litellm/tests/unit/integrations/datadog/test_datadog.py

import gzip
import json
import os
from datetime import datetime
from typing import Coroutine, Final
from unittest.mock import AsyncMock, patch

import pytest
from httpx import Request, Response

import litellm
import litellm.integrations.datadog.datadog as datadog_module
from litellm.integrations.datadog.datadog import DataDogLogger
from litellm.integrations.datadog.datadog_handler import (
    get_datadog_env,
    get_datadog_hostname,
    get_datadog_pod_name,
    get_datadog_service,
    get_datadog_source,
    get_datadog_tags,
)
from litellm.types.integrations.datadog import DatadogInitParams, DatadogPayload, DataDogStatus
from litellm.types.utils import (
    StandardLoggingHiddenParams,
    StandardLoggingMetadata,
    StandardLoggingModelInformation,
    StandardLoggingPayload,
)
import asyncio
import io
from datetime import datetime as datetime_class

STANDARD_START_TIME: Final[datetime] = datetime(2025, 1, 1)
STANDARD_END_TIME: Final[datetime] = datetime(2025, 1, 1, 0, 0, 1)


def _discard_periodic_flush(coroutine: Coroutine[object, object, None]) -> None:
    coroutine.close()


class _DummySpan:
    def __init__(self, trace_id=None, span_id=None):
        self.trace_id = trace_id
        self.span_id = span_id


class _DummyTracer:
    def __init__(self, current_span=None, current_root_span=None):
        self._current_span = current_span
        self._current_root_span = current_root_span

    def current_span(self):
        return self._current_span

    def current_root_span(self):
        return self._current_root_span


def _standard_logging_payload() -> StandardLoggingPayload:
    return StandardLoggingPayload(
        id="test_id",
        trace_id="trace-id",
        session_id="session-id",
        litellm_call_id="call-id",
        call_type="completion",
        stream=False,
        response_cost=0.1,
        cost_breakdown=None,
        autorouter_savings=None,
        autorouter_savings_estimate=None,
        autorouter_baseline_observation=None,
        response_cost_failure_debug_info=None,
        status="success",
        status_fields={},
        custom_llm_provider="openai",
        total_tokens=30,
        prompt_tokens=20,
        completion_tokens=10,
        startTime=1234567890.0,
        endTime=1234567891.0,
        completionStartTime=1234567890.5,
        response_time=1.0,
        model_map_information=StandardLoggingModelInformation(
            model_map_key="gpt-4.1-mini",
            model_map_value=None,
        ),
        model="gpt-4.1-mini",
        model_id="model-123",
        model_group="openai-gpt",
        api_base="https://api.openai.com",
        user_agent=None,
        metadata=StandardLoggingMetadata(
            user_api_key_hash="test_hash",
            user_api_key_org_id=None,
            user_api_key_alias="test_alias",
            user_api_key_team_id="test_team",
            user_api_key_user_id="test_user",
            user_api_key_team_alias="test_team_alias",
            spend_logs_metadata=None,
            requester_ip_address="127.0.0.1",
            requester_metadata=None,
        ),
        cache_hit=False,
        cache_key=None,
        saved_cache_cost=0.0,
        request_tags=[],
        end_user=None,
        requester_ip_address="127.0.0.1",
        messages=[{"role": "user", "content": "Hello, world!"}],
        response={"choices": [{"message": {"content": "Hi there!"}}]},
        error_str=None,
        error_information=None,
        model_parameters={"stream": True},
        hidden_params=StandardLoggingHiddenParams(
            model_id="model-123",
            cache_key=None,
            api_base="https://api.openai.com",
            response_cost="0.1",
            additional_headers=None,
        ),
        guardrail_information=None,
        standard_built_in_tools_params=None,
    )


@pytest.fixture
def datadog_env(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setenv("DD_API_KEY", "test_api_key")
    monkeypatch.setenv("DD_SITE", "test.datadoghq.com")
    monkeypatch.delenv("LITELLM_DD_AGENT_HOST", raising=False)
    monkeypatch.delenv("LITELLM_DD_AGENT_PORT", raising=False)
    monkeypatch.delenv("DD_BASE_URL", raising=False)
    monkeypatch.setattr(litellm, "datadog_params", None)
    monkeypatch.setattr(litellm, "datadog_use_v1", False)


@pytest.fixture
def datadog_logger(datadog_env: None) -> DataDogLogger:
    with patch("asyncio.create_task", side_effect=_discard_periodic_flush):
        logger: Final = DataDogLogger()
    return logger


@pytest.mark.asyncio
async def test_add_trace_context_uses_current_span(monkeypatch):
    monkeypatch.setenv("DD_SITE", "https://fake.datadoghq.com")
    monkeypatch.setenv("DD_API_KEY", "anything")
    tracer = _DummyTracer(current_span=_DummySpan(trace_id=123, span_id=456))
    monkeypatch.setattr(datadog_module, "tracer", tracer)

    dd_logger = DataDogLogger()
    payload = DatadogPayload(
        ddsource="litellm",
        ddtags="env:test",
        hostname="host",
        message="{}",
        service="svc",
        status="info",
    )

    dd_logger._add_trace_context_to_payload(payload)
    assert payload["dd.trace_id"] == "123"
    assert payload["dd.span_id"] == "456"


@pytest.mark.asyncio
async def test_add_trace_context_falls_back_to_root_span(monkeypatch):
    monkeypatch.setenv("DD_SITE", "https://fake.datadoghq.com")
    monkeypatch.setenv("DD_API_KEY", "anything")
    tracer = _DummyTracer(
        current_span=None,
        current_root_span=_DummySpan(trace_id=789, span_id=None),
    )
    monkeypatch.setattr(datadog_module, "tracer", tracer)

    dd_logger = DataDogLogger()
    payload = DatadogPayload(
        ddsource="litellm",
        ddtags="env:test",
        hostname="host",
        message="{}",
        service="svc",
        status="info",
    )

    dd_logger._add_trace_context_to_payload(payload)
    assert payload["dd.trace_id"] == "789"
    assert "dd.span_id" not in payload


@pytest.mark.asyncio
async def test_add_trace_context_handles_missing_tracer(monkeypatch):
    monkeypatch.setenv("DD_SITE", "https://fake.datadoghq.com")
    monkeypatch.setenv("DD_API_KEY", "anything")
    monkeypatch.setattr(datadog_module, "tracer", object())

    dd_logger = DataDogLogger()
    payload = DatadogPayload(
        ddsource="litellm",
        ddtags="env:test",
        hostname="host",
        message="{}",
        service="svc",
        status="info",
    )

    dd_logger._add_trace_context_to_payload(payload)
    assert "dd.trace_id" not in payload
    assert "dd.span_id" not in payload


@pytest.mark.asyncio
async def test_add_trace_context_ignores_span_without_trace_id(monkeypatch):
    monkeypatch.setenv("DD_SITE", "https://fake.datadoghq.com")
    monkeypatch.setenv("DD_API_KEY", "anything")
    tracer = _DummyTracer(current_span=_DummySpan(trace_id=None, span_id=555))
    monkeypatch.setattr(datadog_module, "tracer", tracer)

    dd_logger = DataDogLogger()
    payload = DatadogPayload(
        ddsource="litellm",
        ddtags="env:test",
        hostname="host",
        message="{}",
        service="svc",
        status="info",
    )

    dd_logger._add_trace_context_to_payload(payload)
    assert "dd.trace_id" not in payload
    assert "dd.span_id" not in payload


@pytest.mark.asyncio
@pytest.mark.usefixtures("datadog_env", "drained_logging_worker")
async def test_datadog_logging_http_request():
    """
    - Test that the HTTP request is made to Datadog
    - sent to the /api/v2/logs endpoint
    - the payload is batched
    - each element in the payload is a DatadogPayload
    - each element in a DatadogPayload.message contains all the valid fields
    """
    try:
        from litellm.integrations.datadog.datadog import DataDogLogger

        os.environ["DD_SITE"] = "https://fake.datadoghq.com"
        os.environ["DD_API_KEY"] = "anything"
        dd_logger = DataDogLogger()

        litellm.callbacks = [dd_logger]

        litellm.set_verbose = True

        # Create a mock for the async_client's post method
        mock_post = AsyncMock()
        mock_post.return_value.status_code = 202
        mock_post.return_value.text = "Accepted"
        dd_logger.async_client.post = mock_post

        # Make the completion call
        for _ in range(5):
            response = await litellm.acompletion(
                model="gpt-4.1-mini",
                messages=[{"role": "user", "content": "what llm are u"}],
                max_tokens=10,
                temperature=0.2,
                mock_response="Accepted",
            )
            print(response)

        # Wait for 5 seconds
        await asyncio.sleep(6)

        # Assert that the mock was called
        assert mock_post.called, "HTTP request was not made"

        # Get the arguments of the last call
        args, kwargs = mock_post.call_args

        print("CAll args and kwargs", args, kwargs)

        # Print the request body

        # You can add more specific assertions here if needed
        # For example, checking if the URL is correct
        assert kwargs["url"].endswith("/api/v2/logs"), "Incorrect DataDog endpoint"

        body = kwargs["data"]

        # use gzip to unzip the body
        with gzip.open(io.BytesIO(body), "rb") as f:
            body = f.read().decode("utf-8")
        print(body)

        # body is string parse it to dict
        body = json.loads(body)
        print(body)

        assert len(body) == 5  # 5 logs should be sent to DataDog

        # Assert that the first element in body has the expected fields and shape
        assert isinstance(body[0], dict), "First element in body should be a dictionary"

        # Get the expected fields and their types from DatadogPayload
        expected_fields = DatadogPayload.__annotations__
        required_fields = {
            "ddsource": str,
            "ddtags": str,
            "hostname": str,
            "message": str,
            "service": str,
            "status": str,
        }
        optional_fields = set(expected_fields.keys()) - set(required_fields.keys())

        # Assert that all elements in body have the required fields with correct types
        for log in body:
            assert isinstance(log, dict), "Each log should be a dictionary"
            for field, expected_type in required_fields.items():
                assert field in log, f"Field '{field}' is missing from the log"
                assert isinstance(
                    log[field], expected_type
                ), f"Field '{field}' has incorrect type. Expected {expected_type}, got {type(log[field])}"

            for optional_field in optional_fields:
                if optional_field in log:
                    assert isinstance(
                        log[optional_field], str
                    ), f"Optional field '{optional_field}' must be a string"

            unexpected_fields = set(log.keys()) - set(expected_fields.keys())
            assert (
                not unexpected_fields
            ), f"Log contains unexpected fields: {unexpected_fields}"

        # Parse the 'message' field as JSON and check its structure
        message = json.loads(body[0]["message"])
        print("logged message", json.dumps(message, indent=4))

        expected_message_fields = StandardLoggingPayload.__required_keys__

        for field in expected_message_fields:
            assert field in message, f"Field '{field}' is missing from the message"

        # Check specific fields
        assert message["call_type"] == "acompletion"
        assert message["model"] == "gpt-4.1-mini"
        assert isinstance(message["model_parameters"], dict)
        assert "temperature" in message["model_parameters"]
        assert "max_tokens" in message["model_parameters"]
        assert isinstance(message["response"], dict)
        assert isinstance(message["metadata"], dict)

    except Exception as e:
        pytest.fail(f"Test failed with exception: {str(e)}")


@pytest.mark.asyncio
async def test_datadog_payload_environment_variables():
    """Test that DataDog payload correctly includes environment variables in the payload structure"""
    try:
        # Set test environment variables
        test_env = {
            "DD_ENV": "test-env",
            "DD_SERVICE": "test-service",
            "DD_VERSION": "1.0.0",
            "DD_SOURCE": "test-source",
            "DD_API_KEY": "fake-key",
            "DD_SITE": "datadoghq.com",
        }

        with patch.dict(os.environ, test_env):
            dd_logger = DataDogLogger()
            standard_payload = create_standard_logging_payload()

            # Create the payload
            dd_payload = dd_logger.create_datadog_logging_payload(
                kwargs={"standard_logging_object": standard_payload},
                response_obj=None,
                start_time=datetime_class.now(),
                end_time=datetime_class.now(),
            )

            print("dd payload=", json.dumps(dd_payload, indent=2))

            # Verify payload structure and environment variables
            assert (
                dd_payload["ddsource"] == "test-source"
            ), "Incorrect source in payload"
            assert (
                dd_payload["service"] == "test-service"
            ), "Incorrect service in payload"

            assert (
                "env:test-env,service:test-service,version:1.0.0,HOSTNAME:"
                in dd_payload["ddtags"]
            ), "Incorrect tags in payload"

    except Exception as e:
        pytest.fail(f"Test failed with exception: {str(e)}")


@pytest.mark.asyncio
@pytest.mark.usefixtures("fake_provider_credentials")
async def test_datadog_payload_content_truncation():
    """
    Test that DataDog payload correctly truncates long content

    DataDog has a limit of 1MB for the logged payload size.
    """
    dd_logger = DataDogLogger()

    # Create a standard payload with very long content
    standard_payload = create_standard_logging_payload()
    long_content = "x" * 80_000  # Create string longer than MAX_STR_LENGTH (10_000)

    # Modify payload with long content
    standard_payload["error_str"] = long_content
    standard_payload["messages"] = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": long_content,
                        "detail": "low",
                    },
                }
            ],
        }
    ]
    standard_payload["response"] = {"choices": [{"message": {"content": long_content}}]}

    # Create the payload
    dd_payload = dd_logger.create_datadog_logging_payload(
        kwargs={"standard_logging_object": standard_payload},
        response_obj=None,
        start_time=datetime_class.now(),
        end_time=datetime_class.now(),
    )

    print("dd_payload", json.dumps(dd_payload, indent=2))

    # Parse the message back to dict to verify truncation
    message_dict = json.loads(dd_payload["message"])

    # Verify truncation of fields
    assert len(message_dict["error_str"]) < 10_100, "error_str not truncated correctly"
    assert (
        len(str(message_dict["messages"])) < 10_100
    ), "messages not truncated correctly"
    assert (
        len(str(message_dict["response"])) < 10_100
    ), "response not truncated correctly"


@pytest.mark.asyncio
async def test_datadog_payload_truncation_leaves_shared_payload_intact(monkeypatch):
    """
    Every callback of a request shares one standard logging object, so the datadog truncation
    must not turn its messages into a string for the callbacks that run after it (the prompt
    caching router check reads `messages` as a list to pin the deployment holding the cache)
    """
    monkeypatch.setenv("DD_SITE", "https://fake.datadoghq.com")
    monkeypatch.setenv("DD_API_KEY", "anything")
    dd_logger = DataDogLogger()
    standard_payload = create_standard_logging_payload()
    original_messages = [{"role": "user", "content": "x" * 80_000}]
    standard_payload["messages"] = original_messages
    kwargs = {"standard_logging_object": standard_payload}

    dd_payload = dd_logger.create_datadog_logging_payload(
        kwargs=kwargs,
        response_obj=None,
        start_time=datetime_class.now(),
        end_time=datetime_class.now(),
    )

    assert kwargs["standard_logging_object"]["messages"] is original_messages
    assert len(json.loads(dd_payload["message"])["messages"]) < 10_100


def test_datadog_static_methods():
    """Test the static helper methods in DataDogLogger class"""

    # Test with default environment variables
    assert get_datadog_source() == "litellm"
    assert get_datadog_service() == "litellm-server"
    assert get_datadog_hostname() is not None
    assert get_datadog_env() == "unknown"
    assert get_datadog_pod_name() == "unknown"

    # Test tags format with default values
    assert "env:unknown,service:litellm-server,version:unknown,HOSTNAME:" in ",".join(
        get_datadog_tags()
    )

    # Test with custom environment variables
    test_env = {
        "DD_SOURCE": "custom-source",
        "DD_SERVICE": "custom-service",
        "HOSTNAME": "test-host",
        "DD_ENV": "production",
        "DD_VERSION": "1.0.0",
        "POD_NAME": "pod-123",
    }

    with patch.dict(os.environ, test_env):
        assert get_datadog_source() == "custom-source"
        print("DataDogLogger._get_datadog_source()", get_datadog_source())
        assert get_datadog_service() == "custom-service"
        print("DataDogLogger._get_datadog_service()", get_datadog_service())
        assert get_datadog_hostname() == "test-host"
        print(
            "DataDogLogger._get_datadog_hostname()",
            get_datadog_hostname(),
        )
        assert get_datadog_env() == "production"
        print("DataDogLogger._get_datadog_env()", get_datadog_env())
        assert get_datadog_pod_name() == "pod-123"
        print(
            "DataDogLogger._get_datadog_pod_name()",
            get_datadog_pod_name(),
        )

        # Test tags format with custom values
        expected_custom_tags = "env:production,service:custom-service,version:1.0.0,HOSTNAME:test-host,POD_NAME:pod-123"
        print("DataDogLogger._get_datadog_tags()", get_datadog_tags())
        assert ",".join(get_datadog_tags()) == expected_custom_tags


@pytest.mark.asyncio
@pytest.mark.usefixtures("fake_provider_credentials")
async def test_datadog_non_serializable_messages():
    """Test logging events with non-JSON-serializable messages"""
    dd_logger = DataDogLogger()

    # Create payload with non-serializable content
    standard_payload = create_standard_logging_payload()
    non_serializable_obj = datetime_class.now()  # datetime objects aren't JSON serializable
    standard_payload["messages"] = [{"role": "user", "content": non_serializable_obj}]
    standard_payload["response"] = {
        "choices": [{"message": {"content": non_serializable_obj}}]
    }

    kwargs = {"standard_logging_object": standard_payload}

    # Test payload creation
    dd_payload = dd_logger.create_datadog_logging_payload(
        kwargs=kwargs,
        response_obj=None,
        start_time=datetime_class.now(),
        end_time=datetime_class.now(),
    )

    # Verify payload can be serialized
    assert dd_payload["status"] == DataDogStatus.INFO

    # Verify the message can be parsed back to dict
    dict_payload = json.loads(dd_payload["message"])

    # Check that the non-serializable objects were converted to strings
    assert isinstance(dict_payload["messages"][0]["content"], str)
    assert isinstance(dict_payload["response"]["choices"][0]["message"]["content"], str)


def test_get_datadog_tags():
    """Test the _get_datadog_tags static method with various inputs"""
    # Test with no standard_logging_object and default env vars
    base_tags = get_datadog_tags()
    assert any("env:" in t for t in base_tags)
    assert any("service:" in t for t in base_tags)
    assert any("version:" in t for t in base_tags)
    assert any("POD_NAME:" in t for t in base_tags)
    assert any("HOSTNAME:" in t for t in base_tags)

    # Test with custom env vars
    test_env = {
        "DD_ENV": "production",
        "DD_SERVICE": "custom-service",
        "DD_VERSION": "1.0.0",
        "HOSTNAME": "test-host",
        "POD_NAME": "pod-123",
    }
    with patch.dict(os.environ, test_env):
        custom_tags = get_datadog_tags()
        assert "env:production" in custom_tags
        assert "service:custom-service" in custom_tags
        assert "version:1.0.0" in custom_tags
        assert "HOSTNAME:test-host" in custom_tags
        assert "POD_NAME:pod-123" in custom_tags

    # Test with standard_logging_object containing request_tags
    standard_logging_obj = create_standard_logging_payload()
    standard_logging_obj["request_tags"] = ["tag1", "tag2"]

    tags_with_request = get_datadog_tags(standard_logging_obj)
    assert "request_tag:tag1" in tags_with_request
    assert "request_tag:tag2" in tags_with_request

    # Test with empty request_tags
    standard_logging_obj["request_tags"] = []
    tags_empty_request = get_datadog_tags(standard_logging_obj)
    assert not any(t.startswith("request_tag:") for t in tags_empty_request)

    # Test with None request_tags
    standard_logging_obj["request_tags"] = None
    tags_none_request = get_datadog_tags(standard_logging_obj)
    assert not any(t.startswith("request_tag:") for t in tags_none_request)


@pytest.mark.asyncio
async def test_datadog_message_redaction():
    """
    Test that DataDog logger correctly initializes with turn_off_message_logging=True
    from litellm.datadog_params
    """
    try:
        # Test using litellm.datadog_params pattern
        litellm.datadog_params = DatadogInitParams(turn_off_message_logging=True)

        os.environ["DD_SITE"] = "https://fake.datadoghq.com"
        os.environ["DD_API_KEY"] = "anything"

        # Mock the periodic flush to avoid async issues
        with patch("asyncio.create_task"):
            dd_logger = DataDogLogger()

        # Verify that turn_off_message_logging was set correctly from litellm.datadog_params
        assert hasattr(
            dd_logger, "turn_off_message_logging"
        ), "DataDogLogger should have turn_off_message_logging attribute"
        assert (
            dd_logger.turn_off_message_logging is True
        ), f"Expected turn_off_message_logging=True, got {dd_logger.turn_off_message_logging}"

        # Test the redaction method inherited from CustomLogger
        model_call_details = {
            "standard_logging_object": {
                "messages": [
                    {
                        "role": "user",
                        "content": "This is sensitive information that should be redacted",
                    }
                ],
                "response": {
                    "choices": [
                        {
                            "message": {
                                "content": "This is a sensitive response that should be redacted"
                            }
                        }
                    ]
                },
            }
        }

        # Apply redaction using the inherited method
        redacted_details = (
            dd_logger.redact_standard_logging_payload_from_model_call_details(
                model_call_details
            )
        )
        redacted_str = "redacted-by-litellm"

        # Verify that messages are redacted
        redacted_standard_obj = redacted_details["standard_logging_object"]
        assert (
            redacted_standard_obj["messages"][0]["content"] == redacted_str
        ), f"Messages not redacted. Got: {redacted_standard_obj['messages'][0]['content']}"

        # Verify that response is redacted
        assert (
            redacted_standard_obj["response"]["choices"][0]["message"]["content"]
            == redacted_str
        ), f"Response not redacted. Got: {redacted_standard_obj['response']['choices'][0]['message']['content']}"

        print("✅ DataDog message redaction test passed")

    except Exception as e:
        pytest.fail(f"Test failed with exception: {str(e)}")
    finally:
        # Clean up
        litellm.datadog_params = None
        litellm.callbacks = []


def test_datadog_agent_configuration():
    """
    Test that DataDog logger correctly configures agent endpoint when LITELLM_DD_AGENT_HOST is set.

    Note: We use LITELLM_DD_AGENT_HOST instead of DD_AGENT_HOST to avoid conflicts
    with ddtrace which automatically sets DD_AGENT_HOST for APM tracing.
    """
    test_env = {
        "LITELLM_DD_AGENT_HOST": "localhost",
        "LITELLM_DD_AGENT_PORT": "10518",
    }

    # Remove DD_SITE and DD_API_KEY to verify they're not required for agent mode
    env_to_remove = ["DD_SITE", "DD_API_KEY"]

    with patch.dict(os.environ, test_env, clear=False):
        for key in env_to_remove:
            os.environ.pop(key, None)

        with patch("asyncio.create_task"):
            dd_logger = DataDogLogger()

        # Verify agent endpoint is configured correctly
        assert (
            dd_logger.intake_url == "http://localhost:10518/api/v2/logs"
        ), f"Expected agent URL, got {dd_logger.intake_url}"

        # Verify DD_API_KEY is optional (can be None)
        assert dd_logger.DD_API_KEY is None or isinstance(dd_logger.DD_API_KEY, str)


def test_datadog_ignores_ddtrace_agent_host():
    """
    Regression test: Ensure DD_AGENT_HOST set by ddtrace doesn't interfere with LiteLLM logging.

    When users have ddtrace installed for APM tracing, it automatically sets DD_AGENT_HOST.
    LiteLLM should ignore DD_AGENT_HOST and only use LITELLM_DD_AGENT_HOST for agent mode.

    This prevents the 404 error when ddtrace's DD_AGENT_HOST points to an APM endpoint
    that doesn't support /api/v2/logs.

    Regression test for: https://github.com/BerriAI/litellm/issues/16379
    """
    test_env = {
        # User's explicit config for LiteLLM logging (direct API)
        "DD_API_KEY": "fake-api-key",
        "DD_SITE": "us5.datadoghq.com",
        # ddtrace automatically sets these for APM tracing
        "DD_AGENT_HOST": "10.176.100.40",
        "DD_AGENT_PORT": "8126",
    }

    with patch.dict(os.environ, test_env, clear=False):
        with patch("asyncio.create_task"):
            dd_logger = DataDogLogger()

        # Verify direct API endpoint is used (DD_AGENT_HOST should be ignored)
        expected_url = "https://http-intake.logs.us5.datadoghq.com/api/v2/logs"
        assert dd_logger.intake_url == expected_url, (
            f"Expected direct API URL '{expected_url}', got '{dd_logger.intake_url}'. "
            "DD_AGENT_HOST (set by ddtrace) should be ignored - only LITELLM_DD_AGENT_HOST should trigger agent mode."
        )

        # Verify API key is set correctly
        assert dd_logger.DD_API_KEY == "fake-api-key"


def create_standard_logging_payload() -> StandardLoggingPayload:
    return StandardLoggingPayload(
        id="test_id",
        call_type="completion",
        response_cost=0.1,
        response_cost_failure_debug_info=None,
        status="success",
        total_tokens=30,
        prompt_tokens=20,
        completion_tokens=10,
        startTime=1234567890.0,
        endTime=1234567891.0,
        completionStartTime=1234567890.5,
        model_map_information=StandardLoggingModelInformation(
            model_map_key="gpt-4.1-mini", model_map_value=None
        ),
        model="gpt-4.1-mini",
        model_id="model-123",
        model_group="openai-gpt",
        api_base="https://api.openai.com",
        metadata=StandardLoggingMetadata(
            user_api_key_hash="test_hash",
            user_api_key_org_id=None,
            user_api_key_alias="test_alias",
            user_api_key_team_id="test_team",
            user_api_key_user_id="test_user",
            user_api_key_team_alias="test_team_alias",
            spend_logs_metadata=None,
            requester_ip_address="127.0.0.1",
            requester_metadata=None,
        ),
        cache_hit=False,
        cache_key=None,
        saved_cache_cost=0.0,
        request_tags=[],
        end_user=None,
        requester_ip_address="127.0.0.1",
        messages=[{"role": "user", "content": "Hello, world!"}],
        response={"choices": [{"message": {"content": "Hi there!"}}]},
        error_str=None,
        model_parameters={"stream": True},
        hidden_params=StandardLoggingHiddenParams(
            model_id="model-123",
            cache_key=None,
            api_base="https://api.openai.com",
            response_cost="0.1",
            additional_headers=None,
        ),
    )