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Merge 2f6a949f49 into 02dcc4d347

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Samarth Mukhija 2026-08-27 23:02:44 +05:30 • committed by GitHub
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2 changed files with 176 additions and 38 deletions
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142
litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
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@ -69,11 +69,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
"speed",
"output_config",
"reasoning_effort",
# TODO: Add Anthropic `metadata` support
# "metadata",
"metadata",
]
def _remove_scope_from_cache_control(self, anthropic_messages_request: dict) -> None:
def _remove_scope_from_cache_control(
self, anthropic_messages_request: dict
) -> None:
"""
Remove `scope` field from cache_control blocks.
@ -136,7 +137,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
text = content_block.get("text", "")
content_type = content_block.get("type", "")
# Skip text blocks that start with billing header
if content_type == "text" and text.startswith("x-anthropic-billing-header:"):
if content_type == "text" and text.startswith(
"x-anthropic-billing-header:"
):
continue
filtered_list.append(content_block)
else:
@ -160,6 +163,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
def _is_system_role_message(message: Any) -> bool:
return isinstance(message, dict) and message.get("role") == "system"
_CONVERTED_SYSTEM_NOTE: Final = (
"Operator note (not from the user): the following was originally a mid-conversation system-role reminder."
)
@ -215,6 +219,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
def _normalize_system_role_messages(self, anthropic_messages_request: dict, model: str) -> None:
"""Normalize ``role: "system"`` entries in ``messages`` per the Anthropic
``/v1/messages`` contract, which the first-party API, Bedrock Invoke,
Vertex, and Azure Foundry all enforce identically.
@ -248,6 +253,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
messages: Final = anthropic_messages_request.get("messages")
if not isinstance(messages, list):
return
leading_count: Final = next(
(i for i, m in enumerate(messages) if not self._is_system_role_message(m)),
len(messages),
@ -275,7 +281,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
)
for block in self._as_system_content_blocks(source)
]
filtered_system: Final = self._filter_billing_headers_from_system(system_content)
filtered_system: Final = self._filter_billing_headers_from_system(
system_content
)
if filtered_system:
anthropic_messages_request["system"] = filtered_system
else:
@ -290,7 +298,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
litellm_params: dict,
stream: bool | None = None,
) -> str:
api_base = AnthropicModelInfo.get_api_base(api_base) or "https://api.anthropic.com"
api_base = (
AnthropicModelInfo.get_api_base(api_base) or "https://api.anthropic.com"
)
if not api_base.endswith("/v1/messages"):
api_base = f"{api_base}/v1/messages"
return api_base
@ -306,7 +316,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
api_base: str | None = None,
) -> tuple[dict, str | None]:
# Check for Anthropic OAuth token in Authorization header
headers, api_key = optionally_handle_anthropic_oauth(headers=headers, api_key=api_key)
headers, api_key = optionally_handle_anthropic_oauth(
headers=headers, api_key=api_key
)
header_names: Final = frozenset(name.lower() for name in headers)
if "x-api-key" not in header_names and "authorization" not in header_names:
@ -335,7 +347,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
return headers, api_base
@staticmethod
def _translate_reasoning_effort_to_anthropic(model: str, optional_params: dict, custom_llm_provider: str) -> None:
def _translate_reasoning_effort_to_anthropic(
model: str, optional_params: dict, custom_llm_provider: str
) -> None:
"""Map OpenAI-style ``reasoning_effort`` to native Anthropic params.
Caller-supplied ``thinking`` / ``output_config`` win over the alias.
@ -367,7 +381,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
optional_params.setdefault("thinking", mapped_thinking)
if AnthropicModelInfo._is_adaptive_thinking_model(model, custom_llm_provider):
mapped_effort: Final = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(reasoning_effort)
mapped_effort: Final = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(
reasoning_effort
)
if mapped_effort is None:
raise AnthropicError(
message=(
@ -377,7 +393,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
),
status_code=400,
)
gate_error: Final = AnthropicConfig._validate_effort_for_model(model, mapped_effort, custom_llm_provider)
gate_error: Final = AnthropicConfig._validate_effort_for_model(
model, mapped_effort, custom_llm_provider
)
if gate_error is not None:
raise AnthropicError(message=gate_error, status_code=400)
existing_output_config = optional_params.get("output_config")
@ -399,7 +417,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
"""
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
if not AnthropicModelInfo._is_adaptive_thinking_model(model, custom_llm_provider):
if not AnthropicModelInfo._is_adaptive_thinking_model(
model, custom_llm_provider
):
return
if AnthropicModelInfo._supports_legacy_thinking(model, custom_llm_provider):
return
@ -428,7 +448,10 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
@staticmethod
def _translate_adaptive_effort_for_non_adaptive_model(
model: str, optional_params: dict, max_tokens: int | None, custom_llm_provider: str
model: str,
optional_params: dict,
max_tokens: int | None,
custom_llm_provider: str,
) -> None:
"""Translate the 4.6+ adaptive-thinking interface (``thinking.type=adaptive``
and/or ``output_config.effort``) down to what an older Anthropic model
@ -476,8 +499,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
output_config: Final = optional_params.get("output_config")
thinking: Final = optional_params.get("thinking")
effort: Final = output_config.get("effort") if isinstance(output_config, dict) else None
adaptive_thinking: Final = isinstance(thinking, dict) and thinking.get("type") == "adaptive"
effort: Final = (
output_config.get("effort") if isinstance(output_config, dict) else None
)
adaptive_thinking: Final = (
isinstance(thinking, dict) and thinking.get("type") == "adaptive"
)
if effort is None and not adaptive_thinking:
return
@ -486,9 +513,14 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
# reject. Effort-only requests pass through so provider subclasses (bedrock/vertex) keep
# owning level clamping; an adaptive request only stays here when its effort level is one
# the model supports, otherwise it falls through to the legacy budget translation below.
if AnthropicConfig._model_supports_effort_param(model, custom_llm_provider) and (
if AnthropicConfig._model_supports_effort_param(
model, custom_llm_provider
) and (
not adaptive_thinking
or AnthropicConfig._validate_effort_for_model(model, effort, custom_llm_provider) is None
or AnthropicConfig._validate_effort_for_model(
model, effort, custom_llm_provider
)
is None
):
if adaptive_thinking:
optional_params.pop("thinking", None)
@ -510,7 +542,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
except _BadRequestError as e:
raise AnthropicError(message=str(e.message), status_code=400)
capped_thinking: Final = (
AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens)
AnthropicConfig._cap_thinking_budget_to_max_tokens(
legacy_thinking, max_tokens
)
if legacy_thinking is not None
else None
)
@ -553,8 +587,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
return
thinking: Final = optional_params.get("thinking")
output_config: Final = optional_params.get("output_config")
thinking_enabled: Final = isinstance(thinking, dict) and thinking.get("type") == "enabled"
effort_enabled: Final = isinstance(output_config, dict) and output_config.get("effort") is not None
thinking_enabled: Final = (
isinstance(thinking, dict) and thinking.get("type") == "enabled"
)
effort_enabled: Final = (
isinstance(output_config, dict) and output_config.get("effort") is not None
)
if thinking_enabled or effort_enabled:
optional_params.pop("temperature", None)
@ -572,7 +610,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
This takes in a request in the Anthropic /v1/messages API spec -> transforms it to /v1/messages API spec (i.e) no transformation is needed
"""
max_tokens: Final = anthropic_messages_optional_request_params.pop("max_tokens", None)
max_tokens: Final = anthropic_messages_optional_request_params.pop(
"max_tokens", None
)
if max_tokens is None:
raise AnthropicError(
message="max_tokens is required for Anthropic /v1/messages API",
@ -612,22 +652,30 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
system_param: Final = anthropic_messages_optional_request_params.get("system")
if self.should_strip_billing_metadata() and system_param is not None:
filtered_system: Final = self._filter_billing_headers_from_system(system_param)
filtered_system: Final = self._filter_billing_headers_from_system(
system_param
)
if filtered_system is not None and len(filtered_system) > 0:
anthropic_messages_optional_request_params["system"] = filtered_system
else:
anthropic_messages_optional_request_params.pop("system", None)
# Transform context_management from OpenAI format to Anthropic format if needed
context_management_param: Final = anthropic_messages_optional_request_params.get("context_management")
context_management_param: Final = (
anthropic_messages_optional_request_params.get("context_management")
)
if context_management_param is not None:
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
transformed_context_management: Final = AnthropicConfig.map_openai_context_management_to_anthropic(
context_management_param
transformed_context_management: Final = (
AnthropicConfig.map_openai_context_management_to_anthropic(
context_management_param
)
)
if transformed_context_management is not None:
anthropic_messages_optional_request_params["context_management"] = transformed_context_management
anthropic_messages_optional_request_params["context_management"] = (
transformed_context_management
)
####### get required params for all anthropic messages requests ######
# Lazy %s: the f-string previously stringified the entire messages
@ -638,15 +686,20 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
# Auto-strip advisor blocks from history if advisor tool is absent.
# Prevents Anthropic 400: advisor_tool_result in history requires advisor tool.
_tools: Final = anthropic_messages_optional_request_params.get("tools") or []
_has_advisor: Final = any(isinstance(t, dict) and t.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE for t in _tools)
_has_advisor: Final = any(
isinstance(t, dict) and t.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE
for t in _tools
)
if not _has_advisor:
messages = strip_advisor_blocks_from_messages(messages)
anthropic_messages_request: Final[AnthropicMessagesRequest] = AnthropicMessagesRequest(
messages=messages,
max_tokens=max_tokens,
model=model,
**anthropic_messages_optional_request_params,
anthropic_messages_request: Final[AnthropicMessagesRequest] = (
AnthropicMessagesRequest(
messages=messages,
max_tokens=max_tokens,
model=model,
**anthropic_messages_optional_request_params,
)
)
return dict(anthropic_messages_request)
@ -661,8 +714,10 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
"""
try:
raw_response_json: Final = raw_response.json()
except Exception:
raise AnthropicError(message=raw_response.text, status_code=raw_response.status_code)
except Exception: # noqa: BLE001
raise AnthropicError(
message=raw_response.text, status_code=raw_response.status_code
)
return AnthropicMessagesResponse(**raw_response_json)
def get_async_streaming_response_iterator(
@ -736,7 +791,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
# Add context management header if any other edits exist
if has_other:
beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)
beta_values.add(
ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
)
# Check for structured outputs. Anthropic's newer request shape nests
# the schema under output_config.format; the older top-level
@ -745,7 +802,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
if optional_params.get("output_format") is not None or (
isinstance(output_config, dict) and output_config.get("format") is not None
):
beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value)
beta_values.add(
ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value
)
# Check for fast mode
if optional_params.get("speed") == "fast":
@ -755,8 +814,13 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
tools = optional_params.get("tools")
if tools:
for tool in tools:
if isinstance(tool, dict) and tool.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE:
beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.ADVISOR_TOOL_2026_03_01.value)
if (
isinstance(tool, dict)
and tool.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE
):
beta_values.add(
ANTHROPIC_BETA_HEADER_VALUES.ADVISOR_TOOL_2026_03_01.value
)
break
# Check for tool search tools
@ -765,7 +829,9 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
anthropic_model_info: Final = AnthropicModelInfo()
if anthropic_model_info.is_tool_search_used(tools):
# Use provider-specific tool search header
tool_search_header: Final = get_tool_search_beta_header(custom_llm_provider)
tool_search_header: Final = get_tool_search_beta_header(
custom_llm_provider
)
beta_values.add(tool_search_header)
if beta_values:

72
tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_metadata.py Normal file
View file

@ -0,0 +1,72 @@
"""
Tests for Anthropic Messages passthrough metadata support.
Related issue: https://github.com/BerriAI/litellm/issues/30663
"""
from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
AnthropicMessagesConfig,
)
from litellm.types.router import GenericLiteLLMParams
class TestAnthropicMessagesMetadataSupport:
"""Test that metadata is properly supported in Anthropic Messages passthrough."""
def setup_method(self):
self.config = AnthropicMessagesConfig()
def test_metadata_in_supported_params(self):
"""Verify 'metadata' is listed in supported Anthropic Messages params."""
supported_params = self.config.get_supported_anthropic_messages_params(
model="claude-sonnet-4-20250514"
)
assert (
"metadata" in supported_params
), "'metadata' should be in supported params for Anthropic Messages passthrough"
def test_metadata_appears_exactly_once(self):
"""Verify 'metadata' is not duplicated in the supported params list."""
supported_params = self.config.get_supported_anthropic_messages_params(
model="claude-sonnet-4-20250514"
)
assert supported_params.count("metadata") == 1
def test_core_params_still_present(self):
"""Regression: ensure adding metadata did not remove existing params."""
supported_params = self.config.get_supported_anthropic_messages_params(
model="claude-sonnet-4-20250514"
)
expected_core_params = [
"messages",
"model",
"system",
"max_tokens",
"temperature",
]
for param in expected_core_params:
assert param in supported_params
def test_metadata_forwarded_in_transformed_request(self):
"""Verify 'metadata' is actually forwarded in the final transformed Anthropic Messages request body."""
result = self.config.transform_anthropic_messages_request(
model="claude-sonnet-4-20250514",
messages=[
{
"role": "user",
"content": "Hello",
}
],
anthropic_messages_optional_request_params={
"max_tokens": 10,
"metadata": {
"user_id": "test-user-123",
},
},
litellm_params=GenericLiteLLMParams(),
headers={},
)
assert result["metadata"] == {
"user_id": "test-user-123",
}