berriai-litellm-20666-22884

AGENTS.md
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Makefile
README.md
ci_cd
codecov.yaml
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db_scripts
deploy
dist
docker
docker-compose.hardened.yml
docker-compose.yml
docs
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index.yaml
litellm
litellm-js
litellm-proxy-extras
mcp_servers.json
model_prices_and_context_window.json
package-lock.json
package.json
poetry.lock
prometheus.yml
provider_endpoints_support.json
proxy_server_config.yaml
pyproject.toml
pyrightconfig.json
render.yaml
requirements.txt
ruff.toml
schema.prisma
scripts
security.md
taplo.toml
tests
ui
uv.lock
bash: line 6: rg: command not found

litellm/anthropic_beta_headers_config.json
litellm/anthropic_beta_headers_manager.py
litellm/anthropic_interface
litellm/integrations/anthropic_cache_control_hook.py
litellm/litellm_core_utils/tokenizers/anthropic_tokenizer.json
litellm/llms/anthropic
litellm/llms/azure_ai/anthropic
litellm/llms/base_llm/anthropic_messages
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
litellm/llms/vertex_ai

litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py
litellm/llms/vertex_ai
…[927 chars omitted]…
k.py
litellm/types/llms/anthropic.py
litellm/types/llms/anthropic_messages
litellm/types/llms/anthropic_messages/anthropic_request.py
litellm/types/llms/anthropic_messages/anthropic_response.py
litellm/types/llms/anthropic_skills.py
litellm/types/llms/anthropic_tool_search.py
litellm/types/llms/vertex_ai.py
litellm/types/llms/vertex_ai_text_to_speech.py
litellm/types/passthrough_endpoints/vertex_ai.py

tests/llm_translation/test_optional_params.py:910:def test_unmapped_vertex_anthropic_model():
tests/llm_translation/test_optional_params.py:1147:def test_is_vertex_anthropic_model():
tests/local_testing/test_amazing_vertex_completion.py:174:def test_vertex_ai_anthropic_streaming():
tests/local_testing/test_amazing_vertex_completion.py:207:# test_vertex_ai_anthropic_streaming()
tests/local_testing/test_amazing_vertex_completion.py:215:async def test_aavertex_ai_anthropic_async():
tests/local_testing/test_amazing_vertex_completion.py:241:# asyncio.run(test_vertex_ai_anthropic_async())
tests/local_testing/test_amazing_vertex_completion.py:249:async def test_aaavertex_ai_anthropic_async_streaming():
tests/local_testing/test_amazing_vertex_completion.py:280:# asyncio.run(test_vertex_ai_anthropic_async_streaming())
tests/local_testing/test_amazing_vertex_completion.py:1195:def vertex_httpx_
…[8630 chars omitted]…
 def test_vertex_anthropic_streaming_mocked():
tests/unified_google_tests/test_vertex_anthropic.py:230:        mock_post.return_value = await vertex_anthropic_streaming_mock_response()

tests/unified_google_tests/test_vertex_anthropic.py:310:        assert request_body["anthropic_version"] == "vertex-2023-10-16", f"Expected anthropic_version 'vertex-2023-10-16', got {request_body['anthropic_version']}"

# What is this?
## Handler file for calling claude-3 on vertex ai
from typing import Any, List, Optional

import httpx

import litellm
from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse

from ....anthropic.chat.transformation import AnthropicConfig

class VertexAIError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        self.request = httpx.Request(
            method="POST", url=" https://cloud.google.com/vertex-ai/"
        )
        self.response = httpx.Response(status_code=status_code, request=self.request)
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs


class VertexAIAnthropicConfig(AnthropicConfig
…[8630 chars omitted]…
-03-05" in (beta_header or ""), \
        "Other non-excluded beta headers should remain"
    # If prompt-caching was the only value, header should be removed completely
    headers2 = {"anthropic-beta": PROMPT_CACHING_BETA_HEADER}
    headers2 = update_headers_with_filtered_beta(headers2, "vertex_ai")
    assert "anthropic-beta" not in headers2, "Header should be removed if no supported values remain"

model: str,
        messages: List[AllMessageValues],
        optional_params: dict,
        litellm_params: dict,
        headers: dict,
    ) -> dict:
        data = super().transform_request(
            model=model,
            messages=messages,
            optional_params=optional_params,
            litellm_params=litellm_params,
            headers=headers,

        )

        data.pop("model", None)  # vertex anthropic doesn't accept 'model' parameter
        
        # VertexAI doesn't support output_format parameter, remove it if present
        data.pop("output_format", None)
        
        tools = optional_params.get("tools")
        tool_search_used = self.is_tool_search_used(tools)
        auto_betas = self.get_anthropic_beta_list(
            model=model,
            optional_params=optional_params,
            computer_tool_used=self.is_computer_tool_used(too
…[7988 chars omitted]…
ss model in request body to vertex ai

        anthropic_messages_request.pop(
            "output_format", None
        )  # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet

        return anthropic_messages_request
1066:    def update_headers_with_optional_anthropic_beta(
1156:        headers = self.update_headers_with_optional_anthropic_beta(

- For all other edits, add context-management-2025-06-27 header
        """
        edits = context_management.get("edits", [])
        
        has_compact = False
        has_other = False
        
        for edit in edits:
            edit_type = edit.get("type", "")
            if edit_type == "compact_20260112":
                has_compact = True
            else:

                has_other = True
        
        # Add compact header if any compact edits exist
        if has_compact:
            self._ensure_beta_header(
                headers, ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value
            )
        
        # Add context management header if any other edits exist
        if has_other:
            self._ensure_beta_header(
                headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
            )

    def update_headers_w
…[8629 chars omitted]…
form_request(
        model="claude-opus-4-6",
        messages=messages,
        optional_params=optional_params,
        litellm_params={},
        headers={}
    )
    
    # Verify context_management is included
    assert "context_management" in result
    assert result["context_management"]["edits"][0]["type"] == "compact_20260112"
    
    # Verify compact beta header is in anthropic_beta field

litellm/llms/anthropic/chat/transformation.py:188:            "extra_headers",
litellm/llms/anthropic/chat/transformation.py:880:            elif param == "extra_headers":
litellm/llms/anthropic/chat/transformation.py:881:                optional_params["extra_headers"] = value
litellm/llms/anthropic/completion/transformation.py:145:            "extra_headers",
litellm/llms/anthropic/skills/readme.md:112:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:129:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:144:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:159:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:231:    extra_headers={
"""
Centralized manager for Anthropic beta headers ac
…[8629 chars omitted]…

rtex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:3:from litellm.anthropic_beta_headers_manager import (
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:111:        auto_betas = self.get_anthropic_beta_list(
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:130:            data["anthropic_beta"] = list(beta_set)

Note: This does NOT handle header transformations/mappings.
    Those are handled in each provider's transformation code.
    
    Args:
        anthropic_beta_header: The Anthropic beta header value
        provider: Provider name
        
    Returns:
        The original header if supported, or None if unsupported
    """
    config = _load_beta_headers_config()
    provider = get_provider_name(provider)

    
    # Check if unsupported
    unsupported_headers = set(config.get(provider, []))
    if anthropic_beta_header in unsupported_headers:
        return None
    
    return anthropic_beta_header


def update_headers_with_filtered_beta(
    headers: dict,
    provider: str,
) -> dict:
    """
    Update headers dict by filtering and transforming anthropic-beta header values.
    Modifies the headers dict in place and returns it.
    
    Args:
        headers: Request header
…[4908 chars omitted]…
is_vertex_request", False)
litellm/llms/azure_ai/anthropic/handler.py:73:            optional_params={**optional_params, "is_vertex_request": is_vertex_request},
litellm/llms/azure_ai/anthropic/transformation.py:82:            is_vertex_request=optional_params.get("is_vertex_request", False),
litellm/llms/vertex_ai/vertex_ai_partner_models/main.py:206:                        "is_vertex_request": True,

cast(
                Optional[List[Union[AllAnthropicToolsValues, Dict]]],
                optional_params.get("tools"),
            )
            or []
        )
        tools = self.add_code_execution_tool(messages=anthropic_messages, tools=_tools)
        if len(tools) > 1:
            optional_params["tools"] = tools

        ## Load Config
        config = litellm.AnthropicConfig.get_config(model=model)

        for k, v in config.items():
            if (
                k not in optional_params
            ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
                optional_params[k] = v

        ## Handle user_id in metadata
        _litellm_metadata = litellm_params.get("metadata", None)
        if (
            _litellm_metadata
            and isinstance(_litellm_metadata, dict)
            and "user_id" 
…[5640 chars omitted]…
 litellm_logging_obj: LiteLLMLoggingObj,
    ) -> AsyncIterator:
        aws_decoder = AmazonAnthropicClaudeMessagesStreamDecoder(
            model=model,
        )
        completion_stream = aws_decoder.aiter_bytes(
            httpx_response.aiter_bytes(chunk_size=aws_decoder.DEFAULT_CHUNK_SIZE)
        )
        # Convert decoded Bedrock events to Server-Sent Events expected by Anthropic clients.

5:from litellm.anthropic_beta_headers_manager import (
91:        headers = self._update_headers_with_anthropic_beta(
174:    def _update_headers_with_anthropic_beta(

model: str,
        messages: List[Any],
        optional_params: dict,
        litellm_params: dict,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
    ) -> Tuple[dict, Optional[str]]:
        import os

        # Check for Anthropic OAuth token in Authorization header
        headers, api_key = optionally_handle_anthropic_oauth(headers=headers, api_key=api_key)
        if api_key is None:

            api_key = os.getenv("ANTHROPIC_API_KEY")

        if "x-api-key" not in headers and api_key:
            headers["x-api-key"] = api_key
        if "anthropic-version" not in headers:
            headers["anthropic-version"] = DEFAULT_ANTHROPIC_API_VERSION
        if "content-type" not in headers:
            headers["content-type"] = "application/json"

        headers = self._update_headers_with_anthropic_beta(
            headers=headers,
      
…[4109 chars omitted]…
 # Check edits array for compact_20260112 type
            edits = context_management_param.get("edits", [])
            has_compact = False
            has_other = False
            
            for edit in edits:
                edit_type = edit.get("type", "")
                if edit_type == "compact_20260112":
                    has_compact = True
103:    def transform_anthropic_messages_request(

(command exceeded 600s)

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'extra_headers': {'anthropic-beta': 'one,two'}, 'output_config': {'effort': 'low'}, 'tools': [{'name': 'x'}], 'anthropic_beta': ['effort-2025-11-24']}

106:        # VertexAI doesn't support output_format parameter, remove it if present
107:        data.pop("output_format", None)
143:        VertexAI doesn't support the output_format parameter, so we force all models
147:        # This ensures Claude Sonnet 4.5 uses tools instead of output_format
            litellm_params=litellm_params,
            headers=headers,
        )

        data.pop("model", None)  # vertex anthropic doesn't accept 'model' parameter
        
        # VertexAI doesn't support output_format parameter, remove it if present
        data.pop("output_format", None)

        
        tools = optional_params.get("tools")
        tool_search_used = self.is_tool_search_used(tools)
        auto_betas = self.get_anthropic_beta_list(
            model=model,
            optional_params=optional_params,
            computer_tool_used=self.is_computer_tool_used(tools),

…[801 chars omitted]…
       self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        """
        Override parent method to ensure VertexAI always uses tool-based structured outputs.
        VertexAI doesn't support the output_format parameter, so we force all models
        to use the tool-based approach for structured outputs.
        """

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'extra_headers': {'anthropic-beta': 'one,two'}, 'anthropic_beta': ['one', 'three', 'two', 'effort-2025-11-24']}
{}

.../anthropic/transformation.py                    | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
index 0b728d8..9d3af65 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
@@ -103,9 +103,24 @@ class VertexAIAnthropicConfig(AnthropicConfig):
 
         data.pop("model", None)  # vertex anthropic doesn't accept 'model' parameter
         
-        # VertexAI doesn't support output_format parameter, remove it if present
+        # Vertex AI uses tool-based structured output and rejects Anthropic's

+        # native structured-output fields.
         data.pop("output_format",
…[1023 chars omitted]…
_server_used=self.is_mcp_server_used(optional_params.get("mcp_servers")),
         )
 
-        beta_set = set(auto_betas)
+        beta_set = {beta.strip() for beta in beta_values if isinstance(beta, str) and beta.strip()}
+        beta_set.update(auto_betas)
         if tool_search_used:
             beta_set.add("tool-search-tool-2025-10-19")  # Vertex requires this header for tool search

126-        anthropic_messages_request.pop(
127-            "model", None
128-        )  # do not pass model in request body to vertex ai
129-
130-        anthropic_messages_request.pop(
131:            "output_format", None
132:        )  # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet
133-
134-        return anthropic_messages_request
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
19	3	litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

.../experimental_pass_through/transformation.py    | 24 +++++++++++++++++++---
 .../anthropic/transformation.py                    | 22 +++++++++++++++++---
 2 files changed, 40 insertions(+), 6 deletions(-)

Provider List: https://docs.litellm.ai/docs/providers

completion Vertex check passed: ['three', 'effort-2025-11-24', 'one', 'two']
 .../experimental_pass_through/transformation.py    | 29 +++++++++++++++++++---
 .../anthropic/transformation.py                    | 22 +++++++++++++---
 2 files changed, 45 insertions(+), 6 deletions(-)

diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
index 918b8ec..f904c30 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
@@ -127,8 +127,31 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert
             "model", None
         )  # do not pass model in request body to vertex ai
 
-        anthropic_messages_request.pop(
-            "output_format", None
-        )  # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet
+        # Vertex AI uses tool-based structur

…[1380 chars omitted]…
rip() for beta in beta_values if isinstance(beta, str) and beta.strip()}
+        if beta_values:
+            anthropic_messages_request["anthropic_beta"] = list(beta_values)
 
         return anthropic_messages_request
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

…[58 chars omitted]…
l-based structured output. Direct reproducer passed; py_compile and git diff --check passed. Focused pytest invocation exceeded the environment's 600s startup limit."}
submitted.

total 2468
drwxr-xr-x 1 root root    4096 Sep 30 18:47 .
drwxr-xr-x 1 root root    4096 Sep 30 18:51 ..
drwxr-xr-x 1 root root    4096 Sep 30 18:35 .circleci
drwxr-xr-x 1 root root    4096 Sep 30 18:35 .devcontainer
-rw-r--r-- 1 root root     635 Sep 30 18:35 .dockerignore
-rw-r--r-- 1 root root     624 Sep 30 18:35 .env.example
-rw-r--r-- 1 root root    1477 Sep 30 18:35 .flake8
drwxr-xr-x 1 root root    4096 Sep 30 18:51 .git
-rw-r--r-- 1 root root     386 Sep 30 18:35 .git-blame-ignore-revs
-rw-r--r-- 1 root root      25 Sep 30 18:35 .gitattributes
-rw-r--r-- 1 root root    3970 Sep 30 18:35 .gitguardian.yaml

drwxr-xr-x 1 root root    4096 Sep 30 18:35 .github
-rw-r--r-- 1 root root    2511 Sep 30 18:35 .gitignore
-rw-r--r-- 1 root root    1309 Sep 30 18:35 .pre-commit-config.yaml
-rw-r--r-- 1 root root     346 Sep 30 18:35 .trivyignore
-rw-r--r-- 1 root root    7699 Sep 30 18:35 A
…[2098 chars omitted]…
rw-r--r-- 1 root root   35118 Sep 30 18:36 schema.prisma
drwxr-xr-x 1 root root    4096 Sep 30 18:36 scripts
-rw-r--r-- 1 root root    1874 Sep 30 18:36 security.md
-rw-r--r-- 1 root root     574 Sep 30 18:33 taplo.toml
drwxr-xr-x 1 root root    4096 Sep 30 18:36 tests
drwxr-xr-x 1 root root    4096 Sep 30 18:35 ui
-rw-r--r-- 1 root root      52 Sep 30 18:36 uv.lock
bash: line 6: rg: command not found

litellm/anthropic_beta_headers_config.json
litellm/anthropic_beta_headers_manager.py
litellm/anthropic_interface
litellm/integrations/anthropic_cache_control_hook.py
litellm/litellm_core_utils/tokenizers/anthropic_tokenizer.json
litellm/llms/anthropic
litellm/llms/azure_ai/anthropic
litellm/llms/base_llm/anthropic_messages
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
litellm/llms/vertex_ai

litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py
litellm/llms/vertex_ai
…[7891 chars omitted]…
x_ai_partner_models_anthropic_messages_config.py
tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py
tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py
tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/test_vertex_ai_partner_models_llama3_transformation.py

litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:3:from litellm.anthropic_beta_headers_manager import (
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:67:        existing_beta = headers.get("anthropic-beta")
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:83:            headers["anthropic-beta"] = ",".join(beta_values)
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:131:            "output_format", None
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:132:        )  # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet
litellm/llms/vertex_ai/vertex_ai_partner_models/an
…[2628 chars omitted]…
nthropic/transformation.py:130:            data["anthropic_beta"] = list(beta_set)
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:143:        VertexAI doesn't support the output_format parameter, so we force all models
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:147:        # This ensures Claude Sonnet 4.5 uses tools instead of output_format

tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_anthropic_image_url_handling.py
tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py
tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py
from typing import Any, Dict, List, Optional, Tuple

from litellm.anthropic_beta_headers_manager import (
    update_headers_with_filtered_beta,
)
from litellm.llms.anthropic.common_utils import AnthropicModelInfo
from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
    AnthropicMessagesConfig,
)

from litellm.types.llms.anthropic import (
    ANTHROPIC_BETA_HEADER_VALUES,
    ANTHROPIC_HOSTED_TOOLS,
)
from litellm.types.llms.anthropic_tool_search import get_tool_search_beta_header
from l
…[5628 chars omitted]…
ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py:306:    config = VertexAIAnthropicConfig()
tests/test_litellm/test_utils.py:319:        VertexAIAnthropicConfig,
tests/test_litellm/test_utils.py:324:        in VertexAIAnthropicConfig().get_supported_openai_params(
tests/test_litellm/test_utils.py:329:    assert VertexAIAnthropicConfig().map_openai_params(

import os
import sys

import pytest

sys.path.insert(
    0, os.path.abspath("../../../../../..")
)  # Adds the parent directory to the system path
from litellm.anthropic_beta_headers_manager import (
    update_headers_with_filtered_beta,
)
from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (

    VertexAIAnthropicConfig,
)


@pytest.mark.parametrize(
    "model, expected_thinking",
    [
        ("claude-sonnet-4@20250514", True),
    ],
)
def test_vertex_ai_anthropic_thinking_param(model, expected_thinking):
    supported_openai_params = VertexAIAnthropicConfig().get_supported_openai_params(
        model=model
    )

    if expected_thinking:
        assert "thinking" in supported_openai_params
    else:
        assert "thinking" not in supported_openai_params


def test_get_supported_params_thinking():
    config = VertexAIAnthropicConfig()
    pa
…[8629 chars omitted]…
 context_management: dict
    ) -> None:
        """
        Add appropriate beta headers based on context_management edits.
        - If any edit has type "compact_20260112", add compact-2026-01-12 header
        - For all other edits, add context-management-2025-06-27 header
        """
        edits = context_management.get("edits", [])
        
        has_compact = False
        has_other = False

# Add context management header if any other edits exist
        if has_other:
            self._ensure_beta_header(
                headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
            )

    def update_headers_with_optional_anthropic_beta(
        self, headers: dict, optional_params: dict
    ) -> dict:
        """Update headers with optional anthropic beta."""
        
        # Skip adding beta headers for Vertex requests

        # Vertex AI handles these headers differently
        is_vertex_request = optional_params.get("is_vertex_request", False)
        if is_vertex_request:
            return headers

        _tools = optional_params.get("tools", [])
        for tool in _tools:
            if tool.get("type", None) and tool.get("type").startswith(
                ANTHROPIC_HOSTED_TOOLS.WEB_FETCH.value
            ):
                se
…[7455 chars omitted]…
al): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:129:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:144:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:159:- `extra_headers` (dict, optional): Additional HTTP headers
litellm/llms/anthropic/skills/readme.md:231:    extra_headers={

result_params = config.map_openai_params(
        non_default_params=non_default_params,
        optional_params=optional_params,
        model="claude-3-5-sonnet-20241022",  # Claude Sonnet 4.5 model
        drop_params=False,
    )
    
    # Should have tools and tool_choice (tool-based approach)
    assert "tools" in result_params, "Tools should be present for structured output"
    assert "tool_choice" in result_params, "Tool choice should be present for structured output"
    assert "json_mode" in result_params, "JSON mode should be enabled"
    

    # Verify the tool is the response format tool
    tools = result_params["tools"]
    assert len(tools) == 1, "Should have exactly one tool for response format"
    assert tools[0]["name"] == "json_tool_call", "Tool should be named json_tool_call"
    
    # Test 2: Verify transform_request removes output_format parameter
    # S
…[6517 chars omitted]…
o = AnthropicModelInfo()
            if anthropic_model_info.is_tool_search_used(tools):
                # Use provider-specific tool search header
                tool_search_header = get_tool_search_beta_header(custom_llm_provider)
                beta_values.add(tool_search_header)

        if beta_values:
            headers["anthropic-beta"] = ",".join(sorted(beta_values))

        return headers

litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:3:from litellm.anthropic_beta_headers_manager import (
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:111:        auto_betas = self.get_anthropic_beta_list(
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:130:            data["anthropic_beta"] = list(beta_set)
litellm/llms/vertex_ai/vertex_llm_base.py:666:        self, auth_header: Optional[str], extra_headers: Optional[dict]
litellm/llms/vertex_ai/vertex_llm_base.py:673:        if extra_headers is not None:
litellm/llms/vertex_ai/vertex_llm_base.py:674:            headers.update(extra_headers)

litellm/a2a_protocol/client.py:53:        extra_headers: Optional[Dict[str, str]] = None,
litellm/a2a_protocol/client.py:61:            extra_headers: Optional additional headers to include in requests
litellm/a2a_protocol/client.py:65:        self.extra_headers = extra_headers
litellm/a2a_protocol/client.py:76:                extra_headers=self.extra_headers,
litellm/a2a_protocol/client.py:87:            extra_headers=self.extra_headers,
litellm/a2a_protocol/main.py:240:        extra_headers = {"X-LiteLLM-Trace-Id": trace_id}
litellm/a2a_protocol/main.py:242:            extra_headers["X-LiteLLM-Agent-Id"] = agent_id
litellm/a2a_protocol/main.py:243:        a2a_client = await create_a2a_client(base_url=api_base, extra_headers=extra_headers)
litellm/a2a_protocol/main.py:564:    extra_headers: Optional[Dict[str, str]] = None,
litellm/a2a_protocol/main.py:575:        extra_headers: Optio
…[7518 chars omitted]…
ormation.py:76:    def transform_request(self, model, messages, optional_params, litellm_params, headers):

litellm/llms/clarifai/chat/transformation.py:78:        return super().transform_request(model, messages, optional_params, litellm_params, headers)
litellm/llms/cohere/chat/v2_transformation.py:175:        data = super().transform_request(model, messages, optional_params, litellm_params, headers)

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'extra_headers': {}}

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'extra_headers': {'anthropic-beta': 'user-one,user-two'}}

from typing import Any, AsyncIterator, Dict, List, Optional, Tuple

import httpx

from litellm.anthropic_beta_headers_manager import (
    update_headers_with_filtered_beta,
)
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.litellm_logging import verbose_logger
from litellm.llms.base_llm.anthropic_messages.transformation import (
    BaseAnthropicMessagesConfig,
)

from litellm.types.llms.anthropic import (
    ANTHROPIC_BETA_HEADER_VALUES,
    AnthropicMessagesRequest,
)
from litellm.types.llms.anthropic_messages.anthropic_response import (
    AnthropicMessagesResponse,
)
from litellm.types.llms.anthropic_tool_search import get_tool_search_beta_header
from litellm.types.router import GenericLiteLLMParams

from ...common_utils import (
    AnthropicError,
    AnthropicModelInfo,
    optionally_handle_anthropic_oauth,

…[7306 chars omitted]…
"anthropic-beta")
+                if isinstance(user_beta, str):
+                    beta_set.update(
+                        beta.strip() for beta in user_beta.split(",") if beta.strip()
+                    )
+        headers.pop("anthropic-beta", None)
+
         if tool_search_used:
             beta_set.add("tool-search-tool-2025-10-19")  # Vertex requires this header for tool search

ERROR: usage: pytest [options] [file_or_dir] [file_or_dir] [...]
pytest: error: argument --maxfail: invalid int value: '1”,'

ERROR: usage: pytest [options] [file_or_dir] [file_or_dir] [...]
pytest: error: argument --maxfail: invalid int value: '1”,'

............                                                             [100%]
12 passed, 2 warnings in 1.91s

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'anthropic_beta': ['user-one', 'effort-2025-11-24', 'user-two']}
Traceback (most recent call last):
  File "<stdin>", line 6, in <module>
AssertionError

Provider List: https://docs.litellm.ai/docs/providers

standard ok ['effort-2025-11-24', 'user-two', 'user-one']
messages ok {'messages': [{'role': 'user', 'content': 'x'}], 'max_tokens': 10, 'anthropic_beta': ['one', 'two'], 'anthropic_version': 'vertex-2023-10-16'}
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

.....................                                                    [100%]
21 passed, 2 warnings in 1.98s
 .../experimental_pass_through/transformation.py    | 16 +++++++++++++
 .../anthropic/transformation.py                    | 28 ++++++++++++++++++++--
 2 files changed, 42 insertions(+), 2 deletions(-)

diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
index 918b8ec..5acf4f7 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
@@ -121,6 +121,20 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert
             headers=headers,
         )
 
+        # Vertex carries beta capabilities in the body rather than as an HTTP
+        # header.  Preserve user supplied comma-separated values together with
+        # values selected by the validation step.
+        beta_header = headers.pop("anthropic-beta", None)

+        if isinst
…[3067 chars omitted]…
a.split(",") if beta.strip()
+                    )
+        headers.pop("anthropic-beta", None)
+        extra_headers = optional_params.get("extra_headers")
+        if isinstance(extra_headers, dict):
+            extra_headers.pop("anthropic-beta", None)
+
         if tool_search_used:
             beta_set.add("tool-search-tool-2025-10-19")  # Vertex requires this header for tool search

submitted.

total 2468
drwxr-xr-x 1 root root    4096 Sep 30 19:39 .
drwxr-xr-x 1 root root    4096 Sep 30 19:42 ..
drwxr-xr-x 1 root root    4096 Sep 30 19:26 .circleci
drwxr-xr-x 1 root root    4096 Sep 30 19:26 .devcontainer
-rw-r--r-- 1 root root     635 Sep 30 19:26 .dockerignore
-rw-r--r-- 1 root root     624 Sep 30 19:26 .env.example
-rw-r--r-- 1 root root    1477 Sep 30 19:26 .flake8
drwxr-xr-x 1 root root    4096 Sep 30 19:43 .git
-rw-r--r-- 1 root root     386 Sep 30 19:26 .git-blame-ignore-revs
-rw-r--r-- 1 root root      25 Sep 30 19:26 .gitattributes
-rw-r--r-- 1 root root    3970 Sep 30 19:26 .gitguardian.yaml

drwxr-xr-x 1 root root    4096 Sep 30 19:26 .github
-rw-r--r-- 1 root root    2511 Sep 30 19:26 .gitignore
-rw-r--r-- 1 root root    1309 Sep 30 19:26 .pre-commit-config.yaml
-rw-r--r-- 1 root root     346 Sep 30 19:26 .trivyignore
-rw-r--r-- 1 root root    7699 Sep 30 19:26 A
…[2098 chars omitted]…
rw-r--r-- 1 root root   35118 Sep 30 19:26 schema.prisma
drwxr-xr-x 1 root root    4096 Sep 30 19:26 scripts
-rw-r--r-- 1 root root    1874 Sep 30 19:26 security.md
-rw-r--r-- 1 root root     574 Sep 30 19:24 taplo.toml
drwxr-xr-x 1 root root    4096 Sep 30 19:27 tests
drwxr-xr-x 1 root root    4096 Sep 30 19:26 ui
-rw-r--r-- 1 root root      52 Sep 30 19:27 uv.lock
bash: line 6: rg: command not found

litellm/anthropic_beta_headers_manager.py:176:    Update headers dict by filtering and transforming anthropic-beta header values.
litellm/anthropic_beta_headers_manager.py:186:    existing_beta = headers.get("anthropic-beta")
litellm/anthropic_beta_headers_manager.py:201:        headers["anthropic-beta"] = ",".join(filtered_beta_values)
litellm/anthropic_beta_headers_manager.py:204:        headers.pop("anthropic-beta", None)
litellm/constants.py:1171:    "anthropic-beta",  # Required for Anthropic features like extended context windows
litellm/constants.py:1176:# e.g., 'x-pass-anthropic-beta: value' becomes 'anthropic-beta: value'
litellm/integrations/dotprompt/prompt_manager.py:30:        self.output_format = self.metadata.get("output", {}).get("format")
litellm/integrations/dotprompt/README.md:258:- **`output_format: str`**: Expected output format
litellm/llms/aiml/image_generation/
…[8631 chars omitted]…
k.py
litellm/types/llms/anthropic.py

litellm/types/llms/anthropic_messages
litellm/types/llms/anthropic_messages/anthropic_request.py
litellm/types/llms/anthropic_messages/anthropic_response.py
litellm/types/llms/anthropic_skills.py
litellm/types/llms/anthropic_tool_search.py
litellm/types/llms/vertex_ai.py
litellm/types/llms/vertex_ai_text_to_speech.py
litellm/types/passthrough_endpoints/vertex_ai.py

]

        return hosted_web_search_tool

    def map_openai_params(  # noqa: PLR0915
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        is_thinking_enabled = self.is_thinking_enabled(

            non_default_params=non_default_params
        )

        for param, value in non_default_params.items():
            if param == "max_tokens":
                optional_params["max_tokens"] = value
            if param == "max_completion_tokens":
                optional_params["max_tokens"] = value
            if param == "tools":
                # check if optional params already has tools
                anthropic_tools, mcp_servers = self._map_tools(value)
                optional_params = self._add_tools_to_optional_params(
                    optional_params=optional_params, too
…[8629 chars omitted]…
sform_response_for_json_mode(
        self,
        json_mode: Optional[bool],
        tool_calls: List[ChatCompletionToolCallChunk],
    ) -> Optional[LitellmMessage]:
        _message: Optional[LitellmMessage] = None
        if json_mode is True and len(tool_calls) == 1:
            # check if tool name is the default tool name
            json_mode_content_str: Optional[str] = None
            if (

import os
import sys

import pytest

sys.path.insert(
    0, os.path.abspath("../../../../../..")
)  # Adds the parent directory to the system path
from litellm.anthropic_beta_headers_manager import (
    update_headers_with_filtered_beta,
)
from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (

    VertexAIAnthropicConfig,
)


@pytest.mark.parametrize(
    "model, expected_thinking",
    [
        ("claude-sonnet-4@20250514", True),
    ],
)
def test_vertex_ai_anthropic_thinking_param(model, expected_thinking):
    supported_openai_params = VertexAIAnthropicConfig().get_supported_openai_params(
        model=model
    )

    if expected_thinking:
        assert "thinking" in supported_openai_params
    else:
        assert "thinking" not in supported_openai_params


def test_get_supported_params_thinking():
    config = VertexAIAnthropicConfig()
    pa
…[8629 chars omitted]…
    self._ensure_beta_header(
                    headers, ANTHROPIC_BETA_HEADER_VALUES.WEB_FETCH_2025_09_10.value
                )
            elif tool.get("type", None) and tool.get("type").startswith(
188:            "extra_headers",
880:            elif param == "extra_headers":
881:                optional_params["extra_headers"] = value
1066:    def update_headers_with_optional_anthropic_beta(

cast(OpenAIWebSearchOptions, value)
                )
                self._add_tools_to_optional_params(
                    optional_params=optional_params, tools=[hosted_web_search_tool]
                )
            elif param == "extra_headers":
                optional_params["extra_headers"] = value
            elif param == "context_management" and isinstance(value, dict):
                # Pass through Anthropic-specific context_management parameter
                optional_params["context_management"] = value

        ## handle thinking tokens

        self.update_optional_params_with_thinking_tokens(
            non_default_params=non_default_params, optional_params=optional_params
        )

        return optional_params

    def _create_json_tool_call_for_response_format(
        self,
        json_schema: Optional[dict] = None,
    ) -> AnthropicMessa
…[5751 chars omitted]…
rtex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:3:from litellm.anthropic_beta_headers_manager import (
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:111:        auto_betas = self.get_anthropic_beta_list(
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:130:            data["anthropic_beta"] = list(beta_set)

}
    }
    
    messages = [
        {"role": "user", "content": "Generate a question and answer about AI."}
    ]
    
    # Test parameters that would trigger the issue
    non_default_params = {
        "response_format": response_format,
        "max_tokens": 1000,
    }

    
    # Test 1: Verify map_openai_params forces tool-based approach for Claude Sonnet 4.5
    optional_params = {}
    result_params = config.map_openai_params(
        non_default_params=non_default_params,
        optional_params=optional_params,
        model="claude-3-5-sonnet-20241022",  # Claude Sonnet 4.5 model
        drop_params=False,
    )
    
    # Should have tools and tool_choice (tool-based approach)
    assert "tools" in result_params, "Tools should be present for structured output"
    assert "tool_choice" in result_params, "Tool choice should be present for structured output"
    ass
…[7799 chars omitted]…
is_vertex_request", False)
litellm/llms/azure_ai/anthropic/handler.py:73:            optional_params={**optional_params, "is_vertex_request": is_vertex_request},
litellm/llms/azure_ai/anthropic/transformation.py:82:            is_vertex_request=optional_params.get("is_vertex_request", False),
litellm/llms/vertex_ai/vertex_ai_partner_models/main.py:206:                        "is_vertex_request": True,

model=model,
            raw_response=response,
            model_response=model_response,
            logging_obj=logging_obj,
            api_key=api_key,
            request_data=data,
            messages=messages,
            optional_params=optional_params,
            litellm_params=litellm_params,
            encoding=encoding,
            json_mode=json_mode,
        )

    def completion(
        self,
        model: str,
        messages: list,
        api_base: str,
        custom_llm_provider: str,
        custom_prompt_dict: dict,
        model_response: ModelResponse,
        print_verbose: Callable,
        encoding,
        api_key,
        logging_obj,
        optional_params: dict,
        timeout: Union[float, httpx.Timeout],
        litellm_params: dict,
        acompletion=None,
        logger_fn=None,
        headers={},
        client=None,
    ):
 
…[6020 chars omitted]…
beta': 'user-beta'} {'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'extra_headers': {'anthropic-beta': 'user-beta'}}

Provider List: https://docs.litellm.ai/docs/providers

{'anthropic-beta': 'one,two'} {'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'extra_headers': {'anthropic-beta': 'one,two'}}

litellm/a2a_protocol/client.py:53:        extra_headers: Optional[Dict[str, str]] = None,
litellm/a2a_protocol/client.py:61:            extra_headers: Optional additional headers to include in requests
litellm/a2a_protocol/client.py:65:        self.extra_headers = extra_headers
litellm/a2a_protocol/client.py:76:                extra_headers=self.extra_headers,
litellm/a2a_protocol/client.py:87:            extra_headers=self.extra_headers,
litellm/a2a_protocol/main.py:240:        extra_headers = {"X-LiteLLM-Trace-Id": trace_id}
litellm/a2a_protocol/main.py:242:            extra_headers["X-LiteLLM-Agent-Id"] = agent_id
litellm/a2a_protocol/main.py:243:        a2a_client = await create_a2a_client(base_url=api_base, extra_headers=extra_headers)
litellm/a2a_protocol/main.py:564:    extra_headers: Optional[Dict[str, str]] = None,
litellm/a2a_protocol/main.py:575:        extra_headers: Optio
…[4928 chars omitted]…
tils.py:377:            betas.update(user_anthropic_beta_headers)

litellm/llms/anthropic/common_utils.py:424:        user_anthropic_beta_headers = self._get_user_anthropic_beta_headers(
litellm/llms/anthropic/common_utils.py:425:            anthropic_beta_header=headers.get("anthropic-beta")
litellm/llms/anthropic/common_utils.py:435:            user_anthropic_beta_headers=user_anthropic_beta_headers,

Provider List: https://docs.litellm.ai/docs/providers

user-beta body= None headers unchanged= {'anthropic-beta': 'user-beta'}

Provider List: https://docs.litellm.ai/docs/providers

one,two body= None headers unchanged= {'anthropic-beta': 'one,two'}
.........                                                                [100%]

Provider List: https://docs.litellm.ai/docs/providers

user-beta ['user-beta'] None {}

Provider List: https://docs.litellm.ai/docs/providers

one,two ['two', 'one'] None {}

.........                                                                [100%]
9 passed, 2 warnings in 1.66s

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'x'}]}], 'max_tokens': 10, 'anthropic_beta': ['second-beta', 'effort-2025-11-24', 'header-beta', 'user-beta']}
{'max_tokens': 10, 'extra_headers': {'anthropic-beta': 'user-beta, second-beta', 'x-test': 'kept-out'}, 'output_config': {'effort': 'high'}, 'output_format': {'type': 'json_schema'}}
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
index 0b728d8..8a5e884 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.
…[2070 chars omitted]…

+        # inferred from the request.  Values may be supplied as one comma-
+        # separated header value or as a sequence by integrations.
+        beta_set.update(
+            beta.strip() for beta in user_beta_values if beta and beta.strip()
+        )
         if tool_search_used:
             beta_set.add("tool-search-tool-2025-10-19")  # Vertex requires this header for tool search

....................................................................F
=================================== FAILURES ===================================
_____________________ test_get_optional_params_num_retries _____________________

self = <litellm.llms.openai.openai.OpenAIChatCompletion object at 0x7ebe29a8e510>
model_response = ModelResponse(id='chatcmpl-22151345-efb0-4e38-a353-58cb13f3e7da', created=1790797570, model=None, object='chat.complet...sage(completion_tokens=0, prompt_tokens=0, total_tokens=0, completion_tokens_details=None, prompt_tokens_details=None))
timeout = 600.0, optional_params = {'max_retries': 0}
litellm_params = {'acompletion': False, 'api_key': None, 'force_timeout': 600, 'logger_fn': None, ...}
logging_obj = <litellm.litellm_core_utils.litellm_logging.Logging object at 0x7ebe28c24b00>
model = 'gpt-3.5-turbo', messages = [{'role': 'user', 'content': 'Hello wor
…[8631 chars omitted]…
 Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new

LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'.

=========================== short test summary info ============================
FAILED tests/llm_translation/test_optional_params.py::test_get_optional_params_num_retries
!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!

M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

.........                                                                [100%]
9 passed, 2 warnings in 1.59s
 .../anthropic/transformation.py                    | 35 ++++++++++++++++++++--
 1 file changed, 32 insertions(+), 3 deletions(-)

submitted.

AGENTS.md
ARCHITECTURE.md
CLAUDE.md
CONTRIBUTING.md
Dockerfile
GEMINI.md
LICENSE
Makefile
README.md
ci_cd
codecov.yaml
cookbook

db_scripts
deploy
dist
docker
docker-compose.hardened.yml
docker-compose.yml
docs
enterprise
index.yaml
litellm
litellm-js
litellm-proxy-extras
mcp_servers.json
model_prices_and_context_window.json
package-lock.json
package.json
poetry.lock
prometheus.yml
provider_endpoints_support.json
proxy_server_config.yaml
pyproject.toml
pyrightconfig.json
render.yaml
requirements.txt
ruff.toml
schema.prisma
scripts
security.md
taplo.toml
tests
ui
uv.lock
bash: line 6: rg: command not found

litellm/anthropic_beta_headers_config.json
litellm/anthropic_beta_headers_manager.py
litellm/anthropic_interface
litellm/integrations/anthropic_cache_control_hook.py
litellm/litellm_core_utils/tokenizers/anthropic_tokenizer.json
litellm/llms/anthropic
litellm/llms/azure_ai/anthropic
litellm/llms/base_llm/anthropic_messages
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
litellm/llms/vertex_ai

litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py
litellm/llms/vertex_ai
…[927 chars omitted]…
k.py
litellm/types/llms/anthropic.py
litellm/types/llms/anthropic_messages
litellm/types/llms/anthropic_messages/anthropic_request.py
litellm/types/llms/anthropic_messages/anthropic_response.py
litellm/types/llms/anthropic_skills.py
litellm/types/llms/anthropic_tool_search.py
litellm/types/llms/vertex_ai.py
litellm/types/llms/vertex_ai_text_to_speech.py
litellm/types/passthrough_endpoints/vertex_ai.py

]

        return hosted_web_search_tool

    def map_openai_params(  # noqa: PLR0915
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        is_thinking_enabled = self.is_thinking_enabled(

            non_default_params=non_default_params
        )

        for param, value in non_default_params.items():
            if param == "max_tokens":
                optional_params["max_tokens"] = value
            if param == "max_completion_tokens":
                optional_params["max_tokens"] = value
            if param == "tools":
                # check if optional params already has tools
                anthropic_tools, mcp_servers = self._map_tools(value)
                optional_params = self._add_tools_to_optional_params(
                    optional_params=optional_params, too
…[8629 chars omitted]…
   # check if tool name is the default tool name
            json_mode_content_str: Optional[str] = None
            if (
                "name" in tool_calls[0]["function"]
                and tool_calls[0]["function"]["name"] == RESPONSE_FORMAT_TOOL_NAME
            ):
                json_mode_content_str = tool_calls[0]["function"].get("arguments")
            if json_mode_content_str is not None:

# What is this?
## Handler file for calling claude-3 on vertex ai
from typing import Any, List, Optional

import httpx

import litellm
from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse

from ....anthropic.chat.transformation import AnthropicConfig

class VertexAIError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        self.request = httpx.Request(
            method="POST", url=" https://cloud.google.com/vertex-ai/"
        )
        self.response = httpx.Response(status_code=status_code, request=self.request)
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs


class VertexAIAnthropicConfig(AnthropicConfig
…[8630 chars omitted]…
essages_optional_request_params=anthropic_messages_optional_request_params,
            litellm_params=litellm_params,
            headers=headers,
        )

        anthropic_messages_request["anthropic_version"] = "vertex-2023-10-16"

        anthropic_messages_request.pop(
            "model", None
        )  # do not pass model in request body to vertex ai

        anthropic_messages_request.pop(

import os
import sys

import pytest

sys.path.insert(
    0, os.path.abspath("../../../../../..")
)  # Adds the parent directory to the system path
from litellm.anthropic_beta_headers_manager import (
    update_headers_with_filtered_beta,
)
from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (

    VertexAIAnthropicConfig,
)


@pytest.mark.parametrize(
    "model, expected_thinking",
    [
        ("claude-sonnet-4@20250514", True),
    ],
)
def test_vertex_ai_anthropic_thinking_param(model, expected_thinking):
    supported_openai_params = VertexAIAnthropicConfig().get_supported_openai_params(
        model=model
    )

    if expected_thinking:
        assert "thinking" in supported_openai_params
    else:
        assert "thinking" not in supported_openai_params


def test_get_supported_params_thinking():
    config = VertexAIAnthropicConfig()
    pa
…[8629 chars omitted]…
erimental_pass_through/messages/transformation.py:180:        Auto-inject anthropic-beta headers based on features used.
litellm/llms/anthropic/experimental_pass_through/messages/transformation.py:195:        existing_beta = headers.get("anthropic-beta")
litellm/llms/anthropic/experimental_pass_through/messages/transformation.py:236:            headers["anthropic-beta"] = ",".join(sorted(beta_values))

- For all other edits, add context-management-2025-06-27 header
        """
        edits = context_management.get("edits", [])
        
        has_compact = False
        has_other = False
        
        for edit in edits:
            edit_type = edit.get("type", "")
            if edit_type == "compact_20260112":
                has_compact = True
            else:

                has_other = True
        
        # Add compact header if any compact edits exist
        if has_compact:
            self._ensure_beta_header(
                headers, ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value
            )
        
        # Add context management header if any other edits exist
        if has_other:
            self._ensure_beta_header(
                headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
            )

    def update_headers_w
…[8629 chars omitted]…
rtex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:3:from litellm.anthropic_beta_headers_manager import (
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:111:        auto_betas = self.get_anthropic_beta_list(
litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:130:            data["anthropic_beta"] = list(beta_set)

Provider List: https://docs.litellm.ai/docs/providers

{'anthropic-beta': 'foo'} {'max_tokens': 1} => {'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'hi'}]}], 'max_tokens': 1}

Provider List: https://docs.litellm.ai/docs/providers

{} {'max_tokens': 1, 'extra_headers': {'anthropic-beta': 'foo,bar'}} => {'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'hi'}]}], 'max_tokens': 1, 'extra_headers': {'anthropic-beta': 'foo,bar'}}

Provider List: https://docs.litellm.ai/docs/providers

{'anthropic-beta': 'foo'} {'max_tokens': 1, 'context_management': {'edits': []}} => {'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'hi'}]}], 'max_tokens': 1, 'context_management': {'edits': []}}

stream: Optional[bool] = None,
    ) -> str:
        if api_base is None:
            raise ValueError(
                "api_base is required. Unable to determine the correct api_base for the request."
            )
        return api_base  # no transformation is needed - handled in validate_environment

    def transform_anthropic_messages_request(
        self,
        model: str,
        messages: List[Dict],

        anthropic_messages_optional_request_params: Dict,
        litellm_params: GenericLiteLLMParams,
        headers: dict,
    ) -> Dict:
        anthropic_messages_request = super().transform_anthropic_messages_request(
            model=model,
            messages=messages,
            anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
            litellm_params=litellm_params,
            headers=headers,
        )

        an
…[1707 chars omitted]…
headers = update_headers_with_filtered_beta(
            headers=headers,
            provider="anthropic",
        )

        return headers, api_base

    def transform_anthropic_messages_request(
        self,
        model: str,
        messages: List[Dict],
        anthropic_messages_optional_request_params: Dict,
        litellm_params: GenericLiteLLMParams,
        headers: dict,
    ) -> Dict:

.....................                                                    [100%]
21 passed, 2 warnings in 1.99s

diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
index 918b8ec..7b3568d 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
@@ -105,6 +105,24 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert
             )
         return api_base  # no transformation is needed - handled in validate_environment
 
+    @staticmethod
+    def _get_user_beta_values(optional_params: Dict, headers: dict) -> set[str]:
+        """Convert user beta headers to the beta list required by Vertex."""
+        values: set[str] = set()

+
+       
…[3959 chars omitted]…
        

Provider List: https://docs.litellm.ai/docs/providers

one ['one'] False False

Provider List: https://docs.litellm.ai/docs/providers

one,two ['two', 'one'] False False

Provider List: https://docs.litellm.ai/docs/providers

{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': 'hi'}]}], 'max_tokens': 1, 'anthropic_beta': ['effort-2025-11-24']}

beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)

        # Check for structured outputs
        if optional_params.get("output_format") is not None:
            beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value)

        # Check for tool search tools
        tools = optional_params.get("tools")
        if tools:
            anthropic_model_info = AnthropicModelInfo()
            if anthropic_model_info.is_tool_search_used(tools):
                # Use provider-specific tool search header

                tool_search_header = get_tool_search_beta_header(custom_llm_provider)
                beta_values.add(tool_search_header)

        if beta_values:
            headers["anthropic-beta"] = ",".join(sorted(beta_values))

        return headers

==================================== ERRORS ======================
…[757 chars omitted]…
===========================
ERROR tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
2 warnings, 1 error in 6.39s
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
 M litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

return headers, api_base

    def transform_anthropic_messages_request(
        self,
        model: str,
        messages: List[Dict],
        anthropic_messages_optional_request_params: Dict,
        litellm_params: GenericLiteLLMParams,
        headers: dict,
    ) -> Dict:
        """
        No transformation is needed for Anthropic messages

        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 = 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",
                status_code=400,
            )
        ####### get required params for all anthropic messages requests ######
        verbose_lo
…[3809 chars omitted]…
o = AnthropicModelInfo()
            if anthropic_model_info.is_tool_search_used(tools):
                # Use provider-specific tool search header
                tool_search_header = get_tool_search_beta_header(custom_llm_provider)
                beta_values.add(tool_search_header)

        if beta_values:
            headers["anthropic-beta"] = ",".join(sorted(beta_values))

        return headers

.....................                                                    [100%]
21 passed, 2 warnings in 2.76s

.../experimental_pass_through/transformation.py    | 28 +++++++++++++++++
 .../anthropic/transformation.py                    | 35 ++++++++++++++++++++--
 2 files changed, 61 insertions(+), 2 deletions(-)
         return api_base  # no transformation is needed - handled in validate_environment
 
+    @staticmethod
+    def _get_user_beta_values(optional_params: Dict, headers: dict) -> set[str]:
+        """Convert user beta headers to the beta list required by Vertex."""
+        values: set[str] = set()
+
+        def add(value: Any) -> None:
+            if isinstance(value, str):

+                values.update(item.strip() for item in value.split(",") if item.strip())
+            elif isinstance(value, (list, tuple, set)):
+                for item in value:
+                    add(item)
+
+        add(headers.get("anthropic-beta"))
+        add(optional_params.get("anthropic_be
…[3291 chars omitted]…
ist(
@@ -117,7 +148,7 @@ class VertexAIAnthropicConfig(AnthropicConfig):
             mcp_server_used=self.is_mcp_server_used(optional_params.get("mcp_servers")),
         )
 
-        beta_set = set(auto_betas)
+        beta_set = user_betas | set(auto_betas)
         if tool_search_used:
             beta_set.add("tool-search-tool-2025-10-19")  # Vertex requires this header for tool search

submitted.

AGENTS.md
ARCHITECTURE.md
CLAUDE.md
CONTRIBUTING.md
Dockerfile
GEMINI.md
LICENSE
Makefile
README.md
ci_cd
codecov.yaml
cookbook

db_scripts
deploy
dist
docker
docker-compose.hardened.yml
docker-compose.yml
docs
enterprise
index.yaml
litellm
litellm-js
litellm-proxy-extras
mcp_servers.json
model_prices_and_context_window.json
package-lock.json
package.json
poetry.lock
prometheus.yml
provider_endpoints_support.json
proxy_server_config.yaml
pyproject.toml
pyrightconfig.json
render.yaml
requirements.txt
ruff.toml
schema.prisma
scripts
security.md
taplo.toml
tests
ui
uv.lock
grep: /repo/src: No such file or directory

agent_engine
batches
common_utils.py
context_caching
cost_calculator.py
count_tokens
files
fine_tuning
gemini
gemini_embeddings
google_genai
image_edit

image_generation
multimodal_embeddings
ocr
rag_engine
rerank
text_to_speech
vector_stores
vertex_ai_non_gemini.py
vertex_ai_partner_models
vertex_embeddings
vertex_gemma_models
vertex_llm_base.py
vertex_model_garden
videos
/repo/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:3:from litellm.anthropic_beta_headers_manager import (
/repo/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:67:        existing_beta = headers.get("anthropic-beta")
/repo/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py:83:            headers["anthropic-beta"] = ",".join(beta_values)
/repo/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:111:        auto_betas = self.get_anthropic_beta_list(
/repo/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py:130:            data["anthropic_beta"] = list(beta_set)

# What is this?
## Handler file for calling claude-3 on vertex ai
from typing import Any, List, Optional

import httpx

import litellm
from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse

from ....anthropic.chat.transformation import AnthropicConfig

class VertexAIError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        self.request = httpx.Request(
            method="POST", url=" https://cloud.google.com/vertex-ai/"
        )
        self.response = httpx.Response(status_code=status_code, request=self.request)
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs


class VertexAIAnthropicConfig(AnthropicConfig
…[6144 chars omitted]…
,
            messages,
            optional_params,
            litellm_params,
            encoding,
            api_key,
            json_mode,
        )
        response.model = model

        return response

    @classmethod
    def is_supported_model(cls, model: str, custom_llm_provider: str) -> bool:
        """
        Check if the model is supported by the VertexAI Anthropic API.
        """

207:        Filter out unsupported fields from JSON schema for Anthropic's output_format API.
209:        Anthropic's output_format doesn't support certain JSON schema properties:
709:    def map_response_format_to_anthropic_output_format(
718:        # Filter out unsupported fields for Anthropic's output_format API
838:                    _output_format = (
839:                        self.map_response_format_to_anthropic_output_format(value)
841:                    if _output_format is not None:
842:                        optional_params["output_format"] = _output_format
1019:        Ensure a beta header value is present in the anthropic-beta header.
1026:        existing_beta = headers.get("anthropic-beta")
1028:            headers["anthropic-beta"] = beta_value
1032:            headers["anthropic-beta"] = f"{existing_beta}, {beta_value}"

1066:    def update_headers_with_optional_
…[128 chars omitted]…
h_optional_anthropic_beta(
1218:        ## Handle output_config (Anthropic-specific parameter)
1219:        if "output_config" in optional_params:
1220:            output_config = optional_params.get("output_config")
1221:            if output_config and isinstance(output_config, dict):
1222:                effort = output_config.get("effort")
1227:                data["output_config"] = output_config

## check if code_execution tool is already in tools
            for tool in tools:
                tool_type = tool.get("type", None)
                if (
                    tool_type
                    and isinstance(tool_type, str)
                    and tool_type.startswith("code_execution")
                ):
                    return tools
            tools.append(
                AnthropicCodeExecutionTool(
                    name="code_execution",

                    type="code_execution_20250522",
                )
            )
        return tools

    def _ensure_beta_header(self, headers: dict, beta_value: str) -> None:
        """
        Ensure a beta header value is present in the anthropic-beta header.
        Merges with existing values instead of overriding them.
        
        Args:
            headers: Dictionary of headers to update
            
…[8616 chars omitted]…
sform_response_for_json_mode(
        self,
        json_mode: Optional[bool],
        tool_calls: List[ChatCompletionToolCallChunk],
    ) -> Optional[LitellmMessage]:
        _message: Optional[LitellmMessage] = None
        if json_mode is True and len(tool_calls) == 1:
            # check if tool name is the default tool name
            json_mode_content_str: Optional[str] = None
            if (

README.MD
__init__.py
agent_tests
audio_tests
basic_proxy_startup_tests
batches_tests
code_coverage_tests
documentation_tests
enterprise
gettysburg.wav
guardrails_tests
image_gen_tests

large_text.py
litellm
litellm-proxy-extras
litellm_core_utils
litellm_utils_tests
llm_responses_api_testing
llm_translation
load_tests
local_testing
logging_callback_tests
mcp_tests
multi_instance_e2e_tests
ocr_tests
old_proxy_tests
openai_batch_completions.jsonl
openai_endpoints_tests
otel_tests
pass_through_tests

/repo/tests/llm_translation/test_anthropic_completion.py
/repo/tests/llm_translation/test_bedrock_anthropic_regression.py
/repo/tests/llm_translation/test_bedrock_completion.py
/repo/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
/repo/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_structured_outputs.py
/repo/tests/test_litellm/llms/azure_ai/claude/test_azure_anthropic_transformation.py
/repo/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
/repo/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
/repo/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py
/repo/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py
/repo/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_passthrough_load_balancing.py
/repo/tests/test_litellm/test_anthropic_beta_headers_manager.py

import os
import sys

import pytest

sys.path.insert(
    0, os.path.abspath("../../../../../..")
)  # Adds the parent directory to the system path
from litellm.anthropic_beta_headers_manager import (
    update_headers_with_filtered_beta,
)
from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (

    VertexAIAnthropicConfig,
)


@pytest.mark.parametrize(
    "model, expected_thinking",
    [
        ("claude-sonnet-4@20250514", True),
    ],
)
def test_vertex_ai_anthropic_thinking_param(model, expected_thinking):
    supported_openai_params = VertexAIAnthropicConfig().get_supported_openai_params(
        model=model
    )

    if expected_thinking:
        assert "thinking" in supported_openai_params
    else:
        assert "thinking" not in supported_openai_params


def test_get_supported_params_thinking():
    config = VertexAIAnthropicConfig()
    pa
…[5823 chars omitted]…
ed 'structured-outputs-2025-11-13', got: {result_non_vertex.get('anthropic-beta')}"


def test_vertex_ai_claude_sonnet_4_5_structured_output_fix():
    """
    Test fix for issue #18625: Claude Sonnet 4.5 on VertexAI should use tool-based 
    structured outputs instead of output_format parameter.
    
    This test verifies that:
    1. Claude Sonnet 4.5 uses tool-based structured outputs on VertexAI

1. Claude Sonnet 4.5 uses tool-based structured outputs on VertexAI
    2. output_format parameter is removed from the final request
    3. The fix prevents "Extra inputs are not permitted" error
    """
    config = VertexAIAnthropicConfig()
    
    # Test data matching the issue report
    response_format = {
        "type": "json_schema",
        "json_schema": {
            "name": "questions",
            "strict": True,

            "schema": {
                "type": "object",
                "properties": {
                    "question": {
                        "type": "string"
                    },
                    "response": {
                        "type": "string"
                    }
                },
                "required": ["question", "response"],
                "additionalProperties": False
            }
        }
    }
    
    messages = [
      
…[5236 chars omitted]…
-03-05" in (beta_header or ""), \
        "Other non-excluded beta headers should remain"
    # If prompt-caching was the only value, header should be removed completely
    headers2 = {"anthropic-beta": PROMPT_CACHING_BETA_HEADER}
    headers2 = update_headers_with_filtered_beta(headers2, "vertex_ai")
    assert "anthropic-beta" not in headers2, "Header should be removed if no supported values remain"

55:        self, beta_set: set, context_management: dict
58:        Add context_management beta headers to the beta_set.
64:            beta_set: Set of beta headers to modify in-place
82:            beta_set.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value)
86:            beta_set.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)
104:        data.pop("model", None)  # vertex anthropic doesn't accept 'model' parameter
107:        data.pop("output_format", None)
111:        auto_betas = self.get_anthropic_beta_list(
120:        beta_set = set(auto_betas)
122:            beta_set.add("tool-search-tool-2025-10-19")  # Vertex requires this header for tool search
127:            self._add_context_management_beta_headers(beta_set, context_management)
129:        if beta_set:

130:            data["anthropic_beta"] = list(beta_set)

return None
        json_schema: Optional[dict] = None
        if "response_schema" in value:
            json_schema = value["response_schema"]
        elif "json_schema" in value:
            json_schema = value["json_schema"]["schema"]

        return json_schema

    def map_response_format_to_anthropic_output_format(
        self, value: Optional[dict]
    ) -> Optional[AnthropicOutputSchema]:

        json_schema: Optional[dict] = self._extract_json_schema_from_response_format(
            value
        )
        if json_schema is None:
            return None
        
        # Filter out unsupported fields for Anthropic's output_format API
        filtered_schema = self.filter_anthropic_output_schema(json_schema)
        
        return AnthropicOutputSchema(
            type="json_schema",
            schema=filtered_schema,
        )

    def map_response_format_t
…[4363 chars omitted]…
e, str)
                and _valid_user_id(value)  # anthropic fails on emails
            ):
                optional_params["metadata"] = {"user_id": value}
            if param == "thinking":
                optional_params["thinking"] = value
            elif param == "reasoning_effort" and isinstance(value, str):
                optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(

Provider List: https://docs.litellm.ai/docs/providers

['interleaved-thinking-2025-05-14', 'context-management-2025-06-27', 'structured-outputs-2025-11-13', 'effort-2025-11-24']
False False
header after: None

self._warn_or_fail_if_strict(f"Unknown config option: {key}\n")

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
9 passed, 2 warnings in 1.07s

ERROR tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!
2 warnings, 1 error in 3.06s
 .../anthropic/transformation.py                    | 27 +++++++++++++++++++---
 1 file changed, 24 insertions(+), 3 deletions(-)

ImportError while importing test module '/repo/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py'.
E   ModuleNotFoundError: No module named 'fastapi'

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
29 passed, 2 warnings in 0.73s

diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
index 0b728d8..08d0edb 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
@@ -102,9 +102,10 @@ class VertexAIAnthropicConfig(AnthropicConfig):
         )
 
         data.pop("model", None)  # vertex anthropic doesn't accept 'model' parameter
-        
-        # VertexAI doesn't support output_format parameter, remove it if present
+
+        # VertexAI doesn't support output_format / output_config parameters, remove them if present

         data.pop("output_format", None)
+        data.pop("output_config", None)
         
         tools = optional_params.get("tools")
         tool_search_used = self.is
…[921 chars omitted]…
eader.
+
+        Supports a single value as well as multiple comma-separated values.
+        """
+        beta_header = headers.get("anthropic-beta")
+        if not beta_header or not isinstance(beta_header, str):
+            return set()
+        return {beta.strip() for beta in beta_header.split(",") if beta.strip()}
+
     def map_openai_params(
         self,
         non_default_params: dict,

…[5 chars omitted]…
 existing `output_format` pop. Non-Vertex Anthropic behavior untouched. Vertex anthropic transformation tests (9) and anthropic beta headers manager tests (29) pass."}
submitted.