From 2820a5195000400de582a6fef14772bcbc516c67 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 23 Jan 2026 19:40:15 +0530 Subject: [PATCH 01/70] Add otel providers and langfuse for litellm_callback_logging_failures_metric_total --- .../integrations/langfuse/langfuse_otel.py | 2 +- .../langfuse/langfuse_prompt_management.py | 92 ++++++++------ litellm/integrations/opentelemetry.py | 9 +- .../integrations/test_prometheus.py | 118 ++++++++++++++++++ 4 files changed, 180 insertions(+), 41 deletions(-) diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index 08493a0e8ec..aed9c1f7ae3 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -46,7 +46,7 @@ class LangfuseOtelLogger(OpenTelemetry): Sets OpenTelemetry span attributes for Langfuse observability. Uses the same attribute setting logic as Arize Phoenix for consistency. """ - + raise Exception("Error raised by LangfuseOtelLogger.set_langfuse_otel_attributes") _utils.set_attributes(span, kwargs, response_obj, LangfuseLLMObsOTELAttributes) ######################################################### diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py index 8f73eabad44..3986fc6a6ef 100644 --- a/litellm/integrations/langfuse/langfuse_prompt_management.py +++ b/litellm/integrations/langfuse/langfuse_prompt_management.py @@ -300,43 +300,59 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge ) async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - standard_callback_dynamic_params = kwargs.get( - "standard_callback_dynamic_params" - ) - langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( - globalLangfuseLogger=self, - standard_callback_dynamic_params=standard_callback_dynamic_params, - in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, - ) - langfuse_logger_to_use.log_event_on_langfuse( - kwargs=kwargs, - response_obj=response_obj, - start_time=start_time, - end_time=end_time, - user_id=kwargs.get("user", None), - ) + try: + standard_callback_dynamic_params = kwargs.get( + "standard_callback_dynamic_params" + ) + langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( + globalLangfuseLogger=self, + standard_callback_dynamic_params=standard_callback_dynamic_params, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + langfuse_logger_to_use.log_event_on_langfuse( + kwargs=kwargs, + response_obj=response_obj, + start_time=start_time, + end_time=end_time, + user_id=kwargs.get("user", None), + ) + except Exception as e: + from litellm._logging import verbose_logger + + verbose_logger.exception( + f"Langfuse Layer Error - Exception occurred while logging success event: {str(e)}" + ) + self.handle_callback_failure(callback_name="langfuse") async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - standard_callback_dynamic_params = kwargs.get( - "standard_callback_dynamic_params" - ) - langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( - globalLangfuseLogger=self, - standard_callback_dynamic_params=standard_callback_dynamic_params, - in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, - ) - standard_logging_object = cast( - Optional[StandardLoggingPayload], - kwargs.get("standard_logging_object", None), - ) - if standard_logging_object is None: - return - langfuse_logger_to_use.log_event_on_langfuse( - start_time=start_time, - end_time=end_time, - response_obj=None, - user_id=kwargs.get("user", None), - status_message=standard_logging_object["error_str"], - level="ERROR", - kwargs=kwargs, - ) + try: + standard_callback_dynamic_params = kwargs.get( + "standard_callback_dynamic_params" + ) + langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( + globalLangfuseLogger=self, + standard_callback_dynamic_params=standard_callback_dynamic_params, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + standard_logging_object = cast( + Optional[StandardLoggingPayload], + kwargs.get("standard_logging_object", None), + ) + if standard_logging_object is None: + return + langfuse_logger_to_use.log_event_on_langfuse( + start_time=start_time, + end_time=end_time, + response_obj=None, + user_id=kwargs.get("user", None), + status_message=standard_logging_object["error_str"], + level="ERROR", + kwargs=kwargs, + ) + except Exception as e: + from litellm._logging import verbose_logger + + verbose_logger.exception( + f"Langfuse Layer Error - Exception occurred while logging failure event: {str(e)}" + ) + self.handle_callback_failure(callback_name="langfuse") diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 93d631eb0f2..dbc1cb0d6b0 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -988,9 +988,13 @@ class OpenTelemetry(CustomLogger): from opentelemetry._logs import SeverityNumber, get_logger, get_logger_provider try: - from opentelemetry.sdk._logs import LogRecord as SdkLogRecord # type: ignore[attr-defined] # OTEL < 1.39.0 + from opentelemetry.sdk._logs import ( + LogRecord as SdkLogRecord, # type: ignore[attr-defined] # OTEL < 1.39.0 + ) except ImportError: - from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord # OTEL >= 1.39.0 + from opentelemetry.sdk._logs._internal import ( + LogRecord as SdkLogRecord, # OTEL >= 1.39.0 + ) otel_logger = get_logger(LITELLM_LOGGER_NAME) @@ -1605,6 +1609,7 @@ class OpenTelemetry(CustomLogger): ) except Exception as e: + self.handle_callback_failure(callback_name= self.callback_name) verbose_logger.exception( "OpenTelemetry logging error in set_attributes %s", str(e) ) diff --git a/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py b/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py index 53757390a44..4a08c208fda 100644 --- a/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py +++ b/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py @@ -937,6 +937,124 @@ def test_callback_failure_metric_different_callbacks(prometheus_logger): ) +@pytest.mark.asyncio +async def test_langfuse_callback_failure_metric(prometheus_logger): + """ + Test that Langfuse callback failures are properly tracked in Prometheus metrics. + + This test verifies that when Langfuse logging fails, the + litellm_callback_logging_failures_metric is incremented with callback_name="langfuse". + """ + from unittest.mock import MagicMock, patch + + from litellm.integrations.langfuse.langfuse_prompt_management import ( + LangfusePromptManagement, + ) + + # Get initial value + initial_value = 0 + try: + initial_value = prometheus_logger.litellm_callback_logging_failures_metric.labels( + callback_name="langfuse" + )._value.get() + except Exception: + initial_value = 0 + + # Create Langfuse logger with mocked initialization + with patch("litellm.integrations.langfuse.langfuse_prompt_management.langfuse_client_init"): + langfuse_logger = LangfusePromptManagement() + + # Mock the log_event_on_langfuse to raise an exception + with patch( + "litellm.integrations.langfuse.langfuse_prompt_management.LangFuseHandler.get_langfuse_logger_for_request" + ) as mock_get_logger: + mock_logger = MagicMock() + mock_logger.log_event_on_langfuse.side_effect = Exception("Langfuse API error") + mock_get_logger.return_value = mock_logger + + # Mock handle_callback_failure to track calls + with patch.object(prometheus_logger, "increment_callback_logging_failure") as mock_increment: + # Inject prometheus logger into the langfuse logger + langfuse_logger.handle_callback_failure = lambda callback_name: mock_increment( + callback_name=callback_name + ) + + # Call async_log_success_event - should catch exception and increment metric + await langfuse_logger.async_log_success_event( + kwargs={}, + response_obj={}, + start_time=None, + end_time=None, + ) + + # Verify that increment was called with correct callback name + mock_increment.assert_called_once_with(callback_name="langfuse") + + print("✓ Langfuse callback failure metric test passed") + + +@pytest.mark.asyncio +async def test_langfuse_otel_callback_failure_metric(prometheus_logger): + """ + Test that Langfuse OTEL callback failures are properly tracked in Prometheus metrics. + + This test verifies that when Langfuse OTEL logging fails, the + litellm_callback_logging_failures_metric is incremented with callback_name="langfuse_otel". + """ + from unittest.mock import MagicMock, patch + + from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger + + # Get initial value + initial_value = 0 + try: + initial_value = prometheus_logger.litellm_callback_logging_failures_metric.labels( + callback_name="langfuse_otel" + )._value.get() + except Exception: + initial_value = 0 + + # Create Langfuse OTEL logger with mocked initialization + with patch("litellm.integrations.opentelemetry.OpenTelemetry.__init__", return_value=None): + langfuse_otel_logger = LangfuseOtelLogger(callback_name="langfuse_otel") + langfuse_otel_logger.callback_name = "langfuse_otel" + + # Mock handle_callback_failure to track calls + with patch.object(prometheus_logger, "increment_callback_logging_failure") as mock_increment: + # Inject prometheus logger into the langfuse otel logger + langfuse_otel_logger.handle_callback_failure = lambda callback_name: mock_increment( + callback_name=callback_name + ) + + # Test that the OpenTelemetry base class set_attributes exception handler works + # This is where langfuse_otel failures are caught and tracked + with patch.object(langfuse_otel_logger, "set_attributes") as mock_set_attributes: + # Simulate the exception handling in set_attributes + def set_attributes_with_error(*args, **kwargs): + # This simulates what happens in the real set_attributes method + try: + raise Exception("Attribute error") + except Exception as e: + langfuse_otel_logger.handle_callback_failure(callback_name=langfuse_otel_logger.callback_name) + + mock_set_attributes.side_effect = set_attributes_with_error + + # Call set_attributes + try: + langfuse_otel_logger.set_attributes( + span=MagicMock(), + kwargs={}, + response_obj={} + ) + except Exception: + pass + + # Verify that increment was called with correct callback name + mock_increment.assert_called_with(callback_name="langfuse_otel") + + print("✓ Langfuse OTEL callback failure metric test passed") + + # ============================================================================== # END CALLBACK FAILURE METRICS TESTS # ============================================================================== From 11b4c456b47fca8d5654a763972c0630aa78262c Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 23 Jan 2026 19:42:59 +0530 Subject: [PATCH 02/70] Add documentation --- docs/my-website/docs/proxy/prometheus.md | 7 ++++++- litellm/integrations/langfuse/langfuse_otel.py | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index cd2b3b68f37..e69cfe114cc 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -130,7 +130,12 @@ Monitor failures while shipping logs to downstream callbacks like `s3_v3` cold s | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`. | +| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`, `langfuse`, or `langfuse_otel` and other otel providers | + +**Supported Callbacks:** +- `S3Logger` - S3 v2 cold storage failures +- `langfuse` - Langfuse logging failures +- `otel` - OpenTelemetry logging failures ## LLM Provider Metrics diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index aed9c1f7ae3..08493a0e8ec 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -46,7 +46,7 @@ class LangfuseOtelLogger(OpenTelemetry): Sets OpenTelemetry span attributes for Langfuse observability. Uses the same attribute setting logic as Arize Phoenix for consistency. """ - raise Exception("Error raised by LangfuseOtelLogger.set_langfuse_otel_attributes") + _utils.set_attributes(span, kwargs, response_obj, LangfuseLLMObsOTELAttributes) ######################################################### From e63537c6c1c266b1fb171dbd77190eb298702f7d Mon Sep 17 00:00:00 2001 From: Nikita Timofeev Date: Fri, 16 Jan 2026 16:11:14 +0000 Subject: [PATCH 03/70] Fix: ensure function content is valid JSON for GigaChat --- litellm/llms/gigachat/chat/transformation.py | 56 +++++++--- tests/llm_translation/test_gigachat.py | 108 +++++++++++++------ 2 files changed, 116 insertions(+), 48 deletions(-) diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py index 90cf67da6b2..ba14de1f65d 100644 --- a/litellm/llms/gigachat/chat/transformation.py +++ b/litellm/llms/gigachat/chat/transformation.py @@ -31,6 +31,16 @@ else: GIGACHAT_BASE_URL = "https://gigachat.devices.sberbank.ru/api/v1" +def is_valid_json(value: str) -> bool: + """Checks whether the value passed is a valid serialized JSON string""" + try: + json.loads(value) + except json.JSONDecodeError: + return False + else: + return True + + class GigaChatError(BaseLLMException): """GigaChat API error.""" @@ -101,7 +111,11 @@ class GigaChatConfig(BaseConfig): Set up headers with OAuth token. """ # Get access token - credentials = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY") + credentials = ( + api_key + or get_secret_str("GIGACHAT_CREDENTIALS") + or get_secret_str("GIGACHAT_API_KEY") + ) access_token = get_access_token(credentials=credentials) # Store credentials for image uploads @@ -193,11 +207,13 @@ class GigaChatConfig(BaseConfig): for tool in tools: if tool.get("type") == "function": func = tool.get("function", {}) - functions.append({ - "name": func.get("name", ""), - "description": func.get("description", ""), - "parameters": func.get("parameters", {}), - }) + functions.append( + { + "name": func.get("name", ""), + "description": func.get("description", ""), + "parameters": func.get("parameters", {}), + } + ) return functions def _map_tool_choice( @@ -281,8 +297,14 @@ class GigaChatConfig(BaseConfig): } # Add optional params - for key in ["temperature", "top_p", "max_tokens", "stream", - "repetition_penalty", "profanity_check"]: + for key in [ + "temperature", + "top_p", + "max_tokens", + "stream", + "repetition_penalty", + "profanity_check", + ]: if key in optional_params: request_data[key] = optional_params[key] @@ -314,7 +336,7 @@ class GigaChatConfig(BaseConfig): elif role == "tool": message["role"] = "function" content = message.get("content", "") - if not isinstance(content, str): + if not isinstance(content, str) or not is_valid_json(content): message["content"] = json.dumps(content, ensure_ascii=False) # Handle None content @@ -441,14 +463,16 @@ class GigaChatConfig(BaseConfig): # Convert to tool_calls format if isinstance(args, dict): args = json.dumps(args, ensure_ascii=False) - message_data["tool_calls"] = [{ - "id": f"call_{uuid.uuid4().hex[:24]}", - "type": "function", - "function": { - "name": func_call.get("name", ""), - "arguments": args, + message_data["tool_calls"] = [ + { + "id": f"call_{uuid.uuid4().hex[:24]}", + "type": "function", + "function": { + "name": func_call.get("name", ""), + "arguments": args, + }, } - }] + ] message_data.pop("function_call", None) finish_reason = "tool_calls" diff --git a/tests/llm_translation/test_gigachat.py b/tests/llm_translation/test_gigachat.py index dd8d56ff54a..b69a5428e42 100644 --- a/tests/llm_translation/test_gigachat.py +++ b/tests/llm_translation/test_gigachat.py @@ -5,9 +5,7 @@ Tests message transformation, parameter handling, and response transformation. Run with: pytest tests/llm_translation/test_gigachat.py -v """ -import json import pytest -from unittest.mock import Mock, MagicMock class TestGigaChatMessageTransformation: @@ -16,6 +14,7 @@ class TestGigaChatMessageTransformation: @pytest.fixture def config(self): from litellm.llms.gigachat.chat.transformation import GigaChatConfig + return GigaChatConfig() def test_simple_user_message(self, config): @@ -52,20 +51,46 @@ class TestGigaChatMessageTransformation: assert result[0]["role"] == "function" + def test_tool_content_convertation_non_string_value(self, config): + """Non string tool content should be serialized""" + messages = [{"role": "tool", "content": {"output": 42}}] + result = config._transform_messages(messages) + + assert result[0]["content"] == '{"output": 42}' + + def test_tool_content_convertation_json_string_value(self, config): + """JSON string tool content left unchanged""" + valid_json = '{"output": "red car"}' + messages = [{"role": "tool", "content": valid_json}] + result = config._transform_messages(messages) + + assert result[0]["content"] == valid_json + + def test_tool_content_convertation_random_string_value(self, config): + """Non JSON tool content should be serialized""" + messages = [{"role": "tool", "content": "random string"}] + result = config._transform_messages(messages) + + assert result[0]["content"] == '"random string"' + def test_tool_calls_to_function_call(self, config): """tool_calls should be converted to function_call""" - messages = [{ - "role": "assistant", - "content": "", - "tool_calls": [{ - "id": "call_123", - "type": "function", - "function": { - "name": "get_weather", - "arguments": '{"city": "Moscow"}' - } - }] - }] + messages = [ + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_123", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"city": "Moscow"}', + }, + } + ], + } + ] result = config._transform_messages(messages) assert "function_call" in result[0] @@ -94,6 +119,7 @@ class TestGigaChatCollapseUserMessages: @pytest.fixture def config(self): from litellm.llms.gigachat.chat.transformation import GigaChatConfig + return GigaChatConfig() def test_no_collapse_single_message(self, config): @@ -136,23 +162,24 @@ class TestGigaChatToolsTransformation: @pytest.fixture def config(self): from litellm.llms.gigachat.chat.transformation import GigaChatConfig + return GigaChatConfig() def test_single_tool_conversion(self, config): """Single tool should be converted correctly""" - tools = [{ - "type": "function", - "function": { - "name": "get_weather", - "description": "Get weather for a city", - "parameters": { - "type": "object", - "properties": { - "city": {"type": "string"} - } - } + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather for a city", + "parameters": { + "type": "object", + "properties": {"city": {"type": "string"}}, + }, + }, } - }] + ] result = config._convert_tools_to_functions(tools) assert len(result) == 1 @@ -162,8 +189,22 @@ class TestGigaChatToolsTransformation: def test_multiple_tools_conversion(self, config): """Multiple tools should all be converted""" tools = [ - {"type": "function", "function": {"name": "func1", "description": "First", "parameters": {"type": "object", "properties": {}}}}, - {"type": "function", "function": {"name": "func2", "description": "Second", "parameters": {"type": "object", "properties": {}}}}, + { + "type": "function", + "function": { + "name": "func1", + "description": "First", + "parameters": {"type": "object", "properties": {}}, + }, + }, + { + "type": "function", + "function": { + "name": "func2", + "description": "Second", + "parameters": {"type": "object", "properties": {}}, + }, + }, ] result = config._convert_tools_to_functions(tools) @@ -178,6 +219,7 @@ class TestGigaChatParamsTransformation: @pytest.fixture def config(self): from litellm.llms.gigachat.chat.transformation import GigaChatConfig + return GigaChatConfig() def test_temperature_zero_becomes_top_p_zero(self, config): @@ -229,10 +271,10 @@ class TestGigaChatParamsTransformation: "type": "object", "properties": { "name": {"type": "string"}, - "age": {"type": "integer"} - } - } - } + "age": {"type": "integer"}, + }, + }, + }, } } result = config.map_openai_params( @@ -283,6 +325,7 @@ class TestGigaChatTransformRequest: @pytest.fixture def config(self): from litellm.llms.gigachat.chat.transformation import GigaChatConfig + return GigaChatConfig() def test_basic_request(self, config): @@ -335,6 +378,7 @@ class TestGigaChatSupportedParams: @pytest.fixture def config(self): from litellm.llms.gigachat.chat.transformation import GigaChatConfig + return GigaChatConfig() def test_supported_params(self, config): From 1ac32992dec19bef4a35f9c403690085843ec04a Mon Sep 17 00:00:00 2001 From: Chesars Date: Fri, 23 Jan 2026 15:25:40 -0300 Subject: [PATCH 04/70] fix(oci): serialize imageUrl as object for OCI GenAI API OCI GenAI expects imageUrl to be an object with a 'url' property, not a plain string. This was causing 400 errors when sending images. Fixes #19589 --- litellm/llms/oci/chat/transformation.py | 3 +- litellm/types/llms/oci.py | 9 ++++- .../oci/chat/test_oci_chat_transformation.py | 37 ++++++++++++++++++- 3 files changed, 45 insertions(+), 4 deletions(-) diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 7af7be2094a..84f39ef2525 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -32,6 +32,7 @@ from litellm.types.llms.oci import ( OCICompletionResponse, OCIContentPartUnion, OCIImageContentPart, + OCIImageUrl, OCIMessage, OCIRoles, OCIServingMode, @@ -1129,7 +1130,7 @@ def adapt_messages_to_generic_oci_standard_content_message( image_url = image_url.get("url") if not isinstance(image_url, str): raise Exception("Prop `image_url` must be a string or an object with a `url` property") - new_content.append(OCIImageContentPart(imageUrl=image_url)) + new_content.append(OCIImageContentPart(imageUrl=OCIImageUrl(url=image_url))) return OCIMessage( role=open_ai_to_generic_oci_role_map[role], diff --git a/litellm/types/llms/oci.py b/litellm/types/llms/oci.py index b9a82cc8b73..9a654bc0f6c 100644 --- a/litellm/types/llms/oci.py +++ b/litellm/types/llms/oci.py @@ -35,11 +35,18 @@ class OCITextContentPart(OCIContentPart): text: str +class OCIImageUrl(BaseModel): + """ImageUrl object for OCI API. See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/generative_ai_inference/models/oci.generative_ai_inference.models.ImageUrl.html""" + + url: str + detail: Optional[Literal["AUTO", "HIGH", "LOW"]] = None + + class OCIImageContentPart(OCIContentPart): """Image content part for the OCI API.""" type: Literal["IMAGE"] = "IMAGE" - imageUrl: str + imageUrl: OCIImageUrl OCIContentPartUnion = Union[OCITextContentPart, OCIImageContentPart] diff --git a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py index f706a025a09..0a6c59d1b44 100644 --- a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py +++ b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py @@ -203,6 +203,7 @@ class TestOCIImageUrlTransformation: """Tests for OCI image_url format handling in multimodal messages. Fixes: https://github.com/BerriAI/litellm/issues/18270 + Fixes: https://github.com/BerriAI/litellm/issues/19589 """ def test_image_url_as_string(self): @@ -224,7 +225,8 @@ class TestOCIImageUrlTransformation: assert len(result) == 1 assert result[0].role == "USER" assert len(result[0].content) == 2 - assert result[0].content[1].imageUrl == "https://example.com/image.png" + # imageUrl is now an OCIImageUrl object with a 'url' property + assert result[0].content[1].imageUrl.url == "https://example.com/image.png" def test_image_url_as_openai_object(self): """Test that image_url as OpenAI-style object {"url": "..."} works.""" @@ -245,7 +247,38 @@ class TestOCIImageUrlTransformation: assert len(result) == 1 assert result[0].role == "USER" assert len(result[0].content) == 2 - assert result[0].content[1].imageUrl == "https://example.com/image.png" + # imageUrl is now an OCIImageUrl object with a 'url' property + assert result[0].content[1].imageUrl.url == "https://example.com/image.png" + + def test_image_url_serializes_as_object(self): + """Test that imageUrl serializes as {"url": "..."} for OCI API. + + Fixes: https://github.com/BerriAI/litellm/issues/19589 + OCI expects imageUrl to be an object with a 'url' property, not a plain string. + """ + from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this image."}, + {"type": "image_url", "image_url": {"url": "data:image/png;base64,ABC123"}}, + ], + } + ] + + result = adapt_messages_to_generic_oci_standard(messages) + image_part = result[0].content[1] + + # Serialize as OCI would receive it (with exclude_none=True) + serialized = image_part.model_dump(exclude_none=True) + + # Verify the structure matches OCI's expected format + assert serialized == { + "type": "IMAGE", + "imageUrl": {"url": "data:image/png;base64,ABC123"} + } def test_image_url_invalid_type_raises_error(self): """Test that invalid image_url type raises an error.""" From caa2c57619ad658c16af79706b7eaf970ef1b3e9 Mon Sep 17 00:00:00 2001 From: Ephrim Stanley Date: Mon, 26 Jan 2026 10:36:43 -0500 Subject: [PATCH 05/70] Fix /batches to return encoded ids (from managed objects table) --- .../proxy/hooks/managed_files.py | 87 +++++++ litellm/proxy/batches_endpoints/endpoints.py | 22 +- .../proxy/hooks/test_managed_files.py | 218 ++++++++++++++++++ 3 files changed, 322 insertions(+), 5 deletions(-) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 445d2b242b4..776706bdd1b 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -244,6 +244,93 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return managed_object.created_by == user_id return True # don't raise error if managed object is not found + async def list_user_batches( + self, + user_api_key_dict: UserAPIKeyAuth, + limit: Optional[int] = None, + after: Optional[str] = None, + provider: Optional[str] = None, + target_model_names: Optional[str] = None, + ) -> Dict[str, Any]: + # Provider filtering is not supported for managed batches + # This is because the encoded object ids stored in the managed objects table do not contain the provider information + # To support provider filtering, we would need to store the provider information in the encoded object ids + if provider: + raise Exception( + "Filtering by 'provider' is not supported when using managed batches. " + "Use 'target_model_names' to filter by specific model names instead." + ) + + where_clause: Dict[str, Any] = {"file_purpose": "batch"} + + # Filter by user who created the batch + if user_api_key_dict.user_id: + where_clause["created_by"] = user_api_key_dict.user_id + + if after: + where_clause["id"] = {"gt": after} + + # Fetch more than needed to allow for post-fetch filtering + fetch_limit = limit or 20 + if target_model_names: + # Fetch extra to account for filtering + fetch_limit = max(fetch_limit * 3, 100) + + batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + where=where_clause, + take=fetch_limit, + order={"created_at": "desc"}, + ) + + # Parse target_model_names filter + target_models_filter: List[str] = [] + if target_model_names: + target_models_filter = [m.strip() for m in target_model_names.split(",") if m.strip()] + + batch_objects: List[LiteLLMBatch] = [] + for batch in batches: + try: + # Stop once we have enough after filtering + if len(batch_objects) >= (limit or 20): + break + + batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object + batch_obj = LiteLLMBatch(**batch_data) + batch_obj.id = batch.unified_object_id + + # If no target_model_names filter, add the batch to the list + if not target_models_filter: + batch_objects.append(batch_obj) + continue + + # Filter by target_model_names + decoded_id = _is_base64_encoded_unified_file_id(batch.unified_object_id) + model_id = None + if decoded_id: + model_id = get_model_id_from_unified_batch_id(decoded_id) + + # Skip batches without decodable IDs if filtering is requested + if not model_id: + continue + + if any(target.lower() in model_id.lower() for target in target_models_filter): + batch_objects.append(batch_obj) + continue + + except Exception as e: + verbose_logger.warning( + f"Failed to parse batch object {batch.unified_object_id}: {e}" + ) + continue + + return { + "object": "list", + "data": batch_objects, + "first_id": batch_objects[0].id if batch_objects else None, + "last_id": batch_objects[-1].id if batch_objects else None, + "has_more": len(batch_objects) == (limit or 20), + } + async def get_user_created_file_ids( self, user_api_key_dict: UserAPIKeyAuth, model_object_ids: List[str] ) -> List[OpenAIFileObject]: diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py index 086105042e8..078e21f9bb4 100644 --- a/litellm/proxy/batches_endpoints/endpoints.py +++ b/litellm/proxy/batches_endpoints/endpoints.py @@ -542,14 +542,26 @@ async def list_batches( route_type="alist_batches", ) - model_param = ( + # Try to use managed objects table for listing batches (returns encoded IDs) + managed_files_obj = proxy_logging_obj.get_proxy_hook("managed_files") + if managed_files_obj is not None and hasattr(managed_files_obj, "list_user_batches"): + verbose_proxy_logger.debug( + "Using managed objects table for batch listing" + ) + response = await managed_files_obj.list_user_batches( + user_api_key_dict=user_api_key_dict, + limit=limit, + after=after, + provider=provider, + target_model_names=target_model_names, + llm_router=llm_router, + ) + elif (model_param := ( data.get("model") or request.query_params.get("model") or request.headers.get("x-litellm-model") - ) - - # SCENARIO 2: Use model-based routing from header/query/body - if model_param: + )): + # SCENARIO 2: Use model-based routing from header/query/body credentials = get_credentials_for_model( llm_router=llm_router, model_id=model_param, 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 9a6e153a22b..e70e0640aa6 100644 --- a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py +++ b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py @@ -827,3 +827,221 @@ async def test_afile_retrieve_raises_error_for_non_managed_file(): ) assert "not found" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_list_batches_from_managed_objects_table(): + from litellm.proxy._types import UserAPIKeyAuth + from openai.types.batch import BatchRequestCounts + + prisma_client = AsyncMock() + + batch_record_1 = MagicMock() + batch_record_1.unified_object_id = "unified-batch-id-1" + batch_record_1.file_object = json.dumps({ + "id": "batch_abc123", + "object": "batch", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "status": "completed", + "created_at": 1234567890, + "input_file_id": "file-input-1", + "request_counts": {"total": 1, "completed": 1, "failed": 0}, + }) + + batch_record_2 = MagicMock() + batch_record_2.unified_object_id = "unified-batch-id-2" + batch_record_2.file_object = json.dumps({ + "id": "batch_xyz789", + "object": "batch", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "status": "in_progress", + "created_at": 1234567891, + "input_file_id": "file-input-2", + "request_counts": {"total": 5, "completed": 2, "failed": 0}, + }) + + prisma_client.db.litellm_managedobjecttable.find_many.return_value = [ + batch_record_1, + batch_record_2, + ] + + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + result = await proxy_managed_files.list_user_batches( + user_api_key_dict=UserAPIKeyAuth(user_id="test-user"), + limit=10, + ) + + assert result["object"] == "list" + assert len(result["data"]) == 2 + assert result["data"][0].id == "unified-batch-id-1" + assert result["data"][1].id == "unified-batch-id-2" + assert result["first_id"] == "unified-batch-id-1" + assert result["last_id"] == "unified-batch-id-2" + + # Should filter by user_id (created_by) + prisma_client.db.litellm_managedobjecttable.find_many.assert_called_once_with( + where={"file_purpose": "batch", "created_by": "test-user"}, + take=10, + order={"created_at": "desc"}, + ) + + +@pytest.mark.asyncio +async def test_list_batches_from_managed_objects_table_empty_list(): + from litellm.proxy._types import UserAPIKeyAuth + + prisma_client = AsyncMock() + prisma_client.db.litellm_managedobjecttable.find_many.return_value = [] + + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + result = await proxy_managed_files.list_user_batches( + user_api_key_dict=UserAPIKeyAuth(user_id="test-user"), + ) + + assert result["object"] == "list" + assert len(result["data"]) == 0 + assert result["first_id"] is None + assert result["last_id"] is None + assert result["has_more"] is False + + # Verify where clause includes created_by filter + # Default take is 20 when no limit is provided + prisma_client.db.litellm_managedobjecttable.find_many.assert_called_once_with( + where={"file_purpose": "batch", "created_by": "test-user"}, + take=20, + order={"created_at": "desc"}, + ) + + +def _create_unified_batch_id(model_id: str, batch_id: str) -> str: + import base64 + unified_str = f"litellm_proxy;model_id:{model_id};llm_batch_id:{batch_id}" + return base64.urlsafe_b64encode(unified_str.encode()).decode().rstrip("=") + + +@pytest.mark.asyncio +async def test_list_batches_from_managed_objects_table_provider_filter_raises_exception(): + from litellm.proxy._types import UserAPIKeyAuth + + prisma_client = AsyncMock() + + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + # Filtering by provider should raise Exception + with pytest.raises(Exception) as exc_info: + await proxy_managed_files.list_user_batches( + user_api_key_dict=UserAPIKeyAuth(user_id="test-user"), + limit=10, + provider="openai", + ) + + assert str(exc_info.value) == ( + "Filtering by 'provider' is not supported when using managed batches." + ) + + # Verify find_many was NOT called since exception is raised before database query + prisma_client.db.litellm_managedobjecttable.find_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_list_batches_from_managed_objects_table_target_model_name_filter_raises_exception(): + from litellm.proxy._types import UserAPIKeyAuth + + prisma_client = AsyncMock() + + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + # Filtering by provider should raise Exception + with pytest.raises(Exception) as exc_info: + await proxy_managed_files.list_user_batches( + user_api_key_dict=UserAPIKeyAuth(user_id="test-user"), + limit=10, + target_model_names="gpt-4o,gpt-3.5", + ) + + assert str(exc_info.value) == ( + "Filtering by 'target_model_names' is not supported when using managed batches." + ) + + # Verify find_many was NOT called since exception is raised before database query + prisma_client.db.litellm_managedobjecttable.find_many.assert_not_called() + +@pytest.mark.asyncio +async def test_list_batches_from_managed_objects_table_filters_by_created_by(): + from litellm.proxy._types import UserAPIKeyAuth + + prisma_client = AsyncMock() + + # Create batch for user1 + batch_user1 = MagicMock() + batch_user1.unified_object_id = "unified-batch-user1" + batch_user1.file_object = json.dumps({ + "id": "batch_user1_abc", + "object": "batch", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "status": "completed", + "created_at": 1234567890, + "input_file_id": "file-input-user1", + "request_counts": {"total": 1, "completed": 1, "failed": 0}, + }) + + # Create batch for user2 + batch_user2 = MagicMock() + batch_user2.unified_object_id = "unified-batch-user2" + batch_user2.file_object = json.dumps({ + "id": "batch_user2_xyz", + "object": "batch", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "status": "completed", + "created_at": 1234567891, + "input_file_id": "file-input-user2", + "request_counts": {"total": 2, "completed": 2, "failed": 0}, + }) + + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + # Query with user1's API key - should only return user1's batch + prisma_client.db.litellm_managedobjecttable.find_many.return_value = [batch_user1] + result_user1 = await proxy_managed_files.list_user_batches( + user_api_key_dict=UserAPIKeyAuth(user_id="user1"), + limit=10, + ) + + assert len(result_user1["data"]) == 1 + assert result_user1["data"][0].id == "unified-batch-user1" + prisma_client.db.litellm_managedobjecttable.find_many.assert_called_with( + where={"file_purpose": "batch", "created_by": "user1"}, + take=10, + order={"created_at": "desc"}, + ) + + # Query with user2's API key - should only return user2's batch + prisma_client.db.litellm_managedobjecttable.find_many.return_value = [batch_user2] + result_user2 = await proxy_managed_files.list_user_batches( + user_api_key_dict=UserAPIKeyAuth(user_id="user2"), + limit=10, + ) + + assert len(result_user2["data"]) == 1 + assert result_user2["data"][0].id == "unified-batch-user2" + prisma_client.db.litellm_managedobjecttable.find_many.assert_called_with( + where={"file_purpose": "batch", "created_by": "user2"}, + take=10, + order={"created_at": "desc"}, + ) \ No newline at end of file From e382351d4a9ce8979df53234ae9af823acd0d56b Mon Sep 17 00:00:00 2001 From: Chesars Date: Mon, 26 Jan 2026 13:49:55 -0300 Subject: [PATCH 06/70] feat(hosted_vllm): support thinking parameter for /v1/messages endpoint Adds support for Anthropic-style 'thinking' parameter in hosted_vllm, converting it to OpenAI-style 'reasoning_effort' since vLLM is OpenAI-compatible. This enables users to use Claude Code CLI with hosted vLLM models like GLM-4.6/4.7 through the /v1/messages endpoint. Mapping (same as Anthropic adapter): - budget_tokens >= 10000 -> "high" - budget_tokens >= 5000 -> "medium" - budget_tokens >= 2000 -> "low" - budget_tokens < 2000 -> "minimal" Fixes #19761 --- .../llms/hosted_vllm/chat/transformation.py | 23 ++++++++- .../test_hosted_vllm_chat_transformation.py | 47 +++++++++++++++++++ 2 files changed, 69 insertions(+), 1 deletion(-) diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index 1d21490ea31..e955800b947 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -23,7 +23,7 @@ from ...openai.chat.gpt_transformation import OpenAIGPTConfig class HostedVLLMChatConfig(OpenAIGPTConfig): def get_supported_openai_params(self, model: str) -> List[str]: params = super().get_supported_openai_params(model) - params.append("reasoning_effort") + params.extend(["reasoning_effort", "thinking"]) return params def map_openai_params( @@ -41,6 +41,27 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): _tools = _remove_strict_from_schema(_tools) if _tools is not None: non_default_params["tools"] = _tools + + # Handle thinking parameter - convert Anthropic-style to OpenAI-style reasoning_effort + # vLLM is OpenAI-compatible, so it understands reasoning_effort, not thinking + # Reference: https://github.com/BerriAI/litellm/issues/19761 + thinking = non_default_params.pop("thinking", None) + if thinking is not None and isinstance(thinking, dict): + if thinking.get("type") == "enabled": + # Only convert if reasoning_effort not already set + if "reasoning_effort" not in non_default_params: + budget_tokens = thinking.get("budget_tokens", 0) + # Map budget_tokens to reasoning_effort level + # Same logic as Anthropic adapter (translate_anthropic_thinking_to_reasoning_effort) + if budget_tokens >= 10000: + non_default_params["reasoning_effort"] = "high" + elif budget_tokens >= 5000: + non_default_params["reasoning_effort"] = "medium" + elif budget_tokens >= 2000: + non_default_params["reasoning_effort"] = "low" + else: + non_default_params["reasoning_effort"] = "minimal" + return super().map_openai_params( non_default_params, optional_params, model, drop_params ) diff --git a/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py b/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py index 3749a5a8ca4..9b3b6aeaea1 100644 --- a/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py +++ b/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py @@ -101,3 +101,50 @@ def test_hosted_vllm_supports_reasoning_effort(): drop_params=False, ) assert optional_params["reasoning_effort"] == "high" + + +def test_hosted_vllm_supports_thinking(): + """ + Test that hosted_vllm supports the 'thinking' parameter. + + Anthropic-style thinking is converted to OpenAI-style reasoning_effort + since vLLM is OpenAI-compatible. + + Related issue: https://github.com/BerriAI/litellm/issues/19761 + """ + config = HostedVLLMChatConfig() + supported_params = config.get_supported_openai_params( + model="hosted_vllm/GLM-4.6-FP8" + ) + assert "thinking" in supported_params + + # Test thinking with low budget_tokens -> "minimal" (for < 2000) + optional_params = config.map_openai_params( + non_default_params={"thinking": {"type": "enabled", "budget_tokens": 1024}}, + optional_params={}, + model="hosted_vllm/GLM-4.6-FP8", + drop_params=False, + ) + assert "thinking" not in optional_params # thinking should NOT be passed + assert optional_params["reasoning_effort"] == "minimal" + + # Test thinking with high budget_tokens -> "high" + optional_params = config.map_openai_params( + non_default_params={"thinking": {"type": "enabled", "budget_tokens": 15000}}, + optional_params={}, + model="hosted_vllm/GLM-4.6-FP8", + drop_params=False, + ) + assert optional_params["reasoning_effort"] == "high" + + # Test that existing reasoning_effort is not overwritten + optional_params = config.map_openai_params( + non_default_params={ + "thinking": {"type": "enabled", "budget_tokens": 15000}, + "reasoning_effort": "low", + }, + optional_params={}, + model="hosted_vllm/GLM-4.6-FP8", + drop_params=False, + ) + assert optional_params["reasoning_effort"] == "low" From 88280d9cca4b7fbd28fa25cf24881a639d2990af Mon Sep 17 00:00:00 2001 From: Ephrim Stanley Date: Mon, 26 Jan 2026 12:02:35 -0500 Subject: [PATCH 07/70] Fix batch creation to return the input file's expires_at attribute --- .../proxy/hooks/managed_files.py | 40 ++++++----------- .../proxy/hooks/test_managed_files.py | 43 ++++++++++++++++++- 2 files changed, 55 insertions(+), 28 deletions(-) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 776706bdd1b..dd0613f87ff 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -251,14 +251,22 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): after: Optional[str] = None, provider: Optional[str] = None, target_model_names: Optional[str] = None, + llm_router: Optional[Router] = None, ) -> Dict[str, Any]: # Provider filtering is not supported for managed batches # This is because the encoded object ids stored in the managed objects table do not contain the provider information # To support provider filtering, we would need to store the provider information in the encoded object ids if provider: raise Exception( - "Filtering by 'provider' is not supported when using managed batches. " - "Use 'target_model_names' to filter by specific model names instead." + "Filtering by 'provider' is not supported when using managed batches." + ) + + # Model name filtering is not supported for managed batches + # This is because the encoded object ids stored in the managed objects table do not contain the model name + # A hash of the model name + litellm_params for the model name is encoded as the model id. This is not sufficient to reliably map the target model names to the model ids. + if target_model_names: + raise Exception( + "Filtering by 'target_model_names' is not supported when using managed batches." ) where_clause: Dict[str, Any] = {"file_purpose": "batch"} @@ -281,12 +289,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): take=fetch_limit, order={"created_at": "desc"}, ) - - # Parse target_model_names filter - target_models_filter: List[str] = [] - if target_model_names: - target_models_filter = [m.strip() for m in target_model_names.split(",") if m.strip()] - + batch_objects: List[LiteLLMBatch] = [] for batch in batches: try: @@ -297,26 +300,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object batch_obj = LiteLLMBatch(**batch_data) batch_obj.id = batch.unified_object_id + batch_objects.append(batch_obj) - # If no target_model_names filter, add the batch to the list - if not target_models_filter: - batch_objects.append(batch_obj) - continue - - # Filter by target_model_names - decoded_id = _is_base64_encoded_unified_file_id(batch.unified_object_id) - model_id = None - if decoded_id: - model_id = get_model_id_from_unified_batch_id(decoded_id) - - # Skip batches without decodable IDs if filtering is requested - if not model_id: - continue - - if any(target.lower() in model_id.lower() for target in target_models_filter): - batch_objects.append(batch_obj) - continue - except Exception as e: verbose_logger.warning( f"Failed to parse batch object {batch.unified_object_id}: {e}" @@ -760,6 +745,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): bytes=file_objects[0].bytes, filename=file_objects[0].filename, status="uploaded", + expires_at=file_objects[0].expires_at, ) return response 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 e70e0640aa6..4fa16066e4b 100644 --- a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py +++ b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py @@ -1044,4 +1044,45 @@ async def test_list_batches_from_managed_objects_table_filters_by_created_by(): where={"file_purpose": "batch", "created_by": "user2"}, take=10, order={"created_at": "desc"}, - ) \ No newline at end of file + ) + + +@pytest.mark.asyncio +async def test_return_unified_file_id_includes_expires_at(): + from litellm.types.llms.openai import OpenAIFileObject + + # Create a mock file object with expires_at set + file_object = OpenAIFileObject( + id="file-abc123", + object="file", + bytes=1234, + created_at=1234567890, + filename="test.jsonl", + purpose="batch", + status="uploaded", + expires_at=1234657890, + ) + file_object._hidden_params = {"model_id": "test-model-id"} + + create_file_request = { + "file": ("test.jsonl", b"test content", "application/jsonl"), + "purpose": "batch", + } + + internal_usage_cache = MagicMock() + + result = await _PROXY_LiteLLMManagedFiles.return_unified_file_id( + file_objects=[file_object], + create_file_request=create_file_request, + internal_usage_cache=internal_usage_cache, + litellm_parent_otel_span=None, + target_model_names_list=["gpt-4o"], + ) + + # Verify expires_at is passed through + assert result.expires_at == 1234657890 + assert result.purpose == "batch" + assert result.filename == "test.jsonl" + assert result.bytes == 1234 + assert result.created_at == 1234567890 + assert _is_base64_encoded_unified_file_id(result.id) \ No newline at end of file From 6a54dcfa934671e0b9d6698a80cc5dfb8c6ccf2b Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Mon, 26 Jan 2026 20:32:08 -0800 Subject: [PATCH 08/70] feat: Add model_id label to Prometheus metrics (#18048) (#19678) Co-authored-by: Cursor Agent --- docs/my-website/docs/proxy/prometheus.md | 8 ++--- litellm/integrations/prometheus.py | 12 ++++--- litellm/types/integrations/prometheus.py | 7 ++++ .../test_prometheus_logging_callbacks.py | 14 +++++++- .../integrations/test_prometheus_labels.py | 35 +++++++++++++------ 5 files changed, 55 insertions(+), 21 deletions(-) diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index cd2b3b68f37..d5a3466a84c 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -121,8 +121,8 @@ Use this to track overall LiteLLM Proxy usage. | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class", "route"` | -| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route"` | +| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "user_email", "exception_status", "exception_class", "route", "model_id"` | +| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route", "model_id"` | ### Callback Logging Metrics @@ -191,10 +191,10 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_request_total_latency_metric` | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" | +| `litellm_request_total_latency_metric` | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model", "model_id" | | `litellm_overhead_latency_metric` | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias" | | `litellm_llm_api_latency_metric` | Latency (seconds) for just the LLM API call - tracked for labels "model", "hashed_api_key", "api_key_alias", "team", "team_alias", "requested_model", "end_user", "user" | -| `litellm_llm_api_time_to_first_token_metric` | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias` [Note: only emitted for streaming requests] | +| `litellm_llm_api_time_to_first_token_metric` | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias`, `requested_model`, `end_user`, `user`, `model_id` [Note: only emitted for streaming requests] | ## Tracking `end_user` on Prometheus diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index bafb0d88c82..8710df05b4c 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -1296,12 +1296,14 @@ class PrometheusLogger(CustomLogger): time_to_first_token_seconds is not None and kwargs.get("stream", False) is True # only emit for streaming requests ): + _ttft_labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_llm_api_time_to_first_token_metric" + ), + enum_values=enum_values, + ) self.litellm_llm_api_time_to_first_token_metric.labels( - model, - user_api_key, - user_api_key_alias, - user_api_team, - user_api_team_alias, + **_ttft_labels ).observe(time_to_first_token_seconds) else: verbose_logger.debug( diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index fd9b722287d..670f8215d89 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -217,6 +217,10 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.API_KEY_ALIAS.value, UserAPIKeyLabelNames.TEAM.value, UserAPIKeyLabelNames.TEAM_ALIAS.value, + UserAPIKeyLabelNames.REQUESTED_MODEL.value, + UserAPIKeyLabelNames.END_USER.value, + UserAPIKeyLabelNames.USER.value, + UserAPIKeyLabelNames.MODEL_ID.value, ] litellm_request_total_latency_metric = [ @@ -228,6 +232,7 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.TEAM_ALIAS.value, UserAPIKeyLabelNames.USER.value, UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value, + UserAPIKeyLabelNames.MODEL_ID.value, ] litellm_request_queue_time_seconds = [ @@ -258,6 +263,7 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.STATUS_CODE.value, UserAPIKeyLabelNames.USER_EMAIL.value, UserAPIKeyLabelNames.ROUTE.value, + UserAPIKeyLabelNames.MODEL_ID.value, ] litellm_proxy_failed_requests_metric = [ @@ -272,6 +278,7 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.EXCEPTION_STATUS.value, UserAPIKeyLabelNames.EXCEPTION_CLASS.value, UserAPIKeyLabelNames.ROUTE.value, + UserAPIKeyLabelNames.MODEL_ID.value, ] litellm_deployment_latency_per_output_token = [ diff --git a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py index 2419c61c25c..a479d1a9fc9 100644 --- a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py +++ b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py @@ -411,7 +411,15 @@ def test_set_latency_metrics(prometheus_logger): # completion_start_time - api_call_start_time prometheus_logger.litellm_llm_api_time_to_first_token_metric.labels.assert_called_once_with( - "gpt-3.5-turbo", "key1", "alias1", "team1", "team_alias1" + end_user=None, + user="test_user", + hashed_api_key="test_hash", + api_key_alias="test_alias", + team="test_team", + team_alias="test_team_alias", + requested_model="openai-gpt", + model="gpt-3.5-turbo", + model_id="model-123", ) prometheus_logger.litellm_llm_api_time_to_first_token_metric.labels().observe.assert_called_once_with( 0.5 @@ -442,6 +450,7 @@ def test_set_latency_metrics(prometheus_logger): team_alias="test_team_alias", requested_model="openai-gpt", model="gpt-3.5-turbo", + model_id="model-123", ) prometheus_logger.litellm_request_total_latency_metric.labels().observe.assert_called_once_with( 2.0 @@ -737,6 +746,7 @@ async def test_async_post_call_failure_hook(prometheus_logger): exception_status="429", exception_class="Openai.RateLimitError", route=user_api_key_dict.request_route, + model_id=None, ) prometheus_logger.litellm_proxy_failed_requests_metric.labels().inc.assert_called_once() @@ -752,6 +762,7 @@ async def test_async_post_call_failure_hook(prometheus_logger): status_code="429", user_email=None, route=user_api_key_dict.request_route, + model_id=None, ) prometheus_logger.litellm_proxy_total_requests_metric.labels().inc.assert_called_once() @@ -798,6 +809,7 @@ async def test_async_post_call_success_hook(prometheus_logger): status_code="200", user_email=None, route=user_api_key_dict.request_route, + model_id=None, ) prometheus_logger.litellm_proxy_total_requests_metric.labels().inc.assert_called_once() diff --git a/tests/test_litellm/integrations/test_prometheus_labels.py b/tests/test_litellm/integrations/test_prometheus_labels.py index c0b863ef6ee..d9a57df1617 100644 --- a/tests/test_litellm/integrations/test_prometheus_labels.py +++ b/tests/test_litellm/integrations/test_prometheus_labels.py @@ -70,6 +70,30 @@ def test_prometheus_metric_labels_structure(): print(f"✅ {metric_name} has proper label structure with user_email") +def test_model_id_in_required_metrics(): + """ + Test that model_id label is present in all the metrics that should have it: + - litellm_proxy_total_requests_metric + - litellm_proxy_failed_requests_metric + - litellm_request_total_latency_metric + - litellm_llm_api_time_to_first_token_metric + """ + model_id_label = UserAPIKeyLabelNames.MODEL_ID.value + + # Metrics that should have model_id + metrics_with_model_id = [ + "litellm_proxy_total_requests_metric", + "litellm_proxy_failed_requests_metric", + "litellm_request_total_latency_metric", + "litellm_llm_api_time_to_first_token_metric" + ] + + for metric_name in metrics_with_model_id: + labels = PrometheusMetricLabels.get_labels(metric_name) + assert model_id_label in labels, f"Metric {metric_name} should contain model_id label" + print(f"✅ {metric_name} contains model_id label") + + def test_route_normalization_for_responses_api(): """ Test that route normalization prevents high cardinality in Prometheus metrics @@ -217,14 +241,3 @@ def test_prometheus_metrics_use_normalized_routes(): print("✅ Prometheus metrics use normalized routes in labels") - -if __name__ == "__main__": - test_user_email_in_required_metrics() - test_user_email_label_exists() - test_prometheus_metric_labels_structure() - test_route_normalization_for_responses_api() - test_route_normalization_for_sub_routes() - test_route_normalization_preserves_static_routes() - test_route_normalization_other_dynamic_apis() - test_prometheus_metrics_use_normalized_routes() - print("\n✅ All prometheus label tests passed!") \ No newline at end of file From 0d45b010691578a9fafb4ac241193c17b8d10401 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 27 Jan 2026 01:36:10 -0300 Subject: [PATCH 09/70] fix(models): set gpt-5.2-codex mode to responses for Azure and OpenRouter (#19770) Fixes #19754 The gpt-5.2-codex model only supports the responses API, not chat completions. Updated azure/gpt-5.2-codex and openrouter/openai/gpt-5.2-codex entries to use mode: "responses" and supported_endpoints: ["/v1/responses"]. --- model_prices_and_context_window.json | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index d958ea4503a..244a679bc27 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -3653,10 +3653,9 @@ "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 1.4e-05, "supported_endpoints": [ - "/v1/chat/completions", "/v1/responses" ], "supported_modalities": [ @@ -23813,8 +23812,11 @@ "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 1.4e-05, + "supported_endpoints": [ + "/v1/responses" + ], "supported_modalities": [ "text", "image" From b1968a8e3372773123872d534c0faaf92fb9ede7 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 27 Jan 2026 01:47:35 -0300 Subject: [PATCH 10/70] fix(responses): update local_vars with detected provider (#19782) (#19798) When using the responses API with provider-specific params (aws_*, vertex_*) without explicitly passing custom_llm_provider, the code crashed with: AttributeError: 'NoneType' object has no attribute 'startswith' Root cause: local_vars was captured via locals() before get_llm_provider() detected the provider from the model string (e.g., "bedrock/..."), so custom_llm_provider remained None when processing provider-specific params. Fix: Update local_vars["custom_llm_provider"] after get_llm_provider() call so the detected provider is available for param processing. Affected provider-specific params: - aws_* (aws_region_name, aws_access_key_id, etc.) for Bedrock/SageMaker - vertex_* (vertex_project, vertex_location, etc.) for Vertex AI --- litellm/responses/main.py | 10 +++++ .../responses/test_responses_utils.py | 43 +++++++++++++++++++ 2 files changed, 53 insertions(+) diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 83c23a58500..b2c2493c812 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -434,6 +434,8 @@ async def aresponses( _, custom_llm_provider, _, _ = litellm.get_llm_provider( model=model, api_base=local_vars.get("base_url", None) ) + # Update local_vars with detected provider (fixes #19782) + local_vars["custom_llm_provider"] = custom_llm_provider func = partial( responses, @@ -583,6 +585,9 @@ def responses( api_key=litellm_params.api_key, ) + # Update local_vars with detected provider (fixes #19782) + local_vars["custom_llm_provider"] = custom_llm_provider + # Use dynamic credentials from get_llm_provider (e.g., when use_litellm_proxy=True) if dynamic_api_key is not None: litellm_params.api_key = dynamic_api_key @@ -1411,6 +1416,8 @@ async def acompact_responses( _, custom_llm_provider, _, _ = litellm.get_llm_provider( model=model, api_base=local_vars.get("base_url", None) ) + # Update local_vars with detected provider (fixes #19782) + local_vars["custom_llm_provider"] = custom_llm_provider func = partial( compact_responses, @@ -1498,6 +1505,9 @@ def compact_responses( api_key=litellm_params.api_key, ) + # Update local_vars with detected provider (fixes #19782) + local_vars["custom_llm_provider"] = custom_llm_provider + # Use dynamic credentials from get_llm_provider (e.g., when use_litellm_proxy=True) if dynamic_api_key is not None: litellm_params.api_key = dynamic_api_key diff --git a/tests/test_litellm/responses/test_responses_utils.py b/tests/test_litellm/responses/test_responses_utils.py index 3eb9c63e1be..8f7acb6c120 100644 --- a/tests/test_litellm/responses/test_responses_utils.py +++ b/tests/test_litellm/responses/test_responses_utils.py @@ -309,3 +309,46 @@ class TestResponseAPILoggingUtils: assert result.completion_tokens_details.reasoning_tokens == 30 assert result.completion_tokens_details.image_tokens == 100 assert result.completion_tokens_details.text_tokens == 70 + + +class TestResponsesAPIProviderSpecificParams: + """ + Tests for fix #19782: provider-specific params (aws_*, vertex_*) should work + without explicitly passing custom_llm_provider. + """ + + def test_provider_specific_params_no_crash_with_bedrock(self): + """Test that processing aws_* params with bedrock provider doesn't crash.""" + params = { + "temperature": 0.7, + "custom_llm_provider": "bedrock", + "kwargs": {"aws_region_name": "eu-central-1"}, + } + + # Should not raise any exception + result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(params) + assert "temperature" in result + + def test_provider_specific_params_no_crash_with_openai(self): + """Test that processing aws_* params with openai provider doesn't crash.""" + params = { + "temperature": 0.7, + "custom_llm_provider": "openai", + "kwargs": {"aws_region_name": "eu-central-1"}, + } + + # Should not raise any exception + result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(params) + assert "temperature" in result + + def test_provider_specific_params_no_crash_with_vertex_ai(self): + """Test that processing vertex_* params with vertex_ai provider doesn't crash.""" + params = { + "temperature": 0.7, + "custom_llm_provider": "vertex_ai", + "kwargs": {"vertex_project": "my-project"}, + } + + # Should not raise any exception + result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(params) + assert "temperature" in result From 16f456ad822f08643d373c4c539df74fcc5dba72 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 27 Jan 2026 02:00:03 -0300 Subject: [PATCH 11/70] fix(azure): use generic cost calculator for audio token pricing (#19771) Azure audio models were charging audio output tokens at the text token rate instead of the correct audio token rate. This resulted in costs being ~6.65x lower than expected. The fix replaces Azure's custom cost calculation logic with the generic cost calculator that properly handles text, audio, cached, reasoning, and image tokens. Fixes #19764 --- litellm/llms/azure/cost_calculation.py | 37 ++++------ tests/test_litellm/test_cost_calculator.py | 84 ++++++++++++++++++++++ 2 files changed, 97 insertions(+), 24 deletions(-) diff --git a/litellm/llms/azure/cost_calculation.py b/litellm/llms/azure/cost_calculation.py index 96c58d95ff2..5b411095ea1 100644 --- a/litellm/llms/azure/cost_calculation.py +++ b/litellm/llms/azure/cost_calculation.py @@ -1,11 +1,12 @@ """ Helper util for handling azure openai-specific cost calculation -- e.g.: prompt caching +- e.g.: prompt caching, audio tokens """ from typing import Optional, Tuple from litellm._logging import verbose_logger +from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token from litellm.types.utils import Usage from litellm.utils import get_model_info @@ -18,34 +19,15 @@ def cost_per_token( Input: - model: str, the model name without provider prefix - - usage: LiteLLM Usage block, containing anthropic caching information + - usage: LiteLLM Usage block, containing caching and audio token information Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ ## GET MODEL INFO model_info = get_model_info(model=model, custom_llm_provider="azure") - cached_tokens: Optional[int] = None - ## CALCULATE INPUT COST - non_cached_text_tokens = usage.prompt_tokens - if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens: - cached_tokens = usage.prompt_tokens_details.cached_tokens - non_cached_text_tokens = non_cached_text_tokens - cached_tokens - prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"] - ## CALCULATE OUTPUT COST - completion_cost: float = ( - usage["completion_tokens"] * model_info["output_cost_per_token"] - ) - - ## Prompt Caching cost calculation - if model_info.get("cache_read_input_token_cost") is not None and cached_tokens: - # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens - prompt_cost += cached_tokens * ( - model_info.get("cache_read_input_token_cost", 0) or 0 - ) - - ## Speech / Audio cost calculation + ## Speech / Audio cost calculation (cost per second for TTS models) if ( "output_cost_per_second" in model_info and model_info["output_cost_per_second"] is not None @@ -55,7 +37,14 @@ def cost_per_token( f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}" ) ## COST PER SECOND ## - prompt_cost = 0 + prompt_cost = 0.0 completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000 + return prompt_cost, completion_cost - return prompt_cost, completion_cost + ## Use generic cost calculator for all other cases + ## This properly handles: text tokens, audio tokens, cached tokens, reasoning tokens, etc. + return generic_cost_per_token( + model=model, + usage=usage, + custom_llm_provider="azure", + ) diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index 9d968d482c6..f727ca6f6c4 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -357,6 +357,90 @@ def test_azure_realtime_cost_calculator(): assert cost > 0 +def test_azure_audio_output_cost_calculation(): + """ + Test that Azure audio models correctly calculate costs for audio output tokens. + + Reproduces issue: https://github.com/BerriAI/litellm/issues/19764 + Audio tokens should be charged at output_cost_per_audio_token rate, + not at the text token rate (output_cost_per_token). + """ + from litellm.types.utils import ( + Choices, + CompletionTokensDetailsWrapper, + Message, + ) + + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") + + # Scenario from issue #19764: + # Input: 17 text tokens, 0 audio tokens + # Output: 110 text tokens, 482 audio tokens + usage_object = Usage( + prompt_tokens=17, + completion_tokens=592, # 110 text + 482 audio + total_tokens=609, + prompt_tokens_details=PromptTokensDetailsWrapper( + audio_tokens=0, + cached_tokens=0, + text_tokens=17, + image_tokens=0, + ), + completion_tokens_details=CompletionTokensDetailsWrapper( + audio_tokens=482, + reasoning_tokens=0, + text_tokens=110, + ), + ) + + completion = ModelResponse( + id="test-azure-audio-cost", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="Test response", + role="assistant", + ), + ) + ], + created=1729282652, + model="azure/gpt-audio-2025-08-28", + object="chat.completion", + usage=usage_object, + ) + + cost = completion_cost(completion, model="azure/gpt-audio-2025-08-28") + + model_info = litellm.get_model_info("azure/gpt-audio-2025-08-28") + + # Calculate expected cost + expected_input_cost = ( + model_info["input_cost_per_token"] * 17 # text tokens + ) + expected_output_cost = ( + model_info["output_cost_per_token"] * 110 # text tokens + + model_info["output_cost_per_audio_token"] * 482 # audio tokens + ) + expected_total_cost = expected_input_cost + expected_output_cost + + # The bug was: all output tokens charged at text rate + wrong_output_cost = model_info["output_cost_per_token"] * 592 + wrong_total_cost = expected_input_cost + wrong_output_cost + + # Verify audio tokens are NOT charged at text rate (the bug) + assert abs(cost - wrong_total_cost) > 0.001, ( + "Bug: Audio tokens are being charged at text token rate" + ) + + # Verify cost matches + assert abs(cost - expected_total_cost) < 0.0000001, ( + f"Expected cost {expected_total_cost}, got {cost}" + ) + + def test_default_image_cost_calculator(monkeypatch): from litellm.cost_calculator import default_image_cost_calculator From e4a557d95fe74e3a08a79ce20c3fe83ad5a5888f Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 27 Jan 2026 02:00:35 -0300 Subject: [PATCH 12/70] fix(xai): correct cached token cost calculation for xAI models (#19772) * fix(azure): use generic cost calculator for audio token pricing Azure audio models were charging audio output tokens at the text token rate instead of the correct audio token rate. This resulted in costs being ~6.65x lower than expected. The fix replaces Azure's custom cost calculation logic with the generic cost calculator that properly handles text, audio, cached, reasoning, and image tokens. Fixes #19764 * fix(xai): correct cached token cost calculation for xAI models - Fix double-counting issue where xAI reports text_tokens = prompt_tokens (including cached), causing tokens to be charged twice - Add cache_read_input_token_cost to xAI grok-3 and grok-3-mini model variants - Detection: when text_tokens + cached_tokens > prompt_tokens, recalculate text_tokens = prompt_tokens - cached_tokens xAI pricing (25% of input for cached): - grok-3 variants: $0.75/M cached (input $3/M) - grok-3-mini variants: $0.075/M cached (input $0.30/M) --- .../litellm_core_utils/llm_cost_calc/utils.py | 24 +++++++++++++++---- ...odel_prices_and_context_window_backup.json | 11 +++++++++ model_prices_and_context_window.json | 11 +++++++++ 3 files changed, 41 insertions(+), 5 deletions(-) diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 785976ed319..642cbbb7922 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -566,14 +566,28 @@ def generic_cost_per_token( # noqa: PLR0915 if usage.prompt_tokens_details: prompt_tokens_details = _parse_prompt_tokens_details(usage) - ## EDGE CASE - text tokens not set inside PromptTokensDetails + ## EDGE CASE - text tokens not set or includes cached tokens (double-counting) + ## Some providers (like xAI) report text_tokens = prompt_tokens (including cached) + ## We detect this when: text_tokens + cached_tokens + other > prompt_tokens + ## Ref: https://github.com/BerriAI/litellm/issues/19680, #14874, #14875 - if prompt_tokens_details["text_tokens"] == 0: + cache_hit = prompt_tokens_details["cache_hit_tokens"] + text_tokens = prompt_tokens_details["text_tokens"] + audio_tokens = prompt_tokens_details["audio_tokens"] + cache_creation = prompt_tokens_details["cache_creation_tokens"] + image_tokens = prompt_tokens_details["image_tokens"] + + # Check for double-counting: sum of details > prompt_tokens means overlap + total_details = text_tokens + cache_hit + audio_tokens + cache_creation + image_tokens + has_double_counting = cache_hit > 0 and total_details > usage.prompt_tokens + + if text_tokens == 0 or has_double_counting: text_tokens = ( usage.prompt_tokens - - prompt_tokens_details["cache_hit_tokens"] - - prompt_tokens_details["audio_tokens"] - - prompt_tokens_details["cache_creation_tokens"] + - cache_hit + - audio_tokens + - cache_creation + - image_tokens ) prompt_tokens_details["text_tokens"] = text_tokens diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index d958ea4503a..cf6b0e7e3f2 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -30382,6 +30382,7 @@ "supports_web_search": true }, "xai/grok-3": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30396,6 +30397,7 @@ "supports_web_search": true }, "xai/grok-3-beta": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30410,6 +30412,7 @@ "supports_web_search": true }, "xai/grok-3-fast-beta": { + "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30424,6 +30427,7 @@ "supports_web_search": true }, "xai/grok-3-fast-latest": { + "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30438,6 +30442,7 @@ "supports_web_search": true }, "xai/grok-3-latest": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30452,6 +30457,7 @@ "supports_web_search": true }, "xai/grok-3-mini": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30467,6 +30473,7 @@ "supports_web_search": true }, "xai/grok-3-mini-beta": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30482,6 +30489,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30497,6 +30505,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast-beta": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30512,6 +30521,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast-latest": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30527,6 +30537,7 @@ "supports_web_search": true }, "xai/grok-3-mini-latest": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 244a679bc27..d0a3bec9009 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -30384,6 +30384,7 @@ "supports_web_search": true }, "xai/grok-3": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30398,6 +30399,7 @@ "supports_web_search": true }, "xai/grok-3-beta": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30412,6 +30414,7 @@ "supports_web_search": true }, "xai/grok-3-fast-beta": { + "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30426,6 +30429,7 @@ "supports_web_search": true }, "xai/grok-3-fast-latest": { + "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30440,6 +30444,7 @@ "supports_web_search": true }, "xai/grok-3-latest": { + "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30454,6 +30459,7 @@ "supports_web_search": true }, "xai/grok-3-mini": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30469,6 +30475,7 @@ "supports_web_search": true }, "xai/grok-3-mini-beta": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30484,6 +30491,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30499,6 +30507,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast-beta": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30514,6 +30523,7 @@ "supports_web_search": true }, "xai/grok-3-mini-fast-latest": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 6e-07, "litellm_provider": "xai", "max_input_tokens": 131072, @@ -30529,6 +30539,7 @@ "supports_web_search": true }, "xai/grok-3-mini-latest": { + "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 3e-07, "litellm_provider": "xai", "max_input_tokens": 131072, From 885a02e6c85442e76cef5b237c1f5efd051ebe3c Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Tue, 27 Jan 2026 11:38:17 +0530 Subject: [PATCH 13/70] fix: token calculations and refactor (#19696) --- litellm/cost_calculator.py | 56 +++-- .../litellm_core_utils/llm_cost_calc/utils.py | 16 +- tests/test_litellm/test_cost_calculator.py | 231 +++++++++++------- 3 files changed, 202 insertions(+), 101 deletions(-) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 1ab7e260a83..490f0288b00 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -23,7 +23,11 @@ from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import from litellm.litellm_core_utils.llm_cost_calc.utils import ( CostCalculatorUtils, _generic_cost_per_character, + _get_service_tier_cost_key, + _parse_prompt_tokens_details, + calculate_cost_component, generic_cost_per_token, + get_billable_input_tokens, select_cost_metric_for_model, ) from litellm.llms.anthropic.cost_calculation import ( @@ -431,12 +435,18 @@ def cost_per_token( # noqa: PLR0915 model=model, custom_llm_provider=custom_llm_provider ) - if model_info["input_cost_per_token"] > 0: - ## COST PER TOKEN ## - prompt_tokens_cost_usd_dollar = ( - model_info["input_cost_per_token"] * prompt_tokens + if ( + model_info.get("input_cost_per_token", 0) > 0 + or model_info.get("output_cost_per_token", 0) > 0 + ): + return generic_cost_per_token( + model=model, + usage=usage_block, + custom_llm_provider=custom_llm_provider, + service_tier=service_tier, ) - elif ( + + if ( model_info.get("input_cost_per_second", None) is not None and response_time_ms is not None ): @@ -451,11 +461,7 @@ def cost_per_token( # noqa: PLR0915 model_info["input_cost_per_second"] * response_time_ms / 1000 # type: ignore ) - if model_info["output_cost_per_token"] > 0: - completion_tokens_cost_usd_dollar = ( - model_info["output_cost_per_token"] * completion_tokens - ) - elif ( + if ( model_info.get("output_cost_per_second", None) is not None and response_time_ms is not None ): @@ -955,7 +961,10 @@ def completion_cost( # noqa: PLR0915 router_model_id=router_model_id, ) - potential_model_names = [selected_model, _get_response_model(completion_response)] + potential_model_names = [ + selected_model, + _get_response_model(completion_response), + ] if model is not None: potential_model_names.append(model) @@ -1710,10 +1719,16 @@ def default_image_cost_calculator( ) # Priority 1: Use per-image pricing if available (for gpt-image-1 and similar models) - if "input_cost_per_image" in cost_info and cost_info["input_cost_per_image"] is not None: + if ( + "input_cost_per_image" in cost_info + and cost_info["input_cost_per_image"] is not None + ): return cost_info["input_cost_per_image"] * n # Priority 2: Fall back to per-pixel pricing for backward compatibility - elif "input_cost_per_pixel" in cost_info and cost_info["input_cost_per_pixel"] is not None: + elif ( + "input_cost_per_pixel" in cost_info + and cost_info["input_cost_per_pixel"] is not None + ): return cost_info["input_cost_per_pixel"] * height * width * n else: raise Exception( @@ -1833,9 +1848,22 @@ def batch_cost_calculator( if input_cost_per_token_batches: total_prompt_cost = usage.prompt_tokens * input_cost_per_token_batches elif input_cost_per_token: + # Subtract cached tokens from prompt_tokens before calculating cost + # Fixes issue where cached tokens are being charged again total_prompt_cost = ( - usage.prompt_tokens * (input_cost_per_token) / 2 + get_billable_input_tokens(usage) * (input_cost_per_token) / 2 ) # batch cost is usually half of the regular token cost + + # Add cache read cost if applicable + details = _parse_prompt_tokens_details(usage) + cache_read_tokens = details["cache_hit_tokens"] + cache_read_cost_key = _get_service_tier_cost_key( + "cache_read_input_token_cost", None + ) + total_prompt_cost += ( + calculate_cost_component(model_info, cache_read_cost_key, cache_read_tokens) + / 2 + ) if output_cost_per_token_batches: total_completion_cost = usage.completion_tokens * output_cost_per_token_batches elif output_cost_per_token: diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 642cbbb7922..fe06641a389 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -23,6 +23,15 @@ def _is_above_128k(tokens: float) -> bool: return False +def get_billable_input_tokens(usage: Usage) -> int: + """ + Returns the number of billable input tokens. + Subtracts cached tokens from prompt tokens if applicable. + """ + details = _parse_prompt_tokens_details(usage) + return usage.prompt_tokens - details["cache_hit_tokens"] + + def select_cost_metric_for_model( model_info: ModelInfo, ) -> Literal["cost_per_character", "cost_per_token"]: @@ -190,7 +199,6 @@ def _get_token_base_cost( 1000 if "k" in threshold_str else 1 ) if usage.prompt_tokens > threshold: - prompt_base_cost = cast( float, _get_cost_per_unit(model_info, key, prompt_base_cost) ) @@ -633,7 +641,11 @@ def generic_cost_per_token( # noqa: PLR0915 # Calculate text tokens as remainder when we have a breakdown # This handles cases like OpenAI's reasoning models where text_tokens isn't provided text_tokens = max( - 0, usage.completion_tokens - reasoning_tokens - audio_tokens - image_tokens + 0, + usage.completion_tokens + - reasoning_tokens + - audio_tokens + - image_tokens, ) else: # No breakdown at all, all tokens are text tokens diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index f727ca6f6c4..e277baf0b0c 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -1,4 +1,3 @@ -import json import os import sys @@ -8,7 +7,6 @@ sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path -from unittest.mock import MagicMock, patch from pydantic import BaseModel @@ -77,7 +75,9 @@ def test_cost_calculator_with_usage(monkeypatch): prompt_tokens=120, completion_tokens=100, prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=10, audio_tokens=90, image_tokens=20, + text_tokens=10, + audio_tokens=90, + image_tokens=20, ), ) mr = ModelResponse(usage=usage, model="gemini-2.0-flash-001") @@ -96,7 +96,9 @@ def test_cost_calculator_with_usage(monkeypatch): # Step 1: Test a model where input_cost_per_image_token is not set. # In this case the calculation should use input_cost_per_token as fallback. - assert model_info.get("input_cost_per_image_token") is None, "Test case expects that input_cost_per_image_token is not set" + assert ( + model_info.get("input_cost_per_image_token") is None + ), "Test case expects that input_cost_per_image_token is not set" expected_cost = ( usage.prompt_tokens_details.audio_tokens @@ -116,9 +118,7 @@ def test_cost_calculator_with_usage(monkeypatch): monkeypatch.setattr( litellm, "model_cost", - { - "gemini-2.0-flash-001": temp_model_info_object - }, + {"gemini-2.0-flash-001": temp_model_info_object}, ) # Invalidate caches after modifying litellm.model_cost @@ -138,8 +138,10 @@ def test_cost_calculator_with_usage(monkeypatch): expected_cost = ( usage.prompt_tokens_details.audio_tokens * temp_model_info_object["input_cost_per_audio_token"] - + usage.prompt_tokens_details.text_tokens * temp_model_info_object["input_cost_per_token"] - + usage.prompt_tokens_details.image_tokens * temp_model_info_object["input_cost_per_image_token"] + + usage.prompt_tokens_details.text_tokens + * temp_model_info_object["input_cost_per_token"] + + usage.prompt_tokens_details.image_tokens + * temp_model_info_object["input_cost_per_image_token"] + usage.completion_tokens * temp_model_info_object["output_cost_per_token"] ) @@ -331,8 +333,6 @@ def test_custom_pricing_with_router_model_id(): def test_azure_realtime_cost_calculator(): - from litellm import get_model_info - os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") @@ -473,9 +473,7 @@ def test_cost_calculator_with_cache_creation(): from litellm import completion_cost from litellm.types.utils import ( Choices, - CompletionTokensDetailsWrapper, Message, - PromptTokensDetailsWrapper, Usage, ) @@ -531,7 +529,7 @@ def test_cost_calculator_with_cache_creation(): def test_bedrock_cost_calculator_comparison_with_without_cache(): """Test that Bedrock caching reduces costs compared to non-cached requests""" from litellm import completion_cost - from litellm.types.utils import Choices, Message, PromptTokensDetailsWrapper, Usage + from litellm.types.utils import Choices, Message, Usage # Response WITHOUT caching response_no_cache = ModelResponse( @@ -782,7 +780,7 @@ def test_log_context_cost_calculation(): f"DEBUG: Tiered input cost per token (>200k): ${input_cost_above_200k:.2e}" ) else: - print(f"DEBUG: No tiered input pricing available, using base pricing") + print("DEBUG: No tiered input pricing available, using base pricing") input_cost_above_200k = input_cost_per_token if output_cost_above_200k is not None: @@ -790,7 +788,7 @@ def test_log_context_cost_calculation(): f"DEBUG: Tiered output cost per token (>200k): ${output_cost_above_200k:.2e}" ) else: - print(f"DEBUG: No tiered output pricing available, using base pricing") + print("DEBUG: No tiered output pricing available, using base pricing") output_cost_above_200k = output_cost_per_token if cache_creation_above_200k is not None: @@ -798,7 +796,7 @@ def test_log_context_cost_calculation(): f"DEBUG: Tiered cache creation cost per token (>200k): ${cache_creation_above_200k:.2e}" ) else: - print(f"DEBUG: No tiered cache creation pricing available, using base pricing") + print("DEBUG: No tiered cache creation pricing available, using base pricing") cache_creation_above_200k = cache_creation_cost_per_token # Since we're above 200k tokens, we should use tiered pricing if available @@ -1007,7 +1005,7 @@ def test_cost_discount_vertex_ai(): expected_cost = cost_without_discount * 0.95 assert cost_with_discount == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost discount test passed:") + print("✓ Cost discount test passed:") print(f" - Original cost: ${cost_without_discount:.6f}") print(f" - Discounted cost (5% off): ${cost_with_discount:.6f}") print(f" - Savings: ${cost_without_discount - cost_with_discount:.6f}") @@ -1057,7 +1055,7 @@ def test_cost_discount_not_applied_to_other_providers(): # Costs should be the same (no discount applied to OpenAI) assert cost_with_selective_discount == cost_without_discount - print(f"✓ Selective discount test passed:") + print("✓ Selective discount test passed:") print(f" - OpenAI cost (no discount configured): ${cost_without_discount:.6f}") print(f" - Cost remains unchanged: ${cost_with_selective_discount:.6f}") @@ -1107,7 +1105,7 @@ def test_cost_margin_percentage(): expected_cost = cost_without_margin * 1.10 assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost margin percentage test passed:") + print("✓ Cost margin percentage test passed:") print(f" - Original cost: ${cost_without_margin:.6f}") print(f" - Cost with margin (10%): ${cost_with_margin:.6f}") print(f" - Margin added: ${cost_with_margin - cost_without_margin:.6f}") @@ -1158,7 +1156,7 @@ def test_cost_margin_fixed_amount(): expected_cost = cost_without_margin + 0.001 assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost margin fixed amount test passed:") + print("✓ Cost margin fixed amount test passed:") print(f" - Original cost: ${cost_without_margin:.6f}") print(f" - Cost with margin ($0.001): ${cost_with_margin:.6f}") print(f" - Margin added: ${cost_with_margin - cost_without_margin:.6f}") @@ -1193,7 +1191,9 @@ def test_cost_margin_combined(): ) # Set 8% margin + $0.0005 fixed for openai - litellm.cost_margin_config = {"openai": {"percentage": 0.08, "fixed_amount": 0.0005}} + litellm.cost_margin_config = { + "openai": {"percentage": 0.08, "fixed_amount": 0.0005} + } # Calculate cost with margin cost_with_margin = completion_cost( @@ -1209,7 +1209,7 @@ def test_cost_margin_combined(): expected_cost = cost_without_margin * 1.08 + 0.0005 assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost margin combined test passed:") + print("✓ Cost margin combined test passed:") print(f" - Original cost: ${cost_without_margin:.6f}") print(f" - Cost with margin (8% + $0.0005): ${cost_with_margin:.6f}") print(f" - Margin added: ${cost_with_margin - cost_without_margin:.6f}") @@ -1260,7 +1260,7 @@ def test_cost_margin_global(): expected_cost = cost_without_margin * 1.05 assert cost_with_global_margin == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost margin global test passed:") + print("✓ Cost margin global test passed:") print(f" - Original cost: ${cost_without_margin:.6f}") print(f" - Cost with global margin (5%): ${cost_with_global_margin:.6f}") print(f" - Margin added: ${cost_with_global_margin - cost_without_margin:.6f}") @@ -1311,9 +1311,11 @@ def test_cost_margin_provider_overrides_global(): expected_cost = cost_without_margin * 1.10 # 10% from provider, not 5% from global assert cost_with_provider_margin == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost margin provider override test passed:") + print("✓ Cost margin provider override test passed:") print(f" - Original cost: ${cost_without_margin:.6f}") - print(f" - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}") + print( + f" - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}" + ) print(f" - Margin added: ${cost_with_provider_margin - cost_without_margin:.6f}") @@ -1367,7 +1369,7 @@ def test_cost_margin_with_discount(): expected_cost = base_cost * 0.95 * 1.10 assert cost_with_both == pytest.approx(expected_cost, rel=1e-9) - print(f"✓ Cost margin with discount test passed:") + print("✓ Cost margin with discount test passed:") print(f" - Base cost: ${base_cost:.6f}") print(f" - Cost with 5% discount + 10% margin: ${cost_with_both:.6f}") print(f" - Expected: ${expected_cost:.6f}") @@ -1436,14 +1438,10 @@ def test_completion_cost_extracts_service_tier_from_response(): # Test with gpt-5-nano which has flex pricing model = "gpt-5-nano" - + # Create usage object - usage = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500 - ) - + usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) + # Create ModelResponse with service_tier in the response object response_with_service_tier = ModelResponse( usage=usage, @@ -1451,34 +1449,36 @@ def test_completion_cost_extracts_service_tier_from_response(): ) # Set service_tier as an attribute on the response setattr(response_with_service_tier, "service_tier", "flex") - + # Test that flex pricing is used when service_tier is in response flex_cost = completion_cost( completion_response=response_with_service_tier, model=model, custom_llm_provider="openai", ) - + # Create ModelResponse without service_tier (should use standard pricing) response_without_service_tier = ModelResponse( usage=usage, model=model, ) - + # Test that standard pricing is used when service_tier is not in response standard_cost = completion_cost( completion_response=response_without_service_tier, model=model, custom_llm_provider="openai", ) - + # Flex should be approximately 50% of standard assert flex_cost > 0, "Flex cost should be greater than 0" assert standard_cost > 0, "Standard cost should be greater than 0" assert flex_cost < standard_cost, "Flex cost should be less than standard cost" - + flex_ratio = flex_cost / standard_cost - assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" + assert ( + 0.45 <= flex_ratio <= 0.55 + ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" def test_completion_cost_extracts_service_tier_from_usage(): @@ -1490,56 +1490,54 @@ def test_completion_cost_extracts_service_tier_from_usage(): # Test with gpt-5-nano which has flex pricing model = "gpt-5-nano" - + # Create usage object with service_tier usage_with_service_tier = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500 + prompt_tokens=1000, completion_tokens=500, total_tokens=1500 ) # Set service_tier as an attribute on the usage object setattr(usage_with_service_tier, "service_tier", "flex") - + # Create ModelResponse with usage containing service_tier response = ModelResponse( usage=usage_with_service_tier, model=model, ) - + # Test that flex pricing is used when service_tier is in usage flex_cost = completion_cost( completion_response=response, model=model, custom_llm_provider="openai", ) - + # Create usage object without service_tier usage_without_service_tier = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500 + prompt_tokens=1000, completion_tokens=500, total_tokens=1500 ) - + # Create ModelResponse with usage without service_tier response_standard = ModelResponse( usage=usage_without_service_tier, model=model, ) - + # Test that standard pricing is used when service_tier is not in usage standard_cost = completion_cost( completion_response=response_standard, model=model, custom_llm_provider="openai", ) - + # Flex should be approximately 50% of standard assert flex_cost > 0, "Flex cost should be greater than 0" assert standard_cost > 0, "Standard cost should be greater than 0" assert flex_cost < standard_cost, "Flex cost should be less than standard cost" - + flex_ratio = flex_cost / standard_cost - assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" + assert ( + 0.45 <= flex_ratio <= 0.55 + ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" def test_completion_cost_service_tier_priority(): @@ -1551,22 +1549,18 @@ def test_completion_cost_service_tier_priority(): # Test with gpt-5-nano which has flex pricing model = "gpt-5-nano" - + # Create usage object with service_tier="flex" - usage = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500 - ) + usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) setattr(usage, "service_tier", "flex") - + # Create response with service_tier="priority" response = ModelResponse( usage=usage, model=model, ) setattr(response, "service_tier", "priority") - + # Test that optional_params takes priority over response and usage cost_from_params = completion_cost( completion_response=response, @@ -1574,14 +1568,14 @@ def test_completion_cost_service_tier_priority(): custom_llm_provider="openai", optional_params={"service_tier": "flex"}, ) - + # Test that response takes priority over usage when optional_params is not provided - cost_from_response = completion_cost( + completion_cost( completion_response=response, model=model, custom_llm_provider="openai", ) - + # Test that usage is used when neither optional_params nor response have service_tier # Create a new response without service_tier attribute response_no_tier = ModelResponse( @@ -1589,25 +1583,27 @@ def test_completion_cost_service_tier_priority(): model=model, ) # Don't set service_tier on response, so it will fall back to usage - + cost_from_usage = completion_cost( completion_response=response_no_tier, model=model, custom_llm_provider="openai", ) - + # All should use flex pricing (from different sources) assert cost_from_params > 0, "Cost from params should be greater than 0" assert cost_from_usage > 0, "Cost from usage should be greater than 0" - + # Costs should be similar (all using flex) - assert abs(cost_from_params - cost_from_usage) < 1e-6, "Costs from params and usage should be similar (both flex)" + assert ( + abs(cost_from_params - cost_from_usage) < 1e-6 + ), "Costs from params and usage should be similar (both flex)" def test_gemini_cache_tokens_details_no_negative_values(): """ Test for Issue #18750: Negative text_tokens with Gemini caching - + When using Gemini with explicit caching, the response includes cacheTokensDetails which breaks down cached tokens by modality. This test ensures that: 1. text_tokens is never negative @@ -1628,41 +1624,47 @@ def test_gemini_cache_tokens_details_no_negative_values(): # Total tokens by modality (includes cached + non-cached) "promptTokensDetails": [ {"modality": "TEXT", "tokenCount": 9402}, - {"modality": "IMAGE", "tokenCount": 258} + {"modality": "IMAGE", "tokenCount": 258}, ], # Breakdown of cached tokens by modality "cacheTokensDetails": [ {"modality": "TEXT", "tokenCount": 9393}, - {"modality": "IMAGE", "tokenCount": 258} - ] + {"modality": "IMAGE", "tokenCount": 258}, + ], } } usage = VertexGeminiConfig._calculate_usage(completion_response) # Text tokens should be non-cached text only: 9402 - 9393 = 9 - assert usage.prompt_tokens_details.text_tokens == 9, \ - f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}" + assert ( + usage.prompt_tokens_details.text_tokens == 9 + ), f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}" # Image tokens should be non-cached image only: 258 - 258 = 0 - assert usage.prompt_tokens_details.image_tokens == 0, \ - f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}" + assert ( + usage.prompt_tokens_details.image_tokens == 0 + ), f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}" # Total cached should match - assert usage.prompt_tokens_details.cached_tokens == 9651, \ - f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}" + assert ( + usage.prompt_tokens_details.cached_tokens == 9651 + ), f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}" # MOST IMPORTANT: text_tokens should NEVER be negative - assert usage.prompt_tokens_details.text_tokens >= 0, \ - f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750" + assert ( + usage.prompt_tokens_details.text_tokens >= 0 + ), f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750" - print("✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative") + print( + "✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative" + ) def test_gemini_without_cache_tokens_details(): """ Test Gemini response without cacheTokensDetails (implicit caching or no cache) - + When cacheTokensDetails is not present, we should use promptTokensDetails as-is without subtracting anything. """ @@ -1677,7 +1679,7 @@ def test_gemini_without_cache_tokens_details(): "totalTokenCount": 279, "promptTokensDetails": [ {"modality": "TEXT", "tokenCount": 6}, - {"modality": "IMAGE", "tokenCount": 258} + {"modality": "IMAGE", "tokenCount": 258}, ] # No cacheTokensDetails } @@ -1691,3 +1693,62 @@ def test_gemini_without_cache_tokens_details(): assert usage.prompt_tokens_details.text_tokens >= 0 print("✅ Gemini without cacheTokensDetails works correctly") + + +def test_generic_provider_cached_token_cost(): + """ + Test that the generic cost calculator correctly handles cached tokens + for providers like z.ai/deepseek that are not explicitly handled. + """ + from litellm.cost_calculator import completion_cost + from litellm.types.utils import ModelResponse, PromptTokensDetailsWrapper, Usage + + # Setup model cost for a generic provider + # We use a name that will bypass complex provider mapping logic + model_name = "custom-cached-model" + litellm.model_cost[model_name] = { + "input_cost_per_token": 0.0000006, + "output_cost_per_token": 0.0000006, + "cache_read_input_token_cost": 0.0000001, + "litellm_provider": "openai", + } + + # Case 1: Standard nested cached tokens (prompt_tokens_details.cached_tokens) + usage = Usage( + prompt_tokens=10000, + completion_tokens=0, + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=9000), + ) + response = ModelResponse(usage=usage, model=model_name) + + cost = completion_cost( + completion_response=response, + model=model_name, + custom_llm_provider="openai", # Explicitly set provider to trigger generic path + ) + + # Expected: (1000 * 0.0000006) + (9000 * 0.0000001) = 0.0006 + 0.0009 = 0.0015 + expected_cost = 0.0015 + assert ( + abs(cost - expected_cost) < 1e-9 + ), f"Nested cache cost failed. Got {cost}, expected {expected_cost}" + + # Case 2: Top-level cached tokens (cache_read_input_tokens) + usage_top = Usage( + prompt_tokens=10000, + completion_tokens=0, + cache_read_input_tokens=9000, + ) + response_top = ModelResponse(usage=usage_top, model=model_name) + + cost_top = completion_cost( + completion_response=response_top, + model=model_name, + custom_llm_provider="openai", + ) + + assert ( + abs(cost_top - expected_cost) < 1e-9 + ), f"Top-level cache cost failed. Got {cost_top}, expected {expected_cost}" + + print("✅ Generic provider cached token cost verified") From a5bc98a18a0e512f2f479b7071404736446d6186 Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Tue, 27 Jan 2026 11:40:54 +0530 Subject: [PATCH 14/70] =?UTF-8?q?fix(prometheus):=20safely=20handle=20None?= =?UTF-8?q?=20metadata=20in=20logging=20to=20prevent=20At=E2=80=A6=20(#196?= =?UTF-8?q?91)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix(prometheus): safely handle None metadata in logging to prevent AttributeError * fix: lint issues --- litellm/integrations/prometheus.py | 99 +++++++++++++++--------------- 1 file changed, 48 insertions(+), 51 deletions(-) diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 8710df05b4c..af569b9ed69 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -891,7 +891,7 @@ class PrometheusLogger(CustomLogger): model = kwargs.get("model", "") litellm_params = kwargs.get("litellm_params", {}) or {} - _metadata = litellm_params.get("metadata", {}) + _metadata = litellm_params.get("metadata") or {} get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() end_user_id = get_end_user_id_for_cost_tracking( @@ -1165,26 +1165,15 @@ class PrometheusLogger(CustomLogger): response_cost: float, user_id: Optional[str] = None, ): - _team_spend = litellm_params.get("metadata", {}).get( - "user_api_key_team_spend", None - ) - _team_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_team_max_budget", None - ) + _metadata = litellm_params.get("metadata") or {} + _team_spend = _metadata.get("user_api_key_team_spend", None) + _team_max_budget = _metadata.get("user_api_key_team_max_budget", None) - _api_key_spend = litellm_params.get("metadata", {}).get( - "user_api_key_spend", None - ) - _api_key_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_max_budget", None - ) + _api_key_spend = _metadata.get("user_api_key_spend", None) + _api_key_max_budget = _metadata.get("user_api_key_max_budget", None) - _user_spend = litellm_params.get("metadata", {}).get( - "user_api_key_user_spend", None - ) - _user_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_user_max_budget", None - ) + _user_spend = _metadata.get("user_api_key_user_spend", None) + _user_max_budget = _metadata.get("user_api_key_user_max_budget", None) await self._set_api_key_budget_metrics_after_api_request( user_api_key=user_api_key, @@ -1343,7 +1332,7 @@ class PrometheusLogger(CustomLogger): # request queue time (time from arrival to processing start) _litellm_params = kwargs.get("litellm_params", {}) or {} - queue_time_seconds = _litellm_params.get("metadata", {}).get( + queue_time_seconds = (_litellm_params.get("metadata") or {}).get( "queue_time_seconds" ) if queue_time_seconds is not None and queue_time_seconds >= 0: @@ -1367,14 +1356,14 @@ class PrometheusLogger(CustomLogger): standard_logging_payload: StandardLoggingPayload = kwargs.get( "standard_logging_object", {} ) - + if self._should_skip_metrics_for_invalid_key( kwargs=kwargs, standard_logging_payload=standard_logging_payload ): return - + model = kwargs.get("model", "") - + litellm_params = kwargs.get("litellm_params", {}) or {} get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() @@ -1415,49 +1404,57 @@ class PrometheusLogger(CustomLogger): ) -> Optional[int]: """ Extract HTTP status code from various input formats for validation. - + This is a centralized helper to extract status code from different callback function signatures. Handles both ProxyException (uses 'code') and standard exceptions (uses 'status_code'). - + Args: kwargs: Dictionary potentially containing 'exception' key enum_values: Object with 'status_code' attribute exception: Exception object to extract status code from directly - + Returns: Status code as integer if found, None otherwise """ status_code = None - + # Try from enum_values first (most common in our callbacks) - if enum_values and hasattr(enum_values, "status_code") and enum_values.status_code: + if ( + enum_values + and hasattr(enum_values, "status_code") + and enum_values.status_code + ): try: status_code = int(enum_values.status_code) except (ValueError, TypeError): pass - + if not status_code and exception: # ProxyException uses 'code' attribute, other exceptions may use 'status_code' - status_code = getattr(exception, "status_code", None) or getattr(exception, "code", None) + status_code = getattr(exception, "status_code", None) or getattr( + exception, "code", None + ) if status_code is not None: try: status_code = int(status_code) except (ValueError, TypeError): status_code = None - + if not status_code and kwargs: exception_in_kwargs = kwargs.get("exception") if exception_in_kwargs: - status_code = getattr(exception_in_kwargs, "status_code", None) or getattr(exception_in_kwargs, "code", None) + status_code = getattr( + exception_in_kwargs, "status_code", None + ) or getattr(exception_in_kwargs, "code", None) if status_code is not None: try: status_code = int(status_code) except (ValueError, TypeError): status_code = None - + return status_code - + def _is_invalid_api_key_request( self, status_code: Optional[int], @@ -1465,23 +1462,23 @@ class PrometheusLogger(CustomLogger): ) -> bool: """ Determine if a request has an invalid API key based on status code and exception. - + This method prevents invalid authentication attempts from being recorded in Prometheus metrics. A 401 status code is the definitive indicator of authentication failure. Additionally, we check exception messages for authentication error patterns to catch cases where the exception hasn't been converted to a ProxyException yet. - + Args: status_code: HTTP status code (401 indicates authentication error) exception: Exception object to check for auth-related error messages - + Returns: True if the request has an invalid API key and metrics should be skipped, False otherwise """ if status_code == 401: return True - + # Handle cases where AssertionError is raised before conversion to ProxyException if exception is not None: exception_str = str(exception).lower() @@ -1494,9 +1491,9 @@ class PrometheusLogger(CustomLogger): ] if any(pattern in exception_str for pattern in auth_error_patterns): return True - + return False - + def _should_skip_metrics_for_invalid_key( self, kwargs: Optional[dict] = None, @@ -1507,18 +1504,18 @@ class PrometheusLogger(CustomLogger): ) -> bool: """ Determine if Prometheus metrics should be skipped for invalid API key requests. - + This is a centralized validation method that extracts status code and exception information from various callback function signatures and determines if the request represents an invalid API key attempt that should be filtered from metrics. - + Args: kwargs: Dictionary potentially containing exception and other data user_api_key_dict: User API key authentication object (currently unused) enum_values: Object with status_code attribute standard_logging_payload: Standard logging payload dictionary exception: Exception object to check directly - + Returns: True if metrics should be skipped (invalid key detected), False otherwise """ @@ -1527,17 +1524,17 @@ class PrometheusLogger(CustomLogger): enum_values=enum_values, exception=exception, ) - + if exception is None and kwargs: exception = kwargs.get("exception") - + if self._is_invalid_api_key_request(status_code, exception=exception): verbose_logger.debug( "Skipping Prometheus metrics for invalid API key request: " f"status_code={status_code}, exception={type(exception).__name__ if exception else None}" ) return True - + return False async def async_post_call_failure_hook( @@ -1686,7 +1683,7 @@ class PrometheusLogger(CustomLogger): exception = request_kwargs.get("exception", None) llm_provider = _litellm_params.get("custom_llm_provider", None) - + if self._should_skip_metrics_for_invalid_key( kwargs=request_kwargs, standard_logging_payload=standard_logging_payload, @@ -2414,8 +2411,8 @@ class PrometheusLogger(CustomLogger): self, user_api_team: Optional[str], user_api_team_alias: Optional[str], - team_spend: float, - team_max_budget: float, + team_spend: Optional[float], + team_max_budget: Optional[float], response_cost: float, ): """ @@ -2577,7 +2574,7 @@ class PrometheusLogger(CustomLogger): user_api_key: Optional[str], user_api_key_alias: Optional[str], response_cost: float, - key_max_budget: float, + key_max_budget: Optional[float], key_spend: Optional[float], ): if user_api_key: @@ -2594,7 +2591,7 @@ class PrometheusLogger(CustomLogger): self, user_api_key: str, user_api_key_alias: str, - key_max_budget: float, + key_max_budget: Optional[float], key_spend: Optional[float], response_cost: float, ) -> UserAPIKeyAuth: From fd2f1481610f03227d6a036749cc8009b18ec8d0 Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Tue, 27 Jan 2026 11:41:36 +0530 Subject: [PATCH 15/70] fix: resolve 'does not exist' migration errors as applied in setup_database (#19281) --- .../litellm_proxy_extras/utils.py | 109 +++++++++++------- .../test_litellm_proxy_extras_utils.py | 5 + 2 files changed, 74 insertions(+), 40 deletions(-) diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py index 7ffbe95be13..f3155722187 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -18,14 +18,15 @@ def str_to_bool(value: Optional[str]) -> bool: return value.lower() in ("true", "1", "t", "y", "yes") - def _get_prisma_env() -> dict: """Get environment variables for Prisma, handling offline mode if configured.""" prisma_env = os.environ.copy() if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")): # These env vars prevent Prisma from attempting downloads prisma_env["NPM_CONFIG_PREFER_OFFLINE"] = "true" - prisma_env["NPM_CONFIG_CACHE"] = os.getenv("NPM_CONFIG_CACHE", "/app/.cache/npm") + prisma_env["NPM_CONFIG_CACHE"] = os.getenv( + "NPM_CONFIG_CACHE", "/app/.cache/npm" + ) return prisma_env @@ -34,29 +35,28 @@ def _get_prisma_command() -> str: if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")): # Primary location where Prisma Python package installs the CLI default_cli_path = "/app/.cache/prisma-python/binaries/node_modules/.bin/prisma" - + # Check if custom path is provided (for flexibility) custom_cli_path = os.getenv("PRISMA_CLI_PATH") if custom_cli_path and os.path.exists(custom_cli_path): logger.info(f"Using custom Prisma CLI at {custom_cli_path}") return custom_cli_path - + # Check the default location if os.path.exists(default_cli_path): logger.info(f"Using cached Prisma CLI at {default_cli_path}") return default_cli_path - + # If not found, log warning and fall back logger.warning( f"Prisma CLI not found at {default_cli_path}. " "Falling back to Python wrapper (may attempt downloads)" ) - + # Fall back to the Python wrapper (will work in online mode) return "prisma" - class ProxyExtrasDBManager: @staticmethod def _get_prisma_dir() -> str: @@ -119,7 +119,7 @@ class ProxyExtrasDBManager: stdout=open(migration_file, "w"), check=True, timeout=30, - env=prisma_env + env=prisma_env, ) # 3. Mark the migration as applied since it represents current state @@ -134,7 +134,7 @@ class ProxyExtrasDBManager: ], check=True, timeout=30, - env=prisma_env + env=prisma_env, ) return True @@ -159,14 +159,20 @@ class ProxyExtrasDBManager: @staticmethod def _roll_back_migration(migration_name: str): """Mark a specific migration as rolled back""" - # Set up environment for offline mode if configured + # Set up environment for offline mode if configured prisma_env = _get_prisma_env() subprocess.run( - [_get_prisma_command(), "migrate", "resolve", "--rolled-back", migration_name], + [ + _get_prisma_command(), + "migrate", + "resolve", + "--rolled-back", + migration_name, + ], timeout=60, check=True, capture_output=True, - env=prisma_env + env=prisma_env, ) @staticmethod @@ -178,7 +184,7 @@ class ProxyExtrasDBManager: timeout=60, check=True, capture_output=True, - env=prisma_env + env=prisma_env, ) @staticmethod @@ -228,6 +234,8 @@ class ProxyExtrasDBManager: r"duplicate key value violates", r"relation .* already exists", r"constraint .* already exists", + r"does not exist", + r"Can't drop database.* because it doesn't exist", ] for pattern in idempotent_patterns: @@ -248,7 +256,7 @@ class ProxyExtrasDBManager: if not database_url: logger.error("DATABASE_URL not set") return - + diff_dir = ( Path(migrations_dir) / "migrations" @@ -283,7 +291,7 @@ class ProxyExtrasDBManager: check=True, timeout=60, stdout=f, - env=_get_prisma_env() + env=_get_prisma_env(), ) except subprocess.CalledProcessError as e: logger.warning(f"Failed to generate migration diff: {e.stderr}") @@ -313,7 +321,7 @@ class ProxyExtrasDBManager: check=True, capture_output=True, text=True, - env=_get_prisma_env() + env=_get_prisma_env(), ) logger.info(f"prisma db execute stdout: {result.stdout}") logger.info("✅ Migration diff applied successfully") @@ -331,12 +339,18 @@ class ProxyExtrasDBManager: try: logger.info(f"Resolving migration: {migration_name}") subprocess.run( - [_get_prisma_command(), "migrate", "resolve", "--applied", migration_name], + [ + _get_prisma_command(), + "migrate", + "resolve", + "--applied", + migration_name, + ], timeout=60, check=True, capture_output=True, text=True, - env=_get_prisma_env() + env=_get_prisma_env(), ) logger.debug(f"Resolved migration: {migration_name}") except subprocess.CalledProcessError as e: @@ -375,7 +389,7 @@ class ProxyExtrasDBManager: check=True, capture_output=True, text=True, - env=_get_prisma_env() + env=_get_prisma_env(), ) logger.info(f"prisma migrate deploy stdout: {result.stdout}") @@ -397,27 +411,42 @@ class ProxyExtrasDBManager: ) if migration_match: failed_migration = migration_match.group(1) - logger.info( - f"Found failed migration: {failed_migration}, marking as rolled back" - ) - # Mark the failed migration as rolled back - subprocess.run( - [ - _get_prisma_command(), - "migrate", - "resolve", - "--rolled-back", - failed_migration, - ], - timeout=60, - check=True, - capture_output=True, - text=True, - env=_get_prisma_env() - ) - logger.info( - f"✅ Migration {failed_migration} marked as rolled back... retrying" - ) + if ProxyExtrasDBManager._is_idempotent_error(e.stderr): + logger.info( + f"Migration {failed_migration} failed due to idempotent error (e.g., column already exists), resolving as applied" + ) + ProxyExtrasDBManager._roll_back_migration( + failed_migration + ) + ProxyExtrasDBManager._resolve_specific_migration( + failed_migration + ) + logger.info( + f"✅ Migration {failed_migration} resolved." + ) + return True + else: + logger.info( + f"Found failed migration: {failed_migration}, marking as rolled back" + ) + # Mark the failed migration as rolled back + subprocess.run( + [ + _get_prisma_command(), + "migrate", + "resolve", + "--rolled-back", + failed_migration, + ], + timeout=60, + check=True, + capture_output=True, + text=True, + env=_get_prisma_env(), + ) + logger.info( + f"✅ Migration {failed_migration} marked as rolled back... retrying" + ) elif ( "P3005" in e.stderr and "database schema is not empty" in e.stderr diff --git a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py index 7c151c80ae3..597c0845d43 100644 --- a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py +++ b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py @@ -97,6 +97,11 @@ class TestIdempotentErrorDetection: error_message = "constraint 'fk_user_id' already exists" assert ProxyExtrasDBManager._is_idempotent_error(error_message) is True + def test_is_idempotent_error_does_not_exist(self): + """Test detection of 'does not exist' error""" + error_message = "ERROR: index 'idx' does not exist" + assert ProxyExtrasDBManager._is_idempotent_error(error_message) is True + def test_is_idempotent_error_case_insensitive(self): """Test that idempotent error detection is case insensitive""" error_message = "COLUMN 'ID' ALREADY EXISTS" From 9883c2fd64148674c0c5edf75e1d4611f3b1f344 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 12:32:37 +0530 Subject: [PATCH 16/70] Fix: timeout exception raised eror --- litellm/main.py | 24 +---- tests/local_testing/test_timeout.py | 134 +++------------------------- 2 files changed, 15 insertions(+), 143 deletions(-) diff --git a/litellm/main.py b/litellm/main.py index ce84c8988e0..1fc4521b296 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -599,16 +599,8 @@ async def acompletion( # noqa: PLR0915 # Add the context to the function ctx = contextvars.copy_context() func_with_context = partial(ctx.run, func) - - if timeout is not None and isinstance(timeout, (int, float)): - timeout_value = float(timeout) - init_response = await asyncio.wait_for( - loop.run_in_executor(None, func_with_context), - timeout=timeout_value - ) - else: - init_response = await loop.run_in_executor(None, func_with_context) + init_response = await loop.run_in_executor(None, func_with_context) if isinstance(init_response, dict) or isinstance( init_response, ModelResponse ): ## CACHING SCENARIO @@ -616,11 +608,7 @@ async def acompletion( # noqa: PLR0915 response = ModelResponse(**init_response) response = init_response elif asyncio.iscoroutine(init_response): - if timeout is not None and isinstance(timeout, (int, float)): - timeout_value = float(timeout) - response = await asyncio.wait_for(init_response, timeout=timeout_value) - else: - response = await init_response + response = await init_response else: response = init_response # type: ignore @@ -637,14 +625,6 @@ async def acompletion( # noqa: PLR0915 loop=loop ) # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls) return response - except asyncio.TimeoutError: - custom_llm_provider = custom_llm_provider or "openai" - from litellm.exceptions import Timeout - raise Timeout( - message=f"Request timed out after {timeout} seconds", - model=model, - llm_provider=custom_llm_provider, - ) except Exception as e: custom_llm_provider = custom_llm_provider or "openai" raise exception_type( diff --git a/tests/local_testing/test_timeout.py b/tests/local_testing/test_timeout.py index 622c47644a7..7f9837e81dd 100644 --- a/tests/local_testing/test_timeout.py +++ b/tests/local_testing/test_timeout.py @@ -96,7 +96,6 @@ def test_bedrock_timeout(): def test_hanging_request_azure(): litellm.set_verbose = True import asyncio - from unittest.mock import AsyncMock, patch, MagicMock try: router = litellm.Router( @@ -105,8 +104,8 @@ def test_hanging_request_azure(): "model_name": "azure-gpt", "litellm_params": { "model": "azure/gpt-4o-new-test", - "api_base": os.environ.get("AZURE_API_BASE", "https://test.openai.azure.com"), - "api_key": os.environ.get("AZURE_API_KEY", "test-key"), + "api_base": os.environ["AZURE_API_BASE"], + "api_key": os.environ["AZURE_API_KEY"], }, }, { @@ -120,25 +119,16 @@ def test_hanging_request_azure(): encoded = litellm.utils.encode(model="gpt-3.5-turbo", text="blue")[0] async def _test(): - # Mock the Azure OpenAI client's create method to simulate a hanging request - with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create: - # Simulate a hanging request that takes longer than the timeout - async def hanging_request(*args, **kwargs): - await asyncio.sleep(10) # Sleep much longer than the 0.01s timeout - return MagicMock() - - mock_create.side_effect = hanging_request - - response = await router.acompletion( - model="azure-gpt", - messages=[ - {"role": "user", "content": f"what color is red {uuid.uuid4()}"} - ], - logit_bias={encoded: 100}, - timeout=0.01, - ) - print(response) - return response + response = await router.acompletion( + model="azure-gpt", + messages=[ + {"role": "user", "content": f"what color is red {uuid.uuid4()}"} + ], + logit_bias={encoded: 100}, + timeout=0.01, + ) + print(response) + return response response = asyncio.run(_test()) @@ -150,16 +140,9 @@ def test_hanging_request_azure(): ) print(type(e)) pass - except litellm.exceptions.APIError as e: - # Azure may convert CancelledError to APIError - this is also acceptable for timeout scenarios - print( - "Passed: Raised APIError due to timeout (CancelledError). This is acceptable.", e - ) - print(type(e)) - pass except Exception as e: pytest.fail( - f"Did not raise error `openai.APITimeoutError` or `litellm.exceptions.APIError`. Instead raised error type: {type(e)}, Error: {e}" + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" ) @@ -302,94 +285,3 @@ async def test_anthropic_timeout(streaming, sync_mode): ) print(type(e)) pass - - -@pytest.mark.asyncio -async def test_timeout_respects_total_time_not_per_retry(): - """ - Test that timeout applies to the TOTAL operation time, not per-retry. - - This test ensures that when a user sets timeout=2, the entire operation - (including all retries) times out at ~2 seconds, not at 2s * num_retries. - - This is a regression test for the issue where timeout was being applied - per-retry attempt, causing the total time to be much longer than expected. - """ - litellm.set_verbose = False - - timeout_value = 2.0 - # Allow for some overhead (network, processing, etc.) - # but ensure we don't wait for multiple retries - max_allowed_time = timeout_value + 1.0 # 3 seconds max - - start_time = time.time() - - try: - # This should timeout because we're asking for a long response - # with a very short timeout - response = await litellm.acompletion( - model="gpt-3.5-turbo", - timeout=timeout_value, - messages=[{"role": "user", "content": "Write a very long detailed essay about the history of computing, at least 5000 words."}], - ) - pytest.fail("Expected timeout error but got a response") - except (openai.APITimeoutError, litellm.exceptions.Timeout) as e: - elapsed_time = time.time() - start_time - - print(f"Timeout occurred after {elapsed_time:.2f} seconds") - print(f"Expected timeout: {timeout_value} seconds") - print(f"Max allowed time: {max_allowed_time} seconds") - - # Verify that the timeout happened within the expected time window - # It should be close to timeout_value, not timeout_value * num_retries - assert elapsed_time < max_allowed_time, ( - f"Timeout took too long! Expected ~{timeout_value}s, " - f"got {elapsed_time:.2f}s. This suggests timeout is being " - f"applied per-retry instead of to the total operation." - ) - - # Also verify it's not TOO fast (sanity check) - assert elapsed_time >= timeout_value * 0.5, ( - f"Timeout happened too quickly: {elapsed_time:.2f}s. " - f"Expected at least {timeout_value * 0.5}s" - ) - - print("✓ Timeout correctly applied to total operation time, not per-retry") - except Exception as e: - pytest.fail( - f"Expected timeout error but got different error: {type(e).__name__}: {e}" - ) - - -@pytest.mark.asyncio -async def test_timeout_with_retries_disabled(): - """ - Test that timeout works correctly when retries are explicitly disabled. - This should timeout even faster since there are no retry attempts. - """ - litellm.set_verbose = False - - timeout_value = 2.0 - max_allowed_time = timeout_value + 0.5 # Even tighter bound with no retries - - start_time = time.time() - - try: - response = await litellm.acompletion( - model="gpt-3.5-turbo", - timeout=timeout_value, - max_retries=0, # Disable retries - messages=[{"role": "user", "content": "Write a very long detailed essay about the history of computing, at least 5000 words."}], - ) - pytest.fail("Expected timeout error but got a response") - except (openai.APITimeoutError, litellm.exceptions.Timeout) as e: - elapsed_time = time.time() - start_time - - print(f"Timeout with no retries occurred after {elapsed_time:.2f} seconds") - - assert elapsed_time < max_allowed_time, ( - f"Timeout took too long even with retries disabled! " - f"Expected ~{timeout_value}s, got {elapsed_time:.2f}s" - ) - - print("✓ Timeout works correctly with retries disabled") From 3f99a91a47e5fc19840d8bd02e181e2de6981e64 Mon Sep 17 00:00:00 2001 From: Milan Date: Tue, 27 Jan 2026 22:05:04 +0200 Subject: [PATCH 17/70] initialize tiktoken environment at import time to support offline usage --- litellm/__init__.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/litellm/__init__.py b/litellm/__init__.py index e5c09702b9b..46124bf8363 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -80,6 +80,10 @@ import dotenv litellm_mode = os.getenv("LITELLM_MODE", "DEV") # "PRODUCTION", "DEV" if litellm_mode == "DEV": dotenv.load_dotenv() + +# Import default_encoding to ensure environment variables are initialized at import time +from litellm.litellm_core_utils import default_encoding # noqa: F401 + #################################################### if set_verbose: _turn_on_debug() From 0b8c10c48828d13317154a25886211f719e6e2f1 Mon Sep 17 00:00:00 2001 From: Xianzong Xie Date: Tue, 27 Jan 2026 15:54:46 -0800 Subject: [PATCH 18/70] Add native_background_mode to override polling_via_cache for specific models This follow-up to PR #16862 allows users to specify models that should use the native provider's background mode instead of polling via cache. Config example: litellm_settings: responses: background_mode: polling_via_cache: ["openai"] native_background_mode: ["o4-mini-deep-research"] ttl: 3600 When a model is in native_background_mode list, should_use_polling_for_request returns False, allowing the request to fall through to native provider handling. Committed-By-Agent: cursor --- litellm/proxy/proxy_server.py | 8 ++++++-- litellm/proxy/response_api_endpoints/endpoints.py | 2 ++ litellm/proxy/response_polling/polling_handler.py | 12 +++++++++++- 3 files changed, 19 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 183c25ed463..cf5d133bc33 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -1224,6 +1224,7 @@ redis_usage_cache: Optional[ RedisCache ] = None # redis cache used for tracking spend, tpm/rpm limits polling_via_cache_enabled: Union[Literal["all"], List[str], bool] = False +native_background_mode: List[str] = [] # Models that should use native provider background mode instead of polling polling_cache_ttl: int = 3600 # Default 1 hour TTL for polling cache user_custom_auth = None user_custom_key_generate = None @@ -2456,14 +2457,17 @@ class ProxyConfig: pass elif key == "responses": # Initialize global polling via cache settings - global polling_via_cache_enabled, polling_cache_ttl + global polling_via_cache_enabled, native_background_mode, polling_cache_ttl background_mode = value.get("background_mode", {}) polling_via_cache_enabled = background_mode.get( "polling_via_cache", False ) + native_background_mode = background_mode.get( + "native_background_mode", [] + ) polling_cache_ttl = background_mode.get("ttl", 3600) verbose_proxy_logger.debug( - f"{blue_color_code} Initialized polling via cache: enabled={polling_via_cache_enabled}, ttl={polling_cache_ttl}{reset_color_code}" + f"{blue_color_code} Initialized polling via cache: enabled={polling_via_cache_enabled}, native_background_mode={native_background_mode}, ttl={polling_cache_ttl}{reset_color_code}" ) elif key == "default_team_settings": for idx, team_setting in enumerate( diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py index ec1bc5497bd..44e8c42b2c1 100644 --- a/litellm/proxy/response_api_endpoints/endpoints.py +++ b/litellm/proxy/response_api_endpoints/endpoints.py @@ -68,6 +68,7 @@ async def responses_api( _read_request_body, general_settings, llm_router, + native_background_mode, polling_cache_ttl, polling_via_cache_enabled, proxy_config, @@ -95,6 +96,7 @@ async def responses_api( redis_cache=redis_usage_cache, model=data.get("model", ""), llm_router=llm_router, + native_background_mode=native_background_mode, ) # If polling is enabled, use polling mode diff --git a/litellm/proxy/response_polling/polling_handler.py b/litellm/proxy/response_polling/polling_handler.py index c47578c8d7b..f0b850049bf 100644 --- a/litellm/proxy/response_polling/polling_handler.py +++ b/litellm/proxy/response_polling/polling_handler.py @@ -3,7 +3,7 @@ Response Polling Handler for Background Responses with Cache """ import json from datetime import datetime, timezone -from typing import Any, Dict, Optional +from typing import Any, Dict, List, Optional from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid4 @@ -257,6 +257,7 @@ def should_use_polling_for_request( redis_cache, # RedisCache or None model: str, llm_router, # Router instance or None + native_background_mode: Optional[List[str]] = None, # List of models that should use native background mode ) -> bool: """ Determine if polling via cache should be used for a request. @@ -267,6 +268,8 @@ def should_use_polling_for_request( redis_cache: Redis cache instance (required for polling) model: Model name from the request (e.g., "gpt-5" or "openai/gpt-4o") llm_router: LiteLLM router instance for looking up model deployments + native_background_mode: List of model names that should use native provider + background mode instead of polling via cache Returns: True if polling should be used, False otherwise @@ -275,6 +278,13 @@ def should_use_polling_for_request( if not (background_mode and polling_via_cache_enabled and redis_cache): return False + # Check if model is in native_background_mode list - these use native provider background mode + if native_background_mode and model in native_background_mode: + verbose_proxy_logger.debug( + f"Model {model} is in native_background_mode list, skipping polling via cache" + ) + return False + # "all" enables polling for all providers if polling_via_cache_enabled == "all": return True From f9eea06a371360972373fa880b87ccad01b756f3 Mon Sep 17 00:00:00 2001 From: Xianzong Xie Date: Tue, 27 Jan 2026 16:48:22 -0800 Subject: [PATCH 19/70] Add tests for native_background_mode feature Added 8 new unit tests for the native_background_mode feature: - test_polling_disabled_when_model_in_native_background_mode - test_polling_disabled_for_native_background_mode_with_provider_list - test_polling_enabled_when_model_not_in_native_background_mode - test_polling_enabled_when_native_background_mode_is_none - test_polling_enabled_when_native_background_mode_is_empty_list - test_native_background_mode_exact_match_required - test_native_background_mode_with_provider_prefix_in_request - test_native_background_mode_with_router_lookup Committed-By-Agent: cursor --- .../test_response_polling_handler.py | 136 ++++++++++++++++++ 1 file changed, 136 insertions(+) diff --git a/tests/proxy_unit_tests/test_response_polling_handler.py b/tests/proxy_unit_tests/test_response_polling_handler.py index cb4cd0efe57..26f8ac24adc 100644 --- a/tests/proxy_unit_tests/test_response_polling_handler.py +++ b/tests/proxy_unit_tests/test_response_polling_handler.py @@ -1005,6 +1005,142 @@ class TestPollingConditionChecks: assert result is False + # ==================== Native Background Mode Tests ==================== + + def test_polling_disabled_when_model_in_native_background_mode(self): + """Test that polling is disabled when model is in native_background_mode list""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled="all", + redis_cache=Mock(), + model="o4-mini-deep-research", + llm_router=None, + native_background_mode=["o4-mini-deep-research", "o3-deep-research"], + ) + + assert result is False + + def test_polling_disabled_for_native_background_mode_with_provider_list(self): + """Test that native_background_mode takes precedence even when provider matches""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled=["openai"], + redis_cache=Mock(), + model="openai/o4-mini-deep-research", + llm_router=None, + native_background_mode=["openai/o4-mini-deep-research"], + ) + + assert result is False + + def test_polling_enabled_when_model_not_in_native_background_mode(self): + """Test that polling is enabled when model is not in native_background_mode list""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled="all", + redis_cache=Mock(), + model="gpt-4o", + llm_router=None, + native_background_mode=["o4-mini-deep-research"], + ) + + assert result is True + + def test_polling_enabled_when_native_background_mode_is_none(self): + """Test that polling works normally when native_background_mode is None""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled="all", + redis_cache=Mock(), + model="gpt-4o", + llm_router=None, + native_background_mode=None, + ) + + assert result is True + + def test_polling_enabled_when_native_background_mode_is_empty_list(self): + """Test that polling works normally when native_background_mode is empty list""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled="all", + redis_cache=Mock(), + model="gpt-4o", + llm_router=None, + native_background_mode=[], + ) + + assert result is True + + def test_native_background_mode_exact_match_required(self): + """Test that native_background_mode uses exact model name matching""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + # "o4-mini" should not match "o4-mini-deep-research" + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled="all", + redis_cache=Mock(), + model="o4-mini", + llm_router=None, + native_background_mode=["o4-mini-deep-research"], + ) + + assert result is True + + def test_native_background_mode_with_provider_prefix_in_request(self): + """Test native_background_mode matching when request model has provider prefix""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + # Model in native_background_mode without provider prefix + # Request comes in with provider prefix - should not match + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled=["openai"], + redis_cache=Mock(), + model="openai/o4-mini-deep-research", + llm_router=None, + native_background_mode=["o4-mini-deep-research"], # Without prefix + ) + + # Should return True because "openai/o4-mini-deep-research" != "o4-mini-deep-research" + assert result is True + + def test_native_background_mode_with_router_lookup(self): + """Test that native_background_mode works with router-resolved models""" + from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request + + mock_router = Mock() + mock_router.model_name_to_deployment_indices = {"deep-research": [0]} + mock_router.model_list = [ + { + "model_name": "deep-research", + "litellm_params": {"model": "openai/o4-mini-deep-research"} + } + ] + + # Model alias "deep-research" is in native_background_mode + result = should_use_polling_for_request( + background_mode=True, + polling_via_cache_enabled=["openai"], + redis_cache=Mock(), + model="deep-research", + llm_router=mock_router, + native_background_mode=["deep-research"], + ) + + assert result is False + class TestStreamingEventParsing: """ From 1581bcf9853dae64462ebb1d1ecbe600c65a45cd Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 16:54:52 -0800 Subject: [PATCH 20/70] add sortBy and sortOrder params for /v2/model/info --- litellm/proxy/proxy_server.py | 135 ++++- tests/test_litellm/proxy/test_proxy_server.py | 273 +++++++++ .../components/molecules/models/columns.tsx | 561 +++++++++--------- 3 files changed, 682 insertions(+), 287 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 183c25ed463..fb4a59fd04c 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -7807,6 +7807,7 @@ async def _apply_search_filter_to_models( size: int, prisma_client: Optional[Any], proxy_config: Any, + sort_by: Optional[str] = None, ) -> Tuple[List[Dict[str, Any]], Optional[int]]: """ Apply search filter to models, querying database for additional matching models. @@ -7818,6 +7819,7 @@ async def _apply_search_filter_to_models( size: Page size prisma_client: Prisma client for database queries proxy_config: Proxy config for decrypting models + sort_by: Optional sort field - if provided, fetch all matching models instead of paginating at DB level Returns: Tuple of (filtered_models, total_count). total_count is None if not searching. @@ -7881,17 +7883,15 @@ async def _apply_search_filter_to_models( # Calculate total count for search results search_total_count = router_models_count + db_models_total_count - # Fetch database models if we need more for the current page - if router_models_count < models_needed_for_page: - models_to_fetch = min( - models_needed_for_page - router_models_count, db_models_total_count - ) - - if models_to_fetch > 0: + # If sorting is requested, we need to fetch ALL matching models to sort correctly + # Otherwise, we can optimize by only fetching what's needed for the current page + if sort_by: + # Fetch all matching database models for sorting + if db_models_total_count > 0: db_models_raw = ( await prisma_client.db.litellm_proxymodeltable.find_many( where=db_where_condition, - take=models_to_fetch, + take=db_models_total_count, # Fetch all matching models ) ) @@ -7902,6 +7902,28 @@ async def _apply_search_filter_to_models( ) if decrypted_models: db_models.extend(decrypted_models) + else: + # Fetch database models if we need more for the current page + if router_models_count < models_needed_for_page: + models_to_fetch = min( + models_needed_for_page - router_models_count, db_models_total_count + ) + + if models_to_fetch > 0: + db_models_raw = ( + await prisma_client.db.litellm_proxymodeltable.find_many( + where=db_where_condition, + take=models_to_fetch, + ) + ) + + # Convert database models to router format + for db_model in db_models_raw: + decrypted_models = proxy_config.decrypt_model_list_from_db( + [db_model] + ) + if decrypted_models: + db_models.extend(decrypted_models) except Exception as e: verbose_proxy_logger.exception( f"Error querying database models with search: {str(e)}" @@ -7917,6 +7939,80 @@ async def _apply_search_filter_to_models( return filtered_models, search_total_count +def _sort_models( + all_models: List[Dict[str, Any]], + sort_by: Optional[str], + sort_order: str = "asc", +) -> List[Dict[str, Any]]: + """ + Sort models by the specified field and order. + + Args: + all_models: List of models to sort + sort_by: Field to sort by (model_name, created_at, updated_at, costs, status) + sort_order: Sort order (asc or desc) + + Returns: + Sorted list of models + """ + if not sort_by or sort_by not in ["model_name", "created_at", "updated_at", "costs", "status"]: + return all_models + + reverse = sort_order.lower() == "desc" + + def get_sort_key(model: Dict[str, Any]) -> Any: + model_info = model.get("model_info", {}) + + if sort_by == "model_name": + return model.get("model_name", "").lower() + + elif sort_by == "created_at": + created_at = model_info.get("created_at") + if created_at is None: + # Put None values at the end for asc, at the start for desc + return (datetime.max if not reverse else datetime.min) + if isinstance(created_at, str): + try: + return datetime.fromisoformat(created_at.replace("Z", "+00:00")) + except (ValueError, AttributeError): + return datetime.min if not reverse else datetime.max + return created_at + + elif sort_by == "updated_at": + updated_at = model_info.get("updated_at") + if updated_at is None: + return (datetime.max if not reverse else datetime.min) + if isinstance(updated_at, str): + try: + return datetime.fromisoformat(updated_at.replace("Z", "+00:00")) + except (ValueError, AttributeError): + return datetime.min if not reverse else datetime.max + return updated_at + + elif sort_by == "costs": + input_cost = model_info.get("input_cost_per_token", 0) or 0 + output_cost = model_info.get("output_cost_per_token", 0) or 0 + total_cost = input_cost + output_cost + # Put 0 or None costs at the end for asc, at the start for desc + if total_cost == 0: + return (float("inf") if not reverse else float("-inf")) + return total_cost + + elif sort_by == "status": + # False (config) comes before True (db) for asc + db_model = model_info.get("db_model", False) + return db_model + + return None + + try: + sorted_models = sorted(all_models, key=get_sort_key, reverse=reverse) + return sorted_models + except Exception as e: + verbose_proxy_logger.exception(f"Error sorting models by {sort_by}: {str(e)}") + return all_models + + def _paginate_models_response( all_models: List[Dict[str, Any]], page: int, @@ -8109,6 +8205,14 @@ async def model_info_v2( None, description="Filter models by team ID. Returns models with direct_access=True or teamId in access_via_team_ids", ), + sortBy: Optional[str] = fastapi.Query( + None, + description="Field to sort by. Options: model_name, created_at, updated_at, costs, status", + ), + sortOrder: Optional[str] = fastapi.Query( + "asc", + description="Sort order. Options: asc, desc", + ), ): """ BETA ENDPOINT. Might change unexpectedly. Use `/v1/model/info` for now. @@ -8193,6 +8297,7 @@ async def model_info_v2( size=size, prisma_client=prisma_client, proxy_config=proxy_config, + sort_by=sortBy, ) if user_models_only: @@ -8236,6 +8341,20 @@ async def model_info_v2( if modelId is not None: search_total_count = len(all_models) + # Apply sorting before pagination + if sortBy: + # Validate sortOrder + if sortOrder and sortOrder.lower() not in ["asc", "desc"]: + raise HTTPException( + status_code=400, + detail=f"Invalid sortOrder: {sortOrder}. Must be 'asc' or 'desc'", + ) + all_models = _sort_models( + all_models=all_models, + sort_by=sortBy, + sort_order=sortOrder or "asc", + ) + verbose_proxy_logger.debug("all_models: %s", all_models) return _paginate_models_response( diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 2f6eccaf04d..22a60f220ee 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -4013,6 +4013,279 @@ async def test_model_info_v2_filter_by_team_id(monkeypatch): app.dependency_overrides = original_overrides +@pytest.mark.asyncio +@pytest.mark.parametrize( + "sort_by,sort_order,expected_order", + [ + # Test model_name sorting + ("model_name", "asc", ["a-model", "b-model", "z-model"]), + ("model_name", "desc", ["z-model", "b-model", "a-model"]), + # Test created_at sorting + ("created_at", "asc", ["old-model", "mid-model", "new-model"]), + ("created_at", "desc", ["new-model", "mid-model", "old-model"]), + # Test updated_at sorting + ("updated_at", "asc", ["old-updated", "mid-updated", "new-updated"]), + ("updated_at", "desc", ["new-updated", "mid-updated", "old-updated"]), + # Test costs sorting + ("costs", "asc", ["low-cost", "mid-cost", "high-cost"]), + ("costs", "desc", ["high-cost", "mid-cost", "low-cost"]), + # Test status sorting (False/config models come before True/db models in asc) + ("status", "asc", ["config-model-1", "config-model-2", "db-model"]), + ("status", "desc", ["db-model", "config-model-1", "config-model-2"]), + ], +) +async def test_model_info_v2_sorting(monkeypatch, sort_by, sort_order, expected_order): + """ + Test sorting functionality for /v2/model/info endpoint. + Tests all sortBy fields (model_name, created_at, updated_at, costs, status) + with both asc and desc sort orders. + """ + from datetime import datetime, timedelta + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth + + # Create base time for date comparisons + base_time = datetime(2024, 1, 1, 12, 0, 0) + + # Create mock models with different values for each sort field + mock_models = [] + + if sort_by == "model_name": + # Models with different names + mock_models = [ + { + "model_name": "z-model", + "litellm_params": {"model": "z-model"}, + "model_info": {"id": "z-model"}, + }, + { + "model_name": "a-model", + "litellm_params": {"model": "a-model"}, + "model_info": {"id": "a-model"}, + }, + { + "model_name": "b-model", + "litellm_params": {"model": "b-model"}, + "model_info": {"id": "b-model"}, + }, + ] + elif sort_by == "created_at": + # Models with different created_at timestamps + mock_models = [ + { + "model_name": "new-model", + "litellm_params": {"model": "new-model"}, + "model_info": { + "id": "new-model", + "created_at": (base_time + timedelta(days=3)).isoformat(), + }, + }, + { + "model_name": "old-model", + "litellm_params": {"model": "old-model"}, + "model_info": { + "id": "old-model", + "created_at": (base_time - timedelta(days=3)).isoformat(), + }, + }, + { + "model_name": "mid-model", + "litellm_params": {"model": "mid-model"}, + "model_info": { + "id": "mid-model", + "created_at": base_time.isoformat(), + }, + }, + ] + elif sort_by == "updated_at": + # Models with different updated_at timestamps + mock_models = [ + { + "model_name": "new-updated", + "litellm_params": {"model": "new-updated"}, + "model_info": { + "id": "new-updated", + "updated_at": (base_time + timedelta(days=3)).isoformat(), + }, + }, + { + "model_name": "old-updated", + "litellm_params": {"model": "old-updated"}, + "model_info": { + "id": "old-updated", + "updated_at": (base_time - timedelta(days=3)).isoformat(), + }, + }, + { + "model_name": "mid-updated", + "litellm_params": {"model": "mid-updated"}, + "model_info": { + "id": "mid-updated", + "updated_at": base_time.isoformat(), + }, + }, + ] + elif sort_by == "costs": + # Models with different costs (input_cost + output_cost) + mock_models = [ + { + "model_name": "high-cost", + "litellm_params": {"model": "high-cost"}, + "model_info": { + "id": "high-cost", + "input_cost_per_token": 0.00005, + "output_cost_per_token": 0.00015, + }, + }, + { + "model_name": "low-cost", + "litellm_params": {"model": "low-cost"}, + "model_info": { + "id": "low-cost", + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00003, + }, + }, + { + "model_name": "mid-cost", + "litellm_params": {"model": "mid-cost"}, + "model_info": { + "id": "mid-cost", + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00007, + }, + }, + ] + elif sort_by == "status": + # Models with different db_model status (False = config, True = db) + mock_models = [ + { + "model_name": "db-model", + "litellm_params": {"model": "db-model"}, + "model_info": {"id": "db-model", "db_model": True}, + }, + { + "model_name": "config-model-1", + "litellm_params": {"model": "config-model-1"}, + "model_info": {"id": "config-model-1", "db_model": False}, + }, + { + "model_name": "config-model-2", + "litellm_params": {"model": "config-model-2"}, + "model_info": {"id": "config-model-2", "db_model": False}, + }, + ] + + # Mock llm_router + mock_router = MagicMock() + mock_router.model_list = mock_models + + # Mock prisma_client + mock_prisma_client = MagicMock() + + # Mock proxy_config.get_config + mock_get_config = AsyncMock(return_value={}) + + # Mock user authentication + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.user_id = "test-user" + mock_user_api_key_dict.api_key = "test-key" + mock_user_api_key_dict.team_models = [] + mock_user_api_key_dict.models = [] + + # Apply monkeypatches + monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None) + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + + # Override auth dependency + original_overrides = app.dependency_overrides.copy() + app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict + + client = TestClient(app) + try: + # Test sorting with specified sortBy and sortOrder + response = client.get( + "/v2/model/info", params={"sortBy": sort_by, "sortOrder": sort_order} + ) + assert response.status_code == 200 + data = response.json() + assert len(data["data"]) == len(expected_order) + + # Verify models are in expected order + actual_order = [m["model_name"] for m in data["data"]] + assert actual_order == expected_order, ( + f"Sorting failed for sortBy={sort_by}, sortOrder={sort_order}. " + f"Expected: {expected_order}, Got: {actual_order}" + ) + + finally: + app.dependency_overrides = original_overrides + + +@pytest.mark.asyncio +async def test_model_info_v2_sorting_invalid_sort_order(monkeypatch): + """ + Test that invalid sortOrder values return a 400 error. + """ + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth + + # Create mock models + mock_models = [ + { + "model_name": "test-model", + "litellm_params": {"model": "test-model"}, + "model_info": {"id": "test-model"}, + } + ] + + # Mock llm_router + mock_router = MagicMock() + mock_router.model_list = mock_models + + # Mock prisma_client + mock_prisma_client = MagicMock() + + # Mock proxy_config.get_config + mock_get_config = AsyncMock(return_value={}) + + # Mock user authentication + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.user_id = "test-user" + mock_user_api_key_dict.api_key = "test-key" + mock_user_api_key_dict.team_models = [] + mock_user_api_key_dict.models = [] + + # Apply monkeypatches + monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None) + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + + # Override auth dependency + original_overrides = app.dependency_overrides.copy() + app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict + + client = TestClient(app) + try: + # Test invalid sortOrder + response = client.get( + "/v2/model/info", params={"sortBy": "model_name", "sortOrder": "invalid"} + ) + assert response.status_code == 400 + data = response.json() + assert "Invalid sortOrder" in data["detail"] + + finally: + app.dependency_overrides = original_overrides + + @pytest.mark.asyncio async def test_apply_search_filter_to_models(monkeypatch): """ diff --git a/ui/litellm-dashboard/src/components/molecules/models/columns.tsx b/ui/litellm-dashboard/src/components/molecules/models/columns.tsx index 43813852f8f..1fc08e502ac 100644 --- a/ui/litellm-dashboard/src/components/molecules/models/columns.tsx +++ b/ui/litellm-dashboard/src/components/molecules/models/columns.tsx @@ -17,297 +17,300 @@ export const columns = ( expandedRows: Set, setExpandedRows: (expandedRows: Set) => void, ): ColumnDef[] => [ - { - header: () => Model ID, - accessorKey: "model_info.id", - cell: ({ row }) => { - const model = row.original; - return ( - -
setSelectedModelId(model.model_info.id)} - > - {model.model_info.id} -
- - ); - }, - }, - { - header: () => Model Information, - accessorKey: "model_name", - size: 250, // Fixed column width - cell: ({ row }) => { - const model = row.original; - const displayName = getDisplayModelName(row.original) || "-"; - const tooltipContent = ( -
-
- Provider: {model.provider || "-"} -
-
- Public Model Name: {displayName} -
-
- LiteLLM Model Name: {model.litellm_model_name || "-"} -
-
- ); - - return ( - -
- {/* Provider Icon */} -
- {model.provider ? ( - - ) : ( -
-
- )} -
- - {/* Model Names Container */} -
- {/* Public Model Name */} -
{displayName}
- {/* LiteLLM Model Name */} -
- {model.litellm_model_name || "-"} -
-
-
- - ); - }, - }, - { - header: () => Credentials, - accessorKey: "litellm_credential_name", - size: 180, // Fixed column width - cell: ({ row }) => { - const model = row.original; - const credentialName = model.litellm_params?.litellm_credential_name; - - return credentialName ? ( - -
- - - {credentialName} - -
- - ) : ( -
- - No credentials -
- ); - }, - }, - { - header: () => Created By, - accessorKey: "model_info.created_by", - sortingFn: "datetime", - size: 160, // Fixed column width - cell: ({ row }) => { - const model = row.original; - const isConfigModel = !model.model_info?.db_model; - const createdBy = model.model_info.created_by; - const createdAt = model.model_info.created_at ? new Date(model.model_info.created_at).toLocaleDateString() : null; - - return ( -
- {/* Created By - Primary */} -
- {isConfigModel ? "Defined in config" : createdBy || "Unknown"} -
- {/* Created At - Secondary */} -
- {isConfigModel ? "-" : createdAt || "Unknown date"} -
-
- ); - }, - }, - { - header: () => Updated At, - accessorKey: "model_info.updated_at", - sortingFn: "datetime", - cell: ({ row }) => { - const model = row.original; - return ( - - {model.model_info.updated_at ? new Date(model.model_info.updated_at).toLocaleDateString() : "-"} - - ); - }, - }, - { - header: () => Costs, - accessorKey: "input_cost", - size: 120, // Fixed column width - cell: ({ row }) => { - const model = row.original; - const inputCost = model.input_cost; - const outputCost = model.output_cost; - - // If both costs are missing or undefined, show "-" - if (!inputCost && !outputCost) { + { + header: () => Model ID, + accessorKey: "model_info.id", + enableSorting: false, + cell: ({ row }) => { + const model = row.original; return ( -
- - + +
setSelectedModelId(model.model_info.id)} + > + {model.model_info.id} +
+ + ); + }, + }, + { + header: () => Model Information, + accessorKey: "model_name", + size: 250, // Fixed column width + cell: ({ row }) => { + const model = row.original; + const displayName = getDisplayModelName(row.original) || "-"; + const tooltipContent = ( +
+
+ Provider: {model.provider || "-"} +
+
+ Public Model Name: {displayName} +
+
+ LiteLLM Model Name: {model.litellm_model_name || "-"} +
); - } - return ( - -
- {/* Input Cost - Primary */} - {inputCost &&
In: ${inputCost}
} - {/* Output Cost - Secondary */} - {outputCost &&
Out: ${outputCost}
} -
- - ); - }, - }, - { - header: () => Team ID, - accessorKey: "model_info.team_id", - cell: ({ row }) => { - const model = row.original; - return model.model_info.team_id ? ( -
- - + return ( + +
+ {/* Provider Icon */} +
+ {model.provider ? ( + + ) : ( +
-
+ )} +
+ + {/* Model Names Container */} +
+ {/* Public Model Name */} +
{displayName}
+ {/* LiteLLM Model Name */} +
+ {model.litellm_model_name || "-"} +
+
+
-
- ) : ( - "-" - ); + ); + }, }, - }, - { - header: () => Model Access Group, - accessorKey: "model_info.model_access_group", - enableSorting: false, - cell: ({ row }) => { - const model = row.original; - const accessGroups = model.model_info.access_groups; + { + header: () => Credentials, + accessorKey: "litellm_credential_name", + enableSorting: false, + size: 180, // Fixed column width + cell: ({ row }) => { + const model = row.original; + const credentialName = model.litellm_params?.litellm_credential_name; - if (!accessGroups || accessGroups.length === 0) { - return "-"; - } + return credentialName ? ( + +
+ + + {credentialName} + +
+ + ) : ( +
+ + No credentials +
+ ); + }, + }, + { + header: () => Created By, + accessorKey: "model_info.created_by", + sortingFn: "datetime", + size: 160, // Fixed column width + cell: ({ row }) => { + const model = row.original; + const isConfigModel = !model.model_info?.db_model; + const createdBy = model.model_info.created_by; + const createdAt = model.model_info.created_at ? new Date(model.model_info.created_at).toLocaleDateString() : null; - const modelId = model.model_info.id; - const isExpanded = expandedRows.has(modelId); - const shouldShowExpandButton = accessGroups.length > 1; - - const toggleExpanded = () => { - const newExpanded = new Set(expandedRows); - if (isExpanded) { - newExpanded.delete(modelId); - } else { - newExpanded.add(modelId); - } - setExpandedRows(newExpanded); - }; - - return ( -
- - {accessGroups[0]} - - - {(isExpanded || (!shouldShowExpandButton && accessGroups.length === 2)) && - accessGroups.slice(1).map((group: string, index: number) => ( - - {group} - - ))} - - {shouldShowExpandButton && ( - - )} -
- ); + {isConfigModel ? "Defined in config" : createdBy || "Unknown"} +
+ {/* Created At - Secondary */} +
+ {isConfigModel ? "-" : createdAt || "Unknown date"} +
+ + ); + }, }, - }, - { - header: () => Status, - accessorKey: "model_info.db_model", - cell: ({ row }) => { - const model = row.original; - return ( -
Updated At, + accessorKey: "model_info.updated_at", + sortingFn: "datetime", + cell: ({ row }) => { + const model = row.original; + return ( + + {model.model_info.updated_at ? new Date(model.model_info.updated_at).toLocaleDateString() : "-"} + + ); + }, + }, + { + header: () => Costs, + accessorKey: "input_cost", + size: 120, // Fixed column width + cell: ({ row }) => { + const model = row.original; + const inputCost = model.input_cost; + const outputCost = model.output_cost; + + // If both costs are missing or undefined, show "-" + if (!inputCost && !outputCost) { + return ( +
+ - +
+ ); + } + + return ( + +
+ {/* Input Cost - Primary */} + {inputCost &&
In: ${inputCost}
} + {/* Output Cost - Secondary */} + {outputCost &&
Out: ${outputCost}
} +
+ + ); + }, + }, + { + header: () => Team ID, + accessorKey: "model_info.team_id", + enableSorting: false, + cell: ({ row }) => { + const model = row.original; + return model.model_info.team_id ? ( +
+ + + +
+ ) : ( + "-" + ); + }, + }, + { + header: () => Model Access Group, + accessorKey: "model_info.model_access_group", + enableSorting: false, + cell: ({ row }) => { + const model = row.original; + const accessGroups = model.model_info.access_groups; + + if (!accessGroups || accessGroups.length === 0) { + return "-"; + } + + const modelId = model.model_info.id; + const isExpanded = expandedRows.has(modelId); + const shouldShowExpandButton = accessGroups.length > 1; + + const toggleExpanded = () => { + const newExpanded = new Set(expandedRows); + if (isExpanded) { + newExpanded.delete(modelId); + } else { + newExpanded.add(modelId); + } + setExpandedRows(newExpanded); + }; + + return ( +
+ + {accessGroups[0]} + + + {(isExpanded || (!shouldShowExpandButton && accessGroups.length === 2)) && + accessGroups.slice(1).map((group: string, index: number) => ( + + {group} + + ))} + + {shouldShowExpandButton && ( + + )} +
+ ); + }, + }, + { + header: () => Status, + accessorKey: "model_info.db_model", + cell: ({ row }) => { + const model = row.original; + return ( +
- {model.model_info.db_model ? "DB Model" : "Config Model"} -
- ); + > + {model.model_info.db_model ? "DB Model" : "Config Model"} +
+ ); + }, }, - }, - { - id: "actions", - header: () => Actions, - cell: ({ row }) => { - const model = row.original; - const canEditModel = userRole === "Admin" || model.model_info?.created_by === userID; - const isConfigModel = !model.model_info?.db_model; - return ( -
- {isConfigModel ? ( - - - - ) : ( - - { - if (canEditModel) { - setSelectedModelId(model.model_info.id); - } - }} - className={!canEditModel ? "opacity-50 cursor-not-allowed" : "cursor-pointer hover:text-red-600"} - /> - - )} -
- ); + { + id: "actions", + header: () => Actions, + cell: ({ row }) => { + const model = row.original; + const canEditModel = userRole === "Admin" || model.model_info?.created_by === userID; + const isConfigModel = !model.model_info?.db_model; + return ( +
+ {isConfigModel ? ( + + + + ) : ( + + { + if (canEditModel) { + setSelectedModelId(model.model_info.id); + } + }} + className={!canEditModel ? "opacity-50 cursor-not-allowed" : "cursor-pointer hover:text-red-600"} + /> + + )} +
+ ); + }, }, - }, -]; + ]; From a7ead714995e076d918a00dea21a03378fa128b8 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 17:00:14 -0800 Subject: [PATCH 21/70] ruff check --- litellm/proxy/proxy_server.py | 94 +++++++++++++++++++++-------------- 1 file changed, 56 insertions(+), 38 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index fb4a59fd04c..12e34e221d7 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -8174,6 +8174,55 @@ async def _filter_models_by_team_id( return filtered_models +async def _find_model_by_id( + model_id: str, + search: Optional[str], + llm_router, + prisma_client, + proxy_config, +) -> tuple[list, Optional[int]]: + """Find a model by its ID and optionally filter by search term.""" + found_model = None + + # First, search in config + if llm_router is not None: + found_model = llm_router.get_model_info(id=model_id) + if found_model: + found_model = copy.deepcopy(found_model) + + # If not found in config, search in database + if found_model is None: + try: + db_model = await prisma_client.db.litellm_proxymodeltable.find_unique( + where={"model_id": model_id} + ) + if db_model: + # Convert database model to router format + decrypted_models = proxy_config.decrypt_model_list_from_db( + [db_model] + ) + if decrypted_models: + found_model = decrypted_models[0] + except Exception as e: + verbose_proxy_logger.exception( + f"Error querying database for modelId {model_id}: {str(e)}" + ) + + # If model found, verify search filter if provided + if found_model is not None: + if search is not None and search.strip(): + search_lower = search.lower().strip() + model_name = found_model.get("model_name", "") + if search_lower not in model_name.lower(): + # Model found but doesn't match search filter + found_model = None + + # Set all_models to the found model or empty list + all_models = [found_model] if found_model is not None else [] + search_total_count: Optional[int] = len(all_models) + return all_models, search_total_count + + @router.get( "/v2/model/info", description="v2 - returns models available to the user based on their API key permissions. Shows model info from config.yaml (except api key and api base). Filter to just user-added models with ?user_models_only=true", @@ -8240,44 +8289,13 @@ async def model_info_v2( # If modelId is provided, search for the specific model if modelId is not None: - found_model = None - - # First, search in config - if llm_router is not None: - found_model = llm_router.get_model_info(id=modelId) - if found_model: - found_model = copy.deepcopy(found_model) - - # If not found in config, search in database - if found_model is None: - try: - db_model = await prisma_client.db.litellm_proxymodeltable.find_unique( - where={"model_id": modelId} - ) - if db_model: - # Convert database model to router format - decrypted_models = proxy_config.decrypt_model_list_from_db( - [db_model] - ) - if decrypted_models: - found_model = decrypted_models[0] - except Exception as e: - verbose_proxy_logger.exception( - f"Error querying database for modelId {modelId}: {str(e)}" - ) - - # If model found, verify search filter if provided - if found_model is not None: - if search is not None and search.strip(): - search_lower = search.lower().strip() - model_name = found_model.get("model_name", "") - if search_lower not in model_name.lower(): - # Model found but doesn't match search filter - found_model = None - - # Set all_models to the found model or empty list - all_models = [found_model] if found_model is not None else [] - search_total_count: Optional[int] = len(all_models) + all_models, search_total_count = await _find_model_by_id( + model_id=modelId, + search=search, + llm_router=llm_router, + prisma_client=prisma_client, + proxy_config=proxy_config, + ) else: # Normal flow when modelId is not provided all_models = copy.deepcopy(llm_router.model_list) From 01e22fd5ab6514d5a9ef6648ce37ba086a6da63e Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 17:19:02 -0800 Subject: [PATCH 22/70] Fixing UI tests --- .../vector_store_management/DocumentsTable.test.tsx | 2 +- .../vector_store_management/S3VectorsConfig.test.tsx | 4 ++-- .../vector_store_management/VectorStoreTable.test.tsx | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx index 761bc2b7df7..507ad0537d6 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx @@ -62,7 +62,7 @@ describe("DocumentsTable", () => { render(); expect(screen.getByText(/1000.00 KB/)).toBeInTheDocument(); - expect(screen.getByText(/2.00 MB/)).toBeInTheDocument(); + expect(screen.getByText(/1.95 MB/)).toBeInTheDocument(); expect(screen.getByText(/500.00 KB/)).toBeInTheDocument(); }); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx index beb13b5441e..28f885a9dd6 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx @@ -87,7 +87,7 @@ describe("S3VectorsConfig", () => { render(); - const bucketInput = screen.getByPlaceholderText("my-vector-bucket"); + const bucketInput = screen.getByPlaceholderText("my-vector-bucket (min 3 chars)"); await act(async () => { fireEvent.change(bucketInput, { target: { value: "test-bucket" } }); @@ -150,7 +150,7 @@ describe("S3VectorsConfig", () => { render(); - const indexInput = screen.getByPlaceholderText("my-vector-index"); + const indexInput = screen.getByPlaceholderText("my-vector-index (optional, min 3 chars)"); await act(async () => { fireEvent.change(indexInput, { target: { value: "my-index" } }); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx index 0e2be7f62df..ac790122507 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx @@ -183,7 +183,7 @@ describe("VectorStoreTable", () => { it("should render fallback for missing name", () => { renderComponent(); const fallbackElements = screen.getAllByText("-"); - expect(fallbackElements.length).toBe(3); // One for missing name, one for missing description, one for missing files + expect(fallbackElements.length).toBe(5); // One for missing name, one for missing description, three for missing files (one per store) }); it("should wrap name in tooltip", () => { @@ -203,7 +203,7 @@ describe("VectorStoreTable", () => { it("should render fallback for missing description", () => { renderComponent(); const fallbackElements = screen.getAllByText("-"); - expect(fallbackElements.length).toBe(3); // One for missing name, one for missing description, one for missing files + expect(fallbackElements.length).toBe(5); // One for missing name, one for missing description, three for missing files (one per store) }); it("should wrap description in tooltip", () => { From 37bfab929db88fc610092f015df2bbaca4160a4b Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 18:08:17 -0800 Subject: [PATCH 23/70] All Models Page server side sorting --- .../app/(dashboard)/hooks/models/useModels.ts | 6 +- .../components/AllModelsTab.tsx | 42 +++- .../model_dashboard/all_models_table.tsx | 196 ++++++++++++++++++ .../src/components/networking.tsx | 10 +- 4 files changed, 245 insertions(+), 9 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/model_dashboard/all_models_table.tsx diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts index 13630f19bd2..c57de675e0e 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts @@ -27,7 +27,7 @@ const modelHubKeys = createQueryKeys("modelHub"); const allProxyModelsKeys = createQueryKeys("allProxyModels"); const selectedTeamModelsKeys = createQueryKeys("selectedTeamModels"); -export const useModelsInfo = (page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string) => { +export const useModelsInfo = (page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string, sortBy?: string, sortOrder?: string) => { const { accessToken, userId, userRole } = useAuthorized(); return useQuery({ queryKey: modelKeys.list({ @@ -39,9 +39,11 @@ export const useModelsInfo = (page: number = 1, size: number = 50, search?: stri ...(search && { search }), ...(modelId && { modelId }), ...(teamId && { teamId }), + ...(sortBy && { sortBy }), + ...(sortOrder && { sortOrder }), }, }), - queryFn: async () => await modelInfoCall(accessToken!, userId!, userRole!, page, size, search, modelId, teamId), + queryFn: async () => await modelInfoCall(accessToken!, userId!, userRole!, page, size, search, modelId, teamId, sortBy, sortOrder), enabled: Boolean(accessToken && userId && userRole), }); }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx index 42189e391a7..a5ec9d35787 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx @@ -2,11 +2,11 @@ import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap" import { useTeams } from "@/app/(dashboard)/hooks/teams/useTeams"; import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; import { Team } from "@/components/key_team_helpers/key_list"; -import { ModelDataTable } from "@/components/model_dashboard/table"; +import { AllModelsDataTable } from "@/components/model_dashboard/all_models_table"; import { columns } from "@/components/molecules/models/columns"; import { getDisplayModelName } from "@/components/view_model/model_name_display"; import { InfoCircleOutlined } from "@ant-design/icons"; -import { PaginationState } from "@tanstack/react-table"; +import { PaginationState, SortingState } from "@tanstack/react-table"; import { Grid, Select, SelectItem, TabPanel, Text } from "@tremor/react"; import { Skeleton, Spin } from "antd"; import debounce from "lodash/debounce"; @@ -49,6 +49,7 @@ const AllModelsTab = ({ pageIndex: 0, pageSize: 50, }); + const [sorting, setSorting] = useState([]); // Debounce search input const debouncedUpdateSearch = useMemo( @@ -72,12 +73,33 @@ const AllModelsTab = ({ // Determine teamId to pass to the query - only pass if not "personal" const teamIdForQuery = currentTeam === "personal" ? undefined : currentTeam.team_id; + // Convert sorting state to sortBy and sortOrder for API + const sortBy = useMemo(() => { + if (sorting.length === 0) return undefined; + const sort = sorting[0]; + // Map column IDs to server-side field names + // The server expects field names like "model_name", "created_at", etc. + const columnIdToServerField: Record = { + input_cost: "costs", // Map input_cost column to "costs" for server-side sorting + model_info_db_model: "status", // Map model_info.db_model column to "status" for server-side sorting + }; + return columnIdToServerField[sort.id] || sort.id; + }, [sorting]); + + const sortOrder = useMemo(() => { + if (sorting.length === 0) return undefined; + const sort = sorting[0]; + return sort.desc ? "desc" : "asc"; + }, [sorting]); + const { data: rawModelData, isLoading: isLoadingModelsInfo } = useModelsInfo( currentPage, pageSize, debouncedSearch || undefined, undefined, - teamIdForQuery + teamIdForQuery, + sortBy, + sortOrder ); const isLoading = isLoadingModelsInfo || isLoadingModelCostMap; @@ -139,6 +161,7 @@ const AllModelsTab = ({ useEffect(() => { setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 })); + setCurrentPage(1); }, [selectedModelGroup, selectedModelAccessGroupFilter]); // Reset pagination when team changes @@ -147,6 +170,12 @@ const AllModelsTab = ({ setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 })); }, [teamIdForQuery]); + // Reset pagination when sorting changes + useEffect(() => { + setCurrentPage(1); + setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 })); + }, [sorting]); + const resetFilters = () => { setModelNameSearch(""); setSelectedModelGroup("all"); @@ -155,6 +184,7 @@ const AllModelsTab = ({ setModelViewMode("current_team"); setCurrentPage(1); setPagination({ pageIndex: 0, pageSize: 50 }); + setSorting([]); }; return ( @@ -439,7 +469,7 @@ const AllModelsTab = ({ - { + className?: string; + } +} + +interface AllModelsDataTableProps { + data: TData[]; + columns: ColumnDef[]; + isLoading?: boolean; + sorting?: SortingState; + onSortingChange?: OnChangeFn; + pagination?: PaginationState; + onPaginationChange?: OnChangeFn; + enablePagination?: boolean; +} + +export function AllModelsDataTable({ + data = [], + columns, + isLoading = false, + sorting = [], + onSortingChange, + pagination, + onPaginationChange, + enablePagination = false, +}: AllModelsDataTableProps) { + const [columnResizeMode] = React.useState("onChange"); + const [columnSizing, setColumnSizing] = React.useState({}); + const [columnVisibility, setColumnVisibility] = React.useState({}); + + const tableInstance = useReactTable({ + data, + columns, + state: { + sorting, + columnSizing, + columnVisibility, + ...(enablePagination && pagination ? { pagination } : {}), + }, + columnResizeMode, + onSortingChange: onSortingChange, + onColumnSizingChange: setColumnSizing, + onColumnVisibilityChange: setColumnVisibility, + ...(enablePagination && onPaginationChange ? { onPaginationChange } : {}), + getCoreRowModel: getCoreRowModel(), + // NO getSortedRowModel - sorting is handled server-side + ...(enablePagination ? { getPaginationRowModel: getPaginationRowModel() } : {}), + enableSorting: true, + enableColumnResizing: true, + manualSorting: true, // Enable manual sorting for server-side sorting + defaultColumn: { + minSize: 40, + maxSize: 500, + }, + }); + + const getHeaderText = (header: any): string => { + if (typeof header === "string") { + return header; + } + if (typeof header === "function") { + const headerElement = header(); + if (headerElement && headerElement.props && headerElement.props.children) { + const children = headerElement.props.children; + if (typeof children === "string") { + return children; + } + if (children.props && children.props.children) { + return children.props.children; + } + } + } + return ""; + }; + + return ( +
+
+
+ + + {tableInstance.getHeaderGroups().map((headerGroup) => ( + + {headerGroup.headers.map((header) => ( + +
+
+ {header.isPlaceholder + ? null + : flexRender(header.column.columnDef.header, header.getContext())} +
+ {header.id !== "actions" && header.column.getCanSort() && ( +
+ {header.column.getIsSorted() ? ( + { + asc: , + desc: , + }[header.column.getIsSorted() as string] + ) : ( + + )} +
+ )} +
+ {header.column.getCanResize() && ( +
+ )} + + ))} + + ))} + + + {isLoading ? ( + + +
+

🚅 Loading models...

+
+ + + ) : tableInstance.getRowModel().rows.length > 0 ? ( + tableInstance.getRowModel().rows.map((row) => ( + + {row.getVisibleCells().map((cell) => ( + + {flexRender(cell.column.columnDef.cell, cell.getContext())} + + ))} + + )) + ) : ( + + +
+

No models found

+
+ + + )} + +
+
+
+
+ ); +} diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index e0dccbe6e80..d66ef7c4bc4 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -2007,12 +2007,12 @@ export const regenerateKeyCall = async (accessToken: string, keyToRegenerate: st let ModelListerrorShown = false; let errorTimer: NodeJS.Timeout | null = null; -export const modelInfoCall = async (accessToken: string, userID: string, userRole: string, page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string) => { +export const modelInfoCall = async (accessToken: string, userID: string, userRole: string, page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string, sortBy?: string, sortOrder?: string) => { /** * Get all models on proxy */ try { - console.log("modelInfoCall:", accessToken, userID, userRole, page, size, search, modelId, teamId); + console.log("modelInfoCall:", accessToken, userID, userRole, page, size, search, modelId, teamId, sortBy, sortOrder); let url = proxyBaseUrl ? `${proxyBaseUrl}/v2/model/info` : `/v2/model/info`; const params = new URLSearchParams(); params.append("include_team_models", "true"); @@ -2027,6 +2027,12 @@ export const modelInfoCall = async (accessToken: string, userID: string, userRol if (teamId && teamId.trim()) { params.append("teamId", teamId.trim()); } + if (sortBy && sortBy.trim()) { + params.append("sortBy", sortBy.trim()); + } + if (sortOrder && sortOrder.trim()) { + params.append("sortOrder", sortOrder.trim()); + } if (params.toString()) { url += `?${params.toString()}`; } From b4a27712a17b7e51efbd07b3bd79fd2a72a32f09 Mon Sep 17 00:00:00 2001 From: Pragya Sardana Date: Tue, 27 Jan 2026 21:10:47 -0500 Subject: [PATCH 24/70] Add Init Containers in the community helm chart (#19816) --- deploy/charts/litellm-helm/templates/deployment.yaml | 4 ++++ deploy/charts/litellm-helm/templates/migrations-job.yaml | 4 ++++ deploy/charts/litellm-helm/values.yaml | 1 + 3 files changed, 9 insertions(+) diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml index c3e0055e380..4ac5582d060 100644 --- a/deploy/charts/litellm-helm/templates/deployment.yaml +++ b/deploy/charts/litellm-helm/templates/deployment.yaml @@ -38,6 +38,10 @@ spec: serviceAccountName: {{ include "litellm.serviceAccountName" . }} securityContext: {{- toYaml .Values.podSecurityContext | nindent 8 }} + {{- with .Values.extraInitContainers }} + initContainers: + {{- toYaml . | nindent 8 }} + {{- end }} containers: - name: {{ include "litellm.name" . }} securityContext: diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml index f8893a47afe..3459fa12d1c 100644 --- a/deploy/charts/litellm-helm/templates/migrations-job.yaml +++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml @@ -35,6 +35,10 @@ spec: {{- toYaml . | nindent 8 }} {{- end }} serviceAccountName: {{ include "litellm.serviceAccountName" . }} + {{- with .Values.migrationJob.extraInitContainers }} + initContainers: + {{- toYaml . | nindent 8 }} + {{- end }} containers: - name: prisma-migrations image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}" diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index b75ce640370..cea25974bb0 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -281,6 +281,7 @@ migrationJob: # cpu: 100m # memory: 100Mi extraContainers: [] + extraInitContainers: [] # Hook configuration hooks: From 70eb732b41030aa689e4f1fc94e98afd1eccce88 Mon Sep 17 00:00:00 2001 From: Ryan Wilson <84201908+ryewilson@users.noreply.github.com> Date: Tue, 27 Jan 2026 21:13:04 -0500 Subject: [PATCH 25/70] docs: fix guardrail logging docs (#19833) --- docs/my-website/docs/proxy/guardrails/quick_start.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md index cb6379d49f4..ddb215fcb66 100644 --- a/docs/my-website/docs/proxy/guardrails/quick_start.md +++ b/docs/my-website/docs/proxy/guardrails/quick_start.md @@ -405,14 +405,10 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ## **Proxy Admin Controls** -### ✨ Monitoring Guardrails +### Monitoring Guardrails Monitor which guardrails were executed and whether they passed or failed. e.g. guardrail going rogue and failing requests we don't intend to fail -:::info - -✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial) - ::: #### Setup From 29c841d7f97a9937260d34e3f37c31a39194a1d8 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 18:14:30 -0800 Subject: [PATCH 26/70] Fixing build and tests --- .../src/app/(dashboard)/hooks/models/useModels.test.ts | 4 ++++ .../components/vector_store_management/S3VectorsConfig.tsx | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts index 3db24cea9ae..4985206092f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts @@ -104,6 +104,8 @@ describe("useModelsInfo", () => { 50, undefined, undefined, + undefined, + undefined, undefined ); expect(modelInfoCall).toHaveBeenCalledTimes(1); @@ -126,6 +128,8 @@ describe("useModelsInfo", () => { 25, undefined, undefined, + undefined, + undefined, undefined ); }); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx index e54d3a163c0..bf714923120 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx @@ -53,7 +53,7 @@ const S3VectorsConfig: React.FC = ({

AWS S3 Vectors allows you to store and query vector embeddings directly in S3:

    -
  • Vector buckets and indexes will be automatically created if they don't exist
    • +
  • Vector buckets and indexes will be automatically created if they don't exist
  • Vector dimensions are auto-detected from your selected embedding model
  • Ensure your AWS credentials have permissions for S3 Vectors operations
  • From 3080e041809133bc03e60d48e0ab4b47f546f68a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 27 Jan 2026 19:31:24 -0800 Subject: [PATCH 27/70] [Feat] UI: Allow Admins to control what pages are visible on LeftNav (#19907) * feat: enabled_ui_pages_internal_users * init ui for internal user controsl * fix ui settings * fix build * fix leftnav * fix leftnav * test fixes * fix leftnav * isPageAccessibleToInternalUsers * docs fix * docs ui viz --- .../docs/proxy/ui/page_visibility.md | 121 ++++++++++ docs/my-website/docs/routing.md | 1 + docs/my-website/sidebars.js | 14 +- .../proxy_setting_endpoints.py | 6 + .../components/SidebarProvider.tsx | 44 +++- .../UISettings/PageVisibilitySettings.tsx | 146 ++++++++++++ .../AdminSettings/UISettings/UISettings.tsx | 25 +- .../src/components/leftnav.tsx | 109 ++++++--- .../src/components/networking.tsx | 60 +++++ .../src/components/page_metadata.ts | 44 ++++ .../src/components/page_utils.test.ts | 219 ++++++++++++++++++ .../src/components/page_utils.ts | 75 ++++++ 12 files changed, 827 insertions(+), 37 deletions(-) create mode 100644 docs/my-website/docs/proxy/ui/page_visibility.md create mode 100644 ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx create mode 100644 ui/litellm-dashboard/src/components/page_metadata.ts create mode 100644 ui/litellm-dashboard/src/components/page_utils.test.ts create mode 100644 ui/litellm-dashboard/src/components/page_utils.ts diff --git a/docs/my-website/docs/proxy/ui/page_visibility.md b/docs/my-website/docs/proxy/ui/page_visibility.md new file mode 100644 index 00000000000..06b06f33219 --- /dev/null +++ b/docs/my-website/docs/proxy/ui/page_visibility.md @@ -0,0 +1,121 @@ +import Image from '@theme/IdealImage'; + +# Control Page Visibility for Internal Users + +Configure which navigation tabs and pages are visible to internal users (non-admin developers) in the LiteLLM UI. + +Use this feature to simplify the UI and control which pages your internal users/developers can see when signing in. + +## Overview + +By default, all pages accessible to internal users are visible in the navigation sidebar. The page visibility control allows admins to restrict which pages internal users can see, creating a more focused and streamlined experience. + + +## Configure Page Visibility + +### 1. Navigate to Settings + +Click the **Settings** icon in the sidebar. + +![Navigate to Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/cbb6f272-ab18-4996-b57d-7ed4aad721ea/ascreenshot_ab80f3175b1a41b0bdabdd2cd3980573_text_export.jpeg) + +### 2. Go to Admin Settings + +Click **Admin Settings** from the settings menu. + +![Go to Admin Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/e2b327bf-1cfd-4519-a9ce-8a6ecb2de53a/ascreenshot_23bb1577b3f84d22be78e0faa58dee3d_text_export.jpeg) + +### 3. Select UI Settings + +Click **UI Settings** to access the page visibility controls. + +![Select UI Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/fff0366a-4944-457a-8f6a-e22018dde108/ascreenshot_0e268e8651654e75bb9fb40d2ed366a9_text_export.jpeg) + +### 4. Open Page Visibility Configuration + +Click **Configure Page Visibility** to expand the configuration panel. + +![Open Configuration](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/3a4761d6-145a-4afd-8abf-d92744b9ac9f/ascreenshot_23c16eb79c32481887b879d961f1f00a_text_export.jpeg) + +### 5. Select Pages to Make Visible + +Check the boxes for the pages you want internal users to see. Pages are organized by category for easy navigation. + +![Select Pages](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/b9c96b54-6c20-484f-8b0b-3a86decb5717/ascreenshot_3347ade01ebe4ea390bc7b57e53db43f_text_export.jpeg) + +**Available pages include:** +- Virtual Keys +- Playground +- Models + Endpoints +- Agents +- MCP Servers +- Search Tools +- Vector Stores +- Logs +- Teams +- Organizations +- Usage +- Budgets +- And more... + +### 6. Save Your Configuration + +Click **Save Page Visibility Settings** to apply the changes. + +![Save Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/8a215378-44f5-4bb8-b984-06fa2aa03903/ascreenshot_44e7aeebe25a477ba92f73a3ed3df644_text_export.jpeg) + +### 7. Verify Changes + +Internal users will now only see the selected pages in their navigation sidebar. + +![Verify Changes](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/493a7718-b276-40b9-970f-5814054932d9/ascreenshot_ad23b8691f824095ba60256f91ad24f8_text_export.jpeg) + +## Reset to Default + +To restore all pages to internal users: + +1. Open the Page Visibility configuration +2. Click **Reset to Default (All Pages)** +3. Click **Save Page Visibility Settings** + +This will clear the restriction and show all accessible pages to internal users. + +## API Configuration + +You can also configure page visibility programmatically using the API: + +### Get Current Settings + +```bash +curl -X GET 'http://localhost:4000/ui_settings/get' \ + -H 'Authorization: Bearer ' +``` + +### Update Page Visibility + +```bash +curl -X PATCH 'http://localhost:4000/ui_settings/update' \ + -H 'Authorization: Bearer ' \ + -H 'Content-Type: application/json' \ + -d '{ + "enabled_ui_pages_internal_users": [ + "api-keys", + "agents", + "mcp-servers", + "logs", + "teams" + ] + }' +``` + +### Clear Page Visibility Restrictions + +```bash +curl -X PATCH 'http://localhost:4000/ui_settings/update' \ + -H 'Authorization: Bearer ' \ + -H 'Content-Type: application/json' \ + -d '{ + "enabled_ui_pages_internal_users": null + }' +``` + diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index b5ece7237aa..2b3a28edf75 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -828,6 +828,7 @@ asyncio.run(router_acompletion()) ``` + ## Traffic Mirroring / Silent Experiments diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index ef7573e9cef..4cc46f97727 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -274,11 +274,19 @@ const sidebars = { "proxy/custom_sso", "proxy/ai_hub", "proxy/model_compare_ui", - "proxy/public_teams", - "proxy/self_serve", - "proxy/ui/bulk_edit_users", "proxy/ui_credentials", "tutorials/scim_litellm", + { + type: "category", + label: "UI User/Team Management", + items: [ + "proxy/access_control", + "proxy/public_teams", + "proxy/self_serve", + "proxy/ui/bulk_edit_users", + "proxy/ui/page_visibility", + ] + }, { type: "category", label: "UI Usage Tracking", diff --git a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py index 09c14bc42c1..4a0268eeede 100644 --- a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py +++ b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py @@ -78,6 +78,11 @@ class UISettings(BaseModel): description="Prevents Team Admins from deleting users from the teams they manage. Useful for SCIM provisioning where team membership is defined externally.", ) + enabled_ui_pages_internal_users: Optional[List[str]] = Field( + default=None, + description="List of page keys that internal users (non-admins) can see in the UI sidebar. If not set, all pages are visible based on role permissions.", + ) + class UISettingsResponse(SettingsResponse): """Response model for UI settings""" @@ -89,6 +94,7 @@ class UISettingsResponse(SettingsResponse): ALLOWED_UI_SETTINGS_FIELDS = { "disable_model_add_for_internal_users", "disable_team_admin_delete_team_user", + "enabled_ui_pages_internal_users", } diff --git a/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx b/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx index 8b934e10779..26f5786b413 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx @@ -1,4 +1,9 @@ +"use client"; + import Sidebar from "@/components/leftnav"; +import { getUISettings } from "@/components/networking"; +import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; +import { useEffect, useState } from "react"; interface SidebarProviderProps { setPage: (page: string) => void; @@ -7,7 +12,44 @@ interface SidebarProviderProps { } const SidebarProvider = ({ setPage, defaultSelectedKey, sidebarCollapsed }: SidebarProviderProps) => { - return ; + const { accessToken } = useAuthorized(); + const [enabledPagesInternalUsers, setEnabledPagesInternalUsers] = useState(null); + + useEffect(() => { + const fetchUISettings = async () => { + if (!accessToken) { + console.log("[SidebarProvider] No access token, skipping UI settings fetch"); + return; + } + + try { + console.log("[SidebarProvider] Fetching UI settings from /get/ui_settings"); + const settings = await getUISettings(accessToken); + console.log("[SidebarProvider] UI settings response:", settings); + + // API returns 'values' not 'settings' + if (settings?.values?.enabled_ui_pages_internal_users !== undefined) { + console.log("[SidebarProvider] Setting enabled pages:", settings.values.enabled_ui_pages_internal_users); + setEnabledPagesInternalUsers(settings.values.enabled_ui_pages_internal_users); + } else { + console.log("[SidebarProvider] No enabled_ui_pages_internal_users in response (all pages visible by default)"); + } + } catch (error) { + console.error("[SidebarProvider] Failed to fetch UI settings:", error); + } + }; + + fetchUISettings(); + }, [accessToken]); + + return ( + + ); }; export default SidebarProvider; diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx new file mode 100644 index 00000000000..47e4a8bb7f3 --- /dev/null +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx @@ -0,0 +1,146 @@ +"use client"; + +import { getAvailablePages } from "@/components/page_utils"; +import { Button, Checkbox, Collapse, Space, Tag, Typography } from "antd"; +import { useMemo, useState } from "react"; + +interface PageVisibilitySettingsProps { + enabledPagesInternalUsers: string[] | null | undefined; + enabledPagesPropertyDescription?: string; + isUpdating: boolean; + onUpdate: (settings: { enabled_ui_pages_internal_users: string[] | null }) => void; +} + +export default function PageVisibilitySettings({ + enabledPagesInternalUsers, + enabledPagesPropertyDescription, + isUpdating, + onUpdate, +}: PageVisibilitySettingsProps) { + // Check if page visibility is set (null/undefined means "not set" = all pages visible) + const isPageVisibilitySet = enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined; + + // Get available pages from leftnav configuration + const availablePages = useMemo(() => getAvailablePages(), []); + + // Group pages by their group for better UI + const pagesByGroup = useMemo(() => { + const grouped: Record = {}; + availablePages.forEach((page) => { + if (!grouped[page.group]) { + grouped[page.group] = []; + } + grouped[page.group].push(page); + }); + return grouped; + }, [availablePages]); + + // Local state for page selection + const [selectedPages, setSelectedPages] = useState(enabledPagesInternalUsers || []); + + // Update local state when data changes + useMemo(() => { + if (enabledPagesInternalUsers) { + setSelectedPages(enabledPagesInternalUsers); + } else { + setSelectedPages([]); + } + }, [enabledPagesInternalUsers]); + + const handleSavePageVisibility = () => { + onUpdate({ enabled_ui_pages_internal_users: selectedPages.length > 0 ? selectedPages : null }); + }; + + const handleResetToDefault = () => { + setSelectedPages([]); + onUpdate({ enabled_ui_pages_internal_users: null }); + }; + + return ( + + + + Internal User Page Visibility + {!isPageVisibilitySet && ( + + Not set (all pages visible) + + )} + {isPageVisibilitySet && ( + + {selectedPages.length} page{selectedPages.length !== 1 ? "s" : ""} selected + + )} + + {enabledPagesPropertyDescription && ( + {enabledPagesPropertyDescription} + )} + + By default, all pages are visible to internal users. Select specific pages to restrict visibility. + + + Note: Only pages accessible to internal user roles are shown here. Admin-only pages are excluded as they + cannot be made visible to internal users regardless of this setting. + + + + + + + {Object.entries(pagesByGroup).map(([groupName, pages]) => ( +
    + + {groupName} + + + {pages.map((page) => ( +
    + + + {page.label} + + {page.description} + + + +
    + ))} +     +
    + ))} +     +     + + + + {isPageVisibilitySet && ( + + )} + +     + ), + }, + ]} + /> + + ); +} diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx index 1cc493194e9..6cc9cbf4309 100644 --- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx @@ -4,7 +4,8 @@ import { useUISettings } from "@/app/(dashboard)/hooks/uiSettings/useUISettings" import { useUpdateUISettings } from "@/app/(dashboard)/hooks/uiSettings/useUpdateUISettings"; import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; import NotificationManager from "@/components/molecules/notifications_manager"; -import { Alert, Card, Skeleton, Space, Switch, Typography } from "antd"; +import PageVisibilitySettings from "./PageVisibilitySettings"; +import { Alert, Card, Divider, Skeleton, Space, Switch, Typography } from "antd"; export default function UISettings() { const { accessToken } = useAuthorized(); @@ -14,6 +15,7 @@ export default function UISettings() { const schema = data?.field_schema; const property = schema?.properties?.disable_model_add_for_internal_users; const disableTeamAdminDeleteProperty = schema?.properties?.disable_team_admin_delete_team_user; + const enabledPagesProperty = schema?.properties?.enabled_ui_pages_internal_users; const values = data?.values ?? {}; const isDisabledForInternalUsers = Boolean(values.disable_model_add_for_internal_users); const isDisabledTeamAdminDeleteTeamUser = Boolean(values.disable_team_admin_delete_team_user); @@ -46,6 +48,17 @@ export default function UISettings() { ); }; + const handleUpdatePageVisibility = (settings: { enabled_ui_pages_internal_users: string[] | null }) => { + updateSettings(settings, { + onSuccess: () => { + NotificationManager.success("Page visibility settings updated successfully"); + }, + onError: (error) => { + NotificationManager.fromBackend(error); + }, + }); + }; + return ( {isLoading ? ( @@ -99,6 +112,16 @@ export default function UISettings() { )} + + + + {/* Page Visibility for Internal Users */} + )} diff --git a/ui/litellm-dashboard/src/components/leftnav.tsx b/ui/litellm-dashboard/src/components/leftnav.tsx index f24f4d60219..61071211778 100644 --- a/ui/litellm-dashboard/src/components/leftnav.tsx +++ b/ui/litellm-dashboard/src/components/leftnav.tsx @@ -39,6 +39,7 @@ interface SidebarProps { setPage: (page: string) => void; defaultSelectedKey: string; collapsed?: boolean; + enabledPagesInternalUsers?: string[] | null; } // Menu item configuration @@ -59,28 +60,8 @@ interface MenuGroup { roles?: string[]; } -const Sidebar: React.FC = ({ setPage, defaultSelectedKey, collapsed = false }) => { - const { userId, accessToken, userRole } = useAuthorized(); - const { data: organizations } = useOrganizations(); - - // Check if user is an org_admin - const isOrgAdmin = useMemo(() => { - if (!userId || !organizations) return false; - return organizations.some((org: Organization) => - org.members?.some((member) => member.user_id === userId && member.user_role === "org_admin"), - ); - }, [userId, organizations]); - - // Navigate to page helper - const navigateToPage = (page: string) => { - const newSearchParams = new URLSearchParams(window.location.search); - newSearchParams.set("page", page); - window.history.pushState(null, "", `?${newSearchParams.toString()}`); - setPage(page); - }; - - // Menu groups organized by category - const menuGroups: MenuGroup[] = [ +// Menu groups organized by category - defined outside component for export +const menuGroups: MenuGroup[] = [ { groupLabel: "AI GATEWAY", items: [ @@ -152,7 +133,6 @@ const Sidebar: React.FC = ({ setPage, defaultSelectedKey, collapse page: "vector-stores", label: "Vector Stores", icon: , - roles: all_admin_roles, }, ], }, @@ -337,20 +317,82 @@ const Sidebar: React.FC = ({ setPage, defaultSelectedKey, collapse }, ]; - // Filter items based on user role +const Sidebar: React.FC = ({ setPage, defaultSelectedKey, collapsed = false, enabledPagesInternalUsers }) => { + const { userId, accessToken, userRole } = useAuthorized(); + const { data: organizations } = useOrganizations(); + + // Check if user is an org_admin + const isOrgAdmin = useMemo(() => { + if (!userId || !organizations) return false; + return organizations.some((org: Organization) => + org.members?.some((member) => member.user_id === userId && member.user_role === "org_admin"), + ); + }, [userId, organizations]); + + // Navigate to page helper + const navigateToPage = (page: string) => { + const newSearchParams = new URLSearchParams(window.location.search); + newSearchParams.set("page", page); + window.history.pushState(null, "", `?${newSearchParams.toString()}`); + setPage(page); + }; + + // Filter items based on user role and enabled pages for internal users const filterItemsByRole = (items: MenuItem[]): MenuItem[] => { + const isAdmin = isAdminRole(userRole); + + // Debug logging + if (enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined) { + console.log("[LeftNav] Filtering with enabled pages:", { + userRole, + isAdmin, + enabledPagesInternalUsers, + }); + } + return items - .filter((item) => { - // Special handling for organizations menu item - allow org_admins - if (item.key === "organizations") { - return !item.roles || item.roles.includes(userRole) || isOrgAdmin; - } - return !item.roles || item.roles.includes(userRole); - }) .map((item) => ({ ...item, children: item.children ? filterItemsByRole(item.children) : undefined, - })); + })) + .filter((item) => { + // Special handling for organizations menu item - allow org_admins + if (item.key === "organizations") { + const hasRoleAccess = !item.roles || item.roles.includes(userRole) || isOrgAdmin; + if (!hasRoleAccess) return false; + + // Check enabled pages for internal users (non-admins) + if (!isAdmin && enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined) { + const isIncluded = enabledPagesInternalUsers.includes(item.page); + console.log(`[LeftNav] Page "${item.page}" (${item.key}): ${isIncluded ? "VISIBLE" : "HIDDEN"}`); + return isIncluded; + } + return true; + } + + // Existing role check + if (item.roles && !item.roles.includes(userRole)) return false; + + // Check enabled pages for internal users (non-admins) + if (!isAdmin && enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined) { + // If item has children, check if any children are visible + if (item.children && item.children.length > 0) { + const hasVisibleChildren = item.children.some((child) => + enabledPagesInternalUsers.includes(child.page) + ); + if (hasVisibleChildren) { + console.log(`[LeftNav] Parent "${item.page}" (${item.key}): VISIBLE (has visible children)`); + return true; + } + } + + const isIncluded = enabledPagesInternalUsers.includes(item.page); + console.log(`[LeftNav] Page "${item.page}" (${item.key}): ${isIncluded ? "VISIBLE" : "HIDDEN"}`); + return isIncluded; + } + + return true; + }); }; // Build menu items with groups @@ -485,3 +527,6 @@ const Sidebar: React.FC = ({ setPage, defaultSelectedKey, collapse }; export default Sidebar; + +// Also export menuGroups for advanced use cases +export { menuGroups }; diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index d66ef7c4bc4..b24f8eaddcc 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -5336,6 +5336,66 @@ export const getProxyUISettings = async (accessToken: string) => { } }; +export const getUISettings = async (accessToken: string) => { + /** + * Get UI-specific configuration flags from the database + */ + try { + const url = proxyBaseUrl ? `${proxyBaseUrl}/get/ui_settings` : `/get/ui_settings`; + const response = await fetch(url, { + method: "GET", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + }); + + if (!response.ok) { + const errorData = await response.json(); + const errorMessage = deriveErrorMessage(errorData); + console.error("Failed to get UI settings:", errorMessage); + return null; + } + + const data = await response.json(); + return data; + } catch (error) { + console.error("Failed to get UI settings:", error); + return null; + } +}; + +export const updateUISettings = async (accessToken: string, settings: any) => { + /** + * Update UI-specific configuration flags in the database + * Only proxy admins can update these settings + */ + try { + const url = proxyBaseUrl ? `${proxyBaseUrl}/update/ui_settings` : `/update/ui_settings`; + const response = await fetch(url, { + method: "PATCH", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify(settings), + }); + + if (!response.ok) { + const errorData = await response.json(); + const errorMessage = deriveErrorMessage(errorData); + handleError(errorMessage); + throw new Error(errorMessage); + } + + const data = await response.json(); + return data; + } catch (error) { + console.error("Failed to update UI settings:", error); + throw error; + } +}; + export const getGuardrailsList = async (accessToken: string) => { try { const url = proxyBaseUrl ? `${proxyBaseUrl}/v2/guardrails/list` : `/v2/guardrails/list`; diff --git a/ui/litellm-dashboard/src/components/page_metadata.ts b/ui/litellm-dashboard/src/components/page_metadata.ts new file mode 100644 index 00000000000..0c3b978a0bb --- /dev/null +++ b/ui/litellm-dashboard/src/components/page_metadata.ts @@ -0,0 +1,44 @@ +/** + * Page metadata for UI Settings configuration + * This file contains descriptions and metadata for all navigation pages + */ + +// Page descriptions for UI Settings configuration +export const pageDescriptions: Record = { + "api-keys": "Manage virtual keys for API access and authentication", + "llm-playground": "Interactive playground for testing LLM requests", + models: "Configure and manage LLM models and endpoints", + agents: "Create and manage AI agents", + "mcp-servers": "Configure Model Context Protocol servers", + guardrails: "Set up content moderation and safety guardrails", + policies: "Define access control and usage policies", + "search-tools": "Configure RAG search and retrieval tools", + "vector-stores": "Manage vector databases for embeddings", + new_usage: "View usage analytics and metrics", + logs: "Access request and response logs", + users: "Manage internal user accounts and permissions", + teams: "Create and manage teams for access control", + organizations: "Manage organizations and their members", + budgets: "Set and monitor spending budgets", + api_ref: "Browse API documentation and endpoints", + "model-hub-table": "Explore available AI models and providers", + "learning-resources": "Access tutorials and documentation", + caching: "Configure response caching settings", + "transform-request": "Set up request transformation rules", + "cost-tracking": "Track and analyze API costs", + "ui-theme": "Customize dashboard appearance", + "tag-management": "Organize resources with tags", + prompts: "Manage and version prompt templates", + "claude-code-plugins": "Configure Claude Code plugins", + usage: "View legacy usage dashboard", + "router-settings": "Configure routing and load balancing settings", + "logging-and-alerts": "Set up logging and alert configurations", + "admin-panel": "Access admin panel and settings", +}; + +export interface PageMetadata { + page: string; + label: string; + group: string; + description: string; +} diff --git a/ui/litellm-dashboard/src/components/page_utils.test.ts b/ui/litellm-dashboard/src/components/page_utils.test.ts new file mode 100644 index 00000000000..fed6c381498 --- /dev/null +++ b/ui/litellm-dashboard/src/components/page_utils.test.ts @@ -0,0 +1,219 @@ +/** + * Tests to ensure page metadata stays in sync with leftnav configuration + * This catches issues when leftnav structure changes but page_utils/page_metadata aren't updated + */ + +import { describe, it, expect } from "vitest"; +import { getAvailablePages } from "./page_utils"; +import { menuGroups } from "./leftnav"; +import { pageDescriptions } from "./page_metadata"; + +describe("Page Utils - LeftNav Sync", () => { + it("should return all pages from leftnav configuration", () => { + const availablePages = getAvailablePages(); + + // Should have pages + expect(availablePages.length).toBeGreaterThan(0); + + // Each page should have required fields + availablePages.forEach((page) => { + expect(page).toHaveProperty("page"); + expect(page).toHaveProperty("label"); + expect(page).toHaveProperty("group"); + expect(page).toHaveProperty("description"); + expect(typeof page.page).toBe("string"); + expect(typeof page.label).toBe("string"); + expect(typeof page.group).toBe("string"); + expect(typeof page.description).toBe("string"); + }); + }); + + it("should include all navigable pages from menuGroups", () => { + const availablePages = getAvailablePages(); + const availablePageKeys = availablePages.map((p) => p.page); + + // Collect all page keys from menuGroups (excluding parent containers) + const menuPageKeys: string[] = []; + const excludedParents = ["tools", "experimental", "settings"]; + + menuGroups.forEach((group) => { + group.items.forEach((item) => { + if (item.page && !excludedParents.includes(item.page)) { + menuPageKeys.push(item.page); + } + + // Add children + if (item.children) { + item.children.forEach((child) => { + menuPageKeys.push(child.page); + }); + } + }); + }); + + // Every menu page should be in available pages + menuPageKeys.forEach((pageKey) => { + expect( + availablePageKeys, + `Page "${pageKey}" from menuGroups should be in getAvailablePages() output` + ).toContain(pageKey); + }); + }); + + it("should not include parent container pages (tools, experimental, settings)", () => { + const availablePages = getAvailablePages(); + const availablePageKeys = availablePages.map((p) => p.page); + + const excludedParents = ["tools", "experimental", "settings"]; + + excludedParents.forEach((parent) => { + expect( + availablePageKeys, + `Parent container "${parent}" should not be in available pages` + ).not.toContain(parent); + }); + }); + + it("should have descriptions for all pages", () => { + const availablePages = getAvailablePages(); + + availablePages.forEach((page) => { + expect( + page.description, + `Page "${page.page}" should have a description` + ).toBeTruthy(); + + expect( + page.description, + `Page "${page.page}" should not have placeholder description` + ).not.toBe("No description available"); + }); + }); + + it("should have pageDescriptions entry for all navigable pages in menuGroups", () => { + // Collect all page keys from menuGroups + const menuPageKeys: string[] = []; + const excludedParents = ["tools", "experimental", "settings"]; + + menuGroups.forEach((group) => { + group.items.forEach((item) => { + if (item.page && !excludedParents.includes(item.page)) { + menuPageKeys.push(item.page); + } + + if (item.children) { + item.children.forEach((child) => { + menuPageKeys.push(child.page); + }); + } + }); + }); + + // Every menu page should have a description + const missingDescriptions: string[] = []; + menuPageKeys.forEach((pageKey) => { + if (!pageDescriptions[pageKey]) { + missingDescriptions.push(pageKey); + } + }); + + expect( + missingDescriptions, + `These pages are missing descriptions in page_metadata.ts: ${missingDescriptions.join(", ")}` + ).toHaveLength(0); + }); + + it("should not have orphaned descriptions (descriptions for pages not in menuGroups)", () => { + // Collect all page keys from menuGroups + const menuPageKeys: string[] = []; + const excludedParents = ["tools", "experimental", "settings"]; + + menuGroups.forEach((group) => { + group.items.forEach((item) => { + if (item.page && !excludedParents.includes(item.page)) { + menuPageKeys.push(item.page); + } + + if (item.children) { + item.children.forEach((child) => { + menuPageKeys.push(child.page); + }); + } + }); + }); + + // Check for descriptions that don't match any menu page + const orphanedDescriptions: string[] = []; + Object.keys(pageDescriptions).forEach((descKey) => { + if (!menuPageKeys.includes(descKey)) { + orphanedDescriptions.push(descKey); + } + }); + + expect( + orphanedDescriptions, + `These descriptions don't match any page in menuGroups: ${orphanedDescriptions.join(", ")}. Remove them or add the pages to leftnav.` + ).toHaveLength(0); + }); + + it("should have proper group hierarchy for nested pages", () => { + const availablePages = getAvailablePages(); + + // Find pages that should be nested (children of Tools, Experimental, Settings) + const nestedPages = availablePages.filter((page) => + page.group.includes(" > ") + ); + + // Each nested page should have parent > child format + nestedPages.forEach((page) => { + const parts = page.group.split(" > "); + expect( + parts.length, + `Nested page "${page.page}" should have exactly 2 parts in group hierarchy` + ).toBe(2); + + // Parent should be one of the group labels + const parentGroup = parts[0]; + const groupLabels = menuGroups.map((g) => g.groupLabel); + expect( + groupLabels, + `Parent group "${parentGroup}" for page "${page.page}" should be a valid group label` + ).toContain(parentGroup); + }); + }); + + it("should have unique page keys", () => { + const availablePages = getAvailablePages(); + const pageKeys = availablePages.map((p) => p.page); + const uniquePageKeys = new Set(pageKeys); + + expect( + pageKeys.length, + "All page keys should be unique (no duplicates)" + ).toBe(uniquePageKeys.size); + }); + + it("should match the structure expected by PageVisibilitySettings component", () => { + const availablePages = getAvailablePages(); + + // Group pages by their group (same logic as in PageVisibilitySettings) + const grouped: Record = {}; + availablePages.forEach((page) => { + if (!grouped[page.group]) { + grouped[page.group] = []; + } + grouped[page.group].push(page); + }); + + // Should have multiple groups + expect(Object.keys(grouped).length).toBeGreaterThan(1); + + // Each group should have at least one page + Object.entries(grouped).forEach(([groupName, pages]) => { + expect( + pages.length, + `Group "${groupName}" should have at least one page` + ).toBeGreaterThan(0); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/page_utils.ts b/ui/litellm-dashboard/src/components/page_utils.ts new file mode 100644 index 00000000000..7461f2b4a88 --- /dev/null +++ b/ui/litellm-dashboard/src/components/page_utils.ts @@ -0,0 +1,75 @@ +/** + * Utility functions for working with navigation pages + */ + +import { menuGroups } from "./leftnav"; +import { pageDescriptions, PageMetadata } from "./page_metadata"; +import { internalUserRoles } from "@/utils/roles"; + +/** + * Check if a page is accessible to internal users + * A page is accessible if: + * 1. It has no role restrictions, OR + * 2. Its roles include at least one internal user role + */ +const isPageAccessibleToInternalUsers = (pageRoles?: string[]): boolean => { + if (!pageRoles || pageRoles.length === 0) { + return true; // No role restrictions + } + + // Check if any of the page's roles match internal user roles + return pageRoles.some(role => internalUserRoles.includes(role)); +}; + +/** + * Get all available pages from the navigation menu configuration + * Used by UI Settings to display available pages for visibility control + * + * IMPORTANT: Only returns pages that internal users can access. + * Pages restricted to admin-only roles are excluded because internal users + * cannot see them regardless of the UI visibility setting. + */ +export const getAvailablePages = (): PageMetadata[] => { + const pages: PageMetadata[] = []; + + menuGroups.forEach((group) => { + group.items.forEach((item) => { + // Add top-level items (skip parent containers like 'tools', 'experimental', 'settings') + // Also skip items that internal users cannot access + if ( + item.page && + item.page !== "tools" && + item.page !== "experimental" && + item.page !== "settings" && + isPageAccessibleToInternalUsers(item.roles) + ) { + const label = typeof item.label === "string" ? item.label : item.key; + pages.push({ + page: item.page, + label: label, + group: group.groupLabel, + description: pageDescriptions[item.page] || "No description available", + }); + } + + // Add children items (also skip those internal users cannot access) + if (item.children) { + const parentLabel = typeof item.label === "string" ? item.label : item.key; + item.children.forEach((child) => { + // Include if internal users can access + if (isPageAccessibleToInternalUsers(child.roles)) { + const childLabel = typeof child.label === "string" ? child.label : child.key; + pages.push({ + page: child.page, + label: childLabel, + group: `${group.groupLabel} > ${parentLabel}`, + description: pageDescriptions[child.page] || "No description available", + }); + } + }); + } + }); + }); + + return pages; +}; From d76fb5932a28faeeee9864a747d3632170a478d7 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 09:54:43 +0530 Subject: [PATCH 28/70] Add xai websearch params support --- litellm/llms/xai/responses/transformation.py | 123 +++++++++- litellm/types/llms/xai.py | 23 ++ .../test_xai_responses_transformation.py | 213 +++++++++++++++++- 3 files changed, 346 insertions(+), 13 deletions(-) create mode 100644 litellm/types/llms/xai.py diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py index bd422c8d81e..90a7faf5a7b 100644 --- a/litellm/llms/xai/responses/transformation.py +++ b/litellm/llms/xai/responses/transformation.py @@ -1,10 +1,11 @@ -from typing import TYPE_CHECKING, Any, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import litellm from litellm._logging import verbose_logger from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams +from litellm.types.llms.xai import XAIWebSearchTool, XAIXSearchTool from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders @@ -49,6 +50,85 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): return supported_params + def _transform_web_search_tool(self, tool: Dict[str, Any]) -> Union[XAIWebSearchTool, Dict[str, Any]]: + """ + Transform web_search tool to XAI format. + + XAI supports web_search with specific filters: + - allowed_domains (max 5) + - excluded_domains (max 5) + - enable_image_understanding + + XAI does NOT support search_context_size (OpenAI-specific). + """ + xai_tool: Dict[str, Any] = {"type": "web_search"} + + # Remove search_context_size if present (not supported by XAI) + if "search_context_size" in tool: + verbose_logger.info( + "XAI does not support 'search_context_size' parameter. Removing it from web_search tool." + ) + + # Handle filters (XAI-specific structure) + filters = {} + if "allowed_domains" in tool: + allowed_domains = tool["allowed_domains"] + filters["allowed_domains"] = allowed_domains + + if "excluded_domains" in tool: + excluded_domains = tool["excluded_domains"] + filters["excluded_domains"] = excluded_domains + + # Add filters if any were specified + if filters: + xai_tool["filters"] = filters + + # Handle enable_image_understanding (top-level in XAI format) + if "enable_image_understanding" in tool: + xai_tool["enable_image_understanding"] = tool["enable_image_understanding"] + + return xai_tool + + def _transform_x_search_tool(self, tool: Dict[str, Any]) -> Union[XAIXSearchTool, Dict[str, Any]]: + """ + Transform x_search tool to XAI format. + + XAI supports x_search with specific parameters: + - allowed_x_handles (max 10) + - excluded_x_handles (max 10) + - from_date (ISO8601: YYYY-MM-DD) + - to_date (ISO8601: YYYY-MM-DD) + - enable_image_understanding + - enable_video_understanding + """ + xai_tool: Dict[str, Any] = {"type": "x_search"} + + # Handle allowed_x_handles + if "allowed_x_handles" in tool: + allowed_handles = tool["allowed_x_handles"] + xai_tool["allowed_x_handles"] = allowed_handles + + # Handle excluded_x_handles + if "excluded_x_handles" in tool: + excluded_handles = tool["excluded_x_handles"] + xai_tool["excluded_x_handles"] = excluded_handles + + # Handle date range + if "from_date" in tool: + xai_tool["from_date"] = tool["from_date"] + + if "to_date" in tool: + xai_tool["to_date"] = tool["to_date"] + + # Handle media understanding flags + if "enable_image_understanding" in tool: + xai_tool["enable_image_understanding"] = tool["enable_image_understanding"] + + if "enable_video_understanding" in tool: + xai_tool["enable_video_understanding"] = tool["enable_video_understanding"] + + return xai_tool + def map_openai_params( self, response_api_optional_params: ResponsesAPIOptionalRequestParams, @@ -61,7 +141,9 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): Handles XAI-specific transformations: 1. Drops 'instructions' parameter (not supported) 2. Transforms code_interpreter tools to remove 'container' field - 3. Sets store=false when images are detected (recommended by XAI) + 3. Transforms web_search tools to XAI format (removes search_context_size, adds filters) + 4. Transforms x_search tools to XAI format + 5. Sets store=false when images are detected (recommended by XAI) """ params = dict(response_api_optional_params) @@ -72,7 +154,7 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): ) params.pop("instructions") - # Transform code_interpreter tools - remove container field + # Transform tools if "tools" in params and params["tools"]: tools_list = params["tools"] # Ensure tools is a list for iteration @@ -81,15 +163,36 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): transformed_tools: List[Any] = [] for tool in tools_list: - if isinstance(tool, dict) and tool.get("type") == "code_interpreter": - # XAI supports code_interpreter but doesn't use the container field - # Keep only the type field - verbose_logger.debug( - "XAI: Transforming code_interpreter tool, removing container field" - ) - transformed_tools.append({"type": "code_interpreter"}) + if isinstance(tool, dict): + tool_type = tool.get("type") + + if tool_type == "code_interpreter": + # XAI supports code_interpreter but doesn't use the container field + verbose_logger.debug( + "XAI: Transforming code_interpreter tool, removing container field" + ) + transformed_tools.append({"type": "code_interpreter"}) + + elif tool_type == "web_search": + # Transform web_search to XAI format + verbose_logger.debug( + "XAI: Transforming web_search tool to XAI format" + ) + transformed_tools.append(self._transform_web_search_tool(tool)) + + elif tool_type == "x_search": + # Transform x_search to XAI format + verbose_logger.debug( + "XAI: Transforming x_search tool to XAI format" + ) + transformed_tools.append(self._transform_x_search_tool(tool)) + + else: + # Keep other tools as-is + transformed_tools.append(tool) else: transformed_tools.append(tool) + params["tools"] = transformed_tools return params diff --git a/litellm/types/llms/xai.py b/litellm/types/llms/xai.py new file mode 100644 index 00000000000..9de8dfc7add --- /dev/null +++ b/litellm/types/llms/xai.py @@ -0,0 +1,23 @@ +from typing import List, Literal, Optional, TypedDict + + +class XAIWebSearchFilters(TypedDict, total=False): + """Filters for XAI web search tool""" + allowed_domains: Optional[List[str]] # Max 5 domains + excluded_domains: Optional[List[str]] # Max 5 domains + +class XAIWebSearchTool(TypedDict, total=False): + """XAI web search tool configuration""" + type: Literal["web_search"] + filters: Optional[XAIWebSearchFilters] + enable_image_understanding: Optional[bool] + +class XAIXSearchTool(TypedDict, total=False): + """XAI X (Twitter) search tool configuration""" + type: Literal["x_search"] + allowed_x_handles: Optional[List[str]] # Max 10 handles + excluded_x_handles: Optional[List[str]] # Max 10 handles + from_date: Optional[str] # ISO8601 format: YYYY-MM-DD + to_date: Optional[str] # ISO8601 format: YYYY-MM-DD + enable_image_understanding: Optional[bool] + enable_video_understanding: Optional[bool] \ No newline at end of file diff --git a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py index c0871d3b9b7..dc06d6a1b0d 100644 --- a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py +++ b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py @@ -6,16 +6,17 @@ transformations for the Responses API. Source: litellm/llms/xai/responses/transformation.py """ -import sys import os +import sys sys.path.insert(0, os.path.abspath("../../../../..")) import pytest -from litellm.types.utils import LlmProviders -from litellm.utils import ProviderConfigManager + from litellm.llms.xai.responses.transformation import XAIResponsesAPIConfig from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams +from litellm.types.utils import LlmProviders +from litellm.utils import ProviderConfigManager class TestXAIResponsesAPITransformation: @@ -110,3 +111,209 @@ class TestXAIResponsesAPITransformation: ) assert url_with_slash == "https://api.x.ai/v1/responses", "Should handle trailing slash" + def test_web_search_tool_transformation(self): + """Test that web_search tools are transformed to XAI format""" + config = XAIResponsesAPIConfig() + + # Test with allowed_domains + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "web_search", + "allowed_domains": ["wikipedia.org", "x.ai"], + "enable_image_understanding": True + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + assert "tools" in result + assert len(result["tools"]) == 1 + tool = result["tools"][0] + assert tool["type"] == "web_search" + assert "filters" in tool + assert tool["filters"]["allowed_domains"] == ["wikipedia.org", "x.ai"] + assert tool["enable_image_understanding"] is True + + def test_web_search_search_context_size_removed(self): + """Test that search_context_size is removed from web_search tools""" + config = XAIResponsesAPIConfig() + + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "web_search", + "search_context_size": "high" # Not supported by XAI + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + assert "tools" in result + assert len(result["tools"]) == 1 + tool = result["tools"][0] + assert tool["type"] == "web_search" + assert "search_context_size" not in tool + + def test_web_search_excluded_domains(self): + """Test web_search with excluded_domains""" + config = XAIResponsesAPIConfig() + + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "web_search", + "excluded_domains": ["example.com", "test.com"] + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + tool = result["tools"][0] + assert "filters" in tool + assert tool["filters"]["excluded_domains"] == ["example.com", "test.com"] + + def test_web_search_domains_limit(self): + """Test that allowed_domains and excluded_domains are limited to 5""" + config = XAIResponsesAPIConfig() + + # Test with more than 5 allowed_domains + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "web_search", + "allowed_domains": ["d1.com", "d2.com", "d3.com", "d4.com", "d5.com", "d6.com", "d7.com"] + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + tool = result["tools"][0] + assert len(tool["filters"]["allowed_domains"]) == 7 + + def test_x_search_tool_transformation(self): + """Test that x_search tools are transformed correctly""" + config = XAIResponsesAPIConfig() + + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "x_search", + "allowed_x_handles": ["elonmusk", "xai"], + "from_date": "2025-01-01", + "to_date": "2025-01-28", + "enable_image_understanding": True, + "enable_video_understanding": True + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + assert "tools" in result + assert len(result["tools"]) == 1 + tool = result["tools"][0] + assert tool["type"] == "x_search" + assert tool["allowed_x_handles"] == ["elonmusk", "xai"] + assert tool["from_date"] == "2025-01-01" + assert tool["to_date"] == "2025-01-28" + assert tool["enable_image_understanding"] is True + assert tool["enable_video_understanding"] is True + + def test_x_search_excluded_handles(self): + """Test x_search with excluded_x_handles""" + config = XAIResponsesAPIConfig() + + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "x_search", + "excluded_x_handles": ["spam_account", "bot_account"] + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + tool = result["tools"][0] + assert tool["excluded_x_handles"] == ["spam_account", "bot_account"] + + def test_mixed_tools(self): + """Test transformation with multiple tool types""" + config = XAIResponsesAPIConfig() + + params = ResponsesAPIOptionalRequestParams( + tools=[ + { + "type": "code_interpreter", + "container": {"type": "auto"} + }, + { + "type": "web_search", + "allowed_domains": ["wikipedia.org"] + }, + { + "type": "x_search", + "allowed_x_handles": ["elonmusk"] + }, + { + "type": "function", + "name": "get_weather", + "description": "Get weather", + "parameters": {"type": "object"} + } + ] + ) + + result = config.map_openai_params( + response_api_optional_params=params, + model="grok-4-1-fast", + drop_params=False + ) + + assert len(result["tools"]) == 4 + + # Verify code_interpreter + assert result["tools"][0]["type"] == "code_interpreter" + assert "container" not in result["tools"][0] + + # Verify web_search + assert result["tools"][1]["type"] == "web_search" + assert "filters" in result["tools"][1] + + # Verify x_search + assert result["tools"][2]["type"] == "x_search" + assert result["tools"][2]["allowed_x_handles"] == ["elonmusk"] + + # Verify function tool is unchanged + assert result["tools"][3]["type"] == "function" + assert result["tools"][3]["name"] == "get_weather" + From 28ca9912967093f6ee1c41a93c31370c85fc7e83 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 20:52:07 -0800 Subject: [PATCH 29/70] Allow dynamic setting of store_prompts_in_spend_logs --- litellm/proxy/_types.py | 8 + litellm/proxy/proxy_server.py | 101 ++++++++- .../spend_tracking/spend_tracking_utils.py | 17 +- .../test_spend_tracking_utils.py | 46 +++++ tests/test_litellm/proxy/test_proxy_server.py | 194 ++++++++++++++++++ 5 files changed, 361 insertions(+), 5 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index c854d81ec71..e15179a8e29 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2073,6 +2073,14 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase): None, description="Controls how non-admin users interact with MCP servers in the dashboard. 'restricted' shows only accessible servers, 'view_all' lists every server in read-only mode.", ) + store_prompts_in_spend_logs: Optional[bool] = Field( + None, + description="If True, stores request messages and responses in spend logs. Default is False.", + ) + maximum_spend_logs_retention_period: Optional[str] = Field( + None, + description="Maximum retention period for spend logs (e.g., '7d' for 7 days). Logs older than this will be deleted.", + ) class ConfigYAML(LiteLLMPydanticObjectBase): diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 12e34e221d7..e7fcd5f4422 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -3540,6 +3540,79 @@ class ProxyConfig: llm_router=llm_router, ) + async def _reschedule_spend_log_cleanup_job(self): + """ + Reschedule the spend log cleanup job based on current general_settings. + This is called when maximum_spend_logs_retention_period is updated dynamically. + If the retention period is None, the job will be removed. + """ + global scheduler, general_settings, prisma_client + if scheduler is None: + return + + # Remove existing job if it exists + try: + scheduler.remove_job("spend_log_cleanup_job") + verbose_proxy_logger.info("Removed existing spend log cleanup job") + except Exception: + pass # Job might not exist, which is fine + + # Schedule new job if retention period is set (not None) + retention_period = general_settings.get("maximum_spend_logs_retention_period") + if retention_period is not None: + from litellm.proxy.db.db_transaction_queue.spend_log_cleanup import ( + SpendLogCleanup, + ) + + spend_log_cleanup = SpendLogCleanup() + cleanup_cron = general_settings.get("maximum_spend_logs_cleanup_cron") + + if cleanup_cron: + from apscheduler.triggers.cron import CronTrigger + + try: + cron_trigger = CronTrigger.from_crontab(cleanup_cron) + scheduler.add_job( + spend_log_cleanup.cleanup_old_spend_logs, + cron_trigger, + args=[prisma_client], + id="spend_log_cleanup_job", + replace_existing=True, + misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME, + ) + verbose_proxy_logger.info( + f"Spend log cleanup rescheduled with cron: {cleanup_cron}" + ) + except ValueError: + verbose_proxy_logger.error( + f"Invalid maximum_spend_logs_cleanup_cron value: {cleanup_cron}" + ) + else: + # Interval-based scheduling (existing behavior) + from litellm.litellm_core_utils.duration_parser import duration_in_seconds + + retention_interval = general_settings.get( + "maximum_spend_logs_retention_interval", "1d" + ) + try: + interval_seconds = duration_in_seconds(retention_interval) + scheduler.add_job( + spend_log_cleanup.cleanup_old_spend_logs, + "interval", + seconds=interval_seconds + random.randint(0, 60), + args=[prisma_client], + id="spend_log_cleanup_job", + replace_existing=True, + misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME, + ) + verbose_proxy_logger.info( + f"Spend log cleanup rescheduled with interval: {retention_interval}" + ) + except ValueError: + verbose_proxy_logger.error( + "Invalid maximum_spend_logs_retention_interval value" + ) + async def _update_general_settings(self, db_general_settings: Optional[Json]): """ Pull from DB, read general settings value @@ -3579,6 +3652,32 @@ class ProxyConfig: if "ui_access_mode" in _general_settings: general_settings["ui_access_mode"] = _general_settings["ui_access_mode"] + ## STORE PROMPTS IN SPEND LOGS ## + if "store_prompts_in_spend_logs" in _general_settings: + value = _general_settings["store_prompts_in_spend_logs"] + # Normalize case: handle True/true/TRUE, False/false/FALSE, None/null + if value is None: + general_settings["store_prompts_in_spend_logs"] = None + elif isinstance(value, bool): + general_settings["store_prompts_in_spend_logs"] = value + elif isinstance(value, str): + # Case-insensitive string comparison + general_settings["store_prompts_in_spend_logs"] = ( + value.lower() == "true" + ) + else: + # For other types, convert to bool + general_settings["store_prompts_in_spend_logs"] = bool(value) + + ## MAXIMUM SPEND LOGS RETENTION PERIOD ## + if "maximum_spend_logs_retention_period" in _general_settings: + old_value = general_settings.get("maximum_spend_logs_retention_period") + new_value = _general_settings["maximum_spend_logs_retention_period"] + general_settings["maximum_spend_logs_retention_period"] = new_value + # Reschedule cleanup job if value changed (including when set to None) + if old_value != new_value: + await self._reschedule_spend_log_cleanup_job() + def _update_config_fields( self, current_config: dict, @@ -4688,7 +4787,7 @@ class ProxyStartupEvent: proxy_logging_obj: ProxyLogging, ): """Initializes scheduled background jobs""" - global store_model_in_db + global store_model_in_db, scheduler # MEMORY LEAK FIX: Configure scheduler with optimized settings # Memray analysis showed APScheduler's normalize() and _apply_jitter() causing diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py index db4e4beec21..bd148ecb481 100644 --- a/litellm/proxy/spend_tracking/spend_tracking_utils.py +++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py @@ -759,10 +759,19 @@ def _should_store_prompts_and_responses_in_spend_logs() -> bool: from litellm.proxy.proxy_server import general_settings from litellm.secret_managers.main import get_secret_bool - return ( - general_settings.get("store_prompts_in_spend_logs") is True - or get_secret_bool("STORE_PROMPTS_IN_SPEND_LOGS") is True - ) + # Check general_settings (from DB or proxy_config.yaml) + store_prompts_value = general_settings.get("store_prompts_in_spend_logs") + + # Normalize case: handle True/true/TRUE, False/false/FALSE, None/null + if store_prompts_value is True: + return True + elif isinstance(store_prompts_value, str): + # Case-insensitive string comparison + if store_prompts_value.lower() == "true": + return True + + # Also check environment variable + return get_secret_bool("STORE_PROMPTS_IN_SPEND_LOGS") is True def _get_status_for_spend_log( diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py index dd4cbfa2e7f..1972103c3d2 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py @@ -23,6 +23,7 @@ from litellm.proxy.spend_tracking.spend_tracking_utils import ( _get_response_for_spend_logs_payload, _get_vector_store_request_for_spend_logs_payload, _sanitize_request_body_for_spend_logs_payload, + _should_store_prompts_and_responses_in_spend_logs, get_logging_payload, ) from litellm.types.utils import ( @@ -911,3 +912,48 @@ def test_spend_logs_redacts_request_and_response_when_turn_off_message_logging_e parsed_response = json.loads(response_result) assert parsed_response == {"text": "redacted-by-litellm"} + +@patch("litellm.secret_managers.main.get_secret_bool") +def test_should_store_prompts_and_responses_in_spend_logs_case_insensitive_string( + mock_get_secret_bool, +): + """ + Test that _should_store_prompts_and_responses_in_spend_logs handles + case-insensitive string values for store_prompts_in_spend_logs in general_settings. + """ + # Test case-insensitive string "true" variations + for true_value in ["true", "TRUE", "True", "TrUe"]: + with patch("litellm.proxy.proxy_server.general_settings", {"store_prompts_in_spend_logs": true_value}): + mock_get_secret_bool.return_value = False # Ensure env var is False + result = _should_store_prompts_and_responses_in_spend_logs() + assert result is True, f"Expected True for '{true_value}', got {result}" + + # Test boolean True + with patch("litellm.proxy.proxy_server.general_settings", {"store_prompts_in_spend_logs": True}): + mock_get_secret_bool.return_value = False + result = _should_store_prompts_and_responses_in_spend_logs() + assert result is True, f"Expected True for boolean True, got {result}" + + # Test that non-true values fall back to environment variable + for false_value in [False, None, "false", "FALSE", "False", "anything"]: + with patch("litellm.proxy.proxy_server.general_settings", {"store_prompts_in_spend_logs": false_value}): + # When env var is True, should return True + mock_get_secret_bool.return_value = True + result = _should_store_prompts_and_responses_in_spend_logs() + assert result is True, f"Expected True (from env var) for '{false_value}', got {result}" + + # When env var is False, should return False + mock_get_secret_bool.return_value = False + result = _should_store_prompts_and_responses_in_spend_logs() + assert result is False, f"Expected False (from env var) for '{false_value}', got {result}" + + # Test when general_settings doesn't have the key at all + with patch("litellm.proxy.proxy_server.general_settings", {}): + mock_get_secret_bool.return_value = True + result = _should_store_prompts_and_responses_in_spend_logs() + assert result is True, "Expected True (from env var) when key missing, got False" + + mock_get_secret_bool.return_value = False + result = _should_store_prompts_and_responses_in_spend_logs() + assert result is False, "Expected False (from env var) when key missing, got True" + diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 22a60f220ee..9069a57643b 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -5121,3 +5121,197 @@ async def test_model_list_no_scope_parameter(monkeypatch): # Verify router methods were NOT called (normal path) mock_router.get_model_names.assert_not_called() mock_router.get_model_access_groups.assert_not_called() + + +@pytest.mark.asyncio +async def test_update_general_settings_store_prompts_in_spend_logs(monkeypatch): + """ + Test that _update_general_settings correctly normalizes store_prompts_in_spend_logs + values (handles bool, string, None, and other types). + """ + from unittest.mock import patch + + from litellm.proxy.proxy_server import ProxyConfig + + proxy_config = ProxyConfig() + + # Test Case 1: None value + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": None} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is None + + # Test Case 2: bool True + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": True} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is True + + # Test Case 3: bool False + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": False} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is False + + # Test Case 4: string "true" (lowercase) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "true"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is True + + # Test Case 5: string "True" (capitalized) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "True"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is True + + # Test Case 6: string "TRUE" (uppercase) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "TRUE"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is True + + # Test Case 7: string "false" (lowercase) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "false"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is False + + # Test Case 8: string "False" (capitalized) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "False"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is False + + # Test Case 9: string "FALSE" (uppercase) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "FALSE"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is False + + # Test Case 10: other string value (should be False) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": "invalid"} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is False + + # Test Case 11: integer 1 (should be True) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": 1} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is True + + # Test Case 12: integer 0 (should be False) + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs): + await proxy_config._update_general_settings( + {"store_prompts_in_spend_logs": 0} + ) + assert mock_gs.get("store_prompts_in_spend_logs") is False + + +@pytest.mark.asyncio +async def test_update_general_settings_maximum_spend_logs_retention_period(monkeypatch): + """ + Test that _update_general_settings correctly handles maximum_spend_logs_retention_period + and reschedules cleanup job when value changes. + """ + from unittest.mock import AsyncMock, patch + + from litellm.proxy.proxy_server import ProxyConfig + + proxy_config = ProxyConfig() + + # Test Case 1: Setting a new value should reschedule cleanup job + mock_reschedule = AsyncMock() + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object( + proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule + ): + await proxy_config._update_general_settings( + {"maximum_spend_logs_retention_period": "7d"} + ) + assert mock_gs.get("maximum_spend_logs_retention_period") == "7d" + mock_reschedule.assert_called_once() + + # Test Case 2: Setting the same value should not reschedule + mock_reschedule.reset_mock() + mock_gs = {"maximum_spend_logs_retention_period": "7d"} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object( + proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule + ): + await proxy_config._update_general_settings( + {"maximum_spend_logs_retention_period": "7d"} + ) + assert mock_gs.get("maximum_spend_logs_retention_period") == "7d" + mock_reschedule.assert_not_called() + + # Test Case 3: Changing value should reschedule + mock_reschedule.reset_mock() + mock_gs = {"maximum_spend_logs_retention_period": "7d"} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object( + proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule + ): + await proxy_config._update_general_settings( + {"maximum_spend_logs_retention_period": "30d"} + ) + assert mock_gs.get("maximum_spend_logs_retention_period") == "30d" + mock_reschedule.assert_called_once() + + # Test Case 4: Setting to None should reschedule + mock_reschedule.reset_mock() + mock_gs = {"maximum_spend_logs_retention_period": "7d"} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object( + proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule + ): + await proxy_config._update_general_settings( + {"maximum_spend_logs_retention_period": None} + ) + assert mock_gs.get("maximum_spend_logs_retention_period") is None + mock_reschedule.assert_called_once() + + # Test Case 5: Changing from None to a value should reschedule + mock_reschedule.reset_mock() + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object( + proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule + ): + await proxy_config._update_general_settings( + {"maximum_spend_logs_retention_period": "24h"} + ) + assert mock_gs.get("maximum_spend_logs_retention_period") == "24h" + mock_reschedule.assert_called_once() + + # Test Case 6: Setting None when already None should not reschedule + mock_reschedule.reset_mock() + mock_gs = {} + with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object( + proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule + ): + await proxy_config._update_general_settings( + {"maximum_spend_logs_retention_period": None} + ) + assert mock_gs.get("maximum_spend_logs_retention_period") is None + mock_reschedule.assert_not_called() From 6fb2a0d11f3e42a087d281db555253cfe84f43fd Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 11:35:18 +0530 Subject: [PATCH 30/70] Fix: output_tokens_details.reasoning_tokens None --- litellm/llms/anthropic/chat/transformation.py | 2 +- .../transformation.py | 4 + litellm/types/llms/openai.py | 6 +- .../test_responses_api_bridge_non_stream.py | 380 +++++++++++++++++- 4 files changed, 387 insertions(+), 5 deletions(-) diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 82eccee596d..5ba901f9729 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -1369,7 +1369,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): else 0 ) completion_token_details = CompletionTokensDetailsWrapper( - reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None, + reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else 0, text_tokens=completion_tokens - reasoning_tokens if reasoning_tokens > 0 else completion_tokens, ) total_tokens = prompt_tokens + completion_tokens diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index 3badbc50578..41abbca755c 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -1750,6 +1750,8 @@ class LiteLLMCompletionResponsesConfig: and prompt_details.cached_tokens is not None ): input_details_dict["cached_tokens"] = prompt_details.cached_tokens + else: + input_details_dict["cached_tokens"] = 0 if ( hasattr(prompt_details, "text_tokens") @@ -1782,6 +1784,8 @@ class LiteLLMCompletionResponsesConfig: output_details_dict["reasoning_tokens"] = ( completion_details.reasoning_tokens ) + else: + output_details_dict["reasoning_tokens"] = 0 if ( hasattr(completion_details, "text_tokens") diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index 9fea1a8b937..b79f098c0cb 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -1114,7 +1114,7 @@ class ResponsesAPIRequestParams(ResponsesAPIOptionalRequestParams, total=False): class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject): - reasoning_tokens: Optional[int] = None + reasoning_tokens: int text_tokens: Optional[int] = None @@ -1123,7 +1123,7 @@ class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject): class InputTokensDetails(BaseLiteLLMOpenAIResponseObject): audio_tokens: Optional[int] = None - cached_tokens: Optional[int] = None + cached_tokens: int text_tokens: Optional[int] = None model_config = {"extra": "allow"} @@ -1193,7 +1193,7 @@ class ResponsesAPIResponse(BaseLiteLLMOpenAIResponseObject): status: Optional[str] = None text: Optional[Union["ResponseText", Dict[str, Any]]] = None truncation: Optional[Literal["auto", "disabled"]] = None - usage: Optional[Any] = None + usage: Optional[ResponseAPIUsage] = None user: Optional[str] = None store: Optional[bool] = None # Define private attributes using PrivateAttr diff --git a/tests/test_litellm/test_responses_api_bridge_non_stream.py b/tests/test_litellm/test_responses_api_bridge_non_stream.py index 8f72debfc5d..9efe7d3378d 100644 --- a/tests/test_litellm/test_responses_api_bridge_non_stream.py +++ b/tests/test_litellm/test_responses_api_bridge_non_stream.py @@ -1,9 +1,32 @@ +import os +import sys +from typing import Optional +from unittest.mock import Mock + +import pytest + +sys.path.insert(0, os.path.abspath("../..")) + from litellm.completion_extras.litellm_responses_transformation.handler import ( ResponsesToCompletionBridgeHandler, ) -from litellm.types.llms.openai import ResponsesAPIResponse +from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, +) +from litellm.types.llms.openai import ( + InputTokensDetails, + OutputTokensDetails, + ResponsesAPIResponse, +) +from litellm.types.utils import Choices, Message, ModelResponse, Usage +""" +Test that all providers can transform completion responses to Responses API format +without breaking due to required fields in InputTokensDetails and OutputTokensDetails. +This is a regression test for the change where reasoning_tokens and cached_tokens +were made non-optional (must be int, not Optional[int]). +""" class _CompletedEvent: def __init__(self, response): self.response = response @@ -42,3 +65,358 @@ def test_should_collect_response_from_stream(): assert collected.id == "resp-1" assert collected._hidden_params.get("headers") == {"x-test": "1"} + + +def create_mock_completion_response( + model: str = "gpt-4", + prompt_tokens: int = 10, + completion_tokens: int = 20, + total_tokens: int = 30, + reasoning_tokens: Optional[int] = None, + cached_tokens: Optional[int] = None, + text_tokens: Optional[int] = None, +) -> ModelResponse: + """ + Create a mock ModelResponse (chat completion) with various token details. + + This simulates responses from different providers that may or may not include + reasoning_tokens, cached_tokens, etc. + """ + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ) + + # Add prompt_tokens_details if we have cached_tokens or text_tokens + if cached_tokens is not None or text_tokens is not None: + from litellm.types.utils import PromptTokensDetails + usage.prompt_tokens_details = PromptTokensDetails( + cached_tokens=cached_tokens, + text_tokens=text_tokens, + ) + + # Add completion_tokens_details if we have reasoning_tokens or text_tokens + if reasoning_tokens is not None or text_tokens is not None: + from litellm.types.utils import CompletionTokensDetails + usage.completion_tokens_details = CompletionTokensDetails( + reasoning_tokens=reasoning_tokens, + text_tokens=text_tokens, + ) + + return ModelResponse( + id="chatcmpl-test", + created=1234567890, + model=model, + object="chat.completion", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="Test response", + role="assistant", + ), + ) + ], + usage=usage, + ) + + +def test_transform_usage_no_token_details(): + """ + Test that transformation works when completion response has NO token details. + + This simulates providers that don't return detailed token breakdowns. + """ + completion_response = create_mock_completion_response( + model="gpt-4", + prompt_tokens=10, + completion_tokens=20, + total_tokens=30, + ) + + # Transform to Responses API usage format + responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + completion_response + ) + + # Should succeed without errors + assert responses_usage.input_tokens == 10 + assert responses_usage.output_tokens == 20 + assert responses_usage.total_tokens == 30 + + # Token details should not be present when not provided + assert responses_usage.input_tokens_details is None + assert responses_usage.output_tokens_details is None + + print("✓ Transformation works with no token details") + + +def test_transform_usage_with_cached_tokens_only(): + """ + Test transformation when only cached_tokens is provided (no reasoning_tokens). + + This simulates providers like Anthropic that support prompt caching but not reasoning. + """ + completion_response = create_mock_completion_response( + model="claude-3-opus", + prompt_tokens=100, + completion_tokens=50, + total_tokens=150, + cached_tokens=80, # Has cached tokens + reasoning_tokens=None, # No reasoning tokens + ) + + responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + completion_response + ) + + # Should succeed and default reasoning_tokens to 0 + assert responses_usage.input_tokens == 100 + assert responses_usage.output_tokens == 50 + assert responses_usage.total_tokens == 150 + + # Input details should be present with cached_tokens + assert responses_usage.input_tokens_details is not None + assert isinstance(responses_usage.input_tokens_details, InputTokensDetails) + assert responses_usage.input_tokens_details.cached_tokens == 80 + + # Output details should not be present (no reasoning_tokens provided) + assert responses_usage.output_tokens_details is None + + print("✓ Transformation works with cached_tokens only") + + +def test_transform_usage_with_reasoning_tokens_only(): + """ + Test transformation when only reasoning_tokens is provided (no cached_tokens). + + This simulates providers like OpenAI o1 that support reasoning but not caching. + """ + completion_response = create_mock_completion_response( + model="o1-preview", + prompt_tokens=50, + completion_tokens=100, + total_tokens=150, + cached_tokens=None, # No cached tokens + reasoning_tokens=60, # Has reasoning tokens + ) + + responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + completion_response + ) + + # Should succeed and default cached_tokens to 0 + assert responses_usage.input_tokens == 50 + assert responses_usage.output_tokens == 100 + assert responses_usage.total_tokens == 150 + + # Input details should not be present (no cached_tokens provided) + assert responses_usage.input_tokens_details is None + + # Output details should be present with reasoning_tokens + assert responses_usage.output_tokens_details is not None + assert isinstance(responses_usage.output_tokens_details, OutputTokensDetails) + assert responses_usage.output_tokens_details.reasoning_tokens == 60 + + print("✓ Transformation works with reasoning_tokens only") + + +def test_transform_usage_with_both_token_details(): + """ + Test transformation when both cached_tokens and reasoning_tokens are provided. + + This simulates advanced providers that support both features. + """ + completion_response = create_mock_completion_response( + model="gpt-4o", + prompt_tokens=100, + completion_tokens=80, + total_tokens=180, + cached_tokens=50, + reasoning_tokens=30, + text_tokens=50, # Also include text_tokens + ) + + responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + completion_response + ) + + # Should succeed with all details + assert responses_usage.input_tokens == 100 + assert responses_usage.output_tokens == 80 + assert responses_usage.total_tokens == 180 + + # Input details should have cached_tokens + assert responses_usage.input_tokens_details is not None + assert responses_usage.input_tokens_details.cached_tokens == 50 + assert responses_usage.input_tokens_details.text_tokens == 50 + + # Output details should have reasoning_tokens + assert responses_usage.output_tokens_details is not None + assert responses_usage.output_tokens_details.reasoning_tokens == 30 + assert responses_usage.output_tokens_details.text_tokens == 50 + + print("✓ Transformation works with both cached_tokens and reasoning_tokens") + + +def test_transform_usage_with_zero_values(): + """ + Test transformation when token details are explicitly set to 0. + + This ensures 0 values are preserved and not treated as None. + """ + completion_response = create_mock_completion_response( + model="gpt-4", + prompt_tokens=100, + completion_tokens=50, + total_tokens=150, + cached_tokens=0, # Explicitly 0 + reasoning_tokens=0, # Explicitly 0 + ) + + responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + completion_response + ) + + # Should preserve 0 values + assert responses_usage.input_tokens_details is not None + assert responses_usage.input_tokens_details.cached_tokens == 0 + + assert responses_usage.output_tokens_details is not None + assert responses_usage.output_tokens_details.reasoning_tokens == 0 + + print("✓ Transformation preserves explicit 0 values") + + +def test_input_tokens_details_requires_cached_tokens(): + """ + Test that InputTokensDetails requires cached_tokens to be an int (not Optional). + + This is the core requirement that was changed. + """ + # Should work with cached_tokens=0 + details1 = InputTokensDetails(cached_tokens=0) + assert details1.cached_tokens == 0 + + # Should work with cached_tokens=100 + details2 = InputTokensDetails(cached_tokens=100) + assert details2.cached_tokens == 100 + + # Should fail without cached_tokens (this is the breaking change) + with pytest.raises((TypeError, ValueError)): + InputTokensDetails() # Missing required field + + print("✓ InputTokensDetails correctly requires cached_tokens") + + +def test_output_tokens_details_requires_reasoning_tokens(): + """ + Test that OutputTokensDetails requires reasoning_tokens to be an int (not Optional). + + This is the core requirement that was changed. + """ + # Should work with reasoning_tokens=0 + details1 = OutputTokensDetails(reasoning_tokens=0) + assert details1.reasoning_tokens == 0 + + # Should work with reasoning_tokens=100 + details2 = OutputTokensDetails(reasoning_tokens=100) + assert details2.reasoning_tokens == 100 + + # Should fail without reasoning_tokens (this is the breaking change) + with pytest.raises((TypeError, ValueError)): + OutputTokensDetails() # Missing required field + + print("✓ OutputTokensDetails correctly requires reasoning_tokens") + + +def test_all_providers_transformation_scenarios(): + """ + Test various provider scenarios to ensure none break after the field requirement change. + + This tests the most common scenarios across different providers: + - OpenAI: may have reasoning_tokens + - Anthropic: may have cached_tokens + - Azure: similar to OpenAI + - Other providers: basic usage only + """ + test_scenarios = [ + { + "name": "Basic provider (no details)", + "model": "gpt-3.5-turbo", + "kwargs": {}, + }, + { + "name": "OpenAI with reasoning", + "model": "o1-preview", + "kwargs": {"reasoning_tokens": 100}, + }, + { + "name": "Anthropic with caching", + "model": "claude-3-opus", + "kwargs": {"cached_tokens": 50}, + }, + { + "name": "OpenAI with caching", + "model": "gpt-4o", + "kwargs": {"cached_tokens": 30}, + }, + { + "name": "Full details (both)", + "model": "gpt-4o", + "kwargs": {"cached_tokens": 40, "reasoning_tokens": 60, "text_tokens": 100}, + }, + { + "name": "Zero values", + "model": "gpt-4", + "kwargs": {"cached_tokens": 0, "reasoning_tokens": 0}, + }, + ] + + for scenario in test_scenarios: + print(f"\nTesting: {scenario['name']}") + + completion_response = create_mock_completion_response( + model=scenario["model"], + **scenario["kwargs"] + ) + + # This should not raise any errors + responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + completion_response + ) + + # Basic assertions + assert responses_usage.input_tokens >= 0 + assert responses_usage.output_tokens >= 0 + assert responses_usage.total_tokens >= 0 + + # If input_tokens_details exists, cached_tokens must be an int + if responses_usage.input_tokens_details is not None: + assert isinstance(responses_usage.input_tokens_details.cached_tokens, int) + + # If output_tokens_details exists, reasoning_tokens must be an int + if responses_usage.output_tokens_details is not None: + assert isinstance(responses_usage.output_tokens_details.reasoning_tokens, int) + + print(f" ✓ {scenario['name']} transformation successful") + + print("\n✓ All provider scenarios work correctly") + + +if __name__ == "__main__": + # Run all tests + test_transform_usage_no_token_details() + test_transform_usage_with_cached_tokens_only() + test_transform_usage_with_reasoning_tokens_only() + test_transform_usage_with_both_token_details() + test_transform_usage_with_zero_values() + test_input_tokens_details_requires_cached_tokens() + test_output_tokens_details_requires_reasoning_tokens() + test_all_providers_transformation_scenarios() + + print("\n" + "="*60) + print("ALL TESTS PASSED!") + print("="*60) From bd15ebba849585859642153a61f9138bdd910486 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 11:51:26 +0530 Subject: [PATCH 31/70] fix: Pydantic will fail to parse it because cached_tokens is required but not provided --- litellm/types/llms/openai.py | 4 ++-- .../test_responses_api_bridge_non_stream.py | 24 +++++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index b79f098c0cb..299b47199ed 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -1114,7 +1114,7 @@ class ResponsesAPIRequestParams(ResponsesAPIOptionalRequestParams, total=False): class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject): - reasoning_tokens: int + reasoning_tokens: int = 0 text_tokens: Optional[int] = None @@ -1123,7 +1123,7 @@ class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject): class InputTokensDetails(BaseLiteLLMOpenAIResponseObject): audio_tokens: Optional[int] = None - cached_tokens: int + cached_tokens: int = 0 text_tokens: Optional[int] = None model_config = {"extra": "allow"} diff --git a/tests/test_litellm/test_responses_api_bridge_non_stream.py b/tests/test_litellm/test_responses_api_bridge_non_stream.py index 9efe7d3378d..c35ca1046fd 100644 --- a/tests/test_litellm/test_responses_api_bridge_non_stream.py +++ b/tests/test_litellm/test_responses_api_bridge_non_stream.py @@ -292,9 +292,9 @@ def test_transform_usage_with_zero_values(): def test_input_tokens_details_requires_cached_tokens(): """ - Test that InputTokensDetails requires cached_tokens to be an int (not Optional). + Test that InputTokensDetails has cached_tokens as an int with default value 0. - This is the core requirement that was changed. + This ensures backward compatibility while making the field non-optional. """ # Should work with cached_tokens=0 details1 = InputTokensDetails(cached_tokens=0) @@ -304,18 +304,18 @@ def test_input_tokens_details_requires_cached_tokens(): details2 = InputTokensDetails(cached_tokens=100) assert details2.cached_tokens == 100 - # Should fail without cached_tokens (this is the breaking change) - with pytest.raises((TypeError, ValueError)): - InputTokensDetails() # Missing required field + # Should work without cached_tokens (defaults to 0) + details3 = InputTokensDetails() + assert details3.cached_tokens == 0 - print("✓ InputTokensDetails correctly requires cached_tokens") + print("✓ InputTokensDetails correctly defaults cached_tokens to 0") def test_output_tokens_details_requires_reasoning_tokens(): """ - Test that OutputTokensDetails requires reasoning_tokens to be an int (not Optional). + Test that OutputTokensDetails has reasoning_tokens as an int with default value 0. - This is the core requirement that was changed. + This ensures backward compatibility while making the field non-optional. """ # Should work with reasoning_tokens=0 details1 = OutputTokensDetails(reasoning_tokens=0) @@ -325,11 +325,11 @@ def test_output_tokens_details_requires_reasoning_tokens(): details2 = OutputTokensDetails(reasoning_tokens=100) assert details2.reasoning_tokens == 100 - # Should fail without reasoning_tokens (this is the breaking change) - with pytest.raises((TypeError, ValueError)): - OutputTokensDetails() # Missing required field + # Should work without reasoning_tokens (defaults to 0) + details3 = OutputTokensDetails() + assert details3.reasoning_tokens == 0 - print("✓ OutputTokensDetails correctly requires reasoning_tokens") + print("✓ OutputTokensDetails correctly defaults reasoning_tokens to 0") def test_all_providers_transformation_scenarios(): From 58eca8fb28587512e2b6ce9825298dc9b52a7274 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 23:34:50 -0800 Subject: [PATCH 32/70] Spend logs setting modal --- .../useStoreRequestInSpendLogs.ts | 63 +++++++++++ .../SpendLogsSettingsModal.tsx | 100 ++++++++++++++++++ .../src/components/view_logs/index.tsx | 17 ++- 3 files changed, 179 insertions(+), 1 deletion(-) create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts create mode 100644 ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts new file mode 100644 index 00000000000..9c6211c3086 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts @@ -0,0 +1,63 @@ +import { useMutation, UseMutationResult } from "@tanstack/react-query"; +import { getProxyBaseUrl, getGlobalLitellmHeaderName } from "@/components/networking"; +import useAuthorized from "../useAuthorized"; + +export interface StoreRequestInSpendLogsParams { + store_prompts_in_spend_logs: boolean; + maximum_spend_logs_retention_period?: string; +} + +export interface StoreRequestInSpendLogsResponse { + message: string; +} + +const performStoreRequestInSpendLogs = async ( + accessToken: string, + params: StoreRequestInSpendLogsParams +): Promise => { + const proxyBaseUrl = getProxyBaseUrl(); + const url = proxyBaseUrl ? `${proxyBaseUrl}/config/update` : `/config/update`; + + const response = await fetch(url, { + method: "POST", + headers: { + [getGlobalLitellmHeaderName()]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + general_settings: { + store_prompts_in_spend_logs: params.store_prompts_in_spend_logs, + ...(params.maximum_spend_logs_retention_period && { + maximum_spend_logs_retention_period: params.maximum_spend_logs_retention_period, + }), + }, + }), + }); + + if (!response.ok) { + const errorData = await response.json().catch(() => ({})); + const errorMessage = + errorData?.error?.message || errorData?.message || errorData?.detail || "Failed to update spend logs settings"; + throw new Error(errorMessage); + } + + const data = await response.json(); + return data; +}; + +export const useStoreRequestInSpendLogs = (): UseMutationResult< + StoreRequestInSpendLogsResponse, + Error, + StoreRequestInSpendLogsParams +> => { + const { accessToken } = useAuthorized(); + + return useMutation({ + mutationFn: async (params: StoreRequestInSpendLogsParams) => { + if (!accessToken) { + throw new Error("Access token is required"); + } + return await performStoreRequestInSpendLogs(accessToken, params); + }, + }); +}; diff --git a/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx b/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx new file mode 100644 index 00000000000..cf8e51be94b --- /dev/null +++ b/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx @@ -0,0 +1,100 @@ +"use client"; + +import { StoreRequestInSpendLogsParams, useStoreRequestInSpendLogs } from "@/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs"; +import NotificationsManager from "@/components/molecules/notifications_manager"; +import { parseErrorMessage } from "@/components/shared/errorUtils"; +import { ClockCircleOutlined } from "@ant-design/icons"; +import { Button, Form, Input, Modal, Space, Switch } from "antd"; +import React from "react"; + +interface SpendLogsSettingsModalProps { + isVisible: boolean; + onCancel: () => void; + onSuccess?: () => void; +} + +const SpendLogsSettingsModal: React.FC = ({ isVisible, onCancel, onSuccess }) => { + const [form] = Form.useForm(); + const { mutateAsync, isPending } = useStoreRequestInSpendLogs(); + const storePromptsValue = Form.useWatch('store_prompts_in_spend_logs', form); + + const handleFormSubmit = async (formValues: StoreRequestInSpendLogsParams) => { + try { + await mutateAsync(formValues, { + onSuccess: () => { + NotificationsManager.success("Spend logs settings updated successfully"); + form.resetFields(); + onSuccess?.(); + }, + onError: (error) => { + NotificationsManager.fromBackend("Failed to save spend logs settings: " + parseErrorMessage(error)); + }, + }); + } catch (error) { + NotificationsManager.fromBackend("Failed to save spend logs settings: " + parseErrorMessage(error)); + } + }; + + const handleCancel = () => { + form.resetFields(); + onCancel(); + }; + + return ( + + + + + } + onCancel={handleCancel} + > +
    + +
    + Store Prompts in Spend Logs + form.setFieldValue('store_prompts_in_spend_logs', checked)} /> +
    +     + + + + } + /> + +
    +     + ); +}; + +export default SpendLogsSettingsModal; diff --git a/ui/litellm-dashboard/src/components/view_logs/index.tsx b/ui/litellm-dashboard/src/components/view_logs/index.tsx index 904d052073a..e25925f13b8 100644 --- a/ui/litellm-dashboard/src/components/view_logs/index.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/index.tsx @@ -12,7 +12,7 @@ import { RequestResponsePanel } from "./RequestResponsePanel"; import { ErrorViewer } from "./ErrorViewer"; import { internalUserRoles } from "../../utils/roles"; import { ConfigInfoMessage } from "./ConfigInfoMessage"; -import { Tooltip } from "antd"; +import { Button, Tooltip } from "antd"; import { KeyResponse, Team } from "../key_team_helpers/key_list"; import KeyInfoView from "../templates/key_info_view"; import { SessionView } from "./SessionView"; @@ -31,6 +31,8 @@ import { truncateString } from "@/utils/textUtils"; import DeletedKeysPage from "../DeletedKeysPage/DeletedKeysPage"; import DeletedTeamsPage from "../DeletedTeamsPage/DeletedTeamsPage"; import NewBadge from "../common_components/NewBadge"; +import SpendLogsSettingsModal from "./SpendLogsSettingsModal/SpendLogsSettingsModal"; +import { SettingOutlined } from "@ant-design/icons"; interface SpendLogsTableProps { accessToken: string | null; @@ -91,6 +93,7 @@ export default function SpendLogsTable({ const [expandedRequestId, setExpandedRequestId] = useState(null); const [selectedSessionId, setSelectedSessionId] = useState(null); + const [isSpendLogsSettingsModalVisible, setIsSpendLogsSettingsModalVisible] = useState(false); const queryClient = useQueryClient(); @@ -526,6 +529,13 @@ export default function SpendLogsTable({ "Request Logs" )} + {!selectedSessionId && ( +
{selectedKeyInfo && selectedKeyIdInfoView && selectedKeyInfo.api_key === selectedKeyIdInfoView ? ( + setIsSpendLogsSettingsModalVisible(false)} + onSuccess={() => setIsSpendLogsSettingsModalVisible(false)} + />
From c4d3750601c788b820b7e97e749658bee2eba78e Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 23:38:23 -0800 Subject: [PATCH 33/70] adding tests --- .../SpendLogsSettingsModal.test.tsx | 348 ++++++++++++++++++ 1 file changed, 348 insertions(+) create mode 100644 ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx diff --git a/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx b/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx new file mode 100644 index 00000000000..b244736540f --- /dev/null +++ b/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx @@ -0,0 +1,348 @@ +import { render, screen, waitFor } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { beforeEach, describe, expect, it, vi, Mock } from "vitest"; +import SpendLogsSettingsModal from "./SpendLogsSettingsModal"; +import { useStoreRequestInSpendLogs } from "@/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs"; +import NotificationsManager from "@/components/molecules/notifications_manager"; +import { parseErrorMessage } from "@/components/shared/errorUtils"; +import { renderWithProviders } from "../../../../tests/test-utils"; + +vi.mock("@/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs"); +vi.mock("@/components/molecules/notifications_manager", () => ({ + default: { + success: vi.fn(), + fromBackend: vi.fn(), + }, +})); +vi.mock("@/components/shared/errorUtils", () => ({ + parseErrorMessage: vi.fn(), +})); + +const mockUseStoreRequestInSpendLogs = vi.mocked(useStoreRequestInSpendLogs); +const mockNotificationsManager = vi.mocked(NotificationsManager); +const mockParseErrorMessage = vi.mocked(parseErrorMessage); + +describe("SpendLogsSettingsModal", () => { + const mockOnCancel = vi.fn(); + const mockOnSuccess = vi.fn(); + const mockMutateAsync = vi.fn(); + + const defaultProps = { + isVisible: true, + onCancel: mockOnCancel, + onSuccess: mockOnSuccess, + }; + + beforeEach(() => { + vi.clearAllMocks(); + mockUseStoreRequestInSpendLogs.mockReturnValue({ + mutateAsync: mockMutateAsync, + isPending: false, + } as any); + mockParseErrorMessage.mockImplementation((error: any) => error?.message || String(error)); + }); + + it("should render the modal", () => { + renderWithProviders(); + expect(screen.getByRole("dialog")).toBeInTheDocument(); + expect(screen.getByText("Spend Logs Settings")).toBeInTheDocument(); + }); + + it("should render form fields with initial values", () => { + renderWithProviders(); + + expect(screen.getByText("Store Prompts in Spend Logs")).toBeInTheDocument(); + expect(screen.getByLabelText("Maximum Spend Logs Retention Period (Optional)")).toBeInTheDocument(); + expect(screen.getByPlaceholderText("e.g., 7d, 30d")).toBeInTheDocument(); + }); + + it("should render cancel and save buttons", () => { + renderWithProviders(); + + expect(screen.getByRole("button", { name: "Cancel" })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Save Settings" })).toBeInTheDocument(); + }); + + it("should call onCancel when cancel button is clicked", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const cancelButton = screen.getByRole("button", { name: "Cancel" }); + await user.click(cancelButton); + + expect(mockOnCancel).toHaveBeenCalledTimes(1); + }); + + it("should call onCancel when modal close button is clicked", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const closeButton = screen.getByRole("button", { name: /close/i }); + await user.click(closeButton); + + expect(mockOnCancel).toHaveBeenCalledTimes(1); + }); + + it("should toggle store prompts switch", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const switchElement = screen.getByRole("switch"); + expect(switchElement).not.toBeChecked(); + + await user.click(switchElement); + + await waitFor(() => { + expect(switchElement).toBeChecked(); + }); + }); + + it("should update retention period input", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d"); + await user.type(retentionInput, "30d"); + + expect(retentionInput).toHaveValue("30d"); + }); + + it("should submit form with store prompts enabled and retention period", async () => { + const user = userEvent.setup(); + mockMutateAsync.mockImplementation(async (params, options) => { + await Promise.resolve(); + options?.onSuccess?.(); + return { message: "Success" }; + }); + + renderWithProviders(); + + const switchElement = screen.getByRole("switch"); + await user.click(switchElement); + + const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d"); + await user.type(retentionInput, "30d"); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockMutateAsync).toHaveBeenCalledWith( + { + store_prompts_in_spend_logs: true, + maximum_spend_logs_retention_period: "30d", + }, + expect.any(Object) + ); + }); + }); + + it("should submit form with store prompts disabled and no retention period", async () => { + const user = userEvent.setup(); + mockMutateAsync.mockImplementation(async (params, options) => { + await Promise.resolve(); + options?.onSuccess?.(); + return { message: "Success" }; + }); + + renderWithProviders(); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockMutateAsync).toHaveBeenCalledWith( + { + store_prompts_in_spend_logs: false, + maximum_spend_logs_retention_period: undefined, + }, + expect.any(Object) + ); + }); + }); + + it("should show success notification and call onSuccess on successful submission", async () => { + const user = userEvent.setup(); + mockMutateAsync.mockImplementation(async (params, options) => { + await Promise.resolve(); + options?.onSuccess?.(); + return { message: "Success" }; + }); + + renderWithProviders(); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockNotificationsManager.success).toHaveBeenCalledWith("Spend logs settings updated successfully"); + expect(mockOnSuccess).toHaveBeenCalledTimes(1); + }); + }); + + it("should show error notification when submission fails", async () => { + const user = userEvent.setup(); + const error = new Error("Network error"); + mockMutateAsync.mockRejectedValue(error); + mockParseErrorMessage.mockReturnValue("Network error"); + + renderWithProviders(); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to save spend logs settings: Network error"); + }); + }); + + it("should show error notification from onError callback", async () => { + const user = userEvent.setup(); + const error = new Error("Backend error"); + mockMutateAsync.mockImplementation((params, options) => { + options?.onError?.(error); + return Promise.reject(error); + }); + mockParseErrorMessage.mockReturnValue("Backend error"); + + renderWithProviders(); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to save spend logs settings: Backend error"); + }); + }); + + it("should disable cancel button when pending", () => { + mockUseStoreRequestInSpendLogs.mockReturnValue({ + mutateAsync: mockMutateAsync, + isPending: true, + } as any); + + renderWithProviders(); + + const cancelButton = screen.getByRole("button", { name: "Cancel" }); + expect(cancelButton).toBeDisabled(); + }); + + it("should show loading state on save button when pending", () => { + mockUseStoreRequestInSpendLogs.mockReturnValue({ + mutateAsync: mockMutateAsync, + isPending: true, + } as any); + + renderWithProviders(); + + const saveButton = screen.getByRole("button", { name: /Saving/i }); + expect(saveButton).toBeInTheDocument(); + expect(saveButton.className).toContain("ant-btn-loading"); + }); + + it("should call onCancel when cancel button is clicked after modifying form", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const switchElement = screen.getByRole("switch"); + await user.click(switchElement); + + const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d"); + await user.type(retentionInput, "30d"); + + expect(switchElement).toBeChecked(); + expect(retentionInput).toHaveValue("30d"); + + const cancelButton = screen.getByRole("button", { name: "Cancel" }); + await user.click(cancelButton); + + expect(mockOnCancel).toHaveBeenCalledTimes(1); + }); + + it("should reset form fields after successful submission", async () => { + const user = userEvent.setup(); + mockMutateAsync.mockImplementation(async (params, options) => { + await Promise.resolve(); + options?.onSuccess?.(); + return { message: "Success" }; + }); + + const { rerender } = renderWithProviders(); + + const switchElement = screen.getByRole("switch"); + await user.click(switchElement); + + const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d"); + await user.type(retentionInput, "30d"); + + expect(switchElement).toBeChecked(); + expect(retentionInput).toHaveValue("30d"); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockNotificationsManager.success).toHaveBeenCalled(); + }); + + rerender(); + + await waitFor(() => { + const updatedSwitchElement = screen.getByRole("switch"); + const updatedRetentionInput = screen.getByPlaceholderText("e.g., 7d, 30d"); + expect(updatedSwitchElement).not.toBeChecked(); + expect(updatedRetentionInput).toHaveValue(""); + }); + }); + + it("should not call onSuccess when it is not provided", async () => { + const user = userEvent.setup(); + mockMutateAsync.mockImplementation(async (params, options) => { + await Promise.resolve(); + options?.onSuccess?.(); + return { message: "Success" }; + }); + + renderWithProviders(); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockNotificationsManager.success).toHaveBeenCalled(); + }); + }); + + it("should not render modal when isVisible is false", () => { + renderWithProviders(); + + expect(screen.queryByRole("dialog")).not.toBeInTheDocument(); + }); + + it("should submit form with only store prompts enabled and no retention period", async () => { + const user = userEvent.setup(); + mockMutateAsync.mockImplementation(async (params, options) => { + await Promise.resolve(); + options?.onSuccess?.(); + return { message: "Success" }; + }); + + renderWithProviders(); + + const switchElement = screen.getByRole("switch"); + await user.click(switchElement); + + const saveButton = screen.getByRole("button", { name: "Save Settings" }); + await user.click(saveButton); + + await waitFor(() => { + expect(mockMutateAsync).toHaveBeenCalledWith( + { + store_prompts_in_spend_logs: true, + maximum_spend_logs_retention_period: undefined, + }, + expect.any(Object) + ); + }); + }); +}); From e4cb28aa07846709b3e4818c31825af5623b1d55 Mon Sep 17 00:00:00 2001 From: lizhen Date: Wed, 28 Jan 2026 15:39:01 +0800 Subject: [PATCH 34/70] fix(anthropic): remove explicit cache_control null in tool_result content Fixes issue where tool_result content blocks include explicit 'cache_control': null which breaks some Anthropic API channels. Changes: - Only include cache_control field when explicitly set and not None - Prevents serialization of null values in tool_result text content - Maintains backward compatibility with existing cache_control usage Related issue: Anthropic tool_result conversion adds explicit null values that cause compatibility issues with certain API implementations. Co-Authored-By: Claude (claude-4.5-sonnet) --- .../prompt_templates/factory.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 30263543fc6..034089080f7 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1677,13 +1677,16 @@ def convert_to_anthropic_tool_result( ] = [] for content in content_list: if content["type"] == "text": - anthropic_content_list.append( - AnthropicMessagesToolResultContent( - type="text", - text=content["text"], - cache_control=content.get("cache_control", None), - ) - ) + # Only include cache_control if explicitly set and not None + # to avoid sending "cache_control": null which breaks some API channels + text_content: AnthropicMessagesToolResultContent = { + "type": "text", + "text": content["text"], + } + cache_control_value = content.get("cache_control") + if cache_control_value is not None: + text_content["cache_control"] = cache_control_value + anthropic_content_list.append(text_content) elif content["type"] == "image_url": format = ( content["image_url"].get("format") From 07c8618227fb31e038d15c185d0bdbe8c0e8e80f Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 27 Jan 2026 23:47:47 -0800 Subject: [PATCH 35/70] Fixing tests --- .../src/components/page_utils.test.ts | 32 ++++++++++++++++--- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/ui/litellm-dashboard/src/components/page_utils.test.ts b/ui/litellm-dashboard/src/components/page_utils.test.ts index fed6c381498..12085735d33 100644 --- a/ui/litellm-dashboard/src/components/page_utils.test.ts +++ b/ui/litellm-dashboard/src/components/page_utils.test.ts @@ -7,6 +7,22 @@ import { describe, it, expect } from "vitest"; import { getAvailablePages } from "./page_utils"; import { menuGroups } from "./leftnav"; import { pageDescriptions } from "./page_metadata"; +import { internalUserRoles } from "@/utils/roles"; + +/** + * Check if a page is accessible to internal users + * A page is accessible if: + * 1. It has no role restrictions, OR + * 2. Its roles include at least one internal user role + */ +const isPageAccessibleToInternalUsers = (pageRoles?: string[]): boolean => { + if (!pageRoles || pageRoles.length === 0) { + return true; // No role restrictions + } + + // Check if any of the page's roles match internal user roles + return pageRoles.some(role => internalUserRoles.includes(role)); +}; describe("Page Utils - LeftNav Sync", () => { it("should return all pages from leftnav configuration", () => { @@ -32,26 +48,32 @@ describe("Page Utils - LeftNav Sync", () => { const availablePages = getAvailablePages(); const availablePageKeys = availablePages.map((p) => p.page); - // Collect all page keys from menuGroups (excluding parent containers) + // Collect all page keys from menuGroups (excluding parent containers and pages not accessible to internal users) const menuPageKeys: string[] = []; const excludedParents = ["tools", "experimental", "settings"]; menuGroups.forEach((group) => { group.items.forEach((item) => { - if (item.page && !excludedParents.includes(item.page)) { + if ( + item.page && + !excludedParents.includes(item.page) && + isPageAccessibleToInternalUsers(item.roles) + ) { menuPageKeys.push(item.page); } - // Add children + // Add children (only if accessible to internal users) if (item.children) { item.children.forEach((child) => { - menuPageKeys.push(child.page); + if (isPageAccessibleToInternalUsers(child.roles)) { + menuPageKeys.push(child.page); + } }); } }); }); - // Every menu page should be in available pages + // Every menu page accessible to internal users should be in available pages menuPageKeys.forEach((pageKey) => { expect( availablePageKeys, From f6ead49afe215970176919947ada37ff7e80c8d3 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 17:25:26 +0530 Subject: [PATCH 36/70] Add Prompt caching and reasoning support for MiniMax, GLM, Xiaomi --- litellm/llms/minimax/chat/transformation.py | 29 +- .../llms/openrouter/chat/transformation.py | 4 + litellm/llms/zai/chat/transformation.py | 16 +- model_prices_and_context_window.json | 12 +- .../llms/test_cache_control_and_reasoning.py | 281 ++++++++++++++++++ 5 files changed, 337 insertions(+), 5 deletions(-) create mode 100644 tests/test_litellm/llms/test_cache_control_and_reasoning.py diff --git a/litellm/llms/minimax/chat/transformation.py b/litellm/llms/minimax/chat/transformation.py index ed80ff8aed1..3e9dc0209f2 100644 --- a/litellm/llms/minimax/chat/transformation.py +++ b/litellm/llms/minimax/chat/transformation.py @@ -1,11 +1,12 @@ """ MiniMax OpenAI transformation config - extends OpenAI chat config for MiniMax's OpenAI-compatible API """ -from typing import Optional +from typing import List, Optional, Tuple import litellm from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam class MinimaxChatConfig(OpenAIGPTConfig): @@ -73,11 +74,33 @@ class MinimaxChatConfig(OpenAIGPTConfig): else: return f"{base_url}/v1/chat/completions" + def remove_cache_control_flag_from_messages_and_tools( + self, + model: str, + messages: List[AllMessageValues], + tools: Optional[List[ChatCompletionToolParam]] = None, + ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]: + """ + Override to preserve cache_control for MiniMax. + MiniMax supports cache_control - don't strip it. + """ + # MiniMax supports cache_control, so return messages and tools unchanged + return messages, tools + def get_supported_openai_params(self, model: str) -> list: """ Get supported OpenAI parameters for MiniMax. - Adds reasoning_split to the list of supported params. + Adds reasoning_split and thinking to the list of supported params. """ base_params = super().get_supported_openai_params(model=model) - return base_params + ["reasoning_split"] + additional_params = ["reasoning_split"] + + # Add thinking parameter if model supports reasoning + try: + if litellm.supports_reasoning(model=model, custom_llm_provider="minimax"): + additional_params.append("thinking") + except Exception: + pass + + return base_params + additional_params diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index b5610852fd2..e3770dbbf49 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -26,6 +26,9 @@ class CacheControlSupportedModels(str, Enum): """Models that support cache_control in content blocks.""" CLAUDE = "claude" GEMINI = "gemini" + MINIMAX = "minimax" + GLM = "glm" + ZAI = "z-ai" class OpenrouterConfig(OpenAIGPTConfig): @@ -39,6 +42,7 @@ class OpenrouterConfig(OpenAIGPTConfig): model=model, custom_llm_provider="openrouter" ) or litellm.supports_reasoning(model=model): supported_params.append("reasoning_effort") + supported_params.append("thinking") except Exception: pass return list(dict.fromkeys(supported_params)) diff --git a/litellm/llms/zai/chat/transformation.py b/litellm/llms/zai/chat/transformation.py index 4380256f0a4..fb1d67df357 100644 --- a/litellm/llms/zai/chat/transformation.py +++ b/litellm/llms/zai/chat/transformation.py @@ -1,6 +1,7 @@ -from typing import Optional, Tuple +from typing import List, Optional, Tuple from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -19,6 +20,19 @@ class ZAIChatConfig(OpenAIGPTConfig): dynamic_api_key = api_key or get_secret_str("ZAI_API_KEY") return api_base, dynamic_api_key + def remove_cache_control_flag_from_messages_and_tools( + self, + model: str, + messages: List[AllMessageValues], + tools: Optional[List[ChatCompletionToolParam]] = None, + ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]: + """ + Override to preserve cache_control for GLM/ZAI. + GLM supports cache_control - don't strip it. + """ + # GLM/ZAI supports cache_control, so return messages and tools unchanged + return messages, tools + def get_supported_openai_params(self, model: str) -> list: base_params = [ "max_tokens", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 4ac4159558f..b9ed7b7bb76 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -20628,6 +20628,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 1000000, "max_output_tokens": 8192 @@ -20642,6 +20643,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 1000000, "max_output_tokens": 8192 @@ -20656,6 +20658,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 200000, "max_output_tokens": 8192 @@ -23540,7 +23543,7 @@ "mode": "chat", "output_cost_per_token": 1.02e-06, "supports_function_calling": true, - "supports_prompt_caching": false, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -24232,6 +24235,7 @@ "output_cost_per_token": 1.75e-06, "source": "https://openrouter.ai/z-ai/glm-4.6", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -24245,6 +24249,7 @@ "output_cost_per_token": 1.9e-06, "source": "https://openrouter.ai/z-ai/glm-4.6:exacto", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -30872,11 +30877,14 @@ "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-4.6": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 6e-07, "output_cost_per_token": 2.2e-06, "litellm_provider": "zai", @@ -30884,6 +30892,8 @@ "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" }, diff --git a/tests/test_litellm/llms/test_cache_control_and_reasoning.py b/tests/test_litellm/llms/test_cache_control_and_reasoning.py new file mode 100644 index 00000000000..468927cdd49 --- /dev/null +++ b/tests/test_litellm/llms/test_cache_control_and_reasoning.py @@ -0,0 +1,281 @@ +""" +Test cache_control and reasoning parameter support for MiniMax, GLM/ZAI, and OpenRouter. + +This test file verifies the fixes for Issue #19923: +- cache_control is preserved (not stripped) for MiniMax, GLM, and OpenRouter variants +- thinking parameter is supported for reasoning-capable models +- Model metadata correctly reflects capabilities +""" +import os +import sys + +import pytest + +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path + +from litellm.llms.minimax.chat.transformation import MinimaxChatConfig +from litellm.llms.openrouter.chat.transformation import OpenrouterConfig +from litellm.llms.zai.chat.transformation import ZAIChatConfig + + +def test_minimax_preserves_cache_control_in_messages(): + """MiniMax should NOT strip cache_control from messages.""" + config = MinimaxChatConfig() + + messages = [ + { + "role": "system", + "content": "You are a helpful assistant.", + "cache_control": {"type": "ephemeral"}, + }, + { + "role": "user", + "content": "Hello, world!", + }, + ] + + transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools( + model="minimax/MiniMax-M2.1", messages=messages + ) + + # cache_control should be preserved + assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"} + + +def test_minimax_preserves_cache_control_in_tools(): + """MiniMax should NOT strip cache_control from tools.""" + config = MinimaxChatConfig() + + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather information", + "parameters": {"type": "object", "properties": {}}, + }, + "cache_control": {"type": "ephemeral"}, + } + ] + + _, transformed_tools = config.remove_cache_control_flag_from_messages_and_tools( + model="minimax/MiniMax-M2.1", messages=[], tools=tools + ) + + # cache_control should be preserved + assert transformed_tools[0].get("cache_control") == {"type": "ephemeral"} + + +def test_minimax_supports_thinking_param(): + """MiniMax reasoning models should support thinking parameter.""" + config = MinimaxChatConfig() + + supported_params = config.get_supported_openai_params( + model="minimax/MiniMax-M2.1" + ) + + # thinking should be in supported params for reasoning models + assert "thinking" in supported_params + # reasoning_split should also be supported + assert "reasoning_split" in supported_params + + +def test_zai_preserves_cache_control_in_messages(): + """ZAI should NOT strip cache_control from messages.""" + config = ZAIChatConfig() + + messages = [ + { + "role": "system", + "content": "You are a helpful assistant.", + "cache_control": {"type": "ephemeral"}, + }, + { + "role": "user", + "content": "Hello, world!", + }, + ] + + transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools( + model="zai/glm-4.7", messages=messages + ) + + # cache_control should be preserved + assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"} + + +def test_zai_preserves_cache_control_in_tools(): + """ZAI should NOT strip cache_control from tools.""" + config = ZAIChatConfig() + + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather information", + "parameters": {"type": "object", "properties": {}}, + }, + "cache_control": {"type": "ephemeral"}, + } + ] + + _, transformed_tools = config.remove_cache_control_flag_from_messages_and_tools( + model="zai/glm-4.7", messages=[], tools=tools + ) + + # cache_control should be preserved + assert transformed_tools[0].get("cache_control") == {"type": "ephemeral"} + + +def test_zai_supports_thinking_param_for_reasoning_models(): + """ZAI reasoning models (glm-4.7, glm-4.6) should support thinking parameter.""" + config = ZAIChatConfig() + + # glm-4.7 supports reasoning + supported_params_47 = config.get_supported_openai_params(model="zai/glm-4.7") + assert "thinking" in supported_params_47 + + # glm-4.6 supports reasoning + supported_params_46 = config.get_supported_openai_params(model="zai/glm-4.6") + assert "thinking" in supported_params_46 + + +def test_openrouter_minimax_supports_cache_control(): + """OpenRouter should preserve cache_control for MiniMax models.""" + config = OpenrouterConfig() + + messages = [ + { + "role": "user", + "content": "Hello, world!", + "cache_control": {"type": "ephemeral"}, + } + ] + + # Test that cache_control is not removed + transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools( + model="openrouter/minimax/minimax-m2", messages=messages + ) + + # The method should preserve cache_control for minimax models + assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"} + + +def test_openrouter_glm_supports_cache_control(): + """OpenRouter should preserve cache_control for GLM models.""" + config = OpenrouterConfig() + + messages = [ + { + "role": "user", + "content": "Hello, world!", + "cache_control": {"type": "ephemeral"}, + } + ] + + # Test that cache_control is not removed for GLM models + transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools( + model="openrouter/z-ai/glm-4.6", messages=messages + ) + + # The method should preserve cache_control for GLM models + assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"} + + +def test_openrouter_deepseek_strips_cache_control(): + """OpenRouter should still strip cache_control for non-supported models.""" + config = OpenrouterConfig() + + messages = [ + { + "role": "user", + "content": "Hello, world!", + "cache_control": {"type": "ephemeral"}, + } + ] + + # DeepSeek doesn't support cache_control, so it should be stripped + transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools( + model="openrouter/deepseek/deepseek-chat", messages=messages + ) + + # cache_control should be removed for non-supported models + assert transformed_messages[0].get("cache_control") is None + + +def test_openrouter_minimax_transform_moves_cache_control_to_content(): + """OpenRouter should move cache_control to content blocks for MiniMax.""" + config = OpenrouterConfig() + + messages = [ + { + "role": "user", + "content": "Analyze this data", + "cache_control": {"type": "ephemeral"}, + } + ] + + transformed_request = config.transform_request( + model="openrouter/minimax/minimax-m2", + messages=messages, + optional_params={}, + litellm_params={}, + headers={}, + ) + + # cache_control should be moved to content blocks + assert "messages" in transformed_request + user_message = transformed_request["messages"][0] + assert isinstance(user_message["content"], list) + assert user_message["content"][0]["cache_control"] == {"type": "ephemeral"} + # Message-level cache_control should be removed + assert "cache_control" not in user_message + + +def test_openrouter_glm_transform_moves_cache_control_to_content(): + """OpenRouter should move cache_control to content blocks for GLM.""" + config = OpenrouterConfig() + + messages = [ + { + "role": "user", + "content": "Analyze this data", + "cache_control": {"type": "ephemeral"}, + } + ] + + transformed_request = config.transform_request( + model="openrouter/z-ai/glm-4.6", + messages=messages, + optional_params={}, + litellm_params={}, + headers={}, + ) + + # cache_control should be moved to content blocks + assert "messages" in transformed_request + user_message = transformed_request["messages"][0] + assert isinstance(user_message["content"], list) + assert user_message["content"][0]["cache_control"] == {"type": "ephemeral"} + + +def test_openrouter_supports_thinking_param_for_reasoning_models(): + """OpenRouter should support thinking parameter for reasoning-capable models.""" + config = OpenrouterConfig() + + # Test MiniMax (supports reasoning) + supported_params_minimax = config.get_supported_openai_params( + model="openrouter/minimax/minimax-m2" + ) + assert "thinking" in supported_params_minimax + assert "reasoning_effort" in supported_params_minimax + + # Test GLM (supports reasoning) + supported_params_glm = config.get_supported_openai_params( + model="openrouter/z-ai/glm-4.6" + ) + assert "thinking" in supported_params_glm + assert "reasoning_effort" in supported_params_glm From c5c1fbc5a25ab2fae255a8039501537f0bb7f4a3 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 18:00:42 +0530 Subject: [PATCH 37/70] Fix test_calculate_usage_completion_tokens_details_always_populated and logging object test --- litellm/litellm_core_utils/litellm_logging.py | 14 +++++++++++++- .../chat/test_anthropic_chat_transformation.py | 2 +- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 66b14946c32..0d9245a686a 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -1638,11 +1638,19 @@ class Logging(LiteLLMLoggingBaseClass): "standard_logging_object" ) ) is not None: - standard_logging_payload["response"] = ( + response_dict = ( result.model_dump() if hasattr(result, "model_dump") else dict(result) ) + # Ensure usage is properly included with transformed chat format + if transformed_usage is not None: + response_dict["usage"] = ( + transformed_usage.model_dump() + if hasattr(transformed_usage, "model_dump") + else dict(transformed_usage) + ) + standard_logging_payload["response"] = response_dict elif isinstance(result, TranscriptionResponse): from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( TranscriptionUsageObjectTransformation, @@ -4540,6 +4548,10 @@ class StandardLoggingPayloadSetup: ) elif isinstance(usage, Usage): return usage + elif isinstance(usage, ResponseAPIUsage): + return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ) elif isinstance(usage, dict): if ResponseAPILoggingUtils._is_response_api_usage(usage): return ( diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index 7e96c4634fd..bd229dedfaa 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -1842,7 +1842,7 @@ def test_calculate_usage_completion_tokens_details_always_populated(): # completion_tokens_details should NOT be None assert usage.completion_tokens_details is not None - assert usage.completion_tokens_details.reasoning_tokens is None + assert usage.completion_tokens_details.reasoning_tokens is 0 assert usage.completion_tokens_details.text_tokens == 248 assert usage.completion_tokens == 248 assert usage.prompt_tokens == 37 From 1cdda28b6ca2c98b2e64114477345db484d830d1 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 21:10:44 +0530 Subject: [PATCH 38/70] Fix gemini-robotics-er-1.5-preview name --- ...odel_prices_and_context_window_backup.json | 20 +++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 8cb488c170d..3f9542193cf 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -3653,10 +3653,9 @@ "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 1.4e-05, "supported_endpoints": [ - "/v1/chat/completions", "/v1/responses" ], "supported_modalities": [ @@ -20628,6 +20627,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 1000000, "max_output_tokens": 8192 @@ -20642,6 +20642,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 1000000, "max_output_tokens": 8192 @@ -20656,6 +20657,7 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_prompt_caching": true, + "supports_reasoning": true, "supports_system_messages": true, "max_input_tokens": 200000, "max_output_tokens": 8192 @@ -23540,7 +23542,7 @@ "mode": "chat", "output_cost_per_token": 1.02e-06, "supports_function_calling": true, - "supports_prompt_caching": false, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -23892,8 +23894,11 @@ "max_input_tokens": 400000, "max_output_tokens": 128000, "max_tokens": 128000, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 1.4e-05, + "supported_endpoints": [ + "/v1/responses" + ], "supported_modalities": [ "text", "image" @@ -24232,6 +24237,7 @@ "output_cost_per_token": 1.75e-06, "source": "https://openrouter.ai/z-ai/glm-4.6", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -24245,6 +24251,7 @@ "output_cost_per_token": 1.9e-06, "source": "https://openrouter.ai/z-ai/glm-4.6:exacto", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -30883,11 +30890,14 @@ "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-4.6": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 6e-07, "output_cost_per_token": 2.2e-06, "litellm_provider": "zai", @@ -30895,6 +30905,8 @@ "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" }, From 3ab1b9f543e03af3ed1492eb5b17680307141e3a Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 28 Jan 2026 21:13:37 +0530 Subject: [PATCH 39/70] Fix gemini-robotics-er-1.5-preview name --- litellm/model_prices_and_context_window_backup.json | 2 +- model_prices_and_context_window.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 3f9542193cf..64f90048493 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -13521,7 +13521,7 @@ "supports_vision": true, "supports_web_search": true }, - "gemini-robotics-er-1.5-preview": { + "gemini/gemini-robotics-er-1.5-preview": { "cache_read_input_token_cost": 0, "input_cost_per_token": 3e-07, "input_cost_per_audio_token": 1e-06, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 3f9542193cf..64f90048493 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -13521,7 +13521,7 @@ "supports_vision": true, "supports_web_search": true }, - "gemini-robotics-er-1.5-preview": { + "gemini/gemini-robotics-er-1.5-preview": { "cache_read_input_token_cost": 0, "input_cost_per_token": 3e-07, "input_cost_per_audio_token": 1e-06, From 8e4f06583a7634f4ee1858c36cd347fd1aa20293 Mon Sep 17 00:00:00 2001 From: boarder7395 <37314943+boarder7395@users.noreply.github.com> Date: Wed, 28 Jan 2026 11:52:52 -0500 Subject: [PATCH 40/70] Fix team cli auth flow (#19666) * Cleanup code for user cli auth, and make sure not to prompt user for team multiple times while polling * Adding tests * Cleanup normalize teams some more --- litellm/proxy/client/cli/commands/auth.py | 233 +++++++++--------- .../test_litellm/proxy/auth/test_cli_auth.py | 203 +++++++++++++++ 2 files changed, 326 insertions(+), 110 deletions(-) create mode 100644 tests/test_litellm/proxy/auth/test_cli_auth.py diff --git a/litellm/proxy/client/cli/commands/auth.py b/litellm/proxy/client/cli/commands/auth.py index 2345b0263e3..64b3233536f 100644 --- a/litellm/proxy/client/cli/commands/auth.py +++ b/litellm/proxy/client/cli/commands/auth.py @@ -276,6 +276,71 @@ def prompt_team_selection_fallback( # Polling-based authentication - no local server needed +def _poll_for_ready_data( + url: str, + *, + total_timeout: int = 300, + poll_interval: int = 2, + request_timeout: int = 10, + pending_message: Optional[str] = None, + pending_log_every: int = 10, + other_status_message: Optional[str] = None, + other_status_log_every: int = 10, + http_error_log_every: int = 10, + connection_error_log_every: int = 10, +) -> Optional[Dict[str, Any]]: + for attempt in range(total_timeout // poll_interval): + try: + response = requests.get(url, timeout=request_timeout) + if response.status_code == 200: + data = response.json() + status = data.get("status") + if status == "ready": + return data + if status == "pending": + if ( + pending_message + and pending_log_every > 0 + and attempt % pending_log_every == 0 + ): + click.echo(pending_message) + elif ( + other_status_message + and other_status_log_every > 0 + and attempt % other_status_log_every == 0 + ): + click.echo(other_status_message) + elif http_error_log_every > 0 and attempt % http_error_log_every == 0: + click.echo(f"Polling error: HTTP {response.status_code}") + except requests.RequestException as e: + if ( + connection_error_log_every > 0 + and attempt % connection_error_log_every == 0 + ): + click.echo(f"Connection error (will retry): {e}") + time.sleep(poll_interval) + return None + + +def _normalize_teams(teams, team_details): + """If team_details are a + + Args: + teams (_type_): _description_ + team_details (_type_): _description_ + + Returns: + _type_: _description_ + """ + if isinstance(team_details, list) and team_details: + return [ + {"team_id": i.get("team_id") or i.get("id"), "team_alias": i.get("team_alias")} + for i in team_details + if isinstance(i, dict) and (i.get("team_id") or i.get("id")) + ] + if isinstance(teams, list): + return [{"team_id": str(t), "team_alias": None} for t in teams] + return [] def _poll_for_authentication(base_url: str, key_id: str) -> Optional[dict]: @@ -286,106 +351,58 @@ def _poll_for_authentication(base_url: str, key_id: str) -> Optional[dict]: Dictionary with authentication data if successful, None otherwise """ poll_url = f"{base_url}/sso/cli/poll/{key_id}" - timeout = 300 # 5 minute timeout - poll_interval = 2 # Poll every 2 seconds + data = _poll_for_ready_data( + poll_url, + pending_message="Still waiting for authentication...", + ) + if not data: + return None + if data.get("requires_team_selection"): + teams = data.get("teams", []) + team_details = data.get("team_details") + user_id = data.get("user_id") + normalized_teams: List[Dict[str, Any]] = _normalize_teams(teams, team_details) + if not normalized_teams: + click.echo("⚠️ No teams available for selection.") + return None - for attempt in range(timeout // poll_interval): - try: - response = requests.get(poll_url, timeout=10) - if response.status_code == 200: - data = response.json() - if data.get("status") == "ready": - # Check if we need team selection first - if data.get("requires_team_selection"): - # Server returned teams list without JWT - need to select team. - # Newer servers may also return "team_details" containing - # objects with both team_id and team_alias. We prefer those - # for display, but continue to support the legacy list of - # team IDs for backwards compatibility. - teams = data.get("teams", []) - team_details = data.get("team_details") - user_id = data.get("user_id") + # User has multiple teams - let them select + jwt_with_team = _handle_team_selection_during_polling( + base_url=base_url, + key_id=key_id, + teams=normalized_teams, + ) - # Build a normalized list of team objects that always have - # "team_id" and optionally "team_alias". - normalized_teams: List[Dict[str, Any]] = [] - if isinstance(team_details, list) and team_details: - for item in team_details: - if isinstance(item, dict): - team_id = item.get("team_id") or item.get("id") - if team_id is None: - continue - normalized_teams.append( - { - "team_id": team_id, - "team_alias": item.get("team_alias"), - } - ) - elif isinstance(teams, list): - for t in teams: - normalized_teams.append( - { - "team_id": str(t), - "team_alias": None, - } - ) + # Use the team-specific JWT if selection succeeded + if jwt_with_team: + return { + "api_key": jwt_with_team, + "user_id": user_id, + "teams": teams, + "team_id": None, # Set by server in JWT + } - if normalized_teams and len(normalized_teams) > 1: - # User has multiple teams - let them select - jwt_with_team = _handle_team_selection_during_polling( - base_url=base_url, - key_id=key_id, - teams=normalized_teams, - ) + click.echo("❌ Team selection cancelled or JWT generation failed.") + return None - # Use the team-specific JWT if selection succeeded - if jwt_with_team: - return { - "api_key": jwt_with_team, - "user_id": user_id, - "teams": teams, - "team_id": None, # Set by server in JWT - } - else: - # Selection failed or was skipped - poll again without team_id - click.echo("⚠️ Team selection skipped, retrying...") - continue - else: - # Shouldn't happen, but fallback - click.echo("⚠️ No teams available, retrying...") - continue - else: - # JWT is ready (single team or team already selected) - api_key = data.get("key") - user_id = data.get("user_id") - teams = data.get("teams", []) - team_id = data.get("team_id") + # JWT is ready (single team or team already selected) + api_key = data.get("key") + user_id = data.get("user_id") + teams = data.get("teams", []) + team_id = data.get("team_id") - # Show which team was assigned - if team_id and len(teams) == 1: - click.echo(f"\n✅ Automatically assigned to team: {team_id}") + # Show which team was assigned + if team_id and len(teams) == 1: + click.echo(f"\n✅ Automatically assigned to team: {team_id}") - if api_key: - return { - "api_key": api_key, - "user_id": user_id, - "teams": teams, - "team_id": team_id, - } - elif data.get("status") == "pending": - # Still pending - if attempt % 10 == 0: # Show progress every 20 seconds - click.echo("Still waiting for authentication...") - else: - click.echo(f"Polling error: HTTP {response.status_code}") + if api_key: + return { + "api_key": api_key, + "user_id": user_id, + "teams": teams, + "team_id": team_id, + } - except requests.RequestException as e: - if attempt % 10 == 0: - click.echo(f"Connection error (will retry): {e}") - - time.sleep(poll_interval) - - # Timeout reached return None @@ -418,25 +435,21 @@ def _handle_team_selection_during_polling( click.echo(f"\n🔄 Generating JWT for team: {team_id}") - # Re-poll with team_id to get JWT with correct team - try: - poll_url = f"{base_url}/sso/cli/poll/{key_id}?team_id={team_id}" - response = requests.get(poll_url, timeout=10) - - if response.status_code == 200: - data = response.json() - if data.get("status") == "ready": - jwt_token = data.get("key") - if jwt_token: - click.echo(f"✅ Successfully generated JWT for team: {team_id}") - return jwt_token - - click.echo(f"❌ Failed to get JWT with team. Status: {response.status_code}") + poll_url = f"{base_url}/sso/cli/poll/{key_id}?team_id={team_id}" + data = _poll_for_ready_data( + poll_url, + pending_message="Still waiting for team authentication...", + other_status_message="Waiting for team authentication to complete...", + http_error_log_every=10, + ) + if not data: return None + jwt_token = data.get("key") + if jwt_token: + click.echo(f"✅ Successfully generated JWT for team: {team_id}") + return jwt_token - except Exception as e: - click.echo(f"❌ Error getting JWT with team: {e}") - return None + return None def _render_and_prompt_for_team_selection(teams: List[Dict[str, Any]]) -> Optional[str]: diff --git a/tests/test_litellm/proxy/auth/test_cli_auth.py b/tests/test_litellm/proxy/auth/test_cli_auth.py new file mode 100644 index 00000000000..2faf6436523 --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_cli_auth.py @@ -0,0 +1,203 @@ +""" +Tests for litellm/proxy/client/cli/commands/auth.py + +This module tests the auth commands and their associated functionality. +""" + +import pytest +import requests +from unittest.mock import AsyncMock, patch, Mock, call +from litellm.proxy.client.cli.commands.auth import _normalize_teams, _poll_for_ready_data, _poll_for_authentication + +@pytest.mark.asyncio +async def test_normalize_teams_teams_only(): + """Test normalize teams helper function""" + teams = ["1", "2", "3"] + team_details = [] + result = _normalize_teams(teams, team_details) + assert result == [{"team_id": "1", "team_alias": None}, {"team_id": "2", "team_alias": None}, {"team_id": "3", "team_alias": None}] + +@pytest.mark.asyncio +async def test_normalize_teams_with_details_no_aliases(): + """Test normalize teams helper function""" + teams = ["4", "5", "6"] + team_details = [{"team_id": "1"}, {"team_id": "2"}, {"team_id": "3"}] + result = _normalize_teams(teams, team_details) + assert result == [{"team_id": "1", "team_alias": None}, {"team_id": "2", "team_alias": None}, {"team_id": "3", "team_alias": None}] + +@pytest.mark.asyncio +async def test_normalize_teams_with_details_with_aliases(): + """Test normalize teams helper function""" + teams = ["4", "5", "6"] + team_details = [{"team_id": "1", "team_alias": "A"}, {"team_id": "2", "team_alias": "B"}, {"team_id": "3", "team_alias": "C"}] + result = _normalize_teams(teams, team_details) + assert result == [{"team_id": "1", "team_alias": "A"}, {"team_id": "2", "team_alias": "B"}, {"team_id": "3", "team_alias": "C"}] + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=404)]) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +@patch("litellm.proxy.client.cli.commands.auth.time.sleep") +async def test_poll_for_ready_404(sleep_mock, click_mock, request_mock): + """Test poll_for_ready function""" + actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=1, request_timeout=42) + assert actual is None + click_mock.assert_called_once_with("Polling error: HTTP 404") + request_mock.assert_called_once_with("https://litellm.com", timeout=42) + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=200, json=Mock(return_value={"status": "ready","json": "data"}))]) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +@patch("litellm.proxy.client.cli.commands.auth.time.sleep") +async def test_poll_for_ready_200_ready(sleep_mock, click_mock, request_mock): + """Test poll_for_ready function""" + actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=1, request_timeout=42) + assert actual == {"status": "ready", "json": "data"} + click_mock.assert_not_called() + request_mock.assert_called_once_with("https://litellm.com", timeout=42) + sleep_mock.assert_not_called() + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=200, json=Mock(return_value={"status": "pending","json": "data"})), Mock(status_code=200, json=Mock(return_value={"status": "ready","json": "data"}))]) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +@patch("litellm.proxy.client.cli.commands.auth.time.sleep") +async def test_poll_for_ready_single_pending(sleep_mock, click_mock, request_mock): + """Test poll_for_ready function""" + actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=2, request_timeout=42) + assert actual == {"status": "ready", "json": "data"} + click_mock.assert_not_called() + request_mock.assert_has_calls([ + call("https://litellm.com", timeout=42), + call("https://litellm.com", timeout=42) + ]) + sleep_mock.assert_called_once_with(1) + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=200, json=Mock(return_value={"status": "pending","json": "data"})), Mock(status_code=200, json=Mock(return_value={"status": "pending","json": "data"}))]) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +@patch("litellm.proxy.client.cli.commands.auth.time.sleep") +async def test_poll_for_ready_pending(sleep_mock, click_mock, request_mock): + """Test poll_for_ready function""" + actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=2, request_timeout=42, pending_message="Pending message", pending_log_every=1) + assert actual is None + click_mock.assert_has_calls([ + call("Pending message"), + call("Pending message") + ]) + request_mock.assert_has_calls([ + call("https://litellm.com", timeout=42), + call("https://litellm.com", timeout=42) + ]) + sleep_mock.assert_has_calls([ + call(1), + call(1) + ]) + + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[requests.RequestException("ERROR"), + requests.RequestException("ERROR")]) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +@patch("litellm.proxy.client.cli.commands.auth.time.sleep") +async def test_poll_for_ready_connection_failure(sleep_mock, click_mock, request_mock): + """Test poll_for_ready function""" + actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=2, request_timeout=42) + assert actual is None + click_mock.assert_called_once_with("Connection error (will retry): ERROR") + request_mock.assert_has_calls([ + call("https://litellm.com", timeout=42), + ]) + sleep_mock.assert_has_calls([ + call(1), + call(1) + ]) + + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling") +@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value=None) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +async def test_poll_for_authentication_no_data(click_mock, poll_mock, handle_mock): + """Test poll_for_authentication function""" + actual = _poll_for_authentication("https://litellm.com", "key-123") + assert actual is None + poll_mock.assert_called_once_with( + "https://litellm.com/sso/cli/poll/key-123", + pending_message="Still waiting for authentication...", + ) + handle_mock.assert_not_called() + click_mock.assert_not_called() + + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling") +@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"requires_team_selection": True, "teams": [], "team_details": []}) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +async def test_poll_for_authentication_no_teams(click_mock, poll_mock, handle_mock): + """Test poll_for_authentication function""" + actual = _poll_for_authentication("https://litellm.com", "key-123") + assert actual is None + poll_mock.assert_called_once_with( + "https://litellm.com/sso/cli/poll/key-123", + pending_message="Still waiting for authentication...", + ) + handle_mock.assert_not_called() + click_mock.assert_called_once() + assert "No teams available for selection." in click_mock.call_args[0][0] + + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling", return_value="jwt-123") +@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"requires_team_selection": True, "teams": [1, 2], "user_id": "user-123"}) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +async def test_poll_for_authentication_team_selection_success(click_mock, poll_mock, handle_mock): + """Test poll_for_authentication function""" + actual = _poll_for_authentication("https://litellm.com", "key-123") + assert actual == {"api_key": "jwt-123", "user_id": "user-123", "teams": [1, 2], "team_id": None} + poll_mock.assert_called_once_with( + "https://litellm.com/sso/cli/poll/key-123", + pending_message="Still waiting for authentication...", + ) + handle_mock.assert_called_once_with( + base_url="https://litellm.com", + key_id="key-123", + teams=[{"team_id": "1", "team_alias": None}, {"team_id": "2", "team_alias": None}], + ) + click_mock.assert_not_called() + + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling", return_value=None) +@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"requires_team_selection": True, "teams": ["team-1"], "user_id": "user-123"}) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +async def test_poll_for_authentication_team_selection_cancelled(click_mock, poll_mock, handle_mock): + """Test poll_for_authentication function""" + actual = _poll_for_authentication("https://litellm.com", "key-123") + assert actual is None + poll_mock.assert_called_once_with( + "https://litellm.com/sso/cli/poll/key-123", + pending_message="Still waiting for authentication...", + ) + handle_mock.assert_called_once_with( + base_url="https://litellm.com", + key_id="key-123", + teams=[{"team_id": "team-1", "team_alias": None}], + ) + click_mock.assert_called_once() + assert "Team selection cancelled" in click_mock.call_args[0][0] + + +@pytest.mark.asyncio +@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling") +@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"key": "jwt-456", "user_id": "user-456", "teams": ["team-1"], "team_id": "team-1"}) +@patch("litellm.proxy.client.cli.commands.auth.click.echo") +async def test_poll_for_authentication_auto_assigned_team(click_mock, poll_mock, handle_mock): + """Test poll_for_authentication function""" + actual = _poll_for_authentication("https://litellm.com", "key-123") + assert actual == {"api_key": "jwt-456", "user_id": "user-456", "teams": ["team-1"], "team_id": "team-1"} + poll_mock.assert_called_once_with( + "https://litellm.com/sso/cli/poll/key-123", + pending_message="Still waiting for authentication...", + ) + handle_mock.assert_not_called() + click_mock.assert_called_once() + assert "Automatically assigned to team: team-1" in click_mock.call_args[0][0] From ea3853e9772f432f173a6106aab8daf40ea516f8 Mon Sep 17 00:00:00 2001 From: michelligabriele Date: Wed, 28 Jan 2026 18:33:53 +0100 Subject: [PATCH 41/70] fix(vertex_ai): support model names with slashes in passthrough URLs (#19944) The regex in get_vertex_model_id_from_url() was using [^/:]+ which stopped at the first slash, truncating model names like 'gcp/google/gemini-2.5-flash' to just 'gcp'. This caused access_groups checks to fail for custom model names. Changed the pattern to [^:]+ to allow slashes in model names, only stopping at the colon before the action (e.g., :generateContent). --- litellm/llms/vertex_ai/common_utils.py | 2 +- .../vertex_ai/test_vertex_ai_common_utils.py | 30 +++++++++++++++++++ 2 files changed, 31 insertions(+), 1 deletion(-) diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 152b99ca4db..a0e2ddf5e98 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -849,7 +849,7 @@ def get_vertex_model_id_from_url(url: str) -> Optional[str]: `https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:streamGenerateContent` """ - match = re.search(r"/models/([^/:]+)", url) + match = re.search(r"/models/([^:]+)", url) return match.group(1) if match else None diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py index 953f54e4a54..94323e06901 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py @@ -812,6 +812,36 @@ def test_get_vertex_model_id_from_url(): assert model_id is None +def test_get_vertex_model_id_from_url_with_slashes(): + """Test get_vertex_model_id_from_url with model names containing slashes (e.g., gcp/google/gemini-2.5-flash) + + Regression test for NVIDIA issue: custom model names with slashes in passthrough URLs + were being truncated (e.g., 'gcp/google/gemini-2.5-flash' -> 'gcp'), causing access_groups + checks to fail. + """ + from litellm.llms.vertex_ai.common_utils import get_vertex_model_id_from_url + + # Test with model name containing slashes: gcp/google/gemini-2.5-flash + url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gcp/google/gemini-2.5-flash:generateContent" + model_id = get_vertex_model_id_from_url(url) + assert model_id == "gcp/google/gemini-2.5-flash" + + # Test with model name containing slashes: gcp/google/gemini-3-flash-preview + url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/global/publishers/google/models/gcp/google/gemini-3-flash-preview:streamGenerateContent" + model_id = get_vertex_model_id_from_url(url) + assert model_id == "gcp/google/gemini-3-flash-preview" + + # Test with custom model path: custom/model + url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/custom/model:generateContent" + model_id = get_vertex_model_id_from_url(url) + assert model_id == "custom/model" + + # Test passthrough URL format (without host) + url = "v1/projects/my-project/locations/us-central1/publishers/google/models/gcp/google/gemini-2.5-flash:generateContent" + model_id = get_vertex_model_id_from_url(url) + assert model_id == "gcp/google/gemini-2.5-flash" + + def test_construct_target_url_with_version_prefix(): """Test construct_target_url with version prefixes""" from litellm.llms.vertex_ai.common_utils import construct_target_url From 4c1b24eed9f21711472c23d2521361f01efb918d Mon Sep 17 00:00:00 2001 From: Alexsander Hamir Date: Wed, 28 Jan 2026 10:35:37 -0800 Subject: [PATCH 42/70] Fix thread leak in OpenTelemetry dynamic header path (#19946) --- litellm/integrations/opentelemetry.py | 9 +++ tests/test_otel_thread_leak.py | 90 +++++++++++++++++++++++++++ 2 files changed, 99 insertions(+) create mode 100644 tests/test_otel_thread_leak.py diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 7aea333ee95..997dd044a65 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -144,6 +144,7 @@ class OpenTelemetry(CustomLogger): self.OTEL_EXPORTER = self.config.exporter self.OTEL_ENDPOINT = self.config.endpoint self.OTEL_HEADERS = self.config.headers + self._tracer_provider_cache: Dict[str, Any] = {} self._init_tracing(tracer_provider) _debug_otel = str(os.getenv("DEBUG_OTEL", "False")).lower() @@ -615,12 +616,20 @@ class OpenTelemetry(CustomLogger): """Create a temporary tracer with dynamic headers for this request only.""" from opentelemetry.sdk.trace import TracerProvider + # Prevents thread exhaustion by reusing providers for the same credential sets (e.g. per-team keys) + cache_key = str(sorted(dynamic_headers.items())) + if cache_key in self._tracer_provider_cache: + return self._tracer_provider_cache[cache_key].get_tracer(LITELLM_TRACER_NAME) + # Create a temporary tracer provider with dynamic headers temp_provider = TracerProvider(resource=self._get_litellm_resource(self.config)) temp_provider.add_span_processor( self._get_span_processor(dynamic_headers=dynamic_headers) ) + # Store in cache for reuse + self._tracer_provider_cache[cache_key] = temp_provider + return temp_provider.get_tracer(LITELLM_TRACER_NAME) def construct_dynamic_otel_headers( diff --git a/tests/test_otel_thread_leak.py b/tests/test_otel_thread_leak.py new file mode 100644 index 00000000000..34f6b299caa --- /dev/null +++ b/tests/test_otel_thread_leak.py @@ -0,0 +1,90 @@ +import sys +import os +import threading +import time +import pytest + +# Add the project root to the path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) + +from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig +from litellm.types.utils import StandardCallbackDynamicParams + +def get_thread_count() -> int: + """Helper to get active thread count""" + return threading.active_count() + +@pytest.fixture +def otel_logger(): + """Fixture to provide a clean OTEL logger for each test""" + config = OpenTelemetryConfig( + exporter="console", + enable_metrics=False, + service_name="litellm-unit-test" + ) + return OpenTelemetry(config=config) + +def test_otel_thread_leak_dynamic_headers(otel_logger): + """ + Unit test to verify that calling get_tracer_to_use_for_request with + dynamic headers doesn't cause a linear thread leak. + + This test reproduces the issue where each unique team/key credential + set causes a new TracerProvider (and its background threads) to be + spawned but never closed. + """ + + # 1. Setup dynamic header simulation (monkey-patch) + # This simulates what LangfuseOtelLogger does for per-team keys + def mock_construct_dynamic_headers(standard_callback_dynamic_params): + if standard_callback_dynamic_params: + return {"Authorization": "Bearer fake_token"} + return None + + otel_logger.construct_dynamic_otel_headers = mock_construct_dynamic_headers + + # 2. Establish Baseline + initial_threads = get_thread_count() + + # 3. Simulate requests + num_requests = 10 + latencies = [] + + print("\n🚀 Simulating requests with dynamic headers:") + for i in range(num_requests): + kwargs = { + "standard_callback_dynamic_params": StandardCallbackDynamicParams( + langfuse_public_key=f"key_{i}", + langfuse_secret_key=f"secret_{i}", + ) + } + + # Measure latency + start_time = time.perf_counter() + tracer = otel_logger.get_tracer_to_use_for_request(kwargs) + end_time = time.perf_counter() + + latency_ms = (end_time - start_time) * 1000 + latencies.append(latency_ms) + print(f" Request {i+1:2d}: Latency = {latency_ms:6.2f} ms") + + # Verify a tracer was actually returned + assert tracer is not None + + avg_latency = sum(latencies) / len(latencies) + print(f"\n📊 Average Latency: {avg_latency:.2f} ms") + + # 4. Check for leaks + # Allow for a small constant increase (OTEL might start a few shared threads) + # but a linear leak would result in +10 or more threads here. + final_threads = get_thread_count() + thread_delta = final_threads - initial_threads + + print(f"\nThread growth: {thread_delta} threads across {num_requests} requests") + + # ASSERTION: The growth should be significantly less than 1 thread per request. + # If the bug exists, thread_delta will be >= num_requests. + assert thread_delta < (num_requests / 2), ( + f"Thread leak detected! Threads grew by {thread_delta} over {num_requests} requests. " + "Each request with dynamic headers appears to be leaking background threads." + ) From e444199d95cc9a45079f470dd035283a4704bf0f Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 28 Jan 2026 12:05:36 -0800 Subject: [PATCH 43/70] UI: New build --- litellm/proxy/_experimental/out/404.html | 2 +- .../_next/static/CLoTmxFwcBe0ryZN5lcGE/_buildManifest.js | 1 - .../out/_next/static/CLoTmxFwcBe0ryZN5lcGE/_ssgManifest.js | 1 - .../out/_next/static/chunks/1098-02ecd9e604bf89a1.js | 1 - .../out/_next/static/chunks/1108-43b967097e41bd10.js | 1 - .../out/_next/static/chunks/1112-8d095bb73a8ed62a.js | 1 - .../out/_next/static/chunks/1567-872f98a963ad6892.js | 1 - .../out/_next/static/chunks/1572-d039561b5597b5d5.js | 1 - .../out/_next/static/chunks/1850-5ecb3a54ee006e51.js | 1 - .../out/_next/static/chunks/1901-9d6d72bdecc0e0c8.js | 1 - .../out/_next/static/chunks/2117-26a589a1115bdd0a.js | 2 -- .../out/_next/static/chunks/2344-03dd7ba935a2a2f3.js | 1 - 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n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tM(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,l,t,"f")}},C=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,l,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,l,void 0,"f"),e},R=function(e){let t=en(this,l,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tM(n)){let t=n[tx]||"";if(Object.defineProperty(n,tx,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tS(t)}catch(n){let e=new ei(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${n}. JSON: ${t}`);en(this,x,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tC={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tR={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tP extends tc{constructor(){super(...arguments),this.batches=new tv(this._client)}create(e,t){let{betas:n,...r}=e;r.model in tR&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tR[r.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tC[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tE.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:th([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tP.Batches=tv;class tO extends tc{constructor(){super(...arguments),this.models=new tm(this._client),this.messages=new tP(this._client),this.files=new tg(this._client)}}tO.Models=tm,tO.Messages=tP,tO.Files=tg;class tj extends tc{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tI="__json_buf";function tA(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tL{constructor(){P.add(this),this.messages=[],this.receivedMessages=[],O.set(this,void 0),this.controller=new AbortController,j.set(this,void 0),I.set(this,()=>{}),A.set(this,()=>{}),L.set(this,void 0),Z.set(this,()=>{}),T.set(this,()=>{}),N.set(this,{}),z.set(this,!1),$.set(this,!1),q.set(this,!1),D.set(this,!1),B.set(this,void 0),H.set(this,void 0),F.set(this,e=>{if(et(this,$,!0,"f"),ea(e)&&(e=new el),e instanceof el)return et(this,q,!0,"f"),this._emit("abort",e);if(e instanceof ei)return this._emit("error",e);if(e instanceof Error){let t=new ei(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ei(String(e)))}),et(this,j,new Promise((e,t)=>{et(this,I,e,"f"),et(this,A,t,"f")}),"f"),et(this,L,new Promise((e,t)=>{et(this,Z,e,"f"),et(this,T,t,"f")}),"f"),en(this,j,"f").catch(()=>{}),en(this,L,"f").catch(()=>{})}get response(){return en(this,B,"f")}get request_id(){return en(this,H,"f")}async withResponse(){let e=await en(this,j,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tL;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tL;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,F,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,P,"m",V).call(this);let{response:a,data:s}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),s))en(this,P,"m",X).call(this,e);if(s.controller.signal?.aborted)throw new el;en(this,P,"m",J).call(this)}_connected(e){this.ended||(et(this,B,e,"f"),et(this,H,e?.headers.get("request-id"),"f"),en(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,z,"f")}get errored(){return en(this,$,"f")}get aborted(){return en(this,q,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,N,"f")[e]||(en(this,N,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,N,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,N,"f")[e]||(en(this,N,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,D,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,D,!0,"f"),await en(this,L,"f")}get currentMessage(){return en(this,O,"f")}async finalMessage(){return await this.done(),en(this,P,"m",U).call(this)}async finalText(){return await this.done(),en(this,P,"m",W).call(this)}_emit(e,...t){if(en(this,z,"f"))return;"end"===e&&(et(this,z,!0,"f"),en(this,Z,"f").call(this));let n=en(this,N,"f")[e];if(n&&(en(this,N,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,D,"f")||n?.length||Promise.reject(e),en(this,A,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,D,"f")||n?.length||Promise.reject(e),en(this,A,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,P,"m",U).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,P,"m",V).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,P,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,P,"m",J).call(this)}[(O=new WeakMap,j=new WeakMap,I=new WeakMap,A=new WeakMap,L=new WeakMap,Z=new WeakMap,T=new WeakMap,N=new WeakMap,z=new WeakMap,$=new WeakMap,q=new WeakMap,D=new WeakMap,B=new WeakMap,H=new WeakMap,F=new WeakMap,P=new WeakSet,U=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},W=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},V=function(){this.ended||et(this,O,void 0,"f")},X=function(e){if(this.ended)return;let t=en(this,P,"m",G).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tA(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,O,t,"f")}},J=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,O,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,O,void 0,"f"),e},G=function(e){let t=en(this,O,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tA(n)){let t=n[tI]||"";Object.defineProperty(n,tI,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tS(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tZ extends tc{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tp`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(tp`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tp`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new ei(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:th([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tb.fromResponse(t.response,t.controller))}}class tT extends tc{constructor(){super(...arguments),this.batches=new tZ(this._client)}create(e,t){e.model in tN&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tN[e.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let n=this._client._options.timeout;if(!e.stream&&null==n){let t=tC[e.model]??void 0;n=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:n??6e5,...t,stream:e.stream??!1})}stream(e,t){return tL.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tN={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tT.Batches=tZ;class tz extends tc{retrieve(e,t={},n){let{betas:r}=t??{};return this._client.get(tp`/v1/models/${e}`,{...n,headers:th([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},n?.headers])})}list(e={},t){let{betas:n,...r}=e??{};return this._client.getAPIList("/v1/models",e2,{query:r,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers])})}}let t$=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tq{constructor({baseURL:e=t$("ANTHROPIC_BASE_URL"),apiKey:t=t$("ANTHROPIC_API_KEY")??null,authToken:n=t$("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){K.set(this,void 0);let a={apiKey:t,authToken:n,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&eL())throw new ei("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tD.DEFAULT_TIMEOUT,this.logger=a.logger??console;let s="warn";this.logLevel=s,this.logLevel=eE(a.logLevel,"ClientOptions.logLevel",this)??eE(t$("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??s,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("undefined"!=typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),et(this,K,eH,"f"),this._options=a,this.apiKey=t,this.authToken=n}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization")||t.has("authorization")))throw Error('Could not resolve authentication method. Expected either apiKey or authToken to be set. Or for one of the "X-Api-Key" or "Authorization" headers to be explicitly omitted')}authHeaders(e){return th([this.apiKeyAuth(e),this.bearerAuth(e)])}apiKeyAuth(e){if(null!=this.apiKey)return th([{"X-Api-Key":this.apiKey}])}bearerAuth(e){if(null!=this.authToken)return th([{Authorization:`Bearer ${this.authToken}`}])}stringifyQuery(e){return Object.entries(e).filter(([e,t])=>void 0!==t).map(([e,t])=>{if("string"==typeof t||"number"==typeof t||"boolean"==typeof t)return`${encodeURIComponent(e)}=${encodeURIComponent(t)}`;if(null===t)return`${encodeURIComponent(e)}=`;throw new ei(`Cannot stringify type ${typeof t}; Expected string, number, boolean, or null. 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