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6 changed files with 222 additions and 11 deletions
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6
litellm/llms/anthropic/chat/handler.py
View file

@ -333,6 +333,7 @@ class AnthropicChatCompletion(BaseLLM):
model: str,
messages: list,
api_base: str,
custom_llm_provider: str,
custom_prompt_dict: dict,
model_response: ModelResponse,
print_verbose: Callable,
@ -375,7 +376,7 @@ class AnthropicChatCompletion(BaseLLM):
streamwrapper: Final = CustomStreamWrapper(
completion_stream=completion_stream,
model=model,
custom_llm_provider="anthropic",
custom_llm_provider=custom_llm_provider,
logging_obj=logging_obj,
_response_headers=process_anthropic_headers(headers),
)
@ -543,6 +544,7 @@ class AnthropicChatCompletion(BaseLLM):
messages=messages,
data=data,
api_base=api_base,
custom_llm_provider=custom_llm_provider,
custom_prompt_dict=custom_prompt_dict,
model_response=model_response,
print_verbose=print_verbose,
@ -622,7 +624,7 @@ class AnthropicChatCompletion(BaseLLM):
return CustomStreamWrapper(
completion_stream=completion_stream,
model=model,
custom_llm_provider="anthropic",
custom_llm_provider=custom_llm_provider,
logging_obj=logging_obj,
_response_headers=process_anthropic_headers(headers),
)

10
litellm/llms/anthropic/chat/transformation.py
View file

@ -2979,8 +2979,6 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
tool_name_reverse_map: dict[str, str] | None = None,
response_format_schema: ResponseFormatSchema | None = None,
):
_hidden_params: Final[dict] = {}
_hidden_params["additional_headers"] = process_anthropic_headers(dict(raw_response.headers))
if "error" in completion_response:
response_headers: Final = getattr(raw_response, "headers", None)
raise AnthropicError(
@ -3063,7 +3061,6 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
_message = json_mode_message
model_response.choices[0].message = _message
model_response.hidden_params["original_response"] = completion_response["content"]
model_response.choices[0].finish_reason = cast(
OpenAIChatCompletionFinishReason,
map_finish_reason(completion_response["stop_reason"]),
@ -3080,8 +3077,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
model_response.created = int(time.time())
model_response.model = completion_response["model"]
_hidden_params["provider_specific_fields"] = provider_specific_fields
model_response.hidden_params = _hidden_params
model_response.hidden_params = {
**model_response.hidden_params,
"additional_headers": process_anthropic_headers(dict(raw_response.headers)),
"provider_specific_fields": provider_specific_fields,
}
return model_response
def get_prefix_prompt(self, messages: list[AllMessageValues]) -> str | None:

1
litellm/llms/azure_ai/anthropic/handler.py
View file

@ -105,6 +105,7 @@ class AzureAnthropicChatCompletion(AnthropicChatCompletion):
messages=messages,
data=data,
api_base=api_base,
custom_llm_provider=custom_llm_provider,
custom_prompt_dict=custom_prompt_dict,
model_response=model_response,
print_verbose=print_verbose,

72
tests/unit/llms/anthropic/chat/test_anthropic_chat_handler.py
View file

@ -15,7 +15,7 @@ import litellm
from litellm._uuid import uuid
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.litellm_core_utils.prompt_templates.factory import anthropic_messages_pt
from litellm.llms.anthropic.chat.handler import ModelResponseIterator, _envelope_verdict, make_call
from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion, ModelResponseIterator, _envelope_verdict, make_call
from litellm.llms.base_llm.base_utils import response_format_schema, unwrap_response_format_envelope
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.types.llms.openai import (
@ -23,7 +23,7 @@ from litellm.types.llms.openai import (
ChatCompletionToolCallFunctionChunk,
)
from litellm.types.responses.main import OutputCodeInterpreterCall
from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponseStream
from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse, ModelResponseStream
from tests.unit.llms.anthropic.anthropic_docs_refusal import ANTHROPIC_DOCS_REFUSAL_MESSAGE
@ -2796,6 +2796,74 @@ def test_served_model_reaches_assembled_stream_through_custom_stream_wrapper():
assert assembled._hidden_params["provider_response_model"] == served_model
def test_streaming_completion_preserves_custom_llm_provider():
response = MagicMock()
response.status_code = 200
response.headers = {}
response.iter_lines.return_value = iter(())
client = MagicMock(spec=HTTPHandler)
client.post.return_value = response
logging_obj = MagicMock()
logging_obj.model_call_details = {
"custom_llm_provider": "vertex_ai",
"litellm_params": {},
}
result = AnthropicChatCompletion().completion(
model="claude-sonnet-4-5@20250929",
messages=[{"role": "user", "content": "Hello"}],
api_base="https://example.com/v1/messages",
custom_llm_provider="vertex_ai",
custom_prompt_dict={},
model_response=ModelResponse(),
print_verbose=MagicMock(),
encoding=None,
api_key="test-key",
logging_obj=logging_obj,
optional_params={"stream": True, "max_tokens": 16, "is_vertex_request": True},
timeout=60.0,
litellm_params={},
client=client,
)
assert result.custom_llm_provider == "vertex_ai"
@pytest.mark.asyncio
async def test_async_streaming_completion_preserves_custom_llm_provider():
response = MagicMock()
response.headers = {}
response.aiter_lines.return_value = iter(())
client = MagicMock(spec=AsyncHTTPHandler)
client.post = AsyncMock(return_value=response)
logging_obj = MagicMock()
logging_obj.model_call_details = {
"custom_llm_provider": "vertex_ai",
"litellm_params": {},
}
completion = AnthropicChatCompletion().completion(
model="claude-sonnet-4-5@20250929",
messages=[{"role": "user", "content": "Hello"}],
api_base="https://example.com/v1/messages",
custom_llm_provider="vertex_ai",
custom_prompt_dict={},
model_response=ModelResponse(),
print_verbose=MagicMock(),
encoding=None,
api_key="test-key",
logging_obj=logging_obj,
optional_params={"stream": True, "max_tokens": 16, "is_vertex_request": True},
timeout=60.0,
litellm_params={},
acompletion=True,
client=client,
)
result = await completion
assert result.custom_llm_provider == "vertex_ai"
_STREAMED_RISK_SCHEMA: Final = {
"type": "object",
"properties": {"tier": {"type": "integer"}, "summary": {"type": "string"}},

86
tests/unit/llms/anthropic/chat/test_anthropic_chat_transformation.py
View file

@ -1,6 +1,7 @@
import copy
import json
from itertools import chain
from queue import Queue
from typing import Final
from unittest.mock import MagicMock, patch
@ -20,6 +21,7 @@ from litellm.constants import (
DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET,
RESPONSE_FORMAT_TOOL_NAME,
)
from litellm.integrations.datadog.datadog import DataDogLogger
from litellm.litellm_core_utils.prompt_templates.common_utils import encrypted_reasoning_signature
from litellm.litellm_core_utils.prompt_templates.factory import anthropic_messages_pt
from litellm.llms.anthropic.chat import ModelResponseIterator
@ -34,6 +36,7 @@ from litellm.llms.bedrock.claude_platform.transformation import BedrockClaudePla
from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import (
AmazonAnthropicClaudeConfig,
)
from litellm.llms.custom_httpx.http_handler import HTTPHandler
from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (
VertexAIAnthropicConfig,
)
@ -7198,6 +7201,89 @@ def test_chat_dummy_tool_result_for_an_orphaned_tool_call_replays_a_byte_identic
assert requests[0]["messages"][2]["content"][0]["type"] == "tool_result"
def test_transform_parsed_response_preserves_existing_hidden_params():
from litellm.types.utils import ModelResponse
config = AnthropicConfig()
raw_response = MagicMock()
raw_response.headers = {"request-id": "req_vertex"}
raw_response.status_code = 200
completion_response = {
"id": "msg_vertex",
"model": "claude-sonnet-4-5@20250929",
"stop_reason": "end_turn",
"usage": {"input_tokens": 10, "output_tokens": 5},
"content": [{"type": "text", "text": "Hello"}],
}
model_response = ModelResponse()
model_response._hidden_params = {
"custom_llm_provider": "vertex_ai",
"region_name": "us-east5",
}
result = config.transform_parsed_response(
completion_response=completion_response,
raw_response=raw_response,
model_response=model_response,
)
assert result._hidden_params["custom_llm_provider"] == "vertex_ai"
assert result._hidden_params["region_name"] == "us-east5"
assert result.choices[0].message.content == "Hello"
@pytest.mark.asyncio
@pytest.mark.parametrize("turn_off_message_logging", [True, False])
async def test_anthropic_completion_respects_message_redaction_in_datadog_v1(
monkeypatch: pytest.MonkeyPatch, turn_off_message_logging: bool
) -> None:
monkeypatch.setattr(litellm, "datadog_use_v1", True)
private_output: Final = "private-claude-output-for-redaction-regression"
logged_requests: Final[Queue[httpx.Request]] = Queue()
def provider_response(request: httpx.Request) -> httpx.Response:
return httpx.Response(
200,
json={
"id": "msg_redaction",
"type": "message",
"role": "assistant",
"model": "claude-fable-5-1",
"content": [{"type": "text", "text": private_output}],
"stop_reason": "end_turn",
"usage": {"input_tokens": 10, "output_tokens": 5},
},
)
def datadog_response(request: httpx.Request) -> httpx.Response:
logged_requests.put(request)
return httpx.Response(202)
logger: Final = DataDogLogger(dd_api_key="test-key", dd_site="datadoghq.com", allow_env_credentials=False)
provider_client: Final = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(provider_response)))
datadog_client: Final = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(datadog_response)))
logger.sync_client = datadog_client
try:
response: Final = litellm.completion(
model="anthropic/claude-fable-5-1",
messages=[{"role": "user", "content": "Hello"}],
api_key="test-key",
client=provider_client,
success_callback=[logger],
turn_off_message_logging=turn_off_message_logging,
)
request: Final = logged_requests.get(timeout=10)
finally:
provider_client.close()
datadog_client.close()
assert response.choices[0].message.content == private_output
assert (private_output in request.content.decode()) is (not turn_off_message_logging)
payload: Final = json.loads(json.loads(request.content)["message"])
assert payload["usage"]["prompt_tokens"] == 10
assert payload["usage"]["completion_tokens"] == 5
anthropic_chunk_list: Final = [
{
"type": "content_block_start",

58
tests/unit/llms/azure_ai/claude/test_azure_anthropic_handler.py
View file

@ -6,7 +6,7 @@ sys.path.insert(
)
import json
from unittest.mock import MagicMock, patch
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
import respx
@ -14,6 +14,7 @@ from httpx import Response
import litellm
from litellm.llms.azure_ai.anthropic.handler import AzureAnthropicChatCompletion
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
from litellm.types.utils import ModelResponse
@ -67,7 +68,10 @@ class TestAzureAnthropicChatCompletion:
headers = {}
with patch.object(
handler, "acompletion_function", return_value=ModelResponse()
handler,
"acompletion_function",
new_callable=MagicMock,
return_value=ModelResponse(),
) as mock_acompletion:
handler.completion(
model=model,
@ -90,6 +94,7 @@ class TestAzureAnthropicChatCompletion:
# Verify AzureAnthropicConfig was used
mock_azure_config.assert_called_once()
mock_config_instance.validate_environment.assert_called_once()
mock_acompletion.assert_called_once()
@patch("litellm.llms.anthropic.chat.handler.make_sync_call")
@patch("litellm.utils.ProviderConfigManager")
@ -161,6 +166,55 @@ class TestAzureAnthropicChatCompletion:
mock_make_sync_call.assert_called_once()
assert result is not None
@pytest.mark.asyncio
@patch("litellm.llms.azure_ai.anthropic.handler.AzureAnthropicConfig")
async def test_completion_async_streaming_preserves_provider(self, mock_azure_config):
handler = AzureAnthropicChatCompletion()
mock_config = MagicMock()
mock_config.validate_environment.return_value = {
"api-key": "test-api-key",
"anthropic-version": "2023-06-01",
}
mock_config.transform_request.return_value = {
"model": "claude-sonnet-4-5",
"messages": [],
"stream": True,
}
mock_azure_config.return_value = mock_config
response = MagicMock()
response.headers = {}
response.aiter_lines.return_value = iter(())
client = MagicMock(spec=AsyncHTTPHandler)
client.post = AsyncMock(return_value=response)
logging_obj = MagicMock()
logging_obj.model_call_details = {
"custom_llm_provider": "azure_ai",
"litellm_params": {},
}
completion = handler.completion(
model="claude-sonnet-4-5",
messages=[{"role": "user", "content": "Hello"}],
api_base="https://test.services.ai.azure.com/anthropic/v1/messages",
custom_llm_provider="azure_ai",
custom_prompt_dict={},
model_response=ModelResponse(),
print_verbose=MagicMock(),
encoding=None,
api_key="test-api-key",
logging_obj=logging_obj,
optional_params={"stream": True},
timeout=60.0,
litellm_params={"api_key": "test-api-key"},
acompletion=True,
client=client,
)
result = await completion
client.post.assert_awaited_once()
assert result.custom_llm_provider == "azure_ai"
@patch("litellm.llms.custom_httpx.http_handler.get_httpx_client")
@patch("litellm.utils.ProviderConfigManager")
@patch("litellm.llms.azure_ai.anthropic.handler.AzureAnthropicConfig")