Merge branch 'main' into litellm_compliance_checker_ui

.github/workflows/test-litellm-matrix.yml

@@ -18,10 +18,15 @@ jobs:
       matrix:
         test-group:
           # tests/test_litellm split by subdirectory (~560 files total)
-          - name: "llms"
-            path: "tests/test_litellm/llms"
-            workers: 2  # Reduced from 4 to 2 to avoid race conditions
-            reruns: 2   # Retry flaky tests twice
+          # Vertex AI tests separated for better isolation (prevent auth/env pollution)
+          - name: "llms-vertex"
+            path: "tests/test_litellm/llms/vertex_ai"
+            workers: 1
+            reruns: 2
+          - name: "llms-other"
+            path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai"
+            workers: 2
+            reruns: 2
           # tests/test_litellm/proxy split by subdirectory (~180 files total)
           - name: "proxy-guardrails"
             path: "tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/management_helpers"
@@ -105,4 +110,5 @@ jobs:
             -n ${{ matrix.test-group.workers }} \
             --reruns ${{ matrix.test-group.reruns }} \
             --reruns-delay 1 \
+            --dist=loadscope \
             --durations=20

litellm/policy_templates_backup.json

@@ -772,13 +772,14 @@
   {
     "id": "eu-ai-act-article5",
     "title": "EU AI Act Article 5 — Prohibited Practices",
-    "description": "EU AI Act Article 5 compliance for prohibited AI practices. Blocks requests related to social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation. Uses conditional matching (identifier word + context word).",
+    "description": "EU AI Act Article 5 compliance for prohibited AI practices. Blocks requests related to social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation. Includes both English and French keyword detection. Uses conditional matching (identifier word + context word).",
     "region": "EU",
     "icon": "ShieldExclamationIcon",
     "iconColor": "text-red-500",
     "iconBg": "bg-red-50",
     "guardrails": [
-      "eu-ai-act-prohibited-practices"
+      "eu-ai-act-prohibited-practices",
+      "eu-ai-act-prohibited-practices-fr"
     ],
     "complexity": "High",
     "guardrailDefinitions": [
@@ -794,7 +795,19 @@
               "enabled": true,
               "action": "BLOCK",
               "severity_threshold": "medium"
-            },
+            }
+          ]
+        },
+        "guardrail_info": {
+          "description": "Blocks EU AI Act Article 5 prohibited practices in English: social scoring systems, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation"
+        }
+      },
+      {
+        "guardrail_name": "eu-ai-act-prohibited-practices-fr",
+        "litellm_params": {
+          "guardrail": "litellm_content_filter",
+          "mode": "pre_call",
+          "categories": [
             {
               "category": "eu_ai_act_article5_prohibited_practices_fr",
               "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_article5_fr.yaml",
@@ -805,15 +818,16 @@
           ]
         },
         "guardrail_info": {
-          "description": "Blocks EU AI Act Article 5 prohibited practices: social scoring systems, emotion recognition in workplace/education, biometric categorization for sensitive attributes, predictive profiling, manipulation, and vulnerability exploitation"
+          "description": "Blocks EU AI Act Article 5 prohibited practices in French: detects and blocks French-language keywords related to social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation"
         }
       }
     ],
     "templateData": {
       "policy_name": "eu-ai-act-article5",
-      "description": "EU AI Act Article 5 compliance policy for prohibited AI practices. Blocks social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation.",
+      "description": "EU AI Act Article 5 compliance policy for prohibited AI practices. Blocks social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation. Includes English and French detection.",
       "guardrails_add": [
-        "eu-ai-act-prohibited-practices"
+        "eu-ai-act-prohibited-practices",
+        "eu-ai-act-prohibited-practices-fr"
       ],
       "guardrails_remove": []
     }

litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py

@@ -264,6 +264,52 @@ class ContentFilterGuardrail(CustomGuardrail):
             f"{len(self.category_keywords)} keywords"
         )
 
+    @staticmethod
+    def _resolve_category_file_path(file_path: str) -> str:
+        """
+        Resolve a category file path that may be relative.
+
+        Paths in policy templates (e.g. category_file) are often stored as
+        relative paths like "litellm/proxy/.../policy_templates/file.yaml".
+        These only work when the CWD is the project root. In production
+        (Docker, installed packages, etc.) the CWD is different, so the
+        file isn't found.
+
+        Resolution order:
+        1. Return as-is if absolute or already exists.
+        2. Try joining the full path relative to this module's directory.
+        3. Progressively strip leading path components and try each suffix
+           relative to this module's directory (handles paths like
+           "litellm/proxy/.../policy_templates/file.yaml" by finding the
+           "policy_templates/file.yaml" suffix that exists).
+
+        Args:
+            file_path: The file path to resolve (absolute or relative).
+
+        Returns:
+            The resolved absolute-ish path, or the original path if
+            resolution fails (caller should check existence).
+        """
+        if os.path.isabs(file_path) or os.path.exists(file_path):
+            return file_path
+
+        module_dir = os.path.dirname(__file__)
+
+        # Try the full relative path joined to the module directory
+        candidate = os.path.join(module_dir, file_path)
+        if os.path.exists(candidate):
+            return candidate
+
+        # Progressively strip leading components to find a matching suffix
+        parts = file_path.split("/")
+        for i in range(1, len(parts)):
+            suffix = os.path.join(*parts[i:])
+            candidate = os.path.join(module_dir, suffix)
+            if os.path.exists(candidate):
+                return candidate
+
+        return file_path
+
     def _load_categories(self, categories: List[ContentFilterCategoryConfig]) -> None:
         """
         Load content categories from configuration.
@@ -302,7 +348,7 @@ class ContentFilterGuardrail(CustomGuardrail):
 
             # Load category file (custom or default)
             if custom_file:
-                category_file_path = custom_file
+                category_file_path = self._resolve_category_file_path(custom_file)
             else:
                 # Try .yaml first, then .json (e.g. harm_toxic_abuse.json)
                 yaml_path = os.path.join(categories_dir, f"{category_name}.yaml")

litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_article5_fr.yaml

@@ -2,7 +2,7 @@
 # Utilise une logique conditionnelle : BLOQUER si un mot identificateur + un mot de blocage apparaissent ensemble
 # Référence : https://artificialintelligenceact.eu/article/5/
 category_name: "eu_ai_act_article5_prohibited_practices_fr"
-description: "Détecte les pratiques interdites de l'Article 5 de la loi sur l'IA de l'UE (français)"
+description: "Detects EU AI Act Article 5 prohibited practices using conditional keyword matching in French"
 default_action: "BLOCK"
 
 # MOTS IDENTIFICATEURS - Actions qui pourraient créer des systèmes interdits

litellm/proxy/spend_tracking/spend_management_endpoints.py

@@ -3278,7 +3278,11 @@ async def _build_ui_spend_logs_response(
     count_map: dict[str, int] = {}
     if enrich_session_counts:
         session_ids = list(
-            {row.session_id for row in data if getattr(row, "session_id", None)}
+            {
+                (row.get("session_id") if isinstance(row, dict) else getattr(row, "session_id", None))
+                for row in data
+                if (row.get("session_id") if isinstance(row, dict) else getattr(row, "session_id", None))
+            }
         )
         if session_ids:
             # NOTE: This GROUP BY runs on every v1/UI page load. The IN clause

litellm/proxy/spend_tracking/spend_tracking_utils.py

@@ -352,6 +352,8 @@ def get_logging_payload(  # noqa: PLR0915
         guardrail_information=(
             standard_logging_payload.get("guardrail_information", None)
             if standard_logging_payload is not None
+            else metadata.get("standard_logging_guardrail_information", None)
+            if metadata is not None
             else None
         ),
         cold_storage_object_key=(

policy_templates.json

@@ -772,13 +772,14 @@
   {
     "id": "eu-ai-act-article5",
     "title": "EU AI Act Article 5 — Prohibited Practices",
-    "description": "EU AI Act Article 5 compliance for prohibited AI practices. Blocks requests related to social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation. Uses conditional matching (identifier word + context word).",
+    "description": "EU AI Act Article 5 compliance for prohibited AI practices. Blocks requests related to social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation. Includes both English and French keyword detection. Uses conditional matching (identifier word + context word).",
     "region": "EU",
     "icon": "ShieldExclamationIcon",
     "iconColor": "text-red-500",
     "iconBg": "bg-red-50",
     "guardrails": [
-      "eu-ai-act-prohibited-practices"
+      "eu-ai-act-prohibited-practices",
+      "eu-ai-act-prohibited-practices-fr"
     ],
     "complexity": "High",
     "guardrailDefinitions": [
@@ -794,7 +795,19 @@
               "enabled": true,
               "action": "BLOCK",
               "severity_threshold": "medium"
-            },
+            }
+          ]
+        },
+        "guardrail_info": {
+          "description": "Blocks EU AI Act Article 5 prohibited practices in English: social scoring systems, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation"
+        }
+      },
+      {
+        "guardrail_name": "eu-ai-act-prohibited-practices-fr",
+        "litellm_params": {
+          "guardrail": "litellm_content_filter",
+          "mode": "pre_call",
+          "categories": [
             {
               "category": "eu_ai_act_article5_prohibited_practices_fr",
               "category_file": "litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/policy_templates/eu_ai_act_article5_fr.yaml",
@@ -805,15 +818,16 @@
           ]
         },
         "guardrail_info": {
-          "description": "Blocks EU AI Act Article 5 prohibited practices: social scoring systems, emotion recognition in workplace/education, biometric categorization for sensitive attributes, predictive profiling, manipulation, and vulnerability exploitation"
+          "description": "Blocks EU AI Act Article 5 prohibited practices in French: detects and blocks French-language keywords related to social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation"
         }
       }
     ],
     "templateData": {
       "policy_name": "eu-ai-act-article5",
-      "description": "EU AI Act Article 5 compliance policy for prohibited AI practices. Blocks social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation.",
+      "description": "EU AI Act Article 5 compliance policy for prohibited AI practices. Blocks social scoring, emotion recognition in workplace/education, biometric categorization, predictive profiling, manipulation, and vulnerability exploitation. Includes English and French detection.",
       "guardrails_add": [
-        "eu-ai-act-prohibited-practices"
+        "eu-ai-act-prohibited-practices",
+        "eu-ai-act-prohibited-practices-fr"
       ],
       "guardrails_remove": []
     }

tests/proxy_unit_tests/test_user_api_key_auth.py

@@ -1044,8 +1044,13 @@ async def test_jwt_non_admin_team_route_access(monkeypatch):
         litellm.proxy.proxy_server, "general_settings", {"enable_jwt_auth": True}
     )
 
-    # Mock JWTAuthManager.auth_builder
+    # Mock enterprise license check and JWTAuthManager.auth_builder
+    # License check must be mocked to avoid environment variable pollution
+    # in parallel test execution
     with patch(
+        "litellm.proxy.proxy_server.premium_user",
+        True,
+    ), patch(
         "litellm.proxy.auth.handle_jwt.JWTAuthManager.auth_builder",
         return_value=mock_jwt_response,
     ):

tests/test_litellm/integrations/test_langfuse_otel.py

@@ -157,8 +157,12 @@ class TestLangfuseOtelIntegration:
 
         stub_module.LangFuseLogger = StubLFLogger  # type: ignore
 
-        # Register stub in sys.modules so import inside method succeeds
-        sys.modules["litellm.integrations.langfuse.langfuse"] = stub_module  # type: ignore
+        # Register stub in sys.modules so import inside method succeeds.
+        # Use monkeypatch so the real module is restored after the test runs,
+        # preventing sys.modules corruption that would break patch() targets in
+        # later tests (the patch would hit the stub while the real module's
+        # globals remain unpatch-ed).
+        monkeypatch.setitem(sys.modules, "litellm.integrations.langfuse.langfuse", stub_module)  # type: ignore
 
         kwargs = {"litellm_params": {"metadata": {"foo": "bar"}}}
         extracted = LangfuseOtelLogger._extract_langfuse_metadata(kwargs)

tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py

@@ -33,7 +33,7 @@ def test_anthropic_experimental_pass_through_messages_handler():
         except Exception as e:
             print(f"Error: {e}")
         mock_completion.assert_called_once()
-        mock_completion.call_args.kwargs["api_key"] == "test-api-key"
+        assert mock_completion.call_args.kwargs["api_key"] == "test-api-key"
 
 
 def test_anthropic_experimental_pass_through_messages_handler_dynamic_api_key_and_api_base_and_custom_values():
@@ -57,9 +57,9 @@ def test_anthropic_experimental_pass_through_messages_handler_dynamic_api_key_an
         except Exception as e:
             print(f"Error: {e}")
         mock_completion.assert_called_once()
-        mock_completion.call_args.kwargs["api_key"] == "test-api-key"
-        mock_completion.call_args.kwargs["api_base"] == "test-api-base"
-        mock_completion.call_args.kwargs["custom_key"] == "custom_value"
+        assert mock_completion.call_args.kwargs["api_key"] == "test-api-key"
+        assert mock_completion.call_args.kwargs["api_base"] == "test-api-base"
+        assert mock_completion.call_args.kwargs["custom_key"] == "custom_value"
 
 
 def test_anthropic_experimental_pass_through_messages_handler_custom_llm_provider():

tests/test_litellm/llms/huggingface/embedding/test_huggingface_embedding_handler.py

@@ -1,3 +1,4 @@
+import importlib
 import json
 import os
 import sys
@@ -15,7 +16,22 @@ MOCK_EMBEDDING_RESPONSE = [[0.1, 0.2, 0.3, 0.4, 0.5]]
 
 
 @pytest.fixture
-def mock_embedding_http_handler():
+def reload_huggingface_modules():
+    """
+    Reload modules to ensure fresh references after conftest reloads litellm.
+    This ensures the HTTPHandler class being patched is the same one used by
+    the embedding handler during parallel test execution.
+    """
+    import litellm.llms.custom_httpx.http_handler as http_handler_module
+    import litellm.llms.huggingface.embedding.handler as hf_embedding_handler_module
+
+    importlib.reload(http_handler_module)
+    importlib.reload(hf_embedding_handler_module)
+    yield
+
+
+@pytest.fixture
+def mock_embedding_http_handler(reload_huggingface_modules):
     """Fixture to mock the HTTP handler for embedding tests"""
     with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
         mock_response = MagicMock()
@@ -27,7 +43,7 @@ def mock_embedding_http_handler():
 
 
 @pytest.fixture
-def mock_embedding_async_http_handler():
+def mock_embedding_async_http_handler(reload_huggingface_modules):
     """Fixture to mock the async HTTP handler for embedding tests"""
     with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock) as mock_post:
         mock_response = MagicMock()

tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py

@@ -2,6 +2,7 @@
 Integration tests for Vertex AI rerank functionality.
 These tests demonstrate end-to-end usage of the Vertex AI rerank feature.
 """
+import importlib
 import os
 from unittest.mock import MagicMock, patch
 
@@ -13,7 +14,14 @@ from litellm.llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
 
 class TestVertexAIRerankIntegration:
     def setup_method(self):
-        self.config = VertexAIRerankConfig()
+        # Reload modules to ensure fresh references after conftest reloads litellm.
+        # This ensures the class being patched is the same one used by the tests.
+        import litellm.llms.vertex_ai.rerank.transformation as rerank_transformation_module
+        importlib.reload(rerank_transformation_module)
+
+        # Re-import after reload to get the fresh class
+        from litellm.llms.vertex_ai.rerank.transformation import VertexAIRerankConfig as FreshConfig
+        self.config = FreshConfig()
         self.model = "semantic-ranker-default@latest"
 
     @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token')

tests/test_litellm/proxy/guardrails/test_pillar_guardrails.py

@@ -6,6 +6,7 @@ and following LiteLLM testing patterns and best practices.
 """
 
 # Standard library imports
+import importlib
 import os
 import sys
 from typing import Dict
@@ -49,16 +50,14 @@ def setup_and_teardown():
     to speed up testing by removing callbacks being chained.
     """
     import asyncio
-    import importlib
-    import sys
+    global litellm
 
-    # Reload litellm to ensure clean state
-    # During parallel test execution, another worker might have removed litellm from sys.modules
-    # so we need to ensure it's imported before reloading
-    if "litellm" not in sys.modules:
-        import litellm as _litellm
-    else:
-        importlib.reload(litellm)
+    # Always import then reload to ensure fresh state
+    # This handles both cases uniformly:
+    # 1. litellm not in sys.modules (parallel worker removed it)
+    # 2. litellm already imported (normal case)
+    _module = importlib.import_module("litellm")
+    litellm = importlib.reload(_module)
 
     # Set up async loop
     loop = asyncio.get_event_loop_policy().new_event_loop()

tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py

@@ -15,7 +15,6 @@ sys.path.insert(
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import litellm
-
 import litellm.proxy.proxy_server as ps
 
 
@@ -2150,3 +2149,58 @@ async def test_ui_view_spend_logs_with_error_code_and_key_alias(client):
         assert metadata["user_api_key_alias"] == "test-key-1"
         assert "error_information" in metadata
         assert metadata["error_information"]["error_code"] == "500"
+
+
+@pytest.mark.asyncio
+async def test_build_ui_spend_logs_response_dict_rows_session_counts():
+    """
+    Regression test: _build_ui_spend_logs_response must enrich session_total_count
+    even when rows are plain dicts (as returned by query_raw) rather than Prisma
+    model instances.  Previously getattr(dict, "session_id", None) silently
+    returned None, so every row got session_total_count=1 and the UI never
+    grouped session rows.
+    """
+    from litellm.proxy.spend_tracking.spend_management_endpoints import (
+        _build_ui_spend_logs_response,
+    )
+
+    session_id = "sess-abc-123"
+    dict_rows = [
+        {"request_id": "req-1", "session_id": session_id, "call_type": "completion"},
+        {"request_id": "req-2", "session_id": session_id, "call_type": "mcp_tool_call"},
+        {"request_id": "req-3", "session_id": None, "call_type": "completion"},
+    ]
+
+    mock_prisma = MagicMock()
+    mock_prisma.db.litellm_spendlogs.group_by = AsyncMock(
+        return_value=[
+            {"session_id": session_id, "_count": {"session_id": 2}},
+        ]
+    )
+
+    result = await _build_ui_spend_logs_response(
+        prisma_client=mock_prisma,
+        data=dict_rows,
+        total_records=3,
+        page=1,
+        page_size=50,
+        total_pages=1,
+        enrich_session_counts=True,
+    )
+
+    rows = result["data"]
+    assert len(rows) == 3
+
+    # Rows with the shared session_id should have session_total_count=2
+    assert rows[0]["session_total_count"] == 2
+    assert rows[1]["session_total_count"] == 2
+
+    # Row without a session_id defaults to 1
+    assert rows[2]["session_total_count"] == 1
+
+    # group_by should have been called with the session_id
+    mock_prisma.db.litellm_spendlogs.group_by.assert_called_once_with(
+        by=["session_id"],
+        where={"session_id": {"in": [session_id]}},
+        count={"session_id": True},
+    )

tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py

@@ -21,6 +21,7 @@ from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.proxy.spend_tracking.spend_tracking_utils import (
     _get_proxy_server_request_for_spend_logs_payload,
     _get_response_for_spend_logs_payload,
+    _get_spend_logs_metadata,
     _get_vector_store_request_for_spend_logs_payload,
     _sanitize_request_body_for_spend_logs_payload,
     _should_store_prompts_and_responses_in_spend_logs,
@@ -957,3 +958,76 @@ def test_should_store_prompts_and_responses_in_spend_logs_case_insensitive_strin
         result = _should_store_prompts_and_responses_in_spend_logs()
         assert result is False, "Expected False (from env var) when key missing, got True"
 
+
+def test_get_spend_logs_metadata_guardrail_info_fallback_from_metadata():
+    """
+    When standard_logging_payload is None (e.g. guardrail blocks before LLM call),
+    guardrail_information should fall back to reading from metadata's
+    standard_logging_guardrail_information field.
+    """
+    guardrail_info = [
+        {
+            "guardrail_name": "content_filter",
+            "guardrail_provider": "litellm",
+            "guardrail_mode": "pre_call",
+            "guardrail_status": "guardrail_intervened",
+            "guardrail_response": "Content blocked",
+        }
+    ]
+    metadata = {
+        "user_api_key": "test-key",
+        "standard_logging_guardrail_information": guardrail_info,
+    }
+
+    result = _get_spend_logs_metadata(
+        metadata=metadata,
+        guardrail_information=None,
+    )
+    # When guardrail_information param is None, should NOT fall back
+    # (the caller is responsible for passing it)
+    assert result["guardrail_information"] is None
+
+
+def test_get_logging_payload_guardrail_info_when_no_standard_logging_payload():
+    """
+    When a guardrail blocks a request before the LLM call, the standard_logging_object
+    is not set on request_data. In this case, get_logging_payload should still include
+    guardrail_information from the metadata.
+
+    This is the bug fix for: guardrail failures not showing GuardrailViewer in the UI.
+    """
+    guardrail_info = [
+        {
+            "guardrail_name": "content_filter",
+            "guardrail_provider": "litellm",
+            "guardrail_mode": "pre_call",
+            "guardrail_status": "guardrail_intervened",
+            "guardrail_response": "Content blocked",
+        }
+    ]
+    # Simulate request_data as it looks when a guardrail blocks before LLM call
+    kwargs = {
+        "model": "gpt-4",
+        "litellm_call_id": "test-call-id",
+        "litellm_params": {
+            "metadata": {
+                "user_api_key": "test-key",
+                "standard_logging_guardrail_information": guardrail_info,
+            },
+            "proxy_server_request": {},
+        },
+        # No "standard_logging_object" key - this is the failure case
+    }
+
+    with patch("litellm.proxy.proxy_server.master_key", "sk-master"):
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            payload = get_logging_payload(
+                kwargs=kwargs,
+                response_obj={},
+                start_time=datetime.datetime.now(tz=timezone.utc),
+                end_time=datetime.datetime.now(tz=timezone.utc),
+            )
+
+    metadata_result = json.loads(payload["metadata"])
+    assert metadata_result["guardrail_information"] == guardrail_info
+

tests/test_litellm/proxy/test_proxy_server.py

@@ -681,10 +681,12 @@ def test_embedding_input_array_of_tokens(client_no_auth):
     """
     from litellm.proxy import proxy_server
 
-    # Apply the mock AFTER client_no_auth fixture has initialized the router
-    # This avoids issues with llm_router being None during parallel test execution
-    if proxy_server.llm_router is None:
-        pytest.skip("llm_router not initialized - skipping test")
+    # The client_no_auth fixture should initialize the router
+    # Assert this to catch any router initialization regressions
+    assert proxy_server.llm_router is not None, (
+        "llm_router is None after client_no_auth fixture initialized. "
+        "This indicates a router initialization issue that should be investigated."
+    )
 
     try:
         with mock.patch.object(

tests/test_litellm/responses/mcp/test_chat_completions_handler.py

@@ -427,16 +427,14 @@ async def test_acompletion_with_mcp_adds_metadata_to_streaming(monkeypatch):
     assert len(all_chunks) > 0
 
     # Verify mcp_list_tools is in the first chunk
-    first_chunk = all_chunks[0] if all_chunks else None
-    assert first_chunk is not None, "Should have a first chunk"
-    if hasattr(first_chunk, "choices") and first_chunk.choices:
-        choice = first_chunk.choices[0]
-        if hasattr(choice, "delta") and choice.delta:
-            provider_fields = getattr(choice.delta, "provider_specific_fields", None)
-            # mcp_list_tools should be added to the first chunk
-            assert provider_fields is not None, f"First chunk should have provider_specific_fields. Delta: {choice.delta}"
-            assert "mcp_list_tools" in provider_fields, f"First chunk should have mcp_list_tools. Fields: {provider_fields}"
-            assert provider_fields["mcp_list_tools"] == openai_tools
+    first_chunk = all_chunks[0]
+    assert hasattr(first_chunk, "choices") and first_chunk.choices, "First chunk must have choices"
+    choice = first_chunk.choices[0]
+    assert hasattr(choice, "delta") and choice.delta, "First choice must have delta"
+    provider_fields = getattr(choice.delta, "provider_specific_fields", None)
+    assert provider_fields is not None, f"First chunk should have provider_specific_fields. Delta: {choice.delta}"
+    assert "mcp_list_tools" in provider_fields, f"First chunk should have mcp_list_tools. Fields: {provider_fields}"
+    assert provider_fields["mcp_list_tools"] == openai_tools
 
 
 @pytest.mark.asyncio
@@ -625,7 +623,7 @@ async def test_acompletion_with_mcp_streaming_metadata_in_correct_chunks(monkeyp
             ],
         ),  # Final chunk with tool_calls
     ]
-    
+
     follow_up_chunks = [
         create_chunk("Hello"),
         create_chunk(" world", finish_reason="stop"),
@@ -785,21 +783,21 @@ async def test_acompletion_with_mcp_streaming_metadata_in_correct_chunks(monkeyp
         assert initial_final_chunk is not None, "Should have a final chunk from initial response"
 
         # Verify mcp_list_tools is in the first chunk
-        if hasattr(first_chunk, "choices") and first_chunk.choices:
-            choice = first_chunk.choices[0]
-            if hasattr(choice, "delta") and choice.delta:
-                provider_fields = getattr(choice.delta, "provider_specific_fields", None)
-                assert provider_fields is not None, "First chunk should have provider_specific_fields"
-                assert "mcp_list_tools" in provider_fields, "First chunk should have mcp_list_tools"
+        assert hasattr(first_chunk, "choices") and first_chunk.choices, "First chunk must have choices"
+        first_choice = first_chunk.choices[0]
+        assert hasattr(first_choice, "delta") and first_choice.delta, "First choice must have delta"
+        first_provider_fields = getattr(first_choice.delta, "provider_specific_fields", None)
+        assert first_provider_fields is not None, "First chunk should have provider_specific_fields"
+        assert "mcp_list_tools" in first_provider_fields, "First chunk should have mcp_list_tools"
 
         # Verify mcp_tool_calls and mcp_call_results are in the final chunk of initial response
-        if hasattr(initial_final_chunk, "choices") and initial_final_chunk.choices:
-            choice = initial_final_chunk.choices[0]
-            if hasattr(choice, "delta") and choice.delta:
-                provider_fields = getattr(choice.delta, "provider_specific_fields", None)
-                assert provider_fields is not None, "Final chunk should have provider_specific_fields"
-                assert "mcp_tool_calls" in provider_fields, "Should have mcp_tool_calls"
-                assert "mcp_call_results" in provider_fields, "Should have mcp_call_results"
+        assert hasattr(initial_final_chunk, "choices") and initial_final_chunk.choices, "Final chunk must have choices"
+        final_choice = initial_final_chunk.choices[0]
+        assert hasattr(final_choice, "delta") and final_choice.delta, "Final choice must have delta"
+        final_provider_fields = getattr(final_choice.delta, "provider_specific_fields", None)
+        assert final_provider_fields is not None, "Final chunk should have provider_specific_fields"
+        assert "mcp_tool_calls" in final_provider_fields, "Should have mcp_tool_calls"
+        assert "mcp_call_results" in final_provider_fields, "Should have mcp_call_results"
 
 
 @pytest.mark.asyncio

ui/litellm-dashboard/src/components/policies/guardrail_selection_modal.tsx

@@ -201,6 +201,11 @@ const GuardrailSelectionModal: React.FC<GuardrailSelectionModalProps> = ({
                         {guardrail.definition.litellm_params.patterns.length} pattern(s)
                       </Tag>
                     )}
+                    {guardrail.definition?.litellm_params?.categories && (
+                      <Tag className="text-xs" color="orange">
+                        {guardrail.definition.litellm_params.categories.length} category/categories
+                      </Tag>
+                    )}
                   </div>
                 </div>
               </div>

ui/litellm-dashboard/src/components/view_logs/GuardrailViewer/GuardrailViewer.tsx

@@ -507,20 +507,6 @@ const EvaluationCard = ({ entry }: { entry: GuardrailInformation }) => {
       {/* Expanded details */}
       {expanded && (
         <div className="border-t border-gray-100 px-4 py-3">
-          {/* View Policy Configuration link */}
-          {entry.policy_template && (
-            <div className="flex justify-end mb-3">
-              <a
-                href="/ui/policies"
-                target="_blank"
-                rel="noopener noreferrer"
-                className="text-sm text-blue-600 hover:text-blue-800 font-medium"
-              >
-                View Policy Configuration
-                <ExternalLinkIcon />
-              </a>
-            </div>
-          )}
 
           {/* Classification details for llm-judge */}
           {entry.classification && (

ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.tsx

@@ -66,9 +66,6 @@ export function LogDetailContent({ logEntry, onOpenSettings, isLoadingDetails =
   const hasGuardrailData = guardrailEntries.length > 0;
   const totalMaskedEntities = calculateTotalMaskedEntities(guardrailEntries);
   const primaryGuardrailLabel = getGuardrailLabel(guardrailEntries);
-  const guardrailPolicyNames = Array.from(
-    new Set(guardrailEntries.map((e: any) => e?.policy_template).filter(Boolean))
-  ) as string[];
 
   // Vector store data
   const hasVectorStoreData = checkHasVectorStoreData(metadata);
@@ -127,7 +124,7 @@ export function LogDetailContent({ logEntry, onOpenSettings, isLoadingDetails =
             )}
             {hasGuardrailData && (
               <Descriptions.Item label="Guardrail">
-                <GuardrailLabel label={primaryGuardrailLabel} maskedCount={totalMaskedEntities} policyNames={guardrailPolicyNames} />
+                <GuardrailLabel label={primaryGuardrailLabel} maskedCount={totalMaskedEntities} />
               </Descriptions.Item>
             )}
           </Descriptions>
@@ -225,7 +222,7 @@ function TagsSection({ tags }: { tags: Record<string, any> }) {
   );
 }
 
-function GuardrailLabel({ label, maskedCount, policyNames }: { label: string; maskedCount: number; policyNames: string[] }) {
+function GuardrailLabel({ label, maskedCount }: { label: string; maskedCount: number }) {
   const handleClick = () => {
     const el = document.getElementById("guardrail-section");
     if (el) el.scrollIntoView({ behavior: "smooth" });
@@ -239,9 +236,6 @@ function GuardrailLabel({ label, maskedCount, policyNames }: { label: string; ma
           {maskedCount} masked
         </Tag>
       )}
-      {policyNames.map((name) => (
-        <Tag key={name} color="purple">{name}</Tag>
-      ))}
     </Space>
   );
 }
@@ -424,6 +418,42 @@ function RequestResponseSection({
   );
 }
 
+export function GuardrailJumpLink({ guardrailEntries }: { guardrailEntries: any[] }) {
+  const allPassed = guardrailEntries.every((e) => {
+    const status = e?.guardrail_status || e?.status;
+    return status === "pass" || status === "passed" || status === "success";
+  });
+
+  const handleClick = () => {
+    const el = document.getElementById("guardrail-section");
+    if (el) el.scrollIntoView({ behavior: "smooth" });
+  };
+
+  return (
+    <div style={{ textAlign: "left", marginBottom: 12 }}>
+      <div
+        onClick={handleClick}
+        style={{
+          display: "inline-flex",
+          alignItems: "center",
+          gap: 6,
+          padding: "4px 12px",
+          borderRadius: 16,
+          cursor: "pointer",
+          fontSize: 13,
+          fontWeight: 500,
+          backgroundColor: allPassed ? "#f0fdf4" : "#fef2f2",
+          color: allPassed ? "#15803d" : "#b91c1c",
+          border: `1px solid ${allPassed ? "#bbf7d0" : "#fecaca"}`,
+        }}
+      >
+        {allPassed ? "\u2713" : "\u2717"} {guardrailEntries.length} guardrail{guardrailEntries.length !== 1 ? "s" : ""} evaluated
+        <span style={{ fontSize: 11, opacity: 0.7 }}>{"\u2193"}</span>
+      </div>
+    </div>
+  );
+}
+
 function MetadataSection({ metadata }: { metadata: Record<string, any> }) {
   return (
     <div className="bg-white rounded-lg shadow w-full max-w-full overflow-hidden mb-6">

ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailsDrawer.tsx

@@ -12,10 +12,11 @@ import { MCP_CALL_TYPES } from "../constants";
 import { getEventDisplayName } from "../utils";
 import { DrawerHeader } from "./DrawerHeader";
 import { useKeyboardNavigation } from "./useKeyboardNavigation";
-import { LogDetailContent } from "./LogDetailContent";
+import { LogDetailContent, GuardrailJumpLink } from "./LogDetailContent";
 import { sessionSpendLogsCall } from "../../networking";
 import { useQuery } from "@tanstack/react-query";
 import { getSpendString } from "@/utils/dataUtils";
+import { normalizeGuardrailEntries } from "./utils";
 import { DRAWER_WIDTH } from "./constants";
 import { useLogDetails } from "@/app/(dashboard)/hooks/logDetails/useLogDetails";
 
@@ -323,6 +324,11 @@ export function LogDetailsDrawer({
             </div>
 
             <div className="flex-1 overflow-y-auto">
+              {normalizeGuardrailEntries(metadata?.guardrail_information).length > 0 && (
+                <div className="px-3 pt-2">
+                  <GuardrailJumpLink guardrailEntries={normalizeGuardrailEntries(metadata?.guardrail_information)} />
+                </div>
+              )}
               {isSessionMode ? (
                 <div className="py-1">
                   {/* Child events — vertical tree line with horizontal connectors */}