diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py
index 4a7c33ed204..92e72a594a0 100644
--- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py
@@ -3,14 +3,15 @@ Transformation for Bedrock Invoke Agent
 
 https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent-runtime_InvokeAgent.html
 """
+
 import base64
 import json
-from litellm._uuid import uuid
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 import httpx
 
 from litellm._logging import verbose_logger
+from litellm._uuid import uuid
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     convert_content_list_to_str,
 )
@@ -22,6 +23,11 @@ from litellm.types.llms.bedrock_invoke_agents import (
     InvokeAgentEvent,
     InvokeAgentEventHeaders,
     InvokeAgentEventList,
+    InvokeAgentMetadata,
+    InvokeAgentModelInvocationInput,
+    InvokeAgentModelInvocationOutput,
+    InvokeAgentOrchestrationTrace,
+    InvokeAgentPreProcessingTrace,
     InvokeAgentTrace,
     InvokeAgentTracePayload,
     InvokeAgentUsage,
@@ -389,15 +395,19 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
         self, trace_data: InvokeAgentTrace, usage_info: InvokeAgentUsage
     ) -> None:
         """Extract usage information from preprocessing trace."""
-        pre_processing = trace_data.get("preProcessingTrace", {})
+        pre_processing: Optional[InvokeAgentPreProcessingTrace] = trace_data.get(
+            "preProcessingTrace"
+        )
         if not pre_processing:
             return
 
-        model_output = pre_processing.get("modelInvocationOutput", {})
+        model_output: Optional[InvokeAgentModelInvocationOutput] = pre_processing.get(
+            "modelInvocationOutput", {}
+        )
         if not model_output:
             return
 
-        metadata = model_output.get("metadata", {})
+        metadata: Optional[InvokeAgentMetadata] = model_output.get("metadata", {})
         if not metadata:
             return
 
@@ -412,11 +422,15 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
         self, trace_data: InvokeAgentTrace
     ) -> Optional[str]:
         """Extract model information from orchestration trace."""
-        orchestration_trace = trace_data.get("orchestrationTrace", {})
+        orchestration_trace: Optional[InvokeAgentOrchestrationTrace] = trace_data.get(
+            "orchestrationTrace"
+        )
         if not orchestration_trace:
             return None
 
-        model_invocation = orchestration_trace.get("modelInvocationInput", {})
+        model_invocation: Optional[InvokeAgentModelInvocationInput] = (
+            orchestration_trace.get("modelInvocationInput", {})
+        )
         if not model_invocation:
             return None
 
diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py
index f38c772e355..94dfea5f58a 100644
--- a/litellm/llms/gemini/google_genai/transformation.py
+++ b/litellm/llms/gemini/google_genai/transformation.py
@@ -1,6 +1,7 @@
 """
 Transformation for Calling Google models in their native format.
 """
+
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
 
 import httpx
@@ -25,27 +26,29 @@ else:
     GenerateContentContentListUnionDict = Any
     GenerateContentResponse = Any
     ToolConfigDict = Any
-    
+
 from ..common_utils import get_api_key_from_env
 
+
 class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
     """
     Configuration for calling Google models in their native format.
     """
+
     ##############################
     # Constants
     ##############################
     XGOOGLE_API_KEY = "x-goog-api-key"
     ##############################
-    
+
     @property
     def custom_llm_provider(self) -> Literal["gemini", "vertex_ai"]:
         return "gemini"
-    
+
     def __init__(self):
         super().__init__()
         VertexLLM.__init__(self)
-    
+
     def get_supported_generate_content_optional_params(self, model: str) -> List[str]:
         """
         Get the list of supported Google GenAI parameters for the model.
@@ -58,7 +61,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         """
         return [
             "http_options",
-            "system_instruction", 
+            "system_instruction",
             "temperature",
             "top_p",
             "top_k",
@@ -84,10 +87,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             "speech_config",
             "audio_timestamp",
             "automatic_function_calling",
-            "thinking_config"
+            "thinking_config",
         ]
 
-
     def map_generate_content_optional_params(
         self,
         generate_content_config_dict: GenerateContentConfigDict,
@@ -103,26 +105,29 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         Returns:
             Mapped parameters for the provider
         """
-        from litellm.types.google_genai.main import GenerateContentConfigDict
-        _generate_content_config_dict = GenerateContentConfigDict()
-        supported_google_genai_params = self.get_supported_generate_content_optional_params(model)
+        _generate_content_config_dict: Dict[str, Any] = {}
+        supported_google_genai_params = (
+            self.get_supported_generate_content_optional_params(model)
+        )
         for param, value in generate_content_config_dict.items():
             if param in supported_google_genai_params:
                 _generate_content_config_dict[param] = value
-        return dict(_generate_content_config_dict)
-    
+        return _generate_content_config_dict
+
     def validate_environment(
-        self, 
+        self,
         api_key: Optional[str],
         headers: Optional[dict],
         model: str,
-        litellm_params: Optional[Union[GenericLiteLLMParams, dict]]
+        litellm_params: Optional[Union[GenericLiteLLMParams, dict]],
     ) -> dict:
         default_headers = {
             "Content-Type": "application/json",
         }
         # Use the passed api_key first, then fall back to litellm_params and environment
-        gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {}))
+        gemini_api_key = api_key or self._get_google_ai_studio_api_key(
+            dict(litellm_params or {})
+        )
         if gemini_api_key is not None:
             default_headers[self.XGOOGLE_API_KEY] = gemini_api_key
         if headers is not None:
@@ -137,14 +142,14 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             or get_api_key_from_env()
             or litellm.api_key
         )
-    
+
     def _get_common_auth_components(
         self,
         litellm_params: dict,
     ) -> Tuple[Any, Optional[str], Optional[str]]:
         """
         Get common authentication components used by both sync and async methods.
-        
+
         Returns:
             Tuple of (vertex_credentials, vertex_project, vertex_location)
         """
@@ -152,7 +157,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         vertex_project = self.get_vertex_ai_project(litellm_params)
         vertex_location = self.get_vertex_ai_location(litellm_params)
         return vertex_credentials, vertex_project, vertex_location
-    
+
     def _build_final_headers_and_url(
         self,
         model: str,
@@ -168,7 +173,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         Build final headers and API URL from auth components.
         """
         gemini_api_key = self._get_google_ai_studio_api_key(litellm_params)
-        
+
         auth_header, api_base = self._get_token_and_url(
             model=model,
             gemini_api_key=gemini_api_key,
@@ -201,7 +206,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         """
         Sync version of get_auth_token_and_url.
         """
-        vertex_credentials, vertex_project, vertex_location = self._get_common_auth_components(litellm_params)
+        vertex_credentials, vertex_project, vertex_location = (
+            self._get_common_auth_components(litellm_params)
+        )
 
         _auth_header, vertex_project = self._ensure_access_token(
             credentials=vertex_credentials,
@@ -238,7 +245,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         Returns:
             Tuple of headers and API base
         """
-        vertex_credentials, vertex_project, vertex_location = self._get_common_auth_components(litellm_params)
+        vertex_credentials, vertex_project, vertex_location = (
+            self._get_common_auth_components(litellm_params)
+        )
 
         _auth_header, vertex_project = await self._ensure_access_token_async(
             credentials=vertex_credentials,
@@ -256,7 +265,6 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             api_base=api_base,
             litellm_params=litellm_params,
         )
-    
 
     def transform_generate_content_request(
         self,
@@ -269,6 +277,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             GenerateContentConfigDict,
             GenerateContentRequestDict,
         )
+
         typed_generate_content_request = GenerateContentRequestDict(
             model=model,
             contents=contents,
@@ -279,7 +288,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         request_dict = cast(dict, typed_generate_content_request)
 
         return request_dict
-    
+
     def transform_generate_content_response(
         self,
         model: str,
@@ -297,6 +306,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             Transformed response data
         """
         from litellm.types.google_genai.main import GenerateContentResponse
+
         try:
             response = raw_response.json()
         except Exception as e:
@@ -305,7 +315,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
                 status_code=raw_response.status_code,
                 headers=raw_response.headers,
             )
-        
+
         logging_obj.model_call_details["httpx_response"] = raw_response
-        
-        return GenerateContentResponse(**response)
\ No newline at end of file
+
+        return GenerateContentResponse(**response)
diff --git a/litellm/llms/vertex_ai/google_genai/transformation.py b/litellm/llms/vertex_ai/google_genai/transformation.py
index 574000e6bca..d7a4ceeb3e7 100644
--- a/litellm/llms/vertex_ai/google_genai/transformation.py
+++ b/litellm/llms/vertex_ai/google_genai/transformation.py
@@ -1,7 +1,8 @@
 """
 Transformation for Calling Google models in their native format.
 """
-from typing import Dict, Literal, Optional, Union
+
+from typing import Any, Dict, Literal, Optional, Union
 
 from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig
 from litellm.types.router import GenericLiteLLMParams
@@ -58,22 +59,21 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig):
         Returns:
             Mapped parameters for the provider
         """
-        from litellm.types.google_genai.main import GenerateContentConfigDict
 
-        _generate_content_config_dict = GenerateContentConfigDict()
+        _generate_content_config_dict: Dict = {}
 
         for param, value in generate_content_config_dict.items():
             camel_case_key = self._camel_to_snake(param)
             _generate_content_config_dict[camel_case_key] = value
-        return dict(_generate_content_config_dict)
+        return _generate_content_config_dict
 
     def transform_generate_content_request(
         self,
         model: str,
-        contents: any,
-        tools: Optional[any],
+        contents: Any,
+        tools: Optional[Any],
         generate_content_config_dict: Dict,
-        system_instruction: Optional[any] = None,
+        system_instruction: Optional[Any] = None,
     ) -> dict:
         """
         Transform the generate content request for Vertex AI.
diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
index 628784f6f1f..c0042133b47 100644
--- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
@@ -3,7 +3,6 @@ import asyncio
 import copy
 import json
 import traceback
-from litellm._uuid import uuid
 from base64 import b64encode
 from datetime import datetime
 from typing import Dict, List, Optional, Tuple, Union
@@ -25,6 +24,7 @@ from starlette.datastructures import UploadFile as StarletteUploadFile
 
 import litellm
 from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
 from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -424,10 +424,10 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
 
         for field_name, field_value in form_data.items():
             if isinstance(field_value, (StarletteUploadFile, UploadFile)):
-                files[
-                    field_name
-                ] = await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file(
-                    upload_file=field_value
+                files[field_name] = (
+                    await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file(
+                        upload_file=field_value
+                    )
                 )
             else:
                 form_data_dict[field_name] = field_value
@@ -476,7 +476,11 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
                 user_api_key_request_route=user_api_key_dict.request_route,
                 user_api_key_spend=user_api_key_dict.spend,
                 user_api_key_max_budget=user_api_key_dict.max_budget,
-                user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None,
+                user_api_key_budget_reset_at=(
+                    user_api_key_dict.budget_reset_at.isoformat()
+                    if user_api_key_dict.budget_reset_at
+                    else None
+                ),
             )
         )
 
@@ -496,7 +500,7 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
 
         kwargs = {
             "litellm_params": {
-                **litellm_params_in_body,
+                **litellm_params_in_body,  # type: ignore
                 "metadata": _metadata,
                 "proxy_server_request": {
                     "url": str(request.url),
@@ -509,9 +513,9 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
             "passthrough_logging_payload": passthrough_logging_payload,
         }
 
-        logging_obj.model_call_details[
-            "passthrough_logging_payload"
-        ] = passthrough_logging_payload
+        logging_obj.model_call_details["passthrough_logging_payload"] = (
+            passthrough_logging_payload
+        )
 
         return kwargs
 
@@ -923,7 +927,6 @@ def create_pass_through_route(
 ):
     # check if target is an adapter.py or a url
     from litellm._uuid import uuid
-
     from litellm.proxy.types_utils.utils import get_instance_fn
 
     try:
@@ -1367,7 +1370,6 @@ async def create_pass_through_endpoints(
     Create new pass-through endpoint
     """
     from litellm._uuid import uuid
-
     from litellm.proxy.proxy_server import (
         get_config_general_settings,
         update_config_general_settings,
diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py
index 7b78200b043..a0c2c929cb2 100644
--- a/litellm/proxy/spend_tracking/spend_tracking_utils.py
+++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py
@@ -10,7 +10,7 @@ from pydantic import BaseModel
 
 import litellm
 from litellm._logging import verbose_proxy_logger
-from litellm.constants import REDACTED_BY_LITELM_STRING, MAX_STRING_LENGTH_PROMPT_IN_DB
+from litellm.constants import MAX_STRING_LENGTH_PROMPT_IN_DB, REDACTED_BY_LITELM_STRING
 from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
@@ -21,6 +21,7 @@ from litellm.types.utils import (
     StandardLoggingModelInformation,
     StandardLoggingPayload,
     StandardLoggingVectorStoreRequest,
+    VectorStoreSearchResponse,
 )
 from litellm.utils import get_end_user_id_for_cost_tracking
 
@@ -297,7 +298,9 @@ def get_logging_payload(  # noqa: PLR0915
         id = f"{id}_cache_hit{time.time()}"  # SpendLogs does not allow duplicate request_id
 
     mcp_namespaced_tool_name = None
-    mcp_tool_call_metadata = clean_metadata.get("mcp_tool_call_metadata", {})
+    mcp_tool_call_metadata: Optional[StandardLoggingMCPToolCall] = clean_metadata.get(
+        "mcp_tool_call_metadata"
+    )
     if mcp_tool_call_metadata is not None:
         mcp_namespaced_tool_name = mcp_tool_call_metadata.get(
             "namespaced_tool_name", None
@@ -505,23 +508,23 @@ def _sanitize_request_body_for_spend_logs_payload(
                 # This split ensures we keep more context from the end of conversations
                 start_ratio = 0.35
                 end_ratio = 0.65
-                
+
                 # Calculate character distribution
                 start_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * start_ratio)
                 end_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * end_ratio)
-                
+
                 # Ensure we don't exceed the total limit
                 total_keep = start_chars + end_chars
                 if total_keep > MAX_STRING_LENGTH_PROMPT_IN_DB:
                     end_chars = MAX_STRING_LENGTH_PROMPT_IN_DB - start_chars
-                
+
                 # If the string length is less than what we want to keep, just truncate normally
                 if len(value) <= MAX_STRING_LENGTH_PROMPT_IN_DB:
                     return value
-                
+
                 # Calculate how many characters are being skipped
                 skipped_chars = len(value) - total_keep
-                
+
                 # Build the truncated string: beginning + truncation marker + end
                 truncated_value = (
                     f"{value[:start_chars]}"
@@ -567,8 +570,9 @@ def _get_vector_store_request_for_spend_logs_payload(
     if vector_store_request_metadata is None:
         return None
     for vector_store_request in vector_store_request_metadata:
-        vector_store_search_response = (
-            vector_store_request.get("vector_store_search_response", {}) or {}
+        vector_store_search_response: VectorStoreSearchResponse = (
+            vector_store_request.get("vector_store_search_response")
+            or VectorStoreSearchResponse()
         )
         response_data = vector_store_search_response.get("data", []) or []
         for response_item in response_data:
diff --git a/litellm/router.py b/litellm/router.py
index 20f0b92d30c..75b7835bb52 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -3442,7 +3442,7 @@ class Router:
             *[try_retrieve_batch(model) for model in filtered_model_list]
         )
 
-        final_results = {
+        final_results: Dict = {
             "object": "list",
             "data": [],
             "first_id": None,
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index 4adb751d905..33607ad933d 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -1308,7 +1308,7 @@ class MCPListToolsFailedEvent(BaseLiteLLMOpenAIResponseObject):
     item_id: str
 
 
-# MCP Call Events  
+# MCP Call Events
 class MCPCallInProgressEvent(BaseLiteLLMOpenAIResponseObject):
     type: Literal[ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS]
     sequence_number: int
diff --git a/tests/test_litellm/log.txt b/tests/test_litellm/log.txt
index 84fcbd137dc..6accec7665d 100644
--- a/tests/test_litellm/log.txt
+++ b/tests/test_litellm/log.txt
@@ -1,10 +1,14 @@
-main.py:1503: error: Argument "api_key" to "completion" of "AzureChatCompletion" has incompatible type "str | None"; expected "str"  [arg-type]
-main.py:1508: error: Argument "azure_ad_token" to "completion" of "AzureChatCompletion" has incompatible type "Any | str | None"; expected "str"  [arg-type]
-main.py:1509: error: Argument "azure_ad_token_provider" to "completion" of "AzureChatCompletion" has incompatible type "Any | None"; expected "Callable[..., Any]"  [arg-type]
-main.py:1579: error: Argument "api_key" to "completion" of "AzureTextCompletion" has incompatible type "str | None"; expected "str"  [arg-type]
-main.py:1580: error: Argument "api_base" to "completion" of "AzureTextCompletion" has incompatible type "str | None"; expected "str"  [arg-type]
-main.py:1583: error: Argument "azure_ad_token" to "completion" of "AzureTextCompletion" has incompatible type "Any | str | None"; expected "str"  [arg-type]
-proxy/hooks/parallel_request_limiter_v3.py:383: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get"  [union-attr]
-proxy/hooks/parallel_request_limiter_v3.py:384: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get"  [union-attr]
-proxy/hooks/parallel_request_limiter_v3.py:385: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get"  [union-attr]
-proxy/hooks/parallel_request_limiter_v3.py:386: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get"  [union-attr]
\ No newline at end of file
+types/llms/openai.py:46: error: Module "openai.types.responses.response_create_params" has no attribute "Text"  [attr-defined]
+router.py:3445: error: Need type annotation for "final_results"  [var-annotated]
+main.py:1581: error: Argument "api_base" to "completion" of "AzureTextCompletion" has incompatible type "str | None"; expected "str"  [arg-type]
+llms/gemini/google_genai/transformation.py:111: error: TypedDict key must be a string literal; expected one of ("http_options", "system_instruction", "temperature", "top_p", "top_k", ...)  [literal-required]
+llms/bedrock/chat/invoke_agent/transformation.py:392: error: Need type annotation for "pre_processing"  [var-annotated]
+llms/bedrock/chat/invoke_agent/transformation.py:396: error: Need type annotation for "model_output"  [var-annotated]
+llms/bedrock/chat/invoke_agent/transformation.py:400: error: Need type annotation for "metadata"  [var-annotated]
+llms/bedrock/chat/invoke_agent/transformation.py:415: error: Need type annotation for "orchestration_trace"  [var-annotated]
+llms/bedrock/chat/invoke_agent/transformation.py:419: error: Need type annotation for "model_invocation"  [var-annotated]
+llms/vertex_ai/google_genai/transformation.py:67: error: TypedDict key must be a string literal; expected one of ("http_options", "system_instruction", "temperature", "top_p", "top_k", ...)  [literal-required]
+proxy/spend_tracking/spend_tracking_utils.py:300: error: Need type annotation for "mcp_tool_call_metadata"  [var-annotated]
+proxy/spend_tracking/spend_tracking_utils.py:571: error: Need type annotation for "vector_store_search_response"  [var-annotated]
+proxy/pass_through_endpoints/pass_through_endpoints.py:499: error: Unsupported type "dict[str, Any]" for ** expansion in TypedDict  [typeddict-item]
+Found 13 errors in 8 files (checked 1114 source files)