fix: resolve CodeQL module-level cyclic import errors

Made-with: Cursor

litellm/integrations/arize/arize.py

@@ -10,7 +10,6 @@ from typing import TYPE_CHECKING, Any, Optional, Union
 
 from litellm.integrations.arize import _utils
 from litellm.integrations.arize._utils import ArizeOTELAttributes
-from litellm.integrations.opentelemetry import OpenTelemetry
 from litellm.types.integrations.arize import ArizeConfig
 from litellm.types.services import ServiceLoggerPayload
 from litellm.types.utils import StandardCallbackDynamicParams
@@ -18,13 +17,19 @@ from litellm.types.utils import StandardCallbackDynamicParams
 if TYPE_CHECKING:
     from opentelemetry.trace import Span as _Span
 
+    from litellm.integrations.opentelemetry import OpenTelemetry as _OpenTelemetry
     from litellm.types.integrations.arize import Protocol as _Protocol
 
     Protocol = _Protocol
     Span = Union[_Span, Any]
+    OpenTelemetry = _OpenTelemetry
 else:
     Protocol = Any
     Span = Any
+    try:
+        from litellm.integrations.opentelemetry import OpenTelemetry
+    except ImportError:
+        OpenTelemetry = None  # type: ignore
 
 
 class ArizeLogger(OpenTelemetry):

litellm/integrations/datadog/datadog.py

@@ -28,7 +28,6 @@ from litellm._logging import verbose_logger
 from litellm._uuid import uuid
 from litellm.integrations.custom_batch_logger import CustomBatchLogger
 from litellm.integrations.datadog.datadog_handler import (
-    get_datadog_base_url_from_env,
     get_datadog_hostname,
     get_datadog_service,
     get_datadog_source,
@@ -120,6 +119,10 @@ class DataDogLogger(
                 self._configure_dd_direct_api()
 
             # Optional override for testing
+            from litellm.integrations.datadog.datadog_handler import (
+                get_datadog_base_url_from_env,
+            )
+
             dd_base_url = get_datadog_base_url_from_env()
             if dd_base_url:
                 self.intake_url = f"{dd_base_url}/api/v2/logs"

litellm/integrations/datadog/datadog_llm_obs.py

@@ -19,7 +19,6 @@ from litellm._logging import verbose_logger
 from litellm._uuid import uuid
 from litellm.integrations.custom_batch_logger import CustomBatchLogger
 from litellm.integrations.datadog.datadog_handler import (
-    get_datadog_base_url_from_env,
     get_datadog_service,
     get_datadog_tags,
 )
@@ -80,6 +79,10 @@ class DataDogLLMObsLogger(CustomBatchLogger):
                 self._configure_dd_direct_api()
 
             # Optional override for testing
+            from litellm.integrations.datadog.datadog_handler import (
+                get_datadog_base_url_from_env,
+            )
+
             dd_base_url = get_datadog_base_url_from_env()
             if dd_base_url:
                 self.intake_url = f"{dd_base_url}/api/intake/llm-obs/v1/trace/spans"
@@ -341,9 +344,9 @@ class DataDogLLMObsLogger(CustomBatchLogger):
 
         if standard_logging_payload.get("status") == "failure":
             # Try to get structured error information first
-            error_information: Optional[
-                StandardLoggingPayloadErrorInformation
-            ] = standard_logging_payload.get("error_information")
+            error_information: Optional[StandardLoggingPayloadErrorInformation] = (
+                standard_logging_payload.get("error_information")
+            )
 
             if error_information:
                 error_info = DDLLMObsError(
@@ -613,9 +616,9 @@ class DataDogLLMObsLogger(CustomBatchLogger):
             latency_metrics["litellm_overhead_time_ms"] = litellm_overhead_ms
 
         # Guardrail overhead latency
-        guardrail_info: Optional[
-            list[StandardLoggingGuardrailInformation]
-        ] = standard_logging_payload.get("guardrail_information")
+        guardrail_info: Optional[list[StandardLoggingGuardrailInformation]] = (
+            standard_logging_payload.get("guardrail_information")
+        )
         if guardrail_info is not None:
             total_duration = 0.0
             for info in guardrail_info:
@@ -785,15 +788,15 @@ class DataDogLLMObsLogger(CustomBatchLogger):
                     if function_arguments:
                         # Store arguments as JSON string for Datadog
                         if isinstance(function_arguments, str):
-                            kv_pairs[
-                                f"tool_calls.{idx}.function.arguments"
-                            ] = function_arguments
+                            kv_pairs[f"tool_calls.{idx}.function.arguments"] = (
+                                function_arguments
+                            )
                         else:
                             import json
 
-                            kv_pairs[
-                                f"tool_calls.{idx}.function.arguments"
-                            ] = json.dumps(function_arguments)
+                            kv_pairs[f"tool_calls.{idx}.function.arguments"] = (
+                                json.dumps(function_arguments)
+                            )
             except (KeyError, TypeError, ValueError) as e:
                 verbose_logger.debug(
                     f"DataDogLLMObs: Error processing tool call {idx}: {str(e)}"

litellm/integrations/opentelemetry.py

@@ -1,7 +1,7 @@
 import os
-import types
 from dataclasses import dataclass
 from datetime import datetime
+from types import MethodType
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
 
 import litellm
@@ -2112,7 +2112,7 @@ class OpenTelemetry(CustomLogger):
         setattr(
             exporter,
             "export",
-            types.MethodType(_export_with_failure_tracking, exporter),
+            MethodType(_export_with_failure_tracking, exporter),
         )
         setattr(exporter, "_litellm_failure_tracking_wrapped", True)
         return exporter

litellm/llms/bedrock/chat/invoke_handler.py

@@ -70,7 +70,6 @@ from ..base_aws_llm import BaseAWSLLM
 from ..common_utils import (
     BedrockError,
     ModelResponseIterator,
-    apply_embedded_bedrock_region_from_model_path,
     get_bedrock_tool_name,
 )
 
@@ -204,11 +203,13 @@ async def make_call(
         if client is None:
             client = get_async_httpx_client(
                 llm_provider=litellm.LlmProviders.BEDROCK,
-                params={"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
-                if logging_obj
-                and logging_obj.litellm_params
-                and logging_obj.litellm_params.get("ssl_verify")
-                else None,
+                params=(
+                    {"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
+                    if logging_obj
+                    and logging_obj.litellm_params
+                    and logging_obj.litellm_params.get("ssl_verify")
+                    else None
+                ),
             )  # Create a new client if none provided
 
         response = await client.post(
@@ -298,11 +299,13 @@ def make_sync_call(
     try:
         if client is None:
             client = _get_httpx_client(
-                params={"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
-                if logging_obj
-                and logging_obj.litellm_params
-                and logging_obj.litellm_params.get("ssl_verify")
-                else None
+                params=(
+                    {"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
+                    if logging_obj
+                    and logging_obj.litellm_params
+                    and logging_obj.litellm_params.get("ssl_verify")
+                    else None
+                )
             )
 
         response = client.post(
@@ -552,9 +555,9 @@ class BedrockLLM(BaseAWSLLM):
                             content=None,
                         )
                         model_response.choices[0].message = _message  # type: ignore
-                        model_response._hidden_params[
-                            "original_response"
-                        ] = outputText  # allow user to access raw anthropic tool calling response
+                        model_response._hidden_params["original_response"] = (
+                            outputText  # allow user to access raw anthropic tool calling response
+                        )
                     if (
                         _is_function_call is True
                         and stream is not None
@@ -887,9 +890,9 @@ class BedrockLLM(BaseAWSLLM):
                     ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
                         inference_params[k] = v
                 if stream is True:
-                    inference_params[
-                        "stream"
-                    ] = True  # cohere requires stream = True in inference params
+                    inference_params["stream"] = (
+                        True  # cohere requires stream = True in inference params
+                    )
                 data = json.dumps({"prompt": prompt, **inference_params})
         elif provider == "anthropic":
             if self.is_claude_messages_api_model(model):
@@ -1284,6 +1287,8 @@ class BedrockLLM(BaseAWSLLM):
         else:
             modelId = model
 
+        from ..common_utils import apply_embedded_bedrock_region_from_model_path
+
         modelId = apply_embedded_bedrock_region_from_model_path(
             modelId, optional_params
         )

litellm/llms/openai/openai.py

@@ -36,7 +36,6 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
 from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
-from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
 from litellm.types.utils import (
     EmbeddingResponse,
     ImageResponse,
@@ -562,9 +561,9 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
                         kwargs_with_provider = (
                             litellm_params.copy() if litellm_params else {}
                         )
-                        kwargs_with_provider[
-                            "custom_llm_provider"
-                        ] = custom_llm_provider
+                        kwargs_with_provider["custom_llm_provider"] = (
+                            custom_llm_provider
+                        )
 
                         # For OpenAI Chat Completions, use the chat completion agentic loop method
                         agentic_response = (
@@ -596,6 +595,8 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
         model: str,
         stream_options: Optional[dict] = None,
     ) -> CustomStreamWrapper:
+        from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
+
         completion_stream = MockResponseIterator(model_response=response)
         streaming_response = CustomStreamWrapper(
             completion_stream=completion_stream,

litellm/llms/vertex_ai/gemini/transformation.py

@@ -3,6 +3,7 @@ Transformation logic from OpenAI format to Gemini format.
 
 Why separate file? Make it easy to see how transformation works
 """
+
 import json
 import os
 from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Union, cast
@@ -23,7 +24,6 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     response_schema_prompt,
 )
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
-from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels
 from litellm.types.files import (
     get_file_mime_type_for_file_type,
     get_file_type_from_extension,
@@ -713,6 +713,8 @@ def _transform_request_body(  # noqa: PLR0915
         config_fields = GenerationConfig.__annotations__.keys()
 
         # labels: optional explicit param and/or metadata.requester_metadata (OpenAI metadata)
+        from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels
+
         labels = pop_vertex_request_labels(optional_params, litellm_params)
 
         filtered_params = {

litellm/proxy/_experimental/mcp_server/rest_endpoints.py

@@ -9,7 +9,7 @@ from litellm.proxy._experimental.mcp_server.ui_session_utils import (
     build_effective_auth_contexts,
 )
 from litellm.proxy._experimental.mcp_server.utils import merge_mcp_headers
-from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
+from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.auth.ip_address_utils import IPAddressUtils
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.common_utils.http_parsing_utils import _safe_get_request_headers
@@ -1027,6 +1027,8 @@ if MCP_AVAILABLE:
         """
         Test if we can connect to the provided MCP server before adding it
         """
+        from litellm.proxy._types import LitellmUserRoles
+
         if LitellmUserRoles.PROXY_ADMIN != user_api_key_dict.user_role:
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,
@@ -1057,6 +1059,8 @@ if MCP_AVAILABLE:
         """
         Preview tools available from MCP server before adding it
         """
+        from litellm.proxy._types import LitellmUserRoles
+
         if LitellmUserRoles.PROXY_ADMIN != user_api_key_dict.user_role:
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,