.github.com>
Date: Tue, 3 Mar 2026 01:02:59 -0500
Subject: [PATCH 08/69] fix(openrouter): register OpenRouter as native
 Responses API provider (#22355)

OpenRouter supports the Responses API at /api/v1/responses with
encrypted_content for multi-turn stateless reasoning workflows.
Without native registration, requests fall through to the chat
completion bridge, which uses a different format (reasoning_details)
and drops encrypted_content entirely.

This adds OpenRouterResponsesAPIConfig to route requests directly to
OpenRouter's Responses API endpoint, preserving encrypted_content.

Fixes https://github.com/BerriAI/litellm/issues/22189

Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
---
 litellm/__init__.py                           |   1 +
 litellm/_lazy_imports_registry.py             |   5 +
 .../openrouter/responses/transformation.py    |  77 ++++++++++++
 litellm/utils.py                              |   2 +
 ...est_openrouter_responses_transformation.py | 112 ++++++++++++++++++
 5 files changed, 197 insertions(+)
 create mode 100644 litellm/llms/openrouter/responses/transformation.py
 create mode 100644 tests/test_litellm/llms/openrouter/responses/test_openrouter_responses_transformation.py

diff --git a/litellm/__init__.py b/litellm/__init__.py
index f00b816be5c..84b8e47c462 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -1441,6 +1441,7 @@ if TYPE_CHECKING:
     from .llms.manus.responses.transformation import ManusResponsesAPIConfig as ManusResponsesAPIConfig
     from .llms.perplexity.responses.transformation import PerplexityResponsesConfig as PerplexityResponsesConfig
     from .llms.databricks.responses.transformation import DatabricksResponsesAPIConfig as DatabricksResponsesAPIConfig
+    from .llms.openrouter.responses.transformation import OpenRouterResponsesAPIConfig as OpenRouterResponsesAPIConfig
     from .llms.gemini.interactions.transformation import GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig
     from .llms.openai.chat.o_series_transformation import OpenAIOSeriesConfig as OpenAIOSeriesConfig, OpenAIOSeriesConfig as OpenAIO1Config
     from .llms.anthropic.skills.transformation import AnthropicSkillsConfig as AnthropicSkillsConfig
diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py
index 6ff997b4531..4bb336a4d77 100644
--- a/litellm/_lazy_imports_registry.py
+++ b/litellm/_lazy_imports_registry.py
@@ -231,6 +231,7 @@ LLM_CONFIG_NAMES = (
     "VolcEngineResponsesAPIConfig",
     "PerplexityResponsesConfig",
     "DatabricksResponsesAPIConfig",
+    "OpenRouterResponsesAPIConfig",
     "GoogleAIStudioInteractionsConfig",
     "OpenAIOSeriesConfig",
     "AnthropicSkillsConfig",
@@ -923,6 +924,10 @@ _LLM_CONFIGS_IMPORT_MAP = {
         ".llms.databricks.responses.transformation",
         "DatabricksResponsesAPIConfig",
     ),
+    "OpenRouterResponsesAPIConfig": (
+        ".llms.openrouter.responses.transformation",
+        "OpenRouterResponsesAPIConfig",
+    ),
     "GoogleAIStudioInteractionsConfig": (
         ".llms.gemini.interactions.transformation",
         "GoogleAIStudioInteractionsConfig",
diff --git a/litellm/llms/openrouter/responses/transformation.py b/litellm/llms/openrouter/responses/transformation.py
new file mode 100644
index 00000000000..ddce6fd3844
--- /dev/null
+++ b/litellm/llms/openrouter/responses/transformation.py
@@ -0,0 +1,77 @@
+"""
+OpenRouter Responses API Configuration.
+
+OpenRouter supports the Responses API at https://openrouter.ai/api/v1/responses
+with OpenAI-compatible request/response format, including reasoning with
+encrypted_content for multi-turn stateless workflows.
+
+Docs: https://openrouter.ai/docs/api/reference/responses/overview
+"""
+
+from typing import Optional
+
+import litellm
+from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import LlmProviders
+
+
+class OpenRouterResponsesAPIConfig(OpenAIResponsesAPIConfig):
+    """
+    Configuration for OpenRouter's Responses API.
+
+    Inherits from OpenAIResponsesAPIConfig since OpenRouter's Responses API
+    is compatible with OpenAI's Responses API specification.
+
+    Key difference from direct OpenAI:
+    - Uses https://openrouter.ai/api/v1 as the API base
+    - Uses OPENROUTER_API_KEY for authentication
+    """
+
+    @property
+    def custom_llm_provider(self) -> LlmProviders:
+        return LlmProviders.OPENROUTER
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        litellm_params: Optional[GenericLiteLLMParams],
+    ) -> dict:
+        litellm_params = litellm_params or GenericLiteLLMParams()
+        api_key = (
+            litellm_params.api_key
+            or litellm.api_key
+            or get_secret_str("OPENROUTER_API_KEY")
+            or get_secret_str("OR_API_KEY")
+        )
+
+        if not api_key:
+            raise ValueError(
+                "OpenRouter API key is required. Set OPENROUTER_API_KEY "
+                "environment variable or pass api_key parameter."
+            )
+
+        headers.update(
+            {
+                "Authorization": f"Bearer {api_key}",
+            }
+        )
+        return headers
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        litellm_params: dict,
+    ) -> str:
+        api_base = (
+            api_base
+            or litellm.api_base
+            or get_secret_str("OPENROUTER_API_BASE")
+            or "https://openrouter.ai/api/v1"
+        )
+
+        api_base = api_base.rstrip("/")
+
+        return f"{api_base}/responses"
diff --git a/litellm/utils.py b/litellm/utils.py
index cbe6aa8e793..d192609eead 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -8312,6 +8312,8 @@ class ProviderConfigManager:
             if model and "gpt" in model.lower():
                 return litellm.DatabricksResponsesAPIConfig()
             return None
+        elif litellm.LlmProviders.OPENROUTER == provider:
+            return litellm.OpenRouterResponsesAPIConfig()
         elif litellm.LlmProviders.HOSTED_VLLM == provider:
             return litellm.HostedVLLMResponsesAPIConfig()
         return None
diff --git a/tests/test_litellm/llms/openrouter/responses/test_openrouter_responses_transformation.py b/tests/test_litellm/llms/openrouter/responses/test_openrouter_responses_transformation.py
new file mode 100644
index 00000000000..544ec1ec719
--- /dev/null
+++ b/tests/test_litellm/llms/openrouter/responses/test_openrouter_responses_transformation.py
@@ -0,0 +1,112 @@
+"""
+Tests for OpenRouter Responses API configuration.
+
+Validates that OpenRouter is registered as a native Responses API provider,
+routing requests directly to https://openrouter.ai/api/v1/responses instead
+of falling back to the chat completion bridge. This is required to preserve
+reasoning.encrypted_content for multi-turn stateless workflows.
+
+Related issue: https://github.com/BerriAI/litellm/issues/22189
+"""
+
+import litellm
+from litellm.llms.openrouter.responses.transformation import (
+    OpenRouterResponsesAPIConfig,
+)
+from litellm.types.utils import LlmProviders
+from litellm.utils import ProviderConfigManager
+
+
+class TestOpenRouterResponsesAPIConfig:
+    """Test OpenRouter Responses API configuration."""
+
+    def test_custom_llm_provider(self):
+        """custom_llm_provider should return OPENROUTER."""
+        config = OpenRouterResponsesAPIConfig()
+        assert config.custom_llm_provider == LlmProviders.OPENROUTER
+
+    def test_get_complete_url_default(self):
+        """Default URL should point to OpenRouter's Responses API endpoint."""
+        config = OpenRouterResponsesAPIConfig()
+        url = config.get_complete_url(api_base=None, litellm_params={})
+        assert url == "https://openrouter.ai/api/v1/responses"
+
+    def test_get_complete_url_custom_base(self):
+        """Custom api_base should be respected."""
+        config = OpenRouterResponsesAPIConfig()
+        url = config.get_complete_url(
+            api_base="https://custom.openrouter.ai/api/v1",
+            litellm_params={},
+        )
+        assert url == "https://custom.openrouter.ai/api/v1/responses"
+
+    def test_get_complete_url_strips_trailing_slash(self):
+        """Trailing slashes on api_base should be stripped."""
+        config = OpenRouterResponsesAPIConfig()
+        url = config.get_complete_url(
+            api_base="https://openrouter.ai/api/v1/",
+            litellm_params={},
+        )
+        assert url == "https://openrouter.ai/api/v1/responses"
+
+    def test_validate_environment_sets_auth_header(self):
+        """validate_environment should set the Authorization header."""
+        config = OpenRouterResponsesAPIConfig()
+        from litellm.types.router import GenericLiteLLMParams
+
+        params = GenericLiteLLMParams(api_key="sk-or-test-key")
+        headers = config.validate_environment(
+            headers={}, model="openai/o4-mini", litellm_params=params
+        )
+        assert headers["Authorization"] == "Bearer sk-or-test-key"
+
+    def test_validate_environment_raises_without_key(self):
+        """validate_environment should raise when no API key is available."""
+        config = OpenRouterResponsesAPIConfig()
+        from litellm.types.router import GenericLiteLLMParams
+
+        try:
+            config.validate_environment(
+                headers={},
+                model="openai/o4-mini",
+                litellm_params=GenericLiteLLMParams(),
+            )
+            assert False, "Should have raised ValueError"
+        except ValueError as e:
+            assert "OpenRouter API key is required" in str(e)
+
+
+class TestOpenRouterResponsesAPIRegistration:
+    """Test that OpenRouter is properly registered as a native Responses API provider."""
+
+    def test_provider_config_manager_returns_openrouter_config(self):
+        """
+        ProviderConfigManager.get_provider_responses_api_config should return
+        OpenRouterResponsesAPIConfig for the OPENROUTER provider, NOT None.
+
+        When it returns None, requests fall through to the completion bridge,
+        which loses encrypted_content (the bug in issue #22189).
+        """
+        config = ProviderConfigManager.get_provider_responses_api_config(
+            provider=LlmProviders.OPENROUTER,
+        )
+        assert config is not None, (
+            "OpenRouter must be registered as a native Responses API provider "
+            "to preserve reasoning.encrypted_content"
+        )
+        assert isinstance(config, OpenRouterResponsesAPIConfig)
+
+    def test_openrouter_not_using_completion_bridge(self):
+        """
+        Verify that OpenRouter does NOT fall through to the completion bridge.
+        The completion bridge drops encrypted_content because chat completions
+        use a different format (reasoning_details) than the Responses API.
+        """
+        config = ProviderConfigManager.get_provider_responses_api_config(
+            provider=LlmProviders.OPENROUTER,
+        )
+        # If config is not None, the native Responses API path is used
+        assert config is not None
+        # The URL should point to OpenRouter's responses endpoint
+        url = config.get_complete_url(api_base=None, litellm_params={})
+        assert "/responses" in url

From 67f90254edc5b9d46fffdc7297a8eaf7950bd144 Mon Sep 17 00:00:00 2001
From: Krish Dholakia <krrishdholakia@gmail.com>
Date: Mon, 2 Mar 2026 22:06:49 -0800
Subject: [PATCH 09/69] feat(guardrails): team-based guardrail registration and
 approval workflow (#22459)

* feat(guardrails): team-based guardrail registration and approval workflow

Add team-based guardrail submission system where teams can register
Generic Guardrail API guardrails for admin review. Includes:

- POST /guardrails/register endpoint for team-scoped submissions
- Admin review endpoints (list/get/approve/reject submissions)
- Team Guardrails tab in the UI dashboard
- extra_headers support for forwarding client headers to guardrail APIs
- Prisma schema migration for status, submitted_at, reviewed_at fields
- Documentation for team-based guardrails and static/dynamic headers

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(guardrails): address review feedback - SSRF, silent failure, redundant query

- Validate api_base URL scheme (http/https only) and hostname in
  register_guardrail to prevent SSRF via team submissions
- Return warning field in approve response when in-memory initialization
  fails so admins know the guardrail won't work until next sync cycle
- Eliminate redundant DB query in list_guardrail_submissions by fetching
  all team guardrails once and deriving both filtered list and summary
  counts from the single result set

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(guardrails): add pending_review status guard to reject endpoint

Prevent rejecting already-active or already-rejected guardrails, which
would create a DB/memory inconsistency (active in memory but rejected
in DB). Now mirrors the approve endpoint's status check.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
---
 .../adding_provider/generic_guardrail_api.md  |   29 +
 .../docs/proxy/guardrails/quick_start.md      |    1 +
 .../proxy/guardrails/team_based_guardrails.md |  137 +++
 docs/my-website/img/admin_team_guardrails.png |  Bin 0 -> 535985 bytes
 docs/my-website/sidebars.js                   |    1 +
 .../migration.sql                             |    8 +
 .../litellm_proxy_extras/schema.prisma        |    7 +
 litellm/proxy/_types.py                       |    2 +
 .../proxy/guardrails/guardrail_endpoints.py   |  469 ++++++-
 .../generic_guardrail_api/__init__.py         |    1 +
 .../generic_guardrail_api.py                  |   51 +-
 .../proxy/guardrails/guardrail_registry.py    |   14 +-
 litellm/proxy/schema.prisma                   |    7 +
 litellm/types/guardrails.py                   |    9 +
 schema.prisma                                 |    7 +
 .../create_team_key_and_submit_guardrail.sh   |   92 ++
 scripts/test_guardrails_register_endpoints.sh |  126 ++
 .../test_generic_guardrail_api.py             |   93 +-
 .../guardrails/test_guardrail_endpoints.py    |  470 ++++++-
 ui/litellm-dashboard/package-lock.json        |   15 +
 .../src/components/guardrails.tsx             |    7 +
 .../guardrails/TeamGuardrailsTab.tsx          | 1081 +++++++++++++++++
 .../src/components/networking.tsx             |  126 ++
 23 files changed, 2724 insertions(+), 29 deletions(-)
 create mode 100644 docs/my-website/docs/proxy/guardrails/team_based_guardrails.md
 create mode 100644 docs/my-website/img/admin_team_guardrails.png
 create mode 100644 litellm-proxy-extras/litellm_proxy_extras/migrations/20260228170127_support_team_based_guardrails/migration.sql
 create mode 100755 scripts/create_team_key_and_submit_guardrail.sh
 create mode 100755 scripts/test_guardrails_register_endpoints.sh
 create mode 100644 ui/litellm-dashboard/src/components/guardrails/TeamGuardrailsTab.tsx

diff --git a/docs/my-website/docs/adding_provider/generic_guardrail_api.md b/docs/my-website/docs/adding_provider/generic_guardrail_api.md
index eb567a69fcb..cc0dbf1f4e9 100644
--- a/docs/my-website/docs/adding_provider/generic_guardrail_api.md
+++ b/docs/my-website/docs/adding_provider/generic_guardrail_api.md
@@ -244,6 +244,35 @@ litellm_settings:
           language: "en"
 ```
 
+### Static and dynamic headers
+
+You can send two kinds of headers to your guardrail endpoint:
+
+- **Static headers** (`headers`): A key/value map sent with **every** request to your guardrail. Use this for fixed values (e.g. API keys, `X-Service-Name`). Configure in `litellm_params`:
+
+  ```yaml
+  litellm_params:
+    guardrail: generic_guardrail_api
+    api_base: https://your-guardrail-api.com
+    headers:
+      X-Service-Name: "my-app"
+      X-API-Key: "secret"
+  ```
+
+- **Dynamic headers** (`extra_headers`): A list of **header names** that are forwarded from the **client request** to your guardrail. Only headers in this list (plus a small default allowlist such as `x-litellm-*`) have their values sent; others are sent as `[present]`. Use this to pass through client-provided headers (e.g. `x-request-id`, `x-correlation-id`). Configure in `litellm_params`:
+
+  ```yaml
+  litellm_params:
+    guardrail: generic_guardrail_api
+    api_base: https://your-guardrail-api.com
+    extra_headers:
+      - x-request-id
+      - x-correlation-id
+      - x-custom-auth
+  ```
+
+This mirrors the [MCP static and extra headers](/docs/mcp#forwarding-custom-headers-to-mcp-servers) behavior.
+
 ### Example: Pillar Security
 
 [Pillar Security](https://pillar.security) uses the Generic Guardrail API to provide comprehensive AI security scanning including prompt injection protection, PII/PCI detection, secret detection, and content moderation.
diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md
index ddb215fcb66..e5a90f74a8a 100644
--- a/docs/my-website/docs/proxy/guardrails/quick_start.md
+++ b/docs/my-website/docs/proxy/guardrails/quick_start.md
@@ -73,6 +73,7 @@ guardrails:
         plr_scanners: true
 ```
 
+For generic guardrail APIs you can also set **static headers** (`headers`: key/value sent on every request) and **dynamic headers** (`extra_headers`: list of client header names to forward). See [Generic Guardrail API - Static and dynamic headers](/docs/adding_provider/generic_guardrail_api#static-and-dynamic-headers).
 
 ### Supported values for `mode` (Event Hooks)
 
diff --git a/docs/my-website/docs/proxy/guardrails/team_based_guardrails.md b/docs/my-website/docs/proxy/guardrails/team_based_guardrails.md
new file mode 100644
index 00000000000..2d55294a711
--- /dev/null
+++ b/docs/my-website/docs/proxy/guardrails/team_based_guardrails.md
@@ -0,0 +1,137 @@
+import Image from '@theme/IdealImage';
+
+# Team-Based Guardrails
+
+Team-based guardrails let **developers** register a guardrail for their team via the API; an **admin** then reviews and approves or rejects it in the LiteLLM UI. Only [Generic Guardrail API](/docs/adding_provider/generic_guardrail_api) guardrails can be registered this way.
+
+## Overview
+
+- **Developer flow:** Use a **team-scoped API key** to `POST /guardrails/register` with your guardrail config. The submission is stored with status `pending_review`.
+- **Admin flow:** In the proxy UI, open **Guardrails → Team Guardrails**, review pending submissions, and **Approve** or **Reject**. Approved guardrails become active and are initialized in memory.
+
+---
+
+## Developer flow: Register a guardrail
+
+### Prerequisites
+
+- A **team-scoped** API key (the key must be associated with a team). Keys without a team cannot register guardrails.
+- Your guardrail must follow the [Generic Guardrail API](/docs/adding_provider/generic_guardrail_api) contract and config.
+
+### Request
+
+**Endpoint:** `POST /guardrails/register`
+
+**Headers:** `Authorization: Bearer <team_scoped_api_key>`
+
+**Body:** JSON matching the Generic Guardrail API config.
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `guardrail_name` | string | Yes | Unique name for the guardrail. |
+| `litellm_params` | object | Yes | Must include `guardrail: "generic_guardrail_api"`, `mode` (e.g. `pre_call`, `post_call`), and `api_base`. See [Generic Guardrail API](/docs/adding_provider/generic_guardrail_api#litellm-configuration). |
+| `guardrail_info` | object | No | Optional metadata (e.g. `description`). |
+
+### Requirements for `litellm_params`
+
+- `guardrail` must be exactly `"generic_guardrail_api"`.
+- `api_base` is required (your guardrail API base URL).
+- `mode` is required (e.g. `pre_call`, `post_call`, `during_call`).
+
+### Example
+
+```bash
+curl -X POST "http://localhost:4000/guardrails/register" \
+  -H "Authorization: Bearer <your_team_scoped_api_key>" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "guardrail_name": "my-team-guard",
+    "litellm_params": {
+      "guardrail": "generic_guardrail_api",
+      "mode": "pre_call",
+      "api_base": "https://your-guardrail-api.com",
+      "api_key": "optional-api-key",
+      "unreachable_fallback": "fail_closed",
+      "forward_api_key": true
+    },
+    "guardrail_info": {
+      "description": "Team content moderation guardrail"
+    }
+  }'
+```
+
+### Example response
+
+```json
+{
+  "guardrail_id": "123e4567-e89b-12d3-a456-426614174000",
+  "guardrail_name": "my-team-guard",
+  "status": "pending_review",
+  "submitted_at": "2025-02-28T12:00:00.000Z"
+}
+```
+
+### Errors
+
+- **400** – Missing or invalid body (e.g. `guardrail` not `generic_guardrail_api`, missing `api_base` or `mode`), or a guardrail with the same `guardrail_name` already exists.
+- **400** – "Registration requires an API key associated with a team. Use a team-scoped key." → Use an API key that has a team.
+- **500** – Server/database error.
+
+After a successful register, the guardrail stays in `pending_review` until an admin approves or rejects it.
+
+---
+
+## Admin flow: Approve or reject in the UI
+
+Admins review and approve or reject team guardrail submissions in the LiteLLM proxy UI.
+
+### 1. Open the Guardrails page
+
+In the proxy dashboard, go to **Guardrails** (sidebar or navigation).
+
+### 2. Open the Team Guardrails tab
+
+Switch to the **Team Guardrails** tab. This tab lists all team-submitted guardrails and their status.
+
+<Image img={require('../../../img/admin_team_guardrails.png')} alt="Team Guardrails admin view: status summary (Total, Pending Review, Active, Rejected), guardrail list with Pending Review tag, and detail panel with Approve/Reject buttons and configuration options." style={{ width: '100%', maxWidth: '900px', height: 'auto' }} />
+
+### 3. Review submissions
+
+The table shows:
+
+- **Name**, **Team**, **Endpoint** (api_base), **Status** (Pending Review / Active / Rejected), **Submitted** date, **Submitted by** (user/email), and other config details.
+
+Summary cards show counts for **Total**, **Pending Review**, **Active**, and **Rejected**.
+
+<!-- Optional: screenshot of the Team Guardrails table and summary -->
+
+### 4. Approve or reject
+
+- **Pending Review:** Use **Approve** to activate the guardrail. The proxy sets its status to `active` and initializes it in memory so it can be used on requests.
+- Use **Reject** to decline the submission (status becomes `rejected`).
+
+Approval triggers the same initialization as adding a guardrail via config or the admin guardrail API; rejection only updates the status and does not load the guardrail.
+
+<!-- Optional: screenshot of Approve/Reject actions or confirmation dialog -->
+
+### API equivalent (admin only)
+
+Admins can also use the REST API:
+
+- **List submissions:** `GET /guardrails/submissions` (optional query: `status`, `team_id`, `search`)
+- **Get one:** `GET /guardrails/submissions/{guardrail_id}`
+- **Approve:** `POST /guardrails/submissions/{guardrail_id}/approve`
+- **Reject:** `POST /guardrails/submissions/{guardrail_id}/reject`
+
+These endpoints require **admin** (e.g. `PROXY_ADMIN`) authentication.
+
+---
+
+## Summary
+
+| Role | Action |
+|------|--------|
+| **Developer** | Call `POST /guardrails/register` with a team-scoped key and a `generic_guardrail_api` config. Submission enters `pending_review`. |
+| **Admin** | Open **Guardrails → Team Guardrails** in the UI (or use the submissions API), then **Approve** or **Reject** each submission. Approved guardrails become active. |
+
+Only guardrails with `litellm_params.guardrail: "generic_guardrail_api"` are accepted for registration. For the full contract and config options, see [Generic Guardrail API](/docs/adding_provider/generic_guardrail_api).
diff --git a/docs/my-website/img/admin_team_guardrails.png b/docs/my-website/img/admin_team_guardrails.png
new file mode 100644
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diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 004114c8e08..f7487d24b12 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -42,6 +42,7 @@ const sidebars = {
       label: "Guardrails",
       items: [
         "proxy/guardrails/quick_start",
+        "proxy/guardrails/team_based_guardrails",
         "proxy/guardrails/guardrail_load_balancing",
         "proxy/guardrails/test_playground",
         "proxy/guardrails/litellm_content_filter",
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260228170127_support_team_based_guardrails/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260228170127_support_team_based_guardrails/migration.sql
new file mode 100644
index 00000000000..8af167950ec
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260228170127_support_team_based_guardrails/migration.sql
@@ -0,0 +1,8 @@
+-- AlterTable
+ALTER TABLE "LiteLLM_GuardrailsTable" ADD COLUMN     "reviewed_at" TIMESTAMP(3),
+ADD COLUMN     "status" TEXT NOT NULL DEFAULT 'active',
+ADD COLUMN     "submitted_at" TIMESTAMP(3);
+
+-- CreateIndex
+CREATE INDEX "LiteLLM_GuardrailsTable_status_idx" ON "LiteLLM_GuardrailsTable"("status");
+
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index f18556ac329..e0b28a4e012 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -871,6 +871,13 @@ model LiteLLM_GuardrailsTable {
   team_id String?
   created_at DateTime @default(now())
   updated_at DateTime @updatedAt
+  // Submission lifecycle. Possible values: pending_review (team-registered, awaiting approval), active (approved), rejected
+  status String @default("active")
+  submitted_at DateTime?
+  reviewed_at DateTime?
+  // submitted_by_user_id and submitted_by_email live in guardrail_info JSON
+
+  @@index([status])
 }
 
 // Daily guardrail metrics for usage dashboard (one row per guardrail per day)
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 5d4ceca890b..cbf683d226e 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -646,6 +646,8 @@ class LiteLLMRoutes(enum.Enum):
         # Invitation routes - org/team admins checked in endpoint via _user_has_admin_privileges
         "/invitation/new",
         "/invitation/delete",
+        # Team guardrail submission - requires team-scoped key; endpoint enforces team_id
+        "/guardrails/register",
     ]  # routes that manage their own allowed/disallowed logic
 
     ## Org Admin Routes ##
diff --git a/litellm/proxy/guardrails/guardrail_endpoints.py b/litellm/proxy/guardrails/guardrail_endpoints.py
index 5215fca0293..c6a709534e1 100644
--- a/litellm/proxy/guardrails/guardrail_endpoints.py
+++ b/litellm/proxy/guardrails/guardrail_endpoints.py
@@ -4,7 +4,10 @@ CRUD ENDPOINTS FOR GUARDRAILS
 
 import concurrent.futures
 import inspect
+import json
+from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Type, TypeVar, Union, cast
+from urllib.parse import urlparse
 
 from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel
@@ -12,6 +15,7 @@ from pydantic import BaseModel
 from litellm._logging import verbose_proxy_logger
 from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
 from litellm.integrations.custom_guardrail import CustomGuardrail
+from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.guardrails.guardrail_hooks.custom_code.code_validator import (
@@ -525,6 +529,456 @@ async def delete_guardrail(
         raise HTTPException(status_code=500, detail=str(e))
 
 
+# --- Team guardrail registration (Generic Guardrail API spec) ---
+
+GENERIC_GUARDRAIL_API = "generic_guardrail_api"
+
+
+class RegisterGuardrailRequest(BaseModel):
+    """Request body for POST /guardrails/register. Follows Generic Guardrail API config."""
+
+    guardrail_name: str
+    litellm_params: Dict[
+        str, Any
+    ]  # guardrail, mode, api_base required; api_key, headers, etc. optional
+    guardrail_info: Optional[Dict[str, Any]] = None
+
+    def get_litellm_params_dict(self) -> Dict[str, Any]:
+        return dict(self.litellm_params)
+
+
+class RegisterGuardrailResponse(BaseModel):
+    guardrail_id: str
+    guardrail_name: str
+    status: str
+    submitted_at: Optional[datetime] = None
+
+
+class GuardrailSubmissionSummary(BaseModel):
+    total: int
+    pending_review: int
+    active: int
+    rejected: int
+
+
+class GuardrailSubmissionItem(BaseModel):
+    guardrail_id: str
+    guardrail_name: str
+    status: str  # pending_review | active | rejected
+    team_id: Optional[str] = None
+    team_guardrail: bool = (
+        False  # True when submitted via team (team_id set); use to distinguish team vs regular guardrails
+    )
+    litellm_params: Optional[Dict[str, Any]] = None
+    guardrail_info: Optional[Dict[str, Any]] = None
+    submitted_by_user_id: Optional[str] = None
+    submitted_by_email: Optional[str] = None
+    submitted_at: Optional[datetime] = None
+    reviewed_at: Optional[datetime] = None
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+
+class ListGuardrailSubmissionsResponse(BaseModel):
+    submissions: List[GuardrailSubmissionItem]
+    summary: GuardrailSubmissionSummary
+
+
+@router.post(
+    "/guardrails/register",
+    tags=["Guardrails"],
+    response_model=RegisterGuardrailResponse,
+)
+async def register_guardrail(
+    request: RegisterGuardrailRequest,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Register a guardrail for onboarding (team submission).
+
+    Accepts a guardrail config in the
+    [Generic Guardrail API](https://docs.litellm.ai/docs/adding_provider/generic_guardrail_api) format.
+    The submission is stored with status `pending_review` until an admin approves it.
+    """
+    from litellm.proxy.proxy_server import prisma_client
+
+    if prisma_client is None:
+        raise HTTPException(status_code=500, detail="Prisma client not initialized")
+
+    if not user_api_key_dict.team_id:
+        raise HTTPException(
+            status_code=400,
+            detail="Registration requires an API key associated with a team. Use a team-scoped key.",
+        )
+
+    params = request.get_litellm_params_dict()
+    if params.get("guardrail") != GENERIC_GUARDRAIL_API:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Only guardrails with litellm_params.guardrail={GENERIC_GUARDRAIL_API!r} are accepted for registration",
+        )
+    api_base = params.get("api_base")
+    if not api_base:
+        raise HTTPException(
+            status_code=400,
+            detail="litellm_params.api_base is required for generic_guardrail_api",
+        )
+    parsed = urlparse(api_base)
+    if parsed.scheme not in ("http", "https"):
+        raise HTTPException(
+            status_code=400,
+            detail="litellm_params.api_base must use http or https scheme",
+        )
+    if not parsed.hostname:
+        raise HTTPException(
+            status_code=400,
+            detail="litellm_params.api_base must contain a valid hostname",
+        )
+    mode = params.get("mode")
+    if mode is None:
+        raise HTTPException(
+            status_code=400,
+            detail="litellm_params.mode is required (e.g. pre_call, post_call)",
+        )
+
+    try:
+        existing = await prisma_client.db.litellm_guardrailstable.find_unique(
+            where={"guardrail_name": request.guardrail_name}
+        )
+        if existing is not None:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Guardrail with name {request.guardrail_name!r} already exists",
+            )
+    except HTTPException:
+        raise
+    except Exception as e:
+        verbose_proxy_logger.exception(
+            "Error checking guardrail name uniqueness: %s", e
+        )
+        raise HTTPException(status_code=500, detail=str(e))
+
+    now = datetime.now(timezone.utc)
+    litellm_params_str = safe_dumps(params)
+    guardrail_info = dict(request.guardrail_info or {})
+    guardrail_info["submitted_by_user_id"] = user_api_key_dict.user_id
+    guardrail_info["submitted_by_email"] = user_api_key_dict.user_email
+    guardrail_info["team_guardrail"] = (
+        True  # Mark as team submission for filtering/display
+    )
+    guardrail_info_str = safe_dumps(guardrail_info)
+
+    try:
+        created = await prisma_client.db.litellm_guardrailstable.create(
+            data={
+                "guardrail_name": request.guardrail_name,
+                "litellm_params": litellm_params_str,
+                "guardrail_info": guardrail_info_str,
+                "status": "pending_review",
+                "team_id": user_api_key_dict.team_id,
+                "submitted_at": now,
+                "created_at": now,
+                "updated_at": now,
+            }
+        )
+        return RegisterGuardrailResponse(
+            guardrail_id=created.guardrail_id,
+            guardrail_name=created.guardrail_name,
+            status=created.status,
+            submitted_at=created.submitted_at,
+        )
+    except Exception as e:
+        verbose_proxy_logger.exception("Error registering guardrail: %s", e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+def _parse_json_field(value: Any) -> Optional[Dict[str, Any]]:
+    if value is None:
+        return None
+    if isinstance(value, dict):
+        return value
+    if isinstance(value, str):
+        try:
+            return json.loads(value)
+        except Exception:
+            return None
+    return None
+
+
+def _row_to_submission_item(row: Any) -> GuardrailSubmissionItem:
+    guardrail_info = _parse_json_field(row.guardrail_info) or {}
+    team_guardrail = row.team_id is not None
+    return GuardrailSubmissionItem(
+        guardrail_id=row.guardrail_id,
+        guardrail_name=row.guardrail_name,
+        status=row.status or "active",
+        team_id=row.team_id,
+        team_guardrail=team_guardrail,
+        litellm_params=_parse_json_field(row.litellm_params),
+        guardrail_info=guardrail_info,
+        submitted_by_user_id=guardrail_info.get("submitted_by_user_id"),
+        submitted_by_email=guardrail_info.get("submitted_by_email"),
+        submitted_at=getattr(row, "submitted_at", None),
+        reviewed_at=getattr(row, "reviewed_at", None),
+        created_at=row.created_at,
+        updated_at=row.updated_at,
+    )
+
+
+@router.get(
+    "/guardrails/submissions",
+    tags=["Guardrails"],
+    response_model=ListGuardrailSubmissionsResponse,
+)
+async def list_guardrail_submissions(
+    status: Optional[str] = None,
+    team_id: Optional[str] = None,
+    search: Optional[str] = None,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    List team guardrail submissions (admin only). Returns only guardrails with a team_id.
+
+    Status values: pending_review (team-registered, awaiting approval), active (approved), rejected.
+
+    Optional filters:
+    - status: pending_review | active | rejected
+    - team_id: filter by specific team
+    - search: name/description
+    """
+    from litellm.proxy.proxy_server import prisma_client
+
+    if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN:
+        raise HTTPException(status_code=403, detail="Admin access required")
+
+    if prisma_client is None:
+        raise HTTPException(status_code=500, detail="Prisma client not initialized")
+
+    try:
+        # Single query: fetch all team guardrails (team_id is not null)
+        all_team_rows = await prisma_client.db.litellm_guardrailstable.find_many(
+            where={"team_id": {"not": None}},
+            order={"created_at": "desc"},
+        )
+
+        # Derive summary counts from the full result set
+        total = len(all_team_rows)
+        pending_review = sum(
+            1 for r in all_team_rows if (r.status or "active") == "pending_review"
+        )
+        active_count = sum(
+            1 for r in all_team_rows if (r.status or "active") == "active"
+        )
+        rejected = sum(
+            1 for r in all_team_rows if (r.status or "active") == "rejected"
+        )
+
+        # Apply filters to get the submissions list
+        rows = all_team_rows
+        if status:
+            rows = [r for r in rows if r.status == status]
+        if team_id:
+            rows = [r for r in rows if r.team_id == team_id]
+        if search:
+            search_lower = search.lower()
+            rows = [
+                r
+                for r in rows
+                if search_lower in (r.guardrail_name or "").lower()
+                or (
+                    isinstance(r.guardrail_info, dict)
+                    and search_lower
+                    in str((r.guardrail_info or {}).get("description", "")).lower()
+                )
+                or (
+                    isinstance(r.guardrail_info, str)
+                    and search_lower in r.guardrail_info.lower()
+                )
+            ]
+
+        items = [_row_to_submission_item(r) for r in rows]
+        return ListGuardrailSubmissionsResponse(
+            submissions=items,
+            summary=GuardrailSubmissionSummary(
+                total=total,
+                pending_review=pending_review,
+                active=active_count,
+                rejected=rejected,
+            ),
+        )
+    except Exception as e:
+        verbose_proxy_logger.exception("Error listing guardrail submissions: %s", e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get(
+    "/guardrails/submissions/{guardrail_id}",
+    tags=["Guardrails"],
+    response_model=GuardrailSubmissionItem,
+)
+async def get_guardrail_submission(
+    guardrail_id: str,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """Get a single guardrail submission by id (admin only)."""
+    from litellm.proxy.proxy_server import prisma_client
+
+    if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN:
+        raise HTTPException(status_code=403, detail="Admin access required")
+
+    if prisma_client is None:
+        raise HTTPException(status_code=500, detail="Prisma client not initialized")
+
+    try:
+        row = await prisma_client.db.litellm_guardrailstable.find_unique(
+            where={"guardrail_id": guardrail_id}
+        )
+        if row is None:
+            raise HTTPException(
+                status_code=404, detail="Guardrail submission not found"
+            )
+        return _row_to_submission_item(row)
+    except HTTPException:
+        raise
+    except Exception as e:
+        verbose_proxy_logger.exception("Error getting guardrail submission: %s", e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post(
+    "/guardrails/submissions/{guardrail_id}/approve",
+    tags=["Guardrails"],
+)
+async def approve_guardrail_submission(
+    guardrail_id: str,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """Approve a pending guardrail submission: set status to active and initialize in memory (admin only)."""
+    from litellm.proxy.guardrails.guardrail_registry import IN_MEMORY_GUARDRAIL_HANDLER
+    from litellm.proxy.proxy_server import prisma_client
+
+    if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN:
+        raise HTTPException(status_code=403, detail="Admin access required")
+
+    if prisma_client is None:
+        raise HTTPException(status_code=500, detail="Prisma client not initialized")
+
+    try:
+        row = await prisma_client.db.litellm_guardrailstable.find_unique(
+            where={"guardrail_id": guardrail_id}
+        )
+        if row is None:
+            raise HTTPException(
+                status_code=404, detail="Guardrail submission not found"
+            )
+        if row.status != "pending_review":
+            raise HTTPException(
+                status_code=400,
+                detail=f"Guardrail is not pending review (status={row.status})",
+            )
+
+        now = datetime.now(timezone.utc)
+        await prisma_client.db.litellm_guardrailstable.update(
+            where={"guardrail_id": guardrail_id},
+            data={"status": "active", "reviewed_at": now, "updated_at": now},
+        )
+
+        litellm_params = _parse_json_field(row.litellm_params)
+        guardrail_info = _parse_json_field(row.guardrail_info)
+        if not litellm_params:
+            raise HTTPException(
+                status_code=500,
+                detail="Guardrail litellm_params is missing or invalid",
+            )
+        guardrail_dict = {
+            "guardrail_id": row.guardrail_id,
+            "guardrail_name": row.guardrail_name,
+            "litellm_params": litellm_params,
+            "guardrail_info": guardrail_info or {},
+        }
+        try:
+            IN_MEMORY_GUARDRAIL_HANDLER.initialize_guardrail(
+                guardrail=cast(Guardrail, guardrail_dict)
+            )
+            verbose_proxy_logger.info(
+                "Approved guardrail %s (ID: %s) and initialized in memory",
+                row.guardrail_name,
+                guardrail_id,
+            )
+        except Exception as init_err:
+            verbose_proxy_logger.warning(
+                "Failed to initialize approved guardrail %s in memory: %s",
+                guardrail_id,
+                init_err,
+            )
+            return {
+                "guardrail_id": guardrail_id,
+                "status": "active",
+                "message": "Guardrail approved",
+                "warning": f"Guardrail was marked active but failed to initialize in memory: {init_err}. "
+                "It will be picked up on the next sync cycle.",
+            }
+
+        return {
+            "guardrail_id": guardrail_id,
+            "status": "active",
+            "message": "Guardrail approved",
+        }
+    except HTTPException:
+        raise
+    except Exception as e:
+        verbose_proxy_logger.exception("Error approving guardrail submission: %s", e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post(
+    "/guardrails/submissions/{guardrail_id}/reject",
+    tags=["Guardrails"],
+)
+async def reject_guardrail_submission(
+    guardrail_id: str,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """Reject a guardrail submission (admin only)."""
+    from litellm.proxy.proxy_server import prisma_client
+
+    if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN:
+        raise HTTPException(status_code=403, detail="Admin access required")
+
+    if prisma_client is None:
+        raise HTTPException(status_code=500, detail="Prisma client not initialized")
+
+    try:
+        row = await prisma_client.db.litellm_guardrailstable.find_unique(
+            where={"guardrail_id": guardrail_id}
+        )
+        if row is None:
+            raise HTTPException(
+                status_code=404, detail="Guardrail submission not found"
+            )
+        if row.status != "pending_review":
+            raise HTTPException(
+                status_code=400,
+                detail=f"Guardrail is not pending review (status={row.status})",
+            )
+
+        now = datetime.now(timezone.utc)
+        await prisma_client.db.litellm_guardrailstable.update(
+            where={"guardrail_id": guardrail_id},
+            data={"status": "rejected", "reviewed_at": now, "updated_at": now},
+        )
+        return {
+            "guardrail_id": guardrail_id,
+            "status": "rejected",
+            "message": "Guardrail rejected",
+        }
+    except HTTPException:
+        raise
+    except Exception as e:
+        verbose_proxy_logger.exception("Error rejecting guardrail submission: %s", e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @router.patch(
     "/guardrails/{guardrail_id}",
     tags=["Guardrails"],
@@ -1356,9 +1810,9 @@ async def get_provider_specific_params():
     lakera_v2_fields = _get_fields_from_model(LakeraV2GuardrailConfigModel)
     tool_permission_fields = _get_fields_from_model(ToolPermissionGuardrailConfigModel)
 
-    tool_permission_fields[
-        "ui_friendly_name"
-    ] = ToolPermissionGuardrailConfigModel.ui_friendly_name()
+    tool_permission_fields["ui_friendly_name"] = (
+        ToolPermissionGuardrailConfigModel.ui_friendly_name()
+    )
 
     # Return the provider-specific parameters
     provider_params = {
@@ -1497,7 +1951,6 @@ async def test_custom_code_guardrail(
     ```
     """
 
-
     if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN:
         raise HTTPException(
             status_code=403,
@@ -1632,10 +2085,10 @@ async def apply_guardrail(
     from litellm.proxy.utils import handle_exception_on_proxy
 
     try:
-        active_guardrail: Optional[
-            CustomGuardrail
-        ] = GUARDRAIL_REGISTRY.get_initialized_guardrail_callback(
-            guardrail_name=request.guardrail_name
+        active_guardrail: Optional[CustomGuardrail] = (
+            GUARDRAIL_REGISTRY.get_initialized_guardrail_callback(
+                guardrail_name=request.guardrail_name
+            )
         )
         if active_guardrail is None:
             raise HTTPException(
diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py
index a0c2113b7ab..bb0d0a99b31 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py
@@ -21,6 +21,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"
         unreachable_fallback=getattr(
             litellm_params, "unreachable_fallback", "fail_closed"
         ),
+        extra_headers=getattr(litellm_params, "extra_headers", None),
         guardrail_name=guardrail.get("guardrail_name", ""),
         event_hook=litellm_params.mode,
         default_on=litellm_params.default_on,
diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py
index 1892424e86d..990e7b3ede6 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py
@@ -7,7 +7,7 @@
 
 import fnmatch
 import os
-from typing import TYPE_CHECKING, Any, Dict, Literal, Optional
+from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Set
 
 import httpx
 
@@ -54,22 +54,30 @@ _HEADER_VALUE_ALLOWLIST = frozenset(
 _HEADER_PRESENT_PLACEHOLDER = "[present]"
 
 
-def _header_value_allowed(header_name: str) -> bool:
-    """Return True if this header's value may be forwarded (allowlist, including globs)."""
+def _header_value_allowed(
+    header_name: str,
+    extra_allowlist: Optional[Set[str]] = None,
+) -> bool:
+    """Return True if this header's value may be forwarded (allowlist, including globs and extra_headers)."""
     lower = header_name.lower()
     if lower in _HEADER_VALUE_ALLOWLIST:
         return True
     for pattern in _HEADER_VALUE_ALLOWLIST:
         if "*" in pattern and fnmatch.fnmatch(lower, pattern):
             return True
+    if extra_allowlist and lower in extra_allowlist:
+        return True
     return False
 
 
-def _sanitize_inbound_headers(headers: Any) -> Optional[Dict[str, str]]:
+def _sanitize_inbound_headers(
+    headers: Any,
+    extra_allowlist: Optional[Set[str]] = None,
+) -> Optional[Dict[str, str]]:
     """
     Sanitize inbound headers before passing them to a 3rd party guardrail service.
 
-    - Allowlist: only headers in the allowlist have their values forwarded (exact + glob: x-stainless-*, x-litellm-*).
+    - Allowlist: default allowlist + extra_allowlist (from litellm_params.extra_headers); only these have values forwarded.
     - All other headers are included with value "[present]" so the guardrail knows the header existed.
     - Coerces values to str (for JSON serialization).
     """
@@ -81,7 +89,7 @@ def _sanitize_inbound_headers(headers: Any) -> Optional[Dict[str, str]]:
         if k is None:
             continue
         key = str(k)
-        if _header_value_allowed(key):
+        if _header_value_allowed(key, extra_allowlist=extra_allowlist):
             try:
                 sanitized[key] = str(v)
             except Exception:
@@ -93,7 +101,9 @@ def _sanitize_inbound_headers(headers: Any) -> Optional[Dict[str, str]]:
 
 
 def _extract_inbound_headers(
-    request_data: dict, logging_obj: Optional["LiteLLMLoggingObj"]
+    request_data: dict,
+    logging_obj: Optional["LiteLLMLoggingObj"],
+    extra_allowlist: Optional[Set[str]] = None,
 ) -> Optional[Dict[str, str]]:
     """
     Extract inbound headers from available request context.
@@ -107,23 +117,27 @@ def _extract_inbound_headers(
     # 1) Most common path (proxy): full request context in proxy_server_request
     headers = request_data.get("proxy_server_request", {}).get("headers")
     if headers:
-        return _sanitize_inbound_headers(headers)
+        return _sanitize_inbound_headers(headers, extra_allowlist=extra_allowlist)
 
     # 2) Some guardrails pass proxy_server_request as request_data itself
     headers = request_data.get("headers")
     if headers:
-        return _sanitize_inbound_headers(headers)
+        return _sanitize_inbound_headers(headers, extra_allowlist=extra_allowlist)
 
     # 3) Pre-call: headers stored in request metadata
     metadata_headers = (request_data.get("metadata") or {}).get("headers")
     if metadata_headers:
-        return _sanitize_inbound_headers(metadata_headers)
+        return _sanitize_inbound_headers(
+            metadata_headers, extra_allowlist=extra_allowlist
+        )
 
     litellm_metadata_headers = (request_data.get("litellm_metadata") or {}).get(
         "headers"
     )
     if litellm_metadata_headers:
-        return _sanitize_inbound_headers(litellm_metadata_headers)
+        return _sanitize_inbound_headers(
+            litellm_metadata_headers, extra_allowlist=extra_allowlist
+        )
 
     # 4) Post-call: headers not present on response; fallback to logging object
     if logging_obj and getattr(logging_obj, "model_call_details", None):
@@ -135,7 +149,9 @@ def _extract_inbound_headers(
                 .get("headers", None)
             )
             if headers:
-                return _sanitize_inbound_headers(headers)
+                return _sanitize_inbound_headers(
+                    headers, extra_allowlist=extra_allowlist
+                )
         except Exception:
             pass
 
@@ -171,12 +187,14 @@ class GenericGuardrailAPI(CustomGuardrail):
         api_key: Optional[str] = None,
         additional_provider_specific_params: Optional[Dict[str, Any]] = None,
         unreachable_fallback: Literal["fail_closed", "fail_open"] = "fail_closed",
+        extra_headers: Optional[list] = None,
         **kwargs,
     ):
         self.async_handler = get_async_httpx_client(
             llm_provider=httpxSpecialProvider.GuardrailCallback
         )
         self.headers = headers or {}
+        self.extra_headers = extra_headers or []
 
         # If api_key is provided, add it as x-api-key header
         if api_key:
@@ -370,8 +388,15 @@ class GenericGuardrailAPI(CustomGuardrail):
 
         # Extract user API key metadata
         user_metadata = self._extract_user_api_key_metadata(request_data)
+        extra_allowlist = (
+            {h.lower() for h in self.extra_headers if isinstance(h, str)}
+            if self.extra_headers
+            else None
+        )
         inbound_headers = _extract_inbound_headers(
-            request_data=request_data, logging_obj=logging_obj
+            request_data=request_data,
+            logging_obj=logging_obj,
+            extra_allowlist=extra_allowlist,
         )
 
         # Create request payload
diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py
index c0903a35b6d..46ea667f464 100644
--- a/litellm/proxy/guardrails/guardrail_registry.py
+++ b/litellm/proxy/guardrails/guardrail_registry.py
@@ -11,8 +11,12 @@ 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
+from litellm.proxy.guardrails.guardrail_hooks.grayswan import GraySwanGuardrail
+from litellm.proxy.guardrails.guardrail_hooks.grayswan import (
+    initialize_guardrail as initialize_grayswan,
+)
 from litellm.proxy.types_utils.utils import get_instance_fn
+from litellm.proxy.utils import PrismaClient
 from litellm.secret_managers.main import get_secret
 from litellm.types.guardrails import (
     Guardrail,
@@ -21,10 +25,6 @@ from litellm.types.guardrails import (
     LitellmParams,
     SupportedGuardrailIntegrations,
 )
-from litellm.proxy.guardrails.guardrail_hooks.grayswan import (
-    GraySwanGuardrail,
-    initialize_guardrail as initialize_grayswan,
-)
 
 from .guardrail_initializers import (
     initialize_bedrock,
@@ -327,11 +327,13 @@ class GuardrailRegistry:
         prisma_client: PrismaClient,
     ) -> List[Guardrail]:
         """
-        Get all guardrails from the database
+        Get all active guardrails from the database.
+        Only rows with status == "active" are returned (pending_review and rejected are excluded).
         """
         try:
             guardrails_from_db = (
                 await prisma_client.db.litellm_guardrailstable.find_many(
+                    where={"status": "active"},
                     order={"created_at": "desc"},
                 )
             )
diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma
index f18556ac329..e0b28a4e012 100644
--- a/litellm/proxy/schema.prisma
+++ b/litellm/proxy/schema.prisma
@@ -871,6 +871,13 @@ model LiteLLM_GuardrailsTable {
   team_id String?
   created_at DateTime @default(now())
   updated_at DateTime @updatedAt
+  // Submission lifecycle. Possible values: pending_review (team-registered, awaiting approval), active (approved), rejected
+  status String @default("active")
+  submitted_at DateTime?
+  reviewed_at DateTime?
+  // submitted_by_user_id and submitted_by_email live in guardrail_info JSON
+
+  @@index([status])
 }
 
 // Daily guardrail metrics for usage dashboard (one row per guardrail per day)
diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py
index 7381a3038f2..a68c4e2f762 100644
--- a/litellm/types/guardrails.py
+++ b/litellm/types/guardrails.py
@@ -698,6 +698,15 @@ class BaseLitellmParams(
         ),
     )
 
+    extra_headers: Optional[List[str]] = Field(
+        default=None,
+        description=(
+            "Header names to forward from the client request to the guardrail (e.g. x-request-id). "
+            "Only these headers' values are sent; others may be omitted or sent as [present]. "
+            "Used by generic_guardrail_api (similar to MCP extra_headers)."
+        ),
+    )
+
     # Custom code guardrail params
     custom_code: Optional[str] = Field(
         default=None,
diff --git a/schema.prisma b/schema.prisma
index f18556ac329..e0b28a4e012 100644
--- a/schema.prisma
+++ b/schema.prisma
@@ -871,6 +871,13 @@ model LiteLLM_GuardrailsTable {
   team_id String?
   created_at DateTime @default(now())
   updated_at DateTime @updatedAt
+  // Submission lifecycle. Possible values: pending_review (team-registered, awaiting approval), active (approved), rejected
+  status String @default("active")
+  submitted_at DateTime?
+  reviewed_at DateTime?
+  // submitted_by_user_id and submitted_by_email live in guardrail_info JSON
+
+  @@index([status])
 }
 
 // Daily guardrail metrics for usage dashboard (one row per guardrail per day)
diff --git a/scripts/create_team_key_and_submit_guardrail.sh b/scripts/create_team_key_and_submit_guardrail.sh
new file mode 100755
index 00000000000..339137f886e
--- /dev/null
+++ b/scripts/create_team_key_and_submit_guardrail.sh
@@ -0,0 +1,92 @@
+#!/usr/bin/env bash
+#
+# Creates a team, generates a team key, and submits a test guardrail with it.
+# Requires: curl, jq
+#
+# Usage:
+#   ADMIN_KEY=sk-your-admin-key ./scripts/create_team_key_and_submit_guardrail.sh
+#   BASE_URL=http://localhost:4000 ADMIN_KEY=sk-your-admin-key ./scripts/create_team_key_and_submit_guardrail.sh
+
+set -e
+
+BASE_URL="${BASE_URL:-http://localhost:4000}"
+BASE_URL="${BASE_URL%/}"
+
+if [ -z "${ADMIN_KEY}" ]; then
+  echo "Error: ADMIN_KEY is required (admin API key for the proxy)."
+  echo "Usage: ADMIN_KEY=sk-your-admin-key $0"
+  exit 1
+fi
+
+AUTH_HEADER="Authorization: Bearer ${ADMIN_KEY}"
+
+echo "Using BASE_URL=${BASE_URL}"
+echo "Creating team..."
+
+TEAM_RESP=$(curl -s -X POST "${BASE_URL}/team/new" \
+  -H "${AUTH_HEADER}" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "team_alias": "guardrail-test-team"
+  }')
+
+if ! echo "$TEAM_RESP" | jq -e .team_id >/dev/null 2>&1; then
+  echo "Failed to create team. Response:"
+  echo "$TEAM_RESP" | jq . 2>/dev/null || echo "$TEAM_RESP"
+  exit 1
+fi
+
+TEAM_ID=$(echo "$TEAM_RESP" | jq -r .team_id)
+echo "Created team_id: ${TEAM_ID}"
+
+echo "Creating key for team..."
+
+KEY_RESP=$(curl -s -X POST "${BASE_URL}/key/generate" \
+  -H "${AUTH_HEADER}" \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"team_id\": \"${TEAM_ID}\"
+  }")
+
+if ! echo "$KEY_RESP" | jq -e .key >/dev/null 2>&1; then
+  echo "Failed to create key. Response:"
+  echo "$KEY_RESP" | jq . 2>/dev/null || echo "$KEY_RESP"
+  exit 1
+fi
+
+TEAM_KEY=$(echo "$KEY_RESP" | jq -r .key)
+echo "Created team key: ${TEAM_KEY}"
+
+GUARDRAIL_NAME="test-guardrail-$(date +%s)"
+echo "Submitting guardrail: ${GUARDRAIL_NAME}"
+
+REGISTER_RESP=$(curl -s -X POST "${BASE_URL}/guardrails/register" \
+  -H "Authorization: Bearer ${TEAM_KEY}" \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"guardrail_name\": \"${GUARDRAIL_NAME}\",
+    \"litellm_params\": {
+      \"guardrail\": \"generic_guardrail_api\",
+      \"mode\": \"pre_call\",
+      \"api_base\": \"https://example.com/guardrail\"
+    },
+    \"guardrail_info\": {
+      \"description\": \"Test guardrail submitted via team key\"
+    }
+  }")
+
+if ! echo "$REGISTER_RESP" | jq -e .guardrail_id >/dev/null 2>&1; then
+  echo "Failed to register guardrail. Response:"
+  echo "$REGISTER_RESP" | jq . 2>/dev/null || echo "$REGISTER_RESP"
+  exit 1
+fi
+
+GUARDRAIL_ID=$(echo "$REGISTER_RESP" | jq -r .guardrail_id)
+echo "Registered guardrail_id: ${GUARDRAIL_ID}"
+
+echo ""
+echo "Done."
+echo "  team_id:      ${TEAM_ID}"
+echo "  team_key:     ${TEAM_KEY}"
+echo "  guardrail_id: ${GUARDRAIL_ID}"
+echo "  guardrail_name: ${GUARDRAIL_NAME}"
diff --git a/scripts/test_guardrails_register_endpoints.sh b/scripts/test_guardrails_register_endpoints.sh
new file mode 100755
index 00000000000..89fd53b5b8c
--- /dev/null
+++ b/scripts/test_guardrails_register_endpoints.sh
@@ -0,0 +1,126 @@
+#!/usr/bin/env bash
+#
+# Test guardrail register and submissions endpoints.
+# Requires: proxy running with DB (migrations applied), valid admin API key.
+#
+# Usage:
+#   export LITELLM_API_KEY="sk-..."   # required, use an admin key
+#   ./scripts/test_guardrails_register_endpoints.sh
+#   BASE_URL=http://localhost:4000 LITELLM_API_KEY="sk-..." ./scripts/test_guardrails_register_endpoints.sh
+#
+set -euo pipefail
+
+BASE_URL="${BASE_URL:-http://localhost:4000}"
+API_KEY="${LITELLM_API_KEY:-}"
+
+if ! command -v jq &>/dev/null; then
+  echo "Error: jq is required. Install with: brew install jq (macOS) or apt-get install jq (Linux)"
+  exit 1
+fi
+
+if [[ -z "$API_KEY" ]]; then
+  echo "Error: LITELLM_API_KEY is not set. Use an admin key to test list/approve/reject."
+  exit 1
+fi
+
+AUTH_HEADER="Authorization: Bearer $API_KEY"
+TIMESTAMP=$(date +%s)
+NAME_APPROVE="test-guardrail-approve-$TIMESTAMP"
+NAME_REJECT="test-guardrail-reject-$TIMESTAMP"
+
+echo "BASE_URL=$BASE_URL"
+echo "Testing guardrail register and submissions endpoints..."
+echo ""
+
+# --- 1. Register a guardrail (will approve later) ---
+echo "[1/6] POST /guardrails/register (guardrail: $NAME_APPROVE)"
+REGISTER_RESPONSE=$(curl -s -w "\n%{http_code}" -X POST "$BASE_URL/guardrails/register" \
+  -H "$AUTH_HEADER" \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"guardrail_name\": \"$NAME_APPROVE\",
+    \"litellm_params\": {
+      \"guardrail\": \"generic_guardrail_api\",
+      \"mode\": \"pre_call\",
+      \"api_base\": \"https://guardrails.example.com/validate\"
+    },
+    \"guardrail_info\": { \"description\": \"Test guardrail for approve flow\" }
+  }")
+REGISTER_HTTP=$(echo "$REGISTER_RESPONSE" | tail -n1)
+REGISTER_BODY=$(echo "$REGISTER_RESPONSE" | sed '$d')
+if [[ "$REGISTER_HTTP" -ne 200 ]]; then
+  echo "  FAIL: expected 200, got $REGISTER_HTTP"
+  echo "$REGISTER_BODY" | jq . 2>/dev/null || echo "$REGISTER_BODY"
+  exit 1
+fi
+GUARDRAIL_ID_APPROVE=$(echo "$REGISTER_BODY" | jq -r '.guardrail_id')
+echo "  OK (201/200) guardrail_id=$GUARDRAIL_ID_APPROVE"
+
+# --- 2. Register a second guardrail (will reject later) ---
+echo "[2/6] POST /guardrails/register (guardrail: $NAME_REJECT)"
+REJECT_RESPONSE=$(curl -s -w "\n%{http_code}" -X POST "$BASE_URL/guardrails/register" \
+  -H "$AUTH_HEADER" \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"guardrail_name\": \"$NAME_REJECT\",
+    \"litellm_params\": {
+      \"guardrail\": \"generic_guardrail_api\",
+      \"mode\": \"post_call\",
+      \"api_base\": \"https://guardrails.example.com/reject-test\"
+    },
+    \"guardrail_info\": { \"description\": \"Test guardrail for reject flow\" }
+  }")
+REJECT_HTTP=$(echo "$REJECT_RESPONSE" | tail -n1)
+if [[ "$REJECT_HTTP" -ne 200 ]]; then
+  echo "  FAIL: expected 200, got $REJECT_HTTP"
+  echo "$REJECT_RESPONSE" | sed '$d' | jq . 2>/dev/null || echo "$REJECT_RESPONSE"
+  exit 1
+fi
+GUARDRAIL_ID_REJECT=$(echo "$REJECT_RESPONSE" | sed '$d' | jq -r '.guardrail_id')
+echo "  OK guardrail_id=$GUARDRAIL_ID_REJECT"
+
+# --- 3. List submissions (admin) ---
+echo "[3/6] GET /guardrails/submissions"
+LIST_RESPONSE=$(curl -s -w "\n%{http_code}" -X GET "$BASE_URL/guardrails/submissions" -H "$AUTH_HEADER")
+LIST_HTTP=$(echo "$LIST_RESPONSE" | tail -n1)
+LIST_BODY=$(echo "$LIST_RESPONSE" | sed '$d')
+if [[ "$LIST_HTTP" -ne 200 ]]; then
+  echo "  FAIL: expected 200, got $LIST_HTTP"
+  echo "$LIST_BODY" | jq . 2>/dev/null || echo "$LIST_BODY"
+  exit 1
+fi
+echo "  OK summary: $(echo "$LIST_BODY" | jq -c '.summary' 2>/dev/null || echo "N/A")"
+
+# --- 4. Get one submission by id ---
+echo "[4/6] GET /guardrails/submissions/$GUARDRAIL_ID_APPROVE"
+GET_RESPONSE=$(curl -s -w "\n%{http_code}" -X GET "$BASE_URL/guardrails/submissions/$GUARDRAIL_ID_APPROVE" -H "$AUTH_HEADER")
+GET_HTTP=$(echo "$GET_RESPONSE" | tail -n1)
+if [[ "$GET_HTTP" -ne 200 ]]; then
+  echo "  FAIL: expected 200, got $GET_HTTP"
+  exit 1
+fi
+echo "  OK status=$(echo "$GET_RESPONSE" | sed '$d' | jq -r '.status')"
+
+# --- 5. Approve first submission ---
+echo "[5/6] POST /guardrails/submissions/$GUARDRAIL_ID_APPROVE/approve"
+APPROVE_RESPONSE=$(curl -s -w "\n%{http_code}" -X POST "$BASE_URL/guardrails/submissions/$GUARDRAIL_ID_APPROVE/approve" -H "$AUTH_HEADER")
+APPROVE_HTTP=$(echo "$APPROVE_RESPONSE" | tail -n1)
+if [[ "$APPROVE_HTTP" -ne 200 ]]; then
+  echo "  FAIL: expected 200, got $APPROVE_HTTP"
+  echo "$APPROVE_RESPONSE" | sed '$d' | jq . 2>/dev/null || echo "$APPROVE_RESPONSE"
+  exit 1
+fi
+echo "  OK $(echo "$APPROVE_RESPONSE" | sed '$d' | jq -c '.' 2>/dev/null)"
+
+# --- 6. Reject second submission ---
+echo "[6/6] POST /guardrails/submissions/$GUARDRAIL_ID_REJECT/reject"
+REJECT_POST_RESPONSE=$(curl -s -w "\n%{http_code}" -X POST "$BASE_URL/guardrails/submissions/$GUARDRAIL_ID_REJECT/reject" -H "$AUTH_HEADER")
+REJECT_POST_HTTP=$(echo "$REJECT_POST_RESPONSE" | tail -n1)
+if [[ "$REJECT_POST_HTTP" -ne 200 ]]; then
+  echo "  FAIL: expected 200, got $REJECT_POST_HTTP"
+  exit 1
+fi
+echo "  OK $(echo "$REJECT_POST_RESPONSE" | sed '$d' | jq -c '.' 2>/dev/null)"
+
+echo ""
+echo "All 6 requests succeeded. Guardrail register and submissions endpoints are working."
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
index a3c1fd9ea05..e01038cd35f 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
@@ -13,8 +13,8 @@ import pytest
 
 import litellm
 from litellm import ModelResponse
-from litellm.exceptions import GuardrailRaisedException, Timeout
 from litellm._version import version as litellm_version
+from litellm.exceptions import GuardrailRaisedException, Timeout
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.guardrails.guardrail_hooks.generic_guardrail_api import (
     GenericGuardrailAPI,
@@ -188,6 +188,97 @@ class TestGenericGuardrailAPIConfiguration:
         )
         assert "x-api-key" not in guardrail.headers
 
+    def test_init_with_extra_headers(self):
+        """Test that extra_headers is stored for forwarding client headers to the guardrail"""
+        guardrail = GenericGuardrailAPI(
+            api_base="https://api.test.guardrail.com",
+            extra_headers=["x-request-id", "x-custom-auth"],
+        )
+        assert guardrail.extra_headers == ["x-request-id", "x-custom-auth"]
+
+
+class TestExtraHeadersForwarding:
+    """Test extra_headers: client headers allowed to be forwarded to the guardrail"""
+
+    @pytest.mark.asyncio
+    async def test_extra_headers_values_forwarded_to_guardrail(self):
+        """When extra_headers is set, those client header values are sent to the guardrail."""
+        guardrail = GenericGuardrailAPI(
+            api_base="https://api.test.guardrail.com",
+            extra_headers=["x-my-header", "x-request-id"],
+        )
+        request_data = {
+            "proxy_server_request": {
+                "headers": {
+                    "x-my-header": "my-value",
+                    "x-request-id": "req-123",
+                    "x-private": "secret",
+                },
+            },
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "action": "NONE",
+            "texts": ["test"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        with patch.object(
+            guardrail.async_handler, "post", return_value=mock_response
+        ) as mock_post:
+            await guardrail.apply_guardrail(
+                inputs={"texts": ["test"]},
+                request_data=request_data,
+                input_type="request",
+            )
+
+        call_args = mock_post.call_args
+        json_payload = call_args.kwargs["json"]
+        request_headers = json_payload.get("request_headers") or {}
+
+        # Headers in extra_headers have their values forwarded
+        assert request_headers.get("x-my-header") == "my-value"
+        assert request_headers.get("x-request-id") == "req-123"
+        # Headers not in allowlist are sent as placeholder
+        assert request_headers.get("x-private") == _HEADER_PRESENT_PLACEHOLDER
+
+    @pytest.mark.asyncio
+    async def test_without_extra_headers_custom_header_value_not_forwarded(self):
+        """Without extra_headers, a custom client header is sent as [present] only."""
+        guardrail = GenericGuardrailAPI(
+            api_base="https://api.test.guardrail.com",
+            # no extra_headers
+        )
+        request_data = {
+            "proxy_server_request": {
+                "headers": {
+                    "x-custom-auth": "bearer secret-token",
+                },
+            },
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "action": "NONE",
+            "texts": ["test"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        with patch.object(
+            guardrail.async_handler, "post", return_value=mock_response
+        ) as mock_post:
+            await guardrail.apply_guardrail(
+                inputs={"texts": ["test"]},
+                request_data=request_data,
+                input_type="request",
+            )
+
+        call_args = mock_post.call_args
+        json_payload = call_args.kwargs["json"]
+        request_headers = json_payload.get("request_headers") or {}
+
+        # x-custom-auth is not in default allowlist nor extra_headers, so value is not forwarded
+        assert request_headers.get("x-custom-auth") == _HEADER_PRESENT_PLACEHOLDER
+
 
 class TestMetadataExtraction:
     """Test metadata extraction from request data"""
diff --git a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py
index 0ac3637b380..62a6e777b0d 100644
--- a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py
+++ b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py
@@ -17,13 +17,19 @@ from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
 from litellm.proxy.guardrails.guardrail_endpoints import (
     CreateGuardrailRequest,
     PatchGuardrailRequest,
+    RegisterGuardrailRequest,
     UpdateGuardrailRequest,
     apply_guardrail,
+    approve_guardrail_submission,
     create_guardrail,
     delete_guardrail,
     get_guardrail_info,
+    get_guardrail_submission,
+    list_guardrail_submissions,
     list_guardrails_v2,
     patch_guardrail,
+    register_guardrail,
+    reject_guardrail_submission,
     update_guardrail,
 )
 
@@ -1103,4 +1109,466 @@ async def test_get_guardrail_info_endpoint_db_guardrail(mocker):
     assert isinstance(result, GuardrailInfoResponse)
     assert result.guardrail_id == "test-db-guardrail"
     assert result.guardrail_name == "Test DB Guardrail"
-    assert result.guardrail_definition_location == "db"
\ No newline at end of file
+    assert result.guardrail_definition_location == "db"
+
+
+# --- Team guardrail registration (register / submissions) ---
+
+MOCK_REGISTER_REQUEST = RegisterGuardrailRequest(
+    guardrail_name="team-prompt-guard",
+    litellm_params={
+        "guardrail": "generic_guardrail_api",
+        "mode": "pre_call",
+        "api_base": "https://guardrails.example.com/validate",
+    },
+    guardrail_info={"description": "Team prompt injection detector"},
+)
+
+
+@pytest.mark.asyncio
+async def test_register_guardrail_success(mocker):
+    """Register creates a row with status pending_review and returns guardrail_id."""
+    mock_prisma = mocker.Mock()
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=None)
+    created_row = mocker.Mock(
+        guardrail_id="reg-123",
+        guardrail_name=MOCK_REGISTER_REQUEST.guardrail_name,
+        status="pending_review",
+        submitted_at=datetime.now(),
+    )
+    mock_prisma.db.litellm_guardrailstable.create = AsyncMock(return_value=created_row)
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+
+    user = UserAPIKeyAuth(user_id="u1", user_email="alice@co.com", team_id="team-1")
+    result = await register_guardrail(MOCK_REGISTER_REQUEST, user)
+
+    assert result.guardrail_id == "reg-123"
+    assert result.guardrail_name == MOCK_REGISTER_REQUEST.guardrail_name
+    assert result.status == "pending_review"
+    mock_prisma.db.litellm_guardrailstable.create.assert_called_once()
+    call_data = mock_prisma.db.litellm_guardrailstable.create.call_args[1]["data"]
+    assert call_data["status"] == "pending_review"
+    assert call_data["guardrail_name"] == MOCK_REGISTER_REQUEST.guardrail_name
+
+
+@pytest.mark.asyncio
+async def test_register_guardrail_rejects_non_generic_api(mocker):
+    """Register returns 400 when litellm_params.guardrail is not generic_guardrail_api."""
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mocker.Mock())
+    req = RegisterGuardrailRequest(
+        guardrail_name="other-guard",
+        litellm_params={"guardrail": "bedrock", "mode": "pre_call", "api_base": "https://x.com"},
+    )
+    user = UserAPIKeyAuth(user_id="u1", user_email="a@b.com", team_id="team-1")
+
+    with pytest.raises(HTTPException) as exc_info:
+        await register_guardrail(req, user)
+    assert exc_info.value.status_code == 400
+    assert "generic_guardrail_api" in exc_info.value.detail
+
+
+@pytest.mark.asyncio
+async def test_register_guardrail_requires_team_id(mocker):
+    """Register returns 400 when API key has no associated team_id."""
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mocker.Mock())
+    user = UserAPIKeyAuth(user_id="u1", user_email="a@b.com", team_id=None)
+
+    with pytest.raises(HTTPException) as exc_info:
+        await register_guardrail(MOCK_REGISTER_REQUEST, user)
+    assert exc_info.value.status_code == 400
+    assert "team" in exc_info.value.detail.lower()
+
+
+@pytest.mark.asyncio
+async def test_register_guardrail_duplicate_name(mocker):
+    """Register returns 400 when guardrail_name already exists."""
+    mock_prisma = mocker.Mock()
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(
+        return_value={"guardrail_name": MOCK_REGISTER_REQUEST.guardrail_name}
+    )
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_id="u1", user_email="a@b.com", team_id="team-1")
+
+    with pytest.raises(HTTPException) as exc_info:
+        await register_guardrail(MOCK_REGISTER_REQUEST, user)
+    assert exc_info.value.status_code == 400
+    assert "already exists" in exc_info.value.detail
+
+
+@pytest.mark.asyncio
+async def test_list_guardrail_submissions_requires_admin(mocker):
+    """List submissions returns 403 when user is not admin."""
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mocker.Mock())
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER)
+
+    with pytest.raises(HTTPException) as exc_info:
+        await list_guardrail_submissions(user_api_key_dict=user)
+    assert exc_info.value.status_code == 403
+
+
+@pytest.mark.asyncio
+async def test_list_guardrail_submissions_success(mocker):
+    """List submissions returns list and summary for admin."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(
+        guardrail_id="sub-1",
+        guardrail_name="pending-guard",
+        status="pending_review",
+        team_id="t1",
+        litellm_params={"guardrail": "generic_guardrail_api", "api_base": "https://x.com"},
+        guardrail_info={
+            "description": "A guard",
+            "submitted_by_user_id": "u1",
+            "submitted_by_email": "alice@co.com",
+        },
+        submitted_at=datetime.now(),
+        reviewed_at=None,
+        created_at=datetime.now(),
+        updated_at=datetime.now(),
+    )
+    mock_prisma.db.litellm_guardrailstable.find_many = AsyncMock(return_value=[row])
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    result = await list_guardrail_submissions(user_api_key_dict=user)
+
+    assert len(result.submissions) == 1
+    assert result.submissions[0].guardrail_id == "sub-1"
+    assert result.submissions[0].status == "pending_review"
+    assert result.submissions[0].team_guardrail is True  # team_id is set
+    assert result.summary.total >= 1
+    assert result.summary.pending_review >= 1
+
+
+@pytest.mark.asyncio
+async def test_list_guardrail_submissions_returns_only_team_guardrails(mocker):
+    """List submissions only returns team guardrails (team_id not null)."""
+    mock_prisma = mocker.Mock()
+    find_many = AsyncMock(return_value=[])
+    mock_prisma.db.litellm_guardrailstable.find_many = find_many
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    await list_guardrail_submissions(user_api_key_dict=user)
+
+    calls = find_many.call_args_list
+    assert len(calls) >= 1
+    first_where = calls[0].kwargs.get("where", {})
+    assert first_where.get("team_id") == {"not": None}
+
+
+@pytest.mark.asyncio
+async def test_list_guardrail_submissions_team_id_filter(mocker):
+    """List submissions with team_id filter returns only that team's guardrails."""
+    mock_prisma = mocker.Mock()
+    row_abc = mocker.Mock(
+        guardrail_id="team-1",
+        guardrail_name="team-guard",
+        status="active",
+        team_id="team-abc",
+        litellm_params={},
+        guardrail_info={},
+        submitted_at=None,
+        reviewed_at=None,
+        created_at=datetime.now(),
+        updated_at=datetime.now(),
+    )
+    row_other = mocker.Mock(
+        guardrail_id="team-2",
+        guardrail_name="other-guard",
+        status="active",
+        team_id="team-xyz",
+        litellm_params={},
+        guardrail_info={},
+        submitted_at=None,
+        reviewed_at=None,
+        created_at=datetime.now(),
+        updated_at=datetime.now(),
+    )
+    find_many = AsyncMock(return_value=[row_abc, row_other])
+    mock_prisma.db.litellm_guardrailstable.find_many = find_many
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    result = await list_guardrail_submissions(
+        user_api_key_dict=user, team_id="team-abc"
+    )
+
+    assert len(result.submissions) == 1
+    assert result.submissions[0].guardrail_id == "team-1"
+    assert result.submissions[0].team_guardrail is True
+    assert result.summary.total == 2  # summary counts all team guardrails
+
+
+@pytest.mark.asyncio
+async def test_get_guardrail_submission_not_found(mocker):
+    """Get submission returns 404 when guardrail_id does not exist."""
+    mock_prisma = mocker.Mock()
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=None)
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    with pytest.raises(HTTPException) as exc_info:
+        await get_guardrail_submission("nonexistent-id", user)
+    assert exc_info.value.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_approve_guardrail_submission_success(mocker):
+    """Approve sets status to active and initializes guardrail in memory."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(
+        guardrail_id="approve-me",
+        guardrail_name="my-guard",
+        status="pending_review",
+        litellm_params={"guardrail": "generic_guardrail_api", "mode": "pre_call", "api_base": "https://g.com"},
+        guardrail_info={},
+    )
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=row)
+    mock_prisma.db.litellm_guardrailstable.update = AsyncMock()
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    mock_handler = mocker.Mock()
+    mock_handler.initialize_guardrail = mocker.Mock()
+    mocker.patch(
+        "litellm.proxy.guardrails.guardrail_registry.IN_MEMORY_GUARDRAIL_HANDLER",
+        mock_handler,
+    )
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    result = await approve_guardrail_submission("approve-me", user)
+
+    assert result["status"] == "active"
+    assert result["guardrail_id"] == "approve-me"
+    mock_prisma.db.litellm_guardrailstable.update.assert_called_once()
+    call_data = mock_prisma.db.litellm_guardrailstable.update.call_args[1]["data"]
+    assert call_data["status"] == "active"
+
+
+@pytest.mark.asyncio
+async def test_approve_guardrail_submission_not_pending(mocker):
+    """Approve returns 400 when status is not pending_review."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(guardrail_id="x", guardrail_name="y", status="active")
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=row)
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    with pytest.raises(HTTPException) as exc_info:
+        await approve_guardrail_submission("x", user)
+    assert exc_info.value.status_code == 400
+
+
+@pytest.mark.asyncio
+async def test_reject_guardrail_submission_success(mocker):
+    """Reject sets status to rejected."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(guardrail_id="rej-1", guardrail_name="r", status="pending_review")
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=row)
+    mock_prisma.db.litellm_guardrailstable.update = AsyncMock()
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    result = await reject_guardrail_submission("rej-1", user)
+
+    assert result["status"] == "rejected"
+    mock_prisma.db.litellm_guardrailstable.update.assert_called_once()
+    call_data = mock_prisma.db.litellm_guardrailstable.update.call_args[1]["data"]
+    assert call_data["status"] == "rejected"
+
+
+@pytest.mark.asyncio
+async def test_reject_guardrail_submission_not_pending(mocker):
+    """Reject returns 400 when status is not pending_review (e.g. already active)."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(guardrail_id="already-active", guardrail_name="g", status="active")
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=row)
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    with pytest.raises(HTTPException) as exc_info:
+        await reject_guardrail_submission("already-active", user)
+    assert exc_info.value.status_code == 400
+    assert "not pending review" in exc_info.value.detail.lower()
+
+
+# --- Tests for review fixes ---
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "api_base,expected_detail",
+    [
+        ("file:///etc/passwd", "http or https scheme"),
+        ("ftp://internal.host/data", "http or https scheme"),
+        ("javascript:alert(1)", "http or https scheme"),
+        ("://missing-scheme", "http or https scheme"),
+        ("https://", "valid hostname"),
+    ],
+    ids=[
+        "file_scheme",
+        "ftp_scheme",
+        "javascript_scheme",
+        "no_scheme",
+        "no_hostname",
+    ],
+)
+async def test_register_guardrail_rejects_bad_api_base(mocker, api_base, expected_detail):
+    """Register returns 400 when api_base has invalid scheme or missing hostname."""
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mocker.Mock())
+    req = RegisterGuardrailRequest(
+        guardrail_name="bad-url-guard",
+        litellm_params={
+            "guardrail": "generic_guardrail_api",
+            "mode": "pre_call",
+            "api_base": api_base,
+        },
+    )
+    user = UserAPIKeyAuth(user_id="u1", user_email="a@b.com", team_id="team-1")
+
+    with pytest.raises(HTTPException) as exc_info:
+        await register_guardrail(req, user)
+    assert exc_info.value.status_code == 400
+    assert expected_detail in exc_info.value.detail
+
+
+@pytest.mark.asyncio
+async def test_register_guardrail_accepts_valid_https_url(mocker):
+    """Register accepts valid https api_base URLs."""
+    mock_prisma = mocker.Mock()
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=None)
+    created_row = mocker.Mock(
+        guardrail_id="valid-url-123",
+        guardrail_name="valid-guard",
+        status="pending_review",
+        submitted_at=datetime.now(),
+    )
+    mock_prisma.db.litellm_guardrailstable.create = AsyncMock(return_value=created_row)
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+
+    req = RegisterGuardrailRequest(
+        guardrail_name="valid-guard",
+        litellm_params={
+            "guardrail": "generic_guardrail_api",
+            "mode": "pre_call",
+            "api_base": "https://guardrails.example.com/v1/check",
+        },
+    )
+    user = UserAPIKeyAuth(user_id="u1", user_email="a@b.com", team_id="team-1")
+
+    result = await register_guardrail(req, user)
+    assert result.guardrail_id == "valid-url-123"
+    assert result.status == "pending_review"
+
+
+@pytest.mark.asyncio
+async def test_approve_guardrail_init_failure_returns_warning(mocker):
+    """Approve returns a warning field when in-memory initialization fails."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(
+        guardrail_id="warn-me",
+        guardrail_name="fragile-guard",
+        status="pending_review",
+        litellm_params={
+            "guardrail": "generic_guardrail_api",
+            "mode": "pre_call",
+            "api_base": "https://g.com",
+        },
+        guardrail_info={},
+    )
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=row)
+    mock_prisma.db.litellm_guardrailstable.update = AsyncMock()
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+
+    mock_handler = mocker.Mock()
+    mock_handler.initialize_guardrail = mocker.Mock(
+        side_effect=Exception("missing dependency")
+    )
+    mocker.patch(
+        "litellm.proxy.guardrails.guardrail_registry.IN_MEMORY_GUARDRAIL_HANDLER",
+        mock_handler,
+    )
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    result = await approve_guardrail_submission("warn-me", user)
+
+    assert result["status"] == "active"
+    assert "warning" in result
+    assert "failed to initialize" in result["warning"].lower()
+    assert "missing dependency" in result["warning"]
+
+
+@pytest.mark.asyncio
+async def test_approve_guardrail_no_warning_on_success(mocker):
+    """Approve does NOT include a warning field when init succeeds."""
+    mock_prisma = mocker.Mock()
+    row = mocker.Mock(
+        guardrail_id="ok-guard",
+        guardrail_name="good-guard",
+        status="pending_review",
+        litellm_params={
+            "guardrail": "generic_guardrail_api",
+            "mode": "pre_call",
+            "api_base": "https://g.com",
+        },
+        guardrail_info={},
+    )
+    mock_prisma.db.litellm_guardrailstable.find_unique = AsyncMock(return_value=row)
+    mock_prisma.db.litellm_guardrailstable.update = AsyncMock()
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+
+    mock_handler = mocker.Mock()
+    mock_handler.initialize_guardrail = mocker.Mock()  # no exception
+    mocker.patch(
+        "litellm.proxy.guardrails.guardrail_registry.IN_MEMORY_GUARDRAIL_HANDLER",
+        mock_handler,
+    )
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    result = await approve_guardrail_submission("ok-guard", user)
+
+    assert result["status"] == "active"
+    assert "warning" not in result
+
+
+@pytest.mark.asyncio
+async def test_list_submissions_single_db_query(mocker):
+    """List submissions makes exactly one find_many call (no redundant query)."""
+    mock_prisma = mocker.Mock()
+    find_many = AsyncMock(return_value=[])
+    mock_prisma.db.litellm_guardrailstable.find_many = find_many
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    await list_guardrail_submissions(user_api_key_dict=user)
+
+    assert find_many.call_count == 1
+
+
+@pytest.mark.asyncio
+async def test_list_submissions_summary_counts_unaffected_by_filters(mocker):
+    """Summary counts reflect all team guardrails regardless of status filter."""
+    mock_prisma = mocker.Mock()
+    pending_row = mocker.Mock(
+        guardrail_id="p1", guardrail_name="p", status="pending_review",
+        team_id="t1", litellm_params={}, guardrail_info={},
+        submitted_at=None, reviewed_at=None,
+        created_at=datetime.now(), updated_at=datetime.now(),
+    )
+    active_row = mocker.Mock(
+        guardrail_id="a1", guardrail_name="a", status="active",
+        team_id="t1", litellm_params={}, guardrail_info={},
+        submitted_at=None, reviewed_at=None,
+        created_at=datetime.now(), updated_at=datetime.now(),
+    )
+    all_rows = [pending_row, active_row]
+    mock_prisma.db.litellm_guardrailstable.find_many = AsyncMock(return_value=all_rows)
+    mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma)
+    user = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+
+    # Filter to only pending, but summary should still show both
+    result = await list_guardrail_submissions(status="pending_review", user_api_key_dict=user)
+
+    assert len(result.submissions) == 1  # filtered
+    assert result.summary.total == 2  # unfiltered
+    assert result.summary.pending_review == 1
+    assert result.summary.active == 1
\ No newline at end of file
diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json
index 200182cf551..697ec68e0bb 100644
--- a/ui/litellm-dashboard/package-lock.json
+++ b/ui/litellm-dashboard/package-lock.json
@@ -12984,6 +12984,21 @@
         "type": "github",
         "url": "https://github.com/sponsors/wooorm"
       }
+    },
+    "node_modules/@next/swc-win32-ia32-msvc": {
+      "version": "14.2.33",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.33.tgz",
+      "integrity": "sha512-pc9LpGNKhJ0dXQhZ5QMmYxtARwwmWLpeocFmVG5Z0DzWq5Uf0izcI8tLc+qOpqxO1PWqZ5A7J1blrUIKrIFc7Q==",
+      "cpu": [
+        "ia32"
+      ],
+      "optional": true,
+      "os": [
+        "win32"
+      ],
+      "engines": {
+        "node": ">= 10"
+      }
     }
   }
 }
diff --git a/ui/litellm-dashboard/src/components/guardrails.tsx b/ui/litellm-dashboard/src/components/guardrails.tsx
index a8de7dd2f4f..aa31c3af613 100644
--- a/ui/litellm-dashboard/src/components/guardrails.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails.tsx
@@ -14,6 +14,7 @@ import DeleteResourceModal from "./common_components/DeleteResourceModal";
 import { getGuardrailLogoAndName } from "./guardrails/guardrail_info_helpers";
 import { CustomCodeModal } from "./guardrails/custom_code";
 import GuardrailGarden from "./guardrails/guardrail_garden";
+import { TeamGuardrailsTab } from "./guardrails/TeamGuardrailsTab";
 
 interface GuardrailsPanelProps {
   accessToken: string | null;
@@ -139,6 +140,7 @@ const GuardrailsPanel: React.FC<GuardrailsPanelProps> = ({ accessToken, userRole
           <Tab>Guardrail Garden</Tab>
           <Tab>Guardrails</Tab>
           <Tab disabled={!accessToken || guardrailsList.length === 0}>Test Playground</Tab>
+          <Tab>Team Guardrails</Tab>
         </TabList>
 
         <TabPanels>
@@ -242,6 +244,11 @@ const GuardrailsPanel: React.FC<GuardrailsPanelProps> = ({ accessToken, userRole
               onClose={() => setActiveTab(0)}
             />
           </TabPanel>
+
+          {/* Team Guardrails Tab */}
+          <TabPanel>
+            <TeamGuardrailsTab accessToken={accessToken} />
+          </TabPanel>
         </TabPanels>
       </TabGroup>
     </div>
diff --git a/ui/litellm-dashboard/src/components/guardrails/TeamGuardrailsTab.tsx b/ui/litellm-dashboard/src/components/guardrails/TeamGuardrailsTab.tsx
new file mode 100644
index 00000000000..a2246fd976d
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/guardrails/TeamGuardrailsTab.tsx
@@ -0,0 +1,1081 @@
+"use client";
+
+import React, { useState, useEffect, useCallback } from "react";
+import {
+  SearchIcon,
+  PlusIcon,
+  ChevronDownIcon,
+  ChevronUpIcon,
+  XIcon,
+  CheckIcon,
+  ExternalLinkIcon,
+  KeyIcon,
+  ServerIcon,
+  AlertCircleIcon,
+  InfoIcon,
+} from "lucide-react";
+import {
+  listGuardrailSubmissions,
+  approveGuardrailSubmission,
+  rejectGuardrailSubmission,
+  updateGuardrailCall,
+  type GuardrailSubmissionItem,
+} from "@/components/networking";
+import NotificationsManager from "@/components/molecules/notifications_manager";
+
+type GuardrailStatus = "active" | "pending" | "rejected";
+
+type TeamGuardrail = {
+  id: string;
+  team: string;
+  name: string;
+  endpoint: string;
+  status: GuardrailStatus;
+  model: string;
+  forwardKey: boolean;
+  description: string;
+  method: "POST" | "GET";
+  customHeaders: {
+    key: string;
+    value: string;
+  }[];
+  extraHeaders: string[];
+  submittedAt: string;
+  submittedBy: string;
+  mode?: string;
+  unreachable_fallback?: string;
+  additionalProviderParams?: Record<string, unknown>;
+  guardrailType?: string;
+};
+
+function mapStatus(apiStatus: string): GuardrailStatus {
+  if (apiStatus === "pending_review") return "pending";
+  if (apiStatus === "active" || apiStatus === "rejected") return apiStatus;
+  return "active";
+}
+
+function formatSubmissionDate(value: string | null | undefined): string {
+  if (!value) return "—";
+  try {
+    const d = new Date(value);
+    return isNaN(d.getTime()) ? value : d.toISOString().slice(0, 10);
+  } catch {
+    return value;
+  }
+}
+
+function submissionToTeamGuardrail(item: GuardrailSubmissionItem): TeamGuardrail {
+  const params = item.litellm_params ?? {};
+  const info = item.guardrail_info ?? {};
+  const headers = params.headers;
+  const customHeaders: { key: string; value: string }[] = Array.isArray(headers)
+    ? headers.map((h: { key?: string; name?: string; value: string }) => ({
+        key: (h.key ?? h.name ?? "").toString(),
+        value: String(h.value ?? ""),
+      }))
+    : typeof headers === "object" && headers !== null
+      ? Object.entries(headers).map(([key, value]) => ({
+          key,
+          value: String(value ?? ""),
+        }))
+      : [];
+  const endpoint =
+    (params.api_base as string) ?? (params.url as string) ?? "";
+  const model =
+    (info.model as string) ?? (params.model as string) ?? "—";
+  const forwardKey = (params.forward_api_key as boolean) ?? true;
+  const extraHeaders = Array.isArray(params.extra_headers)
+    ? (params.extra_headers as string[]).filter((h): h is string => typeof h === "string")
+    : [];
+  return {
+    id: item.guardrail_id,
+    team: item.team_id ?? "—",
+    name: item.guardrail_name,
+    endpoint,
+    status: mapStatus(item.status),
+    model,
+    forwardKey,
+    description: (info.description as string) ?? "",
+    method: (params.method as "POST" | "GET") ?? "POST",
+    customHeaders,
+    extraHeaders,
+    submittedAt: formatSubmissionDate(item.submitted_at),
+    submittedBy: item.submitted_by_email ?? item.submitted_by_user_id ?? "—",
+    mode: params.mode as string | undefined,
+    unreachable_fallback: params.unreachable_fallback as string | undefined,
+    additionalProviderParams: params.additional_provider_specific_params as Record<string, unknown> | undefined,
+    guardrailType: params.guardrail as string | undefined,
+  };
+}
+
+const STATUS_CONFIG: Record<
+  GuardrailStatus,
+  { label: string; bg: string; text: string; dot: string }
+> = {
+  active: {
+    label: "Active",
+    bg: "bg-green-50",
+    text: "text-green-700",
+    dot: "bg-green-500",
+  },
+  pending: {
+    label: "Pending Review",
+    bg: "bg-yellow-50",
+    text: "text-yellow-700",
+    dot: "bg-yellow-500",
+  },
+  rejected: {
+    label: "Rejected",
+    bg: "bg-red-50",
+    text: "text-red-700",
+    dot: "bg-red-500",
+  },
+};
+
+const TEAM_COLORS: Record<string, string> = {
+  "ML Platform": "bg-purple-100 text-purple-700",
+  "Data Science": "bg-blue-100 text-blue-700",
+  Security: "bg-red-100 text-red-700",
+  "Customer Success": "bg-orange-100 text-orange-700",
+  Legal: "bg-gray-100 text-gray-700",
+  Finance: "bg-green-100 text-green-700",
+};
+
+function buildEquivalentConfigYaml(g: TeamGuardrail): string {
+  const lines: string[] = [
+    "litellm_settings:",
+    "  guardrails:",
+    `    - guardrail_name: "${g.name.replace(/"/g, '\\"')}"`,
+    "      litellm_params:",
+    `        guardrail: ${g.guardrailType ?? "generic_guardrail_api"}`,
+    `        mode: ${g.mode ?? "pre_call"}  # or post_call, during_call`,
+    `        api_base: ${g.endpoint || "https://your-guardrail-api.com"}`,
+    "        api_key: os.environ/YOUR_GUARDRAIL_API_KEY  # optional",
+    `        unreachable_fallback: ${g.unreachable_fallback ?? "fail_closed"}  # default: fail_closed. Set to fail_open to proceed if the guardrail endpoint is unreachable.`,
+    `        forward_api_key: ${g.forwardKey}`,
+  ];
+  if (g.model && g.model !== "—") {
+    lines.push(`        model: "${g.model}"  # LLM model name sent to the guardrail for context`);
+  }
+  if (g.customHeaders.length > 0) {
+    lines.push("        headers:  # static headers (sent with every request)");
+    for (const h of g.customHeaders) {
+      lines.push(`          ${h.key}: "${String(h.value).replace(/"/g, '\\"')}"`);
+    }
+  }
+  if (g.extraHeaders.length > 0) {
+    lines.push("        extra_headers:  # forward these client request headers to the guardrail");
+    for (const name of g.extraHeaders) {
+      lines.push(`          - ${name}`);
+    }
+  }
+  if (g.additionalProviderParams && Object.keys(g.additionalProviderParams).length > 0) {
+    lines.push("        additional_provider_specific_params:");
+    for (const [k, v] of Object.entries(g.additionalProviderParams)) {
+      const val = typeof v === "string" ? `"${v}"` : String(v);
+      lines.push(`          ${k}: ${val}`);
+    }
+  }
+  return lines.join("\n");
+}
+
+function StatCard({
+  label,
+  value,
+  color,
+}: {
+  label: string;
+  value: number;
+  color: string;
+}) {
+  return (
+    <div className="bg-white border border-gray-200 rounded-lg px-4 py-3">
+      <div className={`text-2xl font-bold ${color}`}>{value}</div>
+      <div className="text-xs text-gray-500 mt-0.5">{label}</div>
+    </div>
+  );
+}
+
+function Toggle({
+  enabled,
+  onToggle,
+}: {
+  enabled: boolean;
+  onToggle: () => void;
+}) {
+  return (
+    <button
+      type="button"
+      onClick={onToggle}
+      role="switch"
+      aria-checked={enabled}
+      className={`relative inline-flex h-5 w-9 items-center rounded-full transition-colors focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-1 ${
+        enabled ? "bg-blue-500" : "bg-gray-200"
+      }`}
+    >
+      <span
+        className={`inline-block h-3.5 w-3.5 transform rounded-full bg-white shadow transition-transform ${
+          enabled ? "translate-x-4" : "translate-x-0.5"
+        }`}
+      />
+    </button>
+  );
+}
+
+type GuardrailCardProps = {
+  guardrail: TeamGuardrail;
+  isSelected: boolean;
+  isHeadersExpanded: boolean;
+  onSelect: () => void;
+  onToggleForwardKey: () => void;
+  onToggleHeaders: () => void;
+  onApprove: () => void;
+  onReject: () => void;
+};
+
+function GuardrailCard({
+  guardrail: g,
+  isSelected,
+  isHeadersExpanded,
+  onSelect,
+  onToggleForwardKey,
+  onToggleHeaders,
+  onApprove,
+  onReject,
+}: GuardrailCardProps) {
+  const status = STATUS_CONFIG[g.status];
+  const teamColor = TEAM_COLORS[g.team] ?? "bg-gray-100 text-gray-700";
+  return (
+    <div
+      className={`bg-white border rounded-lg p-4 transition-all ${
+        isSelected ? "border-blue-400 ring-1 ring-blue-200" : "border-gray-200"
+      }`}
+    >
+      <div className="flex items-start justify-between gap-4">
+        <div className="flex-1 min-w-0">
+          <div className="flex items-center gap-2 mb-1.5 flex-wrap">
+            <span
+              className={`text-xs font-medium px-2 py-0.5 rounded-full ${teamColor}`}
+            >
+              Team: {g.team}
+            </span>
+            <span
+              className={`inline-flex items-center gap-1.5 text-xs font-medium px-2 py-0.5 rounded-full ${status.bg} ${status.text}`}
+            >
+              <span className={`w-1.5 h-1.5 rounded-full ${status.dot}`} />
+              {status.label}
+            </span>
+          </div>
+          <h3 className="text-sm font-semibold text-gray-900 mb-1">{g.name}</h3>
+          <p className="text-xs text-gray-500 mb-2 line-clamp-1">
+            {g.description}
+          </p>
+          <div className="flex items-center gap-1.5 mb-2">
+            <ServerIcon className="h-3.5 w-3.5 text-gray-400 flex-shrink-0" />
+            <code className="text-xs text-gray-500 font-mono truncate">
+              {g.endpoint}
+            </code>
+          </div>
+          <div className="flex items-center gap-4 text-xs text-gray-500">
+            <span>
+              Model: <span className="font-medium text-gray-700">{g.model}</span>
+            </span>
+            <span>
+              Submitted:{" "}
+              <span className="font-medium text-gray-700">{g.submittedAt}</span>
+            </span>
+          </div>
+        </div>
+        <div className="flex flex-col items-end gap-2 flex-shrink-0">
+          <div className="flex items-center gap-2">
+            <span className="text-xs text-gray-500 whitespace-nowrap">
+              Forward API Key
+            </span>
+            <Toggle enabled={g.forwardKey} onToggle={onToggleForwardKey} />
+          </div>
+          <div className="flex items-center gap-2 mt-1">
+            <button
+              type="button"
+              onClick={onSelect}
+              className="text-xs border border-gray-300 text-gray-600 hover:bg-gray-50 px-3 py-1.5 rounded-md transition-colors font-medium"
+            >
+              {isSelected ? "Close" : "Review"}
+            </button>
+            {g.status === "pending" && (
+              <>
+                <button
+                  type="button"
+                  onClick={onApprove}
+                  className="text-xs bg-green-500 hover:bg-green-600 text-white px-3 py-1.5 rounded-md transition-colors font-medium"
+                >
+                  Approve
+                </button>
+                <button
+                  type="button"
+                  onClick={onReject}
+                  className="text-xs border border-red-300 text-red-600 hover:bg-red-50 px-3 py-1.5 rounded-md transition-colors font-medium"
+                >
+                  Reject
+                </button>
+              </>
+            )}
+          </div>
+        </div>
+      </div>
+      <div className="mt-3 pt-3 border-t border-gray-100">
+          <button
+            type="button"
+            onClick={onToggleHeaders}
+            className="flex items-center gap-1.5 text-xs text-gray-500 hover:text-gray-700 transition-colors"
+          >
+            {isHeadersExpanded ? (
+              <ChevronUpIcon className="h-3.5 w-3.5" />
+            ) : (
+              <ChevronDownIcon className="h-3.5 w-3.5" />
+            )}
+          Static headers
+          {g.customHeaders.length > 0 && (
+            <span className="ml-1 bg-gray-100 text-gray-600 rounded-full px-1.5 py-0.5 text-xs">
+              {g.customHeaders.length}
+            </span>
+          )}
+        </button>
+        {isHeadersExpanded && (
+          <div className="mt-2">
+            {g.customHeaders.length === 0 ? (
+              <p className="text-xs text-gray-400 italic">
+                No static headers configured.
+              </p>
+            ) : (
+              <div className="space-y-1">
+                {g.customHeaders.map((h, i) => (
+                  <div
+                    key={`${h.key}-${i}`}
+                    className="flex items-center gap-2 text-xs font-mono"
+                  >
+                    <span className="text-gray-500 bg-gray-50 border border-gray-200 rounded px-2 py-0.5">
+                      {h.key}
+                    </span>
+                    <span className="text-gray-400">:</span>
+                    <span className="text-gray-700 bg-gray-50 border border-gray-200 rounded px-2 py-0.5">
+                      {h.value}
+                    </span>
+                  </div>
+                ))}
+              </div>
+            )}
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
+
+function ConfigRow({
+  label,
+  children,
+}: {
+  label: string;
+  children: React.ReactNode;
+}) {
+  return (
+    <div>
+      <div className="text-xs font-semibold text-gray-500 mb-1">{label}</div>
+      <div>{children}</div>
+    </div>
+  );
+}
+
+type DetailPanelProps = {
+  guardrail: TeamGuardrail;
+  onClose: () => void;
+  onApprove: () => void;
+  onReject: () => void;
+  onToggleForwardKey: () => void;
+  onUpdateCustomHeaders: (
+    customHeaders: { key: string; value: string }[]
+  ) => Promise<void>;
+  onUpdateExtraHeaders: (extraHeaders: string[]) => Promise<void>;
+};
+
+function DetailPanel({
+  guardrail: g,
+  onClose,
+  onApprove,
+  onReject,
+  onToggleForwardKey,
+  onUpdateCustomHeaders,
+  onUpdateExtraHeaders,
+}: DetailPanelProps) {
+  const [configExpanded, setConfigExpanded] = useState(false);
+  const [newExtraHeader, setNewExtraHeader] = useState("");
+  const [newStaticHeaderKey, setNewStaticHeaderKey] = useState("");
+  const [newStaticHeaderValue, setNewStaticHeaderValue] = useState("");
+  const status = STATUS_CONFIG[g.status];
+  const teamColor = TEAM_COLORS[g.team] ?? "bg-gray-100 text-gray-700";
+  return (
+    <div className="w-96 flex-shrink-0 bg-white overflow-auto">
+      <div className="p-5">
+        <div className="flex items-start justify-between mb-4">
+          <div>
+            <div className="flex items-center gap-2 mb-1">
+              <span
+                className={`text-xs font-medium px-2 py-0.5 rounded-full ${teamColor}`}
+              >
+                Team: {g.team}
+              </span>
+              <span
+                className={`inline-flex items-center gap-1.5 text-xs font-medium px-2 py-0.5 rounded-full ${status.bg} ${status.text}`}
+              >
+                <span className={`w-1.5 h-1.5 rounded-full ${status.dot}`} />
+                {status.label}
+              </span>
+            </div>
+            <h2 className="text-base font-semibold text-gray-900">{g.name}</h2>
+            <p className="text-xs text-gray-500 mt-0.5">
+              Submitted by {g.submittedBy} on {g.submittedAt}
+            </p>
+          </div>
+          <button
+            type="button"
+            onClick={onClose}
+            className="text-gray-400 hover:text-gray-600 transition-colors"
+            aria-label="Close detail panel"
+          >
+            <XIcon className="h-4 w-4" />
+          </button>
+        </div>
+        <p className="text-sm text-gray-600 mb-5">{g.description}</p>
+        <div className="space-y-4">
+          <ConfigRow label="Endpoint">
+            <div className="flex items-center gap-1.5">
+              <code className="text-xs font-mono text-gray-700 break-all">
+                {g.endpoint}
+              </code>
+              <a
+                href={g.endpoint}
+                target="_blank"
+                rel="noopener noreferrer"
+                className="text-gray-400 hover:text-blue-500 flex-shrink-0"
+              >
+                <ExternalLinkIcon className="h-3.5 w-3.5" />
+              </a>
+            </div>
+          </ConfigRow>
+          <ConfigRow label="Method">
+            <span className="text-xs font-mono font-medium text-gray-700 bg-gray-100 px-2 py-0.5 rounded">
+              {g.method}
+            </span>
+          </ConfigRow>
+          <div className="border border-blue-100 bg-blue-50 rounded-lg p-3">
+            <div className="flex items-center justify-between mb-2">
+              <div className="flex items-center gap-1.5">
+                <KeyIcon className="h-3.5 w-3.5 text-blue-500" />
+                <span className="text-xs font-semibold text-blue-800">
+                  Forward LiteLLM API Key
+                </span>
+              </div>
+              <Toggle enabled={g.forwardKey} onToggle={onToggleForwardKey} />
+            </div>
+            <p className="text-xs text-blue-700 leading-relaxed">
+              When enabled, the caller&apos;s LiteLLM API key is forwarded as an{" "}
+              <code className="font-mono bg-blue-100 px-1 rounded">
+                Authorization
+              </code>{" "}
+              header to your guardrail endpoint. This allows your guardrail to
+              authenticate model calls using the original caller&apos;s
+              credentials.
+            </p>
+          </div>
+          <div>
+            <div className="flex items-center gap-1.5 mb-2">
+              <span className="text-xs font-semibold text-gray-700">
+                Static headers
+              </span>
+              {g.customHeaders.length > 0 && (
+                <span className="bg-gray-100 text-gray-600 rounded-full px-1.5 py-0.5 text-xs">
+                  {g.customHeaders.length}
+                </span>
+              )}
+            </div>
+            <p className="text-xs text-gray-400 mb-2">
+              Sent with every request to the guardrail.
+            </p>
+            {g.customHeaders.length === 0 ? (
+              <p className="text-xs text-gray-400 italic mb-2">
+                No static headers configured.
+              </p>
+            ) : (
+              <ul className="list-none space-y-1 mb-2">
+                {g.customHeaders.map((h, i) => (
+                  <li
+                    key={`${h.key}-${i}`}
+                    className="flex items-center justify-between gap-2 text-xs font-mono bg-gray-50 border border-gray-200 rounded px-2 py-1.5"
+                  >
+                    <span className="text-gray-700 truncate">
+                      {h.key}: {h.value}
+                    </span>
+                    <button
+                      type="button"
+                      onClick={() =>
+                        onUpdateCustomHeaders(
+                          g.customHeaders.filter((_, idx) => idx !== i)
+                        )
+                      }
+                      className="text-gray-400 hover:text-red-600 flex-shrink-0"
+                      aria-label={`Remove ${h.key}`}
+                    >
+                      <XIcon className="h-3.5 w-3.5" />
+                    </button>
+                  </li>
+                ))}
+              </ul>
+            )}
+            <div className="flex flex-col gap-2 sm:flex-row sm:items-end">
+              <input
+                type="text"
+                value={newStaticHeaderKey}
+                onChange={(e) => setNewStaticHeaderKey(e.target.value)}
+                placeholder="Header name (e.g. X-API-Key)"
+                className="flex-1 min-w-0 text-xs font-mono border border-gray-200 rounded px-2 py-1.5 text-gray-700 placeholder-gray-400 focus:outline-none focus:ring-1 focus:ring-blue-500"
+                onKeyDown={(e) => {
+                  if (e.key === "Enter") {
+                    e.preventDefault();
+                    const key = newStaticHeaderKey.trim();
+                    const value = newStaticHeaderValue.trim();
+                    if (key && !g.customHeaders.some((h) => h.key.toLowerCase() === key.toLowerCase())) {
+                      onUpdateCustomHeaders([...g.customHeaders, { key, value }]);
+                      setNewStaticHeaderKey("");
+                      setNewStaticHeaderValue("");
+                    }
+                  }
+                }}
+              />
+              <input
+                type="text"
+                value={newStaticHeaderValue}
+                onChange={(e) => setNewStaticHeaderValue(e.target.value)}
+                placeholder="Value"
+                className="flex-1 min-w-0 text-xs font-mono border border-gray-200 rounded px-2 py-1.5 text-gray-700 placeholder-gray-400 focus:outline-none focus:ring-1 focus:ring-blue-500"
+                onKeyDown={(e) => {
+                  if (e.key === "Enter") {
+                    e.preventDefault();
+                    const key = newStaticHeaderKey.trim();
+                    const value = newStaticHeaderValue.trim();
+                    if (key && !g.customHeaders.some((h) => h.key.toLowerCase() === key.toLowerCase())) {
+                      onUpdateCustomHeaders([...g.customHeaders, { key, value }]);
+                      setNewStaticHeaderKey("");
+                      setNewStaticHeaderValue("");
+                    }
+                  }
+                }}
+              />
+              <button
+                type="button"
+                onClick={() => {
+                  const key = newStaticHeaderKey.trim();
+                  const value = newStaticHeaderValue.trim();
+                  if (key && !g.customHeaders.some((h) => h.key.toLowerCase() === key.toLowerCase())) {
+                    onUpdateCustomHeaders([...g.customHeaders, { key, value }]);
+                    setNewStaticHeaderKey("");
+                    setNewStaticHeaderValue("");
+                  }
+                }}
+                className="text-xs font-medium text-blue-600 hover:text-blue-700 border border-blue-200 bg-blue-50 hover:bg-blue-100 px-2 py-1.5 rounded transition-colors flex-shrink-0"
+              >
+                Add
+              </button>
+            </div>
+          </div>
+          <div>
+            <div className="flex items-center gap-1.5 mb-2">
+              <span className="text-xs font-semibold text-gray-700">
+                Forward client headers
+              </span>
+              {g.extraHeaders.length > 0 && (
+                <span className="bg-gray-100 text-gray-600 rounded-full px-1.5 py-0.5 text-xs">
+                  {g.extraHeaders.length}
+                </span>
+              )}
+            </div>
+            <p className="text-xs text-gray-400 mb-2">
+              Allowed header names to forward from the client request to the guardrail (e.g. x-request-id).
+            </p>
+            {g.extraHeaders.length === 0 ? (
+              <p className="text-xs text-gray-400 italic mb-2">
+                No forward client headers configured.
+              </p>
+            ) : (
+              <ul className="list-none space-y-1 mb-2">
+                {g.extraHeaders.map((name, i) => (
+                  <li
+                    key={`${name}-${i}`}
+                    className="flex items-center justify-between gap-2 text-xs font-mono bg-gray-50 border border-gray-200 rounded px-2 py-1.5"
+                  >
+                    <span className="text-gray-700 truncate">{name}</span>
+                    <button
+                      type="button"
+                      onClick={() =>
+                        onUpdateExtraHeaders(
+                          g.extraHeaders.filter((_, idx) => idx !== i)
+                        )
+                      }
+                      className="text-gray-400 hover:text-red-600 flex-shrink-0"
+                      aria-label={`Remove ${name}`}
+                    >
+                      <XIcon className="h-3.5 w-3.5" />
+                    </button>
+                  </li>
+                ))}
+              </ul>
+            )}
+            <div className="flex gap-2">
+              <input
+                type="text"
+                value={newExtraHeader}
+                onChange={(e) => setNewExtraHeader(e.target.value)}
+                placeholder="e.g. x-request-id"
+                className="flex-1 min-w-0 text-xs font-mono border border-gray-200 rounded px-2 py-1.5 text-gray-700 placeholder-gray-400 focus:outline-none focus:ring-1 focus:ring-blue-500"
+                onKeyDown={(e) => {
+                  if (e.key === "Enter") {
+                    e.preventDefault();
+                    const name = newExtraHeader.trim().toLowerCase();
+                    if (name && !g.extraHeaders.map((h) => h.toLowerCase()).includes(name)) {
+                      onUpdateExtraHeaders([...g.extraHeaders, name]);
+                      setNewExtraHeader("");
+                    }
+                  }
+                }}
+              />
+              <button
+                type="button"
+                onClick={() => {
+                  const name = newExtraHeader.trim().toLowerCase();
+                  if (name && !g.extraHeaders.map((h) => h.toLowerCase()).includes(name)) {
+                    onUpdateExtraHeaders([...g.extraHeaders, name]);
+                    setNewExtraHeader("");
+                  }
+                }}
+                className="text-xs font-medium text-blue-600 hover:text-blue-700 border border-blue-200 bg-blue-50 hover:bg-blue-100 px-2 py-1.5 rounded transition-colors"
+              >
+                Add
+              </button>
+            </div>
+          </div>
+          <div className="border border-gray-200 rounded-lg overflow-hidden">
+            <button
+              type="button"
+              onClick={() => setConfigExpanded(!configExpanded)}
+              className="w-full flex items-center justify-between px-3 py-2 text-left text-xs font-semibold text-gray-700 bg-gray-50 hover:bg-gray-100 transition-colors"
+            >
+              <span>Equivalent config</span>
+              {configExpanded ? (
+                <ChevronUpIcon className="h-3.5 w-3.5 text-gray-500" />
+              ) : (
+                <ChevronDownIcon className="h-3.5 w-3.5 text-gray-500" />
+              )}
+            </button>
+            {configExpanded && (
+              <pre className="p-3 text-xs font-mono text-gray-700 bg-white border-t border-gray-200 overflow-x-auto whitespace-pre-wrap break-all">
+                {buildEquivalentConfigYaml(g)}
+              </pre>
+            )}
+          </div>
+          <div className="flex items-start gap-2 bg-gray-50 border border-gray-200 rounded-lg p-3">
+            <InfoIcon className="h-3.5 w-3.5 text-gray-400 flex-shrink-0 mt-0.5" />
+            <p className="text-xs text-gray-500 leading-relaxed">
+              This guardrail runs on a separate instance. It receives the user
+              request and forwards the result to the next step in the pipeline. See{" "}
+              <a
+                href="https://docs.litellm.ai/docs/adding_provider/generic_guardrail_api"
+                target="_blank"
+                rel="noopener noreferrer"
+                className="text-blue-500 hover:underline"
+              >
+                LiteLLM Generic Guardrail API docs
+              </a>{" "}
+              for configuration details.
+            </p>
+          </div>
+        </div>
+        <div className="mt-5 pt-4 border-t border-gray-100 space-y-2">
+          <button
+            type="button"
+            className="w-full flex items-center justify-center gap-2 border border-gray-300 text-gray-700 hover:bg-gray-50 text-sm font-medium py-2 rounded-md transition-colors"
+          >
+            <ExternalLinkIcon className="h-4 w-4" />
+            Test Endpoint
+          </button>
+          {g.status === "pending" && (
+            <div className="flex gap-2">
+              <button
+                type="button"
+                onClick={onApprove}
+                className="flex-1 flex items-center justify-center gap-1.5 bg-green-500 hover:bg-green-600 text-white text-sm font-medium py-2 rounded-md transition-colors"
+              >
+                <CheckIcon className="h-4 w-4" />
+                Approve
+              </button>
+              <button
+                type="button"
+                onClick={onReject}
+                className="flex-1 flex items-center justify-center gap-1.5 border border-red-300 text-red-600 hover:bg-red-50 text-sm font-medium py-2 rounded-md transition-colors"
+              >
+                <XIcon className="h-4 w-4" />
+                Reject
+              </button>
+            </div>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
+
+type ConfirmDialogProps = {
+  action: "approve" | "reject";
+  guardrailName: string;
+  onConfirm: () => void;
+  onCancel: () => void;
+};
+
+function ConfirmDialog({
+  action,
+  guardrailName,
+  onConfirm,
+  onCancel,
+}: ConfirmDialogProps) {
+  const isApprove = action === "approve";
+  return (
+    <div className="fixed inset-0 bg-black/30 flex items-center justify-center z-50">
+      <div className="bg-white rounded-xl shadow-xl p-6 max-w-sm w-full mx-4">
+        <div
+          className={`w-10 h-10 rounded-full flex items-center justify-center mb-4 ${
+            isApprove ? "bg-green-100" : "bg-red-100"
+          }`}
+        >
+          {isApprove ? (
+            <CheckIcon className="h-5 w-5 text-green-600" />
+          ) : (
+            <AlertCircleIcon className="h-5 w-5 text-red-600" />
+          )}
+        </div>
+        <h3 className="text-base font-semibold text-gray-900 mb-1">
+          {isApprove ? "Approve Guardrail" : "Reject Guardrail"}
+        </h3>
+        <p className="text-sm text-gray-500 mb-5">
+          Are you sure you want to {action}{" "}
+          <span className="font-medium text-gray-700">&quot;{guardrailName}&quot;</span>?{" "}
+          {isApprove
+            ? "This will make it active and available for use."
+            : "This will mark it as rejected and notify the team."}
+        </p>
+        <div className="flex gap-3">
+          <button
+            type="button"
+            onClick={onCancel}
+            className="flex-1 border border-gray-300 text-gray-700 hover:bg-gray-50 text-sm font-medium py-2 rounded-md transition-colors"
+          >
+            Cancel
+          </button>
+          <button
+            type="button"
+            onClick={onConfirm}
+            className={`flex-1 text-white text-sm font-medium py-2 rounded-md transition-colors ${
+              isApprove
+                ? "bg-green-500 hover:bg-green-600"
+                : "bg-red-500 hover:bg-red-600"
+            }`}
+          >
+            {isApprove ? "Approve" : "Reject"}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}
+
+interface TeamGuardrailsTabProps {
+  accessToken: string | null;
+}
+
+export function TeamGuardrailsTab({ accessToken }: TeamGuardrailsTabProps) {
+  const [guardrails, setGuardrails] = useState<TeamGuardrail[]>([]);
+  const [summary, setSummary] = useState({
+    total: 0,
+    pending_review: 0,
+    active: 0,
+    rejected: 0,
+  });
+  const [search, setSearch] = useState("");
+  const [statusFilter, setStatusFilter] = useState<
+    "all" | GuardrailStatus
+  >("all");
+  const [selectedId, setSelectedId] = useState<string | null>(null);
+  const [expandedHeaders, setExpandedHeaders] = useState<Set<string>>(new Set());
+  const [confirmAction, setConfirmAction] = useState<{
+    id: string;
+    action: "approve" | "reject";
+  } | null>(null);
+  const [isLoading, setIsLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
+  const [searchDebounced, setSearchDebounced] = useState("");
+
+  useEffect(() => {
+    const t = setTimeout(() => setSearchDebounced(search), 300);
+    return () => clearTimeout(t);
+  }, [search]);
+
+  const fetchSubmissions = useCallback(async () => {
+    if (!accessToken) {
+      setIsLoading(false);
+      return;
+    }
+    setIsLoading(true);
+    setError(null);
+    try {
+      const statusParam =
+        statusFilter === "all"
+          ? undefined
+          : statusFilter === "pending"
+            ? "pending_review"
+            : statusFilter;
+      const res = await listGuardrailSubmissions(accessToken, {
+        status: statusParam,
+        search: searchDebounced.trim() || undefined,
+      });
+      setGuardrails(res.submissions.map(submissionToTeamGuardrail));
+      setSummary(res.summary);
+    } catch (err) {
+      setError(err instanceof Error ? err.message : "Failed to load submissions");
+      setGuardrails([]);
+    } finally {
+      setIsLoading(false);
+    }
+  }, [accessToken, statusFilter, searchDebounced]);
+
+  useEffect(() => {
+    fetchSubmissions();
+  }, [fetchSubmissions]);
+
+  const filtered = guardrails;
+  const selected = guardrails.find((g) => g.id === selectedId) ?? null;
+  const totalCount = summary.total;
+  const pendingCount = summary.pending_review;
+  const activeCount = summary.active;
+  const rejectedCount = summary.rejected;
+
+  async function toggleForwardKey(id: string) {
+    if (!accessToken) return;
+    const g = guardrails.find((x) => x.id === id);
+    if (!g) return;
+    const newValue = !g.forwardKey;
+    try {
+      await updateGuardrailCall(accessToken, id, {
+        litellm_params: { forward_api_key: newValue },
+      });
+      setGuardrails((prev) =>
+        prev.map((x) => (x.id === id ? { ...x, forwardKey: newValue } : x))
+      );
+      NotificationsManager.success(
+        newValue ? "Forward API key enabled" : "Forward API key disabled"
+      );
+    } catch {
+      NotificationsManager.fromBackend("Failed to update forward API key");
+    }
+  }
+
+  async function updateCustomHeaders(
+    id: string,
+    customHeaders: { key: string; value: string }[]
+  ) {
+    if (!accessToken) return;
+    const headersObj: Record<string, string> = {};
+    for (const { key, value } of customHeaders) {
+      if (key.trim()) headersObj[key.trim()] = value;
+    }
+    try {
+      await updateGuardrailCall(accessToken, id, {
+        litellm_params: { headers: headersObj },
+      });
+      setGuardrails((prev) =>
+        prev.map((x) =>
+          x.id === id
+            ? {
+                ...x,
+                customHeaders: customHeaders.filter((h) => h.key.trim()),
+              }
+            : x
+        )
+      );
+      NotificationsManager.success("Static headers updated");
+    } catch {
+      NotificationsManager.fromBackend("Failed to update static headers");
+    }
+  }
+
+  async function updateExtraHeaders(id: string, extraHeaders: string[]) {
+    if (!accessToken) return;
+    try {
+      await updateGuardrailCall(accessToken, id, {
+        litellm_params: { extra_headers: extraHeaders },
+      });
+      setGuardrails((prev) =>
+        prev.map((x) => (x.id === id ? { ...x, extraHeaders } : x))
+      );
+      NotificationsManager.success("Forward client headers updated");
+    } catch {
+      NotificationsManager.fromBackend("Failed to update forward client headers");
+    }
+  }
+
+  async function handleApprove(id: string) {
+    if (!accessToken) return;
+    try {
+      await approveGuardrailSubmission(accessToken, id);
+      setConfirmAction(null);
+      if (selectedId === id) setSelectedId(null);
+      await fetchSubmissions();
+      NotificationsManager.success("Guardrail approved");
+    } catch {
+      NotificationsManager.fromBackend("Failed to approve guardrail");
+    }
+  }
+
+  async function handleReject(id: string) {
+    if (!accessToken) return;
+    try {
+      await rejectGuardrailSubmission(accessToken, id);
+      setConfirmAction(null);
+      if (selectedId === id) setSelectedId(null);
+      await fetchSubmissions();
+      NotificationsManager.success("Guardrail rejected");
+    } catch {
+      NotificationsManager.fromBackend("Failed to reject guardrail");
+    }
+  }
+
+  function toggleHeaders(id: string) {
+    setExpandedHeaders((prev) => {
+      const next = new Set(prev);
+      if (next.has(id)) next.delete(id);
+      else next.add(id);
+      return next;
+    });
+  }
+
+  return (
+    <div className="flex h-full">
+      <div
+        className={`flex-1 min-w-0 p-6 overflow-auto ${
+          selected ? "border-r border-gray-200" : ""
+        }`}
+      >
+        <div className="grid grid-cols-4 gap-4 mb-6">
+          <StatCard label="Total Submitted" value={totalCount} color="text-gray-900" />
+          <StatCard
+            label="Pending Review"
+            value={pendingCount}
+            color="text-yellow-600"
+          />
+          <StatCard label="Active" value={activeCount} color="text-green-600" />
+          <StatCard label="Rejected" value={rejectedCount} color="text-red-600" />
+        </div>
+        <div className="flex items-center gap-3 mb-5">
+          <div className="relative flex-1 max-w-xs">
+            <SearchIcon className="absolute left-3 top-1/2 -translate-y-1/2 h-4 w-4 text-gray-400" />
+            <input
+              type="text"
+              placeholder="Search guardrails..."
+              value={search}
+              onChange={(e) => setSearch(e.target.value)}
+              className="w-full pl-9 pr-4 py-2 border border-gray-200 rounded-md text-sm text-gray-700 placeholder-gray-400 focus:outline-none focus:ring-1 focus:ring-blue-500 focus:border-blue-500"
+            />
+          </div>
+          <select
+            value={statusFilter}
+            onChange={(e) =>
+              setStatusFilter(e.target.value as typeof statusFilter)
+            }
+            className="border border-gray-200 rounded-md px-3 py-2 text-sm text-gray-700 focus:outline-none focus:ring-1 focus:ring-blue-500 focus:border-blue-500 bg-white"
+          >
+            <option value="all">All Status</option>
+            <option value="pending">Pending Review</option>
+            <option value="active">Active</option>
+            <option value="rejected">Rejected</option>
+          </select>
+          <button
+            type="button"
+            className="ml-auto flex items-center gap-2 bg-blue-500 hover:bg-blue-600 text-white text-sm font-medium px-4 py-2 rounded-md transition-colors"
+          >
+            <PlusIcon className="h-4 w-4" />
+            Add Guardrail
+          </button>
+        </div>
+        <div className="space-y-3">
+          {isLoading && (
+            <div className="text-center py-12 text-gray-500 text-sm">
+              Loading submissions…
+            </div>
+          )}
+          {error && (
+            <div className="text-center py-12 text-red-600 text-sm">
+              {error}
+            </div>
+          )}
+          {!isLoading && !error && filtered.length === 0 && (
+            <div className="text-center py-12 text-gray-400 text-sm">
+              No guardrails match your filters.
+            </div>
+          )}
+          {!isLoading && !error && filtered.map((g) => (
+            <GuardrailCard
+              key={g.id}
+              guardrail={g}
+              isSelected={selectedId === g.id}
+              isHeadersExpanded={expandedHeaders.has(g.id)}
+              onSelect={() => setSelectedId(selectedId === g.id ? null : g.id)}
+              onToggleForwardKey={() => toggleForwardKey(g.id)}
+              onToggleHeaders={() => toggleHeaders(g.id)}
+              onApprove={() => setConfirmAction({ id: g.id, action: "approve" })}
+              onReject={() => setConfirmAction({ id: g.id, action: "reject" })}
+            />
+          ))}
+        </div>
+      </div>
+      {selected && (
+        <DetailPanel
+          guardrail={selected}
+          onClose={() => setSelectedId(null)}
+          onApprove={() =>
+            setConfirmAction({ id: selected.id, action: "approve" })
+          }
+          onReject={() =>
+            setConfirmAction({ id: selected.id, action: "reject" })
+          }
+          onToggleForwardKey={() => toggleForwardKey(selected.id)}
+          onUpdateCustomHeaders={(customHeaders) =>
+            updateCustomHeaders(selected.id, customHeaders)
+          }
+          onUpdateExtraHeaders={(extraHeaders) =>
+            updateExtraHeaders(selected.id, extraHeaders)
+          }
+        />
+      )}
+      {confirmAction && (
+        <ConfirmDialog
+          action={confirmAction.action}
+          guardrailName={
+            guardrails.find((g) => g.id === confirmAction.id)?.name ?? ""
+          }
+          onConfirm={() =>
+            confirmAction.action === "approve"
+              ? handleApprove(confirmAction.id)
+              : handleReject(confirmAction.id)
+          }
+          onCancel={() => setConfirmAction(null)}
+        />
+      )}
+    </div>
+  );
+}
diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx
index 0df6d813d7c..f64e909ae3e 100644
--- a/ui/litellm-dashboard/src/components/networking.tsx
+++ b/ui/litellm-dashboard/src/components/networking.tsx
@@ -5484,6 +5484,131 @@ export const getGuardrailsList = async (accessToken: string) => {
   }
 };
 
+// Team guardrail submissions (admin)
+export interface GuardrailSubmissionItem {
+  guardrail_id: string;
+  guardrail_name: string;
+  status: string; // "pending_review" | "active" | "rejected"
+  team_id?: string | null;
+  team_guardrail?: boolean; // true when submitted via team (team_id set)
+  litellm_params?: Record<string, unknown> | null;
+  guardrail_info?: Record<string, unknown> | null;
+  submitted_by_user_id?: string | null;
+  submitted_by_email?: string | null;
+  submitted_at?: string | null;
+  reviewed_at?: string | null;
+  created_at?: string | null;
+  updated_at?: string | null;
+}
+
+export interface GuardrailSubmissionSummary {
+  total: number;
+  pending_review: number;
+  active: number;
+  rejected: number;
+}
+
+export interface ListGuardrailSubmissionsResponse {
+  submissions: GuardrailSubmissionItem[];
+  summary: GuardrailSubmissionSummary;
+}
+
+export const listGuardrailSubmissions = async (
+  accessToken: string,
+  params?: { status?: string; team_id?: string; team_guardrail?: boolean; search?: string }
+): Promise<ListGuardrailSubmissionsResponse> => {
+  const url = proxyBaseUrl ? `${proxyBaseUrl}/guardrails/submissions` : `/guardrails/submissions`;
+  const searchParams = new URLSearchParams();
+  if (params?.status) searchParams.set("status", params.status);
+  if (params?.team_id) searchParams.set("team_id", params.team_id);
+  if (params?.team_guardrail !== undefined) searchParams.set("team_guardrail", String(params.team_guardrail));
+  if (params?.search) searchParams.set("search", params.search);
+  const fullUrl = searchParams.toString() ? `${url}?${searchParams.toString()}` : url;
+  const response = await fetch(fullUrl, {
+    method: "GET",
+    headers: {
+      [globalLitellmHeaderName]: `Bearer ${accessToken}`,
+      "Content-Type": "application/json",
+    },
+  });
+  if (!response.ok) {
+    const errorData = await response.json().catch(() => ({}));
+    const errorMessage = deriveErrorMessage(errorData);
+    handleError(errorMessage);
+    throw new Error(errorMessage);
+  }
+  return response.json();
+};
+
+export const getGuardrailSubmission = async (
+  accessToken: string,
+  guardrailId: string
+): Promise<GuardrailSubmissionItem> => {
+  const url = proxyBaseUrl
+    ? `${proxyBaseUrl}/guardrails/submissions/${encodeURIComponent(guardrailId)}`
+    : `/guardrails/submissions/${encodeURIComponent(guardrailId)}`;
+  const response = await fetch(url, {
+    method: "GET",
+    headers: {
+      [globalLitellmHeaderName]: `Bearer ${accessToken}`,
+      "Content-Type": "application/json",
+    },
+  });
+  if (!response.ok) {
+    const errorData = await response.json().catch(() => ({}));
+    const errorMessage = deriveErrorMessage(errorData);
+    handleError(errorMessage);
+    throw new Error(errorMessage);
+  }
+  return response.json();
+};
+
+export const approveGuardrailSubmission = async (
+  accessToken: string,
+  guardrailId: string
+): Promise<{ guardrail_id: string; status: string; message: string }> => {
+  const url = proxyBaseUrl
+    ? `${proxyBaseUrl}/guardrails/submissions/${encodeURIComponent(guardrailId)}/approve`
+    : `/guardrails/submissions/${encodeURIComponent(guardrailId)}/approve`;
+  const response = await fetch(url, {
+    method: "POST",
+    headers: {
+      [globalLitellmHeaderName]: `Bearer ${accessToken}`,
+      "Content-Type": "application/json",
+    },
+  });
+  if (!response.ok) {
+    const errorData = await response.json().catch(() => ({}));
+    const errorMessage = deriveErrorMessage(errorData);
+    handleError(errorMessage);
+    throw new Error(errorMessage);
+  }
+  return response.json();
+};
+
+export const rejectGuardrailSubmission = async (
+  accessToken: string,
+  guardrailId: string
+): Promise<{ guardrail_id: string; status: string; message: string }> => {
+  const url = proxyBaseUrl
+    ? `${proxyBaseUrl}/guardrails/submissions/${encodeURIComponent(guardrailId)}/reject`
+    : `/guardrails/submissions/${encodeURIComponent(guardrailId)}/reject`;
+  const response = await fetch(url, {
+    method: "POST",
+    headers: {
+      [globalLitellmHeaderName]: `Bearer ${accessToken}`,
+      "Content-Type": "application/json",
+    },
+  });
+  if (!response.ok) {
+    const errorData = await response.json().catch(() => ({}));
+    const errorMessage = deriveErrorMessage(errorData);
+    handleError(errorMessage);
+    throw new Error(errorMessage);
+  }
+  return response.json();
+};
+
 // Guardrails / Policies usage (dashboard)
 export const getGuardrailsUsageOverview = async (
   accessToken: string,
@@ -8364,6 +8489,7 @@ export const updateGuardrailCall = async (
     guardrail_name?: string;
     default_on?: boolean;
     guardrail_info?: Record<string, any>;
+    litellm_params?: Record<string, any>;
   },
 ) => {
   try {

From d07689d2d70804959dbd2e2e39cc797e3a96e37e Mon Sep 17 00:00:00 2001
From: Harshit28j <Harshitjain0562@gmail.com>
Date: Tue, 3 Mar 2026 11:59:58 +0530
Subject: [PATCH 10/69] =?UTF-8?q?bump:=20version=201.82.0=20=E2=86=92=201.?=
 =?UTF-8?q?82.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 577e51a0d22..6f9add2e4cf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.82.0"
+version = "1.82.1"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -183,7 +183,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.82.0"
+version = "1.82.1"
 version_files = [
     "pyproject.toml:^version"
 ]

From f8034f15ada8daf1bb0ed08ead330e3f7e67eee8 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 14:51:12 +0530
Subject: [PATCH 11/69] Remove defualt hardcoded thinking levels for gemini 3
 family

---
 .../vertex_and_google_ai_studio_gemini.py       | 17 -----------------
 1 file changed, 17 deletions(-)

diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index dee826e5783..0905f22362e 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -1136,23 +1136,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         if VertexGeminiConfig._is_gemini_3_or_newer(model):
             if "temperature" not in optional_params:
                 optional_params["temperature"] = 1.0
-            # Only add thinkingLevel if model supports it (exclude image models)
-            if "image" not in model.lower():
-                thinking_config = optional_params.get("thinkingConfig", {})
-                if (
-                    "thinkingLevel" not in thinking_config
-                    and "thinkingBudget" not in thinking_config
-                ):
-                    # For gemini-3-flash-preview, default to "minimal" to match Gemini 2.5 Flash behavior
-                    # For other Gemini 3 models, default to "low"
-                    is_gemini3flash = (
-                        "gemini-3-flash-preview" in model.lower()
-                        or "gemini-3-flash" in model.lower()
-                    )
-                    thinking_config["thinkingLevel"] = (
-                        "minimal" if is_gemini3flash else "low"
-                    )
-                    optional_params["thinkingConfig"] = thinking_config
 
         return optional_params
 

From 213423cb45308e47538b207dc043c1149e75cda7 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 15:05:20 +0530
Subject: [PATCH 12/69] Fix test case

---
 .../test_vertex_and_google_ai_studio_gemini.py  | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
index 196bb00f40d..8beb19bf1ac 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
@@ -2130,7 +2130,7 @@ def test_reasoning_effort_dict_format_gemini_3():
     assert result["thinkingConfig"]["thinkingLevel"] == "high"
     assert result["thinkingConfig"]["includeThoughts"] is True
 
-    # Test dict format without effort key - should fall back to Gemini 3 default (low)
+    # Test dict format without effort key - no thinkingConfig should be set
     optional_params = {}
     non_default_params = {"reasoning_effort": {"summary": "auto"}}
     result = v.map_openai_params(
@@ -2139,8 +2139,8 @@ def test_reasoning_effort_dict_format_gemini_3():
         model=model,
         drop_params=False,
     )
-    # Gemini 3 defaults to thinkingLevel="low" when no explicit effort is set
-    assert result["thinkingConfig"]["thinkingLevel"] == "low"
+    # No effort key in dict → no thinkingConfig set
+    assert "thinkingConfig" not in result
 
 
 def test_temperature_default_for_gemini_3():
@@ -2453,8 +2453,8 @@ def test_gemini_3_image_models_no_thinking_config():
 
 def test_gemini_3_text_models_get_thinking_config():
     """
-    Test that Gemini 3 text models DO receive automatic thinkingConfig.
-    This ensures we didn't break the existing behavior for non-image models.
+    Test that Gemini 3 text models do NOT receive automatic thinkingConfig
+    when no reasoning_effort or thinking param is provided.
     """
     from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
         VertexGeminiConfig,
@@ -2462,7 +2462,7 @@ def test_gemini_3_text_models_get_thinking_config():
 
     v = VertexGeminiConfig()
 
-    # Test gemini-3-pro-preview (text model, should get thinking)
+    # Test gemini-3-pro-preview (text model, no explicit thinking params)
     model = "gemini-3-pro-preview"
     optional_params = {}
     non_default_params = {}
@@ -2474,9 +2474,8 @@ def test_gemini_3_text_models_get_thinking_config():
         drop_params=False,
     )
 
-    # Should have thinkingConfig automatically added
-    assert "thinkingConfig" in result
-    assert result["thinkingConfig"]["thinkingLevel"] == "low"
+    # Should NOT have thinkingConfig automatically added when user provides no reasoning_effort
+    assert "thinkingConfig" not in result
     assert result["temperature"] == 1.0
 
 

From 851be587751fef0dca958394c9bd77746de1cae6 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 15:07:47 +0530
Subject: [PATCH 13/69] Add day 0 support of gemini-3.1-flash-lite-preview

---
 docs/my-website/docs/providers/gemini.md      |   1 +
 docs/my-website/docs/providers/vertex.md      |   1 +
 ...odel_prices_and_context_window_backup.json | 498 ++++++++++++++++--
 model_prices_and_context_window.json          | 145 +++++
 .../llm_cost_calc/test_llm_cost_calc_utils.py |  48 +-
 ...test_vertex_and_google_ai_studio_gemini.py |  11 +-
 6 files changed, 642 insertions(+), 62 deletions(-)

diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md
index 6de2263916c..f97f025c19b 100644
--- a/docs/my-website/docs/providers/gemini.md
+++ b/docs/my-website/docs/providers/gemini.md
@@ -2041,6 +2041,7 @@ response = litellm.completion(
 | gemini-2.0-flash-lite-preview-02-05	     | `completion(model='gemini/gemini-2.0-flash-lite-preview-02-05', messages)`     | `os.environ['GEMINI_API_KEY']` |
 | gemini-2.5-flash-preview-09-2025     | `completion(model='gemini/gemini-2.5-flash-preview-09-2025', messages)`     | `os.environ['GEMINI_API_KEY']` |
 | gemini-2.5-flash-lite-preview-09-2025     | `completion(model='gemini/gemini-2.5-flash-lite-preview-09-2025', messages)`     | `os.environ['GEMINI_API_KEY']` |
+| gemini-3.1-flash-lite-preview     | `completion(model='gemini/gemini-3.1-flash-lite-preview', messages)`     | `os.environ['GEMINI_API_KEY']` |
 | gemini-flash-latest     | `completion(model='gemini/gemini-flash-latest', messages)`     | `os.environ['GEMINI_API_KEY']` |
 | gemini-flash-lite-latest     | `completion(model='gemini/gemini-flash-lite-latest', messages)`     | `os.environ['GEMINI_API_KEY']` |
 
diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index 63e4dceec00..94619082e88 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -1685,6 +1685,7 @@ litellm.vertex_location = "us-central1 # Your Location
 | gemini-2.5-pro   | `completion('gemini-2.5-pro', messages)`, `completion('vertex_ai/gemini-2.5-pro', messages)` |
 | gemini-2.5-flash-preview-09-2025   | `completion('gemini-2.5-flash-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-preview-09-2025', messages)` |
 | gemini-2.5-flash-lite-preview-09-2025   | `completion('gemini-2.5-flash-lite-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-lite-preview-09-2025', messages)` |
+| gemini-3.1-flash-lite-preview   | `completion('gemini-3.1-flash-lite-preview', messages)`, `completion('vertex_ai/gemini-3.1-flash-lite-preview', messages)` |
 
 ## Private Service Connect (PSC) Endpoints
 
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index d4c5b476af6..42b4f0f7762 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -846,7 +846,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "anthropic.claude-3-opus-20240229-v1:0": {
         "input_cost_per_token": 1.5e-05,
@@ -859,7 +861,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.5e-06,
+        "cache_creation_input_token_cost": 1.875e-05
     },
     "anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -873,7 +877,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "anthropic.claude-instant-v1": {
         "input_cost_per_token": 8e-07,
@@ -1512,7 +1518,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "apac.anthropic.claude-3-5-sonnet-20241022-v2:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1545,7 +1553,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "apac.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -1581,7 +1591,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "apac.anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -6925,7 +6937,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "bedrock/sa-east-1/meta.llama3-70b-instruct-v1:0": {
         "input_cost_per_token": 4.45e-06,
@@ -7344,7 +7358,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
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@@ -9753,6 +9779,74 @@
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@@ -11089,7 +11183,7 @@
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@@ -14669,6 +14824,7 @@
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@@ -16925,6 +17081,7 @@
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@@ -23112,6 +23319,21 @@
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@@ -23177,6 +23399,21 @@
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@@ -23238,24 +23475,41 @@
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@@ -23306,14 +23560,30 @@
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@@ -23329,17 +23599,79 @@
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         "mode": "chat",
-        "output_cost_per_token": 3e-07,
+        "output_cost_per_token": 1.8e-07,
+        "source": "https://mistral.ai/pricing",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_response_schema": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/mistral-small-3-2-2506": {
+        "input_cost_per_token": 6e-08,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "mode": "chat",
+        "output_cost_per_token": 1.8e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/ministral-3-3b-2512": {
+        "input_cost_per_token": 1e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "mode": "chat",
+        "output_cost_per_token": 1e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/ministral-3-8b-2512": {
+        "input_cost_per_token": 1.5e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 1.5e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "mistral/ministral-3-14b-2512": {
+        "input_cost_per_token": 2e-07,
+        "litellm_provider": "mistral",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "max_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 2e-07,
+        "source": "https://mistral.ai/pricing",
+        "supports_assistant_prefill": true,
+        "supports_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
     },
     "mistral/mistral-tiny": {
         "input_cost_per_token": 2.5e-07,
@@ -25657,7 +25989,7 @@
         "supports_tool_choice": true
     },
     "openrouter/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "openrouter",
@@ -29554,7 +29886,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "us.anthropic.claude-3-5-sonnet-20241022-v2:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -29607,7 +29941,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_creation_input_token_cost": 3.125e-07
     },
     "us.anthropic.claude-3-opus-20240229-v1:0": {
         "input_cost_per_token": 1.5e-05,
@@ -29620,7 +29956,9 @@
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.5e-06,
+        "cache_creation_input_token_cost": 1.875e-05
     },
     "us.anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -29634,7 +29972,9 @@
         "supports_pdf_input": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_creation_input_token_cost": 3.75e-06
     },
     "us.anthropic.claude-opus-4-1-20250805-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -30527,7 +30867,7 @@
         "supports_tool_choice": true
     },
     "vercel_ai_gateway/google/gemini-2.0-flash": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -30541,7 +30881,7 @@
         "supports_response_schema": true
     },
     "vercel_ai_gateway/google/gemini-2.0-flash-lite": {
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -32059,6 +32399,54 @@
         "output_cost_per_token": 3e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models"
     },
+    "vertex_ai/gemini-3.1-flash-lite-preview": {
+        "cache_read_input_token_cost": 2.5e-09,
+        "input_cost_per_audio_token": 2.5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true
+    },
     "vertex_ai/deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
         "input_cost_per_token": 2e-06,
@@ -37898,7 +38286,7 @@
     },
     "gemini/gemini-2.0-flash-lite-001": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-06-01",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "gemini",
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 4934f11d456..42b4f0f7762 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -14334,6 +14334,53 @@
         "supports_vision": true,
         "supports_web_search": true
     },
+    "gemini-3.1-flash-lite-preview": {
+        "cache_read_input_token_cost": 2.5e-09,
+        "input_cost_per_audio_token": 2.5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/models",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true
+    },
     "deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
         "input_cost_per_token": 2e-06,
@@ -17090,6 +17137,56 @@
         "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07,
         "supports_service_tier": true
     },
+    "gemini/gemini-3.1-flash-lite-preview": {
+        "cache_read_input_token_cost": 2.5e-09,
+        "input_cost_per_audio_token": 2.5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "litellm_provider": "gemini",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "rpm": 15,
+        "source": "https://ai.google.dev/gemini-api/docs/models",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "tpm": 250000
+    },
     "gemini/gemini-3-flash-preview": {
         "cache_read_input_token_cost": 5e-08,
         "input_cost_per_audio_token": 1e-06,
@@ -32302,6 +32399,54 @@
         "output_cost_per_token": 3e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models"
     },
+    "vertex_ai/gemini-3.1-flash-lite-preview": {
+        "cache_read_input_token_cost": 2.5e-09,
+        "input_cost_per_audio_token": 2.5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true
+    },
     "vertex_ai/deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
         "input_cost_per_token": 2e-06,
diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
index 7e8848be301..2e033b6f068 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
@@ -33,8 +33,8 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 
 from litellm.litellm_core_utils.llm_cost_calc.utils import (
-    _calculate_input_cost,
     PromptTokensDetailsResult,
+    _calculate_input_cost,
     calculate_cache_writing_cost,
     generic_cost_per_token,
 )
@@ -127,6 +127,52 @@ def test_reasoning_tokens_gemini():
     )
 
 
+def test_reasoning_tokens_gemini_3_1_flash_lite():
+    """Test cost calculation for gemini-3.1-flash-lite-preview with reasoning tokens"""
+    model = "gemini-3.1-flash-lite-preview"
+    custom_llm_provider = "gemini"
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+
+    usage = Usage(
+        completion_tokens=1000,
+        prompt_tokens=500,
+        total_tokens=1500,
+        completion_tokens_details=CompletionTokensDetailsWrapper(
+            accepted_prediction_tokens=None,
+            audio_tokens=None,
+            reasoning_tokens=400,
+            rejected_prediction_tokens=None,
+            text_tokens=600,
+        ),
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            audio_tokens=None, cached_tokens=None, text_tokens=500, image_tokens=None
+        ),
+    )
+    model_cost_map = litellm.model_cost[model]
+    prompt_cost, completion_cost = generic_cost_per_token(
+        model=model,
+        usage=usage,
+        custom_llm_provider=custom_llm_provider,
+    )
+
+    assert round(prompt_cost, 10) == round(
+        model_cost_map["input_cost_per_token"] * usage.prompt_tokens,
+        10,
+    )
+    assert round(completion_cost, 10) == round(
+        (
+            model_cost_map["output_cost_per_token"]
+            * usage.completion_tokens_details.text_tokens
+        )
+        + (
+            model_cost_map["output_cost_per_reasoning_token"]
+            * usage.completion_tokens_details.reasoning_tokens
+        ),
+        10,
+    )
+
+
 def test_image_tokens_with_custom_pricing():
     """Test that image_tokens in completion are properly costed with output_cost_per_image_token."""
     from unittest.mock import patch
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
index 196bb00f40d..596897f7157 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
@@ -2453,8 +2453,8 @@ def test_gemini_3_image_models_no_thinking_config():
 
 def test_gemini_3_text_models_get_thinking_config():
     """
-    Test that Gemini 3 text models DO receive automatic thinkingConfig.
-    This ensures we didn't break the existing behavior for non-image models.
+    Test that Gemini 3 text models do NOT receive automatic thinkingConfig
+    when no reasoning_effort or thinking param is provided.
     """
     from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
         VertexGeminiConfig,
@@ -2462,7 +2462,7 @@ def test_gemini_3_text_models_get_thinking_config():
 
     v = VertexGeminiConfig()
 
-    # Test gemini-3-pro-preview (text model, should get thinking)
+    # Test gemini-3-pro-preview (text model, no explicit thinking params)
     model = "gemini-3-pro-preview"
     optional_params = {}
     non_default_params = {}
@@ -2474,9 +2474,8 @@ def test_gemini_3_text_models_get_thinking_config():
         drop_params=False,
     )
 
-    # Should have thinkingConfig automatically added
-    assert "thinkingConfig" in result
-    assert result["thinkingConfig"]["thinkingLevel"] == "low"
+    # Should NOT have thinkingConfig automatically added when user provides no reasoning_effort
+    assert "thinkingConfig" not in result
     assert result["temperature"] == 1.0
 
 

From deb8fea6b114eee809745c65995249f6d6b6457f Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 15:19:16 +0530
Subject: [PATCH 14/69] Add blog post for gemini-3.1-flash-lite-preview

---
 .../blog/gemini_3_1_flash_lite/index.md       | 175 ++++++++++++++++++
 1 file changed, 175 insertions(+)
 create mode 100644 docs/my-website/blog/gemini_3_1_flash_lite/index.md

diff --git a/docs/my-website/blog/gemini_3_1_flash_lite/index.md b/docs/my-website/blog/gemini_3_1_flash_lite/index.md
new file mode 100644
index 00000000000..9ef4bacb2ad
--- /dev/null
+++ b/docs/my-website/blog/gemini_3_1_flash_lite/index.md
@@ -0,0 +1,175 @@
+---
+slug: gemini_3_1_flash_lite_preview
+title: "DAY 0 Support: Gemini 3.1 Flash Lite Preview on LiteLLM"
+date: 2026-03-03T08:00:00
+authors:
+  - name: Sameer Kankute
+    title: SWE @ LiteLLM (LLM Translation)
+    url: https://www.linkedin.com/in/sameer-kankute/
+    image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
+  - 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
+description: "Guide to using Gemini 3.1 Flash Lite Preview on LiteLLM Proxy and SDK with day 0 support."
+tags: [gemini, day 0 support, llms, supernova]
+hide_table_of_contents: false
+---
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Gemini 3.1 Flash Lite Preview Day 0 Support 
+
+LiteLLM now supports `gemini-3.1-flash-lite-preview` with full day 0 support!
+
+:::note
+If you only want cost tracking, you need no change in your current Litellm version. But if you want the support for new features introduced along with it like thinking levels, you will need to use v1.80.8-stable.1 or above.
+:::
+
+## 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:main-v1.80.8-stable.1
+```
+
+</TabItem>
+
+<TabItem value="pip" label="Pip">
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==v1.80.8-stable.1
+```
+
+</TabItem>
+</Tabs>
+
+## What's New
+
+Supports all four thinking levels:
+- **MINIMAL**: Ultra-fast responses with minimal reasoning
+- **LOW**: Simple instruction following
+- **MEDIUM**: Balanced reasoning for complex tasks
+- **HIGH**: Maximum reasoning depth (dynamic)
+
+---
+
+## Quick Start
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+**Basic Usage**
+
+```python
+from litellm import completion
+
+response = completion(
+    model="gemini/gemini-3.1-flash-lite-preview",
+    messages=[{"role": "user", "content": "Extract key entities from this text: ..."}],
+)
+
+print(response.choices[0].message.content)
+```
+
+**With Thinking Levels**
+
+```python
+from litellm import completion
+
+# Use MEDIUM thinking for complex reasoning tasks
+response = completion(
+    model="gemini/gemini-3.1-flash-lite-preview",
+    messages=[{"role": "user", "content": "Analyze this dataset and identify patterns"}],
+    reasoning_effort="medium",  # low, medium , high
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+
+<TabItem value="proxy" label="PROXY">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: gemini-3.1-flash-lite
+    litellm_params:
+      model: gemini/gemini-3.1-flash-lite-preview
+      api_key: os.environ/GEMINI_API_KEY
+  
+  # Or use Vertex AI
+  - model_name: vertex-gemini-3.1-flash-lite
+    litellm_params:
+      model: vertex_ai/gemini-3.1-flash-lite-preview
+      vertex_project: your-project-id
+      vertex_location: us-central1
+```
+
+**2. Start proxy**
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+**3. Make requests**
+
+```bash
+curl -X POST http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
+  -d '{
+    "model": "gemini-3.1-flash-lite",
+    "messages": [{"role": "user", "content": "Extract structured data from this text"}],
+    "reasoning_effort": "low"
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+---
+
+## Supported Endpoints
+
+LiteLLM provides **full end-to-end support** for Gemini 3.1 Flash Lite Preview on:
+
+- ✅ `/v1/chat/completions` - OpenAI-compatible chat completions endpoint
+- ✅ `/v1/responses` - OpenAI Responses API endpoint (streaming and non-streaming)
+- ✅ [`/v1/messages`](../../docs/anthropic_unified) - Anthropic-compatible messages endpoint
+- ✅ `/v1/generateContent` – [Google Gemini API](../../docs/generateContent.md) compatible endpoint 
+
+All endpoints support:
+- Streaming and non-streaming responses
+- Function calling with thought signatures
+- Multi-turn conversations
+- All Gemini 3-specific features (thinking levels, thought signatures)
+- Full multimodal support (text, image, audio, video)
+
+---
+
+## `reasoning_effort` Mapping for Gemini 3.1
+
+LiteLLM automatically maps OpenAI's `reasoning_effort` parameter to Gemini's `thinkingLevel`:
+
+| reasoning_effort | thinking_level | Use Case |
+|------------------|----------------|----------|
+| `minimal` | `minimal` | Ultra-fast responses, simple queries |
+| `low` | `low` | Basic instruction following |
+| `medium` | `medium` | Balanced reasoning for moderate complexity |
+| `high` | `high` | Maximum reasoning depth, complex problems |
+| `disable` | `minimal` | Disable extended reasoning |
+| `none` | `minimal` | No extended reasoning |
\ No newline at end of file

From 409208771e6fa8bb596f580e6efd7bcb36b0a308 Mon Sep 17 00:00:00 2001
From: Chesars <cesarponce19544@gmail.com>
Date: Tue, 3 Mar 2026 09:24:49 -0300
Subject: [PATCH 15/69] fix(pricing): add 5 missing OpenRouter model pricing
 entries

Fixes #22609

Adds pricing for OpenRouter models that were routing correctly but
returning $0 for spend tracking due to missing cost map entries:

- openrouter/anthropic/claude-sonnet-4.6 ($3.00/$15.00 per 1M tokens)
- openrouter/google/gemini-3.1-pro-preview ($2.00/$12.00 per 1M tokens)
- openrouter/openai/gpt-5.1-codex-max ($1.25/$10.00 per 1M tokens)
- openrouter/qwen/qwen3-coder-plus ($1.00/$5.00 per 1M tokens)
- openrouter/z-ai/glm-5 ($0.80/$2.56 per 1M tokens)
---
 model_prices_and_context_window.json | 106 +++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)

diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 4c0694db371..07b44fb7809 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -25373,6 +25373,30 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 159
     },
+    "openrouter/anthropic/claude-sonnet-4.6": {
+        "cache_creation_input_token_cost": 3.75e-06,
+        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
+        "cache_read_input_token_cost": 3e-07,
+        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
+        "input_cost_per_token": 3e-06,
+        "input_cost_per_token_above_200k_tokens": 6e-06,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 1.5e-05,
+        "output_cost_per_token_above_200k_tokens": 2.25e-05,
+        "source": "https://openrouter.ai/anthropic/claude-sonnet-4.6",
+        "supports_assistant_prefill": true,
+        "supports_computer_use": true,
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "tool_use_system_prompt_tokens": 159
+    },
     "openrouter/anthropic/claude-opus-4.5": {
         "cache_creation_input_token_cost": 6.25e-06,
         "cache_read_input_token_cost": 5e-07,
@@ -25723,6 +25747,39 @@
         "supports_web_search": true,
         "tpm": 800000
     },
+    "openrouter/google/gemini-3.1-pro-preview": {
+        "cache_read_input_token_cost": 2e-07,
+        "cache_read_input_token_cost_above_200k_tokens": 4e-07,
+        "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07,
+        "input_cost_per_token": 2e-06,
+        "input_cost_per_token_above_200k_tokens": 4e-06,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_token": 1.2e-05,
+        "output_cost_per_token_above_200k_tokens": 1.8e-05,
+        "source": "https://openrouter.ai/google/gemini-3.1-pro-preview",
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
     "openrouter/gryphe/mythomax-l2-13b": {
         "input_cost_per_token": 1.875e-06,
         "litellm_provider": "openrouter",
@@ -26100,6 +26157,29 @@
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
+    "openrouter/openai/gpt-5.1-codex-max": {
+        "cache_read_input_token_cost": 1.25e-07,
+        "input_cost_per_token": 1.25e-06,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 400000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 1e-05,
+        "source": "https://openrouter.ai/openai/gpt-5.1-codex-max",
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
     "openrouter/openai/gpt-5.2": {
         "input_cost_per_image": 0,
         "cache_read_input_token_cost": 1.75e-07,
@@ -26254,6 +26334,19 @@
         "supports_tool_choice": true,
         "supports_function_calling": true
     },
+    "openrouter/qwen/qwen3-coder-plus": {
+        "input_cost_per_token": 1e-06,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 997952,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_token": 5e-06,
+        "source": "https://openrouter.ai/qwen/qwen3-coder-plus",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true
+    },
     "openrouter/qwen/qwen3-235b-a22b-2507": {
         "input_cost_per_token": 7.1e-08,
         "litellm_provider": "openrouter",
@@ -26389,6 +26482,19 @@
         "supports_vision": true,
         "supports_prompt_caching": false
     },
+    "openrouter/z-ai/glm-5": {
+        "input_cost_per_token": 8e-07,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 202752,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 2.56e-06,
+        "source": "https://openrouter.ai/z-ai/glm-5",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true
+    },
     "openrouter/minimax/minimax-m2.1": {
         "input_cost_per_token": 2.7e-07,
         "output_cost_per_token": 1.2e-06,

From 79771261819774fbe40c4a4201148b872479b5a3 Mon Sep 17 00:00:00 2001
From: Chesars <cesarponce19544@gmail.com>
Date: Tue, 3 Mar 2026 09:52:17 -0300
Subject: [PATCH 16/69] fix(pricing): add 7 missing DashScope model pricing
 entries

Fixes #22646

Adds pricing for DashScope models that were missing from the cost map,
causing $0 spend tracking in the proxy dashboard:

- dashscope/qwen3-max-2026-01-23 (tiered, same as qwen3-max)
- dashscope/qwen3-next-80b-a3b-instruct ($0.15/$1.20 per 1M)
- dashscope/qwen3-next-80b-a3b-thinking ($0.15/$1.20 per 1M)
- dashscope/qwen3-vl-235b-a22b-instruct ($0.40/$1.60 per 1M)
- dashscope/qwen3-vl-235b-a22b-thinking ($0.40/$4.00 per 1M)
- dashscope/qwen3-vl-32b-instruct ($0.16/$0.64 per 1M)
- dashscope/qwen3-vl-32b-thinking ($0.16/$2.87 per 1M)
---
 model_prices_and_context_window.json | 116 +++++++++++++++++++++++++++
 1 file changed, 116 insertions(+)

diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 4c0694db371..7f59fcb0818 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -9779,6 +9779,122 @@
             }
         ]
     },
+    "dashscope/qwen3-max-2026-01-23": {
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 258048,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "source": "https://www.alibabacloud.com/help/en/model-studio/models",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "tiered_pricing": [
+            {
+                "input_cost_per_token": 1.2e-06,
+                "output_cost_per_token": 6e-06,
+                "range": [
+                    0,
+                    32000.0
+                ]
+            },
+            {
+                "input_cost_per_token": 2.4e-06,
+                "output_cost_per_token": 1.2e-05,
+                "range": [
+                    32000.0,
+                    128000.0
+                ]
+            },
+            {
+                "input_cost_per_token": 3e-06,
+                "output_cost_per_token": 1.5e-05,
+                "range": [
+                    128000.0,
+                    252000.0
+                ]
+            }
+        ]
+    },
+    "dashscope/qwen3-next-80b-a3b-instruct": {
+        "input_cost_per_token": 1.5e-07,
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_token": 1.2e-06,
+        "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "dashscope/qwen3-next-80b-a3b-thinking": {
+        "input_cost_per_token": 1.5e-07,
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 262144,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_token": 1.2e-06,
+        "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true
+    },
+    "dashscope/qwen3-vl-235b-a22b-instruct": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "mode": "chat",
+        "output_cost_per_token": 1.6e-06,
+        "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "dashscope/qwen3-vl-235b-a22b-thinking": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "mode": "chat",
+        "output_cost_per_token": 4e-06,
+        "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "dashscope/qwen3-vl-32b-instruct": {
+        "input_cost_per_token": 1.6e-07,
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "mode": "chat",
+        "output_cost_per_token": 6.4e-07,
+        "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "dashscope/qwen3-vl-32b-thinking": {
+        "input_cost_per_token": 1.6e-07,
+        "litellm_provider": "dashscope",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "mode": "chat",
+        "output_cost_per_token": 2.87e-06,
+        "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
     "dashscope/qwen3-vl-plus": {
         "litellm_provider": "dashscope",
         "max_input_tokens": 260096,

From 058fac848e0f7e16ea0985e9ed7eae897523bb60 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 16:20:51 +0530
Subject: [PATCH 17/69] Add Encrypted-content-aware deployment affinity for the
 Router

---
 .../encrypted_content_affinity_check.py       | 267 ++++++++++++++++++
 1 file changed, 267 insertions(+)
 create mode 100644 litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py

diff --git a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
new file mode 100644
index 00000000000..cdf69e4b24c
--- /dev/null
+++ b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
@@ -0,0 +1,267 @@
+"""
+Encrypted-content-aware deployment affinity for the Router.
+
+When Codex or other models use `store: false` with `include: ["reasoning.encrypted_content"]`,
+the response output items contain encrypted reasoning tokens tied to the originating
+organization's API key. If a follow-up request containing those items is routed to a
+different deployment (different org), OpenAI rejects it with an `invalid_encrypted_content`
+error because the organization_id doesn't match.
+
+This callback solves the problem by:
+1. Tracking output item IDs from Responses API responses and mapping them to the
+   deployment (model_id) that produced them.
+2. On subsequent requests, scanning the `input` field for known item IDs and pinning
+   the request to the originating deployment.
+
+Safe to enable globally:
+- Only activates when known item IDs appear in the request `input`.
+- No effect on embedding models, chat completions, or first-time requests.
+- No quota reduction -- first requests are fully load balanced.
+"""
+
+from typing import Any, List, Optional, cast
+
+from litellm._logging import verbose_router_logger
+from litellm.caching.dual_cache import DualCache
+from litellm.integrations.custom_logger import CustomLogger, Span
+from litellm.types.llms.openai import AllMessageValues, ResponsesAPIResponse
+
+_DEFAULT_TTL_SECONDS = 86400  # 24 hours
+
+
+class EncryptedContentAffinityCheck(CustomLogger):
+    """
+    Routes follow-up Responses API requests to the deployment that produced
+    the encrypted output items they reference.
+
+    Wired via ``Router(optional_pre_call_checks=["encrypted_content_affinity"])``.
+    """
+
+    CACHE_KEY_PREFIX = "encrypted_content_affinity:v1"
+
+    def __init__(
+        self,
+        cache: DualCache,
+        ttl_seconds: int = _DEFAULT_TTL_SECONDS,
+    ):
+        super().__init__()
+        self.cache = cache
+        self.ttl_seconds = ttl_seconds
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _get_output_from_response(
+        response_obj: Any,
+    ) -> Optional[list]:
+        """
+        Extract the ``output`` list from a Responses API response, handling
+        both ``ResponsesAPIResponse`` objects and plain dicts.
+        """
+        if isinstance(response_obj, ResponsesAPIResponse):
+            return response_obj.output
+        if isinstance(response_obj, dict) and "output" in response_obj:
+            output = response_obj["output"]
+            if isinstance(output, list):
+                return output
+        if hasattr(response_obj, "output"):
+            output = response_obj.output
+            if isinstance(output, list):
+                return output
+        return None
+
+    @staticmethod
+    def _extract_item_ids_from_output(
+        output: list,
+    ) -> List[str]:
+        """Extract all item IDs from a Responses API output list."""
+        item_ids: List[str] = []
+        for item in output:
+            item_id: Optional[str] = None
+            if isinstance(item, dict):
+                item_id = item.get("id")
+            else:
+                item_id = getattr(item, "id", None)
+            if item_id and isinstance(item_id, str):
+                item_ids.append(item_id)
+        return item_ids
+
+    @staticmethod
+    def _extract_item_ids_from_input(request_input: Any) -> List[str]:
+        """
+        Extract item IDs from the ``input`` field of a Responses API request.
+
+        ``input`` can be:
+        - a plain string  -> no item IDs
+        - a list of items -> each item may have an ``id`` field
+        """
+        if not isinstance(request_input, list):
+            return []
+
+        item_ids: List[str] = []
+        for item in request_input:
+            if isinstance(item, dict):
+                item_id = item.get("id")
+                if item_id and isinstance(item_id, str):
+                    item_ids.append(item_id)
+        return item_ids
+
+    @classmethod
+    def _cache_key(cls, item_id: str) -> str:
+        return f"{cls.CACHE_KEY_PREFIX}:{item_id}"
+
+    @staticmethod
+    def _find_deployment_by_model_id(
+        healthy_deployments: List[dict], model_id: str
+    ) -> Optional[dict]:
+        for deployment in healthy_deployments:
+            model_info = deployment.get("model_info")
+            if not isinstance(model_info, dict):
+                continue
+            deployment_model_id = model_info.get("id")
+            if deployment_model_id is not None and str(deployment_model_id) == str(
+                model_id
+            ):
+                return deployment
+        return None
+
+    @staticmethod
+    def _get_model_id_from_kwargs(kwargs: dict) -> Optional[str]:
+        """
+        Extract the deployment model_id from success-callback kwargs.
+
+        The Router populates ``litellm_params.metadata.model_info.id`` after
+        selecting a deployment. Also check top-level ``model_info`` as a
+        fallback (some call paths set it there).
+        """
+        # Primary path: litellm_params -> metadata -> model_info -> id
+        litellm_params = kwargs.get("litellm_params")
+        if isinstance(litellm_params, dict):
+            metadata = litellm_params.get("metadata")
+            if isinstance(metadata, dict):
+                model_info = metadata.get("model_info")
+                if isinstance(model_info, dict):
+                    model_id = model_info.get("id")
+                    if model_id is not None:
+                        return str(model_id)
+
+        # Fallback: top-level model_info (set by some router call paths)
+        model_info = kwargs.get("model_info")
+        if isinstance(model_info, dict):
+            model_id = model_info.get("id")
+            if model_id is not None:
+                return str(model_id)
+
+        return None
+
+    # ------------------------------------------------------------------
+    # Response tracking  (success callback)
+    # ------------------------------------------------------------------
+
+    async def async_log_success_event(
+        self, kwargs: dict, response_obj: Any, start_time: Any, end_time: Any
+    ) -> None:
+        """
+        After a successful Responses API call, cache each output item ID
+        mapped to the deployment that produced it.
+        """
+        output = self._get_output_from_response(response_obj)
+        if output is None:
+            return
+
+        model_id = self._get_model_id_from_kwargs(kwargs)
+        if not model_id:
+            verbose_router_logger.debug(
+                "EncryptedContentAffinityCheck: model_id not found in kwargs, skipping tracking",
+            )
+            return
+
+        item_ids = self._extract_item_ids_from_output(output)
+        if not item_ids:
+            return
+
+        for item_id in item_ids:
+            try:
+                cache_key = self._cache_key(item_id)
+                await self.cache.async_set_cache(
+                    cache_key,
+                    model_id,
+                    ttl=self.ttl_seconds,
+                )
+            except Exception as e:
+                verbose_router_logger.debug(
+                    "EncryptedContentAffinityCheck: failed to cache item_id=%s error=%s",
+                    item_id,
+                    e,
+                )
+
+        verbose_router_logger.info(
+            "EncryptedContentAffinityCheck: cached %d item IDs -> deployment=%s",
+            len(item_ids),
+            model_id,
+        )
+
+    # ------------------------------------------------------------------
+    # Request routing  (pre-call filter)
+    # ------------------------------------------------------------------
+
+    async def async_filter_deployments(
+        self,
+        model: str,
+        healthy_deployments: List,
+        messages: Optional[List[AllMessageValues]],
+        request_kwargs: Optional[dict] = None,
+        parent_otel_span: Optional[Span] = None,
+    ) -> List[dict]:
+        """
+        If the request ``input`` contains items whose IDs were previously
+        tracked, pin the request to the deployment that produced them.
+        """
+        request_kwargs = request_kwargs or {}
+        typed_healthy_deployments = cast(List[dict], healthy_deployments)
+
+        request_input = request_kwargs.get("input")
+        input_item_ids = self._extract_item_ids_from_input(request_input)
+        if not input_item_ids:
+            return typed_healthy_deployments
+
+        verbose_router_logger.debug(
+            "EncryptedContentAffinityCheck: found %d item IDs in input, checking cache",
+            len(input_item_ids),
+        )
+
+        for item_id in input_item_ids:
+            cache_key = self._cache_key(item_id)
+            try:
+                cached_model_id = await self.cache.async_get_cache(key=cache_key)
+            except Exception:
+                continue
+
+            if not cached_model_id or not isinstance(cached_model_id, str):
+                continue
+
+            deployment = self._find_deployment_by_model_id(
+                healthy_deployments=typed_healthy_deployments,
+                model_id=cached_model_id,
+            )
+            if deployment is not None:
+                verbose_router_logger.info(
+                    "EncryptedContentAffinityCheck: item_id=%s pinning -> deployment=%s",
+                    item_id,
+                    cached_model_id,
+                )
+                request_kwargs[
+                    "_encrypted_content_affinity_pinned"
+                ] = True
+                return [deployment]
+
+            verbose_router_logger.info(
+                "EncryptedContentAffinityCheck: cached deployment=%s for item_id=%s "
+                "not found in healthy_deployments",
+                cached_model_id,
+                item_id,
+            )
+
+        return typed_healthy_deployments

From 92b255628216fe6550928eaa53fbf18b7780c519 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 16:21:20 +0530
Subject: [PATCH 18/69] Add encrypted_content_affinity in router

---
 litellm/router.py       | 30 ++++++++++++++++++++++++++++++
 litellm/types/router.py |  1 +
 2 files changed, 31 insertions(+)

diff --git a/litellm/router.py b/litellm/router.py
index 5c8d27e76d7..7dee4fa83de 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -115,6 +115,9 @@ from litellm.router_utils.handle_error import (
 from litellm.router_utils.pre_call_checks.deployment_affinity_check import (
     DeploymentAffinityCheck,
 )
+from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
+    EncryptedContentAffinityCheck,
+)
 from litellm.router_utils.pre_call_checks.model_rate_limit_check import (
     ModelRateLimitingCheck,
 )
@@ -1248,6 +1251,25 @@ class Router:
                 self.optional_callbacks.append(affinity_callback)
                 litellm.logging_callback_manager.add_litellm_callback(affinity_callback)
 
+        # ---------------------------------------------------------------------
+        # Encrypted content affinity
+        # ---------------------------------------------------------------------
+        if "encrypted_content_affinity" in optional_pre_call_checks:
+            if self.optional_callbacks is None:
+                self.optional_callbacks = []
+
+            already_registered = any(
+                isinstance(cb, EncryptedContentAffinityCheck)
+                for cb in self.optional_callbacks
+            )
+            if not already_registered:
+                ec_callback = EncryptedContentAffinityCheck(
+                    cache=self.cache,
+                    ttl_seconds=self.deployment_affinity_ttl_seconds,
+                )
+                self.optional_callbacks.append(ec_callback)
+                litellm.logging_callback_manager.add_litellm_callback(ec_callback)
+
         # ---------------------------------------------------------------------
         # Remaining optional pre-call checks
         # ---------------------------------------------------------------------
@@ -1257,6 +1279,7 @@ class Router:
                 "deployment_affinity",
                 "responses_api_deployment_check",
                 "session_affinity",
+                "encrypted_content_affinity",
             ):
                 continue
             if pre_call_check == "prompt_caching":
@@ -8808,6 +8831,13 @@ class Router:
             if isinstance(healthy_deployments, dict):
                 return healthy_deployments
 
+            # When encrypted content affinity pins to a specific deployment,
+            if (
+                request_kwargs.get("_encrypted_content_affinity_pinned")
+                and len(healthy_deployments) == 1
+            ):
+                return healthy_deployments[0]
+
             start_time = time.time()
             if (
                 self.routing_strategy == "usage-based-routing-v2"
diff --git a/litellm/types/router.py b/litellm/types/router.py
index aa4d7bd9a97..fca731d1f91 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -813,6 +813,7 @@ OptionalPreCallChecks = List[
         "session_affinity",
         "forward_client_headers_by_model_group",
         "enforce_model_rate_limits",
+        "encrypted_content_affinity",
     ]
 ]
 

From 394c49d3037a9cc05cc942cc73c13a9f7acb4eb6 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 16:21:49 +0530
Subject: [PATCH 19/69] Add tests for encrypted_content_affinity

---
 .../test_encrypted_content_affinity_check.py  | 335 ++++++++++++++++++
 1 file changed, 335 insertions(+)
 create mode 100644 tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py

diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
new file mode 100644
index 00000000000..a266df749c1
--- /dev/null
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
@@ -0,0 +1,335 @@
+import asyncio
+import os
+import sys
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import json
+
+import litellm
+from litellm.caching.dual_cache import DualCache
+from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
+    EncryptedContentAffinityCheck,
+)
+
+
+class MockResponse:
+    def __init__(self, json_data, status_code):
+        self._json_data = json_data
+        self.status_code = status_code
+        self.text = json.dumps(json_data)
+        self.headers = {}
+
+    def json(self):
+        return self._json_data
+
+
+@pytest.mark.asyncio
+async def test_encrypted_content_affinity_tracks_and_routes():
+    """
+    When encrypted_content_affinity is enabled, output item IDs from responses
+    are tracked, and follow-up requests containing those IDs route to the same
+    deployment.
+    """
+    mock_response_data = {
+        "id": "resp_mock-123",
+        "object": "response",
+        "created_at": 1741476542,
+        "status": "completed",
+        "model": "openai/gpt-5.1-codex",
+        "output": [
+            {
+                "type": "message",
+                "id": "msg_abc123",
+                "status": "completed",
+                "role": "assistant",
+                "content": [{"type": "output_text", "text": "Hello!", "annotations": []}],
+            },
+            {
+                "type": "reasoning",
+                "id": "rs_encrypted_item_456",
+                "status": "completed",
+            },
+        ],
+        "parallel_tool_calls": True,
+        "usage": {
+            "input_tokens": 5,
+            "output_tokens": 10,
+            "total_tokens": 15,
+        },
+        "error": None,
+    }
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-1",
+                },
+                "model_info": {"id": "deployment-1"},
+            },
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-2",
+                },
+                "model_info": {"id": "deployment-2"},
+            },
+        ],
+        optional_pre_call_checks=["encrypted_content_affinity"],
+    )
+
+    model_group = "openai.gpt-5.1-codex"
+
+    # Track which deployment was selected
+    selected_deployments = []
+
+    def deterministic_choice(seq):
+        # First call: select deployment-1
+        # Second call: would select deployment-2, but affinity should override
+        if len(selected_deployments) == 0:
+            return seq[0]
+        return seq[1] if len(seq) > 1 else seq[0]
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post, patch(
+        "litellm.router_strategy.simple_shuffle.random.choice",
+        side_effect=deterministic_choice,
+    ):
+        mock_post.return_value = MockResponse(mock_response_data, 200)
+
+        # First request: no encrypted items in input
+        first_response = await router.aresponses(
+            model=model_group,
+            input="Hello, how are you?",
+        )
+        first_model_id = first_response._hidden_params["model_id"]
+        selected_deployments.append(first_model_id)
+
+        # Give async callbacks time to run
+        await asyncio.sleep(0.2)
+
+        # Second request: includes encrypted item IDs from first response
+        second_response = await router.aresponses(
+            model=model_group,
+            input=[
+                {"type": "message", "id": "msg_abc123", "role": "assistant"},
+                {"type": "reasoning", "id": "rs_encrypted_item_456"},
+            ],
+        )
+        second_model_id = second_response._hidden_params["model_id"]
+
+        # Affinity should route to the same deployment
+        assert second_model_id == first_model_id, (
+            f"Expected affinity to route to {first_model_id}, "
+            f"but got {second_model_id}"
+        )
+
+
+@pytest.mark.asyncio
+async def test_encrypted_content_affinity_no_effect_on_chat_completions():
+    """
+    Encrypted content affinity should not affect regular chat completions
+    (they don't use the Responses API).
+    """
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "gpt-3.5-turbo",
+                    "api_key": os.environ.get("OPENAI_API_KEY", "test-key"),
+                },
+                "model_info": {"id": "chat-deployment-1"},
+            },
+        ],
+        optional_pre_call_checks=["encrypted_content_affinity"],
+    )
+
+    mock_chat_response = {
+        "id": "chatcmpl-123",
+        "object": "chat.completion",
+        "created": 1677652288,
+        "model": "gpt-3.5-turbo",
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": "Hello!"},
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 5, "completion_tokens": 7, "total_tokens": 12},
+    }
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post:
+        mock_post.return_value = MockResponse(mock_chat_response, 200)
+
+        # Multiple chat completion requests should work normally
+        response1 = await router.acompletion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hello"}],
+        )
+        response2 = await router.acompletion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hello again"}],
+        )
+
+        # Both should succeed (no affinity interference)
+        # Check that responses have IDs (litellm may modify them)
+        assert response1.id is not None
+        assert response2.id is not None
+
+
+@pytest.mark.asyncio
+async def test_encrypted_content_affinity_bypasses_rpm_limits():
+    """
+    When encrypted content affinity pins to a deployment, it should bypass
+    RPM limits since the encrypted content will fail on any other deployment.
+    """
+    mock_response_data = {
+        "id": "resp_mock-rpm-test",
+        "object": "response",
+        "created_at": 1741476542,
+        "status": "completed",
+        "model": "openai/gpt-5.1-codex",
+        "output": [
+            {
+                "type": "reasoning",
+                "id": "rs_encrypted_must_pin",
+                "status": "completed",
+            },
+        ],
+        "usage": {"input_tokens": 5, "output_tokens": 10, "total_tokens": 15},
+        "error": None,
+    }
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-1",
+                    "rpm": 1,  # Very low limit
+                },
+                "model_info": {"id": "rpm-limited-deployment"},
+            },
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-2",
+                    "rpm": 100,
+                },
+                "model_info": {"id": "high-rpm-deployment"},
+            },
+        ],
+        optional_pre_call_checks=["encrypted_content_affinity"],
+        routing_strategy="usage-based-routing-v2",
+    )
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post:
+        mock_post.return_value = MockResponse(mock_response_data, 200)
+
+        # First request goes to the low-RPM deployment
+        first_response = await router.aresponses(
+            model="openai.gpt-5.1-codex",
+            input="Initial request",
+        )
+        first_model_id = first_response._hidden_params["model_id"]
+
+        await asyncio.sleep(0.2)
+
+        # Second request with encrypted content should pin to the same deployment
+        # even though it's at RPM limit
+        second_response = await router.aresponses(
+            model="openai.gpt-5.1-codex",
+            input=[
+                {"type": "reasoning", "id": "rs_encrypted_must_pin"},
+            ],
+        )
+        second_model_id = second_response._hidden_params["model_id"]
+
+        # Should route to the same deployment despite RPM limit
+        assert second_model_id == first_model_id
+
+
+@pytest.mark.asyncio
+async def test_encrypted_content_affinity_no_match_normal_routing():
+    """
+    When input contains item IDs that aren't tracked, normal load balancing
+    should occur.
+    """
+    mock_response_data = {
+        "id": "resp_mock-no-match",
+        "object": "response",
+        "created_at": 1741476542,
+        "status": "completed",
+        "model": "openai/gpt-5.1-codex",
+        "output": [
+            {
+                "type": "message",
+                "id": "msg_new",
+                "status": "completed",
+                "role": "assistant",
+                "content": [{"type": "output_text", "text": "Response"}],
+            },
+        ],
+        "usage": {"input_tokens": 5, "output_tokens": 10, "total_tokens": 15},
+        "error": None,
+    }
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-1",
+                },
+                "model_info": {"id": "deployment-a"},
+            },
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-2",
+                },
+                "model_info": {"id": "deployment-b"},
+            },
+        ],
+        optional_pre_call_checks=["encrypted_content_affinity"],
+    )
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post:
+        mock_post.return_value = MockResponse(mock_response_data, 200)
+
+        # Request with unknown item IDs should use normal routing
+        response = await router.aresponses(
+            model="openai.gpt-5.1-codex",
+            input=[
+                {"type": "message", "id": "unknown_item_id_12345"},
+            ],
+        )
+
+        # Should succeed with normal routing (litellm may modify the ID)
+        assert response.id is not None
+        # Verify it contains the original response ID in some form
+        assert "resp_mock-no-match" in str(response.id) or response.id.startswith("resp_")

From 9f627c67d83bf8216d2637ba9eb877e0588a42ca Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 16:22:00 +0530
Subject: [PATCH 20/69] Add tests for encrypted_content_affinity

---
 .../pre_call_checks/test_encrypted_content_affinity_check.py  | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
index a266df749c1..c70290b674d 100644
--- a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
@@ -10,10 +10,6 @@ sys.path.insert(0, os.path.abspath("../.."))
 import json
 
 import litellm
-from litellm.caching.dual_cache import DualCache
-from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
-    EncryptedContentAffinityCheck,
-)
 
 
 class MockResponse:

From fbec5c5ccf6c3633cae358f50da695203a76a624 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 16:22:15 +0530
Subject: [PATCH 21/69] Add docs for encrypted_content_affinity

---
 docs/my-website/docs/proxy/config_settings.md |   2 +-
 docs/my-website/docs/proxy/load_balancing.md  |  33 ++++
 docs/my-website/docs/response_api.md          | 155 ++++++++++++++++++
 3 files changed, 189 insertions(+), 1 deletion(-)

diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index 7b2011e45dd..af868bc9f9d 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -360,7 +360,7 @@ router_settings:
 | redis_url | str | URL for Redis server. **Known performance issue with Redis URL.** |
 | cache_responses | boolean | Flag to enable caching LLM Responses, if cache set under `router_settings`. If true, caches responses. Defaults to False. |
 | router_general_settings | RouterGeneralSettings | [SDK-Only] Router general settings - contains optimizations like 'async_only_mode'. [Docs](../routing.md#router-general-settings) |
-| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Supported: `router_budget_limiting`, `prompt_caching`, `responses_api_deployment_check`, `deployment_affinity`, `forward_client_headers_by_model_group` |
+| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Supported: `router_budget_limiting`, `prompt_caching`, `responses_api_deployment_check`, `encrypted_content_affinity`, `deployment_affinity`, `session_affinity`, `forward_client_headers_by_model_group` |
 | deployment_affinity_ttl_seconds | int | TTL (seconds) for user-key → deployment affinity mapping when `deployment_affinity` is enabled (configured at Router init / proxy startup). Defaults to `3600` (1 hour). |
 | ignore_invalid_deployments | boolean | If true, ignores invalid deployments. Default for proxy is True - to prevent invalid models from blocking other models from being loaded. |
 | search_tools | List[SearchToolTypedDict] | List of search tool configurations for Search API integration. Each tool specifies a search_tool_name and litellm_params with search_provider, api_key, api_base, etc. [Further Docs](../search.md) |
diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md
index 186307d6498..5bf39d179f6 100644
--- a/docs/my-website/docs/proxy/load_balancing.md
+++ b/docs/my-website/docs/proxy/load_balancing.md
@@ -347,3 +347,36 @@ If `order=1` deployment is unavailable (e.g., rate-limited), the router falls ba
 - **Higher throughput**: More requests handled simultaneously across deployments
 - **Improved reliability**: If one deployment fails, traffic automatically routes to healthy ones
 - **Better resource utilization**: Load spread evenly across all available deployments
+
+## Special Considerations for Responses API
+
+When load balancing OpenAI's Responses API across deployments with **different API keys** (e.g., different Azure regions or organizations), encrypted content items (like `rs_...` reasoning items) can only be decrypted by the originating API key.
+
+**Solution:** Use the `encrypted_content_affinity` pre-call check to automatically route follow-up requests containing encrypted items to the correct deployment:
+
+```yaml
+model_list:
+  - model_name: gpt-5.1-codex
+    litellm_params:
+      model: azure/gpt-5.1-codex
+      api_base: https://eastus.openai.azure.com/
+      api_key: os.environ/AZURE_API_KEY_EASTUS
+    model_info:
+      id: "deployment-eastus"
+  
+  - model_name: gpt-5.1-codex
+    litellm_params:
+      model: azure/gpt-5.1-codex
+      api_base: https://westeurope.openai.azure.com/
+      api_key: os.environ/AZURE_API_KEY_WESTEUROPE
+    model_info:
+      id: "deployment-westeurope"
+
+router_settings:
+  optional_pre_call_checks:
+    - encrypted_content_affinity  # 👈 Prevents invalid_encrypted_content errors
+```
+
+This ensures requests containing encrypted content are routed to the deployment that created them, while other requests continue to load balance normally.
+
+**[Learn more about Encrypted Content Affinity →](../response_api.md#encrypted-content-affinity-multi-region-load-balancing)**
diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md
index b37be2b5bc2..85144a80245 100644
--- a/docs/my-website/docs/response_api.md
+++ b/docs/my-website/docs/response_api.md
@@ -920,9 +920,14 @@ follow_up = await router.aresponses(
 To enable session continuity for Responses API in your LiteLLM proxy, set `optional_pre_call_checks` in your proxy config.yaml.
 
 - `responses_api_deployment_check`: high priority routing when `previous_response_id` is provided
+- `encrypted_content_affinity`: **[Recommended]** content-aware routing for encrypted items (e.g., `rs_...` reasoning items)
 - `session_affinity`: sticky sessions based on session id (takes priority over `deployment_affinity`)
 - `deployment_affinity`: sticky sessions based on user key (applies even without `previous_response_id`)
 
+:::tip Recommended: Use `encrypted_content_affinity`
+For Responses API with load balancing across deployments with **different API keys**, use `encrypted_content_affinity` instead of `deployment_affinity`. It only pins requests that contain encrypted content, avoiding quota reduction while preventing `invalid_encrypted_content` errors.
+:::
+
 Notes:
 - User-key affinity is keyed on `metadata.user_api_key_hash` (the API key hash). The OpenAI `user` request parameter is an end-user identifier and is intentionally not used for deployment affinity.
 - Session-ID affinity is keyed on `metadata.session_id`. For proxy requests, this can be passed via the `x-litellm-session-id` HTTP header. For Python SDK requests, you can pass it via `litellm_metadata={"session_id": "value"}` in request args.
@@ -983,6 +988,156 @@ follow_up = client.responses.create(
 </TabItem>
 </Tabs>
 
+## Encrypted Content Affinity (Multi-Region Load Balancing)
+
+When load balancing Responses API across deployments with **different API keys** (e.g., different Azure regions or OpenAI organizations), encrypted content items (like `rs_...` reasoning items) can only be decrypted by the API key that created them.
+
+### The Problem
+
+```json
+{
+  "error": {
+    "message": "The encrypted content for item rs_0d09d6e56879e76500699d6feee41c8197bd268aae76141f87 could not be verified. Reason: Encrypted content organization_id did not match the target organization.",
+    "type": "invalid_request_error",
+    "code": "invalid_encrypted_content"
+  }
+}
+```
+
+This error occurs when:
+1. Initial request goes to Deployment A (API Key 1) → produces encrypted item `rs_xyz`
+2. Follow-up request with `rs_xyz` in input gets load balanced to Deployment B (API Key 2)
+3. Deployment B cannot decrypt content created by Deployment A → **request fails**
+
+### The Solution: `encrypted_content_affinity`
+
+The `encrypted_content_affinity` pre-call check intelligently tracks encrypted content and routes follow-up requests to the originating deployment **only when necessary**.
+
+**Key Benefits:**
+- ✅ **No quota reduction**: Unlike `deployment_affinity`, only pins requests that contain tracked encrypted items
+- ✅ **Bypasses rate limits**: When encrypted content requires a specific deployment, RPM/TPM limits are bypassed (the request would fail on any other deployment anyway)
+- ✅ **No `previous_response_id` required**: Works by tracking item IDs in response output and matching them in request input
+- ✅ **Globally safe**: Can be enabled for all models; non-Responses-API calls (chat, embeddings) are unaffected
+
+### How It Works
+
+1. **Tracking Phase** (after successful response):
+   - Extracts all item IDs from response `output` (e.g., `msg_abc`, `rs_xyz`)
+   - Caches mapping: `item_id` → `deployment_id` (default TTL: 24 hours)
+
+2. **Routing Phase** (before request):
+   - Scans request `input` for item IDs
+   - If tracked item found → pins to originating deployment, bypasses rate limits
+   - If no tracked items → normal load balancing
+
+### Configuration
+
+<Tabs>
+<TabItem value="sdk" label="Python SDK">
+
+```python
+from litellm import Router
+
+router = Router(
+    model_list=[
+        {
+            "model_name": "gpt-5.1-codex",
+            "litellm_params": {
+                "model": "openai/gpt-5.1-codex",
+                "api_key": "org-1-api-key",  # Different API key
+            },
+            "model_info": {"id": "deployment-us-east"},
+        },
+        {
+            "model_name": "gpt-5.1-codex",
+            "litellm_params": {
+                "model": "openai/gpt-5.1-codex",
+                "api_key": "org-2-api-key",  # Different API key
+            },
+            "model_info": {"id": "deployment-eu-west"},
+        },
+    ],
+    optional_pre_call_checks=["encrypted_content_affinity"],
+    deployment_affinity_ttl_seconds=86400,  # 24 hours (default)
+)
+
+# Initial request - routes to any deployment
+response1 = await router.aresponses(
+    model="gpt-5.1-codex",
+    input="Explain quantum computing",
+)
+
+# Follow-up with encrypted items - automatically routes to same deployment
+response2 = await router.aresponses(
+    model="gpt-5.1-codex",
+    input=response1.output,  # Contains encrypted items from response1
+)
+```
+
+</TabItem>
+<TabItem value="proxy" label="Proxy Server">
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: gpt-5.1-codex
+    litellm_params:
+      model: azure/gpt-5.1-codex
+      api_base: https://eastus.openai.azure.com/
+      api_key: os.environ/AZURE_API_KEY_EASTUS
+      rpm: 600
+      tpm: 100000
+    model_info:
+      id: "gpt-5.1-codex-eastus"
+
+  - model_name: gpt-5.1-codex
+    litellm_params:
+      model: azure/gpt-5.1-codex
+      api_base: https://westeurope.openai.azure.com/
+      api_key: os.environ/AZURE_API_KEY_WESTEUROPE
+      rpm: 600
+      tpm: 100000
+    model_info:
+      id: "gpt-5.1-codex-westeurope"
+
+router_settings:
+  routing_strategy: usage-based-routing-v2
+  enable_pre_call_checks: true
+  optional_pre_call_checks:
+    - encrypted_content_affinity
+  deployment_affinity_ttl_seconds: 86400  # Optional, default is 86400 (24 hours)
+```
+
+**Start proxy:**
+```bash
+litellm --config config.yaml
+```
+
+</TabItem>
+</Tabs>
+
+### When to Use Each Affinity Type
+
+| Affinity Type | Use Case | Scope | Quota Impact |
+|---------------|----------|-------|--------------|
+| **`encrypted_content_affinity`** | **[Recommended]** Multi-region Responses API with different API keys | Only requests with tracked encrypted items | ✅ None (surgical pinning) |
+| `responses_api_deployment_check` | When `previous_response_id` is available | Requests with `previous_response_id` | ✅ None |
+| `session_affinity` | Session-based applications | All requests with same `session_id` | ⚠️ Reduces quota by # of sessions |
+| `deployment_affinity` | Simple sticky sessions | All requests from same API key | ❌ Reduces quota by # of users |
+
+### Multi-Instance Deployment (Redis)
+
+For multiple LiteLLM proxy instances, use Redis to share affinity state:
+
+```yaml
+router_settings:
+  optional_pre_call_checks:
+    - encrypted_content_affinity
+  redis_host: redis.example.com
+  redis_port: 6379
+  redis_password: your-password
+```
+
+
 ## Calling non-Responses API endpoints (`/responses` to `/chat/completions` Bridge)
 
 LiteLLM allows you to call non-Responses API models via a bridge to LiteLLM's `/chat/completions` endpoint. This is useful for calling Anthropic, Gemini and even non-Responses API OpenAI models.

From 37612bdf56349635b1c7d97066d9ee66988fb4d6 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 16:24:57 +0530
Subject: [PATCH 22/69] ADd incident report

---
 .../index.md                                  | 231 ++++++++++++++++++
 1 file changed, 231 insertions(+)
 create mode 100644 docs/my-website/blog/responses_api_encrypted_content_incident/index.md

diff --git a/docs/my-website/blog/responses_api_encrypted_content_incident/index.md b/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
new file mode 100644
index 00000000000..8422f6d3cd0
--- /dev/null
+++ b/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
@@ -0,0 +1,231 @@
+---
+slug: responses-api-encrypted-content-incident
+title: "Incident Report: Encrypted Content Failures in Multi-Region Responses API Load Balancing"
+date: 2026-02-24T10:00:00
+authors:
+  - name: Sameer Kankute
+    title: SWE @ LiteLLM (LLM Translation)
+    url: https://www.linkedin.com/in/sameer-kankute/
+    image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
+  - 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
+tags: [incident-report, proxy, responses-api, load-balancing]
+hide_table_of_contents: false
+---
+
+**Date:** Feb 24, 2026  
+**Duration:** Ongoing (until fix deployed)  
+**Severity:** High (for users load balancing Responses API across different API keys)  
+**Status:** Resolved
+
+## Summary
+
+When load balancing OpenAI's Responses API across deployments with **different API keys** (e.g., different Azure regions or OpenAI organizations), follow-up requests containing encrypted content items (like `rs_...` reasoning items) would fail with:
+
+```json
+{
+  "error": {
+    "message": "The encrypted content for item rs_0d09d6e56879e76500699d6feee41c8197bd268aae76141f87 could not be verified. Reason: Encrypted content organization_id did not match the target organization.",
+    "type": "invalid_request_error",
+    "code": "invalid_encrypted_content"
+  }
+}
+```
+
+Encrypted content items are cryptographically tied to the API key's organization that created them. When the router load balanced a follow-up request to a deployment with a different API key, decryption failed.
+
+- **Responses API calls with encrypted content:** Complete failure when routed to wrong deployment
+- **Initial requests:** Unaffected — only follow-up requests containing encrypted items failed
+- **Other API endpoints:** No impact — chat completions, embeddings, etc. functioned normally
+
+{/* truncate */}
+
+---
+
+## Background
+
+OpenAI's Responses API can return encrypted "reasoning items" (with IDs like `rs_...`) that contain intermediate reasoning steps. These items are encrypted with the organization's key and can only be decrypted by the same organization's API key.
+
+When load balancing across deployments with different API keys, the existing affinity mechanisms were insufficient:
+
+- **`responses_api_deployment_check`**: Requires `previous_response_id` which some clients (like Codex) don't provide
+- **`deployment_affinity`**: Too broad — pins *all* requests from a user to one deployment, reducing effective quota by the number of users
+- **`session_affinity`**: Requires explicit session IDs and still reduces quota
+
+```mermaid
+flowchart TD
+    A["1. Initial request to Responses API
+    router.aresponses()"] --> B["2. Router load balances to Deployment A
+    (API Key 1, Azure East US)"]
+    B --> C["3. Response contains encrypted item
+    rs_abc123 (encrypted with Org 1 key)"]
+    C --> D["4. Follow-up request includes rs_abc123 in input"]
+    D --> E["5. Router load balances to Deployment B
+    (API Key 2, Azure West Europe)"]
+    E -->|"Different API key"| F["6. ❌ Deployment B cannot decrypt rs_abc123
+    Error: invalid_encrypted_content"]
+    
+    D -.->|"With encrypted_content_affinity"| G["5b. Router detects rs_abc123 was created by Deployment A"]
+    G --> H["6b. ✅ Routes to Deployment A (bypasses rate limits)
+    Request succeeds"]
+
+    style F fill:#f8d7da,stroke:#dc3545
+    style H fill:#d4edda,stroke:#28a745
+    style E fill:#fff3cd,stroke:#ffc107
+    style G fill:#d4edda,stroke:#28a745
+```
+
+---
+
+## Root Cause
+
+LiteLLM's router had no mechanism to track which deployment created specific encrypted content items and route follow-up requests accordingly. The router treated all deployments as interchangeable, leading to decryption failures when encrypted content crossed organizational boundaries.
+
+**The Problem Flow:**
+
+1. User calls `router.aresponses()` with model `gpt-5.1-codex`
+2. Router load balances to Deployment A (Azure East US, API Key 1)
+3. Response contains encrypted reasoning item `rs_abc123` (encrypted with Org 1's key)
+4. User makes follow-up request with `rs_abc123` in the input
+5. Router load balances to Deployment B (Azure West Europe, API Key 2)
+6. Deployment B tries to decrypt `rs_abc123` with Org 2's key → **fails**
+
+**Why Existing Solutions Didn't Work:**
+
+- **`previous_response_id`**: Not provided by all clients (e.g., Codex)
+- **`deployment_affinity`**: Pins *all* user requests to one deployment → reduces quota to 1/N where N = number of deployments
+- **`session_affinity`**: Requires explicit session management and still reduces quota
+
+**Timeline:**
+
+1. Users configured multi-region Responses API load balancing with different API keys
+2. Initial requests succeeded, but follow-up requests with encrypted content failed intermittently
+3. Error rate correlated with number of deployments (more deployments = higher chance of routing to wrong one)
+4. Investigation revealed encrypted content was organization-bound
+5. Existing affinity mechanisms deemed unsuitable (quota reduction, missing `previous_response_id`)
+6. New solution designed and implemented: `encrypted_content_affinity`
+
+---
+
+## The Fix
+
+Implemented a new `encrypted_content_affinity` pre-call check that intelligently tracks encrypted content and routes follow-up requests **only when necessary**.
+
+### Implementation
+
+**1. New `EncryptedContentAffinityCheck` Class** ([`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py))
+
+```python
+class EncryptedContentAffinityCheck(CustomLogger):
+    """
+    Routes follow-up Responses API requests to the deployment that produced
+    the encrypted output items they reference.
+    """
+    
+    async def async_log_success_event(self, kwargs, response_obj, ...):
+        """Track: Extract item IDs from response output, cache item_id → deployment_id"""
+        output = self._get_output_from_response(response_obj)
+        item_ids = self._extract_item_ids_from_output(output)
+        model_id = self._get_model_id_from_kwargs(kwargs)
+        
+        for item_id in item_ids:
+            await self.cache.async_set_cache(
+                f"encrypted_content_affinity:v1:{item_id}",
+                model_id,
+                ttl=86400,  # 24 hours
+            )
+    
+    async def async_filter_deployments(self, model, healthy_deployments, ...):
+        """Route: Check if input contains tracked items, pin to originating deployment"""
+        input_item_ids = self._extract_item_ids_from_input(request_kwargs.get("input"))
+        
+        for item_id in input_item_ids:
+            cached_model_id = await self.cache.async_get_cache(f"...:{item_id}")
+            if cached_model_id:
+                deployment = self._find_deployment_by_model_id(
+                    healthy_deployments, cached_model_id
+                )
+                if deployment:
+                    # Signal to bypass rate limits (encrypted content must go here)
+                    request_kwargs["_encrypted_content_affinity_pinned"] = True
+                    return [deployment]
+        
+        return healthy_deployments  # Normal load balancing
+```
+
+**2. Rate Limit Bypass** ([`router.py#L8656-L8660`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py#L8656-L8660))
+
+When encrypted content requires a specific deployment, RPM/TPM limits are bypassed (the request would fail on any other deployment anyway):
+
+```python
+# In async_get_available_deployment, after filtering healthy deployments:
+if (
+    request_kwargs.get("_encrypted_content_affinity_pinned")
+    and len(healthy_deployments) == 1
+):
+    return healthy_deployments[0]  # Bypass routing strategy (RPM/TPM checks)
+```
+
+**3. Configuration**
+
+```yaml
+router_settings:
+  routing_strategy: usage-based-routing-v2
+  enable_pre_call_checks: true
+  optional_pre_call_checks:
+    - encrypted_content_affinity
+  deployment_affinity_ttl_seconds: 86400  # 24 hours
+```
+
+### Key Benefits
+
+✅ **No quota reduction**: Only pins requests containing tracked encrypted items  
+✅ **Bypasses rate limits**: When encrypted content requires a specific deployment, RPM/TPM limits don't block it  
+✅ **No `previous_response_id` required**: Works by tracking item IDs in response output  
+✅ **Globally safe**: Can be enabled for all models; non-Responses-API calls are unaffected  
+✅ **Surgical precision**: Normal requests continue to load balance freely
+
+---
+
+## Remediation
+
+| # | Action | Status | Code |
+|---|---|---|---|
+| 1 | Create `EncryptedContentAffinityCheck` class with tracking and routing logic | ✅ Done | [`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py) |
+| 2 | Add `encrypted_content_affinity` to `OptionalPreCallChecks` type | ✅ Done | [`router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/types/router.py) |
+| 3 | Wire up check in `Router.add_optional_pre_call_checks` | ✅ Done | [`router.py#L8656-L8660`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py#L8656-L8660) |
+| 4 | Implement rate limit bypass for affinity-pinned requests | ✅ Done | [`router.py#L8656-L8660`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py#L8656-L8660) |
+| 5 | Unit tests: tracking, routing, no-op for non-Responses-API, RPM bypass | ✅ Done | [`test_encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py) |
+| 6 | Documentation: Responses API guide, load balancing guide, config reference | ✅ Done | [Docs](https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing) |
+
+---
+
+## Migration Guide
+
+### Before (Using `deployment_affinity`)
+
+```yaml
+router_settings:
+  optional_pre_call_checks:
+    - deployment_affinity  # ❌ Reduces quota by number of users
+```
+
+**Problem:** All requests from a user pin to one deployment, reducing effective quota to 1/N.
+
+### After (Using `encrypted_content_affinity`)
+
+```yaml
+router_settings:
+  optional_pre_call_checks:
+    - encrypted_content_affinity  # ✅ Only pins requests with encrypted content
+```
+
+**Benefit:** Normal requests load balance freely, only encrypted content requests pin when necessary.
+
+---

From a88a17796b385ad4321a39e75523613095b5f078 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 22:56:57 +0530
Subject: [PATCH 23/69] Fix logging and encrypted content extraction

---
 .../encrypted_content_affinity_check.py       | 24 ++++++++++++-------
 .../test_encrypted_content_affinity_check.py  |  6 +++--
 2 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
index cdf69e4b24c..9cab59ea0e0 100644
--- a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
+++ b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
@@ -76,26 +76,31 @@ class EncryptedContentAffinityCheck(CustomLogger):
     def _extract_item_ids_from_output(
         output: list,
     ) -> List[str]:
-        """Extract all item IDs from a Responses API output list."""
+        """Extract item IDs from output items that contain encrypted_content."""
         item_ids: List[str] = []
         for item in output:
             item_id: Optional[str] = None
+            has_encrypted_content = False
+            
             if isinstance(item, dict):
                 item_id = item.get("id")
+                has_encrypted_content = "encrypted_content" in item
             else:
                 item_id = getattr(item, "id", None)
-            if item_id and isinstance(item_id, str):
+                has_encrypted_content = hasattr(item, "encrypted_content")
+            
+            if item_id and isinstance(item_id, str) and has_encrypted_content:
                 item_ids.append(item_id)
         return item_ids
 
     @staticmethod
     def _extract_item_ids_from_input(request_input: Any) -> List[str]:
         """
-        Extract item IDs from the ``input`` field of a Responses API request.
+        Extract item IDs from input items that contain encrypted_content.
 
         ``input`` can be:
         - a plain string  -> no item IDs
-        - a list of items -> each item may have an ``id`` field
+        - a list of items -> only extract IDs from items with encrypted_content
         """
         if not isinstance(request_input, list):
             return []
@@ -104,7 +109,8 @@ class EncryptedContentAffinityCheck(CustomLogger):
         for item in request_input:
             if isinstance(item, dict):
                 item_id = item.get("id")
-                if item_id and isinstance(item_id, str):
+                has_encrypted_content = "encrypted_content" in item
+                if item_id and isinstance(item_id, str) and has_encrypted_content:
                     item_ids.append(item_id)
         return item_ids
 
@@ -191,13 +197,13 @@ class EncryptedContentAffinityCheck(CustomLogger):
                     ttl=self.ttl_seconds,
                 )
             except Exception as e:
-                verbose_router_logger.debug(
+                verbose_router_logger.error(
                     "EncryptedContentAffinityCheck: failed to cache item_id=%s error=%s",
                     item_id,
                     e,
                 )
 
-        verbose_router_logger.info(
+        verbose_router_logger.debug(
             "EncryptedContentAffinityCheck: cached %d item IDs -> deployment=%s",
             len(item_ids),
             model_id,
@@ -247,7 +253,7 @@ class EncryptedContentAffinityCheck(CustomLogger):
                 model_id=cached_model_id,
             )
             if deployment is not None:
-                verbose_router_logger.info(
+                verbose_router_logger.debug(
                     "EncryptedContentAffinityCheck: item_id=%s pinning -> deployment=%s",
                     item_id,
                     cached_model_id,
@@ -257,7 +263,7 @@ class EncryptedContentAffinityCheck(CustomLogger):
                 ] = True
                 return [deployment]
 
-            verbose_router_logger.info(
+            verbose_router_logger.debug(
                 "EncryptedContentAffinityCheck: cached deployment=%s for item_id=%s "
                 "not found in healthy_deployments",
                 cached_model_id,
diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
index c70290b674d..17cd2162d99 100644
--- a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
@@ -48,6 +48,7 @@ async def test_encrypted_content_affinity_tracks_and_routes():
                 "type": "reasoning",
                 "id": "rs_encrypted_item_456",
                 "status": "completed",
+                "encrypted_content": "gAAAAABpnW_yEYmSNEyOG...",
             },
         ],
         "parallel_tool_calls": True,
@@ -118,7 +119,7 @@ async def test_encrypted_content_affinity_tracks_and_routes():
             model=model_group,
             input=[
                 {"type": "message", "id": "msg_abc123", "role": "assistant"},
-                {"type": "reasoning", "id": "rs_encrypted_item_456"},
+                {"type": "reasoning", "id": "rs_encrypted_item_456", "encrypted_content": "gAAAAABpnW_yEYmSNEyOG..."},
             ],
         )
         second_model_id = second_response._hidden_params["model_id"]
@@ -204,6 +205,7 @@ async def test_encrypted_content_affinity_bypasses_rpm_limits():
                 "type": "reasoning",
                 "id": "rs_encrypted_must_pin",
                 "status": "completed",
+                "encrypted_content": "gAAAAABpnW_yEYmSNEyOG...",
             },
         ],
         "usage": {"input_tokens": 5, "output_tokens": 10, "total_tokens": 15},
@@ -255,7 +257,7 @@ async def test_encrypted_content_affinity_bypasses_rpm_limits():
         second_response = await router.aresponses(
             model="openai.gpt-5.1-codex",
             input=[
-                {"type": "reasoning", "id": "rs_encrypted_must_pin"},
+                {"type": "reasoning", "id": "rs_encrypted_must_pin", "encrypted_content": "gAAAAABpnW_yEYmSNEyOG..."},
             ],
         )
         second_model_id = second_response._hidden_params["model_id"]

From 18bf3f2df649c53486bf4e15c353e8464f112afa Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 23:06:54 +0530
Subject: [PATCH 24/69] Fix mock github test

---
 .../test_encrypted_content_affinity_check.py  | 52 ++++++-------------
 1 file changed, 17 insertions(+), 35 deletions(-)

diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
index 17cd2162d99..cfebaab346f 100644
--- a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
@@ -4,6 +4,8 @@ import sys
 from unittest.mock import AsyncMock, patch
 
 import pytest
+import respx
+from httpx import Response
 
 sys.path.insert(0, os.path.abspath("../.."))
 
@@ -143,7 +145,8 @@ async def test_encrypted_content_affinity_no_effect_on_chat_completions():
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
                     "model": "gpt-3.5-turbo",
-                    "api_key": os.environ.get("OPENAI_API_KEY", "test-key"),
+                    "api_key": "test-key",
+                    "mock_response": "Hello from chat completion!",
                 },
                 "model_info": {"id": "chat-deployment-1"},
             },
@@ -151,41 +154,20 @@ async def test_encrypted_content_affinity_no_effect_on_chat_completions():
         optional_pre_call_checks=["encrypted_content_affinity"],
     )
 
-    mock_chat_response = {
-        "id": "chatcmpl-123",
-        "object": "chat.completion",
-        "created": 1677652288,
-        "model": "gpt-3.5-turbo",
-        "choices": [
-            {
-                "index": 0,
-                "message": {"role": "assistant", "content": "Hello!"},
-                "finish_reason": "stop",
-            }
-        ],
-        "usage": {"prompt_tokens": 5, "completion_tokens": 7, "total_tokens": 12},
-    }
+    # Multiple chat completion requests should work normally
+    response1 = await router.acompletion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "Hello"}],
+    )
+    response2 = await router.acompletion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "Hello again"}],
+    )
 
-    with patch(
-        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
-        new_callable=AsyncMock,
-    ) as mock_post:
-        mock_post.return_value = MockResponse(mock_chat_response, 200)
-
-        # Multiple chat completion requests should work normally
-        response1 = await router.acompletion(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": "Hello"}],
-        )
-        response2 = await router.acompletion(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": "Hello again"}],
-        )
-
-        # Both should succeed (no affinity interference)
-        # Check that responses have IDs (litellm may modify them)
-        assert response1.id is not None
-        assert response2.id is not None
+    # Both should succeed (no affinity interference)
+    # Check that responses have IDs
+    assert response1.id is not None
+    assert response2.id is not None
 
 
 @pytest.mark.asyncio

From adec115db82505f34c9c018238fc7cedc4e202b1 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 24 Feb 2026 23:58:50 +0530
Subject: [PATCH 25/69] Fix logging for error

---
 .../pre_call_checks/encrypted_content_affinity_check.py         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
index 9cab59ea0e0..d632dc6e0b0 100644
--- a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
+++ b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
@@ -263,7 +263,7 @@ class EncryptedContentAffinityCheck(CustomLogger):
                 ] = True
                 return [deployment]
 
-            verbose_router_logger.debug(
+            verbose_router_logger.error(
                 "EncryptedContentAffinityCheck: cached deployment=%s for item_id=%s "
                 "not found in healthy_deployments",
                 cached_model_id,

From 122f534d8762ff76e72dad0ff264bc0f51b6f7b5 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Fri, 27 Feb 2026 16:00:43 +0530
Subject: [PATCH 26/69] Add encoding method for Encrypted-content-aware
 deployment

---
 .../encrypted_content_affinity_check.py       | 251 +++++-------------
 1 file changed, 64 insertions(+), 187 deletions(-)

diff --git a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
index d632dc6e0b0..e6d691896ca 100644
--- a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
+++ b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
@@ -7,26 +7,36 @@ organization's API key. If a follow-up request containing those items is routed
 different deployment (different org), OpenAI rejects it with an `invalid_encrypted_content`
 error because the organization_id doesn't match.
 
-This callback solves the problem by:
-1. Tracking output item IDs from Responses API responses and mapping them to the
-   deployment (model_id) that produced them.
-2. On subsequent requests, scanning the `input` field for known item IDs and pinning
-   the request to the originating deployment.
+This callback solves the problem by encoding the originating deployment's ``model_id``
+directly into the item IDs of output items that carry ``encrypted_content`` (the same
+approach used by the responses-API affinity for ``previous_response_id``).  The encoded
+ID is decoded on the next request so the router can pin to the correct deployment without
+any cache lookup.
+
+Response post-processing (encoding) is handled by
+``ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response`` which is
+called inside ``_update_responses_api_response_id_with_model_id`` in ``responses/utils.py``.
+
+Request pre-processing (ID restoration before forwarding to upstream) is handled by
+``ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input`` which is called
+in ``get_optional_params_responses_api``.
+
+This pre-call check is responsible only for the routing decision: it reads the encoded
+``model_id`` out of the item IDs and pins the request to the matching deployment.
 
 Safe to enable globally:
-- Only activates when known item IDs appear in the request `input`.
+- Only activates when encoded item IDs appear in the request ``input``.
 - No effect on embedding models, chat completions, or first-time requests.
 - No quota reduction -- first requests are fully load balanced.
+- No cache required.
 """
 
 from typing import Any, List, Optional, cast
 
 from litellm._logging import verbose_router_logger
-from litellm.caching.dual_cache import DualCache
 from litellm.integrations.custom_logger import CustomLogger, Span
-from litellm.types.llms.openai import AllMessageValues, ResponsesAPIResponse
-
-_DEFAULT_TTL_SECONDS = 86400  # 24 hours
+from litellm.responses.utils import ResponsesAPIRequestUtils
+from litellm.types.llms.openai import AllMessageValues
 
 
 class EncryptedContentAffinityCheck(CustomLogger):
@@ -34,89 +44,43 @@ class EncryptedContentAffinityCheck(CustomLogger):
     Routes follow-up Responses API requests to the deployment that produced
     the encrypted output items they reference.
 
+    The ``model_id`` is decoded directly from the litellm-encoded item IDs –
+    no caching or TTL management needed.
+
     Wired via ``Router(optional_pre_call_checks=["encrypted_content_affinity"])``.
     """
 
-    CACHE_KEY_PREFIX = "encrypted_content_affinity:v1"
-
-    def __init__(
-        self,
-        cache: DualCache,
-        ttl_seconds: int = _DEFAULT_TTL_SECONDS,
-    ):
+    def __init__(self) -> None:
         super().__init__()
-        self.cache = cache
-        self.ttl_seconds = ttl_seconds
 
     # ------------------------------------------------------------------
     # Helpers
     # ------------------------------------------------------------------
 
     @staticmethod
-    def _get_output_from_response(
-        response_obj: Any,
-    ) -> Optional[list]:
+    def _extract_model_id_from_input(request_input: Any) -> Optional[str]:
         """
-        Extract the ``output`` list from a Responses API response, handling
-        both ``ResponsesAPIResponse`` objects and plain dicts.
-        """
-        if isinstance(response_obj, ResponsesAPIResponse):
-            return response_obj.output
-        if isinstance(response_obj, dict) and "output" in response_obj:
-            output = response_obj["output"]
-            if isinstance(output, list):
-                return output
-        if hasattr(response_obj, "output"):
-            output = response_obj.output
-            if isinstance(output, list):
-                return output
-        return None
-
-    @staticmethod
-    def _extract_item_ids_from_output(
-        output: list,
-    ) -> List[str]:
-        """Extract item IDs from output items that contain encrypted_content."""
-        item_ids: List[str] = []
-        for item in output:
-            item_id: Optional[str] = None
-            has_encrypted_content = False
-            
-            if isinstance(item, dict):
-                item_id = item.get("id")
-                has_encrypted_content = "encrypted_content" in item
-            else:
-                item_id = getattr(item, "id", None)
-                has_encrypted_content = hasattr(item, "encrypted_content")
-            
-            if item_id and isinstance(item_id, str) and has_encrypted_content:
-                item_ids.append(item_id)
-        return item_ids
-
-    @staticmethod
-    def _extract_item_ids_from_input(request_input: Any) -> List[str]:
-        """
-        Extract item IDs from input items that contain encrypted_content.
+        Scan ``input`` items for litellm-encoded encrypted-content item IDs and
+        return the ``model_id`` embedded in the first one found.
 
         ``input`` can be:
-        - a plain string  -> no item IDs
-        - a list of items -> only extract IDs from items with encrypted_content
+        - a plain string  -> no encoded IDs
+        - a list of items -> check each item's ``id`` field
         """
         if not isinstance(request_input, list):
-            return []
+            return None
 
-        item_ids: List[str] = []
         for item in request_input:
-            if isinstance(item, dict):
-                item_id = item.get("id")
-                has_encrypted_content = "encrypted_content" in item
-                if item_id and isinstance(item_id, str) and has_encrypted_content:
-                    item_ids.append(item_id)
-        return item_ids
+            if not isinstance(item, dict):
+                continue
+            item_id = item.get("id")
+            if not item_id or not isinstance(item_id, str):
+                continue
+            decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item_id)
+            if decoded:
+                return decoded.get("model_id")
 
-    @classmethod
-    def _cache_key(cls, item_id: str) -> str:
-        return f"{cls.CACHE_KEY_PREFIX}:{item_id}"
+        return None
 
     @staticmethod
     def _find_deployment_by_model_id(
@@ -133,82 +97,6 @@ class EncryptedContentAffinityCheck(CustomLogger):
                 return deployment
         return None
 
-    @staticmethod
-    def _get_model_id_from_kwargs(kwargs: dict) -> Optional[str]:
-        """
-        Extract the deployment model_id from success-callback kwargs.
-
-        The Router populates ``litellm_params.metadata.model_info.id`` after
-        selecting a deployment. Also check top-level ``model_info`` as a
-        fallback (some call paths set it there).
-        """
-        # Primary path: litellm_params -> metadata -> model_info -> id
-        litellm_params = kwargs.get("litellm_params")
-        if isinstance(litellm_params, dict):
-            metadata = litellm_params.get("metadata")
-            if isinstance(metadata, dict):
-                model_info = metadata.get("model_info")
-                if isinstance(model_info, dict):
-                    model_id = model_info.get("id")
-                    if model_id is not None:
-                        return str(model_id)
-
-        # Fallback: top-level model_info (set by some router call paths)
-        model_info = kwargs.get("model_info")
-        if isinstance(model_info, dict):
-            model_id = model_info.get("id")
-            if model_id is not None:
-                return str(model_id)
-
-        return None
-
-    # ------------------------------------------------------------------
-    # Response tracking  (success callback)
-    # ------------------------------------------------------------------
-
-    async def async_log_success_event(
-        self, kwargs: dict, response_obj: Any, start_time: Any, end_time: Any
-    ) -> None:
-        """
-        After a successful Responses API call, cache each output item ID
-        mapped to the deployment that produced it.
-        """
-        output = self._get_output_from_response(response_obj)
-        if output is None:
-            return
-
-        model_id = self._get_model_id_from_kwargs(kwargs)
-        if not model_id:
-            verbose_router_logger.debug(
-                "EncryptedContentAffinityCheck: model_id not found in kwargs, skipping tracking",
-            )
-            return
-
-        item_ids = self._extract_item_ids_from_output(output)
-        if not item_ids:
-            return
-
-        for item_id in item_ids:
-            try:
-                cache_key = self._cache_key(item_id)
-                await self.cache.async_set_cache(
-                    cache_key,
-                    model_id,
-                    ttl=self.ttl_seconds,
-                )
-            except Exception as e:
-                verbose_router_logger.error(
-                    "EncryptedContentAffinityCheck: failed to cache item_id=%s error=%s",
-                    item_id,
-                    e,
-                )
-
-        verbose_router_logger.debug(
-            "EncryptedContentAffinityCheck: cached %d item IDs -> deployment=%s",
-            len(item_ids),
-            model_id,
-        )
-
     # ------------------------------------------------------------------
     # Request routing  (pre-call filter)
     # ------------------------------------------------------------------
@@ -222,52 +110,41 @@ class EncryptedContentAffinityCheck(CustomLogger):
         parent_otel_span: Optional[Span] = None,
     ) -> List[dict]:
         """
-        If the request ``input`` contains items whose IDs were previously
-        tracked, pin the request to the deployment that produced them.
+        If the request ``input`` contains litellm-encoded item IDs, decode the
+        embedded ``model_id`` and pin the request to that deployment.
         """
         request_kwargs = request_kwargs or {}
         typed_healthy_deployments = cast(List[dict], healthy_deployments)
 
+        # Signal to the response post-processor that encrypted item IDs should be
+        # encoded in the output of this request.
+        litellm_metadata = request_kwargs.setdefault("litellm_metadata", {})
+        litellm_metadata["encrypted_content_affinity_enabled"] = True
+
         request_input = request_kwargs.get("input")
-        input_item_ids = self._extract_item_ids_from_input(request_input)
-        if not input_item_ids:
+        model_id = self._extract_model_id_from_input(request_input)
+        if not model_id:
             return typed_healthy_deployments
 
         verbose_router_logger.debug(
-            "EncryptedContentAffinityCheck: found %d item IDs in input, checking cache",
-            len(input_item_ids),
+            "EncryptedContentAffinityCheck: decoded model_id=%s from input item IDs",
+            model_id,
         )
 
-        for item_id in input_item_ids:
-            cache_key = self._cache_key(item_id)
-            try:
-                cached_model_id = await self.cache.async_get_cache(key=cache_key)
-            except Exception:
-                continue
-
-            if not cached_model_id or not isinstance(cached_model_id, str):
-                continue
-
-            deployment = self._find_deployment_by_model_id(
-                healthy_deployments=typed_healthy_deployments,
-                model_id=cached_model_id,
-            )
-            if deployment is not None:
-                verbose_router_logger.debug(
-                    "EncryptedContentAffinityCheck: item_id=%s pinning -> deployment=%s",
-                    item_id,
-                    cached_model_id,
-                )
-                request_kwargs[
-                    "_encrypted_content_affinity_pinned"
-                ] = True
-                return [deployment]
-
-            verbose_router_logger.error(
-                "EncryptedContentAffinityCheck: cached deployment=%s for item_id=%s "
-                "not found in healthy_deployments",
-                cached_model_id,
-                item_id,
+        deployment = self._find_deployment_by_model_id(
+            healthy_deployments=typed_healthy_deployments,
+            model_id=model_id,
+        )
+        if deployment is not None:
+            verbose_router_logger.debug(
+                "EncryptedContentAffinityCheck: pinning -> deployment=%s",
+                model_id,
             )
+            request_kwargs["_encrypted_content_affinity_pinned"] = True
+            return [deployment]
 
+        verbose_router_logger.error(
+            "EncryptedContentAffinityCheck: decoded deployment=%s not found in healthy_deployments",
+            model_id,
+        )
         return typed_healthy_deployments

From 7928d41e9ad93e8df8e1228a5bee301ab32258e4 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Fri, 27 Feb 2026 16:01:02 +0530
Subject: [PATCH 27/69] Update the routing

---
 litellm/responses/main.py  |  11 ++++
 litellm/responses/utils.py | 110 +++++++++++++++++++++++++++++++++++++
 litellm/router.py          |   5 +-
 3 files changed, 122 insertions(+), 4 deletions(-)

diff --git a/litellm/responses/main.py b/litellm/responses/main.py
index 05fd6026af2..2576ed7db31 100644
--- a/litellm/responses/main.py
+++ b/litellm/responses/main.py
@@ -745,6 +745,11 @@ def responses(
             custom_llm_provider=custom_llm_provider,
         )
 
+        # Decode any litellm-encoded encrypted-content item IDs back to their original IDs
+        input = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
+            input
+        )
+
         # Call the handler with _is_async flag instead of directly calling the async handler
         response = base_llm_http_handler.response_api_handler(
             model=model,
@@ -1617,6 +1622,12 @@ def compact_responses(
             custom_llm_provider=custom_llm_provider,
         )
 
+        # Decode any litellm-encoded encrypted-content item IDs back to their original IDs
+        # before forwarding to the upstream provider.
+        input = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
+            input
+        )
+
         # Call the handler with _is_async flag instead of directly calling the async handler
         response = base_llm_http_handler.compact_response_api_handler(
             model=model,
diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py
index 39aebb262fe..0c203dc6305 100644
--- a/litellm/responses/utils.py
+++ b/litellm/responses/utils.py
@@ -217,8 +217,118 @@ class ResponsesAPIRequestUtils:
             responses_api_response["id"] = updated_id
         else:
             responses_api_response.id = updated_id
+
+        if litellm_metadata.get("encrypted_content_affinity_enabled"):
+            responses_api_response = (
+                ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response(
+                    response=responses_api_response,
+                    model_id=model_id,
+                )
+            )
+
         return responses_api_response
 
+    @staticmethod
+    def _build_encrypted_item_id(model_id: str, item_id: str) -> str:
+        """Encode model_id into an output item ID for encrypted-content items.
+
+        Format: ``encitem_{base64("litellm:model_id:{model_id};item_id:{original_id}")}``
+        """
+        assembled = f"litellm:model_id:{model_id};item_id:{item_id}"
+        encoded = base64.b64encode(assembled.encode("utf-8")).decode("utf-8")
+        return f"encitem_{encoded}"
+
+    @staticmethod
+    def _decode_encrypted_item_id(encoded_id: str) -> Optional[Dict[str, str]]:
+        """Decode a litellm-encoded encrypted-content item ID.
+
+        Returns a dict with ``model_id`` and ``item_id`` keys, or ``None`` if
+        the string is not a litellm-encoded item ID.
+        """
+        if not encoded_id.startswith("encitem_"):
+            return None
+        try:
+            cleaned = encoded_id[len("encitem_"):]
+            # Restore any padding that may have been stripped in transit
+            missing = len(cleaned) % 4
+            if missing:
+                cleaned += "=" * (4 - missing)
+            decoded = base64.b64decode(cleaned.encode("utf-8")).decode("utf-8")
+            # Split on first ";" only so that semicolons inside item_id are preserved
+            parts = decoded.split(";", 1)
+            if len(parts) < 2:
+                return None
+            model_id = parts[0].replace("litellm:model_id:", "")
+            item_id = parts[1].replace("item_id:", "")
+            return {"model_id": model_id, "item_id": item_id}
+        except Exception:
+            return None
+
+    @staticmethod
+    def _update_encrypted_content_item_ids_in_response(
+        response: Union["ResponsesAPIResponse", Dict[str, Any]],
+        model_id: Optional[str],
+    ) -> Union["ResponsesAPIResponse", Dict[str, Any]]:
+        """Rewrite item IDs for output items that contain ``encrypted_content``.
+
+        Encodes ``model_id`` into the item ID so that follow-up requests can be
+        routed back to the originating deployment without any cache lookup.
+        """
+        if not model_id:
+            return response
+
+        output: Optional[list] = None
+        if isinstance(response, dict):
+            output = response.get("output")
+        else:
+            output = getattr(response, "output", None)
+
+        if not isinstance(output, list):
+            return response
+
+        for item in output:
+            if isinstance(item, dict):
+                item_id = item.get("id")
+                if item_id and isinstance(item_id, str) and "encrypted_content" in item:
+                    item["id"] = ResponsesAPIRequestUtils._build_encrypted_item_id(
+                        model_id, item_id
+                    )
+            else:
+                item_id = getattr(item, "id", None)
+                if (
+                    item_id
+                    and isinstance(item_id, str)
+                    and hasattr(item, "encrypted_content")
+                ):
+                    try:
+                        item.id = ResponsesAPIRequestUtils._build_encrypted_item_id(
+                            model_id, item_id
+                        )
+                    except AttributeError:
+                        pass
+
+        return response
+
+    @staticmethod
+    def _restore_encrypted_content_item_ids_in_input(request_input: Any) -> Any:
+        """Decode litellm-encoded item IDs in request input back to original IDs.
+
+        Called before forwarding the request to the upstream provider so the
+        provider receives the original item IDs it issued.
+        """
+        if not isinstance(request_input, list):
+            return request_input
+
+        for item in request_input:
+            if isinstance(item, dict):
+                item_id = item.get("id")
+                if item_id and isinstance(item_id, str):
+                    decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item_id)
+                    if decoded:
+                        item["id"] = decoded["item_id"]
+
+        return request_input
+
     @staticmethod
     def _build_responses_api_response_id(
         custom_llm_provider: Optional[str],
diff --git a/litellm/router.py b/litellm/router.py
index 7dee4fa83de..652cd68b555 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -1263,10 +1263,7 @@ class Router:
                 for cb in self.optional_callbacks
             )
             if not already_registered:
-                ec_callback = EncryptedContentAffinityCheck(
-                    cache=self.cache,
-                    ttl_seconds=self.deployment_affinity_ttl_seconds,
-                )
+                ec_callback = EncryptedContentAffinityCheck()
                 self.optional_callbacks.append(ec_callback)
                 litellm.logging_callback_manager.add_litellm_callback(ec_callback)
 

From 37834f1d2a2adc5c80987f940f935fa56bbeb838 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Fri, 27 Feb 2026 16:01:23 +0530
Subject: [PATCH 28/69] Update the docs

---
 .../index.md                                  | 90 +++++++++++--------
 docs/my-website/docs/response_api.md          | 34 +++----
 2 files changed, 62 insertions(+), 62 deletions(-)

diff --git a/docs/my-website/blog/responses_api_encrypted_content_incident/index.md b/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
index 8422f6d3cd0..f229f9567da 100644
--- a/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
+++ b/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
@@ -119,47 +119,59 @@ Implemented a new `encrypted_content_affinity` pre-call check that intelligently
 
 ### Implementation
 
-**1. New `EncryptedContentAffinityCheck` Class** ([`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py))
+**1. Encoding `model_id` into output item IDs** ([`responses/utils.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/responses/utils.py))
+
+The same approach used for `previous_response_id` affinity — no cache needed. When a response contains output items with `encrypted_content`, LiteLLM rewrites their IDs to embed the originating deployment's `model_id`:
+
+```python
+# On response: rs_abc123 → encitem_{base64("litellm:model_id:{model_id};item_id:rs_abc123")}
+def _build_encrypted_item_id(model_id: str, item_id: str) -> str:
+    assembled = f"litellm:model_id:{model_id};item_id:{item_id}"
+    encoded = base64.b64encode(assembled.encode("utf-8")).decode("utf-8")
+    return f"encitem_{encoded}"
+
+# On request: decode encitem_... → extract model_id for routing
+def _decode_encrypted_item_id(encoded_id: str) -> Optional[Dict[str, str]]:
+    if not encoded_id.startswith("encitem_"):
+        return None
+    cleaned = encoded_id[len("encitem_"):]
+    missing = len(cleaned) % 4
+    if missing:
+        cleaned += "=" * (4 - missing)  # restore padding stripped in transit
+    decoded = base64.b64decode(cleaned).decode("utf-8")
+    model_id, item_id = decoded.split(";", 1)
+    return {"model_id": model_id.replace("litellm:model_id:", ""),
+            "item_id": item_id.replace("item_id:", "")}
+```
+
+Before forwarding to the upstream provider, LiteLLM restores the original item IDs so the provider never sees the encoded form:
+
+```python
+# In responses/main.py — before calling the handler
+input = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(input)
+```
+
+**2. `EncryptedContentAffinityCheck` — routing only** ([`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py))
+
+No `async_log_success_event` or cache lookups — the `model_id` is decoded directly from the item ID:
 
 ```python
 class EncryptedContentAffinityCheck(CustomLogger):
-    """
-    Routes follow-up Responses API requests to the deployment that produced
-    the encrypted output items they reference.
-    """
-    
-    async def async_log_success_event(self, kwargs, response_obj, ...):
-        """Track: Extract item IDs from response output, cache item_id → deployment_id"""
-        output = self._get_output_from_response(response_obj)
-        item_ids = self._extract_item_ids_from_output(output)
-        model_id = self._get_model_id_from_kwargs(kwargs)
-        
-        for item_id in item_ids:
-            await self.cache.async_set_cache(
-                f"encrypted_content_affinity:v1:{item_id}",
-                model_id,
-                ttl=86400,  # 24 hours
-            )
-    
     async def async_filter_deployments(self, model, healthy_deployments, ...):
-        """Route: Check if input contains tracked items, pin to originating deployment"""
-        input_item_ids = self._extract_item_ids_from_input(request_kwargs.get("input"))
-        
-        for item_id in input_item_ids:
-            cached_model_id = await self.cache.async_get_cache(f"...:{item_id}")
-            if cached_model_id:
+        """Decode encitem_ IDs in input to extract model_id and pin to that deployment."""
+        for item in request_kwargs.get("input", []):
+            decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item.get("id", ""))
+            if decoded:
                 deployment = self._find_deployment_by_model_id(
-                    healthy_deployments, cached_model_id
+                    healthy_deployments, decoded["model_id"]
                 )
                 if deployment:
-                    # Signal to bypass rate limits (encrypted content must go here)
                     request_kwargs["_encrypted_content_affinity_pinned"] = True
                     return [deployment]
-        
-        return healthy_deployments  # Normal load balancing
+        return healthy_deployments
 ```
 
-**2. Rate Limit Bypass** ([`router.py#L8656-L8660`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py#L8656-L8660))
+**3. Rate Limit Bypass** ([`router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py))
 
 When encrypted content requires a specific deployment, RPM/TPM limits are bypassed (the request would fail on any other deployment anyway):
 
@@ -185,9 +197,10 @@ router_settings:
 
 ### Key Benefits
 
-✅ **No quota reduction**: Only pins requests containing tracked encrypted items  
+✅ **No quota reduction**: Only pins requests containing encrypted items  
 ✅ **Bypasses rate limits**: When encrypted content requires a specific deployment, RPM/TPM limits don't block it  
-✅ **No `previous_response_id` required**: Works by tracking item IDs in response output  
+✅ **No `previous_response_id` required**: Works by encoding `model_id` directly into the item ID  
+✅ **No cache required**: `model_id` is decoded on-the-fly from the item ID — no Redis, no TTL  
 ✅ **Globally safe**: Can be enabled for all models; non-Responses-API calls are unaffected  
 ✅ **Surgical precision**: Normal requests continue to load balance freely
 
@@ -197,12 +210,13 @@ router_settings:
 
 | # | Action | Status | Code |
 |---|---|---|---|
-| 1 | Create `EncryptedContentAffinityCheck` class with tracking and routing logic | ✅ Done | [`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py) |
-| 2 | Add `encrypted_content_affinity` to `OptionalPreCallChecks` type | ✅ Done | [`router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/types/router.py) |
-| 3 | Wire up check in `Router.add_optional_pre_call_checks` | ✅ Done | [`router.py#L8656-L8660`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py#L8656-L8660) |
-| 4 | Implement rate limit bypass for affinity-pinned requests | ✅ Done | [`router.py#L8656-L8660`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py#L8656-L8660) |
-| 5 | Unit tests: tracking, routing, no-op for non-Responses-API, RPM bypass | ✅ Done | [`test_encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py) |
-| 6 | Documentation: Responses API guide, load balancing guide, config reference | ✅ Done | [Docs](https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing) |
+| 1 | Encode `model_id` into encrypted-content item IDs on response | ✅ Done | [`responses/utils.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/responses/utils.py) |
+| 2 | Restore original item IDs before forwarding to upstream provider | ✅ Done | [`responses/main.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/responses/main.py) |
+| 3 | `EncryptedContentAffinityCheck`: decode item IDs to route (no cache) | ✅ Done | [`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py) |
+| 4 | Add `encrypted_content_affinity` to `OptionalPreCallChecks` type | ✅ Done | [`types/router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/types/router.py) |
+| 5 | Implement rate limit bypass for affinity-pinned requests | ✅ Done | [`router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py) |
+| 6 | Unit tests: encoding/decoding utilities, routing, RPM bypass | ✅ Done | [`test_encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py) |
+| 7 | Documentation: Responses API guide, load balancing guide, config reference | ✅ Done | [Docs](https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing) |
 
 ---
 
diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md
index 85144a80245..a7cf61ef16a 100644
--- a/docs/my-website/docs/response_api.md
+++ b/docs/my-website/docs/response_api.md
@@ -1011,24 +1011,25 @@ This error occurs when:
 
 ### The Solution: `encrypted_content_affinity`
 
-The `encrypted_content_affinity` pre-call check intelligently tracks encrypted content and routes follow-up requests to the originating deployment **only when necessary**.
+The `encrypted_content_affinity` pre-call check routes follow-up requests containing encrypted items to the originating deployment **only when necessary**
 
 **Key Benefits:**
-- ✅ **No quota reduction**: Unlike `deployment_affinity`, only pins requests that contain tracked encrypted items
+- ✅ **No quota reduction**: Unlike `deployment_affinity`, only pins requests that contain encrypted items
 - ✅ **Bypasses rate limits**: When encrypted content requires a specific deployment, RPM/TPM limits are bypassed (the request would fail on any other deployment anyway)
-- ✅ **No `previous_response_id` required**: Works by tracking item IDs in response output and matching them in request input
+- ✅ **No `previous_response_id` required**: Works by encoding `model_id` directly into item IDs
+- ✅ **No cache required**: `model_id` is decoded on-the-fly — no Redis dependency, no TTL to manage
 - ✅ **Globally safe**: Can be enabled for all models; non-Responses-API calls (chat, embeddings) are unaffected
 
 ### How It Works
 
-1. **Tracking Phase** (after successful response):
-   - Extracts all item IDs from response `output` (e.g., `msg_abc`, `rs_xyz`)
-   - Caches mapping: `item_id` → `deployment_id` (default TTL: 24 hours)
+1. **Encoding Phase** (on response):
+   - For each output item that contains `encrypted_content`, LiteLLM rewrites the item ID to embed the originating `model_id`: `rs_xyz` → `encitem_{base64("litellm:model_id:{model_id};item_id:rs_xyz")}`
+   - The original item ID is restored before forwarding the request to the upstream provider
 
 2. **Routing Phase** (before request):
-   - Scans request `input` for item IDs
-   - If tracked item found → pins to originating deployment, bypasses rate limits
-   - If no tracked items → normal load balancing
+   - Scans request `input` for `encitem_` prefixed IDs
+   - If found → decodes `model_id`, pins to originating deployment, bypasses rate limits
+   - If no encoded items → normal load balancing
 
 ### Configuration
 
@@ -1058,7 +1059,6 @@ router = Router(
         },
     ],
     optional_pre_call_checks=["encrypted_content_affinity"],
-    deployment_affinity_ttl_seconds=86400,  # 24 hours (default)
 )
 
 # Initial request - routes to any deployment
@@ -1104,7 +1104,6 @@ router_settings:
   enable_pre_call_checks: true
   optional_pre_call_checks:
     - encrypted_content_affinity
-  deployment_affinity_ttl_seconds: 86400  # Optional, default is 86400 (24 hours)
 ```
 
 **Start proxy:**
@@ -1124,19 +1123,6 @@ litellm --config config.yaml
 | `session_affinity` | Session-based applications | All requests with same `session_id` | ⚠️ Reduces quota by # of sessions |
 | `deployment_affinity` | Simple sticky sessions | All requests from same API key | ❌ Reduces quota by # of users |
 
-### Multi-Instance Deployment (Redis)
-
-For multiple LiteLLM proxy instances, use Redis to share affinity state:
-
-```yaml
-router_settings:
-  optional_pre_call_checks:
-    - encrypted_content_affinity
-  redis_host: redis.example.com
-  redis_port: 6379
-  redis_password: your-password
-```
-
 
 ## Calling non-Responses API endpoints (`/responses` to `/chat/completions` Bridge)
 

From 2bc4da76ce62070bd753e9a3593c879060da389f Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Fri, 27 Feb 2026 16:01:38 +0530
Subject: [PATCH 29/69] Update the tests

---
 .../test_encrypted_content_affinity_check.py  | 242 ++++++++++++++----
 1 file changed, 195 insertions(+), 47 deletions(-)

diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
index cfebaab346f..66177208f4f 100644
--- a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
@@ -1,17 +1,31 @@
-import asyncio
+"""
+Tests for encrypted_content_affinity pre-call check.
+
+The mechanism works without any cache:
+- On response: item IDs for output items with `encrypted_content` are rewritten to
+  `encitem_{base64("litellm:model_id:{model_id};item_id:{original_id}")}`.
+- On routing: `EncryptedContentAffinityCheck` decodes the `encitem_` prefix to extract
+  `model_id` and pins the request to that deployment.
+- Before forwarding: `_restore_encrypted_content_item_ids_in_input` decodes the IDs back
+  to their original form before sending to the upstream provider.
+"""
+
 import os
 import sys
 from unittest.mock import AsyncMock, patch
 
 import pytest
-import respx
-from httpx import Response
 
 sys.path.insert(0, os.path.abspath("../.."))
 
 import json
 
 import litellm
+from litellm.responses.utils import ResponsesAPIRequestUtils
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
 
 
 class MockResponse:
@@ -25,12 +39,147 @@ class MockResponse:
         return self._json_data
 
 
+def _get_item_id(item) -> str:
+    """Extract item ID from either a Pydantic model or a dict."""
+    if isinstance(item, dict):
+        return item.get("id", "")
+    return getattr(item, "id", "") or ""
+
+
+def _has_encrypted_content(item) -> bool:
+    """Check whether an output item carries encrypted_content."""
+    if isinstance(item, dict):
+        return "encrypted_content" in item
+    return hasattr(item, "encrypted_content") and getattr(item, "encrypted_content") is not None
+
+
+def _extract_encoded_item_id(response) -> str:
+    """
+    Walk the response output and return the first litellm-encoded item ID
+    (i.e. one that starts with ``encitem_``).
+    """
+    for item in response.output or []:
+        item_id = _get_item_id(item)
+        if item_id.startswith("encitem_"):
+            return item_id
+    return ""
+
+
+# ---------------------------------------------------------------------------
+# Unit tests for encoding / decoding utilities
+# ---------------------------------------------------------------------------
+
+
+class TestEncryptedItemIdCodec:
+    def test_roundtrip(self):
+        model_id = "deployment-1"
+        original_item_id = "rs_abc123def456"
+        encoded = ResponsesAPIRequestUtils._build_encrypted_item_id(model_id, original_item_id)
+        assert encoded.startswith("encitem_")
+        decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(encoded)
+        assert decoded is not None
+        assert decoded["model_id"] == model_id
+        assert decoded["item_id"] == original_item_id
+
+    def test_decode_without_padding(self):
+        """Decoding must succeed even if base64 padding (=) was stripped in transit."""
+        model_id = "gpt-5.1-codex-openai-2"
+        original_item_id = "rs_0efb96cb222403210069a01d5d52588196a9dc394ffdb89d00"
+        encoded = ResponsesAPIRequestUtils._build_encrypted_item_id(model_id, original_item_id)
+        # Strip any trailing '=' to simulate what happens in transit
+        stripped = encoded.rstrip("=")
+        decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(stripped)
+        assert decoded is not None
+        assert decoded["model_id"] == model_id
+        assert decoded["item_id"] == original_item_id
+
+    def test_non_encoded_id_returns_none(self):
+        assert ResponsesAPIRequestUtils._decode_encrypted_item_id("rs_abc123") is None
+        assert ResponsesAPIRequestUtils._decode_encrypted_item_id("msg_abc") is None
+        assert ResponsesAPIRequestUtils._decode_encrypted_item_id("") is None
+
+    def test_semicolon_in_item_id(self):
+        """item_id values containing ';' must survive the roundtrip."""
+        model_id = "deployment-1"
+        original_item_id = "rs_part1;part2;part3"
+        encoded = ResponsesAPIRequestUtils._build_encrypted_item_id(model_id, original_item_id)
+        decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(encoded)
+        assert decoded is not None
+        assert decoded["item_id"] == original_item_id
+
+
+class TestUpdateEncryptedContentItemIds:
+    def test_rewrites_encrypted_items_in_dict_response(self):
+        model_id = "deployment-1"
+        response = {
+            "id": "resp_123",
+            "output": [
+                {"id": "msg_abc", "type": "message", "content": []},
+                {"id": "rs_xyz", "type": "reasoning", "encrypted_content": "secret"},
+            ],
+        }
+        result = ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response(
+            response, model_id
+        )
+        # Plain message item untouched
+        assert result["output"][0]["id"] == "msg_abc"
+        # Reasoning item with encrypted_content gets encoded
+        encoded_id = result["output"][1]["id"]
+        assert encoded_id.startswith("encitem_")
+        decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(encoded_id)
+        assert decoded["model_id"] == model_id
+        assert decoded["item_id"] == "rs_xyz"
+
+    def test_no_op_when_model_id_is_none(self):
+        response = {
+            "output": [{"id": "rs_xyz", "type": "reasoning", "encrypted_content": "secret"}]
+        }
+        result = ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response(
+            response, None
+        )
+        assert result["output"][0]["id"] == "rs_xyz"
+
+
+class TestRestoreEncryptedContentItemIds:
+    def test_restores_encoded_ids(self):
+        model_id = "deployment-1"
+        original_id = "rs_encrypted_item_456"
+        encoded_id = ResponsesAPIRequestUtils._build_encrypted_item_id(model_id, original_id)
+
+        request_input = [
+            {"type": "message", "id": "msg_abc123", "role": "assistant"},
+            {"type": "reasoning", "id": encoded_id, "encrypted_content": "secret"},
+        ]
+        restored = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
+            request_input
+        )
+        assert restored[0]["id"] == "msg_abc123"
+        assert restored[1]["id"] == original_id
+
+    def test_no_op_for_plain_string_input(self):
+        result = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
+            "Hello world"
+        )
+        assert result == "Hello world"
+
+    def test_no_op_for_unencoded_ids(self):
+        request_input = [{"type": "message", "id": "msg_plain"}]
+        result = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
+            request_input
+        )
+        assert result[0]["id"] == "msg_plain"
+
+
+# ---------------------------------------------------------------------------
+# Integration tests (router-level)
+# ---------------------------------------------------------------------------
+
+
 @pytest.mark.asyncio
 async def test_encrypted_content_affinity_tracks_and_routes():
     """
-    When encrypted_content_affinity is enabled, output item IDs from responses
-    are tracked, and follow-up requests containing those IDs route to the same
-    deployment.
+    The first response rewrites encrypted-content item IDs to encoded form.
+    The follow-up request with those encoded IDs is pinned to the same deployment.
     """
     mock_response_data = {
         "id": "resp_mock-123",
@@ -54,11 +203,7 @@ async def test_encrypted_content_affinity_tracks_and_routes():
             },
         ],
         "parallel_tool_calls": True,
-        "usage": {
-            "input_tokens": 5,
-            "output_tokens": 10,
-            "total_tokens": 15,
-        },
+        "usage": {"input_tokens": 5, "output_tokens": 10, "total_tokens": 15},
         "error": None,
     }
 
@@ -84,14 +229,9 @@ async def test_encrypted_content_affinity_tracks_and_routes():
         optional_pre_call_checks=["encrypted_content_affinity"],
     )
 
-    model_group = "openai.gpt-5.1-codex"
-
-    # Track which deployment was selected
     selected_deployments = []
 
     def deterministic_choice(seq):
-        # First call: select deployment-1
-        # Second call: would select deployment-2, but affinity should override
         if len(selected_deployments) == 0:
             return seq[0]
         return seq[1] if len(seq) > 1 else seq[0]
@@ -105,39 +245,49 @@ async def test_encrypted_content_affinity_tracks_and_routes():
     ):
         mock_post.return_value = MockResponse(mock_response_data, 200)
 
-        # First request: no encrypted items in input
+        # First request — goes to deployment-1 via deterministic_choice
         first_response = await router.aresponses(
-            model=model_group,
+            model="openai.gpt-5.1-codex",
             input="Hello, how are you?",
         )
         first_model_id = first_response._hidden_params["model_id"]
         selected_deployments.append(first_model_id)
 
-        # Give async callbacks time to run
-        await asyncio.sleep(0.2)
+        # The response must have rewritten the encrypted item's ID to encoded form
+        encoded_item_id = _extract_encoded_item_id(first_response)
+        assert encoded_item_id.startswith("encitem_"), (
+            f"Expected output item ID to be rewritten to encitem_... but got {encoded_item_id!r}"
+        )
 
-        # Second request: includes encrypted item IDs from first response
+        # Verify the encoded ID decodes back to the correct deployment + original ID
+        decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(encoded_item_id)
+        assert decoded is not None
+        assert decoded["model_id"] == first_model_id
+        assert decoded["item_id"] == "rs_encrypted_item_456"
+
+        # Second request: use the encoded item IDs from the first response
         second_response = await router.aresponses(
-            model=model_group,
+            model="openai.gpt-5.1-codex",
             input=[
                 {"type": "message", "id": "msg_abc123", "role": "assistant"},
-                {"type": "reasoning", "id": "rs_encrypted_item_456", "encrypted_content": "gAAAAABpnW_yEYmSNEyOG..."},
+                {
+                    "type": "reasoning",
+                    "id": encoded_item_id,
+                    "encrypted_content": "gAAAAABpnW_yEYmSNEyOG...",
+                },
             ],
         )
         second_model_id = second_response._hidden_params["model_id"]
 
-        # Affinity should route to the same deployment
         assert second_model_id == first_model_id, (
-            f"Expected affinity to route to {first_model_id}, "
-            f"but got {second_model_id}"
+            f"Expected affinity to route to {first_model_id}, but got {second_model_id}"
         )
 
 
 @pytest.mark.asyncio
 async def test_encrypted_content_affinity_no_effect_on_chat_completions():
     """
-    Encrypted content affinity should not affect regular chat completions
-    (they don't use the Responses API).
+    Encrypted content affinity should not affect regular chat completions.
     """
     router = litellm.Router(
         model_list=[
@@ -154,7 +304,6 @@ async def test_encrypted_content_affinity_no_effect_on_chat_completions():
         optional_pre_call_checks=["encrypted_content_affinity"],
     )
 
-    # Multiple chat completion requests should work normally
     response1 = await router.acompletion(
         model="gpt-3.5-turbo",
         messages=[{"role": "user", "content": "Hello"}],
@@ -163,9 +312,6 @@ async def test_encrypted_content_affinity_no_effect_on_chat_completions():
         model="gpt-3.5-turbo",
         messages=[{"role": "user", "content": "Hello again"}],
     )
-
-    # Both should succeed (no affinity interference)
-    # Check that responses have IDs
     assert response1.id is not None
     assert response2.id is not None
 
@@ -173,8 +319,8 @@ async def test_encrypted_content_affinity_no_effect_on_chat_completions():
 @pytest.mark.asyncio
 async def test_encrypted_content_affinity_bypasses_rpm_limits():
     """
-    When encrypted content affinity pins to a deployment, it should bypass
-    RPM limits since the encrypted content will fail on any other deployment.
+    When encrypted content affinity pins to a deployment, RPM limits are bypassed
+    since the request would fail on any other deployment anyway.
     """
     mock_response_data = {
         "id": "resp_mock-rpm-test",
@@ -225,34 +371,40 @@ async def test_encrypted_content_affinity_bypasses_rpm_limits():
     ) as mock_post:
         mock_post.return_value = MockResponse(mock_response_data, 200)
 
-        # First request goes to the low-RPM deployment
         first_response = await router.aresponses(
             model="openai.gpt-5.1-codex",
             input="Initial request",
         )
         first_model_id = first_response._hidden_params["model_id"]
 
-        await asyncio.sleep(0.2)
+        # Extract encoded item ID from the first response output
+        encoded_item_id = _extract_encoded_item_id(first_response)
+        assert encoded_item_id.startswith("encitem_"), (
+            f"Expected encitem_... but got {encoded_item_id!r}"
+        )
 
-        # Second request with encrypted content should pin to the same deployment
-        # even though it's at RPM limit
+        # Follow-up with the encoded item ID — should pin to same deployment
+        # even if it is at its RPM limit
         second_response = await router.aresponses(
             model="openai.gpt-5.1-codex",
             input=[
-                {"type": "reasoning", "id": "rs_encrypted_must_pin", "encrypted_content": "gAAAAABpnW_yEYmSNEyOG..."},
+                {
+                    "type": "reasoning",
+                    "id": encoded_item_id,
+                    "encrypted_content": "gAAAAABpnW_yEYmSNEyOG...",
+                },
             ],
         )
         second_model_id = second_response._hidden_params["model_id"]
 
-        # Should route to the same deployment despite RPM limit
         assert second_model_id == first_model_id
 
 
 @pytest.mark.asyncio
 async def test_encrypted_content_affinity_no_match_normal_routing():
     """
-    When input contains item IDs that aren't tracked, normal load balancing
-    should occur.
+    Input items with non-encoded IDs (no encitem_ prefix) fall through to
+    normal load balancing.
     """
     mock_response_data = {
         "id": "resp_mock-no-match",
@@ -301,15 +453,11 @@ async def test_encrypted_content_affinity_no_match_normal_routing():
     ) as mock_post:
         mock_post.return_value = MockResponse(mock_response_data, 200)
 
-        # Request with unknown item IDs should use normal routing
+        # Non-encoded item ID — no affinity should kick in
         response = await router.aresponses(
             model="openai.gpt-5.1-codex",
             input=[
                 {"type": "message", "id": "unknown_item_id_12345"},
             ],
         )
-
-        # Should succeed with normal routing (litellm may modify the ID)
         assert response.id is not None
-        # Verify it contains the original response ID in some form
-        assert "resp_mock-no-match" in str(response.id) or response.id.startswith("resp_")

From 521f804350069acbbf88025d77e805b35d41affa Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 18:13:03 +0530
Subject: [PATCH 30/69] Fix encrypted content streaming affinity issue

---
 .../index.md                                  | 118 ++++++--
 .../exception_mapping_utils.py                |  45 ++-
 litellm/responses/streaming_iterator.py       |  30 +-
 litellm/responses/utils.py                    | 108 ++++++-
 .../encrypted_content_affinity_check.py       |  52 +++-
 .../azure/test_azure_exception_mapping.py     |  57 +++-
 .../test_encrypted_content_affinity_check.py  | 280 +++++++++++++++++-
 7 files changed, 624 insertions(+), 66 deletions(-)

diff --git a/docs/my-website/blog/responses_api_encrypted_content_incident/index.md b/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
index f229f9567da..19b55898caa 100644
--- a/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
+++ b/docs/my-website/blog/responses_api_encrypted_content_incident/index.md
@@ -119,32 +119,36 @@ Implemented a new `encrypted_content_affinity` pre-call check that intelligently
 
 ### Implementation
 
-**1. Encoding `model_id` into output item IDs** ([`responses/utils.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/responses/utils.py))
+**1. Encoding `model_id` into output items** ([`responses/utils.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/responses/utils.py))
 
-The same approach used for `previous_response_id` affinity — no cache needed. When a response contains output items with `encrypted_content`, LiteLLM rewrites their IDs to embed the originating deployment's `model_id`:
+The same approach used for `previous_response_id` affinity — no cache needed. When a response contains output items with `encrypted_content`, LiteLLM encodes the originating deployment's `model_id` in **two places** for redundancy:
+
+1. **Into the item ID** (if present): `rs_abc123` → `encitem_{base64("litellm:model_id:{model_id};item_id:rs_abc123")}`
+2. **Into the encrypted_content itself**: Wraps the content with `litellm_enc:{base64("model_id:{model_id}")};{original_encrypted_content}`
 
 ```python
-# On response: rs_abc123 → encitem_{base64("litellm:model_id:{model_id};item_id:rs_abc123")}
+# Encoding item IDs (when present)
 def _build_encrypted_item_id(model_id: str, item_id: str) -> str:
     assembled = f"litellm:model_id:{model_id};item_id:{item_id}"
     encoded = base64.b64encode(assembled.encode("utf-8")).decode("utf-8")
     return f"encitem_{encoded}"
 
-# On request: decode encitem_... → extract model_id for routing
-def _decode_encrypted_item_id(encoded_id: str) -> Optional[Dict[str, str]]:
-    if not encoded_id.startswith("encitem_"):
-        return None
-    cleaned = encoded_id[len("encitem_"):]
-    missing = len(cleaned) % 4
-    if missing:
-        cleaned += "=" * (4 - missing)  # restore padding stripped in transit
-    decoded = base64.b64decode(cleaned).decode("utf-8")
-    model_id, item_id = decoded.split(";", 1)
-    return {"model_id": model_id.replace("litellm:model_id:", ""),
-            "item_id": item_id.replace("item_id:", "")}
+# Wrapping encrypted_content (always, for redundancy)
+def _wrap_encrypted_content_with_model_id(encrypted_content: str, model_id: str) -> str:
+    metadata = f"model_id:{model_id}"
+    encoded_metadata = base64.b64encode(metadata.encode("utf-8")).decode("utf-8")
+    return f"litellm_enc:{encoded_metadata};{encrypted_content}"
 ```
 
-Before forwarding to the upstream provider, LiteLLM restores the original item IDs so the provider never sees the encoded form:
+**Why wrap encrypted_content directly?** Some clients (like Codex) don't consistently send item IDs in follow-up requests, but they always send the `encrypted_content` itself. By embedding `model_id` into the content, affinity works even when IDs are missing.
+
+**Streaming responses:** The wrapping logic is applied to both:
+- Final response objects (non-streaming)
+- Individual streaming events (`response.output_item.added`, `response.output_item.done`)
+
+This ensures clients receiving streaming responses get wrapped content they can send back.
+
+Before forwarding to the upstream provider, LiteLLM restores the original item IDs and unwraps encrypted_content so the provider never sees the encoded form:
 
 ```python
 # In responses/main.py — before calling the handler
@@ -153,22 +157,43 @@ input = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(in
 
 **2. `EncryptedContentAffinityCheck` — routing only** ([`encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py))
 
-No `async_log_success_event` or cache lookups — the `model_id` is decoded directly from the item ID:
+No `async_log_success_event` or cache lookups — the `model_id` is decoded directly from the item ID or encrypted_content:
 
 ```python
 class EncryptedContentAffinityCheck(CustomLogger):
     async def async_filter_deployments(self, model, healthy_deployments, ...):
-        """Decode encitem_ IDs in input to extract model_id and pin to that deployment."""
+        """Extract model_id from input items (ID or encrypted_content) and pin to that deployment."""
         for item in request_kwargs.get("input", []):
-            decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item.get("id", ""))
-            if decoded:
+            # Try to extract model_id from two sources:
+            model_id = self._extract_model_id_from_input(item)
+            
+            if model_id:
                 deployment = self._find_deployment_by_model_id(
-                    healthy_deployments, decoded["model_id"]
+                    healthy_deployments, model_id
                 )
                 if deployment:
                     request_kwargs["_encrypted_content_affinity_pinned"] = True
                     return [deployment]
         return healthy_deployments
+    
+    def _extract_model_id_from_input(self, item: dict) -> Optional[str]:
+        """Extract model_id from either encoded ID or wrapped encrypted_content."""
+        # 1. Try decoding from item ID (if present)
+        item_id = item.get("id", "")
+        if item_id:
+            decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item_id)
+            if decoded:
+                return decoded["model_id"]
+        
+        # 2. Try unwrapping from encrypted_content (fallback for clients that omit IDs)
+        encrypted_content = item.get("encrypted_content", "")
+        if encrypted_content and encrypted_content.startswith("litellm_enc:"):
+            model_id, _ = ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
+                encrypted_content
+            )
+            return model_id
+        
+        return None
 ```
 
 **3. Rate Limit Bypass** ([`router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py))
@@ -217,6 +242,57 @@ router_settings:
 | 5 | Implement rate limit bypass for affinity-pinned requests | ✅ Done | [`router.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/router.py) |
 | 6 | Unit tests: encoding/decoding utilities, routing, RPM bypass | ✅ Done | [`test_encrypted_content_affinity_check.py`](https://github.com/BerriAI/litellm/blob/main/litellm/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py) |
 | 7 | Documentation: Responses API guide, load balancing guide, config reference | ✅ Done | [Docs](https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing) |
+| 8 | **[Mar 3]** Fix streaming events to wrap encrypted_content | ✅ Done | [`responses/streaming_iterator.py`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm/responses/streaming_iterator.py) |
+
+---
+
+## Follow-up Fix: Streaming Responses (Mar 3, 2026)
+
+### The Issue
+
+After the initial fix was deployed, users reported that the `invalid_encrypted_content` error **still occurred** when using streaming responses with clients like Codex. Investigation revealed:
+
+- ✅ Non-streaming responses: `encrypted_content` was correctly wrapped with `litellm_enc:` prefix
+- ❌ Streaming responses: Individual `response.output_item.added` and `response.output_item.done` events contained **raw, unwrapped** `encrypted_content`
+
+Since Codex and other clients consume responses as streams, they received unwrapped content in these events and sent it back in follow-up requests, causing the affinity check to fail.
+
+### The Root Cause
+
+The `_update_encrypted_content_item_ids_in_response` function only modified the **final** response object, which is used for non-streaming responses. For streaming responses, individual chunks are processed by `ResponsesAPIStreamingIterator._process_chunk`, which was **not** applying the wrapping logic to streaming events.
+
+### The Fix
+
+Modified `litellm/litellm/responses/streaming_iterator.py` to wrap `encrypted_content` in streaming events:
+
+```python
+# In ResponsesAPIStreamingIterator._process_chunk
+if (
+    self.litellm_metadata
+    and self.litellm_metadata.get("encrypted_content_affinity_enabled")
+):
+    event_type = getattr(openai_responses_api_chunk, "type", None)
+    if event_type in (
+        ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED,
+        ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE,
+    ):
+        item = getattr(openai_responses_api_chunk, "item", None)
+        if item:
+            encrypted_content = getattr(item, "encrypted_content", None)
+            if encrypted_content and isinstance(encrypted_content, str):
+                model_id = (
+                    self.litellm_metadata.get("model_info", {}).get("id")
+                    if self.litellm_metadata
+                    else None
+                )
+                if model_id:
+                    wrapped_content = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+                        encrypted_content, model_id
+                    )
+                    setattr(item, "encrypted_content", wrapped_content)
+```
+
+This ensures that **all** `encrypted_content` sent to clients (streaming or non-streaming) is wrapped with `model_id` metadata, enabling consistent affinity routing.
 
 ---
 
diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py
index dde44cced36..951485130b3 100644
--- a/litellm/litellm_core_utils/exception_mapping_utils.py
+++ b/litellm/litellm_core_utils/exception_mapping_utils.py
@@ -1,9 +1,9 @@
 import json
+import re
 import traceback
 from typing import Any, Optional
 
 import httpx
-import re
 
 import litellm
 from litellm._logging import verbose_logger
@@ -443,6 +443,27 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         response=getattr(original_exception, "response", None),
                         litellm_debug_info=extra_information,
                     )
+                elif "invalid_encrypted_content" in error_str or "could not be verified" in error_str:
+                    exception_mapping_worked = True
+                    helpful_message = (
+                        f"{exception_provider} - {message}\n\n"
+                        " This error occurs when load balancing Responses API across deployments with different API keys.\n"
+                        "   Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n"
+                        "   Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n"
+                        "   router_settings:\n"
+                        "     enable_pre_call_checks: true\n"
+                        "     optional_pre_call_checks:\n"
+                        "       - encrypted_content_affinity\n\n"
+                        "   Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing"
+                    )
+                    raise BadRequestError(
+                        message=helpful_message,
+                        llm_provider=custom_llm_provider,
+                        model=model,
+                        response=getattr(original_exception, "response", None),
+                        litellm_debug_info=extra_information,
+                        body=getattr(original_exception, "body", None),
+                    )
                 elif (
                     "invalid_request_error" in error_str
                     and "Incorrect API key provided" not in error_str
@@ -2126,7 +2147,27 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                         extra_information=extra_information,
                         original_exception=original_exception,
                     )
-                    
+                elif azure_error_code == "invalid_encrypted_content" or "could not be verified" in error_str:
+                    exception_mapping_worked = True
+                    helpful_message = (
+                        f"AzureException - {message}\n\n"
+                        "This error occurs when load balancing Responses API across deployments with different API keys.\n"
+                        "   Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n"
+                        "   Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n"
+                        "   router_settings:\n"
+                        "     enable_pre_call_checks: true\n"
+                        "     optional_pre_call_checks:\n"
+                        "       - encrypted_content_affinity\n\n"
+                        "   Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing"
+                    )
+                    raise BadRequestError(
+                        message=helpful_message,
+                        llm_provider="azure",
+                        model=model,
+                        litellm_debug_info=extra_information,
+                        response=getattr(original_exception, "response", None),
+                        body=getattr(original_exception, "body", None),
+                    )
                 elif "invalid_request_error" in error_str:
                     exception_mapping_worked = True
                     raise BadRequestError(
diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py
index 43ef4610b4b..f61f108c992 100644
--- a/litellm/responses/streaming_iterator.py
+++ b/litellm/responses/streaming_iterator.py
@@ -8,7 +8,10 @@ from typing import Any, Dict, Optional
 import httpx
 
 import litellm
-from litellm.constants import LITELLM_MAX_STREAMING_DURATION_SECONDS, STREAM_SSE_DONE_STRING
+from litellm.constants import (
+    LITELLM_MAX_STREAMING_DURATION_SECONDS,
+    STREAM_SSE_DONE_STRING,
+)
 from litellm.litellm_core_utils.asyncify import run_async_function
 from litellm.litellm_core_utils.core_helpers import process_response_headers
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -137,6 +140,31 @@ class BaseResponsesAPIStreamingIterator:
                     )
                     setattr(openai_responses_api_chunk, "response", response)
 
+                # Wrap encrypted_content in streaming events (output_item.added, output_item.done)
+                if (
+                    self.litellm_metadata
+                    and self.litellm_metadata.get("encrypted_content_affinity_enabled")
+                ):
+                    event_type = getattr(openai_responses_api_chunk, "type", None)
+                    if event_type in (
+                        ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED,
+                        ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE,
+                    ):
+                        item = getattr(openai_responses_api_chunk, "item", None)
+                        if item:
+                            encrypted_content = getattr(item, "encrypted_content", None)
+                            if encrypted_content and isinstance(encrypted_content, str):
+                                model_id = (
+                                    self.litellm_metadata.get("model_info", {}).get("id")
+                                    if self.litellm_metadata
+                                    else None
+                                )
+                                if model_id:
+                                    wrapped_content = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+                                        encrypted_content, model_id
+                                    )
+                                    setattr(item, "encrypted_content", wrapped_content)
+
                 # Store the completed response
                 if (
                     openai_responses_api_chunk
diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py
index 0c203dc6305..89e89711706 100644
--- a/litellm/responses/utils.py
+++ b/litellm/responses/utils.py
@@ -264,6 +264,56 @@ class ResponsesAPIRequestUtils:
         except Exception:
             return None
 
+    @staticmethod
+    def _wrap_encrypted_content_with_model_id(
+        encrypted_content: str, model_id: str
+    ) -> str:
+        """Wrap encrypted_content with model_id metadata for affinity routing.
+
+        When Codex or other clients send items with encrypted_content but no ID,
+        we encode the model_id directly into the encrypted_content itself.
+
+        Format: ``litellm_enc:{base64("model_id:{model_id}")};{original_encrypted_content}``
+        """
+        metadata = f"model_id:{model_id}"
+        encoded_metadata = base64.b64encode(metadata.encode("utf-8")).decode("utf-8")
+        return f"litellm_enc:{encoded_metadata};{encrypted_content}"
+
+    @staticmethod
+    def _unwrap_encrypted_content_with_model_id(
+        wrapped_content: str,
+    ) -> tuple[Optional[str], str]:
+        """Unwrap encrypted_content to extract model_id and original content.
+
+        Returns:
+            Tuple of (model_id, original_encrypted_content).
+            If not wrapped, returns (None, original_content).
+        """
+        if not wrapped_content.startswith("litellm_enc:"):
+            return None, wrapped_content
+
+        try:
+            # Split on first ";" to separate metadata from content
+            parts = wrapped_content.split(";", 1)
+            if len(parts) < 2:
+                return None, wrapped_content
+
+            metadata_b64 = parts[0].replace("litellm_enc:", "")
+            original_content = parts[1]
+
+            # Restore padding if needed
+            missing = len(metadata_b64) % 4
+            if missing:
+                metadata_b64 += "=" * (4 - missing)
+
+            decoded_metadata = base64.b64decode(metadata_b64.encode("utf-8")).decode(
+                "utf-8"
+            )
+            model_id = decoded_metadata.replace("model_id:", "")
+            return model_id, original_content
+        except Exception:
+            return None, wrapped_content
+
     @staticmethod
     def _update_encrypted_content_item_ids_in_response(
         response: Union["ResponsesAPIResponse", Dict[str, Any]],
@@ -273,6 +323,9 @@ class ResponsesAPIRequestUtils:
 
         Encodes ``model_id`` into the item ID so that follow-up requests can be
         routed back to the originating deployment without any cache lookup.
+
+        For items without an ID (e.g., from Codex), encodes model_id directly
+        into the encrypted_content itself.
         """
         if not model_id:
             return response
@@ -289,23 +342,42 @@ class ResponsesAPIRequestUtils:
         for item in output:
             if isinstance(item, dict):
                 item_id = item.get("id")
-                if item_id and isinstance(item_id, str) and "encrypted_content" in item:
-                    item["id"] = ResponsesAPIRequestUtils._build_encrypted_item_id(
-                        model_id, item_id
+                encrypted_content = item.get("encrypted_content")
+
+                if encrypted_content and isinstance(encrypted_content, str):
+                    # Always wrap encrypted_content with model_id for redundancy
+                    item["encrypted_content"] = (
+                        ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+                            encrypted_content, model_id
+                        )
                     )
+                    # Also encode the ID if present
+                    if item_id and isinstance(item_id, str):
+                        item["id"] = ResponsesAPIRequestUtils._build_encrypted_item_id(
+                            model_id, item_id
+                        )
             else:
                 item_id = getattr(item, "id", None)
-                if (
-                    item_id
-                    and isinstance(item_id, str)
-                    and hasattr(item, "encrypted_content")
-                ):
+                encrypted_content = getattr(item, "encrypted_content", None)
+
+                if encrypted_content and isinstance(encrypted_content, str):
+                    # Always wrap encrypted_content with model_id for redundancy
                     try:
-                        item.id = ResponsesAPIRequestUtils._build_encrypted_item_id(
-                            model_id, item_id
+                        item.encrypted_content = (
+                            ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+                                encrypted_content, model_id
+                            )
                         )
                     except AttributeError:
                         pass
+                    # Also encode the ID if present
+                    if item_id and isinstance(item_id, str):
+                        try:
+                            item.id = ResponsesAPIRequestUtils._build_encrypted_item_id(
+                                model_id, item_id
+                            )
+                        except AttributeError:
+                            pass
 
         return response
 
@@ -314,7 +386,11 @@ class ResponsesAPIRequestUtils:
         """Decode litellm-encoded item IDs in request input back to original IDs.
 
         Called before forwarding the request to the upstream provider so the
-        provider receives the original item IDs it issued.
+        provider receives the original item IDs and unwrapped encrypted_content.
+
+        Handles both:
+        1. Items with encoded IDs (encitem_...)
+        2. Items with wrapped encrypted_content (litellm_enc:...)
         """
         if not isinstance(request_input, list):
             return request_input
@@ -327,6 +403,16 @@ class ResponsesAPIRequestUtils:
                     if decoded:
                         item["id"] = decoded["item_id"]
 
+                encrypted_content = item.get("encrypted_content")
+                if encrypted_content and isinstance(encrypted_content, str):
+                    _, unwrapped = (
+                        ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
+                            encrypted_content
+                        )
+                    )
+                    if unwrapped != encrypted_content:
+                        item["encrypted_content"] = unwrapped
+
         return request_input
 
     @staticmethod
diff --git a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
index e6d691896ca..dc44ef13b7c 100644
--- a/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
+++ b/litellm/router_utils/pre_call_checks/encrypted_content_affinity_check.py
@@ -8,24 +8,29 @@ different deployment (different org), OpenAI rejects it with an `invalid_encrypt
 error because the organization_id doesn't match.
 
 This callback solves the problem by encoding the originating deployment's ``model_id``
-directly into the item IDs of output items that carry ``encrypted_content`` (the same
-approach used by the responses-API affinity for ``previous_response_id``).  The encoded
-ID is decoded on the next request so the router can pin to the correct deployment without
-any cache lookup.
+into the response output items that carry ``encrypted_content``. Two encoding strategies:
+
+1. **Items with IDs**: Encode model_id into the item ID itself (e.g., ``encitem_...``)
+2. **Items without IDs** (Codex): Wrap the encrypted_content with model_id metadata
+   (e.g., ``litellm_enc:{base64_metadata};{original_encrypted_content}``)
+
+The encoded model_id is decoded on the next request so the router can pin to the correct
+deployment without any cache lookup.
 
 Response post-processing (encoding) is handled by
 ``ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response`` which is
 called inside ``_update_responses_api_response_id_with_model_id`` in ``responses/utils.py``.
 
-Request pre-processing (ID restoration before forwarding to upstream) is handled by
+Request pre-processing (ID/content restoration before forwarding to upstream) is handled by
 ``ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input`` which is called
 in ``get_optional_params_responses_api``.
 
 This pre-call check is responsible only for the routing decision: it reads the encoded
-``model_id`` out of the item IDs and pins the request to the matching deployment.
+``model_id`` from either item IDs or wrapped encrypted_content and pins the request to
+the matching deployment.
 
 Safe to enable globally:
-- Only activates when encoded item IDs appear in the request ``input``.
+- Only activates when encoded markers appear in the request ``input``.
 - No effect on embedding models, chat completions, or first-time requests.
 - No quota reduction -- first requests are fully load balanced.
 - No cache required.
@@ -60,12 +65,16 @@ class EncryptedContentAffinityCheck(CustomLogger):
     @staticmethod
     def _extract_model_id_from_input(request_input: Any) -> Optional[str]:
         """
-        Scan ``input`` items for litellm-encoded encrypted-content item IDs and
+        Scan ``input`` items for litellm-encoded encrypted-content markers and
         return the ``model_id`` embedded in the first one found.
 
+        Checks both:
+        1. Encoded item IDs (encitem_...) - for clients that send IDs
+        2. Wrapped encrypted_content (litellm_enc:...) - for clients like Codex that don't send IDs
+
         ``input`` can be:
-        - a plain string  -> no encoded IDs
-        - a list of items -> check each item's ``id`` field
+        - a plain string  -> no encoded markers
+        - a list of items -> check each item's ``id`` and ``encrypted_content`` fields
         """
         if not isinstance(request_input, list):
             return None
@@ -73,12 +82,25 @@ class EncryptedContentAffinityCheck(CustomLogger):
         for item in request_input:
             if not isinstance(item, dict):
                 continue
+
+            # First, try to decode from item ID (if present)
             item_id = item.get("id")
-            if not item_id or not isinstance(item_id, str):
-                continue
-            decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item_id)
-            if decoded:
-                return decoded.get("model_id")
+            if item_id and isinstance(item_id, str):
+                decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item_id)
+                if decoded:
+                    return decoded.get("model_id")
+
+            # If no encoded ID, check if encrypted_content itself is wrapped
+            encrypted_content = item.get("encrypted_content")
+            if encrypted_content and isinstance(encrypted_content, str):
+                (
+                    model_id,
+                    _,
+                ) = ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
+                    encrypted_content
+                )
+                if model_id:
+                    return model_id
 
         return None
 
diff --git a/tests/test_litellm/llms/azure/test_azure_exception_mapping.py b/tests/test_litellm/llms/azure/test_azure_exception_mapping.py
index 495ca958cf5..249b9349c54 100644
--- a/tests/test_litellm/llms/azure/test_azure_exception_mapping.py
+++ b/tests/test_litellm/llms/azure/test_azure_exception_mapping.py
@@ -384,4 +384,59 @@ class TestAzureExceptionMapping:
                 model="azure/dall-e-3",
                 original_exception=mock_exception,
                 custom_llm_provider="azure",
-            )
\ No newline at end of file
+            )
+
+    def test_invalid_encrypted_content_error_with_helpful_message(self):
+        """Test that invalid_encrypted_content errors include helpful guidance
+        about enabling encrypted_content_affinity."""
+        from litellm.exceptions import BadRequestError
+
+        mock_exception = Exception(
+            "The encrypted content gAAAAABpnW_yEYmSNEyOG... could not be verified. "
+            "Reason: Encrypted content organization_id did not match the target organization."
+        )
+        mock_exception.body = {
+            "error": {
+                "message": "The encrypted content could not be verified.",
+                "type": "invalid_request_error",
+                "code": "invalid_encrypted_content",
+            }
+        }
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_exception.response = mock_response
+
+        with pytest.raises(BadRequestError) as exc_info:
+            exception_type(
+                model="azure/gpt-5.1-codex",
+                original_exception=mock_exception,
+                custom_llm_provider="azure",
+            )
+
+        error = exc_info.value
+        assert "encrypted_content_affinity" in error.message
+        assert "enable_pre_call_checks" in error.message
+        assert "optional_pre_call_checks" in error.message
+        assert "docs.litellm.ai" in error.message
+
+    def test_openai_invalid_encrypted_content_error(self):
+        """Test that OpenAI invalid_encrypted_content errors also get helpful guidance."""
+        from litellm.exceptions import BadRequestError
+
+        mock_exception = Exception(
+            "The encrypted content could not be verified."
+        )
+        mock_response = MagicMock()
+        mock_response.status_code = 400
+        mock_exception.response = mock_response
+
+        with pytest.raises(BadRequestError) as exc_info:
+            exception_type(
+                model="gpt-5.1-codex",
+                original_exception=mock_exception,
+                custom_llm_provider="openai",
+            )
+
+        error = exc_info.value
+        assert "encrypted_content_affinity" in error.message
+        assert "enable_pre_call_checks" in error.message
\ No newline at end of file
diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
index 66177208f4f..6e845e9d050 100644
--- a/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_encrypted_content_affinity_check.py
@@ -1,13 +1,18 @@
 """
 Tests for encrypted_content_affinity pre-call check.
 
-The mechanism works without any cache:
-- On response: item IDs for output items with `encrypted_content` are rewritten to
-  `encitem_{base64("litellm:model_id:{model_id};item_id:{original_id}")}`.
-- On routing: `EncryptedContentAffinityCheck` decodes the `encitem_` prefix to extract
-  `model_id` and pins the request to that deployment.
-- Before forwarding: `_restore_encrypted_content_item_ids_in_input` decodes the IDs back
-  to their original form before sending to the upstream provider.
+The mechanism works without any cache and supports two encoding strategies:
+
+1. **Items with IDs**: item IDs for output items with `encrypted_content` are rewritten to
+   `encitem_{base64("litellm:model_id:{model_id};item_id:{original_id}")}`.
+
+2. **Items without IDs** (Codex): encrypted_content itself is wrapped with model_id metadata:
+   `litellm_enc:{base64("model_id:{model_id}")};{original_encrypted_content}`.
+
+- On routing: `EncryptedContentAffinityCheck` decodes from either item IDs or wrapped
+  encrypted_content to extract `model_id` and pins the request to that deployment.
+- Before forwarding: `_restore_encrypted_content_item_ids_in_input` decodes IDs and unwraps
+  encrypted_content back to their original forms before sending to the upstream provider.
 """
 
 import os
@@ -140,6 +145,59 @@ class TestUpdateEncryptedContentItemIds:
         assert result["output"][0]["id"] == "rs_xyz"
 
 
+class TestEncryptedContentWrapping:
+    def test_wrap_and_unwrap_encrypted_content(self):
+        """Test wrapping encrypted_content with model_id metadata."""
+        model_id = "deployment-1"
+        original_content = "gAAAAABpnW_yEYmSNEyOG_original_encrypted_data"
+        wrapped = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+            original_content, model_id
+        )
+        assert wrapped.startswith("litellm_enc:")
+        assert wrapped != original_content
+
+        unwrapped_model_id, unwrapped_content = (
+            ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(wrapped)
+        )
+        assert unwrapped_model_id == model_id
+        assert unwrapped_content == original_content
+
+    def test_unwrap_plain_encrypted_content(self):
+        """Unwrapping plain encrypted_content returns None for model_id."""
+        plain_content = "gAAAAABpnW_yEYmSNEyOG_plain_content"
+        model_id, content = ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
+            plain_content
+        )
+        assert model_id is None
+        assert content == plain_content
+
+    def test_update_response_wraps_encrypted_content_without_id(self):
+        """Items with encrypted_content but no ID get the content wrapped."""
+        model_id = "deployment-1"
+        response = {
+            "id": "resp_123",
+            "output": [
+                {"type": "message", "content": []},
+                {
+                    "type": "reasoning",
+                    "encrypted_content": "gAAAAABpnW_yEYmSNEyOG_secret",
+                },
+            ],
+        }
+        result = ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response(
+            response, model_id
+        )
+        assert result["output"][0].get("encrypted_content") is None
+        wrapped = result["output"][1]["encrypted_content"]
+        assert wrapped.startswith("litellm_enc:")
+
+        model_id_extracted, unwrapped = (
+            ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(wrapped)
+        )
+        assert model_id_extracted == model_id
+        assert unwrapped == "gAAAAABpnW_yEYmSNEyOG_secret"
+
+
 class TestRestoreEncryptedContentItemIds:
     def test_restores_encoded_ids(self):
         model_id = "deployment-1"
@@ -156,6 +214,22 @@ class TestRestoreEncryptedContentItemIds:
         assert restored[0]["id"] == "msg_abc123"
         assert restored[1]["id"] == original_id
 
+    def test_unwraps_encrypted_content(self):
+        """Test that wrapped encrypted_content is unwrapped before forwarding."""
+        model_id = "deployment-1"
+        original_content = "gAAAAABpnW_yEYmSNEyOG_original"
+        wrapped_content = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+            original_content, model_id
+        )
+
+        request_input = [
+            {"type": "reasoning", "encrypted_content": wrapped_content},
+        ]
+        restored = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
+            request_input
+        )
+        assert restored[0]["encrypted_content"] == original_content
+
     def test_no_op_for_plain_string_input(self):
         result = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
             "Hello world"
@@ -319,8 +393,8 @@ async def test_encrypted_content_affinity_no_effect_on_chat_completions():
 @pytest.mark.asyncio
 async def test_encrypted_content_affinity_bypasses_rpm_limits():
     """
-    When encrypted content affinity pins to a deployment, RPM limits are bypassed
-    since the request would fail on any other deployment anyway.
+    When encrypted content affinity pins to a deployment, the request
+    goes through even if normal routing would avoid it.
     """
     mock_response_data = {
         "id": "resp_mock-rpm-test",
@@ -347,28 +421,36 @@ async def test_encrypted_content_affinity_bypasses_rpm_limits():
                 "litellm_params": {
                     "model": "openai/gpt-5.1-codex",
                     "api_key": "mock-api-key-1",
-                    "rpm": 1,  # Very low limit
                 },
-                "model_info": {"id": "rpm-limited-deployment"},
+                "model_info": {"id": "deployment-alpha"},
             },
             {
                 "model_name": "openai.gpt-5.1-codex",
                 "litellm_params": {
                     "model": "openai/gpt-5.1-codex",
                     "api_key": "mock-api-key-2",
-                    "rpm": 100,
                 },
-                "model_info": {"id": "high-rpm-deployment"},
+                "model_info": {"id": "deployment-beta"},
             },
         ],
         optional_pre_call_checks=["encrypted_content_affinity"],
         routing_strategy="usage-based-routing-v2",
     )
 
+    selected_deployments = []
+
+    def deterministic_choice(seq):
+        if len(selected_deployments) == 0:
+            return seq[0]
+        return seq[1] if len(seq) > 1 else seq[0]
+
     with patch(
         "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
         new_callable=AsyncMock,
-    ) as mock_post:
+    ) as mock_post, patch(
+        "litellm.router_strategy.simple_shuffle.random.choice",
+        side_effect=deterministic_choice,
+    ):
         mock_post.return_value = MockResponse(mock_response_data, 200)
 
         first_response = await router.aresponses(
@@ -376,6 +458,7 @@ async def test_encrypted_content_affinity_bypasses_rpm_limits():
             input="Initial request",
         )
         first_model_id = first_response._hidden_params["model_id"]
+        selected_deployments.append(first_model_id)
 
         # Extract encoded item ID from the first response output
         encoded_item_id = _extract_encoded_item_id(first_response)
@@ -384,7 +467,6 @@ async def test_encrypted_content_affinity_bypasses_rpm_limits():
         )
 
         # Follow-up with the encoded item ID — should pin to same deployment
-        # even if it is at its RPM limit
         second_response = await router.aresponses(
             model="openai.gpt-5.1-codex",
             input=[
@@ -461,3 +543,171 @@ async def test_encrypted_content_affinity_no_match_normal_routing():
             ],
         )
         assert response.id is not None
+
+
+@pytest.mark.asyncio
+async def test_encrypted_content_affinity_with_wrapped_content_no_id():
+    """
+    Test affinity routing when items have wrapped encrypted_content but no ID.
+    This simulates Codex client behavior where IDs are omitted.
+    """
+    mock_response_data = {
+        "id": "resp_mock-wrapped-content",
+        "object": "response",
+        "created_at": 1741476542,
+        "status": "completed",
+        "model": "openai/gpt-5.1-codex",
+        "output": [
+            {
+                "type": "reasoning",
+                "status": "completed",
+                "encrypted_content": "gAAAAABpnW_yEYmSNEyOG_original_content",
+            },
+        ],
+        "usage": {"input_tokens": 5, "output_tokens": 10, "total_tokens": 15},
+        "error": None,
+    }
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-1",
+                },
+                "model_info": {"id": "deployment-1"},
+            },
+            {
+                "model_name": "openai.gpt-5.1-codex",
+                "litellm_params": {
+                    "model": "openai/gpt-5.1-codex",
+                    "api_key": "mock-api-key-2",
+                },
+                "model_info": {"id": "deployment-2"},
+            },
+        ],
+        optional_pre_call_checks=["encrypted_content_affinity"],
+    )
+
+    selected_deployments = []
+
+    def deterministic_choice(seq):
+        if len(selected_deployments) == 0:
+            return seq[0]
+        return seq[1] if len(seq) > 1 else seq[0]
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post, patch(
+        "litellm.router_strategy.simple_shuffle.random.choice",
+        side_effect=deterministic_choice,
+    ):
+        mock_post.return_value = MockResponse(mock_response_data, 200)
+
+        # First request — goes to deployment-1
+        first_response = await router.aresponses(
+            model="openai.gpt-5.1-codex",
+            input="Hello, how are you?",
+        )
+        first_model_id = first_response._hidden_params["model_id"]
+        selected_deployments.append(first_model_id)
+
+        # Extract wrapped encrypted_content from first response
+        first_item = first_response.output[0]
+        wrapped_content = (
+            first_item.encrypted_content
+            if hasattr(first_item, "encrypted_content")
+            else first_item.get("encrypted_content")
+        )
+        assert wrapped_content.startswith("litellm_enc:"), (
+            f"Expected wrapped content but got {wrapped_content[:50]}..."
+        )
+
+        # Verify we can extract model_id from wrapped content
+        extracted_model_id, _ = (
+            ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
+                wrapped_content
+            )
+        )
+        assert extracted_model_id == first_model_id
+
+        # Second request: use wrapped encrypted_content WITHOUT an ID (Codex behavior)
+        second_response = await router.aresponses(
+            model="openai.gpt-5.1-codex",
+            input=[
+                {
+                    "type": "reasoning",
+                    "encrypted_content": wrapped_content,
+                },
+            ],
+        )
+        second_model_id = second_response._hidden_params["model_id"]
+
+        assert second_model_id == first_model_id, (
+            f"Expected affinity to route to {first_model_id}, but got {second_model_id}"
+        )
+
+
+def test_encrypted_content_wrapping_preserves_original_content():
+    """
+    Test that wrapping and unwrapping encrypted_content preserves the original content.
+    This is critical for streaming responses where content must round-trip correctly.
+    """
+    model_id = "test-deployment-1"
+    original_encrypted_content = "gAAAAABpnW_yEYmSNEyOG_streaming_test_content_with_special_chars==+/"
+
+    wrapped = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+        original_encrypted_content, model_id
+    )
+    
+    assert wrapped.startswith("litellm_enc:")
+    assert wrapped != original_encrypted_content
+
+    extracted_model_id, unwrapped_content = (
+        ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(wrapped)
+    )
+    
+    assert extracted_model_id == model_id
+    assert unwrapped_content == original_encrypted_content
+
+
+def test_encrypted_content_wrapping_with_multiple_semicolons():
+    """
+    Test that encrypted_content containing semicolons is handled correctly.
+    """
+    model_id = "deployment-with-semicolons"
+    original_content = "gAAAAAB;some;content;with;semicolons"
+
+    wrapped = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+        original_content, model_id
+    )
+    
+    extracted_model_id, unwrapped = (
+        ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(wrapped)
+    )
+    
+    assert extracted_model_id == model_id
+    assert unwrapped == original_content
+
+
+def test_encrypted_content_wrapping_empty_string():
+    """
+    Test that empty encrypted_content is handled gracefully.
+    """
+    model_id = "test-deployment"
+    original_content = ""
+
+    wrapped = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
+        original_content, model_id
+    )
+    
+    assert wrapped.startswith("litellm_enc:")
+
+    extracted_model_id, unwrapped = (
+        ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(wrapped)
+    )
+    
+    assert extracted_model_id == model_id
+    assert unwrapped == original_content

From ca597e18c8a0ca173d45ff1fd56ee39f4d1aa80b Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 18:32:41 +0530
Subject: [PATCH 31/69] Fix routing of encrypted content

---
 litellm/router.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/litellm/router.py b/litellm/router.py
index 652cd68b555..67ccec4c6ef 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -1289,6 +1289,11 @@ class Router:
                 )
             elif pre_call_check == "enforce_model_rate_limits":
                 _callback = ModelRateLimitingCheck(dual_cache=self.cache)
+            elif pre_call_check == "encrypted_content_affinity":
+                from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
+                    EncryptedContentAffinityCheck,
+                )
+                _callback = EncryptedContentAffinityCheck()
 
             if _callback is None:
                 continue

From 2f6279d1894ce630afcbf80c1e96231ec06d1e82 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 18:41:15 +0530
Subject: [PATCH 32/69] Fix import issue

---
 litellm/router.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/litellm/router.py b/litellm/router.py
index 67ccec4c6ef..8eb2c417511 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -1255,6 +1255,10 @@ class Router:
         # Encrypted content affinity
         # ---------------------------------------------------------------------
         if "encrypted_content_affinity" in optional_pre_call_checks:
+            from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
+                EncryptedContentAffinityCheck,
+            )
+
             if self.optional_callbacks is None:
                 self.optional_callbacks = []
 
@@ -1289,11 +1293,6 @@ class Router:
                 )
             elif pre_call_check == "enforce_model_rate_limits":
                 _callback = ModelRateLimitingCheck(dual_cache=self.cache)
-            elif pre_call_check == "encrypted_content_affinity":
-                from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
-                    EncryptedContentAffinityCheck,
-                )
-                _callback = EncryptedContentAffinityCheck()
 
             if _callback is None:
                 continue

From d66f8bc15d8f22c52d81785ab047ff65208da35f Mon Sep 17 00:00:00 2001
From: Varad Khonde <varadkhonde@gmail.com>
Date: Tue, 3 Mar 2026 19:17:28 +0530
Subject: [PATCH 33/69] feat(togetherai): add support for
 togetherai/Qwen3.5-397B-A17B model

---
 litellm/model_prices_and_context_window_backup.json | 12 ++++++++++++
 model_prices_and_context_window.json                | 12 ++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index d4c5b476af6..e476d0c6b2a 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -29397,6 +29397,18 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "together_ai/Qwen/Qwen/Qwen3.5-397B-A17B": {
+        "input_cost_per_token": 6e-07,
+        "litellm_provider": "together_ai",
+        "max_input_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 3.6e-06,
+        "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B",
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true
+    },
     "tts-1": {
         "input_cost_per_character": 1.5e-05,
         "litellm_provider": "openai",
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 4934f11d456..cf55ddf6e25 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -29632,6 +29632,18 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "together_ai/Qwen/Qwen/Qwen3.5-397B-A17B": {
+        "input_cost_per_token": 6e-07,
+        "litellm_provider": "together_ai",
+        "max_input_tokens": 262144,
+        "mode": "chat",
+        "output_cost_per_token": 3.6e-06,
+        "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B",
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true
+    },
     "tts-1": {
         "input_cost_per_character": 1.5e-05,
         "litellm_provider": "openai",

From 6c0387d170a2caab45393222bcc32e769a2c5124 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 19:31:55 +0530
Subject: [PATCH 34/69] Add support for Attaching knowledge base to model via
 UI

---
 .../src/components/add_model/AddModelForm.tsx |  1 +
 .../add_model/advanced_settings.test.tsx      |  3 +
 .../add_model/advanced_settings.tsx           | 30 +++++++++
 .../src/components/model_info_view.tsx        | 61 +++++++++++++++++++
 4 files changed, 95 insertions(+)

diff --git a/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx b/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx
index 59ac63cffe6..2b3f23a35ae 100644
--- a/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx
@@ -358,6 +358,7 @@ const AddModelForm: React.FC<AddModelFormProps> = ({
                   teams={teams}
                   guardrailsList={guardrailsList || []}
                   tagsList={tagsList || {}}
+                  accessToken={accessToken || ""}
                 />
               </>
             )}
diff --git a/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx b/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx
index 6515c67c292..9fe36e13998 100644
--- a/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/advanced_settings.test.tsx
@@ -13,6 +13,7 @@ describe("AdvancedSettings", () => {
         setShowAdvancedSettings={() => {}}
         guardrailsList={[]}
         tagsList={{}}
+        accessToken="test-token"
       />,
     );
   });
@@ -24,6 +25,7 @@ describe("AdvancedSettings", () => {
         setShowAdvancedSettings={() => {}}
         guardrailsList={[]}
         tagsList={{}}
+        accessToken="test-token"
       />,
     );
     fireEvent.click(getByText("Advanced Settings"));
@@ -39,6 +41,7 @@ describe("AdvancedSettings", () => {
         setShowAdvancedSettings={() => {}}
         guardrailsList={[]}
         tagsList={{}}
+        accessToken="test-token"
       />,
     );
     act(() => {
diff --git a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx
index c9f5ef8a4b1..8ae90c1cbbc 100644
--- a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx
@@ -6,6 +6,7 @@ import TextArea from "antd/es/input/TextArea";
 import { InfoCircleOutlined } from "@ant-design/icons";
 import { Team } from "../key_team_helpers/key_list";
 import CacheControlSettings from "./cache_control_settings";
+import VectorStoreSelector from "../vector_store_management/VectorStoreSelector";
 import { Tag } from "../tag_management/types";
 import { formItemValidateJSON } from "../../utils/textUtils";
 const { Link } = Typography;
@@ -16,6 +17,7 @@ interface AdvancedSettingsProps {
   teams?: Team[] | null;
   guardrailsList: string[];
   tagsList: Record<string, Tag>;
+  accessToken: string;
 }
 
 const AdvancedSettings: React.FC<AdvancedSettingsProps> = ({
@@ -24,6 +26,7 @@ const AdvancedSettings: React.FC<AdvancedSettingsProps> = ({
   teams,
   guardrailsList,
   tagsList,
+  accessToken,
 }) => {
   const [form] = Form.useForm();
   const [customPricing, setCustomPricing] = React.useState(false);
@@ -109,6 +112,33 @@ const AdvancedSettings: React.FC<AdvancedSettingsProps> = ({
               <Switch onChange={handleCustomPricingChange} className="bg-gray-600" />
             </Form.Item>
 
+            <Form.Item
+              label={
+                <span>
+                  Attached Knowledge Bases (RAG){" "}
+                  <Tooltip title="Vector stores to use for RAG. Every request to this model will automatically retrieve context from these knowledge bases.">
+                    <a
+                      href="https://docs.litellm.ai/docs/completion/knowledgebase"
+                      target="_blank"
+                      rel="noopener noreferrer"
+                      onClick={(e) => e.stopPropagation()}
+                    >
+                      <InfoCircleOutlined style={{ marginLeft: "4px" }} />
+                    </a>
+                  </Tooltip>
+                </span>
+              }
+              name="vector_store_ids"
+              className="mt-4"
+              help="Select vector stores to attach. Requests to this model will automatically use these for RAG. Set up vector stores in Tools > Vector Stores."
+            >
+              <VectorStoreSelector
+                onChange={() => {}}
+                accessToken={accessToken}
+                placeholder="Select knowledge bases (optional)"
+              />
+            </Form.Item>
+
             <Form.Item
               label={
                 <span>
diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx
index e2fc8caa21c..40c1a3a386a 100644
--- a/ui/litellm-dashboard/src/components/model_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/model_info_view.tsx
@@ -17,6 +17,7 @@ import {
   Button as TremorButton,
 } from "@tremor/react";
 import { Button, Form, Input, Modal, Select, Tooltip } from "antd";
+import VectorStoreSelector from "./vector_store_management/VectorStoreSelector";
 import { CheckIcon, CopyIcon } from "lucide-react";
 import { useEffect, useMemo, useState } from "react";
 import { copyToClipboard as utilCopyToClipboard } from "../utils/dataUtils";
@@ -245,6 +246,11 @@ export default function ModelInfoView({
       if (values.guardrails) {
         updatedLitellmParams.guardrails = values.guardrails;
       }
+      if (values.vector_store_ids !== undefined) {
+        updatedLitellmParams.vector_store_ids = Array.isArray(values.vector_store_ids)
+          ? values.vector_store_ids
+          : [];
+      }
 
       // Handle cache control settings
       if (values.cache_control && values.cache_control_injection_points?.length > 0) {
@@ -606,6 +612,9 @@ export default function ModelInfoView({
                     guardrails: Array.isArray(localModelData.litellm_params?.guardrails)
                       ? localModelData.litellm_params.guardrails
                       : [],
+                    vector_store_ids: Array.isArray(localModelData.litellm_params?.vector_store_ids)
+                      ? localModelData.litellm_params.vector_store_ids
+                      : [],
                     tags: Array.isArray(localModelData.litellm_params?.tags) ? localModelData.litellm_params.tags : [],
                     health_check_model: isWildcardModel ? localModelData.model_info?.health_check_model : null,
                     litellm_extra_params: JSON.stringify(localModelData.litellm_params || {}, null, 2),
@@ -883,6 +892,58 @@ export default function ModelInfoView({
                         )}
                       </div>
 
+                      <div>
+                        <Text className="font-medium">
+                          Attached Knowledge Bases (RAG)
+                          <Tooltip title="Vector stores used for RAG. Every request to this model will automatically retrieve context from these knowledge bases.">
+                            <a
+                              href="https://docs.litellm.ai/docs/completion/knowledgebase"
+                              target="_blank"
+                              rel="noopener noreferrer"
+                              onClick={(e) => e.stopPropagation()}
+                            >
+                              <InfoCircleOutlined style={{ marginLeft: "4px" }} />
+                            </a>
+                          </Tooltip>
+                        </Text>
+                        {isEditing ? (
+                          <Form.Item name="vector_store_ids" className="mb-0">
+                            <VectorStoreSelector
+                              onChange={() => {}}
+                              accessToken={accessToken || ""}
+                              placeholder="Select knowledge bases (optional)"
+                            />
+                          </Form.Item>
+                        ) : (
+                          <div className="mt-1 p-2 bg-gray-50 rounded">
+                            {localModelData.litellm_params?.vector_store_ids ? (
+                              Array.isArray(localModelData.litellm_params.vector_store_ids) ? (
+                                localModelData.litellm_params.vector_store_ids.length > 0 ? (
+                                  <div className="flex flex-wrap gap-1">
+                                    {localModelData.litellm_params.vector_store_ids.map(
+                                      (vsId: string, index: number) => (
+                                        <span
+                                          key={index}
+                                          className="inline-flex items-center px-2 py-1 rounded-full text-xs font-medium bg-blue-100 text-blue-800"
+                                        >
+                                          {vsId}
+                                        </span>
+                                      )
+                                    )}
+                                  </div>
+                                ) : (
+                                  "No knowledge bases attached"
+                                )
+                              ) : (
+                                String(localModelData.litellm_params.vector_store_ids)
+                              )
+                            ) : (
+                              "Not Set"
+                            )}
+                          </div>
+                        )}
+                      </div>
+
                       <div>
                         <Text className="font-medium">Tags</Text>
                         {isEditing ? (

From 24ec7f882f5e3036d47544f8ef96f899fb4606aa Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 19:35:26 +0530
Subject: [PATCH 35/69] Revert "feat(togetherai): add support for
 togetherai/Qwen3.5-397B-A17B model"

---
 litellm/model_prices_and_context_window_backup.json | 12 ------------
 model_prices_and_context_window.json                | 12 ------------
 2 files changed, 24 deletions(-)

diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index e476d0c6b2a..d4c5b476af6 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -29397,18 +29397,6 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
-    "together_ai/Qwen/Qwen/Qwen3.5-397B-A17B": {
-        "input_cost_per_token": 6e-07,
-        "litellm_provider": "together_ai",
-        "max_input_tokens": 262144,
-        "mode": "chat",
-        "output_cost_per_token": 3.6e-06,
-        "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B",
-        "supports_function_calling": true,
-        "supports_parallel_function_calling": true,
-        "supports_response_schema": true,
-        "supports_tool_choice": true
-    },
     "tts-1": {
         "input_cost_per_character": 1.5e-05,
         "litellm_provider": "openai",
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index cf55ddf6e25..4934f11d456 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -29632,18 +29632,6 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
-    "together_ai/Qwen/Qwen/Qwen3.5-397B-A17B": {
-        "input_cost_per_token": 6e-07,
-        "litellm_provider": "together_ai",
-        "max_input_tokens": 262144,
-        "mode": "chat",
-        "output_cost_per_token": 3.6e-06,
-        "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B",
-        "supports_function_calling": true,
-        "supports_parallel_function_calling": true,
-        "supports_response_schema": true,
-        "supports_tool_choice": true
-    },
     "tts-1": {
         "input_cost_per_character": 1.5e-05,
         "litellm_provider": "openai",

From 6d535e56395782de8841497aa228729e16354872 Mon Sep 17 00:00:00 2001
From: Harshit28j <Harshitjain0562@gmail.com>
Date: Tue, 3 Mar 2026 20:14:18 +0530
Subject: [PATCH 36/69] fix(proxy): allow custom auth routes to bypass route
 authorization checks

Custom user-added routes (e.g. /ldap/ngs/ready) used with Depends(user_api_key_auth) were being rejected as admin-only after _run_post_custom_auth_checks was introduced in commit 14badde13c.

The route authorization check in common_checks is designed for LiteLLM's own management routes. Custom auth flows that add their own routes should be trusted since the custom auth function already validated the request. Budget and expiry checks still run.

Add skip_route_check parameter to common_checks() and pass skip_route_check=True from _run_post_custom_auth_checks() to skip route authorization while preserving budget/team/model checks.

Regression test added: test_common_checks_skip_route_check_for_custom_auth

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
---
 litellm/proxy/auth/auth_checks.py             | 28 +++++-----
 litellm/proxy/auth/user_api_key_auth.py       |  1 +
 .../proxy/auth/test_auth_checks.py            | 52 +++++++++++++++++++
 3 files changed, 69 insertions(+), 12 deletions(-)

diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py
index ef6b0ac462c..91ac58215ab 100644
--- a/litellm/proxy/auth/auth_checks.py
+++ b/litellm/proxy/auth/auth_checks.py
@@ -234,6 +234,7 @@ async def common_checks(
     request: Request,
     skip_budget_checks: bool = False,
     project_object: Optional[LiteLLM_ProjectTableCachedObj] = None,
+    skip_route_check: bool = False,
 ) -> bool:
     """
     Common checks across jwt + key-based auth.
@@ -453,18 +454,21 @@ async def common_checks(
         user_object=user_object, route=route, request_body=request_body
     )
 
-    token_team = getattr(valid_token, "team_id", None)
-    token_type: Literal["ui", "api"] = (
-        "ui" if token_team is not None and token_team == "litellm-dashboard" else "api"
-    )
-    _is_route_allowed = _is_allowed_route(
-        route=route,
-        token_type=token_type,
-        user_obj=user_object,
-        request=request,
-        request_data=request_body,
-        valid_token=valid_token,
-    )
+    if not skip_route_check:
+        token_team = getattr(valid_token, "team_id", None)
+        token_type: Literal["ui", "api"] = (
+            "ui"
+            if token_team is not None and token_team == "litellm-dashboard"
+            else "api"
+        )
+        _is_route_allowed = _is_allowed_route(
+            route=route,
+            token_type=token_type,
+            user_obj=user_object,
+            request=request,
+            request_data=request_body,
+            valid_token=valid_token,
+        )
 
     # 11. [OPTIONAL] Vector store checks - is the object allowed to access the vector store
     await vector_store_access_check(
diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py
index a2705ceb7da..1d575fb5131 100644
--- a/litellm/proxy/auth/user_api_key_auth.py
+++ b/litellm/proxy/auth/user_api_key_auth.py
@@ -1766,6 +1766,7 @@ async def _run_post_custom_auth_checks(
         valid_token=valid_token,
         skip_budget_checks=False,
         project_object=_project_obj,
+        skip_route_check=True,
     )
 
     return valid_token
diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py
index 4cdca2d0617..501c2285d1e 100644
--- a/tests/test_litellm/proxy/auth/test_auth_checks.py
+++ b/tests/test_litellm/proxy/auth/test_auth_checks.py
@@ -1519,3 +1519,55 @@ async def test_get_fuzzy_user_object_case_insensitive_email():
     assert call_args.kwargs["where"]["user_email"]["equals"] == "test@example.com"
     assert call_args.kwargs["where"]["user_email"]["mode"] == "insensitive"
     assert call_args.kwargs["include"] == {"organization_memberships": True}
+
+
+@pytest.mark.asyncio
+async def test_common_checks_skip_route_check_for_custom_auth():
+    """
+    Test that custom routes (e.g. /ldap/ngs/ready) pass common_checks when
+    skip_route_check=True, which is the case for custom auth flows.
+
+    Regression test for: custom user-added routes being rejected as admin-only
+    after _run_post_custom_auth_checks was introduced.
+    """
+    from fastapi import Request
+
+    from litellm.proxy.auth.auth_checks import common_checks
+
+    mock_request = MagicMock(spec=Request)
+    valid_token = UserAPIKeyAuth(token="test-token")
+
+    # Without skip_route_check, a custom route with unknown user should fail
+    with pytest.raises(Exception):
+        await common_checks(
+            request_body={},
+            team_object=None,
+            user_object=None,
+            end_user_object=None,
+            global_proxy_spend=None,
+            general_settings={},
+            route="/ldap/ngs/ready",
+            llm_router=None,
+            proxy_logging_obj=MagicMock(),
+            valid_token=valid_token,
+            request=mock_request,
+            skip_route_check=False,
+        )
+
+    # With skip_route_check=True (custom auth path), the same route should pass
+    result = await common_checks(
+        request_body={},
+        team_object=None,
+        user_object=None,
+        end_user_object=None,
+        global_proxy_spend=None,
+        general_settings={},
+        route="/ldap/ngs/ready",
+        llm_router=None,
+        proxy_logging_obj=MagicMock(),
+        valid_token=valid_token,
+        request=mock_request,
+        skip_route_check=True,
+    )
+
+    assert result is True

From 75518c3ca73626b457a7697fea456a0cabc1a341 Mon Sep 17 00:00:00 2001
From: Chesars <cesarponce19544@gmail.com>
Date: Tue, 3 Mar 2026 12:03:40 -0300
Subject: [PATCH 37/69] feat(models): add zai/glm-5 and zai/glm-5-code to model
 cost map

Add native ZhipuAI GLM-5 and GLM-5-Code model entries with pricing
from docs.z.ai/guides/overview/pricing.
---
 model_prices_and_context_window.json | 30 ++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 4c0694db371..ac0e9dfaa7a 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -33811,6 +33811,36 @@
         "supports_tool_choice": true,
         "source": "https://aws.amazon.com/bedrock/pricing/"
     },
+    "zai/glm-5": {
+        "cache_creation_input_token_cost": 0,
+        "cache_read_input_token_cost": 2e-07,
+        "input_cost_per_token": 1e-06,
+        "output_cost_per_token": 3.2e-06,
+        "litellm_provider": "zai",
+        "max_input_tokens": 200000,
+        "max_output_tokens": 128000,
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "source": "https://docs.z.ai/guides/overview/pricing"
+    },
+    "zai/glm-5-code": {
+        "cache_creation_input_token_cost": 0,
+        "cache_read_input_token_cost": 3e-07,
+        "input_cost_per_token": 1.2e-06,
+        "output_cost_per_token": 5e-06,
+        "litellm_provider": "zai",
+        "max_input_tokens": 200000,
+        "max_output_tokens": 128000,
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "source": "https://docs.z.ai/guides/overview/pricing"
+    },
     "zai/glm-4.7": {
         "cache_creation_input_token_cost": 0,
         "cache_read_input_token_cost": 1.1e-07,

From c3fe4634b62599cea71856d351571a681f3a8e12 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 22:06:16 +0530
Subject: [PATCH 38/69] Add correct pricing for gemini 3.1 flash lite

---
 ...odel_prices_and_context_window_backup.json | 33 ++++++++++++-------
 model_prices_and_context_window.json          | 33 ++++++++++++-------
 2 files changed, 42 insertions(+), 24 deletions(-)

diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 42b4f0f7762..8f5fd26e3e8 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -14335,17 +14335,18 @@
         "supports_web_search": true
     },
     "gemini-3.1-flash-lite-preview": {
-        "cache_read_input_token_cost": 2.5e-09,
-        "input_cost_per_audio_token": 2.5e-07,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
         "max_audio_length_hours": 8.4,
         "max_audio_per_prompt": 1,
         "max_images_per_prompt": 3000,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 65535,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -14368,6 +14369,8 @@
         ],
         "supports_audio_input": true,
         "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_pdf_input": true,
@@ -17138,17 +17141,18 @@
         "supports_service_tier": true
     },
     "gemini/gemini-3.1-flash-lite-preview": {
-        "cache_read_input_token_cost": 2.5e-09,
-        "input_cost_per_audio_token": 2.5e-07,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "gemini",
         "max_audio_length_hours": 8.4,
         "max_audio_per_prompt": 1,
         "max_images_per_prompt": 3000,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 65535,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -17172,6 +17176,8 @@
         ],
         "supports_audio_input": true,
         "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_pdf_input": true,
@@ -32400,17 +32406,18 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models"
     },
     "vertex_ai/gemini-3.1-flash-lite-preview": {
-        "cache_read_input_token_cost": 2.5e-09,
-        "input_cost_per_audio_token": 2.5e-07,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
         "max_audio_length_hours": 8.4,
         "max_audio_per_prompt": 1,
         "max_images_per_prompt": 3000,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 65535,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -32433,6 +32440,8 @@
         ],
         "supports_audio_input": true,
         "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_pdf_input": true,
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 42b4f0f7762..8f5fd26e3e8 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -14335,17 +14335,18 @@
         "supports_web_search": true
     },
     "gemini-3.1-flash-lite-preview": {
-        "cache_read_input_token_cost": 2.5e-09,
-        "input_cost_per_audio_token": 2.5e-07,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
         "max_audio_length_hours": 8.4,
         "max_audio_per_prompt": 1,
         "max_images_per_prompt": 3000,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 65535,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -14368,6 +14369,8 @@
         ],
         "supports_audio_input": true,
         "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_pdf_input": true,
@@ -17138,17 +17141,18 @@
         "supports_service_tier": true
     },
     "gemini/gemini-3.1-flash-lite-preview": {
-        "cache_read_input_token_cost": 2.5e-09,
-        "input_cost_per_audio_token": 2.5e-07,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "gemini",
         "max_audio_length_hours": 8.4,
         "max_audio_per_prompt": 1,
         "max_images_per_prompt": 3000,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 65535,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -17172,6 +17176,8 @@
         ],
         "supports_audio_input": true,
         "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_pdf_input": true,
@@ -32400,17 +32406,18 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models"
     },
     "vertex_ai/gemini-3.1-flash-lite-preview": {
-        "cache_read_input_token_cost": 2.5e-09,
-        "input_cost_per_audio_token": 2.5e-07,
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "input_cost_per_audio_token": 5e-07,
         "input_cost_per_token": 2.5e-07,
         "litellm_provider": "vertex_ai-language-models",
         "max_audio_length_hours": 8.4,
         "max_audio_per_prompt": 1,
         "max_images_per_prompt": 3000,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 65535,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -32433,6 +32440,8 @@
         ],
         "supports_audio_input": true,
         "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_pdf_input": true,

From 9d06106af077b0996c154c43c014b5db0b509094 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Tue, 3 Mar 2026 22:22:57 +0530
Subject: [PATCH 39/69] Fix gemini-3.1-flash-lite-preview for streaming

---
 litellm/model_prices_and_context_window_backup.json | 3 ++-
 model_prices_and_context_window.json                | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 8f5fd26e3e8..5de764c5cec 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -14382,7 +14382,8 @@
         "supports_url_context": true,
         "supports_video_input": true,
         "supports_vision": true,
-        "supports_web_search": true
+        "supports_web_search": true,
+        "supports_native_streaming": true
     },
     "deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 8f5fd26e3e8..5de764c5cec 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -14382,7 +14382,8 @@
         "supports_url_context": true,
         "supports_video_input": true,
         "supports_vision": true,
-        "supports_web_search": true
+        "supports_web_search": true,
+        "supports_native_streaming": true
     },
     "deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,

From 22e682b1e89c1c1795b45910b2cea988f1525124 Mon Sep 17 00:00:00 2001
From: Harshit28j <Harshitjain0562@gmail.com>
Date: Tue, 3 Mar 2026 22:39:06 +0530
Subject: [PATCH 40/69] feat: guardrail-mode-default-list

---
 .../docs/proxy/guardrails/quick_start.md      | 40 +++++++++++++++++++
 litellm/integrations/custom_guardrail.py      | 18 ++++++++-
 litellm/types/guardrails.py                   |  2 +-
 3 files changed, 57 insertions(+), 3 deletions(-)

diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md
index e5a90f74a8a..eb56c27f876 100644
--- a/docs/my-website/docs/proxy/guardrails/quick_start.md
+++ b/docs/my-website/docs/proxy/guardrails/quick_start.md
@@ -499,6 +499,11 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 
 Run guardrails based on the user-agent header. This is useful for running pre-call checks on OpenWebUI but only masking in logs for Claude CLI.
 
+`default` can be a single mode string or a list of modes.
+
+<Tabs>
+<TabItem value="single" label="Single Default Mode">
+
 ```yaml
 model_list:
   - model_name: gpt-3.5-turbo
@@ -519,6 +524,32 @@ guardrails:
       default_on: true # run on every request
 ```
 
+</TabItem>
+<TabItem value="multi" label="Multiple Default Modes">
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+      api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+  - guardrail_name: "guardrails_ai-guard"
+    litellm_params:
+      guardrail: guardrails_ai
+      guard_name: "pii_detect"
+      mode:
+        tags:
+            "User-Agent: claude-cli": "logging_only"
+        default: ["pre_call", "post_call"]  # Run on both pre and post call when no tags match
+      api_base: os.environ/GUARDRAILS_AI_API_BASE
+      default_on: true
+```
+
+</TabItem>
+</Tabs>
+
 
 ### ✨ Model-level Guardrails
 
@@ -640,13 +671,22 @@ guardrails:
 
 Mode Specification
 
+`default` accepts either a single string or a list of strings.
+
 ```python
 from litellm.types.guardrails import Mode
 
+# Single default mode
 mode = Mode(
     tags={"User-Agent: claude-cli": "logging_only"},
     default="logging_only"
 )
+
+# Multiple default modes
+mode = Mode(
+    tags={"User-Agent: claude-cli": "logging_only"},
+    default=["pre_call", "post_call"]
+)
 ```
 
 ### `guardrails` Request Parameter
diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py
index 5d11fd68475..3fd179bb411 100644
--- a/litellm/integrations/custom_guardrail.py
+++ b/litellm/integrations/custom_guardrail.py
@@ -235,8 +235,13 @@ class CustomGuardrail(CustomLogger):
                 list(event_hook.tags.values()), supported_event_hooks
             )
             if event_hook.default:
+                default_list = (
+                    event_hook.default
+                    if isinstance(event_hook.default, list)
+                    else [event_hook.default]
+                )
                 _validate_event_hook_list_is_in_supported_event_hooks(
-                    [event_hook.default], supported_event_hooks
+                    default_list, supported_event_hooks
                 )
         elif isinstance(event_hook, GuardrailEventHooks):
             if event_hook not in supported_event_hooks:
@@ -461,7 +466,16 @@ class CustomGuardrail(CustomLogger):
         if isinstance(self.event_hook, list):
             return event_type.value in self.event_hook
         if isinstance(self.event_hook, Mode):
-            return event_type.value in self.event_hook.tags.values()
+            if event_type.value in self.event_hook.tags.values():
+                return True
+            if self.event_hook.default:
+                default_list = (
+                    self.event_hook.default
+                    if isinstance(self.event_hook.default, list)
+                    else [self.event_hook.default]
+                )
+                return event_type.value in default_list
+            return False
         return self.event_hook == event_type.value
 
     def get_guardrail_dynamic_request_body_params(self, request_data: dict) -> dict:
diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py
index a68c4e2f762..dc95ed3314a 100644
--- a/litellm/types/guardrails.py
+++ b/litellm/types/guardrails.py
@@ -718,7 +718,7 @@ class BaseLitellmParams(
 
 class Mode(BaseModel):
     tags: Dict[str, str] = Field(description="Tags for the guardrail mode")
-    default: Optional[str] = Field(
+    default: Optional[Union[str, List[str]]] = Field(
         default=None, description="Default mode when no tags match"
     )
 

From 76e3dba0f88929a82c673b7c586cc4aba8b8f34a Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 09:41:45 -0800
Subject: [PATCH 41/69] fix mcp server created_at and updated_at timestamps
 being overwritten with current time

- Add created_at field to MCPServer type (was missing)
- Map created_at from LiteLLM_MCPServerTable in build_mcp_server_from_table()
- Use server.created_at and server.updated_at instead of datetime.now() in _build_mcp_server_table() and health check table builder
- Add regression tests to verify timestamps are preserved through round-trip conversions

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../mcp_server/mcp_server_manager.py          | 11 ++-
 .../types/mcp_server/mcp_server_manager.py    |  1 +
 .../mcp_server/test_mcp_server_manager.py     | 86 +++++++++++++++++++
 3 files changed, 92 insertions(+), 6 deletions(-)

diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index da29c7804a1..b7c013e9f20 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -642,6 +642,7 @@ class MCPServerManager:
             available_on_public_internet=bool(
                 getattr(mcp_server, "available_on_public_internet", True)
             ),
+            created_at=getattr(mcp_server, "created_at", None),
             updated_at=getattr(mcp_server, "updated_at", None),
         )
         return new_server
@@ -2540,8 +2541,8 @@ class MCPServerManager:
             url=server.url,
             transport=server.transport,
             auth_type=server.auth_type,
-            created_at=datetime.now(),
-            updated_at=datetime.now(),
+            created_at=server.created_at,
+            updated_at=server.updated_at,
             teams=[],
             mcp_access_groups=server.access_groups or [],
             allowed_tools=server.allowed_tools or [],
@@ -2620,8 +2621,6 @@ class MCPServerManager:
         return list_mcp_servers
 
     def _build_mcp_server_table(self, server: MCPServer) -> LiteLLM_MCPServerTable:
-        from datetime import datetime
-
         return LiteLLM_MCPServerTable(
             server_id=server.server_id,
             server_name=server.server_name,
@@ -2633,8 +2632,8 @@ class MCPServerManager:
             spec_path=server.spec_path,
             transport=server.transport,
             auth_type=server.auth_type,
-            created_at=datetime.now(),
-            updated_at=datetime.now(),
+            created_at=server.created_at,
+            updated_at=server.updated_at,
             teams=[],
             mcp_access_groups=server.access_groups or [],
             allowed_tools=server.allowed_tools or [],
diff --git a/litellm/types/mcp_server/mcp_server_manager.py b/litellm/types/mcp_server/mcp_server_manager.py
index 69b34a25a21..cabac6b9d51 100644
--- a/litellm/types/mcp_server/mcp_server_manager.py
+++ b/litellm/types/mcp_server/mcp_server_manager.py
@@ -53,6 +53,7 @@ class MCPServer(BaseModel):
     access_groups: Optional[List[str]] = None
     allow_all_keys: bool = False
     available_on_public_internet: bool = True
+    created_at: Optional[datetime] = None
     updated_at: Optional[datetime] = None
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
index c105052479d..acc76221cbb 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
@@ -2307,5 +2307,91 @@ class TestMCPServerManager:
         assert resolved_server.server_name == "test_server"  # server_name matches
 
 
+class TestMCPServerTimestamps:
+    """Regression tests: created_at/updated_at must be preserved, not overwritten with datetime.now()."""
+
+    @pytest.mark.asyncio
+    async def test_build_mcp_server_from_table_preserves_timestamps(self):
+        """build_mcp_server_from_table must carry created_at and updated_at into MCPServer."""
+        manager = MCPServerManager()
+
+        created = datetime(2024, 1, 15, 10, 0, 0)
+        updated = datetime(2024, 6, 20, 12, 30, 0)
+
+        table_record = LiteLLM_MCPServerTable(
+            server_id="ts-server-1",
+            server_name="ts_server",
+            url="https://example.com/mcp",
+            transport=MCPTransport.http,
+            created_at=created,
+            updated_at=updated,
+        )
+
+        mcp_server = await manager.build_mcp_server_from_table(table_record)
+
+        assert mcp_server.created_at == created
+        assert mcp_server.updated_at == updated
+
+    def test_build_mcp_server_table_preserves_timestamps(self):
+        """_build_mcp_server_table must use the MCPServer's stored timestamps, not datetime.now()."""
+        manager = MCPServerManager()
+
+        created = datetime(2024, 1, 15, 10, 0, 0)
+        updated = datetime(2024, 6, 20, 12, 30, 0)
+
+        server = MCPServer(
+            server_id="ts-server-2",
+            name="ts_server",
+            url="https://example.com/mcp",
+            transport=MCPTransport.http,
+            created_at=created,
+            updated_at=updated,
+        )
+
+        table = manager._build_mcp_server_table(server)
+
+        assert table.created_at == created
+        assert table.updated_at == updated
+
+    def test_build_mcp_server_table_none_timestamps_when_not_set(self):
+        """_build_mcp_server_table must return None timestamps when not set on MCPServer."""
+        manager = MCPServerManager()
+
+        server = MCPServer(
+            server_id="ts-server-3",
+            name="ts_server",
+            url="https://example.com/mcp",
+            transport=MCPTransport.http,
+        )
+
+        table = manager._build_mcp_server_table(server)
+
+        assert table.created_at is None
+        assert table.updated_at is None
+
+    @pytest.mark.asyncio
+    async def test_round_trip_timestamps_preserved(self):
+        """Timestamps survive the full round-trip: LiteLLM_MCPServerTable -> MCPServer -> LiteLLM_MCPServerTable."""
+        manager = MCPServerManager()
+
+        created = datetime(2023, 3, 10, 8, 0, 0)
+        updated = datetime(2023, 9, 5, 16, 45, 0)
+
+        table_record = LiteLLM_MCPServerTable(
+            server_id="ts-server-4",
+            server_name="ts_server_rt",
+            url="https://example.com/mcp",
+            transport=MCPTransport.http,
+            created_at=created,
+            updated_at=updated,
+        )
+
+        mcp_server = await manager.build_mcp_server_from_table(table_record)
+        rebuilt_table = manager._build_mcp_server_table(mcp_server)
+
+        assert rebuilt_table.created_at == created
+        assert rebuilt_table.updated_at == updated
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

From 224c61711948fd1508b59083542f022d1f37b99c Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 10:12:08 -0800
Subject: [PATCH 42/69] Fix spend log cleanup: lock tracking, integer
 retention, skip log level
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Only release distributed lock in finally if it was actually acquired;
  prevents spurious Redis release_lock calls on early returns
- Treat bare integer maximum_spend_logs_retention_period as days (e.g. 3 → "3d")
  instead of silently failing with a ValueError
- Elevate "Skipping cleanup" log from info to error so misconfigured
  retention settings are visible without verbose logging
- Add tests for all three fixes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../db_transaction_queue/spend_log_cleanup.py | 13 ++--
 .../proxy/test_spend_log_cleanup.py           | 59 +++++++++++++++++++
 2 files changed, 68 insertions(+), 4 deletions(-)

diff --git a/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py b/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py
index 8c59c79ff0a..6db7d6dd43c 100644
--- a/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py
+++ b/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py
@@ -49,7 +49,11 @@ class SpendLogCleanup:
 
         try:
             if isinstance(retention_setting, int):
-                retention_setting = str(retention_setting)
+                verbose_proxy_logger.warning(
+                    f"maximum_spend_logs_retention_period is an integer ({retention_setting}); treating as days. "
+                    "Use a string like '3d' to be explicit."
+                )
+                retention_setting = f"{retention_setting}d"
             self.retention_seconds = duration_in_seconds(retention_setting)
             verbose_proxy_logger.info(
                 f"Retention period set to {self.retention_seconds} seconds"
@@ -112,11 +116,12 @@ class SpendLogCleanup:
         If pod_lock_manager is available, ensures only one pod runs cleanup.
         If no pod_lock_manager, runs cleanup without distributed locking.
         """
+        lock_acquired = False
         try:
             verbose_proxy_logger.info(f"Cleanup job triggered at {datetime.now()}")
 
             if not self._should_delete_spend_logs():
-                verbose_proxy_logger.info(
+                verbose_proxy_logger.error(
                     "Skipping cleanup — invalid or missing retention setting."
                 )
                 return
@@ -155,8 +160,8 @@ class SpendLogCleanup:
             verbose_proxy_logger.error(f"Error during cleanup: {str(e)}")
             return  # Return after error handling
         finally:
-            # Always release the lock if we have a pod lock manager
-            if self.pod_lock_manager and self.pod_lock_manager.redis_cache:
+            # Only release the lock if it was actually acquired
+            if lock_acquired and self.pod_lock_manager and self.pod_lock_manager.redis_cache:
                 await self.pod_lock_manager.release_lock(
                     cronjob_id=SPEND_LOG_CLEANUP_JOB_NAME
                 )
diff --git a/tests/test_litellm/proxy/test_spend_log_cleanup.py b/tests/test_litellm/proxy/test_spend_log_cleanup.py
index c1fa3ad0c43..3a01437908d 100644
--- a/tests/test_litellm/proxy/test_spend_log_cleanup.py
+++ b/tests/test_litellm/proxy/test_spend_log_cleanup.py
@@ -233,6 +233,65 @@ async def test_cleanup_old_spend_logs_no_retention_period():
     mock_prisma_client.db.execute_raw.assert_not_called()
 
 
+@pytest.mark.asyncio
+async def test_lock_not_released_when_not_acquired():
+    """
+    Lock release should be skipped when _should_delete_spend_logs returns False
+    before the lock is ever acquired.
+    """
+    mock_prisma_client = MagicMock()
+    mock_prisma_client.db.execute_raw = AsyncMock()
+
+    mock_redis_cache = MagicMock()
+    mock_pod_lock_manager = MagicMock()
+    mock_pod_lock_manager.redis_cache = mock_redis_cache
+    mock_pod_lock_manager.acquire_lock = AsyncMock(return_value=True)
+    mock_pod_lock_manager.release_lock = AsyncMock()
+
+    # No retention setting → _should_delete_spend_logs() returns False before lock is acquired
+    cleaner = SpendLogCleanup(general_settings={})
+    cleaner.pod_lock_manager = mock_pod_lock_manager
+
+    await cleaner.cleanup_old_spend_logs(mock_prisma_client)
+
+    mock_pod_lock_manager.acquire_lock.assert_not_called()
+    mock_pod_lock_manager.release_lock.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_integer_retention_treated_as_days():
+    """
+    An integer value for maximum_spend_logs_retention_period should be treated
+    as days (e.g., 3 → '3d' → 259200 seconds).
+    """
+    cleaner = SpendLogCleanup(
+        general_settings={"maximum_spend_logs_retention_period": 3}
+    )
+    result = cleaner._should_delete_spend_logs()
+    assert result is True
+    assert cleaner.retention_seconds == 3 * 86400  # 3 days in seconds
+
+
+def test_string_retention_still_works():
+    """
+    String values like '3d', '24h', '3600s' should continue to parse correctly.
+    """
+    cases = [
+        ("3d", 3 * 86400),
+        ("24h", 24 * 3600),
+        ("3600s", 3600),
+        ("2w", 2 * 604800),
+    ]
+    for setting, expected_seconds in cases:
+        cleaner = SpendLogCleanup(
+            general_settings={"maximum_spend_logs_retention_period": setting}
+        )
+        assert cleaner._should_delete_spend_logs() is True, f"Failed for {setting}"
+        assert cleaner.retention_seconds == expected_seconds, (
+            f"Expected {expected_seconds} for {setting}, got {cleaner.retention_seconds}"
+        )
+
+
 def test_cleanup_batch_size_env_var(monkeypatch):
     """Ensure batch size is configurable via environment variable"""
     import importlib

From a1ba6c9fa643c04492d274b2a664d2de88ce1603 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 10:21:31 -0800
Subject: [PATCH 43/69] Fix log levels: info for unconfigured, warning for
 misconfigured

Suppress noisy error log fired every cron tick when spend log cleanup
is simply not configured. _should_delete_spend_logs already logs the
specific reason at the right level (info for None, warning for
invalid value), so the redundant blanket error log in
cleanup_old_spend_logs is removed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py b/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py
index 6db7d6dd43c..a538e411b68 100644
--- a/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py
+++ b/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py
@@ -60,7 +60,7 @@ class SpendLogCleanup:
             )
             return True
         except ValueError as e:
-            verbose_proxy_logger.error(
+            verbose_proxy_logger.warning(
                 f"Invalid maximum_spend_logs_retention_period value: {retention_setting}, error: {str(e)}"
             )
             return False
@@ -121,9 +121,6 @@ class SpendLogCleanup:
             verbose_proxy_logger.info(f"Cleanup job triggered at {datetime.now()}")
 
             if not self._should_delete_spend_logs():
-                verbose_proxy_logger.error(
-                    "Skipping cleanup — invalid or missing retention setting."
-                )
                 return
 
             if self.retention_seconds is None:

From 43cec8c980abf699b3664a239393f408b9ca2f90 Mon Sep 17 00:00:00 2001
From: Ryan Crabbe <rcrabbe@berkeley.edu>
Date: Tue, 3 Mar 2026 10:46:45 -0800
Subject: [PATCH 44/69] feat(batches): support output_expires_after passthrough

---
 litellm/batches/main.py                       |  5 ++
 litellm/types/llms/openai.py                  |  1 +
 tests/test_litellm/proxy/test_batch_expiry.py | 74 +++++++++++++++++++
 3 files changed, 80 insertions(+)
 create mode 100644 tests/test_litellm/proxy/test_batch_expiry.py

diff --git a/litellm/batches/main.py b/litellm/batches/main.py
index 9553d2c5246..e73f73d2f33 100644
--- a/litellm/batches/main.py
+++ b/litellm/batches/main.py
@@ -112,6 +112,7 @@ async def acreate_batch(
     metadata: Optional[Dict[str, str]] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
+    output_expires_after: Optional[Dict[str, Any]] = None,
     **kwargs,
 ) -> LiteLLMBatch:
     """
@@ -133,6 +134,7 @@ async def acreate_batch(
             metadata,
             extra_headers,
             extra_body,
+            output_expires_after,
             **kwargs,
         )
 
@@ -160,6 +162,7 @@ def create_batch(
     metadata: Optional[Dict[str, str]] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
+    output_expires_after: Optional[Dict[str, Any]] = None,
     **kwargs,
 ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
     """
@@ -215,6 +218,8 @@ def create_batch(
             extra_headers=extra_headers,
             extra_body=extra_body,
         )
+        if output_expires_after is not None:
+            _create_batch_request["output_expires_after"] = output_expires_after
         if model is not None:
             provider_config = ProviderConfigManager.get_provider_batches_config(
                 model=model,
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index d06d879dad1..c5d610e639b 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -424,6 +424,7 @@ class CreateBatchRequest(TypedDict, total=False):
     endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"]
     input_file_id: str
     metadata: Optional[Dict[str, str]]
+    output_expires_after: Optional[FileExpiresAfter]
     extra_headers: Optional[Dict[str, str]]
     extra_body: Optional[Dict[str, str]]
     timeout: Optional[float]
diff --git a/tests/test_litellm/proxy/test_batch_expiry.py b/tests/test_litellm/proxy/test_batch_expiry.py
new file mode 100644
index 00000000000..25b1631792e
--- /dev/null
+++ b/tests/test_litellm/proxy/test_batch_expiry.py
@@ -0,0 +1,74 @@
+"""
+Tests for batch output_expires_after passthrough and team-level expiry enforcement.
+"""
+
+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
+
+import litellm
+from litellm.types.llms.openai import CreateBatchRequest
+
+
+class TestCreateBatchOutputExpiresAfterPassthrough:
+    """Verify output_expires_after flows through create_batch to the provider."""
+
+    def test_output_expires_after_included_in_request(self):
+        """When output_expires_after is provided, it reaches the openai batches instance."""
+        captured = {}
+
+        original_create = None
+
+        def capturing_create(**kwargs):
+            captured.update(kwargs)
+            mock_response = MagicMock()
+            mock_response.id = "batch_123"
+            return mock_response
+
+        with patch(
+            "litellm.batches.main.openai_batches_instance"
+        ) as mock_instance:
+            mock_instance.create_batch.side_effect = capturing_create
+            litellm.create_batch(
+                completion_window="24h",
+                endpoint="/v1/chat/completions",
+                input_file_id="file-abc123",
+                output_expires_after={"anchor": "created_at", "seconds": 86400},
+                custom_llm_provider="openai",
+            )
+
+        create_batch_data = captured["create_batch_data"]
+        assert create_batch_data["output_expires_after"] == {
+            "anchor": "created_at",
+            "seconds": 86400,
+        }
+
+    def test_output_expires_after_absent_when_not_provided(self):
+        """Backward compat: output_expires_after not in request when omitted."""
+        captured = {}
+
+        def capturing_create(**kwargs):
+            captured.update(kwargs)
+            mock_response = MagicMock()
+            mock_response.id = "batch_123"
+            return mock_response
+
+        with patch(
+            "litellm.batches.main.openai_batches_instance"
+        ) as mock_instance:
+            mock_instance.create_batch.side_effect = capturing_create
+            litellm.create_batch(
+                completion_window="24h",
+                endpoint="/v1/chat/completions",
+                input_file_id="file-abc123",
+                custom_llm_provider="openai",
+            )
+
+        create_batch_data = captured["create_batch_data"]
+        assert "output_expires_after" not in create_batch_data

From 3d15bcdb115731cd0b4dd93f19be880e2128f1bb Mon Sep 17 00:00:00 2001
From: Ryan Crabbe <rcrabbe@berkeley.edu>
Date: Tue, 3 Mar 2026 10:58:31 -0800
Subject: [PATCH 45/69] feat(proxy): add team-level batch output expiry
 enforcement

---
 litellm/proxy/_types.py                       |   6 +
 litellm/proxy/batches_endpoints/endpoints.py  |   9 +
 tests/test_litellm/proxy/test_batch_expiry.py | 188 +++++++++++++-----
 3 files changed, 153 insertions(+), 50 deletions(-)

diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index cbf683d226e..5122c64ea64 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -1551,6 +1551,8 @@ class NewTeamRequest(TeamBase):
     ] = None  # allow user to set TPM limit for all team members
     team_member_key_duration: Optional[str] = None  # e.g. "1d", "1w", "1m"
     allowed_vector_store_indexes: Optional[List[AllowedVectorStoreIndexItem]] = None
+    enforced_batch_output_expires_after: Optional[dict] = None
+    enforced_file_expires_after: Optional[dict] = None
 
     model_config = ConfigDict(protected_namespaces=())
 
@@ -1606,6 +1608,8 @@ class UpdateTeamRequest(LiteLLMPydanticObjectBase):
     model_rpm_limit: Optional[Dict[str, int]] = None
     model_tpm_limit: Optional[Dict[str, int]] = None
     allowed_vector_store_indexes: Optional[List[AllowedVectorStoreIndexItem]] = None
+    enforced_batch_output_expires_after: Optional[dict] = None
+    enforced_file_expires_after: Optional[dict] = None
     router_settings: Optional[dict] = None
     access_group_ids: Optional[List[str]] = None
 
@@ -3783,6 +3787,8 @@ LiteLLM_ManagementEndpoint_MetadataFields = [
     "temp_budget_increase",
     "temp_budget_expiry",
     "allowed_vector_store_indexes",
+    "enforced_batch_output_expires_after",
+    "enforced_file_expires_after",
 ]
 
 LiteLLM_ManagementEndpoint_MetadataFields_Premium = [
diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py
index 1c9ba6cb248..60905243369 100644
--- a/litellm/proxy/batches_endpoints/endpoints.py
+++ b/litellm/proxy/batches_endpoints/endpoints.py
@@ -118,6 +118,15 @@ async def create_batch(  # noqa: PLR0915
             or "openai"
         )
         _create_batch_data = LiteLLMBatchCreateRequest(**data)
+
+        # Apply team-level batch output expiry enforcement
+        team_metadata = user_api_key_dict.team_metadata or {}
+        enforced_batch_expiry = team_metadata.get(
+            "enforced_batch_output_expires_after"
+        )
+        if enforced_batch_expiry is not None:
+            _create_batch_data["output_expires_after"] = enforced_batch_expiry
+
         input_file_id = _create_batch_data.get("input_file_id", None)
         unified_file_id: Union[str, Literal[False]] = False
         
diff --git a/tests/test_litellm/proxy/test_batch_expiry.py b/tests/test_litellm/proxy/test_batch_expiry.py
index 25b1631792e..1f54f190c63 100644
--- a/tests/test_litellm/proxy/test_batch_expiry.py
+++ b/tests/test_litellm/proxy/test_batch_expiry.py
@@ -4,7 +4,7 @@ Tests for batch output_expires_after passthrough and team-level expiry enforceme
 
 import os
 import sys
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
 import pytest
 
@@ -13,62 +13,150 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 
 import litellm
-from litellm.types.llms.openai import CreateBatchRequest
+from litellm.caching.caching import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.proxy_server import app
+from litellm.proxy.utils import ProxyLogging
+from litellm.router import Router
+from litellm.types.utils import LiteLLMBatch
+
+from fastapi.testclient import TestClient
+
+client = TestClient(app)
+
+TEAM_EXPIRY = {"anchor": "created_at", "seconds": 3600}
+CALLER_EXPIRY = {"anchor": "created_at", "seconds": 86400}
 
 
-class TestCreateBatchOutputExpiresAfterPassthrough:
-    """Verify output_expires_after flows through create_batch to the provider."""
+@pytest.fixture
+def llm_router() -> Router:
+    return Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "openai/gpt-3.5-turbo",
+                    "api_key": "test-key",
+                },
+                "model_info": {"id": "gpt-3.5-turbo-id"},
+            },
+        ]
+    )
 
-    def test_output_expires_after_included_in_request(self):
-        """When output_expires_after is provided, it reaches the openai batches instance."""
-        captured = {}
 
-        original_create = None
+def _setup_proxy(monkeypatch, llm_router: Router):
+    proxy_logging_obj = ProxyLogging(
+        user_api_key_cache=DualCache(default_in_memory_ttl=1)
+    )
+    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", llm_router)
+    monkeypatch.setattr(
+        "litellm.proxy.proxy_server.proxy_logging_obj", proxy_logging_obj
+    )
 
-        def capturing_create(**kwargs):
-            captured.update(kwargs)
-            mock_response = MagicMock()
-            mock_response.id = "batch_123"
-            return mock_response
 
-        with patch(
-            "litellm.batches.main.openai_batches_instance"
-        ) as mock_instance:
-            mock_instance.create_batch.side_effect = capturing_create
-            litellm.create_batch(
-                completion_window="24h",
-                endpoint="/v1/chat/completions",
-                input_file_id="file-abc123",
-                output_expires_after={"anchor": "created_at", "seconds": 86400},
-                custom_llm_provider="openai",
+def _make_batch_response() -> LiteLLMBatch:
+    return LiteLLMBatch(
+        id="batch_abc123",
+        completion_window="24h",
+        created_at=1234567890,
+        endpoint="/v1/chat/completions",
+        input_file_id="file-abc123",
+        object="batch",
+        status="validating",
+    )
+
+
+def test_output_expires_after_passthrough():
+    """output_expires_after flows through create_batch to the provider."""
+    captured = {}
+
+    def capturing_create(**kwargs):
+        captured.update(kwargs)
+        mock_response = MagicMock()
+        mock_response.id = "batch_123"
+        return mock_response
+
+    with patch("litellm.batches.main.openai_batches_instance") as mock_instance:
+        mock_instance.create_batch.side_effect = capturing_create
+        litellm.create_batch(
+            completion_window="24h",
+            endpoint="/v1/chat/completions",
+            input_file_id="file-abc123",
+            output_expires_after=CALLER_EXPIRY,
+            custom_llm_provider="openai",
+        )
+
+    assert captured["create_batch_data"]["output_expires_after"] == CALLER_EXPIRY
+
+
+class TestBatchEndpointTeamOverride:
+    """Verify team-level enforced_batch_output_expires_after in the proxy endpoint."""
+
+    def _post_batch(
+        self,
+        monkeypatch,
+        llm_router: Router,
+        team_metadata: dict,
+        request_body: dict,
+    ) -> dict:
+        """POST /v1/batches with given team_metadata and body, return captured kwargs."""
+        _setup_proxy(monkeypatch, llm_router)
+
+        user_key = UserAPIKeyAuth(
+            api_key="test-key",
+            team_metadata=team_metadata,
+        )
+        app.dependency_overrides[user_api_key_auth] = lambda: user_key
+
+        captured_kwargs = {}
+
+        async def mock_acreate_batch(**kwargs):
+            captured_kwargs.update(kwargs)
+            return _make_batch_response()
+
+        monkeypatch.setattr(litellm, "acreate_batch", mock_acreate_batch)
+
+        try:
+            response = client.post(
+                "/v1/batches",
+                json=request_body,
+                headers={"Authorization": "Bearer test-key"},
             )
+            assert response.status_code == 200
+        finally:
+            app.dependency_overrides.clear()
 
-        create_batch_data = captured["create_batch_data"]
-        assert create_batch_data["output_expires_after"] == {
-            "anchor": "created_at",
-            "seconds": 86400,
-        }
+        return captured_kwargs
 
-    def test_output_expires_after_absent_when_not_provided(self):
-        """Backward compat: output_expires_after not in request when omitted."""
-        captured = {}
+    def test_team_override_overrides_caller(self, monkeypatch, llm_router):
+        """Team enforcement wins over caller-provided value."""
+        kwargs = self._post_batch(
+            monkeypatch,
+            llm_router,
+            team_metadata={
+                "enforced_batch_output_expires_after": TEAM_EXPIRY,
+            },
+            request_body={
+                "input_file_id": "file-abc123",
+                "endpoint": "/v1/chat/completions",
+                "completion_window": "24h",
+                "output_expires_after": CALLER_EXPIRY,
+            },
+        )
+        assert kwargs["output_expires_after"] == TEAM_EXPIRY
 
-        def capturing_create(**kwargs):
-            captured.update(kwargs)
-            mock_response = MagicMock()
-            mock_response.id = "batch_123"
-            return mock_response
-
-        with patch(
-            "litellm.batches.main.openai_batches_instance"
-        ) as mock_instance:
-            mock_instance.create_batch.side_effect = capturing_create
-            litellm.create_batch(
-                completion_window="24h",
-                endpoint="/v1/chat/completions",
-                input_file_id="file-abc123",
-                custom_llm_provider="openai",
-            )
-
-        create_batch_data = captured["create_batch_data"]
-        assert "output_expires_after" not in create_batch_data
+    def test_no_team_setting_preserves_caller(self, monkeypatch, llm_router):
+        """No team setting = caller value passes through."""
+        kwargs = self._post_batch(
+            monkeypatch,
+            llm_router,
+            team_metadata={},
+            request_body={
+                "input_file_id": "file-abc123",
+                "endpoint": "/v1/chat/completions",
+                "completion_window": "24h",
+                "output_expires_after": CALLER_EXPIRY,
+            },
+        )
+        assert kwargs["output_expires_after"] == CALLER_EXPIRY

From 08613b24cbbf0315249a305b743e43cc5e73411f Mon Sep 17 00:00:00 2001
From: Ryan Crabbe <rcrabbe@berkeley.edu>
Date: Tue, 3 Mar 2026 11:03:14 -0800
Subject: [PATCH 46/69] feat(proxy): add team-level file expiry enforcement

---
 .../openai_files_endpoints/files_endpoints.py |  11 +-
 .../test_files_endpoint.py                    | 135 ++++++++++++++++++
 2 files changed, 145 insertions(+), 1 deletion(-)

diff --git a/litellm/proxy/openai_files_endpoints/files_endpoints.py b/litellm/proxy/openai_files_endpoints/files_endpoints.py
index ec6e9733344..a641fb1c69f 100644
--- a/litellm/proxy/openai_files_endpoints/files_endpoints.py
+++ b/litellm/proxy/openai_files_endpoints/files_endpoints.py
@@ -454,8 +454,17 @@ async def create_file(  # noqa: PLR0915
                     model=router_model, llm_router=llm_router
                 )
 
+        # Apply team-level file expiry enforcement
+        team_metadata = user_api_key_dict.team_metadata or {}
+        enforced_file_expiry = team_metadata.get("enforced_file_expires_after")
+        if enforced_file_expiry is not None:
+            expires_after = FileExpiresAfter(
+                anchor=enforced_file_expiry["anchor"],
+                seconds=enforced_file_expiry["seconds"],
+            )
+
         _create_file_request = CreateFileRequest(
-            file=file_data, 
+            file=file_data,
             purpose=cast(CREATE_FILE_REQUESTS_PURPOSE, purpose),
             expires_after=expires_after,
             **data
diff --git a/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py b/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py
index fb063ef8ee7..0239c39e67f 100644
--- a/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py
+++ b/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py
@@ -1168,3 +1168,138 @@ def test_create_file_with_deep_nested_litellm_metadata(
     assert captured_litellm_metadata["config"]["database"]["port"] == "5432"
     assert "cache" in captured_litellm_metadata["config"]
     assert captured_litellm_metadata["config"]["cache"]["enabled"] == "true"
+
+
+# ---------------------------------------------------------------------------
+# Team-level enforced_file_expires_after tests
+# ---------------------------------------------------------------------------
+
+
+def _make_capturing_managed_files():
+    """Create a DummyManagedFiles that captures the expires_after from the request."""
+    from litellm.llms.base_llm.files.transformation import BaseFileEndpoints
+
+    captured = {}
+
+    class CapturingManagedFiles(BaseFileEndpoints):
+        async def acreate_file(
+            self,
+            llm_router,
+            create_file_request,
+            target_model_names_list,
+            litellm_parent_otel_span,
+            user_api_key_dict,
+        ):
+            if isinstance(create_file_request, dict):
+                captured["expires_after"] = create_file_request.get("expires_after")
+            else:
+                captured["expires_after"] = getattr(
+                    create_file_request, "expires_after", None
+                )
+            return OpenAIFileObject(
+                id="file-abc123",
+                object="file",
+                bytes=100,
+                created_at=1234567890,
+                filename="mydata.jsonl",
+                purpose="batch",
+                status="uploaded",
+            )
+
+        async def afile_retrieve(self, file_id, litellm_parent_otel_span, llm_router):
+            raise NotImplementedError
+
+        async def afile_list(self, purpose, litellm_parent_otel_span):
+            raise NotImplementedError
+
+        async def afile_delete(
+            self, file_id, litellm_parent_otel_span, llm_router, **data
+        ):
+            raise NotImplementedError
+
+        async def afile_content(
+            self, file_id, litellm_parent_otel_span, llm_router, **data
+        ):
+            raise NotImplementedError
+
+    return CapturingManagedFiles(), captured
+
+
+def _post_file_with_team_metadata(
+    monkeypatch,
+    llm_router: Router,
+    team_metadata: dict,
+    form_data: dict,
+):
+    """POST /v1/files with given team_metadata, return captured expires_after."""
+    from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+
+    proxy_logging_obj = ProxyLogging(
+        user_api_key_cache=DualCache(default_in_memory_ttl=1)
+    )
+    dummy, captured = _make_capturing_managed_files()
+    proxy_logging_obj.proxy_hook_mapping["managed_files"] = dummy
+    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", llm_router)
+    monkeypatch.setattr(
+        "litellm.proxy.proxy_server.proxy_logging_obj", proxy_logging_obj
+    )
+
+    user_key = UserAPIKeyAuth(api_key="test-key", team_metadata=team_metadata)
+    app.dependency_overrides[user_api_key_auth] = lambda: user_key
+
+    test_file = ("mydata.jsonl", b'{"prompt": "Hello"}', "application/json")
+    try:
+        response = client.post(
+            "/v1/files",
+            files={"file": test_file},
+            data=form_data,
+            headers={"Authorization": "Bearer test-key"},
+        )
+        assert response.status_code == 200
+    finally:
+        app.dependency_overrides.clear()
+
+    return captured["expires_after"]
+
+
+def test_file_team_override_overrides_caller(
+    mocker: MockerFixture, monkeypatch, llm_router: Router
+):
+    """Team enforced_file_expires_after wins over caller-provided value."""
+    expires_after = _post_file_with_team_metadata(
+        monkeypatch,
+        llm_router,
+        team_metadata={
+            "enforced_file_expires_after": {
+                "anchor": "created_at",
+                "seconds": 3600,
+            }
+        },
+        form_data={
+            "purpose": "batch",
+            "target_model_names": "gpt-3.5-turbo",
+            "expires_after[anchor]": "created_at",
+            "expires_after[seconds]": "86400",
+        },
+    )
+    assert expires_after["anchor"] == "created_at"
+    assert expires_after["seconds"] == 3600
+
+
+def test_file_no_team_setting_preserves_caller(
+    mocker: MockerFixture, monkeypatch, llm_router: Router
+):
+    """No team setting = caller-provided expires_after passes through."""
+    expires_after = _post_file_with_team_metadata(
+        monkeypatch,
+        llm_router,
+        team_metadata={},
+        form_data={
+            "purpose": "batch",
+            "target_model_names": "gpt-3.5-turbo",
+            "expires_after[anchor]": "created_at",
+            "expires_after[seconds]": "86400",
+        },
+    )
+    assert expires_after["anchor"] == "created_at"
+    assert expires_after["seconds"] == 86400

From d6614191096d2a9e06e7d9a36281ce71ad4170ab Mon Sep 17 00:00:00 2001
From: Harshit28j <Harshitjain0562@gmail.com>
Date: Wed, 4 Mar 2026 01:50:28 +0530
Subject: [PATCH 47/69] fix: support list of modes in Mode.default for
 tag-based guardrails

---
 .../integrations/custom_guardrail.py          |  37 ++++-
 litellm/integrations/custom_guardrail.py      |   4 +-
 .../integrations/test_custom_guardrail.py     | 130 +++++++++++++++++-
 3 files changed, 158 insertions(+), 13 deletions(-)

diff --git a/enterprise/litellm_enterprise/integrations/custom_guardrail.py b/enterprise/litellm_enterprise/integrations/custom_guardrail.py
index b165d788f35..8ed3bfcac4c 100644
--- a/enterprise/litellm_enterprise/integrations/custom_guardrail.py
+++ b/enterprise/litellm_enterprise/integrations/custom_guardrail.py
@@ -10,10 +10,15 @@ class EnterpriseCustomGuardrailHelper:
         event_hook: Optional[
             Union[GuardrailEventHooks, List[GuardrailEventHooks], Mode]
         ],
+        event_type: Optional[GuardrailEventHooks] = None,
     ) -> Optional[bool]:
         """
-        Assumes check for event match is done in `should_run_guardrail`
-        Returns True if the guardrail should be run by tag
+        Returns True if the guardrail should be run for this request and event_type.
+
+        Logic:
+        - If a request tag matches a Mode tag key, only run if event_type matches
+          the tag's value (the mode for that tag).
+        - If no request tag matches, fall back to default mode(s).
         """
         from litellm.litellm_core_utils.litellm_logging import (
             StandardLoggingPayloadSetup,
@@ -36,11 +41,29 @@ class EnterpriseCustomGuardrailHelper:
             proxy_server_request=proxy_server_request,
         )
 
-        if request_tags and any(tag in event_hook.tags for tag in request_tags):
-            return True
-        elif event_hook.default and any(
-            tag in event_hook.default for tag in request_tags
-        ):
+        # Check if any request tag matches a Mode tag key
+        matched_mode = None
+        if request_tags:
+            for tag in request_tags:
+                if tag in event_hook.tags:
+                    matched_mode = event_hook.tags[tag]
+                    break
+
+        if matched_mode is not None:
+            # Tag matched: only run if event_type matches the tag's mode value
+            if event_type is not None:
+                return event_type.value == matched_mode
             return True
 
+        # No tag matched: fall back to default mode(s)
+        if event_hook.default is not None:
+            if event_type is not None:
+                default_list = (
+                    event_hook.default
+                    if isinstance(event_hook.default, list)
+                    else [event_hook.default]
+                )
+                return event_type.value in default_list
+            return False
+
         return False
diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py
index 3fd179bb411..269797b9873 100644
--- a/litellm/integrations/custom_guardrail.py
+++ b/litellm/integrations/custom_guardrail.py
@@ -420,7 +420,7 @@ class CustomGuardrail(CustomLogger):
                             "Setting tag-based guardrails is only available in litellm-enterprise. You must be a premium user to use this feature."
                         )
                     result = EnterpriseCustomGuardrailHelper._should_run_if_mode_by_tag(
-                        data, self.event_hook
+                        data, self.event_hook, event_type
                     )
                     if result is not None:
                         return result
@@ -447,7 +447,7 @@ class CustomGuardrail(CustomLogger):
                     "Setting tag-based guardrails is only available in litellm-enterprise. You must be a premium user to use this feature."
                 )
             result = EnterpriseCustomGuardrailHelper._should_run_if_mode_by_tag(
-                data, self.event_hook
+                data, self.event_hook, event_type
             )
             if result is not None:
                 return result
diff --git a/tests/enterprise/litellm_enterprise/integrations/test_custom_guardrail.py b/tests/enterprise/litellm_enterprise/integrations/test_custom_guardrail.py
index 6feaca6f0b7..f4e06f9f317 100644
--- a/tests/enterprise/litellm_enterprise/integrations/test_custom_guardrail.py
+++ b/tests/enterprise/litellm_enterprise/integrations/test_custom_guardrail.py
@@ -1,9 +1,5 @@
-import datetime
-import json
 import os
 import sys
-import unittest
-from unittest.mock import ANY, MagicMock, patch
 
 sys.path.insert(
     0, os.path.abspath("../..")
@@ -12,6 +8,132 @@ from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.types.guardrails import GuardrailEventHooks, Mode
 
 
+def test_custom_guardrail_with_mode_default_list(monkeypatch):
+    """Test Mode with default as a list of modes (e.g. default: ["pre_call", "post_call"])"""
+    monkeypatch.setattr("litellm.proxy.proxy_server.premium_user", True)
+    cg = CustomGuardrail(
+        guardrail_name="test_guardrail",
+        supported_event_hooks=[
+            GuardrailEventHooks.pre_call,
+            GuardrailEventHooks.post_call,
+            GuardrailEventHooks.logging_only,
+        ],
+        event_hook=Mode(
+            tags={"test_tag": "logging_only"},
+            default=["pre_call", "post_call"],
+        ),
+        default_on=True,
+    )
+
+    # No tag match → default fires for pre_call
+    assert (
+        cg.should_run_guardrail(
+            data={"messages": [{"role": "user", "content": "test"}]},
+            event_type=GuardrailEventHooks.pre_call,
+        )
+        is True
+    )
+
+    # No tag match → default fires for post_call
+    assert (
+        cg.should_run_guardrail(
+            data={"messages": [{"role": "user", "content": "test"}]},
+            event_type=GuardrailEventHooks.post_call,
+        )
+        is True
+    )
+
+    # No tag match → logging_only NOT in default list, should not fire
+    assert (
+        cg.should_run_guardrail(
+            data={"messages": [{"role": "user", "content": "test"}]},
+            event_type=GuardrailEventHooks.logging_only,
+        )
+        is False
+    )
+
+    # Tag matches → only logging_only should fire
+    assert (
+        cg.should_run_guardrail(
+            data={
+                "messages": [{"role": "user", "content": "test"}],
+                "litellm_metadata": {"tags": ["test_tag"]},
+            },
+            event_type=GuardrailEventHooks.logging_only,
+        )
+        is True
+    )
+
+    # Tag matches → pre_call should NOT fire (tag says logging_only)
+    assert (
+        cg.should_run_guardrail(
+            data={
+                "messages": [{"role": "user", "content": "test"}],
+                "litellm_metadata": {"tags": ["test_tag"]},
+            },
+            event_type=GuardrailEventHooks.pre_call,
+        )
+        is False
+    )
+
+    # Tag matches → post_call should NOT fire (tag says logging_only)
+    assert (
+        cg.should_run_guardrail(
+            data={
+                "messages": [{"role": "user", "content": "test"}],
+                "litellm_metadata": {"tags": ["test_tag"]},
+            },
+            event_type=GuardrailEventHooks.post_call,
+        )
+        is False
+    )
+
+
+def test_custom_guardrail_with_mode_no_default(monkeypatch):
+    """Test Mode with no default — guardrail only fires when tag matches"""
+    monkeypatch.setattr("litellm.proxy.proxy_server.premium_user", True)
+    cg = CustomGuardrail(
+        guardrail_name="test_guardrail",
+        supported_event_hooks=[
+            GuardrailEventHooks.pre_call,
+            GuardrailEventHooks.logging_only,
+        ],
+        event_hook=Mode(
+            tags={"test_tag": "logging_only"},
+        ),
+        default_on=True,
+    )
+
+    # No tag, no default → nothing fires
+    assert (
+        cg.should_run_guardrail(
+            data={"messages": [{"role": "user", "content": "test"}]},
+            event_type=GuardrailEventHooks.pre_call,
+        )
+        is False
+    )
+
+    assert (
+        cg.should_run_guardrail(
+            data={"messages": [{"role": "user", "content": "test"}]},
+            event_type=GuardrailEventHooks.logging_only,
+        )
+        is False
+    )
+
+    # Tag matches → only logging_only fires
+    assert (
+        cg.should_run_guardrail(
+            data={
+                "messages": [{"role": "user", "content": "test"}],
+                "litellm_metadata": {"tags": ["test_tag"]},
+            },
+            event_type=GuardrailEventHooks.logging_only,
+        )
+        is True
+    )
+
+
 def test_custom_guardrail_with_mode(monkeypatch):
     monkeypatch.setattr(
         "litellm.proxy.proxy_server.premium_user", True

From 4edb1e00c60bf620ec6a179e29224298b000e081 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 12:36:26 -0800
Subject: [PATCH 48/69] [Test] UI - Add unit tests for project hooks

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../hooks/projects/useCreateProject.test.ts   | 111 ++++++++++++++
 .../hooks/projects/useDeleteProject.test.ts   |  88 +++++++++++
 .../hooks/projects/useProjectDetails.test.ts  | 144 ++++++++++++++++++
 .../hooks/projects/useProjects.test.ts        | 124 +++++++++++++++
 .../hooks/projects/useUpdateProject.test.ts   | 116 ++++++++++++++
 5 files changed, 583 insertions(+)
 create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useCreateProject.test.ts
 create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useDeleteProject.test.ts
 create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjectDetails.test.ts
 create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjects.test.ts
 create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useUpdateProject.test.ts

diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useCreateProject.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useCreateProject.test.ts
new file mode 100644
index 00000000000..64d950d59ee
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useCreateProject.test.ts
@@ -0,0 +1,111 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { renderHook, waitFor } from "@testing-library/react";
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import React, { ReactNode } from "react";
+import { useCreateProject, ProjectCreateParams } from "./useCreateProject";
+import { projectKeys, ProjectResponse } from "./useProjects";
+
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => ""),
+  getGlobalLitellmHeaderName: vi.fn(() => "Authorization"),
+  deriveErrorMessage: vi.fn((data: any) => data?.error || "Error"),
+  handleError: vi.fn(),
+}));
+
+const mockUseAuthorized = vi.fn();
+vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+  default: () => mockUseAuthorized(),
+}));
+
+const mockProject: ProjectResponse = {
+  project_id: "proj-1",
+  project_alias: "Test Project",
+  description: "A test project",
+  team_id: "team-1",
+  budget_id: null,
+  metadata: null,
+  models: ["gpt-4"],
+  spend: 25.0,
+  model_spend: null,
+  model_rpm_limit: null,
+  model_tpm_limit: null,
+  blocked: false,
+  object_permission_id: null,
+  created_at: "2024-01-01T00:00:00Z",
+  created_by: "user-1",
+  updated_at: "2024-01-02T00:00:00Z",
+  updated_by: "user-1",
+  litellm_budget_table: null,
+};
+
+function makeWrapper(queryClient: QueryClient) {
+  return ({ children }: { children: ReactNode }) =>
+    React.createElement(QueryClientProvider, { client: queryClient }, children);
+}
+
+describe("useCreateProject", () => {
+  let queryClient: QueryClient;
+
+  beforeEach(() => {
+    queryClient = new QueryClient({
+      defaultOptions: { queries: { retry: false }, mutations: { retry: false } },
+    });
+    vi.clearAllMocks();
+    global.fetch = vi.fn();
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Admin" });
+  });
+
+  it("should render", () => {
+    const { result } = renderHook(() => useCreateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.mutate).toBeDefined();
+  });
+
+  it("should POST to /project/new and return the created project", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    const { result } = renderHook(() => useCreateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    const params: ProjectCreateParams = { team_id: "team-1", project_alias: "New Project" };
+    const data = await result.current.mutateAsync(params);
+    expect(data).toEqual(mockProject);
+    const [url, init] = (global.fetch as any).mock.calls[0];
+    expect(url).toContain("/project/new");
+    expect(init.method).toBe("POST");
+    expect(JSON.parse(init.body)).toMatchObject(params);
+  });
+
+  it("should invalidate project queries on success", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    const invalidateSpy = vi.spyOn(queryClient, "invalidateQueries");
+    const { result } = renderHook(() => useCreateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await result.current.mutateAsync({ team_id: "team-1" });
+    expect(invalidateSpy).toHaveBeenCalledWith({ queryKey: projectKeys.all });
+  });
+
+  it("should set isError when the request fails", async () => {
+    (global.fetch as any).mockResolvedValue({
+      ok: false,
+      json: async () => ({ error: "Server error" }),
+    });
+    const { result } = renderHook(() => useCreateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    result.current.mutateAsync({ team_id: "team-1" }).catch(() => {});
+    await waitFor(() => expect(result.current.isError).toBe(true));
+  });
+
+  it("should throw when accessToken is missing", async () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: null, userRole: "Admin" });
+    const { result } = renderHook(() => useCreateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await expect(result.current.mutateAsync({ team_id: "team-1" })).rejects.toThrow(
+      "Access token is required"
+    );
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+});
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useDeleteProject.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useDeleteProject.test.ts
new file mode 100644
index 00000000000..85a9f3e0b10
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useDeleteProject.test.ts
@@ -0,0 +1,88 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { renderHook, waitFor } from "@testing-library/react";
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import React, { ReactNode } from "react";
+import { useDeleteProject } from "./useDeleteProject";
+import { projectKeys } from "./useProjects";
+
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => ""),
+  getGlobalLitellmHeaderName: vi.fn(() => "Authorization"),
+  deriveErrorMessage: vi.fn((data: any) => data?.error || "Error"),
+  handleError: vi.fn(),
+}));
+
+const mockUseAuthorized = vi.fn();
+vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+  default: () => mockUseAuthorized(),
+}));
+
+function makeWrapper(queryClient: QueryClient) {
+  return ({ children }: { children: ReactNode }) =>
+    React.createElement(QueryClientProvider, { client: queryClient }, children);
+}
+
+describe("useDeleteProject", () => {
+  let queryClient: QueryClient;
+
+  beforeEach(() => {
+    queryClient = new QueryClient({
+      defaultOptions: { queries: { retry: false }, mutations: { retry: false } },
+    });
+    vi.clearAllMocks();
+    global.fetch = vi.fn();
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Admin" });
+  });
+
+  it("should render", () => {
+    const { result } = renderHook(() => useDeleteProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.mutate).toBeDefined();
+  });
+
+  it("should send DELETE to /project/delete with the given project IDs", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => ({}) });
+    const { result } = renderHook(() => useDeleteProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await result.current.mutateAsync(["proj-1", "proj-2"]);
+    const [url, init] = (global.fetch as any).mock.calls[0];
+    expect(url).toContain("/project/delete");
+    expect(init.method).toBe("DELETE");
+    expect(JSON.parse(init.body)).toEqual({ project_ids: ["proj-1", "proj-2"] });
+  });
+
+  it("should invalidate project queries on success", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => ({}) });
+    const invalidateSpy = vi.spyOn(queryClient, "invalidateQueries");
+    const { result } = renderHook(() => useDeleteProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await result.current.mutateAsync(["proj-1"]);
+    expect(invalidateSpy).toHaveBeenCalledWith({ queryKey: projectKeys.all });
+  });
+
+  it("should set isError when the request fails", async () => {
+    (global.fetch as any).mockResolvedValue({
+      ok: false,
+      json: async () => ({ error: "Not found" }),
+    });
+    const { result } = renderHook(() => useDeleteProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    result.current.mutateAsync(["proj-1"]).catch(() => {});
+    await waitFor(() => expect(result.current.isError).toBe(true));
+  });
+
+  it("should throw when accessToken is missing", async () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: null, userRole: "Admin" });
+    const { result } = renderHook(() => useDeleteProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await expect(result.current.mutateAsync(["proj-1"])).rejects.toThrow(
+      "Access token is required"
+    );
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+});
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjectDetails.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjectDetails.test.ts
new file mode 100644
index 00000000000..426abfe9bb6
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjectDetails.test.ts
@@ -0,0 +1,144 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { renderHook, waitFor } from "@testing-library/react";
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import React, { ReactNode } from "react";
+import { useProjectDetails } from "./useProjectDetails";
+import { projectKeys, ProjectResponse } from "./useProjects";
+
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => ""),
+  getGlobalLitellmHeaderName: vi.fn(() => "Authorization"),
+  deriveErrorMessage: vi.fn((data: any) => data?.error || "Error"),
+  handleError: vi.fn(),
+}));
+
+const mockUseAuthorized = vi.fn();
+vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+  default: () => mockUseAuthorized(),
+}));
+
+const mockProject: ProjectResponse = {
+  project_id: "proj-1",
+  project_alias: "Test Project",
+  description: "A test project",
+  team_id: "team-1",
+  budget_id: null,
+  metadata: null,
+  models: ["gpt-4"],
+  spend: 25.0,
+  model_spend: null,
+  model_rpm_limit: null,
+  model_tpm_limit: null,
+  blocked: false,
+  object_permission_id: null,
+  created_at: "2024-01-01T00:00:00Z",
+  created_by: "user-1",
+  updated_at: "2024-01-02T00:00:00Z",
+  updated_by: "user-1",
+  litellm_budget_table: null,
+};
+
+const mockProjects: ProjectResponse[] = [
+  mockProject,
+  { ...mockProject, project_id: "proj-2", project_alias: "Test Project 2" },
+];
+
+function makeWrapper(queryClient: QueryClient) {
+  return ({ children }: { children: ReactNode }) =>
+    React.createElement(QueryClientProvider, { client: queryClient }, children);
+}
+
+describe("useProjectDetails", () => {
+  let queryClient: QueryClient;
+
+  beforeEach(() => {
+    queryClient = new QueryClient({
+      defaultOptions: { queries: { retry: false } },
+    });
+    vi.clearAllMocks();
+    global.fetch = vi.fn();
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Admin" });
+  });
+
+  it("should render", () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    const { result } = renderHook(() => useProjectDetails("proj-1"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current).toBeDefined();
+  });
+
+  it("should return project details when the request succeeds", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    const { result } = renderHook(() => useProjectDetails("proj-1"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await waitFor(() => expect(result.current.isSuccess).toBe(true));
+    expect(result.current.data).toEqual(mockProject);
+  });
+
+  it("should call /project/info with the projectId encoded as a query param", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    renderHook(() => useProjectDetails("proj-1"), { wrapper: makeWrapper(queryClient) });
+    await waitFor(() => expect(global.fetch).toHaveBeenCalled());
+    const [url] = (global.fetch as any).mock.calls[0];
+    expect(url).toContain("/project/info");
+    expect(url).toContain("project_id=proj-1");
+  });
+
+  it("should not fetch when projectId is missing", () => {
+    const { result } = renderHook(() => useProjectDetails(undefined), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.isFetched).toBe(false);
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+
+  it("should not fetch when accessToken is missing", () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: null, userRole: "Admin" });
+    const { result } = renderHook(() => useProjectDetails("proj-1"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.isFetched).toBe(false);
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+
+  it("should not fetch when userRole is not an admin role", () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Internal User" });
+    const { result } = renderHook(() => useProjectDetails("proj-1"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.isFetched).toBe(false);
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+
+  it("should seed initialData from the projects list cache", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    queryClient.setQueryData(projectKeys.list({}), mockProjects);
+    const { result } = renderHook(() => useProjectDetails("proj-1"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.data).toEqual(mockProject);
+    expect(result.current.isLoading).toBe(false);
+    await waitFor(() => expect(result.current.isFetching).toBe(false));
+  });
+
+  it("should return undefined initialData when projectId is not in the cache", () => {
+    queryClient.setQueryData(projectKeys.list({}), mockProjects);
+    const { result } = renderHook(() => useProjectDetails("non-existent"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.data).toBeUndefined();
+  });
+
+  it("should set isError when the request fails", async () => {
+    (global.fetch as any).mockResolvedValue({
+      ok: false,
+      json: async () => ({ error: "Not found" }),
+    });
+    const { result } = renderHook(() => useProjectDetails("proj-1"), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await waitFor(() => expect(result.current.isError).toBe(true));
+  });
+});
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjects.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjects.test.ts
new file mode 100644
index 00000000000..13b9107bdc1
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useProjects.test.ts
@@ -0,0 +1,124 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { renderHook, waitFor } from "@testing-library/react";
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import React, { ReactNode } from "react";
+import { useProjects, ProjectResponse } from "./useProjects";
+
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => ""),
+  getGlobalLitellmHeaderName: vi.fn(() => "Authorization"),
+  deriveErrorMessage: vi.fn((data: any) => data?.error || "Error"),
+  handleError: vi.fn(),
+}));
+
+const mockUseAuthorized = vi.fn();
+vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+  default: () => mockUseAuthorized(),
+}));
+
+const mockProjects: ProjectResponse[] = [
+  {
+    project_id: "proj-1",
+    project_alias: "Test Project",
+    description: "A test project",
+    team_id: "team-1",
+    budget_id: null,
+    metadata: null,
+    models: ["gpt-4"],
+    spend: 25.0,
+    model_spend: null,
+    model_rpm_limit: null,
+    model_tpm_limit: null,
+    blocked: false,
+    object_permission_id: null,
+    created_at: "2024-01-01T00:00:00Z",
+    created_by: "user-1",
+    updated_at: "2024-01-02T00:00:00Z",
+    updated_by: "user-1",
+    litellm_budget_table: null,
+  },
+  {
+    project_id: "proj-2",
+    project_alias: "Test Project 2",
+    description: null,
+    team_id: "team-1",
+    budget_id: null,
+    metadata: null,
+    models: [],
+    spend: 0,
+    model_spend: null,
+    model_rpm_limit: null,
+    model_tpm_limit: null,
+    blocked: false,
+    object_permission_id: null,
+    created_at: "2024-01-03T00:00:00Z",
+    created_by: "user-1",
+    updated_at: "2024-01-03T00:00:00Z",
+    updated_by: "user-1",
+    litellm_budget_table: null,
+  },
+];
+
+function makeWrapper(queryClient: QueryClient) {
+  return ({ children }: { children: ReactNode }) =>
+    React.createElement(QueryClientProvider, { client: queryClient }, children);
+}
+
+describe("useProjects", () => {
+  let queryClient: QueryClient;
+
+  beforeEach(() => {
+    queryClient = new QueryClient({
+      defaultOptions: { queries: { retry: false } },
+    });
+    vi.clearAllMocks();
+    global.fetch = vi.fn();
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Admin" });
+  });
+
+  it("should render", () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProjects });
+    const { result } = renderHook(() => useProjects(), { wrapper: makeWrapper(queryClient) });
+    expect(result.current).toBeDefined();
+  });
+
+  it("should return projects when the request succeeds", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProjects });
+    const { result } = renderHook(() => useProjects(), { wrapper: makeWrapper(queryClient) });
+    await waitFor(() => expect(result.current.isSuccess).toBe(true));
+    expect(result.current.data).toEqual(mockProjects);
+  });
+
+  it("should call GET /project/list with the auth header", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProjects });
+    renderHook(() => useProjects(), { wrapper: makeWrapper(queryClient) });
+    await waitFor(() => expect(global.fetch).toHaveBeenCalled());
+    const [url, init] = (global.fetch as any).mock.calls[0];
+    expect(url).toContain("/project/list");
+    expect(init.headers["Authorization"]).toBe("Bearer test-token");
+  });
+
+  it("should set isError when the request fails", async () => {
+    (global.fetch as any).mockResolvedValue({
+      ok: false,
+      json: async () => ({ error: "Not authorized" }),
+    });
+    const { result } = renderHook(() => useProjects(), { wrapper: makeWrapper(queryClient) });
+    await waitFor(() => expect(result.current.isError).toBe(true));
+    expect(result.current.data).toBeUndefined();
+  });
+
+  it("should not fetch when accessToken is missing", () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: null, userRole: "Admin" });
+    const { result } = renderHook(() => useProjects(), { wrapper: makeWrapper(queryClient) });
+    expect(result.current.isFetched).toBe(false);
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+
+  it("should not fetch when userRole is not an admin role", () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Internal User" });
+    const { result } = renderHook(() => useProjects(), { wrapper: makeWrapper(queryClient) });
+    expect(result.current.isFetched).toBe(false);
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+});
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useUpdateProject.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useUpdateProject.test.ts
new file mode 100644
index 00000000000..31d1a5fb352
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/projects/useUpdateProject.test.ts
@@ -0,0 +1,116 @@
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { renderHook, waitFor } from "@testing-library/react";
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import React, { ReactNode } from "react";
+import { useUpdateProject } from "./useUpdateProject";
+import { projectKeys, ProjectResponse } from "./useProjects";
+
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => ""),
+  getGlobalLitellmHeaderName: vi.fn(() => "Authorization"),
+  deriveErrorMessage: vi.fn((data: any) => data?.error || "Error"),
+  handleError: vi.fn(),
+}));
+
+const mockUseAuthorized = vi.fn();
+vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
+  default: () => mockUseAuthorized(),
+}));
+
+const mockProject: ProjectResponse = {
+  project_id: "proj-1",
+  project_alias: "Test Project",
+  description: "A test project",
+  team_id: "team-1",
+  budget_id: null,
+  metadata: null,
+  models: ["gpt-4"],
+  spend: 25.0,
+  model_spend: null,
+  model_rpm_limit: null,
+  model_tpm_limit: null,
+  blocked: false,
+  object_permission_id: null,
+  created_at: "2024-01-01T00:00:00Z",
+  created_by: "user-1",
+  updated_at: "2024-01-02T00:00:00Z",
+  updated_by: "user-1",
+  litellm_budget_table: null,
+};
+
+function makeWrapper(queryClient: QueryClient) {
+  return ({ children }: { children: ReactNode }) =>
+    React.createElement(QueryClientProvider, { client: queryClient }, children);
+}
+
+describe("useUpdateProject", () => {
+  let queryClient: QueryClient;
+
+  beforeEach(() => {
+    queryClient = new QueryClient({
+      defaultOptions: { queries: { retry: false }, mutations: { retry: false } },
+    });
+    vi.clearAllMocks();
+    global.fetch = vi.fn();
+    mockUseAuthorized.mockReturnValue({ accessToken: "test-token", userRole: "Admin" });
+  });
+
+  it("should render", () => {
+    const { result } = renderHook(() => useUpdateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    expect(result.current.mutate).toBeDefined();
+  });
+
+  it("should POST to /project/update and return the updated project", async () => {
+    const updated = { ...mockProject, project_alias: "Updated Name" };
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => updated });
+    const { result } = renderHook(() => useUpdateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    const data = await result.current.mutateAsync({
+      projectId: "proj-1",
+      params: { project_alias: "Updated Name" },
+    });
+    expect(data).toEqual(updated);
+    const [url, init] = (global.fetch as any).mock.calls[0];
+    expect(url).toContain("/project/update");
+    expect(JSON.parse(init.body)).toMatchObject({
+      project_id: "proj-1",
+      project_alias: "Updated Name",
+    });
+  });
+
+  it("should invalidate project queries on success", async () => {
+    (global.fetch as any).mockResolvedValue({ ok: true, json: async () => mockProject });
+    const invalidateSpy = vi.spyOn(queryClient, "invalidateQueries");
+    const { result } = renderHook(() => useUpdateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await result.current.mutateAsync({ projectId: "proj-1", params: {} });
+    expect(invalidateSpy).toHaveBeenCalledWith({ queryKey: projectKeys.all });
+  });
+
+  it("should set isError when the request fails", async () => {
+    (global.fetch as any).mockResolvedValue({
+      ok: false,
+      json: async () => ({ error: "Server error" }),
+    });
+    const { result } = renderHook(() => useUpdateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    result.current.mutateAsync({ projectId: "proj-1", params: {} }).catch(() => {});
+    await waitFor(() => expect(result.current.isError).toBe(true));
+  });
+
+  it("should throw when accessToken is missing", async () => {
+    mockUseAuthorized.mockReturnValue({ accessToken: null, userRole: "Admin" });
+    const { result } = renderHook(() => useUpdateProject(), {
+      wrapper: makeWrapper(queryClient),
+    });
+    await expect(
+      result.current.mutateAsync({ projectId: "proj-1", params: {} })
+    ).rejects.toThrow("Access token is required");
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+});

From 279b4f16cb3c5d783294928f1018bf05320dfb50 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 17:57:32 -0300
Subject: [PATCH 49/69] Fix mypy override errors in count_tokens signatures

Replace **kwargs with explicit tools and system parameters to match
the BaseTokenCounter.count_tokens abstract method signature.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 litellm/llms/gemini/common_utils.py    | 3 ++-
 litellm/llms/vertex_ai/common_utils.py | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/litellm/llms/gemini/common_utils.py b/litellm/llms/gemini/common_utils.py
index f99548c2c45..17b9c78123f 100644
--- a/litellm/llms/gemini/common_utils.py
+++ b/litellm/llms/gemini/common_utils.py
@@ -166,7 +166,8 @@ class GoogleAIStudioTokenCounter(BaseTokenCounter):
         contents: Optional[List[Dict[str, Any]]],
         deployment: Optional[Dict[str, Any]] = None,
         request_model: str = "",
-        **kwargs,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        system: Optional[Any] = None,
     ) -> Optional[TokenCountResponse]:
         import copy
 
diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py
index 791878c9700..3c5cbb65437 100644
--- a/litellm/llms/vertex_ai/common_utils.py
+++ b/litellm/llms/vertex_ai/common_utils.py
@@ -1030,7 +1030,8 @@ class VertexAITokenCounter(BaseTokenCounter):
         contents: Optional[List[Dict[str, Any]]],
         deployment: Optional[Dict[str, Any]] = None,
         request_model: str = "",
-        **kwargs,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        system: Optional[Any] = None,
     ) -> Optional[TokenCountResponse]:
         import copy
 

From 6b4bc99202c1fad64920c260f437ca52116966a5 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 18:12:49 -0300
Subject: [PATCH 50/69] Fix Anthropic streaming sync __next__ and Azure GPT-5.1
 logprobs

Two independent fixes for pre-existing test failures on main:

1. Anthropic streaming: The sync __next__ method used a simple
   holding_chunk pattern that lost chunks when multiple events needed
   to be returned. Refactored to use the same chunk_queue approach as
   the async __anext__ method. Also fixed tests that used ModelResponse
   (which defaults finish_reason to 'stop') instead of ModelResponseStream.

2. Azure GPT-5.1 logprobs: The base OpenAI class includes logprobs for
   gpt-5.1+ models, but Azure hasn't verified support for gpt-5.1.
   Added explicit removal of logprobs/top_logprobs for gpt-5.1 (non-5.2)
   models in the Azure config.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 .../adapters/streaming_iterator.py            | 135 ++++++++++--------
 .../llms/azure/chat/gpt_5_transformation.py   |   8 +-
 .../test_content_after_stop_reason.py         |  14 +-
 .../messages/test_parallel_tool_calls.py      |  26 ++--
 .../messages/test_sse_wrapper.py              |  17 +--
 5 files changed, 105 insertions(+), 95 deletions(-)

diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
index de634ff9ecf..cdf8ac5ca82 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
@@ -80,38 +80,40 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
         from .transformation import LiteLLMAnthropicMessagesAdapter
 
         try:
+            # Always return queued chunks first
+            if self.chunk_queue:
+                return self.chunk_queue.popleft()
+
+            # Queue initial chunks if not sent yet
             if self.sent_first_chunk is False:
                 self.sent_first_chunk = True
-                return {
-                    "type": "message_start",
-                    "message": {
-                        "id": "msg_{}".format(uuid.uuid4()),
-                        "type": "message",
-                        "role": "assistant",
-                        "content": [],
-                        "model": self.model,
-                        "stop_reason": None,
-                        "stop_sequence": None,
-                        "usage": self._create_initial_usage_delta(),
-                    },
-                }
+                self.chunk_queue.append(
+                    {
+                        "type": "message_start",
+                        "message": {
+                            "id": "msg_{}".format(uuid.uuid4()),
+                            "type": "message",
+                            "role": "assistant",
+                            "content": [],
+                            "model": self.model,
+                            "stop_reason": None,
+                            "stop_sequence": None,
+                            "usage": self._create_initial_usage_delta(),
+                        },
+                    }
+                )
+                return self.chunk_queue.popleft()
+
             if self.sent_content_block_start is False:
                 self.sent_content_block_start = True
-                return {
-                    "type": "content_block_start",
-                    "index": self.current_content_block_index,
-                    "content_block": {"type": "text", "text": ""},
-                }
-
-            # Handle pending new content block start
-            if self.pending_new_content_block:
-                self.pending_new_content_block = False
-                self.sent_content_block_finish = False  # Reset for new block
-                return {
-                    "type": "content_block_start",
-                    "index": self.current_content_block_index,
-                    "content_block": self.current_content_block_start,
-                }
+                self.chunk_queue.append(
+                    {
+                        "type": "content_block_start",
+                        "index": self.current_content_block_index,
+                        "content_block": {"type": "text", "text": ""},
+                    }
+                )
+                return self.chunk_queue.popleft()
 
             for chunk in self.completion_stream:
                 if chunk == "None" or chunk is None:
@@ -126,45 +128,65 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
                     current_content_block_index=self.current_content_block_index,
                 )
 
-                # Check if we need to start a new content block
-                # This is where you'd add your logic to detect when a new content block should start
-                # For example, if the chunk indicates a tool call or different content type
-
                 if should_start_new_block and not self.sent_content_block_finish:
-                    # End current content block and prepare for new one
-                    self.holding_chunk = processed_chunk
-                    self.sent_content_block_finish = True
-                    self.pending_new_content_block = True
-                    return {
-                        "type": "content_block_stop",
-                        "index": max(self.current_content_block_index - 1, 0),
-                    }
+                    # Queue the sequence: content_block_stop -> content_block_start
+                    # The trigger chunk itself is not emitted as a delta since the
+                    # content_block_start already carries the relevant information.
+                    self.chunk_queue.append(
+                        {
+                            "type": "content_block_stop",
+                            "index": max(self.current_content_block_index - 1, 0),
+                        }
+                    )
+                    self.chunk_queue.append(
+                        {
+                            "type": "content_block_start",
+                            "index": self.current_content_block_index,
+                            "content_block": self.current_content_block_start,
+                        }
+                    )
+                    self.sent_content_block_finish = False
+                    return self.chunk_queue.popleft()
 
                 if (
                     processed_chunk["type"] == "message_delta"
                     and self.sent_content_block_finish is False
                 ):
-                    self.holding_chunk = processed_chunk
+                    # Queue both the content_block_stop and the message_delta
+                    self.chunk_queue.append(
+                        {
+                            "type": "content_block_stop",
+                            "index": self.current_content_block_index,
+                        }
+                    )
                     self.sent_content_block_finish = True
-                    return {
-                        "type": "content_block_stop",
-                        "index": self.current_content_block_index,
-                    }
+                    self.chunk_queue.append(processed_chunk)
+                    return self.chunk_queue.popleft()
                 elif self.holding_chunk is not None:
-                    return_chunk = self.holding_chunk
-                    self.holding_chunk = processed_chunk
-                    return return_chunk
+                    self.chunk_queue.append(self.holding_chunk)
+                    self.chunk_queue.append(processed_chunk)
+                    self.holding_chunk = None
+                    return self.chunk_queue.popleft()
                 else:
-                    return processed_chunk
+                    self.chunk_queue.append(processed_chunk)
+                    return self.chunk_queue.popleft()
+
+            # Handle any remaining held chunks after stream ends
             if self.holding_chunk is not None:
-                return_chunk = self.holding_chunk
+                self.chunk_queue.append(self.holding_chunk)
                 self.holding_chunk = None
-                return return_chunk
-            if self.sent_last_message is False:
+
+            if not self.sent_last_message:
                 self.sent_last_message = True
-                return {"type": "message_stop"}
+                self.chunk_queue.append({"type": "message_stop"})
+
+            if self.chunk_queue:
+                return self.chunk_queue.popleft()
+
             raise StopIteration
         except StopIteration:
+            if self.chunk_queue:
+                return self.chunk_queue.popleft()
             if self.sent_last_message is False:
                 self.sent_last_message = True
                 return {"type": "message_stop"}
@@ -265,7 +287,9 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
 
                 if not self.queued_usage_chunk:
                     if should_start_new_block and not self.sent_content_block_finish:
-                        # Queue the sequence: content_block_stop -> content_block_start -> current_chunk
+                        # Queue the sequence: content_block_stop -> content_block_start
+                        # The trigger chunk itself is not emitted as a delta since the
+                        # content_block_start already carries the relevant information.
 
                         # 1. Stop current content block
                         self.chunk_queue.append(
@@ -284,9 +308,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
                             }
                         )
 
-                        # 3. Queue the current chunk (don't lose it!)
-                        self.chunk_queue.append(processed_chunk)
-
                         # Reset state for new block
                         self.sent_content_block_finish = False
 
diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py
index eeb55911ecf..2a2955fca37 100644
--- a/litellm/llms/azure/chat/gpt_5_transformation.py
+++ b/litellm/llms/azure/chat/gpt_5_transformation.py
@@ -43,8 +43,12 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
         if "tool_choice" not in params:
             params.append("tool_choice")
 
-        # Only gpt-5.2 has been verified to support logprobs on Azure
-        if self.is_model_gpt_5_2_model(model):
+        # Only gpt-5.2 has been verified to support logprobs on Azure.
+        # The base OpenAI class includes logprobs for gpt-5.1+, but Azure
+        # hasn't verified support for gpt-5.1, so remove them unless gpt-5.2.
+        if self.is_model_gpt_5_1_model(model) and not self.is_model_gpt_5_2_model(model):
+            params = [p for p in params if p not in ["logprobs", "top_logprobs"]]
+        elif self.is_model_gpt_5_2_model(model):
             azure_supported_params = ["logprobs", "top_logprobs"]
             params.extend(azure_supported_params)
 
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py
index 4a170d666f5..eadc0da2f1f 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py
@@ -23,7 +23,7 @@ sys.path.insert(0, os.path.abspath("../../../../.."))
 from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import (
     AnthropicStreamWrapper,
 )
-from litellm.types.utils import Delta, ModelResponse, StreamingChoices, Usage
+from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices, Usage
 
 
 class MockCompletionStreamWithContentAfterStopReason:
@@ -32,16 +32,14 @@ class MockCompletionStreamWithContentAfterStopReason:
     def __init__(self):
         self.responses = [
             # Initial text content
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content="Hello"), index=0, finish_reason=None
                     )
                 ],
             ),
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content=" world"), index=0, finish_reason=None
@@ -49,8 +47,7 @@ class MockCompletionStreamWithContentAfterStopReason:
                 ],
             ),
             # Message delta with stop_reason AND usage (this is how it actually comes from the API)
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content=""), index=0, finish_reason="stop"
@@ -60,8 +57,7 @@ class MockCompletionStreamWithContentAfterStopReason:
             ),
             # Additional content after the stop_reason - this simulates the scenario
             # where there might be additional content blocks after the main response
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content=" Additional content"),
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_parallel_tool_calls.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_parallel_tool_calls.py
index 9d4e58f3c88..1d25d719384 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_parallel_tool_calls.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_parallel_tool_calls.py
@@ -10,7 +10,7 @@ from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterato
 )
 from litellm.types.utils import (
     Delta,
-    ModelResponse,
+    ModelResponseStream,
     StreamingChoices,
     Usage,
     ChatCompletionDeltaToolCall,
@@ -19,7 +19,7 @@ from litellm.types.utils import (
 
 
 class MockCompletionStream:
-    def __init__(self, responses: List[ModelResponse]):
+    def __init__(self, responses: List[ModelResponseStream]):
         self.responses = responses
         self.index = 0
 
@@ -44,9 +44,8 @@ class MockCompletionStream:
         return response
 
 
-def construct_text_chunk(text: str) -> ModelResponse:
-    return ModelResponse(
-        stream=True,
+def construct_text_chunk(text: str) -> ModelResponseStream:
+    return ModelResponseStream(
         choices=[
             StreamingChoices(
                 delta=Delta(content=text),
@@ -59,11 +58,10 @@ def construct_text_chunk(text: str) -> ModelResponse:
 
 def construct_split_tool_call(
     id: str, function_name: str, function_arg_parts: List[str]
-) -> List[ModelResponse]:
+) -> List[ModelResponseStream]:
     return [
         # https://platform.openai.com/docs/guides/function-calling#streaming
-        ModelResponse(
-            stream=True,
+        ModelResponseStream(
             choices=[
                 StreamingChoices(
                     delta=Delta(
@@ -82,8 +80,7 @@ def construct_split_tool_call(
             ],
         ),
         *[
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(
@@ -109,8 +106,7 @@ def construct_split_tool_call(
 def test_anthropic_stream_wrapper_single_tool_call():
     responses = [
         *construct_split_tool_call("tooluse_foo", "get_weather", ['{"city":', '"NY"}']),
-        ModelResponse(
-            stream=True,
+        ModelResponseStream(
             choices=[
                 StreamingChoices(
                     delta=Delta(content="", stop_reason="tool_calls"),
@@ -172,8 +168,7 @@ def test_anthropic_stream_wrapper_back_to_back_tool_calls():
     responses = [
         *construct_split_tool_call("tooluse_foo", "get_weather", ['{"city":', '"NY"}']),
         *construct_split_tool_call("tooluse_bar", "get_weather", ['{"city":', '"SF"}']),
-        ModelResponse(
-            stream=True,
+        ModelResponseStream(
             choices=[
                 StreamingChoices(
                     delta=Delta(content="", stop_reason="tool_calls"),
@@ -244,8 +239,7 @@ def test_anthropic_stream_wrapper_interleaved_tool_calls_and_text():
             "tooluse_bar", "get_weather", ['{"city":', '"CHI"}']
         ),
         construct_text_chunk("The weather is not so nice today."),
-        ModelResponse(
-            stream=True,
+        ModelResponseStream(
             choices=[
                 StreamingChoices(
                     delta=Delta(content="", stop_reason="tool_calls"),
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_sse_wrapper.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_sse_wrapper.py
index dfcb9b3eb74..63fed907c3c 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_sse_wrapper.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_sse_wrapper.py
@@ -9,31 +9,28 @@ sys.path.insert(0, os.path.abspath("../../../../.."))
 from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import (
     AnthropicStreamWrapper,
 )
-from litellm.types.utils import Delta, ModelResponse, StreamingChoices
+from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
 
 
 # Create a simple test
 class MockCompletionStream:
     def __init__(self):
         self.responses = [
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content="Hello"), index=0, finish_reason=None
                     )
                 ],
             ),
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content=" World"), index=0, finish_reason=None
                     )
                 ],
             ),
-            ModelResponse(
-                stream=True,
+            ModelResponseStream(
                 choices=[
                     StreamingChoices(
                         delta=Delta(content=""), index=0, finish_reason="stop"
@@ -109,16 +106,14 @@ async def test_async_anthropic_sse_wrapper():
     class AsyncMockCompletionStream:
         def __init__(self):
             self.responses = [
-                ModelResponse(
-                    stream=True,
+                ModelResponseStream(
                     choices=[
                         StreamingChoices(
                             delta=Delta(content="Hello"), index=0, finish_reason=None
                         )
                     ],
                 ),
-                ModelResponse(
-                    stream=True,
+                ModelResponseStream(
                     choices=[
                         StreamingChoices(
                             delta=Delta(content=" World"), index=0, finish_reason=None

From ab718444c57db2bc88f078d82a8821ca7f9ba7d7 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 18:28:34 -0300
Subject: [PATCH 51/69] Remove dead pending_new_content_block attribute

Cleanup per review: this class attribute is no longer used after the
__next__ refactor to queue-based approach.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 .../experimental_pass_through/adapters/streaming_iterator.py     | 1 -
 1 file changed, 1 deletion(-)

diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
index cdf8ac5ca82..7f17526e75c 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
@@ -41,7 +41,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
         type="text",
         text="",
     )
-    pending_new_content_block: bool = False
     chunk_queue: deque = deque()  # Queue for buffering multiple chunks
 
     def __init__(

From d6ad312a4c2a2d14625210a9bd6daceb14940f97 Mon Sep 17 00:00:00 2001
From: Ryan Crabbe <rcrabbe@berkeley.edu>
Date: Tue, 3 Mar 2026 13:37:11 -0800
Subject: [PATCH 52/69] fix(proxy): validate enforced_file_expires_after keys
 before access

Add key validation for enforced_file_expires_after to return a clear
400 error instead of an unhandled KeyError 500.
---
 .../proxy/openai_files_endpoints/files_endpoints.py   | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/litellm/proxy/openai_files_endpoints/files_endpoints.py b/litellm/proxy/openai_files_endpoints/files_endpoints.py
index a641fb1c69f..386ab2bf044 100644
--- a/litellm/proxy/openai_files_endpoints/files_endpoints.py
+++ b/litellm/proxy/openai_files_endpoints/files_endpoints.py
@@ -458,11 +458,22 @@ async def create_file(  # noqa: PLR0915
         team_metadata = user_api_key_dict.team_metadata or {}
         enforced_file_expiry = team_metadata.get("enforced_file_expires_after")
         if enforced_file_expiry is not None:
+            if "anchor" not in enforced_file_expiry or "seconds" not in enforced_file_expiry:
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": "enforced_file_expires_after must contain 'anchor' and 'seconds' keys",
+                    },
+                )
             expires_after = FileExpiresAfter(
                 anchor=enforced_file_expiry["anchor"],
                 seconds=enforced_file_expiry["seconds"],
             )
 
+        verbose_proxy_logger.info(
+            "create_file expires_after: %s", expires_after
+        )
+
         _create_file_request = CreateFileRequest(
             file=file_data,
             purpose=cast(CREATE_FILE_REQUESTS_PURPOSE, purpose),

From 903ade4a1b3d10c83c2b7b91ffdc22322739e55e Mon Sep 17 00:00:00 2001
From: Ryan Crabbe <rcrabbe@berkeley.edu>
Date: Tue, 3 Mar 2026 13:51:41 -0800
Subject: [PATCH 53/69] fix(proxy): add anchor validation for file expiry, key
 validation for batch expiry

Validate anchor is "created_at" in enforced_file_expires_after (matching
user-provided path). Add key existence validation to batch endpoint for
enforced_batch_output_expires_after.
---
 litellm/proxy/batches_endpoints/endpoints.py            | 7 +++++++
 litellm/proxy/openai_files_endpoints/files_endpoints.py | 9 ++++++++-
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py
index 60905243369..850134b649c 100644
--- a/litellm/proxy/batches_endpoints/endpoints.py
+++ b/litellm/proxy/batches_endpoints/endpoints.py
@@ -125,6 +125,13 @@ async def create_batch(  # noqa: PLR0915
             "enforced_batch_output_expires_after"
         )
         if enforced_batch_expiry is not None:
+            if "anchor" not in enforced_batch_expiry or "seconds" not in enforced_batch_expiry:
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": "enforced_batch_output_expires_after must contain 'anchor' and 'seconds' keys",
+                    },
+                )
             _create_batch_data["output_expires_after"] = enforced_batch_expiry
 
         input_file_id = _create_batch_data.get("input_file_id", None)
diff --git a/litellm/proxy/openai_files_endpoints/files_endpoints.py b/litellm/proxy/openai_files_endpoints/files_endpoints.py
index 386ab2bf044..82cae8c64ea 100644
--- a/litellm/proxy/openai_files_endpoints/files_endpoints.py
+++ b/litellm/proxy/openai_files_endpoints/files_endpoints.py
@@ -465,8 +465,15 @@ async def create_file(  # noqa: PLR0915
                         "error": "enforced_file_expires_after must contain 'anchor' and 'seconds' keys",
                     },
                 )
+            if enforced_file_expiry["anchor"] != "created_at":
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": f"enforced_file_expires_after anchor must be 'created_at', got '{enforced_file_expiry['anchor']}'",
+                    },
+                )
             expires_after = FileExpiresAfter(
-                anchor=enforced_file_expiry["anchor"],
+                anchor="created_at",
                 seconds=enforced_file_expiry["seconds"],
             )
 

From 657a60ea5b55fa3497e4222b12ec782fc54f2df8 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 14:24:55 -0800
Subject: [PATCH 54/69] fix(audit): AND semantics for combined JSON filters;
 remove unused allTeams prop
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Fix object_team_id + object_key_hash combining incorrectly as OR — each
  filter now adds an AND clause wrapping an internal OR over before_value
  and updated_values, so both conditions must be satisfied simultaneously
- Rename helper to _build_json_field_or_condition to reflect its purpose
- Remove allTeams from AuditLogsProps and its call site in index.tsx

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../proxy/audit_logging_endpoints.py          | 42 ++++++++++---------
 .../src/components/view_logs/audit_logs.tsx   |  2 -
 .../src/components/view_logs/index.tsx        |  1 -
 3 files changed, 22 insertions(+), 23 deletions(-)

diff --git a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py
index 5ab3669b50c..18ac29b9781 100644
--- a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py
+++ b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py
@@ -22,19 +22,25 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 router = APIRouter()
 
 
-def _build_json_field_conditions(
-    field: str, json_key: str, value: str
-) -> List[Dict[str, Any]]:
+def _build_json_field_or_condition(json_key: str, value: str) -> Dict[str, Any]:
     """
-    Build OR conditions to match a value inside a JSON column at the given key.
+    Build an OR condition that matches a value inside a JSON column at the
+    given key, checking both before_value and updated_values.
 
-    Uses Prisma's JSON path filtering (PostgreSQL only). Returns a list of
-    two conditions — one for `before_value` and one for `updated_values` — to
-    be merged into the caller's top-level OR list.
+    Uses Prisma's JSON path filtering (PostgreSQL only).
+
+    Example result (team_id="t1"):
+      {"OR": [
+          {"before_value":    {"path": ["team_id"], "string_contains": "t1"}},
+          {"updated_values":  {"path": ["team_id"], "string_contains": "t1"}},
+      ]}
     """
-    return [
-        {field: {"path": [json_key], "string_contains": value}},
-    ]
+    return {
+        "OR": [
+            {"before_value": {"path": [json_key], "string_contains": value}},
+            {"updated_values": {"path": [json_key], "string_contains": value}},
+        ]
+    }
 
 
 @router.get(
@@ -115,19 +121,15 @@ async def get_audit_logs(
             date_filter["lte"] = end_date
         where_conditions["updated_at"] = date_filter
 
-    # JSON field filters (PostgreSQL only) — search inside before_value and
-    # updated_values for a matching key/value pair.
+    # JSON field filters (PostgreSQL only) — each filter is AND'd with the
+    # others, but checks both before_value and updated_values internally (OR).
     if object_team_id:
-        where_conditions["OR"] = [
-            *_build_json_field_conditions("before_value", "team_id", object_team_id),
-            *_build_json_field_conditions("updated_values", "team_id", object_team_id),
+        where_conditions["AND"] = where_conditions.get("AND", []) + [
+            _build_json_field_or_condition("team_id", object_team_id)
         ]
     if object_key_hash:
-        existing_or: List[Dict[str, Any]] = where_conditions.get("OR", [])
-        where_conditions["OR"] = [
-            *existing_or,
-            *_build_json_field_conditions("before_value", "token", object_key_hash),
-            *_build_json_field_conditions("updated_values", "token", object_key_hash),
+        where_conditions["AND"] = where_conditions.get("AND", []) + [
+            _build_json_field_or_condition("token", object_key_hash)
         ]
 
     # Build sort conditions
diff --git a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
index d3372eca6ff..693318acca8 100644
--- a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
@@ -7,7 +7,6 @@ import moment from "moment";
 import { uiAuditLogsCall } from "../networking";
 import { AuditLogEntry } from "./columns";
 import { AuditLogDrawer } from "./AuditLogDrawer/AuditLogDrawer";
-import { Team } from "../key_team_helpers/key_list";
 
 const { Search } = Input;
 
@@ -18,7 +17,6 @@ interface AuditLogsProps {
   userID: string | null;
   isActive: boolean;
   premiumUser: boolean;
-  allTeams: Team[];
 }
 
 const asset_logos_folder = "../ui/assets/";
diff --git a/ui/litellm-dashboard/src/components/view_logs/index.tsx b/ui/litellm-dashboard/src/components/view_logs/index.tsx
index 22cc06a73e9..64dc53c0e77 100644
--- a/ui/litellm-dashboard/src/components/view_logs/index.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/index.tsx
@@ -714,7 +714,6 @@ export default function SpendLogsTable({
               accessToken={accessToken}
               isActive={activeTab === "audit logs"}
               premiumUser={premiumUser}
-              allTeams={allTeams}
             />
           </TabPanel>
           <TabPanel><DeletedKeysPage /></TabPanel>

From 35d2bc382f0185850fbe6c9e505a69d940fb1619 Mon Sep 17 00:00:00 2001
From: Ryan Crabbe <rcrabbe@berkeley.edu>
Date: Tue, 3 Mar 2026 14:28:25 -0800
Subject: [PATCH 55/69] fix(batches): suppress PLR0915 lint for create_batch
 dispatch function

---
 litellm/batches/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/litellm/batches/main.py b/litellm/batches/main.py
index e73f73d2f33..e69c5a5c377 100644
--- a/litellm/batches/main.py
+++ b/litellm/batches/main.py
@@ -154,7 +154,7 @@ async def acreate_batch(
 
 
 @client
-def create_batch(
+def create_batch(  # noqa: PLR0915
     completion_window: Literal["24h"],
     endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],
     input_file_id: str,

From c8e6428eb77196030d65f68de9e8a09aab6ce72c Mon Sep 17 00:00:00 2001
From: ryan-crabbe <128659760+ryan-crabbe@users.noreply.github.com>
Date: Tue, 3 Mar 2026 14:35:58 -0800
Subject: [PATCH 56/69] Update
 litellm/proxy/openai_files_endpoints/files_endpoints.py

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---
 litellm/proxy/openai_files_endpoints/files_endpoints.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/litellm/proxy/openai_files_endpoints/files_endpoints.py b/litellm/proxy/openai_files_endpoints/files_endpoints.py
index 82cae8c64ea..44bd9b09d8e 100644
--- a/litellm/proxy/openai_files_endpoints/files_endpoints.py
+++ b/litellm/proxy/openai_files_endpoints/files_endpoints.py
@@ -477,7 +477,7 @@ async def create_file(  # noqa: PLR0915
                 seconds=enforced_file_expiry["seconds"],
             )
 
-        verbose_proxy_logger.info(
+        verbose_proxy_logger.debug(
             "create_file expires_after: %s", expires_after
         )
 

From 5da7fa9ac18a0b82a1a294389d6cf146fa443dd9 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 14:39:39 -0800
Subject: [PATCH 57/69] [Feature] UI - Virtual Keys: Add manual spend reset to
 unblock keys

Adds a "Reset Spend" button to the key detail view so proxy admins and team
admins can immediately reset a key's spend to $0, unblocking keys that have
hit their budget limit without waiting for the next scheduled budget reset.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../hooks/keys/useResetKeySpend.ts            |  66 ++++++++
 .../components/templates/KeyInfoHeader.tsx    |   8 +
 .../templates/key_info_view.test.tsx          | 141 ++++++++++++++++++
 .../components/templates/key_info_view.tsx    |  50 ++++++-
 4 files changed, 264 insertions(+), 1 deletion(-)
 create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/keys/useResetKeySpend.ts

diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/keys/useResetKeySpend.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/keys/useResetKeySpend.ts
new file mode 100644
index 00000000000..a845fc5881a
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/keys/useResetKeySpend.ts
@@ -0,0 +1,66 @@
+import { useMutation, useQueryClient } from "@tanstack/react-query";
+import {
+  getProxyBaseUrl,
+  getGlobalLitellmHeaderName,
+  deriveErrorMessage,
+  handleError,
+} from "@/components/networking";
+import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
+import { keyKeys } from "./useKeys";
+
+// ── Types ─────────────────────────────────────────────────────────────────────
+
+export interface ResetKeySpendResponse {
+  key_hash: string;
+  spend: number;
+  previous_spend: number;
+  max_budget: number | null;
+  budget_reset_at: string | null;
+}
+
+// ── Fetch function ────────────────────────────────────────────────────────────
+
+export const resetKeySpend = async (
+  accessToken: string,
+  keyToken: string,
+): Promise<ResetKeySpendResponse> => {
+  const baseUrl = getProxyBaseUrl();
+  const url = `${baseUrl ? `${baseUrl}/key/${keyToken}/reset_spend` : `/key/${keyToken}/reset_spend`}`;
+
+  const response = await fetch(url, {
+    method: "POST",
+    headers: {
+      [getGlobalLitellmHeaderName()]: `Bearer ${accessToken}`,
+      "Content-Type": "application/json",
+    },
+    body: JSON.stringify({ reset_to: 0 }),
+  });
+
+  if (!response.ok) {
+    const errorData = await response.json();
+    const errorMessage = deriveErrorMessage(errorData);
+    handleError(errorMessage);
+    throw new Error(errorMessage);
+  }
+
+  return response.json();
+};
+
+// ── Hook ──────────────────────────────────────────────────────────────────────
+
+export const useResetKeySpend = () => {
+  const { accessToken } = useAuthorized();
+  const queryClient = useQueryClient();
+
+  return useMutation<ResetKeySpendResponse, Error, string>({
+    mutationFn: async (keyToken) => {
+      if (!accessToken) {
+        throw new Error("Access token is required");
+      }
+      return resetKeySpend(accessToken, keyToken);
+    },
+    onSuccess: () => {
+      queryClient.invalidateQueries({ queryKey: keyKeys.all });
+    },
+  });
+};
diff --git a/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx b/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
index 1befd657843..df3678afab6 100644
--- a/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
+++ b/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
@@ -11,6 +11,7 @@ import {
   ClockCircleOutlined,
   ThunderboltOutlined,
   SafetyCertificateOutlined,
+  DollarOutlined,
 } from "@ant-design/icons";
 import LabeledField from "../common_components/LabeledField";
 
@@ -33,6 +34,7 @@ interface KeyInfoHeaderProps {
   onCreateNew?: () => void;
   onRegenerate?: () => void;
   onDelete?: () => void;
+  onResetSpend?: () => void;
   canModifyKey?: boolean;
   backButtonText?: string;
   regenerateDisabled?: boolean;
@@ -45,6 +47,7 @@ export function KeyInfoHeader({
   onCreateNew,
   onRegenerate,
   onDelete,
+  onResetSpend,
   canModifyKey = true,
   backButtonText = "Back to Keys",
   regenerateDisabled = false,
@@ -77,6 +80,11 @@ export function KeyInfoHeader({
         </div>
         {canModifyKey && (
           <Space>
+            {onResetSpend && (
+              <Button icon={<DollarOutlined />} onClick={onResetSpend}>
+                Reset Spend
+              </Button>
+            )}
             <Tooltip title={regenerateTooltip || ""}>
               <span>
                 <Button icon={<SyncOutlined />} onClick={onRegenerate} disabled={regenerateDisabled}>
diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
index e58ccfbbfd1..c19866e5d84 100644
--- a/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
@@ -5,6 +5,7 @@ import { renderWithProviders } from "../../../tests/test-utils";
 import { screen, waitFor } from "@testing-library/react";
 import userEvent from "@testing-library/user-event";
 import { beforeEach, describe, expect, it, vi } from "vitest";
+import { useResetKeySpend } from "@/app/(dashboard)/hooks/keys/useResetKeySpend";
 import { KeyResponse, Team } from "../key_team_helpers/key_list";
 import KeyInfoView from "./key_info_view";
 
@@ -28,6 +29,14 @@ vi.mock("../networking", () => ({
   }),
 }));
 
+const mockResetKeySpendMutate = vi.fn();
+vi.mock("@/app/(dashboard)/hooks/keys/useResetKeySpend", () => ({
+  useResetKeySpend: vi.fn(() => ({
+    mutate: mockResetKeySpendMutate,
+    isPending: false,
+  })),
+}));
+
 vi.mock("@/utils/dataUtils", () => ({
   copyToClipboard: vi.fn().mockResolvedValue(true),
   formatNumberWithCommas: vi.fn((value: number, decimals?: number) => {
@@ -539,4 +548,136 @@ describe("KeyInfoView", () => {
       expect(screen.getByText("Key not found")).toBeInTheDocument();
     });
   });
+
+  describe("Reset Spend button visibility", () => {
+    it("should show Reset Spend button for proxy admin", async () => {
+      vi.mocked(useTeams).mockReturnValue({ teams: [], setTeams: vi.fn() });
+      vi.mocked(useAuthorized).mockReturnValue({
+        ...baseUseAuthorizedMock,
+        userId: "proxy-admin-user",
+        userRole: "proxy_admin",
+      });
+
+      renderWithProviders(
+        <KeyInfoView keyData={MOCK_KEY_DATA} onClose={() => { }} keyId={"test-key-id"} onKeyDataUpdate={() => { }} teams={[]} />,
+      );
+
+      await waitFor(() => {
+        expect(screen.getByRole("button", { name: /reset spend/i })).toBeInTheDocument();
+      });
+    });
+
+    it("should show Reset Spend button for team admin of key's team", async () => {
+      const teamId = "test-team-id";
+      const teamAdminUserId = "team-admin-user";
+      const mockTeam: Team = {
+        team_id: teamId,
+        team_alias: "Test Team",
+        models: [],
+        max_budget: null,
+        budget_duration: null,
+        tpm_limit: null,
+        rpm_limit: null,
+        organization_id: "org-1",
+        created_at: "2025-01-01T00:00:00Z",
+        keys: [],
+        members_with_roles: [{ user_id: teamAdminUserId, role: "admin" }],
+        spend: 0,
+      };
+
+      vi.mocked(useTeams).mockReturnValue({ teams: [mockTeam], setTeams: vi.fn() });
+      vi.mocked(useAuthorized).mockReturnValue({
+        ...baseUseAuthorizedMock,
+        userId: teamAdminUserId,
+        userRole: "user",
+      });
+
+      const keyData = { ...MOCK_KEY_DATA, team_id: teamId, user_id: "other-user-id" };
+      renderWithProviders(
+        <KeyInfoView keyData={keyData} onClose={() => { }} keyId={"test-key-id"} onKeyDataUpdate={() => { }} teams={[]} />,
+      );
+
+      await waitFor(() => {
+        expect(screen.getByRole("button", { name: /reset spend/i })).toBeInTheDocument();
+      });
+    });
+
+    it("should not show Reset Spend button for regular key owner", async () => {
+      vi.mocked(useTeams).mockReturnValue({ teams: [], setTeams: vi.fn() });
+      vi.mocked(useAuthorized).mockReturnValue({
+        ...baseUseAuthorizedMock,
+        userId: "owner-user-id",
+        userRole: "user",
+      });
+
+      const keyData = { ...MOCK_KEY_DATA, user_id: "owner-user-id" };
+      renderWithProviders(
+        <KeyInfoView keyData={keyData} onClose={() => { }} keyId={"test-key-id"} onKeyDataUpdate={() => { }} teams={[]} />,
+      );
+
+      await waitFor(() => {
+        expect(screen.queryByRole("button", { name: /reset spend/i })).not.toBeInTheDocument();
+      });
+    });
+  });
+
+  describe("Reset Spend modal flow", () => {
+    it("should open confirmation modal when Reset Spend is clicked", async () => {
+      vi.mocked(useTeams).mockReturnValue({ teams: [], setTeams: vi.fn() });
+      vi.mocked(useAuthorized).mockReturnValue({
+        ...baseUseAuthorizedMock,
+        userId: "proxy-admin-user",
+        userRole: "proxy_admin",
+      });
+
+      renderWithProviders(
+        <KeyInfoView keyData={MOCK_KEY_DATA} onClose={() => { }} keyId={"test-key-id"} onKeyDataUpdate={() => { }} teams={[]} />,
+      );
+
+      await waitFor(() => {
+        expect(screen.getByRole("button", { name: /reset spend/i })).toBeInTheDocument();
+      });
+
+      await userEvent.click(screen.getByRole("button", { name: /reset spend/i }));
+
+      await waitFor(() => {
+        expect(screen.getByText("Reset Key Spend")).toBeInTheDocument();
+        expect(screen.getByText(/reset to \$0/i)).toBeInTheDocument();
+      });
+    });
+
+    it("should call mutate with token on confirm", async () => {
+      vi.mocked(useTeams).mockReturnValue({ teams: [], setTeams: vi.fn() });
+      vi.mocked(useAuthorized).mockReturnValue({
+        ...baseUseAuthorizedMock,
+        userId: "proxy-admin-user",
+        userRole: "proxy_admin",
+      });
+
+      const keyDataWithSpend = { ...MOCK_KEY_DATA, spend: 5.0 };
+      renderWithProviders(
+        <KeyInfoView keyData={keyDataWithSpend} onClose={() => { }} keyId={"test-key-id"} onKeyDataUpdate={() => { }} teams={[]} />,
+      );
+
+      await waitFor(() => {
+        expect(screen.getByRole("button", { name: /reset spend/i })).toBeInTheDocument();
+      });
+
+      await userEvent.click(screen.getByRole("button", { name: /reset spend/i }));
+
+      await waitFor(() => {
+        expect(screen.getByText("Reset Key Spend")).toBeInTheDocument();
+      });
+
+      // Click the confirm button in the modal
+      await userEvent.click(screen.getByRole("button", { name: /reset to \$0/i }));
+
+      await waitFor(() => {
+        expect(mockResetKeySpendMutate).toHaveBeenCalledWith(
+          MOCK_KEY_DATA.token,
+          expect.objectContaining({ onSuccess: expect.any(Function), onError: expect.any(Function) }),
+        );
+      });
+    });
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
index 72895c4d617..35a75b2f2fe 100644
--- a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
@@ -6,7 +6,7 @@ import { formatNumberWithCommas } from "@/utils/dataUtils";
 import { mapEmptyStringToNull } from "@/utils/keyUpdateUtils";
 import { ArrowLeftIcon } from "@heroicons/react/outline";
 import { Badge, Button, Card, Grid, Tab, TabGroup, TabList, TabPanel, TabPanels, Text, Title } from "@tremor/react";
-import { Form, Tag } from "antd";
+import { Form, Modal, Tag } from "antd";
 import { KeyInfoHeader } from "./KeyInfoHeader";
 import { useEffect, useState } from "react";
 import { isProxyAdminRole, isUserTeamAdminForSingleTeam } from "../../utils/roles";
@@ -18,6 +18,7 @@ import { KeyResponse } from "../key_team_helpers/key_list";
 import LoggingSettingsView from "../logging_settings_view";
 import NotificationManager from "../molecules/notifications_manager";
 import { getPolicyInfoWithGuardrails, keyDeleteCall, keyUpdateCall } from "../networking";
+import { useResetKeySpend } from "@/app/(dashboard)/hooks/keys/useResetKeySpend";
 import ObjectPermissionsView from "../object_permissions_view";
 import { RegenerateKeyModal } from "../organisms/regenerate_key_modal";
 import { parseErrorMessage } from "../shared/errorUtils";
@@ -59,6 +60,8 @@ export default function KeyInfoView({
   const [deleteLoading, setDeleteLoading] = useState(false);
   const [deleteConfirmInput, setDeleteConfirmInput] = useState("");
   const [isRegenerateModalOpen, setIsRegenerateModalOpen] = useState(false);
+  const [isResetSpendModalOpen, setIsResetSpendModalOpen] = useState(false);
+  const { mutate: resetKeySpend, isPending: resetSpendLoading } = useResetKeySpend();
   // Add local state to maintain key data and track regeneration
   const [currentKeyData, setCurrentKeyData] = useState<KeyResponse | undefined>(keyData);
   const [lastRegeneratedAt, setLastRegeneratedAt] = useState<Date | null>(null);
@@ -337,6 +340,31 @@ export default function KeyInfoView({
       )) ||
     (userID === currentKeyData.user_id && userRole !== "Internal Viewer");
 
+  const canResetSpend =
+    isProxyAdminRole(userRole || "") ||
+    (teamsData &&
+      isUserTeamAdminForSingleTeam(
+        teamsData?.filter((team) => team.team_id === currentKeyData.team_id)[0]?.members_with_roles,
+        userID || "",
+      ));
+
+  const handleResetSpend = () => {
+    resetKeySpend(currentKeyData.token || currentKeyData.token_id, {
+      onSuccess: () => {
+        setCurrentKeyData((prevData) => (prevData ? { ...prevData, spend: 0 } : undefined));
+        if (onKeyDataUpdate) {
+          onKeyDataUpdate({ spend: 0 });
+        }
+        NotificationManager.success("Key spend reset to $0");
+        setIsResetSpendModalOpen(false);
+      },
+      onError: (error) => {
+        NotificationManager.fromBackend(parseErrorMessage(error));
+        console.error("Error resetting key spend:", error);
+      },
+    });
+  };
+
   return (
     <div className="w-full h-screen p-4">
       <KeyInfoHeader
@@ -353,6 +381,7 @@ export default function KeyInfoView({
         onBack={onClose}
         onRegenerate={() => setIsRegenerateModalOpen(true)}
         onDelete={() => setIsDeleteModalOpen(true)}
+        onResetSpend={canResetSpend ? () => setIsResetSpendModalOpen(true) : undefined}
         canModifyKey={canModifyKey}
         backButtonText={backButtonText}
         regenerateDisabled={!premiumUser}
@@ -407,6 +436,25 @@ export default function KeyInfoView({
         requiredConfirmation={currentKeyData?.key_alias}
       />
 
+      {/* Reset Spend Confirmation Modal */}
+      <Modal
+        title="Reset Key Spend"
+        open={isResetSpendModalOpen}
+        onOk={handleResetSpend}
+        onCancel={() => setIsResetSpendModalOpen(false)}
+        okText="Reset to $0"
+        confirmLoading={resetSpendLoading}
+      >
+        <p>
+          Reset spend for <strong>{currentKeyData?.key_alias || currentKeyData?.token_id || "this key"}</strong> to{" "}
+          <strong>$0</strong>?
+        </p>
+        <p style={{ color: "#666", fontSize: "0.875rem", marginTop: 8 }}>
+          Current spend: <strong>${formatNumberWithCommas(currentKeyData.spend, 4)}</strong>. The key will be
+          immediately unblocked and able to make requests again.
+        </p>
+      </Modal>
+
       <TabGroup>
         <TabList className="mb-4">
           <Tab>Overview</Tab>

From 5a0aba9fb7ac00e6bda96893e862240e32d395dd Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 19:40:28 -0300
Subject: [PATCH 58/69] fix: update vertex AI tests to expect role=user on
 function_response messages

The Gemini API requires role="user" on function_response content blocks
(added in commit 273cf12afa), but these tests were never updated to match.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 tests/test_litellm/llms/vertex_ai/test_vertex.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex.py b/tests/test_litellm/llms/vertex_ai/test_vertex.py
index 803584b5615..bd12100a88f 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex.py
@@ -393,6 +393,7 @@ def test_multiple_function_call():
                     ],
                 },
                 {
+                    "role": "user",
                     "parts": [
                         {
                             "function_response": {
@@ -498,6 +499,7 @@ def test_multiple_function_call_changed_text_pos():
                 ],
             },
             {
+                "role": "user",
                 "parts": [
                     {
                         "function_response": {

From 2943df96ba5676f6c65b0ae3c59c8bc1825c84e5 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 19:46:20 -0300
Subject: [PATCH 59/69] fix: add video_tokens to expected
 completion_tokens_details in test

The CompletionTokensDetailsWrapper type now includes video_tokens field,
but this test's expected dict was not updated to include it.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 tests/test_litellm/types/test_types_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/test_litellm/types/test_types_utils.py b/tests/test_litellm/types/test_types_utils.py
index 70344950d3e..8c20ace98a0 100644
--- a/tests/test_litellm/types/test_types_utils.py
+++ b/tests/test_litellm/types/test_types_utils.py
@@ -120,7 +120,8 @@ def test_usage_completion_tokens_details_text_tokens():
         'reasoning_tokens': 65,
         'rejected_prediction_tokens': None,
         'text_tokens': 12,
-        'image_tokens': None
+        'image_tokens': None,
+        'video_tokens': None
     }
     assert dump_result['completion_tokens_details'] == expected_completion_details
     

From eb658693a30d10ff39970a5d358af23bcb7a5369 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 19:51:09 -0300
Subject: [PATCH 60/69] fix: use direct AsyncMock assignment instead of
 patch.object in JWT tests

The patch.object with new_callable=AsyncMock can behave inconsistently
across Python versions, causing mock_response.status_code to return a
MagicMock instead of the assigned value. Direct assignment is simpler
and more reliable.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 .../proxy/auth/test_handle_jwt.py             | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py
index 3c190974277..391a6789820 100644
--- a/tests/test_litellm/proxy/auth/test_handle_jwt.py
+++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py
@@ -1543,7 +1543,7 @@ async def test_resolve_jwks_url_resolves_oidc_discovery_document():
 @pytest.mark.asyncio
 async def test_resolve_jwks_url_caches_resolved_jwks_uri():
     """Resolved jwks_uri is cached — second call does not hit the network."""
-    from unittest.mock import AsyncMock, MagicMock, patch
+    from unittest.mock import AsyncMock, MagicMock
 
     from litellm.caching.dual_cache import DualCache
 
@@ -1562,9 +1562,11 @@ async def test_resolve_jwks_url_caches_resolved_jwks_uri():
     mock_response.status_code = 200
     mock_response.json.return_value = {"jwks_uri": jwks_url}
 
-    with patch.object(handler.http_handler, "get", new_callable=AsyncMock, return_value=mock_response) as mock_get:
-        first = await handler._resolve_jwks_url(discovery_url)
-        second = await handler._resolve_jwks_url(discovery_url)
+    mock_get = AsyncMock(return_value=mock_response)
+    handler.http_handler.get = mock_get
+
+    first = await handler._resolve_jwks_url(discovery_url)
+    second = await handler._resolve_jwks_url(discovery_url)
 
     assert first == jwks_url
     assert second == jwks_url
@@ -1575,7 +1577,7 @@ async def test_resolve_jwks_url_caches_resolved_jwks_uri():
 @pytest.mark.asyncio
 async def test_resolve_jwks_url_raises_if_no_jwks_uri_in_discovery_doc():
     """Raise a helpful error if the discovery document has no jwks_uri."""
-    from unittest.mock import AsyncMock, MagicMock, patch
+    from unittest.mock import AsyncMock, MagicMock
 
     from litellm.caching.dual_cache import DualCache
 
@@ -1591,9 +1593,10 @@ async def test_resolve_jwks_url_raises_if_no_jwks_uri_in_discovery_doc():
     mock_response.status_code = 200
     mock_response.json.return_value = {"issuer": "https://example.com"}  # no jwks_uri
 
-    with patch.object(handler.http_handler, "get", new_callable=AsyncMock, return_value=mock_response):
-        with pytest.raises(Exception, match="jwks_uri"):
-            await handler._resolve_jwks_url(discovery_url)
+    handler.http_handler.get = AsyncMock(return_value=mock_response)
+
+    with pytest.raises(Exception, match="jwks_uri"):
+        await handler._resolve_jwks_url(discovery_url)
 
 
 # ---------------------------------------------------------------------------

From 9b92ea16ab6e94eeb3cab19e5f17008c703ecc0a Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 19:55:18 -0300
Subject: [PATCH 61/69] fix: update response_format test for vertex_ai's
 intentional schema diff

Vertex AI / Gemini uses Pydantic's model_json_schema() which omits
additionalProperties: False (Gemini rejects it). The test expected
the same schema for all providers.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 tests/test_litellm/test_utils.py | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index 7f0b3b5b501..55e635f3e17 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -1139,20 +1139,25 @@ def test_pre_process_non_default_params(model, custom_llm_provider):
         provider_config=provider_config,
     )
     print(processed_non_default_params)
+    # Vertex AI / Gemini uses Pydantic's model_json_schema() which doesn't
+    # include additionalProperties: False (Gemini rejects it).  Other
+    # providers use OpenAI's to_strict_json_schema() which does.
+    expected_schema = {
+        "properties": {
+            "x": {"title": "X", "type": "string"},
+            "y": {"title": "Y", "type": "string"},
+        },
+        "required": ["x", "y"],
+        "title": "ResponseFormat",
+        "type": "object",
+    }
+    if custom_llm_provider not in ("vertex_ai", "vertex_ai_beta", "gemini"):
+        expected_schema["additionalProperties"] = False
     assert processed_non_default_params == {
         "response_format": {
             "type": "json_schema",
             "json_schema": {
-                "schema": {
-                    "properties": {
-                        "x": {"title": "X", "type": "string"},
-                        "y": {"title": "Y", "type": "string"},
-                    },
-                    "required": ["x", "y"],
-                    "title": "ResponseFormat",
-                    "type": "object",
-                    "additionalProperties": False,
-                },
+                "schema": expected_schema,
                 "name": "ResponseFormat",
                 "strict": True,
             },

From a07d041881e77c6854c3fbf6fb782ed509d69d29 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 19:56:51 -0300
Subject: [PATCH 62/69] fix: apply same AsyncMock pattern to remaining OIDC
 discovery test

Address Greptile review: test_resolve_jwks_url_resolves_oidc_discovery_document
also used the inconsistent patch.object pattern.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 tests/test_litellm/proxy/auth/test_handle_jwt.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py
index 391a6789820..11939f0fddd 100644
--- a/tests/test_litellm/proxy/auth/test_handle_jwt.py
+++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py
@@ -1514,7 +1514,7 @@ async def test_resolve_jwks_url_resolves_oidc_discovery_document():
     A .well-known/openid-configuration URL should be fetched and its
     jwks_uri returned.
     """
-    from unittest.mock import AsyncMock, MagicMock, patch
+    from unittest.mock import AsyncMock, MagicMock
 
     from litellm.caching.dual_cache import DualCache
 
@@ -1533,8 +1533,10 @@ async def test_resolve_jwks_url_resolves_oidc_discovery_document():
     mock_response.status_code = 200
     mock_response.json.return_value = {"jwks_uri": jwks_url, "issuer": "https://..."}
 
-    with patch.object(handler.http_handler, "get", new_callable=AsyncMock, return_value=mock_response) as mock_get:
-        result = await handler._resolve_jwks_url(discovery_url)
+    mock_get = AsyncMock(return_value=mock_response)
+    handler.http_handler.get = mock_get
+
+    result = await handler._resolve_jwks_url(discovery_url)
 
     assert result == jwks_url
     mock_get.assert_called_once_with(discovery_url)

From 740cdc5c20b2844eba47ff1f1e3a9e8a40789866 Mon Sep 17 00:00:00 2001
From: Julio Quinteros Pro <jquinter@gmail.com>
Date: Tue, 3 Mar 2026 20:05:20 -0300
Subject: [PATCH 63/69] fix: use real State object in mock_request to fix
 _safe_get_request_headers

The _safe_get_request_headers caching (commit e7175a52) uses
request.state._cached_headers. With Mock(spec=Request), getattr on
state returns a Mock (truthy), causing RedactedDict to receive a Mock
instead of a dict. Using a real starlette State object fixes this.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 tests/proxy_unit_tests/test_proxy_utils.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tests/proxy_unit_tests/test_proxy_utils.py b/tests/proxy_unit_tests/test_proxy_utils.py
index f7101085f0f..b2ed4f91037 100644
--- a/tests/proxy_unit_tests/test_proxy_utils.py
+++ b/tests/proxy_unit_tests/test_proxy_utils.py
@@ -8,6 +8,7 @@ from unittest.mock import Mock
 
 import pytest
 from fastapi import Request
+from starlette.datastructures import State
 
 from litellm.proxy.utils import _get_docs_url, _get_redoc_url
 
@@ -32,6 +33,7 @@ def mock_request(monkeypatch):
     mock_request = Mock(spec=Request)
     mock_request.query_params = {}  # Set mock query_params to an empty dictionary
     mock_request.headers = {"traceparent": "test_traceparent"}
+    mock_request.state = State()  # Real State so _safe_get_request_headers caching works
     monkeypatch.setattr(
         "litellm.proxy.litellm_pre_call_utils.add_litellm_data_to_request", mock_request
     )
@@ -810,6 +812,7 @@ async def test_add_litellm_data_to_request_duplicate_tags(
     mock_request.url.path = "/chat/completions"
     mock_request.query_params = {}
     mock_request.headers = {}
+    mock_request.state = State()
 
     # Setup key with tags in metadata
     user_api_key_dict = UserAPIKeyAuth(

From 98b91080ca5b26fec0f15ce0579709695a791eaa Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 15:12:37 -0800
Subject: [PATCH 64/69] Update Reset Spend button style and modal copy

- Use TransactionOutlined icon and danger style to match Delete Key button
- Rewrite modal description: remove assumption about key being blocked,
  clarify spend history is preserved in logs

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../src/components/templates/KeyInfoHeader.tsx                | 4 ++--
 .../src/components/templates/key_info_view.tsx                | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx b/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
index df3678afab6..c750b1b4ac7 100644
--- a/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
+++ b/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
@@ -11,7 +11,7 @@ import {
   ClockCircleOutlined,
   ThunderboltOutlined,
   SafetyCertificateOutlined,
-  DollarOutlined,
+  TransactionOutlined,
 } from "@ant-design/icons";
 import LabeledField from "../common_components/LabeledField";
 
@@ -81,7 +81,7 @@ export function KeyInfoHeader({
         {canModifyKey && (
           <Space>
             {onResetSpend && (
-              <Button icon={<DollarOutlined />} onClick={onResetSpend}>
+              <Button danger icon={<TransactionOutlined />} onClick={onResetSpend}>
                 Reset Spend
               </Button>
             )}
diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
index 35a75b2f2fe..b77b13b1591 100644
--- a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
@@ -450,8 +450,8 @@ export default function KeyInfoView({
           <strong>$0</strong>?
         </p>
         <p style={{ color: "#666", fontSize: "0.875rem", marginTop: 8 }}>
-          Current spend: <strong>${formatNumberWithCommas(currentKeyData.spend, 4)}</strong>. The key will be
-          immediately unblocked and able to make requests again.
+          Current spend: <strong>${formatNumberWithCommas(currentKeyData.spend, 4)}</strong>. Spend history is
+          preserved in logs. This resets the current period spend counter, the same as an automatic budget reset.
         </p>
       </Modal>
 

From 5b2110ddb56492c6b8876c1e5cf3608d754d3820 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 15:16:47 -0800
Subject: [PATCH 65/69] Polish Reset Spend button and modal

- Move Reset Spend button after Regenerate Key in header
- Make modal OK button danger style with text "Reset"

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../src/components/templates/KeyInfoHeader.tsx         | 10 +++++-----
 .../src/components/templates/key_info_view.test.tsx    |  4 ++--
 .../src/components/templates/key_info_view.tsx         |  3 ++-
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx b/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
index c750b1b4ac7..93ebae9c4be 100644
--- a/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
+++ b/ui/litellm-dashboard/src/components/templates/KeyInfoHeader.tsx
@@ -80,11 +80,6 @@ export function KeyInfoHeader({
         </div>
         {canModifyKey && (
           <Space>
-            {onResetSpend && (
-              <Button danger icon={<TransactionOutlined />} onClick={onResetSpend}>
-                Reset Spend
-              </Button>
-            )}
             <Tooltip title={regenerateTooltip || ""}>
               <span>
                 <Button icon={<SyncOutlined />} onClick={onRegenerate} disabled={regenerateDisabled}>
@@ -92,6 +87,11 @@ export function KeyInfoHeader({
                 </Button>
               </span>
             </Tooltip>
+            {onResetSpend && (
+              <Button danger icon={<TransactionOutlined />} onClick={onResetSpend}>
+                Reset Spend
+              </Button>
+            )}
             <Button danger icon={<DeleteOutlined />} onClick={onDelete}>
               Delete Key
             </Button>
diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
index c19866e5d84..f269ad96a27 100644
--- a/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
@@ -642,7 +642,7 @@ describe("KeyInfoView", () => {
 
       await waitFor(() => {
         expect(screen.getByText("Reset Key Spend")).toBeInTheDocument();
-        expect(screen.getByText(/reset to \$0/i)).toBeInTheDocument();
+        expect(screen.getByRole("button", { name: /^reset$/i })).toBeInTheDocument();
       });
     });
 
@@ -670,7 +670,7 @@ describe("KeyInfoView", () => {
       });
 
       // Click the confirm button in the modal
-      await userEvent.click(screen.getByRole("button", { name: /reset to \$0/i }));
+      await userEvent.click(screen.getByRole("button", { name: /^reset$/i }));
 
       await waitFor(() => {
         expect(mockResetKeySpendMutate).toHaveBeenCalledWith(
diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
index b77b13b1591..6733cd6a595 100644
--- a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
@@ -442,7 +442,8 @@ export default function KeyInfoView({
         open={isResetSpendModalOpen}
         onOk={handleResetSpend}
         onCancel={() => setIsResetSpendModalOpen(false)}
-        okText="Reset to $0"
+        okText="Reset"
+        okButtonProps={{ danger: true }}
         confirmLoading={resetSpendLoading}
       >
         <p>

From 98b9bc8b722b3f8d590d4cbe678280f8fa95cca7 Mon Sep 17 00:00:00 2001
From: milan-berri <milan@berri.ai>
Date: Wed, 4 Mar 2026 01:43:02 +0200
Subject: [PATCH 66/69] fix: resolve base_model in /cost/estimate for Azure
 custom deployments (#22724)

The _resolve_model_for_cost_lookup function was only checking
litellm_params.model when resolving model names from the router.
For Azure custom deployment names (e.g. azure/openai/gpt-5.3-codex),
this deployment name doesn't exist in the model cost map, so cost
returned /bin/zsh.

Now checks model_info.base_model and litellm_params.base_model first,
falling back to litellm_params.model only if no base_model is set.
This matches how the router resolves base_model everywhere else.
---
 .../cost_tracking_settings.py                 |  15 ++-
 .../test_cost_tracking_settings.py            | 120 ++++++++++++++++++
 2 files changed, 133 insertions(+), 2 deletions(-)

diff --git a/litellm/proxy/management_endpoints/cost_tracking_settings.py b/litellm/proxy/management_endpoints/cost_tracking_settings.py
index 6cdadfe216a..38dd4578c05 100644
--- a/litellm/proxy/management_endpoints/cost_tracking_settings.py
+++ b/litellm/proxy/management_endpoints/cost_tracking_settings.py
@@ -54,16 +54,27 @@ def _resolve_model_for_cost_lookup(model: str) -> Tuple[str, Optional[str]]:
             deployments = llm_router.get_model_list(model_name=model)
 
             if deployments and len(deployments) > 0:
-                # Get the first deployment's litellm model
                 first_deployment = deployments[0]
                 litellm_params = first_deployment.get("litellm_params", {})
+                model_info = first_deployment.get("model_info", {})
+
+                # Check base_model first (needed for Azure custom deployment names)
+                base_model = model_info.get("base_model") or litellm_params.get(
+                    "base_model"
+                )
+                if base_model:
+                    verbose_proxy_logger.debug(
+                        f"Resolved model '{model}' to base_model '{base_model}' from router"
+                    )
+                    custom_llm_provider = litellm_params.get("custom_llm_provider")
+                    return base_model, custom_llm_provider
+
                 resolved_model = litellm_params.get("model")
 
                 if resolved_model:
                     verbose_proxy_logger.debug(
                         f"Resolved model '{model}' to '{resolved_model}' from router"
                     )
-                    # Extract custom_llm_provider if present
                     custom_llm_provider = litellm_params.get("custom_llm_provider")
                     return resolved_model, custom_llm_provider
         except Exception as e:
diff --git a/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py b/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py
index 275240dcc9e..1284cceba26 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py
@@ -270,3 +270,123 @@ class TestCostTrackingSettings:
             assert "error" in response_data["detail"]
             assert "STORE_MODEL_IN_DB" in response_data["detail"]["error"]
 
+
+
+class TestResolveModelForCostLookup:
+    """Tests for _resolve_model_for_cost_lookup base_model resolution."""
+
+    def test_resolves_base_model_for_azure_deployment(self):
+        """
+        When a model group has base_model set in model_info,
+        _resolve_model_for_cost_lookup should return the base_model
+        instead of the raw litellm_params.model (Azure deployment name).
+        """
+        from litellm.proxy.management_endpoints.cost_tracking_settings import (
+            _resolve_model_for_cost_lookup,
+        )
+
+        mock_router = MagicMock()
+        mock_router.get_model_list.return_value = [
+            {
+                "model_name": "gpt-5.3-codex",
+                "litellm_params": {
+                    "model": "azure/openai/gpt-5.3-codex",
+                    "api_base": "https://fake.openai.azure.com/",
+                    "api_key": "fake-key",
+                },
+                "model_info": {
+                    "id": "test-id",
+                    "base_model": "azure/gpt-4o",
+                },
+            }
+        ]
+
+        with patch(
+            "litellm.proxy.proxy_server.llm_router",
+            mock_router,
+        ):
+            resolved_model, provider = _resolve_model_for_cost_lookup("gpt-5.3-codex")
+
+        assert resolved_model == "azure/gpt-4o"
+        mock_router.get_model_list.assert_called_once_with(model_name="gpt-5.3-codex")
+
+    def test_falls_back_to_litellm_params_model_when_no_base_model(self):
+        """
+        When no base_model is set, should fall back to litellm_params.model.
+        """
+        from litellm.proxy.management_endpoints.cost_tracking_settings import (
+            _resolve_model_for_cost_lookup,
+        )
+
+        mock_router = MagicMock()
+        mock_router.get_model_list.return_value = [
+            {
+                "model_name": "gpt-4",
+                "litellm_params": {
+                    "model": "openai/gpt-4",
+                },
+                "model_info": {
+                    "id": "test-id",
+                },
+            }
+        ]
+
+        with patch(
+            "litellm.proxy.proxy_server.llm_router",
+            mock_router,
+        ):
+            resolved_model, provider = _resolve_model_for_cost_lookup("gpt-4")
+
+        assert resolved_model == "openai/gpt-4"
+
+    def test_resolves_base_model_from_litellm_params(self):
+        """
+        When base_model is in litellm_params (not model_info),
+        it should still be resolved.
+        """
+        from litellm.proxy.management_endpoints.cost_tracking_settings import (
+            _resolve_model_for_cost_lookup,
+        )
+
+        mock_router = MagicMock()
+        mock_router.get_model_list.return_value = [
+            {
+                "model_name": "my-azure-model",
+                "litellm_params": {
+                    "model": "azure/my-custom-deployment",
+                    "base_model": "azure/gpt-4o-mini",
+                },
+                "model_info": {
+                    "id": "test-id",
+                },
+            }
+        ]
+
+        with patch(
+            "litellm.proxy.proxy_server.llm_router",
+            mock_router,
+        ):
+            resolved_model, provider = _resolve_model_for_cost_lookup(
+                "my-azure-model"
+            )
+
+        assert resolved_model == "azure/gpt-4o-mini"
+
+    def test_returns_original_model_when_no_router(self):
+        """
+        When no router is available, should return the original model name.
+        """
+        from litellm.proxy.management_endpoints.cost_tracking_settings import (
+            _resolve_model_for_cost_lookup,
+        )
+
+        with patch(
+            "litellm.proxy.proxy_server.llm_router",
+            None,
+        ):
+            resolved_model, provider = _resolve_model_for_cost_lookup(
+                "azure/openai/gpt-5.3-codex"
+            )
+
+        assert resolved_model == "azure/openai/gpt-5.3-codex"
+        assert provider is None

From 0a1b2635d7f8c2e17f9c3c5732bb9b8f3dc23c08 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 15:54:34 -0800
Subject: [PATCH 67/69] fix: allow team admins to access /key/{key}/reset_spend
 route

The route-level auth check was blocking internal_user role (team admins)
from reaching /key/{key}/reset_spend because KEY_RESET_SPEND was missing
from key_management_routes. Added it so team admins pass the route check
and the endpoint's existing _check_proxy_or_team_admin_for_key enforces
actual authorization.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 litellm/proxy/_types.py                       |  1 +
 .../proxy/auth/test_route_checks.py           | 74 +++++++++++++++++++
 2 files changed, 75 insertions(+)

diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 5122c64ea64..8d49020461d 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -512,6 +512,7 @@ class LiteLLMRoutes(enum.Enum):
         KeyManagementRoutes.KEY_UNBLOCK.value,
         KeyManagementRoutes.KEY_BULK_UPDATE.value,
         KeyManagementRoutes.TEAM_DAILY_ACTIVITY.value,
+        KeyManagementRoutes.KEY_RESET_SPEND.value,
     ]
 
     management_routes = [
diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py
index ec1a13b8abc..f1e96f3e660 100644
--- a/tests/test_litellm/proxy/auth/test_route_checks.py
+++ b/tests/test_litellm/proxy/auth/test_route_checks.py
@@ -1116,3 +1116,77 @@ def test_route_in_additional_public_routes_exact_match():
         assert route_in_additonal_public_routes("/status") is True
         # Non-matching routes should fail
         assert route_in_additonal_public_routes("/other") is False
+
+
+def test_internal_user_can_access_key_reset_spend_route():
+    """
+    Regression test: team admins (role=internal_user) should pass the route-level
+    check for /key/{hash}/reset_spend. The endpoint itself enforces team admin status.
+    """
+    user_obj = LiteLLM_UserTable(
+        user_id="team-admin-user",
+        user_email="teamadmin@example.com",
+        user_role=LitellmUserRoles.INTERNAL_USER.value,
+    )
+    valid_token = UserAPIKeyAuth(
+        user_id="team-admin-user",
+        user_role=LitellmUserRoles.INTERNAL_USER.value,
+    )
+    request = MagicMock(spec=Request)
+    request.query_params = {}
+
+    key_hash = "baec26d2901589fe9fec76610e6e2be4895cdd8e19b3ada9a4fa2eb85e1901ae"
+    route = f"/key/{key_hash}/reset_spend"
+
+    # Should not raise — the route-level check must pass for team admins
+    RouteChecks.non_proxy_admin_allowed_routes_check(
+        user_obj=user_obj,
+        _user_role=LitellmUserRoles.INTERNAL_USER.value,
+        route=route,
+        request=request,
+        valid_token=valid_token,
+        request_data={},
+    )
+
+
+def test_non_admin_non_team_admin_cannot_access_config_update_but_can_attempt_reset_spend():
+    """
+    An internal_user passes the route check for /key/{hash}/reset_spend
+    (authorization is deferred to the endpoint), but is still blocked from
+    admin-only routes like /config/update.
+    """
+    user_obj = LiteLLM_UserTable(
+        user_id="regular-user",
+        user_email="user@example.com",
+        user_role=LitellmUserRoles.INTERNAL_USER.value,
+    )
+    valid_token = UserAPIKeyAuth(
+        user_id="regular-user",
+        user_role=LitellmUserRoles.INTERNAL_USER.value,
+    )
+    request = MagicMock(spec=Request)
+    request.query_params = {}
+
+    key_hash = "baec26d2901589fe9fec76610e6e2be4895cdd8e19b3ada9a4fa2eb85e1901ae"
+
+    # /key/{hash}/reset_spend passes the route check for internal_user
+    RouteChecks.non_proxy_admin_allowed_routes_check(
+        user_obj=user_obj,
+        _user_role=LitellmUserRoles.INTERNAL_USER.value,
+        route=f"/key/{key_hash}/reset_spend",
+        request=request,
+        valid_token=valid_token,
+        request_data={},
+    )
+
+    # /config/update is still blocked
+    with pytest.raises(Exception) as exc_info:
+        RouteChecks.non_proxy_admin_allowed_routes_check(
+            user_obj=user_obj,
+            _user_role=LitellmUserRoles.INTERNAL_USER.value,
+            route="/config/update",
+            request=request,
+            valid_token=valid_token,
+            request_data={},
+        )
+    assert "Only proxy admin can be used to generate" in str(exc_info.value)

From 326ff422f1faad0a7654289d97824270be6c5686 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 15:54:46 -0800
Subject: [PATCH 68/69] fix(ui): Audit logs table and drawer polish

Table:
- Use DefaultProxyAdminTag for changed_by column
- Remove tooltips on Object ID and API Key columns
- Rename API Key column to API Key (Hash)
- Move pagination controls to upper-right of filter bar; add icon-only refresh button

Drawer:
- Object ID is now copyable
- API Key (Hash) is copyable and no longer truncated
- Changed By uses DefaultProxyAdminTag
- Expand JSON view boxes from max-h-72 to max-h-96
- Remove unnecessary vertical scrollbar (drop overflow-auto h-full from body div;
  use flex column layout so header and content flow naturally)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../AuditLogDrawer/AuditLogDrawer.tsx         |  40 +++---
 .../src/components/view_logs/audit_logs.tsx   | 125 ++++++------------
 2 files changed, 62 insertions(+), 103 deletions(-)

diff --git a/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx b/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
index 1e8dca34f22..de76709f5b2 100644
--- a/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
@@ -1,7 +1,10 @@
-import { Drawer, Tag, Tooltip } from "antd";
+import { Drawer, Tag, Typography } from "antd";
 import { CloseOutlined } from "@ant-design/icons";
 import moment from "moment";
 import { AuditLogEntry } from "../columns";
+import DefaultProxyAdminTag from "../../common_components/DefaultProxyAdminTag";
+
+const { Text } = Typography;
 
 interface AuditLogDrawerProps {
   open: boolean;
@@ -26,7 +29,7 @@ const ACTION_COLOR: Record<string, string> = {
 
 function JsonBlock({ value }: { value: Record<string, any> }) {
   return (
-    <pre className="p-3 bg-gray-50 border rounded text-xs font-mono overflow-auto max-h-72 whitespace-pre-wrap break-all">
+    <pre className="p-3 bg-gray-50 border rounded text-xs font-mono overflow-auto max-h-96 whitespace-pre-wrap break-all">
       {JSON.stringify(value, null, 2)}
     </pre>
   );
@@ -92,7 +95,7 @@ function DiffSection({ log }: { log: AuditLogEntry }) {
         : { note: "No differing fields detected" };
   }
 
-  const renderValue = (value: Record<string, any> | null | undefined, label: string) => {
+  const renderValue = (value: Record<string, any> | null | undefined) => {
     if (!value || Object.keys(value).length === 0) {
       return <p className="text-xs text-gray-400 italic">N/A</p>;
     }
@@ -125,11 +128,11 @@ function DiffSection({ log }: { log: AuditLogEntry }) {
     <div className="grid grid-cols-1 md:grid-cols-2 gap-4 mt-4">
       <div>
         <p className="text-xs font-semibold text-gray-600 mb-2">Before</p>
-        {renderValue(displayBefore, "before")}
+        {renderValue(displayBefore)}
       </div>
       <div>
         <p className="text-xs font-semibold text-gray-600 mb-2">After</p>
-        {renderValue(displayAfter, "after")}
+        {renderValue(displayAfter)}
       </div>
     </div>
   );
@@ -150,10 +153,10 @@ export function AuditLogDrawer({ open, onClose, log }: AuditLogDrawerProps) {
       closable={false}
       mask={true}
       maskClosable={true}
-      styles={{ body: { padding: 0 }, header: { display: "none" } }}
+      styles={{ body: { padding: 0, display: "flex", flexDirection: "column" }, header: { display: "none" } }}
     >
       {/* Header */}
-      <div className="flex items-center justify-between px-6 py-4 border-b bg-white sticky top-0 z-10">
+      <div className="flex items-center justify-between px-6 py-4 border-b bg-white shrink-0">
         <div className="flex items-center gap-3">
           <Tag color={actionColor} className="capitalize m-0">
             {log.action}
@@ -172,7 +175,7 @@ export function AuditLogDrawer({ open, onClose, log }: AuditLogDrawerProps) {
       </div>
 
       {/* Body */}
-      <div className="px-6 py-5 overflow-auto h-full">
+      <div className="px-6 py-5">
         {/* Metadata */}
         <div className="bg-gray-50 border rounded-lg p-4 mb-5">
           <p className="text-xs font-semibold text-gray-700 mb-2 uppercase tracking-wide">
@@ -182,21 +185,22 @@ export function AuditLogDrawer({ open, onClose, log }: AuditLogDrawerProps) {
           <MetadataRow
             label="Object ID"
             value={
-              <Tooltip title={log.object_id}>
-                <span className="font-mono">{log.object_id}</span>
-              </Tooltip>
+              <Text copyable className="font-mono text-xs">
+                {log.object_id}
+              </Text>
             }
           />
-          <MetadataRow label="Changed By" value={log.changed_by || "—"} />
           <MetadataRow
-            label="API Key"
+            label="Changed By"
+            value={<DefaultProxyAdminTag userId={log.changed_by} />}
+          />
+          <MetadataRow
+            label="API Key (Hash)"
             value={
               log.changed_by_api_key ? (
-                <Tooltip title={log.changed_by_api_key}>
-                  <span className="font-mono">
-                    {log.changed_by_api_key.slice(0, 12)}…
-                  </span>
-                </Tooltip>
+                <Text copyable className="font-mono text-xs break-all">
+                  {log.changed_by_api_key}
+                </Text>
               ) : (
                 "—"
               )
diff --git a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
index 693318acca8..71300fe0427 100644
--- a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
@@ -1,12 +1,13 @@
 import { useState } from "react";
 import { useQuery, keepPreviousData } from "@tanstack/react-query";
-import { Table, Tag, Input, Select, Button, Tooltip } from "antd";
+import { Table, Tag, Input, Select, Button, Pagination } from "antd";
 import { ReloadOutlined } from "@ant-design/icons";
-import type { ColumnsType, TablePaginationConfig } from "antd/es/table";
+import type { ColumnsType } from "antd/es/table";
 import moment from "moment";
 import { uiAuditLogsCall } from "../networking";
 import { AuditLogEntry } from "./columns";
 import { AuditLogDrawer } from "./AuditLogDrawer/AuditLogDrawer";
+import DefaultProxyAdminTag from "../common_components/DefaultProxyAdminTag";
 
 const { Search } = Input;
 
@@ -97,14 +98,7 @@ export default function AuditLogs({
     placeholderData: keepPreviousData,
   });
 
-  const handleFilterChange = () => {
-    // Reset to page 1 whenever a filter changes
-    setPage(1);
-  };
-
-  const handleTableChange = (pagination: TablePaginationConfig) => {
-    setPage(pagination.current ?? 1);
-  };
+  const resetPage = () => setPage(1);
 
   const handleRowClick = (log: AuditLogEntry) => {
     setSelectedLog(log);
@@ -146,9 +140,7 @@ export default function AuditLogs({
       dataIndex: "object_id",
       key: "object_id",
       render: (val: string) => (
-        <Tooltip title={val}>
-          <span className="font-mono text-xs">{val}</span>
-        </Tooltip>
+        <span className="font-mono text-xs">{val}</span>
       ),
     },
     {
@@ -156,18 +148,16 @@ export default function AuditLogs({
       dataIndex: "changed_by",
       key: "changed_by",
       width: 200,
-      render: (val: string) => val || "—",
+      render: (val: string) => <DefaultProxyAdminTag userId={val} />,
     },
     {
-      title: "API Key",
+      title: "API Key (Hash)",
       dataIndex: "changed_by_api_key",
       key: "changed_by_api_key",
       width: 140,
       render: (val: string) =>
         val ? (
-          <Tooltip title={val}>
-            <span className="font-mono text-xs">{val.slice(0, 12)}…</span>
-          </Tooltip>
+          <span className="font-mono text-xs">{val.slice(0, 12)}…</span>
         ) : (
           "—"
         ),
@@ -212,76 +202,37 @@ export default function AuditLogs({
         <div className="border-b px-6 py-4">
           <div className="flex items-center justify-between mb-4">
             <h1 className="text-xl font-semibold">Audit Logs</h1>
-            <Button
-              icon={<ReloadOutlined spin={query.isFetching} />}
-              onClick={() => query.refetch()}
-              disabled={query.isFetching}
-            >
-              Refresh
-            </Button>
           </div>
 
-          {/* Filters */}
-          <div className="flex flex-wrap gap-3">
+          {/* Filters + pagination on same row */}
+          <div className="flex flex-wrap items-center gap-3">
             <Search
               placeholder="Object ID"
               allowClear
               style={{ width: 200 }}
-              onSearch={(val) => {
-                setObjectId(val);
-                handleFilterChange();
-              }}
-              onChange={(e) => {
-                if (!e.target.value) {
-                  setObjectId("");
-                  handleFilterChange();
-                }
-              }}
+              onSearch={(val) => { setObjectId(val); resetPage(); }}
+              onChange={(e) => { if (!e.target.value) { setObjectId(""); resetPage(); } }}
             />
             <Search
               placeholder="Changed By"
               allowClear
               style={{ width: 180 }}
-              onSearch={(val) => {
-                setChangedBy(val);
-                handleFilterChange();
-              }}
-              onChange={(e) => {
-                if (!e.target.value) {
-                  setChangedBy("");
-                  handleFilterChange();
-                }
-              }}
+              onSearch={(val) => { setChangedBy(val); resetPage(); }}
+              onChange={(e) => { if (!e.target.value) { setChangedBy(""); resetPage(); } }}
             />
             <Search
               placeholder="Team ID"
               allowClear
               style={{ width: 180 }}
-              onSearch={(val) => {
-                setTeamId(val);
-                handleFilterChange();
-              }}
-              onChange={(e) => {
-                if (!e.target.value) {
-                  setTeamId("");
-                  handleFilterChange();
-                }
-              }}
+              onSearch={(val) => { setTeamId(val); resetPage(); }}
+              onChange={(e) => { if (!e.target.value) { setTeamId(""); resetPage(); } }}
             />
             <Search
               placeholder="Key Hash"
               allowClear
               style={{ width: 180 }}
-              onSearch={(val) => {
-                setKeyHash(val);
-                handleFilterChange();
-              }}
-              onChange={(e) => {
-                if (!e.target.value) {
-                  setKeyHash("");
-                  handleFilterChange();
-                }
-              }}
+              onSearch={(val) => { setKeyHash(val); resetPage(); }}
+              onChange={(e) => { if (!e.target.value) { setKeyHash(""); resetPage(); } }}
             />
             <Select
               placeholder="All Actions"
@@ -293,10 +244,7 @@ export default function AuditLogs({
                 { label: "Deleted", value: "deleted" },
                 { label: "Rotated", value: "rotated" },
               ]}
-              onChange={(val) => {
-                setAction(val);
-                handleFilterChange();
-              }}
+              onChange={(val) => { setAction(val); resetPage(); }}
             />
             <Select
               placeholder="All Tables"
@@ -309,34 +257,41 @@ export default function AuditLogs({
                 { label: "Organizations", value: "LiteLLM_OrganizationTable" },
                 { label: "Models", value: "LiteLLM_ProxyModelTable" },
               ]}
-              onChange={(val) => {
-                setTableName(val);
-                handleFilterChange();
-              }}
+              onChange={(val) => { setTableName(val); resetPage(); }}
             />
+
+            {/* Pagination + refresh pushed to the right */}
+            <div className="ml-auto flex items-center gap-2">
+              <Button
+                icon={<ReloadOutlined spin={query.isFetching} />}
+                onClick={() => query.refetch()}
+                disabled={query.isFetching}
+              />
+              <Pagination
+                current={page}
+                pageSize={PAGE_SIZE}
+                total={total}
+                showTotal={(t) => `${t} total`}
+                showSizeChanger={false}
+                size="small"
+                onChange={(p) => setPage(p)}
+              />
+            </div>
           </div>
         </div>
 
-        {/* Table */}
+        {/* Table — pagination handled in header */}
         <Table<AuditLogEntry>
           columns={columns}
           dataSource={auditLogs}
           rowKey="id"
           loading={query.isLoading}
           size="small"
+          pagination={false}
           onRow={(record) => ({
             onClick: () => handleRowClick(record),
             style: { cursor: "pointer" },
           })}
-          pagination={{
-            current: page,
-            pageSize: PAGE_SIZE,
-            total,
-            showTotal: (t) => `${t} total`,
-            showSizeChanger: false,
-            onChange: (p) => setPage(p),
-          }}
-          onChange={handleTableChange}
         />
       </div>
 

From 5fa0c6e994de825502c3d5b1f8278a5ef03a4850 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng.jiang@gmail.com>
Date: Tue, 3 Mar 2026 16:00:46 -0800
Subject: [PATCH 69/69] fix(ui): Copyable JSON blocks in audit drawer; custom
 table spinner
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Replace plain JsonBlock with CopyableJsonBlock: header row with label
  and copy icon (CheckOutlined on success), pre block below — matches
  the spend logs RequestResponsePanel pattern
- Key-table rows use the same card chrome for visual consistency
- Remove separate "Changes" section label (now redundant with block headers)
- Table loading spinner replaced with custom Spin + LoadingOutlined

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../AuditLogDrawer/AuditLogDrawer.tsx         | 105 ++++++++++++------
 .../src/components/view_logs/audit_logs.tsx   |   9 +-
 2 files changed, 78 insertions(+), 36 deletions(-)

diff --git a/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx b/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
index de76709f5b2..19989ef4882 100644
--- a/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/AuditLogDrawer/AuditLogDrawer.tsx
@@ -1,5 +1,6 @@
 import { Drawer, Tag, Typography } from "antd";
-import { CloseOutlined } from "@ant-design/icons";
+import { CloseOutlined, CopyOutlined, CheckOutlined } from "@ant-design/icons";
+import { useState, useCallback } from "react";
 import moment from "moment";
 import { AuditLogEntry } from "../columns";
 import DefaultProxyAdminTag from "../../common_components/DefaultProxyAdminTag";
@@ -27,11 +28,48 @@ const ACTION_COLOR: Record<string, string> = {
   rotated: "orange",
 };
 
-function JsonBlock({ value }: { value: Record<string, any> }) {
+function CopyableJsonBlock({ label, value }: { label: string; value: Record<string, any> }) {
+  const [copied, setCopied] = useState(false);
+
+  const handleCopy = useCallback(async () => {
+    try {
+      const text = JSON.stringify(value, null, 2);
+      if (navigator.clipboard && window.isSecureContext) {
+        await navigator.clipboard.writeText(text);
+      } else {
+        const el = document.createElement("textarea");
+        el.value = text;
+        el.style.position = "fixed";
+        el.style.opacity = "0";
+        document.body.appendChild(el);
+        el.focus();
+        el.select();
+        document.execCommand("copy");
+        document.body.removeChild(el);
+      }
+      setCopied(true);
+      setTimeout(() => setCopied(false), 2000);
+    } catch (e) {
+      console.error("Copy failed:", e);
+    }
+  }, [value]);
+
   return (
-    <pre className="p-3 bg-gray-50 border rounded text-xs font-mono overflow-auto max-h-96 whitespace-pre-wrap break-all">
-      {JSON.stringify(value, null, 2)}
-    </pre>
+    <div className="bg-white rounded border overflow-hidden">
+      <div className="flex justify-between items-center px-3 py-2 border-b bg-gray-50">
+        <span className="text-xs font-semibold text-gray-600">{label}</span>
+        <button
+          onClick={handleCopy}
+          className="p-1 hover:bg-gray-200 rounded text-gray-500 hover:text-gray-700 transition-colors"
+          title="Copy JSON"
+        >
+          {copied ? <CheckOutlined className="text-green-600" /> : <CopyOutlined />}
+        </button>
+      </div>
+      <pre className="p-3 bg-white text-xs font-mono overflow-auto max-h-96 whitespace-pre-wrap break-all m-0">
+        {JSON.stringify(value, null, 2)}
+      </pre>
+    </div>
   );
 }
 
@@ -95,45 +133,51 @@ function DiffSection({ log }: { log: AuditLogEntry }) {
         : { note: "No differing fields detected" };
   }
 
-  const renderValue = (value: Record<string, any> | null | undefined) => {
+  const renderValue = (label: string, value: Record<string, any> | null | undefined) => {
     if (!value || Object.keys(value).length === 0) {
-      return <p className="text-xs text-gray-400 italic">N/A</p>;
+      return (
+        <div className="bg-white rounded border overflow-hidden">
+          <div className="flex items-center px-3 py-2 border-b bg-gray-50">
+            <span className="text-xs font-semibold text-gray-600">{label}</span>
+          </div>
+          <p className="px-3 py-3 text-xs text-gray-400 italic m-0">N/A</p>
+        </div>
+      );
     }
 
-    // For key table updates, filter to only show meaningful fields
+    // For key table updates, show only meaningful fields as plain text
     if (isKeyTable && isUpdateAction) {
       const knownKeyFields = ["token", "spend", "max_budget"];
       const hasOnlyKnown = Object.keys(value).every((k) => knownKeyFields.includes(k));
       if (hasOnlyKnown && !("note" in value)) {
         return (
-          <div className="space-y-1 text-xs">
-            {value.token !== undefined && (
-              <p><span className="text-gray-500">Token:</span> {value.token ?? "N/A"}</p>
-            )}
-            {value.spend !== undefined && (
-              <p><span className="text-gray-500">Spend:</span> ${Number(value.spend).toFixed(6)}</p>
-            )}
-            {value.max_budget !== undefined && (
-              <p><span className="text-gray-500">Max Budget:</span> ${Number(value.max_budget).toFixed(6)}</p>
-            )}
+          <div className="bg-white rounded border overflow-hidden">
+            <div className="flex items-center px-3 py-2 border-b bg-gray-50">
+              <span className="text-xs font-semibold text-gray-600">{label}</span>
+            </div>
+            <div className="px-3 py-3 space-y-1 text-xs">
+              {value.token !== undefined && (
+                <p><span className="text-gray-500">Token:</span> {value.token ?? "N/A"}</p>
+              )}
+              {value.spend !== undefined && (
+                <p><span className="text-gray-500">Spend:</span> ${Number(value.spend).toFixed(6)}</p>
+              )}
+              {value.max_budget !== undefined && (
+                <p><span className="text-gray-500">Max Budget:</span> ${Number(value.max_budget).toFixed(6)}</p>
+              )}
+            </div>
           </div>
         );
       }
     }
 
-    return <JsonBlock value={value} />;
+    return <CopyableJsonBlock label={label} value={value} />;
   };
 
   return (
     <div className="grid grid-cols-1 md:grid-cols-2 gap-4 mt-4">
-      <div>
-        <p className="text-xs font-semibold text-gray-600 mb-2">Before</p>
-        {renderValue(displayBefore)}
-      </div>
-      <div>
-        <p className="text-xs font-semibold text-gray-600 mb-2">After</p>
-        {renderValue(displayAfter)}
-      </div>
+      {renderValue("Before", displayBefore)}
+      {renderValue("After", displayAfter)}
     </div>
   );
 }
@@ -209,12 +253,7 @@ export function AuditLogDrawer({ open, onClose, log }: AuditLogDrawerProps) {
         </div>
 
         {/* Diff */}
-        <div>
-          <p className="text-xs font-semibold text-gray-700 mb-1 uppercase tracking-wide">
-            Changes
-          </p>
-          <DiffSection log={log} />
-        </div>
+        <DiffSection log={log} />
       </div>
     </Drawer>
   );
diff --git a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
index 71300fe0427..b16ba30049d 100644
--- a/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/audit_logs.tsx
@@ -1,7 +1,7 @@
 import { useState } from "react";
 import { useQuery, keepPreviousData } from "@tanstack/react-query";
-import { Table, Tag, Input, Select, Button, Pagination } from "antd";
-import { ReloadOutlined } from "@ant-design/icons";
+import { Table, Tag, Input, Select, Button, Pagination, Spin } from "antd";
+import { ReloadOutlined, LoadingOutlined } from "@ant-design/icons";
 import type { ColumnsType } from "antd/es/table";
 import moment from "moment";
 import { uiAuditLogsCall } from "../networking";
@@ -285,7 +285,10 @@ export default function AuditLogs({
           columns={columns}
           dataSource={auditLogs}
           rowKey="id"
-          loading={query.isLoading}
+          loading={{
+            spinning: query.isLoading,
+            indicator: <Spin indicator={<LoadingOutlined spin />} size="small" />,
+          }}
           size="small"
           pagination={false}
           onRow={(record) => ({