chore: initial public snapshot for github upload
This commit is contained in:
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"""
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This file contains common utils for anthropic calls.
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"""
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from typing import Dict, List, Optional, Union
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import httpx
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import litellm
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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get_file_ids_from_messages,
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)
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from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.types.llms.anthropic import (
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ANTHROPIC_HOSTED_TOOLS,
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ANTHROPIC_OAUTH_BETA_HEADER,
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ANTHROPIC_OAUTH_TOKEN_PREFIX,
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AllAnthropicToolsValues,
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AnthropicMcpServerTool,
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)
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from litellm.types.llms.openai import AllMessageValues
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def is_anthropic_oauth_key(value: Optional[str]) -> bool:
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"""Check if a value contains an Anthropic OAuth token (sk-ant-oat*)."""
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if value is None:
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return False
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# Handle both raw token and "Bearer <token>" format
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if value.startswith("Bearer "):
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value = value[7:]
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return value.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX)
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def _merge_beta_headers(existing: Optional[str], new_beta: str) -> str:
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"""Merge a new beta value into an existing comma-separated anthropic-beta header."""
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if not existing:
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return new_beta
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betas = {b.strip() for b in existing.split(",") if b.strip()}
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betas.add(new_beta)
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return ",".join(sorted(betas))
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def optionally_handle_anthropic_oauth(
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headers: dict, api_key: Optional[str]
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) -> tuple[dict, Optional[str]]:
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"""
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Handle Anthropic OAuth token detection and header setup.
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If an OAuth token is detected in the Authorization header, extracts it
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and sets the required OAuth headers.
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Args:
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headers: Request headers dict
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api_key: Current API key (may be None)
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Returns:
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Tuple of (updated headers, api_key)
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"""
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# Check Authorization header (passthrough / forwarded requests)
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auth_header = headers.get("authorization", "")
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if auth_header and auth_header.startswith(f"Bearer {ANTHROPIC_OAUTH_TOKEN_PREFIX}"):
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api_key = auth_header.replace("Bearer ", "")
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headers.pop("x-api-key", None)
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headers["anthropic-beta"] = _merge_beta_headers(
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headers.get("anthropic-beta"), ANTHROPIC_OAUTH_BETA_HEADER
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)
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headers["anthropic-dangerous-direct-browser-access"] = "true"
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return headers, api_key
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# Check api_key directly (standard chat/completion flow)
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if api_key and api_key.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX):
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headers.pop("x-api-key", None)
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headers["authorization"] = f"Bearer {api_key}"
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headers["anthropic-beta"] = _merge_beta_headers(
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headers.get("anthropic-beta"), ANTHROPIC_OAUTH_BETA_HEADER
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)
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headers["anthropic-dangerous-direct-browser-access"] = "true"
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return headers, api_key
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class AnthropicError(BaseLLMException):
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def __init__(
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self,
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status_code: int,
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message,
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headers: Optional[httpx.Headers] = None,
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):
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super().__init__(status_code=status_code, message=message, headers=headers)
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class AnthropicModelInfo(BaseLLMModelInfo):
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def is_cache_control_set(self, messages: List[AllMessageValues]) -> bool:
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"""
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Return if {"cache_control": ..} in message content block
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Used to check if anthropic prompt caching headers need to be set.
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"""
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for message in messages:
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if message.get("cache_control", None) is not None:
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return True
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_message_content = message.get("content")
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if _message_content is not None and isinstance(_message_content, list):
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for content in _message_content:
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if "cache_control" in content:
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return True
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return False
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def is_file_id_used(self, messages: List[AllMessageValues]) -> bool:
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"""
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Return if {"source": {"type": "file", "file_id": ..}} in message content block
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"""
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file_ids = get_file_ids_from_messages(messages)
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return len(file_ids) > 0
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def is_mcp_server_used(
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self, mcp_servers: Optional[List[AnthropicMcpServerTool]]
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) -> bool:
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if mcp_servers is None:
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return False
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if mcp_servers:
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return True
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return False
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def is_computer_tool_used(
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self, tools: Optional[List[AllAnthropicToolsValues]]
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) -> Optional[str]:
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"""Returns the computer tool version if used, e.g. 'computer_20250124' or None"""
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if tools is None:
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return None
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for tool in tools:
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if "type" in tool and tool["type"].startswith("computer_"):
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return tool["type"]
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return None
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def is_web_search_tool_used(
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self, tools: Optional[List[AllAnthropicToolsValues]]
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) -> bool:
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"""Returns True if web_search tool is used"""
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if tools is None:
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return False
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for tool in tools:
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if "type" in tool and tool["type"].startswith(
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ANTHROPIC_HOSTED_TOOLS.WEB_SEARCH.value
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):
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return True
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return False
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def is_pdf_used(self, messages: List[AllMessageValues]) -> bool:
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"""
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Set to true if media passed into messages.
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"""
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for message in messages:
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if (
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"content" in message
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and message["content"] is not None
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and isinstance(message["content"], list)
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):
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for content in message["content"]:
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if "type" in content and content["type"] != "text":
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return True
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return False
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def is_tool_search_used(self, tools: Optional[List]) -> bool:
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"""
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Check if tool search tools are present in the tools list.
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"""
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if not tools:
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return False
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for tool in tools:
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tool_type = tool.get("type", "")
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if tool_type in [
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"tool_search_tool_regex_20251119",
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"tool_search_tool_bm25_20251119",
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]:
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return True
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return False
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def is_programmatic_tool_calling_used(self, tools: Optional[List]) -> bool:
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"""
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Check if programmatic tool calling is being used (tools with allowed_callers field).
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Returns True if any tool has allowed_callers containing 'code_execution_20250825'.
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"""
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if not tools:
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return False
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for tool in tools:
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# Check top-level allowed_callers
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allowed_callers = tool.get("allowed_callers", None)
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if allowed_callers and isinstance(allowed_callers, list):
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if "code_execution_20250825" in allowed_callers:
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return True
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# Check function.allowed_callers for OpenAI format tools
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function = tool.get("function", {})
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if isinstance(function, dict):
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function_allowed_callers = function.get("allowed_callers", None)
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if function_allowed_callers and isinstance(
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function_allowed_callers, list
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):
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if "code_execution_20250825" in function_allowed_callers:
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return True
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return False
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def is_input_examples_used(self, tools: Optional[List]) -> bool:
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"""
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Check if input_examples is being used in any tools.
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Returns True if any tool has input_examples field.
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"""
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if not tools:
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return False
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for tool in tools:
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# Check top-level input_examples
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input_examples = tool.get("input_examples", None)
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if (
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input_examples
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and isinstance(input_examples, list)
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and len(input_examples) > 0
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):
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return True
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# Check function.input_examples for OpenAI format tools
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function = tool.get("function", {})
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if isinstance(function, dict):
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function_input_examples = function.get("input_examples", None)
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if (
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function_input_examples
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and isinstance(function_input_examples, list)
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and len(function_input_examples) > 0
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):
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return True
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return False
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@staticmethod
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def _is_claude_4_6_model(model: str) -> bool:
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"""Check if the model is a Claude 4.6 model (Opus 4.6 or Sonnet 4.6)."""
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model_lower = model.lower()
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return any(
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v in model_lower
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for v in (
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"opus-4-6",
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"opus_4_6",
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"opus-4.6",
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"opus_4.6",
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"sonnet-4-6",
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"sonnet_4_6",
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"sonnet-4.6",
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"sonnet_4.6",
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)
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)
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def is_effort_used(
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self, optional_params: Optional[dict], model: Optional[str] = None
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) -> bool:
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"""
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Check if effort parameter is being used and requires a beta header.
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Returns True if effort-related parameters are present and
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the model requires the effort beta header. Claude 4.6 models
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use output_config as a stable API feature — no beta header needed.
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"""
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if not optional_params:
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return False
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# Claude 4.6 models use output_config as a stable API feature — no beta header needed
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if model and self._is_claude_4_6_model(model):
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return False
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# Check if reasoning_effort is provided for Claude Opus 4.5
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if model and ("opus-4-5" in model.lower() or "opus_4_5" in model.lower()):
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reasoning_effort = optional_params.get("reasoning_effort")
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if reasoning_effort and isinstance(reasoning_effort, str):
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return True
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# Check if output_config is directly provided (for non-4.6 models)
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output_config = optional_params.get("output_config")
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if output_config and isinstance(output_config, dict):
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effort = output_config.get("effort")
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if effort and isinstance(effort, str):
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return True
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return False
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def is_code_execution_tool_used(self, tools: Optional[List]) -> bool:
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"""
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Check if code execution tool is being used.
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Returns True if any tool has type "code_execution_20250825".
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"""
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if not tools:
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return False
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for tool in tools:
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tool_type = tool.get("type", "")
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if tool_type == "code_execution_20250825":
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return True
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return False
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def is_container_with_skills_used(self, optional_params: Optional[dict]) -> bool:
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"""
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Check if container with skills is being used.
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Returns True if optional_params contains container with skills.
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"""
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if not optional_params:
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return False
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container = optional_params.get("container")
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if container and isinstance(container, dict):
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skills = container.get("skills")
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if skills and isinstance(skills, list) and len(skills) > 0:
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return True
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return False
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def _get_user_anthropic_beta_headers(
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self, anthropic_beta_header: Optional[str]
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) -> Optional[List[str]]:
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if anthropic_beta_header is None:
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return None
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return anthropic_beta_header.split(",")
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def get_computer_tool_beta_header(self, computer_tool_version: str) -> str:
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"""
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Get the appropriate beta header for a given computer tool version.
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Args:
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computer_tool_version: The computer tool version (e.g., 'computer_20250124', 'computer_20241022')
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Returns:
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The corresponding beta header string
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"""
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computer_tool_beta_mapping = {
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"computer_20250124": "computer-use-2025-01-24",
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"computer_20241022": "computer-use-2024-10-22",
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}
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return computer_tool_beta_mapping.get(
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computer_tool_version, "computer-use-2024-10-22" # Default fallback
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)
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def get_anthropic_beta_list(
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self,
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model: str,
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optional_params: Optional[dict] = None,
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computer_tool_used: Optional[str] = None,
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prompt_caching_set: bool = False,
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file_id_used: bool = False,
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mcp_server_used: bool = False,
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) -> List[str]:
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"""
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Get list of common beta headers based on the features that are active.
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Returns:
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List of beta header strings
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"""
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from litellm.types.llms.anthropic import (
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ANTHROPIC_EFFORT_BETA_HEADER,
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)
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betas = []
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# Detect features
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effort_used = self.is_effort_used(optional_params, model)
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if effort_used:
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betas.append(ANTHROPIC_EFFORT_BETA_HEADER) # effort-2025-11-24
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if computer_tool_used:
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beta_header = self.get_computer_tool_beta_header(computer_tool_used)
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betas.append(beta_header)
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# Anthropic no longer requires the prompt-caching beta header
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# Prompt caching now works automatically when cache_control is used in messages
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# Reference: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
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if file_id_used:
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betas.append("files-api-2025-04-14")
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betas.append("code-execution-2025-05-22")
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if mcp_server_used:
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betas.append("mcp-client-2025-04-04")
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return list(set(betas))
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def get_anthropic_headers(
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self,
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api_key: str,
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anthropic_version: Optional[str] = None,
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computer_tool_used: Optional[str] = None,
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prompt_caching_set: bool = False,
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pdf_used: bool = False,
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file_id_used: bool = False,
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mcp_server_used: bool = False,
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web_search_tool_used: bool = False,
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tool_search_used: bool = False,
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programmatic_tool_calling_used: bool = False,
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input_examples_used: bool = False,
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effort_used: bool = False,
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is_vertex_request: bool = False,
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user_anthropic_beta_headers: Optional[List[str]] = None,
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code_execution_tool_used: bool = False,
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container_with_skills_used: bool = False,
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) -> dict:
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betas = set()
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# Anthropic no longer requires the prompt-caching beta header
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# Prompt caching now works automatically when cache_control is used in messages
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# Reference: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
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if computer_tool_used:
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beta_header = self.get_computer_tool_beta_header(computer_tool_used)
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betas.add(beta_header)
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# if pdf_used:
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# betas.add("pdfs-2024-09-25")
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if file_id_used:
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betas.add("files-api-2025-04-14")
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betas.add("code-execution-2025-05-22")
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if mcp_server_used:
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betas.add("mcp-client-2025-04-04")
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# Tool search, programmatic tool calling, and input_examples all use the same beta header
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if tool_search_used or programmatic_tool_calling_used or input_examples_used:
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from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
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||||
betas.add(ANTHROPIC_TOOL_SEARCH_BETA_HEADER)
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||||
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||||
# Effort parameter uses a separate beta header
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||||
if effort_used:
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||||
from litellm.types.llms.anthropic import ANTHROPIC_EFFORT_BETA_HEADER
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||||
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||||
betas.add(ANTHROPIC_EFFORT_BETA_HEADER)
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||||
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||||
# Code execution tool uses a separate beta header
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||||
if code_execution_tool_used:
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betas.add("code-execution-2025-08-25")
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||||
|
||||
# Container with skills uses a separate beta header
|
||||
if container_with_skills_used:
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||||
betas.add("skills-2025-10-02")
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||||
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||||
_is_oauth = api_key and api_key.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX)
|
||||
headers = {
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||||
"anthropic-version": anthropic_version or "2023-06-01",
|
||||
"accept": "application/json",
|
||||
"content-type": "application/json",
|
||||
}
|
||||
if _is_oauth:
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||||
headers["authorization"] = f"Bearer {api_key}"
|
||||
headers["anthropic-dangerous-direct-browser-access"] = "true"
|
||||
betas.add(ANTHROPIC_OAUTH_BETA_HEADER)
|
||||
else:
|
||||
headers["x-api-key"] = api_key
|
||||
|
||||
if user_anthropic_beta_headers is not None:
|
||||
betas.update(user_anthropic_beta_headers)
|
||||
|
||||
# Don't send any beta headers to Vertex, except web search which is required
|
||||
if is_vertex_request is True:
|
||||
# Vertex AI requires web search beta header for web search to work
|
||||
if web_search_tool_used:
|
||||
from litellm.types.llms.anthropic import ANTHROPIC_BETA_HEADER_VALUES
|
||||
|
||||
headers[
|
||||
"anthropic-beta"
|
||||
] = ANTHROPIC_BETA_HEADER_VALUES.WEB_SEARCH_2025_03_05.value
|
||||
elif len(betas) > 0:
|
||||
headers["anthropic-beta"] = ",".join(betas)
|
||||
|
||||
return headers
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: dict,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
api_key: Optional[str] = None,
|
||||
api_base: Optional[str] = None,
|
||||
) -> Dict:
|
||||
# Check for Anthropic OAuth token in headers
|
||||
headers, api_key = optionally_handle_anthropic_oauth(
|
||||
headers=headers, api_key=api_key
|
||||
)
|
||||
if api_key is None:
|
||||
raise litellm.AuthenticationError(
|
||||
message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars",
|
||||
llm_provider="anthropic",
|
||||
model=model,
|
||||
)
|
||||
|
||||
tools = optional_params.get("tools")
|
||||
prompt_caching_set = self.is_cache_control_set(messages=messages)
|
||||
computer_tool_used = self.is_computer_tool_used(tools=tools)
|
||||
mcp_server_used = self.is_mcp_server_used(
|
||||
mcp_servers=optional_params.get("mcp_servers")
|
||||
)
|
||||
pdf_used = self.is_pdf_used(messages=messages)
|
||||
file_id_used = self.is_file_id_used(messages=messages)
|
||||
web_search_tool_used = self.is_web_search_tool_used(tools=tools)
|
||||
tool_search_used = self.is_tool_search_used(tools=tools)
|
||||
programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(
|
||||
tools=tools
|
||||
)
|
||||
input_examples_used = self.is_input_examples_used(tools=tools)
|
||||
effort_used = self.is_effort_used(optional_params=optional_params, model=model)
|
||||
code_execution_tool_used = self.is_code_execution_tool_used(tools=tools)
|
||||
container_with_skills_used = self.is_container_with_skills_used(
|
||||
optional_params=optional_params
|
||||
)
|
||||
user_anthropic_beta_headers = self._get_user_anthropic_beta_headers(
|
||||
anthropic_beta_header=headers.get("anthropic-beta")
|
||||
)
|
||||
anthropic_headers = self.get_anthropic_headers(
|
||||
computer_tool_used=computer_tool_used,
|
||||
prompt_caching_set=prompt_caching_set,
|
||||
pdf_used=pdf_used,
|
||||
api_key=api_key,
|
||||
file_id_used=file_id_used,
|
||||
web_search_tool_used=web_search_tool_used,
|
||||
is_vertex_request=optional_params.get("is_vertex_request", False),
|
||||
user_anthropic_beta_headers=user_anthropic_beta_headers,
|
||||
mcp_server_used=mcp_server_used,
|
||||
tool_search_used=tool_search_used,
|
||||
programmatic_tool_calling_used=programmatic_tool_calling_used,
|
||||
input_examples_used=input_examples_used,
|
||||
effort_used=effort_used,
|
||||
code_execution_tool_used=code_execution_tool_used,
|
||||
container_with_skills_used=container_with_skills_used,
|
||||
)
|
||||
|
||||
headers = {**headers, **anthropic_headers}
|
||||
|
||||
return headers
|
||||
|
||||
@staticmethod
|
||||
def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
|
||||
return (
|
||||
api_base
|
||||
or get_secret_str("ANTHROPIC_API_BASE")
|
||||
or "https://api.anthropic.com"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def get_api_key(api_key: Optional[str] = None) -> Optional[str]:
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
|
||||
return api_key or get_secret_str("ANTHROPIC_API_KEY")
|
||||
|
||||
@staticmethod
|
||||
def get_base_model(model: Optional[str] = None) -> Optional[str]:
|
||||
return model.replace("anthropic/", "") if model else None
|
||||
|
||||
def get_models(
|
||||
self, api_key: Optional[str] = None, api_base: Optional[str] = None
|
||||
) -> List[str]:
|
||||
api_base = AnthropicModelInfo.get_api_base(api_base)
|
||||
api_key = AnthropicModelInfo.get_api_key(api_key)
|
||||
if api_base is None or api_key is None:
|
||||
raise ValueError(
|
||||
"ANTHROPIC_API_BASE or ANTHROPIC_API_KEY is not set. Please set the environment variable, to query Anthropic's `/models` endpoint."
|
||||
)
|
||||
response = litellm.module_level_client.get(
|
||||
url=f"{api_base}/v1/models",
|
||||
headers={"x-api-key": api_key, "anthropic-version": "2023-06-01"},
|
||||
)
|
||||
|
||||
try:
|
||||
response.raise_for_status()
|
||||
except httpx.HTTPStatusError:
|
||||
raise Exception(
|
||||
f"Failed to fetch models from Anthropic. Status code: {response.status_code}, Response: {response.text}"
|
||||
)
|
||||
|
||||
models = response.json()["data"]
|
||||
|
||||
litellm_model_names = []
|
||||
for model in models:
|
||||
stripped_model_name = model["id"]
|
||||
litellm_model_name = "anthropic/" + stripped_model_name
|
||||
litellm_model_names.append(litellm_model_name)
|
||||
return litellm_model_names
|
||||
|
||||
def get_token_counter(self) -> Optional[BaseTokenCounter]:
|
||||
"""
|
||||
Factory method to create an Anthropic token counter.
|
||||
|
||||
Returns:
|
||||
AnthropicTokenCounter instance for this provider.
|
||||
"""
|
||||
from litellm.llms.anthropic.count_tokens.token_counter import (
|
||||
AnthropicTokenCounter,
|
||||
)
|
||||
|
||||
return AnthropicTokenCounter()
|
||||
|
||||
|
||||
def process_anthropic_headers(headers: Union[httpx.Headers, dict]) -> dict:
|
||||
openai_headers = {}
|
||||
if "anthropic-ratelimit-requests-limit" in headers:
|
||||
openai_headers["x-ratelimit-limit-requests"] = headers[
|
||||
"anthropic-ratelimit-requests-limit"
|
||||
]
|
||||
if "anthropic-ratelimit-requests-remaining" in headers:
|
||||
openai_headers["x-ratelimit-remaining-requests"] = headers[
|
||||
"anthropic-ratelimit-requests-remaining"
|
||||
]
|
||||
if "anthropic-ratelimit-tokens-limit" in headers:
|
||||
openai_headers["x-ratelimit-limit-tokens"] = headers[
|
||||
"anthropic-ratelimit-tokens-limit"
|
||||
]
|
||||
if "anthropic-ratelimit-tokens-remaining" in headers:
|
||||
openai_headers["x-ratelimit-remaining-tokens"] = headers[
|
||||
"anthropic-ratelimit-tokens-remaining"
|
||||
]
|
||||
|
||||
llm_response_headers = {
|
||||
"{}-{}".format("llm_provider", k): v for k, v in headers.items()
|
||||
}
|
||||
|
||||
additional_headers = {**llm_response_headers, **openai_headers}
|
||||
return additional_headers
|
||||
Reference in New Issue
Block a user